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The Human Urine Metabolome
The Human Urine Metabolome
Abstract
Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.
Introduction
Metabolomics is a relatively young branch of “omics” science concerned with the systematic study of the chemical products or metabolites that cells and organisms generate. Because metabolites are the downstream products of numerous genome-wide or proteome-wide interactions, the metabolome (the sum of all metabolites in an organism) can be a very sensitive measure of an organism’s phenotype. This fact has made metabolomics particularly useful in the study of environment-gene interactions [1], [2], [3], [4], the identification of disease biomarkers [5], [6], [7], [8], [9] and the discovery of drugs [10]. Unlike its older “omics” cousins, where complete or near-complete coverage of the genome or proteome is fairly routine, metabolomics still struggles to cover even a tiny fraction of the metabolome. Indeed, most human metabolomic studies published today, even those exploiting the latest and most sensitive LC-MS/MS technologies, typically succeed in identifying or characterizing fewer than 100 compounds [11], [12], [13], [14]. This corresponds to less than 1% of the known human metabolome [15], [16]. In an effort to help improve this situation, we (and others) have started to undertake the systematic characterization of various human biofluid metabolomes. This includes the human cerebrospinal fluid metabolome [17], [18], the human saliva metabolome [19], and the human serum metabolome [20]. We have now turned our attention to characterizing the human urine metabolome.
Urine, as produced by mammals, is a transparent, sterile, amber-colored fluid generated by the kidneys. The kidneys extract the soluble wastes from the bloodstream, as well as excess water, sugars, and a variety of other compounds. The resulting urine contains high concentrations of urea (from amino acid metabolism), inorganic salts (chloride, sodium, and potassium), creatinine, ammonia, organic acids, various water-soluble toxins and pigmented products of hemoglobin breakdown, including urobilin, which gives urine its characteristic color. Urination is the primary route by which the body eliminates water-soluble waste products. The average adult generates between 1.5–2.0 liters of urine per day, which over the course of their lifetime would be enough to fill a small backyard swimming pool (5 X 8 X 1.5 m).
While largely viewed as a waste product, urine has considerable value as a diagnostic biofluid. Indeed the analysis of urine for medical purposes dates back to ancient Egypt [21], [22], [23], [24]. Hippocrates largely legitimized the medical practice of uroscopy (the study of urine for medical diagnostics) where examination of the color, cloudiness, smell and even the taste of urine was used to identify a variety of diseases. Throughout the Byzantine era and well into the Middle Ages, urine color wheels (a diagram that linked the color of urine to a particular disease) were commonly used by physicians to make diagnoses [21], [25]. A brownish color would indicate jaundice, a red hue (blood) might indicate urinary tract tumors, absence of color would be indicative of diabetes and foamy urine would indicate proteinuria. With the advent of modern clinical techniques in the middle of the 19th century, uroscopy largely disappeared. However, urine has continued to be an important cornerstone to modern medical practice. In fact, it was the first biofluid to be used to clinically diagnose a human genetic disease - alkaptonuria [26]. Even today urine analysis is routinely performed with dipstick tests that can readily measure urinary glucose, bilirubin, ketone bodies, nitrates, leukocyte esterase, specific gravity, hemoglobin, urobilinogen and protein. More detailed urinalysis can be also used to study a variety of renal conditions, such as bladder, ovarian and kidney diseases [27], [28], [29], [30].
Being an important and easily accessible biological fluid, urine has been the subject of detailed chemical analysis for more than 100 years [31], [32]. Extensive tables of normal reference ranges have been published for more than 100 urine ions and metabolites [33], [34], [35], [36], [37]. In addition to these referential clinical chemistry studies, several groups have applied various “global” metabolomic or metabolite profiling methods to urine, such as high resolution nuclear magnetic resolution (NMR) spectroscopy [38], [39], [40], high performance liquid chromatography (HPLC) [41], high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) [42], ultra high performance liquid chromatography-high resolution orbitrap mass spectrometry (UHPLC-HRMS) [43], ultra high performance liquid chromatography (UPLC-RP) [31], [44], fast atom bombardment ionization coupled with mass spectroscopy (FAB-MS) [35], two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GCxGC-MS) [32] or time-of-flight mass spectrometry (GCxGC-TOF-MS) [37], nanoflow liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) [33] and liquid chromatography coupled with electrospray quadruple time-of-flight mass spectrometry (LC/QTOF-MS) [36]. A combination of two or more profiling methods has also been used by many research groups to help improve metabolite coverage [34], [40].
A comprehensive list of methods used to analyze urine and the numbers of metabolites identified and/or quantified by these methods (along with references) is provided in Table 1. As seen from this table, it is possible to (tentatively) identify up to 294 different metabolites in human urine. However, quantification is somewhat more difficult, with the largest number of quantified metabolites ever reported in human urine being slightly less than 100. In addition to these global metabolomic studies, hundreds of other “targeted” or single-metabolite studies have been conducted on human urine that have led to the identification and quantification of hundreds of other urine metabolites. Unfortunately, this information is not located in any central repository. Instead it is highly dispersed across numerous textbooks and periodicals [16].
Table 1 Historic data on metabolite characterization of human urine by different platforms.
Method(s) # Identified # Quantified Year Reference
Multiple 51 51 1971 Putnam [105]
GC-MS 52 0 1976 Lawson [85]
GC-MS 72 72 1978 Gates [106]
GC-MS 36 36 1989 Hoffman [107]
GC-MS 137 0 1990 Liebich [108]
GC-MS 131 95 1991 Shoemaker [84]
GC-MS 69 69 1994 Guneral [56]
GC-MS 75 75 1996 Matsumoto [109]
ICP-MS 33 33 2005 Goulle [110]
SPE/NMR 73 0 2008 Yang [40]
SPE/LC/NMR 72 0 2008 Rezzi [78]
LC-MS/NMR 50 0 2008 Law [34]
LC-MS 93 93 2009 Guo [111]
NMR 50 50 2009 Shaykhutdinov [112]
GC-MS-MS 48 48 2011 Kvitvang [113]
SPE-GC-MS 79 0 2011 Silva [114]
LC-MS 167 0 2011 Guo [115]
NMR 42 42 2011 Saude [116]
NMR/GC-MS 77 0 2012 Zhang [117]
LC-MS 219 (25 listed) 0 2012 Van der Kloet [118]
GC-TOF/LC-MS 261 0 2012 Cheng [119]
GC-MS 258 0 2012 Roux [83]
GCxGC-TOF-MS 294 0 2012 Rocha [120] To facilitate future research into urine chemistry and urine metabolomics, we believe it is critical to establish a comprehensive, electronically accessible database of the detectable metabolites in human urine. This document describes just such a database, one that contains the metabolites that can, with today’s technology, be detected in human urine along with their respective concentrations and disease associations. This resource was assembled using a combination of both our own experimental and literature-based research. Experimentally, we used high-resolution NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection tandem mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) with ultraviolet (UV) or fluorescence detection (FD) techniques performed on multiple human urine samples to identify 445 metabolites or metabolite species and quantify 378 of these compounds. To complement these “global” metabolic profiling efforts, our team also surveyed and extracted metabolite and disease-association data from more than 3000 books and journal articles that had been identified through computer-aided literature and in-house developed text-mining software. This “bibliomic” effort yielded data for another 2206 metabolites. The resulting Urine Metabolome Database (UMDB - http://www.urinemetabolome.ca) is a comprehensive, web-accessible resource containing a total of 2651 confirmed urine metabolites or metabolite species (corresponding to 3079 defined structures), their corresponding concentrations and their disease associations that were revealed or identified from these combined experimental and literature mining efforts.
Results and Discussion
The Content of the Human Urine Metabolome – The Urine Metabolome Database
The Urine Metabolome Database (UMDB: http://www.urinemetabolome.ca) contains a complete list of all (to the best of our knowledge) possible metabolites that have been detected in human urine using current technologies. The UMDB is freely available, easily queried, web-enabled database which provides a list of the metabolite names, level of verification (confirmed or probable), normal and disease-associated concentration ranges, associated diseases and corresponding literature references for all human urine metabolites that have ever been detected and/or quantified in the literature. The UMDB also contains concentration data compiled from the experimental studies described here. Each urine metabolite entry in this database is linked to a MetaboCard button [15], [16] that, when clicked, brings up detailed information about that particular entry. This detailed information includes nomenclature, chemical, clinical and molecular/biochemical data. Each MetaboCard entry contains up to 120 data fields many of which are hyperlinked to other databases (KEGG [45], PubChem [46], MetaCyc [47], ChEBI [48], PDB [49], UniProt [50], and GenBank [51] as well as to GeneCard IDs [52], GeneAtlas IDs [53] and HGNC IDs [54] for each of the corresponding enzymes or proteins known to act on that metabolite). Additionally, the UMDB through its MetaboCard/HMDB links includes nearly 450 hand-drawn, zoomable and fully hyperlinked human metabolic pathway maps (SMPDB: http://www.smpdb.ca/). These maps are intended to help users visualize the chemical structures on metabolic pathways and to get detailed information about metabolic processes [55]. These UMDB pathway maps are quite specific to human metabolism and explicitly show the subcellular compartments where specific reactions are known to take place.
The UMDB’s simple text query (TextQuery) supports general text queries including names, synonyms, conditions and disorders. Clicking on the Browse button (on the UMDB navigation panel) generates a tabular view that allows users to casually scroll through the database or re-sort its contents by compound name or by concentration. Users can choose either the “Metabolite View”, “Concentration View” or “Diseases View” to facilitate their browsing or searching. Clicking on a given MetaboCard button brings up the full data content (from the HMDB) for the corresponding metabolite. Users may also search the database using a variety of options listed uner the “Search” menu. For instance, the ChemQuery button allows users to draw or write (using a SMILES string) a chemical compound to search the UMDB for chemicals similar or identical to the query compound. ChemQuery also supports chemical formula and molecular weight searches. The Sequence Search button allows users to conduct BLAST sequence searches of the 4075 protein sequences contained in the UMDB. Both single and multiple sequence BLAST queries are supported. “Advanced Search” which is also located under the “Search” menu is the most sophisticated search tool in the UMDB and opens an easy-to-use query search tool that allows users to select or search over various combinations of subfields. The UMDB’s “MS Search” allows users to submit mass spectral peak lists that will be searched against the Human Metabolome Database (HMDB)’s library of MS/MS spectra. This potentially allows facile identification of urine metabolites from mixtures via MS/MS spectroscopy. UMDB’s NMR Search allows users to submit peak lists from 1H or 13C NMR spectra (both pure and mixtures) and have these peak lists compared to the NMR libraries contained in the HMDB. This allows the identification of metabolites from mixtures via NMR spectral data. The Download button provides links to collected sequence, image and text files associated with the UMDB. In the About menu, the “Data Fields Explained” button lists source data used to assemble the UMDB.
Currently the UMDB contains information on 2651 detectable metabolites or metabolite species (which corresponds to 3079 metabolites with precisely defined structures) and 3832 concentration ranges or values associated with 220 different conditions, diseases and disorders. The number of metabolites in the UMDB is not a number that will remain unchanged. Rather it reflects the total number of metabolites – most of which are endogenous - that have ever been detected and/or quantified by ourselves and others. Certainly as technology improves, we anticipate this number will increase as other, lower abundance, metabolites are detected and added to future versions of the UMDB. Likewise, if the list was expanded to include intermittent, exogenous compounds such as all possible drugs or drug metabolites or rare food additives and food-derived phytochemicals, the database could be substantially larger.
Inspection of the on-line tables in UMDB generally shows that human urine contains a substantial number of hydrophilic molecules. This is further reiterated in Table 2, which provides a listing of the chemical “superclasses” (using the HMDB definitions) in human urine and the number of representative compounds that can be found in this biofluid. Excluding lipids (which are in very low concentration), human urine is dominated by amino acids and derivatives, carbohydrates and carbohydrate conjugates. This simply reinforces the fact that urine is a key carrier of hydrophilic waste products. Other small molecule components found in high abundance in urine include hydroxy acids and derivatives (such as citric acid), urea, ammonia, creatinine and hippuric acid. A more detailed description of both our literature and experimental findings is given in the following 7 sections covering: 1) Literature Review/Text Mining; 2) NMR; 3) DFI/LC-MS/MS; 4) GC-MS; 5) ICP-MS; 6) HPLC/UV and 7) HPLC/FD.
Table 2 Chemical superclasses in the urine metabolome database.
Chemical superclass # Compounds
Aliphatic Acyclic Compounds 93
Aliphatic Heteromonocyclic Compounds 43
Aliphatic Heteropolycyclic Compounds 40
Aliphatic Homomonocyclic Compounds 18
Aliphatic Homopolycyclic Compounds 5
Alkaloids and Derivatives 45
Amino Acids, Peptides, and Analogues 286
Aromatic Heteromonocyclic Compounds 67
Aromatic Heteropolycyclic Compounds 728
Aromatic Homomonocyclic Compounds 432
Aromatic Homopolycyclic Compounds 6
Carbohydrates and Carbohydrate Conjugates 116
Homogeneous Metal Compounds 45
Homogeneous Non-metal Compounds 15
Inorganic compounds 1
Lignans and Norlignans 12
Lipids 866
Mixed Metal/Non-metal Compounds 7
Nucleosides, Nucleotides, and Analogues 49
Organic Acids and Derivatives 108
Organic Halides 3
Organometallic Compounds 1
Organophosphorus Compounds 17
Polyketides 74
Tannins 2
Metabolite Concentration in Urine – Literature Survey
In addition to the experimentally derived values obtained for this study, the urine metabolome database (UMDB) also presents literature-derived concentrations of urine metabolites with references to either PubMed IDs or clinical textbooks. In many cases, multiple concentration values are given for “normal” conditions. This is done to provide users/readers with a better estimate of the potential concentration variations that different technologies or laboratories may measure. As a general rule, there is good agreement between most laboratories and methods. However, the literature results presented in the UMDB do not reflect the true state of the raw literature. A number of literature-derived concentration values were eliminated through the curation process after being deemed mistaken, disproven (by subsequent published studies), mis-typed or physiologically impossible. Much of the curation process involved having multiple curators carefully reading and re-reading the primary literature to check for unit type, unit conversion and typographical inconsistencies.
Other than the inorganic ions and gases such as sodium (14.7 ± 9.0 mM/mM creatinine – average value), chlorine (8.8 ± 6.2 mM/mM creatinine), potassium (4.6 ± 0.1 mM/mM creatinine), ammonia (2.8 ± 0.9 mM/mM creatinine), the 4 most abundant organic metabolites found in urine (based on average values) are urea (22.5 ± 4.4 mM/mM creatinine), creatinine (10.4 ± 2.0 mM), hippuric acid (298.5 ± 276.8 µM/mM creatinine) and citric acid (280.6 ± 115.2 µM/mM creatinine). The least abundant (detectable) metabolites in urine include oxytocin (0.9 ± 0.1 pM/mM creatinine), angiotensin II (1.2 ± 0.2 pM/mM creatinine), 15-deoxy-d-12,14-PGJ2 (2.3 ± 1.0 pM/mM creatinine) and melatonin (3.3 ± 2.7 pM/mM creatinine). This shows that the current lower limit of detection for metabolites in urine is in the low pM/mM creatinine range and that the concentration range of analytes in urine spans nearly 11 orders of magnitude.
One point that is particularly interesting is the fact that the concentration (scaled to creatinine) of the average metabolite in normal urine varies by about ± 50%, with some metabolites varying by as much as ± 350% (such as normetanephrine (0.00085 ± 0.00317 µM/mM creatinine), pipecolic acid (0.03 ± 0.07 µM/mM creatinine), enterodiol (0.032 ± 0.072 µM/mM creatinine), tungsten (0.010 ± 0.022 µM/mM creatinine) and chlorogenic acid (0.0014 ± 0.0029 µM/mM creatinine). Therefore, drawing conclusions about potential disease biomarkers without properly taking into account this variation would be ill-advised. We believe that these relatively large metabolite concentration ranges are due to a number of factors, including age, gender, genetic background, diurnal variation, health status, activity level and diet [56], [57], [58], [59]. Indeed, some UMDB entries explicitly show such variations based on the populations (age, gender) from which these metabolite concentrations were derived. Clearly more study on the contributions to the observed variations in urine is warranted, although with thousands of metabolites to measure for dozens of conditions, these studies will obviously require significant technical and human resources.
Experimental Quantification and Identification of Urine Metabolites – NMR
A representative high-resolution NMR spectrum of urine from a healthy individual is shown in Figure 1. As can be readily seen from this figure, urine NMR spectra are very information-rich and surprisingly complex, with thousands of resolved peaks. From the 22 healthy control urine samples analyzed, we could identify a total of 209 unique compounds with an average of 167 ± 19 compounds being identified per sample. Every compound was unequivocally identified and quantified using spectral fitting (via Chenomx) and/or spike-in experiments with authentic standards. The concentration of each metabolite was normalized to each urine sample’s corresponding creatinine value to compensate for variations in urine volume (the concentration of metabolites is expressed as µM/mM creatinine).
Figure 1 Typical 500 MHz 1H-NMR spectra of urine from human urine.
Numbers indicates the following metabolites: 1: creatinine; 2: citric acid; 3: glycine; 4: formic acid; 5: methanol; 6: guanidoacetic acid; 7: acetic acid; 8: L-cysteine; 9: glycolic acid; 10: creatine; 11: isocitric acid; 12: hippuric acid; 13: L-glutamine; 14: L-alanine; 15: L-lysine; 16: gluconic acid; 17∶2- hydroxyglutaric acid; 18: D-glucose; 19: indoxyl sulfate; 20: trimethyl-N-oxide; 21: ethanolamine; 22: L-lactic acid; 23: taurine; 24: L-threonine; 25: dimethylamine; 26: pyroglutamic acid; 27: trigonelline; 28: sucrose; 29: trimethylamine; 30: mannitol; 31: L-serine; 32: acetone; 33: L-cystine; 34: adipic acid; 35: L-histidine; 36: L-tyrosine; 37: imidazole; 38: mandelic acid; 39: dimethylglycine; 40: Cis-aconitic acid; 41: urea; 42∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 43: phenol; 45: isobutyric acid; 46: methylsuccinic acid; 47∶3-aminoisobutyric acid; 48: L-fucose; 49: N-acetylaspartic acid; 50: N-acetylneuraminic acid; 51: acetoacetic acid; 52: Alpha-aminoadipic acid; 53: methylguanidine; 54: phenylacetylglutamine. The 209 compounds identified and quantified from these NMR studies represent a “high-water” mark for NMR-based metabolomics. Previous studies have reported up to 70 compounds being identified and/or quantified in human urine [40]. Indeed, compared to other platforms previously used to analyze human urine (Table 1), it appears that NMR may currently be the most comprehensive and certainly the most quantitative approach to characterizing this biofluid. Based on the fitted area under each urinary NMR spectrum and the number of unidentified peaks we estimate that more than 96% of the spectral area and more than 92% of all NMR-detectable compounds in our human urine samples are listed in Table 3. In other words, for NMR-based metabolomics, human urine is essentially “solved”. The same “solved” status has already been achieved for human serum (with 49 definitive compounds, [20]) and for human cerebrospinal fluid (with 53 definitive compounds, [17]). Knowing the expected or detectable composition of these biofluids should open the door to automated NMR-based metabolomics [60].
Table 3 Concentrations [Mean (range)] and % occurrence of urine metabolites as determined by NMR.
Compounds HMDB ID Concentration (µM/mM creatinine) Oc (%) Literature Values (µM/mM creatinine)
1,3-Diaminopropane HMDB00002 1.2 5 2.55 (0.18–5.85)
1,3-Dimethyluric acid HMDB01857 3.1 (1.3–6.8) 68 1.5 (1.3–2.3)
1-Methyladenosine HMDB03331 1.7 (0.9–3.6) 82 2.03±0.20
1-Methylhistidine HMDB00001 8.3 (2.4–28.4) 100 15.9±19.5
1-Methylnicotinamide HMDB00699 5.8 (1.2–15.0) 77 6.1 (0.2–12.0)
2-Furoylglycine HMDB00439 4.0 (0.9–8.4) 100 9.95 (2.0–18.66)
2-Hydroxy-3-methylpentanoic acid HMDB00317 3.9 (1.7–7.9) 100 NA
2-Hydroxybutyric acid HMDB00008 2.8 (1.2–6.9) 77 2.4 (0.0–4.9)
2-Hydroxyglutaric acid HMDB00694 33.0 (13.3–77.9) 100 33.0 (20.0–46.0)
2-Ketobutyric acid HMDB00005 2.9 (0.7–5.4) 100 2.6 (1.1–4.1)
2-Methyl-3-hydroxybutyric acid HMDB00354 4.2 (1.6–6.7) 95 5.5 (0.0–11.0)
2-Methyl-3-ketovaleric acid HMDB00408 1.1 (0.8–1.3) 18 NA
2-Methylbutyrylglycine HMDB00339 2.0 (1.2–3.3) 32 1 (0–2)
2-Methylerythritol HMDB11659 25.3 (17.0–49.0) 14 NA
2-Methylglutaric acid HMDB00422 0.8 (0.3–1.7) 45 NA**
3-Aminoisobutanoic acid HMDB03911 26.0 (2.2–140.0) 100 2.91–116.43
3-Hydroxybutyric acid HMDB00357 3.6 (1.3–6.4) 77 1.4±1.3
3-Hydroxyhippuric acid HMDB06116 7.4 (5.7–9.1) 32 2.5 (0.2–5.0)
3-Hydroxyisovaleric acid HMDB00754 6.8 (3.2–21.8) 100 8.5±3.2
3-Hydroxymethylglutaric acid HMDB00355 2.8 (1.1–5.8) 68 3 (0–10)
3-Hydroxyphenylacetic acid HMDB00440 7.7 (1.4–22.1) 91 6.1±4.1
3-Methyl-2-oxovaleric acid HMDB00491 2.2 (0.4–4.8) 91 2.0±2.0
3-Methyladipic acid HMDB00555 2.9 (1.1–7.2) 86 2.9 (0.7–10.5)
3-Methylglutaconic acid HMDB00522 7.5 (3.4–9.5) 22 4.5 (0.0–9.0)
3-Methylhistidine HMDB00479 16.5 (2.8–59.8) 100 15.1 (3.9–26.3)
3-Methylxanthine HMDB01886 6.2 (1.4–17.8) 100 4.0 (2.3–6.3)
4-Aminobutyric acid HMDB00112 2.9 (2.7–3.3) 9 0.75 (0.0–1.50)
4-Aminohippuric acid HMDB01867 3.3 (1.0–7.2) 86 0.3
4-Deoxythreonic acid HMDB02453 12.3 (4.4–19.8) 100 25±10
4-Ethylphenol HMDB29306 0.9 (0.6–1.2) 18 0.409±0.174
4-Hydroxybenzoic acid HMDB00500 1.7 (0.9–3.0) 77 1.8±1.8
4-Hydroxybutyric acid HMDB00710 7.2 (2.7–22.3) 100 3.3 (0.0–10.8)
4-Hydroxyhippuric acid HMDB13678 2.8 (1.4–6.0) 100 0–14
4-Hydroxyphenyllactic acid HMDB00755 3.2 (1.5–3.9) 50 1.1 (0.2–2.6)
4-Pyridoxic acid HMDB00017 3.1 (0.4–7.5) 95 0.78±0.45a
5,6-Dihydrouracil HMDB00076 3.8 (2.1–8.3) 68 3.0±2.7
5-Aminolevulinic acid HMDB01149 2.9 (1.2–4.4) 95 1.45±0.72
5-Aminopentanoic acid HMDB03355 1.1 5 2
7-Methylxanthine HMDB01991 10.0 (3.9–16.1) 9 10.0 (6.7–14.6)
Acetaldehyde HMDB00990 2.6 (0.8–4.2) 32 NA**
Acetaminophen HMDB01859 11.1 5 5.3
Acetaminophen glucuronide HMDB10316 33.4 5 NA**
Acetaminophen sulfate HMDB59911 138.6 5 NA**
Acetic acid HMDB00042 13.0 (2.5–106.0) 100 45.3±53.3
Acetoacetic acid HMDB00060 11.1 (2.2–24.9) 100 33 (0–67)
Acetone HMDB01659 3.9 (0.8–17.6) 100 2.24±4.13
Acetyl-L-carnitine HMDB00201 2.8 (0.6–7.5) 100 1.9±0.3
Adenine HMDB00034 2.9 (1.4–5.1) 45 1.17
Adenosine HMDB00050 1.4 (0.9–2.3) 45 0.50 (0.09–0.92)
Adipic acid HMDB00448 4.4 (1.6–14.3) 91 5.1 (0.8–35.0)
ADP HMDB01341 1.8 (1.0–3.4) 77 NA**
Allantoin HMDB00462 15.4 (4.9–29.3) 100 19.7±10.5
Alpha-Aminoadipic acid HMDB00510 7.3 (3.4–11.2) 73 4.6
Alpha-Aminobutyric acid HMDB00452 2.2 (1.1–4.4) 95 1.37±1.05
Alpha-Hydroxyisobutyric acid HMDB00729 3.8 (1.7–5.9) 100 3.8
Alpha-Ketoisovaleric acid HMDB00019 0.5 (0.2–1.1) 50 1 (0–2)
Anserine HMDB00194 7.0 (3.3–20.0) 100 16.16
Arabinitol HMDB01851 31.8 (10.2–64.3) 100 19.28±6.32
Ascorbic acid HMDB00044 32.5 (4.6–78.0) 14 1.70–22.72
Asymmetric dimethylarginine HMDB01539 3.0 (0.2–6.7) 100 4.6 (3.0–7.0)
Azelaic acid HMDB00784 2.8 (1.8–4.8) 64 4.8 (1.3–15.0)
Benzoic acid HMDB01870 4.7 (1.2–10.5) 86 4.2 (1.9–6.5)
Beta-Alanine HMDB00056 5.9 (3.4–13.0) 68 5 (0–10)
Betaine HMDB00043 11.5 (2.7–24.7) 100 4.14
Butyric acid HMDB00039 1.6 (0.5–4.0) 77 0.131±0.013
Caffeine HMDB01847 1.2 5 0.33 (0.0–1.01)
Carnosine HMDB00033 5.5 (1.4–12.6) 91 4.0±2.5
Choline HMDB00097 3.5 (1.4–6.1) 100 5.27
Cinnamic acid HMDB00567 1.6 (0.9–2.2) 9 0.77
Cis-Aconitic acid HMDB00072 20.9 (3.8–95.3) 100 13.0 (2.7–44.0)
Citraconic acid HMDB00634 2.0 (1.2–2.6) 73 2.2
Citramalic acid HMDB00426 2.4 (1.0–4.8) 91 3.1
Citric acid HMDB00094 203 (49–600) 100 242.0±129.6
Creatine HMDB00064 46 (3–448) 100 46 (9–135)
Creatinine* HMDB00562 14,743±9,797 100 10,090±310
Cytosine HMDB00630 4.1 (1.1–10.7) 36 NA**
Dehydroascorbic acid HMDB01264 9.1 (5.8–7.1) 32 1.72–7.47
D-Galactose HMDB00143 11.9 (5.4–25.2) 100 4.4 (0.0–31.0)
D-Glucose HMDB00122 37.5 (12.5–58.4) 100 31.12
Dihydrothymine HMDB00079 2.4 (1.3–4.4) 64 5 (0–10)
Dimethyl sulfone HMDB04983 8.0 (1.3–49.0) 100 4.6 (2.5–7.5)
Dimethylamine HMDB00087 30.8 (20.3–59.2) 100 39.3±8.3
Dimethylglycine HMDB00092 4.4 (1.6–10.4) 100 6.2 (0.8–11.2)
D-Threitol HMDB04136 19.3 (5.3–32.7) 100 10.0 (5.6–15.0)
D-Xylitol HMDB02917 8.4 (6.0–13.8) 95 3.50±2.37
D-Xylose HMDB00098 20 (3.0–102) 91 51.19
Erythritol HMDB02994 33.4 (6.8–64.0) 100 51±18
Ethanol HMDB00108 3.1 5 5–500b
Ethanolamine HMDB00149 37.0 (24.8–56.2) 100 21.4 (6.6–36.2)
Ethyl glucuronide HMDB10325 8.2 5 26.38±15.26
Ethylmalonic acid HMDB00622 3.0 (1.2–5.8) 95 2.0 (0.5–3.9)
Formic acid HMDB00142 26.8 (6.9–120.9) 100 20.39±11.84
Fumaric acid HMDB00134 0.7 (0.2–1.7) 50 1 (0–2)
Glucaric acid HMDB00663 8.4 (5.8–12.3) 82 2.9±0.2
Gluconic acid HMDB00625 21.5 (8.1–38.8) 100 NA**
Glucuronic acid HMDB00127 9.7 (3.7–20.6) 77 NA
Glutaconic acid HMDB00620 3.1 (1.2–3.1) 68 1 (0–2)
Glutaric acid HMDB00661 2.1 (0.7–3.6) 18 1.3 (0.6–2.6)
Glyceric acid HMDB00139 15.9 (7.8–31.1) 100 4.5 (0.0–9.0)
Glycerol HMDB00131 13 (4–19) 100 20 (0–40)
Glycine HMDB00123 106 (44–300) 100 151 (233–248)
Glycolic acid HMDB00115 42.0 (3.7–122.0) 100 35 (18–55)
Guanidoacetic acid HMDB00128 41.8 (10.6–97.3) 100 89.0 (11.0–124.0)
Hippuric acid HMDB00714 229 (19–622) 100 257 (20–770)
Homocitrulline HMDB00679 7.5 (3.8–13.8) 91 6.0 (0.0–8.8)
Homogentisic acid HMDB00130 1.70 (0.55–2.8) 45 1 (0–2)
Homovanillic acid HMDB00118 6.2 (1.8–12.7) 100 4.9±3.4
HPHPA HMDB02643 5.9 (1.4–22.1) 100 0.55–54.93
Hydroxyproline HMDB00725 6.5 (2.4–12.5) 55 2
Hydroxypropionic acid HMDB00700 12.3 (4.4–17.9) 82 6.5 (3.0–10.0)
Hypoxanthine HMDB00157 7.2 (1.8–24.1) 100 4.67 (2.80–6.38)
Indoleacetic acid HMDB00197 3.4 (1.8–6.2) 100 2.0±1.0
Indoxyl sulfate HMDB00682 22.4 (6.0–64.8) 100 19.74±5.26
Inosine HMDB00195 1.4 (0.4–6.5) 50 1.04±0.56
Isobutyric acid HMDB01873 3.5 (1.2–9.0) 95 3.9
Isocitric acid HMDB00193 46.9 (20.4–89.2) 68 58 (36–84)
Isovaleric acid HMDB00718 1.4 (0.8–2.5) 86 0.42±0.02
Isovalerylglycine HMDB00678 2.0 (0.4–4.0) 91 5.5 (1.0–10.0)
Kynurenic acid HMDB00715 2.1 (0.8–4.2) 64 1.53 (0.66–4.13)
Kynurenine HMDB00684 1.7 (1.1–2.5) 23 2.85±1.94
Lactose HMDB00186 11.8 (1.0–24.2) 91 36.31±6.6c
L-Alanine HMDB00161 21.8 (7.1–43.0) 100 22.0±11.0
L-Arabinose HMDB00646 13.3 (1.3–22.0) 100 8.75 (6.12–11.84)
L-Arginine HMDB00517 8.6 (3.8–18.6) 95 6.6 (2.5–10.8)
L-Asparagine HMDB00168 10.0 (4.6–17.8) 95 10.0 (4.6–16.3)
L-Aspartic acid HMDB00191 10.9 (3.5–21.8) 95 4.63 (1.71–7.56)
L-Carnitine HMDB00062 5.0 (0.7–16.4) 100 5.7±4.8
L-Cystathionine HMDB00099 14.7 (3.9–20.7) 82 5.3 (1.0–9.6)
L-Cysteine HMDB00574 65.8 (23.1–134.5) 100 33.4±15.5
L-Cystine HMDB00192 12.5 (5.0–24.6) 100 9.0±3.0
Levoglucosan HMDB00640 10.0 (2.4–29.0) 27 NA**
Levulinic acid HMDB00720 1.3 (0.4–2.0) 64 1.28 (0.33–2.75)
L-Fucose HMDB00174 11.8 (6.7–26.0) 91 9.6 (6.4–16.0)
L-Glutamic acid HMDB00148 8.5 (3.3–18.4) 100 10.0±3.7
L-Glutamine HMDB00641 37.2 (19.1–77.9) 100 33.32±10.78
L-Histidine HMDB00177 43 (17–90) 100 60.75±27.45
L-Isoleucine HMDB00172 1.3 (0.4–2.6) 100 1.64
L-Lactic acid HMDB00190 11.6 (3.5–29.3) 100 12.3±6.2
L-Leucine HMDB00687 2.8 (1.6–5.4) 100 2.5±1.3
L-Lysine HMDB00182 17.2 (3.7–51.3) 100 18 (7–29)
L-Methionine HMDB00696 1.4 (0.5–2.5) 41 1.3±0.97
L-Phenylalanine HMDB00159 6.4 (3.5–11.2) 100 4.50±1.15
L-Serine HMDB00187 25.3 (11.6–53.4) 100 26.0±10.0
L-Threonine HMDB00167 13.3 (6.4–25.2) 100 5.17–24.59
L-Tryptophan HMDB00929 6.3 (3.4–11.1) 100 2.45–29.40
L-Tyrosine HMDB00158 9.5 (4.1–23.5) 100 8.8±4.5
L-Valine HMDB00883 3.4 (2.0–7.7) 100 3.0±1.0
Maleic acid HMDB00176 0.4 (0.3–0.5) 14 0.5
Malonic acid HMDB00691 2.9 (2.0–3.5) 77 1 (0–2)
Maltose HMDB00163 6.0 (1.3–21.4) 73 NA**
Mandelic acid HMDB00703 1.4 (1.1–1.7) 9 0.966±0.669
Mannitol HMDB00765 32.4 (5.2–85.1) 95 10.26±9.14
Methanol HMDB01875 37 (10–117) 100 NA**
Methylamine HMDB00164 4.0 (1.5–11.9) 100 10.5 (9.8–12.5)
Methylglutaric acid HMDB00752 3.1 (2.6–3.6) 9 3.5 (0.0–7.0)
Methylguanidine HMDB01522 2.7 (1.2–6.0) 95 1.25±0.72
Methylmalonic acid HMDB00202 1.9 (0.7–3.5) 95 1.6 (0.0–3.6)
Methylsuccinic acid HMDB01844 3.2 (0.7–9.9) 100 2.3 (0.8–10.8)
Monomethyl glutaric acid HMDB00858 2.3 (0.6–4.4) 68 NA
m-Thymol HMDB01878 2.2 (0.7–4.0) 86 NA**
Myoinositol HMDB00211 22.4 (7.9–36.1) 100 18.8±2.9
N-Acetylaspartic acid HMDB00812 4.0 (1.3–7.0) 100 4.66±1.14
N-Acetylneuraminic acid HMDB00230 5.4 (2.5–8.6) 100 8.60±1.58
N-Acetylputrescine HMDB02064 0.5 (0.4–0.7) 32 1.18 (0.58–2.25)
N-Methylhydantoin HMDB03646 4.9 (1.0–11.0) 55 NA
o-Hydroxyphenylacetic acid HMDB00669 2.0 (0.9–4.5) 91 7.71±3.65d
Oxoglutaric acid HMDB00208 4.8 (2.0–17.0) 100 2.98 (0.42–18.30)
Oxypurinol HMDB00786 13.3 (5.1–29.3) 50 3.29
Pantothenic acid HMDB00210 1.9 (0.6–4.4) 100 2.7±0.9
p-Cresol sulfate HMDB11635 1.3 (0.3–5.5) 91 NA**
Phenol HMDB00228 4.8 (0.6–12.8) 95 7.0 (5.6–9.2)
Phenylacetic acid HMDB00209 1.9 5 4.16±0.91
Phenylacetylglutamine HMDB06344 34.0 (4.5–70.0) 100 47.03 (3.84–85.51)
Phenylglyoxylic acid HMDB01587 7.34 5 36.6±0.48e
Phosphorylcholine HMDB01565 1.1 (0.7–3.0) 41 NA
p-Hydroxyphenylacetic acid HMDB00020 5.5 (1.4–14.6) 100 9.68
Picolinic acid HMDB02243 19.4 (7.2–37.6) 18 1.65–10.28
Pimelic acid HMDB00857 2.2 (0.7–4.0) 77 NA**
Proline-betaine HMDB04827 2.3 (0.8–4.0) 100 12.7 (1.6–23.7)
Propylene glycol HMDB01881 6.7 (1.4–44.3) 100 NA**
Pseudouridine HMDB00767 28.9 (13.3–41.3) 100 26.02±4.62
Pyrocatechol HMDB00957 4.7 (2.0–8.5) 100 6.0±4.3
Pyroglutamic acid HMDB00267 20.7 (10.2–32.6) 100 28.8 (3.4–54.2)
Pyruvic acid HMDB00243 2.7 (0.8–6.4) 100 2.13 (0.54–8.67)
Quinolinic acid HMDB00232 5.2 (1.0–17.5) 100 2.5±1.1
Sarcosine HMDB00271 2.9 (0.5–5.4) 77 2.8 (0.0–5.6)
Scyllitol HMDB06088 4.2 (2.2–8.1) 68 NA
Sebacic acid HMDB00792 1.9 5 1 (0–2)
Sorbitol HMDB00247 9.9 (2.5–18.7) 100 3.50±2.24
Suberic acid HMDB00893 1.6 (0.9–2.4) 32 0.5 (0.0–2.9)
Succinic acid HMDB00254 6.0 (0.3–33.3) 91 5.6±3.8
Succinylacetone HMDB00635 2.8 (0.6–4.7) 36 1 (0–2)
Sucrose HMDB00258 7.4 (1.4–19.5) 100 7.12±3.23
Sumiki’s acid HMDB02432 1.7 5 NA**
Symmetric dimethylarginine HMDB03334 2.7 (1.5–5.2) 100 3.72±0.35
Syringic acid HMDB02085 1.5 5 0.9±0.2
Tartaric acid HMDB00956 11.8 (2.6–64.4) 82 12.0 (1.3–46.0)
Taurine HMDB00251 81 (13–251) 100 4.00–159.98
Threonic acid HMDB00943 20.8 (10.2–39.3) 100 10.0±7.0
Thymidine HMDB00273 2.2 (0.7–3.9) 36 6.87±4.86
Trans-Aconitic acid HMDB00958 6.6 (1.8–25.1) 55 3.38
Trans-Ferulic acid HMDB00954 1.2 5 36.3±11.2f
Trigonelline HMDB00875 31.1 (5.5–109.3) 100 16.08
Trimethylamine HMDB00906 2.5 (0.3–19.4) 86 7.7±7.4
Trimethylamine N-oxide HMDB00925 91.0 (4.8–509.0) 100 118.7 (35.5–202.1)
Uracil HMDB00300 9.5 (2.6–22.8) 100 12 (2–22)
Urea HMDB00294 12,285 (174–49,097) 100 22,566±4,407
Vanillic acid HMDB00484 1.6 (0.6–3.3) 95 1.0 (0.0–2.5)
Vanillylmandelic acid HMDB00291 2.3 (1.0–3.4) 9 1.2±0.5
* Concentration of metabolite (Mean ± SD) is expressed by µM.
a The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman;
b depends greatly on amount of alcohol consumed;
c after administration of lactose;
d after administration of 1 g of coumarin;
e After styrene exposure; and
f after consumption of chocolate.
NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. Based on these NMR studies, the most abundant constituents (based on average values) of urine from a healthy individual are urea (12.3 ± 14.5 mM/mM creatinine), creatinine (14.7 ± 9.8 mM), hippuric acid (229 ± 160 µM/mM creatinine) and citric acid (203 ± 129 µM/mM creatinine). The least abundant compounds were trans-ferulic acid (1.2 µM/mM creatinine), phosphorylcholine (1.1 ± 0.7 µM/mM creatinine), 4-ethylphenol (0.9 ± 0.1 µM/mM creatinine), 2-methylglutaric acid (0.8 ± 0.4 µM/mM creatinine). The lowest concentrations that could be reliably detected using NMR were 0.4 ± 0.1 µM/mM creatinine (for maleic acid), 0.5 ± 0.2 µM/mM creatinine (for alpha-ketoisovaleric acid and N-acetylputrescine) and 0.7 ± 0.4 µM/mM creatinine (for fumaric acid). The complete list of average compound concentrations (including their range), literature-reported concentrations (for healthy adult) and the frequency of their occurrence is shown in Table 3. In general, there is good agreement between the NMR-measured concentrations and those reported in the literature (often obtained by other analytical techniques).
However, not all of the NMR-derived urine concentrations agree with literature-derived values. A total of 34 compounds had average concentrations somewhat higher (>1 SD) than previously reported values (for example, 1, 3-dimethyluric acid, glucaric acid, L-aspartic acid, mannitol), while 21 compounds had average concentrations lower (<1 SD) than previously reported (for example, 1-methylhistidine, phenol, dihydrothymine, thymidine). Another 7 metabolites also exhibited somewhat greater range in concentrations than those previously reported in the literature. These included: acetic acid, butyric acid, isovaleric acid, lactose, phenylglyoxylic acid, proline-betaine and trans-ferulic acid. Some of these discrepancies are likely due to differences in diet, physiological status, pharmacological effects and the age of the different cohorts that were analyzed. Other differences may be due to storage effects, sample preparation methods and the analytical methods being used. As a rule, NMR concentration determinations are very accurate since they involve direct measurement of the compound, as opposed to an indirect measurement of a derivative compound. Therefore we are quite confident in the NMR concentration values reported in Table 3 and would tend to view these as more reliable than those measured via other technologies.
A number of the compounds exhibiting higher-than or lower-than-reported concentrations appear to be associated with dietary intake. For example, mannitol is a sugar alcohol that is poorly absorbed by humans but its presence in urine can be explained by its occurrence in commonly consumed foods such as apples, pineapples, asparagus and carrots. Likewise, the urinary excretion of trans-ferulic acid (a polyphenolic derivative) increases after the ingestion of breakfast cereals [61] and chocolate. The relatively low value we measured is likely due to the fact that the literature value of trans-ferulic acid reported in Table 3 was measured for people on a special diet [62]. Also, the low level of proline-betaine we measured in urine may be due to a lower frequency of exposure to dietary citrus fruits in our population sample [63]. Proline-betaine is an osmoprotectant found in citrus fruit and urinary excretion of this metabolite is increased after consumption of fruits such as orange juice [64]. Similarly the higher levels of dimethyl sulfone we detected in urine could be attributed to dietary sources that contain DMSO [65]. For example, onions contain many sulfoxides including DMSO which can be oxidized in the liver and kidneys to produce dimethyl sulfone [66], [67]. The consumption of meat could also significantly increase the concentration of some metabolites in urine as reported in the case of 1-methylhistidine [68]. 1-methylhistidine is produced from the metabolism of anserine (a dipeptide) which is commonly found in meats [69], [70]. In addition to diet, metabolite levels in urine can also be affected by physiological status. For instance, the level of 3-hydroxybutyric acid in urine increases during fasting and can range from 0 to 200 µM/mM creatinine, with the maximum level reported in the literature (200 µM/mM creatinine) corresponding to healthy male after 35 h of fasting [50].
Some of the metabolites we measured by NMR did not have any previously reported literature values. For example, glucuronic acid is usually reported as total glucuronic acid (the free acid plus glucuronide conjugates) after hydrolysis [71], [72]. Here we report the concentration of free glucuronic acid, as indicated in Table 3. Another example, 2-methylerythritol, was previously detected in human urine but no concentration was reported [73]. The urinary excretion of 2-methylerythritol is most likely a result of dietary consumption of fruits or vegetables containing 2-methylerythritol and/or 2-methylerythritol-4-phosphate. 2-methylerythritol-4-phosphate is an intermediate in isoprenoid biosynthesis [74] and has been found to be quite abundant in certain plants [75].
A number of compounds we measured by NMR appear to be normal constituents of human urine but seem not to have been previously reported as being detectable by NMR (a total of 42 compounds) or reported as detected but not-quantified by any other method (a total of 8 compounds). The identification of these “NMR-novel” compounds was aided by their prior identification by GC-MS and DI-MS (see following sections) and through a careful literature analysis of compounds that had previously been detected in human urine via other methods. The list of detected but not previously quantified by NMR compounds includes: 2-hydroxy-3-methylpentanoic acid, 2-methyl-3-ketovaleric acid, 2-methylerythritol, glucuronic acid, monomethyl glutaric acid, N-methylhydantoin, phosphorylcholine and scyllitol. All of these compounds were confirmed using authentic standards.
This NMR study also revealed a number of common identification errors made in previously published NMR-based human urine metabolomic studies. In particular, several earlier reports identified phenylacetylglycine [76], N-acetylglutamic acid [77], cresol [78], isonicotinic acid [78], yellow 7.1 [79], meta-hydroxyphenylpropionic acid [80], 2-oxoisocaproic acid [81], urocanic acid, glycylproline and ornithine [80] as being detectable by NMR in human urine. Using our NMR instrument and the samples available to us, we were unable to detect any of these compounds, even after performing multiple spike-in experiments using authentic compounds. While some of these metabolites have been previously reported to be in human urine, they were reported at concentrations far below the lower limit of detection of modern NMR instruments (which is ∼ 1 µM). Due to their chemical shift similarity, phenylacetylglycine (which is found only in rats and mice) and N-acetylglutamic acid appear to be commonly mistaken for phenylacetylglutamine. We also noticed that, isonicotinic acid (a breakdown product of isoniazid and hydrazine derivatives, which is found only in individuals that have taken isoniazid and other hydrazine derivatives as a drug) appears to be mistaken for trigonelline. Likewise cresol (water-insoluble) appears to be frequently mistaken for cresol-sulfate (water-soluble), while the compounds yellow 7.1, meta-hydroxyphenylpropionic acid and 3-(p-hydroxyphenyl)-propionic acid appear to be commonly mistaken for 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA).
In addition to correcting these compound identification errors, we also observed some significant gender-related effects on creatinine levels in our urine samples. Since males generally have a greater mass of skeletal muscle than females, they tend to have higher urinary levels of creatinine than women. This was clearly evident in our samples as the average male creatinine level was 20 mM while the average female creatinine level was 11 mM. In addition, increased dietary intake of creatine or a protein-rich diet can increase daily creatinine excretion [82].
Quantification and Identification of Urine Metabolites – GC-MS
As seen in Table 1, GC-MS methods have long been used to comprehensively characterize the chemical content of human urine. For our studies a total of 4 different GC-MS analyses were performed. The first method employed polar solvent extraction and derivatization to achieve broad metabolite coverage of polar metabolites, the second was more selective and targeted organic acids, the third targeted volatiles, while the fourth targeted bile acids. Representative high-resolution GC-MS total ion chromatograms are shown in Figures 2–4 for each of these analyses (except for the bile acids). Combined, the 4 GC-MS methods allowed us to identify 179 and quantify a total of 85 compounds. Table 4 shows the identified polar, organic acid extracts and bile acids (127 in total), Table 5 shows the identified volatile metabolites (52 in total) while Table 6 shows the 85 fully quantified compounds from all 4 techniques. These numbers actually represent the highest number of urine metabolites both identified and quantified by GC-MS to date. As seen in Table 1, previous GC-MS studies have reported up to 258 unique compounds being identified (but none quantified) [83] and approximately 95 compounds quantified in human urine [84]. Relative to NMR (see previous section) and other methods used to analyze human urine (Table 1), it appears that a multi-pronged GC-MS analysis is an excellent approach to characterize this biofluid. However, unlike NMR where nearly all detectable peaks are identifiable, metabolite coverage by GC-MS tends to be relatively incomplete. As seen in Figure 2, only 60% of the peaks could be identified using as reference the 2008 NIST library and other home-made GC-MS metabolite libraries. Likewise, in Figure 3, we see that only 65% of the organic acid peaks could be identified while in Figure 4, just 60% of the volatile compound peaks could be identified.
Figure 2 Typical total ion chromatogram of water-soluble metabolites extracted from human urine.
Numbers indicate the following metabolites (quantified compounds): 1: L-valine; 2: oxalic acid; 3∶3-hydroxyisobutyric acid; 4: L-serine; 5: glycine; 6: succinic acid; 7: glyceric acid; 8∶4-deoxythreonic acid; 9∶2,4-dihydroxybutanoic acid; 10∶3,4-dihydroxybutanoic acid; 11: adipic acid; 12: creatinine; 13: threonic acid; 14: L-phenylalanine; 15: p-hydroxyphenylacetic acid; 16: L-ornithine; 17: L-asparagine; 18: L-arabinose; 19: D-xylitol; 20: D-xylulose; 21: Cis/Trans-aconitic acid; 22: hippuric acid; 23: isocitric acid; 24: D-galactose; 25: D-glucose; 26: L- tyrosine; 27: sorbitol; 28: gluconic acid; 29: scyllitol; 30: myoinositol; 31: uric acid; 32: pseudouridine.
Figure 3 Typical GC-MS total ion chromatogram of organic acids extracted from human urine.
Numbers indicate the following metabolites (quantified compounds): 1: pyruvic acid; 2: L-lactic acid; 3: alpha-hydroxyisobutyric acid; 4: glycolic acid; 5: levulinic acid; 6∶3-hydroxyisovaleric acid; 7∶2-hydroxy-2-methylbutyric acid; 8: hydroxypropionic acid; 9∶2-methyl-3-hydroxybutyric acid; 10: malonic acid; 11∶3-hydroxyisovaleric acid; 12: methylmalonic acid; 13∶2-ethylhydracrylic acid; 14: benzoic acid; 15: phosphoric acid; 16: ethylmalonic acid; 17: succinic acid; 18: methylsuccinic acid; 19∶4-deoxythreonic acid; 20∶5-hydroxyhexanoic acid; 21: citraconic acid; 22: glutaric acid; 23: m-chlorobenzoic acid; 24; 3,4-dihydroxybutanoic acid; 25∶3-methylglutaconic acid; 26: adipic acid; 27: pyroglutamic acid; 28∶3-methyladipic acid; 29: sumiki’s acid; 30: o-hydroxyphenylacetic acid; 31: oxoglutaric acid; 32: pimelic acid; 33∶3-hydroxymethylglutaric acid; 34∶3-hydroxyphenylacetic acid; 35∶4-hydroxybenzoic acid; 36∶2-furoylglycine; 37: suberic acid; 38: quinolinic acid; 39: Cis/Trans-aconitic acid; 40: homovanillic acid; 41: azelaic acid; 42: hippuric acid; 43∶3,4-dihydroxybenzeneacetic acid; 44∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 45: vanillylmandelic acid; 46∶4-hydroxyphenyllactic acid; 47: indoleacetic acid; 48: palmitic acid; 49: kynurenic acid; 50∶3-hydroxyhippuric acid; 51∶3-hydroxysebacic acid; 52: Trans-ferulic acid; 53∶5-hydroxyindoleacetic acid; 54: stearic acid.
Figure 4 Typical gas chromatogram of volatile organic compounds (VOC) from a pooled human urine sample.
Numbers indicate the following metabolites: 1∶3-methyl sulfolane; 2∶3-hexanone; 3∶2-pentanone; 4∶1-hydroxy-2-pentanone; 5: allyl methylsulphide; 6: dimethyl disulfide; 7∶4-heptanone; 8∶1-methylcyclohexanol; 9; 2-hexanone; 10∶3,4-dimethylthiophene; 11: diallyl sulphide; 12∶5-methyl-2-hexanone; 13∶1,3-dithio cyclohexane; 14: dimethyl trisulfide; 15: phenol; 16: o-cymene; 17: p-cymene; 18: m-cymene; 19∶1,4-cineol; 20: p-cresol; 21: linalool oxide; 22: iso-menthol; 23: Alpha-p-dimethylstyrene; 24: L-menthol; 25: undecane; 26: ledene oxide (II); 27: salicylic acid methyl ester; 28: Beta-carvone; 29: piperitone; 30: o-thymol; 31: Beta-cyclocitrol; 32∶4,7-dimethyl-benzofuran; 33: cuminal: 34∶2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene; 35∶4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone; 36∶1,2,3,4-tetrahydro-1,1,6-trimethyl naphthalene; 37: Alpha-cedrene; 38∶1,2,3,4-tetrahydro-1,5,7-trimethylnapthalene; 39∶1,2-diydro-1,1,6-trimethyl-napthalene; 40: Beta-guaiene; 41: Beta-damascenone; 42∶2,5-cyclohexadiene-1,4-dione-2,6-di-tert-butyl; 43: himachalene; 44∶4-(2,6,6-trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one: 45∶1-(2,6,6-trimethyl-1-cyclohexen-1-yl)-1-penten-3-one; 46∶2,4-Bis (1,1-dimethylethyl)-phenol; 47∶1-(2,3,6-trimethyl phenyl)-3-buten-2-one; 48: L-calamenene; 49: Beta-vatirenene; 50∶1,6,7-trimethylnaphthalene; 51: azulol; 52∶3,3,5,6-tetramethyl-1-indanone.
Table 4 List of metabolites identified in human urine extracts by GC-MS (except volatile compounds).
Amino acids HMDB ID Organic acids HMDB ID
Beta-Alaninea HMDB00056 Azelaic acidb HMDB00784
Glycinea HMDB00123 Benzoic acidb HMDB01870
L-Alaninea HMDB00161 Butyric acida HMDB00039
L-Asparaginea HMDB00168 Cis-Aconitic acida,b HMDB00072
L-Aspartic acida HMDB00191 Citraconic acidb HMDB00634
L-Glutaminea HMDB00641 Citramalic acidb HMDB00426
L-Histidinea HMDB00177 Citric acida HMDB00094
L-Isoleucinea HMDB00172 Erythronic acida HMDB00613
L-Leucinea HMDB00687 Ethylmalonic acidb HMDB00622
L-Lysinea HMDB00182 Gluconic acida HMDB00625
L-Ornithinea HMDB00214 Glutaric acidb HMDB00661
L-Phenylalaninea HMDB00159 Glyceric acida HMDB00139
L-Serinea HMDB00187 Glycolic acida,b HMDB00115
L-Threoninea HMDB00167 Glyoxalic acida HMDB00119
L-Tryptophana HMDB00929 Hippuric acida,b HMDB00714
L-Tyrosinea HMDB00158 Homovanillic acidb HMDB00118
L-Valinea HMDB00883 HPHPAb HMDB02643
Bile acids HMDB ID Hydroxypropionic acidb HMDB00700
Cholenic acidc HMDB00308 Indoleacetic acidb HMDB00197
Deoxycholic acidc HMDB00626 Indolelactic acid b HMDB00671
Lithocholic acidc HMDB00761 Isobutyrylglycineb HMDB00730
Ursodeoxycholic acidc HMDB00946 Isocitric acida HMDB00193
Misc HMDB ID Isovalerylglycineb HMDB00678
3-Methylhistidinea HMDB00479 Kynurenic acidb HMDB00715
5-Hydroxyindoleb HMDB59805 Levulinic acidb HMDB00720
Creatininea HMDB00562 L-Lactic acidb HMDB00190
Dimethylglycinea HMDB00092 Malic acida HMDB00744
D-Xylitola HMDB02917 Malonic acida,b HMDB00691
Glycerol 3-phosphatea HMDB00126 m-Chlorobenzoic acidb HMDB01544
Glycerola HMDB00131 m-Coumaric acidb HMDB01713
Mannitola HMDB00765 Methylmalonic acidb HMDB00202
Myoinositola HMDB00211 Methylsuccinic acidb HMDB01844
Pseudouridinea HMDB00767 o-Hydroxyhippuric acidb HMDB00840
Scyllitola HMDB06088 o-Hydroxyphenylacetic acidb HMDB00669
Sorbitola HMDB00247 Oleic acidb HMDB00207
Ureaa HMDB00294 Oxalic acida HMDB02329
Organic acids HMDB ID Oxoglutaric acidb HMDB00208
2,4-Dihydroxybutanoic acida HMDB00360 Palmitic acidb HMDB00220
2-Ethylhydracrylic acidb HMDB00396 Pantothenic acidb HMDB00210
2-Furoylglycineb HMDB00439 Phosphoric acidb HMDB02142
2-Hydroxy-2-methylbutyric acidb HMDB01987 p-Hydroxyphenylacetic acida HMDB00020
2-Hydroxyglutaric acida HMDB00694 Pimelic acidb HMDB00857
2-Hydroxyvaleric acida HMDB01863 Pyroglutamic acida,b HMDB00267
2-Ketobutyric acidb HMDB00005 Pyruvic acidb HMDB00243
2-Methyl-3-hydroxybutyric acidb HMDB00354 Quinolinic acidb HMDB00232
3,4-Dihydroxybenzeneacetic acidb HMDB01336 Stearic acidb HMDB00827
3,4-Dihydroxybutanoic acida,b HMDB00337 Suberic acidb HMDB00893
3,4-Dihydroxyhydrocinnamic acidb HMDB00423 Succinic acida,b HMDB00254
3-Aminoisobutanoic acida,b HMDB03911 Sumiki’s acidb HMDB02432
3-Hydroxyhippuric acidb HMDB06116 Tartaric acida HMDB00956
3-Hydroxyisobutyric acida HMDB00023 Threonic acida HMDB00943
3-Hydroxyisovaleric acida,b HMDB00754 Trans-Aconitic acida HMDB00958
3-Hydroxymethylglutaric acidb HMDB00355 Trans-Ferulic acidb HMDB00954
3-Hydroxyphenylacetic acidb HMDB00440 Uric acida HMDB00289
3-Hydroxysebacic acidb HMDB00350 Vanillyllactic acidb HMDB00913
3-Methyladipic acidb HMDB00555 Vanillylmandelic acidb HMDB00291
3-Methylglutaconic acidb HMDB00522 Sugars HMDB ID
4-Deoxythreonic acida,b HMDB02453 D-Fructosea HMDB00660
4-Hydroxybenzoic acidb HMDB00500 D-Galactosea HMDB00143
4-Hydroxyphenyllactic acidb HMDB00755 D-Glucosea HMDB00122
5-Hydroxyhexanoic acidb HMDB00525 D-Xylosea HMDB00098
5-Hydroxyindoleacetic acidb HMDB00763 D-Xylulosea HMDB01644
Adipic acida,b HMDB00448 L-Arabinosea HMDB00646
Alpha-Hydroxyisobutyric acidb HMDB00729 L-Fucosea HMDB00174
Aminomalonic acida HMDB01147 Sucrosea HMDB00258
Ascorbic acida HMDB00044
a Polar metabolites.
b Targeted organic acids.
c Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid.
Table 5 Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS.
Peak # Compound HMDB ID Uniquemass MSmatch RT(min) RI(Calc) RI(NIST)
1 3-Methyl sulfolane HMDB59667 68 867 0.83 725 654
2 3-Hexanone HMDB00753 43 781 0.99 732 788
3 2-Pentanone HMDB34235 43 894 1.61 758 654
4 1-Hydroxy-2-pentanone HMDB59678 43 780 1.74 763 897
5 Allyl methylsulphide HMDB31653 88 793 1.83 767 701
6 Dimethyl disulfide HMDB05879 94 940 2.26 785 763
7 4-Heptanone HMDB04814 71 919 3.34 831 883
8 1-Methylcyclohexanol HMDB59693 71 821 3.81 850 931
9 2-Hexanone HMDB05842 43 821 3.82 851 889
10 3,4-Dimethylthiophene HMDB29304 111 906 4.15 865 888
11 Diallyl sulphide HMDB36491 45 827 4.86 895 848
12 5-Methyl-2-hexanone HMDB31549 43 816 5.29 913 836
13 1,3-Dithio cyclohexane HMDB31473 120 841 5.85 936 1027
14 Dimethyl trisulfide HMDB13780 126 873 6.01 943 950
15 Phenol HMDB00228 94 859 6.34 957 998
16 o-Cymene HMDB37050 119 928 7.19 993 1014
17 p-Cymene HMDB05805 119 928 7.33 998 1018
18 m-Cymene HMDB13806 119 928 7.43 1009 1022
19 1,4-Cineol HMDB31455 111 912 8.71 1057 1016
20 p-Cresol HMDB01858 107 930 8.99 1068 1082
21 Linalool oxide HMDB31440 59 783 10.49 1131 1074
22 Iso-menthol HMDB35764 57 923 10.99 1152 1141
23 Alpha-p-dimethylstyrene HMDB29641 132 915 11 1153 1088
24 L-Menthol HMDB03352 71 924 11.18 1160 1140
25 Undecane HMDB31445 57 910 11.35 1168 1100
26 Ledene Oxide (II) HMDB60295 43 743 11.54 1176 1201
27 Salicylic acid methyl ester HMDB34172 120 857 11.65 1180 1193
28 Beta-Carvone HMDB35824 82 859 12.81 1229 1247
29 Piperitone HMDB34975 150 873 12.96 1235 1184
30 o-Thymol HMDB35770 135 904 13.9 1275 1305
31 Beta-Cyclocitrol HMDB41011 41 884 14.1 1283 1219
32 4,7-Dimethyl-benzofuran HMDB59897 146 861 14.4 1296 1244
33 Cuminal HMDB02214 133 861 14.82 1313 1235
34 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene HMDB36823 138 911 15.91 1359 1370
35 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone HMDB32913 121 795 15.97 1362 1433
36 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene HMDB59826 159 855 16.02 1364 1349
37 Alpha-Cedrene HMDB59695 119 821 16.1 1367 1415
38 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene HMDB59696 159 795 16.59 1388 1310
39 1,2-Diydro-1,1,6-trimethyl-napthalene HMDB40284 157 947 16.82 1398 1354
40 Beta-Guaiene HMDB38157 161 879 17 1405 1500
41 Beta-Damascenone HMDB13804 69 889 17.35 1420 1365
42 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl HMDB13817 177 843 18.51 1469 1462
43 Himachalene HMDB38168 119 823 18.62 1473 1489
44 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one HMDB37139 136 883 18.73 1478 1497
45 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one HMDB38130 121 788 19.05 1491 1507
46 2,4-Bis (1,1-Dimethylethyl)-phenol HMDB13186 191 863 19.5 1510 1519
47 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one HMDB59697 132 918 19.8 1523 1528
48 L-Calamenene HMDB59910 159 920 20.25 1533 1534
49 Beta-Vatirenene HMDB59676 202 806 20.45 1550 1545
50 1,6,7-Trimethylnaphthalene HMDB59701 170 899 20.82 1564 1559
51 Azulol HMDB59686 183 823 21.18 1581 1572
52 3,3,5,6-Tetramethyl-1-indanone HMDB59683 173 686 21.28 1585 1579
Table 6 Concentrations of metabolites in human urine as measured by GC-MS.
Compounds Observed Concentration(µM/mM creatinine) Literature Values(µM/mM creatinine)
2,4-Dihydroxybutanoic acid 1.0 (0.3–1.8) 2.0±2.0
2-Ethylhydracrylic acid 2.1 (1.3–2.9) NA**
2-Furoylglycine 3.6 (0.7–5.8) 9.95 (2.0–18.66)
2-Hydroxy-2-methylbutyric acid 0.8 (0.4–1.5) 2.8±1.5
2-Methyl-3-hydroxybutyric acid 4.0 (1.3–6.2) 5.5 (0.0–11.0)
3,4-Dihydroxybenzeneacetic acid 0.9 (0.6–1.3) 1.16±0.50
3,4-Dihydroxybutanoic acid 42.2 (21.9–56.1) 34.9 (12.5–57.2)
3-Aminoisobutanoic acid 25.8 (1.4–155.6) 2.91–116.43
3-Hydroxyhippuric acid 6.1 (4.1–7.8) 2.5 (0.2–5.0)
3-Hydroxyisobutyric acid 29.0 (11.8–59.8) 17.5 (2.0–33.0)
3-Hydroxyisovaleric acid 7.4 (4.1–17.2) 8.5±3.2
3-Hydroxymethylglutaric acid 3.2 (1.1–5.2) 3 (0–10)
3-Hydroxyphenylacetic acid 5.6 (1.4–15.0) 6.1±4.1
3-Hydroxysebacic acid 2.9 (1.7–3.8) NA**
3-Methyladipic acid 2.1 (0.9–5.6) 2.9 (0.7–10.5)
3-Methylglutaconic acid 6.2 (2.8–8.3) 4.5 (0.0–9.0)
4-Deoxythreonic acid 18.2 (5.6–23.9) 25±10
4-Hydroxybenzoic acid 2.4 (1.3–3.6) 1.8±1.8
4-Hydroxyphenyllactic acid 2.8 (2.6–3.0) 1.1 (0.2–2.6)
5-Hydroxyhexanoic acid 2.7 (0.8–5.7) 3.5 (0.0–7.0)
5-Hydroxyindoleacetic acid 2.9 (0.4–5.8) 2.4±0.40
Adipic acid 3.0 (1.0–12.9) 5.1 (0.8–35.0)
Alpha-Hydroxyisobutyric acid 2.9 (1.3–4.6) 3.8
Azelaic acid 1.9 (1.2–3.2) 4.8 (1.3–15.0)
Benzoic acid 3.6 (1.4–7.5) 4.2 (1.9–6.5)
Cholenic acid 0.015 (0.004–0.030) NA**
Cis/Trans-Aconitic acid 15.3 (5.2–21.8) NA**
Citraconic acid 1.6 (0.9–2.1) 2.2
Creatinine* 12,246±8,369 10,090±310
Deoxycholic acid 0.055 (0.050–0.060) NA**
D-Galactose 3.3 (1.3–8.5) 4.4 (0.0–31.0)
D-Glucose 35.6 (10.3–56.7) 31.12
D-Xylitol 2.3 (0.9–8.2) 3.50±2. 37
D-Xylulose 19.7 (6.4–32.6) NA**
Ethylmalonic acid 2.4 (0.9–4.0) 2.0 (0.5–3.9)
Gluconic acid 13.3 (8.2–26.4) NA**
Glutaric acid 1.4 (0.5–2.3 ) 1.3 (0.6–2.6)
Glyceric acid 14.4 (7.8–28.8) 4.5 (0.0–9.0)
Glycine 75.7 (21–205) 151 (233–248)
Glycolic acid 39.4 (2.9–78.1) 35 (18–55)
Hippuric acid 217 (28–610) 257 (20–770)
Homovanillic acid 4.3 (0.9–8.9) 4.9±3.4
HPHPA 4.2 (0.9–15.3) 0.55–54.93
Hydroxypropionic acid 8.1 (3.1–11.8) 6.5 (3.0–10.0)
Indoleacetic acid 2.6 (0.6–5.4) 2.0±1.0
Indolelactic acid 0.9 0.460 (0.098–0.980)
Isocitric acid 56.8 (19.4–119.1) 58 (36–84)
Kynurenic acid 2.6 (0.9–4.1) 1.34±0.30
L-Arabinose 8.8 (0.8–19.4) 8.75 (6.12–11.84)
L-Asparagine 9.5 (3.0–26.0) 10.0 (4.6–16.3)
Levulinic acid 1.7 (0.6–2.5) 1.28 (0.33–2.75)
Lithocholic acid 0.16 (0.10–0.20) NA**
L-Lactic acid 7.1 (0.9–16.4) 12.3±6.2
L-Ornithine 5.3 (2.0–8.8) 5.0 (2.0–8.0)
L-Phenylalanine 7.8 (5.0–11.3) 4.50±1.15
L-Serine 19.0 (10.4–35.8) 26.0±10.0
L-Tyrosine 15.1 (6.1–23.2) 8.8±4.5
L-Valine 5.5 (2.7–9.8) 3.0±1.0
Malonic acid 2.3 (1.2–3.1) 1 (0–2)
m-Chlorobenzoic acid 0.7 NA**
Methylmalonic acid 1.3 (0.5–2.1) 1.6 (0.0–3.6)
Methylsuccinic acid 4.7 (1.1–6.2) 2.3 (0.8–10.8)
Myoinositol 12.6 (5.1–15.3) 18.8±2.9
o-Hydroxyphenylacetic acid 2.9 (1.4–3.7) 7.71±3.65
Oxalic acid 8.2 (3.9–14.0) 1.11–33.34
Oxoglutaric acid 3.4 (1.4–12.5) 2.98 (0.42–18.30)
Palmitic acid 6.7 (2.6–24.3) 11 (6–23)
Phosphoric acid 784 (425–1170) 1000–4900
p-Hydroxyphenylacetic acid 6.0 (2.4–9.7) 9.68
Pimelic acid 1.9 (0.5–3.5) NA**
Pseudouridine 29.8 (21.8–47.3) 26.02±4.62
Pyroglutamic acid 18.6 (4.5–24.9) 28.8 (3.4–54.2)
Pyruvic acid 2.1 (1.0–3.7) 2.13 (0.54–8.67)
Quinolinic acid 3.9 (0.9–15.1) 2.5±1.1
Scyllitol 3.6 (1.9–6.9) NA
Sorbitol 3.9 (1.9–5.1) 3.50±2.24
Stearic acid 3.9 (2.3–7.7) 2.9 (1.6–6.6)
Suberic acid 1.2 (0.5–1.9) 0.5 (0.0–2.9)
Succinic acid 6.2 (2.5–13.5) 5.6±3.8
Sumiki’s acid 1.6 NA**
Threonic acid 14.8 (8.6–36.1) 10.0±7.0
Trans-Ferulic acid 1 36.3±11.2
Uric acid 186 (93–329) 188 (79–296)
Ursodeoxycholic acid 0.022 (0.013–0.030) NA**
Vanillylmandelic acid 1.6 (0.6–2.8) 1.2±0.5
* Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. Incomplete compound identification is a common problem with global or untargeted GC-MS metabolomics. This may be due to any number of factors including spectral overlap due to incomplete separation, poor signal to noise for low intensity peaks, the lack of reference GC-MS spectral data for certain metabolites (especially unusual dietary sources), or the presence of spectral artefacts such as derivatization by-products or degraded metabolites in the GC-MS spectrum. For our GC-MS studies we used the NIST library supplemented with a home-made GC-MS reference library of known urine compounds assembled from the Human Metabolome Library [16]. No doubt the use of other commercially available reference GC-MS libraries such as the Fiehn GC-MS library from Agilent or the GOLM metabolome database library [85] might have allowed us to further increase our coverage. Likewise the use of a faster scan rate and/or a more sensitive GC-TOF instrument (instead of a slower scanning quadrupole GC-MS) certainly would have increased overall coverage.
Nearly all of the non-volatile metabolites (87) identified by our GC-MS analyses were also identified by NMR. Some of the exceptions were oxalic acid, phosphate and uric acid, each of which was detected by GC-MS but not by NMR. These compounds do not have NMR-detectable protons at physiological pH, making them essentially “NMR invisible”. Other compounds seen by GC-MS but not by NMR included metabolites that were generally below the detection limit of NMR (∼2 µM/mM creatinine) such as indolelactic acid and 2,4-dihydroxybutanoic acid. For our non-targeted GC-MS analysis, the lower limit of detection was 1 µM/mM creatinine (for 2,4-dihydroxybutanoic acid), while for our targeted organic acid GC-MS analysis the lower limit of detection was 0.7 µM/mM creatinine (for m-chlorobenzoic acid). Overall, our data suggests that the sensitivity of a standard single quadrupole GC-MS instrument is perhaps 1.5–2X better than a 500 MHz NMR instrument for water-soluble metabolites. It is also important to note that the level of water-soluble, non-volatile metabolite coverage obtained by GC-MS is not as great as seen with NMR (127 cmpds vs. 209 cmpds). The limited coverage of GC-MS is partly due to the fact that not all compounds can be readily extracted, easily derivatized or routinely separated on a GC column. Furthermore, when analyzing urine by GC-MS there is a need to pretreat the sample with urease (to reduce urea levels) that can diminish the abundance of some metabolites [86]. While GC-MS may not be the best method for analyzing water-soluble metabolites, it certainly excels at the detection of volatile metabolites. Indeed, only one of the volatile metabolites identified by GC-MS is identified by NMR (phenol). This certainly underlines a key strength of GC-MS relative to other metabolomics platforms. When comparing NMR to GC-MS we found that NMR is capable of detecting 121 compounds that the 4 combined GC-MS methods cannot detect while the combined GC-MS methods can detect 91 compounds that NMR cannot routinely detect. Overall, these data suggest that GC-MS and NMR appear to be complementary methods for the identification and quantification of small molecules in urine.
Among the 58 metabolites quantified by both GC-MS and NMR we found very good overall agreement, with the majority of measured concentration values falling within 20 ± 11% of each other. The concentration patterns and rankings of the most abundant to the least abundant compounds were also largely identical for the two platforms. A total of 12 metabolites exhibited somewhat larger concentration discrepancies between GC-MS and NMR (i.e; L-arabinose, L-serine (lower in GC-MS vs. NMR), 4-hydroxybenzoic acid and tyrosine (higher in GC-MS vs NMR). Some of these concentration differences may be due to the extraction or derivatization process needed to conduct GC-MS analyses. This can lead to unspecified compound losses, unusual derivatives or unrecognized fragmentation patterns. Therefore we would have expected at least a few GC-MS concentration values to be slightly lower than those seen by NMR. Likewise, it is important to remember that there are inherent errors (5–10%) in measuring peak areas (i.e. compound concentrations) both in GC-MS and NMR due to peak overlap, uneven baselines and spectral noise.
Nearly all of the compounds we detected or quantified in human urine by GC-MS have been previously described or mentioned in the GC-MS literature. One compound (scyllitol), however, appears not to have been previously detected by GC-MS. The identification of this compound by our GC-MS method was aided by its prior identification by NMR (see previous section). Additionally, a careful literature analysis also indicated the scyllitol is a normal constituent of human urine and has previously been detected in human urine via other methods.
As we noted with our NMR studies earlier, there are a few previously reported GC-MS detectable metabolites in human urine that appear to be artefacts. These artefactual metabolites may arise from extractions with different solvents, pre-treatment with urease, and chemical derivatization. For example, Shoemaker et al [84], reported the presence of bisethane in human urine. We also detected bisethane, but it appears to be artefact of chemical derivatization and is not a urine metabolite.
Quantification and Identification of Urine Metabolites – Combined Direct Flow Injection and LC-MS/MS Assay
Direct flow injection (DFI) MS/MS or DFI-MS/MS is another commonly used global metabolic profiling method [87]. When isotopic standards are used along with multiple reactions monitoring (MRM), it is also possible to perform targeted metabolomics with very accurate concentration measurements. For our urine studies, we employed a combined DFI/LC-MS/MS approach, based on the commercially available AbsoluteIDQ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck). When applied to urine, we were able to identify and quantify a total of 127 metabolites or metabolite species, including 34 acylcarnitines, 21 amino acids, 15 biogenic amines, creatinine, hexose, 35 phospatidylcholines, 15 sphingomyelins and 5 lysophosphatidylcholines. The amino acids and biogenic amines are analyzed by an LC-MS/MS method, whereas all other metabolites are analyzed by DFI-MS/MS as indicated in Table 7. DFI-MS/MS identifies lipid species (as opposed to specific lipids) using their total acyl/alkyl chain content (i.e. PC (38∶4)) rather than their unique structure. Therefore each lipid species identified by the BIOCRATES kit typically corresponds to 5–10 possible unique lipid structures. Consequently, the total number of phosphatidylcholines, sphingolipids and lysophosphatidylcholines structures identified by this method was 458, 19 and 6, respectively. Therefore, combining these probable lipid structures (483 in total, based on the known fatty acid and lipid composition in human serum) with the other 72 confirmed non-lipid metabolites, the DFI-MS/MS method yields 555 confirmed and probable metabolites or metabolite structures. All of these compounds, along with their corresponding estimated concentrations have all been entered into the UMDB.
Table 7 Concentrations of acylcarnitines, amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and sphingomyelins in human urine by combined DFI/LC-MS/MS (BIOCRATES kit).
Acylcarnitines (DFI-MS/MS) Mean (µM/mM creatinine) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine)
Acetyl-L-carnitine 2.0 (0.4–5.9) Kynurenine 0.41 (0.08–1.31)
Butenyl-L-carnitine 0.014 (0.008 –0.021) L-Dopa 0.02 (0.01–0.04)
Butyryl-L-carnitine 0.76 (0.12–1.81) Methioninesulfoxide 0.31 (0.14–0.66)
Decadienyl-L-carnitine 0.10 (0.04–0.30) Phenylethylamine 0.011 (0.003–0.067)
Decanoyl-L-carnitine 0.04 (0.02–0.06) Putrescine 0.20 (0.04–0.40)
Decenoyl-L-carnitine 0.14 (0.06–0.32) Sarcosine 1.6 (0.2–9.4)
Dodecanoyl-L-carnitine 0.02 (0.01–0.04) Serotonin 0.08 (0.04–0.12)
Dodecenoyl-L-carnitine 0.028 (0.011–0.042) Symmetric dimethylarginine 2.8 (2.0–9.4)
Glutaconyl-L-carnitine 0.04 (0.01–0.06) Lysophosphatidylcholines (DFI-MS/MS) Mean (µM/Mm creatinine)
Glutaryl-L-carnitine 0.04 (0.03–0.10) lysoPC a C16∶0 0.014
Hexadecadienyl-L-carnitine 0.0010 (0.0005–0.0040) lysoPC a C17∶0 0.0032 (0.0008–0.0087)
Hexadecanoyl-L-carnitine 0.0022 (0.0020–0.0030) lysoPC a C18∶0 0.0040 (0.0013–0.0100)
Hexanoyl-L-carnitine 0.05 (0.03–0.07) lysoPC a C20∶4 0.0020 (0.0005–0.0038)
Hexenoyl-L-carnitine 0.019 (0.009–0.050) lysoPC a C6∶0 0.0045 (0.0022–0.0074)
Hydroxyhexadecanoyl-L-carnitine 0.0015 (0.0006–0.0050) Phosphatidylcholines (DFI-LC/MS) Mean (µM/Mm creatinine)
Hydroxypropionyl-L-carnitine 0.008 (0.004–0.012) PC aa C28∶1 0.006 (0.003–0.020)
Hydroxytetradecadienyl-L-carnitine 0.003 (0.001–0.008) PC aa C30∶2 0.0005 (0.0001–0.0022)
Hydroxytetradecenoyl-L-carnitine 0.0020 (0.0008–0.0040) PC aa C32∶0 0.0035 (0.0013–0.0071)
L-Carnitine 4.5 (0.6–15.2) PC aa C32∶3 0.0009 (0.0003–0.0025)
Malonyl-L-carnitine 0.02 (0.01–0.04) PC aa C34∶1 0.0077 (0.0023–0.0500)
Methylglutaryl-L-carnitine 0.030 (0.016–0.052) PC aa C34∶2 0.0047 (0.0016–0.0240)
Methylmalonyl-L-carnitine 0.09 (0.05–0.16) PC aa C34∶3 0.002
Nonayl-L-carnitine 0.20 (0.07–0.46) PC aa C34∶4 0.0023 (0.0002–0.0180)
Octadecadienyl-L-carnitine 0.0007 (0.0003–0.0020) PC aa C36∶1 0.0034 (0.0006–0.0250)
Octanoyl-L-carnitine 0.05 (0.03–0.09) PC aa C36∶2 0.0160 (0.0021–0.0450)
Octenoyl-L-carnitine 0.50 (0.08–1.20) PC aa C36∶3 0.0024 (0.0005–0.0240)
Pimelyl-L-carnitine 0.03 (0.01–0.11) PC aa C36∶4 0.0018 (0.0005–0.0083)
Propenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C36∶6 0.0037
Propionyl-L-carnitine 0.07 (0.01–0.20) PC aa C38∶3 0.0056
Tetradecadienyl-L-carnitine 0.0023 (0.0010–0.0050) PC aa C38∶4 0.0022 (0.0005–0.0065)
Tetradecanoyl-L-carnitine 0.018 (0.001–0.088) PC aa C38∶5 0.0007 (0.0002–0.0020)
Tetradecenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C38∶6 0.0010 (0.0005–0.0036)
Tiglyl-L-carnitine 0.10 (0.05–0.20) PC aa C40∶3 0.00035 (0.0–0.00140)
Valeryl-L-carnitine 0.220 (0.037–0.440) PC aa C42∶2 0.0009 (0.0003–0.0024)
Creatinine Mean PC aa C42∶4 0.0008 (0.0003–0.0025)
Creatinine* 12,475±7,955 PC ae C34∶1 0.0030 (0.0003–0.0110)
Hexose Mean (µM/Mm creatinine) PC ae C34∶2 0.0012 (0.0002–0.0075)
Hexose 110 (37–501) PC ae C36∶1 0.0046 (0.0012–0.0080)
Amino acids (LC-MS/MS) Mean (µM/Mm creatinine) PC ae C36∶2 0.0016 (0.0002–0.0050)
Glycine 101.0 (37.0–250.6) PC ae C36∶3 0.0009 (0.0001–0.0040)
L-Alanine 21.7 (7.2–47.4) PC ae C36∶4 0.0022
L-Arginine 7.4 (3.2–14.6) PC ae C38∶2 0.0015 (0.0007–0.0023)
L-Asparagine 8.8 (4.6–17.7) PC ae C38∶3 0.0020 (0.0012–0.0028)
L-Aspartic acid 10.9 (1.9–26.8) PC ae C38∶4 0.0006 (0.0–0.0024)
L-Citrulline 0.8 (0.2–1.7) PC ae C38∶6 0.0065 (0.0027–0.0170)
L-Glutamic acid 6.4 (0.6–17.5) PC ae C40∶2 0.0012 (0.0006–0.0020)
L-Glutamine 39.9 (18.4–72.5) PC ae C40∶5 0.0006 (0.0001–0.0025)
L-Histidine 45.6 (21.7–71.5) PC ae C42∶3 0.0007 (0.0001–0.0020)
L-Isoleucine 1.3 (0.5–2.7) PC ae C44∶3 0.0010 (0.0002–0.0031)
L-Leucine 3.0 (1.6–6.0) Sphingomyelins (DFI-MS/MS) Mean (µM/Mm creatinine)
L-Lysine 17.9 (3.6–56.1) SM (OH) C14∶1 0.0028 (0.0008–0.0049)
L-Methionine 0.8 (0.4–1.6) SM (OH) C16∶1 0.0006 (0.0001–0.0023)
L-Ornithine 4.4 (1.2–22.1) SM (OH) C22∶1 0.0023 (0.0009–0.0058)
L-Phenylalanine 5.9 (2.8–10.8) SM (OH) C22∶2 0.0007 (0.0001–0.0028)
L-Proline 1.1 (0.1–2.5) SM (OH) C24∶1 0.0010 (0.00034–0.0025)
L-Serine 24.5 (12.3–50.2) SM C16∶0 0.0100 (0.0016–0.0700)
L-Threonine 14.6 (6.6–29.3) SM C16∶1 0.0009 (0.0004–0.0023)
L-Tryptophan 5.6 (9.3–2.1) SM C18∶0 0.0024 (0.0011–0.0070)
L-Tyrosine 8.7 (3.0–20.1) SM C18∶1 0.0004 (0.0001–0.0013)
L-Valine 4.3 (2.4–10.4) SM C20∶2 0.0003 (0.0–0.0016)
Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) SM C22∶3 0.0011 (0.0008–0.0014)
Acetyl-ornithine 1.3 (0.5–2.8) SM C24∶0 0.0085 (0.0047–0.0200)
Alpha-aminoadipic acid 7.2 (2.5–16.4) SM C24∶1 0.0030 (0.0007–0.0120)
Asymmetric dimethylarginine 2.7 (1.4–4.2) SM C26∶0 0.0014 (0.0002–0.0043)
Carnosine 1.2 (0.2–4.1) SM C26∶1 0.0005 (0.0001–0.0012)
Dopamine 0.4 (0.2–0.7)
Histamine 0.03 (0.01–0.10)
Hydroxykynurenine 0.15 (0.06–0.34)
* Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. Our results show very good agreement with the previous studies conducted by BIOCRATES on human urine samples (Biocrates Application Note 1005-1). We found that the lower limit of quantification by DFI MS/MS based on the AbsoluteIDQ kit was 0.1 nM/mM creatinine for certain phosphatidylcholine species (i.e. PC aa C40∶3) and 0.1 nM/mM creatinine for certain sphingomyelin species (i.e. SM 26∶1). Comparison of our lipid results with literature data was difficult as relatively few papers report urine lipid concentration data. Indeed, the presence of lipids in urine is a little unexpected, but not entirely unreasonable. It is likely that urea, a well known chaotrope, facilitates the dissolution of small amounts of fatty acids and other lipid species in urine.
Many of the compounds we measured with this kit assay appear to be normal constituents of human urine but have not been previously reported (quantified and/or detected) in the scientific literature (with the exception of the BIOCRATES Application Note). In total, 53 compounds are being reported here for the first time as being normal constituents of human urine, while 68 compounds are being robustly quantified in human urine for the first time. The vast majority of these compounds are lipids.
Based on our results, the combined DFI/LC-MS/MS method detected 98 compounds or compound species that GC-MS and NMR methods could not detect, while GC-MS detected 161 compounds and NMR detected 181 compounds that DFI/LC-MS/MS could not detect. The 3 methods were able to detect a common set of 17 compounds including creatinine, L-glutamine, L-tryptophan, L-tyrosine and L-valine. Interestingly, the concentrations measured by DFI/LC-MS/MS, NMR and GC-MS (across the shared set of 17 compounds) showed generally good agreement (within 19 ± 7% of each other). The relatively small overlap, in terms of compound coverage, between the 3 platforms is a bit of a surprise and certainly serves to emphasize the tremendous chemical diversity that must exist in urine. Overall, by combining these 3 platforms, we were able to identify 295 and quantify 231 unique or non-overlapping metabolites or metabolite species. These data suggest that DFI/LC-MS/MS, GC-MS and NMR are highly complementary techniques for the identification and quantification of metabolites in human urine.
Quantification and Identification of Urinary Trace Metals – ICP-MS
To determine the trace elemental composition of urine, we used inductively coupled plasma mass spectrometry (ICP-MS). ICP-MS is widely considered to be one of the best techniques for the characterization of the trace element composition of biological samples [88], [89]. Indeed, none of the other methods (1H-NMR, GC-MS and DFI/LC-MS/MS) are suited for measuring trace element composition or concentrations. Our multi-elemental analysis of urine using ICP-MS provided quantitative results for a total of 40 metals or trace minerals (Table 8). Based on their frequency of occurrence and overall abundance, all 40 trace elements appear to be normal constituents of human urine. Of these, 2 have previously not been quantified for healthy adults.
Table 8 Concentrations of ions (µmol/mmol creatinine) as determined by ICP-MS.
Compound Name HMDB Concentration (µM/mM creatinine) Litterature values(µM/mM creatinine)
Aluminum (Al) HMDB01247 0.31 (0.05–1.21) 0.667±0.037
Arsenic (As) HMDB02290 0.10 (0.01–0.52) 0.16±0.16
Barium (Ba) HMDB04142 0.0070 (0.0001–0.0655) 0.0129–0.0320
Cadmium (Cd) HMDB03638 0.0004 (0.0002–0.0010) 0.00060 (0.00035–0.00100)
Calcium (Ca) HMDB00464 200.0 (16.9–520.0) 299.0±99.0
Cerium (Ce) HMDB13672 0.00048 (0.00008–0.00180) 0.0 – 0.0007
Cesium (Cs) HMDB13669 0.0039 (0.0005–0.0110) 0.0061±0.0011
Chromium (Cr) HMDB00599 0.0084 (0.0001–0.0460) 0.0100±0.0065
Cobalt (Co) HMDB00608 0.0014 (0.0001–0.0140) 0.000810±0.00053
Copper (Cu) HMDB00657 0.0163 (0.0006–0.1099) 0.0190 (0.0092–0.038)
Gallium (Ga) HMDB01478 0.0056 (0.0012–0.0150) 0.0003–0.0006
Hafnium (Hf) HMDB29201 0.00031 (0.00006–0.00110) 0.00027±0.00012
Iron (Fe) HMDB00692 0.0890 (0.0025–0.4579) 0.11 (0.0–0.14)
Lanthanum (La) HMDB13711 0.00020 (0.00003–0.00079) 0.00052±0.00039
Lead (Pb) HMDB04628 0.0026 (0.0007–0.0110) 0.011 (0.0–0.020)
Lithium (Li) HMDB05949 0.5 (0.2–0.9) 0.504 (0.058–3.414)
Magnesium (Mg) HMDB00547 262 (42–1,189) 289.0±92.0
Manganese (Mn) HMDB01333 0.013 (0.002–0.051) 0.011 (0.0–0.023)
Molybdenum (Mo) HMDB01302 0.049 (0.006–0.295) 0.06±0.03
Neodymium (Nd) HMDB13715 0.00017 (0.00004–0.00070) 0.0026±0.0013
Nickel (Ni) HMDB02457 0.0080 (0.0015–0.0450) 0.00420 (0.00302–0.00572)
Palladium (Pd) HMDB13670 0.0110 (0.0011–0.1410) < 0.00001–0.2538
Platinum (Pt) HMDB13671 0.000155 (0.000031–0.000560) 0.0000656
Posphorus (P) HMDB01315 1,799 (221–4,936) 1,666.67–4,333.33
Potassium (K) HMDB00586 3,593 (553–8,079) 4,605 (2,631–6,578)
Rhenium (Re) HMDB13719 0.000096 (0.000013–0.000340) NA
Rubidium (Rb) HMDB01327 1.354 (0.187–3.673) 1.84±0.46
Ruthenium (Ru) HMDB13668 0.0002 (0.0001–0.0009) NA
Selenium (Se) HMDB01349 0.149 (0.028–0.345) 0.11346 (0.07724–0.15010)
Sodium (Na) HMDB00588 12,477 (1,863–37,249) 14,737 (5,263–36,842)
Strontium (Sr) HMDB03642 0.163 (0.030–0.415) 0.1333
Tantalum (Ta) HMDB04143 0.00020 (0.00004–0.00074) 0.00008±0.00006
Thallium (Tl) HMDB13724 0.0003 (0.0001–0.0013) < 0.00659
Thorium (Th) HMDB29215 0.00020 (0.00002–0.00100) < 0.00584
Tin (Sn) HMDB01960 0.00110 (0.00016–0.00690) < 0.00552
Titanium (Ti) HMDB01966 0.0013 (0.0005–0.0030) 0.0142
Tungsten (W) HMDB01989 0.0060 (0.0011–0.0280) 0.010±0.022
Vanadium (V) HMDB02503 0.033 (0.004–0.083) 0.00162±0.00144
Zinc (Zn) HMDB01303 0.460 (0.058–0.948) 0.52±0.25
Zirconium (Zr) HMDB01475 0.021 (0.004–0.083) 0.0012–0.0074 As far as we are aware, this is the first multi-elemental study of urine that has been performed by ICP-MS. As seen in Table 8, there is generally good agreement between the values measured by ICP-MS and those previously reported in literature, with differences generally being less than 22 ± 10%. Larger differences are seen for gallium (Ga), lead (Pb), Neodymium (Nd), titanium (Ti) and vanadium (V), but these may be due to the effects of age, diet, local environment (minerals in local water) or diurnal variation. Alternately they may reflect real differences in the sensitivity or accuracy of the instruments being used. As a general rule, ICP-MS is considered as a gold standard for the identification and quantification of trace metals [18], so we would tend to place higher confidence in the values derived via ICP-MS over those measured by other technologies. By our measurements, the most abundant metals/salts are sodium (Na) (12.5 ± 10.6 mM/mM creatinine) and potassium (K) (3.6 ± 2.5 mM/mM creatinine) – as expected, while the least abundant was rhenium (Re) with a lower limit of quantification by ICP-MS of 96 pM/mM creatinine.
Quantification and Identification of Urine Metabolites – Targeted HPLC Assays
The inventory of metabolites we detected and quantified by 1H NMR, GC-MS, DFI/LC-MS/MS and ICP-MS covers a significant portion of all chemical classes. However, these methods sometimes lack the necessary sensitivity, the appropriate instrumental configuration or detection capabilities and therefore fail to detect/quantify a variety of important compound classes. This includes a number of molecules that are normal constituents of urine such as thiols and isoflavones. To identify and quantify these 2 classes of metabolites we decided to employ High Performance Liquid Chromatography (HPLC). HPLC assays are the method of choice for detecting isoflavones and thiols as they are sensitive, precise and can be easily coupled with sensitive detection methodologies such as fluorescence and ultraviolet detection. In our studies, fluorescence and ultraviolet detection were used for the identification and quantification of urinary thiols and isoflavones, respectively.
Biological thiols, or mercaptans, are very active metabolic products of sulfur and play a central role in redox metabolism, cellular homeostasis and a variety of physiological and pathological processes. In urine, the most important thiols are L-cysteine and L-cysteinylglycine [90], [91], [92], [93]. Isoflavones or phytoestrogens form or constitute another important class of urinary metabolites [90]. Humans are exposed to these biologically active phytochemicals mainly through food intake via vegetables, fruit and wheat/bread products [94]. For the detection of thiols we developed assays to measure L-cysteine, L-cysteineglycine, L-glutathione and L-homocysyeine, while for isoflavones we developed assays to measure biochanin A, coumesterol, daidzein, equol, formonentin and genistein. Using these HPLC assays, we measured a total of 4 thiols and 6 isoflavones in urine (Table 9).
Table 9 Concentrations of isoflavones and thiols as determined by HPLC.
Compound Name HMDB ID Mean(µM/mM creatinine) Oc (%) Literature values (µM/mM creatinine)
Biochanin Aa HMDB02338 0.0088 (0.0027–0.0200) 77 NA
Coumesterola HMDB02326 0.0081 (0.0033–0.0120) 45 NA
Daidzeina HMDB03312 0.026 (0.002–0.064) 88 0.036 ± 0.008
Equola HMDB02209 0.06 (0.01–0.10) 67 0.13 ± 0.12
Formonenetina HMDB05808 0.0007 (0.0002–0.0011) 33 NA
Genisteina HMDB03217 0.0280 (0.0077–0.0640) 88 0.032 ± 0.007
L-Cysteineb HMDB00574 81.0 (36.7–147.6) 100 33.4 ± 15.5
L-Cysteinylglycineb HMDB00078 0.6 (0.28–1.54) 100 0.48 ± 0.15
L-Glutathioneb HMDB00125 0.030 (0.005–0.085) 100 0.065
L-Homocysteineb HMDB00742 1.1 (0.6–1.7) 100 1.95 ± 1.47
a Isoflavone metabolites measured by HPLC/UV.
b Thiol metabolites measured by HPLC/FD.
Oc: occurrence;
NA: not available. As seen in Table 9, there is generally good agreement between the values measured by these targeted HPLC assays and those previously reported in literature, with differences generally being less than ∼30%. Only one of these metabolites (L-cysteine) was measured independently on one of our other platforms (NMR) and the NMR concentration was found to be 25% lower than the HPLC assay. A possible explanation for this discrepancy is that cysteine measured by HPLC-FD yields total L-cysteine including the free form and L-cystine reduced to L-cysteine during the reaction [95]. On the other hand, NMR can distinguish between L-cysteine and L-cystine.
The Composition of Human Urine – Comparison with Other Biofluids
By combining a systematic computer-aided literature survey with an extensive, quantitative multiplatform metabolomic analysis we have been able to comprehensively characterize the human urine metabolome. Our data suggests that there are at least 3079 detectable metabolites in human urine, of which 1350 have been quantified. At least 72 of these compounds are of microbial origin, 1453 are endogenous while 2282 are considered exogenous (note some compounds can be both exogenous and endogenous), coming from diet, drugs, cosmetics or environmental exposure. Using a chemical classification system developed for the HMDB [15] we found that human urinary metabolites fall into 230 different chemical classes (25 “super classes”, Table 2). Given that there are only 356 chemical classes in the entire human metabolome [16], this certainly demonstrates the enormous chemical diversity found in urine. As might be expected, most urinary metabolites are very hydrophilic, although there are clearly trace amounts of lipids and fatty acids that contribute a significant number of chemicals to the urinary metabolome (836 fatty acids and lipids). This is in rather striking contrast to the composition of serum [20] which is particularly rich in lipids (i.e. >17000 lipids and fatty acids). Relative to other biofluids such as CSF [18] or saliva [19], urine contains significantly more compounds (5–10X) and exhibits significantly more chemical diversity (2–3X). On the other hand, we know that every compound that is found in human urine is also found in human blood. In other words, the human urine metabolome is a subset of the human serum metabolome, both in terms of composition and chemical diversity [20]. However, more than 484 compounds we identified in urine (either experimentally or via literature review) were not previously reported to be in blood. The fact that so many compounds seem to be unique to urine likely has to do with the fact that the kidneys do an extraordinary job of concentrating certain metabolites from the blood. Consequently compounds that are far below the limit of detection in blood (using today’s instrumentation) are well above the detection limit in urine. In fact, concentration differences between the two biofluids sometimes exceed 1000X for certain compounds, such as histamine, androsterone, normetanephrine, testosterone 13, 14-dihydro-15-keto-PGE2, m-tyramine and aldosterone. So, while the number of water-soluble compounds in blood and urine may be almost identical, the concentrations of these compounds are often profoundly different. This difference, combined with the ability of the kidney to handle abnormally high or abnormally low concentrations of metabolites, makes urine a particularly useful biofluid for medical diagnostics. In fact, according to our data in the UMDB, urinary metabolites have been used to characterize nearly 220 diseases. Furthermore, the ability of the kidneys to filter toxins or xenobiotics makes urine a particularly useful biofluid for diet and drug monitoring and for assessing chemical or pollutant exposure [96].
Method Comparison
One of the central motivations behind this work was to ascertain the strengths and weaknesses of several common metabolomic platforms for characterizing human urine. We employed 6 different analytical platforms: NMR; GC-MS; DFI/LC-MS/MS; HPLC/UV; HPLC/FD and ICP-MS. Using our literature-derived knowledge about the composition of human urine, along with custom-derived spectral libraries and targeted assays we were able to “push the limits” in terms of number of compounds that could be identified and/or quantified via each platform. In total, we identified 445 and quantified 378 distinct metabolites using these 6 different systems. According to Table 1, this is the largest number of urine metabolites ever identified and/or quantified in a single study. NMR spectroscopy was able to identify and quantify 209 compounds; GC-MS was able to identify 179 and quantify 85 compounds; DFI/LC-MS/MS identified and quantified 127 compounds; ICP-MS identified and quantified 40 compounds; while customized HPLC assays (with UV or FD detection) identified and quantified 10 compounds. The number of urinary metabolites we identified/quantified for NMR, GC-MS, DFI/LC-MS/MS and ICP-MS all represent “records” for these platforms.
In terms of platform overlap and compound complementarity, NMR and GC-MS were able to identify a common set of 88 metabolites; NMR and DFI/LC-MS/MS were able to identify and quantify a common set of 28 metabolites, while NMR, GC-MS and DFI/LC-MS/MS were able to identify a common set of 17 metabolites (15 amino acids, creatinine and hexose/glucose). All of these results are summarized in a Venn diagram (Figure 5). As might be expected, metabolite coverage differs from one analytical technique to another. These are difference mostly due to the intrinsic nature of the devices or platforms used. In particular, significant differences exist between these platforms in terms of their sensitivity or separation and/or extraction efficiency. Likewise the use of targeted vs. non-targeted methods along with issues related to compound stability, solubility and volatility led to some significantly different platform-dependent results.
Figure 5 Venn diagram showing the overlap of urine metabolites detected by NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC coupled to UV detection and HPLC coupled to fluorescence detection compared to the detectable urine metabolome. Given that the known, quantifiable urine metabolome consists of ∼2651 known metabolites and metabolite species (corresponding to 3079 distinct structures), we can calculate that NMR is able to measure ∼8% (209/2651) of the human urine metabolome; GC-MS is able to measure ∼7% (179/2651); DFI/LC-MS/MS is able to measure ∼5% (127/2651); ICP-MS/MS is able to measure ∼1.5% (40/2651); HPLC/UV is able to measure ∼0.2% (6/2651); while HPLC/FD is able to measure ∼0.15% (4/2651) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >16% of the known urinary metabolome (>445/2651). If we re-evaluate this fraction in terms of total metabolite structures (corresponding to known and highly probable metabolites), the urine metabolome consists of 3079 compounds. From this total we can calculate that NMR is able to measure ∼7% (209/3079) of the human urine metabolome; GC-MS is able to measure ∼6% (179/3079); DFI/LC-MS/MS is able to measure ∼18% (555/3079); ICP-MS/MS is able to measure ∼1.3% (40/3079); HPLC/UV is able to measure ∼0.2% (6/3079); while HPLC/FD is able to measure ∼0.13% (4/3079) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >28% of the known and probable or putative urinary metabolome (>873/3079). In terms of chemical class coverage, NMR detects compounds from 15 of the 25 major chemical superclasses in urine, GC-MS detects compounds from 14 of the 25, DFI/LC-MS/MS detects 6 of the 25, ICP-MS detects 1 of the 25 while the targeted methods for thiols and isoflavones detect just 1 of each.
From these data we can conclude that NMR is currently the best method for identifying and quantifying urinary compounds. Not only does it permit measurement of the largest number of metabolites (209) but it also yields the greatest chemical diversity. Furthermore, NMR is non-destructive so that the same sample can be subsequently re-used for GC-MS, LC-MS or ICP-MS analyses. The minimal sample preparation and relatively rapid data collection for NMR also make it much more appealing for urine metabolomics, although the spectral analysis can be quite slow (∼1–2 hours per sample). While GC-MS is a close second in terms of overall coverage (179 metabolites, 14 chemical superclasses), these numbers represents the result of 4 different analyses performed on 2 different GC-MS instruments. Many labs would not have these multiple configurations available or the resources to routinely run these types of analyses. Likewise each sample required many hours of preparation, sample collection and data analysis. In this regard, multi-platform GC-MS is definitely not a high-throughput metabolomics technique. Relative to NMR and GC-MS, DFI/LC-MS/MS also performs well, with 127 compounds being quantified. However, DFI/LC-MS/MS provides very limited chemical diversity (only 6 chemical superclasses). On the other hand, DFI/LC-MS/MS requires very little sample volume (10 µL) and it is a very low-cost, largely automated, high-throughput route for measuring metabolites. The other techniques (HPLC, ICP-MS) we employed in this study, while useful, do not come close to matching the coverage or diversity of NMR, GC-MS or DFI/LC-MS/MS.
While we certainly went to considerable lengths to use current or cutting edge technologies to characterize the urine metabolome, it is also important to note that there is always potential for future improvement. Using higher field (900–950 MHz) NMR instruments, employing newer model GC-MS instruments or more sensitive GC-TOF instruments, using more than 3 derivatization or extraction steps for our GC-MS analyses, employing the latest version of the NIST database or a larger collection of GC-MS databases, implementing more sophisticated or targeted detection and separation techniques, using various commercial immunodetection kits or employing the latest LC-MS/MS techniques coupled to FT-MS or orbitraps – all of these could have added to the quantity and diversity of metabolites detected or quantified. However, like many laboratories, our resources are somewhat limited. Furthermore, in this study we wanted to address the question of how well a cross-section of commonly accessible metabolomic methods or platforms could perform in identifying and quantifying metabolites in urine.
While being able to quantitatively compare metabolite coverage and chemical diversity amongst the major metabolomics platforms (NMR, GC-MS and DFI/LC-MS/MS) is important, it is also useful to compare their consistency or reproducibility in terms of metabolite quantification. In particular we decided to assess the 3 major platforms in terms of their ability to identify and quantify a common group of compounds, namely the amino acids. Overall we found that the measured concentrations are in relatively good agreement (Table 10). However, a few exceptions are evident. For example, the NMR and DFI/LC-MS/MS concentrations of glycine and serine are higher than the GC-MS values (note that glycine exhibits the highest concentration among urinary amino acids). For serine, after the silylation reaction using MSTFA, we obtained serine-2TMS (13.9 min) and serine-3TMS derivatives (16.6 min). The chromatographic peak corresponding to serine-2TMS is weak and overlaps slightly with the urea peak. This overlap and the corresponding difficulty in peak integration may explain the quantitation differences compared to other analytical assays. Neither L-glutamine nor L-glutamic acid could be accurately quantified or identified by GC-MS. In our case, the glutamine peak co-elutes with glycerol-3-phosphate. Other investigators have noted that glutamine can also be hydrolyzed and converted to glutamic acid [97], or to pyroglutamic acid during derivatization [95]. As a result, only pyroglutamic acid could be identified in our GC-MS assay. The identification of glutamic acid and pyroglutamic acid can be complicated [97], which explains our failure to identify glutamic acid by GC-MS. L-arginine could not be detected by GC-MS, because it is converted to ornithine during derivatization [98], which probably explains the slightly higher concentration of ornithine measured by GC-MS, compared to the one determined by DFI/LC-MS/MS. Finally, L-cysteine and L-cystine can only be identified and quantified by NMR and targeted HPLC/FD because the identification and quantification of theses metabolites is not possible with the Biocrates kit. With GC-MS it has often been noted that L-cystine can be converted to L-cysteine during derivatization, while L-cysteine might be oxidized to L-cystine during prolonged storage of the standard solution [95], confounding the identification of L-cystine and L-cysteine.
Table 10 Comparison of urine concentrations of 23 quantified amino acids by different assays: DFI/LC-MS/MS (BIOCRATES kit), NMR and GC-MS.
Amino acids* DFI/LC-MS/MS NMR GC-MS
Glycine 101.0±53.5 106±66 75.7±51.7
L-Alanine 21.7±10.6 21.8±10.3 NA
L-Arginine 7.4±3.0 8.6±3.6 NA
L-Asparagine 8.8±3.5 10.1±4.0 9.5±5.5
L-Aspartic acid 10.9±7.7 10.9±5.8 NA
L-Citrulline 0.8±0.4 NA NA
L-Cysteine NA 65.8±24.0 NA
L-Cystine NA 12.5±5.4 NA
L-Glutamic acid 6.4±4.9 8.5±3.6 NA
L-Glutamine 39.9±14.3 37.2±14.8 NA
L-Histidine 45.6±15.6 43.9±19.5 NA
L-Isoleucine 1.3±0.4 1.3±0.5 NA
L-Leucine 3.0±1.0 2.8±0.9 NA
L-Lysine 17.9±12.2 17.2±11.5 NA
L-Methionine 0.8±0.3 1.4±0.6 NA
L-Ornithine 4.4±5.0 NA 5.3±1.9
L-Phenylalanine 5.9±2.0 6.4±2.3 7.8±2.0
L-Proline 1.1±0.6 NA NA
L-Serine 24.5±9.2 25.3±9.7 19.0±7.0
L-Threonine 14.6±6.1 13.3±5.3 NA
L-Tryptophan 5.6±2.1 6.3±2.2 NA
L-Tyrosine 8.7±4.2 9.5±4.5 15.1±5.4
L-Valine 4.3±1.9 3.4±1.4 5.5±2.0
* Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available.
Conclusion
Using a combination of multiple experimental assays supplemented with an extensive computer–assisted literature survey we were able to identify a total of 2651 metabolites or metabolite species (corresponding to 3079 distinct structures) that can be or have been identified and/or quantified in human urine using today’s technology. This information, which includes both normal and abnormal (disease or exposure-associated) metabolites has been placed into a publicly accessible web-enabled database called the Urine Metabolome Database (UMDB). To assess the validity of the literature data and to further investigate the capabilities of existing metabolomics technologies we conducted a comprehensive, quantitative analysis of human urine from 22 healthy volunteers. A total of 6 different platforms: NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC/UV and HPLC/FD were used in this analysis. From this experimental work we were able to identify a total of 445 and quantify 378 metabolites or metabolite species. This corresponds to 873 unique structures (identified) and 806 unique structures (quantified). A total of 53 compounds or compound species are being reported here for the first time as being normal constituents of human urine, while 77 compounds or compound species are being robustly quantified in human urine for the first time. All of the metabolites that we experimentally identified and/or quantified have been added to the UMDB. Based on the information in the UMDB, our experimentally acquired data corresponds to 16% of the human urine metabolome (or 28% if we include probable or putative metabolites).
Considering the level of coverage, the diversity of chemical species and the ease with which analyses can be performed, we have determined that NMR spectroscopy appears to be the method of choice for global or untargeted metabolomic analysis of urine. On the other hand, the kit-based combined DFI/LC-MS/MS methods appear to be optimal for a targeted metabolomic approach. Using a multi-pronged GC-MS approach for urine metabolomics appears to be very promising in terms of coverage, but is not ideal for high-throughput analyses. All methods used in this study appear to be quite complementary with relatively little compound overlap. This strongly suggests that if sufficient time and resources are available, multiple methods should be used in urine metabolomic studies.
If additional resources had been available, we would have liked to assess other technologies (GCxGC-MS, FT-MS, isotope labeled-LC-MS) and to compare the level of metabolite coverage and chemical diversity attainable with these methods. However, this study is not intended to be the “final” word on urine or urine metabolome. Rather, it should be viewed as a starting point for future studies and future improvements in this field. Indeed, our primary objective for undertaking these studies and compiling this data was to help advance the fields of quantitative metabolomics, especially with regard to clinically important biofluids such as urine. Experimentally, our data should serve as a useful benchmark from which to compare other technologies and to assess coming methodological improvements in human urine characterization. From a clinical standpoint, we think the information contained in the human urine metabolome database (UMDB) should provide metabolomic researchers as well as clinicians and clinical chemists with a convenient, centralized resource from which to learn more about human urine and its unique chemical constituents.
Methods
Ethics Statement
All samples were collected in accordance with the ethical guidelines mandated by the University of Alberta as approved by the University’s Health Research Ethics Board. All individuals were over 18 years of age. All were approached using approved ethical guidelines and those who agreed to participate in this study were required to sign consent forms. All participants provided written consent.
Collection of Urine Samples
Human urine samples (first pass, morning) were collected from 22 healthy adult volunteers (14 male, 8 female) in 120 mL sterile urine specimen cups. The average age of the volunteers was 30 (range 19–65) for females, and 32 (range 21–67) for males. Upon receipt (typically within 1 hour of collection), all samples were immediately treated with sodium azide to a final concentration of 2.5 mM. After centrifugation at 4000 rpm for 10 min to remove particulate matter, the urine samples were stored in 2 mL aliquots in falcon tube at −20°C until further use. Prior to each analysis, the samples were thawed at room temperature for 30 minutes and filtered for a second time via centrifugation.
NMR Compound Identification and Quantification
All 1H-NMR spectra were collected on a 500 MHz Inova (Varian Inc., Palo Alto, CA) spectrometer using the first transient of the tnnoesy-presaturation pulse sequence. The resulting 1H-NMR spectra were processed and analyzed using the Chenomx NMR Suite Professional software package version 7.0 (Chenomx Inc., Edmonton, AB), as previously described [17]. Additional NMR spectra for 39 compounds were added to the Chenomx Spectral Reference Library using the company’s recommended spectral acquisition and formatting protocols. Further details on the NMR sample preparation, NMR data acquisition and the customized spectral library are provided in Method S1.
GC-MS Compound Identification and Quantification
Twenty-two urine samples were extracted separately to obtain separate pools of polar, organic acid, bile acid (3 of the 22 urine samples were chosen for analysis and aliquots from these provided an additional “pooled normal” sample) and volatile metabolites using different protocols. The polar metabolites were extracted with cold HPLC grade methanol and double-distilled water after pretreatment with urease, followed by derivatization with MSTFA (N-Methyl-N-trifluoroacetamide) with 1% TMCS (trimethylchlorosilane). For organic acids, the ketoacids were converted first to methoxime derivatives, followed by derivatization with BSTFA (N,O-Bis(trimethylsilyl)trifluoroacetamide) after two successive extractions by ethyl acetate and diethyl ether. The bile acids were eluted with methanol through a SPE column (Bond Elute C18), followed by two different derivatization steps. First the bile acid extracts were esterified using 2% sulfuric acid in methanol, then after phase separation, the esterified bile acids were converted to the corresponding methyl ester-trimethylsilyl ether derivatives using MSTFA with 1% TMCS. Each set of extracted/derivatized metabolites (polar metabolites, organic acids, bile acids) was separated and analyzed using an Agilent 5890 Series II GC-MS operating in electron impact (EI) ionization mode.
The volatile compound extraction and analysis by GC-MS was far different from the other protocols. The pooled urine samples were acidified using HCl and transferred to a SPME (solid phase microextraction) vial (75 µm Carboxen/PDMS from Supelco), and then introducing the SPME fiber assembly into the GC-MS injector port according to procedures described elsewhere [99], [100]. The fibers were conditioned prior to use according to the manufacturer’s instructions by inserting them into the GC injector port. Further details on the extraction, derivatization, separation and GC-MS data analysis for the 4 separate groups of urine metabolites are provided in Method S2.
Direct Flow Injection/LC-MS/MS Compound Identification and Quantification
To assess the performance of direct flow injection DFI-MS/MS methods in urine metabolomics and to determine the concentration ranges of a number of metabolites not measurable by other methods, we used the commercially available Absolute-IDQ p180 Kit (BIOCRATES Life Sciences AG - Austria). The kit, in combination with an ABI 4000 Q-Trap (Applied Biosystems/MDS Sciex) mass spectrometer, can be used for the targeted identification and quantification of 187 different metabolites or metabolite species including amino acids, biogenic amines, creatinine, acylcarnitines, glycerophospholipids, sphingolipids and hexoses. This method involves derivatization and extraction of analytes from the biofluid of interest, along with selective mass spectrometric detection and quantification via multiple reactions monitoring (MRM). Isotope-labeled internal standards are integrated into the kit plate filter to facilitate metabolite quantification. Metabolite concentrations were expressed as ratios relative to creatinine to correct for dilution, assuming a constant rate creatinine excretion for each urine sample (see Method S3 for additional information).
Trace Element Analysis Using ICP-MS
Before trace elemental analysis by ICP-MS was performed, 22 urine samples were processed as described previously [101]. The concentrations of trace elements were determined on a Perkin-Elmer Sciex Elan 6000 quadrupole ICP-MS operating in a dual detector mode. Blank subtraction was applied after internal standard correction (see Method S4 for additional information). The accuracy of the ICP-MS analytical protocol was periodically evaluated via the analysis of certified reference standard materials (whole rock powders) BE-N and DR-N available from the SARM laboratory at the CRPG (Centre de Recherches Pétrographiques et Géologiques).
Characterization of Isoflavones from Urine
We processed 22 urine samples as described previously [102], [103]. The isoflavones were isolated and concentrated by solid-phase extraction (Bond Elut C18 column). The elutes were hydrolyzed enzymatically as the urinary isoflavones occur predominantly as glucuronate and sulfate conjugates. The analysis were performed on an Agilent 1100 HPLC system using NovaPak C18 reversed-phase column connected to Agilent G1315B diode array detector with signals scanned between 190 and 400 nm (see Method S5 for additional information).
Characterization of Thiols from Urine
To extract urinary thiols, we derivatized all 22 urine samples as described previously [104]. A mixture of reagent was used for the reduction and derivatization (with bromobimane) of thiols. The derivatized thiols were injected immediately into a hypersil-ODS HPLC Column connected to Agilent fluorometer operating at an excitation wavelength of 485 nm and emission wavelength of 510 nm (see Method S6 for additional information).
Supporting Information
Method S1 NMR Compound Identification and Quantification.
(DOC)
Click here for additional data file.
Method S2 GC-MS Compound Identification and Quantification.
(DOC)
Click here for additional data file.
Method S3 Direct Flow Injection/LC-MS/MS Compound Identification and Quantification.
(DOC)
Click here for additional data file.
Method S4 Trace Element Analysis Using ICP-MS.
(DOC)
Click here for additional data file.
Method S5 Characterization of Isoflavones from Urine.
(DOC)
Click here for additional data file.
Method S6 Characterization of Thiols from Urine.
(DOC)
Click here for additional data file.
The authors wish to thank Kruti Chaudhary and Hetal Chaudhary for adding concentration values from the literature to the UMDB, Rolando Perez-Pineiro for the synthesis of HPHPA and Beomsoo Han for converting all figures to high resolution images.
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Document structure show
| article-title | The Human Urine Metabolome |
| alt-title | The Human Urine Metabolome |
| abstract | Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca. |
| p | Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca. |
| body | Introduction Metabolomics is a relatively young branch of “omics” science concerned with the systematic study of the chemical products or metabolites that cells and organisms generate. Because metabolites are the downstream products of numerous genome-wide or proteome-wide interactions, the metabolome (the sum of all metabolites in an organism) can be a very sensitive measure of an organism’s phenotype. This fact has made metabolomics particularly useful in the study of environment-gene interactions [1], [2], [3], [4], the identification of disease biomarkers [5], [6], [7], [8], [9] and the discovery of drugs [10]. Unlike its older “omics” cousins, where complete or near-complete coverage of the genome or proteome is fairly routine, metabolomics still struggles to cover even a tiny fraction of the metabolome. Indeed, most human metabolomic studies published today, even those exploiting the latest and most sensitive LC-MS/MS technologies, typically succeed in identifying or characterizing fewer than 100 compounds [11], [12], [13], [14]. This corresponds to less than 1% of the known human metabolome [15], [16]. In an effort to help improve this situation, we (and others) have started to undertake the systematic characterization of various human biofluid metabolomes. This includes the human cerebrospinal fluid metabolome [17], [18], the human saliva metabolome [19], and the human serum metabolome [20]. We have now turned our attention to characterizing the human urine metabolome. Urine, as produced by mammals, is a transparent, sterile, amber-colored fluid generated by the kidneys. The kidneys extract the soluble wastes from the bloodstream, as well as excess water, sugars, and a variety of other compounds. The resulting urine contains high concentrations of urea (from amino acid metabolism), inorganic salts (chloride, sodium, and potassium), creatinine, ammonia, organic acids, various water-soluble toxins and pigmented products of hemoglobin breakdown, including urobilin, which gives urine its characteristic color. Urination is the primary route by which the body eliminates water-soluble waste products. The average adult generates between 1.5–2.0 liters of urine per day, which over the course of their lifetime would be enough to fill a small backyard swimming pool (5 X 8 X 1.5 m). While largely viewed as a waste product, urine has considerable value as a diagnostic biofluid. Indeed the analysis of urine for medical purposes dates back to ancient Egypt [21], [22], [23], [24]. Hippocrates largely legitimized the medical practice of uroscopy (the study of urine for medical diagnostics) where examination of the color, cloudiness, smell and even the taste of urine was used to identify a variety of diseases. Throughout the Byzantine era and well into the Middle Ages, urine color wheels (a diagram that linked the color of urine to a particular disease) were commonly used by physicians to make diagnoses [21], [25]. A brownish color would indicate jaundice, a red hue (blood) might indicate urinary tract tumors, absence of color would be indicative of diabetes and foamy urine would indicate proteinuria. With the advent of modern clinical techniques in the middle of the 19th century, uroscopy largely disappeared. However, urine has continued to be an important cornerstone to modern medical practice. In fact, it was the first biofluid to be used to clinically diagnose a human genetic disease - alkaptonuria [26]. Even today urine analysis is routinely performed with dipstick tests that can readily measure urinary glucose, bilirubin, ketone bodies, nitrates, leukocyte esterase, specific gravity, hemoglobin, urobilinogen and protein. More detailed urinalysis can be also used to study a variety of renal conditions, such as bladder, ovarian and kidney diseases [27], [28], [29], [30]. Being an important and easily accessible biological fluid, urine has been the subject of detailed chemical analysis for more than 100 years [31], [32]. Extensive tables of normal reference ranges have been published for more than 100 urine ions and metabolites [33], [34], [35], [36], [37]. In addition to these referential clinical chemistry studies, several groups have applied various “global” metabolomic or metabolite profiling methods to urine, such as high resolution nuclear magnetic resolution (NMR) spectroscopy [38], [39], [40], high performance liquid chromatography (HPLC) [41], high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) [42], ultra high performance liquid chromatography-high resolution orbitrap mass spectrometry (UHPLC-HRMS) [43], ultra high performance liquid chromatography (UPLC-RP) [31], [44], fast atom bombardment ionization coupled with mass spectroscopy (FAB-MS) [35], two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GCxGC-MS) [32] or time-of-flight mass spectrometry (GCxGC-TOF-MS) [37], nanoflow liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) [33] and liquid chromatography coupled with electrospray quadruple time-of-flight mass spectrometry (LC/QTOF-MS) [36]. A combination of two or more profiling methods has also been used by many research groups to help improve metabolite coverage [34], [40]. A comprehensive list of methods used to analyze urine and the numbers of metabolites identified and/or quantified by these methods (along with references) is provided in Table 1. As seen from this table, it is possible to (tentatively) identify up to 294 different metabolites in human urine. However, quantification is somewhat more difficult, with the largest number of quantified metabolites ever reported in human urine being slightly less than 100. In addition to these global metabolomic studies, hundreds of other “targeted” or single-metabolite studies have been conducted on human urine that have led to the identification and quantification of hundreds of other urine metabolites. Unfortunately, this information is not located in any central repository. Instead it is highly dispersed across numerous textbooks and periodicals [16]. Table 1 Historic data on metabolite characterization of human urine by different platforms. Method(s) # Identified # Quantified Year Reference Multiple 51 51 1971 Putnam [105] GC-MS 52 0 1976 Lawson [85] GC-MS 72 72 1978 Gates [106] GC-MS 36 36 1989 Hoffman [107] GC-MS 137 0 1990 Liebich [108] GC-MS 131 95 1991 Shoemaker [84] GC-MS 69 69 1994 Guneral [56] GC-MS 75 75 1996 Matsumoto [109] ICP-MS 33 33 2005 Goulle [110] SPE/NMR 73 0 2008 Yang [40] SPE/LC/NMR 72 0 2008 Rezzi [78] LC-MS/NMR 50 0 2008 Law [34] LC-MS 93 93 2009 Guo [111] NMR 50 50 2009 Shaykhutdinov [112] GC-MS-MS 48 48 2011 Kvitvang [113] SPE-GC-MS 79 0 2011 Silva [114] LC-MS 167 0 2011 Guo [115] NMR 42 42 2011 Saude [116] NMR/GC-MS 77 0 2012 Zhang [117] LC-MS 219 (25 listed) 0 2012 Van der Kloet [118] GC-TOF/LC-MS 261 0 2012 Cheng [119] GC-MS 258 0 2012 Roux [83] GCxGC-TOF-MS 294 0 2012 Rocha [120] To facilitate future research into urine chemistry and urine metabolomics, we believe it is critical to establish a comprehensive, electronically accessible database of the detectable metabolites in human urine. This document describes just such a database, one that contains the metabolites that can, with today’s technology, be detected in human urine along with their respective concentrations and disease associations. This resource was assembled using a combination of both our own experimental and literature-based research. Experimentally, we used high-resolution NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection tandem mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) with ultraviolet (UV) or fluorescence detection (FD) techniques performed on multiple human urine samples to identify 445 metabolites or metabolite species and quantify 378 of these compounds. To complement these “global” metabolic profiling efforts, our team also surveyed and extracted metabolite and disease-association data from more than 3000 books and journal articles that had been identified through computer-aided literature and in-house developed text-mining software. This “bibliomic” effort yielded data for another 2206 metabolites. The resulting Urine Metabolome Database (UMDB - http://www.urinemetabolome.ca) is a comprehensive, web-accessible resource containing a total of 2651 confirmed urine metabolites or metabolite species (corresponding to 3079 defined structures), their corresponding concentrations and their disease associations that were revealed or identified from these combined experimental and literature mining efforts. Results and Discussion The Content of the Human Urine Metabolome – The Urine Metabolome Database The Urine Metabolome Database (UMDB: http://www.urinemetabolome.ca) contains a complete list of all (to the best of our knowledge) possible metabolites that have been detected in human urine using current technologies. The UMDB is freely available, easily queried, web-enabled database which provides a list of the metabolite names, level of verification (confirmed or probable), normal and disease-associated concentration ranges, associated diseases and corresponding literature references for all human urine metabolites that have ever been detected and/or quantified in the literature. The UMDB also contains concentration data compiled from the experimental studies described here. Each urine metabolite entry in this database is linked to a MetaboCard button [15], [16] that, when clicked, brings up detailed information about that particular entry. This detailed information includes nomenclature, chemical, clinical and molecular/biochemical data. Each MetaboCard entry contains up to 120 data fields many of which are hyperlinked to other databases (KEGG [45], PubChem [46], MetaCyc [47], ChEBI [48], PDB [49], UniProt [50], and GenBank [51] as well as to GeneCard IDs [52], GeneAtlas IDs [53] and HGNC IDs [54] for each of the corresponding enzymes or proteins known to act on that metabolite). Additionally, the UMDB through its MetaboCard/HMDB links includes nearly 450 hand-drawn, zoomable and fully hyperlinked human metabolic pathway maps (SMPDB: http://www.smpdb.ca/). These maps are intended to help users visualize the chemical structures on metabolic pathways and to get detailed information about metabolic processes [55]. These UMDB pathway maps are quite specific to human metabolism and explicitly show the subcellular compartments where specific reactions are known to take place. The UMDB’s simple text query (TextQuery) supports general text queries including names, synonyms, conditions and disorders. Clicking on the Browse button (on the UMDB navigation panel) generates a tabular view that allows users to casually scroll through the database or re-sort its contents by compound name or by concentration. Users can choose either the “Metabolite View”, “Concentration View” or “Diseases View” to facilitate their browsing or searching. Clicking on a given MetaboCard button brings up the full data content (from the HMDB) for the corresponding metabolite. Users may also search the database using a variety of options listed uner the “Search” menu. For instance, the ChemQuery button allows users to draw or write (using a SMILES string) a chemical compound to search the UMDB for chemicals similar or identical to the query compound. ChemQuery also supports chemical formula and molecular weight searches. The Sequence Search button allows users to conduct BLAST sequence searches of the 4075 protein sequences contained in the UMDB. Both single and multiple sequence BLAST queries are supported. “Advanced Search” which is also located under the “Search” menu is the most sophisticated search tool in the UMDB and opens an easy-to-use query search tool that allows users to select or search over various combinations of subfields. The UMDB’s “MS Search” allows users to submit mass spectral peak lists that will be searched against the Human Metabolome Database (HMDB)’s library of MS/MS spectra. This potentially allows facile identification of urine metabolites from mixtures via MS/MS spectroscopy. UMDB’s NMR Search allows users to submit peak lists from 1H or 13C NMR spectra (both pure and mixtures) and have these peak lists compared to the NMR libraries contained in the HMDB. This allows the identification of metabolites from mixtures via NMR spectral data. The Download button provides links to collected sequence, image and text files associated with the UMDB. In the About menu, the “Data Fields Explained” button lists source data used to assemble the UMDB. Currently the UMDB contains information on 2651 detectable metabolites or metabolite species (which corresponds to 3079 metabolites with precisely defined structures) and 3832 concentration ranges or values associated with 220 different conditions, diseases and disorders. The number of metabolites in the UMDB is not a number that will remain unchanged. Rather it reflects the total number of metabolites – most of which are endogenous - that have ever been detected and/or quantified by ourselves and others. Certainly as technology improves, we anticipate this number will increase as other, lower abundance, metabolites are detected and added to future versions of the UMDB. Likewise, if the list was expanded to include intermittent, exogenous compounds such as all possible drugs or drug metabolites or rare food additives and food-derived phytochemicals, the database could be substantially larger. Inspection of the on-line tables in UMDB generally shows that human urine contains a substantial number of hydrophilic molecules. This is further reiterated in Table 2, which provides a listing of the chemical “superclasses” (using the HMDB definitions) in human urine and the number of representative compounds that can be found in this biofluid. Excluding lipids (which are in very low concentration), human urine is dominated by amino acids and derivatives, carbohydrates and carbohydrate conjugates. This simply reinforces the fact that urine is a key carrier of hydrophilic waste products. Other small molecule components found in high abundance in urine include hydroxy acids and derivatives (such as citric acid), urea, ammonia, creatinine and hippuric acid. A more detailed description of both our literature and experimental findings is given in the following 7 sections covering: 1) Literature Review/Text Mining; 2) NMR; 3) DFI/LC-MS/MS; 4) GC-MS; 5) ICP-MS; 6) HPLC/UV and 7) HPLC/FD. Table 2 Chemical superclasses in the urine metabolome database. Chemical superclass # Compounds Aliphatic Acyclic Compounds 93 Aliphatic Heteromonocyclic Compounds 43 Aliphatic Heteropolycyclic Compounds 40 Aliphatic Homomonocyclic Compounds 18 Aliphatic Homopolycyclic Compounds 5 Alkaloids and Derivatives 45 Amino Acids, Peptides, and Analogues 286 Aromatic Heteromonocyclic Compounds 67 Aromatic Heteropolycyclic Compounds 728 Aromatic Homomonocyclic Compounds 432 Aromatic Homopolycyclic Compounds 6 Carbohydrates and Carbohydrate Conjugates 116 Homogeneous Metal Compounds 45 Homogeneous Non-metal Compounds 15 Inorganic compounds 1 Lignans and Norlignans 12 Lipids 866 Mixed Metal/Non-metal Compounds 7 Nucleosides, Nucleotides, and Analogues 49 Organic Acids and Derivatives 108 Organic Halides 3 Organometallic Compounds 1 Organophosphorus Compounds 17 Polyketides 74 Tannins 2 Metabolite Concentration in Urine – Literature Survey In addition to the experimentally derived values obtained for this study, the urine metabolome database (UMDB) also presents literature-derived concentrations of urine metabolites with references to either PubMed IDs or clinical textbooks. In many cases, multiple concentration values are given for “normal” conditions. This is done to provide users/readers with a better estimate of the potential concentration variations that different technologies or laboratories may measure. As a general rule, there is good agreement between most laboratories and methods. However, the literature results presented in the UMDB do not reflect the true state of the raw literature. A number of literature-derived concentration values were eliminated through the curation process after being deemed mistaken, disproven (by subsequent published studies), mis-typed or physiologically impossible. Much of the curation process involved having multiple curators carefully reading and re-reading the primary literature to check for unit type, unit conversion and typographical inconsistencies. Other than the inorganic ions and gases such as sodium (14.7 ± 9.0 mM/mM creatinine – average value), chlorine (8.8 ± 6.2 mM/mM creatinine), potassium (4.6 ± 0.1 mM/mM creatinine), ammonia (2.8 ± 0.9 mM/mM creatinine), the 4 most abundant organic metabolites found in urine (based on average values) are urea (22.5 ± 4.4 mM/mM creatinine), creatinine (10.4 ± 2.0 mM), hippuric acid (298.5 ± 276.8 µM/mM creatinine) and citric acid (280.6 ± 115.2 µM/mM creatinine). The least abundant (detectable) metabolites in urine include oxytocin (0.9 ± 0.1 pM/mM creatinine), angiotensin II (1.2 ± 0.2 pM/mM creatinine), 15-deoxy-d-12,14-PGJ2 (2.3 ± 1.0 pM/mM creatinine) and melatonin (3.3 ± 2.7 pM/mM creatinine). This shows that the current lower limit of detection for metabolites in urine is in the low pM/mM creatinine range and that the concentration range of analytes in urine spans nearly 11 orders of magnitude. One point that is particularly interesting is the fact that the concentration (scaled to creatinine) of the average metabolite in normal urine varies by about ± 50%, with some metabolites varying by as much as ± 350% (such as normetanephrine (0.00085 ± 0.00317 µM/mM creatinine), pipecolic acid (0.03 ± 0.07 µM/mM creatinine), enterodiol (0.032 ± 0.072 µM/mM creatinine), tungsten (0.010 ± 0.022 µM/mM creatinine) and chlorogenic acid (0.0014 ± 0.0029 µM/mM creatinine). Therefore, drawing conclusions about potential disease biomarkers without properly taking into account this variation would be ill-advised. We believe that these relatively large metabolite concentration ranges are due to a number of factors, including age, gender, genetic background, diurnal variation, health status, activity level and diet [56], [57], [58], [59]. Indeed, some UMDB entries explicitly show such variations based on the populations (age, gender) from which these metabolite concentrations were derived. Clearly more study on the contributions to the observed variations in urine is warranted, although with thousands of metabolites to measure for dozens of conditions, these studies will obviously require significant technical and human resources. Experimental Quantification and Identification of Urine Metabolites – NMR A representative high-resolution NMR spectrum of urine from a healthy individual is shown in Figure 1. As can be readily seen from this figure, urine NMR spectra are very information-rich and surprisingly complex, with thousands of resolved peaks. From the 22 healthy control urine samples analyzed, we could identify a total of 209 unique compounds with an average of 167 ± 19 compounds being identified per sample. Every compound was unequivocally identified and quantified using spectral fitting (via Chenomx) and/or spike-in experiments with authentic standards. The concentration of each metabolite was normalized to each urine sample’s corresponding creatinine value to compensate for variations in urine volume (the concentration of metabolites is expressed as µM/mM creatinine). Figure 1 Typical 500 MHz 1H-NMR spectra of urine from human urine. Numbers indicates the following metabolites: 1: creatinine; 2: citric acid; 3: glycine; 4: formic acid; 5: methanol; 6: guanidoacetic acid; 7: acetic acid; 8: L-cysteine; 9: glycolic acid; 10: creatine; 11: isocitric acid; 12: hippuric acid; 13: L-glutamine; 14: L-alanine; 15: L-lysine; 16: gluconic acid; 17∶2- hydroxyglutaric acid; 18: D-glucose; 19: indoxyl sulfate; 20: trimethyl-N-oxide; 21: ethanolamine; 22: L-lactic acid; 23: taurine; 24: L-threonine; 25: dimethylamine; 26: pyroglutamic acid; 27: trigonelline; 28: sucrose; 29: trimethylamine; 30: mannitol; 31: L-serine; 32: acetone; 33: L-cystine; 34: adipic acid; 35: L-histidine; 36: L-tyrosine; 37: imidazole; 38: mandelic acid; 39: dimethylglycine; 40: Cis-aconitic acid; 41: urea; 42∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 43: phenol; 45: isobutyric acid; 46: methylsuccinic acid; 47∶3-aminoisobutyric acid; 48: L-fucose; 49: N-acetylaspartic acid; 50: N-acetylneuraminic acid; 51: acetoacetic acid; 52: Alpha-aminoadipic acid; 53: methylguanidine; 54: phenylacetylglutamine. The 209 compounds identified and quantified from these NMR studies represent a “high-water” mark for NMR-based metabolomics. Previous studies have reported up to 70 compounds being identified and/or quantified in human urine [40]. Indeed, compared to other platforms previously used to analyze human urine (Table 1), it appears that NMR may currently be the most comprehensive and certainly the most quantitative approach to characterizing this biofluid. Based on the fitted area under each urinary NMR spectrum and the number of unidentified peaks we estimate that more than 96% of the spectral area and more than 92% of all NMR-detectable compounds in our human urine samples are listed in Table 3. In other words, for NMR-based metabolomics, human urine is essentially “solved”. The same “solved” status has already been achieved for human serum (with 49 definitive compounds, [20]) and for human cerebrospinal fluid (with 53 definitive compounds, [17]). Knowing the expected or detectable composition of these biofluids should open the door to automated NMR-based metabolomics [60]. Table 3 Concentrations [Mean (range)] and % occurrence of urine metabolites as determined by NMR. Compounds HMDB ID Concentration (µM/mM creatinine) Oc (%) Literature Values (µM/mM creatinine) 1,3-Diaminopropane HMDB00002 1.2 5 2.55 (0.18–5.85) 1,3-Dimethyluric acid HMDB01857 3.1 (1.3–6.8) 68 1.5 (1.3–2.3) 1-Methyladenosine HMDB03331 1.7 (0.9–3.6) 82 2.03±0.20 1-Methylhistidine HMDB00001 8.3 (2.4–28.4) 100 15.9±19.5 1-Methylnicotinamide HMDB00699 5.8 (1.2–15.0) 77 6.1 (0.2–12.0) 2-Furoylglycine HMDB00439 4.0 (0.9–8.4) 100 9.95 (2.0–18.66) 2-Hydroxy-3-methylpentanoic acid HMDB00317 3.9 (1.7–7.9) 100 NA 2-Hydroxybutyric acid HMDB00008 2.8 (1.2–6.9) 77 2.4 (0.0–4.9) 2-Hydroxyglutaric acid HMDB00694 33.0 (13.3–77.9) 100 33.0 (20.0–46.0) 2-Ketobutyric acid HMDB00005 2.9 (0.7–5.4) 100 2.6 (1.1–4.1) 2-Methyl-3-hydroxybutyric acid HMDB00354 4.2 (1.6–6.7) 95 5.5 (0.0–11.0) 2-Methyl-3-ketovaleric acid HMDB00408 1.1 (0.8–1.3) 18 NA 2-Methylbutyrylglycine HMDB00339 2.0 (1.2–3.3) 32 1 (0–2) 2-Methylerythritol HMDB11659 25.3 (17.0–49.0) 14 NA 2-Methylglutaric acid HMDB00422 0.8 (0.3–1.7) 45 NA** 3-Aminoisobutanoic acid HMDB03911 26.0 (2.2–140.0) 100 2.91–116.43 3-Hydroxybutyric acid HMDB00357 3.6 (1.3–6.4) 77 1.4±1.3 3-Hydroxyhippuric acid HMDB06116 7.4 (5.7–9.1) 32 2.5 (0.2–5.0) 3-Hydroxyisovaleric acid HMDB00754 6.8 (3.2–21.8) 100 8.5±3.2 3-Hydroxymethylglutaric acid HMDB00355 2.8 (1.1–5.8) 68 3 (0–10) 3-Hydroxyphenylacetic acid HMDB00440 7.7 (1.4–22.1) 91 6.1±4.1 3-Methyl-2-oxovaleric acid HMDB00491 2.2 (0.4–4.8) 91 2.0±2.0 3-Methyladipic acid HMDB00555 2.9 (1.1–7.2) 86 2.9 (0.7–10.5) 3-Methylglutaconic acid HMDB00522 7.5 (3.4–9.5) 22 4.5 (0.0–9.0) 3-Methylhistidine HMDB00479 16.5 (2.8–59.8) 100 15.1 (3.9–26.3) 3-Methylxanthine HMDB01886 6.2 (1.4–17.8) 100 4.0 (2.3–6.3) 4-Aminobutyric acid HMDB00112 2.9 (2.7–3.3) 9 0.75 (0.0–1.50) 4-Aminohippuric acid HMDB01867 3.3 (1.0–7.2) 86 0.3 4-Deoxythreonic acid HMDB02453 12.3 (4.4–19.8) 100 25±10 4-Ethylphenol HMDB29306 0.9 (0.6–1.2) 18 0.409±0.174 4-Hydroxybenzoic acid HMDB00500 1.7 (0.9–3.0) 77 1.8±1.8 4-Hydroxybutyric acid HMDB00710 7.2 (2.7–22.3) 100 3.3 (0.0–10.8) 4-Hydroxyhippuric acid HMDB13678 2.8 (1.4–6.0) 100 0–14 4-Hydroxyphenyllactic acid HMDB00755 3.2 (1.5–3.9) 50 1.1 (0.2–2.6) 4-Pyridoxic acid HMDB00017 3.1 (0.4–7.5) 95 0.78±0.45a 5,6-Dihydrouracil HMDB00076 3.8 (2.1–8.3) 68 3.0±2.7 5-Aminolevulinic acid HMDB01149 2.9 (1.2–4.4) 95 1.45±0.72 5-Aminopentanoic acid HMDB03355 1.1 5 2 7-Methylxanthine HMDB01991 10.0 (3.9–16.1) 9 10.0 (6.7–14.6) Acetaldehyde HMDB00990 2.6 (0.8–4.2) 32 NA** Acetaminophen HMDB01859 11.1 5 5.3 Acetaminophen glucuronide HMDB10316 33.4 5 NA** Acetaminophen sulfate HMDB59911 138.6 5 NA** Acetic acid HMDB00042 13.0 (2.5–106.0) 100 45.3±53.3 Acetoacetic acid HMDB00060 11.1 (2.2–24.9) 100 33 (0–67) Acetone HMDB01659 3.9 (0.8–17.6) 100 2.24±4.13 Acetyl-L-carnitine HMDB00201 2.8 (0.6–7.5) 100 1.9±0.3 Adenine HMDB00034 2.9 (1.4–5.1) 45 1.17 Adenosine HMDB00050 1.4 (0.9–2.3) 45 0.50 (0.09–0.92) Adipic acid HMDB00448 4.4 (1.6–14.3) 91 5.1 (0.8–35.0) ADP HMDB01341 1.8 (1.0–3.4) 77 NA** Allantoin HMDB00462 15.4 (4.9–29.3) 100 19.7±10.5 Alpha-Aminoadipic acid HMDB00510 7.3 (3.4–11.2) 73 4.6 Alpha-Aminobutyric acid HMDB00452 2.2 (1.1–4.4) 95 1.37±1.05 Alpha-Hydroxyisobutyric acid HMDB00729 3.8 (1.7–5.9) 100 3.8 Alpha-Ketoisovaleric acid HMDB00019 0.5 (0.2–1.1) 50 1 (0–2) Anserine HMDB00194 7.0 (3.3–20.0) 100 16.16 Arabinitol HMDB01851 31.8 (10.2–64.3) 100 19.28±6.32 Ascorbic acid HMDB00044 32.5 (4.6–78.0) 14 1.70–22.72 Asymmetric dimethylarginine HMDB01539 3.0 (0.2–6.7) 100 4.6 (3.0–7.0) Azelaic acid HMDB00784 2.8 (1.8–4.8) 64 4.8 (1.3–15.0) Benzoic acid HMDB01870 4.7 (1.2–10.5) 86 4.2 (1.9–6.5) Beta-Alanine HMDB00056 5.9 (3.4–13.0) 68 5 (0–10) Betaine HMDB00043 11.5 (2.7–24.7) 100 4.14 Butyric acid HMDB00039 1.6 (0.5–4.0) 77 0.131±0.013 Caffeine HMDB01847 1.2 5 0.33 (0.0–1.01) Carnosine HMDB00033 5.5 (1.4–12.6) 91 4.0±2.5 Choline HMDB00097 3.5 (1.4–6.1) 100 5.27 Cinnamic acid HMDB00567 1.6 (0.9–2.2) 9 0.77 Cis-Aconitic acid HMDB00072 20.9 (3.8–95.3) 100 13.0 (2.7–44.0) Citraconic acid HMDB00634 2.0 (1.2–2.6) 73 2.2 Citramalic acid HMDB00426 2.4 (1.0–4.8) 91 3.1 Citric acid HMDB00094 203 (49–600) 100 242.0±129.6 Creatine HMDB00064 46 (3–448) 100 46 (9–135) Creatinine* HMDB00562 14,743±9,797 100 10,090±310 Cytosine HMDB00630 4.1 (1.1–10.7) 36 NA** Dehydroascorbic acid HMDB01264 9.1 (5.8–7.1) 32 1.72–7.47 D-Galactose HMDB00143 11.9 (5.4–25.2) 100 4.4 (0.0–31.0) D-Glucose HMDB00122 37.5 (12.5–58.4) 100 31.12 Dihydrothymine HMDB00079 2.4 (1.3–4.4) 64 5 (0–10) Dimethyl sulfone HMDB04983 8.0 (1.3–49.0) 100 4.6 (2.5–7.5) Dimethylamine HMDB00087 30.8 (20.3–59.2) 100 39.3±8.3 Dimethylglycine HMDB00092 4.4 (1.6–10.4) 100 6.2 (0.8–11.2) D-Threitol HMDB04136 19.3 (5.3–32.7) 100 10.0 (5.6–15.0) D-Xylitol HMDB02917 8.4 (6.0–13.8) 95 3.50±2.37 D-Xylose HMDB00098 20 (3.0–102) 91 51.19 Erythritol HMDB02994 33.4 (6.8–64.0) 100 51±18 Ethanol HMDB00108 3.1 5 5–500b Ethanolamine HMDB00149 37.0 (24.8–56.2) 100 21.4 (6.6–36.2) Ethyl glucuronide HMDB10325 8.2 5 26.38±15.26 Ethylmalonic acid HMDB00622 3.0 (1.2–5.8) 95 2.0 (0.5–3.9) Formic acid HMDB00142 26.8 (6.9–120.9) 100 20.39±11.84 Fumaric acid HMDB00134 0.7 (0.2–1.7) 50 1 (0–2) Glucaric acid HMDB00663 8.4 (5.8–12.3) 82 2.9±0.2 Gluconic acid HMDB00625 21.5 (8.1–38.8) 100 NA** Glucuronic acid HMDB00127 9.7 (3.7–20.6) 77 NA Glutaconic acid HMDB00620 3.1 (1.2–3.1) 68 1 (0–2) Glutaric acid HMDB00661 2.1 (0.7–3.6) 18 1.3 (0.6–2.6) Glyceric acid HMDB00139 15.9 (7.8–31.1) 100 4.5 (0.0–9.0) Glycerol HMDB00131 13 (4–19) 100 20 (0–40) Glycine HMDB00123 106 (44–300) 100 151 (233–248) Glycolic acid HMDB00115 42.0 (3.7–122.0) 100 35 (18–55) Guanidoacetic acid HMDB00128 41.8 (10.6–97.3) 100 89.0 (11.0–124.0) Hippuric acid HMDB00714 229 (19–622) 100 257 (20–770) Homocitrulline HMDB00679 7.5 (3.8–13.8) 91 6.0 (0.0–8.8) Homogentisic acid HMDB00130 1.70 (0.55–2.8) 45 1 (0–2) Homovanillic acid HMDB00118 6.2 (1.8–12.7) 100 4.9±3.4 HPHPA HMDB02643 5.9 (1.4–22.1) 100 0.55–54.93 Hydroxyproline HMDB00725 6.5 (2.4–12.5) 55 2 Hydroxypropionic acid HMDB00700 12.3 (4.4–17.9) 82 6.5 (3.0–10.0) Hypoxanthine HMDB00157 7.2 (1.8–24.1) 100 4.67 (2.80–6.38) Indoleacetic acid HMDB00197 3.4 (1.8–6.2) 100 2.0±1.0 Indoxyl sulfate HMDB00682 22.4 (6.0–64.8) 100 19.74±5.26 Inosine HMDB00195 1.4 (0.4–6.5) 50 1.04±0.56 Isobutyric acid HMDB01873 3.5 (1.2–9.0) 95 3.9 Isocitric acid HMDB00193 46.9 (20.4–89.2) 68 58 (36–84) Isovaleric acid HMDB00718 1.4 (0.8–2.5) 86 0.42±0.02 Isovalerylglycine HMDB00678 2.0 (0.4–4.0) 91 5.5 (1.0–10.0) Kynurenic acid HMDB00715 2.1 (0.8–4.2) 64 1.53 (0.66–4.13) Kynurenine HMDB00684 1.7 (1.1–2.5) 23 2.85±1.94 Lactose HMDB00186 11.8 (1.0–24.2) 91 36.31±6.6c L-Alanine HMDB00161 21.8 (7.1–43.0) 100 22.0±11.0 L-Arabinose HMDB00646 13.3 (1.3–22.0) 100 8.75 (6.12–11.84) L-Arginine HMDB00517 8.6 (3.8–18.6) 95 6.6 (2.5–10.8) L-Asparagine HMDB00168 10.0 (4.6–17.8) 95 10.0 (4.6–16.3) L-Aspartic acid HMDB00191 10.9 (3.5–21.8) 95 4.63 (1.71–7.56) L-Carnitine HMDB00062 5.0 (0.7–16.4) 100 5.7±4.8 L-Cystathionine HMDB00099 14.7 (3.9–20.7) 82 5.3 (1.0–9.6) L-Cysteine HMDB00574 65.8 (23.1–134.5) 100 33.4±15.5 L-Cystine HMDB00192 12.5 (5.0–24.6) 100 9.0±3.0 Levoglucosan HMDB00640 10.0 (2.4–29.0) 27 NA** Levulinic acid HMDB00720 1.3 (0.4–2.0) 64 1.28 (0.33–2.75) L-Fucose HMDB00174 11.8 (6.7–26.0) 91 9.6 (6.4–16.0) L-Glutamic acid HMDB00148 8.5 (3.3–18.4) 100 10.0±3.7 L-Glutamine HMDB00641 37.2 (19.1–77.9) 100 33.32±10.78 L-Histidine HMDB00177 43 (17–90) 100 60.75±27.45 L-Isoleucine HMDB00172 1.3 (0.4–2.6) 100 1.64 L-Lactic acid HMDB00190 11.6 (3.5–29.3) 100 12.3±6.2 L-Leucine HMDB00687 2.8 (1.6–5.4) 100 2.5±1.3 L-Lysine HMDB00182 17.2 (3.7–51.3) 100 18 (7–29) L-Methionine HMDB00696 1.4 (0.5–2.5) 41 1.3±0.97 L-Phenylalanine HMDB00159 6.4 (3.5–11.2) 100 4.50±1.15 L-Serine HMDB00187 25.3 (11.6–53.4) 100 26.0±10.0 L-Threonine HMDB00167 13.3 (6.4–25.2) 100 5.17–24.59 L-Tryptophan HMDB00929 6.3 (3.4–11.1) 100 2.45–29.40 L-Tyrosine HMDB00158 9.5 (4.1–23.5) 100 8.8±4.5 L-Valine HMDB00883 3.4 (2.0–7.7) 100 3.0±1.0 Maleic acid HMDB00176 0.4 (0.3–0.5) 14 0.5 Malonic acid HMDB00691 2.9 (2.0–3.5) 77 1 (0–2) Maltose HMDB00163 6.0 (1.3–21.4) 73 NA** Mandelic acid HMDB00703 1.4 (1.1–1.7) 9 0.966±0.669 Mannitol HMDB00765 32.4 (5.2–85.1) 95 10.26±9.14 Methanol HMDB01875 37 (10–117) 100 NA** Methylamine HMDB00164 4.0 (1.5–11.9) 100 10.5 (9.8–12.5) Methylglutaric acid HMDB00752 3.1 (2.6–3.6) 9 3.5 (0.0–7.0) Methylguanidine HMDB01522 2.7 (1.2–6.0) 95 1.25±0.72 Methylmalonic acid HMDB00202 1.9 (0.7–3.5) 95 1.6 (0.0–3.6) Methylsuccinic acid HMDB01844 3.2 (0.7–9.9) 100 2.3 (0.8–10.8) Monomethyl glutaric acid HMDB00858 2.3 (0.6–4.4) 68 NA m-Thymol HMDB01878 2.2 (0.7–4.0) 86 NA** Myoinositol HMDB00211 22.4 (7.9–36.1) 100 18.8±2.9 N-Acetylaspartic acid HMDB00812 4.0 (1.3–7.0) 100 4.66±1.14 N-Acetylneuraminic acid HMDB00230 5.4 (2.5–8.6) 100 8.60±1.58 N-Acetylputrescine HMDB02064 0.5 (0.4–0.7) 32 1.18 (0.58–2.25) N-Methylhydantoin HMDB03646 4.9 (1.0–11.0) 55 NA o-Hydroxyphenylacetic acid HMDB00669 2.0 (0.9–4.5) 91 7.71±3.65d Oxoglutaric acid HMDB00208 4.8 (2.0–17.0) 100 2.98 (0.42–18.30) Oxypurinol HMDB00786 13.3 (5.1–29.3) 50 3.29 Pantothenic acid HMDB00210 1.9 (0.6–4.4) 100 2.7±0.9 p-Cresol sulfate HMDB11635 1.3 (0.3–5.5) 91 NA** Phenol HMDB00228 4.8 (0.6–12.8) 95 7.0 (5.6–9.2) Phenylacetic acid HMDB00209 1.9 5 4.16±0.91 Phenylacetylglutamine HMDB06344 34.0 (4.5–70.0) 100 47.03 (3.84–85.51) Phenylglyoxylic acid HMDB01587 7.34 5 36.6±0.48e Phosphorylcholine HMDB01565 1.1 (0.7–3.0) 41 NA p-Hydroxyphenylacetic acid HMDB00020 5.5 (1.4–14.6) 100 9.68 Picolinic acid HMDB02243 19.4 (7.2–37.6) 18 1.65–10.28 Pimelic acid HMDB00857 2.2 (0.7–4.0) 77 NA** Proline-betaine HMDB04827 2.3 (0.8–4.0) 100 12.7 (1.6–23.7) Propylene glycol HMDB01881 6.7 (1.4–44.3) 100 NA** Pseudouridine HMDB00767 28.9 (13.3–41.3) 100 26.02±4.62 Pyrocatechol HMDB00957 4.7 (2.0–8.5) 100 6.0±4.3 Pyroglutamic acid HMDB00267 20.7 (10.2–32.6) 100 28.8 (3.4–54.2) Pyruvic acid HMDB00243 2.7 (0.8–6.4) 100 2.13 (0.54–8.67) Quinolinic acid HMDB00232 5.2 (1.0–17.5) 100 2.5±1.1 Sarcosine HMDB00271 2.9 (0.5–5.4) 77 2.8 (0.0–5.6) Scyllitol HMDB06088 4.2 (2.2–8.1) 68 NA Sebacic acid HMDB00792 1.9 5 1 (0–2) Sorbitol HMDB00247 9.9 (2.5–18.7) 100 3.50±2.24 Suberic acid HMDB00893 1.6 (0.9–2.4) 32 0.5 (0.0–2.9) Succinic acid HMDB00254 6.0 (0.3–33.3) 91 5.6±3.8 Succinylacetone HMDB00635 2.8 (0.6–4.7) 36 1 (0–2) Sucrose HMDB00258 7.4 (1.4–19.5) 100 7.12±3.23 Sumiki’s acid HMDB02432 1.7 5 NA** Symmetric dimethylarginine HMDB03334 2.7 (1.5–5.2) 100 3.72±0.35 Syringic acid HMDB02085 1.5 5 0.9±0.2 Tartaric acid HMDB00956 11.8 (2.6–64.4) 82 12.0 (1.3–46.0) Taurine HMDB00251 81 (13–251) 100 4.00–159.98 Threonic acid HMDB00943 20.8 (10.2–39.3) 100 10.0±7.0 Thymidine HMDB00273 2.2 (0.7–3.9) 36 6.87±4.86 Trans-Aconitic acid HMDB00958 6.6 (1.8–25.1) 55 3.38 Trans-Ferulic acid HMDB00954 1.2 5 36.3±11.2f Trigonelline HMDB00875 31.1 (5.5–109.3) 100 16.08 Trimethylamine HMDB00906 2.5 (0.3–19.4) 86 7.7±7.4 Trimethylamine N-oxide HMDB00925 91.0 (4.8–509.0) 100 118.7 (35.5–202.1) Uracil HMDB00300 9.5 (2.6–22.8) 100 12 (2–22) Urea HMDB00294 12,285 (174–49,097) 100 22,566±4,407 Vanillic acid HMDB00484 1.6 (0.6–3.3) 95 1.0 (0.0–2.5) Vanillylmandelic acid HMDB00291 2.3 (1.0–3.4) 9 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. a The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman; b depends greatly on amount of alcohol consumed; c after administration of lactose; d after administration of 1 g of coumarin; e After styrene exposure; and f after consumption of chocolate. NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. Based on these NMR studies, the most abundant constituents (based on average values) of urine from a healthy individual are urea (12.3 ± 14.5 mM/mM creatinine), creatinine (14.7 ± 9.8 mM), hippuric acid (229 ± 160 µM/mM creatinine) and citric acid (203 ± 129 µM/mM creatinine). The least abundant compounds were trans-ferulic acid (1.2 µM/mM creatinine), phosphorylcholine (1.1 ± 0.7 µM/mM creatinine), 4-ethylphenol (0.9 ± 0.1 µM/mM creatinine), 2-methylglutaric acid (0.8 ± 0.4 µM/mM creatinine). The lowest concentrations that could be reliably detected using NMR were 0.4 ± 0.1 µM/mM creatinine (for maleic acid), 0.5 ± 0.2 µM/mM creatinine (for alpha-ketoisovaleric acid and N-acetylputrescine) and 0.7 ± 0.4 µM/mM creatinine (for fumaric acid). The complete list of average compound concentrations (including their range), literature-reported concentrations (for healthy adult) and the frequency of their occurrence is shown in Table 3. In general, there is good agreement between the NMR-measured concentrations and those reported in the literature (often obtained by other analytical techniques). However, not all of the NMR-derived urine concentrations agree with literature-derived values. A total of 34 compounds had average concentrations somewhat higher (>1 SD) than previously reported values (for example, 1, 3-dimethyluric acid, glucaric acid, L-aspartic acid, mannitol), while 21 compounds had average concentrations lower (<1 SD) than previously reported (for example, 1-methylhistidine, phenol, dihydrothymine, thymidine). Another 7 metabolites also exhibited somewhat greater range in concentrations than those previously reported in the literature. These included: acetic acid, butyric acid, isovaleric acid, lactose, phenylglyoxylic acid, proline-betaine and trans-ferulic acid. Some of these discrepancies are likely due to differences in diet, physiological status, pharmacological effects and the age of the different cohorts that were analyzed. Other differences may be due to storage effects, sample preparation methods and the analytical methods being used. As a rule, NMR concentration determinations are very accurate since they involve direct measurement of the compound, as opposed to an indirect measurement of a derivative compound. Therefore we are quite confident in the NMR concentration values reported in Table 3 and would tend to view these as more reliable than those measured via other technologies. A number of the compounds exhibiting higher-than or lower-than-reported concentrations appear to be associated with dietary intake. For example, mannitol is a sugar alcohol that is poorly absorbed by humans but its presence in urine can be explained by its occurrence in commonly consumed foods such as apples, pineapples, asparagus and carrots. Likewise, the urinary excretion of trans-ferulic acid (a polyphenolic derivative) increases after the ingestion of breakfast cereals [61] and chocolate. The relatively low value we measured is likely due to the fact that the literature value of trans-ferulic acid reported in Table 3 was measured for people on a special diet [62]. Also, the low level of proline-betaine we measured in urine may be due to a lower frequency of exposure to dietary citrus fruits in our population sample [63]. Proline-betaine is an osmoprotectant found in citrus fruit and urinary excretion of this metabolite is increased after consumption of fruits such as orange juice [64]. Similarly the higher levels of dimethyl sulfone we detected in urine could be attributed to dietary sources that contain DMSO [65]. For example, onions contain many sulfoxides including DMSO which can be oxidized in the liver and kidneys to produce dimethyl sulfone [66], [67]. The consumption of meat could also significantly increase the concentration of some metabolites in urine as reported in the case of 1-methylhistidine [68]. 1-methylhistidine is produced from the metabolism of anserine (a dipeptide) which is commonly found in meats [69], [70]. In addition to diet, metabolite levels in urine can also be affected by physiological status. For instance, the level of 3-hydroxybutyric acid in urine increases during fasting and can range from 0 to 200 µM/mM creatinine, with the maximum level reported in the literature (200 µM/mM creatinine) corresponding to healthy male after 35 h of fasting [50]. Some of the metabolites we measured by NMR did not have any previously reported literature values. For example, glucuronic acid is usually reported as total glucuronic acid (the free acid plus glucuronide conjugates) after hydrolysis [71], [72]. Here we report the concentration of free glucuronic acid, as indicated in Table 3. Another example, 2-methylerythritol, was previously detected in human urine but no concentration was reported [73]. The urinary excretion of 2-methylerythritol is most likely a result of dietary consumption of fruits or vegetables containing 2-methylerythritol and/or 2-methylerythritol-4-phosphate. 2-methylerythritol-4-phosphate is an intermediate in isoprenoid biosynthesis [74] and has been found to be quite abundant in certain plants [75]. A number of compounds we measured by NMR appear to be normal constituents of human urine but seem not to have been previously reported as being detectable by NMR (a total of 42 compounds) or reported as detected but not-quantified by any other method (a total of 8 compounds). The identification of these “NMR-novel” compounds was aided by their prior identification by GC-MS and DI-MS (see following sections) and through a careful literature analysis of compounds that had previously been detected in human urine via other methods. The list of detected but not previously quantified by NMR compounds includes: 2-hydroxy-3-methylpentanoic acid, 2-methyl-3-ketovaleric acid, 2-methylerythritol, glucuronic acid, monomethyl glutaric acid, N-methylhydantoin, phosphorylcholine and scyllitol. All of these compounds were confirmed using authentic standards. This NMR study also revealed a number of common identification errors made in previously published NMR-based human urine metabolomic studies. In particular, several earlier reports identified phenylacetylglycine [76], N-acetylglutamic acid [77], cresol [78], isonicotinic acid [78], yellow 7.1 [79], meta-hydroxyphenylpropionic acid [80], 2-oxoisocaproic acid [81], urocanic acid, glycylproline and ornithine [80] as being detectable by NMR in human urine. Using our NMR instrument and the samples available to us, we were unable to detect any of these compounds, even after performing multiple spike-in experiments using authentic compounds. While some of these metabolites have been previously reported to be in human urine, they were reported at concentrations far below the lower limit of detection of modern NMR instruments (which is ∼ 1 µM). Due to their chemical shift similarity, phenylacetylglycine (which is found only in rats and mice) and N-acetylglutamic acid appear to be commonly mistaken for phenylacetylglutamine. We also noticed that, isonicotinic acid (a breakdown product of isoniazid and hydrazine derivatives, which is found only in individuals that have taken isoniazid and other hydrazine derivatives as a drug) appears to be mistaken for trigonelline. Likewise cresol (water-insoluble) appears to be frequently mistaken for cresol-sulfate (water-soluble), while the compounds yellow 7.1, meta-hydroxyphenylpropionic acid and 3-(p-hydroxyphenyl)-propionic acid appear to be commonly mistaken for 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA). In addition to correcting these compound identification errors, we also observed some significant gender-related effects on creatinine levels in our urine samples. Since males generally have a greater mass of skeletal muscle than females, they tend to have higher urinary levels of creatinine than women. This was clearly evident in our samples as the average male creatinine level was 20 mM while the average female creatinine level was 11 mM. In addition, increased dietary intake of creatine or a protein-rich diet can increase daily creatinine excretion [82]. Quantification and Identification of Urine Metabolites – GC-MS As seen in Table 1, GC-MS methods have long been used to comprehensively characterize the chemical content of human urine. For our studies a total of 4 different GC-MS analyses were performed. The first method employed polar solvent extraction and derivatization to achieve broad metabolite coverage of polar metabolites, the second was more selective and targeted organic acids, the third targeted volatiles, while the fourth targeted bile acids. Representative high-resolution GC-MS total ion chromatograms are shown in Figures 2–4 for each of these analyses (except for the bile acids). Combined, the 4 GC-MS methods allowed us to identify 179 and quantify a total of 85 compounds. Table 4 shows the identified polar, organic acid extracts and bile acids (127 in total), Table 5 shows the identified volatile metabolites (52 in total) while Table 6 shows the 85 fully quantified compounds from all 4 techniques. These numbers actually represent the highest number of urine metabolites both identified and quantified by GC-MS to date. As seen in Table 1, previous GC-MS studies have reported up to 258 unique compounds being identified (but none quantified) [83] and approximately 95 compounds quantified in human urine [84]. Relative to NMR (see previous section) and other methods used to analyze human urine (Table 1), it appears that a multi-pronged GC-MS analysis is an excellent approach to characterize this biofluid. However, unlike NMR where nearly all detectable peaks are identifiable, metabolite coverage by GC-MS tends to be relatively incomplete. As seen in Figure 2, only 60% of the peaks could be identified using as reference the 2008 NIST library and other home-made GC-MS metabolite libraries. Likewise, in Figure 3, we see that only 65% of the organic acid peaks could be identified while in Figure 4, just 60% of the volatile compound peaks could be identified. Figure 2 Typical total ion chromatogram of water-soluble metabolites extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: L-valine; 2: oxalic acid; 3∶3-hydroxyisobutyric acid; 4: L-serine; 5: glycine; 6: succinic acid; 7: glyceric acid; 8∶4-deoxythreonic acid; 9∶2,4-dihydroxybutanoic acid; 10∶3,4-dihydroxybutanoic acid; 11: adipic acid; 12: creatinine; 13: threonic acid; 14: L-phenylalanine; 15: p-hydroxyphenylacetic acid; 16: L-ornithine; 17: L-asparagine; 18: L-arabinose; 19: D-xylitol; 20: D-xylulose; 21: Cis/Trans-aconitic acid; 22: hippuric acid; 23: isocitric acid; 24: D-galactose; 25: D-glucose; 26: L- tyrosine; 27: sorbitol; 28: gluconic acid; 29: scyllitol; 30: myoinositol; 31: uric acid; 32: pseudouridine. Figure 3 Typical GC-MS total ion chromatogram of organic acids extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: pyruvic acid; 2: L-lactic acid; 3: alpha-hydroxyisobutyric acid; 4: glycolic acid; 5: levulinic acid; 6∶3-hydroxyisovaleric acid; 7∶2-hydroxy-2-methylbutyric acid; 8: hydroxypropionic acid; 9∶2-methyl-3-hydroxybutyric acid; 10: malonic acid; 11∶3-hydroxyisovaleric acid; 12: methylmalonic acid; 13∶2-ethylhydracrylic acid; 14: benzoic acid; 15: phosphoric acid; 16: ethylmalonic acid; 17: succinic acid; 18: methylsuccinic acid; 19∶4-deoxythreonic acid; 20∶5-hydroxyhexanoic acid; 21: citraconic acid; 22: glutaric acid; 23: m-chlorobenzoic acid; 24; 3,4-dihydroxybutanoic acid; 25∶3-methylglutaconic acid; 26: adipic acid; 27: pyroglutamic acid; 28∶3-methyladipic acid; 29: sumiki’s acid; 30: o-hydroxyphenylacetic acid; 31: oxoglutaric acid; 32: pimelic acid; 33∶3-hydroxymethylglutaric acid; 34∶3-hydroxyphenylacetic acid; 35∶4-hydroxybenzoic acid; 36∶2-furoylglycine; 37: suberic acid; 38: quinolinic acid; 39: Cis/Trans-aconitic acid; 40: homovanillic acid; 41: azelaic acid; 42: hippuric acid; 43∶3,4-dihydroxybenzeneacetic acid; 44∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 45: vanillylmandelic acid; 46∶4-hydroxyphenyllactic acid; 47: indoleacetic acid; 48: palmitic acid; 49: kynurenic acid; 50∶3-hydroxyhippuric acid; 51∶3-hydroxysebacic acid; 52: Trans-ferulic acid; 53∶5-hydroxyindoleacetic acid; 54: stearic acid. Figure 4 Typical gas chromatogram of volatile organic compounds (VOC) from a pooled human urine sample. Numbers indicate the following metabolites: 1∶3-methyl sulfolane; 2∶3-hexanone; 3∶2-pentanone; 4∶1-hydroxy-2-pentanone; 5: allyl methylsulphide; 6: dimethyl disulfide; 7∶4-heptanone; 8∶1-methylcyclohexanol; 9; 2-hexanone; 10∶3,4-dimethylthiophene; 11: diallyl sulphide; 12∶5-methyl-2-hexanone; 13∶1,3-dithio cyclohexane; 14: dimethyl trisulfide; 15: phenol; 16: o-cymene; 17: p-cymene; 18: m-cymene; 19∶1,4-cineol; 20: p-cresol; 21: linalool oxide; 22: iso-menthol; 23: Alpha-p-dimethylstyrene; 24: L-menthol; 25: undecane; 26: ledene oxide (II); 27: salicylic acid methyl ester; 28: Beta-carvone; 29: piperitone; 30: o-thymol; 31: Beta-cyclocitrol; 32∶4,7-dimethyl-benzofuran; 33: cuminal: 34∶2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene; 35∶4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone; 36∶1,2,3,4-tetrahydro-1,1,6-trimethyl naphthalene; 37: Alpha-cedrene; 38∶1,2,3,4-tetrahydro-1,5,7-trimethylnapthalene; 39∶1,2-diydro-1,1,6-trimethyl-napthalene; 40: Beta-guaiene; 41: Beta-damascenone; 42∶2,5-cyclohexadiene-1,4-dione-2,6-di-tert-butyl; 43: himachalene; 44∶4-(2,6,6-trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one: 45∶1-(2,6,6-trimethyl-1-cyclohexen-1-yl)-1-penten-3-one; 46∶2,4-Bis (1,1-dimethylethyl)-phenol; 47∶1-(2,3,6-trimethyl phenyl)-3-buten-2-one; 48: L-calamenene; 49: Beta-vatirenene; 50∶1,6,7-trimethylnaphthalene; 51: azulol; 52∶3,3,5,6-tetramethyl-1-indanone. Table 4 List of metabolites identified in human urine extracts by GC-MS (except volatile compounds). Amino acids HMDB ID Organic acids HMDB ID Beta-Alaninea HMDB00056 Azelaic acidb HMDB00784 Glycinea HMDB00123 Benzoic acidb HMDB01870 L-Alaninea HMDB00161 Butyric acida HMDB00039 L-Asparaginea HMDB00168 Cis-Aconitic acida,b HMDB00072 L-Aspartic acida HMDB00191 Citraconic acidb HMDB00634 L-Glutaminea HMDB00641 Citramalic acidb HMDB00426 L-Histidinea HMDB00177 Citric acida HMDB00094 L-Isoleucinea HMDB00172 Erythronic acida HMDB00613 L-Leucinea HMDB00687 Ethylmalonic acidb HMDB00622 L-Lysinea HMDB00182 Gluconic acida HMDB00625 L-Ornithinea HMDB00214 Glutaric acidb HMDB00661 L-Phenylalaninea HMDB00159 Glyceric acida HMDB00139 L-Serinea HMDB00187 Glycolic acida,b HMDB00115 L-Threoninea HMDB00167 Glyoxalic acida HMDB00119 L-Tryptophana HMDB00929 Hippuric acida,b HMDB00714 L-Tyrosinea HMDB00158 Homovanillic acidb HMDB00118 L-Valinea HMDB00883 HPHPAb HMDB02643 Bile acids HMDB ID Hydroxypropionic acidb HMDB00700 Cholenic acidc HMDB00308 Indoleacetic acidb HMDB00197 Deoxycholic acidc HMDB00626 Indolelactic acid b HMDB00671 Lithocholic acidc HMDB00761 Isobutyrylglycineb HMDB00730 Ursodeoxycholic acidc HMDB00946 Isocitric acida HMDB00193 Misc HMDB ID Isovalerylglycineb HMDB00678 3-Methylhistidinea HMDB00479 Kynurenic acidb HMDB00715 5-Hydroxyindoleb HMDB59805 Levulinic acidb HMDB00720 Creatininea HMDB00562 L-Lactic acidb HMDB00190 Dimethylglycinea HMDB00092 Malic acida HMDB00744 D-Xylitola HMDB02917 Malonic acida,b HMDB00691 Glycerol 3-phosphatea HMDB00126 m-Chlorobenzoic acidb HMDB01544 Glycerola HMDB00131 m-Coumaric acidb HMDB01713 Mannitola HMDB00765 Methylmalonic acidb HMDB00202 Myoinositola HMDB00211 Methylsuccinic acidb HMDB01844 Pseudouridinea HMDB00767 o-Hydroxyhippuric acidb HMDB00840 Scyllitola HMDB06088 o-Hydroxyphenylacetic acidb HMDB00669 Sorbitola HMDB00247 Oleic acidb HMDB00207 Ureaa HMDB00294 Oxalic acida HMDB02329 Organic acids HMDB ID Oxoglutaric acidb HMDB00208 2,4-Dihydroxybutanoic acida HMDB00360 Palmitic acidb HMDB00220 2-Ethylhydracrylic acidb HMDB00396 Pantothenic acidb HMDB00210 2-Furoylglycineb HMDB00439 Phosphoric acidb HMDB02142 2-Hydroxy-2-methylbutyric acidb HMDB01987 p-Hydroxyphenylacetic acida HMDB00020 2-Hydroxyglutaric acida HMDB00694 Pimelic acidb HMDB00857 2-Hydroxyvaleric acida HMDB01863 Pyroglutamic acida,b HMDB00267 2-Ketobutyric acidb HMDB00005 Pyruvic acidb HMDB00243 2-Methyl-3-hydroxybutyric acidb HMDB00354 Quinolinic acidb HMDB00232 3,4-Dihydroxybenzeneacetic acidb HMDB01336 Stearic acidb HMDB00827 3,4-Dihydroxybutanoic acida,b HMDB00337 Suberic acidb HMDB00893 3,4-Dihydroxyhydrocinnamic acidb HMDB00423 Succinic acida,b HMDB00254 3-Aminoisobutanoic acida,b HMDB03911 Sumiki’s acidb HMDB02432 3-Hydroxyhippuric acidb HMDB06116 Tartaric acida HMDB00956 3-Hydroxyisobutyric acida HMDB00023 Threonic acida HMDB00943 3-Hydroxyisovaleric acida,b HMDB00754 Trans-Aconitic acida HMDB00958 3-Hydroxymethylglutaric acidb HMDB00355 Trans-Ferulic acidb HMDB00954 3-Hydroxyphenylacetic acidb HMDB00440 Uric acida HMDB00289 3-Hydroxysebacic acidb HMDB00350 Vanillyllactic acidb HMDB00913 3-Methyladipic acidb HMDB00555 Vanillylmandelic acidb HMDB00291 3-Methylglutaconic acidb HMDB00522 Sugars HMDB ID 4-Deoxythreonic acida,b HMDB02453 D-Fructosea HMDB00660 4-Hydroxybenzoic acidb HMDB00500 D-Galactosea HMDB00143 4-Hydroxyphenyllactic acidb HMDB00755 D-Glucosea HMDB00122 5-Hydroxyhexanoic acidb HMDB00525 D-Xylosea HMDB00098 5-Hydroxyindoleacetic acidb HMDB00763 D-Xylulosea HMDB01644 Adipic acida,b HMDB00448 L-Arabinosea HMDB00646 Alpha-Hydroxyisobutyric acidb HMDB00729 L-Fucosea HMDB00174 Aminomalonic acida HMDB01147 Sucrosea HMDB00258 Ascorbic acida HMDB00044 a Polar metabolites. b Targeted organic acids. c Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid. Table 5 Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS. Peak # Compound HMDB ID Uniquemass MSmatch RT(min) RI(Calc) RI(NIST) 1 3-Methyl sulfolane HMDB59667 68 867 0.83 725 654 2 3-Hexanone HMDB00753 43 781 0.99 732 788 3 2-Pentanone HMDB34235 43 894 1.61 758 654 4 1-Hydroxy-2-pentanone HMDB59678 43 780 1.74 763 897 5 Allyl methylsulphide HMDB31653 88 793 1.83 767 701 6 Dimethyl disulfide HMDB05879 94 940 2.26 785 763 7 4-Heptanone HMDB04814 71 919 3.34 831 883 8 1-Methylcyclohexanol HMDB59693 71 821 3.81 850 931 9 2-Hexanone HMDB05842 43 821 3.82 851 889 10 3,4-Dimethylthiophene HMDB29304 111 906 4.15 865 888 11 Diallyl sulphide HMDB36491 45 827 4.86 895 848 12 5-Methyl-2-hexanone HMDB31549 43 816 5.29 913 836 13 1,3-Dithio cyclohexane HMDB31473 120 841 5.85 936 1027 14 Dimethyl trisulfide HMDB13780 126 873 6.01 943 950 15 Phenol HMDB00228 94 859 6.34 957 998 16 o-Cymene HMDB37050 119 928 7.19 993 1014 17 p-Cymene HMDB05805 119 928 7.33 998 1018 18 m-Cymene HMDB13806 119 928 7.43 1009 1022 19 1,4-Cineol HMDB31455 111 912 8.71 1057 1016 20 p-Cresol HMDB01858 107 930 8.99 1068 1082 21 Linalool oxide HMDB31440 59 783 10.49 1131 1074 22 Iso-menthol HMDB35764 57 923 10.99 1152 1141 23 Alpha-p-dimethylstyrene HMDB29641 132 915 11 1153 1088 24 L-Menthol HMDB03352 71 924 11.18 1160 1140 25 Undecane HMDB31445 57 910 11.35 1168 1100 26 Ledene Oxide (II) HMDB60295 43 743 11.54 1176 1201 27 Salicylic acid methyl ester HMDB34172 120 857 11.65 1180 1193 28 Beta-Carvone HMDB35824 82 859 12.81 1229 1247 29 Piperitone HMDB34975 150 873 12.96 1235 1184 30 o-Thymol HMDB35770 135 904 13.9 1275 1305 31 Beta-Cyclocitrol HMDB41011 41 884 14.1 1283 1219 32 4,7-Dimethyl-benzofuran HMDB59897 146 861 14.4 1296 1244 33 Cuminal HMDB02214 133 861 14.82 1313 1235 34 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene HMDB36823 138 911 15.91 1359 1370 35 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone HMDB32913 121 795 15.97 1362 1433 36 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene HMDB59826 159 855 16.02 1364 1349 37 Alpha-Cedrene HMDB59695 119 821 16.1 1367 1415 38 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene HMDB59696 159 795 16.59 1388 1310 39 1,2-Diydro-1,1,6-trimethyl-napthalene HMDB40284 157 947 16.82 1398 1354 40 Beta-Guaiene HMDB38157 161 879 17 1405 1500 41 Beta-Damascenone HMDB13804 69 889 17.35 1420 1365 42 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl HMDB13817 177 843 18.51 1469 1462 43 Himachalene HMDB38168 119 823 18.62 1473 1489 44 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one HMDB37139 136 883 18.73 1478 1497 45 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one HMDB38130 121 788 19.05 1491 1507 46 2,4-Bis (1,1-Dimethylethyl)-phenol HMDB13186 191 863 19.5 1510 1519 47 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one HMDB59697 132 918 19.8 1523 1528 48 L-Calamenene HMDB59910 159 920 20.25 1533 1534 49 Beta-Vatirenene HMDB59676 202 806 20.45 1550 1545 50 1,6,7-Trimethylnaphthalene HMDB59701 170 899 20.82 1564 1559 51 Azulol HMDB59686 183 823 21.18 1581 1572 52 3,3,5,6-Tetramethyl-1-indanone HMDB59683 173 686 21.28 1585 1579 Table 6 Concentrations of metabolites in human urine as measured by GC-MS. Compounds Observed Concentration(µM/mM creatinine) Literature Values(µM/mM creatinine) 2,4-Dihydroxybutanoic acid 1.0 (0.3–1.8) 2.0±2.0 2-Ethylhydracrylic acid 2.1 (1.3–2.9) NA** 2-Furoylglycine 3.6 (0.7–5.8) 9.95 (2.0–18.66) 2-Hydroxy-2-methylbutyric acid 0.8 (0.4–1.5) 2.8±1.5 2-Methyl-3-hydroxybutyric acid 4.0 (1.3–6.2) 5.5 (0.0–11.0) 3,4-Dihydroxybenzeneacetic acid 0.9 (0.6–1.3) 1.16±0.50 3,4-Dihydroxybutanoic acid 42.2 (21.9–56.1) 34.9 (12.5–57.2) 3-Aminoisobutanoic acid 25.8 (1.4–155.6) 2.91–116.43 3-Hydroxyhippuric acid 6.1 (4.1–7.8) 2.5 (0.2–5.0) 3-Hydroxyisobutyric acid 29.0 (11.8–59.8) 17.5 (2.0–33.0) 3-Hydroxyisovaleric acid 7.4 (4.1–17.2) 8.5±3.2 3-Hydroxymethylglutaric acid 3.2 (1.1–5.2) 3 (0–10) 3-Hydroxyphenylacetic acid 5.6 (1.4–15.0) 6.1±4.1 3-Hydroxysebacic acid 2.9 (1.7–3.8) NA** 3-Methyladipic acid 2.1 (0.9–5.6) 2.9 (0.7–10.5) 3-Methylglutaconic acid 6.2 (2.8–8.3) 4.5 (0.0–9.0) 4-Deoxythreonic acid 18.2 (5.6–23.9) 25±10 4-Hydroxybenzoic acid 2.4 (1.3–3.6) 1.8±1.8 4-Hydroxyphenyllactic acid 2.8 (2.6–3.0) 1.1 (0.2–2.6) 5-Hydroxyhexanoic acid 2.7 (0.8–5.7) 3.5 (0.0–7.0) 5-Hydroxyindoleacetic acid 2.9 (0.4–5.8) 2.4±0.40 Adipic acid 3.0 (1.0–12.9) 5.1 (0.8–35.0) Alpha-Hydroxyisobutyric acid 2.9 (1.3–4.6) 3.8 Azelaic acid 1.9 (1.2–3.2) 4.8 (1.3–15.0) Benzoic acid 3.6 (1.4–7.5) 4.2 (1.9–6.5) Cholenic acid 0.015 (0.004–0.030) NA** Cis/Trans-Aconitic acid 15.3 (5.2–21.8) NA** Citraconic acid 1.6 (0.9–2.1) 2.2 Creatinine* 12,246±8,369 10,090±310 Deoxycholic acid 0.055 (0.050–0.060) NA** D-Galactose 3.3 (1.3–8.5) 4.4 (0.0–31.0) D-Glucose 35.6 (10.3–56.7) 31.12 D-Xylitol 2.3 (0.9–8.2) 3.50±2. 37 D-Xylulose 19.7 (6.4–32.6) NA** Ethylmalonic acid 2.4 (0.9–4.0) 2.0 (0.5–3.9) Gluconic acid 13.3 (8.2–26.4) NA** Glutaric acid 1.4 (0.5–2.3 ) 1.3 (0.6–2.6) Glyceric acid 14.4 (7.8–28.8) 4.5 (0.0–9.0) Glycine 75.7 (21–205) 151 (233–248) Glycolic acid 39.4 (2.9–78.1) 35 (18–55) Hippuric acid 217 (28–610) 257 (20–770) Homovanillic acid 4.3 (0.9–8.9) 4.9±3.4 HPHPA 4.2 (0.9–15.3) 0.55–54.93 Hydroxypropionic acid 8.1 (3.1–11.8) 6.5 (3.0–10.0) Indoleacetic acid 2.6 (0.6–5.4) 2.0±1.0 Indolelactic acid 0.9 0.460 (0.098–0.980) Isocitric acid 56.8 (19.4–119.1) 58 (36–84) Kynurenic acid 2.6 (0.9–4.1) 1.34±0.30 L-Arabinose 8.8 (0.8–19.4) 8.75 (6.12–11.84) L-Asparagine 9.5 (3.0–26.0) 10.0 (4.6–16.3) Levulinic acid 1.7 (0.6–2.5) 1.28 (0.33–2.75) Lithocholic acid 0.16 (0.10–0.20) NA** L-Lactic acid 7.1 (0.9–16.4) 12.3±6.2 L-Ornithine 5.3 (2.0–8.8) 5.0 (2.0–8.0) L-Phenylalanine 7.8 (5.0–11.3) 4.50±1.15 L-Serine 19.0 (10.4–35.8) 26.0±10.0 L-Tyrosine 15.1 (6.1–23.2) 8.8±4.5 L-Valine 5.5 (2.7–9.8) 3.0±1.0 Malonic acid 2.3 (1.2–3.1) 1 (0–2) m-Chlorobenzoic acid 0.7 NA** Methylmalonic acid 1.3 (0.5–2.1) 1.6 (0.0–3.6) Methylsuccinic acid 4.7 (1.1–6.2) 2.3 (0.8–10.8) Myoinositol 12.6 (5.1–15.3) 18.8±2.9 o-Hydroxyphenylacetic acid 2.9 (1.4–3.7) 7.71±3.65 Oxalic acid 8.2 (3.9–14.0) 1.11–33.34 Oxoglutaric acid 3.4 (1.4–12.5) 2.98 (0.42–18.30) Palmitic acid 6.7 (2.6–24.3) 11 (6–23) Phosphoric acid 784 (425–1170) 1000–4900 p-Hydroxyphenylacetic acid 6.0 (2.4–9.7) 9.68 Pimelic acid 1.9 (0.5–3.5) NA** Pseudouridine 29.8 (21.8–47.3) 26.02±4.62 Pyroglutamic acid 18.6 (4.5–24.9) 28.8 (3.4–54.2) Pyruvic acid 2.1 (1.0–3.7) 2.13 (0.54–8.67) Quinolinic acid 3.9 (0.9–15.1) 2.5±1.1 Scyllitol 3.6 (1.9–6.9) NA Sorbitol 3.9 (1.9–5.1) 3.50±2.24 Stearic acid 3.9 (2.3–7.7) 2.9 (1.6–6.6) Suberic acid 1.2 (0.5–1.9) 0.5 (0.0–2.9) Succinic acid 6.2 (2.5–13.5) 5.6±3.8 Sumiki’s acid 1.6 NA** Threonic acid 14.8 (8.6–36.1) 10.0±7.0 Trans-Ferulic acid 1 36.3±11.2 Uric acid 186 (93–329) 188 (79–296) Ursodeoxycholic acid 0.022 (0.013–0.030) NA** Vanillylmandelic acid 1.6 (0.6–2.8) 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. Incomplete compound identification is a common problem with global or untargeted GC-MS metabolomics. This may be due to any number of factors including spectral overlap due to incomplete separation, poor signal to noise for low intensity peaks, the lack of reference GC-MS spectral data for certain metabolites (especially unusual dietary sources), or the presence of spectral artefacts such as derivatization by-products or degraded metabolites in the GC-MS spectrum. For our GC-MS studies we used the NIST library supplemented with a home-made GC-MS reference library of known urine compounds assembled from the Human Metabolome Library [16]. No doubt the use of other commercially available reference GC-MS libraries such as the Fiehn GC-MS library from Agilent or the GOLM metabolome database library [85] might have allowed us to further increase our coverage. Likewise the use of a faster scan rate and/or a more sensitive GC-TOF instrument (instead of a slower scanning quadrupole GC-MS) certainly would have increased overall coverage. Nearly all of the non-volatile metabolites (87) identified by our GC-MS analyses were also identified by NMR. Some of the exceptions were oxalic acid, phosphate and uric acid, each of which was detected by GC-MS but not by NMR. These compounds do not have NMR-detectable protons at physiological pH, making them essentially “NMR invisible”. Other compounds seen by GC-MS but not by NMR included metabolites that were generally below the detection limit of NMR (∼2 µM/mM creatinine) such as indolelactic acid and 2,4-dihydroxybutanoic acid. For our non-targeted GC-MS analysis, the lower limit of detection was 1 µM/mM creatinine (for 2,4-dihydroxybutanoic acid), while for our targeted organic acid GC-MS analysis the lower limit of detection was 0.7 µM/mM creatinine (for m-chlorobenzoic acid). Overall, our data suggests that the sensitivity of a standard single quadrupole GC-MS instrument is perhaps 1.5–2X better than a 500 MHz NMR instrument for water-soluble metabolites. It is also important to note that the level of water-soluble, non-volatile metabolite coverage obtained by GC-MS is not as great as seen with NMR (127 cmpds vs. 209 cmpds). The limited coverage of GC-MS is partly due to the fact that not all compounds can be readily extracted, easily derivatized or routinely separated on a GC column. Furthermore, when analyzing urine by GC-MS there is a need to pretreat the sample with urease (to reduce urea levels) that can diminish the abundance of some metabolites [86]. While GC-MS may not be the best method for analyzing water-soluble metabolites, it certainly excels at the detection of volatile metabolites. Indeed, only one of the volatile metabolites identified by GC-MS is identified by NMR (phenol). This certainly underlines a key strength of GC-MS relative to other metabolomics platforms. When comparing NMR to GC-MS we found that NMR is capable of detecting 121 compounds that the 4 combined GC-MS methods cannot detect while the combined GC-MS methods can detect 91 compounds that NMR cannot routinely detect. Overall, these data suggest that GC-MS and NMR appear to be complementary methods for the identification and quantification of small molecules in urine. Among the 58 metabolites quantified by both GC-MS and NMR we found very good overall agreement, with the majority of measured concentration values falling within 20 ± 11% of each other. The concentration patterns and rankings of the most abundant to the least abundant compounds were also largely identical for the two platforms. A total of 12 metabolites exhibited somewhat larger concentration discrepancies between GC-MS and NMR (i.e; L-arabinose, L-serine (lower in GC-MS vs. NMR), 4-hydroxybenzoic acid and tyrosine (higher in GC-MS vs NMR). Some of these concentration differences may be due to the extraction or derivatization process needed to conduct GC-MS analyses. This can lead to unspecified compound losses, unusual derivatives or unrecognized fragmentation patterns. Therefore we would have expected at least a few GC-MS concentration values to be slightly lower than those seen by NMR. Likewise, it is important to remember that there are inherent errors (5–10%) in measuring peak areas (i.e. compound concentrations) both in GC-MS and NMR due to peak overlap, uneven baselines and spectral noise. Nearly all of the compounds we detected or quantified in human urine by GC-MS have been previously described or mentioned in the GC-MS literature. One compound (scyllitol), however, appears not to have been previously detected by GC-MS. The identification of this compound by our GC-MS method was aided by its prior identification by NMR (see previous section). Additionally, a careful literature analysis also indicated the scyllitol is a normal constituent of human urine and has previously been detected in human urine via other methods. As we noted with our NMR studies earlier, there are a few previously reported GC-MS detectable metabolites in human urine that appear to be artefacts. These artefactual metabolites may arise from extractions with different solvents, pre-treatment with urease, and chemical derivatization. For example, Shoemaker et al [84], reported the presence of bisethane in human urine. We also detected bisethane, but it appears to be artefact of chemical derivatization and is not a urine metabolite. Quantification and Identification of Urine Metabolites – Combined Direct Flow Injection and LC-MS/MS Assay Direct flow injection (DFI) MS/MS or DFI-MS/MS is another commonly used global metabolic profiling method [87]. When isotopic standards are used along with multiple reactions monitoring (MRM), it is also possible to perform targeted metabolomics with very accurate concentration measurements. For our urine studies, we employed a combined DFI/LC-MS/MS approach, based on the commercially available AbsoluteIDQ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck). When applied to urine, we were able to identify and quantify a total of 127 metabolites or metabolite species, including 34 acylcarnitines, 21 amino acids, 15 biogenic amines, creatinine, hexose, 35 phospatidylcholines, 15 sphingomyelins and 5 lysophosphatidylcholines. The amino acids and biogenic amines are analyzed by an LC-MS/MS method, whereas all other metabolites are analyzed by DFI-MS/MS as indicated in Table 7. DFI-MS/MS identifies lipid species (as opposed to specific lipids) using their total acyl/alkyl chain content (i.e. PC (38∶4)) rather than their unique structure. Therefore each lipid species identified by the BIOCRATES kit typically corresponds to 5–10 possible unique lipid structures. Consequently, the total number of phosphatidylcholines, sphingolipids and lysophosphatidylcholines structures identified by this method was 458, 19 and 6, respectively. Therefore, combining these probable lipid structures (483 in total, based on the known fatty acid and lipid composition in human serum) with the other 72 confirmed non-lipid metabolites, the DFI-MS/MS method yields 555 confirmed and probable metabolites or metabolite structures. All of these compounds, along with their corresponding estimated concentrations have all been entered into the UMDB. Table 7 Concentrations of acylcarnitines, amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and sphingomyelins in human urine by combined DFI/LC-MS/MS (BIOCRATES kit). Acylcarnitines (DFI-MS/MS) Mean (µM/mM creatinine) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) Acetyl-L-carnitine 2.0 (0.4–5.9) Kynurenine 0.41 (0.08–1.31) Butenyl-L-carnitine 0.014 (0.008 –0.021) L-Dopa 0.02 (0.01–0.04) Butyryl-L-carnitine 0.76 (0.12–1.81) Methioninesulfoxide 0.31 (0.14–0.66) Decadienyl-L-carnitine 0.10 (0.04–0.30) Phenylethylamine 0.011 (0.003–0.067) Decanoyl-L-carnitine 0.04 (0.02–0.06) Putrescine 0.20 (0.04–0.40) Decenoyl-L-carnitine 0.14 (0.06–0.32) Sarcosine 1.6 (0.2–9.4) Dodecanoyl-L-carnitine 0.02 (0.01–0.04) Serotonin 0.08 (0.04–0.12) Dodecenoyl-L-carnitine 0.028 (0.011–0.042) Symmetric dimethylarginine 2.8 (2.0–9.4) Glutaconyl-L-carnitine 0.04 (0.01–0.06) Lysophosphatidylcholines (DFI-MS/MS) Mean (µM/Mm creatinine) Glutaryl-L-carnitine 0.04 (0.03–0.10) lysoPC a C16∶0 0.014 Hexadecadienyl-L-carnitine 0.0010 (0.0005–0.0040) lysoPC a C17∶0 0.0032 (0.0008–0.0087) Hexadecanoyl-L-carnitine 0.0022 (0.0020–0.0030) lysoPC a C18∶0 0.0040 (0.0013–0.0100) Hexanoyl-L-carnitine 0.05 (0.03–0.07) lysoPC a C20∶4 0.0020 (0.0005–0.0038) Hexenoyl-L-carnitine 0.019 (0.009–0.050) lysoPC a C6∶0 0.0045 (0.0022–0.0074) Hydroxyhexadecanoyl-L-carnitine 0.0015 (0.0006–0.0050) Phosphatidylcholines (DFI-LC/MS) Mean (µM/Mm creatinine) Hydroxypropionyl-L-carnitine 0.008 (0.004–0.012) PC aa C28∶1 0.006 (0.003–0.020) Hydroxytetradecadienyl-L-carnitine 0.003 (0.001–0.008) PC aa C30∶2 0.0005 (0.0001–0.0022) Hydroxytetradecenoyl-L-carnitine 0.0020 (0.0008–0.0040) PC aa C32∶0 0.0035 (0.0013–0.0071) L-Carnitine 4.5 (0.6–15.2) PC aa C32∶3 0.0009 (0.0003–0.0025) Malonyl-L-carnitine 0.02 (0.01–0.04) PC aa C34∶1 0.0077 (0.0023–0.0500) Methylglutaryl-L-carnitine 0.030 (0.016–0.052) PC aa C34∶2 0.0047 (0.0016–0.0240) Methylmalonyl-L-carnitine 0.09 (0.05–0.16) PC aa C34∶3 0.002 Nonayl-L-carnitine 0.20 (0.07–0.46) PC aa C34∶4 0.0023 (0.0002–0.0180) Octadecadienyl-L-carnitine 0.0007 (0.0003–0.0020) PC aa C36∶1 0.0034 (0.0006–0.0250) Octanoyl-L-carnitine 0.05 (0.03–0.09) PC aa C36∶2 0.0160 (0.0021–0.0450) Octenoyl-L-carnitine 0.50 (0.08–1.20) PC aa C36∶3 0.0024 (0.0005–0.0240) Pimelyl-L-carnitine 0.03 (0.01–0.11) PC aa C36∶4 0.0018 (0.0005–0.0083) Propenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C36∶6 0.0037 Propionyl-L-carnitine 0.07 (0.01–0.20) PC aa C38∶3 0.0056 Tetradecadienyl-L-carnitine 0.0023 (0.0010–0.0050) PC aa C38∶4 0.0022 (0.0005–0.0065) Tetradecanoyl-L-carnitine 0.018 (0.001–0.088) PC aa C38∶5 0.0007 (0.0002–0.0020) Tetradecenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C38∶6 0.0010 (0.0005–0.0036) Tiglyl-L-carnitine 0.10 (0.05–0.20) PC aa C40∶3 0.00035 (0.0–0.00140) Valeryl-L-carnitine 0.220 (0.037–0.440) PC aa C42∶2 0.0009 (0.0003–0.0024) Creatinine Mean PC aa C42∶4 0.0008 (0.0003–0.0025) Creatinine* 12,475±7,955 PC ae C34∶1 0.0030 (0.0003–0.0110) Hexose Mean (µM/Mm creatinine) PC ae C34∶2 0.0012 (0.0002–0.0075) Hexose 110 (37–501) PC ae C36∶1 0.0046 (0.0012–0.0080) Amino acids (LC-MS/MS) Mean (µM/Mm creatinine) PC ae C36∶2 0.0016 (0.0002–0.0050) Glycine 101.0 (37.0–250.6) PC ae C36∶3 0.0009 (0.0001–0.0040) L-Alanine 21.7 (7.2–47.4) PC ae C36∶4 0.0022 L-Arginine 7.4 (3.2–14.6) PC ae C38∶2 0.0015 (0.0007–0.0023) L-Asparagine 8.8 (4.6–17.7) PC ae C38∶3 0.0020 (0.0012–0.0028) L-Aspartic acid 10.9 (1.9–26.8) PC ae C38∶4 0.0006 (0.0–0.0024) L-Citrulline 0.8 (0.2–1.7) PC ae C38∶6 0.0065 (0.0027–0.0170) L-Glutamic acid 6.4 (0.6–17.5) PC ae C40∶2 0.0012 (0.0006–0.0020) L-Glutamine 39.9 (18.4–72.5) PC ae C40∶5 0.0006 (0.0001–0.0025) L-Histidine 45.6 (21.7–71.5) PC ae C42∶3 0.0007 (0.0001–0.0020) L-Isoleucine 1.3 (0.5–2.7) PC ae C44∶3 0.0010 (0.0002–0.0031) L-Leucine 3.0 (1.6–6.0) Sphingomyelins (DFI-MS/MS) Mean (µM/Mm creatinine) L-Lysine 17.9 (3.6–56.1) SM (OH) C14∶1 0.0028 (0.0008–0.0049) L-Methionine 0.8 (0.4–1.6) SM (OH) C16∶1 0.0006 (0.0001–0.0023) L-Ornithine 4.4 (1.2–22.1) SM (OH) C22∶1 0.0023 (0.0009–0.0058) L-Phenylalanine 5.9 (2.8–10.8) SM (OH) C22∶2 0.0007 (0.0001–0.0028) L-Proline 1.1 (0.1–2.5) SM (OH) C24∶1 0.0010 (0.00034–0.0025) L-Serine 24.5 (12.3–50.2) SM C16∶0 0.0100 (0.0016–0.0700) L-Threonine 14.6 (6.6–29.3) SM C16∶1 0.0009 (0.0004–0.0023) L-Tryptophan 5.6 (9.3–2.1) SM C18∶0 0.0024 (0.0011–0.0070) L-Tyrosine 8.7 (3.0–20.1) SM C18∶1 0.0004 (0.0001–0.0013) L-Valine 4.3 (2.4–10.4) SM C20∶2 0.0003 (0.0–0.0016) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) SM C22∶3 0.0011 (0.0008–0.0014) Acetyl-ornithine 1.3 (0.5–2.8) SM C24∶0 0.0085 (0.0047–0.0200) Alpha-aminoadipic acid 7.2 (2.5–16.4) SM C24∶1 0.0030 (0.0007–0.0120) Asymmetric dimethylarginine 2.7 (1.4–4.2) SM C26∶0 0.0014 (0.0002–0.0043) Carnosine 1.2 (0.2–4.1) SM C26∶1 0.0005 (0.0001–0.0012) Dopamine 0.4 (0.2–0.7) Histamine 0.03 (0.01–0.10) Hydroxykynurenine 0.15 (0.06–0.34) * Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. Our results show very good agreement with the previous studies conducted by BIOCRATES on human urine samples (Biocrates Application Note 1005-1). We found that the lower limit of quantification by DFI MS/MS based on the AbsoluteIDQ kit was 0.1 nM/mM creatinine for certain phosphatidylcholine species (i.e. PC aa C40∶3) and 0.1 nM/mM creatinine for certain sphingomyelin species (i.e. SM 26∶1). Comparison of our lipid results with literature data was difficult as relatively few papers report urine lipid concentration data. Indeed, the presence of lipids in urine is a little unexpected, but not entirely unreasonable. It is likely that urea, a well known chaotrope, facilitates the dissolution of small amounts of fatty acids and other lipid species in urine. Many of the compounds we measured with this kit assay appear to be normal constituents of human urine but have not been previously reported (quantified and/or detected) in the scientific literature (with the exception of the BIOCRATES Application Note). In total, 53 compounds are being reported here for the first time as being normal constituents of human urine, while 68 compounds are being robustly quantified in human urine for the first time. The vast majority of these compounds are lipids. Based on our results, the combined DFI/LC-MS/MS method detected 98 compounds or compound species that GC-MS and NMR methods could not detect, while GC-MS detected 161 compounds and NMR detected 181 compounds that DFI/LC-MS/MS could not detect. The 3 methods were able to detect a common set of 17 compounds including creatinine, L-glutamine, L-tryptophan, L-tyrosine and L-valine. Interestingly, the concentrations measured by DFI/LC-MS/MS, NMR and GC-MS (across the shared set of 17 compounds) showed generally good agreement (within 19 ± 7% of each other). The relatively small overlap, in terms of compound coverage, between the 3 platforms is a bit of a surprise and certainly serves to emphasize the tremendous chemical diversity that must exist in urine. Overall, by combining these 3 platforms, we were able to identify 295 and quantify 231 unique or non-overlapping metabolites or metabolite species. These data suggest that DFI/LC-MS/MS, GC-MS and NMR are highly complementary techniques for the identification and quantification of metabolites in human urine. Quantification and Identification of Urinary Trace Metals – ICP-MS To determine the trace elemental composition of urine, we used inductively coupled plasma mass spectrometry (ICP-MS). ICP-MS is widely considered to be one of the best techniques for the characterization of the trace element composition of biological samples [88], [89]. Indeed, none of the other methods (1H-NMR, GC-MS and DFI/LC-MS/MS) are suited for measuring trace element composition or concentrations. Our multi-elemental analysis of urine using ICP-MS provided quantitative results for a total of 40 metals or trace minerals (Table 8). Based on their frequency of occurrence and overall abundance, all 40 trace elements appear to be normal constituents of human urine. Of these, 2 have previously not been quantified for healthy adults. Table 8 Concentrations of ions (µmol/mmol creatinine) as determined by ICP-MS. Compound Name HMDB Concentration (µM/mM creatinine) Litterature values(µM/mM creatinine) Aluminum (Al) HMDB01247 0.31 (0.05–1.21) 0.667±0.037 Arsenic (As) HMDB02290 0.10 (0.01–0.52) 0.16±0.16 Barium (Ba) HMDB04142 0.0070 (0.0001–0.0655) 0.0129–0.0320 Cadmium (Cd) HMDB03638 0.0004 (0.0002–0.0010) 0.00060 (0.00035–0.00100) Calcium (Ca) HMDB00464 200.0 (16.9–520.0) 299.0±99.0 Cerium (Ce) HMDB13672 0.00048 (0.00008–0.00180) 0.0 – 0.0007 Cesium (Cs) HMDB13669 0.0039 (0.0005–0.0110) 0.0061±0.0011 Chromium (Cr) HMDB00599 0.0084 (0.0001–0.0460) 0.0100±0.0065 Cobalt (Co) HMDB00608 0.0014 (0.0001–0.0140) 0.000810±0.00053 Copper (Cu) HMDB00657 0.0163 (0.0006–0.1099) 0.0190 (0.0092–0.038) Gallium (Ga) HMDB01478 0.0056 (0.0012–0.0150) 0.0003–0.0006 Hafnium (Hf) HMDB29201 0.00031 (0.00006–0.00110) 0.00027±0.00012 Iron (Fe) HMDB00692 0.0890 (0.0025–0.4579) 0.11 (0.0–0.14) Lanthanum (La) HMDB13711 0.00020 (0.00003–0.00079) 0.00052±0.00039 Lead (Pb) HMDB04628 0.0026 (0.0007–0.0110) 0.011 (0.0–0.020) Lithium (Li) HMDB05949 0.5 (0.2–0.9) 0.504 (0.058–3.414) Magnesium (Mg) HMDB00547 262 (42–1,189) 289.0±92.0 Manganese (Mn) HMDB01333 0.013 (0.002–0.051) 0.011 (0.0–0.023) Molybdenum (Mo) HMDB01302 0.049 (0.006–0.295) 0.06±0.03 Neodymium (Nd) HMDB13715 0.00017 (0.00004–0.00070) 0.0026±0.0013 Nickel (Ni) HMDB02457 0.0080 (0.0015–0.0450) 0.00420 (0.00302–0.00572) Palladium (Pd) HMDB13670 0.0110 (0.0011–0.1410) < 0.00001–0.2538 Platinum (Pt) HMDB13671 0.000155 (0.000031–0.000560) 0.0000656 Posphorus (P) HMDB01315 1,799 (221–4,936) 1,666.67–4,333.33 Potassium (K) HMDB00586 3,593 (553–8,079) 4,605 (2,631–6,578) Rhenium (Re) HMDB13719 0.000096 (0.000013–0.000340) NA Rubidium (Rb) HMDB01327 1.354 (0.187–3.673) 1.84±0.46 Ruthenium (Ru) HMDB13668 0.0002 (0.0001–0.0009) NA Selenium (Se) HMDB01349 0.149 (0.028–0.345) 0.11346 (0.07724–0.15010) Sodium (Na) HMDB00588 12,477 (1,863–37,249) 14,737 (5,263–36,842) Strontium (Sr) HMDB03642 0.163 (0.030–0.415) 0.1333 Tantalum (Ta) HMDB04143 0.00020 (0.00004–0.00074) 0.00008±0.00006 Thallium (Tl) HMDB13724 0.0003 (0.0001–0.0013) < 0.00659 Thorium (Th) HMDB29215 0.00020 (0.00002–0.00100) < 0.00584 Tin (Sn) HMDB01960 0.00110 (0.00016–0.00690) < 0.00552 Titanium (Ti) HMDB01966 0.0013 (0.0005–0.0030) 0.0142 Tungsten (W) HMDB01989 0.0060 (0.0011–0.0280) 0.010±0.022 Vanadium (V) HMDB02503 0.033 (0.004–0.083) 0.00162±0.00144 Zinc (Zn) HMDB01303 0.460 (0.058–0.948) 0.52±0.25 Zirconium (Zr) HMDB01475 0.021 (0.004–0.083) 0.0012–0.0074 As far as we are aware, this is the first multi-elemental study of urine that has been performed by ICP-MS. As seen in Table 8, there is generally good agreement between the values measured by ICP-MS and those previously reported in literature, with differences generally being less than 22 ± 10%. Larger differences are seen for gallium (Ga), lead (Pb), Neodymium (Nd), titanium (Ti) and vanadium (V), but these may be due to the effects of age, diet, local environment (minerals in local water) or diurnal variation. Alternately they may reflect real differences in the sensitivity or accuracy of the instruments being used. As a general rule, ICP-MS is considered as a gold standard for the identification and quantification of trace metals [18], so we would tend to place higher confidence in the values derived via ICP-MS over those measured by other technologies. By our measurements, the most abundant metals/salts are sodium (Na) (12.5 ± 10.6 mM/mM creatinine) and potassium (K) (3.6 ± 2.5 mM/mM creatinine) – as expected, while the least abundant was rhenium (Re) with a lower limit of quantification by ICP-MS of 96 pM/mM creatinine. Quantification and Identification of Urine Metabolites – Targeted HPLC Assays The inventory of metabolites we detected and quantified by 1H NMR, GC-MS, DFI/LC-MS/MS and ICP-MS covers a significant portion of all chemical classes. However, these methods sometimes lack the necessary sensitivity, the appropriate instrumental configuration or detection capabilities and therefore fail to detect/quantify a variety of important compound classes. This includes a number of molecules that are normal constituents of urine such as thiols and isoflavones. To identify and quantify these 2 classes of metabolites we decided to employ High Performance Liquid Chromatography (HPLC). HPLC assays are the method of choice for detecting isoflavones and thiols as they are sensitive, precise and can be easily coupled with sensitive detection methodologies such as fluorescence and ultraviolet detection. In our studies, fluorescence and ultraviolet detection were used for the identification and quantification of urinary thiols and isoflavones, respectively. Biological thiols, or mercaptans, are very active metabolic products of sulfur and play a central role in redox metabolism, cellular homeostasis and a variety of physiological and pathological processes. In urine, the most important thiols are L-cysteine and L-cysteinylglycine [90], [91], [92], [93]. Isoflavones or phytoestrogens form or constitute another important class of urinary metabolites [90]. Humans are exposed to these biologically active phytochemicals mainly through food intake via vegetables, fruit and wheat/bread products [94]. For the detection of thiols we developed assays to measure L-cysteine, L-cysteineglycine, L-glutathione and L-homocysyeine, while for isoflavones we developed assays to measure biochanin A, coumesterol, daidzein, equol, formonentin and genistein. Using these HPLC assays, we measured a total of 4 thiols and 6 isoflavones in urine (Table 9). Table 9 Concentrations of isoflavones and thiols as determined by HPLC. Compound Name HMDB ID Mean(µM/mM creatinine) Oc (%) Literature values (µM/mM creatinine) Biochanin Aa HMDB02338 0.0088 (0.0027–0.0200) 77 NA Coumesterola HMDB02326 0.0081 (0.0033–0.0120) 45 NA Daidzeina HMDB03312 0.026 (0.002–0.064) 88 0.036 ± 0.008 Equola HMDB02209 0.06 (0.01–0.10) 67 0.13 ± 0.12 Formonenetina HMDB05808 0.0007 (0.0002–0.0011) 33 NA Genisteina HMDB03217 0.0280 (0.0077–0.0640) 88 0.032 ± 0.007 L-Cysteineb HMDB00574 81.0 (36.7–147.6) 100 33.4 ± 15.5 L-Cysteinylglycineb HMDB00078 0.6 (0.28–1.54) 100 0.48 ± 0.15 L-Glutathioneb HMDB00125 0.030 (0.005–0.085) 100 0.065 L-Homocysteineb HMDB00742 1.1 (0.6–1.7) 100 1.95 ± 1.47 a Isoflavone metabolites measured by HPLC/UV. b Thiol metabolites measured by HPLC/FD. Oc: occurrence; NA: not available. As seen in Table 9, there is generally good agreement between the values measured by these targeted HPLC assays and those previously reported in literature, with differences generally being less than ∼30%. Only one of these metabolites (L-cysteine) was measured independently on one of our other platforms (NMR) and the NMR concentration was found to be 25% lower than the HPLC assay. A possible explanation for this discrepancy is that cysteine measured by HPLC-FD yields total L-cysteine including the free form and L-cystine reduced to L-cysteine during the reaction [95]. On the other hand, NMR can distinguish between L-cysteine and L-cystine. The Composition of Human Urine – Comparison with Other Biofluids By combining a systematic computer-aided literature survey with an extensive, quantitative multiplatform metabolomic analysis we have been able to comprehensively characterize the human urine metabolome. Our data suggests that there are at least 3079 detectable metabolites in human urine, of which 1350 have been quantified. At least 72 of these compounds are of microbial origin, 1453 are endogenous while 2282 are considered exogenous (note some compounds can be both exogenous and endogenous), coming from diet, drugs, cosmetics or environmental exposure. Using a chemical classification system developed for the HMDB [15] we found that human urinary metabolites fall into 230 different chemical classes (25 “super classes”, Table 2). Given that there are only 356 chemical classes in the entire human metabolome [16], this certainly demonstrates the enormous chemical diversity found in urine. As might be expected, most urinary metabolites are very hydrophilic, although there are clearly trace amounts of lipids and fatty acids that contribute a significant number of chemicals to the urinary metabolome (836 fatty acids and lipids). This is in rather striking contrast to the composition of serum [20] which is particularly rich in lipids (i.e. >17000 lipids and fatty acids). Relative to other biofluids such as CSF [18] or saliva [19], urine contains significantly more compounds (5–10X) and exhibits significantly more chemical diversity (2–3X). On the other hand, we know that every compound that is found in human urine is also found in human blood. In other words, the human urine metabolome is a subset of the human serum metabolome, both in terms of composition and chemical diversity [20]. However, more than 484 compounds we identified in urine (either experimentally or via literature review) were not previously reported to be in blood. The fact that so many compounds seem to be unique to urine likely has to do with the fact that the kidneys do an extraordinary job of concentrating certain metabolites from the blood. Consequently compounds that are far below the limit of detection in blood (using today’s instrumentation) are well above the detection limit in urine. In fact, concentration differences between the two biofluids sometimes exceed 1000X for certain compounds, such as histamine, androsterone, normetanephrine, testosterone 13, 14-dihydro-15-keto-PGE2, m-tyramine and aldosterone. So, while the number of water-soluble compounds in blood and urine may be almost identical, the concentrations of these compounds are often profoundly different. This difference, combined with the ability of the kidney to handle abnormally high or abnormally low concentrations of metabolites, makes urine a particularly useful biofluid for medical diagnostics. In fact, according to our data in the UMDB, urinary metabolites have been used to characterize nearly 220 diseases. Furthermore, the ability of the kidneys to filter toxins or xenobiotics makes urine a particularly useful biofluid for diet and drug monitoring and for assessing chemical or pollutant exposure [96]. Method Comparison One of the central motivations behind this work was to ascertain the strengths and weaknesses of several common metabolomic platforms for characterizing human urine. We employed 6 different analytical platforms: NMR; GC-MS; DFI/LC-MS/MS; HPLC/UV; HPLC/FD and ICP-MS. Using our literature-derived knowledge about the composition of human urine, along with custom-derived spectral libraries and targeted assays we were able to “push the limits” in terms of number of compounds that could be identified and/or quantified via each platform. In total, we identified 445 and quantified 378 distinct metabolites using these 6 different systems. According to Table 1, this is the largest number of urine metabolites ever identified and/or quantified in a single study. NMR spectroscopy was able to identify and quantify 209 compounds; GC-MS was able to identify 179 and quantify 85 compounds; DFI/LC-MS/MS identified and quantified 127 compounds; ICP-MS identified and quantified 40 compounds; while customized HPLC assays (with UV or FD detection) identified and quantified 10 compounds. The number of urinary metabolites we identified/quantified for NMR, GC-MS, DFI/LC-MS/MS and ICP-MS all represent “records” for these platforms. In terms of platform overlap and compound complementarity, NMR and GC-MS were able to identify a common set of 88 metabolites; NMR and DFI/LC-MS/MS were able to identify and quantify a common set of 28 metabolites, while NMR, GC-MS and DFI/LC-MS/MS were able to identify a common set of 17 metabolites (15 amino acids, creatinine and hexose/glucose). All of these results are summarized in a Venn diagram (Figure 5). As might be expected, metabolite coverage differs from one analytical technique to another. These are difference mostly due to the intrinsic nature of the devices or platforms used. In particular, significant differences exist between these platforms in terms of their sensitivity or separation and/or extraction efficiency. Likewise the use of targeted vs. non-targeted methods along with issues related to compound stability, solubility and volatility led to some significantly different platform-dependent results. Figure 5 Venn diagram showing the overlap of urine metabolites detected by NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC coupled to UV detection and HPLC coupled to fluorescence detection compared to the detectable urine metabolome. Given that the known, quantifiable urine metabolome consists of ∼2651 known metabolites and metabolite species (corresponding to 3079 distinct structures), we can calculate that NMR is able to measure ∼8% (209/2651) of the human urine metabolome; GC-MS is able to measure ∼7% (179/2651); DFI/LC-MS/MS is able to measure ∼5% (127/2651); ICP-MS/MS is able to measure ∼1.5% (40/2651); HPLC/UV is able to measure ∼0.2% (6/2651); while HPLC/FD is able to measure ∼0.15% (4/2651) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >16% of the known urinary metabolome (>445/2651). If we re-evaluate this fraction in terms of total metabolite structures (corresponding to known and highly probable metabolites), the urine metabolome consists of 3079 compounds. From this total we can calculate that NMR is able to measure ∼7% (209/3079) of the human urine metabolome; GC-MS is able to measure ∼6% (179/3079); DFI/LC-MS/MS is able to measure ∼18% (555/3079); ICP-MS/MS is able to measure ∼1.3% (40/3079); HPLC/UV is able to measure ∼0.2% (6/3079); while HPLC/FD is able to measure ∼0.13% (4/3079) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >28% of the known and probable or putative urinary metabolome (>873/3079). In terms of chemical class coverage, NMR detects compounds from 15 of the 25 major chemical superclasses in urine, GC-MS detects compounds from 14 of the 25, DFI/LC-MS/MS detects 6 of the 25, ICP-MS detects 1 of the 25 while the targeted methods for thiols and isoflavones detect just 1 of each. From these data we can conclude that NMR is currently the best method for identifying and quantifying urinary compounds. Not only does it permit measurement of the largest number of metabolites (209) but it also yields the greatest chemical diversity. Furthermore, NMR is non-destructive so that the same sample can be subsequently re-used for GC-MS, LC-MS or ICP-MS analyses. The minimal sample preparation and relatively rapid data collection for NMR also make it much more appealing for urine metabolomics, although the spectral analysis can be quite slow (∼1–2 hours per sample). While GC-MS is a close second in terms of overall coverage (179 metabolites, 14 chemical superclasses), these numbers represents the result of 4 different analyses performed on 2 different GC-MS instruments. Many labs would not have these multiple configurations available or the resources to routinely run these types of analyses. Likewise each sample required many hours of preparation, sample collection and data analysis. In this regard, multi-platform GC-MS is definitely not a high-throughput metabolomics technique. Relative to NMR and GC-MS, DFI/LC-MS/MS also performs well, with 127 compounds being quantified. However, DFI/LC-MS/MS provides very limited chemical diversity (only 6 chemical superclasses). On the other hand, DFI/LC-MS/MS requires very little sample volume (10 µL) and it is a very low-cost, largely automated, high-throughput route for measuring metabolites. The other techniques (HPLC, ICP-MS) we employed in this study, while useful, do not come close to matching the coverage or diversity of NMR, GC-MS or DFI/LC-MS/MS. While we certainly went to considerable lengths to use current or cutting edge technologies to characterize the urine metabolome, it is also important to note that there is always potential for future improvement. Using higher field (900–950 MHz) NMR instruments, employing newer model GC-MS instruments or more sensitive GC-TOF instruments, using more than 3 derivatization or extraction steps for our GC-MS analyses, employing the latest version of the NIST database or a larger collection of GC-MS databases, implementing more sophisticated or targeted detection and separation techniques, using various commercial immunodetection kits or employing the latest LC-MS/MS techniques coupled to FT-MS or orbitraps – all of these could have added to the quantity and diversity of metabolites detected or quantified. However, like many laboratories, our resources are somewhat limited. Furthermore, in this study we wanted to address the question of how well a cross-section of commonly accessible metabolomic methods or platforms could perform in identifying and quantifying metabolites in urine. While being able to quantitatively compare metabolite coverage and chemical diversity amongst the major metabolomics platforms (NMR, GC-MS and DFI/LC-MS/MS) is important, it is also useful to compare their consistency or reproducibility in terms of metabolite quantification. In particular we decided to assess the 3 major platforms in terms of their ability to identify and quantify a common group of compounds, namely the amino acids. Overall we found that the measured concentrations are in relatively good agreement (Table 10). However, a few exceptions are evident. For example, the NMR and DFI/LC-MS/MS concentrations of glycine and serine are higher than the GC-MS values (note that glycine exhibits the highest concentration among urinary amino acids). For serine, after the silylation reaction using MSTFA, we obtained serine-2TMS (13.9 min) and serine-3TMS derivatives (16.6 min). The chromatographic peak corresponding to serine-2TMS is weak and overlaps slightly with the urea peak. This overlap and the corresponding difficulty in peak integration may explain the quantitation differences compared to other analytical assays. Neither L-glutamine nor L-glutamic acid could be accurately quantified or identified by GC-MS. In our case, the glutamine peak co-elutes with glycerol-3-phosphate. Other investigators have noted that glutamine can also be hydrolyzed and converted to glutamic acid [97], or to pyroglutamic acid during derivatization [95]. As a result, only pyroglutamic acid could be identified in our GC-MS assay. The identification of glutamic acid and pyroglutamic acid can be complicated [97], which explains our failure to identify glutamic acid by GC-MS. L-arginine could not be detected by GC-MS, because it is converted to ornithine during derivatization [98], which probably explains the slightly higher concentration of ornithine measured by GC-MS, compared to the one determined by DFI/LC-MS/MS. Finally, L-cysteine and L-cystine can only be identified and quantified by NMR and targeted HPLC/FD because the identification and quantification of theses metabolites is not possible with the Biocrates kit. With GC-MS it has often been noted that L-cystine can be converted to L-cysteine during derivatization, while L-cysteine might be oxidized to L-cystine during prolonged storage of the standard solution [95], confounding the identification of L-cystine and L-cysteine. Table 10 Comparison of urine concentrations of 23 quantified amino acids by different assays: DFI/LC-MS/MS (BIOCRATES kit), NMR and GC-MS. Amino acids* DFI/LC-MS/MS NMR GC-MS Glycine 101.0±53.5 106±66 75.7±51.7 L-Alanine 21.7±10.6 21.8±10.3 NA L-Arginine 7.4±3.0 8.6±3.6 NA L-Asparagine 8.8±3.5 10.1±4.0 9.5±5.5 L-Aspartic acid 10.9±7.7 10.9±5.8 NA L-Citrulline 0.8±0.4 NA NA L-Cysteine NA 65.8±24.0 NA L-Cystine NA 12.5±5.4 NA L-Glutamic acid 6.4±4.9 8.5±3.6 NA L-Glutamine 39.9±14.3 37.2±14.8 NA L-Histidine 45.6±15.6 43.9±19.5 NA L-Isoleucine 1.3±0.4 1.3±0.5 NA L-Leucine 3.0±1.0 2.8±0.9 NA L-Lysine 17.9±12.2 17.2±11.5 NA L-Methionine 0.8±0.3 1.4±0.6 NA L-Ornithine 4.4±5.0 NA 5.3±1.9 L-Phenylalanine 5.9±2.0 6.4±2.3 7.8±2.0 L-Proline 1.1±0.6 NA NA L-Serine 24.5±9.2 25.3±9.7 19.0±7.0 L-Threonine 14.6±6.1 13.3±5.3 NA L-Tryptophan 5.6±2.1 6.3±2.2 NA L-Tyrosine 8.7±4.2 9.5±4.5 15.1±5.4 L-Valine 4.3±1.9 3.4±1.4 5.5±2.0 * Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available. Conclusion Using a combination of multiple experimental assays supplemented with an extensive computer–assisted literature survey we were able to identify a total of 2651 metabolites or metabolite species (corresponding to 3079 distinct structures) that can be or have been identified and/or quantified in human urine using today’s technology. This information, which includes both normal and abnormal (disease or exposure-associated) metabolites has been placed into a publicly accessible web-enabled database called the Urine Metabolome Database (UMDB). To assess the validity of the literature data and to further investigate the capabilities of existing metabolomics technologies we conducted a comprehensive, quantitative analysis of human urine from 22 healthy volunteers. A total of 6 different platforms: NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC/UV and HPLC/FD were used in this analysis. From this experimental work we were able to identify a total of 445 and quantify 378 metabolites or metabolite species. This corresponds to 873 unique structures (identified) and 806 unique structures (quantified). A total of 53 compounds or compound species are being reported here for the first time as being normal constituents of human urine, while 77 compounds or compound species are being robustly quantified in human urine for the first time. All of the metabolites that we experimentally identified and/or quantified have been added to the UMDB. Based on the information in the UMDB, our experimentally acquired data corresponds to 16% of the human urine metabolome (or 28% if we include probable or putative metabolites). Considering the level of coverage, the diversity of chemical species and the ease with which analyses can be performed, we have determined that NMR spectroscopy appears to be the method of choice for global or untargeted metabolomic analysis of urine. On the other hand, the kit-based combined DFI/LC-MS/MS methods appear to be optimal for a targeted metabolomic approach. Using a multi-pronged GC-MS approach for urine metabolomics appears to be very promising in terms of coverage, but is not ideal for high-throughput analyses. All methods used in this study appear to be quite complementary with relatively little compound overlap. This strongly suggests that if sufficient time and resources are available, multiple methods should be used in urine metabolomic studies. If additional resources had been available, we would have liked to assess other technologies (GCxGC-MS, FT-MS, isotope labeled-LC-MS) and to compare the level of metabolite coverage and chemical diversity attainable with these methods. However, this study is not intended to be the “final” word on urine or urine metabolome. Rather, it should be viewed as a starting point for future studies and future improvements in this field. Indeed, our primary objective for undertaking these studies and compiling this data was to help advance the fields of quantitative metabolomics, especially with regard to clinically important biofluids such as urine. Experimentally, our data should serve as a useful benchmark from which to compare other technologies and to assess coming methodological improvements in human urine characterization. From a clinical standpoint, we think the information contained in the human urine metabolome database (UMDB) should provide metabolomic researchers as well as clinicians and clinical chemists with a convenient, centralized resource from which to learn more about human urine and its unique chemical constituents. Methods Ethics Statement All samples were collected in accordance with the ethical guidelines mandated by the University of Alberta as approved by the University’s Health Research Ethics Board. All individuals were over 18 years of age. All were approached using approved ethical guidelines and those who agreed to participate in this study were required to sign consent forms. All participants provided written consent. Collection of Urine Samples Human urine samples (first pass, morning) were collected from 22 healthy adult volunteers (14 male, 8 female) in 120 mL sterile urine specimen cups. The average age of the volunteers was 30 (range 19–65) for females, and 32 (range 21–67) for males. Upon receipt (typically within 1 hour of collection), all samples were immediately treated with sodium azide to a final concentration of 2.5 mM. After centrifugation at 4000 rpm for 10 min to remove particulate matter, the urine samples were stored in 2 mL aliquots in falcon tube at −20°C until further use. Prior to each analysis, the samples were thawed at room temperature for 30 minutes and filtered for a second time via centrifugation. NMR Compound Identification and Quantification All 1H-NMR spectra were collected on a 500 MHz Inova (Varian Inc., Palo Alto, CA) spectrometer using the first transient of the tnnoesy-presaturation pulse sequence. The resulting 1H-NMR spectra were processed and analyzed using the Chenomx NMR Suite Professional software package version 7.0 (Chenomx Inc., Edmonton, AB), as previously described [17]. Additional NMR spectra for 39 compounds were added to the Chenomx Spectral Reference Library using the company’s recommended spectral acquisition and formatting protocols. Further details on the NMR sample preparation, NMR data acquisition and the customized spectral library are provided in Method S1. GC-MS Compound Identification and Quantification Twenty-two urine samples were extracted separately to obtain separate pools of polar, organic acid, bile acid (3 of the 22 urine samples were chosen for analysis and aliquots from these provided an additional “pooled normal” sample) and volatile metabolites using different protocols. The polar metabolites were extracted with cold HPLC grade methanol and double-distilled water after pretreatment with urease, followed by derivatization with MSTFA (N-Methyl-N-trifluoroacetamide) with 1% TMCS (trimethylchlorosilane). For organic acids, the ketoacids were converted first to methoxime derivatives, followed by derivatization with BSTFA (N,O-Bis(trimethylsilyl)trifluoroacetamide) after two successive extractions by ethyl acetate and diethyl ether. The bile acids were eluted with methanol through a SPE column (Bond Elute C18), followed by two different derivatization steps. First the bile acid extracts were esterified using 2% sulfuric acid in methanol, then after phase separation, the esterified bile acids were converted to the corresponding methyl ester-trimethylsilyl ether derivatives using MSTFA with 1% TMCS. Each set of extracted/derivatized metabolites (polar metabolites, organic acids, bile acids) was separated and analyzed using an Agilent 5890 Series II GC-MS operating in electron impact (EI) ionization mode. The volatile compound extraction and analysis by GC-MS was far different from the other protocols. The pooled urine samples were acidified using HCl and transferred to a SPME (solid phase microextraction) vial (75 µm Carboxen/PDMS from Supelco), and then introducing the SPME fiber assembly into the GC-MS injector port according to procedures described elsewhere [99], [100]. The fibers were conditioned prior to use according to the manufacturer’s instructions by inserting them into the GC injector port. Further details on the extraction, derivatization, separation and GC-MS data analysis for the 4 separate groups of urine metabolites are provided in Method S2. Direct Flow Injection/LC-MS/MS Compound Identification and Quantification To assess the performance of direct flow injection DFI-MS/MS methods in urine metabolomics and to determine the concentration ranges of a number of metabolites not measurable by other methods, we used the commercially available Absolute-IDQ p180 Kit (BIOCRATES Life Sciences AG - Austria). The kit, in combination with an ABI 4000 Q-Trap (Applied Biosystems/MDS Sciex) mass spectrometer, can be used for the targeted identification and quantification of 187 different metabolites or metabolite species including amino acids, biogenic amines, creatinine, acylcarnitines, glycerophospholipids, sphingolipids and hexoses. This method involves derivatization and extraction of analytes from the biofluid of interest, along with selective mass spectrometric detection and quantification via multiple reactions monitoring (MRM). Isotope-labeled internal standards are integrated into the kit plate filter to facilitate metabolite quantification. Metabolite concentrations were expressed as ratios relative to creatinine to correct for dilution, assuming a constant rate creatinine excretion for each urine sample (see Method S3 for additional information). Trace Element Analysis Using ICP-MS Before trace elemental analysis by ICP-MS was performed, 22 urine samples were processed as described previously [101]. The concentrations of trace elements were determined on a Perkin-Elmer Sciex Elan 6000 quadrupole ICP-MS operating in a dual detector mode. Blank subtraction was applied after internal standard correction (see Method S4 for additional information). The accuracy of the ICP-MS analytical protocol was periodically evaluated via the analysis of certified reference standard materials (whole rock powders) BE-N and DR-N available from the SARM laboratory at the CRPG (Centre de Recherches Pétrographiques et Géologiques). Characterization of Isoflavones from Urine We processed 22 urine samples as described previously [102], [103]. The isoflavones were isolated and concentrated by solid-phase extraction (Bond Elut C18 column). The elutes were hydrolyzed enzymatically as the urinary isoflavones occur predominantly as glucuronate and sulfate conjugates. The analysis were performed on an Agilent 1100 HPLC system using NovaPak C18 reversed-phase column connected to Agilent G1315B diode array detector with signals scanned between 190 and 400 nm (see Method S5 for additional information). Characterization of Thiols from Urine To extract urinary thiols, we derivatized all 22 urine samples as described previously [104]. A mixture of reagent was used for the reduction and derivatization (with bromobimane) of thiols. The derivatized thiols were injected immediately into a hypersil-ODS HPLC Column connected to Agilent fluorometer operating at an excitation wavelength of 485 nm and emission wavelength of 510 nm (see Method S6 for additional information). Supporting Information Method S1 NMR Compound Identification and Quantification. (DOC) Click here for additional data file. Method S2 GC-MS Compound Identification and Quantification. (DOC) Click here for additional data file. Method S3 Direct Flow Injection/LC-MS/MS Compound Identification and Quantification. (DOC) Click here for additional data file. Method S4 Trace Element Analysis Using ICP-MS. (DOC) Click here for additional data file. Method S5 Characterization of Isoflavones from Urine. (DOC) Click here for additional data file. Method S6 Characterization of Thiols from Urine. (DOC) Click here for additional data file. |
| sec | Introduction Metabolomics is a relatively young branch of “omics” science concerned with the systematic study of the chemical products or metabolites that cells and organisms generate. Because metabolites are the downstream products of numerous genome-wide or proteome-wide interactions, the metabolome (the sum of all metabolites in an organism) can be a very sensitive measure of an organism’s phenotype. This fact has made metabolomics particularly useful in the study of environment-gene interactions [1], [2], [3], [4], the identification of disease biomarkers [5], [6], [7], [8], [9] and the discovery of drugs [10]. Unlike its older “omics” cousins, where complete or near-complete coverage of the genome or proteome is fairly routine, metabolomics still struggles to cover even a tiny fraction of the metabolome. Indeed, most human metabolomic studies published today, even those exploiting the latest and most sensitive LC-MS/MS technologies, typically succeed in identifying or characterizing fewer than 100 compounds [11], [12], [13], [14]. This corresponds to less than 1% of the known human metabolome [15], [16]. In an effort to help improve this situation, we (and others) have started to undertake the systematic characterization of various human biofluid metabolomes. This includes the human cerebrospinal fluid metabolome [17], [18], the human saliva metabolome [19], and the human serum metabolome [20]. We have now turned our attention to characterizing the human urine metabolome. Urine, as produced by mammals, is a transparent, sterile, amber-colored fluid generated by the kidneys. The kidneys extract the soluble wastes from the bloodstream, as well as excess water, sugars, and a variety of other compounds. The resulting urine contains high concentrations of urea (from amino acid metabolism), inorganic salts (chloride, sodium, and potassium), creatinine, ammonia, organic acids, various water-soluble toxins and pigmented products of hemoglobin breakdown, including urobilin, which gives urine its characteristic color. Urination is the primary route by which the body eliminates water-soluble waste products. The average adult generates between 1.5–2.0 liters of urine per day, which over the course of their lifetime would be enough to fill a small backyard swimming pool (5 X 8 X 1.5 m). While largely viewed as a waste product, urine has considerable value as a diagnostic biofluid. Indeed the analysis of urine for medical purposes dates back to ancient Egypt [21], [22], [23], [24]. Hippocrates largely legitimized the medical practice of uroscopy (the study of urine for medical diagnostics) where examination of the color, cloudiness, smell and even the taste of urine was used to identify a variety of diseases. Throughout the Byzantine era and well into the Middle Ages, urine color wheels (a diagram that linked the color of urine to a particular disease) were commonly used by physicians to make diagnoses [21], [25]. A brownish color would indicate jaundice, a red hue (blood) might indicate urinary tract tumors, absence of color would be indicative of diabetes and foamy urine would indicate proteinuria. With the advent of modern clinical techniques in the middle of the 19th century, uroscopy largely disappeared. However, urine has continued to be an important cornerstone to modern medical practice. In fact, it was the first biofluid to be used to clinically diagnose a human genetic disease - alkaptonuria [26]. Even today urine analysis is routinely performed with dipstick tests that can readily measure urinary glucose, bilirubin, ketone bodies, nitrates, leukocyte esterase, specific gravity, hemoglobin, urobilinogen and protein. More detailed urinalysis can be also used to study a variety of renal conditions, such as bladder, ovarian and kidney diseases [27], [28], [29], [30]. Being an important and easily accessible biological fluid, urine has been the subject of detailed chemical analysis for more than 100 years [31], [32]. Extensive tables of normal reference ranges have been published for more than 100 urine ions and metabolites [33], [34], [35], [36], [37]. In addition to these referential clinical chemistry studies, several groups have applied various “global” metabolomic or metabolite profiling methods to urine, such as high resolution nuclear magnetic resolution (NMR) spectroscopy [38], [39], [40], high performance liquid chromatography (HPLC) [41], high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) [42], ultra high performance liquid chromatography-high resolution orbitrap mass spectrometry (UHPLC-HRMS) [43], ultra high performance liquid chromatography (UPLC-RP) [31], [44], fast atom bombardment ionization coupled with mass spectroscopy (FAB-MS) [35], two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GCxGC-MS) [32] or time-of-flight mass spectrometry (GCxGC-TOF-MS) [37], nanoflow liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) [33] and liquid chromatography coupled with electrospray quadruple time-of-flight mass spectrometry (LC/QTOF-MS) [36]. A combination of two or more profiling methods has also been used by many research groups to help improve metabolite coverage [34], [40]. A comprehensive list of methods used to analyze urine and the numbers of metabolites identified and/or quantified by these methods (along with references) is provided in Table 1. As seen from this table, it is possible to (tentatively) identify up to 294 different metabolites in human urine. However, quantification is somewhat more difficult, with the largest number of quantified metabolites ever reported in human urine being slightly less than 100. In addition to these global metabolomic studies, hundreds of other “targeted” or single-metabolite studies have been conducted on human urine that have led to the identification and quantification of hundreds of other urine metabolites. Unfortunately, this information is not located in any central repository. Instead it is highly dispersed across numerous textbooks and periodicals [16]. Table 1 Historic data on metabolite characterization of human urine by different platforms. Method(s) # Identified # Quantified Year Reference Multiple 51 51 1971 Putnam [105] GC-MS 52 0 1976 Lawson [85] GC-MS 72 72 1978 Gates [106] GC-MS 36 36 1989 Hoffman [107] GC-MS 137 0 1990 Liebich [108] GC-MS 131 95 1991 Shoemaker [84] GC-MS 69 69 1994 Guneral [56] GC-MS 75 75 1996 Matsumoto [109] ICP-MS 33 33 2005 Goulle [110] SPE/NMR 73 0 2008 Yang [40] SPE/LC/NMR 72 0 2008 Rezzi [78] LC-MS/NMR 50 0 2008 Law [34] LC-MS 93 93 2009 Guo [111] NMR 50 50 2009 Shaykhutdinov [112] GC-MS-MS 48 48 2011 Kvitvang [113] SPE-GC-MS 79 0 2011 Silva [114] LC-MS 167 0 2011 Guo [115] NMR 42 42 2011 Saude [116] NMR/GC-MS 77 0 2012 Zhang [117] LC-MS 219 (25 listed) 0 2012 Van der Kloet [118] GC-TOF/LC-MS 261 0 2012 Cheng [119] GC-MS 258 0 2012 Roux [83] GCxGC-TOF-MS 294 0 2012 Rocha [120] To facilitate future research into urine chemistry and urine metabolomics, we believe it is critical to establish a comprehensive, electronically accessible database of the detectable metabolites in human urine. This document describes just such a database, one that contains the metabolites that can, with today’s technology, be detected in human urine along with their respective concentrations and disease associations. This resource was assembled using a combination of both our own experimental and literature-based research. Experimentally, we used high-resolution NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection tandem mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) with ultraviolet (UV) or fluorescence detection (FD) techniques performed on multiple human urine samples to identify 445 metabolites or metabolite species and quantify 378 of these compounds. To complement these “global” metabolic profiling efforts, our team also surveyed and extracted metabolite and disease-association data from more than 3000 books and journal articles that had been identified through computer-aided literature and in-house developed text-mining software. This “bibliomic” effort yielded data for another 2206 metabolites. The resulting Urine Metabolome Database (UMDB - http://www.urinemetabolome.ca) is a comprehensive, web-accessible resource containing a total of 2651 confirmed urine metabolites or metabolite species (corresponding to 3079 defined structures), their corresponding concentrations and their disease associations that were revealed or identified from these combined experimental and literature mining efforts. |
| title | Introduction |
| p | Metabolomics is a relatively young branch of “omics” science concerned with the systematic study of the chemical products or metabolites that cells and organisms generate. Because metabolites are the downstream products of numerous genome-wide or proteome-wide interactions, the metabolome (the sum of all metabolites in an organism) can be a very sensitive measure of an organism’s phenotype. This fact has made metabolomics particularly useful in the study of environment-gene interactions [1], [2], [3], [4], the identification of disease biomarkers [5], [6], [7], [8], [9] and the discovery of drugs [10]. Unlike its older “omics” cousins, where complete or near-complete coverage of the genome or proteome is fairly routine, metabolomics still struggles to cover even a tiny fraction of the metabolome. Indeed, most human metabolomic studies published today, even those exploiting the latest and most sensitive LC-MS/MS technologies, typically succeed in identifying or characterizing fewer than 100 compounds [11], [12], [13], [14]. This corresponds to less than 1% of the known human metabolome [15], [16]. In an effort to help improve this situation, we (and others) have started to undertake the systematic characterization of various human biofluid metabolomes. This includes the human cerebrospinal fluid metabolome [17], [18], the human saliva metabolome [19], and the human serum metabolome [20]. We have now turned our attention to characterizing the human urine metabolome. |
| p | Urine, as produced by mammals, is a transparent, sterile, amber-colored fluid generated by the kidneys. The kidneys extract the soluble wastes from the bloodstream, as well as excess water, sugars, and a variety of other compounds. The resulting urine contains high concentrations of urea (from amino acid metabolism), inorganic salts (chloride, sodium, and potassium), creatinine, ammonia, organic acids, various water-soluble toxins and pigmented products of hemoglobin breakdown, including urobilin, which gives urine its characteristic color. Urination is the primary route by which the body eliminates water-soluble waste products. The average adult generates between 1.5–2.0 liters of urine per day, which over the course of their lifetime would be enough to fill a small backyard swimming pool (5 X 8 X 1.5 m). |
| p | While largely viewed as a waste product, urine has considerable value as a diagnostic biofluid. Indeed the analysis of urine for medical purposes dates back to ancient Egypt [21], [22], [23], [24]. Hippocrates largely legitimized the medical practice of uroscopy (the study of urine for medical diagnostics) where examination of the color, cloudiness, smell and even the taste of urine was used to identify a variety of diseases. Throughout the Byzantine era and well into the Middle Ages, urine color wheels (a diagram that linked the color of urine to a particular disease) were commonly used by physicians to make diagnoses [21], [25]. A brownish color would indicate jaundice, a red hue (blood) might indicate urinary tract tumors, absence of color would be indicative of diabetes and foamy urine would indicate proteinuria. With the advent of modern clinical techniques in the middle of the 19th century, uroscopy largely disappeared. However, urine has continued to be an important cornerstone to modern medical practice. In fact, it was the first biofluid to be used to clinically diagnose a human genetic disease - alkaptonuria [26]. Even today urine analysis is routinely performed with dipstick tests that can readily measure urinary glucose, bilirubin, ketone bodies, nitrates, leukocyte esterase, specific gravity, hemoglobin, urobilinogen and protein. More detailed urinalysis can be also used to study a variety of renal conditions, such as bladder, ovarian and kidney diseases [27], [28], [29], [30]. |
| p | Being an important and easily accessible biological fluid, urine has been the subject of detailed chemical analysis for more than 100 years [31], [32]. Extensive tables of normal reference ranges have been published for more than 100 urine ions and metabolites [33], [34], [35], [36], [37]. In addition to these referential clinical chemistry studies, several groups have applied various “global” metabolomic or metabolite profiling methods to urine, such as high resolution nuclear magnetic resolution (NMR) spectroscopy [38], [39], [40], high performance liquid chromatography (HPLC) [41], high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) [42], ultra high performance liquid chromatography-high resolution orbitrap mass spectrometry (UHPLC-HRMS) [43], ultra high performance liquid chromatography (UPLC-RP) [31], [44], fast atom bombardment ionization coupled with mass spectroscopy (FAB-MS) [35], two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GCxGC-MS) [32] or time-of-flight mass spectrometry (GCxGC-TOF-MS) [37], nanoflow liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) [33] and liquid chromatography coupled with electrospray quadruple time-of-flight mass spectrometry (LC/QTOF-MS) [36]. A combination of two or more profiling methods has also been used by many research groups to help improve metabolite coverage [34], [40]. |
| p | A comprehensive list of methods used to analyze urine and the numbers of metabolites identified and/or quantified by these methods (along with references) is provided in Table 1. As seen from this table, it is possible to (tentatively) identify up to 294 different metabolites in human urine. However, quantification is somewhat more difficult, with the largest number of quantified metabolites ever reported in human urine being slightly less than 100. In addition to these global metabolomic studies, hundreds of other “targeted” or single-metabolite studies have been conducted on human urine that have led to the identification and quantification of hundreds of other urine metabolites. Unfortunately, this information is not located in any central repository. Instead it is highly dispersed across numerous textbooks and periodicals [16]. |
| table-wrap | Table 1 Historic data on metabolite characterization of human urine by different platforms. Method(s) # Identified # Quantified Year Reference Multiple 51 51 1971 Putnam [105] GC-MS 52 0 1976 Lawson [85] GC-MS 72 72 1978 Gates [106] GC-MS 36 36 1989 Hoffman [107] GC-MS 137 0 1990 Liebich [108] GC-MS 131 95 1991 Shoemaker [84] GC-MS 69 69 1994 Guneral [56] GC-MS 75 75 1996 Matsumoto [109] ICP-MS 33 33 2005 Goulle [110] SPE/NMR 73 0 2008 Yang [40] SPE/LC/NMR 72 0 2008 Rezzi [78] LC-MS/NMR 50 0 2008 Law [34] LC-MS 93 93 2009 Guo [111] NMR 50 50 2009 Shaykhutdinov [112] GC-MS-MS 48 48 2011 Kvitvang [113] SPE-GC-MS 79 0 2011 Silva [114] LC-MS 167 0 2011 Guo [115] NMR 42 42 2011 Saude [116] NMR/GC-MS 77 0 2012 Zhang [117] LC-MS 219 (25 listed) 0 2012 Van der Kloet [118] GC-TOF/LC-MS 261 0 2012 Cheng [119] GC-MS 258 0 2012 Roux [83] GCxGC-TOF-MS 294 0 2012 Rocha [120] |
| label | Table 1 |
| caption | Historic data on metabolite characterization of human urine by different platforms. |
| title | Historic data on metabolite characterization of human urine by different platforms. |
| table | Method(s) # Identified # Quantified Year Reference Multiple 51 51 1971 Putnam [105] GC-MS 52 0 1976 Lawson [85] GC-MS 72 72 1978 Gates [106] GC-MS 36 36 1989 Hoffman [107] GC-MS 137 0 1990 Liebich [108] GC-MS 131 95 1991 Shoemaker [84] GC-MS 69 69 1994 Guneral [56] GC-MS 75 75 1996 Matsumoto [109] ICP-MS 33 33 2005 Goulle [110] SPE/NMR 73 0 2008 Yang [40] SPE/LC/NMR 72 0 2008 Rezzi [78] LC-MS/NMR 50 0 2008 Law [34] LC-MS 93 93 2009 Guo [111] NMR 50 50 2009 Shaykhutdinov [112] GC-MS-MS 48 48 2011 Kvitvang [113] SPE-GC-MS 79 0 2011 Silva [114] LC-MS 167 0 2011 Guo [115] NMR 42 42 2011 Saude [116] NMR/GC-MS 77 0 2012 Zhang [117] LC-MS 219 (25 listed) 0 2012 Van der Kloet [118] GC-TOF/LC-MS 261 0 2012 Cheng [119] GC-MS 258 0 2012 Roux [83] GCxGC-TOF-MS 294 0 2012 Rocha [120] |
| tr | Method(s) # Identified # Quantified Year Reference |
| td | Method(s) |
| td | # Identified |
| td | # Quantified |
| td | Year |
| td | Reference |
| tr | Multiple 51 51 1971 Putnam [105] |
| td | Multiple |
| td | 51 |
| td | 51 |
| td | 1971 |
| td | Putnam [105] |
| tr | GC-MS 52 0 1976 Lawson [85] |
| td | GC-MS |
| td | 52 |
| td | 0 |
| td | 1976 |
| td | Lawson [85] |
| tr | GC-MS 72 72 1978 Gates [106] |
| td | GC-MS |
| td | 72 |
| td | 72 |
| td | 1978 |
| td | Gates [106] |
| tr | GC-MS 36 36 1989 Hoffman [107] |
| td | GC-MS |
| td | 36 |
| td | 36 |
| td | 1989 |
| td | Hoffman [107] |
| tr | GC-MS 137 0 1990 Liebich [108] |
| td | GC-MS |
| td | 137 |
| td | 0 |
| td | 1990 |
| td | Liebich [108] |
| tr | GC-MS 131 95 1991 Shoemaker [84] |
| td | GC-MS |
| td | 131 |
| td | 95 |
| td | 1991 |
| td | Shoemaker [84] |
| tr | GC-MS 69 69 1994 Guneral [56] |
| td | GC-MS |
| td | 69 |
| td | 69 |
| td | 1994 |
| td | Guneral [56] |
| tr | GC-MS 75 75 1996 Matsumoto [109] |
| td | GC-MS |
| td | 75 |
| td | 75 |
| td | 1996 |
| td | Matsumoto [109] |
| tr | ICP-MS 33 33 2005 Goulle [110] |
| td | ICP-MS |
| td | 33 |
| td | 33 |
| td | 2005 |
| td | Goulle [110] |
| tr | SPE/NMR 73 0 2008 Yang [40] |
| td | SPE/NMR |
| td | 73 |
| td | 0 |
| td | 2008 |
| td | Yang [40] |
| tr | SPE/LC/NMR 72 0 2008 Rezzi [78] |
| td | SPE/LC/NMR |
| td | 72 |
| td | 0 |
| td | 2008 |
| td | Rezzi [78] |
| tr | LC-MS/NMR 50 0 2008 Law [34] |
| td | LC-MS/NMR |
| td | 50 |
| td | 0 |
| td | 2008 |
| td | Law [34] |
| tr | LC-MS 93 93 2009 Guo [111] |
| td | LC-MS |
| td | 93 |
| td | 93 |
| td | 2009 |
| td | Guo [111] |
| tr | NMR 50 50 2009 Shaykhutdinov [112] |
| td | NMR |
| td | 50 |
| td | 50 |
| td | 2009 |
| td | Shaykhutdinov [112] |
| tr | GC-MS-MS 48 48 2011 Kvitvang [113] |
| td | GC-MS-MS |
| td | 48 |
| td | 48 |
| td | 2011 |
| td | Kvitvang [113] |
| tr | SPE-GC-MS 79 0 2011 Silva [114] |
| td | SPE-GC-MS |
| td | 79 |
| td | 0 |
| td | 2011 |
| td | Silva [114] |
| tr | LC-MS 167 0 2011 Guo [115] |
| td | LC-MS |
| td | 167 |
| td | 0 |
| td | 2011 |
| td | Guo [115] |
| tr | NMR 42 42 2011 Saude [116] |
| td | NMR |
| td | 42 |
| td | 42 |
| td | 2011 |
| td | Saude [116] |
| tr | NMR/GC-MS 77 0 2012 Zhang [117] |
| td | NMR/GC-MS |
| td | 77 |
| td | 0 |
| td | 2012 |
| td | Zhang [117] |
| tr | LC-MS 219 (25 listed) 0 2012 Van der Kloet [118] |
| td | LC-MS |
| td | 219 (25 listed) |
| td | 0 |
| td | 2012 |
| td | Van der Kloet [118] |
| tr | GC-TOF/LC-MS 261 0 2012 Cheng [119] |
| td | GC-TOF/LC-MS |
| td | 261 |
| td | 0 |
| td | 2012 |
| td | Cheng [119] |
| tr | GC-MS 258 0 2012 Roux [83] |
| td | GC-MS |
| td | 258 |
| td | 0 |
| td | 2012 |
| td | Roux [83] |
| tr | GCxGC-TOF-MS 294 0 2012 Rocha [120] |
| td | GCxGC-TOF-MS |
| td | 294 |
| td | 0 |
| td | 2012 |
| td | Rocha [120] |
| p | To facilitate future research into urine chemistry and urine metabolomics, we believe it is critical to establish a comprehensive, electronically accessible database of the detectable metabolites in human urine. This document describes just such a database, one that contains the metabolites that can, with today’s technology, be detected in human urine along with their respective concentrations and disease associations. This resource was assembled using a combination of both our own experimental and literature-based research. Experimentally, we used high-resolution NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection tandem mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) with ultraviolet (UV) or fluorescence detection (FD) techniques performed on multiple human urine samples to identify 445 metabolites or metabolite species and quantify 378 of these compounds. To complement these “global” metabolic profiling efforts, our team also surveyed and extracted metabolite and disease-association data from more than 3000 books and journal articles that had been identified through computer-aided literature and in-house developed text-mining software. This “bibliomic” effort yielded data for another 2206 metabolites. The resulting Urine Metabolome Database (UMDB - http://www.urinemetabolome.ca) is a comprehensive, web-accessible resource containing a total of 2651 confirmed urine metabolites or metabolite species (corresponding to 3079 defined structures), their corresponding concentrations and their disease associations that were revealed or identified from these combined experimental and literature mining efforts. |
| sec | Results and Discussion The Content of the Human Urine Metabolome – The Urine Metabolome Database The Urine Metabolome Database (UMDB: http://www.urinemetabolome.ca) contains a complete list of all (to the best of our knowledge) possible metabolites that have been detected in human urine using current technologies. The UMDB is freely available, easily queried, web-enabled database which provides a list of the metabolite names, level of verification (confirmed or probable), normal and disease-associated concentration ranges, associated diseases and corresponding literature references for all human urine metabolites that have ever been detected and/or quantified in the literature. The UMDB also contains concentration data compiled from the experimental studies described here. Each urine metabolite entry in this database is linked to a MetaboCard button [15], [16] that, when clicked, brings up detailed information about that particular entry. This detailed information includes nomenclature, chemical, clinical and molecular/biochemical data. Each MetaboCard entry contains up to 120 data fields many of which are hyperlinked to other databases (KEGG [45], PubChem [46], MetaCyc [47], ChEBI [48], PDB [49], UniProt [50], and GenBank [51] as well as to GeneCard IDs [52], GeneAtlas IDs [53] and HGNC IDs [54] for each of the corresponding enzymes or proteins known to act on that metabolite). Additionally, the UMDB through its MetaboCard/HMDB links includes nearly 450 hand-drawn, zoomable and fully hyperlinked human metabolic pathway maps (SMPDB: http://www.smpdb.ca/). These maps are intended to help users visualize the chemical structures on metabolic pathways and to get detailed information about metabolic processes [55]. These UMDB pathway maps are quite specific to human metabolism and explicitly show the subcellular compartments where specific reactions are known to take place. The UMDB’s simple text query (TextQuery) supports general text queries including names, synonyms, conditions and disorders. Clicking on the Browse button (on the UMDB navigation panel) generates a tabular view that allows users to casually scroll through the database or re-sort its contents by compound name or by concentration. Users can choose either the “Metabolite View”, “Concentration View” or “Diseases View” to facilitate their browsing or searching. Clicking on a given MetaboCard button brings up the full data content (from the HMDB) for the corresponding metabolite. Users may also search the database using a variety of options listed uner the “Search” menu. For instance, the ChemQuery button allows users to draw or write (using a SMILES string) a chemical compound to search the UMDB for chemicals similar or identical to the query compound. ChemQuery also supports chemical formula and molecular weight searches. The Sequence Search button allows users to conduct BLAST sequence searches of the 4075 protein sequences contained in the UMDB. Both single and multiple sequence BLAST queries are supported. “Advanced Search” which is also located under the “Search” menu is the most sophisticated search tool in the UMDB and opens an easy-to-use query search tool that allows users to select or search over various combinations of subfields. The UMDB’s “MS Search” allows users to submit mass spectral peak lists that will be searched against the Human Metabolome Database (HMDB)’s library of MS/MS spectra. This potentially allows facile identification of urine metabolites from mixtures via MS/MS spectroscopy. UMDB’s NMR Search allows users to submit peak lists from 1H or 13C NMR spectra (both pure and mixtures) and have these peak lists compared to the NMR libraries contained in the HMDB. This allows the identification of metabolites from mixtures via NMR spectral data. The Download button provides links to collected sequence, image and text files associated with the UMDB. In the About menu, the “Data Fields Explained” button lists source data used to assemble the UMDB. Currently the UMDB contains information on 2651 detectable metabolites or metabolite species (which corresponds to 3079 metabolites with precisely defined structures) and 3832 concentration ranges or values associated with 220 different conditions, diseases and disorders. The number of metabolites in the UMDB is not a number that will remain unchanged. Rather it reflects the total number of metabolites – most of which are endogenous - that have ever been detected and/or quantified by ourselves and others. Certainly as technology improves, we anticipate this number will increase as other, lower abundance, metabolites are detected and added to future versions of the UMDB. Likewise, if the list was expanded to include intermittent, exogenous compounds such as all possible drugs or drug metabolites or rare food additives and food-derived phytochemicals, the database could be substantially larger. Inspection of the on-line tables in UMDB generally shows that human urine contains a substantial number of hydrophilic molecules. This is further reiterated in Table 2, which provides a listing of the chemical “superclasses” (using the HMDB definitions) in human urine and the number of representative compounds that can be found in this biofluid. Excluding lipids (which are in very low concentration), human urine is dominated by amino acids and derivatives, carbohydrates and carbohydrate conjugates. This simply reinforces the fact that urine is a key carrier of hydrophilic waste products. Other small molecule components found in high abundance in urine include hydroxy acids and derivatives (such as citric acid), urea, ammonia, creatinine and hippuric acid. A more detailed description of both our literature and experimental findings is given in the following 7 sections covering: 1) Literature Review/Text Mining; 2) NMR; 3) DFI/LC-MS/MS; 4) GC-MS; 5) ICP-MS; 6) HPLC/UV and 7) HPLC/FD. Table 2 Chemical superclasses in the urine metabolome database. Chemical superclass # Compounds Aliphatic Acyclic Compounds 93 Aliphatic Heteromonocyclic Compounds 43 Aliphatic Heteropolycyclic Compounds 40 Aliphatic Homomonocyclic Compounds 18 Aliphatic Homopolycyclic Compounds 5 Alkaloids and Derivatives 45 Amino Acids, Peptides, and Analogues 286 Aromatic Heteromonocyclic Compounds 67 Aromatic Heteropolycyclic Compounds 728 Aromatic Homomonocyclic Compounds 432 Aromatic Homopolycyclic Compounds 6 Carbohydrates and Carbohydrate Conjugates 116 Homogeneous Metal Compounds 45 Homogeneous Non-metal Compounds 15 Inorganic compounds 1 Lignans and Norlignans 12 Lipids 866 Mixed Metal/Non-metal Compounds 7 Nucleosides, Nucleotides, and Analogues 49 Organic Acids and Derivatives 108 Organic Halides 3 Organometallic Compounds 1 Organophosphorus Compounds 17 Polyketides 74 Tannins 2 Metabolite Concentration in Urine – Literature Survey In addition to the experimentally derived values obtained for this study, the urine metabolome database (UMDB) also presents literature-derived concentrations of urine metabolites with references to either PubMed IDs or clinical textbooks. In many cases, multiple concentration values are given for “normal” conditions. This is done to provide users/readers with a better estimate of the potential concentration variations that different technologies or laboratories may measure. As a general rule, there is good agreement between most laboratories and methods. However, the literature results presented in the UMDB do not reflect the true state of the raw literature. A number of literature-derived concentration values were eliminated through the curation process after being deemed mistaken, disproven (by subsequent published studies), mis-typed or physiologically impossible. Much of the curation process involved having multiple curators carefully reading and re-reading the primary literature to check for unit type, unit conversion and typographical inconsistencies. Other than the inorganic ions and gases such as sodium (14.7 ± 9.0 mM/mM creatinine – average value), chlorine (8.8 ± 6.2 mM/mM creatinine), potassium (4.6 ± 0.1 mM/mM creatinine), ammonia (2.8 ± 0.9 mM/mM creatinine), the 4 most abundant organic metabolites found in urine (based on average values) are urea (22.5 ± 4.4 mM/mM creatinine), creatinine (10.4 ± 2.0 mM), hippuric acid (298.5 ± 276.8 µM/mM creatinine) and citric acid (280.6 ± 115.2 µM/mM creatinine). The least abundant (detectable) metabolites in urine include oxytocin (0.9 ± 0.1 pM/mM creatinine), angiotensin II (1.2 ± 0.2 pM/mM creatinine), 15-deoxy-d-12,14-PGJ2 (2.3 ± 1.0 pM/mM creatinine) and melatonin (3.3 ± 2.7 pM/mM creatinine). This shows that the current lower limit of detection for metabolites in urine is in the low pM/mM creatinine range and that the concentration range of analytes in urine spans nearly 11 orders of magnitude. One point that is particularly interesting is the fact that the concentration (scaled to creatinine) of the average metabolite in normal urine varies by about ± 50%, with some metabolites varying by as much as ± 350% (such as normetanephrine (0.00085 ± 0.00317 µM/mM creatinine), pipecolic acid (0.03 ± 0.07 µM/mM creatinine), enterodiol (0.032 ± 0.072 µM/mM creatinine), tungsten (0.010 ± 0.022 µM/mM creatinine) and chlorogenic acid (0.0014 ± 0.0029 µM/mM creatinine). Therefore, drawing conclusions about potential disease biomarkers without properly taking into account this variation would be ill-advised. We believe that these relatively large metabolite concentration ranges are due to a number of factors, including age, gender, genetic background, diurnal variation, health status, activity level and diet [56], [57], [58], [59]. Indeed, some UMDB entries explicitly show such variations based on the populations (age, gender) from which these metabolite concentrations were derived. Clearly more study on the contributions to the observed variations in urine is warranted, although with thousands of metabolites to measure for dozens of conditions, these studies will obviously require significant technical and human resources. Experimental Quantification and Identification of Urine Metabolites – NMR A representative high-resolution NMR spectrum of urine from a healthy individual is shown in Figure 1. As can be readily seen from this figure, urine NMR spectra are very information-rich and surprisingly complex, with thousands of resolved peaks. From the 22 healthy control urine samples analyzed, we could identify a total of 209 unique compounds with an average of 167 ± 19 compounds being identified per sample. Every compound was unequivocally identified and quantified using spectral fitting (via Chenomx) and/or spike-in experiments with authentic standards. The concentration of each metabolite was normalized to each urine sample’s corresponding creatinine value to compensate for variations in urine volume (the concentration of metabolites is expressed as µM/mM creatinine). Figure 1 Typical 500 MHz 1H-NMR spectra of urine from human urine. Numbers indicates the following metabolites: 1: creatinine; 2: citric acid; 3: glycine; 4: formic acid; 5: methanol; 6: guanidoacetic acid; 7: acetic acid; 8: L-cysteine; 9: glycolic acid; 10: creatine; 11: isocitric acid; 12: hippuric acid; 13: L-glutamine; 14: L-alanine; 15: L-lysine; 16: gluconic acid; 17∶2- hydroxyglutaric acid; 18: D-glucose; 19: indoxyl sulfate; 20: trimethyl-N-oxide; 21: ethanolamine; 22: L-lactic acid; 23: taurine; 24: L-threonine; 25: dimethylamine; 26: pyroglutamic acid; 27: trigonelline; 28: sucrose; 29: trimethylamine; 30: mannitol; 31: L-serine; 32: acetone; 33: L-cystine; 34: adipic acid; 35: L-histidine; 36: L-tyrosine; 37: imidazole; 38: mandelic acid; 39: dimethylglycine; 40: Cis-aconitic acid; 41: urea; 42∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 43: phenol; 45: isobutyric acid; 46: methylsuccinic acid; 47∶3-aminoisobutyric acid; 48: L-fucose; 49: N-acetylaspartic acid; 50: N-acetylneuraminic acid; 51: acetoacetic acid; 52: Alpha-aminoadipic acid; 53: methylguanidine; 54: phenylacetylglutamine. The 209 compounds identified and quantified from these NMR studies represent a “high-water” mark for NMR-based metabolomics. Previous studies have reported up to 70 compounds being identified and/or quantified in human urine [40]. Indeed, compared to other platforms previously used to analyze human urine (Table 1), it appears that NMR may currently be the most comprehensive and certainly the most quantitative approach to characterizing this biofluid. Based on the fitted area under each urinary NMR spectrum and the number of unidentified peaks we estimate that more than 96% of the spectral area and more than 92% of all NMR-detectable compounds in our human urine samples are listed in Table 3. In other words, for NMR-based metabolomics, human urine is essentially “solved”. The same “solved” status has already been achieved for human serum (with 49 definitive compounds, [20]) and for human cerebrospinal fluid (with 53 definitive compounds, [17]). Knowing the expected or detectable composition of these biofluids should open the door to automated NMR-based metabolomics [60]. Table 3 Concentrations [Mean (range)] and % occurrence of urine metabolites as determined by NMR. Compounds HMDB ID Concentration (µM/mM creatinine) Oc (%) Literature Values (µM/mM creatinine) 1,3-Diaminopropane HMDB00002 1.2 5 2.55 (0.18–5.85) 1,3-Dimethyluric acid HMDB01857 3.1 (1.3–6.8) 68 1.5 (1.3–2.3) 1-Methyladenosine HMDB03331 1.7 (0.9–3.6) 82 2.03±0.20 1-Methylhistidine HMDB00001 8.3 (2.4–28.4) 100 15.9±19.5 1-Methylnicotinamide HMDB00699 5.8 (1.2–15.0) 77 6.1 (0.2–12.0) 2-Furoylglycine HMDB00439 4.0 (0.9–8.4) 100 9.95 (2.0–18.66) 2-Hydroxy-3-methylpentanoic acid HMDB00317 3.9 (1.7–7.9) 100 NA 2-Hydroxybutyric acid HMDB00008 2.8 (1.2–6.9) 77 2.4 (0.0–4.9) 2-Hydroxyglutaric acid HMDB00694 33.0 (13.3–77.9) 100 33.0 (20.0–46.0) 2-Ketobutyric acid HMDB00005 2.9 (0.7–5.4) 100 2.6 (1.1–4.1) 2-Methyl-3-hydroxybutyric acid HMDB00354 4.2 (1.6–6.7) 95 5.5 (0.0–11.0) 2-Methyl-3-ketovaleric acid HMDB00408 1.1 (0.8–1.3) 18 NA 2-Methylbutyrylglycine HMDB00339 2.0 (1.2–3.3) 32 1 (0–2) 2-Methylerythritol HMDB11659 25.3 (17.0–49.0) 14 NA 2-Methylglutaric acid HMDB00422 0.8 (0.3–1.7) 45 NA** 3-Aminoisobutanoic acid HMDB03911 26.0 (2.2–140.0) 100 2.91–116.43 3-Hydroxybutyric acid HMDB00357 3.6 (1.3–6.4) 77 1.4±1.3 3-Hydroxyhippuric acid HMDB06116 7.4 (5.7–9.1) 32 2.5 (0.2–5.0) 3-Hydroxyisovaleric acid HMDB00754 6.8 (3.2–21.8) 100 8.5±3.2 3-Hydroxymethylglutaric acid HMDB00355 2.8 (1.1–5.8) 68 3 (0–10) 3-Hydroxyphenylacetic acid HMDB00440 7.7 (1.4–22.1) 91 6.1±4.1 3-Methyl-2-oxovaleric acid HMDB00491 2.2 (0.4–4.8) 91 2.0±2.0 3-Methyladipic acid HMDB00555 2.9 (1.1–7.2) 86 2.9 (0.7–10.5) 3-Methylglutaconic acid HMDB00522 7.5 (3.4–9.5) 22 4.5 (0.0–9.0) 3-Methylhistidine HMDB00479 16.5 (2.8–59.8) 100 15.1 (3.9–26.3) 3-Methylxanthine HMDB01886 6.2 (1.4–17.8) 100 4.0 (2.3–6.3) 4-Aminobutyric acid HMDB00112 2.9 (2.7–3.3) 9 0.75 (0.0–1.50) 4-Aminohippuric acid HMDB01867 3.3 (1.0–7.2) 86 0.3 4-Deoxythreonic acid HMDB02453 12.3 (4.4–19.8) 100 25±10 4-Ethylphenol HMDB29306 0.9 (0.6–1.2) 18 0.409±0.174 4-Hydroxybenzoic acid HMDB00500 1.7 (0.9–3.0) 77 1.8±1.8 4-Hydroxybutyric acid HMDB00710 7.2 (2.7–22.3) 100 3.3 (0.0–10.8) 4-Hydroxyhippuric acid HMDB13678 2.8 (1.4–6.0) 100 0–14 4-Hydroxyphenyllactic acid HMDB00755 3.2 (1.5–3.9) 50 1.1 (0.2–2.6) 4-Pyridoxic acid HMDB00017 3.1 (0.4–7.5) 95 0.78±0.45a 5,6-Dihydrouracil HMDB00076 3.8 (2.1–8.3) 68 3.0±2.7 5-Aminolevulinic acid HMDB01149 2.9 (1.2–4.4) 95 1.45±0.72 5-Aminopentanoic acid HMDB03355 1.1 5 2 7-Methylxanthine HMDB01991 10.0 (3.9–16.1) 9 10.0 (6.7–14.6) Acetaldehyde HMDB00990 2.6 (0.8–4.2) 32 NA** Acetaminophen HMDB01859 11.1 5 5.3 Acetaminophen glucuronide HMDB10316 33.4 5 NA** Acetaminophen sulfate HMDB59911 138.6 5 NA** Acetic acid HMDB00042 13.0 (2.5–106.0) 100 45.3±53.3 Acetoacetic acid HMDB00060 11.1 (2.2–24.9) 100 33 (0–67) Acetone HMDB01659 3.9 (0.8–17.6) 100 2.24±4.13 Acetyl-L-carnitine HMDB00201 2.8 (0.6–7.5) 100 1.9±0.3 Adenine HMDB00034 2.9 (1.4–5.1) 45 1.17 Adenosine HMDB00050 1.4 (0.9–2.3) 45 0.50 (0.09–0.92) Adipic acid HMDB00448 4.4 (1.6–14.3) 91 5.1 (0.8–35.0) ADP HMDB01341 1.8 (1.0–3.4) 77 NA** Allantoin HMDB00462 15.4 (4.9–29.3) 100 19.7±10.5 Alpha-Aminoadipic acid HMDB00510 7.3 (3.4–11.2) 73 4.6 Alpha-Aminobutyric acid HMDB00452 2.2 (1.1–4.4) 95 1.37±1.05 Alpha-Hydroxyisobutyric acid HMDB00729 3.8 (1.7–5.9) 100 3.8 Alpha-Ketoisovaleric acid HMDB00019 0.5 (0.2–1.1) 50 1 (0–2) Anserine HMDB00194 7.0 (3.3–20.0) 100 16.16 Arabinitol HMDB01851 31.8 (10.2–64.3) 100 19.28±6.32 Ascorbic acid HMDB00044 32.5 (4.6–78.0) 14 1.70–22.72 Asymmetric dimethylarginine HMDB01539 3.0 (0.2–6.7) 100 4.6 (3.0–7.0) Azelaic acid HMDB00784 2.8 (1.8–4.8) 64 4.8 (1.3–15.0) Benzoic acid HMDB01870 4.7 (1.2–10.5) 86 4.2 (1.9–6.5) Beta-Alanine HMDB00056 5.9 (3.4–13.0) 68 5 (0–10) Betaine HMDB00043 11.5 (2.7–24.7) 100 4.14 Butyric acid HMDB00039 1.6 (0.5–4.0) 77 0.131±0.013 Caffeine HMDB01847 1.2 5 0.33 (0.0–1.01) Carnosine HMDB00033 5.5 (1.4–12.6) 91 4.0±2.5 Choline HMDB00097 3.5 (1.4–6.1) 100 5.27 Cinnamic acid HMDB00567 1.6 (0.9–2.2) 9 0.77 Cis-Aconitic acid HMDB00072 20.9 (3.8–95.3) 100 13.0 (2.7–44.0) Citraconic acid HMDB00634 2.0 (1.2–2.6) 73 2.2 Citramalic acid HMDB00426 2.4 (1.0–4.8) 91 3.1 Citric acid HMDB00094 203 (49–600) 100 242.0±129.6 Creatine HMDB00064 46 (3–448) 100 46 (9–135) Creatinine* HMDB00562 14,743±9,797 100 10,090±310 Cytosine HMDB00630 4.1 (1.1–10.7) 36 NA** Dehydroascorbic acid HMDB01264 9.1 (5.8–7.1) 32 1.72–7.47 D-Galactose HMDB00143 11.9 (5.4–25.2) 100 4.4 (0.0–31.0) D-Glucose HMDB00122 37.5 (12.5–58.4) 100 31.12 Dihydrothymine HMDB00079 2.4 (1.3–4.4) 64 5 (0–10) Dimethyl sulfone HMDB04983 8.0 (1.3–49.0) 100 4.6 (2.5–7.5) Dimethylamine HMDB00087 30.8 (20.3–59.2) 100 39.3±8.3 Dimethylglycine HMDB00092 4.4 (1.6–10.4) 100 6.2 (0.8–11.2) D-Threitol HMDB04136 19.3 (5.3–32.7) 100 10.0 (5.6–15.0) D-Xylitol HMDB02917 8.4 (6.0–13.8) 95 3.50±2.37 D-Xylose HMDB00098 20 (3.0–102) 91 51.19 Erythritol HMDB02994 33.4 (6.8–64.0) 100 51±18 Ethanol HMDB00108 3.1 5 5–500b Ethanolamine HMDB00149 37.0 (24.8–56.2) 100 21.4 (6.6–36.2) Ethyl glucuronide HMDB10325 8.2 5 26.38±15.26 Ethylmalonic acid HMDB00622 3.0 (1.2–5.8) 95 2.0 (0.5–3.9) Formic acid HMDB00142 26.8 (6.9–120.9) 100 20.39±11.84 Fumaric acid HMDB00134 0.7 (0.2–1.7) 50 1 (0–2) Glucaric acid HMDB00663 8.4 (5.8–12.3) 82 2.9±0.2 Gluconic acid HMDB00625 21.5 (8.1–38.8) 100 NA** Glucuronic acid HMDB00127 9.7 (3.7–20.6) 77 NA Glutaconic acid HMDB00620 3.1 (1.2–3.1) 68 1 (0–2) Glutaric acid HMDB00661 2.1 (0.7–3.6) 18 1.3 (0.6–2.6) Glyceric acid HMDB00139 15.9 (7.8–31.1) 100 4.5 (0.0–9.0) Glycerol HMDB00131 13 (4–19) 100 20 (0–40) Glycine HMDB00123 106 (44–300) 100 151 (233–248) Glycolic acid HMDB00115 42.0 (3.7–122.0) 100 35 (18–55) Guanidoacetic acid HMDB00128 41.8 (10.6–97.3) 100 89.0 (11.0–124.0) Hippuric acid HMDB00714 229 (19–622) 100 257 (20–770) Homocitrulline HMDB00679 7.5 (3.8–13.8) 91 6.0 (0.0–8.8) Homogentisic acid HMDB00130 1.70 (0.55–2.8) 45 1 (0–2) Homovanillic acid HMDB00118 6.2 (1.8–12.7) 100 4.9±3.4 HPHPA HMDB02643 5.9 (1.4–22.1) 100 0.55–54.93 Hydroxyproline HMDB00725 6.5 (2.4–12.5) 55 2 Hydroxypropionic acid HMDB00700 12.3 (4.4–17.9) 82 6.5 (3.0–10.0) Hypoxanthine HMDB00157 7.2 (1.8–24.1) 100 4.67 (2.80–6.38) Indoleacetic acid HMDB00197 3.4 (1.8–6.2) 100 2.0±1.0 Indoxyl sulfate HMDB00682 22.4 (6.0–64.8) 100 19.74±5.26 Inosine HMDB00195 1.4 (0.4–6.5) 50 1.04±0.56 Isobutyric acid HMDB01873 3.5 (1.2–9.0) 95 3.9 Isocitric acid HMDB00193 46.9 (20.4–89.2) 68 58 (36–84) Isovaleric acid HMDB00718 1.4 (0.8–2.5) 86 0.42±0.02 Isovalerylglycine HMDB00678 2.0 (0.4–4.0) 91 5.5 (1.0–10.0) Kynurenic acid HMDB00715 2.1 (0.8–4.2) 64 1.53 (0.66–4.13) Kynurenine HMDB00684 1.7 (1.1–2.5) 23 2.85±1.94 Lactose HMDB00186 11.8 (1.0–24.2) 91 36.31±6.6c L-Alanine HMDB00161 21.8 (7.1–43.0) 100 22.0±11.0 L-Arabinose HMDB00646 13.3 (1.3–22.0) 100 8.75 (6.12–11.84) L-Arginine HMDB00517 8.6 (3.8–18.6) 95 6.6 (2.5–10.8) L-Asparagine HMDB00168 10.0 (4.6–17.8) 95 10.0 (4.6–16.3) L-Aspartic acid HMDB00191 10.9 (3.5–21.8) 95 4.63 (1.71–7.56) L-Carnitine HMDB00062 5.0 (0.7–16.4) 100 5.7±4.8 L-Cystathionine HMDB00099 14.7 (3.9–20.7) 82 5.3 (1.0–9.6) L-Cysteine HMDB00574 65.8 (23.1–134.5) 100 33.4±15.5 L-Cystine HMDB00192 12.5 (5.0–24.6) 100 9.0±3.0 Levoglucosan HMDB00640 10.0 (2.4–29.0) 27 NA** Levulinic acid HMDB00720 1.3 (0.4–2.0) 64 1.28 (0.33–2.75) L-Fucose HMDB00174 11.8 (6.7–26.0) 91 9.6 (6.4–16.0) L-Glutamic acid HMDB00148 8.5 (3.3–18.4) 100 10.0±3.7 L-Glutamine HMDB00641 37.2 (19.1–77.9) 100 33.32±10.78 L-Histidine HMDB00177 43 (17–90) 100 60.75±27.45 L-Isoleucine HMDB00172 1.3 (0.4–2.6) 100 1.64 L-Lactic acid HMDB00190 11.6 (3.5–29.3) 100 12.3±6.2 L-Leucine HMDB00687 2.8 (1.6–5.4) 100 2.5±1.3 L-Lysine HMDB00182 17.2 (3.7–51.3) 100 18 (7–29) L-Methionine HMDB00696 1.4 (0.5–2.5) 41 1.3±0.97 L-Phenylalanine HMDB00159 6.4 (3.5–11.2) 100 4.50±1.15 L-Serine HMDB00187 25.3 (11.6–53.4) 100 26.0±10.0 L-Threonine HMDB00167 13.3 (6.4–25.2) 100 5.17–24.59 L-Tryptophan HMDB00929 6.3 (3.4–11.1) 100 2.45–29.40 L-Tyrosine HMDB00158 9.5 (4.1–23.5) 100 8.8±4.5 L-Valine HMDB00883 3.4 (2.0–7.7) 100 3.0±1.0 Maleic acid HMDB00176 0.4 (0.3–0.5) 14 0.5 Malonic acid HMDB00691 2.9 (2.0–3.5) 77 1 (0–2) Maltose HMDB00163 6.0 (1.3–21.4) 73 NA** Mandelic acid HMDB00703 1.4 (1.1–1.7) 9 0.966±0.669 Mannitol HMDB00765 32.4 (5.2–85.1) 95 10.26±9.14 Methanol HMDB01875 37 (10–117) 100 NA** Methylamine HMDB00164 4.0 (1.5–11.9) 100 10.5 (9.8–12.5) Methylglutaric acid HMDB00752 3.1 (2.6–3.6) 9 3.5 (0.0–7.0) Methylguanidine HMDB01522 2.7 (1.2–6.0) 95 1.25±0.72 Methylmalonic acid HMDB00202 1.9 (0.7–3.5) 95 1.6 (0.0–3.6) Methylsuccinic acid HMDB01844 3.2 (0.7–9.9) 100 2.3 (0.8–10.8) Monomethyl glutaric acid HMDB00858 2.3 (0.6–4.4) 68 NA m-Thymol HMDB01878 2.2 (0.7–4.0) 86 NA** Myoinositol HMDB00211 22.4 (7.9–36.1) 100 18.8±2.9 N-Acetylaspartic acid HMDB00812 4.0 (1.3–7.0) 100 4.66±1.14 N-Acetylneuraminic acid HMDB00230 5.4 (2.5–8.6) 100 8.60±1.58 N-Acetylputrescine HMDB02064 0.5 (0.4–0.7) 32 1.18 (0.58–2.25) N-Methylhydantoin HMDB03646 4.9 (1.0–11.0) 55 NA o-Hydroxyphenylacetic acid HMDB00669 2.0 (0.9–4.5) 91 7.71±3.65d Oxoglutaric acid HMDB00208 4.8 (2.0–17.0) 100 2.98 (0.42–18.30) Oxypurinol HMDB00786 13.3 (5.1–29.3) 50 3.29 Pantothenic acid HMDB00210 1.9 (0.6–4.4) 100 2.7±0.9 p-Cresol sulfate HMDB11635 1.3 (0.3–5.5) 91 NA** Phenol HMDB00228 4.8 (0.6–12.8) 95 7.0 (5.6–9.2) Phenylacetic acid HMDB00209 1.9 5 4.16±0.91 Phenylacetylglutamine HMDB06344 34.0 (4.5–70.0) 100 47.03 (3.84–85.51) Phenylglyoxylic acid HMDB01587 7.34 5 36.6±0.48e Phosphorylcholine HMDB01565 1.1 (0.7–3.0) 41 NA p-Hydroxyphenylacetic acid HMDB00020 5.5 (1.4–14.6) 100 9.68 Picolinic acid HMDB02243 19.4 (7.2–37.6) 18 1.65–10.28 Pimelic acid HMDB00857 2.2 (0.7–4.0) 77 NA** Proline-betaine HMDB04827 2.3 (0.8–4.0) 100 12.7 (1.6–23.7) Propylene glycol HMDB01881 6.7 (1.4–44.3) 100 NA** Pseudouridine HMDB00767 28.9 (13.3–41.3) 100 26.02±4.62 Pyrocatechol HMDB00957 4.7 (2.0–8.5) 100 6.0±4.3 Pyroglutamic acid HMDB00267 20.7 (10.2–32.6) 100 28.8 (3.4–54.2) Pyruvic acid HMDB00243 2.7 (0.8–6.4) 100 2.13 (0.54–8.67) Quinolinic acid HMDB00232 5.2 (1.0–17.5) 100 2.5±1.1 Sarcosine HMDB00271 2.9 (0.5–5.4) 77 2.8 (0.0–5.6) Scyllitol HMDB06088 4.2 (2.2–8.1) 68 NA Sebacic acid HMDB00792 1.9 5 1 (0–2) Sorbitol HMDB00247 9.9 (2.5–18.7) 100 3.50±2.24 Suberic acid HMDB00893 1.6 (0.9–2.4) 32 0.5 (0.0–2.9) Succinic acid HMDB00254 6.0 (0.3–33.3) 91 5.6±3.8 Succinylacetone HMDB00635 2.8 (0.6–4.7) 36 1 (0–2) Sucrose HMDB00258 7.4 (1.4–19.5) 100 7.12±3.23 Sumiki’s acid HMDB02432 1.7 5 NA** Symmetric dimethylarginine HMDB03334 2.7 (1.5–5.2) 100 3.72±0.35 Syringic acid HMDB02085 1.5 5 0.9±0.2 Tartaric acid HMDB00956 11.8 (2.6–64.4) 82 12.0 (1.3–46.0) Taurine HMDB00251 81 (13–251) 100 4.00–159.98 Threonic acid HMDB00943 20.8 (10.2–39.3) 100 10.0±7.0 Thymidine HMDB00273 2.2 (0.7–3.9) 36 6.87±4.86 Trans-Aconitic acid HMDB00958 6.6 (1.8–25.1) 55 3.38 Trans-Ferulic acid HMDB00954 1.2 5 36.3±11.2f Trigonelline HMDB00875 31.1 (5.5–109.3) 100 16.08 Trimethylamine HMDB00906 2.5 (0.3–19.4) 86 7.7±7.4 Trimethylamine N-oxide HMDB00925 91.0 (4.8–509.0) 100 118.7 (35.5–202.1) Uracil HMDB00300 9.5 (2.6–22.8) 100 12 (2–22) Urea HMDB00294 12,285 (174–49,097) 100 22,566±4,407 Vanillic acid HMDB00484 1.6 (0.6–3.3) 95 1.0 (0.0–2.5) Vanillylmandelic acid HMDB00291 2.3 (1.0–3.4) 9 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. a The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman; b depends greatly on amount of alcohol consumed; c after administration of lactose; d after administration of 1 g of coumarin; e After styrene exposure; and f after consumption of chocolate. NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. Based on these NMR studies, the most abundant constituents (based on average values) of urine from a healthy individual are urea (12.3 ± 14.5 mM/mM creatinine), creatinine (14.7 ± 9.8 mM), hippuric acid (229 ± 160 µM/mM creatinine) and citric acid (203 ± 129 µM/mM creatinine). The least abundant compounds were trans-ferulic acid (1.2 µM/mM creatinine), phosphorylcholine (1.1 ± 0.7 µM/mM creatinine), 4-ethylphenol (0.9 ± 0.1 µM/mM creatinine), 2-methylglutaric acid (0.8 ± 0.4 µM/mM creatinine). The lowest concentrations that could be reliably detected using NMR were 0.4 ± 0.1 µM/mM creatinine (for maleic acid), 0.5 ± 0.2 µM/mM creatinine (for alpha-ketoisovaleric acid and N-acetylputrescine) and 0.7 ± 0.4 µM/mM creatinine (for fumaric acid). The complete list of average compound concentrations (including their range), literature-reported concentrations (for healthy adult) and the frequency of their occurrence is shown in Table 3. In general, there is good agreement between the NMR-measured concentrations and those reported in the literature (often obtained by other analytical techniques). However, not all of the NMR-derived urine concentrations agree with literature-derived values. A total of 34 compounds had average concentrations somewhat higher (>1 SD) than previously reported values (for example, 1, 3-dimethyluric acid, glucaric acid, L-aspartic acid, mannitol), while 21 compounds had average concentrations lower (<1 SD) than previously reported (for example, 1-methylhistidine, phenol, dihydrothymine, thymidine). Another 7 metabolites also exhibited somewhat greater range in concentrations than those previously reported in the literature. These included: acetic acid, butyric acid, isovaleric acid, lactose, phenylglyoxylic acid, proline-betaine and trans-ferulic acid. Some of these discrepancies are likely due to differences in diet, physiological status, pharmacological effects and the age of the different cohorts that were analyzed. Other differences may be due to storage effects, sample preparation methods and the analytical methods being used. As a rule, NMR concentration determinations are very accurate since they involve direct measurement of the compound, as opposed to an indirect measurement of a derivative compound. Therefore we are quite confident in the NMR concentration values reported in Table 3 and would tend to view these as more reliable than those measured via other technologies. A number of the compounds exhibiting higher-than or lower-than-reported concentrations appear to be associated with dietary intake. For example, mannitol is a sugar alcohol that is poorly absorbed by humans but its presence in urine can be explained by its occurrence in commonly consumed foods such as apples, pineapples, asparagus and carrots. Likewise, the urinary excretion of trans-ferulic acid (a polyphenolic derivative) increases after the ingestion of breakfast cereals [61] and chocolate. The relatively low value we measured is likely due to the fact that the literature value of trans-ferulic acid reported in Table 3 was measured for people on a special diet [62]. Also, the low level of proline-betaine we measured in urine may be due to a lower frequency of exposure to dietary citrus fruits in our population sample [63]. Proline-betaine is an osmoprotectant found in citrus fruit and urinary excretion of this metabolite is increased after consumption of fruits such as orange juice [64]. Similarly the higher levels of dimethyl sulfone we detected in urine could be attributed to dietary sources that contain DMSO [65]. For example, onions contain many sulfoxides including DMSO which can be oxidized in the liver and kidneys to produce dimethyl sulfone [66], [67]. The consumption of meat could also significantly increase the concentration of some metabolites in urine as reported in the case of 1-methylhistidine [68]. 1-methylhistidine is produced from the metabolism of anserine (a dipeptide) which is commonly found in meats [69], [70]. In addition to diet, metabolite levels in urine can also be affected by physiological status. For instance, the level of 3-hydroxybutyric acid in urine increases during fasting and can range from 0 to 200 µM/mM creatinine, with the maximum level reported in the literature (200 µM/mM creatinine) corresponding to healthy male after 35 h of fasting [50]. Some of the metabolites we measured by NMR did not have any previously reported literature values. For example, glucuronic acid is usually reported as total glucuronic acid (the free acid plus glucuronide conjugates) after hydrolysis [71], [72]. Here we report the concentration of free glucuronic acid, as indicated in Table 3. Another example, 2-methylerythritol, was previously detected in human urine but no concentration was reported [73]. The urinary excretion of 2-methylerythritol is most likely a result of dietary consumption of fruits or vegetables containing 2-methylerythritol and/or 2-methylerythritol-4-phosphate. 2-methylerythritol-4-phosphate is an intermediate in isoprenoid biosynthesis [74] and has been found to be quite abundant in certain plants [75]. A number of compounds we measured by NMR appear to be normal constituents of human urine but seem not to have been previously reported as being detectable by NMR (a total of 42 compounds) or reported as detected but not-quantified by any other method (a total of 8 compounds). The identification of these “NMR-novel” compounds was aided by their prior identification by GC-MS and DI-MS (see following sections) and through a careful literature analysis of compounds that had previously been detected in human urine via other methods. The list of detected but not previously quantified by NMR compounds includes: 2-hydroxy-3-methylpentanoic acid, 2-methyl-3-ketovaleric acid, 2-methylerythritol, glucuronic acid, monomethyl glutaric acid, N-methylhydantoin, phosphorylcholine and scyllitol. All of these compounds were confirmed using authentic standards. This NMR study also revealed a number of common identification errors made in previously published NMR-based human urine metabolomic studies. In particular, several earlier reports identified phenylacetylglycine [76], N-acetylglutamic acid [77], cresol [78], isonicotinic acid [78], yellow 7.1 [79], meta-hydroxyphenylpropionic acid [80], 2-oxoisocaproic acid [81], urocanic acid, glycylproline and ornithine [80] as being detectable by NMR in human urine. Using our NMR instrument and the samples available to us, we were unable to detect any of these compounds, even after performing multiple spike-in experiments using authentic compounds. While some of these metabolites have been previously reported to be in human urine, they were reported at concentrations far below the lower limit of detection of modern NMR instruments (which is ∼ 1 µM). Due to their chemical shift similarity, phenylacetylglycine (which is found only in rats and mice) and N-acetylglutamic acid appear to be commonly mistaken for phenylacetylglutamine. We also noticed that, isonicotinic acid (a breakdown product of isoniazid and hydrazine derivatives, which is found only in individuals that have taken isoniazid and other hydrazine derivatives as a drug) appears to be mistaken for trigonelline. Likewise cresol (water-insoluble) appears to be frequently mistaken for cresol-sulfate (water-soluble), while the compounds yellow 7.1, meta-hydroxyphenylpropionic acid and 3-(p-hydroxyphenyl)-propionic acid appear to be commonly mistaken for 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA). In addition to correcting these compound identification errors, we also observed some significant gender-related effects on creatinine levels in our urine samples. Since males generally have a greater mass of skeletal muscle than females, they tend to have higher urinary levels of creatinine than women. This was clearly evident in our samples as the average male creatinine level was 20 mM while the average female creatinine level was 11 mM. In addition, increased dietary intake of creatine or a protein-rich diet can increase daily creatinine excretion [82]. Quantification and Identification of Urine Metabolites – GC-MS As seen in Table 1, GC-MS methods have long been used to comprehensively characterize the chemical content of human urine. For our studies a total of 4 different GC-MS analyses were performed. The first method employed polar solvent extraction and derivatization to achieve broad metabolite coverage of polar metabolites, the second was more selective and targeted organic acids, the third targeted volatiles, while the fourth targeted bile acids. Representative high-resolution GC-MS total ion chromatograms are shown in Figures 2–4 for each of these analyses (except for the bile acids). Combined, the 4 GC-MS methods allowed us to identify 179 and quantify a total of 85 compounds. Table 4 shows the identified polar, organic acid extracts and bile acids (127 in total), Table 5 shows the identified volatile metabolites (52 in total) while Table 6 shows the 85 fully quantified compounds from all 4 techniques. These numbers actually represent the highest number of urine metabolites both identified and quantified by GC-MS to date. As seen in Table 1, previous GC-MS studies have reported up to 258 unique compounds being identified (but none quantified) [83] and approximately 95 compounds quantified in human urine [84]. Relative to NMR (see previous section) and other methods used to analyze human urine (Table 1), it appears that a multi-pronged GC-MS analysis is an excellent approach to characterize this biofluid. However, unlike NMR where nearly all detectable peaks are identifiable, metabolite coverage by GC-MS tends to be relatively incomplete. As seen in Figure 2, only 60% of the peaks could be identified using as reference the 2008 NIST library and other home-made GC-MS metabolite libraries. Likewise, in Figure 3, we see that only 65% of the organic acid peaks could be identified while in Figure 4, just 60% of the volatile compound peaks could be identified. Figure 2 Typical total ion chromatogram of water-soluble metabolites extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: L-valine; 2: oxalic acid; 3∶3-hydroxyisobutyric acid; 4: L-serine; 5: glycine; 6: succinic acid; 7: glyceric acid; 8∶4-deoxythreonic acid; 9∶2,4-dihydroxybutanoic acid; 10∶3,4-dihydroxybutanoic acid; 11: adipic acid; 12: creatinine; 13: threonic acid; 14: L-phenylalanine; 15: p-hydroxyphenylacetic acid; 16: L-ornithine; 17: L-asparagine; 18: L-arabinose; 19: D-xylitol; 20: D-xylulose; 21: Cis/Trans-aconitic acid; 22: hippuric acid; 23: isocitric acid; 24: D-galactose; 25: D-glucose; 26: L- tyrosine; 27: sorbitol; 28: gluconic acid; 29: scyllitol; 30: myoinositol; 31: uric acid; 32: pseudouridine. Figure 3 Typical GC-MS total ion chromatogram of organic acids extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: pyruvic acid; 2: L-lactic acid; 3: alpha-hydroxyisobutyric acid; 4: glycolic acid; 5: levulinic acid; 6∶3-hydroxyisovaleric acid; 7∶2-hydroxy-2-methylbutyric acid; 8: hydroxypropionic acid; 9∶2-methyl-3-hydroxybutyric acid; 10: malonic acid; 11∶3-hydroxyisovaleric acid; 12: methylmalonic acid; 13∶2-ethylhydracrylic acid; 14: benzoic acid; 15: phosphoric acid; 16: ethylmalonic acid; 17: succinic acid; 18: methylsuccinic acid; 19∶4-deoxythreonic acid; 20∶5-hydroxyhexanoic acid; 21: citraconic acid; 22: glutaric acid; 23: m-chlorobenzoic acid; 24; 3,4-dihydroxybutanoic acid; 25∶3-methylglutaconic acid; 26: adipic acid; 27: pyroglutamic acid; 28∶3-methyladipic acid; 29: sumiki’s acid; 30: o-hydroxyphenylacetic acid; 31: oxoglutaric acid; 32: pimelic acid; 33∶3-hydroxymethylglutaric acid; 34∶3-hydroxyphenylacetic acid; 35∶4-hydroxybenzoic acid; 36∶2-furoylglycine; 37: suberic acid; 38: quinolinic acid; 39: Cis/Trans-aconitic acid; 40: homovanillic acid; 41: azelaic acid; 42: hippuric acid; 43∶3,4-dihydroxybenzeneacetic acid; 44∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 45: vanillylmandelic acid; 46∶4-hydroxyphenyllactic acid; 47: indoleacetic acid; 48: palmitic acid; 49: kynurenic acid; 50∶3-hydroxyhippuric acid; 51∶3-hydroxysebacic acid; 52: Trans-ferulic acid; 53∶5-hydroxyindoleacetic acid; 54: stearic acid. Figure 4 Typical gas chromatogram of volatile organic compounds (VOC) from a pooled human urine sample. Numbers indicate the following metabolites: 1∶3-methyl sulfolane; 2∶3-hexanone; 3∶2-pentanone; 4∶1-hydroxy-2-pentanone; 5: allyl methylsulphide; 6: dimethyl disulfide; 7∶4-heptanone; 8∶1-methylcyclohexanol; 9; 2-hexanone; 10∶3,4-dimethylthiophene; 11: diallyl sulphide; 12∶5-methyl-2-hexanone; 13∶1,3-dithio cyclohexane; 14: dimethyl trisulfide; 15: phenol; 16: o-cymene; 17: p-cymene; 18: m-cymene; 19∶1,4-cineol; 20: p-cresol; 21: linalool oxide; 22: iso-menthol; 23: Alpha-p-dimethylstyrene; 24: L-menthol; 25: undecane; 26: ledene oxide (II); 27: salicylic acid methyl ester; 28: Beta-carvone; 29: piperitone; 30: o-thymol; 31: Beta-cyclocitrol; 32∶4,7-dimethyl-benzofuran; 33: cuminal: 34∶2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene; 35∶4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone; 36∶1,2,3,4-tetrahydro-1,1,6-trimethyl naphthalene; 37: Alpha-cedrene; 38∶1,2,3,4-tetrahydro-1,5,7-trimethylnapthalene; 39∶1,2-diydro-1,1,6-trimethyl-napthalene; 40: Beta-guaiene; 41: Beta-damascenone; 42∶2,5-cyclohexadiene-1,4-dione-2,6-di-tert-butyl; 43: himachalene; 44∶4-(2,6,6-trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one: 45∶1-(2,6,6-trimethyl-1-cyclohexen-1-yl)-1-penten-3-one; 46∶2,4-Bis (1,1-dimethylethyl)-phenol; 47∶1-(2,3,6-trimethyl phenyl)-3-buten-2-one; 48: L-calamenene; 49: Beta-vatirenene; 50∶1,6,7-trimethylnaphthalene; 51: azulol; 52∶3,3,5,6-tetramethyl-1-indanone. Table 4 List of metabolites identified in human urine extracts by GC-MS (except volatile compounds). Amino acids HMDB ID Organic acids HMDB ID Beta-Alaninea HMDB00056 Azelaic acidb HMDB00784 Glycinea HMDB00123 Benzoic acidb HMDB01870 L-Alaninea HMDB00161 Butyric acida HMDB00039 L-Asparaginea HMDB00168 Cis-Aconitic acida,b HMDB00072 L-Aspartic acida HMDB00191 Citraconic acidb HMDB00634 L-Glutaminea HMDB00641 Citramalic acidb HMDB00426 L-Histidinea HMDB00177 Citric acida HMDB00094 L-Isoleucinea HMDB00172 Erythronic acida HMDB00613 L-Leucinea HMDB00687 Ethylmalonic acidb HMDB00622 L-Lysinea HMDB00182 Gluconic acida HMDB00625 L-Ornithinea HMDB00214 Glutaric acidb HMDB00661 L-Phenylalaninea HMDB00159 Glyceric acida HMDB00139 L-Serinea HMDB00187 Glycolic acida,b HMDB00115 L-Threoninea HMDB00167 Glyoxalic acida HMDB00119 L-Tryptophana HMDB00929 Hippuric acida,b HMDB00714 L-Tyrosinea HMDB00158 Homovanillic acidb HMDB00118 L-Valinea HMDB00883 HPHPAb HMDB02643 Bile acids HMDB ID Hydroxypropionic acidb HMDB00700 Cholenic acidc HMDB00308 Indoleacetic acidb HMDB00197 Deoxycholic acidc HMDB00626 Indolelactic acid b HMDB00671 Lithocholic acidc HMDB00761 Isobutyrylglycineb HMDB00730 Ursodeoxycholic acidc HMDB00946 Isocitric acida HMDB00193 Misc HMDB ID Isovalerylglycineb HMDB00678 3-Methylhistidinea HMDB00479 Kynurenic acidb HMDB00715 5-Hydroxyindoleb HMDB59805 Levulinic acidb HMDB00720 Creatininea HMDB00562 L-Lactic acidb HMDB00190 Dimethylglycinea HMDB00092 Malic acida HMDB00744 D-Xylitola HMDB02917 Malonic acida,b HMDB00691 Glycerol 3-phosphatea HMDB00126 m-Chlorobenzoic acidb HMDB01544 Glycerola HMDB00131 m-Coumaric acidb HMDB01713 Mannitola HMDB00765 Methylmalonic acidb HMDB00202 Myoinositola HMDB00211 Methylsuccinic acidb HMDB01844 Pseudouridinea HMDB00767 o-Hydroxyhippuric acidb HMDB00840 Scyllitola HMDB06088 o-Hydroxyphenylacetic acidb HMDB00669 Sorbitola HMDB00247 Oleic acidb HMDB00207 Ureaa HMDB00294 Oxalic acida HMDB02329 Organic acids HMDB ID Oxoglutaric acidb HMDB00208 2,4-Dihydroxybutanoic acida HMDB00360 Palmitic acidb HMDB00220 2-Ethylhydracrylic acidb HMDB00396 Pantothenic acidb HMDB00210 2-Furoylglycineb HMDB00439 Phosphoric acidb HMDB02142 2-Hydroxy-2-methylbutyric acidb HMDB01987 p-Hydroxyphenylacetic acida HMDB00020 2-Hydroxyglutaric acida HMDB00694 Pimelic acidb HMDB00857 2-Hydroxyvaleric acida HMDB01863 Pyroglutamic acida,b HMDB00267 2-Ketobutyric acidb HMDB00005 Pyruvic acidb HMDB00243 2-Methyl-3-hydroxybutyric acidb HMDB00354 Quinolinic acidb HMDB00232 3,4-Dihydroxybenzeneacetic acidb HMDB01336 Stearic acidb HMDB00827 3,4-Dihydroxybutanoic acida,b HMDB00337 Suberic acidb HMDB00893 3,4-Dihydroxyhydrocinnamic acidb HMDB00423 Succinic acida,b HMDB00254 3-Aminoisobutanoic acida,b HMDB03911 Sumiki’s acidb HMDB02432 3-Hydroxyhippuric acidb HMDB06116 Tartaric acida HMDB00956 3-Hydroxyisobutyric acida HMDB00023 Threonic acida HMDB00943 3-Hydroxyisovaleric acida,b HMDB00754 Trans-Aconitic acida HMDB00958 3-Hydroxymethylglutaric acidb HMDB00355 Trans-Ferulic acidb HMDB00954 3-Hydroxyphenylacetic acidb HMDB00440 Uric acida HMDB00289 3-Hydroxysebacic acidb HMDB00350 Vanillyllactic acidb HMDB00913 3-Methyladipic acidb HMDB00555 Vanillylmandelic acidb HMDB00291 3-Methylglutaconic acidb HMDB00522 Sugars HMDB ID 4-Deoxythreonic acida,b HMDB02453 D-Fructosea HMDB00660 4-Hydroxybenzoic acidb HMDB00500 D-Galactosea HMDB00143 4-Hydroxyphenyllactic acidb HMDB00755 D-Glucosea HMDB00122 5-Hydroxyhexanoic acidb HMDB00525 D-Xylosea HMDB00098 5-Hydroxyindoleacetic acidb HMDB00763 D-Xylulosea HMDB01644 Adipic acida,b HMDB00448 L-Arabinosea HMDB00646 Alpha-Hydroxyisobutyric acidb HMDB00729 L-Fucosea HMDB00174 Aminomalonic acida HMDB01147 Sucrosea HMDB00258 Ascorbic acida HMDB00044 a Polar metabolites. b Targeted organic acids. c Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid. Table 5 Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS. Peak # Compound HMDB ID Uniquemass MSmatch RT(min) RI(Calc) RI(NIST) 1 3-Methyl sulfolane HMDB59667 68 867 0.83 725 654 2 3-Hexanone HMDB00753 43 781 0.99 732 788 3 2-Pentanone HMDB34235 43 894 1.61 758 654 4 1-Hydroxy-2-pentanone HMDB59678 43 780 1.74 763 897 5 Allyl methylsulphide HMDB31653 88 793 1.83 767 701 6 Dimethyl disulfide HMDB05879 94 940 2.26 785 763 7 4-Heptanone HMDB04814 71 919 3.34 831 883 8 1-Methylcyclohexanol HMDB59693 71 821 3.81 850 931 9 2-Hexanone HMDB05842 43 821 3.82 851 889 10 3,4-Dimethylthiophene HMDB29304 111 906 4.15 865 888 11 Diallyl sulphide HMDB36491 45 827 4.86 895 848 12 5-Methyl-2-hexanone HMDB31549 43 816 5.29 913 836 13 1,3-Dithio cyclohexane HMDB31473 120 841 5.85 936 1027 14 Dimethyl trisulfide HMDB13780 126 873 6.01 943 950 15 Phenol HMDB00228 94 859 6.34 957 998 16 o-Cymene HMDB37050 119 928 7.19 993 1014 17 p-Cymene HMDB05805 119 928 7.33 998 1018 18 m-Cymene HMDB13806 119 928 7.43 1009 1022 19 1,4-Cineol HMDB31455 111 912 8.71 1057 1016 20 p-Cresol HMDB01858 107 930 8.99 1068 1082 21 Linalool oxide HMDB31440 59 783 10.49 1131 1074 22 Iso-menthol HMDB35764 57 923 10.99 1152 1141 23 Alpha-p-dimethylstyrene HMDB29641 132 915 11 1153 1088 24 L-Menthol HMDB03352 71 924 11.18 1160 1140 25 Undecane HMDB31445 57 910 11.35 1168 1100 26 Ledene Oxide (II) HMDB60295 43 743 11.54 1176 1201 27 Salicylic acid methyl ester HMDB34172 120 857 11.65 1180 1193 28 Beta-Carvone HMDB35824 82 859 12.81 1229 1247 29 Piperitone HMDB34975 150 873 12.96 1235 1184 30 o-Thymol HMDB35770 135 904 13.9 1275 1305 31 Beta-Cyclocitrol HMDB41011 41 884 14.1 1283 1219 32 4,7-Dimethyl-benzofuran HMDB59897 146 861 14.4 1296 1244 33 Cuminal HMDB02214 133 861 14.82 1313 1235 34 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene HMDB36823 138 911 15.91 1359 1370 35 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone HMDB32913 121 795 15.97 1362 1433 36 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene HMDB59826 159 855 16.02 1364 1349 37 Alpha-Cedrene HMDB59695 119 821 16.1 1367 1415 38 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene HMDB59696 159 795 16.59 1388 1310 39 1,2-Diydro-1,1,6-trimethyl-napthalene HMDB40284 157 947 16.82 1398 1354 40 Beta-Guaiene HMDB38157 161 879 17 1405 1500 41 Beta-Damascenone HMDB13804 69 889 17.35 1420 1365 42 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl HMDB13817 177 843 18.51 1469 1462 43 Himachalene HMDB38168 119 823 18.62 1473 1489 44 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one HMDB37139 136 883 18.73 1478 1497 45 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one HMDB38130 121 788 19.05 1491 1507 46 2,4-Bis (1,1-Dimethylethyl)-phenol HMDB13186 191 863 19.5 1510 1519 47 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one HMDB59697 132 918 19.8 1523 1528 48 L-Calamenene HMDB59910 159 920 20.25 1533 1534 49 Beta-Vatirenene HMDB59676 202 806 20.45 1550 1545 50 1,6,7-Trimethylnaphthalene HMDB59701 170 899 20.82 1564 1559 51 Azulol HMDB59686 183 823 21.18 1581 1572 52 3,3,5,6-Tetramethyl-1-indanone HMDB59683 173 686 21.28 1585 1579 Table 6 Concentrations of metabolites in human urine as measured by GC-MS. Compounds Observed Concentration(µM/mM creatinine) Literature Values(µM/mM creatinine) 2,4-Dihydroxybutanoic acid 1.0 (0.3–1.8) 2.0±2.0 2-Ethylhydracrylic acid 2.1 (1.3–2.9) NA** 2-Furoylglycine 3.6 (0.7–5.8) 9.95 (2.0–18.66) 2-Hydroxy-2-methylbutyric acid 0.8 (0.4–1.5) 2.8±1.5 2-Methyl-3-hydroxybutyric acid 4.0 (1.3–6.2) 5.5 (0.0–11.0) 3,4-Dihydroxybenzeneacetic acid 0.9 (0.6–1.3) 1.16±0.50 3,4-Dihydroxybutanoic acid 42.2 (21.9–56.1) 34.9 (12.5–57.2) 3-Aminoisobutanoic acid 25.8 (1.4–155.6) 2.91–116.43 3-Hydroxyhippuric acid 6.1 (4.1–7.8) 2.5 (0.2–5.0) 3-Hydroxyisobutyric acid 29.0 (11.8–59.8) 17.5 (2.0–33.0) 3-Hydroxyisovaleric acid 7.4 (4.1–17.2) 8.5±3.2 3-Hydroxymethylglutaric acid 3.2 (1.1–5.2) 3 (0–10) 3-Hydroxyphenylacetic acid 5.6 (1.4–15.0) 6.1±4.1 3-Hydroxysebacic acid 2.9 (1.7–3.8) NA** 3-Methyladipic acid 2.1 (0.9–5.6) 2.9 (0.7–10.5) 3-Methylglutaconic acid 6.2 (2.8–8.3) 4.5 (0.0–9.0) 4-Deoxythreonic acid 18.2 (5.6–23.9) 25±10 4-Hydroxybenzoic acid 2.4 (1.3–3.6) 1.8±1.8 4-Hydroxyphenyllactic acid 2.8 (2.6–3.0) 1.1 (0.2–2.6) 5-Hydroxyhexanoic acid 2.7 (0.8–5.7) 3.5 (0.0–7.0) 5-Hydroxyindoleacetic acid 2.9 (0.4–5.8) 2.4±0.40 Adipic acid 3.0 (1.0–12.9) 5.1 (0.8–35.0) Alpha-Hydroxyisobutyric acid 2.9 (1.3–4.6) 3.8 Azelaic acid 1.9 (1.2–3.2) 4.8 (1.3–15.0) Benzoic acid 3.6 (1.4–7.5) 4.2 (1.9–6.5) Cholenic acid 0.015 (0.004–0.030) NA** Cis/Trans-Aconitic acid 15.3 (5.2–21.8) NA** Citraconic acid 1.6 (0.9–2.1) 2.2 Creatinine* 12,246±8,369 10,090±310 Deoxycholic acid 0.055 (0.050–0.060) NA** D-Galactose 3.3 (1.3–8.5) 4.4 (0.0–31.0) D-Glucose 35.6 (10.3–56.7) 31.12 D-Xylitol 2.3 (0.9–8.2) 3.50±2. 37 D-Xylulose 19.7 (6.4–32.6) NA** Ethylmalonic acid 2.4 (0.9–4.0) 2.0 (0.5–3.9) Gluconic acid 13.3 (8.2–26.4) NA** Glutaric acid 1.4 (0.5–2.3 ) 1.3 (0.6–2.6) Glyceric acid 14.4 (7.8–28.8) 4.5 (0.0–9.0) Glycine 75.7 (21–205) 151 (233–248) Glycolic acid 39.4 (2.9–78.1) 35 (18–55) Hippuric acid 217 (28–610) 257 (20–770) Homovanillic acid 4.3 (0.9–8.9) 4.9±3.4 HPHPA 4.2 (0.9–15.3) 0.55–54.93 Hydroxypropionic acid 8.1 (3.1–11.8) 6.5 (3.0–10.0) Indoleacetic acid 2.6 (0.6–5.4) 2.0±1.0 Indolelactic acid 0.9 0.460 (0.098–0.980) Isocitric acid 56.8 (19.4–119.1) 58 (36–84) Kynurenic acid 2.6 (0.9–4.1) 1.34±0.30 L-Arabinose 8.8 (0.8–19.4) 8.75 (6.12–11.84) L-Asparagine 9.5 (3.0–26.0) 10.0 (4.6–16.3) Levulinic acid 1.7 (0.6–2.5) 1.28 (0.33–2.75) Lithocholic acid 0.16 (0.10–0.20) NA** L-Lactic acid 7.1 (0.9–16.4) 12.3±6.2 L-Ornithine 5.3 (2.0–8.8) 5.0 (2.0–8.0) L-Phenylalanine 7.8 (5.0–11.3) 4.50±1.15 L-Serine 19.0 (10.4–35.8) 26.0±10.0 L-Tyrosine 15.1 (6.1–23.2) 8.8±4.5 L-Valine 5.5 (2.7–9.8) 3.0±1.0 Malonic acid 2.3 (1.2–3.1) 1 (0–2) m-Chlorobenzoic acid 0.7 NA** Methylmalonic acid 1.3 (0.5–2.1) 1.6 (0.0–3.6) Methylsuccinic acid 4.7 (1.1–6.2) 2.3 (0.8–10.8) Myoinositol 12.6 (5.1–15.3) 18.8±2.9 o-Hydroxyphenylacetic acid 2.9 (1.4–3.7) 7.71±3.65 Oxalic acid 8.2 (3.9–14.0) 1.11–33.34 Oxoglutaric acid 3.4 (1.4–12.5) 2.98 (0.42–18.30) Palmitic acid 6.7 (2.6–24.3) 11 (6–23) Phosphoric acid 784 (425–1170) 1000–4900 p-Hydroxyphenylacetic acid 6.0 (2.4–9.7) 9.68 Pimelic acid 1.9 (0.5–3.5) NA** Pseudouridine 29.8 (21.8–47.3) 26.02±4.62 Pyroglutamic acid 18.6 (4.5–24.9) 28.8 (3.4–54.2) Pyruvic acid 2.1 (1.0–3.7) 2.13 (0.54–8.67) Quinolinic acid 3.9 (0.9–15.1) 2.5±1.1 Scyllitol 3.6 (1.9–6.9) NA Sorbitol 3.9 (1.9–5.1) 3.50±2.24 Stearic acid 3.9 (2.3–7.7) 2.9 (1.6–6.6) Suberic acid 1.2 (0.5–1.9) 0.5 (0.0–2.9) Succinic acid 6.2 (2.5–13.5) 5.6±3.8 Sumiki’s acid 1.6 NA** Threonic acid 14.8 (8.6–36.1) 10.0±7.0 Trans-Ferulic acid 1 36.3±11.2 Uric acid 186 (93–329) 188 (79–296) Ursodeoxycholic acid 0.022 (0.013–0.030) NA** Vanillylmandelic acid 1.6 (0.6–2.8) 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. Incomplete compound identification is a common problem with global or untargeted GC-MS metabolomics. This may be due to any number of factors including spectral overlap due to incomplete separation, poor signal to noise for low intensity peaks, the lack of reference GC-MS spectral data for certain metabolites (especially unusual dietary sources), or the presence of spectral artefacts such as derivatization by-products or degraded metabolites in the GC-MS spectrum. For our GC-MS studies we used the NIST library supplemented with a home-made GC-MS reference library of known urine compounds assembled from the Human Metabolome Library [16]. No doubt the use of other commercially available reference GC-MS libraries such as the Fiehn GC-MS library from Agilent or the GOLM metabolome database library [85] might have allowed us to further increase our coverage. Likewise the use of a faster scan rate and/or a more sensitive GC-TOF instrument (instead of a slower scanning quadrupole GC-MS) certainly would have increased overall coverage. Nearly all of the non-volatile metabolites (87) identified by our GC-MS analyses were also identified by NMR. Some of the exceptions were oxalic acid, phosphate and uric acid, each of which was detected by GC-MS but not by NMR. These compounds do not have NMR-detectable protons at physiological pH, making them essentially “NMR invisible”. Other compounds seen by GC-MS but not by NMR included metabolites that were generally below the detection limit of NMR (∼2 µM/mM creatinine) such as indolelactic acid and 2,4-dihydroxybutanoic acid. For our non-targeted GC-MS analysis, the lower limit of detection was 1 µM/mM creatinine (for 2,4-dihydroxybutanoic acid), while for our targeted organic acid GC-MS analysis the lower limit of detection was 0.7 µM/mM creatinine (for m-chlorobenzoic acid). Overall, our data suggests that the sensitivity of a standard single quadrupole GC-MS instrument is perhaps 1.5–2X better than a 500 MHz NMR instrument for water-soluble metabolites. It is also important to note that the level of water-soluble, non-volatile metabolite coverage obtained by GC-MS is not as great as seen with NMR (127 cmpds vs. 209 cmpds). The limited coverage of GC-MS is partly due to the fact that not all compounds can be readily extracted, easily derivatized or routinely separated on a GC column. Furthermore, when analyzing urine by GC-MS there is a need to pretreat the sample with urease (to reduce urea levels) that can diminish the abundance of some metabolites [86]. While GC-MS may not be the best method for analyzing water-soluble metabolites, it certainly excels at the detection of volatile metabolites. Indeed, only one of the volatile metabolites identified by GC-MS is identified by NMR (phenol). This certainly underlines a key strength of GC-MS relative to other metabolomics platforms. When comparing NMR to GC-MS we found that NMR is capable of detecting 121 compounds that the 4 combined GC-MS methods cannot detect while the combined GC-MS methods can detect 91 compounds that NMR cannot routinely detect. Overall, these data suggest that GC-MS and NMR appear to be complementary methods for the identification and quantification of small molecules in urine. Among the 58 metabolites quantified by both GC-MS and NMR we found very good overall agreement, with the majority of measured concentration values falling within 20 ± 11% of each other. The concentration patterns and rankings of the most abundant to the least abundant compounds were also largely identical for the two platforms. A total of 12 metabolites exhibited somewhat larger concentration discrepancies between GC-MS and NMR (i.e; L-arabinose, L-serine (lower in GC-MS vs. NMR), 4-hydroxybenzoic acid and tyrosine (higher in GC-MS vs NMR). Some of these concentration differences may be due to the extraction or derivatization process needed to conduct GC-MS analyses. This can lead to unspecified compound losses, unusual derivatives or unrecognized fragmentation patterns. Therefore we would have expected at least a few GC-MS concentration values to be slightly lower than those seen by NMR. Likewise, it is important to remember that there are inherent errors (5–10%) in measuring peak areas (i.e. compound concentrations) both in GC-MS and NMR due to peak overlap, uneven baselines and spectral noise. Nearly all of the compounds we detected or quantified in human urine by GC-MS have been previously described or mentioned in the GC-MS literature. One compound (scyllitol), however, appears not to have been previously detected by GC-MS. The identification of this compound by our GC-MS method was aided by its prior identification by NMR (see previous section). Additionally, a careful literature analysis also indicated the scyllitol is a normal constituent of human urine and has previously been detected in human urine via other methods. As we noted with our NMR studies earlier, there are a few previously reported GC-MS detectable metabolites in human urine that appear to be artefacts. These artefactual metabolites may arise from extractions with different solvents, pre-treatment with urease, and chemical derivatization. For example, Shoemaker et al [84], reported the presence of bisethane in human urine. We also detected bisethane, but it appears to be artefact of chemical derivatization and is not a urine metabolite. Quantification and Identification of Urine Metabolites – Combined Direct Flow Injection and LC-MS/MS Assay Direct flow injection (DFI) MS/MS or DFI-MS/MS is another commonly used global metabolic profiling method [87]. When isotopic standards are used along with multiple reactions monitoring (MRM), it is also possible to perform targeted metabolomics with very accurate concentration measurements. For our urine studies, we employed a combined DFI/LC-MS/MS approach, based on the commercially available AbsoluteIDQ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck). When applied to urine, we were able to identify and quantify a total of 127 metabolites or metabolite species, including 34 acylcarnitines, 21 amino acids, 15 biogenic amines, creatinine, hexose, 35 phospatidylcholines, 15 sphingomyelins and 5 lysophosphatidylcholines. The amino acids and biogenic amines are analyzed by an LC-MS/MS method, whereas all other metabolites are analyzed by DFI-MS/MS as indicated in Table 7. DFI-MS/MS identifies lipid species (as opposed to specific lipids) using their total acyl/alkyl chain content (i.e. PC (38∶4)) rather than their unique structure. Therefore each lipid species identified by the BIOCRATES kit typically corresponds to 5–10 possible unique lipid structures. Consequently, the total number of phosphatidylcholines, sphingolipids and lysophosphatidylcholines structures identified by this method was 458, 19 and 6, respectively. Therefore, combining these probable lipid structures (483 in total, based on the known fatty acid and lipid composition in human serum) with the other 72 confirmed non-lipid metabolites, the DFI-MS/MS method yields 555 confirmed and probable metabolites or metabolite structures. All of these compounds, along with their corresponding estimated concentrations have all been entered into the UMDB. Table 7 Concentrations of acylcarnitines, amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and sphingomyelins in human urine by combined DFI/LC-MS/MS (BIOCRATES kit). Acylcarnitines (DFI-MS/MS) Mean (µM/mM creatinine) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) Acetyl-L-carnitine 2.0 (0.4–5.9) Kynurenine 0.41 (0.08–1.31) Butenyl-L-carnitine 0.014 (0.008 –0.021) L-Dopa 0.02 (0.01–0.04) Butyryl-L-carnitine 0.76 (0.12–1.81) Methioninesulfoxide 0.31 (0.14–0.66) Decadienyl-L-carnitine 0.10 (0.04–0.30) Phenylethylamine 0.011 (0.003–0.067) Decanoyl-L-carnitine 0.04 (0.02–0.06) Putrescine 0.20 (0.04–0.40) Decenoyl-L-carnitine 0.14 (0.06–0.32) Sarcosine 1.6 (0.2–9.4) Dodecanoyl-L-carnitine 0.02 (0.01–0.04) Serotonin 0.08 (0.04–0.12) Dodecenoyl-L-carnitine 0.028 (0.011–0.042) Symmetric dimethylarginine 2.8 (2.0–9.4) Glutaconyl-L-carnitine 0.04 (0.01–0.06) Lysophosphatidylcholines (DFI-MS/MS) Mean (µM/Mm creatinine) Glutaryl-L-carnitine 0.04 (0.03–0.10) lysoPC a C16∶0 0.014 Hexadecadienyl-L-carnitine 0.0010 (0.0005–0.0040) lysoPC a C17∶0 0.0032 (0.0008–0.0087) Hexadecanoyl-L-carnitine 0.0022 (0.0020–0.0030) lysoPC a C18∶0 0.0040 (0.0013–0.0100) Hexanoyl-L-carnitine 0.05 (0.03–0.07) lysoPC a C20∶4 0.0020 (0.0005–0.0038) Hexenoyl-L-carnitine 0.019 (0.009–0.050) lysoPC a C6∶0 0.0045 (0.0022–0.0074) Hydroxyhexadecanoyl-L-carnitine 0.0015 (0.0006–0.0050) Phosphatidylcholines (DFI-LC/MS) Mean (µM/Mm creatinine) Hydroxypropionyl-L-carnitine 0.008 (0.004–0.012) PC aa C28∶1 0.006 (0.003–0.020) Hydroxytetradecadienyl-L-carnitine 0.003 (0.001–0.008) PC aa C30∶2 0.0005 (0.0001–0.0022) Hydroxytetradecenoyl-L-carnitine 0.0020 (0.0008–0.0040) PC aa C32∶0 0.0035 (0.0013–0.0071) L-Carnitine 4.5 (0.6–15.2) PC aa C32∶3 0.0009 (0.0003–0.0025) Malonyl-L-carnitine 0.02 (0.01–0.04) PC aa C34∶1 0.0077 (0.0023–0.0500) Methylglutaryl-L-carnitine 0.030 (0.016–0.052) PC aa C34∶2 0.0047 (0.0016–0.0240) Methylmalonyl-L-carnitine 0.09 (0.05–0.16) PC aa C34∶3 0.002 Nonayl-L-carnitine 0.20 (0.07–0.46) PC aa C34∶4 0.0023 (0.0002–0.0180) Octadecadienyl-L-carnitine 0.0007 (0.0003–0.0020) PC aa C36∶1 0.0034 (0.0006–0.0250) Octanoyl-L-carnitine 0.05 (0.03–0.09) PC aa C36∶2 0.0160 (0.0021–0.0450) Octenoyl-L-carnitine 0.50 (0.08–1.20) PC aa C36∶3 0.0024 (0.0005–0.0240) Pimelyl-L-carnitine 0.03 (0.01–0.11) PC aa C36∶4 0.0018 (0.0005–0.0083) Propenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C36∶6 0.0037 Propionyl-L-carnitine 0.07 (0.01–0.20) PC aa C38∶3 0.0056 Tetradecadienyl-L-carnitine 0.0023 (0.0010–0.0050) PC aa C38∶4 0.0022 (0.0005–0.0065) Tetradecanoyl-L-carnitine 0.018 (0.001–0.088) PC aa C38∶5 0.0007 (0.0002–0.0020) Tetradecenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C38∶6 0.0010 (0.0005–0.0036) Tiglyl-L-carnitine 0.10 (0.05–0.20) PC aa C40∶3 0.00035 (0.0–0.00140) Valeryl-L-carnitine 0.220 (0.037–0.440) PC aa C42∶2 0.0009 (0.0003–0.0024) Creatinine Mean PC aa C42∶4 0.0008 (0.0003–0.0025) Creatinine* 12,475±7,955 PC ae C34∶1 0.0030 (0.0003–0.0110) Hexose Mean (µM/Mm creatinine) PC ae C34∶2 0.0012 (0.0002–0.0075) Hexose 110 (37–501) PC ae C36∶1 0.0046 (0.0012–0.0080) Amino acids (LC-MS/MS) Mean (µM/Mm creatinine) PC ae C36∶2 0.0016 (0.0002–0.0050) Glycine 101.0 (37.0–250.6) PC ae C36∶3 0.0009 (0.0001–0.0040) L-Alanine 21.7 (7.2–47.4) PC ae C36∶4 0.0022 L-Arginine 7.4 (3.2–14.6) PC ae C38∶2 0.0015 (0.0007–0.0023) L-Asparagine 8.8 (4.6–17.7) PC ae C38∶3 0.0020 (0.0012–0.0028) L-Aspartic acid 10.9 (1.9–26.8) PC ae C38∶4 0.0006 (0.0–0.0024) L-Citrulline 0.8 (0.2–1.7) PC ae C38∶6 0.0065 (0.0027–0.0170) L-Glutamic acid 6.4 (0.6–17.5) PC ae C40∶2 0.0012 (0.0006–0.0020) L-Glutamine 39.9 (18.4–72.5) PC ae C40∶5 0.0006 (0.0001–0.0025) L-Histidine 45.6 (21.7–71.5) PC ae C42∶3 0.0007 (0.0001–0.0020) L-Isoleucine 1.3 (0.5–2.7) PC ae C44∶3 0.0010 (0.0002–0.0031) L-Leucine 3.0 (1.6–6.0) Sphingomyelins (DFI-MS/MS) Mean (µM/Mm creatinine) L-Lysine 17.9 (3.6–56.1) SM (OH) C14∶1 0.0028 (0.0008–0.0049) L-Methionine 0.8 (0.4–1.6) SM (OH) C16∶1 0.0006 (0.0001–0.0023) L-Ornithine 4.4 (1.2–22.1) SM (OH) C22∶1 0.0023 (0.0009–0.0058) L-Phenylalanine 5.9 (2.8–10.8) SM (OH) C22∶2 0.0007 (0.0001–0.0028) L-Proline 1.1 (0.1–2.5) SM (OH) C24∶1 0.0010 (0.00034–0.0025) L-Serine 24.5 (12.3–50.2) SM C16∶0 0.0100 (0.0016–0.0700) L-Threonine 14.6 (6.6–29.3) SM C16∶1 0.0009 (0.0004–0.0023) L-Tryptophan 5.6 (9.3–2.1) SM C18∶0 0.0024 (0.0011–0.0070) L-Tyrosine 8.7 (3.0–20.1) SM C18∶1 0.0004 (0.0001–0.0013) L-Valine 4.3 (2.4–10.4) SM C20∶2 0.0003 (0.0–0.0016) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) SM C22∶3 0.0011 (0.0008–0.0014) Acetyl-ornithine 1.3 (0.5–2.8) SM C24∶0 0.0085 (0.0047–0.0200) Alpha-aminoadipic acid 7.2 (2.5–16.4) SM C24∶1 0.0030 (0.0007–0.0120) Asymmetric dimethylarginine 2.7 (1.4–4.2) SM C26∶0 0.0014 (0.0002–0.0043) Carnosine 1.2 (0.2–4.1) SM C26∶1 0.0005 (0.0001–0.0012) Dopamine 0.4 (0.2–0.7) Histamine 0.03 (0.01–0.10) Hydroxykynurenine 0.15 (0.06–0.34) * Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. Our results show very good agreement with the previous studies conducted by BIOCRATES on human urine samples (Biocrates Application Note 1005-1). We found that the lower limit of quantification by DFI MS/MS based on the AbsoluteIDQ kit was 0.1 nM/mM creatinine for certain phosphatidylcholine species (i.e. PC aa C40∶3) and 0.1 nM/mM creatinine for certain sphingomyelin species (i.e. SM 26∶1). Comparison of our lipid results with literature data was difficult as relatively few papers report urine lipid concentration data. Indeed, the presence of lipids in urine is a little unexpected, but not entirely unreasonable. It is likely that urea, a well known chaotrope, facilitates the dissolution of small amounts of fatty acids and other lipid species in urine. Many of the compounds we measured with this kit assay appear to be normal constituents of human urine but have not been previously reported (quantified and/or detected) in the scientific literature (with the exception of the BIOCRATES Application Note). In total, 53 compounds are being reported here for the first time as being normal constituents of human urine, while 68 compounds are being robustly quantified in human urine for the first time. The vast majority of these compounds are lipids. Based on our results, the combined DFI/LC-MS/MS method detected 98 compounds or compound species that GC-MS and NMR methods could not detect, while GC-MS detected 161 compounds and NMR detected 181 compounds that DFI/LC-MS/MS could not detect. The 3 methods were able to detect a common set of 17 compounds including creatinine, L-glutamine, L-tryptophan, L-tyrosine and L-valine. Interestingly, the concentrations measured by DFI/LC-MS/MS, NMR and GC-MS (across the shared set of 17 compounds) showed generally good agreement (within 19 ± 7% of each other). The relatively small overlap, in terms of compound coverage, between the 3 platforms is a bit of a surprise and certainly serves to emphasize the tremendous chemical diversity that must exist in urine. Overall, by combining these 3 platforms, we were able to identify 295 and quantify 231 unique or non-overlapping metabolites or metabolite species. These data suggest that DFI/LC-MS/MS, GC-MS and NMR are highly complementary techniques for the identification and quantification of metabolites in human urine. Quantification and Identification of Urinary Trace Metals – ICP-MS To determine the trace elemental composition of urine, we used inductively coupled plasma mass spectrometry (ICP-MS). ICP-MS is widely considered to be one of the best techniques for the characterization of the trace element composition of biological samples [88], [89]. Indeed, none of the other methods (1H-NMR, GC-MS and DFI/LC-MS/MS) are suited for measuring trace element composition or concentrations. Our multi-elemental analysis of urine using ICP-MS provided quantitative results for a total of 40 metals or trace minerals (Table 8). Based on their frequency of occurrence and overall abundance, all 40 trace elements appear to be normal constituents of human urine. Of these, 2 have previously not been quantified for healthy adults. Table 8 Concentrations of ions (µmol/mmol creatinine) as determined by ICP-MS. Compound Name HMDB Concentration (µM/mM creatinine) Litterature values(µM/mM creatinine) Aluminum (Al) HMDB01247 0.31 (0.05–1.21) 0.667±0.037 Arsenic (As) HMDB02290 0.10 (0.01–0.52) 0.16±0.16 Barium (Ba) HMDB04142 0.0070 (0.0001–0.0655) 0.0129–0.0320 Cadmium (Cd) HMDB03638 0.0004 (0.0002–0.0010) 0.00060 (0.00035–0.00100) Calcium (Ca) HMDB00464 200.0 (16.9–520.0) 299.0±99.0 Cerium (Ce) HMDB13672 0.00048 (0.00008–0.00180) 0.0 – 0.0007 Cesium (Cs) HMDB13669 0.0039 (0.0005–0.0110) 0.0061±0.0011 Chromium (Cr) HMDB00599 0.0084 (0.0001–0.0460) 0.0100±0.0065 Cobalt (Co) HMDB00608 0.0014 (0.0001–0.0140) 0.000810±0.00053 Copper (Cu) HMDB00657 0.0163 (0.0006–0.1099) 0.0190 (0.0092–0.038) Gallium (Ga) HMDB01478 0.0056 (0.0012–0.0150) 0.0003–0.0006 Hafnium (Hf) HMDB29201 0.00031 (0.00006–0.00110) 0.00027±0.00012 Iron (Fe) HMDB00692 0.0890 (0.0025–0.4579) 0.11 (0.0–0.14) Lanthanum (La) HMDB13711 0.00020 (0.00003–0.00079) 0.00052±0.00039 Lead (Pb) HMDB04628 0.0026 (0.0007–0.0110) 0.011 (0.0–0.020) Lithium (Li) HMDB05949 0.5 (0.2–0.9) 0.504 (0.058–3.414) Magnesium (Mg) HMDB00547 262 (42–1,189) 289.0±92.0 Manganese (Mn) HMDB01333 0.013 (0.002–0.051) 0.011 (0.0–0.023) Molybdenum (Mo) HMDB01302 0.049 (0.006–0.295) 0.06±0.03 Neodymium (Nd) HMDB13715 0.00017 (0.00004–0.00070) 0.0026±0.0013 Nickel (Ni) HMDB02457 0.0080 (0.0015–0.0450) 0.00420 (0.00302–0.00572) Palladium (Pd) HMDB13670 0.0110 (0.0011–0.1410) < 0.00001–0.2538 Platinum (Pt) HMDB13671 0.000155 (0.000031–0.000560) 0.0000656 Posphorus (P) HMDB01315 1,799 (221–4,936) 1,666.67–4,333.33 Potassium (K) HMDB00586 3,593 (553–8,079) 4,605 (2,631–6,578) Rhenium (Re) HMDB13719 0.000096 (0.000013–0.000340) NA Rubidium (Rb) HMDB01327 1.354 (0.187–3.673) 1.84±0.46 Ruthenium (Ru) HMDB13668 0.0002 (0.0001–0.0009) NA Selenium (Se) HMDB01349 0.149 (0.028–0.345) 0.11346 (0.07724–0.15010) Sodium (Na) HMDB00588 12,477 (1,863–37,249) 14,737 (5,263–36,842) Strontium (Sr) HMDB03642 0.163 (0.030–0.415) 0.1333 Tantalum (Ta) HMDB04143 0.00020 (0.00004–0.00074) 0.00008±0.00006 Thallium (Tl) HMDB13724 0.0003 (0.0001–0.0013) < 0.00659 Thorium (Th) HMDB29215 0.00020 (0.00002–0.00100) < 0.00584 Tin (Sn) HMDB01960 0.00110 (0.00016–0.00690) < 0.00552 Titanium (Ti) HMDB01966 0.0013 (0.0005–0.0030) 0.0142 Tungsten (W) HMDB01989 0.0060 (0.0011–0.0280) 0.010±0.022 Vanadium (V) HMDB02503 0.033 (0.004–0.083) 0.00162±0.00144 Zinc (Zn) HMDB01303 0.460 (0.058–0.948) 0.52±0.25 Zirconium (Zr) HMDB01475 0.021 (0.004–0.083) 0.0012–0.0074 As far as we are aware, this is the first multi-elemental study of urine that has been performed by ICP-MS. As seen in Table 8, there is generally good agreement between the values measured by ICP-MS and those previously reported in literature, with differences generally being less than 22 ± 10%. Larger differences are seen for gallium (Ga), lead (Pb), Neodymium (Nd), titanium (Ti) and vanadium (V), but these may be due to the effects of age, diet, local environment (minerals in local water) or diurnal variation. Alternately they may reflect real differences in the sensitivity or accuracy of the instruments being used. As a general rule, ICP-MS is considered as a gold standard for the identification and quantification of trace metals [18], so we would tend to place higher confidence in the values derived via ICP-MS over those measured by other technologies. By our measurements, the most abundant metals/salts are sodium (Na) (12.5 ± 10.6 mM/mM creatinine) and potassium (K) (3.6 ± 2.5 mM/mM creatinine) – as expected, while the least abundant was rhenium (Re) with a lower limit of quantification by ICP-MS of 96 pM/mM creatinine. Quantification and Identification of Urine Metabolites – Targeted HPLC Assays The inventory of metabolites we detected and quantified by 1H NMR, GC-MS, DFI/LC-MS/MS and ICP-MS covers a significant portion of all chemical classes. However, these methods sometimes lack the necessary sensitivity, the appropriate instrumental configuration or detection capabilities and therefore fail to detect/quantify a variety of important compound classes. This includes a number of molecules that are normal constituents of urine such as thiols and isoflavones. To identify and quantify these 2 classes of metabolites we decided to employ High Performance Liquid Chromatography (HPLC). HPLC assays are the method of choice for detecting isoflavones and thiols as they are sensitive, precise and can be easily coupled with sensitive detection methodologies such as fluorescence and ultraviolet detection. In our studies, fluorescence and ultraviolet detection were used for the identification and quantification of urinary thiols and isoflavones, respectively. Biological thiols, or mercaptans, are very active metabolic products of sulfur and play a central role in redox metabolism, cellular homeostasis and a variety of physiological and pathological processes. In urine, the most important thiols are L-cysteine and L-cysteinylglycine [90], [91], [92], [93]. Isoflavones or phytoestrogens form or constitute another important class of urinary metabolites [90]. Humans are exposed to these biologically active phytochemicals mainly through food intake via vegetables, fruit and wheat/bread products [94]. For the detection of thiols we developed assays to measure L-cysteine, L-cysteineglycine, L-glutathione and L-homocysyeine, while for isoflavones we developed assays to measure biochanin A, coumesterol, daidzein, equol, formonentin and genistein. Using these HPLC assays, we measured a total of 4 thiols and 6 isoflavones in urine (Table 9). Table 9 Concentrations of isoflavones and thiols as determined by HPLC. Compound Name HMDB ID Mean(µM/mM creatinine) Oc (%) Literature values (µM/mM creatinine) Biochanin Aa HMDB02338 0.0088 (0.0027–0.0200) 77 NA Coumesterola HMDB02326 0.0081 (0.0033–0.0120) 45 NA Daidzeina HMDB03312 0.026 (0.002–0.064) 88 0.036 ± 0.008 Equola HMDB02209 0.06 (0.01–0.10) 67 0.13 ± 0.12 Formonenetina HMDB05808 0.0007 (0.0002–0.0011) 33 NA Genisteina HMDB03217 0.0280 (0.0077–0.0640) 88 0.032 ± 0.007 L-Cysteineb HMDB00574 81.0 (36.7–147.6) 100 33.4 ± 15.5 L-Cysteinylglycineb HMDB00078 0.6 (0.28–1.54) 100 0.48 ± 0.15 L-Glutathioneb HMDB00125 0.030 (0.005–0.085) 100 0.065 L-Homocysteineb HMDB00742 1.1 (0.6–1.7) 100 1.95 ± 1.47 a Isoflavone metabolites measured by HPLC/UV. b Thiol metabolites measured by HPLC/FD. Oc: occurrence; NA: not available. As seen in Table 9, there is generally good agreement between the values measured by these targeted HPLC assays and those previously reported in literature, with differences generally being less than ∼30%. Only one of these metabolites (L-cysteine) was measured independently on one of our other platforms (NMR) and the NMR concentration was found to be 25% lower than the HPLC assay. A possible explanation for this discrepancy is that cysteine measured by HPLC-FD yields total L-cysteine including the free form and L-cystine reduced to L-cysteine during the reaction [95]. On the other hand, NMR can distinguish between L-cysteine and L-cystine. The Composition of Human Urine – Comparison with Other Biofluids By combining a systematic computer-aided literature survey with an extensive, quantitative multiplatform metabolomic analysis we have been able to comprehensively characterize the human urine metabolome. Our data suggests that there are at least 3079 detectable metabolites in human urine, of which 1350 have been quantified. At least 72 of these compounds are of microbial origin, 1453 are endogenous while 2282 are considered exogenous (note some compounds can be both exogenous and endogenous), coming from diet, drugs, cosmetics or environmental exposure. Using a chemical classification system developed for the HMDB [15] we found that human urinary metabolites fall into 230 different chemical classes (25 “super classes”, Table 2). Given that there are only 356 chemical classes in the entire human metabolome [16], this certainly demonstrates the enormous chemical diversity found in urine. As might be expected, most urinary metabolites are very hydrophilic, although there are clearly trace amounts of lipids and fatty acids that contribute a significant number of chemicals to the urinary metabolome (836 fatty acids and lipids). This is in rather striking contrast to the composition of serum [20] which is particularly rich in lipids (i.e. >17000 lipids and fatty acids). Relative to other biofluids such as CSF [18] or saliva [19], urine contains significantly more compounds (5–10X) and exhibits significantly more chemical diversity (2–3X). On the other hand, we know that every compound that is found in human urine is also found in human blood. In other words, the human urine metabolome is a subset of the human serum metabolome, both in terms of composition and chemical diversity [20]. However, more than 484 compounds we identified in urine (either experimentally or via literature review) were not previously reported to be in blood. The fact that so many compounds seem to be unique to urine likely has to do with the fact that the kidneys do an extraordinary job of concentrating certain metabolites from the blood. Consequently compounds that are far below the limit of detection in blood (using today’s instrumentation) are well above the detection limit in urine. In fact, concentration differences between the two biofluids sometimes exceed 1000X for certain compounds, such as histamine, androsterone, normetanephrine, testosterone 13, 14-dihydro-15-keto-PGE2, m-tyramine and aldosterone. So, while the number of water-soluble compounds in blood and urine may be almost identical, the concentrations of these compounds are often profoundly different. This difference, combined with the ability of the kidney to handle abnormally high or abnormally low concentrations of metabolites, makes urine a particularly useful biofluid for medical diagnostics. In fact, according to our data in the UMDB, urinary metabolites have been used to characterize nearly 220 diseases. Furthermore, the ability of the kidneys to filter toxins or xenobiotics makes urine a particularly useful biofluid for diet and drug monitoring and for assessing chemical or pollutant exposure [96]. Method Comparison One of the central motivations behind this work was to ascertain the strengths and weaknesses of several common metabolomic platforms for characterizing human urine. We employed 6 different analytical platforms: NMR; GC-MS; DFI/LC-MS/MS; HPLC/UV; HPLC/FD and ICP-MS. Using our literature-derived knowledge about the composition of human urine, along with custom-derived spectral libraries and targeted assays we were able to “push the limits” in terms of number of compounds that could be identified and/or quantified via each platform. In total, we identified 445 and quantified 378 distinct metabolites using these 6 different systems. According to Table 1, this is the largest number of urine metabolites ever identified and/or quantified in a single study. NMR spectroscopy was able to identify and quantify 209 compounds; GC-MS was able to identify 179 and quantify 85 compounds; DFI/LC-MS/MS identified and quantified 127 compounds; ICP-MS identified and quantified 40 compounds; while customized HPLC assays (with UV or FD detection) identified and quantified 10 compounds. The number of urinary metabolites we identified/quantified for NMR, GC-MS, DFI/LC-MS/MS and ICP-MS all represent “records” for these platforms. In terms of platform overlap and compound complementarity, NMR and GC-MS were able to identify a common set of 88 metabolites; NMR and DFI/LC-MS/MS were able to identify and quantify a common set of 28 metabolites, while NMR, GC-MS and DFI/LC-MS/MS were able to identify a common set of 17 metabolites (15 amino acids, creatinine and hexose/glucose). All of these results are summarized in a Venn diagram (Figure 5). As might be expected, metabolite coverage differs from one analytical technique to another. These are difference mostly due to the intrinsic nature of the devices or platforms used. In particular, significant differences exist between these platforms in terms of their sensitivity or separation and/or extraction efficiency. Likewise the use of targeted vs. non-targeted methods along with issues related to compound stability, solubility and volatility led to some significantly different platform-dependent results. Figure 5 Venn diagram showing the overlap of urine metabolites detected by NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC coupled to UV detection and HPLC coupled to fluorescence detection compared to the detectable urine metabolome. Given that the known, quantifiable urine metabolome consists of ∼2651 known metabolites and metabolite species (corresponding to 3079 distinct structures), we can calculate that NMR is able to measure ∼8% (209/2651) of the human urine metabolome; GC-MS is able to measure ∼7% (179/2651); DFI/LC-MS/MS is able to measure ∼5% (127/2651); ICP-MS/MS is able to measure ∼1.5% (40/2651); HPLC/UV is able to measure ∼0.2% (6/2651); while HPLC/FD is able to measure ∼0.15% (4/2651) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >16% of the known urinary metabolome (>445/2651). If we re-evaluate this fraction in terms of total metabolite structures (corresponding to known and highly probable metabolites), the urine metabolome consists of 3079 compounds. From this total we can calculate that NMR is able to measure ∼7% (209/3079) of the human urine metabolome; GC-MS is able to measure ∼6% (179/3079); DFI/LC-MS/MS is able to measure ∼18% (555/3079); ICP-MS/MS is able to measure ∼1.3% (40/3079); HPLC/UV is able to measure ∼0.2% (6/3079); while HPLC/FD is able to measure ∼0.13% (4/3079) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >28% of the known and probable or putative urinary metabolome (>873/3079). In terms of chemical class coverage, NMR detects compounds from 15 of the 25 major chemical superclasses in urine, GC-MS detects compounds from 14 of the 25, DFI/LC-MS/MS detects 6 of the 25, ICP-MS detects 1 of the 25 while the targeted methods for thiols and isoflavones detect just 1 of each. From these data we can conclude that NMR is currently the best method for identifying and quantifying urinary compounds. Not only does it permit measurement of the largest number of metabolites (209) but it also yields the greatest chemical diversity. Furthermore, NMR is non-destructive so that the same sample can be subsequently re-used for GC-MS, LC-MS or ICP-MS analyses. The minimal sample preparation and relatively rapid data collection for NMR also make it much more appealing for urine metabolomics, although the spectral analysis can be quite slow (∼1–2 hours per sample). While GC-MS is a close second in terms of overall coverage (179 metabolites, 14 chemical superclasses), these numbers represents the result of 4 different analyses performed on 2 different GC-MS instruments. Many labs would not have these multiple configurations available or the resources to routinely run these types of analyses. Likewise each sample required many hours of preparation, sample collection and data analysis. In this regard, multi-platform GC-MS is definitely not a high-throughput metabolomics technique. Relative to NMR and GC-MS, DFI/LC-MS/MS also performs well, with 127 compounds being quantified. However, DFI/LC-MS/MS provides very limited chemical diversity (only 6 chemical superclasses). On the other hand, DFI/LC-MS/MS requires very little sample volume (10 µL) and it is a very low-cost, largely automated, high-throughput route for measuring metabolites. The other techniques (HPLC, ICP-MS) we employed in this study, while useful, do not come close to matching the coverage or diversity of NMR, GC-MS or DFI/LC-MS/MS. While we certainly went to considerable lengths to use current or cutting edge technologies to characterize the urine metabolome, it is also important to note that there is always potential for future improvement. Using higher field (900–950 MHz) NMR instruments, employing newer model GC-MS instruments or more sensitive GC-TOF instruments, using more than 3 derivatization or extraction steps for our GC-MS analyses, employing the latest version of the NIST database or a larger collection of GC-MS databases, implementing more sophisticated or targeted detection and separation techniques, using various commercial immunodetection kits or employing the latest LC-MS/MS techniques coupled to FT-MS or orbitraps – all of these could have added to the quantity and diversity of metabolites detected or quantified. However, like many laboratories, our resources are somewhat limited. Furthermore, in this study we wanted to address the question of how well a cross-section of commonly accessible metabolomic methods or platforms could perform in identifying and quantifying metabolites in urine. While being able to quantitatively compare metabolite coverage and chemical diversity amongst the major metabolomics platforms (NMR, GC-MS and DFI/LC-MS/MS) is important, it is also useful to compare their consistency or reproducibility in terms of metabolite quantification. In particular we decided to assess the 3 major platforms in terms of their ability to identify and quantify a common group of compounds, namely the amino acids. Overall we found that the measured concentrations are in relatively good agreement (Table 10). However, a few exceptions are evident. For example, the NMR and DFI/LC-MS/MS concentrations of glycine and serine are higher than the GC-MS values (note that glycine exhibits the highest concentration among urinary amino acids). For serine, after the silylation reaction using MSTFA, we obtained serine-2TMS (13.9 min) and serine-3TMS derivatives (16.6 min). The chromatographic peak corresponding to serine-2TMS is weak and overlaps slightly with the urea peak. This overlap and the corresponding difficulty in peak integration may explain the quantitation differences compared to other analytical assays. Neither L-glutamine nor L-glutamic acid could be accurately quantified or identified by GC-MS. In our case, the glutamine peak co-elutes with glycerol-3-phosphate. Other investigators have noted that glutamine can also be hydrolyzed and converted to glutamic acid [97], or to pyroglutamic acid during derivatization [95]. As a result, only pyroglutamic acid could be identified in our GC-MS assay. The identification of glutamic acid and pyroglutamic acid can be complicated [97], which explains our failure to identify glutamic acid by GC-MS. L-arginine could not be detected by GC-MS, because it is converted to ornithine during derivatization [98], which probably explains the slightly higher concentration of ornithine measured by GC-MS, compared to the one determined by DFI/LC-MS/MS. Finally, L-cysteine and L-cystine can only be identified and quantified by NMR and targeted HPLC/FD because the identification and quantification of theses metabolites is not possible with the Biocrates kit. With GC-MS it has often been noted that L-cystine can be converted to L-cysteine during derivatization, while L-cysteine might be oxidized to L-cystine during prolonged storage of the standard solution [95], confounding the identification of L-cystine and L-cysteine. Table 10 Comparison of urine concentrations of 23 quantified amino acids by different assays: DFI/LC-MS/MS (BIOCRATES kit), NMR and GC-MS. Amino acids* DFI/LC-MS/MS NMR GC-MS Glycine 101.0±53.5 106±66 75.7±51.7 L-Alanine 21.7±10.6 21.8±10.3 NA L-Arginine 7.4±3.0 8.6±3.6 NA L-Asparagine 8.8±3.5 10.1±4.0 9.5±5.5 L-Aspartic acid 10.9±7.7 10.9±5.8 NA L-Citrulline 0.8±0.4 NA NA L-Cysteine NA 65.8±24.0 NA L-Cystine NA 12.5±5.4 NA L-Glutamic acid 6.4±4.9 8.5±3.6 NA L-Glutamine 39.9±14.3 37.2±14.8 NA L-Histidine 45.6±15.6 43.9±19.5 NA L-Isoleucine 1.3±0.4 1.3±0.5 NA L-Leucine 3.0±1.0 2.8±0.9 NA L-Lysine 17.9±12.2 17.2±11.5 NA L-Methionine 0.8±0.3 1.4±0.6 NA L-Ornithine 4.4±5.0 NA 5.3±1.9 L-Phenylalanine 5.9±2.0 6.4±2.3 7.8±2.0 L-Proline 1.1±0.6 NA NA L-Serine 24.5±9.2 25.3±9.7 19.0±7.0 L-Threonine 14.6±6.1 13.3±5.3 NA L-Tryptophan 5.6±2.1 6.3±2.2 NA L-Tyrosine 8.7±4.2 9.5±4.5 15.1±5.4 L-Valine 4.3±1.9 3.4±1.4 5.5±2.0 * Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available. |
| title | Results and Discussion |
| sec | The Content of the Human Urine Metabolome – The Urine Metabolome Database The Urine Metabolome Database (UMDB: http://www.urinemetabolome.ca) contains a complete list of all (to the best of our knowledge) possible metabolites that have been detected in human urine using current technologies. The UMDB is freely available, easily queried, web-enabled database which provides a list of the metabolite names, level of verification (confirmed or probable), normal and disease-associated concentration ranges, associated diseases and corresponding literature references for all human urine metabolites that have ever been detected and/or quantified in the literature. The UMDB also contains concentration data compiled from the experimental studies described here. Each urine metabolite entry in this database is linked to a MetaboCard button [15], [16] that, when clicked, brings up detailed information about that particular entry. This detailed information includes nomenclature, chemical, clinical and molecular/biochemical data. Each MetaboCard entry contains up to 120 data fields many of which are hyperlinked to other databases (KEGG [45], PubChem [46], MetaCyc [47], ChEBI [48], PDB [49], UniProt [50], and GenBank [51] as well as to GeneCard IDs [52], GeneAtlas IDs [53] and HGNC IDs [54] for each of the corresponding enzymes or proteins known to act on that metabolite). Additionally, the UMDB through its MetaboCard/HMDB links includes nearly 450 hand-drawn, zoomable and fully hyperlinked human metabolic pathway maps (SMPDB: http://www.smpdb.ca/). These maps are intended to help users visualize the chemical structures on metabolic pathways and to get detailed information about metabolic processes [55]. These UMDB pathway maps are quite specific to human metabolism and explicitly show the subcellular compartments where specific reactions are known to take place. The UMDB’s simple text query (TextQuery) supports general text queries including names, synonyms, conditions and disorders. Clicking on the Browse button (on the UMDB navigation panel) generates a tabular view that allows users to casually scroll through the database or re-sort its contents by compound name or by concentration. Users can choose either the “Metabolite View”, “Concentration View” or “Diseases View” to facilitate their browsing or searching. Clicking on a given MetaboCard button brings up the full data content (from the HMDB) for the corresponding metabolite. Users may also search the database using a variety of options listed uner the “Search” menu. For instance, the ChemQuery button allows users to draw or write (using a SMILES string) a chemical compound to search the UMDB for chemicals similar or identical to the query compound. ChemQuery also supports chemical formula and molecular weight searches. The Sequence Search button allows users to conduct BLAST sequence searches of the 4075 protein sequences contained in the UMDB. Both single and multiple sequence BLAST queries are supported. “Advanced Search” which is also located under the “Search” menu is the most sophisticated search tool in the UMDB and opens an easy-to-use query search tool that allows users to select or search over various combinations of subfields. The UMDB’s “MS Search” allows users to submit mass spectral peak lists that will be searched against the Human Metabolome Database (HMDB)’s library of MS/MS spectra. This potentially allows facile identification of urine metabolites from mixtures via MS/MS spectroscopy. UMDB’s NMR Search allows users to submit peak lists from 1H or 13C NMR spectra (both pure and mixtures) and have these peak lists compared to the NMR libraries contained in the HMDB. This allows the identification of metabolites from mixtures via NMR spectral data. The Download button provides links to collected sequence, image and text files associated with the UMDB. In the About menu, the “Data Fields Explained” button lists source data used to assemble the UMDB. Currently the UMDB contains information on 2651 detectable metabolites or metabolite species (which corresponds to 3079 metabolites with precisely defined structures) and 3832 concentration ranges or values associated with 220 different conditions, diseases and disorders. The number of metabolites in the UMDB is not a number that will remain unchanged. Rather it reflects the total number of metabolites – most of which are endogenous - that have ever been detected and/or quantified by ourselves and others. Certainly as technology improves, we anticipate this number will increase as other, lower abundance, metabolites are detected and added to future versions of the UMDB. Likewise, if the list was expanded to include intermittent, exogenous compounds such as all possible drugs or drug metabolites or rare food additives and food-derived phytochemicals, the database could be substantially larger. Inspection of the on-line tables in UMDB generally shows that human urine contains a substantial number of hydrophilic molecules. This is further reiterated in Table 2, which provides a listing of the chemical “superclasses” (using the HMDB definitions) in human urine and the number of representative compounds that can be found in this biofluid. Excluding lipids (which are in very low concentration), human urine is dominated by amino acids and derivatives, carbohydrates and carbohydrate conjugates. This simply reinforces the fact that urine is a key carrier of hydrophilic waste products. Other small molecule components found in high abundance in urine include hydroxy acids and derivatives (such as citric acid), urea, ammonia, creatinine and hippuric acid. A more detailed description of both our literature and experimental findings is given in the following 7 sections covering: 1) Literature Review/Text Mining; 2) NMR; 3) DFI/LC-MS/MS; 4) GC-MS; 5) ICP-MS; 6) HPLC/UV and 7) HPLC/FD. Table 2 Chemical superclasses in the urine metabolome database. Chemical superclass # Compounds Aliphatic Acyclic Compounds 93 Aliphatic Heteromonocyclic Compounds 43 Aliphatic Heteropolycyclic Compounds 40 Aliphatic Homomonocyclic Compounds 18 Aliphatic Homopolycyclic Compounds 5 Alkaloids and Derivatives 45 Amino Acids, Peptides, and Analogues 286 Aromatic Heteromonocyclic Compounds 67 Aromatic Heteropolycyclic Compounds 728 Aromatic Homomonocyclic Compounds 432 Aromatic Homopolycyclic Compounds 6 Carbohydrates and Carbohydrate Conjugates 116 Homogeneous Metal Compounds 45 Homogeneous Non-metal Compounds 15 Inorganic compounds 1 Lignans and Norlignans 12 Lipids 866 Mixed Metal/Non-metal Compounds 7 Nucleosides, Nucleotides, and Analogues 49 Organic Acids and Derivatives 108 Organic Halides 3 Organometallic Compounds 1 Organophosphorus Compounds 17 Polyketides 74 Tannins 2 |
| title | The Content of the Human Urine Metabolome – The Urine Metabolome Database |
| p | The Urine Metabolome Database (UMDB: http://www.urinemetabolome.ca) contains a complete list of all (to the best of our knowledge) possible metabolites that have been detected in human urine using current technologies. The UMDB is freely available, easily queried, web-enabled database which provides a list of the metabolite names, level of verification (confirmed or probable), normal and disease-associated concentration ranges, associated diseases and corresponding literature references for all human urine metabolites that have ever been detected and/or quantified in the literature. The UMDB also contains concentration data compiled from the experimental studies described here. Each urine metabolite entry in this database is linked to a MetaboCard button [15], [16] that, when clicked, brings up detailed information about that particular entry. This detailed information includes nomenclature, chemical, clinical and molecular/biochemical data. Each MetaboCard entry contains up to 120 data fields many of which are hyperlinked to other databases (KEGG [45], PubChem [46], MetaCyc [47], ChEBI [48], PDB [49], UniProt [50], and GenBank [51] as well as to GeneCard IDs [52], GeneAtlas IDs [53] and HGNC IDs [54] for each of the corresponding enzymes or proteins known to act on that metabolite). Additionally, the UMDB through its MetaboCard/HMDB links includes nearly 450 hand-drawn, zoomable and fully hyperlinked human metabolic pathway maps (SMPDB: http://www.smpdb.ca/). These maps are intended to help users visualize the chemical structures on metabolic pathways and to get detailed information about metabolic processes [55]. These UMDB pathway maps are quite specific to human metabolism and explicitly show the subcellular compartments where specific reactions are known to take place. |
| p | The UMDB’s simple text query (TextQuery) supports general text queries including names, synonyms, conditions and disorders. Clicking on the Browse button (on the UMDB navigation panel) generates a tabular view that allows users to casually scroll through the database or re-sort its contents by compound name or by concentration. Users can choose either the “Metabolite View”, “Concentration View” or “Diseases View” to facilitate their browsing or searching. Clicking on a given MetaboCard button brings up the full data content (from the HMDB) for the corresponding metabolite. Users may also search the database using a variety of options listed uner the “Search” menu. For instance, the ChemQuery button allows users to draw or write (using a SMILES string) a chemical compound to search the UMDB for chemicals similar or identical to the query compound. ChemQuery also supports chemical formula and molecular weight searches. The Sequence Search button allows users to conduct BLAST sequence searches of the 4075 protein sequences contained in the UMDB. Both single and multiple sequence BLAST queries are supported. “Advanced Search” which is also located under the “Search” menu is the most sophisticated search tool in the UMDB and opens an easy-to-use query search tool that allows users to select or search over various combinations of subfields. The UMDB’s “MS Search” allows users to submit mass spectral peak lists that will be searched against the Human Metabolome Database (HMDB)’s library of MS/MS spectra. This potentially allows facile identification of urine metabolites from mixtures via MS/MS spectroscopy. UMDB’s NMR Search allows users to submit peak lists from 1H or 13C NMR spectra (both pure and mixtures) and have these peak lists compared to the NMR libraries contained in the HMDB. This allows the identification of metabolites from mixtures via NMR spectral data. The Download button provides links to collected sequence, image and text files associated with the UMDB. In the About menu, the “Data Fields Explained” button lists source data used to assemble the UMDB. |
| p | Currently the UMDB contains information on 2651 detectable metabolites or metabolite species (which corresponds to 3079 metabolites with precisely defined structures) and 3832 concentration ranges or values associated with 220 different conditions, diseases and disorders. The number of metabolites in the UMDB is not a number that will remain unchanged. Rather it reflects the total number of metabolites – most of which are endogenous - that have ever been detected and/or quantified by ourselves and others. Certainly as technology improves, we anticipate this number will increase as other, lower abundance, metabolites are detected and added to future versions of the UMDB. Likewise, if the list was expanded to include intermittent, exogenous compounds such as all possible drugs or drug metabolites or rare food additives and food-derived phytochemicals, the database could be substantially larger. |
| p | Inspection of the on-line tables in UMDB generally shows that human urine contains a substantial number of hydrophilic molecules. This is further reiterated in Table 2, which provides a listing of the chemical “superclasses” (using the HMDB definitions) in human urine and the number of representative compounds that can be found in this biofluid. Excluding lipids (which are in very low concentration), human urine is dominated by amino acids and derivatives, carbohydrates and carbohydrate conjugates. This simply reinforces the fact that urine is a key carrier of hydrophilic waste products. Other small molecule components found in high abundance in urine include hydroxy acids and derivatives (such as citric acid), urea, ammonia, creatinine and hippuric acid. A more detailed description of both our literature and experimental findings is given in the following 7 sections covering: 1) Literature Review/Text Mining; 2) NMR; 3) DFI/LC-MS/MS; 4) GC-MS; 5) ICP-MS; 6) HPLC/UV and 7) HPLC/FD. |
| table-wrap | Table 2 Chemical superclasses in the urine metabolome database. Chemical superclass # Compounds Aliphatic Acyclic Compounds 93 Aliphatic Heteromonocyclic Compounds 43 Aliphatic Heteropolycyclic Compounds 40 Aliphatic Homomonocyclic Compounds 18 Aliphatic Homopolycyclic Compounds 5 Alkaloids and Derivatives 45 Amino Acids, Peptides, and Analogues 286 Aromatic Heteromonocyclic Compounds 67 Aromatic Heteropolycyclic Compounds 728 Aromatic Homomonocyclic Compounds 432 Aromatic Homopolycyclic Compounds 6 Carbohydrates and Carbohydrate Conjugates 116 Homogeneous Metal Compounds 45 Homogeneous Non-metal Compounds 15 Inorganic compounds 1 Lignans and Norlignans 12 Lipids 866 Mixed Metal/Non-metal Compounds 7 Nucleosides, Nucleotides, and Analogues 49 Organic Acids and Derivatives 108 Organic Halides 3 Organometallic Compounds 1 Organophosphorus Compounds 17 Polyketides 74 Tannins 2 |
| label | Table 2 |
| caption | Chemical superclasses in the urine metabolome database. |
| title | Chemical superclasses in the urine metabolome database. |
| table | Chemical superclass # Compounds Aliphatic Acyclic Compounds 93 Aliphatic Heteromonocyclic Compounds 43 Aliphatic Heteropolycyclic Compounds 40 Aliphatic Homomonocyclic Compounds 18 Aliphatic Homopolycyclic Compounds 5 Alkaloids and Derivatives 45 Amino Acids, Peptides, and Analogues 286 Aromatic Heteromonocyclic Compounds 67 Aromatic Heteropolycyclic Compounds 728 Aromatic Homomonocyclic Compounds 432 Aromatic Homopolycyclic Compounds 6 Carbohydrates and Carbohydrate Conjugates 116 Homogeneous Metal Compounds 45 Homogeneous Non-metal Compounds 15 Inorganic compounds 1 Lignans and Norlignans 12 Lipids 866 Mixed Metal/Non-metal Compounds 7 Nucleosides, Nucleotides, and Analogues 49 Organic Acids and Derivatives 108 Organic Halides 3 Organometallic Compounds 1 Organophosphorus Compounds 17 Polyketides 74 Tannins 2 |
| tr | Chemical superclass # Compounds |
| td | Chemical superclass |
| td | # Compounds |
| tr | Aliphatic Acyclic Compounds 93 |
| td | Aliphatic Acyclic Compounds |
| td | 93 |
| tr | Aliphatic Heteromonocyclic Compounds 43 |
| td | Aliphatic Heteromonocyclic Compounds |
| td | 43 |
| tr | Aliphatic Heteropolycyclic Compounds 40 |
| td | Aliphatic Heteropolycyclic Compounds |
| td | 40 |
| tr | Aliphatic Homomonocyclic Compounds 18 |
| td | Aliphatic Homomonocyclic Compounds |
| td | 18 |
| tr | Aliphatic Homopolycyclic Compounds 5 |
| td | Aliphatic Homopolycyclic Compounds |
| td | 5 |
| tr | Alkaloids and Derivatives 45 |
| td | Alkaloids and Derivatives |
| td | 45 |
| tr | Amino Acids, Peptides, and Analogues 286 |
| td | Amino Acids, Peptides, and Analogues |
| td | 286 |
| tr | Aromatic Heteromonocyclic Compounds 67 |
| td | Aromatic Heteromonocyclic Compounds |
| td | 67 |
| tr | Aromatic Heteropolycyclic Compounds 728 |
| td | Aromatic Heteropolycyclic Compounds |
| td | 728 |
| tr | Aromatic Homomonocyclic Compounds 432 |
| td | Aromatic Homomonocyclic Compounds |
| td | 432 |
| tr | Aromatic Homopolycyclic Compounds 6 |
| td | Aromatic Homopolycyclic Compounds |
| td | 6 |
| tr | Carbohydrates and Carbohydrate Conjugates 116 |
| td | Carbohydrates and Carbohydrate Conjugates |
| td | 116 |
| tr | Homogeneous Metal Compounds 45 |
| td | Homogeneous Metal Compounds |
| td | 45 |
| tr | Homogeneous Non-metal Compounds 15 |
| td | Homogeneous Non-metal Compounds |
| td | 15 |
| tr | Inorganic compounds 1 |
| td | Inorganic compounds |
| td | 1 |
| tr | Lignans and Norlignans 12 |
| td | Lignans and Norlignans |
| td | 12 |
| tr | Lipids 866 |
| td | Lipids |
| td | 866 |
| tr | Mixed Metal/Non-metal Compounds 7 |
| td | Mixed Metal/Non-metal Compounds |
| td | 7 |
| tr | Nucleosides, Nucleotides, and Analogues 49 |
| td | Nucleosides, Nucleotides, and Analogues |
| td | 49 |
| tr | Organic Acids and Derivatives 108 |
| td | Organic Acids and Derivatives |
| td | 108 |
| tr | Organic Halides 3 |
| td | Organic Halides |
| td | 3 |
| tr | Organometallic Compounds 1 |
| td | Organometallic Compounds |
| td | 1 |
| tr | Organophosphorus Compounds 17 |
| td | Organophosphorus Compounds |
| td | 17 |
| tr | Polyketides 74 |
| td | Polyketides |
| td | 74 |
| tr | Tannins 2 |
| td | Tannins |
| td | 2 |
| sec | Metabolite Concentration in Urine – Literature Survey In addition to the experimentally derived values obtained for this study, the urine metabolome database (UMDB) also presents literature-derived concentrations of urine metabolites with references to either PubMed IDs or clinical textbooks. In many cases, multiple concentration values are given for “normal” conditions. This is done to provide users/readers with a better estimate of the potential concentration variations that different technologies or laboratories may measure. As a general rule, there is good agreement between most laboratories and methods. However, the literature results presented in the UMDB do not reflect the true state of the raw literature. A number of literature-derived concentration values were eliminated through the curation process after being deemed mistaken, disproven (by subsequent published studies), mis-typed or physiologically impossible. Much of the curation process involved having multiple curators carefully reading and re-reading the primary literature to check for unit type, unit conversion and typographical inconsistencies. Other than the inorganic ions and gases such as sodium (14.7 ± 9.0 mM/mM creatinine – average value), chlorine (8.8 ± 6.2 mM/mM creatinine), potassium (4.6 ± 0.1 mM/mM creatinine), ammonia (2.8 ± 0.9 mM/mM creatinine), the 4 most abundant organic metabolites found in urine (based on average values) are urea (22.5 ± 4.4 mM/mM creatinine), creatinine (10.4 ± 2.0 mM), hippuric acid (298.5 ± 276.8 µM/mM creatinine) and citric acid (280.6 ± 115.2 µM/mM creatinine). The least abundant (detectable) metabolites in urine include oxytocin (0.9 ± 0.1 pM/mM creatinine), angiotensin II (1.2 ± 0.2 pM/mM creatinine), 15-deoxy-d-12,14-PGJ2 (2.3 ± 1.0 pM/mM creatinine) and melatonin (3.3 ± 2.7 pM/mM creatinine). This shows that the current lower limit of detection for metabolites in urine is in the low pM/mM creatinine range and that the concentration range of analytes in urine spans nearly 11 orders of magnitude. One point that is particularly interesting is the fact that the concentration (scaled to creatinine) of the average metabolite in normal urine varies by about ± 50%, with some metabolites varying by as much as ± 350% (such as normetanephrine (0.00085 ± 0.00317 µM/mM creatinine), pipecolic acid (0.03 ± 0.07 µM/mM creatinine), enterodiol (0.032 ± 0.072 µM/mM creatinine), tungsten (0.010 ± 0.022 µM/mM creatinine) and chlorogenic acid (0.0014 ± 0.0029 µM/mM creatinine). Therefore, drawing conclusions about potential disease biomarkers without properly taking into account this variation would be ill-advised. We believe that these relatively large metabolite concentration ranges are due to a number of factors, including age, gender, genetic background, diurnal variation, health status, activity level and diet [56], [57], [58], [59]. Indeed, some UMDB entries explicitly show such variations based on the populations (age, gender) from which these metabolite concentrations were derived. Clearly more study on the contributions to the observed variations in urine is warranted, although with thousands of metabolites to measure for dozens of conditions, these studies will obviously require significant technical and human resources. |
| title | Metabolite Concentration in Urine – Literature Survey |
| p | In addition to the experimentally derived values obtained for this study, the urine metabolome database (UMDB) also presents literature-derived concentrations of urine metabolites with references to either PubMed IDs or clinical textbooks. In many cases, multiple concentration values are given for “normal” conditions. This is done to provide users/readers with a better estimate of the potential concentration variations that different technologies or laboratories may measure. As a general rule, there is good agreement between most laboratories and methods. However, the literature results presented in the UMDB do not reflect the true state of the raw literature. A number of literature-derived concentration values were eliminated through the curation process after being deemed mistaken, disproven (by subsequent published studies), mis-typed or physiologically impossible. Much of the curation process involved having multiple curators carefully reading and re-reading the primary literature to check for unit type, unit conversion and typographical inconsistencies. |
| p | Other than the inorganic ions and gases such as sodium (14.7 ± 9.0 mM/mM creatinine – average value), chlorine (8.8 ± 6.2 mM/mM creatinine), potassium (4.6 ± 0.1 mM/mM creatinine), ammonia (2.8 ± 0.9 mM/mM creatinine), the 4 most abundant organic metabolites found in urine (based on average values) are urea (22.5 ± 4.4 mM/mM creatinine), creatinine (10.4 ± 2.0 mM), hippuric acid (298.5 ± 276.8 µM/mM creatinine) and citric acid (280.6 ± 115.2 µM/mM creatinine). The least abundant (detectable) metabolites in urine include oxytocin (0.9 ± 0.1 pM/mM creatinine), angiotensin II (1.2 ± 0.2 pM/mM creatinine), 15-deoxy-d-12,14-PGJ2 (2.3 ± 1.0 pM/mM creatinine) and melatonin (3.3 ± 2.7 pM/mM creatinine). This shows that the current lower limit of detection for metabolites in urine is in the low pM/mM creatinine range and that the concentration range of analytes in urine spans nearly 11 orders of magnitude. |
| p | One point that is particularly interesting is the fact that the concentration (scaled to creatinine) of the average metabolite in normal urine varies by about ± 50%, with some metabolites varying by as much as ± 350% (such as normetanephrine (0.00085 ± 0.00317 µM/mM creatinine), pipecolic acid (0.03 ± 0.07 µM/mM creatinine), enterodiol (0.032 ± 0.072 µM/mM creatinine), tungsten (0.010 ± 0.022 µM/mM creatinine) and chlorogenic acid (0.0014 ± 0.0029 µM/mM creatinine). Therefore, drawing conclusions about potential disease biomarkers without properly taking into account this variation would be ill-advised. We believe that these relatively large metabolite concentration ranges are due to a number of factors, including age, gender, genetic background, diurnal variation, health status, activity level and diet [56], [57], [58], [59]. Indeed, some UMDB entries explicitly show such variations based on the populations (age, gender) from which these metabolite concentrations were derived. Clearly more study on the contributions to the observed variations in urine is warranted, although with thousands of metabolites to measure for dozens of conditions, these studies will obviously require significant technical and human resources. |
| sec | Experimental Quantification and Identification of Urine Metabolites – NMR A representative high-resolution NMR spectrum of urine from a healthy individual is shown in Figure 1. As can be readily seen from this figure, urine NMR spectra are very information-rich and surprisingly complex, with thousands of resolved peaks. From the 22 healthy control urine samples analyzed, we could identify a total of 209 unique compounds with an average of 167 ± 19 compounds being identified per sample. Every compound was unequivocally identified and quantified using spectral fitting (via Chenomx) and/or spike-in experiments with authentic standards. The concentration of each metabolite was normalized to each urine sample’s corresponding creatinine value to compensate for variations in urine volume (the concentration of metabolites is expressed as µM/mM creatinine). Figure 1 Typical 500 MHz 1H-NMR spectra of urine from human urine. Numbers indicates the following metabolites: 1: creatinine; 2: citric acid; 3: glycine; 4: formic acid; 5: methanol; 6: guanidoacetic acid; 7: acetic acid; 8: L-cysteine; 9: glycolic acid; 10: creatine; 11: isocitric acid; 12: hippuric acid; 13: L-glutamine; 14: L-alanine; 15: L-lysine; 16: gluconic acid; 17∶2- hydroxyglutaric acid; 18: D-glucose; 19: indoxyl sulfate; 20: trimethyl-N-oxide; 21: ethanolamine; 22: L-lactic acid; 23: taurine; 24: L-threonine; 25: dimethylamine; 26: pyroglutamic acid; 27: trigonelline; 28: sucrose; 29: trimethylamine; 30: mannitol; 31: L-serine; 32: acetone; 33: L-cystine; 34: adipic acid; 35: L-histidine; 36: L-tyrosine; 37: imidazole; 38: mandelic acid; 39: dimethylglycine; 40: Cis-aconitic acid; 41: urea; 42∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 43: phenol; 45: isobutyric acid; 46: methylsuccinic acid; 47∶3-aminoisobutyric acid; 48: L-fucose; 49: N-acetylaspartic acid; 50: N-acetylneuraminic acid; 51: acetoacetic acid; 52: Alpha-aminoadipic acid; 53: methylguanidine; 54: phenylacetylglutamine. The 209 compounds identified and quantified from these NMR studies represent a “high-water” mark for NMR-based metabolomics. Previous studies have reported up to 70 compounds being identified and/or quantified in human urine [40]. Indeed, compared to other platforms previously used to analyze human urine (Table 1), it appears that NMR may currently be the most comprehensive and certainly the most quantitative approach to characterizing this biofluid. Based on the fitted area under each urinary NMR spectrum and the number of unidentified peaks we estimate that more than 96% of the spectral area and more than 92% of all NMR-detectable compounds in our human urine samples are listed in Table 3. In other words, for NMR-based metabolomics, human urine is essentially “solved”. The same “solved” status has already been achieved for human serum (with 49 definitive compounds, [20]) and for human cerebrospinal fluid (with 53 definitive compounds, [17]). Knowing the expected or detectable composition of these biofluids should open the door to automated NMR-based metabolomics [60]. Table 3 Concentrations [Mean (range)] and % occurrence of urine metabolites as determined by NMR. Compounds HMDB ID Concentration (µM/mM creatinine) Oc (%) Literature Values (µM/mM creatinine) 1,3-Diaminopropane HMDB00002 1.2 5 2.55 (0.18–5.85) 1,3-Dimethyluric acid HMDB01857 3.1 (1.3–6.8) 68 1.5 (1.3–2.3) 1-Methyladenosine HMDB03331 1.7 (0.9–3.6) 82 2.03±0.20 1-Methylhistidine HMDB00001 8.3 (2.4–28.4) 100 15.9±19.5 1-Methylnicotinamide HMDB00699 5.8 (1.2–15.0) 77 6.1 (0.2–12.0) 2-Furoylglycine HMDB00439 4.0 (0.9–8.4) 100 9.95 (2.0–18.66) 2-Hydroxy-3-methylpentanoic acid HMDB00317 3.9 (1.7–7.9) 100 NA 2-Hydroxybutyric acid HMDB00008 2.8 (1.2–6.9) 77 2.4 (0.0–4.9) 2-Hydroxyglutaric acid HMDB00694 33.0 (13.3–77.9) 100 33.0 (20.0–46.0) 2-Ketobutyric acid HMDB00005 2.9 (0.7–5.4) 100 2.6 (1.1–4.1) 2-Methyl-3-hydroxybutyric acid HMDB00354 4.2 (1.6–6.7) 95 5.5 (0.0–11.0) 2-Methyl-3-ketovaleric acid HMDB00408 1.1 (0.8–1.3) 18 NA 2-Methylbutyrylglycine HMDB00339 2.0 (1.2–3.3) 32 1 (0–2) 2-Methylerythritol HMDB11659 25.3 (17.0–49.0) 14 NA 2-Methylglutaric acid HMDB00422 0.8 (0.3–1.7) 45 NA** 3-Aminoisobutanoic acid HMDB03911 26.0 (2.2–140.0) 100 2.91–116.43 3-Hydroxybutyric acid HMDB00357 3.6 (1.3–6.4) 77 1.4±1.3 3-Hydroxyhippuric acid HMDB06116 7.4 (5.7–9.1) 32 2.5 (0.2–5.0) 3-Hydroxyisovaleric acid HMDB00754 6.8 (3.2–21.8) 100 8.5±3.2 3-Hydroxymethylglutaric acid HMDB00355 2.8 (1.1–5.8) 68 3 (0–10) 3-Hydroxyphenylacetic acid HMDB00440 7.7 (1.4–22.1) 91 6.1±4.1 3-Methyl-2-oxovaleric acid HMDB00491 2.2 (0.4–4.8) 91 2.0±2.0 3-Methyladipic acid HMDB00555 2.9 (1.1–7.2) 86 2.9 (0.7–10.5) 3-Methylglutaconic acid HMDB00522 7.5 (3.4–9.5) 22 4.5 (0.0–9.0) 3-Methylhistidine HMDB00479 16.5 (2.8–59.8) 100 15.1 (3.9–26.3) 3-Methylxanthine HMDB01886 6.2 (1.4–17.8) 100 4.0 (2.3–6.3) 4-Aminobutyric acid HMDB00112 2.9 (2.7–3.3) 9 0.75 (0.0–1.50) 4-Aminohippuric acid HMDB01867 3.3 (1.0–7.2) 86 0.3 4-Deoxythreonic acid HMDB02453 12.3 (4.4–19.8) 100 25±10 4-Ethylphenol HMDB29306 0.9 (0.6–1.2) 18 0.409±0.174 4-Hydroxybenzoic acid HMDB00500 1.7 (0.9–3.0) 77 1.8±1.8 4-Hydroxybutyric acid HMDB00710 7.2 (2.7–22.3) 100 3.3 (0.0–10.8) 4-Hydroxyhippuric acid HMDB13678 2.8 (1.4–6.0) 100 0–14 4-Hydroxyphenyllactic acid HMDB00755 3.2 (1.5–3.9) 50 1.1 (0.2–2.6) 4-Pyridoxic acid HMDB00017 3.1 (0.4–7.5) 95 0.78±0.45a 5,6-Dihydrouracil HMDB00076 3.8 (2.1–8.3) 68 3.0±2.7 5-Aminolevulinic acid HMDB01149 2.9 (1.2–4.4) 95 1.45±0.72 5-Aminopentanoic acid HMDB03355 1.1 5 2 7-Methylxanthine HMDB01991 10.0 (3.9–16.1) 9 10.0 (6.7–14.6) Acetaldehyde HMDB00990 2.6 (0.8–4.2) 32 NA** Acetaminophen HMDB01859 11.1 5 5.3 Acetaminophen glucuronide HMDB10316 33.4 5 NA** Acetaminophen sulfate HMDB59911 138.6 5 NA** Acetic acid HMDB00042 13.0 (2.5–106.0) 100 45.3±53.3 Acetoacetic acid HMDB00060 11.1 (2.2–24.9) 100 33 (0–67) Acetone HMDB01659 3.9 (0.8–17.6) 100 2.24±4.13 Acetyl-L-carnitine HMDB00201 2.8 (0.6–7.5) 100 1.9±0.3 Adenine HMDB00034 2.9 (1.4–5.1) 45 1.17 Adenosine HMDB00050 1.4 (0.9–2.3) 45 0.50 (0.09–0.92) Adipic acid HMDB00448 4.4 (1.6–14.3) 91 5.1 (0.8–35.0) ADP HMDB01341 1.8 (1.0–3.4) 77 NA** Allantoin HMDB00462 15.4 (4.9–29.3) 100 19.7±10.5 Alpha-Aminoadipic acid HMDB00510 7.3 (3.4–11.2) 73 4.6 Alpha-Aminobutyric acid HMDB00452 2.2 (1.1–4.4) 95 1.37±1.05 Alpha-Hydroxyisobutyric acid HMDB00729 3.8 (1.7–5.9) 100 3.8 Alpha-Ketoisovaleric acid HMDB00019 0.5 (0.2–1.1) 50 1 (0–2) Anserine HMDB00194 7.0 (3.3–20.0) 100 16.16 Arabinitol HMDB01851 31.8 (10.2–64.3) 100 19.28±6.32 Ascorbic acid HMDB00044 32.5 (4.6–78.0) 14 1.70–22.72 Asymmetric dimethylarginine HMDB01539 3.0 (0.2–6.7) 100 4.6 (3.0–7.0) Azelaic acid HMDB00784 2.8 (1.8–4.8) 64 4.8 (1.3–15.0) Benzoic acid HMDB01870 4.7 (1.2–10.5) 86 4.2 (1.9–6.5) Beta-Alanine HMDB00056 5.9 (3.4–13.0) 68 5 (0–10) Betaine HMDB00043 11.5 (2.7–24.7) 100 4.14 Butyric acid HMDB00039 1.6 (0.5–4.0) 77 0.131±0.013 Caffeine HMDB01847 1.2 5 0.33 (0.0–1.01) Carnosine HMDB00033 5.5 (1.4–12.6) 91 4.0±2.5 Choline HMDB00097 3.5 (1.4–6.1) 100 5.27 Cinnamic acid HMDB00567 1.6 (0.9–2.2) 9 0.77 Cis-Aconitic acid HMDB00072 20.9 (3.8–95.3) 100 13.0 (2.7–44.0) Citraconic acid HMDB00634 2.0 (1.2–2.6) 73 2.2 Citramalic acid HMDB00426 2.4 (1.0–4.8) 91 3.1 Citric acid HMDB00094 203 (49–600) 100 242.0±129.6 Creatine HMDB00064 46 (3–448) 100 46 (9–135) Creatinine* HMDB00562 14,743±9,797 100 10,090±310 Cytosine HMDB00630 4.1 (1.1–10.7) 36 NA** Dehydroascorbic acid HMDB01264 9.1 (5.8–7.1) 32 1.72–7.47 D-Galactose HMDB00143 11.9 (5.4–25.2) 100 4.4 (0.0–31.0) D-Glucose HMDB00122 37.5 (12.5–58.4) 100 31.12 Dihydrothymine HMDB00079 2.4 (1.3–4.4) 64 5 (0–10) Dimethyl sulfone HMDB04983 8.0 (1.3–49.0) 100 4.6 (2.5–7.5) Dimethylamine HMDB00087 30.8 (20.3–59.2) 100 39.3±8.3 Dimethylglycine HMDB00092 4.4 (1.6–10.4) 100 6.2 (0.8–11.2) D-Threitol HMDB04136 19.3 (5.3–32.7) 100 10.0 (5.6–15.0) D-Xylitol HMDB02917 8.4 (6.0–13.8) 95 3.50±2.37 D-Xylose HMDB00098 20 (3.0–102) 91 51.19 Erythritol HMDB02994 33.4 (6.8–64.0) 100 51±18 Ethanol HMDB00108 3.1 5 5–500b Ethanolamine HMDB00149 37.0 (24.8–56.2) 100 21.4 (6.6–36.2) Ethyl glucuronide HMDB10325 8.2 5 26.38±15.26 Ethylmalonic acid HMDB00622 3.0 (1.2–5.8) 95 2.0 (0.5–3.9) Formic acid HMDB00142 26.8 (6.9–120.9) 100 20.39±11.84 Fumaric acid HMDB00134 0.7 (0.2–1.7) 50 1 (0–2) Glucaric acid HMDB00663 8.4 (5.8–12.3) 82 2.9±0.2 Gluconic acid HMDB00625 21.5 (8.1–38.8) 100 NA** Glucuronic acid HMDB00127 9.7 (3.7–20.6) 77 NA Glutaconic acid HMDB00620 3.1 (1.2–3.1) 68 1 (0–2) Glutaric acid HMDB00661 2.1 (0.7–3.6) 18 1.3 (0.6–2.6) Glyceric acid HMDB00139 15.9 (7.8–31.1) 100 4.5 (0.0–9.0) Glycerol HMDB00131 13 (4–19) 100 20 (0–40) Glycine HMDB00123 106 (44–300) 100 151 (233–248) Glycolic acid HMDB00115 42.0 (3.7–122.0) 100 35 (18–55) Guanidoacetic acid HMDB00128 41.8 (10.6–97.3) 100 89.0 (11.0–124.0) Hippuric acid HMDB00714 229 (19–622) 100 257 (20–770) Homocitrulline HMDB00679 7.5 (3.8–13.8) 91 6.0 (0.0–8.8) Homogentisic acid HMDB00130 1.70 (0.55–2.8) 45 1 (0–2) Homovanillic acid HMDB00118 6.2 (1.8–12.7) 100 4.9±3.4 HPHPA HMDB02643 5.9 (1.4–22.1) 100 0.55–54.93 Hydroxyproline HMDB00725 6.5 (2.4–12.5) 55 2 Hydroxypropionic acid HMDB00700 12.3 (4.4–17.9) 82 6.5 (3.0–10.0) Hypoxanthine HMDB00157 7.2 (1.8–24.1) 100 4.67 (2.80–6.38) Indoleacetic acid HMDB00197 3.4 (1.8–6.2) 100 2.0±1.0 Indoxyl sulfate HMDB00682 22.4 (6.0–64.8) 100 19.74±5.26 Inosine HMDB00195 1.4 (0.4–6.5) 50 1.04±0.56 Isobutyric acid HMDB01873 3.5 (1.2–9.0) 95 3.9 Isocitric acid HMDB00193 46.9 (20.4–89.2) 68 58 (36–84) Isovaleric acid HMDB00718 1.4 (0.8–2.5) 86 0.42±0.02 Isovalerylglycine HMDB00678 2.0 (0.4–4.0) 91 5.5 (1.0–10.0) Kynurenic acid HMDB00715 2.1 (0.8–4.2) 64 1.53 (0.66–4.13) Kynurenine HMDB00684 1.7 (1.1–2.5) 23 2.85±1.94 Lactose HMDB00186 11.8 (1.0–24.2) 91 36.31±6.6c L-Alanine HMDB00161 21.8 (7.1–43.0) 100 22.0±11.0 L-Arabinose HMDB00646 13.3 (1.3–22.0) 100 8.75 (6.12–11.84) L-Arginine HMDB00517 8.6 (3.8–18.6) 95 6.6 (2.5–10.8) L-Asparagine HMDB00168 10.0 (4.6–17.8) 95 10.0 (4.6–16.3) L-Aspartic acid HMDB00191 10.9 (3.5–21.8) 95 4.63 (1.71–7.56) L-Carnitine HMDB00062 5.0 (0.7–16.4) 100 5.7±4.8 L-Cystathionine HMDB00099 14.7 (3.9–20.7) 82 5.3 (1.0–9.6) L-Cysteine HMDB00574 65.8 (23.1–134.5) 100 33.4±15.5 L-Cystine HMDB00192 12.5 (5.0–24.6) 100 9.0±3.0 Levoglucosan HMDB00640 10.0 (2.4–29.0) 27 NA** Levulinic acid HMDB00720 1.3 (0.4–2.0) 64 1.28 (0.33–2.75) L-Fucose HMDB00174 11.8 (6.7–26.0) 91 9.6 (6.4–16.0) L-Glutamic acid HMDB00148 8.5 (3.3–18.4) 100 10.0±3.7 L-Glutamine HMDB00641 37.2 (19.1–77.9) 100 33.32±10.78 L-Histidine HMDB00177 43 (17–90) 100 60.75±27.45 L-Isoleucine HMDB00172 1.3 (0.4–2.6) 100 1.64 L-Lactic acid HMDB00190 11.6 (3.5–29.3) 100 12.3±6.2 L-Leucine HMDB00687 2.8 (1.6–5.4) 100 2.5±1.3 L-Lysine HMDB00182 17.2 (3.7–51.3) 100 18 (7–29) L-Methionine HMDB00696 1.4 (0.5–2.5) 41 1.3±0.97 L-Phenylalanine HMDB00159 6.4 (3.5–11.2) 100 4.50±1.15 L-Serine HMDB00187 25.3 (11.6–53.4) 100 26.0±10.0 L-Threonine HMDB00167 13.3 (6.4–25.2) 100 5.17–24.59 L-Tryptophan HMDB00929 6.3 (3.4–11.1) 100 2.45–29.40 L-Tyrosine HMDB00158 9.5 (4.1–23.5) 100 8.8±4.5 L-Valine HMDB00883 3.4 (2.0–7.7) 100 3.0±1.0 Maleic acid HMDB00176 0.4 (0.3–0.5) 14 0.5 Malonic acid HMDB00691 2.9 (2.0–3.5) 77 1 (0–2) Maltose HMDB00163 6.0 (1.3–21.4) 73 NA** Mandelic acid HMDB00703 1.4 (1.1–1.7) 9 0.966±0.669 Mannitol HMDB00765 32.4 (5.2–85.1) 95 10.26±9.14 Methanol HMDB01875 37 (10–117) 100 NA** Methylamine HMDB00164 4.0 (1.5–11.9) 100 10.5 (9.8–12.5) Methylglutaric acid HMDB00752 3.1 (2.6–3.6) 9 3.5 (0.0–7.0) Methylguanidine HMDB01522 2.7 (1.2–6.0) 95 1.25±0.72 Methylmalonic acid HMDB00202 1.9 (0.7–3.5) 95 1.6 (0.0–3.6) Methylsuccinic acid HMDB01844 3.2 (0.7–9.9) 100 2.3 (0.8–10.8) Monomethyl glutaric acid HMDB00858 2.3 (0.6–4.4) 68 NA m-Thymol HMDB01878 2.2 (0.7–4.0) 86 NA** Myoinositol HMDB00211 22.4 (7.9–36.1) 100 18.8±2.9 N-Acetylaspartic acid HMDB00812 4.0 (1.3–7.0) 100 4.66±1.14 N-Acetylneuraminic acid HMDB00230 5.4 (2.5–8.6) 100 8.60±1.58 N-Acetylputrescine HMDB02064 0.5 (0.4–0.7) 32 1.18 (0.58–2.25) N-Methylhydantoin HMDB03646 4.9 (1.0–11.0) 55 NA o-Hydroxyphenylacetic acid HMDB00669 2.0 (0.9–4.5) 91 7.71±3.65d Oxoglutaric acid HMDB00208 4.8 (2.0–17.0) 100 2.98 (0.42–18.30) Oxypurinol HMDB00786 13.3 (5.1–29.3) 50 3.29 Pantothenic acid HMDB00210 1.9 (0.6–4.4) 100 2.7±0.9 p-Cresol sulfate HMDB11635 1.3 (0.3–5.5) 91 NA** Phenol HMDB00228 4.8 (0.6–12.8) 95 7.0 (5.6–9.2) Phenylacetic acid HMDB00209 1.9 5 4.16±0.91 Phenylacetylglutamine HMDB06344 34.0 (4.5–70.0) 100 47.03 (3.84–85.51) Phenylglyoxylic acid HMDB01587 7.34 5 36.6±0.48e Phosphorylcholine HMDB01565 1.1 (0.7–3.0) 41 NA p-Hydroxyphenylacetic acid HMDB00020 5.5 (1.4–14.6) 100 9.68 Picolinic acid HMDB02243 19.4 (7.2–37.6) 18 1.65–10.28 Pimelic acid HMDB00857 2.2 (0.7–4.0) 77 NA** Proline-betaine HMDB04827 2.3 (0.8–4.0) 100 12.7 (1.6–23.7) Propylene glycol HMDB01881 6.7 (1.4–44.3) 100 NA** Pseudouridine HMDB00767 28.9 (13.3–41.3) 100 26.02±4.62 Pyrocatechol HMDB00957 4.7 (2.0–8.5) 100 6.0±4.3 Pyroglutamic acid HMDB00267 20.7 (10.2–32.6) 100 28.8 (3.4–54.2) Pyruvic acid HMDB00243 2.7 (0.8–6.4) 100 2.13 (0.54–8.67) Quinolinic acid HMDB00232 5.2 (1.0–17.5) 100 2.5±1.1 Sarcosine HMDB00271 2.9 (0.5–5.4) 77 2.8 (0.0–5.6) Scyllitol HMDB06088 4.2 (2.2–8.1) 68 NA Sebacic acid HMDB00792 1.9 5 1 (0–2) Sorbitol HMDB00247 9.9 (2.5–18.7) 100 3.50±2.24 Suberic acid HMDB00893 1.6 (0.9–2.4) 32 0.5 (0.0–2.9) Succinic acid HMDB00254 6.0 (0.3–33.3) 91 5.6±3.8 Succinylacetone HMDB00635 2.8 (0.6–4.7) 36 1 (0–2) Sucrose HMDB00258 7.4 (1.4–19.5) 100 7.12±3.23 Sumiki’s acid HMDB02432 1.7 5 NA** Symmetric dimethylarginine HMDB03334 2.7 (1.5–5.2) 100 3.72±0.35 Syringic acid HMDB02085 1.5 5 0.9±0.2 Tartaric acid HMDB00956 11.8 (2.6–64.4) 82 12.0 (1.3–46.0) Taurine HMDB00251 81 (13–251) 100 4.00–159.98 Threonic acid HMDB00943 20.8 (10.2–39.3) 100 10.0±7.0 Thymidine HMDB00273 2.2 (0.7–3.9) 36 6.87±4.86 Trans-Aconitic acid HMDB00958 6.6 (1.8–25.1) 55 3.38 Trans-Ferulic acid HMDB00954 1.2 5 36.3±11.2f Trigonelline HMDB00875 31.1 (5.5–109.3) 100 16.08 Trimethylamine HMDB00906 2.5 (0.3–19.4) 86 7.7±7.4 Trimethylamine N-oxide HMDB00925 91.0 (4.8–509.0) 100 118.7 (35.5–202.1) Uracil HMDB00300 9.5 (2.6–22.8) 100 12 (2–22) Urea HMDB00294 12,285 (174–49,097) 100 22,566±4,407 Vanillic acid HMDB00484 1.6 (0.6–3.3) 95 1.0 (0.0–2.5) Vanillylmandelic acid HMDB00291 2.3 (1.0–3.4) 9 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. a The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman; b depends greatly on amount of alcohol consumed; c after administration of lactose; d after administration of 1 g of coumarin; e After styrene exposure; and f after consumption of chocolate. NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. Based on these NMR studies, the most abundant constituents (based on average values) of urine from a healthy individual are urea (12.3 ± 14.5 mM/mM creatinine), creatinine (14.7 ± 9.8 mM), hippuric acid (229 ± 160 µM/mM creatinine) and citric acid (203 ± 129 µM/mM creatinine). The least abundant compounds were trans-ferulic acid (1.2 µM/mM creatinine), phosphorylcholine (1.1 ± 0.7 µM/mM creatinine), 4-ethylphenol (0.9 ± 0.1 µM/mM creatinine), 2-methylglutaric acid (0.8 ± 0.4 µM/mM creatinine). The lowest concentrations that could be reliably detected using NMR were 0.4 ± 0.1 µM/mM creatinine (for maleic acid), 0.5 ± 0.2 µM/mM creatinine (for alpha-ketoisovaleric acid and N-acetylputrescine) and 0.7 ± 0.4 µM/mM creatinine (for fumaric acid). The complete list of average compound concentrations (including their range), literature-reported concentrations (for healthy adult) and the frequency of their occurrence is shown in Table 3. In general, there is good agreement between the NMR-measured concentrations and those reported in the literature (often obtained by other analytical techniques). However, not all of the NMR-derived urine concentrations agree with literature-derived values. A total of 34 compounds had average concentrations somewhat higher (>1 SD) than previously reported values (for example, 1, 3-dimethyluric acid, glucaric acid, L-aspartic acid, mannitol), while 21 compounds had average concentrations lower (<1 SD) than previously reported (for example, 1-methylhistidine, phenol, dihydrothymine, thymidine). Another 7 metabolites also exhibited somewhat greater range in concentrations than those previously reported in the literature. These included: acetic acid, butyric acid, isovaleric acid, lactose, phenylglyoxylic acid, proline-betaine and trans-ferulic acid. Some of these discrepancies are likely due to differences in diet, physiological status, pharmacological effects and the age of the different cohorts that were analyzed. Other differences may be due to storage effects, sample preparation methods and the analytical methods being used. As a rule, NMR concentration determinations are very accurate since they involve direct measurement of the compound, as opposed to an indirect measurement of a derivative compound. Therefore we are quite confident in the NMR concentration values reported in Table 3 and would tend to view these as more reliable than those measured via other technologies. A number of the compounds exhibiting higher-than or lower-than-reported concentrations appear to be associated with dietary intake. For example, mannitol is a sugar alcohol that is poorly absorbed by humans but its presence in urine can be explained by its occurrence in commonly consumed foods such as apples, pineapples, asparagus and carrots. Likewise, the urinary excretion of trans-ferulic acid (a polyphenolic derivative) increases after the ingestion of breakfast cereals [61] and chocolate. The relatively low value we measured is likely due to the fact that the literature value of trans-ferulic acid reported in Table 3 was measured for people on a special diet [62]. Also, the low level of proline-betaine we measured in urine may be due to a lower frequency of exposure to dietary citrus fruits in our population sample [63]. Proline-betaine is an osmoprotectant found in citrus fruit and urinary excretion of this metabolite is increased after consumption of fruits such as orange juice [64]. Similarly the higher levels of dimethyl sulfone we detected in urine could be attributed to dietary sources that contain DMSO [65]. For example, onions contain many sulfoxides including DMSO which can be oxidized in the liver and kidneys to produce dimethyl sulfone [66], [67]. The consumption of meat could also significantly increase the concentration of some metabolites in urine as reported in the case of 1-methylhistidine [68]. 1-methylhistidine is produced from the metabolism of anserine (a dipeptide) which is commonly found in meats [69], [70]. In addition to diet, metabolite levels in urine can also be affected by physiological status. For instance, the level of 3-hydroxybutyric acid in urine increases during fasting and can range from 0 to 200 µM/mM creatinine, with the maximum level reported in the literature (200 µM/mM creatinine) corresponding to healthy male after 35 h of fasting [50]. Some of the metabolites we measured by NMR did not have any previously reported literature values. For example, glucuronic acid is usually reported as total glucuronic acid (the free acid plus glucuronide conjugates) after hydrolysis [71], [72]. Here we report the concentration of free glucuronic acid, as indicated in Table 3. Another example, 2-methylerythritol, was previously detected in human urine but no concentration was reported [73]. The urinary excretion of 2-methylerythritol is most likely a result of dietary consumption of fruits or vegetables containing 2-methylerythritol and/or 2-methylerythritol-4-phosphate. 2-methylerythritol-4-phosphate is an intermediate in isoprenoid biosynthesis [74] and has been found to be quite abundant in certain plants [75]. A number of compounds we measured by NMR appear to be normal constituents of human urine but seem not to have been previously reported as being detectable by NMR (a total of 42 compounds) or reported as detected but not-quantified by any other method (a total of 8 compounds). The identification of these “NMR-novel” compounds was aided by their prior identification by GC-MS and DI-MS (see following sections) and through a careful literature analysis of compounds that had previously been detected in human urine via other methods. The list of detected but not previously quantified by NMR compounds includes: 2-hydroxy-3-methylpentanoic acid, 2-methyl-3-ketovaleric acid, 2-methylerythritol, glucuronic acid, monomethyl glutaric acid, N-methylhydantoin, phosphorylcholine and scyllitol. All of these compounds were confirmed using authentic standards. This NMR study also revealed a number of common identification errors made in previously published NMR-based human urine metabolomic studies. In particular, several earlier reports identified phenylacetylglycine [76], N-acetylglutamic acid [77], cresol [78], isonicotinic acid [78], yellow 7.1 [79], meta-hydroxyphenylpropionic acid [80], 2-oxoisocaproic acid [81], urocanic acid, glycylproline and ornithine [80] as being detectable by NMR in human urine. Using our NMR instrument and the samples available to us, we were unable to detect any of these compounds, even after performing multiple spike-in experiments using authentic compounds. While some of these metabolites have been previously reported to be in human urine, they were reported at concentrations far below the lower limit of detection of modern NMR instruments (which is ∼ 1 µM). Due to their chemical shift similarity, phenylacetylglycine (which is found only in rats and mice) and N-acetylglutamic acid appear to be commonly mistaken for phenylacetylglutamine. We also noticed that, isonicotinic acid (a breakdown product of isoniazid and hydrazine derivatives, which is found only in individuals that have taken isoniazid and other hydrazine derivatives as a drug) appears to be mistaken for trigonelline. Likewise cresol (water-insoluble) appears to be frequently mistaken for cresol-sulfate (water-soluble), while the compounds yellow 7.1, meta-hydroxyphenylpropionic acid and 3-(p-hydroxyphenyl)-propionic acid appear to be commonly mistaken for 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA). In addition to correcting these compound identification errors, we also observed some significant gender-related effects on creatinine levels in our urine samples. Since males generally have a greater mass of skeletal muscle than females, they tend to have higher urinary levels of creatinine than women. This was clearly evident in our samples as the average male creatinine level was 20 mM while the average female creatinine level was 11 mM. In addition, increased dietary intake of creatine or a protein-rich diet can increase daily creatinine excretion [82]. |
| title | Experimental Quantification and Identification of Urine Metabolites – NMR |
| p | A representative high-resolution NMR spectrum of urine from a healthy individual is shown in Figure 1. As can be readily seen from this figure, urine NMR spectra are very information-rich and surprisingly complex, with thousands of resolved peaks. From the 22 healthy control urine samples analyzed, we could identify a total of 209 unique compounds with an average of 167 ± 19 compounds being identified per sample. Every compound was unequivocally identified and quantified using spectral fitting (via Chenomx) and/or spike-in experiments with authentic standards. The concentration of each metabolite was normalized to each urine sample’s corresponding creatinine value to compensate for variations in urine volume (the concentration of metabolites is expressed as µM/mM creatinine). |
| figure | Figure 1 Typical 500 MHz 1H-NMR spectra of urine from human urine. Numbers indicates the following metabolites: 1: creatinine; 2: citric acid; 3: glycine; 4: formic acid; 5: methanol; 6: guanidoacetic acid; 7: acetic acid; 8: L-cysteine; 9: glycolic acid; 10: creatine; 11: isocitric acid; 12: hippuric acid; 13: L-glutamine; 14: L-alanine; 15: L-lysine; 16: gluconic acid; 17∶2- hydroxyglutaric acid; 18: D-glucose; 19: indoxyl sulfate; 20: trimethyl-N-oxide; 21: ethanolamine; 22: L-lactic acid; 23: taurine; 24: L-threonine; 25: dimethylamine; 26: pyroglutamic acid; 27: trigonelline; 28: sucrose; 29: trimethylamine; 30: mannitol; 31: L-serine; 32: acetone; 33: L-cystine; 34: adipic acid; 35: L-histidine; 36: L-tyrosine; 37: imidazole; 38: mandelic acid; 39: dimethylglycine; 40: Cis-aconitic acid; 41: urea; 42∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 43: phenol; 45: isobutyric acid; 46: methylsuccinic acid; 47∶3-aminoisobutyric acid; 48: L-fucose; 49: N-acetylaspartic acid; 50: N-acetylneuraminic acid; 51: acetoacetic acid; 52: Alpha-aminoadipic acid; 53: methylguanidine; 54: phenylacetylglutamine. |
| label | Figure 1 |
| caption | Typical 500 MHz 1H-NMR spectra of urine from human urine. Numbers indicates the following metabolites: 1: creatinine; 2: citric acid; 3: glycine; 4: formic acid; 5: methanol; 6: guanidoacetic acid; 7: acetic acid; 8: L-cysteine; 9: glycolic acid; 10: creatine; 11: isocitric acid; 12: hippuric acid; 13: L-glutamine; 14: L-alanine; 15: L-lysine; 16: gluconic acid; 17∶2- hydroxyglutaric acid; 18: D-glucose; 19: indoxyl sulfate; 20: trimethyl-N-oxide; 21: ethanolamine; 22: L-lactic acid; 23: taurine; 24: L-threonine; 25: dimethylamine; 26: pyroglutamic acid; 27: trigonelline; 28: sucrose; 29: trimethylamine; 30: mannitol; 31: L-serine; 32: acetone; 33: L-cystine; 34: adipic acid; 35: L-histidine; 36: L-tyrosine; 37: imidazole; 38: mandelic acid; 39: dimethylglycine; 40: Cis-aconitic acid; 41: urea; 42∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 43: phenol; 45: isobutyric acid; 46: methylsuccinic acid; 47∶3-aminoisobutyric acid; 48: L-fucose; 49: N-acetylaspartic acid; 50: N-acetylneuraminic acid; 51: acetoacetic acid; 52: Alpha-aminoadipic acid; 53: methylguanidine; 54: phenylacetylglutamine. |
| title | Typical 500 MHz 1H-NMR spectra of urine from human urine. |
| p | Numbers indicates the following metabolites: 1: creatinine; 2: citric acid; 3: glycine; 4: formic acid; 5: methanol; 6: guanidoacetic acid; 7: acetic acid; 8: L-cysteine; 9: glycolic acid; 10: creatine; 11: isocitric acid; 12: hippuric acid; 13: L-glutamine; 14: L-alanine; 15: L-lysine; 16: gluconic acid; 17∶2- hydroxyglutaric acid; 18: D-glucose; 19: indoxyl sulfate; 20: trimethyl-N-oxide; 21: ethanolamine; 22: L-lactic acid; 23: taurine; 24: L-threonine; 25: dimethylamine; 26: pyroglutamic acid; 27: trigonelline; 28: sucrose; 29: trimethylamine; 30: mannitol; 31: L-serine; 32: acetone; 33: L-cystine; 34: adipic acid; 35: L-histidine; 36: L-tyrosine; 37: imidazole; 38: mandelic acid; 39: dimethylglycine; 40: Cis-aconitic acid; 41: urea; 42∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 43: phenol; 45: isobutyric acid; 46: methylsuccinic acid; 47∶3-aminoisobutyric acid; 48: L-fucose; 49: N-acetylaspartic acid; 50: N-acetylneuraminic acid; 51: acetoacetic acid; 52: Alpha-aminoadipic acid; 53: methylguanidine; 54: phenylacetylglutamine. |
| p | The 209 compounds identified and quantified from these NMR studies represent a “high-water” mark for NMR-based metabolomics. Previous studies have reported up to 70 compounds being identified and/or quantified in human urine [40]. Indeed, compared to other platforms previously used to analyze human urine (Table 1), it appears that NMR may currently be the most comprehensive and certainly the most quantitative approach to characterizing this biofluid. Based on the fitted area under each urinary NMR spectrum and the number of unidentified peaks we estimate that more than 96% of the spectral area and more than 92% of all NMR-detectable compounds in our human urine samples are listed in Table 3. In other words, for NMR-based metabolomics, human urine is essentially “solved”. The same “solved” status has already been achieved for human serum (with 49 definitive compounds, [20]) and for human cerebrospinal fluid (with 53 definitive compounds, [17]). Knowing the expected or detectable composition of these biofluids should open the door to automated NMR-based metabolomics [60]. |
| table-wrap | Table 3 Concentrations [Mean (range)] and % occurrence of urine metabolites as determined by NMR. Compounds HMDB ID Concentration (µM/mM creatinine) Oc (%) Literature Values (µM/mM creatinine) 1,3-Diaminopropane HMDB00002 1.2 5 2.55 (0.18–5.85) 1,3-Dimethyluric acid HMDB01857 3.1 (1.3–6.8) 68 1.5 (1.3–2.3) 1-Methyladenosine HMDB03331 1.7 (0.9–3.6) 82 2.03±0.20 1-Methylhistidine HMDB00001 8.3 (2.4–28.4) 100 15.9±19.5 1-Methylnicotinamide HMDB00699 5.8 (1.2–15.0) 77 6.1 (0.2–12.0) 2-Furoylglycine HMDB00439 4.0 (0.9–8.4) 100 9.95 (2.0–18.66) 2-Hydroxy-3-methylpentanoic acid HMDB00317 3.9 (1.7–7.9) 100 NA 2-Hydroxybutyric acid HMDB00008 2.8 (1.2–6.9) 77 2.4 (0.0–4.9) 2-Hydroxyglutaric acid HMDB00694 33.0 (13.3–77.9) 100 33.0 (20.0–46.0) 2-Ketobutyric acid HMDB00005 2.9 (0.7–5.4) 100 2.6 (1.1–4.1) 2-Methyl-3-hydroxybutyric acid HMDB00354 4.2 (1.6–6.7) 95 5.5 (0.0–11.0) 2-Methyl-3-ketovaleric acid HMDB00408 1.1 (0.8–1.3) 18 NA 2-Methylbutyrylglycine HMDB00339 2.0 (1.2–3.3) 32 1 (0–2) 2-Methylerythritol HMDB11659 25.3 (17.0–49.0) 14 NA 2-Methylglutaric acid HMDB00422 0.8 (0.3–1.7) 45 NA** 3-Aminoisobutanoic acid HMDB03911 26.0 (2.2–140.0) 100 2.91–116.43 3-Hydroxybutyric acid HMDB00357 3.6 (1.3–6.4) 77 1.4±1.3 3-Hydroxyhippuric acid HMDB06116 7.4 (5.7–9.1) 32 2.5 (0.2–5.0) 3-Hydroxyisovaleric acid HMDB00754 6.8 (3.2–21.8) 100 8.5±3.2 3-Hydroxymethylglutaric acid HMDB00355 2.8 (1.1–5.8) 68 3 (0–10) 3-Hydroxyphenylacetic acid HMDB00440 7.7 (1.4–22.1) 91 6.1±4.1 3-Methyl-2-oxovaleric acid HMDB00491 2.2 (0.4–4.8) 91 2.0±2.0 3-Methyladipic acid HMDB00555 2.9 (1.1–7.2) 86 2.9 (0.7–10.5) 3-Methylglutaconic acid HMDB00522 7.5 (3.4–9.5) 22 4.5 (0.0–9.0) 3-Methylhistidine HMDB00479 16.5 (2.8–59.8) 100 15.1 (3.9–26.3) 3-Methylxanthine HMDB01886 6.2 (1.4–17.8) 100 4.0 (2.3–6.3) 4-Aminobutyric acid HMDB00112 2.9 (2.7–3.3) 9 0.75 (0.0–1.50) 4-Aminohippuric acid HMDB01867 3.3 (1.0–7.2) 86 0.3 4-Deoxythreonic acid HMDB02453 12.3 (4.4–19.8) 100 25±10 4-Ethylphenol HMDB29306 0.9 (0.6–1.2) 18 0.409±0.174 4-Hydroxybenzoic acid HMDB00500 1.7 (0.9–3.0) 77 1.8±1.8 4-Hydroxybutyric acid HMDB00710 7.2 (2.7–22.3) 100 3.3 (0.0–10.8) 4-Hydroxyhippuric acid HMDB13678 2.8 (1.4–6.0) 100 0–14 4-Hydroxyphenyllactic acid HMDB00755 3.2 (1.5–3.9) 50 1.1 (0.2–2.6) 4-Pyridoxic acid HMDB00017 3.1 (0.4–7.5) 95 0.78±0.45a 5,6-Dihydrouracil HMDB00076 3.8 (2.1–8.3) 68 3.0±2.7 5-Aminolevulinic acid HMDB01149 2.9 (1.2–4.4) 95 1.45±0.72 5-Aminopentanoic acid HMDB03355 1.1 5 2 7-Methylxanthine HMDB01991 10.0 (3.9–16.1) 9 10.0 (6.7–14.6) Acetaldehyde HMDB00990 2.6 (0.8–4.2) 32 NA** Acetaminophen HMDB01859 11.1 5 5.3 Acetaminophen glucuronide HMDB10316 33.4 5 NA** Acetaminophen sulfate HMDB59911 138.6 5 NA** Acetic acid HMDB00042 13.0 (2.5–106.0) 100 45.3±53.3 Acetoacetic acid HMDB00060 11.1 (2.2–24.9) 100 33 (0–67) Acetone HMDB01659 3.9 (0.8–17.6) 100 2.24±4.13 Acetyl-L-carnitine HMDB00201 2.8 (0.6–7.5) 100 1.9±0.3 Adenine HMDB00034 2.9 (1.4–5.1) 45 1.17 Adenosine HMDB00050 1.4 (0.9–2.3) 45 0.50 (0.09–0.92) Adipic acid HMDB00448 4.4 (1.6–14.3) 91 5.1 (0.8–35.0) ADP HMDB01341 1.8 (1.0–3.4) 77 NA** Allantoin HMDB00462 15.4 (4.9–29.3) 100 19.7±10.5 Alpha-Aminoadipic acid HMDB00510 7.3 (3.4–11.2) 73 4.6 Alpha-Aminobutyric acid HMDB00452 2.2 (1.1–4.4) 95 1.37±1.05 Alpha-Hydroxyisobutyric acid HMDB00729 3.8 (1.7–5.9) 100 3.8 Alpha-Ketoisovaleric acid HMDB00019 0.5 (0.2–1.1) 50 1 (0–2) Anserine HMDB00194 7.0 (3.3–20.0) 100 16.16 Arabinitol HMDB01851 31.8 (10.2–64.3) 100 19.28±6.32 Ascorbic acid HMDB00044 32.5 (4.6–78.0) 14 1.70–22.72 Asymmetric dimethylarginine HMDB01539 3.0 (0.2–6.7) 100 4.6 (3.0–7.0) Azelaic acid HMDB00784 2.8 (1.8–4.8) 64 4.8 (1.3–15.0) Benzoic acid HMDB01870 4.7 (1.2–10.5) 86 4.2 (1.9–6.5) Beta-Alanine HMDB00056 5.9 (3.4–13.0) 68 5 (0–10) Betaine HMDB00043 11.5 (2.7–24.7) 100 4.14 Butyric acid HMDB00039 1.6 (0.5–4.0) 77 0.131±0.013 Caffeine HMDB01847 1.2 5 0.33 (0.0–1.01) Carnosine HMDB00033 5.5 (1.4–12.6) 91 4.0±2.5 Choline HMDB00097 3.5 (1.4–6.1) 100 5.27 Cinnamic acid HMDB00567 1.6 (0.9–2.2) 9 0.77 Cis-Aconitic acid HMDB00072 20.9 (3.8–95.3) 100 13.0 (2.7–44.0) Citraconic acid HMDB00634 2.0 (1.2–2.6) 73 2.2 Citramalic acid HMDB00426 2.4 (1.0–4.8) 91 3.1 Citric acid HMDB00094 203 (49–600) 100 242.0±129.6 Creatine HMDB00064 46 (3–448) 100 46 (9–135) Creatinine* HMDB00562 14,743±9,797 100 10,090±310 Cytosine HMDB00630 4.1 (1.1–10.7) 36 NA** Dehydroascorbic acid HMDB01264 9.1 (5.8–7.1) 32 1.72–7.47 D-Galactose HMDB00143 11.9 (5.4–25.2) 100 4.4 (0.0–31.0) D-Glucose HMDB00122 37.5 (12.5–58.4) 100 31.12 Dihydrothymine HMDB00079 2.4 (1.3–4.4) 64 5 (0–10) Dimethyl sulfone HMDB04983 8.0 (1.3–49.0) 100 4.6 (2.5–7.5) Dimethylamine HMDB00087 30.8 (20.3–59.2) 100 39.3±8.3 Dimethylglycine HMDB00092 4.4 (1.6–10.4) 100 6.2 (0.8–11.2) D-Threitol HMDB04136 19.3 (5.3–32.7) 100 10.0 (5.6–15.0) D-Xylitol HMDB02917 8.4 (6.0–13.8) 95 3.50±2.37 D-Xylose HMDB00098 20 (3.0–102) 91 51.19 Erythritol HMDB02994 33.4 (6.8–64.0) 100 51±18 Ethanol HMDB00108 3.1 5 5–500b Ethanolamine HMDB00149 37.0 (24.8–56.2) 100 21.4 (6.6–36.2) Ethyl glucuronide HMDB10325 8.2 5 26.38±15.26 Ethylmalonic acid HMDB00622 3.0 (1.2–5.8) 95 2.0 (0.5–3.9) Formic acid HMDB00142 26.8 (6.9–120.9) 100 20.39±11.84 Fumaric acid HMDB00134 0.7 (0.2–1.7) 50 1 (0–2) Glucaric acid HMDB00663 8.4 (5.8–12.3) 82 2.9±0.2 Gluconic acid HMDB00625 21.5 (8.1–38.8) 100 NA** Glucuronic acid HMDB00127 9.7 (3.7–20.6) 77 NA Glutaconic acid HMDB00620 3.1 (1.2–3.1) 68 1 (0–2) Glutaric acid HMDB00661 2.1 (0.7–3.6) 18 1.3 (0.6–2.6) Glyceric acid HMDB00139 15.9 (7.8–31.1) 100 4.5 (0.0–9.0) Glycerol HMDB00131 13 (4–19) 100 20 (0–40) Glycine HMDB00123 106 (44–300) 100 151 (233–248) Glycolic acid HMDB00115 42.0 (3.7–122.0) 100 35 (18–55) Guanidoacetic acid HMDB00128 41.8 (10.6–97.3) 100 89.0 (11.0–124.0) Hippuric acid HMDB00714 229 (19–622) 100 257 (20–770) Homocitrulline HMDB00679 7.5 (3.8–13.8) 91 6.0 (0.0–8.8) Homogentisic acid HMDB00130 1.70 (0.55–2.8) 45 1 (0–2) Homovanillic acid HMDB00118 6.2 (1.8–12.7) 100 4.9±3.4 HPHPA HMDB02643 5.9 (1.4–22.1) 100 0.55–54.93 Hydroxyproline HMDB00725 6.5 (2.4–12.5) 55 2 Hydroxypropionic acid HMDB00700 12.3 (4.4–17.9) 82 6.5 (3.0–10.0) Hypoxanthine HMDB00157 7.2 (1.8–24.1) 100 4.67 (2.80–6.38) Indoleacetic acid HMDB00197 3.4 (1.8–6.2) 100 2.0±1.0 Indoxyl sulfate HMDB00682 22.4 (6.0–64.8) 100 19.74±5.26 Inosine HMDB00195 1.4 (0.4–6.5) 50 1.04±0.56 Isobutyric acid HMDB01873 3.5 (1.2–9.0) 95 3.9 Isocitric acid HMDB00193 46.9 (20.4–89.2) 68 58 (36–84) Isovaleric acid HMDB00718 1.4 (0.8–2.5) 86 0.42±0.02 Isovalerylglycine HMDB00678 2.0 (0.4–4.0) 91 5.5 (1.0–10.0) Kynurenic acid HMDB00715 2.1 (0.8–4.2) 64 1.53 (0.66–4.13) Kynurenine HMDB00684 1.7 (1.1–2.5) 23 2.85±1.94 Lactose HMDB00186 11.8 (1.0–24.2) 91 36.31±6.6c L-Alanine HMDB00161 21.8 (7.1–43.0) 100 22.0±11.0 L-Arabinose HMDB00646 13.3 (1.3–22.0) 100 8.75 (6.12–11.84) L-Arginine HMDB00517 8.6 (3.8–18.6) 95 6.6 (2.5–10.8) L-Asparagine HMDB00168 10.0 (4.6–17.8) 95 10.0 (4.6–16.3) L-Aspartic acid HMDB00191 10.9 (3.5–21.8) 95 4.63 (1.71–7.56) L-Carnitine HMDB00062 5.0 (0.7–16.4) 100 5.7±4.8 L-Cystathionine HMDB00099 14.7 (3.9–20.7) 82 5.3 (1.0–9.6) L-Cysteine HMDB00574 65.8 (23.1–134.5) 100 33.4±15.5 L-Cystine HMDB00192 12.5 (5.0–24.6) 100 9.0±3.0 Levoglucosan HMDB00640 10.0 (2.4–29.0) 27 NA** Levulinic acid HMDB00720 1.3 (0.4–2.0) 64 1.28 (0.33–2.75) L-Fucose HMDB00174 11.8 (6.7–26.0) 91 9.6 (6.4–16.0) L-Glutamic acid HMDB00148 8.5 (3.3–18.4) 100 10.0±3.7 L-Glutamine HMDB00641 37.2 (19.1–77.9) 100 33.32±10.78 L-Histidine HMDB00177 43 (17–90) 100 60.75±27.45 L-Isoleucine HMDB00172 1.3 (0.4–2.6) 100 1.64 L-Lactic acid HMDB00190 11.6 (3.5–29.3) 100 12.3±6.2 L-Leucine HMDB00687 2.8 (1.6–5.4) 100 2.5±1.3 L-Lysine HMDB00182 17.2 (3.7–51.3) 100 18 (7–29) L-Methionine HMDB00696 1.4 (0.5–2.5) 41 1.3±0.97 L-Phenylalanine HMDB00159 6.4 (3.5–11.2) 100 4.50±1.15 L-Serine HMDB00187 25.3 (11.6–53.4) 100 26.0±10.0 L-Threonine HMDB00167 13.3 (6.4–25.2) 100 5.17–24.59 L-Tryptophan HMDB00929 6.3 (3.4–11.1) 100 2.45–29.40 L-Tyrosine HMDB00158 9.5 (4.1–23.5) 100 8.8±4.5 L-Valine HMDB00883 3.4 (2.0–7.7) 100 3.0±1.0 Maleic acid HMDB00176 0.4 (0.3–0.5) 14 0.5 Malonic acid HMDB00691 2.9 (2.0–3.5) 77 1 (0–2) Maltose HMDB00163 6.0 (1.3–21.4) 73 NA** Mandelic acid HMDB00703 1.4 (1.1–1.7) 9 0.966±0.669 Mannitol HMDB00765 32.4 (5.2–85.1) 95 10.26±9.14 Methanol HMDB01875 37 (10–117) 100 NA** Methylamine HMDB00164 4.0 (1.5–11.9) 100 10.5 (9.8–12.5) Methylglutaric acid HMDB00752 3.1 (2.6–3.6) 9 3.5 (0.0–7.0) Methylguanidine HMDB01522 2.7 (1.2–6.0) 95 1.25±0.72 Methylmalonic acid HMDB00202 1.9 (0.7–3.5) 95 1.6 (0.0–3.6) Methylsuccinic acid HMDB01844 3.2 (0.7–9.9) 100 2.3 (0.8–10.8) Monomethyl glutaric acid HMDB00858 2.3 (0.6–4.4) 68 NA m-Thymol HMDB01878 2.2 (0.7–4.0) 86 NA** Myoinositol HMDB00211 22.4 (7.9–36.1) 100 18.8±2.9 N-Acetylaspartic acid HMDB00812 4.0 (1.3–7.0) 100 4.66±1.14 N-Acetylneuraminic acid HMDB00230 5.4 (2.5–8.6) 100 8.60±1.58 N-Acetylputrescine HMDB02064 0.5 (0.4–0.7) 32 1.18 (0.58–2.25) N-Methylhydantoin HMDB03646 4.9 (1.0–11.0) 55 NA o-Hydroxyphenylacetic acid HMDB00669 2.0 (0.9–4.5) 91 7.71±3.65d Oxoglutaric acid HMDB00208 4.8 (2.0–17.0) 100 2.98 (0.42–18.30) Oxypurinol HMDB00786 13.3 (5.1–29.3) 50 3.29 Pantothenic acid HMDB00210 1.9 (0.6–4.4) 100 2.7±0.9 p-Cresol sulfate HMDB11635 1.3 (0.3–5.5) 91 NA** Phenol HMDB00228 4.8 (0.6–12.8) 95 7.0 (5.6–9.2) Phenylacetic acid HMDB00209 1.9 5 4.16±0.91 Phenylacetylglutamine HMDB06344 34.0 (4.5–70.0) 100 47.03 (3.84–85.51) Phenylglyoxylic acid HMDB01587 7.34 5 36.6±0.48e Phosphorylcholine HMDB01565 1.1 (0.7–3.0) 41 NA p-Hydroxyphenylacetic acid HMDB00020 5.5 (1.4–14.6) 100 9.68 Picolinic acid HMDB02243 19.4 (7.2–37.6) 18 1.65–10.28 Pimelic acid HMDB00857 2.2 (0.7–4.0) 77 NA** Proline-betaine HMDB04827 2.3 (0.8–4.0) 100 12.7 (1.6–23.7) Propylene glycol HMDB01881 6.7 (1.4–44.3) 100 NA** Pseudouridine HMDB00767 28.9 (13.3–41.3) 100 26.02±4.62 Pyrocatechol HMDB00957 4.7 (2.0–8.5) 100 6.0±4.3 Pyroglutamic acid HMDB00267 20.7 (10.2–32.6) 100 28.8 (3.4–54.2) Pyruvic acid HMDB00243 2.7 (0.8–6.4) 100 2.13 (0.54–8.67) Quinolinic acid HMDB00232 5.2 (1.0–17.5) 100 2.5±1.1 Sarcosine HMDB00271 2.9 (0.5–5.4) 77 2.8 (0.0–5.6) Scyllitol HMDB06088 4.2 (2.2–8.1) 68 NA Sebacic acid HMDB00792 1.9 5 1 (0–2) Sorbitol HMDB00247 9.9 (2.5–18.7) 100 3.50±2.24 Suberic acid HMDB00893 1.6 (0.9–2.4) 32 0.5 (0.0–2.9) Succinic acid HMDB00254 6.0 (0.3–33.3) 91 5.6±3.8 Succinylacetone HMDB00635 2.8 (0.6–4.7) 36 1 (0–2) Sucrose HMDB00258 7.4 (1.4–19.5) 100 7.12±3.23 Sumiki’s acid HMDB02432 1.7 5 NA** Symmetric dimethylarginine HMDB03334 2.7 (1.5–5.2) 100 3.72±0.35 Syringic acid HMDB02085 1.5 5 0.9±0.2 Tartaric acid HMDB00956 11.8 (2.6–64.4) 82 12.0 (1.3–46.0) Taurine HMDB00251 81 (13–251) 100 4.00–159.98 Threonic acid HMDB00943 20.8 (10.2–39.3) 100 10.0±7.0 Thymidine HMDB00273 2.2 (0.7–3.9) 36 6.87±4.86 Trans-Aconitic acid HMDB00958 6.6 (1.8–25.1) 55 3.38 Trans-Ferulic acid HMDB00954 1.2 5 36.3±11.2f Trigonelline HMDB00875 31.1 (5.5–109.3) 100 16.08 Trimethylamine HMDB00906 2.5 (0.3–19.4) 86 7.7±7.4 Trimethylamine N-oxide HMDB00925 91.0 (4.8–509.0) 100 118.7 (35.5–202.1) Uracil HMDB00300 9.5 (2.6–22.8) 100 12 (2–22) Urea HMDB00294 12,285 (174–49,097) 100 22,566±4,407 Vanillic acid HMDB00484 1.6 (0.6–3.3) 95 1.0 (0.0–2.5) Vanillylmandelic acid HMDB00291 2.3 (1.0–3.4) 9 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. a The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman; b depends greatly on amount of alcohol consumed; c after administration of lactose; d after administration of 1 g of coumarin; e After styrene exposure; and f after consumption of chocolate. NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. |
| label | Table 3 |
| caption | Concentrations [Mean (range)] and % occurrence of urine metabolites as determined by NMR. |
| title | Concentrations [Mean (range)] and % occurrence of urine metabolites as determined by NMR. |
| table | Compounds HMDB ID Concentration (µM/mM creatinine) Oc (%) Literature Values (µM/mM creatinine) 1,3-Diaminopropane HMDB00002 1.2 5 2.55 (0.18–5.85) 1,3-Dimethyluric acid HMDB01857 3.1 (1.3–6.8) 68 1.5 (1.3–2.3) 1-Methyladenosine HMDB03331 1.7 (0.9–3.6) 82 2.03±0.20 1-Methylhistidine HMDB00001 8.3 (2.4–28.4) 100 15.9±19.5 1-Methylnicotinamide HMDB00699 5.8 (1.2–15.0) 77 6.1 (0.2–12.0) 2-Furoylglycine HMDB00439 4.0 (0.9–8.4) 100 9.95 (2.0–18.66) 2-Hydroxy-3-methylpentanoic acid HMDB00317 3.9 (1.7–7.9) 100 NA 2-Hydroxybutyric acid HMDB00008 2.8 (1.2–6.9) 77 2.4 (0.0–4.9) 2-Hydroxyglutaric acid HMDB00694 33.0 (13.3–77.9) 100 33.0 (20.0–46.0) 2-Ketobutyric acid HMDB00005 2.9 (0.7–5.4) 100 2.6 (1.1–4.1) 2-Methyl-3-hydroxybutyric acid HMDB00354 4.2 (1.6–6.7) 95 5.5 (0.0–11.0) 2-Methyl-3-ketovaleric acid HMDB00408 1.1 (0.8–1.3) 18 NA 2-Methylbutyrylglycine HMDB00339 2.0 (1.2–3.3) 32 1 (0–2) 2-Methylerythritol HMDB11659 25.3 (17.0–49.0) 14 NA 2-Methylglutaric acid HMDB00422 0.8 (0.3–1.7) 45 NA** 3-Aminoisobutanoic acid HMDB03911 26.0 (2.2–140.0) 100 2.91–116.43 3-Hydroxybutyric acid HMDB00357 3.6 (1.3–6.4) 77 1.4±1.3 3-Hydroxyhippuric acid HMDB06116 7.4 (5.7–9.1) 32 2.5 (0.2–5.0) 3-Hydroxyisovaleric acid HMDB00754 6.8 (3.2–21.8) 100 8.5±3.2 3-Hydroxymethylglutaric acid HMDB00355 2.8 (1.1–5.8) 68 3 (0–10) 3-Hydroxyphenylacetic acid HMDB00440 7.7 (1.4–22.1) 91 6.1±4.1 3-Methyl-2-oxovaleric acid HMDB00491 2.2 (0.4–4.8) 91 2.0±2.0 3-Methyladipic acid HMDB00555 2.9 (1.1–7.2) 86 2.9 (0.7–10.5) 3-Methylglutaconic acid HMDB00522 7.5 (3.4–9.5) 22 4.5 (0.0–9.0) 3-Methylhistidine HMDB00479 16.5 (2.8–59.8) 100 15.1 (3.9–26.3) 3-Methylxanthine HMDB01886 6.2 (1.4–17.8) 100 4.0 (2.3–6.3) 4-Aminobutyric acid HMDB00112 2.9 (2.7–3.3) 9 0.75 (0.0–1.50) 4-Aminohippuric acid HMDB01867 3.3 (1.0–7.2) 86 0.3 4-Deoxythreonic acid HMDB02453 12.3 (4.4–19.8) 100 25±10 4-Ethylphenol HMDB29306 0.9 (0.6–1.2) 18 0.409±0.174 4-Hydroxybenzoic acid HMDB00500 1.7 (0.9–3.0) 77 1.8±1.8 4-Hydroxybutyric acid HMDB00710 7.2 (2.7–22.3) 100 3.3 (0.0–10.8) 4-Hydroxyhippuric acid HMDB13678 2.8 (1.4–6.0) 100 0–14 4-Hydroxyphenyllactic acid HMDB00755 3.2 (1.5–3.9) 50 1.1 (0.2–2.6) 4-Pyridoxic acid HMDB00017 3.1 (0.4–7.5) 95 0.78±0.45a 5,6-Dihydrouracil HMDB00076 3.8 (2.1–8.3) 68 3.0±2.7 5-Aminolevulinic acid HMDB01149 2.9 (1.2–4.4) 95 1.45±0.72 5-Aminopentanoic acid HMDB03355 1.1 5 2 7-Methylxanthine HMDB01991 10.0 (3.9–16.1) 9 10.0 (6.7–14.6) Acetaldehyde HMDB00990 2.6 (0.8–4.2) 32 NA** Acetaminophen HMDB01859 11.1 5 5.3 Acetaminophen glucuronide HMDB10316 33.4 5 NA** Acetaminophen sulfate HMDB59911 138.6 5 NA** Acetic acid HMDB00042 13.0 (2.5–106.0) 100 45.3±53.3 Acetoacetic acid HMDB00060 11.1 (2.2–24.9) 100 33 (0–67) Acetone HMDB01659 3.9 (0.8–17.6) 100 2.24±4.13 Acetyl-L-carnitine HMDB00201 2.8 (0.6–7.5) 100 1.9±0.3 Adenine HMDB00034 2.9 (1.4–5.1) 45 1.17 Adenosine HMDB00050 1.4 (0.9–2.3) 45 0.50 (0.09–0.92) Adipic acid HMDB00448 4.4 (1.6–14.3) 91 5.1 (0.8–35.0) ADP HMDB01341 1.8 (1.0–3.4) 77 NA** Allantoin HMDB00462 15.4 (4.9–29.3) 100 19.7±10.5 Alpha-Aminoadipic acid HMDB00510 7.3 (3.4–11.2) 73 4.6 Alpha-Aminobutyric acid HMDB00452 2.2 (1.1–4.4) 95 1.37±1.05 Alpha-Hydroxyisobutyric acid HMDB00729 3.8 (1.7–5.9) 100 3.8 Alpha-Ketoisovaleric acid HMDB00019 0.5 (0.2–1.1) 50 1 (0–2) Anserine HMDB00194 7.0 (3.3–20.0) 100 16.16 Arabinitol HMDB01851 31.8 (10.2–64.3) 100 19.28±6.32 Ascorbic acid HMDB00044 32.5 (4.6–78.0) 14 1.70–22.72 Asymmetric dimethylarginine HMDB01539 3.0 (0.2–6.7) 100 4.6 (3.0–7.0) Azelaic acid HMDB00784 2.8 (1.8–4.8) 64 4.8 (1.3–15.0) Benzoic acid HMDB01870 4.7 (1.2–10.5) 86 4.2 (1.9–6.5) Beta-Alanine HMDB00056 5.9 (3.4–13.0) 68 5 (0–10) Betaine HMDB00043 11.5 (2.7–24.7) 100 4.14 Butyric acid HMDB00039 1.6 (0.5–4.0) 77 0.131±0.013 Caffeine HMDB01847 1.2 5 0.33 (0.0–1.01) Carnosine HMDB00033 5.5 (1.4–12.6) 91 4.0±2.5 Choline HMDB00097 3.5 (1.4–6.1) 100 5.27 Cinnamic acid HMDB00567 1.6 (0.9–2.2) 9 0.77 Cis-Aconitic acid HMDB00072 20.9 (3.8–95.3) 100 13.0 (2.7–44.0) Citraconic acid HMDB00634 2.0 (1.2–2.6) 73 2.2 Citramalic acid HMDB00426 2.4 (1.0–4.8) 91 3.1 Citric acid HMDB00094 203 (49–600) 100 242.0±129.6 Creatine HMDB00064 46 (3–448) 100 46 (9–135) Creatinine* HMDB00562 14,743±9,797 100 10,090±310 Cytosine HMDB00630 4.1 (1.1–10.7) 36 NA** Dehydroascorbic acid HMDB01264 9.1 (5.8–7.1) 32 1.72–7.47 D-Galactose HMDB00143 11.9 (5.4–25.2) 100 4.4 (0.0–31.0) D-Glucose HMDB00122 37.5 (12.5–58.4) 100 31.12 Dihydrothymine HMDB00079 2.4 (1.3–4.4) 64 5 (0–10) Dimethyl sulfone HMDB04983 8.0 (1.3–49.0) 100 4.6 (2.5–7.5) Dimethylamine HMDB00087 30.8 (20.3–59.2) 100 39.3±8.3 Dimethylglycine HMDB00092 4.4 (1.6–10.4) 100 6.2 (0.8–11.2) D-Threitol HMDB04136 19.3 (5.3–32.7) 100 10.0 (5.6–15.0) D-Xylitol HMDB02917 8.4 (6.0–13.8) 95 3.50±2.37 D-Xylose HMDB00098 20 (3.0–102) 91 51.19 Erythritol HMDB02994 33.4 (6.8–64.0) 100 51±18 Ethanol HMDB00108 3.1 5 5–500b Ethanolamine HMDB00149 37.0 (24.8–56.2) 100 21.4 (6.6–36.2) Ethyl glucuronide HMDB10325 8.2 5 26.38±15.26 Ethylmalonic acid HMDB00622 3.0 (1.2–5.8) 95 2.0 (0.5–3.9) Formic acid HMDB00142 26.8 (6.9–120.9) 100 20.39±11.84 Fumaric acid HMDB00134 0.7 (0.2–1.7) 50 1 (0–2) Glucaric acid HMDB00663 8.4 (5.8–12.3) 82 2.9±0.2 Gluconic acid HMDB00625 21.5 (8.1–38.8) 100 NA** Glucuronic acid HMDB00127 9.7 (3.7–20.6) 77 NA Glutaconic acid HMDB00620 3.1 (1.2–3.1) 68 1 (0–2) Glutaric acid HMDB00661 2.1 (0.7–3.6) 18 1.3 (0.6–2.6) Glyceric acid HMDB00139 15.9 (7.8–31.1) 100 4.5 (0.0–9.0) Glycerol HMDB00131 13 (4–19) 100 20 (0–40) Glycine HMDB00123 106 (44–300) 100 151 (233–248) Glycolic acid HMDB00115 42.0 (3.7–122.0) 100 35 (18–55) Guanidoacetic acid HMDB00128 41.8 (10.6–97.3) 100 89.0 (11.0–124.0) Hippuric acid HMDB00714 229 (19–622) 100 257 (20–770) Homocitrulline HMDB00679 7.5 (3.8–13.8) 91 6.0 (0.0–8.8) Homogentisic acid HMDB00130 1.70 (0.55–2.8) 45 1 (0–2) Homovanillic acid HMDB00118 6.2 (1.8–12.7) 100 4.9±3.4 HPHPA HMDB02643 5.9 (1.4–22.1) 100 0.55–54.93 Hydroxyproline HMDB00725 6.5 (2.4–12.5) 55 2 Hydroxypropionic acid HMDB00700 12.3 (4.4–17.9) 82 6.5 (3.0–10.0) Hypoxanthine HMDB00157 7.2 (1.8–24.1) 100 4.67 (2.80–6.38) Indoleacetic acid HMDB00197 3.4 (1.8–6.2) 100 2.0±1.0 Indoxyl sulfate HMDB00682 22.4 (6.0–64.8) 100 19.74±5.26 Inosine HMDB00195 1.4 (0.4–6.5) 50 1.04±0.56 Isobutyric acid HMDB01873 3.5 (1.2–9.0) 95 3.9 Isocitric acid HMDB00193 46.9 (20.4–89.2) 68 58 (36–84) Isovaleric acid HMDB00718 1.4 (0.8–2.5) 86 0.42±0.02 Isovalerylglycine HMDB00678 2.0 (0.4–4.0) 91 5.5 (1.0–10.0) Kynurenic acid HMDB00715 2.1 (0.8–4.2) 64 1.53 (0.66–4.13) Kynurenine HMDB00684 1.7 (1.1–2.5) 23 2.85±1.94 Lactose HMDB00186 11.8 (1.0–24.2) 91 36.31±6.6c L-Alanine HMDB00161 21.8 (7.1–43.0) 100 22.0±11.0 L-Arabinose HMDB00646 13.3 (1.3–22.0) 100 8.75 (6.12–11.84) L-Arginine HMDB00517 8.6 (3.8–18.6) 95 6.6 (2.5–10.8) L-Asparagine HMDB00168 10.0 (4.6–17.8) 95 10.0 (4.6–16.3) L-Aspartic acid HMDB00191 10.9 (3.5–21.8) 95 4.63 (1.71–7.56) L-Carnitine HMDB00062 5.0 (0.7–16.4) 100 5.7±4.8 L-Cystathionine HMDB00099 14.7 (3.9–20.7) 82 5.3 (1.0–9.6) L-Cysteine HMDB00574 65.8 (23.1–134.5) 100 33.4±15.5 L-Cystine HMDB00192 12.5 (5.0–24.6) 100 9.0±3.0 Levoglucosan HMDB00640 10.0 (2.4–29.0) 27 NA** Levulinic acid HMDB00720 1.3 (0.4–2.0) 64 1.28 (0.33–2.75) L-Fucose HMDB00174 11.8 (6.7–26.0) 91 9.6 (6.4–16.0) L-Glutamic acid HMDB00148 8.5 (3.3–18.4) 100 10.0±3.7 L-Glutamine HMDB00641 37.2 (19.1–77.9) 100 33.32±10.78 L-Histidine HMDB00177 43 (17–90) 100 60.75±27.45 L-Isoleucine HMDB00172 1.3 (0.4–2.6) 100 1.64 L-Lactic acid HMDB00190 11.6 (3.5–29.3) 100 12.3±6.2 L-Leucine HMDB00687 2.8 (1.6–5.4) 100 2.5±1.3 L-Lysine HMDB00182 17.2 (3.7–51.3) 100 18 (7–29) L-Methionine HMDB00696 1.4 (0.5–2.5) 41 1.3±0.97 L-Phenylalanine HMDB00159 6.4 (3.5–11.2) 100 4.50±1.15 L-Serine HMDB00187 25.3 (11.6–53.4) 100 26.0±10.0 L-Threonine HMDB00167 13.3 (6.4–25.2) 100 5.17–24.59 L-Tryptophan HMDB00929 6.3 (3.4–11.1) 100 2.45–29.40 L-Tyrosine HMDB00158 9.5 (4.1–23.5) 100 8.8±4.5 L-Valine HMDB00883 3.4 (2.0–7.7) 100 3.0±1.0 Maleic acid HMDB00176 0.4 (0.3–0.5) 14 0.5 Malonic acid HMDB00691 2.9 (2.0–3.5) 77 1 (0–2) Maltose HMDB00163 6.0 (1.3–21.4) 73 NA** Mandelic acid HMDB00703 1.4 (1.1–1.7) 9 0.966±0.669 Mannitol HMDB00765 32.4 (5.2–85.1) 95 10.26±9.14 Methanol HMDB01875 37 (10–117) 100 NA** Methylamine HMDB00164 4.0 (1.5–11.9) 100 10.5 (9.8–12.5) Methylglutaric acid HMDB00752 3.1 (2.6–3.6) 9 3.5 (0.0–7.0) Methylguanidine HMDB01522 2.7 (1.2–6.0) 95 1.25±0.72 Methylmalonic acid HMDB00202 1.9 (0.7–3.5) 95 1.6 (0.0–3.6) Methylsuccinic acid HMDB01844 3.2 (0.7–9.9) 100 2.3 (0.8–10.8) Monomethyl glutaric acid HMDB00858 2.3 (0.6–4.4) 68 NA m-Thymol HMDB01878 2.2 (0.7–4.0) 86 NA** Myoinositol HMDB00211 22.4 (7.9–36.1) 100 18.8±2.9 N-Acetylaspartic acid HMDB00812 4.0 (1.3–7.0) 100 4.66±1.14 N-Acetylneuraminic acid HMDB00230 5.4 (2.5–8.6) 100 8.60±1.58 N-Acetylputrescine HMDB02064 0.5 (0.4–0.7) 32 1.18 (0.58–2.25) N-Methylhydantoin HMDB03646 4.9 (1.0–11.0) 55 NA o-Hydroxyphenylacetic acid HMDB00669 2.0 (0.9–4.5) 91 7.71±3.65d Oxoglutaric acid HMDB00208 4.8 (2.0–17.0) 100 2.98 (0.42–18.30) Oxypurinol HMDB00786 13.3 (5.1–29.3) 50 3.29 Pantothenic acid HMDB00210 1.9 (0.6–4.4) 100 2.7±0.9 p-Cresol sulfate HMDB11635 1.3 (0.3–5.5) 91 NA** Phenol HMDB00228 4.8 (0.6–12.8) 95 7.0 (5.6–9.2) Phenylacetic acid HMDB00209 1.9 5 4.16±0.91 Phenylacetylglutamine HMDB06344 34.0 (4.5–70.0) 100 47.03 (3.84–85.51) Phenylglyoxylic acid HMDB01587 7.34 5 36.6±0.48e Phosphorylcholine HMDB01565 1.1 (0.7–3.0) 41 NA p-Hydroxyphenylacetic acid HMDB00020 5.5 (1.4–14.6) 100 9.68 Picolinic acid HMDB02243 19.4 (7.2–37.6) 18 1.65–10.28 Pimelic acid HMDB00857 2.2 (0.7–4.0) 77 NA** Proline-betaine HMDB04827 2.3 (0.8–4.0) 100 12.7 (1.6–23.7) Propylene glycol HMDB01881 6.7 (1.4–44.3) 100 NA** Pseudouridine HMDB00767 28.9 (13.3–41.3) 100 26.02±4.62 Pyrocatechol HMDB00957 4.7 (2.0–8.5) 100 6.0±4.3 Pyroglutamic acid HMDB00267 20.7 (10.2–32.6) 100 28.8 (3.4–54.2) Pyruvic acid HMDB00243 2.7 (0.8–6.4) 100 2.13 (0.54–8.67) Quinolinic acid HMDB00232 5.2 (1.0–17.5) 100 2.5±1.1 Sarcosine HMDB00271 2.9 (0.5–5.4) 77 2.8 (0.0–5.6) Scyllitol HMDB06088 4.2 (2.2–8.1) 68 NA Sebacic acid HMDB00792 1.9 5 1 (0–2) Sorbitol HMDB00247 9.9 (2.5–18.7) 100 3.50±2.24 Suberic acid HMDB00893 1.6 (0.9–2.4) 32 0.5 (0.0–2.9) Succinic acid HMDB00254 6.0 (0.3–33.3) 91 5.6±3.8 Succinylacetone HMDB00635 2.8 (0.6–4.7) 36 1 (0–2) Sucrose HMDB00258 7.4 (1.4–19.5) 100 7.12±3.23 Sumiki’s acid HMDB02432 1.7 5 NA** Symmetric dimethylarginine HMDB03334 2.7 (1.5–5.2) 100 3.72±0.35 Syringic acid HMDB02085 1.5 5 0.9±0.2 Tartaric acid HMDB00956 11.8 (2.6–64.4) 82 12.0 (1.3–46.0) Taurine HMDB00251 81 (13–251) 100 4.00–159.98 Threonic acid HMDB00943 20.8 (10.2–39.3) 100 10.0±7.0 Thymidine HMDB00273 2.2 (0.7–3.9) 36 6.87±4.86 Trans-Aconitic acid HMDB00958 6.6 (1.8–25.1) 55 3.38 Trans-Ferulic acid HMDB00954 1.2 5 36.3±11.2f Trigonelline HMDB00875 31.1 (5.5–109.3) 100 16.08 Trimethylamine HMDB00906 2.5 (0.3–19.4) 86 7.7±7.4 Trimethylamine N-oxide HMDB00925 91.0 (4.8–509.0) 100 118.7 (35.5–202.1) Uracil HMDB00300 9.5 (2.6–22.8) 100 12 (2–22) Urea HMDB00294 12,285 (174–49,097) 100 22,566±4,407 Vanillic acid HMDB00484 1.6 (0.6–3.3) 95 1.0 (0.0–2.5) Vanillylmandelic acid HMDB00291 2.3 (1.0–3.4) 9 1.2±0.5 |
| tr | Compounds HMDB ID Concentration (µM/mM creatinine) Oc (%) Literature Values (µM/mM creatinine) |
| td | Compounds |
| td | HMDB ID |
| td | Concentration (µM/mM creatinine) |
| td | Oc (%) |
| td | Literature Values (µM/mM creatinine) |
| tr | 1,3-Diaminopropane HMDB00002 1.2 5 2.55 (0.18–5.85) |
| td | 1,3-Diaminopropane |
| td | HMDB00002 |
| td | 1.2 |
| td | 5 |
| td | 2.55 (0.18–5.85) |
| tr | 1,3-Dimethyluric acid HMDB01857 3.1 (1.3–6.8) 68 1.5 (1.3–2.3) |
| td | 1,3-Dimethyluric acid |
| td | HMDB01857 |
| td | 3.1 (1.3–6.8) |
| td | 68 |
| td | 1.5 (1.3–2.3) |
| tr | 1-Methyladenosine HMDB03331 1.7 (0.9–3.6) 82 2.03±0.20 |
| td | 1-Methyladenosine |
| td | HMDB03331 |
| td | 1.7 (0.9–3.6) |
| td | 82 |
| td | 2.03±0.20 |
| tr | 1-Methylhistidine HMDB00001 8.3 (2.4–28.4) 100 15.9±19.5 |
| td | 1-Methylhistidine |
| td | HMDB00001 |
| td | 8.3 (2.4–28.4) |
| td | 100 |
| td | 15.9±19.5 |
| tr | 1-Methylnicotinamide HMDB00699 5.8 (1.2–15.0) 77 6.1 (0.2–12.0) |
| td | 1-Methylnicotinamide |
| td | HMDB00699 |
| td | 5.8 (1.2–15.0) |
| td | 77 |
| td | 6.1 (0.2–12.0) |
| tr | 2-Furoylglycine HMDB00439 4.0 (0.9–8.4) 100 9.95 (2.0–18.66) |
| td | 2-Furoylglycine |
| td | HMDB00439 |
| td | 4.0 (0.9–8.4) |
| td | 100 |
| td | 9.95 (2.0–18.66) |
| tr | 2-Hydroxy-3-methylpentanoic acid HMDB00317 3.9 (1.7–7.9) 100 NA |
| td | 2-Hydroxy-3-methylpentanoic acid |
| td | HMDB00317 |
| td | 3.9 (1.7–7.9) |
| td | 100 |
| td | NA |
| tr | 2-Hydroxybutyric acid HMDB00008 2.8 (1.2–6.9) 77 2.4 (0.0–4.9) |
| td | 2-Hydroxybutyric acid |
| td | HMDB00008 |
| td | 2.8 (1.2–6.9) |
| td | 77 |
| td | 2.4 (0.0–4.9) |
| tr | 2-Hydroxyglutaric acid HMDB00694 33.0 (13.3–77.9) 100 33.0 (20.0–46.0) |
| td | 2-Hydroxyglutaric acid |
| td | HMDB00694 |
| td | 33.0 (13.3–77.9) |
| td | 100 |
| td | 33.0 (20.0–46.0) |
| tr | 2-Ketobutyric acid HMDB00005 2.9 (0.7–5.4) 100 2.6 (1.1–4.1) |
| td | 2-Ketobutyric acid |
| td | HMDB00005 |
| td | 2.9 (0.7–5.4) |
| td | 100 |
| td | 2.6 (1.1–4.1) |
| tr | 2-Methyl-3-hydroxybutyric acid HMDB00354 4.2 (1.6–6.7) 95 5.5 (0.0–11.0) |
| td | 2-Methyl-3-hydroxybutyric acid |
| td | HMDB00354 |
| td | 4.2 (1.6–6.7) |
| td | 95 |
| td | 5.5 (0.0–11.0) |
| tr | 2-Methyl-3-ketovaleric acid HMDB00408 1.1 (0.8–1.3) 18 NA |
| td | 2-Methyl-3-ketovaleric acid |
| td | HMDB00408 |
| td | 1.1 (0.8–1.3) |
| td | 18 |
| td | NA |
| tr | 2-Methylbutyrylglycine HMDB00339 2.0 (1.2–3.3) 32 1 (0–2) |
| td | 2-Methylbutyrylglycine |
| td | HMDB00339 |
| td | 2.0 (1.2–3.3) |
| td | 32 |
| td | 1 (0–2) |
| tr | 2-Methylerythritol HMDB11659 25.3 (17.0–49.0) 14 NA |
| td | 2-Methylerythritol |
| td | HMDB11659 |
| td | 25.3 (17.0–49.0) |
| td | 14 |
| td | NA |
| tr | 2-Methylglutaric acid HMDB00422 0.8 (0.3–1.7) 45 NA** |
| td | 2-Methylglutaric acid |
| td | HMDB00422 |
| td | 0.8 (0.3–1.7) |
| td | 45 |
| td | NA** |
| tr | 3-Aminoisobutanoic acid HMDB03911 26.0 (2.2–140.0) 100 2.91–116.43 |
| td | 3-Aminoisobutanoic acid |
| td | HMDB03911 |
| td | 26.0 (2.2–140.0) |
| td | 100 |
| td | 2.91–116.43 |
| tr | 3-Hydroxybutyric acid HMDB00357 3.6 (1.3–6.4) 77 1.4±1.3 |
| td | 3-Hydroxybutyric acid |
| td | HMDB00357 |
| td | 3.6 (1.3–6.4) |
| td | 77 |
| td | 1.4±1.3 |
| tr | 3-Hydroxyhippuric acid HMDB06116 7.4 (5.7–9.1) 32 2.5 (0.2–5.0) |
| td | 3-Hydroxyhippuric acid |
| td | HMDB06116 |
| td | 7.4 (5.7–9.1) |
| td | 32 |
| td | 2.5 (0.2–5.0) |
| tr | 3-Hydroxyisovaleric acid HMDB00754 6.8 (3.2–21.8) 100 8.5±3.2 |
| td | 3-Hydroxyisovaleric acid |
| td | HMDB00754 |
| td | 6.8 (3.2–21.8) |
| td | 100 |
| td | 8.5±3.2 |
| tr | 3-Hydroxymethylglutaric acid HMDB00355 2.8 (1.1–5.8) 68 3 (0–10) |
| td | 3-Hydroxymethylglutaric acid |
| td | HMDB00355 |
| td | 2.8 (1.1–5.8) |
| td | 68 |
| td | 3 (0–10) |
| tr | 3-Hydroxyphenylacetic acid HMDB00440 7.7 (1.4–22.1) 91 6.1±4.1 |
| td | 3-Hydroxyphenylacetic acid |
| td | HMDB00440 |
| td | 7.7 (1.4–22.1) |
| td | 91 |
| td | 6.1±4.1 |
| tr | 3-Methyl-2-oxovaleric acid HMDB00491 2.2 (0.4–4.8) 91 2.0±2.0 |
| td | 3-Methyl-2-oxovaleric acid |
| td | HMDB00491 |
| td | 2.2 (0.4–4.8) |
| td | 91 |
| td | 2.0±2.0 |
| tr | 3-Methyladipic acid HMDB00555 2.9 (1.1–7.2) 86 2.9 (0.7–10.5) |
| td | 3-Methyladipic acid |
| td | HMDB00555 |
| td | 2.9 (1.1–7.2) |
| td | 86 |
| td | 2.9 (0.7–10.5) |
| tr | 3-Methylglutaconic acid HMDB00522 7.5 (3.4–9.5) 22 4.5 (0.0–9.0) |
| td | 3-Methylglutaconic acid |
| td | HMDB00522 |
| td | 7.5 (3.4–9.5) |
| td | 22 |
| td | 4.5 (0.0–9.0) |
| tr | 3-Methylhistidine HMDB00479 16.5 (2.8–59.8) 100 15.1 (3.9–26.3) |
| td | 3-Methylhistidine |
| td | HMDB00479 |
| td | 16.5 (2.8–59.8) |
| td | 100 |
| td | 15.1 (3.9–26.3) |
| tr | 3-Methylxanthine HMDB01886 6.2 (1.4–17.8) 100 4.0 (2.3–6.3) |
| td | 3-Methylxanthine |
| td | HMDB01886 |
| td | 6.2 (1.4–17.8) |
| td | 100 |
| td | 4.0 (2.3–6.3) |
| tr | 4-Aminobutyric acid HMDB00112 2.9 (2.7–3.3) 9 0.75 (0.0–1.50) |
| td | 4-Aminobutyric acid |
| td | HMDB00112 |
| td | 2.9 (2.7–3.3) |
| td | 9 |
| td | 0.75 (0.0–1.50) |
| tr | 4-Aminohippuric acid HMDB01867 3.3 (1.0–7.2) 86 0.3 |
| td | 4-Aminohippuric acid |
| td | HMDB01867 |
| td | 3.3 (1.0–7.2) |
| td | 86 |
| td | 0.3 |
| tr | 4-Deoxythreonic acid HMDB02453 12.3 (4.4–19.8) 100 25±10 |
| td | 4-Deoxythreonic acid |
| td | HMDB02453 |
| td | 12.3 (4.4–19.8) |
| td | 100 |
| td | 25±10 |
| tr | 4-Ethylphenol HMDB29306 0.9 (0.6–1.2) 18 0.409±0.174 |
| td | 4-Ethylphenol |
| td | HMDB29306 |
| td | 0.9 (0.6–1.2) |
| td | 18 |
| td | 0.409±0.174 |
| tr | 4-Hydroxybenzoic acid HMDB00500 1.7 (0.9–3.0) 77 1.8±1.8 |
| td | 4-Hydroxybenzoic acid |
| td | HMDB00500 |
| td | 1.7 (0.9–3.0) |
| td | 77 |
| td | 1.8±1.8 |
| tr | 4-Hydroxybutyric acid HMDB00710 7.2 (2.7–22.3) 100 3.3 (0.0–10.8) |
| td | 4-Hydroxybutyric acid |
| td | HMDB00710 |
| td | 7.2 (2.7–22.3) |
| td | 100 |
| td | 3.3 (0.0–10.8) |
| tr | 4-Hydroxyhippuric acid HMDB13678 2.8 (1.4–6.0) 100 0–14 |
| td | 4-Hydroxyhippuric acid |
| td | HMDB13678 |
| td | 2.8 (1.4–6.0) |
| td | 100 |
| td | 0–14 |
| tr | 4-Hydroxyphenyllactic acid HMDB00755 3.2 (1.5–3.9) 50 1.1 (0.2–2.6) |
| td | 4-Hydroxyphenyllactic acid |
| td | HMDB00755 |
| td | 3.2 (1.5–3.9) |
| td | 50 |
| td | 1.1 (0.2–2.6) |
| tr | 4-Pyridoxic acid HMDB00017 3.1 (0.4–7.5) 95 0.78±0.45a |
| td | 4-Pyridoxic acid |
| td | HMDB00017 |
| td | 3.1 (0.4–7.5) |
| td | 95 |
| td | 0.78±0.45a |
| tr | 5,6-Dihydrouracil HMDB00076 3.8 (2.1–8.3) 68 3.0±2.7 |
| td | 5,6-Dihydrouracil |
| td | HMDB00076 |
| td | 3.8 (2.1–8.3) |
| td | 68 |
| td | 3.0±2.7 |
| tr | 5-Aminolevulinic acid HMDB01149 2.9 (1.2–4.4) 95 1.45±0.72 |
| td | 5-Aminolevulinic acid |
| td | HMDB01149 |
| td | 2.9 (1.2–4.4) |
| td | 95 |
| td | 1.45±0.72 |
| tr | 5-Aminopentanoic acid HMDB03355 1.1 5 2 |
| td | 5-Aminopentanoic acid |
| td | HMDB03355 |
| td | 1.1 |
| td | 5 |
| td | 2 |
| tr | 7-Methylxanthine HMDB01991 10.0 (3.9–16.1) 9 10.0 (6.7–14.6) |
| td | 7-Methylxanthine |
| td | HMDB01991 |
| td | 10.0 (3.9–16.1) |
| td | 9 |
| td | 10.0 (6.7–14.6) |
| tr | Acetaldehyde HMDB00990 2.6 (0.8–4.2) 32 NA** |
| td | Acetaldehyde |
| td | HMDB00990 |
| td | 2.6 (0.8–4.2) |
| td | 32 |
| td | NA** |
| tr | Acetaminophen HMDB01859 11.1 5 5.3 |
| td | Acetaminophen |
| td | HMDB01859 |
| td | 11.1 |
| td | 5 |
| td | 5.3 |
| tr | Acetaminophen glucuronide HMDB10316 33.4 5 NA** |
| td | Acetaminophen glucuronide |
| td | HMDB10316 |
| td | 33.4 |
| td | 5 |
| td | NA** |
| tr | Acetaminophen sulfate HMDB59911 138.6 5 NA** |
| td | Acetaminophen sulfate |
| td | HMDB59911 |
| td | 138.6 |
| td | 5 |
| td | NA** |
| tr | Acetic acid HMDB00042 13.0 (2.5–106.0) 100 45.3±53.3 |
| td | Acetic acid |
| td | HMDB00042 |
| td | 13.0 (2.5–106.0) |
| td | 100 |
| td | 45.3±53.3 |
| tr | Acetoacetic acid HMDB00060 11.1 (2.2–24.9) 100 33 (0–67) |
| td | Acetoacetic acid |
| td | HMDB00060 |
| td | 11.1 (2.2–24.9) |
| td | 100 |
| td | 33 (0–67) |
| tr | Acetone HMDB01659 3.9 (0.8–17.6) 100 2.24±4.13 |
| td | Acetone |
| td | HMDB01659 |
| td | 3.9 (0.8–17.6) |
| td | 100 |
| td | 2.24±4.13 |
| tr | Acetyl-L-carnitine HMDB00201 2.8 (0.6–7.5) 100 1.9±0.3 |
| td | Acetyl-L-carnitine |
| td | HMDB00201 |
| td | 2.8 (0.6–7.5) |
| td | 100 |
| td | 1.9±0.3 |
| tr | Adenine HMDB00034 2.9 (1.4–5.1) 45 1.17 |
| td | Adenine |
| td | HMDB00034 |
| td | 2.9 (1.4–5.1) |
| td | 45 |
| td | 1.17 |
| tr | Adenosine HMDB00050 1.4 (0.9–2.3) 45 0.50 (0.09–0.92) |
| td | Adenosine |
| td | HMDB00050 |
| td | 1.4 (0.9–2.3) |
| td | 45 |
| td | 0.50 (0.09–0.92) |
| tr | Adipic acid HMDB00448 4.4 (1.6–14.3) 91 5.1 (0.8–35.0) |
| td | Adipic acid |
| td | HMDB00448 |
| td | 4.4 (1.6–14.3) |
| td | 91 |
| td | 5.1 (0.8–35.0) |
| tr | ADP HMDB01341 1.8 (1.0–3.4) 77 NA** |
| td | ADP |
| td | HMDB01341 |
| td | 1.8 (1.0–3.4) |
| td | 77 |
| td | NA** |
| tr | Allantoin HMDB00462 15.4 (4.9–29.3) 100 19.7±10.5 |
| td | Allantoin |
| td | HMDB00462 |
| td | 15.4 (4.9–29.3) |
| td | 100 |
| td | 19.7±10.5 |
| tr | Alpha-Aminoadipic acid HMDB00510 7.3 (3.4–11.2) 73 4.6 |
| td | Alpha-Aminoadipic acid |
| td | HMDB00510 |
| td | 7.3 (3.4–11.2) |
| td | 73 |
| td | 4.6 |
| tr | Alpha-Aminobutyric acid HMDB00452 2.2 (1.1–4.4) 95 1.37±1.05 |
| td | Alpha-Aminobutyric acid |
| td | HMDB00452 |
| td | 2.2 (1.1–4.4) |
| td | 95 |
| td | 1.37±1.05 |
| tr | Alpha-Hydroxyisobutyric acid HMDB00729 3.8 (1.7–5.9) 100 3.8 |
| td | Alpha-Hydroxyisobutyric acid |
| td | HMDB00729 |
| td | 3.8 (1.7–5.9) |
| td | 100 |
| td | 3.8 |
| tr | Alpha-Ketoisovaleric acid HMDB00019 0.5 (0.2–1.1) 50 1 (0–2) |
| td | Alpha-Ketoisovaleric acid |
| td | HMDB00019 |
| td | 0.5 (0.2–1.1) |
| td | 50 |
| td | 1 (0–2) |
| tr | Anserine HMDB00194 7.0 (3.3–20.0) 100 16.16 |
| td | Anserine |
| td | HMDB00194 |
| td | 7.0 (3.3–20.0) |
| td | 100 |
| td | 16.16 |
| tr | Arabinitol HMDB01851 31.8 (10.2–64.3) 100 19.28±6.32 |
| td | Arabinitol |
| td | HMDB01851 |
| td | 31.8 (10.2–64.3) |
| td | 100 |
| td | 19.28±6.32 |
| tr | Ascorbic acid HMDB00044 32.5 (4.6–78.0) 14 1.70–22.72 |
| td | Ascorbic acid |
| td | HMDB00044 |
| td | 32.5 (4.6–78.0) |
| td | 14 |
| td | 1.70–22.72 |
| tr | Asymmetric dimethylarginine HMDB01539 3.0 (0.2–6.7) 100 4.6 (3.0–7.0) |
| td | Asymmetric dimethylarginine |
| td | HMDB01539 |
| td | 3.0 (0.2–6.7) |
| td | 100 |
| td | 4.6 (3.0–7.0) |
| tr | Azelaic acid HMDB00784 2.8 (1.8–4.8) 64 4.8 (1.3–15.0) |
| td | Azelaic acid |
| td | HMDB00784 |
| td | 2.8 (1.8–4.8) |
| td | 64 |
| td | 4.8 (1.3–15.0) |
| tr | Benzoic acid HMDB01870 4.7 (1.2–10.5) 86 4.2 (1.9–6.5) |
| td | Benzoic acid |
| td | HMDB01870 |
| td | 4.7 (1.2–10.5) |
| td | 86 |
| td | 4.2 (1.9–6.5) |
| tr | Beta-Alanine HMDB00056 5.9 (3.4–13.0) 68 5 (0–10) |
| td | Beta-Alanine |
| td | HMDB00056 |
| td | 5.9 (3.4–13.0) |
| td | 68 |
| td | 5 (0–10) |
| tr | Betaine HMDB00043 11.5 (2.7–24.7) 100 4.14 |
| td | Betaine |
| td | HMDB00043 |
| td | 11.5 (2.7–24.7) |
| td | 100 |
| td | 4.14 |
| tr | Butyric acid HMDB00039 1.6 (0.5–4.0) 77 0.131±0.013 |
| td | Butyric acid |
| td | HMDB00039 |
| td | 1.6 (0.5–4.0) |
| td | 77 |
| td | 0.131±0.013 |
| tr | Caffeine HMDB01847 1.2 5 0.33 (0.0–1.01) |
| td | Caffeine |
| td | HMDB01847 |
| td | 1.2 |
| td | 5 |
| td | 0.33 (0.0–1.01) |
| tr | Carnosine HMDB00033 5.5 (1.4–12.6) 91 4.0±2.5 |
| td | Carnosine |
| td | HMDB00033 |
| td | 5.5 (1.4–12.6) |
| td | 91 |
| td | 4.0±2.5 |
| tr | Choline HMDB00097 3.5 (1.4–6.1) 100 5.27 |
| td | Choline |
| td | HMDB00097 |
| td | 3.5 (1.4–6.1) |
| td | 100 |
| td | 5.27 |
| tr | Cinnamic acid HMDB00567 1.6 (0.9–2.2) 9 0.77 |
| td | Cinnamic acid |
| td | HMDB00567 |
| td | 1.6 (0.9–2.2) |
| td | 9 |
| td | 0.77 |
| tr | Cis-Aconitic acid HMDB00072 20.9 (3.8–95.3) 100 13.0 (2.7–44.0) |
| td | Cis-Aconitic acid |
| td | HMDB00072 |
| td | 20.9 (3.8–95.3) |
| td | 100 |
| td | 13.0 (2.7–44.0) |
| tr | Citraconic acid HMDB00634 2.0 (1.2–2.6) 73 2.2 |
| td | Citraconic acid |
| td | HMDB00634 |
| td | 2.0 (1.2–2.6) |
| td | 73 |
| td | 2.2 |
| tr | Citramalic acid HMDB00426 2.4 (1.0–4.8) 91 3.1 |
| td | Citramalic acid |
| td | HMDB00426 |
| td | 2.4 (1.0–4.8) |
| td | 91 |
| td | 3.1 |
| tr | Citric acid HMDB00094 203 (49–600) 100 242.0±129.6 |
| td | Citric acid |
| td | HMDB00094 |
| td | 203 (49–600) |
| td | 100 |
| td | 242.0±129.6 |
| tr | Creatine HMDB00064 46 (3–448) 100 46 (9–135) |
| td | Creatine |
| td | HMDB00064 |
| td | 46 (3–448) |
| td | 100 |
| td | 46 (9–135) |
| tr | Creatinine* HMDB00562 14,743±9,797 100 10,090±310 |
| td | Creatinine* |
| td | HMDB00562 |
| td | 14,743±9,797 |
| td | 100 |
| td | 10,090±310 |
| tr | Cytosine HMDB00630 4.1 (1.1–10.7) 36 NA** |
| td | Cytosine |
| td | HMDB00630 |
| td | 4.1 (1.1–10.7) |
| td | 36 |
| td | NA** |
| tr | Dehydroascorbic acid HMDB01264 9.1 (5.8–7.1) 32 1.72–7.47 |
| td | Dehydroascorbic acid |
| td | HMDB01264 |
| td | 9.1 (5.8–7.1) |
| td | 32 |
| td | 1.72–7.47 |
| tr | D-Galactose HMDB00143 11.9 (5.4–25.2) 100 4.4 (0.0–31.0) |
| td | D-Galactose |
| td | HMDB00143 |
| td | 11.9 (5.4–25.2) |
| td | 100 |
| td | 4.4 (0.0–31.0) |
| tr | D-Glucose HMDB00122 37.5 (12.5–58.4) 100 31.12 |
| td | D-Glucose |
| td | HMDB00122 |
| td | 37.5 (12.5–58.4) |
| td | 100 |
| td | 31.12 |
| tr | Dihydrothymine HMDB00079 2.4 (1.3–4.4) 64 5 (0–10) |
| td | Dihydrothymine |
| td | HMDB00079 |
| td | 2.4 (1.3–4.4) |
| td | 64 |
| td | 5 (0–10) |
| tr | Dimethyl sulfone HMDB04983 8.0 (1.3–49.0) 100 4.6 (2.5–7.5) |
| td | Dimethyl sulfone |
| td | HMDB04983 |
| td | 8.0 (1.3–49.0) |
| td | 100 |
| td | 4.6 (2.5–7.5) |
| tr | Dimethylamine HMDB00087 30.8 (20.3–59.2) 100 39.3±8.3 |
| td | Dimethylamine |
| td | HMDB00087 |
| td | 30.8 (20.3–59.2) |
| td | 100 |
| td | 39.3±8.3 |
| tr | Dimethylglycine HMDB00092 4.4 (1.6–10.4) 100 6.2 (0.8–11.2) |
| td | Dimethylglycine |
| td | HMDB00092 |
| td | 4.4 (1.6–10.4) |
| td | 100 |
| td | 6.2 (0.8–11.2) |
| tr | D-Threitol HMDB04136 19.3 (5.3–32.7) 100 10.0 (5.6–15.0) |
| td | D-Threitol |
| td | HMDB04136 |
| td | 19.3 (5.3–32.7) |
| td | 100 |
| td | 10.0 (5.6–15.0) |
| tr | D-Xylitol HMDB02917 8.4 (6.0–13.8) 95 3.50±2.37 |
| td | D-Xylitol |
| td | HMDB02917 |
| td | 8.4 (6.0–13.8) |
| td | 95 |
| td | 3.50±2.37 |
| tr | D-Xylose HMDB00098 20 (3.0–102) 91 51.19 |
| td | D-Xylose |
| td | HMDB00098 |
| td | 20 (3.0–102) |
| td | 91 |
| td | 51.19 |
| tr | Erythritol HMDB02994 33.4 (6.8–64.0) 100 51±18 |
| td | Erythritol |
| td | HMDB02994 |
| td | 33.4 (6.8–64.0) |
| td | 100 |
| td | 51±18 |
| tr | Ethanol HMDB00108 3.1 5 5–500b |
| td | Ethanol |
| td | HMDB00108 |
| td | 3.1 |
| td | 5 |
| td | 5–500b |
| tr | Ethanolamine HMDB00149 37.0 (24.8–56.2) 100 21.4 (6.6–36.2) |
| td | Ethanolamine |
| td | HMDB00149 |
| td | 37.0 (24.8–56.2) |
| td | 100 |
| td | 21.4 (6.6–36.2) |
| tr | Ethyl glucuronide HMDB10325 8.2 5 26.38±15.26 |
| td | Ethyl glucuronide |
| td | HMDB10325 |
| td | 8.2 |
| td | 5 |
| td | 26.38±15.26 |
| tr | Ethylmalonic acid HMDB00622 3.0 (1.2–5.8) 95 2.0 (0.5–3.9) |
| td | Ethylmalonic acid |
| td | HMDB00622 |
| td | 3.0 (1.2–5.8) |
| td | 95 |
| td | 2.0 (0.5–3.9) |
| tr | Formic acid HMDB00142 26.8 (6.9–120.9) 100 20.39±11.84 |
| td | Formic acid |
| td | HMDB00142 |
| td | 26.8 (6.9–120.9) |
| td | 100 |
| td | 20.39±11.84 |
| tr | Fumaric acid HMDB00134 0.7 (0.2–1.7) 50 1 (0–2) |
| td | Fumaric acid |
| td | HMDB00134 |
| td | 0.7 (0.2–1.7) |
| td | 50 |
| td | 1 (0–2) |
| tr | Glucaric acid HMDB00663 8.4 (5.8–12.3) 82 2.9±0.2 |
| td | Glucaric acid |
| td | HMDB00663 |
| td | 8.4 (5.8–12.3) |
| td | 82 |
| td | 2.9±0.2 |
| tr | Gluconic acid HMDB00625 21.5 (8.1–38.8) 100 NA** |
| td | Gluconic acid |
| td | HMDB00625 |
| td | 21.5 (8.1–38.8) |
| td | 100 |
| td | NA** |
| tr | Glucuronic acid HMDB00127 9.7 (3.7–20.6) 77 NA |
| td | Glucuronic acid |
| td | HMDB00127 |
| td | 9.7 (3.7–20.6) |
| td | 77 |
| td | NA |
| tr | Glutaconic acid HMDB00620 3.1 (1.2–3.1) 68 1 (0–2) |
| td | Glutaconic acid |
| td | HMDB00620 |
| td | 3.1 (1.2–3.1) |
| td | 68 |
| td | 1 (0–2) |
| tr | Glutaric acid HMDB00661 2.1 (0.7–3.6) 18 1.3 (0.6–2.6) |
| td | Glutaric acid |
| td | HMDB00661 |
| td | 2.1 (0.7–3.6) |
| td | 18 |
| td | 1.3 (0.6–2.6) |
| tr | Glyceric acid HMDB00139 15.9 (7.8–31.1) 100 4.5 (0.0–9.0) |
| td | Glyceric acid |
| td | HMDB00139 |
| td | 15.9 (7.8–31.1) |
| td | 100 |
| td | 4.5 (0.0–9.0) |
| tr | Glycerol HMDB00131 13 (4–19) 100 20 (0–40) |
| td | Glycerol |
| td | HMDB00131 |
| td | 13 (4–19) |
| td | 100 |
| td | 20 (0–40) |
| tr | Glycine HMDB00123 106 (44–300) 100 151 (233–248) |
| td | Glycine |
| td | HMDB00123 |
| td | 106 (44–300) |
| td | 100 |
| td | 151 (233–248) |
| tr | Glycolic acid HMDB00115 42.0 (3.7–122.0) 100 35 (18–55) |
| td | Glycolic acid |
| td | HMDB00115 |
| td | 42.0 (3.7–122.0) |
| td | 100 |
| td | 35 (18–55) |
| tr | Guanidoacetic acid HMDB00128 41.8 (10.6–97.3) 100 89.0 (11.0–124.0) |
| td | Guanidoacetic acid |
| td | HMDB00128 |
| td | 41.8 (10.6–97.3) |
| td | 100 |
| td | 89.0 (11.0–124.0) |
| tr | Hippuric acid HMDB00714 229 (19–622) 100 257 (20–770) |
| td | Hippuric acid |
| td | HMDB00714 |
| td | 229 (19–622) |
| td | 100 |
| td | 257 (20–770) |
| tr | Homocitrulline HMDB00679 7.5 (3.8–13.8) 91 6.0 (0.0–8.8) |
| td | Homocitrulline |
| td | HMDB00679 |
| td | 7.5 (3.8–13.8) |
| td | 91 |
| td | 6.0 (0.0–8.8) |
| tr | Homogentisic acid HMDB00130 1.70 (0.55–2.8) 45 1 (0–2) |
| td | Homogentisic acid |
| td | HMDB00130 |
| td | 1.70 (0.55–2.8) |
| td | 45 |
| td | 1 (0–2) |
| tr | Homovanillic acid HMDB00118 6.2 (1.8–12.7) 100 4.9±3.4 |
| td | Homovanillic acid |
| td | HMDB00118 |
| td | 6.2 (1.8–12.7) |
| td | 100 |
| td | 4.9±3.4 |
| tr | HPHPA HMDB02643 5.9 (1.4–22.1) 100 0.55–54.93 |
| td | HPHPA |
| td | HMDB02643 |
| td | 5.9 (1.4–22.1) |
| td | 100 |
| td | 0.55–54.93 |
| tr | Hydroxyproline HMDB00725 6.5 (2.4–12.5) 55 2 |
| td | Hydroxyproline |
| td | HMDB00725 |
| td | 6.5 (2.4–12.5) |
| td | 55 |
| td | 2 |
| tr | Hydroxypropionic acid HMDB00700 12.3 (4.4–17.9) 82 6.5 (3.0–10.0) |
| td | Hydroxypropionic acid |
| td | HMDB00700 |
| td | 12.3 (4.4–17.9) |
| td | 82 |
| td | 6.5 (3.0–10.0) |
| tr | Hypoxanthine HMDB00157 7.2 (1.8–24.1) 100 4.67 (2.80–6.38) |
| td | Hypoxanthine |
| td | HMDB00157 |
| td | 7.2 (1.8–24.1) |
| td | 100 |
| td | 4.67 (2.80–6.38) |
| tr | Indoleacetic acid HMDB00197 3.4 (1.8–6.2) 100 2.0±1.0 |
| td | Indoleacetic acid |
| td | HMDB00197 |
| td | 3.4 (1.8–6.2) |
| td | 100 |
| td | 2.0±1.0 |
| tr | Indoxyl sulfate HMDB00682 22.4 (6.0–64.8) 100 19.74±5.26 |
| td | Indoxyl sulfate |
| td | HMDB00682 |
| td | 22.4 (6.0–64.8) |
| td | 100 |
| td | 19.74±5.26 |
| tr | Inosine HMDB00195 1.4 (0.4–6.5) 50 1.04±0.56 |
| td | Inosine |
| td | HMDB00195 |
| td | 1.4 (0.4–6.5) |
| td | 50 |
| td | 1.04±0.56 |
| tr | Isobutyric acid HMDB01873 3.5 (1.2–9.0) 95 3.9 |
| td | Isobutyric acid |
| td | HMDB01873 |
| td | 3.5 (1.2–9.0) |
| td | 95 |
| td | 3.9 |
| tr | Isocitric acid HMDB00193 46.9 (20.4–89.2) 68 58 (36–84) |
| td | Isocitric acid |
| td | HMDB00193 |
| td | 46.9 (20.4–89.2) |
| td | 68 |
| td | 58 (36–84) |
| tr | Isovaleric acid HMDB00718 1.4 (0.8–2.5) 86 0.42±0.02 |
| td | Isovaleric acid |
| td | HMDB00718 |
| td | 1.4 (0.8–2.5) |
| td | 86 |
| td | 0.42±0.02 |
| tr | Isovalerylglycine HMDB00678 2.0 (0.4–4.0) 91 5.5 (1.0–10.0) |
| td | Isovalerylglycine |
| td | HMDB00678 |
| td | 2.0 (0.4–4.0) |
| td | 91 |
| td | 5.5 (1.0–10.0) |
| tr | Kynurenic acid HMDB00715 2.1 (0.8–4.2) 64 1.53 (0.66–4.13) |
| td | Kynurenic acid |
| td | HMDB00715 |
| td | 2.1 (0.8–4.2) |
| td | 64 |
| td | 1.53 (0.66–4.13) |
| tr | Kynurenine HMDB00684 1.7 (1.1–2.5) 23 2.85±1.94 |
| td | Kynurenine |
| td | HMDB00684 |
| td | 1.7 (1.1–2.5) |
| td | 23 |
| td | 2.85±1.94 |
| tr | Lactose HMDB00186 11.8 (1.0–24.2) 91 36.31±6.6c |
| td | Lactose |
| td | HMDB00186 |
| td | 11.8 (1.0–24.2) |
| td | 91 |
| td | 36.31±6.6c |
| tr | L-Alanine HMDB00161 21.8 (7.1–43.0) 100 22.0±11.0 |
| td | L-Alanine |
| td | HMDB00161 |
| td | 21.8 (7.1–43.0) |
| td | 100 |
| td | 22.0±11.0 |
| tr | L-Arabinose HMDB00646 13.3 (1.3–22.0) 100 8.75 (6.12–11.84) |
| td | L-Arabinose |
| td | HMDB00646 |
| td | 13.3 (1.3–22.0) |
| td | 100 |
| td | 8.75 (6.12–11.84) |
| tr | L-Arginine HMDB00517 8.6 (3.8–18.6) 95 6.6 (2.5–10.8) |
| td | L-Arginine |
| td | HMDB00517 |
| td | 8.6 (3.8–18.6) |
| td | 95 |
| td | 6.6 (2.5–10.8) |
| tr | L-Asparagine HMDB00168 10.0 (4.6–17.8) 95 10.0 (4.6–16.3) |
| td | L-Asparagine |
| td | HMDB00168 |
| td | 10.0 (4.6–17.8) |
| td | 95 |
| td | 10.0 (4.6–16.3) |
| tr | L-Aspartic acid HMDB00191 10.9 (3.5–21.8) 95 4.63 (1.71–7.56) |
| td | L-Aspartic acid |
| td | HMDB00191 |
| td | 10.9 (3.5–21.8) |
| td | 95 |
| td | 4.63 (1.71–7.56) |
| tr | L-Carnitine HMDB00062 5.0 (0.7–16.4) 100 5.7±4.8 |
| td | L-Carnitine |
| td | HMDB00062 |
| td | 5.0 (0.7–16.4) |
| td | 100 |
| td | 5.7±4.8 |
| tr | L-Cystathionine HMDB00099 14.7 (3.9–20.7) 82 5.3 (1.0–9.6) |
| td | L-Cystathionine |
| td | HMDB00099 |
| td | 14.7 (3.9–20.7) |
| td | 82 |
| td | 5.3 (1.0–9.6) |
| tr | L-Cysteine HMDB00574 65.8 (23.1–134.5) 100 33.4±15.5 |
| td | L-Cysteine |
| td | HMDB00574 |
| td | 65.8 (23.1–134.5) |
| td | 100 |
| td | 33.4±15.5 |
| tr | L-Cystine HMDB00192 12.5 (5.0–24.6) 100 9.0±3.0 |
| td | L-Cystine |
| td | HMDB00192 |
| td | 12.5 (5.0–24.6) |
| td | 100 |
| td | 9.0±3.0 |
| tr | Levoglucosan HMDB00640 10.0 (2.4–29.0) 27 NA** |
| td | Levoglucosan |
| td | HMDB00640 |
| td | 10.0 (2.4–29.0) |
| td | 27 |
| td | NA** |
| tr | Levulinic acid HMDB00720 1.3 (0.4–2.0) 64 1.28 (0.33–2.75) |
| td | Levulinic acid |
| td | HMDB00720 |
| td | 1.3 (0.4–2.0) |
| td | 64 |
| td | 1.28 (0.33–2.75) |
| tr | L-Fucose HMDB00174 11.8 (6.7–26.0) 91 9.6 (6.4–16.0) |
| td | L-Fucose |
| td | HMDB00174 |
| td | 11.8 (6.7–26.0) |
| td | 91 |
| td | 9.6 (6.4–16.0) |
| tr | L-Glutamic acid HMDB00148 8.5 (3.3–18.4) 100 10.0±3.7 |
| td | L-Glutamic acid |
| td | HMDB00148 |
| td | 8.5 (3.3–18.4) |
| td | 100 |
| td | 10.0±3.7 |
| tr | L-Glutamine HMDB00641 37.2 (19.1–77.9) 100 33.32±10.78 |
| td | L-Glutamine |
| td | HMDB00641 |
| td | 37.2 (19.1–77.9) |
| td | 100 |
| td | 33.32±10.78 |
| tr | L-Histidine HMDB00177 43 (17–90) 100 60.75±27.45 |
| td | L-Histidine |
| td | HMDB00177 |
| td | 43 (17–90) |
| td | 100 |
| td | 60.75±27.45 |
| tr | L-Isoleucine HMDB00172 1.3 (0.4–2.6) 100 1.64 |
| td | L-Isoleucine |
| td | HMDB00172 |
| td | 1.3 (0.4–2.6) |
| td | 100 |
| td | 1.64 |
| tr | L-Lactic acid HMDB00190 11.6 (3.5–29.3) 100 12.3±6.2 |
| td | L-Lactic acid |
| td | HMDB00190 |
| td | 11.6 (3.5–29.3) |
| td | 100 |
| td | 12.3±6.2 |
| tr | L-Leucine HMDB00687 2.8 (1.6–5.4) 100 2.5±1.3 |
| td | L-Leucine |
| td | HMDB00687 |
| td | 2.8 (1.6–5.4) |
| td | 100 |
| td | 2.5±1.3 |
| tr | L-Lysine HMDB00182 17.2 (3.7–51.3) 100 18 (7–29) |
| td | L-Lysine |
| td | HMDB00182 |
| td | 17.2 (3.7–51.3) |
| td | 100 |
| td | 18 (7–29) |
| tr | L-Methionine HMDB00696 1.4 (0.5–2.5) 41 1.3±0.97 |
| td | L-Methionine |
| td | HMDB00696 |
| td | 1.4 (0.5–2.5) |
| td | 41 |
| td | 1.3±0.97 |
| tr | L-Phenylalanine HMDB00159 6.4 (3.5–11.2) 100 4.50±1.15 |
| td | L-Phenylalanine |
| td | HMDB00159 |
| td | 6.4 (3.5–11.2) |
| td | 100 |
| td | 4.50±1.15 |
| tr | L-Serine HMDB00187 25.3 (11.6–53.4) 100 26.0±10.0 |
| td | L-Serine |
| td | HMDB00187 |
| td | 25.3 (11.6–53.4) |
| td | 100 |
| td | 26.0±10.0 |
| tr | L-Threonine HMDB00167 13.3 (6.4–25.2) 100 5.17–24.59 |
| td | L-Threonine |
| td | HMDB00167 |
| td | 13.3 (6.4–25.2) |
| td | 100 |
| td | 5.17–24.59 |
| tr | L-Tryptophan HMDB00929 6.3 (3.4–11.1) 100 2.45–29.40 |
| td | L-Tryptophan |
| td | HMDB00929 |
| td | 6.3 (3.4–11.1) |
| td | 100 |
| td | 2.45–29.40 |
| tr | L-Tyrosine HMDB00158 9.5 (4.1–23.5) 100 8.8±4.5 |
| td | L-Tyrosine |
| td | HMDB00158 |
| td | 9.5 (4.1–23.5) |
| td | 100 |
| td | 8.8±4.5 |
| tr | L-Valine HMDB00883 3.4 (2.0–7.7) 100 3.0±1.0 |
| td | L-Valine |
| td | HMDB00883 |
| td | 3.4 (2.0–7.7) |
| td | 100 |
| td | 3.0±1.0 |
| tr | Maleic acid HMDB00176 0.4 (0.3–0.5) 14 0.5 |
| td | Maleic acid |
| td | HMDB00176 |
| td | 0.4 (0.3–0.5) |
| td | 14 |
| td | 0.5 |
| tr | Malonic acid HMDB00691 2.9 (2.0–3.5) 77 1 (0–2) |
| td | Malonic acid |
| td | HMDB00691 |
| td | 2.9 (2.0–3.5) |
| td | 77 |
| td | 1 (0–2) |
| tr | Maltose HMDB00163 6.0 (1.3–21.4) 73 NA** |
| td | Maltose |
| td | HMDB00163 |
| td | 6.0 (1.3–21.4) |
| td | 73 |
| td | NA** |
| tr | Mandelic acid HMDB00703 1.4 (1.1–1.7) 9 0.966±0.669 |
| td | Mandelic acid |
| td | HMDB00703 |
| td | 1.4 (1.1–1.7) |
| td | 9 |
| td | 0.966±0.669 |
| tr | Mannitol HMDB00765 32.4 (5.2–85.1) 95 10.26±9.14 |
| td | Mannitol |
| td | HMDB00765 |
| td | 32.4 (5.2–85.1) |
| td | 95 |
| td | 10.26±9.14 |
| tr | Methanol HMDB01875 37 (10–117) 100 NA** |
| td | Methanol |
| td | HMDB01875 |
| td | 37 (10–117) |
| td | 100 |
| td | NA** |
| tr | Methylamine HMDB00164 4.0 (1.5–11.9) 100 10.5 (9.8–12.5) |
| td | Methylamine |
| td | HMDB00164 |
| td | 4.0 (1.5–11.9) |
| td | 100 |
| td | 10.5 (9.8–12.5) |
| tr | Methylglutaric acid HMDB00752 3.1 (2.6–3.6) 9 3.5 (0.0–7.0) |
| td | Methylglutaric acid |
| td | HMDB00752 |
| td | 3.1 (2.6–3.6) |
| td | 9 |
| td | 3.5 (0.0–7.0) |
| tr | Methylguanidine HMDB01522 2.7 (1.2–6.0) 95 1.25±0.72 |
| td | Methylguanidine |
| td | HMDB01522 |
| td | 2.7 (1.2–6.0) |
| td | 95 |
| td | 1.25±0.72 |
| tr | Methylmalonic acid HMDB00202 1.9 (0.7–3.5) 95 1.6 (0.0–3.6) |
| td | Methylmalonic acid |
| td | HMDB00202 |
| td | 1.9 (0.7–3.5) |
| td | 95 |
| td | 1.6 (0.0–3.6) |
| tr | Methylsuccinic acid HMDB01844 3.2 (0.7–9.9) 100 2.3 (0.8–10.8) |
| td | Methylsuccinic acid |
| td | HMDB01844 |
| td | 3.2 (0.7–9.9) |
| td | 100 |
| td | 2.3 (0.8–10.8) |
| tr | Monomethyl glutaric acid HMDB00858 2.3 (0.6–4.4) 68 NA |
| td | Monomethyl glutaric acid |
| td | HMDB00858 |
| td | 2.3 (0.6–4.4) |
| td | 68 |
| td | NA |
| tr | m-Thymol HMDB01878 2.2 (0.7–4.0) 86 NA** |
| td | m-Thymol |
| td | HMDB01878 |
| td | 2.2 (0.7–4.0) |
| td | 86 |
| td | NA** |
| tr | Myoinositol HMDB00211 22.4 (7.9–36.1) 100 18.8±2.9 |
| td | Myoinositol |
| td | HMDB00211 |
| td | 22.4 (7.9–36.1) |
| td | 100 |
| td | 18.8±2.9 |
| tr | N-Acetylaspartic acid HMDB00812 4.0 (1.3–7.0) 100 4.66±1.14 |
| td | N-Acetylaspartic acid |
| td | HMDB00812 |
| td | 4.0 (1.3–7.0) |
| td | 100 |
| td | 4.66±1.14 |
| tr | N-Acetylneuraminic acid HMDB00230 5.4 (2.5–8.6) 100 8.60±1.58 |
| td | N-Acetylneuraminic acid |
| td | HMDB00230 |
| td | 5.4 (2.5–8.6) |
| td | 100 |
| td | 8.60±1.58 |
| tr | N-Acetylputrescine HMDB02064 0.5 (0.4–0.7) 32 1.18 (0.58–2.25) |
| td | N-Acetylputrescine |
| td | HMDB02064 |
| td | 0.5 (0.4–0.7) |
| td | 32 |
| td | 1.18 (0.58–2.25) |
| tr | N-Methylhydantoin HMDB03646 4.9 (1.0–11.0) 55 NA |
| td | N-Methylhydantoin |
| td | HMDB03646 |
| td | 4.9 (1.0–11.0) |
| td | 55 |
| td | NA |
| tr | o-Hydroxyphenylacetic acid HMDB00669 2.0 (0.9–4.5) 91 7.71±3.65d |
| td | o-Hydroxyphenylacetic acid |
| td | HMDB00669 |
| td | 2.0 (0.9–4.5) |
| td | 91 |
| td | 7.71±3.65d |
| tr | Oxoglutaric acid HMDB00208 4.8 (2.0–17.0) 100 2.98 (0.42–18.30) |
| td | Oxoglutaric acid |
| td | HMDB00208 |
| td | 4.8 (2.0–17.0) |
| td | 100 |
| td | 2.98 (0.42–18.30) |
| tr | Oxypurinol HMDB00786 13.3 (5.1–29.3) 50 3.29 |
| td | Oxypurinol |
| td | HMDB00786 |
| td | 13.3 (5.1–29.3) |
| td | 50 |
| td | 3.29 |
| tr | Pantothenic acid HMDB00210 1.9 (0.6–4.4) 100 2.7±0.9 |
| td | Pantothenic acid |
| td | HMDB00210 |
| td | 1.9 (0.6–4.4) |
| td | 100 |
| td | 2.7±0.9 |
| tr | p-Cresol sulfate HMDB11635 1.3 (0.3–5.5) 91 NA** |
| td | p-Cresol sulfate |
| td | HMDB11635 |
| td | 1.3 (0.3–5.5) |
| td | 91 |
| td | NA** |
| tr | Phenol HMDB00228 4.8 (0.6–12.8) 95 7.0 (5.6–9.2) |
| td | Phenol |
| td | HMDB00228 |
| td | 4.8 (0.6–12.8) |
| td | 95 |
| td | 7.0 (5.6–9.2) |
| tr | Phenylacetic acid HMDB00209 1.9 5 4.16±0.91 |
| td | Phenylacetic acid |
| td | HMDB00209 |
| td | 1.9 |
| td | 5 |
| td | 4.16±0.91 |
| tr | Phenylacetylglutamine HMDB06344 34.0 (4.5–70.0) 100 47.03 (3.84–85.51) |
| td | Phenylacetylglutamine |
| td | HMDB06344 |
| td | 34.0 (4.5–70.0) |
| td | 100 |
| td | 47.03 (3.84–85.51) |
| tr | Phenylglyoxylic acid HMDB01587 7.34 5 36.6±0.48e |
| td | Phenylglyoxylic acid |
| td | HMDB01587 |
| td | 7.34 |
| td | 5 |
| td | 36.6±0.48e |
| tr | Phosphorylcholine HMDB01565 1.1 (0.7–3.0) 41 NA |
| td | Phosphorylcholine |
| td | HMDB01565 |
| td | 1.1 (0.7–3.0) |
| td | 41 |
| td | NA |
| tr | p-Hydroxyphenylacetic acid HMDB00020 5.5 (1.4–14.6) 100 9.68 |
| td | p-Hydroxyphenylacetic acid |
| td | HMDB00020 |
| td | 5.5 (1.4–14.6) |
| td | 100 |
| td | 9.68 |
| tr | Picolinic acid HMDB02243 19.4 (7.2–37.6) 18 1.65–10.28 |
| td | Picolinic acid |
| td | HMDB02243 |
| td | 19.4 (7.2–37.6) |
| td | 18 |
| td | 1.65–10.28 |
| tr | Pimelic acid HMDB00857 2.2 (0.7–4.0) 77 NA** |
| td | Pimelic acid |
| td | HMDB00857 |
| td | 2.2 (0.7–4.0) |
| td | 77 |
| td | NA** |
| tr | Proline-betaine HMDB04827 2.3 (0.8–4.0) 100 12.7 (1.6–23.7) |
| td | Proline-betaine |
| td | HMDB04827 |
| td | 2.3 (0.8–4.0) |
| td | 100 |
| td | 12.7 (1.6–23.7) |
| tr | Propylene glycol HMDB01881 6.7 (1.4–44.3) 100 NA** |
| td | Propylene glycol |
| td | HMDB01881 |
| td | 6.7 (1.4–44.3) |
| td | 100 |
| td | NA** |
| tr | Pseudouridine HMDB00767 28.9 (13.3–41.3) 100 26.02±4.62 |
| td | Pseudouridine |
| td | HMDB00767 |
| td | 28.9 (13.3–41.3) |
| td | 100 |
| td | 26.02±4.62 |
| tr | Pyrocatechol HMDB00957 4.7 (2.0–8.5) 100 6.0±4.3 |
| td | Pyrocatechol |
| td | HMDB00957 |
| td | 4.7 (2.0–8.5) |
| td | 100 |
| td | 6.0±4.3 |
| tr | Pyroglutamic acid HMDB00267 20.7 (10.2–32.6) 100 28.8 (3.4–54.2) |
| td | Pyroglutamic acid |
| td | HMDB00267 |
| td | 20.7 (10.2–32.6) |
| td | 100 |
| td | 28.8 (3.4–54.2) |
| tr | Pyruvic acid HMDB00243 2.7 (0.8–6.4) 100 2.13 (0.54–8.67) |
| td | Pyruvic acid |
| td | HMDB00243 |
| td | 2.7 (0.8–6.4) |
| td | 100 |
| td | 2.13 (0.54–8.67) |
| tr | Quinolinic acid HMDB00232 5.2 (1.0–17.5) 100 2.5±1.1 |
| td | Quinolinic acid |
| td | HMDB00232 |
| td | 5.2 (1.0–17.5) |
| td | 100 |
| td | 2.5±1.1 |
| tr | Sarcosine HMDB00271 2.9 (0.5–5.4) 77 2.8 (0.0–5.6) |
| td | Sarcosine |
| td | HMDB00271 |
| td | 2.9 (0.5–5.4) |
| td | 77 |
| td | 2.8 (0.0–5.6) |
| tr | Scyllitol HMDB06088 4.2 (2.2–8.1) 68 NA |
| td | Scyllitol |
| td | HMDB06088 |
| td | 4.2 (2.2–8.1) |
| td | 68 |
| td | NA |
| tr | Sebacic acid HMDB00792 1.9 5 1 (0–2) |
| td | Sebacic acid |
| td | HMDB00792 |
| td | 1.9 |
| td | 5 |
| td | 1 (0–2) |
| tr | Sorbitol HMDB00247 9.9 (2.5–18.7) 100 3.50±2.24 |
| td | Sorbitol |
| td | HMDB00247 |
| td | 9.9 (2.5–18.7) |
| td | 100 |
| td | 3.50±2.24 |
| tr | Suberic acid HMDB00893 1.6 (0.9–2.4) 32 0.5 (0.0–2.9) |
| td | Suberic acid |
| td | HMDB00893 |
| td | 1.6 (0.9–2.4) |
| td | 32 |
| td | 0.5 (0.0–2.9) |
| tr | Succinic acid HMDB00254 6.0 (0.3–33.3) 91 5.6±3.8 |
| td | Succinic acid |
| td | HMDB00254 |
| td | 6.0 (0.3–33.3) |
| td | 91 |
| td | 5.6±3.8 |
| tr | Succinylacetone HMDB00635 2.8 (0.6–4.7) 36 1 (0–2) |
| td | Succinylacetone |
| td | HMDB00635 |
| td | 2.8 (0.6–4.7) |
| td | 36 |
| td | 1 (0–2) |
| tr | Sucrose HMDB00258 7.4 (1.4–19.5) 100 7.12±3.23 |
| td | Sucrose |
| td | HMDB00258 |
| td | 7.4 (1.4–19.5) |
| td | 100 |
| td | 7.12±3.23 |
| tr | Sumiki’s acid HMDB02432 1.7 5 NA** |
| td | Sumiki’s acid |
| td | HMDB02432 |
| td | 1.7 |
| td | 5 |
| td | NA** |
| tr | Symmetric dimethylarginine HMDB03334 2.7 (1.5–5.2) 100 3.72±0.35 |
| td | Symmetric dimethylarginine |
| td | HMDB03334 |
| td | 2.7 (1.5–5.2) |
| td | 100 |
| td | 3.72±0.35 |
| tr | Syringic acid HMDB02085 1.5 5 0.9±0.2 |
| td | Syringic acid |
| td | HMDB02085 |
| td | 1.5 |
| td | 5 |
| td | 0.9±0.2 |
| tr | Tartaric acid HMDB00956 11.8 (2.6–64.4) 82 12.0 (1.3–46.0) |
| td | Tartaric acid |
| td | HMDB00956 |
| td | 11.8 (2.6–64.4) |
| td | 82 |
| td | 12.0 (1.3–46.0) |
| tr | Taurine HMDB00251 81 (13–251) 100 4.00–159.98 |
| td | Taurine |
| td | HMDB00251 |
| td | 81 (13–251) |
| td | 100 |
| td | 4.00–159.98 |
| tr | Threonic acid HMDB00943 20.8 (10.2–39.3) 100 10.0±7.0 |
| td | Threonic acid |
| td | HMDB00943 |
| td | 20.8 (10.2–39.3) |
| td | 100 |
| td | 10.0±7.0 |
| tr | Thymidine HMDB00273 2.2 (0.7–3.9) 36 6.87±4.86 |
| td | Thymidine |
| td | HMDB00273 |
| td | 2.2 (0.7–3.9) |
| td | 36 |
| td | 6.87±4.86 |
| tr | Trans-Aconitic acid HMDB00958 6.6 (1.8–25.1) 55 3.38 |
| td | Trans-Aconitic acid |
| td | HMDB00958 |
| td | 6.6 (1.8–25.1) |
| td | 55 |
| td | 3.38 |
| tr | Trans-Ferulic acid HMDB00954 1.2 5 36.3±11.2f |
| td | Trans-Ferulic acid |
| td | HMDB00954 |
| td | 1.2 |
| td | 5 |
| td | 36.3±11.2f |
| tr | Trigonelline HMDB00875 31.1 (5.5–109.3) 100 16.08 |
| td | Trigonelline |
| td | HMDB00875 |
| td | 31.1 (5.5–109.3) |
| td | 100 |
| td | 16.08 |
| tr | Trimethylamine HMDB00906 2.5 (0.3–19.4) 86 7.7±7.4 |
| td | Trimethylamine |
| td | HMDB00906 |
| td | 2.5 (0.3–19.4) |
| td | 86 |
| td | 7.7±7.4 |
| tr | Trimethylamine N-oxide HMDB00925 91.0 (4.8–509.0) 100 118.7 (35.5–202.1) |
| td | Trimethylamine N-oxide |
| td | HMDB00925 |
| td | 91.0 (4.8–509.0) |
| td | 100 |
| td | 118.7 (35.5–202.1) |
| tr | Uracil HMDB00300 9.5 (2.6–22.8) 100 12 (2–22) |
| td | Uracil |
| td | HMDB00300 |
| td | 9.5 (2.6–22.8) |
| td | 100 |
| td | 12 (2–22) |
| tr | Urea HMDB00294 12,285 (174–49,097) 100 22,566±4,407 |
| td | Urea |
| td | HMDB00294 |
| td | 12,285 (174–49,097) |
| td | 100 |
| td | 22,566±4,407 |
| tr | Vanillic acid HMDB00484 1.6 (0.6–3.3) 95 1.0 (0.0–2.5) |
| td | Vanillic acid |
| td | HMDB00484 |
| td | 1.6 (0.6–3.3) |
| td | 95 |
| td | 1.0 (0.0–2.5) |
| tr | Vanillylmandelic acid HMDB00291 2.3 (1.0–3.4) 9 1.2±0.5 |
| td | Vanillylmandelic acid |
| td | HMDB00291 |
| td | 2.3 (1.0–3.4) |
| td | 9 |
| td | 1.2±0.5 |
| table-wrap-foot | * Concentration of metabolite (Mean ± SD) is expressed by µM. a The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman; b depends greatly on amount of alcohol consumed; c after administration of lactose; d after administration of 1 g of coumarin; e After styrene exposure; and f after consumption of chocolate. NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. |
| footnote | * Concentration of metabolite (Mean ± SD) is expressed by µM. |
| label | * |
| p | Concentration of metabolite (Mean ± SD) is expressed by µM. |
| footnote | a The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman; |
| label | a |
| p | The mean daily vitamin B6 intake was well below the recommended dietary allowance for men and woman; |
| footnote | b depends greatly on amount of alcohol consumed; |
| label | b |
| p | depends greatly on amount of alcohol consumed; |
| footnote | c after administration of lactose; |
| label | c |
| p | after administration of lactose; |
| footnote | d after administration of 1 g of coumarin; |
| label | d |
| p | after administration of 1 g of coumarin; |
| footnote | e After styrene exposure; and |
| label | e |
| p | After styrene exposure; and |
| footnote | f after consumption of chocolate. |
| label | f |
| p | after consumption of chocolate. |
| footnote | NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. |
| p | NA: not available; NA**: not available for healthy adults; ADP: Adenosine-5-diphosphate; HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; Oc: occurrence. |
| p | Based on these NMR studies, the most abundant constituents (based on average values) of urine from a healthy individual are urea (12.3 ± 14.5 mM/mM creatinine), creatinine (14.7 ± 9.8 mM), hippuric acid (229 ± 160 µM/mM creatinine) and citric acid (203 ± 129 µM/mM creatinine). The least abundant compounds were trans-ferulic acid (1.2 µM/mM creatinine), phosphorylcholine (1.1 ± 0.7 µM/mM creatinine), 4-ethylphenol (0.9 ± 0.1 µM/mM creatinine), 2-methylglutaric acid (0.8 ± 0.4 µM/mM creatinine). The lowest concentrations that could be reliably detected using NMR were 0.4 ± 0.1 µM/mM creatinine (for maleic acid), 0.5 ± 0.2 µM/mM creatinine (for alpha-ketoisovaleric acid and N-acetylputrescine) and 0.7 ± 0.4 µM/mM creatinine (for fumaric acid). The complete list of average compound concentrations (including their range), literature-reported concentrations (for healthy adult) and the frequency of their occurrence is shown in Table 3. In general, there is good agreement between the NMR-measured concentrations and those reported in the literature (often obtained by other analytical techniques). |
| p | However, not all of the NMR-derived urine concentrations agree with literature-derived values. A total of 34 compounds had average concentrations somewhat higher (>1 SD) than previously reported values (for example, 1, 3-dimethyluric acid, glucaric acid, L-aspartic acid, mannitol), while 21 compounds had average concentrations lower (<1 SD) than previously reported (for example, 1-methylhistidine, phenol, dihydrothymine, thymidine). Another 7 metabolites also exhibited somewhat greater range in concentrations than those previously reported in the literature. These included: acetic acid, butyric acid, isovaleric acid, lactose, phenylglyoxylic acid, proline-betaine and trans-ferulic acid. Some of these discrepancies are likely due to differences in diet, physiological status, pharmacological effects and the age of the different cohorts that were analyzed. Other differences may be due to storage effects, sample preparation methods and the analytical methods being used. As a rule, NMR concentration determinations are very accurate since they involve direct measurement of the compound, as opposed to an indirect measurement of a derivative compound. Therefore we are quite confident in the NMR concentration values reported in Table 3 and would tend to view these as more reliable than those measured via other technologies. |
| p | A number of the compounds exhibiting higher-than or lower-than-reported concentrations appear to be associated with dietary intake. For example, mannitol is a sugar alcohol that is poorly absorbed by humans but its presence in urine can be explained by its occurrence in commonly consumed foods such as apples, pineapples, asparagus and carrots. Likewise, the urinary excretion of trans-ferulic acid (a polyphenolic derivative) increases after the ingestion of breakfast cereals [61] and chocolate. The relatively low value we measured is likely due to the fact that the literature value of trans-ferulic acid reported in Table 3 was measured for people on a special diet [62]. Also, the low level of proline-betaine we measured in urine may be due to a lower frequency of exposure to dietary citrus fruits in our population sample [63]. Proline-betaine is an osmoprotectant found in citrus fruit and urinary excretion of this metabolite is increased after consumption of fruits such as orange juice [64]. Similarly the higher levels of dimethyl sulfone we detected in urine could be attributed to dietary sources that contain DMSO [65]. For example, onions contain many sulfoxides including DMSO which can be oxidized in the liver and kidneys to produce dimethyl sulfone [66], [67]. The consumption of meat could also significantly increase the concentration of some metabolites in urine as reported in the case of 1-methylhistidine [68]. 1-methylhistidine is produced from the metabolism of anserine (a dipeptide) which is commonly found in meats [69], [70]. In addition to diet, metabolite levels in urine can also be affected by physiological status. For instance, the level of 3-hydroxybutyric acid in urine increases during fasting and can range from 0 to 200 µM/mM creatinine, with the maximum level reported in the literature (200 µM/mM creatinine) corresponding to healthy male after 35 h of fasting [50]. |
| p | Some of the metabolites we measured by NMR did not have any previously reported literature values. For example, glucuronic acid is usually reported as total glucuronic acid (the free acid plus glucuronide conjugates) after hydrolysis [71], [72]. Here we report the concentration of free glucuronic acid, as indicated in Table 3. Another example, 2-methylerythritol, was previously detected in human urine but no concentration was reported [73]. The urinary excretion of 2-methylerythritol is most likely a result of dietary consumption of fruits or vegetables containing 2-methylerythritol and/or 2-methylerythritol-4-phosphate. 2-methylerythritol-4-phosphate is an intermediate in isoprenoid biosynthesis [74] and has been found to be quite abundant in certain plants [75]. |
| p | A number of compounds we measured by NMR appear to be normal constituents of human urine but seem not to have been previously reported as being detectable by NMR (a total of 42 compounds) or reported as detected but not-quantified by any other method (a total of 8 compounds). The identification of these “NMR-novel” compounds was aided by their prior identification by GC-MS and DI-MS (see following sections) and through a careful literature analysis of compounds that had previously been detected in human urine via other methods. The list of detected but not previously quantified by NMR compounds includes: 2-hydroxy-3-methylpentanoic acid, 2-methyl-3-ketovaleric acid, 2-methylerythritol, glucuronic acid, monomethyl glutaric acid, N-methylhydantoin, phosphorylcholine and scyllitol. All of these compounds were confirmed using authentic standards. |
| p | This NMR study also revealed a number of common identification errors made in previously published NMR-based human urine metabolomic studies. In particular, several earlier reports identified phenylacetylglycine [76], N-acetylglutamic acid [77], cresol [78], isonicotinic acid [78], yellow 7.1 [79], meta-hydroxyphenylpropionic acid [80], 2-oxoisocaproic acid [81], urocanic acid, glycylproline and ornithine [80] as being detectable by NMR in human urine. Using our NMR instrument and the samples available to us, we were unable to detect any of these compounds, even after performing multiple spike-in experiments using authentic compounds. While some of these metabolites have been previously reported to be in human urine, they were reported at concentrations far below the lower limit of detection of modern NMR instruments (which is ∼ 1 µM). Due to their chemical shift similarity, phenylacetylglycine (which is found only in rats and mice) and N-acetylglutamic acid appear to be commonly mistaken for phenylacetylglutamine. We also noticed that, isonicotinic acid (a breakdown product of isoniazid and hydrazine derivatives, which is found only in individuals that have taken isoniazid and other hydrazine derivatives as a drug) appears to be mistaken for trigonelline. Likewise cresol (water-insoluble) appears to be frequently mistaken for cresol-sulfate (water-soluble), while the compounds yellow 7.1, meta-hydroxyphenylpropionic acid and 3-(p-hydroxyphenyl)-propionic acid appear to be commonly mistaken for 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA). |
| p | In addition to correcting these compound identification errors, we also observed some significant gender-related effects on creatinine levels in our urine samples. Since males generally have a greater mass of skeletal muscle than females, they tend to have higher urinary levels of creatinine than women. This was clearly evident in our samples as the average male creatinine level was 20 mM while the average female creatinine level was 11 mM. In addition, increased dietary intake of creatine or a protein-rich diet can increase daily creatinine excretion [82]. |
| sec | Quantification and Identification of Urine Metabolites – GC-MS As seen in Table 1, GC-MS methods have long been used to comprehensively characterize the chemical content of human urine. For our studies a total of 4 different GC-MS analyses were performed. The first method employed polar solvent extraction and derivatization to achieve broad metabolite coverage of polar metabolites, the second was more selective and targeted organic acids, the third targeted volatiles, while the fourth targeted bile acids. Representative high-resolution GC-MS total ion chromatograms are shown in Figures 2–4 for each of these analyses (except for the bile acids). Combined, the 4 GC-MS methods allowed us to identify 179 and quantify a total of 85 compounds. Table 4 shows the identified polar, organic acid extracts and bile acids (127 in total), Table 5 shows the identified volatile metabolites (52 in total) while Table 6 shows the 85 fully quantified compounds from all 4 techniques. These numbers actually represent the highest number of urine metabolites both identified and quantified by GC-MS to date. As seen in Table 1, previous GC-MS studies have reported up to 258 unique compounds being identified (but none quantified) [83] and approximately 95 compounds quantified in human urine [84]. Relative to NMR (see previous section) and other methods used to analyze human urine (Table 1), it appears that a multi-pronged GC-MS analysis is an excellent approach to characterize this biofluid. However, unlike NMR where nearly all detectable peaks are identifiable, metabolite coverage by GC-MS tends to be relatively incomplete. As seen in Figure 2, only 60% of the peaks could be identified using as reference the 2008 NIST library and other home-made GC-MS metabolite libraries. Likewise, in Figure 3, we see that only 65% of the organic acid peaks could be identified while in Figure 4, just 60% of the volatile compound peaks could be identified. Figure 2 Typical total ion chromatogram of water-soluble metabolites extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: L-valine; 2: oxalic acid; 3∶3-hydroxyisobutyric acid; 4: L-serine; 5: glycine; 6: succinic acid; 7: glyceric acid; 8∶4-deoxythreonic acid; 9∶2,4-dihydroxybutanoic acid; 10∶3,4-dihydroxybutanoic acid; 11: adipic acid; 12: creatinine; 13: threonic acid; 14: L-phenylalanine; 15: p-hydroxyphenylacetic acid; 16: L-ornithine; 17: L-asparagine; 18: L-arabinose; 19: D-xylitol; 20: D-xylulose; 21: Cis/Trans-aconitic acid; 22: hippuric acid; 23: isocitric acid; 24: D-galactose; 25: D-glucose; 26: L- tyrosine; 27: sorbitol; 28: gluconic acid; 29: scyllitol; 30: myoinositol; 31: uric acid; 32: pseudouridine. Figure 3 Typical GC-MS total ion chromatogram of organic acids extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: pyruvic acid; 2: L-lactic acid; 3: alpha-hydroxyisobutyric acid; 4: glycolic acid; 5: levulinic acid; 6∶3-hydroxyisovaleric acid; 7∶2-hydroxy-2-methylbutyric acid; 8: hydroxypropionic acid; 9∶2-methyl-3-hydroxybutyric acid; 10: malonic acid; 11∶3-hydroxyisovaleric acid; 12: methylmalonic acid; 13∶2-ethylhydracrylic acid; 14: benzoic acid; 15: phosphoric acid; 16: ethylmalonic acid; 17: succinic acid; 18: methylsuccinic acid; 19∶4-deoxythreonic acid; 20∶5-hydroxyhexanoic acid; 21: citraconic acid; 22: glutaric acid; 23: m-chlorobenzoic acid; 24; 3,4-dihydroxybutanoic acid; 25∶3-methylglutaconic acid; 26: adipic acid; 27: pyroglutamic acid; 28∶3-methyladipic acid; 29: sumiki’s acid; 30: o-hydroxyphenylacetic acid; 31: oxoglutaric acid; 32: pimelic acid; 33∶3-hydroxymethylglutaric acid; 34∶3-hydroxyphenylacetic acid; 35∶4-hydroxybenzoic acid; 36∶2-furoylglycine; 37: suberic acid; 38: quinolinic acid; 39: Cis/Trans-aconitic acid; 40: homovanillic acid; 41: azelaic acid; 42: hippuric acid; 43∶3,4-dihydroxybenzeneacetic acid; 44∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 45: vanillylmandelic acid; 46∶4-hydroxyphenyllactic acid; 47: indoleacetic acid; 48: palmitic acid; 49: kynurenic acid; 50∶3-hydroxyhippuric acid; 51∶3-hydroxysebacic acid; 52: Trans-ferulic acid; 53∶5-hydroxyindoleacetic acid; 54: stearic acid. Figure 4 Typical gas chromatogram of volatile organic compounds (VOC) from a pooled human urine sample. Numbers indicate the following metabolites: 1∶3-methyl sulfolane; 2∶3-hexanone; 3∶2-pentanone; 4∶1-hydroxy-2-pentanone; 5: allyl methylsulphide; 6: dimethyl disulfide; 7∶4-heptanone; 8∶1-methylcyclohexanol; 9; 2-hexanone; 10∶3,4-dimethylthiophene; 11: diallyl sulphide; 12∶5-methyl-2-hexanone; 13∶1,3-dithio cyclohexane; 14: dimethyl trisulfide; 15: phenol; 16: o-cymene; 17: p-cymene; 18: m-cymene; 19∶1,4-cineol; 20: p-cresol; 21: linalool oxide; 22: iso-menthol; 23: Alpha-p-dimethylstyrene; 24: L-menthol; 25: undecane; 26: ledene oxide (II); 27: salicylic acid methyl ester; 28: Beta-carvone; 29: piperitone; 30: o-thymol; 31: Beta-cyclocitrol; 32∶4,7-dimethyl-benzofuran; 33: cuminal: 34∶2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene; 35∶4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone; 36∶1,2,3,4-tetrahydro-1,1,6-trimethyl naphthalene; 37: Alpha-cedrene; 38∶1,2,3,4-tetrahydro-1,5,7-trimethylnapthalene; 39∶1,2-diydro-1,1,6-trimethyl-napthalene; 40: Beta-guaiene; 41: Beta-damascenone; 42∶2,5-cyclohexadiene-1,4-dione-2,6-di-tert-butyl; 43: himachalene; 44∶4-(2,6,6-trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one: 45∶1-(2,6,6-trimethyl-1-cyclohexen-1-yl)-1-penten-3-one; 46∶2,4-Bis (1,1-dimethylethyl)-phenol; 47∶1-(2,3,6-trimethyl phenyl)-3-buten-2-one; 48: L-calamenene; 49: Beta-vatirenene; 50∶1,6,7-trimethylnaphthalene; 51: azulol; 52∶3,3,5,6-tetramethyl-1-indanone. Table 4 List of metabolites identified in human urine extracts by GC-MS (except volatile compounds). Amino acids HMDB ID Organic acids HMDB ID Beta-Alaninea HMDB00056 Azelaic acidb HMDB00784 Glycinea HMDB00123 Benzoic acidb HMDB01870 L-Alaninea HMDB00161 Butyric acida HMDB00039 L-Asparaginea HMDB00168 Cis-Aconitic acida,b HMDB00072 L-Aspartic acida HMDB00191 Citraconic acidb HMDB00634 L-Glutaminea HMDB00641 Citramalic acidb HMDB00426 L-Histidinea HMDB00177 Citric acida HMDB00094 L-Isoleucinea HMDB00172 Erythronic acida HMDB00613 L-Leucinea HMDB00687 Ethylmalonic acidb HMDB00622 L-Lysinea HMDB00182 Gluconic acida HMDB00625 L-Ornithinea HMDB00214 Glutaric acidb HMDB00661 L-Phenylalaninea HMDB00159 Glyceric acida HMDB00139 L-Serinea HMDB00187 Glycolic acida,b HMDB00115 L-Threoninea HMDB00167 Glyoxalic acida HMDB00119 L-Tryptophana HMDB00929 Hippuric acida,b HMDB00714 L-Tyrosinea HMDB00158 Homovanillic acidb HMDB00118 L-Valinea HMDB00883 HPHPAb HMDB02643 Bile acids HMDB ID Hydroxypropionic acidb HMDB00700 Cholenic acidc HMDB00308 Indoleacetic acidb HMDB00197 Deoxycholic acidc HMDB00626 Indolelactic acid b HMDB00671 Lithocholic acidc HMDB00761 Isobutyrylglycineb HMDB00730 Ursodeoxycholic acidc HMDB00946 Isocitric acida HMDB00193 Misc HMDB ID Isovalerylglycineb HMDB00678 3-Methylhistidinea HMDB00479 Kynurenic acidb HMDB00715 5-Hydroxyindoleb HMDB59805 Levulinic acidb HMDB00720 Creatininea HMDB00562 L-Lactic acidb HMDB00190 Dimethylglycinea HMDB00092 Malic acida HMDB00744 D-Xylitola HMDB02917 Malonic acida,b HMDB00691 Glycerol 3-phosphatea HMDB00126 m-Chlorobenzoic acidb HMDB01544 Glycerola HMDB00131 m-Coumaric acidb HMDB01713 Mannitola HMDB00765 Methylmalonic acidb HMDB00202 Myoinositola HMDB00211 Methylsuccinic acidb HMDB01844 Pseudouridinea HMDB00767 o-Hydroxyhippuric acidb HMDB00840 Scyllitola HMDB06088 o-Hydroxyphenylacetic acidb HMDB00669 Sorbitola HMDB00247 Oleic acidb HMDB00207 Ureaa HMDB00294 Oxalic acida HMDB02329 Organic acids HMDB ID Oxoglutaric acidb HMDB00208 2,4-Dihydroxybutanoic acida HMDB00360 Palmitic acidb HMDB00220 2-Ethylhydracrylic acidb HMDB00396 Pantothenic acidb HMDB00210 2-Furoylglycineb HMDB00439 Phosphoric acidb HMDB02142 2-Hydroxy-2-methylbutyric acidb HMDB01987 p-Hydroxyphenylacetic acida HMDB00020 2-Hydroxyglutaric acida HMDB00694 Pimelic acidb HMDB00857 2-Hydroxyvaleric acida HMDB01863 Pyroglutamic acida,b HMDB00267 2-Ketobutyric acidb HMDB00005 Pyruvic acidb HMDB00243 2-Methyl-3-hydroxybutyric acidb HMDB00354 Quinolinic acidb HMDB00232 3,4-Dihydroxybenzeneacetic acidb HMDB01336 Stearic acidb HMDB00827 3,4-Dihydroxybutanoic acida,b HMDB00337 Suberic acidb HMDB00893 3,4-Dihydroxyhydrocinnamic acidb HMDB00423 Succinic acida,b HMDB00254 3-Aminoisobutanoic acida,b HMDB03911 Sumiki’s acidb HMDB02432 3-Hydroxyhippuric acidb HMDB06116 Tartaric acida HMDB00956 3-Hydroxyisobutyric acida HMDB00023 Threonic acida HMDB00943 3-Hydroxyisovaleric acida,b HMDB00754 Trans-Aconitic acida HMDB00958 3-Hydroxymethylglutaric acidb HMDB00355 Trans-Ferulic acidb HMDB00954 3-Hydroxyphenylacetic acidb HMDB00440 Uric acida HMDB00289 3-Hydroxysebacic acidb HMDB00350 Vanillyllactic acidb HMDB00913 3-Methyladipic acidb HMDB00555 Vanillylmandelic acidb HMDB00291 3-Methylglutaconic acidb HMDB00522 Sugars HMDB ID 4-Deoxythreonic acida,b HMDB02453 D-Fructosea HMDB00660 4-Hydroxybenzoic acidb HMDB00500 D-Galactosea HMDB00143 4-Hydroxyphenyllactic acidb HMDB00755 D-Glucosea HMDB00122 5-Hydroxyhexanoic acidb HMDB00525 D-Xylosea HMDB00098 5-Hydroxyindoleacetic acidb HMDB00763 D-Xylulosea HMDB01644 Adipic acida,b HMDB00448 L-Arabinosea HMDB00646 Alpha-Hydroxyisobutyric acidb HMDB00729 L-Fucosea HMDB00174 Aminomalonic acida HMDB01147 Sucrosea HMDB00258 Ascorbic acida HMDB00044 a Polar metabolites. b Targeted organic acids. c Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid. Table 5 Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS. Peak # Compound HMDB ID Uniquemass MSmatch RT(min) RI(Calc) RI(NIST) 1 3-Methyl sulfolane HMDB59667 68 867 0.83 725 654 2 3-Hexanone HMDB00753 43 781 0.99 732 788 3 2-Pentanone HMDB34235 43 894 1.61 758 654 4 1-Hydroxy-2-pentanone HMDB59678 43 780 1.74 763 897 5 Allyl methylsulphide HMDB31653 88 793 1.83 767 701 6 Dimethyl disulfide HMDB05879 94 940 2.26 785 763 7 4-Heptanone HMDB04814 71 919 3.34 831 883 8 1-Methylcyclohexanol HMDB59693 71 821 3.81 850 931 9 2-Hexanone HMDB05842 43 821 3.82 851 889 10 3,4-Dimethylthiophene HMDB29304 111 906 4.15 865 888 11 Diallyl sulphide HMDB36491 45 827 4.86 895 848 12 5-Methyl-2-hexanone HMDB31549 43 816 5.29 913 836 13 1,3-Dithio cyclohexane HMDB31473 120 841 5.85 936 1027 14 Dimethyl trisulfide HMDB13780 126 873 6.01 943 950 15 Phenol HMDB00228 94 859 6.34 957 998 16 o-Cymene HMDB37050 119 928 7.19 993 1014 17 p-Cymene HMDB05805 119 928 7.33 998 1018 18 m-Cymene HMDB13806 119 928 7.43 1009 1022 19 1,4-Cineol HMDB31455 111 912 8.71 1057 1016 20 p-Cresol HMDB01858 107 930 8.99 1068 1082 21 Linalool oxide HMDB31440 59 783 10.49 1131 1074 22 Iso-menthol HMDB35764 57 923 10.99 1152 1141 23 Alpha-p-dimethylstyrene HMDB29641 132 915 11 1153 1088 24 L-Menthol HMDB03352 71 924 11.18 1160 1140 25 Undecane HMDB31445 57 910 11.35 1168 1100 26 Ledene Oxide (II) HMDB60295 43 743 11.54 1176 1201 27 Salicylic acid methyl ester HMDB34172 120 857 11.65 1180 1193 28 Beta-Carvone HMDB35824 82 859 12.81 1229 1247 29 Piperitone HMDB34975 150 873 12.96 1235 1184 30 o-Thymol HMDB35770 135 904 13.9 1275 1305 31 Beta-Cyclocitrol HMDB41011 41 884 14.1 1283 1219 32 4,7-Dimethyl-benzofuran HMDB59897 146 861 14.4 1296 1244 33 Cuminal HMDB02214 133 861 14.82 1313 1235 34 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene HMDB36823 138 911 15.91 1359 1370 35 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone HMDB32913 121 795 15.97 1362 1433 36 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene HMDB59826 159 855 16.02 1364 1349 37 Alpha-Cedrene HMDB59695 119 821 16.1 1367 1415 38 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene HMDB59696 159 795 16.59 1388 1310 39 1,2-Diydro-1,1,6-trimethyl-napthalene HMDB40284 157 947 16.82 1398 1354 40 Beta-Guaiene HMDB38157 161 879 17 1405 1500 41 Beta-Damascenone HMDB13804 69 889 17.35 1420 1365 42 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl HMDB13817 177 843 18.51 1469 1462 43 Himachalene HMDB38168 119 823 18.62 1473 1489 44 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one HMDB37139 136 883 18.73 1478 1497 45 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one HMDB38130 121 788 19.05 1491 1507 46 2,4-Bis (1,1-Dimethylethyl)-phenol HMDB13186 191 863 19.5 1510 1519 47 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one HMDB59697 132 918 19.8 1523 1528 48 L-Calamenene HMDB59910 159 920 20.25 1533 1534 49 Beta-Vatirenene HMDB59676 202 806 20.45 1550 1545 50 1,6,7-Trimethylnaphthalene HMDB59701 170 899 20.82 1564 1559 51 Azulol HMDB59686 183 823 21.18 1581 1572 52 3,3,5,6-Tetramethyl-1-indanone HMDB59683 173 686 21.28 1585 1579 Table 6 Concentrations of metabolites in human urine as measured by GC-MS. Compounds Observed Concentration(µM/mM creatinine) Literature Values(µM/mM creatinine) 2,4-Dihydroxybutanoic acid 1.0 (0.3–1.8) 2.0±2.0 2-Ethylhydracrylic acid 2.1 (1.3–2.9) NA** 2-Furoylglycine 3.6 (0.7–5.8) 9.95 (2.0–18.66) 2-Hydroxy-2-methylbutyric acid 0.8 (0.4–1.5) 2.8±1.5 2-Methyl-3-hydroxybutyric acid 4.0 (1.3–6.2) 5.5 (0.0–11.0) 3,4-Dihydroxybenzeneacetic acid 0.9 (0.6–1.3) 1.16±0.50 3,4-Dihydroxybutanoic acid 42.2 (21.9–56.1) 34.9 (12.5–57.2) 3-Aminoisobutanoic acid 25.8 (1.4–155.6) 2.91–116.43 3-Hydroxyhippuric acid 6.1 (4.1–7.8) 2.5 (0.2–5.0) 3-Hydroxyisobutyric acid 29.0 (11.8–59.8) 17.5 (2.0–33.0) 3-Hydroxyisovaleric acid 7.4 (4.1–17.2) 8.5±3.2 3-Hydroxymethylglutaric acid 3.2 (1.1–5.2) 3 (0–10) 3-Hydroxyphenylacetic acid 5.6 (1.4–15.0) 6.1±4.1 3-Hydroxysebacic acid 2.9 (1.7–3.8) NA** 3-Methyladipic acid 2.1 (0.9–5.6) 2.9 (0.7–10.5) 3-Methylglutaconic acid 6.2 (2.8–8.3) 4.5 (0.0–9.0) 4-Deoxythreonic acid 18.2 (5.6–23.9) 25±10 4-Hydroxybenzoic acid 2.4 (1.3–3.6) 1.8±1.8 4-Hydroxyphenyllactic acid 2.8 (2.6–3.0) 1.1 (0.2–2.6) 5-Hydroxyhexanoic acid 2.7 (0.8–5.7) 3.5 (0.0–7.0) 5-Hydroxyindoleacetic acid 2.9 (0.4–5.8) 2.4±0.40 Adipic acid 3.0 (1.0–12.9) 5.1 (0.8–35.0) Alpha-Hydroxyisobutyric acid 2.9 (1.3–4.6) 3.8 Azelaic acid 1.9 (1.2–3.2) 4.8 (1.3–15.0) Benzoic acid 3.6 (1.4–7.5) 4.2 (1.9–6.5) Cholenic acid 0.015 (0.004–0.030) NA** Cis/Trans-Aconitic acid 15.3 (5.2–21.8) NA** Citraconic acid 1.6 (0.9–2.1) 2.2 Creatinine* 12,246±8,369 10,090±310 Deoxycholic acid 0.055 (0.050–0.060) NA** D-Galactose 3.3 (1.3–8.5) 4.4 (0.0–31.0) D-Glucose 35.6 (10.3–56.7) 31.12 D-Xylitol 2.3 (0.9–8.2) 3.50±2. 37 D-Xylulose 19.7 (6.4–32.6) NA** Ethylmalonic acid 2.4 (0.9–4.0) 2.0 (0.5–3.9) Gluconic acid 13.3 (8.2–26.4) NA** Glutaric acid 1.4 (0.5–2.3 ) 1.3 (0.6–2.6) Glyceric acid 14.4 (7.8–28.8) 4.5 (0.0–9.0) Glycine 75.7 (21–205) 151 (233–248) Glycolic acid 39.4 (2.9–78.1) 35 (18–55) Hippuric acid 217 (28–610) 257 (20–770) Homovanillic acid 4.3 (0.9–8.9) 4.9±3.4 HPHPA 4.2 (0.9–15.3) 0.55–54.93 Hydroxypropionic acid 8.1 (3.1–11.8) 6.5 (3.0–10.0) Indoleacetic acid 2.6 (0.6–5.4) 2.0±1.0 Indolelactic acid 0.9 0.460 (0.098–0.980) Isocitric acid 56.8 (19.4–119.1) 58 (36–84) Kynurenic acid 2.6 (0.9–4.1) 1.34±0.30 L-Arabinose 8.8 (0.8–19.4) 8.75 (6.12–11.84) L-Asparagine 9.5 (3.0–26.0) 10.0 (4.6–16.3) Levulinic acid 1.7 (0.6–2.5) 1.28 (0.33–2.75) Lithocholic acid 0.16 (0.10–0.20) NA** L-Lactic acid 7.1 (0.9–16.4) 12.3±6.2 L-Ornithine 5.3 (2.0–8.8) 5.0 (2.0–8.0) L-Phenylalanine 7.8 (5.0–11.3) 4.50±1.15 L-Serine 19.0 (10.4–35.8) 26.0±10.0 L-Tyrosine 15.1 (6.1–23.2) 8.8±4.5 L-Valine 5.5 (2.7–9.8) 3.0±1.0 Malonic acid 2.3 (1.2–3.1) 1 (0–2) m-Chlorobenzoic acid 0.7 NA** Methylmalonic acid 1.3 (0.5–2.1) 1.6 (0.0–3.6) Methylsuccinic acid 4.7 (1.1–6.2) 2.3 (0.8–10.8) Myoinositol 12.6 (5.1–15.3) 18.8±2.9 o-Hydroxyphenylacetic acid 2.9 (1.4–3.7) 7.71±3.65 Oxalic acid 8.2 (3.9–14.0) 1.11–33.34 Oxoglutaric acid 3.4 (1.4–12.5) 2.98 (0.42–18.30) Palmitic acid 6.7 (2.6–24.3) 11 (6–23) Phosphoric acid 784 (425–1170) 1000–4900 p-Hydroxyphenylacetic acid 6.0 (2.4–9.7) 9.68 Pimelic acid 1.9 (0.5–3.5) NA** Pseudouridine 29.8 (21.8–47.3) 26.02±4.62 Pyroglutamic acid 18.6 (4.5–24.9) 28.8 (3.4–54.2) Pyruvic acid 2.1 (1.0–3.7) 2.13 (0.54–8.67) Quinolinic acid 3.9 (0.9–15.1) 2.5±1.1 Scyllitol 3.6 (1.9–6.9) NA Sorbitol 3.9 (1.9–5.1) 3.50±2.24 Stearic acid 3.9 (2.3–7.7) 2.9 (1.6–6.6) Suberic acid 1.2 (0.5–1.9) 0.5 (0.0–2.9) Succinic acid 6.2 (2.5–13.5) 5.6±3.8 Sumiki’s acid 1.6 NA** Threonic acid 14.8 (8.6–36.1) 10.0±7.0 Trans-Ferulic acid 1 36.3±11.2 Uric acid 186 (93–329) 188 (79–296) Ursodeoxycholic acid 0.022 (0.013–0.030) NA** Vanillylmandelic acid 1.6 (0.6–2.8) 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. Incomplete compound identification is a common problem with global or untargeted GC-MS metabolomics. This may be due to any number of factors including spectral overlap due to incomplete separation, poor signal to noise for low intensity peaks, the lack of reference GC-MS spectral data for certain metabolites (especially unusual dietary sources), or the presence of spectral artefacts such as derivatization by-products or degraded metabolites in the GC-MS spectrum. For our GC-MS studies we used the NIST library supplemented with a home-made GC-MS reference library of known urine compounds assembled from the Human Metabolome Library [16]. No doubt the use of other commercially available reference GC-MS libraries such as the Fiehn GC-MS library from Agilent or the GOLM metabolome database library [85] might have allowed us to further increase our coverage. Likewise the use of a faster scan rate and/or a more sensitive GC-TOF instrument (instead of a slower scanning quadrupole GC-MS) certainly would have increased overall coverage. Nearly all of the non-volatile metabolites (87) identified by our GC-MS analyses were also identified by NMR. Some of the exceptions were oxalic acid, phosphate and uric acid, each of which was detected by GC-MS but not by NMR. These compounds do not have NMR-detectable protons at physiological pH, making them essentially “NMR invisible”. Other compounds seen by GC-MS but not by NMR included metabolites that were generally below the detection limit of NMR (∼2 µM/mM creatinine) such as indolelactic acid and 2,4-dihydroxybutanoic acid. For our non-targeted GC-MS analysis, the lower limit of detection was 1 µM/mM creatinine (for 2,4-dihydroxybutanoic acid), while for our targeted organic acid GC-MS analysis the lower limit of detection was 0.7 µM/mM creatinine (for m-chlorobenzoic acid). Overall, our data suggests that the sensitivity of a standard single quadrupole GC-MS instrument is perhaps 1.5–2X better than a 500 MHz NMR instrument for water-soluble metabolites. It is also important to note that the level of water-soluble, non-volatile metabolite coverage obtained by GC-MS is not as great as seen with NMR (127 cmpds vs. 209 cmpds). The limited coverage of GC-MS is partly due to the fact that not all compounds can be readily extracted, easily derivatized or routinely separated on a GC column. Furthermore, when analyzing urine by GC-MS there is a need to pretreat the sample with urease (to reduce urea levels) that can diminish the abundance of some metabolites [86]. While GC-MS may not be the best method for analyzing water-soluble metabolites, it certainly excels at the detection of volatile metabolites. Indeed, only one of the volatile metabolites identified by GC-MS is identified by NMR (phenol). This certainly underlines a key strength of GC-MS relative to other metabolomics platforms. When comparing NMR to GC-MS we found that NMR is capable of detecting 121 compounds that the 4 combined GC-MS methods cannot detect while the combined GC-MS methods can detect 91 compounds that NMR cannot routinely detect. Overall, these data suggest that GC-MS and NMR appear to be complementary methods for the identification and quantification of small molecules in urine. Among the 58 metabolites quantified by both GC-MS and NMR we found very good overall agreement, with the majority of measured concentration values falling within 20 ± 11% of each other. The concentration patterns and rankings of the most abundant to the least abundant compounds were also largely identical for the two platforms. A total of 12 metabolites exhibited somewhat larger concentration discrepancies between GC-MS and NMR (i.e; L-arabinose, L-serine (lower in GC-MS vs. NMR), 4-hydroxybenzoic acid and tyrosine (higher in GC-MS vs NMR). Some of these concentration differences may be due to the extraction or derivatization process needed to conduct GC-MS analyses. This can lead to unspecified compound losses, unusual derivatives or unrecognized fragmentation patterns. Therefore we would have expected at least a few GC-MS concentration values to be slightly lower than those seen by NMR. Likewise, it is important to remember that there are inherent errors (5–10%) in measuring peak areas (i.e. compound concentrations) both in GC-MS and NMR due to peak overlap, uneven baselines and spectral noise. Nearly all of the compounds we detected or quantified in human urine by GC-MS have been previously described or mentioned in the GC-MS literature. One compound (scyllitol), however, appears not to have been previously detected by GC-MS. The identification of this compound by our GC-MS method was aided by its prior identification by NMR (see previous section). Additionally, a careful literature analysis also indicated the scyllitol is a normal constituent of human urine and has previously been detected in human urine via other methods. As we noted with our NMR studies earlier, there are a few previously reported GC-MS detectable metabolites in human urine that appear to be artefacts. These artefactual metabolites may arise from extractions with different solvents, pre-treatment with urease, and chemical derivatization. For example, Shoemaker et al [84], reported the presence of bisethane in human urine. We also detected bisethane, but it appears to be artefact of chemical derivatization and is not a urine metabolite. |
| title | Quantification and Identification of Urine Metabolites – GC-MS |
| p | As seen in Table 1, GC-MS methods have long been used to comprehensively characterize the chemical content of human urine. For our studies a total of 4 different GC-MS analyses were performed. The first method employed polar solvent extraction and derivatization to achieve broad metabolite coverage of polar metabolites, the second was more selective and targeted organic acids, the third targeted volatiles, while the fourth targeted bile acids. Representative high-resolution GC-MS total ion chromatograms are shown in Figures 2–4 for each of these analyses (except for the bile acids). Combined, the 4 GC-MS methods allowed us to identify 179 and quantify a total of 85 compounds. Table 4 shows the identified polar, organic acid extracts and bile acids (127 in total), Table 5 shows the identified volatile metabolites (52 in total) while Table 6 shows the 85 fully quantified compounds from all 4 techniques. These numbers actually represent the highest number of urine metabolites both identified and quantified by GC-MS to date. As seen in Table 1, previous GC-MS studies have reported up to 258 unique compounds being identified (but none quantified) [83] and approximately 95 compounds quantified in human urine [84]. Relative to NMR (see previous section) and other methods used to analyze human urine (Table 1), it appears that a multi-pronged GC-MS analysis is an excellent approach to characterize this biofluid. However, unlike NMR where nearly all detectable peaks are identifiable, metabolite coverage by GC-MS tends to be relatively incomplete. As seen in Figure 2, only 60% of the peaks could be identified using as reference the 2008 NIST library and other home-made GC-MS metabolite libraries. Likewise, in Figure 3, we see that only 65% of the organic acid peaks could be identified while in Figure 4, just 60% of the volatile compound peaks could be identified. |
| figure | Figure 2 Typical total ion chromatogram of water-soluble metabolites extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: L-valine; 2: oxalic acid; 3∶3-hydroxyisobutyric acid; 4: L-serine; 5: glycine; 6: succinic acid; 7: glyceric acid; 8∶4-deoxythreonic acid; 9∶2,4-dihydroxybutanoic acid; 10∶3,4-dihydroxybutanoic acid; 11: adipic acid; 12: creatinine; 13: threonic acid; 14: L-phenylalanine; 15: p-hydroxyphenylacetic acid; 16: L-ornithine; 17: L-asparagine; 18: L-arabinose; 19: D-xylitol; 20: D-xylulose; 21: Cis/Trans-aconitic acid; 22: hippuric acid; 23: isocitric acid; 24: D-galactose; 25: D-glucose; 26: L- tyrosine; 27: sorbitol; 28: gluconic acid; 29: scyllitol; 30: myoinositol; 31: uric acid; 32: pseudouridine. |
| label | Figure 2 |
| caption | Typical total ion chromatogram of water-soluble metabolites extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: L-valine; 2: oxalic acid; 3∶3-hydroxyisobutyric acid; 4: L-serine; 5: glycine; 6: succinic acid; 7: glyceric acid; 8∶4-deoxythreonic acid; 9∶2,4-dihydroxybutanoic acid; 10∶3,4-dihydroxybutanoic acid; 11: adipic acid; 12: creatinine; 13: threonic acid; 14: L-phenylalanine; 15: p-hydroxyphenylacetic acid; 16: L-ornithine; 17: L-asparagine; 18: L-arabinose; 19: D-xylitol; 20: D-xylulose; 21: Cis/Trans-aconitic acid; 22: hippuric acid; 23: isocitric acid; 24: D-galactose; 25: D-glucose; 26: L- tyrosine; 27: sorbitol; 28: gluconic acid; 29: scyllitol; 30: myoinositol; 31: uric acid; 32: pseudouridine. |
| title | Typical total ion chromatogram of water-soluble metabolites extracted from human urine. |
| p | Numbers indicate the following metabolites (quantified compounds): 1: L-valine; 2: oxalic acid; 3∶3-hydroxyisobutyric acid; 4: L-serine; 5: glycine; 6: succinic acid; 7: glyceric acid; 8∶4-deoxythreonic acid; 9∶2,4-dihydroxybutanoic acid; 10∶3,4-dihydroxybutanoic acid; 11: adipic acid; 12: creatinine; 13: threonic acid; 14: L-phenylalanine; 15: p-hydroxyphenylacetic acid; 16: L-ornithine; 17: L-asparagine; 18: L-arabinose; 19: D-xylitol; 20: D-xylulose; 21: Cis/Trans-aconitic acid; 22: hippuric acid; 23: isocitric acid; 24: D-galactose; 25: D-glucose; 26: L- tyrosine; 27: sorbitol; 28: gluconic acid; 29: scyllitol; 30: myoinositol; 31: uric acid; 32: pseudouridine. |
| figure | Figure 3 Typical GC-MS total ion chromatogram of organic acids extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: pyruvic acid; 2: L-lactic acid; 3: alpha-hydroxyisobutyric acid; 4: glycolic acid; 5: levulinic acid; 6∶3-hydroxyisovaleric acid; 7∶2-hydroxy-2-methylbutyric acid; 8: hydroxypropionic acid; 9∶2-methyl-3-hydroxybutyric acid; 10: malonic acid; 11∶3-hydroxyisovaleric acid; 12: methylmalonic acid; 13∶2-ethylhydracrylic acid; 14: benzoic acid; 15: phosphoric acid; 16: ethylmalonic acid; 17: succinic acid; 18: methylsuccinic acid; 19∶4-deoxythreonic acid; 20∶5-hydroxyhexanoic acid; 21: citraconic acid; 22: glutaric acid; 23: m-chlorobenzoic acid; 24; 3,4-dihydroxybutanoic acid; 25∶3-methylglutaconic acid; 26: adipic acid; 27: pyroglutamic acid; 28∶3-methyladipic acid; 29: sumiki’s acid; 30: o-hydroxyphenylacetic acid; 31: oxoglutaric acid; 32: pimelic acid; 33∶3-hydroxymethylglutaric acid; 34∶3-hydroxyphenylacetic acid; 35∶4-hydroxybenzoic acid; 36∶2-furoylglycine; 37: suberic acid; 38: quinolinic acid; 39: Cis/Trans-aconitic acid; 40: homovanillic acid; 41: azelaic acid; 42: hippuric acid; 43∶3,4-dihydroxybenzeneacetic acid; 44∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 45: vanillylmandelic acid; 46∶4-hydroxyphenyllactic acid; 47: indoleacetic acid; 48: palmitic acid; 49: kynurenic acid; 50∶3-hydroxyhippuric acid; 51∶3-hydroxysebacic acid; 52: Trans-ferulic acid; 53∶5-hydroxyindoleacetic acid; 54: stearic acid. |
| label | Figure 3 |
| caption | Typical GC-MS total ion chromatogram of organic acids extracted from human urine. Numbers indicate the following metabolites (quantified compounds): 1: pyruvic acid; 2: L-lactic acid; 3: alpha-hydroxyisobutyric acid; 4: glycolic acid; 5: levulinic acid; 6∶3-hydroxyisovaleric acid; 7∶2-hydroxy-2-methylbutyric acid; 8: hydroxypropionic acid; 9∶2-methyl-3-hydroxybutyric acid; 10: malonic acid; 11∶3-hydroxyisovaleric acid; 12: methylmalonic acid; 13∶2-ethylhydracrylic acid; 14: benzoic acid; 15: phosphoric acid; 16: ethylmalonic acid; 17: succinic acid; 18: methylsuccinic acid; 19∶4-deoxythreonic acid; 20∶5-hydroxyhexanoic acid; 21: citraconic acid; 22: glutaric acid; 23: m-chlorobenzoic acid; 24; 3,4-dihydroxybutanoic acid; 25∶3-methylglutaconic acid; 26: adipic acid; 27: pyroglutamic acid; 28∶3-methyladipic acid; 29: sumiki’s acid; 30: o-hydroxyphenylacetic acid; 31: oxoglutaric acid; 32: pimelic acid; 33∶3-hydroxymethylglutaric acid; 34∶3-hydroxyphenylacetic acid; 35∶4-hydroxybenzoic acid; 36∶2-furoylglycine; 37: suberic acid; 38: quinolinic acid; 39: Cis/Trans-aconitic acid; 40: homovanillic acid; 41: azelaic acid; 42: hippuric acid; 43∶3,4-dihydroxybenzeneacetic acid; 44∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 45: vanillylmandelic acid; 46∶4-hydroxyphenyllactic acid; 47: indoleacetic acid; 48: palmitic acid; 49: kynurenic acid; 50∶3-hydroxyhippuric acid; 51∶3-hydroxysebacic acid; 52: Trans-ferulic acid; 53∶5-hydroxyindoleacetic acid; 54: stearic acid. |
| title | Typical GC-MS total ion chromatogram of organic acids extracted from human urine. |
| p | Numbers indicate the following metabolites (quantified compounds): 1: pyruvic acid; 2: L-lactic acid; 3: alpha-hydroxyisobutyric acid; 4: glycolic acid; 5: levulinic acid; 6∶3-hydroxyisovaleric acid; 7∶2-hydroxy-2-methylbutyric acid; 8: hydroxypropionic acid; 9∶2-methyl-3-hydroxybutyric acid; 10: malonic acid; 11∶3-hydroxyisovaleric acid; 12: methylmalonic acid; 13∶2-ethylhydracrylic acid; 14: benzoic acid; 15: phosphoric acid; 16: ethylmalonic acid; 17: succinic acid; 18: methylsuccinic acid; 19∶4-deoxythreonic acid; 20∶5-hydroxyhexanoic acid; 21: citraconic acid; 22: glutaric acid; 23: m-chlorobenzoic acid; 24; 3,4-dihydroxybutanoic acid; 25∶3-methylglutaconic acid; 26: adipic acid; 27: pyroglutamic acid; 28∶3-methyladipic acid; 29: sumiki’s acid; 30: o-hydroxyphenylacetic acid; 31: oxoglutaric acid; 32: pimelic acid; 33∶3-hydroxymethylglutaric acid; 34∶3-hydroxyphenylacetic acid; 35∶4-hydroxybenzoic acid; 36∶2-furoylglycine; 37: suberic acid; 38: quinolinic acid; 39: Cis/Trans-aconitic acid; 40: homovanillic acid; 41: azelaic acid; 42: hippuric acid; 43∶3,4-dihydroxybenzeneacetic acid; 44∶3-(3-hydroxyphenyl)-3-hydroxypropanoic acid (HPHPA); 45: vanillylmandelic acid; 46∶4-hydroxyphenyllactic acid; 47: indoleacetic acid; 48: palmitic acid; 49: kynurenic acid; 50∶3-hydroxyhippuric acid; 51∶3-hydroxysebacic acid; 52: Trans-ferulic acid; 53∶5-hydroxyindoleacetic acid; 54: stearic acid. |
| figure | Figure 4 Typical gas chromatogram of volatile organic compounds (VOC) from a pooled human urine sample. Numbers indicate the following metabolites: 1∶3-methyl sulfolane; 2∶3-hexanone; 3∶2-pentanone; 4∶1-hydroxy-2-pentanone; 5: allyl methylsulphide; 6: dimethyl disulfide; 7∶4-heptanone; 8∶1-methylcyclohexanol; 9; 2-hexanone; 10∶3,4-dimethylthiophene; 11: diallyl sulphide; 12∶5-methyl-2-hexanone; 13∶1,3-dithio cyclohexane; 14: dimethyl trisulfide; 15: phenol; 16: o-cymene; 17: p-cymene; 18: m-cymene; 19∶1,4-cineol; 20: p-cresol; 21: linalool oxide; 22: iso-menthol; 23: Alpha-p-dimethylstyrene; 24: L-menthol; 25: undecane; 26: ledene oxide (II); 27: salicylic acid methyl ester; 28: Beta-carvone; 29: piperitone; 30: o-thymol; 31: Beta-cyclocitrol; 32∶4,7-dimethyl-benzofuran; 33: cuminal: 34∶2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene; 35∶4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone; 36∶1,2,3,4-tetrahydro-1,1,6-trimethyl naphthalene; 37: Alpha-cedrene; 38∶1,2,3,4-tetrahydro-1,5,7-trimethylnapthalene; 39∶1,2-diydro-1,1,6-trimethyl-napthalene; 40: Beta-guaiene; 41: Beta-damascenone; 42∶2,5-cyclohexadiene-1,4-dione-2,6-di-tert-butyl; 43: himachalene; 44∶4-(2,6,6-trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one: 45∶1-(2,6,6-trimethyl-1-cyclohexen-1-yl)-1-penten-3-one; 46∶2,4-Bis (1,1-dimethylethyl)-phenol; 47∶1-(2,3,6-trimethyl phenyl)-3-buten-2-one; 48: L-calamenene; 49: Beta-vatirenene; 50∶1,6,7-trimethylnaphthalene; 51: azulol; 52∶3,3,5,6-tetramethyl-1-indanone. |
| label | Figure 4 |
| caption | Typical gas chromatogram of volatile organic compounds (VOC) from a pooled human urine sample. Numbers indicate the following metabolites: 1∶3-methyl sulfolane; 2∶3-hexanone; 3∶2-pentanone; 4∶1-hydroxy-2-pentanone; 5: allyl methylsulphide; 6: dimethyl disulfide; 7∶4-heptanone; 8∶1-methylcyclohexanol; 9; 2-hexanone; 10∶3,4-dimethylthiophene; 11: diallyl sulphide; 12∶5-methyl-2-hexanone; 13∶1,3-dithio cyclohexane; 14: dimethyl trisulfide; 15: phenol; 16: o-cymene; 17: p-cymene; 18: m-cymene; 19∶1,4-cineol; 20: p-cresol; 21: linalool oxide; 22: iso-menthol; 23: Alpha-p-dimethylstyrene; 24: L-menthol; 25: undecane; 26: ledene oxide (II); 27: salicylic acid methyl ester; 28: Beta-carvone; 29: piperitone; 30: o-thymol; 31: Beta-cyclocitrol; 32∶4,7-dimethyl-benzofuran; 33: cuminal: 34∶2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene; 35∶4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone; 36∶1,2,3,4-tetrahydro-1,1,6-trimethyl naphthalene; 37: Alpha-cedrene; 38∶1,2,3,4-tetrahydro-1,5,7-trimethylnapthalene; 39∶1,2-diydro-1,1,6-trimethyl-napthalene; 40: Beta-guaiene; 41: Beta-damascenone; 42∶2,5-cyclohexadiene-1,4-dione-2,6-di-tert-butyl; 43: himachalene; 44∶4-(2,6,6-trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one: 45∶1-(2,6,6-trimethyl-1-cyclohexen-1-yl)-1-penten-3-one; 46∶2,4-Bis (1,1-dimethylethyl)-phenol; 47∶1-(2,3,6-trimethyl phenyl)-3-buten-2-one; 48: L-calamenene; 49: Beta-vatirenene; 50∶1,6,7-trimethylnaphthalene; 51: azulol; 52∶3,3,5,6-tetramethyl-1-indanone. |
| title | Typical gas chromatogram of volatile organic compounds (VOC) from a pooled human urine sample. |
| p | Numbers indicate the following metabolites: 1∶3-methyl sulfolane; 2∶3-hexanone; 3∶2-pentanone; 4∶1-hydroxy-2-pentanone; 5: allyl methylsulphide; 6: dimethyl disulfide; 7∶4-heptanone; 8∶1-methylcyclohexanol; 9; 2-hexanone; 10∶3,4-dimethylthiophene; 11: diallyl sulphide; 12∶5-methyl-2-hexanone; 13∶1,3-dithio cyclohexane; 14: dimethyl trisulfide; 15: phenol; 16: o-cymene; 17: p-cymene; 18: m-cymene; 19∶1,4-cineol; 20: p-cresol; 21: linalool oxide; 22: iso-menthol; 23: Alpha-p-dimethylstyrene; 24: L-menthol; 25: undecane; 26: ledene oxide (II); 27: salicylic acid methyl ester; 28: Beta-carvone; 29: piperitone; 30: o-thymol; 31: Beta-cyclocitrol; 32∶4,7-dimethyl-benzofuran; 33: cuminal: 34∶2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene; 35∶4-(2,6,6-trimethyl-1-cyclohexen-1-yl)-2-butanone; 36∶1,2,3,4-tetrahydro-1,1,6-trimethyl naphthalene; 37: Alpha-cedrene; 38∶1,2,3,4-tetrahydro-1,5,7-trimethylnapthalene; 39∶1,2-diydro-1,1,6-trimethyl-napthalene; 40: Beta-guaiene; 41: Beta-damascenone; 42∶2,5-cyclohexadiene-1,4-dione-2,6-di-tert-butyl; 43: himachalene; 44∶4-(2,6,6-trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one: 45∶1-(2,6,6-trimethyl-1-cyclohexen-1-yl)-1-penten-3-one; 46∶2,4-Bis (1,1-dimethylethyl)-phenol; 47∶1-(2,3,6-trimethyl phenyl)-3-buten-2-one; 48: L-calamenene; 49: Beta-vatirenene; 50∶1,6,7-trimethylnaphthalene; 51: azulol; 52∶3,3,5,6-tetramethyl-1-indanone. |
| table-wrap | Table 4 List of metabolites identified in human urine extracts by GC-MS (except volatile compounds). Amino acids HMDB ID Organic acids HMDB ID Beta-Alaninea HMDB00056 Azelaic acidb HMDB00784 Glycinea HMDB00123 Benzoic acidb HMDB01870 L-Alaninea HMDB00161 Butyric acida HMDB00039 L-Asparaginea HMDB00168 Cis-Aconitic acida,b HMDB00072 L-Aspartic acida HMDB00191 Citraconic acidb HMDB00634 L-Glutaminea HMDB00641 Citramalic acidb HMDB00426 L-Histidinea HMDB00177 Citric acida HMDB00094 L-Isoleucinea HMDB00172 Erythronic acida HMDB00613 L-Leucinea HMDB00687 Ethylmalonic acidb HMDB00622 L-Lysinea HMDB00182 Gluconic acida HMDB00625 L-Ornithinea HMDB00214 Glutaric acidb HMDB00661 L-Phenylalaninea HMDB00159 Glyceric acida HMDB00139 L-Serinea HMDB00187 Glycolic acida,b HMDB00115 L-Threoninea HMDB00167 Glyoxalic acida HMDB00119 L-Tryptophana HMDB00929 Hippuric acida,b HMDB00714 L-Tyrosinea HMDB00158 Homovanillic acidb HMDB00118 L-Valinea HMDB00883 HPHPAb HMDB02643 Bile acids HMDB ID Hydroxypropionic acidb HMDB00700 Cholenic acidc HMDB00308 Indoleacetic acidb HMDB00197 Deoxycholic acidc HMDB00626 Indolelactic acid b HMDB00671 Lithocholic acidc HMDB00761 Isobutyrylglycineb HMDB00730 Ursodeoxycholic acidc HMDB00946 Isocitric acida HMDB00193 Misc HMDB ID Isovalerylglycineb HMDB00678 3-Methylhistidinea HMDB00479 Kynurenic acidb HMDB00715 5-Hydroxyindoleb HMDB59805 Levulinic acidb HMDB00720 Creatininea HMDB00562 L-Lactic acidb HMDB00190 Dimethylglycinea HMDB00092 Malic acida HMDB00744 D-Xylitola HMDB02917 Malonic acida,b HMDB00691 Glycerol 3-phosphatea HMDB00126 m-Chlorobenzoic acidb HMDB01544 Glycerola HMDB00131 m-Coumaric acidb HMDB01713 Mannitola HMDB00765 Methylmalonic acidb HMDB00202 Myoinositola HMDB00211 Methylsuccinic acidb HMDB01844 Pseudouridinea HMDB00767 o-Hydroxyhippuric acidb HMDB00840 Scyllitola HMDB06088 o-Hydroxyphenylacetic acidb HMDB00669 Sorbitola HMDB00247 Oleic acidb HMDB00207 Ureaa HMDB00294 Oxalic acida HMDB02329 Organic acids HMDB ID Oxoglutaric acidb HMDB00208 2,4-Dihydroxybutanoic acida HMDB00360 Palmitic acidb HMDB00220 2-Ethylhydracrylic acidb HMDB00396 Pantothenic acidb HMDB00210 2-Furoylglycineb HMDB00439 Phosphoric acidb HMDB02142 2-Hydroxy-2-methylbutyric acidb HMDB01987 p-Hydroxyphenylacetic acida HMDB00020 2-Hydroxyglutaric acida HMDB00694 Pimelic acidb HMDB00857 2-Hydroxyvaleric acida HMDB01863 Pyroglutamic acida,b HMDB00267 2-Ketobutyric acidb HMDB00005 Pyruvic acidb HMDB00243 2-Methyl-3-hydroxybutyric acidb HMDB00354 Quinolinic acidb HMDB00232 3,4-Dihydroxybenzeneacetic acidb HMDB01336 Stearic acidb HMDB00827 3,4-Dihydroxybutanoic acida,b HMDB00337 Suberic acidb HMDB00893 3,4-Dihydroxyhydrocinnamic acidb HMDB00423 Succinic acida,b HMDB00254 3-Aminoisobutanoic acida,b HMDB03911 Sumiki’s acidb HMDB02432 3-Hydroxyhippuric acidb HMDB06116 Tartaric acida HMDB00956 3-Hydroxyisobutyric acida HMDB00023 Threonic acida HMDB00943 3-Hydroxyisovaleric acida,b HMDB00754 Trans-Aconitic acida HMDB00958 3-Hydroxymethylglutaric acidb HMDB00355 Trans-Ferulic acidb HMDB00954 3-Hydroxyphenylacetic acidb HMDB00440 Uric acida HMDB00289 3-Hydroxysebacic acidb HMDB00350 Vanillyllactic acidb HMDB00913 3-Methyladipic acidb HMDB00555 Vanillylmandelic acidb HMDB00291 3-Methylglutaconic acidb HMDB00522 Sugars HMDB ID 4-Deoxythreonic acida,b HMDB02453 D-Fructosea HMDB00660 4-Hydroxybenzoic acidb HMDB00500 D-Galactosea HMDB00143 4-Hydroxyphenyllactic acidb HMDB00755 D-Glucosea HMDB00122 5-Hydroxyhexanoic acidb HMDB00525 D-Xylosea HMDB00098 5-Hydroxyindoleacetic acidb HMDB00763 D-Xylulosea HMDB01644 Adipic acida,b HMDB00448 L-Arabinosea HMDB00646 Alpha-Hydroxyisobutyric acidb HMDB00729 L-Fucosea HMDB00174 Aminomalonic acida HMDB01147 Sucrosea HMDB00258 Ascorbic acida HMDB00044 a Polar metabolites. b Targeted organic acids. c Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid. |
| label | Table 4 |
| caption | List of metabolites identified in human urine extracts by GC-MS (except volatile compounds). |
| title | List of metabolites identified in human urine extracts by GC-MS (except volatile compounds). |
| table | Amino acids HMDB ID Organic acids HMDB ID Beta-Alaninea HMDB00056 Azelaic acidb HMDB00784 Glycinea HMDB00123 Benzoic acidb HMDB01870 L-Alaninea HMDB00161 Butyric acida HMDB00039 L-Asparaginea HMDB00168 Cis-Aconitic acida,b HMDB00072 L-Aspartic acida HMDB00191 Citraconic acidb HMDB00634 L-Glutaminea HMDB00641 Citramalic acidb HMDB00426 L-Histidinea HMDB00177 Citric acida HMDB00094 L-Isoleucinea HMDB00172 Erythronic acida HMDB00613 L-Leucinea HMDB00687 Ethylmalonic acidb HMDB00622 L-Lysinea HMDB00182 Gluconic acida HMDB00625 L-Ornithinea HMDB00214 Glutaric acidb HMDB00661 L-Phenylalaninea HMDB00159 Glyceric acida HMDB00139 L-Serinea HMDB00187 Glycolic acida,b HMDB00115 L-Threoninea HMDB00167 Glyoxalic acida HMDB00119 L-Tryptophana HMDB00929 Hippuric acida,b HMDB00714 L-Tyrosinea HMDB00158 Homovanillic acidb HMDB00118 L-Valinea HMDB00883 HPHPAb HMDB02643 Bile acids HMDB ID Hydroxypropionic acidb HMDB00700 Cholenic acidc HMDB00308 Indoleacetic acidb HMDB00197 Deoxycholic acidc HMDB00626 Indolelactic acid b HMDB00671 Lithocholic acidc HMDB00761 Isobutyrylglycineb HMDB00730 Ursodeoxycholic acidc HMDB00946 Isocitric acida HMDB00193 Misc HMDB ID Isovalerylglycineb HMDB00678 3-Methylhistidinea HMDB00479 Kynurenic acidb HMDB00715 5-Hydroxyindoleb HMDB59805 Levulinic acidb HMDB00720 Creatininea HMDB00562 L-Lactic acidb HMDB00190 Dimethylglycinea HMDB00092 Malic acida HMDB00744 D-Xylitola HMDB02917 Malonic acida,b HMDB00691 Glycerol 3-phosphatea HMDB00126 m-Chlorobenzoic acidb HMDB01544 Glycerola HMDB00131 m-Coumaric acidb HMDB01713 Mannitola HMDB00765 Methylmalonic acidb HMDB00202 Myoinositola HMDB00211 Methylsuccinic acidb HMDB01844 Pseudouridinea HMDB00767 o-Hydroxyhippuric acidb HMDB00840 Scyllitola HMDB06088 o-Hydroxyphenylacetic acidb HMDB00669 Sorbitola HMDB00247 Oleic acidb HMDB00207 Ureaa HMDB00294 Oxalic acida HMDB02329 Organic acids HMDB ID Oxoglutaric acidb HMDB00208 2,4-Dihydroxybutanoic acida HMDB00360 Palmitic acidb HMDB00220 2-Ethylhydracrylic acidb HMDB00396 Pantothenic acidb HMDB00210 2-Furoylglycineb HMDB00439 Phosphoric acidb HMDB02142 2-Hydroxy-2-methylbutyric acidb HMDB01987 p-Hydroxyphenylacetic acida HMDB00020 2-Hydroxyglutaric acida HMDB00694 Pimelic acidb HMDB00857 2-Hydroxyvaleric acida HMDB01863 Pyroglutamic acida,b HMDB00267 2-Ketobutyric acidb HMDB00005 Pyruvic acidb HMDB00243 2-Methyl-3-hydroxybutyric acidb HMDB00354 Quinolinic acidb HMDB00232 3,4-Dihydroxybenzeneacetic acidb HMDB01336 Stearic acidb HMDB00827 3,4-Dihydroxybutanoic acida,b HMDB00337 Suberic acidb HMDB00893 3,4-Dihydroxyhydrocinnamic acidb HMDB00423 Succinic acida,b HMDB00254 3-Aminoisobutanoic acida,b HMDB03911 Sumiki’s acidb HMDB02432 3-Hydroxyhippuric acidb HMDB06116 Tartaric acida HMDB00956 3-Hydroxyisobutyric acida HMDB00023 Threonic acida HMDB00943 3-Hydroxyisovaleric acida,b HMDB00754 Trans-Aconitic acida HMDB00958 3-Hydroxymethylglutaric acidb HMDB00355 Trans-Ferulic acidb HMDB00954 3-Hydroxyphenylacetic acidb HMDB00440 Uric acida HMDB00289 3-Hydroxysebacic acidb HMDB00350 Vanillyllactic acidb HMDB00913 3-Methyladipic acidb HMDB00555 Vanillylmandelic acidb HMDB00291 3-Methylglutaconic acidb HMDB00522 Sugars HMDB ID 4-Deoxythreonic acida,b HMDB02453 D-Fructosea HMDB00660 4-Hydroxybenzoic acidb HMDB00500 D-Galactosea HMDB00143 4-Hydroxyphenyllactic acidb HMDB00755 D-Glucosea HMDB00122 5-Hydroxyhexanoic acidb HMDB00525 D-Xylosea HMDB00098 5-Hydroxyindoleacetic acidb HMDB00763 D-Xylulosea HMDB01644 Adipic acida,b HMDB00448 L-Arabinosea HMDB00646 Alpha-Hydroxyisobutyric acidb HMDB00729 L-Fucosea HMDB00174 Aminomalonic acida HMDB01147 Sucrosea HMDB00258 Ascorbic acida HMDB00044 |
| tr | Amino acids HMDB ID Organic acids HMDB ID |
| td | Amino acids |
| td | HMDB ID |
| td | Organic acids |
| td | HMDB ID |
| tr | Beta-Alaninea HMDB00056 Azelaic acidb HMDB00784 |
| td | Beta-Alaninea |
| td | HMDB00056 |
| td | Azelaic acidb |
| td | HMDB00784 |
| tr | Glycinea HMDB00123 Benzoic acidb HMDB01870 |
| td | Glycinea |
| td | HMDB00123 |
| td | Benzoic acidb |
| td | HMDB01870 |
| tr | L-Alaninea HMDB00161 Butyric acida HMDB00039 |
| td | L-Alaninea |
| td | HMDB00161 |
| td | Butyric acida |
| td | HMDB00039 |
| tr | L-Asparaginea HMDB00168 Cis-Aconitic acida,b HMDB00072 |
| td | L-Asparaginea |
| td | HMDB00168 |
| td | Cis-Aconitic acida,b |
| td | HMDB00072 |
| tr | L-Aspartic acida HMDB00191 Citraconic acidb HMDB00634 |
| td | L-Aspartic acida |
| td | HMDB00191 |
| td | Citraconic acidb |
| td | HMDB00634 |
| tr | L-Glutaminea HMDB00641 Citramalic acidb HMDB00426 |
| td | L-Glutaminea |
| td | HMDB00641 |
| td | Citramalic acidb |
| td | HMDB00426 |
| tr | L-Histidinea HMDB00177 Citric acida HMDB00094 |
| td | L-Histidinea |
| td | HMDB00177 |
| td | Citric acida |
| td | HMDB00094 |
| tr | L-Isoleucinea HMDB00172 Erythronic acida HMDB00613 |
| td | L-Isoleucinea |
| td | HMDB00172 |
| td | Erythronic acida |
| td | HMDB00613 |
| tr | L-Leucinea HMDB00687 Ethylmalonic acidb HMDB00622 |
| td | L-Leucinea |
| td | HMDB00687 |
| td | Ethylmalonic acidb |
| td | HMDB00622 |
| tr | L-Lysinea HMDB00182 Gluconic acida HMDB00625 |
| td | L-Lysinea |
| td | HMDB00182 |
| td | Gluconic acida |
| td | HMDB00625 |
| tr | L-Ornithinea HMDB00214 Glutaric acidb HMDB00661 |
| td | L-Ornithinea |
| td | HMDB00214 |
| td | Glutaric acidb |
| td | HMDB00661 |
| tr | L-Phenylalaninea HMDB00159 Glyceric acida HMDB00139 |
| td | L-Phenylalaninea |
| td | HMDB00159 |
| td | Glyceric acida |
| td | HMDB00139 |
| tr | L-Serinea HMDB00187 Glycolic acida,b HMDB00115 |
| td | L-Serinea |
| td | HMDB00187 |
| td | Glycolic acida,b |
| td | HMDB00115 |
| tr | L-Threoninea HMDB00167 Glyoxalic acida HMDB00119 |
| td | L-Threoninea |
| td | HMDB00167 |
| td | Glyoxalic acida |
| td | HMDB00119 |
| tr | L-Tryptophana HMDB00929 Hippuric acida,b HMDB00714 |
| td | L-Tryptophana |
| td | HMDB00929 |
| td | Hippuric acida,b |
| td | HMDB00714 |
| tr | L-Tyrosinea HMDB00158 Homovanillic acidb HMDB00118 |
| td | L-Tyrosinea |
| td | HMDB00158 |
| td | Homovanillic acidb |
| td | HMDB00118 |
| tr | L-Valinea HMDB00883 HPHPAb HMDB02643 |
| td | L-Valinea |
| td | HMDB00883 |
| td | HPHPAb |
| td | HMDB02643 |
| tr | Bile acids HMDB ID Hydroxypropionic acidb HMDB00700 |
| td | Bile acids |
| td | HMDB ID |
| td | Hydroxypropionic acidb |
| td | HMDB00700 |
| tr | Cholenic acidc HMDB00308 Indoleacetic acidb HMDB00197 |
| td | Cholenic acidc |
| td | HMDB00308 |
| td | Indoleacetic acidb |
| td | HMDB00197 |
| tr | Deoxycholic acidc HMDB00626 Indolelactic acid b HMDB00671 |
| td | Deoxycholic acidc |
| td | HMDB00626 |
| td | Indolelactic acid b |
| td | HMDB00671 |
| tr | Lithocholic acidc HMDB00761 Isobutyrylglycineb HMDB00730 |
| td | Lithocholic acidc |
| td | HMDB00761 |
| td | Isobutyrylglycineb |
| td | HMDB00730 |
| tr | Ursodeoxycholic acidc HMDB00946 Isocitric acida HMDB00193 |
| td | Ursodeoxycholic acidc |
| td | HMDB00946 |
| td | Isocitric acida |
| td | HMDB00193 |
| tr | Misc HMDB ID Isovalerylglycineb HMDB00678 |
| td | Misc |
| td | HMDB ID |
| td | Isovalerylglycineb |
| td | HMDB00678 |
| tr | 3-Methylhistidinea HMDB00479 Kynurenic acidb HMDB00715 |
| td | 3-Methylhistidinea |
| td | HMDB00479 |
| td | Kynurenic acidb |
| td | HMDB00715 |
| tr | 5-Hydroxyindoleb HMDB59805 Levulinic acidb HMDB00720 |
| td | 5-Hydroxyindoleb |
| td | HMDB59805 |
| td | Levulinic acidb |
| td | HMDB00720 |
| tr | Creatininea HMDB00562 L-Lactic acidb HMDB00190 |
| td | Creatininea |
| td | HMDB00562 |
| td | L-Lactic acidb |
| td | HMDB00190 |
| tr | Dimethylglycinea HMDB00092 Malic acida HMDB00744 |
| td | Dimethylglycinea |
| td | HMDB00092 |
| td | Malic acida |
| td | HMDB00744 |
| tr | D-Xylitola HMDB02917 Malonic acida,b HMDB00691 |
| td | D-Xylitola |
| td | HMDB02917 |
| td | Malonic acida,b |
| td | HMDB00691 |
| tr | Glycerol 3-phosphatea HMDB00126 m-Chlorobenzoic acidb HMDB01544 |
| td | Glycerol 3-phosphatea |
| td | HMDB00126 |
| td | m-Chlorobenzoic acidb |
| td | HMDB01544 |
| tr | Glycerola HMDB00131 m-Coumaric acidb HMDB01713 |
| td | Glycerola |
| td | HMDB00131 |
| td | m-Coumaric acidb |
| td | HMDB01713 |
| tr | Mannitola HMDB00765 Methylmalonic acidb HMDB00202 |
| td | Mannitola |
| td | HMDB00765 |
| td | Methylmalonic acidb |
| td | HMDB00202 |
| tr | Myoinositola HMDB00211 Methylsuccinic acidb HMDB01844 |
| td | Myoinositola |
| td | HMDB00211 |
| td | Methylsuccinic acidb |
| td | HMDB01844 |
| tr | Pseudouridinea HMDB00767 o-Hydroxyhippuric acidb HMDB00840 |
| td | Pseudouridinea |
| td | HMDB00767 |
| td | o-Hydroxyhippuric acidb |
| td | HMDB00840 |
| tr | Scyllitola HMDB06088 o-Hydroxyphenylacetic acidb HMDB00669 |
| td | Scyllitola |
| td | HMDB06088 |
| td | o-Hydroxyphenylacetic acidb |
| td | HMDB00669 |
| tr | Sorbitola HMDB00247 Oleic acidb HMDB00207 |
| td | Sorbitola |
| td | HMDB00247 |
| td | Oleic acidb |
| td | HMDB00207 |
| tr | Ureaa HMDB00294 Oxalic acida HMDB02329 |
| td | Ureaa |
| td | HMDB00294 |
| td | Oxalic acida |
| td | HMDB02329 |
| tr | Organic acids HMDB ID Oxoglutaric acidb HMDB00208 |
| td | Organic acids |
| td | HMDB ID |
| td | Oxoglutaric acidb |
| td | HMDB00208 |
| tr | 2,4-Dihydroxybutanoic acida HMDB00360 Palmitic acidb HMDB00220 |
| td | 2,4-Dihydroxybutanoic acida |
| td | HMDB00360 |
| td | Palmitic acidb |
| td | HMDB00220 |
| tr | 2-Ethylhydracrylic acidb HMDB00396 Pantothenic acidb HMDB00210 |
| td | 2-Ethylhydracrylic acidb |
| td | HMDB00396 |
| td | Pantothenic acidb |
| td | HMDB00210 |
| tr | 2-Furoylglycineb HMDB00439 Phosphoric acidb HMDB02142 |
| td | 2-Furoylglycineb |
| td | HMDB00439 |
| td | Phosphoric acidb |
| td | HMDB02142 |
| tr | 2-Hydroxy-2-methylbutyric acidb HMDB01987 p-Hydroxyphenylacetic acida HMDB00020 |
| td | 2-Hydroxy-2-methylbutyric acidb |
| td | HMDB01987 |
| td | p-Hydroxyphenylacetic acida |
| td | HMDB00020 |
| tr | 2-Hydroxyglutaric acida HMDB00694 Pimelic acidb HMDB00857 |
| td | 2-Hydroxyglutaric acida |
| td | HMDB00694 |
| td | Pimelic acidb |
| td | HMDB00857 |
| tr | 2-Hydroxyvaleric acida HMDB01863 Pyroglutamic acida,b HMDB00267 |
| td | 2-Hydroxyvaleric acida |
| td | HMDB01863 |
| td | Pyroglutamic acida,b |
| td | HMDB00267 |
| tr | 2-Ketobutyric acidb HMDB00005 Pyruvic acidb HMDB00243 |
| td | 2-Ketobutyric acidb |
| td | HMDB00005 |
| td | Pyruvic acidb |
| td | HMDB00243 |
| tr | 2-Methyl-3-hydroxybutyric acidb HMDB00354 Quinolinic acidb HMDB00232 |
| td | 2-Methyl-3-hydroxybutyric acidb |
| td | HMDB00354 |
| td | Quinolinic acidb |
| td | HMDB00232 |
| tr | 3,4-Dihydroxybenzeneacetic acidb HMDB01336 Stearic acidb HMDB00827 |
| td | 3,4-Dihydroxybenzeneacetic acidb |
| td | HMDB01336 |
| td | Stearic acidb |
| td | HMDB00827 |
| tr | 3,4-Dihydroxybutanoic acida,b HMDB00337 Suberic acidb HMDB00893 |
| td | 3,4-Dihydroxybutanoic acida,b |
| td | HMDB00337 |
| td | Suberic acidb |
| td | HMDB00893 |
| tr | 3,4-Dihydroxyhydrocinnamic acidb HMDB00423 Succinic acida,b HMDB00254 |
| td | 3,4-Dihydroxyhydrocinnamic acidb |
| td | HMDB00423 |
| td | Succinic acida,b |
| td | HMDB00254 |
| tr | 3-Aminoisobutanoic acida,b HMDB03911 Sumiki’s acidb HMDB02432 |
| td | 3-Aminoisobutanoic acida,b |
| td | HMDB03911 |
| td | Sumiki’s acidb |
| td | HMDB02432 |
| tr | 3-Hydroxyhippuric acidb HMDB06116 Tartaric acida HMDB00956 |
| td | 3-Hydroxyhippuric acidb |
| td | HMDB06116 |
| td | Tartaric acida |
| td | HMDB00956 |
| tr | 3-Hydroxyisobutyric acida HMDB00023 Threonic acida HMDB00943 |
| td | 3-Hydroxyisobutyric acida |
| td | HMDB00023 |
| td | Threonic acida |
| td | HMDB00943 |
| tr | 3-Hydroxyisovaleric acida,b HMDB00754 Trans-Aconitic acida HMDB00958 |
| td | 3-Hydroxyisovaleric acida,b |
| td | HMDB00754 |
| td | Trans-Aconitic acida |
| td | HMDB00958 |
| tr | 3-Hydroxymethylglutaric acidb HMDB00355 Trans-Ferulic acidb HMDB00954 |
| td | 3-Hydroxymethylglutaric acidb |
| td | HMDB00355 |
| td | Trans-Ferulic acidb |
| td | HMDB00954 |
| tr | 3-Hydroxyphenylacetic acidb HMDB00440 Uric acida HMDB00289 |
| td | 3-Hydroxyphenylacetic acidb |
| td | HMDB00440 |
| td | Uric acida |
| td | HMDB00289 |
| tr | 3-Hydroxysebacic acidb HMDB00350 Vanillyllactic acidb HMDB00913 |
| td | 3-Hydroxysebacic acidb |
| td | HMDB00350 |
| td | Vanillyllactic acidb |
| td | HMDB00913 |
| tr | 3-Methyladipic acidb HMDB00555 Vanillylmandelic acidb HMDB00291 |
| td | 3-Methyladipic acidb |
| td | HMDB00555 |
| td | Vanillylmandelic acidb |
| td | HMDB00291 |
| tr | 3-Methylglutaconic acidb HMDB00522 Sugars HMDB ID |
| td | 3-Methylglutaconic acidb |
| td | HMDB00522 |
| td | Sugars |
| td | HMDB ID |
| tr | 4-Deoxythreonic acida,b HMDB02453 D-Fructosea HMDB00660 |
| td | 4-Deoxythreonic acida,b |
| td | HMDB02453 |
| td | D-Fructosea |
| td | HMDB00660 |
| tr | 4-Hydroxybenzoic acidb HMDB00500 D-Galactosea HMDB00143 |
| td | 4-Hydroxybenzoic acidb |
| td | HMDB00500 |
| td | D-Galactosea |
| td | HMDB00143 |
| tr | 4-Hydroxyphenyllactic acidb HMDB00755 D-Glucosea HMDB00122 |
| td | 4-Hydroxyphenyllactic acidb |
| td | HMDB00755 |
| td | D-Glucosea |
| td | HMDB00122 |
| tr | 5-Hydroxyhexanoic acidb HMDB00525 D-Xylosea HMDB00098 |
| td | 5-Hydroxyhexanoic acidb |
| td | HMDB00525 |
| td | D-Xylosea |
| td | HMDB00098 |
| tr | 5-Hydroxyindoleacetic acidb HMDB00763 D-Xylulosea HMDB01644 |
| td | 5-Hydroxyindoleacetic acidb |
| td | HMDB00763 |
| td | D-Xylulosea |
| td | HMDB01644 |
| tr | Adipic acida,b HMDB00448 L-Arabinosea HMDB00646 |
| td | Adipic acida,b |
| td | HMDB00448 |
| td | L-Arabinosea |
| td | HMDB00646 |
| tr | Alpha-Hydroxyisobutyric acidb HMDB00729 L-Fucosea HMDB00174 |
| td | Alpha-Hydroxyisobutyric acidb |
| td | HMDB00729 |
| td | L-Fucosea |
| td | HMDB00174 |
| tr | Aminomalonic acida HMDB01147 Sucrosea HMDB00258 |
| td | Aminomalonic acida |
| td | HMDB01147 |
| td | Sucrosea |
| td | HMDB00258 |
| tr | Ascorbic acida HMDB00044 |
| td | Ascorbic acida |
| td | HMDB00044 |
| table-wrap-foot | a Polar metabolites. b Targeted organic acids. c Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid. |
| footnote | a Polar metabolites. |
| label | a |
| p | Polar metabolites. |
| footnote | b Targeted organic acids. |
| label | b |
| p | Targeted organic acids. |
| footnote | c Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid. |
| label | c |
| p | Targeted bile acids. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid. |
| table-wrap | Table 5 Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS. Peak # Compound HMDB ID Uniquemass MSmatch RT(min) RI(Calc) RI(NIST) 1 3-Methyl sulfolane HMDB59667 68 867 0.83 725 654 2 3-Hexanone HMDB00753 43 781 0.99 732 788 3 2-Pentanone HMDB34235 43 894 1.61 758 654 4 1-Hydroxy-2-pentanone HMDB59678 43 780 1.74 763 897 5 Allyl methylsulphide HMDB31653 88 793 1.83 767 701 6 Dimethyl disulfide HMDB05879 94 940 2.26 785 763 7 4-Heptanone HMDB04814 71 919 3.34 831 883 8 1-Methylcyclohexanol HMDB59693 71 821 3.81 850 931 9 2-Hexanone HMDB05842 43 821 3.82 851 889 10 3,4-Dimethylthiophene HMDB29304 111 906 4.15 865 888 11 Diallyl sulphide HMDB36491 45 827 4.86 895 848 12 5-Methyl-2-hexanone HMDB31549 43 816 5.29 913 836 13 1,3-Dithio cyclohexane HMDB31473 120 841 5.85 936 1027 14 Dimethyl trisulfide HMDB13780 126 873 6.01 943 950 15 Phenol HMDB00228 94 859 6.34 957 998 16 o-Cymene HMDB37050 119 928 7.19 993 1014 17 p-Cymene HMDB05805 119 928 7.33 998 1018 18 m-Cymene HMDB13806 119 928 7.43 1009 1022 19 1,4-Cineol HMDB31455 111 912 8.71 1057 1016 20 p-Cresol HMDB01858 107 930 8.99 1068 1082 21 Linalool oxide HMDB31440 59 783 10.49 1131 1074 22 Iso-menthol HMDB35764 57 923 10.99 1152 1141 23 Alpha-p-dimethylstyrene HMDB29641 132 915 11 1153 1088 24 L-Menthol HMDB03352 71 924 11.18 1160 1140 25 Undecane HMDB31445 57 910 11.35 1168 1100 26 Ledene Oxide (II) HMDB60295 43 743 11.54 1176 1201 27 Salicylic acid methyl ester HMDB34172 120 857 11.65 1180 1193 28 Beta-Carvone HMDB35824 82 859 12.81 1229 1247 29 Piperitone HMDB34975 150 873 12.96 1235 1184 30 o-Thymol HMDB35770 135 904 13.9 1275 1305 31 Beta-Cyclocitrol HMDB41011 41 884 14.1 1283 1219 32 4,7-Dimethyl-benzofuran HMDB59897 146 861 14.4 1296 1244 33 Cuminal HMDB02214 133 861 14.82 1313 1235 34 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene HMDB36823 138 911 15.91 1359 1370 35 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone HMDB32913 121 795 15.97 1362 1433 36 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene HMDB59826 159 855 16.02 1364 1349 37 Alpha-Cedrene HMDB59695 119 821 16.1 1367 1415 38 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene HMDB59696 159 795 16.59 1388 1310 39 1,2-Diydro-1,1,6-trimethyl-napthalene HMDB40284 157 947 16.82 1398 1354 40 Beta-Guaiene HMDB38157 161 879 17 1405 1500 41 Beta-Damascenone HMDB13804 69 889 17.35 1420 1365 42 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl HMDB13817 177 843 18.51 1469 1462 43 Himachalene HMDB38168 119 823 18.62 1473 1489 44 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one HMDB37139 136 883 18.73 1478 1497 45 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one HMDB38130 121 788 19.05 1491 1507 46 2,4-Bis (1,1-Dimethylethyl)-phenol HMDB13186 191 863 19.5 1510 1519 47 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one HMDB59697 132 918 19.8 1523 1528 48 L-Calamenene HMDB59910 159 920 20.25 1533 1534 49 Beta-Vatirenene HMDB59676 202 806 20.45 1550 1545 50 1,6,7-Trimethylnaphthalene HMDB59701 170 899 20.82 1564 1559 51 Azulol HMDB59686 183 823 21.18 1581 1572 52 3,3,5,6-Tetramethyl-1-indanone HMDB59683 173 686 21.28 1585 1579 |
| label | Table 5 |
| caption | Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS. |
| title | Compound names, HMDB identification numbers, unique masses, mean mass spectral match quality, retention times, and NIST retention indices for volatile compounds in urine analyzed by HS-SPME-GC/MS. |
| table | Peak # Compound HMDB ID Uniquemass MSmatch RT(min) RI(Calc) RI(NIST) 1 3-Methyl sulfolane HMDB59667 68 867 0.83 725 654 2 3-Hexanone HMDB00753 43 781 0.99 732 788 3 2-Pentanone HMDB34235 43 894 1.61 758 654 4 1-Hydroxy-2-pentanone HMDB59678 43 780 1.74 763 897 5 Allyl methylsulphide HMDB31653 88 793 1.83 767 701 6 Dimethyl disulfide HMDB05879 94 940 2.26 785 763 7 4-Heptanone HMDB04814 71 919 3.34 831 883 8 1-Methylcyclohexanol HMDB59693 71 821 3.81 850 931 9 2-Hexanone HMDB05842 43 821 3.82 851 889 10 3,4-Dimethylthiophene HMDB29304 111 906 4.15 865 888 11 Diallyl sulphide HMDB36491 45 827 4.86 895 848 12 5-Methyl-2-hexanone HMDB31549 43 816 5.29 913 836 13 1,3-Dithio cyclohexane HMDB31473 120 841 5.85 936 1027 14 Dimethyl trisulfide HMDB13780 126 873 6.01 943 950 15 Phenol HMDB00228 94 859 6.34 957 998 16 o-Cymene HMDB37050 119 928 7.19 993 1014 17 p-Cymene HMDB05805 119 928 7.33 998 1018 18 m-Cymene HMDB13806 119 928 7.43 1009 1022 19 1,4-Cineol HMDB31455 111 912 8.71 1057 1016 20 p-Cresol HMDB01858 107 930 8.99 1068 1082 21 Linalool oxide HMDB31440 59 783 10.49 1131 1074 22 Iso-menthol HMDB35764 57 923 10.99 1152 1141 23 Alpha-p-dimethylstyrene HMDB29641 132 915 11 1153 1088 24 L-Menthol HMDB03352 71 924 11.18 1160 1140 25 Undecane HMDB31445 57 910 11.35 1168 1100 26 Ledene Oxide (II) HMDB60295 43 743 11.54 1176 1201 27 Salicylic acid methyl ester HMDB34172 120 857 11.65 1180 1193 28 Beta-Carvone HMDB35824 82 859 12.81 1229 1247 29 Piperitone HMDB34975 150 873 12.96 1235 1184 30 o-Thymol HMDB35770 135 904 13.9 1275 1305 31 Beta-Cyclocitrol HMDB41011 41 884 14.1 1283 1219 32 4,7-Dimethyl-benzofuran HMDB59897 146 861 14.4 1296 1244 33 Cuminal HMDB02214 133 861 14.82 1313 1235 34 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene HMDB36823 138 911 15.91 1359 1370 35 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone HMDB32913 121 795 15.97 1362 1433 36 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene HMDB59826 159 855 16.02 1364 1349 37 Alpha-Cedrene HMDB59695 119 821 16.1 1367 1415 38 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene HMDB59696 159 795 16.59 1388 1310 39 1,2-Diydro-1,1,6-trimethyl-napthalene HMDB40284 157 947 16.82 1398 1354 40 Beta-Guaiene HMDB38157 161 879 17 1405 1500 41 Beta-Damascenone HMDB13804 69 889 17.35 1420 1365 42 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl HMDB13817 177 843 18.51 1469 1462 43 Himachalene HMDB38168 119 823 18.62 1473 1489 44 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one HMDB37139 136 883 18.73 1478 1497 45 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one HMDB38130 121 788 19.05 1491 1507 46 2,4-Bis (1,1-Dimethylethyl)-phenol HMDB13186 191 863 19.5 1510 1519 47 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one HMDB59697 132 918 19.8 1523 1528 48 L-Calamenene HMDB59910 159 920 20.25 1533 1534 49 Beta-Vatirenene HMDB59676 202 806 20.45 1550 1545 50 1,6,7-Trimethylnaphthalene HMDB59701 170 899 20.82 1564 1559 51 Azulol HMDB59686 183 823 21.18 1581 1572 52 3,3,5,6-Tetramethyl-1-indanone HMDB59683 173 686 21.28 1585 1579 |
| tr | Peak # Compound HMDB ID Uniquemass MSmatch RT(min) RI(Calc) RI(NIST) |
| td | Peak # |
| td | Compound |
| td | HMDB ID |
| td | Uniquemass |
| td | MSmatch |
| td | RT(min) |
| td | RI(Calc) |
| td | RI(NIST) |
| tr | 1 3-Methyl sulfolane HMDB59667 68 867 0.83 725 654 |
| td | 1 |
| td | 3-Methyl sulfolane |
| td | HMDB59667 |
| td | 68 |
| td | 867 |
| td | 0.83 |
| td | 725 |
| td | 654 |
| tr | 2 3-Hexanone HMDB00753 43 781 0.99 732 788 |
| td | 2 |
| td | 3-Hexanone |
| td | HMDB00753 |
| td | 43 |
| td | 781 |
| td | 0.99 |
| td | 732 |
| td | 788 |
| tr | 3 2-Pentanone HMDB34235 43 894 1.61 758 654 |
| td | 3 |
| td | 2-Pentanone |
| td | HMDB34235 |
| td | 43 |
| td | 894 |
| td | 1.61 |
| td | 758 |
| td | 654 |
| tr | 4 1-Hydroxy-2-pentanone HMDB59678 43 780 1.74 763 897 |
| td | 4 |
| td | 1-Hydroxy-2-pentanone |
| td | HMDB59678 |
| td | 43 |
| td | 780 |
| td | 1.74 |
| td | 763 |
| td | 897 |
| tr | 5 Allyl methylsulphide HMDB31653 88 793 1.83 767 701 |
| td | 5 |
| td | Allyl methylsulphide |
| td | HMDB31653 |
| td | 88 |
| td | 793 |
| td | 1.83 |
| td | 767 |
| td | 701 |
| tr | 6 Dimethyl disulfide HMDB05879 94 940 2.26 785 763 |
| td | 6 |
| td | Dimethyl disulfide |
| td | HMDB05879 |
| td | 94 |
| td | 940 |
| td | 2.26 |
| td | 785 |
| td | 763 |
| tr | 7 4-Heptanone HMDB04814 71 919 3.34 831 883 |
| td | 7 |
| td | 4-Heptanone |
| td | HMDB04814 |
| td | 71 |
| td | 919 |
| td | 3.34 |
| td | 831 |
| td | 883 |
| tr | 8 1-Methylcyclohexanol HMDB59693 71 821 3.81 850 931 |
| td | 8 |
| td | 1-Methylcyclohexanol |
| td | HMDB59693 |
| td | 71 |
| td | 821 |
| td | 3.81 |
| td | 850 |
| td | 931 |
| tr | 9 2-Hexanone HMDB05842 43 821 3.82 851 889 |
| td | 9 |
| td | 2-Hexanone |
| td | HMDB05842 |
| td | 43 |
| td | 821 |
| td | 3.82 |
| td | 851 |
| td | 889 |
| tr | 10 3,4-Dimethylthiophene HMDB29304 111 906 4.15 865 888 |
| td | 10 |
| td | 3,4-Dimethylthiophene |
| td | HMDB29304 |
| td | 111 |
| td | 906 |
| td | 4.15 |
| td | 865 |
| td | 888 |
| tr | 11 Diallyl sulphide HMDB36491 45 827 4.86 895 848 |
| td | 11 |
| td | Diallyl sulphide |
| td | HMDB36491 |
| td | 45 |
| td | 827 |
| td | 4.86 |
| td | 895 |
| td | 848 |
| tr | 12 5-Methyl-2-hexanone HMDB31549 43 816 5.29 913 836 |
| td | 12 |
| td | 5-Methyl-2-hexanone |
| td | HMDB31549 |
| td | 43 |
| td | 816 |
| td | 5.29 |
| td | 913 |
| td | 836 |
| tr | 13 1,3-Dithio cyclohexane HMDB31473 120 841 5.85 936 1027 |
| td | 13 |
| td | 1,3-Dithio cyclohexane |
| td | HMDB31473 |
| td | 120 |
| td | 841 |
| td | 5.85 |
| td | 936 |
| td | 1027 |
| tr | 14 Dimethyl trisulfide HMDB13780 126 873 6.01 943 950 |
| td | 14 |
| td | Dimethyl trisulfide |
| td | HMDB13780 |
| td | 126 |
| td | 873 |
| td | 6.01 |
| td | 943 |
| td | 950 |
| tr | 15 Phenol HMDB00228 94 859 6.34 957 998 |
| td | 15 |
| td | Phenol |
| td | HMDB00228 |
| td | 94 |
| td | 859 |
| td | 6.34 |
| td | 957 |
| td | 998 |
| tr | 16 o-Cymene HMDB37050 119 928 7.19 993 1014 |
| td | 16 |
| td | o-Cymene |
| td | HMDB37050 |
| td | 119 |
| td | 928 |
| td | 7.19 |
| td | 993 |
| td | 1014 |
| tr | 17 p-Cymene HMDB05805 119 928 7.33 998 1018 |
| td | 17 |
| td | p-Cymene |
| td | HMDB05805 |
| td | 119 |
| td | 928 |
| td | 7.33 |
| td | 998 |
| td | 1018 |
| tr | 18 m-Cymene HMDB13806 119 928 7.43 1009 1022 |
| td | 18 |
| td | m-Cymene |
| td | HMDB13806 |
| td | 119 |
| td | 928 |
| td | 7.43 |
| td | 1009 |
| td | 1022 |
| tr | 19 1,4-Cineol HMDB31455 111 912 8.71 1057 1016 |
| td | 19 |
| td | 1,4-Cineol |
| td | HMDB31455 |
| td | 111 |
| td | 912 |
| td | 8.71 |
| td | 1057 |
| td | 1016 |
| tr | 20 p-Cresol HMDB01858 107 930 8.99 1068 1082 |
| td | 20 |
| td | p-Cresol |
| td | HMDB01858 |
| td | 107 |
| td | 930 |
| td | 8.99 |
| td | 1068 |
| td | 1082 |
| tr | 21 Linalool oxide HMDB31440 59 783 10.49 1131 1074 |
| td | 21 |
| td | Linalool oxide |
| td | HMDB31440 |
| td | 59 |
| td | 783 |
| td | 10.49 |
| td | 1131 |
| td | 1074 |
| tr | 22 Iso-menthol HMDB35764 57 923 10.99 1152 1141 |
| td | 22 |
| td | Iso-menthol |
| td | HMDB35764 |
| td | 57 |
| td | 923 |
| td | 10.99 |
| td | 1152 |
| td | 1141 |
| tr | 23 Alpha-p-dimethylstyrene HMDB29641 132 915 11 1153 1088 |
| td | 23 |
| td | Alpha-p-dimethylstyrene |
| td | HMDB29641 |
| td | 132 |
| td | 915 |
| td | 11 |
| td | 1153 |
| td | 1088 |
| tr | 24 L-Menthol HMDB03352 71 924 11.18 1160 1140 |
| td | 24 |
| td | L-Menthol |
| td | HMDB03352 |
| td | 71 |
| td | 924 |
| td | 11.18 |
| td | 1160 |
| td | 1140 |
| tr | 25 Undecane HMDB31445 57 910 11.35 1168 1100 |
| td | 25 |
| td | Undecane |
| td | HMDB31445 |
| td | 57 |
| td | 910 |
| td | 11.35 |
| td | 1168 |
| td | 1100 |
| tr | 26 Ledene Oxide (II) HMDB60295 43 743 11.54 1176 1201 |
| td | 26 |
| td | Ledene Oxide (II) |
| td | HMDB60295 |
| td | 43 |
| td | 743 |
| td | 11.54 |
| td | 1176 |
| td | 1201 |
| tr | 27 Salicylic acid methyl ester HMDB34172 120 857 11.65 1180 1193 |
| td | 27 |
| td | Salicylic acid methyl ester |
| td | HMDB34172 |
| td | 120 |
| td | 857 |
| td | 11.65 |
| td | 1180 |
| td | 1193 |
| tr | 28 Beta-Carvone HMDB35824 82 859 12.81 1229 1247 |
| td | 28 |
| td | Beta-Carvone |
| td | HMDB35824 |
| td | 82 |
| td | 859 |
| td | 12.81 |
| td | 1229 |
| td | 1247 |
| tr | 29 Piperitone HMDB34975 150 873 12.96 1235 1184 |
| td | 29 |
| td | Piperitone |
| td | HMDB34975 |
| td | 150 |
| td | 873 |
| td | 12.96 |
| td | 1235 |
| td | 1184 |
| tr | 30 o-Thymol HMDB35770 135 904 13.9 1275 1305 |
| td | 30 |
| td | o-Thymol |
| td | HMDB35770 |
| td | 135 |
| td | 904 |
| td | 13.9 |
| td | 1275 |
| td | 1305 |
| tr | 31 Beta-Cyclocitrol HMDB41011 41 884 14.1 1283 1219 |
| td | 31 |
| td | Beta-Cyclocitrol |
| td | HMDB41011 |
| td | 41 |
| td | 884 |
| td | 14.1 |
| td | 1283 |
| td | 1219 |
| tr | 32 4,7-Dimethyl-benzofuran HMDB59897 146 861 14.4 1296 1244 |
| td | 32 |
| td | 4,7-Dimethyl-benzofuran |
| td | HMDB59897 |
| td | 146 |
| td | 861 |
| td | 14.4 |
| td | 1296 |
| td | 1244 |
| tr | 33 Cuminal HMDB02214 133 861 14.82 1313 1235 |
| td | 33 |
| td | Cuminal |
| td | HMDB02214 |
| td | 133 |
| td | 861 |
| td | 14.82 |
| td | 1313 |
| td | 1235 |
| tr | 34 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene HMDB36823 138 911 15.91 1359 1370 |
| td | 34 |
| td | 2,6,10,10 Tetramethyl-oxa-spiro-4,5-dec-6-ene |
| td | HMDB36823 |
| td | 138 |
| td | 911 |
| td | 15.91 |
| td | 1359 |
| td | 1370 |
| tr | 35 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone HMDB32913 121 795 15.97 1362 1433 |
| td | 35 |
| td | 4-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-2-butanone |
| td | HMDB32913 |
| td | 121 |
| td | 795 |
| td | 15.97 |
| td | 1362 |
| td | 1433 |
| tr | 36 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene HMDB59826 159 855 16.02 1364 1349 |
| td | 36 |
| td | 1,2,3,4-Tetrahydro-1,1,6-trimethyl naphthalene |
| td | HMDB59826 |
| td | 159 |
| td | 855 |
| td | 16.02 |
| td | 1364 |
| td | 1349 |
| tr | 37 Alpha-Cedrene HMDB59695 119 821 16.1 1367 1415 |
| td | 37 |
| td | Alpha-Cedrene |
| td | HMDB59695 |
| td | 119 |
| td | 821 |
| td | 16.1 |
| td | 1367 |
| td | 1415 |
| tr | 38 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene HMDB59696 159 795 16.59 1388 1310 |
| td | 38 |
| td | 1,2,3,4,Tetrahydro-1,5,7-trimethylnapthalene |
| td | HMDB59696 |
| td | 159 |
| td | 795 |
| td | 16.59 |
| td | 1388 |
| td | 1310 |
| tr | 39 1,2-Diydro-1,1,6-trimethyl-napthalene HMDB40284 157 947 16.82 1398 1354 |
| td | 39 |
| td | 1,2-Diydro-1,1,6-trimethyl-napthalene |
| td | HMDB40284 |
| td | 157 |
| td | 947 |
| td | 16.82 |
| td | 1398 |
| td | 1354 |
| tr | 40 Beta-Guaiene HMDB38157 161 879 17 1405 1500 |
| td | 40 |
| td | Beta-Guaiene |
| td | HMDB38157 |
| td | 161 |
| td | 879 |
| td | 17 |
| td | 1405 |
| td | 1500 |
| tr | 41 Beta-Damascenone HMDB13804 69 889 17.35 1420 1365 |
| td | 41 |
| td | Beta-Damascenone |
| td | HMDB13804 |
| td | 69 |
| td | 889 |
| td | 17.35 |
| td | 1420 |
| td | 1365 |
| tr | 42 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl HMDB13817 177 843 18.51 1469 1462 |
| td | 42 |
| td | 2,5-Cyclohexadiene-1,4-dione-2,6-di-tert-butyl |
| td | HMDB13817 |
| td | 177 |
| td | 843 |
| td | 18.51 |
| td | 1469 |
| td | 1462 |
| tr | 43 Himachalene HMDB38168 119 823 18.62 1473 1489 |
| td | 43 |
| td | Himachalene |
| td | HMDB38168 |
| td | 119 |
| td | 823 |
| td | 18.62 |
| td | 1473 |
| td | 1489 |
| tr | 44 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one HMDB37139 136 883 18.73 1478 1497 |
| td | 44 |
| td | 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)-but-3-en-2-one |
| td | HMDB37139 |
| td | 136 |
| td | 883 |
| td | 18.73 |
| td | 1478 |
| td | 1497 |
| tr | 45 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one HMDB38130 121 788 19.05 1491 1507 |
| td | 45 |
| td | 1-(2,6,6-Trimethyl-1-cyclohexen-1-yl)-1-penten-3-one |
| td | HMDB38130 |
| td | 121 |
| td | 788 |
| td | 19.05 |
| td | 1491 |
| td | 1507 |
| tr | 46 2,4-Bis (1,1-Dimethylethyl)-phenol HMDB13186 191 863 19.5 1510 1519 |
| td | 46 |
| td | 2,4-Bis (1,1-Dimethylethyl)-phenol |
| td | HMDB13186 |
| td | 191 |
| td | 863 |
| td | 19.5 |
| td | 1510 |
| td | 1519 |
| tr | 47 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one HMDB59697 132 918 19.8 1523 1528 |
| td | 47 |
| td | 1-(2,3,6-Trimethyl phenyl)-3-Buten-2-one |
| td | HMDB59697 |
| td | 132 |
| td | 918 |
| td | 19.8 |
| td | 1523 |
| td | 1528 |
| tr | 48 L-Calamenene HMDB59910 159 920 20.25 1533 1534 |
| td | 48 |
| td | L-Calamenene |
| td | HMDB59910 |
| td | 159 |
| td | 920 |
| td | 20.25 |
| td | 1533 |
| td | 1534 |
| tr | 49 Beta-Vatirenene HMDB59676 202 806 20.45 1550 1545 |
| td | 49 |
| td | Beta-Vatirenene |
| td | HMDB59676 |
| td | 202 |
| td | 806 |
| td | 20.45 |
| td | 1550 |
| td | 1545 |
| tr | 50 1,6,7-Trimethylnaphthalene HMDB59701 170 899 20.82 1564 1559 |
| td | 50 |
| td | 1,6,7-Trimethylnaphthalene |
| td | HMDB59701 |
| td | 170 |
| td | 899 |
| td | 20.82 |
| td | 1564 |
| td | 1559 |
| tr | 51 Azulol HMDB59686 183 823 21.18 1581 1572 |
| td | 51 |
| td | Azulol |
| td | HMDB59686 |
| td | 183 |
| td | 823 |
| td | 21.18 |
| td | 1581 |
| td | 1572 |
| tr | 52 3,3,5,6-Tetramethyl-1-indanone HMDB59683 173 686 21.28 1585 1579 |
| td | 52 |
| td | 3,3,5,6-Tetramethyl-1-indanone |
| td | HMDB59683 |
| td | 173 |
| td | 686 |
| td | 21.28 |
| td | 1585 |
| td | 1579 |
| table-wrap | Table 6 Concentrations of metabolites in human urine as measured by GC-MS. Compounds Observed Concentration(µM/mM creatinine) Literature Values(µM/mM creatinine) 2,4-Dihydroxybutanoic acid 1.0 (0.3–1.8) 2.0±2.0 2-Ethylhydracrylic acid 2.1 (1.3–2.9) NA** 2-Furoylglycine 3.6 (0.7–5.8) 9.95 (2.0–18.66) 2-Hydroxy-2-methylbutyric acid 0.8 (0.4–1.5) 2.8±1.5 2-Methyl-3-hydroxybutyric acid 4.0 (1.3–6.2) 5.5 (0.0–11.0) 3,4-Dihydroxybenzeneacetic acid 0.9 (0.6–1.3) 1.16±0.50 3,4-Dihydroxybutanoic acid 42.2 (21.9–56.1) 34.9 (12.5–57.2) 3-Aminoisobutanoic acid 25.8 (1.4–155.6) 2.91–116.43 3-Hydroxyhippuric acid 6.1 (4.1–7.8) 2.5 (0.2–5.0) 3-Hydroxyisobutyric acid 29.0 (11.8–59.8) 17.5 (2.0–33.0) 3-Hydroxyisovaleric acid 7.4 (4.1–17.2) 8.5±3.2 3-Hydroxymethylglutaric acid 3.2 (1.1–5.2) 3 (0–10) 3-Hydroxyphenylacetic acid 5.6 (1.4–15.0) 6.1±4.1 3-Hydroxysebacic acid 2.9 (1.7–3.8) NA** 3-Methyladipic acid 2.1 (0.9–5.6) 2.9 (0.7–10.5) 3-Methylglutaconic acid 6.2 (2.8–8.3) 4.5 (0.0–9.0) 4-Deoxythreonic acid 18.2 (5.6–23.9) 25±10 4-Hydroxybenzoic acid 2.4 (1.3–3.6) 1.8±1.8 4-Hydroxyphenyllactic acid 2.8 (2.6–3.0) 1.1 (0.2–2.6) 5-Hydroxyhexanoic acid 2.7 (0.8–5.7) 3.5 (0.0–7.0) 5-Hydroxyindoleacetic acid 2.9 (0.4–5.8) 2.4±0.40 Adipic acid 3.0 (1.0–12.9) 5.1 (0.8–35.0) Alpha-Hydroxyisobutyric acid 2.9 (1.3–4.6) 3.8 Azelaic acid 1.9 (1.2–3.2) 4.8 (1.3–15.0) Benzoic acid 3.6 (1.4–7.5) 4.2 (1.9–6.5) Cholenic acid 0.015 (0.004–0.030) NA** Cis/Trans-Aconitic acid 15.3 (5.2–21.8) NA** Citraconic acid 1.6 (0.9–2.1) 2.2 Creatinine* 12,246±8,369 10,090±310 Deoxycholic acid 0.055 (0.050–0.060) NA** D-Galactose 3.3 (1.3–8.5) 4.4 (0.0–31.0) D-Glucose 35.6 (10.3–56.7) 31.12 D-Xylitol 2.3 (0.9–8.2) 3.50±2. 37 D-Xylulose 19.7 (6.4–32.6) NA** Ethylmalonic acid 2.4 (0.9–4.0) 2.0 (0.5–3.9) Gluconic acid 13.3 (8.2–26.4) NA** Glutaric acid 1.4 (0.5–2.3 ) 1.3 (0.6–2.6) Glyceric acid 14.4 (7.8–28.8) 4.5 (0.0–9.0) Glycine 75.7 (21–205) 151 (233–248) Glycolic acid 39.4 (2.9–78.1) 35 (18–55) Hippuric acid 217 (28–610) 257 (20–770) Homovanillic acid 4.3 (0.9–8.9) 4.9±3.4 HPHPA 4.2 (0.9–15.3) 0.55–54.93 Hydroxypropionic acid 8.1 (3.1–11.8) 6.5 (3.0–10.0) Indoleacetic acid 2.6 (0.6–5.4) 2.0±1.0 Indolelactic acid 0.9 0.460 (0.098–0.980) Isocitric acid 56.8 (19.4–119.1) 58 (36–84) Kynurenic acid 2.6 (0.9–4.1) 1.34±0.30 L-Arabinose 8.8 (0.8–19.4) 8.75 (6.12–11.84) L-Asparagine 9.5 (3.0–26.0) 10.0 (4.6–16.3) Levulinic acid 1.7 (0.6–2.5) 1.28 (0.33–2.75) Lithocholic acid 0.16 (0.10–0.20) NA** L-Lactic acid 7.1 (0.9–16.4) 12.3±6.2 L-Ornithine 5.3 (2.0–8.8) 5.0 (2.0–8.0) L-Phenylalanine 7.8 (5.0–11.3) 4.50±1.15 L-Serine 19.0 (10.4–35.8) 26.0±10.0 L-Tyrosine 15.1 (6.1–23.2) 8.8±4.5 L-Valine 5.5 (2.7–9.8) 3.0±1.0 Malonic acid 2.3 (1.2–3.1) 1 (0–2) m-Chlorobenzoic acid 0.7 NA** Methylmalonic acid 1.3 (0.5–2.1) 1.6 (0.0–3.6) Methylsuccinic acid 4.7 (1.1–6.2) 2.3 (0.8–10.8) Myoinositol 12.6 (5.1–15.3) 18.8±2.9 o-Hydroxyphenylacetic acid 2.9 (1.4–3.7) 7.71±3.65 Oxalic acid 8.2 (3.9–14.0) 1.11–33.34 Oxoglutaric acid 3.4 (1.4–12.5) 2.98 (0.42–18.30) Palmitic acid 6.7 (2.6–24.3) 11 (6–23) Phosphoric acid 784 (425–1170) 1000–4900 p-Hydroxyphenylacetic acid 6.0 (2.4–9.7) 9.68 Pimelic acid 1.9 (0.5–3.5) NA** Pseudouridine 29.8 (21.8–47.3) 26.02±4.62 Pyroglutamic acid 18.6 (4.5–24.9) 28.8 (3.4–54.2) Pyruvic acid 2.1 (1.0–3.7) 2.13 (0.54–8.67) Quinolinic acid 3.9 (0.9–15.1) 2.5±1.1 Scyllitol 3.6 (1.9–6.9) NA Sorbitol 3.9 (1.9–5.1) 3.50±2.24 Stearic acid 3.9 (2.3–7.7) 2.9 (1.6–6.6) Suberic acid 1.2 (0.5–1.9) 0.5 (0.0–2.9) Succinic acid 6.2 (2.5–13.5) 5.6±3.8 Sumiki’s acid 1.6 NA** Threonic acid 14.8 (8.6–36.1) 10.0±7.0 Trans-Ferulic acid 1 36.3±11.2 Uric acid 186 (93–329) 188 (79–296) Ursodeoxycholic acid 0.022 (0.013–0.030) NA** Vanillylmandelic acid 1.6 (0.6–2.8) 1.2±0.5 * Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. |
| label | Table 6 |
| caption | Concentrations of metabolites in human urine as measured by GC-MS. |
| title | Concentrations of metabolites in human urine as measured by GC-MS. |
| table | Compounds Observed Concentration(µM/mM creatinine) Literature Values(µM/mM creatinine) 2,4-Dihydroxybutanoic acid 1.0 (0.3–1.8) 2.0±2.0 2-Ethylhydracrylic acid 2.1 (1.3–2.9) NA** 2-Furoylglycine 3.6 (0.7–5.8) 9.95 (2.0–18.66) 2-Hydroxy-2-methylbutyric acid 0.8 (0.4–1.5) 2.8±1.5 2-Methyl-3-hydroxybutyric acid 4.0 (1.3–6.2) 5.5 (0.0–11.0) 3,4-Dihydroxybenzeneacetic acid 0.9 (0.6–1.3) 1.16±0.50 3,4-Dihydroxybutanoic acid 42.2 (21.9–56.1) 34.9 (12.5–57.2) 3-Aminoisobutanoic acid 25.8 (1.4–155.6) 2.91–116.43 3-Hydroxyhippuric acid 6.1 (4.1–7.8) 2.5 (0.2–5.0) 3-Hydroxyisobutyric acid 29.0 (11.8–59.8) 17.5 (2.0–33.0) 3-Hydroxyisovaleric acid 7.4 (4.1–17.2) 8.5±3.2 3-Hydroxymethylglutaric acid 3.2 (1.1–5.2) 3 (0–10) 3-Hydroxyphenylacetic acid 5.6 (1.4–15.0) 6.1±4.1 3-Hydroxysebacic acid 2.9 (1.7–3.8) NA** 3-Methyladipic acid 2.1 (0.9–5.6) 2.9 (0.7–10.5) 3-Methylglutaconic acid 6.2 (2.8–8.3) 4.5 (0.0–9.0) 4-Deoxythreonic acid 18.2 (5.6–23.9) 25±10 4-Hydroxybenzoic acid 2.4 (1.3–3.6) 1.8±1.8 4-Hydroxyphenyllactic acid 2.8 (2.6–3.0) 1.1 (0.2–2.6) 5-Hydroxyhexanoic acid 2.7 (0.8–5.7) 3.5 (0.0–7.0) 5-Hydroxyindoleacetic acid 2.9 (0.4–5.8) 2.4±0.40 Adipic acid 3.0 (1.0–12.9) 5.1 (0.8–35.0) Alpha-Hydroxyisobutyric acid 2.9 (1.3–4.6) 3.8 Azelaic acid 1.9 (1.2–3.2) 4.8 (1.3–15.0) Benzoic acid 3.6 (1.4–7.5) 4.2 (1.9–6.5) Cholenic acid 0.015 (0.004–0.030) NA** Cis/Trans-Aconitic acid 15.3 (5.2–21.8) NA** Citraconic acid 1.6 (0.9–2.1) 2.2 Creatinine* 12,246±8,369 10,090±310 Deoxycholic acid 0.055 (0.050–0.060) NA** D-Galactose 3.3 (1.3–8.5) 4.4 (0.0–31.0) D-Glucose 35.6 (10.3–56.7) 31.12 D-Xylitol 2.3 (0.9–8.2) 3.50±2. 37 D-Xylulose 19.7 (6.4–32.6) NA** Ethylmalonic acid 2.4 (0.9–4.0) 2.0 (0.5–3.9) Gluconic acid 13.3 (8.2–26.4) NA** Glutaric acid 1.4 (0.5–2.3 ) 1.3 (0.6–2.6) Glyceric acid 14.4 (7.8–28.8) 4.5 (0.0–9.0) Glycine 75.7 (21–205) 151 (233–248) Glycolic acid 39.4 (2.9–78.1) 35 (18–55) Hippuric acid 217 (28–610) 257 (20–770) Homovanillic acid 4.3 (0.9–8.9) 4.9±3.4 HPHPA 4.2 (0.9–15.3) 0.55–54.93 Hydroxypropionic acid 8.1 (3.1–11.8) 6.5 (3.0–10.0) Indoleacetic acid 2.6 (0.6–5.4) 2.0±1.0 Indolelactic acid 0.9 0.460 (0.098–0.980) Isocitric acid 56.8 (19.4–119.1) 58 (36–84) Kynurenic acid 2.6 (0.9–4.1) 1.34±0.30 L-Arabinose 8.8 (0.8–19.4) 8.75 (6.12–11.84) L-Asparagine 9.5 (3.0–26.0) 10.0 (4.6–16.3) Levulinic acid 1.7 (0.6–2.5) 1.28 (0.33–2.75) Lithocholic acid 0.16 (0.10–0.20) NA** L-Lactic acid 7.1 (0.9–16.4) 12.3±6.2 L-Ornithine 5.3 (2.0–8.8) 5.0 (2.0–8.0) L-Phenylalanine 7.8 (5.0–11.3) 4.50±1.15 L-Serine 19.0 (10.4–35.8) 26.0±10.0 L-Tyrosine 15.1 (6.1–23.2) 8.8±4.5 L-Valine 5.5 (2.7–9.8) 3.0±1.0 Malonic acid 2.3 (1.2–3.1) 1 (0–2) m-Chlorobenzoic acid 0.7 NA** Methylmalonic acid 1.3 (0.5–2.1) 1.6 (0.0–3.6) Methylsuccinic acid 4.7 (1.1–6.2) 2.3 (0.8–10.8) Myoinositol 12.6 (5.1–15.3) 18.8±2.9 o-Hydroxyphenylacetic acid 2.9 (1.4–3.7) 7.71±3.65 Oxalic acid 8.2 (3.9–14.0) 1.11–33.34 Oxoglutaric acid 3.4 (1.4–12.5) 2.98 (0.42–18.30) Palmitic acid 6.7 (2.6–24.3) 11 (6–23) Phosphoric acid 784 (425–1170) 1000–4900 p-Hydroxyphenylacetic acid 6.0 (2.4–9.7) 9.68 Pimelic acid 1.9 (0.5–3.5) NA** Pseudouridine 29.8 (21.8–47.3) 26.02±4.62 Pyroglutamic acid 18.6 (4.5–24.9) 28.8 (3.4–54.2) Pyruvic acid 2.1 (1.0–3.7) 2.13 (0.54–8.67) Quinolinic acid 3.9 (0.9–15.1) 2.5±1.1 Scyllitol 3.6 (1.9–6.9) NA Sorbitol 3.9 (1.9–5.1) 3.50±2.24 Stearic acid 3.9 (2.3–7.7) 2.9 (1.6–6.6) Suberic acid 1.2 (0.5–1.9) 0.5 (0.0–2.9) Succinic acid 6.2 (2.5–13.5) 5.6±3.8 Sumiki’s acid 1.6 NA** Threonic acid 14.8 (8.6–36.1) 10.0±7.0 Trans-Ferulic acid 1 36.3±11.2 Uric acid 186 (93–329) 188 (79–296) Ursodeoxycholic acid 0.022 (0.013–0.030) NA** Vanillylmandelic acid 1.6 (0.6–2.8) 1.2±0.5 |
| tr | Compounds Observed Concentration(µM/mM creatinine) Literature Values(µM/mM creatinine) |
| td | Compounds |
| td | Observed Concentration(µM/mM creatinine) |
| td | Literature Values(µM/mM creatinine) |
| tr | 2,4-Dihydroxybutanoic acid 1.0 (0.3–1.8) 2.0±2.0 |
| td | 2,4-Dihydroxybutanoic acid |
| td | 1.0 (0.3–1.8) |
| td | 2.0±2.0 |
| tr | 2-Ethylhydracrylic acid 2.1 (1.3–2.9) NA** |
| td | 2-Ethylhydracrylic acid |
| td | 2.1 (1.3–2.9) |
| td | NA** |
| tr | 2-Furoylglycine 3.6 (0.7–5.8) 9.95 (2.0–18.66) |
| td | 2-Furoylglycine |
| td | 3.6 (0.7–5.8) |
| td | 9.95 (2.0–18.66) |
| tr | 2-Hydroxy-2-methylbutyric acid 0.8 (0.4–1.5) 2.8±1.5 |
| td | 2-Hydroxy-2-methylbutyric acid |
| td | 0.8 (0.4–1.5) |
| td | 2.8±1.5 |
| tr | 2-Methyl-3-hydroxybutyric acid 4.0 (1.3–6.2) 5.5 (0.0–11.0) |
| td | 2-Methyl-3-hydroxybutyric acid |
| td | 4.0 (1.3–6.2) |
| td | 5.5 (0.0–11.0) |
| tr | 3,4-Dihydroxybenzeneacetic acid 0.9 (0.6–1.3) 1.16±0.50 |
| td | 3,4-Dihydroxybenzeneacetic acid |
| td | 0.9 (0.6–1.3) |
| td | 1.16±0.50 |
| tr | 3,4-Dihydroxybutanoic acid 42.2 (21.9–56.1) 34.9 (12.5–57.2) |
| td | 3,4-Dihydroxybutanoic acid |
| td | 42.2 (21.9–56.1) |
| td | 34.9 (12.5–57.2) |
| tr | 3-Aminoisobutanoic acid 25.8 (1.4–155.6) 2.91–116.43 |
| td | 3-Aminoisobutanoic acid |
| td | 25.8 (1.4–155.6) |
| td | 2.91–116.43 |
| tr | 3-Hydroxyhippuric acid 6.1 (4.1–7.8) 2.5 (0.2–5.0) |
| td | 3-Hydroxyhippuric acid |
| td | 6.1 (4.1–7.8) |
| td | 2.5 (0.2–5.0) |
| tr | 3-Hydroxyisobutyric acid 29.0 (11.8–59.8) 17.5 (2.0–33.0) |
| td | 3-Hydroxyisobutyric acid |
| td | 29.0 (11.8–59.8) |
| td | 17.5 (2.0–33.0) |
| tr | 3-Hydroxyisovaleric acid 7.4 (4.1–17.2) 8.5±3.2 |
| td | 3-Hydroxyisovaleric acid |
| td | 7.4 (4.1–17.2) |
| td | 8.5±3.2 |
| tr | 3-Hydroxymethylglutaric acid 3.2 (1.1–5.2) 3 (0–10) |
| td | 3-Hydroxymethylglutaric acid |
| td | 3.2 (1.1–5.2) |
| td | 3 (0–10) |
| tr | 3-Hydroxyphenylacetic acid 5.6 (1.4–15.0) 6.1±4.1 |
| td | 3-Hydroxyphenylacetic acid |
| td | 5.6 (1.4–15.0) |
| td | 6.1±4.1 |
| tr | 3-Hydroxysebacic acid 2.9 (1.7–3.8) NA** |
| td | 3-Hydroxysebacic acid |
| td | 2.9 (1.7–3.8) |
| td | NA** |
| tr | 3-Methyladipic acid 2.1 (0.9–5.6) 2.9 (0.7–10.5) |
| td | 3-Methyladipic acid |
| td | 2.1 (0.9–5.6) |
| td | 2.9 (0.7–10.5) |
| tr | 3-Methylglutaconic acid 6.2 (2.8–8.3) 4.5 (0.0–9.0) |
| td | 3-Methylglutaconic acid |
| td | 6.2 (2.8–8.3) |
| td | 4.5 (0.0–9.0) |
| tr | 4-Deoxythreonic acid 18.2 (5.6–23.9) 25±10 |
| td | 4-Deoxythreonic acid |
| td | 18.2 (5.6–23.9) |
| td | 25±10 |
| tr | 4-Hydroxybenzoic acid 2.4 (1.3–3.6) 1.8±1.8 |
| td | 4-Hydroxybenzoic acid |
| td | 2.4 (1.3–3.6) |
| td | 1.8±1.8 |
| tr | 4-Hydroxyphenyllactic acid 2.8 (2.6–3.0) 1.1 (0.2–2.6) |
| td | 4-Hydroxyphenyllactic acid |
| td | 2.8 (2.6–3.0) |
| td | 1.1 (0.2–2.6) |
| tr | 5-Hydroxyhexanoic acid 2.7 (0.8–5.7) 3.5 (0.0–7.0) |
| td | 5-Hydroxyhexanoic acid |
| td | 2.7 (0.8–5.7) |
| td | 3.5 (0.0–7.0) |
| tr | 5-Hydroxyindoleacetic acid 2.9 (0.4–5.8) 2.4±0.40 |
| td | 5-Hydroxyindoleacetic acid |
| td | 2.9 (0.4–5.8) |
| td | 2.4±0.40 |
| tr | Adipic acid 3.0 (1.0–12.9) 5.1 (0.8–35.0) |
| td | Adipic acid |
| td | 3.0 (1.0–12.9) |
| td | 5.1 (0.8–35.0) |
| tr | Alpha-Hydroxyisobutyric acid 2.9 (1.3–4.6) 3.8 |
| td | Alpha-Hydroxyisobutyric acid |
| td | 2.9 (1.3–4.6) |
| td | 3.8 |
| tr | Azelaic acid 1.9 (1.2–3.2) 4.8 (1.3–15.0) |
| td | Azelaic acid |
| td | 1.9 (1.2–3.2) |
| td | 4.8 (1.3–15.0) |
| tr | Benzoic acid 3.6 (1.4–7.5) 4.2 (1.9–6.5) |
| td | Benzoic acid |
| td | 3.6 (1.4–7.5) |
| td | 4.2 (1.9–6.5) |
| tr | Cholenic acid 0.015 (0.004–0.030) NA** |
| td | Cholenic acid |
| td | 0.015 (0.004–0.030) |
| td | NA** |
| tr | Cis/Trans-Aconitic acid 15.3 (5.2–21.8) NA** |
| td | Cis/Trans-Aconitic acid |
| td | 15.3 (5.2–21.8) |
| td | NA** |
| tr | Citraconic acid 1.6 (0.9–2.1) 2.2 |
| td | Citraconic acid |
| td | 1.6 (0.9–2.1) |
| td | 2.2 |
| tr | Creatinine* 12,246±8,369 10,090±310 |
| td | Creatinine* |
| td | 12,246±8,369 |
| td | 10,090±310 |
| tr | Deoxycholic acid 0.055 (0.050–0.060) NA** |
| td | Deoxycholic acid |
| td | 0.055 (0.050–0.060) |
| td | NA** |
| tr | D-Galactose 3.3 (1.3–8.5) 4.4 (0.0–31.0) |
| td | D-Galactose |
| td | 3.3 (1.3–8.5) |
| td | 4.4 (0.0–31.0) |
| tr | D-Glucose 35.6 (10.3–56.7) 31.12 |
| td | D-Glucose |
| td | 35.6 (10.3–56.7) |
| td | 31.12 |
| tr | D-Xylitol 2.3 (0.9–8.2) 3.50±2. 37 |
| td | D-Xylitol |
| td | 2.3 (0.9–8.2) |
| td | 3.50±2. 37 |
| tr | D-Xylulose 19.7 (6.4–32.6) NA** |
| td | D-Xylulose |
| td | 19.7 (6.4–32.6) |
| td | NA** |
| tr | Ethylmalonic acid 2.4 (0.9–4.0) 2.0 (0.5–3.9) |
| td | Ethylmalonic acid |
| td | 2.4 (0.9–4.0) |
| td | 2.0 (0.5–3.9) |
| tr | Gluconic acid 13.3 (8.2–26.4) NA** |
| td | Gluconic acid |
| td | 13.3 (8.2–26.4) |
| td | NA** |
| tr | Glutaric acid 1.4 (0.5–2.3 ) 1.3 (0.6–2.6) |
| td | Glutaric acid |
| td | 1.4 (0.5–2.3 ) |
| td | 1.3 (0.6–2.6) |
| tr | Glyceric acid 14.4 (7.8–28.8) 4.5 (0.0–9.0) |
| td | Glyceric acid |
| td | 14.4 (7.8–28.8) |
| td | 4.5 (0.0–9.0) |
| tr | Glycine 75.7 (21–205) 151 (233–248) |
| td | Glycine |
| td | 75.7 (21–205) |
| td | 151 (233–248) |
| tr | Glycolic acid 39.4 (2.9–78.1) 35 (18–55) |
| td | Glycolic acid |
| td | 39.4 (2.9–78.1) |
| td | 35 (18–55) |
| tr | Hippuric acid 217 (28–610) 257 (20–770) |
| td | Hippuric acid |
| td | 217 (28–610) |
| td | 257 (20–770) |
| tr | Homovanillic acid 4.3 (0.9–8.9) 4.9±3.4 |
| td | Homovanillic acid |
| td | 4.3 (0.9–8.9) |
| td | 4.9±3.4 |
| tr | HPHPA 4.2 (0.9–15.3) 0.55–54.93 |
| td | HPHPA |
| td | 4.2 (0.9–15.3) |
| td | 0.55–54.93 |
| tr | Hydroxypropionic acid 8.1 (3.1–11.8) 6.5 (3.0–10.0) |
| td | Hydroxypropionic acid |
| td | 8.1 (3.1–11.8) |
| td | 6.5 (3.0–10.0) |
| tr | Indoleacetic acid 2.6 (0.6–5.4) 2.0±1.0 |
| td | Indoleacetic acid |
| td | 2.6 (0.6–5.4) |
| td | 2.0±1.0 |
| tr | Indolelactic acid 0.9 0.460 (0.098–0.980) |
| td | Indolelactic acid |
| td | 0.9 |
| td | 0.460 (0.098–0.980) |
| tr | Isocitric acid 56.8 (19.4–119.1) 58 (36–84) |
| td | Isocitric acid |
| td | 56.8 (19.4–119.1) |
| td | 58 (36–84) |
| tr | Kynurenic acid 2.6 (0.9–4.1) 1.34±0.30 |
| td | Kynurenic acid |
| td | 2.6 (0.9–4.1) |
| td | 1.34±0.30 |
| tr | L-Arabinose 8.8 (0.8–19.4) 8.75 (6.12–11.84) |
| td | L-Arabinose |
| td | 8.8 (0.8–19.4) |
| td | 8.75 (6.12–11.84) |
| tr | L-Asparagine 9.5 (3.0–26.0) 10.0 (4.6–16.3) |
| td | L-Asparagine |
| td | 9.5 (3.0–26.0) |
| td | 10.0 (4.6–16.3) |
| tr | Levulinic acid 1.7 (0.6–2.5) 1.28 (0.33–2.75) |
| td | Levulinic acid |
| td | 1.7 (0.6–2.5) |
| td | 1.28 (0.33–2.75) |
| tr | Lithocholic acid 0.16 (0.10–0.20) NA** |
| td | Lithocholic acid |
| td | 0.16 (0.10–0.20) |
| td | NA** |
| tr | L-Lactic acid 7.1 (0.9–16.4) 12.3±6.2 |
| td | L-Lactic acid |
| td | 7.1 (0.9–16.4) |
| td | 12.3±6.2 |
| tr | L-Ornithine 5.3 (2.0–8.8) 5.0 (2.0–8.0) |
| td | L-Ornithine |
| td | 5.3 (2.0–8.8) |
| td | 5.0 (2.0–8.0) |
| tr | L-Phenylalanine 7.8 (5.0–11.3) 4.50±1.15 |
| td | L-Phenylalanine |
| td | 7.8 (5.0–11.3) |
| td | 4.50±1.15 |
| tr | L-Serine 19.0 (10.4–35.8) 26.0±10.0 |
| td | L-Serine |
| td | 19.0 (10.4–35.8) |
| td | 26.0±10.0 |
| tr | L-Tyrosine 15.1 (6.1–23.2) 8.8±4.5 |
| td | L-Tyrosine |
| td | 15.1 (6.1–23.2) |
| td | 8.8±4.5 |
| tr | L-Valine 5.5 (2.7–9.8) 3.0±1.0 |
| td | L-Valine |
| td | 5.5 (2.7–9.8) |
| td | 3.0±1.0 |
| tr | Malonic acid 2.3 (1.2–3.1) 1 (0–2) |
| td | Malonic acid |
| td | 2.3 (1.2–3.1) |
| td | 1 (0–2) |
| tr | m-Chlorobenzoic acid 0.7 NA** |
| td | m-Chlorobenzoic acid |
| td | 0.7 |
| td | NA** |
| tr | Methylmalonic acid 1.3 (0.5–2.1) 1.6 (0.0–3.6) |
| td | Methylmalonic acid |
| td | 1.3 (0.5–2.1) |
| td | 1.6 (0.0–3.6) |
| tr | Methylsuccinic acid 4.7 (1.1–6.2) 2.3 (0.8–10.8) |
| td | Methylsuccinic acid |
| td | 4.7 (1.1–6.2) |
| td | 2.3 (0.8–10.8) |
| tr | Myoinositol 12.6 (5.1–15.3) 18.8±2.9 |
| td | Myoinositol |
| td | 12.6 (5.1–15.3) |
| td | 18.8±2.9 |
| tr | o-Hydroxyphenylacetic acid 2.9 (1.4–3.7) 7.71±3.65 |
| td | o-Hydroxyphenylacetic acid |
| td | 2.9 (1.4–3.7) |
| td | 7.71±3.65 |
| tr | Oxalic acid 8.2 (3.9–14.0) 1.11–33.34 |
| td | Oxalic acid |
| td | 8.2 (3.9–14.0) |
| td | 1.11–33.34 |
| tr | Oxoglutaric acid 3.4 (1.4–12.5) 2.98 (0.42–18.30) |
| td | Oxoglutaric acid |
| td | 3.4 (1.4–12.5) |
| td | 2.98 (0.42–18.30) |
| tr | Palmitic acid 6.7 (2.6–24.3) 11 (6–23) |
| td | Palmitic acid |
| td | 6.7 (2.6–24.3) |
| td | 11 (6–23) |
| tr | Phosphoric acid 784 (425–1170) 1000–4900 |
| td | Phosphoric acid |
| td | 784 (425–1170) |
| td | 1000–4900 |
| tr | p-Hydroxyphenylacetic acid 6.0 (2.4–9.7) 9.68 |
| td | p-Hydroxyphenylacetic acid |
| td | 6.0 (2.4–9.7) |
| td | 9.68 |
| tr | Pimelic acid 1.9 (0.5–3.5) NA** |
| td | Pimelic acid |
| td | 1.9 (0.5–3.5) |
| td | NA** |
| tr | Pseudouridine 29.8 (21.8–47.3) 26.02±4.62 |
| td | Pseudouridine |
| td | 29.8 (21.8–47.3) |
| td | 26.02±4.62 |
| tr | Pyroglutamic acid 18.6 (4.5–24.9) 28.8 (3.4–54.2) |
| td | Pyroglutamic acid |
| td | 18.6 (4.5–24.9) |
| td | 28.8 (3.4–54.2) |
| tr | Pyruvic acid 2.1 (1.0–3.7) 2.13 (0.54–8.67) |
| td | Pyruvic acid |
| td | 2.1 (1.0–3.7) |
| td | 2.13 (0.54–8.67) |
| tr | Quinolinic acid 3.9 (0.9–15.1) 2.5±1.1 |
| td | Quinolinic acid |
| td | 3.9 (0.9–15.1) |
| td | 2.5±1.1 |
| tr | Scyllitol 3.6 (1.9–6.9) NA |
| td | Scyllitol |
| td | 3.6 (1.9–6.9) |
| td | NA |
| tr | Sorbitol 3.9 (1.9–5.1) 3.50±2.24 |
| td | Sorbitol |
| td | 3.9 (1.9–5.1) |
| td | 3.50±2.24 |
| tr | Stearic acid 3.9 (2.3–7.7) 2.9 (1.6–6.6) |
| td | Stearic acid |
| td | 3.9 (2.3–7.7) |
| td | 2.9 (1.6–6.6) |
| tr | Suberic acid 1.2 (0.5–1.9) 0.5 (0.0–2.9) |
| td | Suberic acid |
| td | 1.2 (0.5–1.9) |
| td | 0.5 (0.0–2.9) |
| tr | Succinic acid 6.2 (2.5–13.5) 5.6±3.8 |
| td | Succinic acid |
| td | 6.2 (2.5–13.5) |
| td | 5.6±3.8 |
| tr | Sumiki’s acid 1.6 NA** |
| td | Sumiki’s acid |
| td | 1.6 |
| td | NA** |
| tr | Threonic acid 14.8 (8.6–36.1) 10.0±7.0 |
| td | Threonic acid |
| td | 14.8 (8.6–36.1) |
| td | 10.0±7.0 |
| tr | Trans-Ferulic acid 1 36.3±11.2 |
| td | Trans-Ferulic acid |
| td | 1 |
| td | 36.3±11.2 |
| tr | Uric acid 186 (93–329) 188 (79–296) |
| td | Uric acid |
| td | 186 (93–329) |
| td | 188 (79–296) |
| tr | Ursodeoxycholic acid 0.022 (0.013–0.030) NA** |
| td | Ursodeoxycholic acid |
| td | 0.022 (0.013–0.030) |
| td | NA** |
| tr | Vanillylmandelic acid 1.6 (0.6–2.8) 1.2±0.5 |
| td | Vanillylmandelic acid |
| td | 1.6 (0.6–2.8) |
| td | 1.2±0.5 |
| table-wrap-foot | * Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. |
| footnote | * Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. |
| label | * |
| p | Concentration of metabolite (Mean ± SD) is expressed by µM. HPHPA: 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid; NA: not available; NA**: not available for healthy adults. |
| p | Incomplete compound identification is a common problem with global or untargeted GC-MS metabolomics. This may be due to any number of factors including spectral overlap due to incomplete separation, poor signal to noise for low intensity peaks, the lack of reference GC-MS spectral data for certain metabolites (especially unusual dietary sources), or the presence of spectral artefacts such as derivatization by-products or degraded metabolites in the GC-MS spectrum. For our GC-MS studies we used the NIST library supplemented with a home-made GC-MS reference library of known urine compounds assembled from the Human Metabolome Library [16]. No doubt the use of other commercially available reference GC-MS libraries such as the Fiehn GC-MS library from Agilent or the GOLM metabolome database library [85] might have allowed us to further increase our coverage. Likewise the use of a faster scan rate and/or a more sensitive GC-TOF instrument (instead of a slower scanning quadrupole GC-MS) certainly would have increased overall coverage. |
| p | Nearly all of the non-volatile metabolites (87) identified by our GC-MS analyses were also identified by NMR. Some of the exceptions were oxalic acid, phosphate and uric acid, each of which was detected by GC-MS but not by NMR. These compounds do not have NMR-detectable protons at physiological pH, making them essentially “NMR invisible”. Other compounds seen by GC-MS but not by NMR included metabolites that were generally below the detection limit of NMR (∼2 µM/mM creatinine) such as indolelactic acid and 2,4-dihydroxybutanoic acid. For our non-targeted GC-MS analysis, the lower limit of detection was 1 µM/mM creatinine (for 2,4-dihydroxybutanoic acid), while for our targeted organic acid GC-MS analysis the lower limit of detection was 0.7 µM/mM creatinine (for m-chlorobenzoic acid). Overall, our data suggests that the sensitivity of a standard single quadrupole GC-MS instrument is perhaps 1.5–2X better than a 500 MHz NMR instrument for water-soluble metabolites. It is also important to note that the level of water-soluble, non-volatile metabolite coverage obtained by GC-MS is not as great as seen with NMR (127 cmpds vs. 209 cmpds). The limited coverage of GC-MS is partly due to the fact that not all compounds can be readily extracted, easily derivatized or routinely separated on a GC column. Furthermore, when analyzing urine by GC-MS there is a need to pretreat the sample with urease (to reduce urea levels) that can diminish the abundance of some metabolites [86]. While GC-MS may not be the best method for analyzing water-soluble metabolites, it certainly excels at the detection of volatile metabolites. Indeed, only one of the volatile metabolites identified by GC-MS is identified by NMR (phenol). This certainly underlines a key strength of GC-MS relative to other metabolomics platforms. When comparing NMR to GC-MS we found that NMR is capable of detecting 121 compounds that the 4 combined GC-MS methods cannot detect while the combined GC-MS methods can detect 91 compounds that NMR cannot routinely detect. Overall, these data suggest that GC-MS and NMR appear to be complementary methods for the identification and quantification of small molecules in urine. |
| p | Among the 58 metabolites quantified by both GC-MS and NMR we found very good overall agreement, with the majority of measured concentration values falling within 20 ± 11% of each other. The concentration patterns and rankings of the most abundant to the least abundant compounds were also largely identical for the two platforms. A total of 12 metabolites exhibited somewhat larger concentration discrepancies between GC-MS and NMR (i.e; L-arabinose, L-serine (lower in GC-MS vs. NMR), 4-hydroxybenzoic acid and tyrosine (higher in GC-MS vs NMR). Some of these concentration differences may be due to the extraction or derivatization process needed to conduct GC-MS analyses. This can lead to unspecified compound losses, unusual derivatives or unrecognized fragmentation patterns. Therefore we would have expected at least a few GC-MS concentration values to be slightly lower than those seen by NMR. Likewise, it is important to remember that there are inherent errors (5–10%) in measuring peak areas (i.e. compound concentrations) both in GC-MS and NMR due to peak overlap, uneven baselines and spectral noise. |
| p | Nearly all of the compounds we detected or quantified in human urine by GC-MS have been previously described or mentioned in the GC-MS literature. One compound (scyllitol), however, appears not to have been previously detected by GC-MS. The identification of this compound by our GC-MS method was aided by its prior identification by NMR (see previous section). Additionally, a careful literature analysis also indicated the scyllitol is a normal constituent of human urine and has previously been detected in human urine via other methods. |
| p | As we noted with our NMR studies earlier, there are a few previously reported GC-MS detectable metabolites in human urine that appear to be artefacts. These artefactual metabolites may arise from extractions with different solvents, pre-treatment with urease, and chemical derivatization. For example, Shoemaker et al [84], reported the presence of bisethane in human urine. We also detected bisethane, but it appears to be artefact of chemical derivatization and is not a urine metabolite. |
| sec | Quantification and Identification of Urine Metabolites – Combined Direct Flow Injection and LC-MS/MS Assay Direct flow injection (DFI) MS/MS or DFI-MS/MS is another commonly used global metabolic profiling method [87]. When isotopic standards are used along with multiple reactions monitoring (MRM), it is also possible to perform targeted metabolomics with very accurate concentration measurements. For our urine studies, we employed a combined DFI/LC-MS/MS approach, based on the commercially available AbsoluteIDQ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck). When applied to urine, we were able to identify and quantify a total of 127 metabolites or metabolite species, including 34 acylcarnitines, 21 amino acids, 15 biogenic amines, creatinine, hexose, 35 phospatidylcholines, 15 sphingomyelins and 5 lysophosphatidylcholines. The amino acids and biogenic amines are analyzed by an LC-MS/MS method, whereas all other metabolites are analyzed by DFI-MS/MS as indicated in Table 7. DFI-MS/MS identifies lipid species (as opposed to specific lipids) using their total acyl/alkyl chain content (i.e. PC (38∶4)) rather than their unique structure. Therefore each lipid species identified by the BIOCRATES kit typically corresponds to 5–10 possible unique lipid structures. Consequently, the total number of phosphatidylcholines, sphingolipids and lysophosphatidylcholines structures identified by this method was 458, 19 and 6, respectively. Therefore, combining these probable lipid structures (483 in total, based on the known fatty acid and lipid composition in human serum) with the other 72 confirmed non-lipid metabolites, the DFI-MS/MS method yields 555 confirmed and probable metabolites or metabolite structures. All of these compounds, along with their corresponding estimated concentrations have all been entered into the UMDB. Table 7 Concentrations of acylcarnitines, amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and sphingomyelins in human urine by combined DFI/LC-MS/MS (BIOCRATES kit). Acylcarnitines (DFI-MS/MS) Mean (µM/mM creatinine) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) Acetyl-L-carnitine 2.0 (0.4–5.9) Kynurenine 0.41 (0.08–1.31) Butenyl-L-carnitine 0.014 (0.008 –0.021) L-Dopa 0.02 (0.01–0.04) Butyryl-L-carnitine 0.76 (0.12–1.81) Methioninesulfoxide 0.31 (0.14–0.66) Decadienyl-L-carnitine 0.10 (0.04–0.30) Phenylethylamine 0.011 (0.003–0.067) Decanoyl-L-carnitine 0.04 (0.02–0.06) Putrescine 0.20 (0.04–0.40) Decenoyl-L-carnitine 0.14 (0.06–0.32) Sarcosine 1.6 (0.2–9.4) Dodecanoyl-L-carnitine 0.02 (0.01–0.04) Serotonin 0.08 (0.04–0.12) Dodecenoyl-L-carnitine 0.028 (0.011–0.042) Symmetric dimethylarginine 2.8 (2.0–9.4) Glutaconyl-L-carnitine 0.04 (0.01–0.06) Lysophosphatidylcholines (DFI-MS/MS) Mean (µM/Mm creatinine) Glutaryl-L-carnitine 0.04 (0.03–0.10) lysoPC a C16∶0 0.014 Hexadecadienyl-L-carnitine 0.0010 (0.0005–0.0040) lysoPC a C17∶0 0.0032 (0.0008–0.0087) Hexadecanoyl-L-carnitine 0.0022 (0.0020–0.0030) lysoPC a C18∶0 0.0040 (0.0013–0.0100) Hexanoyl-L-carnitine 0.05 (0.03–0.07) lysoPC a C20∶4 0.0020 (0.0005–0.0038) Hexenoyl-L-carnitine 0.019 (0.009–0.050) lysoPC a C6∶0 0.0045 (0.0022–0.0074) Hydroxyhexadecanoyl-L-carnitine 0.0015 (0.0006–0.0050) Phosphatidylcholines (DFI-LC/MS) Mean (µM/Mm creatinine) Hydroxypropionyl-L-carnitine 0.008 (0.004–0.012) PC aa C28∶1 0.006 (0.003–0.020) Hydroxytetradecadienyl-L-carnitine 0.003 (0.001–0.008) PC aa C30∶2 0.0005 (0.0001–0.0022) Hydroxytetradecenoyl-L-carnitine 0.0020 (0.0008–0.0040) PC aa C32∶0 0.0035 (0.0013–0.0071) L-Carnitine 4.5 (0.6–15.2) PC aa C32∶3 0.0009 (0.0003–0.0025) Malonyl-L-carnitine 0.02 (0.01–0.04) PC aa C34∶1 0.0077 (0.0023–0.0500) Methylglutaryl-L-carnitine 0.030 (0.016–0.052) PC aa C34∶2 0.0047 (0.0016–0.0240) Methylmalonyl-L-carnitine 0.09 (0.05–0.16) PC aa C34∶3 0.002 Nonayl-L-carnitine 0.20 (0.07–0.46) PC aa C34∶4 0.0023 (0.0002–0.0180) Octadecadienyl-L-carnitine 0.0007 (0.0003–0.0020) PC aa C36∶1 0.0034 (0.0006–0.0250) Octanoyl-L-carnitine 0.05 (0.03–0.09) PC aa C36∶2 0.0160 (0.0021–0.0450) Octenoyl-L-carnitine 0.50 (0.08–1.20) PC aa C36∶3 0.0024 (0.0005–0.0240) Pimelyl-L-carnitine 0.03 (0.01–0.11) PC aa C36∶4 0.0018 (0.0005–0.0083) Propenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C36∶6 0.0037 Propionyl-L-carnitine 0.07 (0.01–0.20) PC aa C38∶3 0.0056 Tetradecadienyl-L-carnitine 0.0023 (0.0010–0.0050) PC aa C38∶4 0.0022 (0.0005–0.0065) Tetradecanoyl-L-carnitine 0.018 (0.001–0.088) PC aa C38∶5 0.0007 (0.0002–0.0020) Tetradecenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C38∶6 0.0010 (0.0005–0.0036) Tiglyl-L-carnitine 0.10 (0.05–0.20) PC aa C40∶3 0.00035 (0.0–0.00140) Valeryl-L-carnitine 0.220 (0.037–0.440) PC aa C42∶2 0.0009 (0.0003–0.0024) Creatinine Mean PC aa C42∶4 0.0008 (0.0003–0.0025) Creatinine* 12,475±7,955 PC ae C34∶1 0.0030 (0.0003–0.0110) Hexose Mean (µM/Mm creatinine) PC ae C34∶2 0.0012 (0.0002–0.0075) Hexose 110 (37–501) PC ae C36∶1 0.0046 (0.0012–0.0080) Amino acids (LC-MS/MS) Mean (µM/Mm creatinine) PC ae C36∶2 0.0016 (0.0002–0.0050) Glycine 101.0 (37.0–250.6) PC ae C36∶3 0.0009 (0.0001–0.0040) L-Alanine 21.7 (7.2–47.4) PC ae C36∶4 0.0022 L-Arginine 7.4 (3.2–14.6) PC ae C38∶2 0.0015 (0.0007–0.0023) L-Asparagine 8.8 (4.6–17.7) PC ae C38∶3 0.0020 (0.0012–0.0028) L-Aspartic acid 10.9 (1.9–26.8) PC ae C38∶4 0.0006 (0.0–0.0024) L-Citrulline 0.8 (0.2–1.7) PC ae C38∶6 0.0065 (0.0027–0.0170) L-Glutamic acid 6.4 (0.6–17.5) PC ae C40∶2 0.0012 (0.0006–0.0020) L-Glutamine 39.9 (18.4–72.5) PC ae C40∶5 0.0006 (0.0001–0.0025) L-Histidine 45.6 (21.7–71.5) PC ae C42∶3 0.0007 (0.0001–0.0020) L-Isoleucine 1.3 (0.5–2.7) PC ae C44∶3 0.0010 (0.0002–0.0031) L-Leucine 3.0 (1.6–6.0) Sphingomyelins (DFI-MS/MS) Mean (µM/Mm creatinine) L-Lysine 17.9 (3.6–56.1) SM (OH) C14∶1 0.0028 (0.0008–0.0049) L-Methionine 0.8 (0.4–1.6) SM (OH) C16∶1 0.0006 (0.0001–0.0023) L-Ornithine 4.4 (1.2–22.1) SM (OH) C22∶1 0.0023 (0.0009–0.0058) L-Phenylalanine 5.9 (2.8–10.8) SM (OH) C22∶2 0.0007 (0.0001–0.0028) L-Proline 1.1 (0.1–2.5) SM (OH) C24∶1 0.0010 (0.00034–0.0025) L-Serine 24.5 (12.3–50.2) SM C16∶0 0.0100 (0.0016–0.0700) L-Threonine 14.6 (6.6–29.3) SM C16∶1 0.0009 (0.0004–0.0023) L-Tryptophan 5.6 (9.3–2.1) SM C18∶0 0.0024 (0.0011–0.0070) L-Tyrosine 8.7 (3.0–20.1) SM C18∶1 0.0004 (0.0001–0.0013) L-Valine 4.3 (2.4–10.4) SM C20∶2 0.0003 (0.0–0.0016) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) SM C22∶3 0.0011 (0.0008–0.0014) Acetyl-ornithine 1.3 (0.5–2.8) SM C24∶0 0.0085 (0.0047–0.0200) Alpha-aminoadipic acid 7.2 (2.5–16.4) SM C24∶1 0.0030 (0.0007–0.0120) Asymmetric dimethylarginine 2.7 (1.4–4.2) SM C26∶0 0.0014 (0.0002–0.0043) Carnosine 1.2 (0.2–4.1) SM C26∶1 0.0005 (0.0001–0.0012) Dopamine 0.4 (0.2–0.7) Histamine 0.03 (0.01–0.10) Hydroxykynurenine 0.15 (0.06–0.34) * Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. Our results show very good agreement with the previous studies conducted by BIOCRATES on human urine samples (Biocrates Application Note 1005-1). We found that the lower limit of quantification by DFI MS/MS based on the AbsoluteIDQ kit was 0.1 nM/mM creatinine for certain phosphatidylcholine species (i.e. PC aa C40∶3) and 0.1 nM/mM creatinine for certain sphingomyelin species (i.e. SM 26∶1). Comparison of our lipid results with literature data was difficult as relatively few papers report urine lipid concentration data. Indeed, the presence of lipids in urine is a little unexpected, but not entirely unreasonable. It is likely that urea, a well known chaotrope, facilitates the dissolution of small amounts of fatty acids and other lipid species in urine. Many of the compounds we measured with this kit assay appear to be normal constituents of human urine but have not been previously reported (quantified and/or detected) in the scientific literature (with the exception of the BIOCRATES Application Note). In total, 53 compounds are being reported here for the first time as being normal constituents of human urine, while 68 compounds are being robustly quantified in human urine for the first time. The vast majority of these compounds are lipids. Based on our results, the combined DFI/LC-MS/MS method detected 98 compounds or compound species that GC-MS and NMR methods could not detect, while GC-MS detected 161 compounds and NMR detected 181 compounds that DFI/LC-MS/MS could not detect. The 3 methods were able to detect a common set of 17 compounds including creatinine, L-glutamine, L-tryptophan, L-tyrosine and L-valine. Interestingly, the concentrations measured by DFI/LC-MS/MS, NMR and GC-MS (across the shared set of 17 compounds) showed generally good agreement (within 19 ± 7% of each other). The relatively small overlap, in terms of compound coverage, between the 3 platforms is a bit of a surprise and certainly serves to emphasize the tremendous chemical diversity that must exist in urine. Overall, by combining these 3 platforms, we were able to identify 295 and quantify 231 unique or non-overlapping metabolites or metabolite species. These data suggest that DFI/LC-MS/MS, GC-MS and NMR are highly complementary techniques for the identification and quantification of metabolites in human urine. |
| title | Quantification and Identification of Urine Metabolites – Combined Direct Flow Injection and LC-MS/MS Assay |
| p | Direct flow injection (DFI) MS/MS or DFI-MS/MS is another commonly used global metabolic profiling method [87]. When isotopic standards are used along with multiple reactions monitoring (MRM), it is also possible to perform targeted metabolomics with very accurate concentration measurements. For our urine studies, we employed a combined DFI/LC-MS/MS approach, based on the commercially available AbsoluteIDQ p180 Kit (BIOCRATES Life Sciences AG, Innsbruck). When applied to urine, we were able to identify and quantify a total of 127 metabolites or metabolite species, including 34 acylcarnitines, 21 amino acids, 15 biogenic amines, creatinine, hexose, 35 phospatidylcholines, 15 sphingomyelins and 5 lysophosphatidylcholines. The amino acids and biogenic amines are analyzed by an LC-MS/MS method, whereas all other metabolites are analyzed by DFI-MS/MS as indicated in Table 7. DFI-MS/MS identifies lipid species (as opposed to specific lipids) using their total acyl/alkyl chain content (i.e. PC (38∶4)) rather than their unique structure. Therefore each lipid species identified by the BIOCRATES kit typically corresponds to 5–10 possible unique lipid structures. Consequently, the total number of phosphatidylcholines, sphingolipids and lysophosphatidylcholines structures identified by this method was 458, 19 and 6, respectively. Therefore, combining these probable lipid structures (483 in total, based on the known fatty acid and lipid composition in human serum) with the other 72 confirmed non-lipid metabolites, the DFI-MS/MS method yields 555 confirmed and probable metabolites or metabolite structures. All of these compounds, along with their corresponding estimated concentrations have all been entered into the UMDB. |
| table-wrap | Table 7 Concentrations of acylcarnitines, amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and sphingomyelins in human urine by combined DFI/LC-MS/MS (BIOCRATES kit). Acylcarnitines (DFI-MS/MS) Mean (µM/mM creatinine) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) Acetyl-L-carnitine 2.0 (0.4–5.9) Kynurenine 0.41 (0.08–1.31) Butenyl-L-carnitine 0.014 (0.008 –0.021) L-Dopa 0.02 (0.01–0.04) Butyryl-L-carnitine 0.76 (0.12–1.81) Methioninesulfoxide 0.31 (0.14–0.66) Decadienyl-L-carnitine 0.10 (0.04–0.30) Phenylethylamine 0.011 (0.003–0.067) Decanoyl-L-carnitine 0.04 (0.02–0.06) Putrescine 0.20 (0.04–0.40) Decenoyl-L-carnitine 0.14 (0.06–0.32) Sarcosine 1.6 (0.2–9.4) Dodecanoyl-L-carnitine 0.02 (0.01–0.04) Serotonin 0.08 (0.04–0.12) Dodecenoyl-L-carnitine 0.028 (0.011–0.042) Symmetric dimethylarginine 2.8 (2.0–9.4) Glutaconyl-L-carnitine 0.04 (0.01–0.06) Lysophosphatidylcholines (DFI-MS/MS) Mean (µM/Mm creatinine) Glutaryl-L-carnitine 0.04 (0.03–0.10) lysoPC a C16∶0 0.014 Hexadecadienyl-L-carnitine 0.0010 (0.0005–0.0040) lysoPC a C17∶0 0.0032 (0.0008–0.0087) Hexadecanoyl-L-carnitine 0.0022 (0.0020–0.0030) lysoPC a C18∶0 0.0040 (0.0013–0.0100) Hexanoyl-L-carnitine 0.05 (0.03–0.07) lysoPC a C20∶4 0.0020 (0.0005–0.0038) Hexenoyl-L-carnitine 0.019 (0.009–0.050) lysoPC a C6∶0 0.0045 (0.0022–0.0074) Hydroxyhexadecanoyl-L-carnitine 0.0015 (0.0006–0.0050) Phosphatidylcholines (DFI-LC/MS) Mean (µM/Mm creatinine) Hydroxypropionyl-L-carnitine 0.008 (0.004–0.012) PC aa C28∶1 0.006 (0.003–0.020) Hydroxytetradecadienyl-L-carnitine 0.003 (0.001–0.008) PC aa C30∶2 0.0005 (0.0001–0.0022) Hydroxytetradecenoyl-L-carnitine 0.0020 (0.0008–0.0040) PC aa C32∶0 0.0035 (0.0013–0.0071) L-Carnitine 4.5 (0.6–15.2) PC aa C32∶3 0.0009 (0.0003–0.0025) Malonyl-L-carnitine 0.02 (0.01–0.04) PC aa C34∶1 0.0077 (0.0023–0.0500) Methylglutaryl-L-carnitine 0.030 (0.016–0.052) PC aa C34∶2 0.0047 (0.0016–0.0240) Methylmalonyl-L-carnitine 0.09 (0.05–0.16) PC aa C34∶3 0.002 Nonayl-L-carnitine 0.20 (0.07–0.46) PC aa C34∶4 0.0023 (0.0002–0.0180) Octadecadienyl-L-carnitine 0.0007 (0.0003–0.0020) PC aa C36∶1 0.0034 (0.0006–0.0250) Octanoyl-L-carnitine 0.05 (0.03–0.09) PC aa C36∶2 0.0160 (0.0021–0.0450) Octenoyl-L-carnitine 0.50 (0.08–1.20) PC aa C36∶3 0.0024 (0.0005–0.0240) Pimelyl-L-carnitine 0.03 (0.01–0.11) PC aa C36∶4 0.0018 (0.0005–0.0083) Propenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C36∶6 0.0037 Propionyl-L-carnitine 0.07 (0.01–0.20) PC aa C38∶3 0.0056 Tetradecadienyl-L-carnitine 0.0023 (0.0010–0.0050) PC aa C38∶4 0.0022 (0.0005–0.0065) Tetradecanoyl-L-carnitine 0.018 (0.001–0.088) PC aa C38∶5 0.0007 (0.0002–0.0020) Tetradecenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C38∶6 0.0010 (0.0005–0.0036) Tiglyl-L-carnitine 0.10 (0.05–0.20) PC aa C40∶3 0.00035 (0.0–0.00140) Valeryl-L-carnitine 0.220 (0.037–0.440) PC aa C42∶2 0.0009 (0.0003–0.0024) Creatinine Mean PC aa C42∶4 0.0008 (0.0003–0.0025) Creatinine* 12,475±7,955 PC ae C34∶1 0.0030 (0.0003–0.0110) Hexose Mean (µM/Mm creatinine) PC ae C34∶2 0.0012 (0.0002–0.0075) Hexose 110 (37–501) PC ae C36∶1 0.0046 (0.0012–0.0080) Amino acids (LC-MS/MS) Mean (µM/Mm creatinine) PC ae C36∶2 0.0016 (0.0002–0.0050) Glycine 101.0 (37.0–250.6) PC ae C36∶3 0.0009 (0.0001–0.0040) L-Alanine 21.7 (7.2–47.4) PC ae C36∶4 0.0022 L-Arginine 7.4 (3.2–14.6) PC ae C38∶2 0.0015 (0.0007–0.0023) L-Asparagine 8.8 (4.6–17.7) PC ae C38∶3 0.0020 (0.0012–0.0028) L-Aspartic acid 10.9 (1.9–26.8) PC ae C38∶4 0.0006 (0.0–0.0024) L-Citrulline 0.8 (0.2–1.7) PC ae C38∶6 0.0065 (0.0027–0.0170) L-Glutamic acid 6.4 (0.6–17.5) PC ae C40∶2 0.0012 (0.0006–0.0020) L-Glutamine 39.9 (18.4–72.5) PC ae C40∶5 0.0006 (0.0001–0.0025) L-Histidine 45.6 (21.7–71.5) PC ae C42∶3 0.0007 (0.0001–0.0020) L-Isoleucine 1.3 (0.5–2.7) PC ae C44∶3 0.0010 (0.0002–0.0031) L-Leucine 3.0 (1.6–6.0) Sphingomyelins (DFI-MS/MS) Mean (µM/Mm creatinine) L-Lysine 17.9 (3.6–56.1) SM (OH) C14∶1 0.0028 (0.0008–0.0049) L-Methionine 0.8 (0.4–1.6) SM (OH) C16∶1 0.0006 (0.0001–0.0023) L-Ornithine 4.4 (1.2–22.1) SM (OH) C22∶1 0.0023 (0.0009–0.0058) L-Phenylalanine 5.9 (2.8–10.8) SM (OH) C22∶2 0.0007 (0.0001–0.0028) L-Proline 1.1 (0.1–2.5) SM (OH) C24∶1 0.0010 (0.00034–0.0025) L-Serine 24.5 (12.3–50.2) SM C16∶0 0.0100 (0.0016–0.0700) L-Threonine 14.6 (6.6–29.3) SM C16∶1 0.0009 (0.0004–0.0023) L-Tryptophan 5.6 (9.3–2.1) SM C18∶0 0.0024 (0.0011–0.0070) L-Tyrosine 8.7 (3.0–20.1) SM C18∶1 0.0004 (0.0001–0.0013) L-Valine 4.3 (2.4–10.4) SM C20∶2 0.0003 (0.0–0.0016) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) SM C22∶3 0.0011 (0.0008–0.0014) Acetyl-ornithine 1.3 (0.5–2.8) SM C24∶0 0.0085 (0.0047–0.0200) Alpha-aminoadipic acid 7.2 (2.5–16.4) SM C24∶1 0.0030 (0.0007–0.0120) Asymmetric dimethylarginine 2.7 (1.4–4.2) SM C26∶0 0.0014 (0.0002–0.0043) Carnosine 1.2 (0.2–4.1) SM C26∶1 0.0005 (0.0001–0.0012) Dopamine 0.4 (0.2–0.7) Histamine 0.03 (0.01–0.10) Hydroxykynurenine 0.15 (0.06–0.34) * Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. |
| label | Table 7 |
| caption | Concentrations of acylcarnitines, amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and sphingomyelins in human urine by combined DFI/LC-MS/MS (BIOCRATES kit). |
| title | Concentrations of acylcarnitines, amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and sphingomyelins in human urine by combined DFI/LC-MS/MS (BIOCRATES kit). |
| table | Acylcarnitines (DFI-MS/MS) Mean (µM/mM creatinine) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) Acetyl-L-carnitine 2.0 (0.4–5.9) Kynurenine 0.41 (0.08–1.31) Butenyl-L-carnitine 0.014 (0.008 –0.021) L-Dopa 0.02 (0.01–0.04) Butyryl-L-carnitine 0.76 (0.12–1.81) Methioninesulfoxide 0.31 (0.14–0.66) Decadienyl-L-carnitine 0.10 (0.04–0.30) Phenylethylamine 0.011 (0.003–0.067) Decanoyl-L-carnitine 0.04 (0.02–0.06) Putrescine 0.20 (0.04–0.40) Decenoyl-L-carnitine 0.14 (0.06–0.32) Sarcosine 1.6 (0.2–9.4) Dodecanoyl-L-carnitine 0.02 (0.01–0.04) Serotonin 0.08 (0.04–0.12) Dodecenoyl-L-carnitine 0.028 (0.011–0.042) Symmetric dimethylarginine 2.8 (2.0–9.4) Glutaconyl-L-carnitine 0.04 (0.01–0.06) Lysophosphatidylcholines (DFI-MS/MS) Mean (µM/Mm creatinine) Glutaryl-L-carnitine 0.04 (0.03–0.10) lysoPC a C16∶0 0.014 Hexadecadienyl-L-carnitine 0.0010 (0.0005–0.0040) lysoPC a C17∶0 0.0032 (0.0008–0.0087) Hexadecanoyl-L-carnitine 0.0022 (0.0020–0.0030) lysoPC a C18∶0 0.0040 (0.0013–0.0100) Hexanoyl-L-carnitine 0.05 (0.03–0.07) lysoPC a C20∶4 0.0020 (0.0005–0.0038) Hexenoyl-L-carnitine 0.019 (0.009–0.050) lysoPC a C6∶0 0.0045 (0.0022–0.0074) Hydroxyhexadecanoyl-L-carnitine 0.0015 (0.0006–0.0050) Phosphatidylcholines (DFI-LC/MS) Mean (µM/Mm creatinine) Hydroxypropionyl-L-carnitine 0.008 (0.004–0.012) PC aa C28∶1 0.006 (0.003–0.020) Hydroxytetradecadienyl-L-carnitine 0.003 (0.001–0.008) PC aa C30∶2 0.0005 (0.0001–0.0022) Hydroxytetradecenoyl-L-carnitine 0.0020 (0.0008–0.0040) PC aa C32∶0 0.0035 (0.0013–0.0071) L-Carnitine 4.5 (0.6–15.2) PC aa C32∶3 0.0009 (0.0003–0.0025) Malonyl-L-carnitine 0.02 (0.01–0.04) PC aa C34∶1 0.0077 (0.0023–0.0500) Methylglutaryl-L-carnitine 0.030 (0.016–0.052) PC aa C34∶2 0.0047 (0.0016–0.0240) Methylmalonyl-L-carnitine 0.09 (0.05–0.16) PC aa C34∶3 0.002 Nonayl-L-carnitine 0.20 (0.07–0.46) PC aa C34∶4 0.0023 (0.0002–0.0180) Octadecadienyl-L-carnitine 0.0007 (0.0003–0.0020) PC aa C36∶1 0.0034 (0.0006–0.0250) Octanoyl-L-carnitine 0.05 (0.03–0.09) PC aa C36∶2 0.0160 (0.0021–0.0450) Octenoyl-L-carnitine 0.50 (0.08–1.20) PC aa C36∶3 0.0024 (0.0005–0.0240) Pimelyl-L-carnitine 0.03 (0.01–0.11) PC aa C36∶4 0.0018 (0.0005–0.0083) Propenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C36∶6 0.0037 Propionyl-L-carnitine 0.07 (0.01–0.20) PC aa C38∶3 0.0056 Tetradecadienyl-L-carnitine 0.0023 (0.0010–0.0050) PC aa C38∶4 0.0022 (0.0005–0.0065) Tetradecanoyl-L-carnitine 0.018 (0.001–0.088) PC aa C38∶5 0.0007 (0.0002–0.0020) Tetradecenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C38∶6 0.0010 (0.0005–0.0036) Tiglyl-L-carnitine 0.10 (0.05–0.20) PC aa C40∶3 0.00035 (0.0–0.00140) Valeryl-L-carnitine 0.220 (0.037–0.440) PC aa C42∶2 0.0009 (0.0003–0.0024) Creatinine Mean PC aa C42∶4 0.0008 (0.0003–0.0025) Creatinine* 12,475±7,955 PC ae C34∶1 0.0030 (0.0003–0.0110) Hexose Mean (µM/Mm creatinine) PC ae C34∶2 0.0012 (0.0002–0.0075) Hexose 110 (37–501) PC ae C36∶1 0.0046 (0.0012–0.0080) Amino acids (LC-MS/MS) Mean (µM/Mm creatinine) PC ae C36∶2 0.0016 (0.0002–0.0050) Glycine 101.0 (37.0–250.6) PC ae C36∶3 0.0009 (0.0001–0.0040) L-Alanine 21.7 (7.2–47.4) PC ae C36∶4 0.0022 L-Arginine 7.4 (3.2–14.6) PC ae C38∶2 0.0015 (0.0007–0.0023) L-Asparagine 8.8 (4.6–17.7) PC ae C38∶3 0.0020 (0.0012–0.0028) L-Aspartic acid 10.9 (1.9–26.8) PC ae C38∶4 0.0006 (0.0–0.0024) L-Citrulline 0.8 (0.2–1.7) PC ae C38∶6 0.0065 (0.0027–0.0170) L-Glutamic acid 6.4 (0.6–17.5) PC ae C40∶2 0.0012 (0.0006–0.0020) L-Glutamine 39.9 (18.4–72.5) PC ae C40∶5 0.0006 (0.0001–0.0025) L-Histidine 45.6 (21.7–71.5) PC ae C42∶3 0.0007 (0.0001–0.0020) L-Isoleucine 1.3 (0.5–2.7) PC ae C44∶3 0.0010 (0.0002–0.0031) L-Leucine 3.0 (1.6–6.0) Sphingomyelins (DFI-MS/MS) Mean (µM/Mm creatinine) L-Lysine 17.9 (3.6–56.1) SM (OH) C14∶1 0.0028 (0.0008–0.0049) L-Methionine 0.8 (0.4–1.6) SM (OH) C16∶1 0.0006 (0.0001–0.0023) L-Ornithine 4.4 (1.2–22.1) SM (OH) C22∶1 0.0023 (0.0009–0.0058) L-Phenylalanine 5.9 (2.8–10.8) SM (OH) C22∶2 0.0007 (0.0001–0.0028) L-Proline 1.1 (0.1–2.5) SM (OH) C24∶1 0.0010 (0.00034–0.0025) L-Serine 24.5 (12.3–50.2) SM C16∶0 0.0100 (0.0016–0.0700) L-Threonine 14.6 (6.6–29.3) SM C16∶1 0.0009 (0.0004–0.0023) L-Tryptophan 5.6 (9.3–2.1) SM C18∶0 0.0024 (0.0011–0.0070) L-Tyrosine 8.7 (3.0–20.1) SM C18∶1 0.0004 (0.0001–0.0013) L-Valine 4.3 (2.4–10.4) SM C20∶2 0.0003 (0.0–0.0016) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) SM C22∶3 0.0011 (0.0008–0.0014) Acetyl-ornithine 1.3 (0.5–2.8) SM C24∶0 0.0085 (0.0047–0.0200) Alpha-aminoadipic acid 7.2 (2.5–16.4) SM C24∶1 0.0030 (0.0007–0.0120) Asymmetric dimethylarginine 2.7 (1.4–4.2) SM C26∶0 0.0014 (0.0002–0.0043) Carnosine 1.2 (0.2–4.1) SM C26∶1 0.0005 (0.0001–0.0012) Dopamine 0.4 (0.2–0.7) Histamine 0.03 (0.01–0.10) Hydroxykynurenine 0.15 (0.06–0.34) |
| tr | Acylcarnitines (DFI-MS/MS) Mean (µM/mM creatinine) Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) |
| td | Acylcarnitines (DFI-MS/MS) |
| td | Mean (µM/mM creatinine) |
| td | Biogenic amines (LC-MS/MS) |
| td | Mean (µM/Mm creatinine) |
| tr | Acetyl-L-carnitine 2.0 (0.4–5.9) Kynurenine 0.41 (0.08–1.31) |
| td | Acetyl-L-carnitine |
| td | 2.0 (0.4–5.9) |
| td | Kynurenine |
| td | 0.41 (0.08–1.31) |
| tr | Butenyl-L-carnitine 0.014 (0.008 –0.021) L-Dopa 0.02 (0.01–0.04) |
| td | Butenyl-L-carnitine |
| td | 0.014 (0.008 –0.021) |
| td | L-Dopa |
| td | 0.02 (0.01–0.04) |
| tr | Butyryl-L-carnitine 0.76 (0.12–1.81) Methioninesulfoxide 0.31 (0.14–0.66) |
| td | Butyryl-L-carnitine |
| td | 0.76 (0.12–1.81) |
| td | Methioninesulfoxide |
| td | 0.31 (0.14–0.66) |
| tr | Decadienyl-L-carnitine 0.10 (0.04–0.30) Phenylethylamine 0.011 (0.003–0.067) |
| td | Decadienyl-L-carnitine |
| td | 0.10 (0.04–0.30) |
| td | Phenylethylamine |
| td | 0.011 (0.003–0.067) |
| tr | Decanoyl-L-carnitine 0.04 (0.02–0.06) Putrescine 0.20 (0.04–0.40) |
| td | Decanoyl-L-carnitine |
| td | 0.04 (0.02–0.06) |
| td | Putrescine |
| td | 0.20 (0.04–0.40) |
| tr | Decenoyl-L-carnitine 0.14 (0.06–0.32) Sarcosine 1.6 (0.2–9.4) |
| td | Decenoyl-L-carnitine |
| td | 0.14 (0.06–0.32) |
| td | Sarcosine |
| td | 1.6 (0.2–9.4) |
| tr | Dodecanoyl-L-carnitine 0.02 (0.01–0.04) Serotonin 0.08 (0.04–0.12) |
| td | Dodecanoyl-L-carnitine |
| td | 0.02 (0.01–0.04) |
| td | Serotonin |
| td | 0.08 (0.04–0.12) |
| tr | Dodecenoyl-L-carnitine 0.028 (0.011–0.042) Symmetric dimethylarginine 2.8 (2.0–9.4) |
| td | Dodecenoyl-L-carnitine |
| td | 0.028 (0.011–0.042) |
| td | Symmetric dimethylarginine |
| td | 2.8 (2.0–9.4) |
| tr | Glutaconyl-L-carnitine 0.04 (0.01–0.06) Lysophosphatidylcholines (DFI-MS/MS) Mean (µM/Mm creatinine) |
| td | Glutaconyl-L-carnitine |
| td | 0.04 (0.01–0.06) |
| td | Lysophosphatidylcholines (DFI-MS/MS) |
| td | Mean (µM/Mm creatinine) |
| tr | Glutaryl-L-carnitine 0.04 (0.03–0.10) lysoPC a C16∶0 0.014 |
| td | Glutaryl-L-carnitine |
| td | 0.04 (0.03–0.10) |
| td | lysoPC a C16∶0 |
| td | 0.014 |
| tr | Hexadecadienyl-L-carnitine 0.0010 (0.0005–0.0040) lysoPC a C17∶0 0.0032 (0.0008–0.0087) |
| td | Hexadecadienyl-L-carnitine |
| td | 0.0010 (0.0005–0.0040) |
| td | lysoPC a C17∶0 |
| td | 0.0032 (0.0008–0.0087) |
| tr | Hexadecanoyl-L-carnitine 0.0022 (0.0020–0.0030) lysoPC a C18∶0 0.0040 (0.0013–0.0100) |
| td | Hexadecanoyl-L-carnitine |
| td | 0.0022 (0.0020–0.0030) |
| td | lysoPC a C18∶0 |
| td | 0.0040 (0.0013–0.0100) |
| tr | Hexanoyl-L-carnitine 0.05 (0.03–0.07) lysoPC a C20∶4 0.0020 (0.0005–0.0038) |
| td | Hexanoyl-L-carnitine |
| td | 0.05 (0.03–0.07) |
| td | lysoPC a C20∶4 |
| td | 0.0020 (0.0005–0.0038) |
| tr | Hexenoyl-L-carnitine 0.019 (0.009–0.050) lysoPC a C6∶0 0.0045 (0.0022–0.0074) |
| td | Hexenoyl-L-carnitine |
| td | 0.019 (0.009–0.050) |
| td | lysoPC a C6∶0 |
| td | 0.0045 (0.0022–0.0074) |
| tr | Hydroxyhexadecanoyl-L-carnitine 0.0015 (0.0006–0.0050) Phosphatidylcholines (DFI-LC/MS) Mean (µM/Mm creatinine) |
| td | Hydroxyhexadecanoyl-L-carnitine |
| td | 0.0015 (0.0006–0.0050) |
| td | Phosphatidylcholines (DFI-LC/MS) |
| td | Mean (µM/Mm creatinine) |
| tr | Hydroxypropionyl-L-carnitine 0.008 (0.004–0.012) PC aa C28∶1 0.006 (0.003–0.020) |
| td | Hydroxypropionyl-L-carnitine |
| td | 0.008 (0.004–0.012) |
| td | PC aa C28∶1 |
| td | 0.006 (0.003–0.020) |
| tr | Hydroxytetradecadienyl-L-carnitine 0.003 (0.001–0.008) PC aa C30∶2 0.0005 (0.0001–0.0022) |
| td | Hydroxytetradecadienyl-L-carnitine |
| td | 0.003 (0.001–0.008) |
| td | PC aa C30∶2 |
| td | 0.0005 (0.0001–0.0022) |
| tr | Hydroxytetradecenoyl-L-carnitine 0.0020 (0.0008–0.0040) PC aa C32∶0 0.0035 (0.0013–0.0071) |
| td | Hydroxytetradecenoyl-L-carnitine |
| td | 0.0020 (0.0008–0.0040) |
| td | PC aa C32∶0 |
| td | 0.0035 (0.0013–0.0071) |
| tr | L-Carnitine 4.5 (0.6–15.2) PC aa C32∶3 0.0009 (0.0003–0.0025) |
| td | L-Carnitine |
| td | 4.5 (0.6–15.2) |
| td | PC aa C32∶3 |
| td | 0.0009 (0.0003–0.0025) |
| tr | Malonyl-L-carnitine 0.02 (0.01–0.04) PC aa C34∶1 0.0077 (0.0023–0.0500) |
| td | Malonyl-L-carnitine |
| td | 0.02 (0.01–0.04) |
| td | PC aa C34∶1 |
| td | 0.0077 (0.0023–0.0500) |
| tr | Methylglutaryl-L-carnitine 0.030 (0.016–0.052) PC aa C34∶2 0.0047 (0.0016–0.0240) |
| td | Methylglutaryl-L-carnitine |
| td | 0.030 (0.016–0.052) |
| td | PC aa C34∶2 |
| td | 0.0047 (0.0016–0.0240) |
| tr | Methylmalonyl-L-carnitine 0.09 (0.05–0.16) PC aa C34∶3 0.002 |
| td | Methylmalonyl-L-carnitine |
| td | 0.09 (0.05–0.16) |
| td | PC aa C34∶3 |
| td | 0.002 |
| tr | Nonayl-L-carnitine 0.20 (0.07–0.46) PC aa C34∶4 0.0023 (0.0002–0.0180) |
| td | Nonayl-L-carnitine |
| td | 0.20 (0.07–0.46) |
| td | PC aa C34∶4 |
| td | 0.0023 (0.0002–0.0180) |
| tr | Octadecadienyl-L-carnitine 0.0007 (0.0003–0.0020) PC aa C36∶1 0.0034 (0.0006–0.0250) |
| td | Octadecadienyl-L-carnitine |
| td | 0.0007 (0.0003–0.0020) |
| td | PC aa C36∶1 |
| td | 0.0034 (0.0006–0.0250) |
| tr | Octanoyl-L-carnitine 0.05 (0.03–0.09) PC aa C36∶2 0.0160 (0.0021–0.0450) |
| td | Octanoyl-L-carnitine |
| td | 0.05 (0.03–0.09) |
| td | PC aa C36∶2 |
| td | 0.0160 (0.0021–0.0450) |
| tr | Octenoyl-L-carnitine 0.50 (0.08–1.20) PC aa C36∶3 0.0024 (0.0005–0.0240) |
| td | Octenoyl-L-carnitine |
| td | 0.50 (0.08–1.20) |
| td | PC aa C36∶3 |
| td | 0.0024 (0.0005–0.0240) |
| tr | Pimelyl-L-carnitine 0.03 (0.01–0.11) PC aa C36∶4 0.0018 (0.0005–0.0083) |
| td | Pimelyl-L-carnitine |
| td | 0.03 (0.01–0.11) |
| td | PC aa C36∶4 |
| td | 0.0018 (0.0005–0.0083) |
| tr | Propenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C36∶6 0.0037 |
| td | Propenoyl-L-carnitine |
| td | 0.003 (0.001–0.006) |
| td | PC aa C36∶6 |
| td | 0.0037 |
| tr | Propionyl-L-carnitine 0.07 (0.01–0.20) PC aa C38∶3 0.0056 |
| td | Propionyl-L-carnitine |
| td | 0.07 (0.01–0.20) |
| td | PC aa C38∶3 |
| td | 0.0056 |
| tr | Tetradecadienyl-L-carnitine 0.0023 (0.0010–0.0050) PC aa C38∶4 0.0022 (0.0005–0.0065) |
| td | Tetradecadienyl-L-carnitine |
| td | 0.0023 (0.0010–0.0050) |
| td | PC aa C38∶4 |
| td | 0.0022 (0.0005–0.0065) |
| tr | Tetradecanoyl-L-carnitine 0.018 (0.001–0.088) PC aa C38∶5 0.0007 (0.0002–0.0020) |
| td | Tetradecanoyl-L-carnitine |
| td | 0.018 (0.001–0.088) |
| td | PC aa C38∶5 |
| td | 0.0007 (0.0002–0.0020) |
| tr | Tetradecenoyl-L-carnitine 0.003 (0.001–0.006) PC aa C38∶6 0.0010 (0.0005–0.0036) |
| td | Tetradecenoyl-L-carnitine |
| td | 0.003 (0.001–0.006) |
| td | PC aa C38∶6 |
| td | 0.0010 (0.0005–0.0036) |
| tr | Tiglyl-L-carnitine 0.10 (0.05–0.20) PC aa C40∶3 0.00035 (0.0–0.00140) |
| td | Tiglyl-L-carnitine |
| td | 0.10 (0.05–0.20) |
| td | PC aa C40∶3 |
| td | 0.00035 (0.0–0.00140) |
| tr | Valeryl-L-carnitine 0.220 (0.037–0.440) PC aa C42∶2 0.0009 (0.0003–0.0024) |
| td | Valeryl-L-carnitine |
| td | 0.220 (0.037–0.440) |
| td | PC aa C42∶2 |
| td | 0.0009 (0.0003–0.0024) |
| tr | Creatinine Mean PC aa C42∶4 0.0008 (0.0003–0.0025) |
| td | Creatinine |
| td | Mean |
| td | PC aa C42∶4 |
| td | 0.0008 (0.0003–0.0025) |
| tr | Creatinine* 12,475±7,955 PC ae C34∶1 0.0030 (0.0003–0.0110) |
| td | Creatinine* |
| td | 12,475±7,955 |
| td | PC ae C34∶1 |
| td | 0.0030 (0.0003–0.0110) |
| tr | Hexose Mean (µM/Mm creatinine) PC ae C34∶2 0.0012 (0.0002–0.0075) |
| td | Hexose |
| td | Mean (µM/Mm creatinine) |
| td | PC ae C34∶2 |
| td | 0.0012 (0.0002–0.0075) |
| tr | Hexose 110 (37–501) PC ae C36∶1 0.0046 (0.0012–0.0080) |
| td | Hexose |
| td | 110 (37–501) |
| td | PC ae C36∶1 |
| td | 0.0046 (0.0012–0.0080) |
| tr | Amino acids (LC-MS/MS) Mean (µM/Mm creatinine) PC ae C36∶2 0.0016 (0.0002–0.0050) |
| td | Amino acids (LC-MS/MS) |
| td | Mean (µM/Mm creatinine) |
| td | PC ae C36∶2 |
| td | 0.0016 (0.0002–0.0050) |
| tr | Glycine 101.0 (37.0–250.6) PC ae C36∶3 0.0009 (0.0001–0.0040) |
| td | Glycine |
| td | 101.0 (37.0–250.6) |
| td | PC ae C36∶3 |
| td | 0.0009 (0.0001–0.0040) |
| tr | L-Alanine 21.7 (7.2–47.4) PC ae C36∶4 0.0022 |
| td | L-Alanine |
| td | 21.7 (7.2–47.4) |
| td | PC ae C36∶4 |
| td | 0.0022 |
| tr | L-Arginine 7.4 (3.2–14.6) PC ae C38∶2 0.0015 (0.0007–0.0023) |
| td | L-Arginine |
| td | 7.4 (3.2–14.6) |
| td | PC ae C38∶2 |
| td | 0.0015 (0.0007–0.0023) |
| tr | L-Asparagine 8.8 (4.6–17.7) PC ae C38∶3 0.0020 (0.0012–0.0028) |
| td | L-Asparagine |
| td | 8.8 (4.6–17.7) |
| td | PC ae C38∶3 |
| td | 0.0020 (0.0012–0.0028) |
| tr | L-Aspartic acid 10.9 (1.9–26.8) PC ae C38∶4 0.0006 (0.0–0.0024) |
| td | L-Aspartic acid |
| td | 10.9 (1.9–26.8) |
| td | PC ae C38∶4 |
| td | 0.0006 (0.0–0.0024) |
| tr | L-Citrulline 0.8 (0.2–1.7) PC ae C38∶6 0.0065 (0.0027–0.0170) |
| td | L-Citrulline |
| td | 0.8 (0.2–1.7) |
| td | PC ae C38∶6 |
| td | 0.0065 (0.0027–0.0170) |
| tr | L-Glutamic acid 6.4 (0.6–17.5) PC ae C40∶2 0.0012 (0.0006–0.0020) |
| td | L-Glutamic acid |
| td | 6.4 (0.6–17.5) |
| td | PC ae C40∶2 |
| td | 0.0012 (0.0006–0.0020) |
| tr | L-Glutamine 39.9 (18.4–72.5) PC ae C40∶5 0.0006 (0.0001–0.0025) |
| td | L-Glutamine |
| td | 39.9 (18.4–72.5) |
| td | PC ae C40∶5 |
| td | 0.0006 (0.0001–0.0025) |
| tr | L-Histidine 45.6 (21.7–71.5) PC ae C42∶3 0.0007 (0.0001–0.0020) |
| td | L-Histidine |
| td | 45.6 (21.7–71.5) |
| td | PC ae C42∶3 |
| td | 0.0007 (0.0001–0.0020) |
| tr | L-Isoleucine 1.3 (0.5–2.7) PC ae C44∶3 0.0010 (0.0002–0.0031) |
| td | L-Isoleucine |
| td | 1.3 (0.5–2.7) |
| td | PC ae C44∶3 |
| td | 0.0010 (0.0002–0.0031) |
| tr | L-Leucine 3.0 (1.6–6.0) Sphingomyelins (DFI-MS/MS) Mean (µM/Mm creatinine) |
| td | L-Leucine |
| td | 3.0 (1.6–6.0) |
| td | Sphingomyelins (DFI-MS/MS) |
| td | Mean (µM/Mm creatinine) |
| tr | L-Lysine 17.9 (3.6–56.1) SM (OH) C14∶1 0.0028 (0.0008–0.0049) |
| td | L-Lysine |
| td | 17.9 (3.6–56.1) |
| td | SM (OH) C14∶1 |
| td | 0.0028 (0.0008–0.0049) |
| tr | L-Methionine 0.8 (0.4–1.6) SM (OH) C16∶1 0.0006 (0.0001–0.0023) |
| td | L-Methionine |
| td | 0.8 (0.4–1.6) |
| td | SM (OH) C16∶1 |
| td | 0.0006 (0.0001–0.0023) |
| tr | L-Ornithine 4.4 (1.2–22.1) SM (OH) C22∶1 0.0023 (0.0009–0.0058) |
| td | L-Ornithine |
| td | 4.4 (1.2–22.1) |
| td | SM (OH) C22∶1 |
| td | 0.0023 (0.0009–0.0058) |
| tr | L-Phenylalanine 5.9 (2.8–10.8) SM (OH) C22∶2 0.0007 (0.0001–0.0028) |
| td | L-Phenylalanine |
| td | 5.9 (2.8–10.8) |
| td | SM (OH) C22∶2 |
| td | 0.0007 (0.0001–0.0028) |
| tr | L-Proline 1.1 (0.1–2.5) SM (OH) C24∶1 0.0010 (0.00034–0.0025) |
| td | L-Proline |
| td | 1.1 (0.1–2.5) |
| td | SM (OH) C24∶1 |
| td | 0.0010 (0.00034–0.0025) |
| tr | L-Serine 24.5 (12.3–50.2) SM C16∶0 0.0100 (0.0016–0.0700) |
| td | L-Serine |
| td | 24.5 (12.3–50.2) |
| td | SM C16∶0 |
| td | 0.0100 (0.0016–0.0700) |
| tr | L-Threonine 14.6 (6.6–29.3) SM C16∶1 0.0009 (0.0004–0.0023) |
| td | L-Threonine |
| td | 14.6 (6.6–29.3) |
| td | SM C16∶1 |
| td | 0.0009 (0.0004–0.0023) |
| tr | L-Tryptophan 5.6 (9.3–2.1) SM C18∶0 0.0024 (0.0011–0.0070) |
| td | L-Tryptophan |
| td | 5.6 (9.3–2.1) |
| td | SM C18∶0 |
| td | 0.0024 (0.0011–0.0070) |
| tr | L-Tyrosine 8.7 (3.0–20.1) SM C18∶1 0.0004 (0.0001–0.0013) |
| td | L-Tyrosine |
| td | 8.7 (3.0–20.1) |
| td | SM C18∶1 |
| td | 0.0004 (0.0001–0.0013) |
| tr | L-Valine 4.3 (2.4–10.4) SM C20∶2 0.0003 (0.0–0.0016) |
| td | L-Valine |
| td | 4.3 (2.4–10.4) |
| td | SM C20∶2 |
| td | 0.0003 (0.0–0.0016) |
| tr | Biogenic amines (LC-MS/MS) Mean (µM/Mm creatinine) SM C22∶3 0.0011 (0.0008–0.0014) |
| td | Biogenic amines (LC-MS/MS) |
| td | Mean (µM/Mm creatinine) |
| td | SM C22∶3 |
| td | 0.0011 (0.0008–0.0014) |
| tr | Acetyl-ornithine 1.3 (0.5–2.8) SM C24∶0 0.0085 (0.0047–0.0200) |
| td | Acetyl-ornithine |
| td | 1.3 (0.5–2.8) |
| td | SM C24∶0 |
| td | 0.0085 (0.0047–0.0200) |
| tr | Alpha-aminoadipic acid 7.2 (2.5–16.4) SM C24∶1 0.0030 (0.0007–0.0120) |
| td | Alpha-aminoadipic acid |
| td | 7.2 (2.5–16.4) |
| td | SM C24∶1 |
| td | 0.0030 (0.0007–0.0120) |
| tr | Asymmetric dimethylarginine 2.7 (1.4–4.2) SM C26∶0 0.0014 (0.0002–0.0043) |
| td | Asymmetric dimethylarginine |
| td | 2.7 (1.4–4.2) |
| td | SM C26∶0 |
| td | 0.0014 (0.0002–0.0043) |
| tr | Carnosine 1.2 (0.2–4.1) SM C26∶1 0.0005 (0.0001–0.0012) |
| td | Carnosine |
| td | 1.2 (0.2–4.1) |
| td | SM C26∶1 |
| td | 0.0005 (0.0001–0.0012) |
| tr | Dopamine 0.4 (0.2–0.7) |
| td | Dopamine |
| td | 0.4 (0.2–0.7) |
| tr | Histamine 0.03 (0.01–0.10) |
| td | Histamine |
| td | 0.03 (0.01–0.10) |
| tr | Hydroxykynurenine 0.15 (0.06–0.34) |
| td | Hydroxykynurenine |
| td | 0.15 (0.06–0.34) |
| table-wrap-foot | * Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. |
| footnote | * Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. |
| label | * |
| p | Concentration of metabolite (Mean±SD) is expressed by µM. aa: diacyl; ae: acyl-alkyl. |
| p | Our results show very good agreement with the previous studies conducted by BIOCRATES on human urine samples (Biocrates Application Note 1005-1). We found that the lower limit of quantification by DFI MS/MS based on the AbsoluteIDQ kit was 0.1 nM/mM creatinine for certain phosphatidylcholine species (i.e. PC aa C40∶3) and 0.1 nM/mM creatinine for certain sphingomyelin species (i.e. SM 26∶1). Comparison of our lipid results with literature data was difficult as relatively few papers report urine lipid concentration data. Indeed, the presence of lipids in urine is a little unexpected, but not entirely unreasonable. It is likely that urea, a well known chaotrope, facilitates the dissolution of small amounts of fatty acids and other lipid species in urine. |
| p | Many of the compounds we measured with this kit assay appear to be normal constituents of human urine but have not been previously reported (quantified and/or detected) in the scientific literature (with the exception of the BIOCRATES Application Note). In total, 53 compounds are being reported here for the first time as being normal constituents of human urine, while 68 compounds are being robustly quantified in human urine for the first time. The vast majority of these compounds are lipids. |
| p | Based on our results, the combined DFI/LC-MS/MS method detected 98 compounds or compound species that GC-MS and NMR methods could not detect, while GC-MS detected 161 compounds and NMR detected 181 compounds that DFI/LC-MS/MS could not detect. The 3 methods were able to detect a common set of 17 compounds including creatinine, L-glutamine, L-tryptophan, L-tyrosine and L-valine. Interestingly, the concentrations measured by DFI/LC-MS/MS, NMR and GC-MS (across the shared set of 17 compounds) showed generally good agreement (within 19 ± 7% of each other). The relatively small overlap, in terms of compound coverage, between the 3 platforms is a bit of a surprise and certainly serves to emphasize the tremendous chemical diversity that must exist in urine. Overall, by combining these 3 platforms, we were able to identify 295 and quantify 231 unique or non-overlapping metabolites or metabolite species. These data suggest that DFI/LC-MS/MS, GC-MS and NMR are highly complementary techniques for the identification and quantification of metabolites in human urine. |
| sec | Quantification and Identification of Urinary Trace Metals – ICP-MS To determine the trace elemental composition of urine, we used inductively coupled plasma mass spectrometry (ICP-MS). ICP-MS is widely considered to be one of the best techniques for the characterization of the trace element composition of biological samples [88], [89]. Indeed, none of the other methods (1H-NMR, GC-MS and DFI/LC-MS/MS) are suited for measuring trace element composition or concentrations. Our multi-elemental analysis of urine using ICP-MS provided quantitative results for a total of 40 metals or trace minerals (Table 8). Based on their frequency of occurrence and overall abundance, all 40 trace elements appear to be normal constituents of human urine. Of these, 2 have previously not been quantified for healthy adults. Table 8 Concentrations of ions (µmol/mmol creatinine) as determined by ICP-MS. Compound Name HMDB Concentration (µM/mM creatinine) Litterature values(µM/mM creatinine) Aluminum (Al) HMDB01247 0.31 (0.05–1.21) 0.667±0.037 Arsenic (As) HMDB02290 0.10 (0.01–0.52) 0.16±0.16 Barium (Ba) HMDB04142 0.0070 (0.0001–0.0655) 0.0129–0.0320 Cadmium (Cd) HMDB03638 0.0004 (0.0002–0.0010) 0.00060 (0.00035–0.00100) Calcium (Ca) HMDB00464 200.0 (16.9–520.0) 299.0±99.0 Cerium (Ce) HMDB13672 0.00048 (0.00008–0.00180) 0.0 – 0.0007 Cesium (Cs) HMDB13669 0.0039 (0.0005–0.0110) 0.0061±0.0011 Chromium (Cr) HMDB00599 0.0084 (0.0001–0.0460) 0.0100±0.0065 Cobalt (Co) HMDB00608 0.0014 (0.0001–0.0140) 0.000810±0.00053 Copper (Cu) HMDB00657 0.0163 (0.0006–0.1099) 0.0190 (0.0092–0.038) Gallium (Ga) HMDB01478 0.0056 (0.0012–0.0150) 0.0003–0.0006 Hafnium (Hf) HMDB29201 0.00031 (0.00006–0.00110) 0.00027±0.00012 Iron (Fe) HMDB00692 0.0890 (0.0025–0.4579) 0.11 (0.0–0.14) Lanthanum (La) HMDB13711 0.00020 (0.00003–0.00079) 0.00052±0.00039 Lead (Pb) HMDB04628 0.0026 (0.0007–0.0110) 0.011 (0.0–0.020) Lithium (Li) HMDB05949 0.5 (0.2–0.9) 0.504 (0.058–3.414) Magnesium (Mg) HMDB00547 262 (42–1,189) 289.0±92.0 Manganese (Mn) HMDB01333 0.013 (0.002–0.051) 0.011 (0.0–0.023) Molybdenum (Mo) HMDB01302 0.049 (0.006–0.295) 0.06±0.03 Neodymium (Nd) HMDB13715 0.00017 (0.00004–0.00070) 0.0026±0.0013 Nickel (Ni) HMDB02457 0.0080 (0.0015–0.0450) 0.00420 (0.00302–0.00572) Palladium (Pd) HMDB13670 0.0110 (0.0011–0.1410) < 0.00001–0.2538 Platinum (Pt) HMDB13671 0.000155 (0.000031–0.000560) 0.0000656 Posphorus (P) HMDB01315 1,799 (221–4,936) 1,666.67–4,333.33 Potassium (K) HMDB00586 3,593 (553–8,079) 4,605 (2,631–6,578) Rhenium (Re) HMDB13719 0.000096 (0.000013–0.000340) NA Rubidium (Rb) HMDB01327 1.354 (0.187–3.673) 1.84±0.46 Ruthenium (Ru) HMDB13668 0.0002 (0.0001–0.0009) NA Selenium (Se) HMDB01349 0.149 (0.028–0.345) 0.11346 (0.07724–0.15010) Sodium (Na) HMDB00588 12,477 (1,863–37,249) 14,737 (5,263–36,842) Strontium (Sr) HMDB03642 0.163 (0.030–0.415) 0.1333 Tantalum (Ta) HMDB04143 0.00020 (0.00004–0.00074) 0.00008±0.00006 Thallium (Tl) HMDB13724 0.0003 (0.0001–0.0013) < 0.00659 Thorium (Th) HMDB29215 0.00020 (0.00002–0.00100) < 0.00584 Tin (Sn) HMDB01960 0.00110 (0.00016–0.00690) < 0.00552 Titanium (Ti) HMDB01966 0.0013 (0.0005–0.0030) 0.0142 Tungsten (W) HMDB01989 0.0060 (0.0011–0.0280) 0.010±0.022 Vanadium (V) HMDB02503 0.033 (0.004–0.083) 0.00162±0.00144 Zinc (Zn) HMDB01303 0.460 (0.058–0.948) 0.52±0.25 Zirconium (Zr) HMDB01475 0.021 (0.004–0.083) 0.0012–0.0074 As far as we are aware, this is the first multi-elemental study of urine that has been performed by ICP-MS. As seen in Table 8, there is generally good agreement between the values measured by ICP-MS and those previously reported in literature, with differences generally being less than 22 ± 10%. Larger differences are seen for gallium (Ga), lead (Pb), Neodymium (Nd), titanium (Ti) and vanadium (V), but these may be due to the effects of age, diet, local environment (minerals in local water) or diurnal variation. Alternately they may reflect real differences in the sensitivity or accuracy of the instruments being used. As a general rule, ICP-MS is considered as a gold standard for the identification and quantification of trace metals [18], so we would tend to place higher confidence in the values derived via ICP-MS over those measured by other technologies. By our measurements, the most abundant metals/salts are sodium (Na) (12.5 ± 10.6 mM/mM creatinine) and potassium (K) (3.6 ± 2.5 mM/mM creatinine) – as expected, while the least abundant was rhenium (Re) with a lower limit of quantification by ICP-MS of 96 pM/mM creatinine. |
| title | Quantification and Identification of Urinary Trace Metals – ICP-MS |
| p | To determine the trace elemental composition of urine, we used inductively coupled plasma mass spectrometry (ICP-MS). ICP-MS is widely considered to be one of the best techniques for the characterization of the trace element composition of biological samples [88], [89]. Indeed, none of the other methods (1H-NMR, GC-MS and DFI/LC-MS/MS) are suited for measuring trace element composition or concentrations. Our multi-elemental analysis of urine using ICP-MS provided quantitative results for a total of 40 metals or trace minerals (Table 8). Based on their frequency of occurrence and overall abundance, all 40 trace elements appear to be normal constituents of human urine. Of these, 2 have previously not been quantified for healthy adults. |
| table-wrap | Table 8 Concentrations of ions (µmol/mmol creatinine) as determined by ICP-MS. Compound Name HMDB Concentration (µM/mM creatinine) Litterature values(µM/mM creatinine) Aluminum (Al) HMDB01247 0.31 (0.05–1.21) 0.667±0.037 Arsenic (As) HMDB02290 0.10 (0.01–0.52) 0.16±0.16 Barium (Ba) HMDB04142 0.0070 (0.0001–0.0655) 0.0129–0.0320 Cadmium (Cd) HMDB03638 0.0004 (0.0002–0.0010) 0.00060 (0.00035–0.00100) Calcium (Ca) HMDB00464 200.0 (16.9–520.0) 299.0±99.0 Cerium (Ce) HMDB13672 0.00048 (0.00008–0.00180) 0.0 – 0.0007 Cesium (Cs) HMDB13669 0.0039 (0.0005–0.0110) 0.0061±0.0011 Chromium (Cr) HMDB00599 0.0084 (0.0001–0.0460) 0.0100±0.0065 Cobalt (Co) HMDB00608 0.0014 (0.0001–0.0140) 0.000810±0.00053 Copper (Cu) HMDB00657 0.0163 (0.0006–0.1099) 0.0190 (0.0092–0.038) Gallium (Ga) HMDB01478 0.0056 (0.0012–0.0150) 0.0003–0.0006 Hafnium (Hf) HMDB29201 0.00031 (0.00006–0.00110) 0.00027±0.00012 Iron (Fe) HMDB00692 0.0890 (0.0025–0.4579) 0.11 (0.0–0.14) Lanthanum (La) HMDB13711 0.00020 (0.00003–0.00079) 0.00052±0.00039 Lead (Pb) HMDB04628 0.0026 (0.0007–0.0110) 0.011 (0.0–0.020) Lithium (Li) HMDB05949 0.5 (0.2–0.9) 0.504 (0.058–3.414) Magnesium (Mg) HMDB00547 262 (42–1,189) 289.0±92.0 Manganese (Mn) HMDB01333 0.013 (0.002–0.051) 0.011 (0.0–0.023) Molybdenum (Mo) HMDB01302 0.049 (0.006–0.295) 0.06±0.03 Neodymium (Nd) HMDB13715 0.00017 (0.00004–0.00070) 0.0026±0.0013 Nickel (Ni) HMDB02457 0.0080 (0.0015–0.0450) 0.00420 (0.00302–0.00572) Palladium (Pd) HMDB13670 0.0110 (0.0011–0.1410) < 0.00001–0.2538 Platinum (Pt) HMDB13671 0.000155 (0.000031–0.000560) 0.0000656 Posphorus (P) HMDB01315 1,799 (221–4,936) 1,666.67–4,333.33 Potassium (K) HMDB00586 3,593 (553–8,079) 4,605 (2,631–6,578) Rhenium (Re) HMDB13719 0.000096 (0.000013–0.000340) NA Rubidium (Rb) HMDB01327 1.354 (0.187–3.673) 1.84±0.46 Ruthenium (Ru) HMDB13668 0.0002 (0.0001–0.0009) NA Selenium (Se) HMDB01349 0.149 (0.028–0.345) 0.11346 (0.07724–0.15010) Sodium (Na) HMDB00588 12,477 (1,863–37,249) 14,737 (5,263–36,842) Strontium (Sr) HMDB03642 0.163 (0.030–0.415) 0.1333 Tantalum (Ta) HMDB04143 0.00020 (0.00004–0.00074) 0.00008±0.00006 Thallium (Tl) HMDB13724 0.0003 (0.0001–0.0013) < 0.00659 Thorium (Th) HMDB29215 0.00020 (0.00002–0.00100) < 0.00584 Tin (Sn) HMDB01960 0.00110 (0.00016–0.00690) < 0.00552 Titanium (Ti) HMDB01966 0.0013 (0.0005–0.0030) 0.0142 Tungsten (W) HMDB01989 0.0060 (0.0011–0.0280) 0.010±0.022 Vanadium (V) HMDB02503 0.033 (0.004–0.083) 0.00162±0.00144 Zinc (Zn) HMDB01303 0.460 (0.058–0.948) 0.52±0.25 Zirconium (Zr) HMDB01475 0.021 (0.004–0.083) 0.0012–0.0074 |
| label | Table 8 |
| caption | Concentrations of ions (µmol/mmol creatinine) as determined by ICP-MS. |
| title | Concentrations of ions (µmol/mmol creatinine) as determined by ICP-MS. |
| table | Compound Name HMDB Concentration (µM/mM creatinine) Litterature values(µM/mM creatinine) Aluminum (Al) HMDB01247 0.31 (0.05–1.21) 0.667±0.037 Arsenic (As) HMDB02290 0.10 (0.01–0.52) 0.16±0.16 Barium (Ba) HMDB04142 0.0070 (0.0001–0.0655) 0.0129–0.0320 Cadmium (Cd) HMDB03638 0.0004 (0.0002–0.0010) 0.00060 (0.00035–0.00100) Calcium (Ca) HMDB00464 200.0 (16.9–520.0) 299.0±99.0 Cerium (Ce) HMDB13672 0.00048 (0.00008–0.00180) 0.0 – 0.0007 Cesium (Cs) HMDB13669 0.0039 (0.0005–0.0110) 0.0061±0.0011 Chromium (Cr) HMDB00599 0.0084 (0.0001–0.0460) 0.0100±0.0065 Cobalt (Co) HMDB00608 0.0014 (0.0001–0.0140) 0.000810±0.00053 Copper (Cu) HMDB00657 0.0163 (0.0006–0.1099) 0.0190 (0.0092–0.038) Gallium (Ga) HMDB01478 0.0056 (0.0012–0.0150) 0.0003–0.0006 Hafnium (Hf) HMDB29201 0.00031 (0.00006–0.00110) 0.00027±0.00012 Iron (Fe) HMDB00692 0.0890 (0.0025–0.4579) 0.11 (0.0–0.14) Lanthanum (La) HMDB13711 0.00020 (0.00003–0.00079) 0.00052±0.00039 Lead (Pb) HMDB04628 0.0026 (0.0007–0.0110) 0.011 (0.0–0.020) Lithium (Li) HMDB05949 0.5 (0.2–0.9) 0.504 (0.058–3.414) Magnesium (Mg) HMDB00547 262 (42–1,189) 289.0±92.0 Manganese (Mn) HMDB01333 0.013 (0.002–0.051) 0.011 (0.0–0.023) Molybdenum (Mo) HMDB01302 0.049 (0.006–0.295) 0.06±0.03 Neodymium (Nd) HMDB13715 0.00017 (0.00004–0.00070) 0.0026±0.0013 Nickel (Ni) HMDB02457 0.0080 (0.0015–0.0450) 0.00420 (0.00302–0.00572) Palladium (Pd) HMDB13670 0.0110 (0.0011–0.1410) < 0.00001–0.2538 Platinum (Pt) HMDB13671 0.000155 (0.000031–0.000560) 0.0000656 Posphorus (P) HMDB01315 1,799 (221–4,936) 1,666.67–4,333.33 Potassium (K) HMDB00586 3,593 (553–8,079) 4,605 (2,631–6,578) Rhenium (Re) HMDB13719 0.000096 (0.000013–0.000340) NA Rubidium (Rb) HMDB01327 1.354 (0.187–3.673) 1.84±0.46 Ruthenium (Ru) HMDB13668 0.0002 (0.0001–0.0009) NA Selenium (Se) HMDB01349 0.149 (0.028–0.345) 0.11346 (0.07724–0.15010) Sodium (Na) HMDB00588 12,477 (1,863–37,249) 14,737 (5,263–36,842) Strontium (Sr) HMDB03642 0.163 (0.030–0.415) 0.1333 Tantalum (Ta) HMDB04143 0.00020 (0.00004–0.00074) 0.00008±0.00006 Thallium (Tl) HMDB13724 0.0003 (0.0001–0.0013) < 0.00659 Thorium (Th) HMDB29215 0.00020 (0.00002–0.00100) < 0.00584 Tin (Sn) HMDB01960 0.00110 (0.00016–0.00690) < 0.00552 Titanium (Ti) HMDB01966 0.0013 (0.0005–0.0030) 0.0142 Tungsten (W) HMDB01989 0.0060 (0.0011–0.0280) 0.010±0.022 Vanadium (V) HMDB02503 0.033 (0.004–0.083) 0.00162±0.00144 Zinc (Zn) HMDB01303 0.460 (0.058–0.948) 0.52±0.25 Zirconium (Zr) HMDB01475 0.021 (0.004–0.083) 0.0012–0.0074 |
| tr | Compound Name HMDB Concentration (µM/mM creatinine) Litterature values(µM/mM creatinine) |
| td | Compound Name |
| td | HMDB |
| td | Concentration (µM/mM creatinine) |
| td | Litterature values(µM/mM creatinine) |
| tr | Aluminum (Al) HMDB01247 0.31 (0.05–1.21) 0.667±0.037 |
| td | Aluminum (Al) |
| td | HMDB01247 |
| td | 0.31 (0.05–1.21) |
| td | 0.667±0.037 |
| tr | Arsenic (As) HMDB02290 0.10 (0.01–0.52) 0.16±0.16 |
| td | Arsenic (As) |
| td | HMDB02290 |
| td | 0.10 (0.01–0.52) |
| td | 0.16±0.16 |
| tr | Barium (Ba) HMDB04142 0.0070 (0.0001–0.0655) 0.0129–0.0320 |
| td | Barium (Ba) |
| td | HMDB04142 |
| td | 0.0070 (0.0001–0.0655) |
| td | 0.0129–0.0320 |
| tr | Cadmium (Cd) HMDB03638 0.0004 (0.0002–0.0010) 0.00060 (0.00035–0.00100) |
| td | Cadmium (Cd) |
| td | HMDB03638 |
| td | 0.0004 (0.0002–0.0010) |
| td | 0.00060 (0.00035–0.00100) |
| tr | Calcium (Ca) HMDB00464 200.0 (16.9–520.0) 299.0±99.0 |
| td | Calcium (Ca) |
| td | HMDB00464 |
| td | 200.0 (16.9–520.0) |
| td | 299.0±99.0 |
| tr | Cerium (Ce) HMDB13672 0.00048 (0.00008–0.00180) 0.0 – 0.0007 |
| td | Cerium (Ce) |
| td | HMDB13672 |
| td | 0.00048 (0.00008–0.00180) |
| td | 0.0 – 0.0007 |
| tr | Cesium (Cs) HMDB13669 0.0039 (0.0005–0.0110) 0.0061±0.0011 |
| td | Cesium (Cs) |
| td | HMDB13669 |
| td | 0.0039 (0.0005–0.0110) |
| td | 0.0061±0.0011 |
| tr | Chromium (Cr) HMDB00599 0.0084 (0.0001–0.0460) 0.0100±0.0065 |
| td | Chromium (Cr) |
| td | HMDB00599 |
| td | 0.0084 (0.0001–0.0460) |
| td | 0.0100±0.0065 |
| tr | Cobalt (Co) HMDB00608 0.0014 (0.0001–0.0140) 0.000810±0.00053 |
| td | Cobalt (Co) |
| td | HMDB00608 |
| td | 0.0014 (0.0001–0.0140) |
| td | 0.000810±0.00053 |
| tr | Copper (Cu) HMDB00657 0.0163 (0.0006–0.1099) 0.0190 (0.0092–0.038) |
| td | Copper (Cu) |
| td | HMDB00657 |
| td | 0.0163 (0.0006–0.1099) |
| td | 0.0190 (0.0092–0.038) |
| tr | Gallium (Ga) HMDB01478 0.0056 (0.0012–0.0150) 0.0003–0.0006 |
| td | Gallium (Ga) |
| td | HMDB01478 |
| td | 0.0056 (0.0012–0.0150) |
| td | 0.0003–0.0006 |
| tr | Hafnium (Hf) HMDB29201 0.00031 (0.00006–0.00110) 0.00027±0.00012 |
| td | Hafnium (Hf) |
| td | HMDB29201 |
| td | 0.00031 (0.00006–0.00110) |
| td | 0.00027±0.00012 |
| tr | Iron (Fe) HMDB00692 0.0890 (0.0025–0.4579) 0.11 (0.0–0.14) |
| td | Iron (Fe) |
| td | HMDB00692 |
| td | 0.0890 (0.0025–0.4579) |
| td | 0.11 (0.0–0.14) |
| tr | Lanthanum (La) HMDB13711 0.00020 (0.00003–0.00079) 0.00052±0.00039 |
| td | Lanthanum (La) |
| td | HMDB13711 |
| td | 0.00020 (0.00003–0.00079) |
| td | 0.00052±0.00039 |
| tr | Lead (Pb) HMDB04628 0.0026 (0.0007–0.0110) 0.011 (0.0–0.020) |
| td | Lead (Pb) |
| td | HMDB04628 |
| td | 0.0026 (0.0007–0.0110) |
| td | 0.011 (0.0–0.020) |
| tr | Lithium (Li) HMDB05949 0.5 (0.2–0.9) 0.504 (0.058–3.414) |
| td | Lithium (Li) |
| td | HMDB05949 |
| td | 0.5 (0.2–0.9) |
| td | 0.504 (0.058–3.414) |
| tr | Magnesium (Mg) HMDB00547 262 (42–1,189) 289.0±92.0 |
| td | Magnesium (Mg) |
| td | HMDB00547 |
| td | 262 (42–1,189) |
| td | 289.0±92.0 |
| tr | Manganese (Mn) HMDB01333 0.013 (0.002–0.051) 0.011 (0.0–0.023) |
| td | Manganese (Mn) |
| td | HMDB01333 |
| td | 0.013 (0.002–0.051) |
| td | 0.011 (0.0–0.023) |
| tr | Molybdenum (Mo) HMDB01302 0.049 (0.006–0.295) 0.06±0.03 |
| td | Molybdenum (Mo) |
| td | HMDB01302 |
| td | 0.049 (0.006–0.295) |
| td | 0.06±0.03 |
| tr | Neodymium (Nd) HMDB13715 0.00017 (0.00004–0.00070) 0.0026±0.0013 |
| td | Neodymium (Nd) |
| td | HMDB13715 |
| td | 0.00017 (0.00004–0.00070) |
| td | 0.0026±0.0013 |
| tr | Nickel (Ni) HMDB02457 0.0080 (0.0015–0.0450) 0.00420 (0.00302–0.00572) |
| td | Nickel (Ni) |
| td | HMDB02457 |
| td | 0.0080 (0.0015–0.0450) |
| td | 0.00420 (0.00302–0.00572) |
| tr | Palladium (Pd) HMDB13670 0.0110 (0.0011–0.1410) < 0.00001–0.2538 |
| td | Palladium (Pd) |
| td | HMDB13670 |
| td | 0.0110 (0.0011–0.1410) |
| td | < 0.00001–0.2538 |
| tr | Platinum (Pt) HMDB13671 0.000155 (0.000031–0.000560) 0.0000656 |
| td | Platinum (Pt) |
| td | HMDB13671 |
| td | 0.000155 (0.000031–0.000560) |
| td | 0.0000656 |
| tr | Posphorus (P) HMDB01315 1,799 (221–4,936) 1,666.67–4,333.33 |
| td | Posphorus (P) |
| td | HMDB01315 |
| td | 1,799 (221–4,936) |
| td | 1,666.67–4,333.33 |
| tr | Potassium (K) HMDB00586 3,593 (553–8,079) 4,605 (2,631–6,578) |
| td | Potassium (K) |
| td | HMDB00586 |
| td | 3,593 (553–8,079) |
| td | 4,605 (2,631–6,578) |
| tr | Rhenium (Re) HMDB13719 0.000096 (0.000013–0.000340) NA |
| td | Rhenium (Re) |
| td | HMDB13719 |
| td | 0.000096 (0.000013–0.000340) |
| td | NA |
| tr | Rubidium (Rb) HMDB01327 1.354 (0.187–3.673) 1.84±0.46 |
| td | Rubidium (Rb) |
| td | HMDB01327 |
| td | 1.354 (0.187–3.673) |
| td | 1.84±0.46 |
| tr | Ruthenium (Ru) HMDB13668 0.0002 (0.0001–0.0009) NA |
| td | Ruthenium (Ru) |
| td | HMDB13668 |
| td | 0.0002 (0.0001–0.0009) |
| td | NA |
| tr | Selenium (Se) HMDB01349 0.149 (0.028–0.345) 0.11346 (0.07724–0.15010) |
| td | Selenium (Se) |
| td | HMDB01349 |
| td | 0.149 (0.028–0.345) |
| td | 0.11346 (0.07724–0.15010) |
| tr | Sodium (Na) HMDB00588 12,477 (1,863–37,249) 14,737 (5,263–36,842) |
| td | Sodium (Na) |
| td | HMDB00588 |
| td | 12,477 (1,863–37,249) |
| td | 14,737 (5,263–36,842) |
| tr | Strontium (Sr) HMDB03642 0.163 (0.030–0.415) 0.1333 |
| td | Strontium (Sr) |
| td | HMDB03642 |
| td | 0.163 (0.030–0.415) |
| td | 0.1333 |
| tr | Tantalum (Ta) HMDB04143 0.00020 (0.00004–0.00074) 0.00008±0.00006 |
| td | Tantalum (Ta) |
| td | HMDB04143 |
| td | 0.00020 (0.00004–0.00074) |
| td | 0.00008±0.00006 |
| tr | Thallium (Tl) HMDB13724 0.0003 (0.0001–0.0013) < 0.00659 |
| td | Thallium (Tl) |
| td | HMDB13724 |
| td | 0.0003 (0.0001–0.0013) |
| td | < 0.00659 |
| tr | Thorium (Th) HMDB29215 0.00020 (0.00002–0.00100) < 0.00584 |
| td | Thorium (Th) |
| td | HMDB29215 |
| td | 0.00020 (0.00002–0.00100) |
| td | < 0.00584 |
| tr | Tin (Sn) HMDB01960 0.00110 (0.00016–0.00690) < 0.00552 |
| td | Tin (Sn) |
| td | HMDB01960 |
| td | 0.00110 (0.00016–0.00690) |
| td | < 0.00552 |
| tr | Titanium (Ti) HMDB01966 0.0013 (0.0005–0.0030) 0.0142 |
| td | Titanium (Ti) |
| td | HMDB01966 |
| td | 0.0013 (0.0005–0.0030) |
| td | 0.0142 |
| tr | Tungsten (W) HMDB01989 0.0060 (0.0011–0.0280) 0.010±0.022 |
| td | Tungsten (W) |
| td | HMDB01989 |
| td | 0.0060 (0.0011–0.0280) |
| td | 0.010±0.022 |
| tr | Vanadium (V) HMDB02503 0.033 (0.004–0.083) 0.00162±0.00144 |
| td | Vanadium (V) |
| td | HMDB02503 |
| td | 0.033 (0.004–0.083) |
| td | 0.00162±0.00144 |
| tr | Zinc (Zn) HMDB01303 0.460 (0.058–0.948) 0.52±0.25 |
| td | Zinc (Zn) |
| td | HMDB01303 |
| td | 0.460 (0.058–0.948) |
| td | 0.52±0.25 |
| tr | Zirconium (Zr) HMDB01475 0.021 (0.004–0.083) 0.0012–0.0074 |
| td | Zirconium (Zr) |
| td | HMDB01475 |
| td | 0.021 (0.004–0.083) |
| td | 0.0012–0.0074 |
| p | As far as we are aware, this is the first multi-elemental study of urine that has been performed by ICP-MS. As seen in Table 8, there is generally good agreement between the values measured by ICP-MS and those previously reported in literature, with differences generally being less than 22 ± 10%. Larger differences are seen for gallium (Ga), lead (Pb), Neodymium (Nd), titanium (Ti) and vanadium (V), but these may be due to the effects of age, diet, local environment (minerals in local water) or diurnal variation. Alternately they may reflect real differences in the sensitivity or accuracy of the instruments being used. As a general rule, ICP-MS is considered as a gold standard for the identification and quantification of trace metals [18], so we would tend to place higher confidence in the values derived via ICP-MS over those measured by other technologies. By our measurements, the most abundant metals/salts are sodium (Na) (12.5 ± 10.6 mM/mM creatinine) and potassium (K) (3.6 ± 2.5 mM/mM creatinine) – as expected, while the least abundant was rhenium (Re) with a lower limit of quantification by ICP-MS of 96 pM/mM creatinine. |
| sec | Quantification and Identification of Urine Metabolites – Targeted HPLC Assays The inventory of metabolites we detected and quantified by 1H NMR, GC-MS, DFI/LC-MS/MS and ICP-MS covers a significant portion of all chemical classes. However, these methods sometimes lack the necessary sensitivity, the appropriate instrumental configuration or detection capabilities and therefore fail to detect/quantify a variety of important compound classes. This includes a number of molecules that are normal constituents of urine such as thiols and isoflavones. To identify and quantify these 2 classes of metabolites we decided to employ High Performance Liquid Chromatography (HPLC). HPLC assays are the method of choice for detecting isoflavones and thiols as they are sensitive, precise and can be easily coupled with sensitive detection methodologies such as fluorescence and ultraviolet detection. In our studies, fluorescence and ultraviolet detection were used for the identification and quantification of urinary thiols and isoflavones, respectively. Biological thiols, or mercaptans, are very active metabolic products of sulfur and play a central role in redox metabolism, cellular homeostasis and a variety of physiological and pathological processes. In urine, the most important thiols are L-cysteine and L-cysteinylglycine [90], [91], [92], [93]. Isoflavones or phytoestrogens form or constitute another important class of urinary metabolites [90]. Humans are exposed to these biologically active phytochemicals mainly through food intake via vegetables, fruit and wheat/bread products [94]. For the detection of thiols we developed assays to measure L-cysteine, L-cysteineglycine, L-glutathione and L-homocysyeine, while for isoflavones we developed assays to measure biochanin A, coumesterol, daidzein, equol, formonentin and genistein. Using these HPLC assays, we measured a total of 4 thiols and 6 isoflavones in urine (Table 9). Table 9 Concentrations of isoflavones and thiols as determined by HPLC. Compound Name HMDB ID Mean(µM/mM creatinine) Oc (%) Literature values (µM/mM creatinine) Biochanin Aa HMDB02338 0.0088 (0.0027–0.0200) 77 NA Coumesterola HMDB02326 0.0081 (0.0033–0.0120) 45 NA Daidzeina HMDB03312 0.026 (0.002–0.064) 88 0.036 ± 0.008 Equola HMDB02209 0.06 (0.01–0.10) 67 0.13 ± 0.12 Formonenetina HMDB05808 0.0007 (0.0002–0.0011) 33 NA Genisteina HMDB03217 0.0280 (0.0077–0.0640) 88 0.032 ± 0.007 L-Cysteineb HMDB00574 81.0 (36.7–147.6) 100 33.4 ± 15.5 L-Cysteinylglycineb HMDB00078 0.6 (0.28–1.54) 100 0.48 ± 0.15 L-Glutathioneb HMDB00125 0.030 (0.005–0.085) 100 0.065 L-Homocysteineb HMDB00742 1.1 (0.6–1.7) 100 1.95 ± 1.47 a Isoflavone metabolites measured by HPLC/UV. b Thiol metabolites measured by HPLC/FD. Oc: occurrence; NA: not available. As seen in Table 9, there is generally good agreement between the values measured by these targeted HPLC assays and those previously reported in literature, with differences generally being less than ∼30%. Only one of these metabolites (L-cysteine) was measured independently on one of our other platforms (NMR) and the NMR concentration was found to be 25% lower than the HPLC assay. A possible explanation for this discrepancy is that cysteine measured by HPLC-FD yields total L-cysteine including the free form and L-cystine reduced to L-cysteine during the reaction [95]. On the other hand, NMR can distinguish between L-cysteine and L-cystine. |
| title | Quantification and Identification of Urine Metabolites – Targeted HPLC Assays |
| p | The inventory of metabolites we detected and quantified by 1H NMR, GC-MS, DFI/LC-MS/MS and ICP-MS covers a significant portion of all chemical classes. However, these methods sometimes lack the necessary sensitivity, the appropriate instrumental configuration or detection capabilities and therefore fail to detect/quantify a variety of important compound classes. This includes a number of molecules that are normal constituents of urine such as thiols and isoflavones. To identify and quantify these 2 classes of metabolites we decided to employ High Performance Liquid Chromatography (HPLC). HPLC assays are the method of choice for detecting isoflavones and thiols as they are sensitive, precise and can be easily coupled with sensitive detection methodologies such as fluorescence and ultraviolet detection. In our studies, fluorescence and ultraviolet detection were used for the identification and quantification of urinary thiols and isoflavones, respectively. |
| p | Biological thiols, or mercaptans, are very active metabolic products of sulfur and play a central role in redox metabolism, cellular homeostasis and a variety of physiological and pathological processes. In urine, the most important thiols are L-cysteine and L-cysteinylglycine [90], [91], [92], [93]. Isoflavones or phytoestrogens form or constitute another important class of urinary metabolites [90]. Humans are exposed to these biologically active phytochemicals mainly through food intake via vegetables, fruit and wheat/bread products [94]. For the detection of thiols we developed assays to measure L-cysteine, L-cysteineglycine, L-glutathione and L-homocysyeine, while for isoflavones we developed assays to measure biochanin A, coumesterol, daidzein, equol, formonentin and genistein. Using these HPLC assays, we measured a total of 4 thiols and 6 isoflavones in urine (Table 9). |
| table-wrap | Table 9 Concentrations of isoflavones and thiols as determined by HPLC. Compound Name HMDB ID Mean(µM/mM creatinine) Oc (%) Literature values (µM/mM creatinine) Biochanin Aa HMDB02338 0.0088 (0.0027–0.0200) 77 NA Coumesterola HMDB02326 0.0081 (0.0033–0.0120) 45 NA Daidzeina HMDB03312 0.026 (0.002–0.064) 88 0.036 ± 0.008 Equola HMDB02209 0.06 (0.01–0.10) 67 0.13 ± 0.12 Formonenetina HMDB05808 0.0007 (0.0002–0.0011) 33 NA Genisteina HMDB03217 0.0280 (0.0077–0.0640) 88 0.032 ± 0.007 L-Cysteineb HMDB00574 81.0 (36.7–147.6) 100 33.4 ± 15.5 L-Cysteinylglycineb HMDB00078 0.6 (0.28–1.54) 100 0.48 ± 0.15 L-Glutathioneb HMDB00125 0.030 (0.005–0.085) 100 0.065 L-Homocysteineb HMDB00742 1.1 (0.6–1.7) 100 1.95 ± 1.47 a Isoflavone metabolites measured by HPLC/UV. b Thiol metabolites measured by HPLC/FD. Oc: occurrence; NA: not available. |
| label | Table 9 |
| caption | Concentrations of isoflavones and thiols as determined by HPLC. |
| title | Concentrations of isoflavones and thiols as determined by HPLC. |
| table | Compound Name HMDB ID Mean(µM/mM creatinine) Oc (%) Literature values (µM/mM creatinine) Biochanin Aa HMDB02338 0.0088 (0.0027–0.0200) 77 NA Coumesterola HMDB02326 0.0081 (0.0033–0.0120) 45 NA Daidzeina HMDB03312 0.026 (0.002–0.064) 88 0.036 ± 0.008 Equola HMDB02209 0.06 (0.01–0.10) 67 0.13 ± 0.12 Formonenetina HMDB05808 0.0007 (0.0002–0.0011) 33 NA Genisteina HMDB03217 0.0280 (0.0077–0.0640) 88 0.032 ± 0.007 L-Cysteineb HMDB00574 81.0 (36.7–147.6) 100 33.4 ± 15.5 L-Cysteinylglycineb HMDB00078 0.6 (0.28–1.54) 100 0.48 ± 0.15 L-Glutathioneb HMDB00125 0.030 (0.005–0.085) 100 0.065 L-Homocysteineb HMDB00742 1.1 (0.6–1.7) 100 1.95 ± 1.47 |
| tr | Compound Name HMDB ID Mean(µM/mM creatinine) Oc (%) Literature values (µM/mM creatinine) |
| td | Compound Name |
| td | HMDB ID |
| td | Mean(µM/mM creatinine) |
| td | Oc (%) |
| td | Literature values (µM/mM creatinine) |
| tr | Biochanin Aa HMDB02338 0.0088 (0.0027–0.0200) 77 NA |
| td | Biochanin Aa |
| td | HMDB02338 |
| td | 0.0088 (0.0027–0.0200) |
| td | 77 |
| td | NA |
| tr | Coumesterola HMDB02326 0.0081 (0.0033–0.0120) 45 NA |
| td | Coumesterola |
| td | HMDB02326 |
| td | 0.0081 (0.0033–0.0120) |
| td | 45 |
| td | NA |
| tr | Daidzeina HMDB03312 0.026 (0.002–0.064) 88 0.036 ± 0.008 |
| td | Daidzeina |
| td | HMDB03312 |
| td | 0.026 (0.002–0.064) |
| td | 88 |
| td | 0.036 ± 0.008 |
| tr | Equola HMDB02209 0.06 (0.01–0.10) 67 0.13 ± 0.12 |
| td | Equola |
| td | HMDB02209 |
| td | 0.06 (0.01–0.10) |
| td | 67 |
| td | 0.13 ± 0.12 |
| tr | Formonenetina HMDB05808 0.0007 (0.0002–0.0011) 33 NA |
| td | Formonenetina |
| td | HMDB05808 |
| td | 0.0007 (0.0002–0.0011) |
| td | 33 |
| td | NA |
| tr | Genisteina HMDB03217 0.0280 (0.0077–0.0640) 88 0.032 ± 0.007 |
| td | Genisteina |
| td | HMDB03217 |
| td | 0.0280 (0.0077–0.0640) |
| td | 88 |
| td | 0.032 ± 0.007 |
| tr | L-Cysteineb HMDB00574 81.0 (36.7–147.6) 100 33.4 ± 15.5 |
| td | L-Cysteineb |
| td | HMDB00574 |
| td | 81.0 (36.7–147.6) |
| td | 100 |
| td | 33.4 ± 15.5 |
| tr | L-Cysteinylglycineb HMDB00078 0.6 (0.28–1.54) 100 0.48 ± 0.15 |
| td | L-Cysteinylglycineb |
| td | HMDB00078 |
| td | 0.6 (0.28–1.54) |
| td | 100 |
| td | 0.48 ± 0.15 |
| tr | L-Glutathioneb HMDB00125 0.030 (0.005–0.085) 100 0.065 |
| td | L-Glutathioneb |
| td | HMDB00125 |
| td | 0.030 (0.005–0.085) |
| td | 100 |
| td | 0.065 |
| tr | L-Homocysteineb HMDB00742 1.1 (0.6–1.7) 100 1.95 ± 1.47 |
| td | L-Homocysteineb |
| td | HMDB00742 |
| td | 1.1 (0.6–1.7) |
| td | 100 |
| td | 1.95 ± 1.47 |
| table-wrap-foot | a Isoflavone metabolites measured by HPLC/UV. b Thiol metabolites measured by HPLC/FD. Oc: occurrence; NA: not available. |
| footnote | a Isoflavone metabolites measured by HPLC/UV. |
| label | a |
| p | Isoflavone metabolites measured by HPLC/UV. |
| footnote | b Thiol metabolites measured by HPLC/FD. |
| label | b |
| p | Thiol metabolites measured by HPLC/FD. |
| footnote | Oc: occurrence; |
| p | Oc: occurrence; |
| footnote | NA: not available. |
| p | NA: not available. |
| p | As seen in Table 9, there is generally good agreement between the values measured by these targeted HPLC assays and those previously reported in literature, with differences generally being less than ∼30%. Only one of these metabolites (L-cysteine) was measured independently on one of our other platforms (NMR) and the NMR concentration was found to be 25% lower than the HPLC assay. A possible explanation for this discrepancy is that cysteine measured by HPLC-FD yields total L-cysteine including the free form and L-cystine reduced to L-cysteine during the reaction [95]. On the other hand, NMR can distinguish between L-cysteine and L-cystine. |
| sec | The Composition of Human Urine – Comparison with Other Biofluids By combining a systematic computer-aided literature survey with an extensive, quantitative multiplatform metabolomic analysis we have been able to comprehensively characterize the human urine metabolome. Our data suggests that there are at least 3079 detectable metabolites in human urine, of which 1350 have been quantified. At least 72 of these compounds are of microbial origin, 1453 are endogenous while 2282 are considered exogenous (note some compounds can be both exogenous and endogenous), coming from diet, drugs, cosmetics or environmental exposure. Using a chemical classification system developed for the HMDB [15] we found that human urinary metabolites fall into 230 different chemical classes (25 “super classes”, Table 2). Given that there are only 356 chemical classes in the entire human metabolome [16], this certainly demonstrates the enormous chemical diversity found in urine. As might be expected, most urinary metabolites are very hydrophilic, although there are clearly trace amounts of lipids and fatty acids that contribute a significant number of chemicals to the urinary metabolome (836 fatty acids and lipids). This is in rather striking contrast to the composition of serum [20] which is particularly rich in lipids (i.e. >17000 lipids and fatty acids). Relative to other biofluids such as CSF [18] or saliva [19], urine contains significantly more compounds (5–10X) and exhibits significantly more chemical diversity (2–3X). On the other hand, we know that every compound that is found in human urine is also found in human blood. In other words, the human urine metabolome is a subset of the human serum metabolome, both in terms of composition and chemical diversity [20]. However, more than 484 compounds we identified in urine (either experimentally or via literature review) were not previously reported to be in blood. The fact that so many compounds seem to be unique to urine likely has to do with the fact that the kidneys do an extraordinary job of concentrating certain metabolites from the blood. Consequently compounds that are far below the limit of detection in blood (using today’s instrumentation) are well above the detection limit in urine. In fact, concentration differences between the two biofluids sometimes exceed 1000X for certain compounds, such as histamine, androsterone, normetanephrine, testosterone 13, 14-dihydro-15-keto-PGE2, m-tyramine and aldosterone. So, while the number of water-soluble compounds in blood and urine may be almost identical, the concentrations of these compounds are often profoundly different. This difference, combined with the ability of the kidney to handle abnormally high or abnormally low concentrations of metabolites, makes urine a particularly useful biofluid for medical diagnostics. In fact, according to our data in the UMDB, urinary metabolites have been used to characterize nearly 220 diseases. Furthermore, the ability of the kidneys to filter toxins or xenobiotics makes urine a particularly useful biofluid for diet and drug monitoring and for assessing chemical or pollutant exposure [96]. |
| title | The Composition of Human Urine – Comparison with Other Biofluids |
| p | By combining a systematic computer-aided literature survey with an extensive, quantitative multiplatform metabolomic analysis we have been able to comprehensively characterize the human urine metabolome. Our data suggests that there are at least 3079 detectable metabolites in human urine, of which 1350 have been quantified. At least 72 of these compounds are of microbial origin, 1453 are endogenous while 2282 are considered exogenous (note some compounds can be both exogenous and endogenous), coming from diet, drugs, cosmetics or environmental exposure. Using a chemical classification system developed for the HMDB [15] we found that human urinary metabolites fall into 230 different chemical classes (25 “super classes”, Table 2). Given that there are only 356 chemical classes in the entire human metabolome [16], this certainly demonstrates the enormous chemical diversity found in urine. As might be expected, most urinary metabolites are very hydrophilic, although there are clearly trace amounts of lipids and fatty acids that contribute a significant number of chemicals to the urinary metabolome (836 fatty acids and lipids). This is in rather striking contrast to the composition of serum [20] which is particularly rich in lipids (i.e. >17000 lipids and fatty acids). Relative to other biofluids such as CSF [18] or saliva [19], urine contains significantly more compounds (5–10X) and exhibits significantly more chemical diversity (2–3X). On the other hand, we know that every compound that is found in human urine is also found in human blood. In other words, the human urine metabolome is a subset of the human serum metabolome, both in terms of composition and chemical diversity [20]. However, more than 484 compounds we identified in urine (either experimentally or via literature review) were not previously reported to be in blood. The fact that so many compounds seem to be unique to urine likely has to do with the fact that the kidneys do an extraordinary job of concentrating certain metabolites from the blood. Consequently compounds that are far below the limit of detection in blood (using today’s instrumentation) are well above the detection limit in urine. In fact, concentration differences between the two biofluids sometimes exceed 1000X for certain compounds, such as histamine, androsterone, normetanephrine, testosterone 13, 14-dihydro-15-keto-PGE2, m-tyramine and aldosterone. So, while the number of water-soluble compounds in blood and urine may be almost identical, the concentrations of these compounds are often profoundly different. This difference, combined with the ability of the kidney to handle abnormally high or abnormally low concentrations of metabolites, makes urine a particularly useful biofluid for medical diagnostics. In fact, according to our data in the UMDB, urinary metabolites have been used to characterize nearly 220 diseases. Furthermore, the ability of the kidneys to filter toxins or xenobiotics makes urine a particularly useful biofluid for diet and drug monitoring and for assessing chemical or pollutant exposure [96]. |
| sec | Method Comparison One of the central motivations behind this work was to ascertain the strengths and weaknesses of several common metabolomic platforms for characterizing human urine. We employed 6 different analytical platforms: NMR; GC-MS; DFI/LC-MS/MS; HPLC/UV; HPLC/FD and ICP-MS. Using our literature-derived knowledge about the composition of human urine, along with custom-derived spectral libraries and targeted assays we were able to “push the limits” in terms of number of compounds that could be identified and/or quantified via each platform. In total, we identified 445 and quantified 378 distinct metabolites using these 6 different systems. According to Table 1, this is the largest number of urine metabolites ever identified and/or quantified in a single study. NMR spectroscopy was able to identify and quantify 209 compounds; GC-MS was able to identify 179 and quantify 85 compounds; DFI/LC-MS/MS identified and quantified 127 compounds; ICP-MS identified and quantified 40 compounds; while customized HPLC assays (with UV or FD detection) identified and quantified 10 compounds. The number of urinary metabolites we identified/quantified for NMR, GC-MS, DFI/LC-MS/MS and ICP-MS all represent “records” for these platforms. In terms of platform overlap and compound complementarity, NMR and GC-MS were able to identify a common set of 88 metabolites; NMR and DFI/LC-MS/MS were able to identify and quantify a common set of 28 metabolites, while NMR, GC-MS and DFI/LC-MS/MS were able to identify a common set of 17 metabolites (15 amino acids, creatinine and hexose/glucose). All of these results are summarized in a Venn diagram (Figure 5). As might be expected, metabolite coverage differs from one analytical technique to another. These are difference mostly due to the intrinsic nature of the devices or platforms used. In particular, significant differences exist between these platforms in terms of their sensitivity or separation and/or extraction efficiency. Likewise the use of targeted vs. non-targeted methods along with issues related to compound stability, solubility and volatility led to some significantly different platform-dependent results. Figure 5 Venn diagram showing the overlap of urine metabolites detected by NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC coupled to UV detection and HPLC coupled to fluorescence detection compared to the detectable urine metabolome. Given that the known, quantifiable urine metabolome consists of ∼2651 known metabolites and metabolite species (corresponding to 3079 distinct structures), we can calculate that NMR is able to measure ∼8% (209/2651) of the human urine metabolome; GC-MS is able to measure ∼7% (179/2651); DFI/LC-MS/MS is able to measure ∼5% (127/2651); ICP-MS/MS is able to measure ∼1.5% (40/2651); HPLC/UV is able to measure ∼0.2% (6/2651); while HPLC/FD is able to measure ∼0.15% (4/2651) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >16% of the known urinary metabolome (>445/2651). If we re-evaluate this fraction in terms of total metabolite structures (corresponding to known and highly probable metabolites), the urine metabolome consists of 3079 compounds. From this total we can calculate that NMR is able to measure ∼7% (209/3079) of the human urine metabolome; GC-MS is able to measure ∼6% (179/3079); DFI/LC-MS/MS is able to measure ∼18% (555/3079); ICP-MS/MS is able to measure ∼1.3% (40/3079); HPLC/UV is able to measure ∼0.2% (6/3079); while HPLC/FD is able to measure ∼0.13% (4/3079) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >28% of the known and probable or putative urinary metabolome (>873/3079). In terms of chemical class coverage, NMR detects compounds from 15 of the 25 major chemical superclasses in urine, GC-MS detects compounds from 14 of the 25, DFI/LC-MS/MS detects 6 of the 25, ICP-MS detects 1 of the 25 while the targeted methods for thiols and isoflavones detect just 1 of each. From these data we can conclude that NMR is currently the best method for identifying and quantifying urinary compounds. Not only does it permit measurement of the largest number of metabolites (209) but it also yields the greatest chemical diversity. Furthermore, NMR is non-destructive so that the same sample can be subsequently re-used for GC-MS, LC-MS or ICP-MS analyses. The minimal sample preparation and relatively rapid data collection for NMR also make it much more appealing for urine metabolomics, although the spectral analysis can be quite slow (∼1–2 hours per sample). While GC-MS is a close second in terms of overall coverage (179 metabolites, 14 chemical superclasses), these numbers represents the result of 4 different analyses performed on 2 different GC-MS instruments. Many labs would not have these multiple configurations available or the resources to routinely run these types of analyses. Likewise each sample required many hours of preparation, sample collection and data analysis. In this regard, multi-platform GC-MS is definitely not a high-throughput metabolomics technique. Relative to NMR and GC-MS, DFI/LC-MS/MS also performs well, with 127 compounds being quantified. However, DFI/LC-MS/MS provides very limited chemical diversity (only 6 chemical superclasses). On the other hand, DFI/LC-MS/MS requires very little sample volume (10 µL) and it is a very low-cost, largely automated, high-throughput route for measuring metabolites. The other techniques (HPLC, ICP-MS) we employed in this study, while useful, do not come close to matching the coverage or diversity of NMR, GC-MS or DFI/LC-MS/MS. While we certainly went to considerable lengths to use current or cutting edge technologies to characterize the urine metabolome, it is also important to note that there is always potential for future improvement. Using higher field (900–950 MHz) NMR instruments, employing newer model GC-MS instruments or more sensitive GC-TOF instruments, using more than 3 derivatization or extraction steps for our GC-MS analyses, employing the latest version of the NIST database or a larger collection of GC-MS databases, implementing more sophisticated or targeted detection and separation techniques, using various commercial immunodetection kits or employing the latest LC-MS/MS techniques coupled to FT-MS or orbitraps – all of these could have added to the quantity and diversity of metabolites detected or quantified. However, like many laboratories, our resources are somewhat limited. Furthermore, in this study we wanted to address the question of how well a cross-section of commonly accessible metabolomic methods or platforms could perform in identifying and quantifying metabolites in urine. While being able to quantitatively compare metabolite coverage and chemical diversity amongst the major metabolomics platforms (NMR, GC-MS and DFI/LC-MS/MS) is important, it is also useful to compare their consistency or reproducibility in terms of metabolite quantification. In particular we decided to assess the 3 major platforms in terms of their ability to identify and quantify a common group of compounds, namely the amino acids. Overall we found that the measured concentrations are in relatively good agreement (Table 10). However, a few exceptions are evident. For example, the NMR and DFI/LC-MS/MS concentrations of glycine and serine are higher than the GC-MS values (note that glycine exhibits the highest concentration among urinary amino acids). For serine, after the silylation reaction using MSTFA, we obtained serine-2TMS (13.9 min) and serine-3TMS derivatives (16.6 min). The chromatographic peak corresponding to serine-2TMS is weak and overlaps slightly with the urea peak. This overlap and the corresponding difficulty in peak integration may explain the quantitation differences compared to other analytical assays. Neither L-glutamine nor L-glutamic acid could be accurately quantified or identified by GC-MS. In our case, the glutamine peak co-elutes with glycerol-3-phosphate. Other investigators have noted that glutamine can also be hydrolyzed and converted to glutamic acid [97], or to pyroglutamic acid during derivatization [95]. As a result, only pyroglutamic acid could be identified in our GC-MS assay. The identification of glutamic acid and pyroglutamic acid can be complicated [97], which explains our failure to identify glutamic acid by GC-MS. L-arginine could not be detected by GC-MS, because it is converted to ornithine during derivatization [98], which probably explains the slightly higher concentration of ornithine measured by GC-MS, compared to the one determined by DFI/LC-MS/MS. Finally, L-cysteine and L-cystine can only be identified and quantified by NMR and targeted HPLC/FD because the identification and quantification of theses metabolites is not possible with the Biocrates kit. With GC-MS it has often been noted that L-cystine can be converted to L-cysteine during derivatization, while L-cysteine might be oxidized to L-cystine during prolonged storage of the standard solution [95], confounding the identification of L-cystine and L-cysteine. Table 10 Comparison of urine concentrations of 23 quantified amino acids by different assays: DFI/LC-MS/MS (BIOCRATES kit), NMR and GC-MS. Amino acids* DFI/LC-MS/MS NMR GC-MS Glycine 101.0±53.5 106±66 75.7±51.7 L-Alanine 21.7±10.6 21.8±10.3 NA L-Arginine 7.4±3.0 8.6±3.6 NA L-Asparagine 8.8±3.5 10.1±4.0 9.5±5.5 L-Aspartic acid 10.9±7.7 10.9±5.8 NA L-Citrulline 0.8±0.4 NA NA L-Cysteine NA 65.8±24.0 NA L-Cystine NA 12.5±5.4 NA L-Glutamic acid 6.4±4.9 8.5±3.6 NA L-Glutamine 39.9±14.3 37.2±14.8 NA L-Histidine 45.6±15.6 43.9±19.5 NA L-Isoleucine 1.3±0.4 1.3±0.5 NA L-Leucine 3.0±1.0 2.8±0.9 NA L-Lysine 17.9±12.2 17.2±11.5 NA L-Methionine 0.8±0.3 1.4±0.6 NA L-Ornithine 4.4±5.0 NA 5.3±1.9 L-Phenylalanine 5.9±2.0 6.4±2.3 7.8±2.0 L-Proline 1.1±0.6 NA NA L-Serine 24.5±9.2 25.3±9.7 19.0±7.0 L-Threonine 14.6±6.1 13.3±5.3 NA L-Tryptophan 5.6±2.1 6.3±2.2 NA L-Tyrosine 8.7±4.2 9.5±4.5 15.1±5.4 L-Valine 4.3±1.9 3.4±1.4 5.5±2.0 * Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available. |
| title | Method Comparison |
| p | One of the central motivations behind this work was to ascertain the strengths and weaknesses of several common metabolomic platforms for characterizing human urine. We employed 6 different analytical platforms: NMR; GC-MS; DFI/LC-MS/MS; HPLC/UV; HPLC/FD and ICP-MS. Using our literature-derived knowledge about the composition of human urine, along with custom-derived spectral libraries and targeted assays we were able to “push the limits” in terms of number of compounds that could be identified and/or quantified via each platform. In total, we identified 445 and quantified 378 distinct metabolites using these 6 different systems. According to Table 1, this is the largest number of urine metabolites ever identified and/or quantified in a single study. NMR spectroscopy was able to identify and quantify 209 compounds; GC-MS was able to identify 179 and quantify 85 compounds; DFI/LC-MS/MS identified and quantified 127 compounds; ICP-MS identified and quantified 40 compounds; while customized HPLC assays (with UV or FD detection) identified and quantified 10 compounds. The number of urinary metabolites we identified/quantified for NMR, GC-MS, DFI/LC-MS/MS and ICP-MS all represent “records” for these platforms. |
| p | In terms of platform overlap and compound complementarity, NMR and GC-MS were able to identify a common set of 88 metabolites; NMR and DFI/LC-MS/MS were able to identify and quantify a common set of 28 metabolites, while NMR, GC-MS and DFI/LC-MS/MS were able to identify a common set of 17 metabolites (15 amino acids, creatinine and hexose/glucose). All of these results are summarized in a Venn diagram (Figure 5). As might be expected, metabolite coverage differs from one analytical technique to another. These are difference mostly due to the intrinsic nature of the devices or platforms used. In particular, significant differences exist between these platforms in terms of their sensitivity or separation and/or extraction efficiency. Likewise the use of targeted vs. non-targeted methods along with issues related to compound stability, solubility and volatility led to some significantly different platform-dependent results. |
| figure | Figure 5 Venn diagram showing the overlap of urine metabolites detected by NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC coupled to UV detection and HPLC coupled to fluorescence detection compared to the detectable urine metabolome. |
| label | Figure 5 |
| caption | Venn diagram showing the overlap of urine metabolites detected by NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC coupled to UV detection and HPLC coupled to fluorescence detection compared to the detectable urine metabolome. |
| title | Venn diagram showing the overlap of urine metabolites detected by NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC coupled to UV detection and HPLC coupled to fluorescence detection compared to the detectable urine metabolome. |
| p | Given that the known, quantifiable urine metabolome consists of ∼2651 known metabolites and metabolite species (corresponding to 3079 distinct structures), we can calculate that NMR is able to measure ∼8% (209/2651) of the human urine metabolome; GC-MS is able to measure ∼7% (179/2651); DFI/LC-MS/MS is able to measure ∼5% (127/2651); ICP-MS/MS is able to measure ∼1.5% (40/2651); HPLC/UV is able to measure ∼0.2% (6/2651); while HPLC/FD is able to measure ∼0.15% (4/2651) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >16% of the known urinary metabolome (>445/2651). If we re-evaluate this fraction in terms of total metabolite structures (corresponding to known and highly probable metabolites), the urine metabolome consists of 3079 compounds. From this total we can calculate that NMR is able to measure ∼7% (209/3079) of the human urine metabolome; GC-MS is able to measure ∼6% (179/3079); DFI/LC-MS/MS is able to measure ∼18% (555/3079); ICP-MS/MS is able to measure ∼1.3% (40/3079); HPLC/UV is able to measure ∼0.2% (6/3079); while HPLC/FD is able to measure ∼0.13% (4/3079) of the urine metabolome. When combined, the 6 analytical techniques are able to cover >28% of the known and probable or putative urinary metabolome (>873/3079). In terms of chemical class coverage, NMR detects compounds from 15 of the 25 major chemical superclasses in urine, GC-MS detects compounds from 14 of the 25, DFI/LC-MS/MS detects 6 of the 25, ICP-MS detects 1 of the 25 while the targeted methods for thiols and isoflavones detect just 1 of each. |
| p | From these data we can conclude that NMR is currently the best method for identifying and quantifying urinary compounds. Not only does it permit measurement of the largest number of metabolites (209) but it also yields the greatest chemical diversity. Furthermore, NMR is non-destructive so that the same sample can be subsequently re-used for GC-MS, LC-MS or ICP-MS analyses. The minimal sample preparation and relatively rapid data collection for NMR also make it much more appealing for urine metabolomics, although the spectral analysis can be quite slow (∼1–2 hours per sample). While GC-MS is a close second in terms of overall coverage (179 metabolites, 14 chemical superclasses), these numbers represents the result of 4 different analyses performed on 2 different GC-MS instruments. Many labs would not have these multiple configurations available or the resources to routinely run these types of analyses. Likewise each sample required many hours of preparation, sample collection and data analysis. In this regard, multi-platform GC-MS is definitely not a high-throughput metabolomics technique. Relative to NMR and GC-MS, DFI/LC-MS/MS also performs well, with 127 compounds being quantified. However, DFI/LC-MS/MS provides very limited chemical diversity (only 6 chemical superclasses). On the other hand, DFI/LC-MS/MS requires very little sample volume (10 µL) and it is a very low-cost, largely automated, high-throughput route for measuring metabolites. The other techniques (HPLC, ICP-MS) we employed in this study, while useful, do not come close to matching the coverage or diversity of NMR, GC-MS or DFI/LC-MS/MS. |
| p | While we certainly went to considerable lengths to use current or cutting edge technologies to characterize the urine metabolome, it is also important to note that there is always potential for future improvement. Using higher field (900–950 MHz) NMR instruments, employing newer model GC-MS instruments or more sensitive GC-TOF instruments, using more than 3 derivatization or extraction steps for our GC-MS analyses, employing the latest version of the NIST database or a larger collection of GC-MS databases, implementing more sophisticated or targeted detection and separation techniques, using various commercial immunodetection kits or employing the latest LC-MS/MS techniques coupled to FT-MS or orbitraps – all of these could have added to the quantity and diversity of metabolites detected or quantified. However, like many laboratories, our resources are somewhat limited. Furthermore, in this study we wanted to address the question of how well a cross-section of commonly accessible metabolomic methods or platforms could perform in identifying and quantifying metabolites in urine. |
| p | While being able to quantitatively compare metabolite coverage and chemical diversity amongst the major metabolomics platforms (NMR, GC-MS and DFI/LC-MS/MS) is important, it is also useful to compare their consistency or reproducibility in terms of metabolite quantification. In particular we decided to assess the 3 major platforms in terms of their ability to identify and quantify a common group of compounds, namely the amino acids. Overall we found that the measured concentrations are in relatively good agreement (Table 10). However, a few exceptions are evident. For example, the NMR and DFI/LC-MS/MS concentrations of glycine and serine are higher than the GC-MS values (note that glycine exhibits the highest concentration among urinary amino acids). For serine, after the silylation reaction using MSTFA, we obtained serine-2TMS (13.9 min) and serine-3TMS derivatives (16.6 min). The chromatographic peak corresponding to serine-2TMS is weak and overlaps slightly with the urea peak. This overlap and the corresponding difficulty in peak integration may explain the quantitation differences compared to other analytical assays. Neither L-glutamine nor L-glutamic acid could be accurately quantified or identified by GC-MS. In our case, the glutamine peak co-elutes with glycerol-3-phosphate. Other investigators have noted that glutamine can also be hydrolyzed and converted to glutamic acid [97], or to pyroglutamic acid during derivatization [95]. As a result, only pyroglutamic acid could be identified in our GC-MS assay. The identification of glutamic acid and pyroglutamic acid can be complicated [97], which explains our failure to identify glutamic acid by GC-MS. L-arginine could not be detected by GC-MS, because it is converted to ornithine during derivatization [98], which probably explains the slightly higher concentration of ornithine measured by GC-MS, compared to the one determined by DFI/LC-MS/MS. Finally, L-cysteine and L-cystine can only be identified and quantified by NMR and targeted HPLC/FD because the identification and quantification of theses metabolites is not possible with the Biocrates kit. With GC-MS it has often been noted that L-cystine can be converted to L-cysteine during derivatization, while L-cysteine might be oxidized to L-cystine during prolonged storage of the standard solution [95], confounding the identification of L-cystine and L-cysteine. |
| table-wrap | Table 10 Comparison of urine concentrations of 23 quantified amino acids by different assays: DFI/LC-MS/MS (BIOCRATES kit), NMR and GC-MS. Amino acids* DFI/LC-MS/MS NMR GC-MS Glycine 101.0±53.5 106±66 75.7±51.7 L-Alanine 21.7±10.6 21.8±10.3 NA L-Arginine 7.4±3.0 8.6±3.6 NA L-Asparagine 8.8±3.5 10.1±4.0 9.5±5.5 L-Aspartic acid 10.9±7.7 10.9±5.8 NA L-Citrulline 0.8±0.4 NA NA L-Cysteine NA 65.8±24.0 NA L-Cystine NA 12.5±5.4 NA L-Glutamic acid 6.4±4.9 8.5±3.6 NA L-Glutamine 39.9±14.3 37.2±14.8 NA L-Histidine 45.6±15.6 43.9±19.5 NA L-Isoleucine 1.3±0.4 1.3±0.5 NA L-Leucine 3.0±1.0 2.8±0.9 NA L-Lysine 17.9±12.2 17.2±11.5 NA L-Methionine 0.8±0.3 1.4±0.6 NA L-Ornithine 4.4±5.0 NA 5.3±1.9 L-Phenylalanine 5.9±2.0 6.4±2.3 7.8±2.0 L-Proline 1.1±0.6 NA NA L-Serine 24.5±9.2 25.3±9.7 19.0±7.0 L-Threonine 14.6±6.1 13.3±5.3 NA L-Tryptophan 5.6±2.1 6.3±2.2 NA L-Tyrosine 8.7±4.2 9.5±4.5 15.1±5.4 L-Valine 4.3±1.9 3.4±1.4 5.5±2.0 * Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available. |
| label | Table 10 |
| caption | Comparison of urine concentrations of 23 quantified amino acids by different assays: DFI/LC-MS/MS (BIOCRATES kit), NMR and GC-MS. |
| title | Comparison of urine concentrations of 23 quantified amino acids by different assays: DFI/LC-MS/MS (BIOCRATES kit), NMR and GC-MS. |
| table | Amino acids* DFI/LC-MS/MS NMR GC-MS Glycine 101.0±53.5 106±66 75.7±51.7 L-Alanine 21.7±10.6 21.8±10.3 NA L-Arginine 7.4±3.0 8.6±3.6 NA L-Asparagine 8.8±3.5 10.1±4.0 9.5±5.5 L-Aspartic acid 10.9±7.7 10.9±5.8 NA L-Citrulline 0.8±0.4 NA NA L-Cysteine NA 65.8±24.0 NA L-Cystine NA 12.5±5.4 NA L-Glutamic acid 6.4±4.9 8.5±3.6 NA L-Glutamine 39.9±14.3 37.2±14.8 NA L-Histidine 45.6±15.6 43.9±19.5 NA L-Isoleucine 1.3±0.4 1.3±0.5 NA L-Leucine 3.0±1.0 2.8±0.9 NA L-Lysine 17.9±12.2 17.2±11.5 NA L-Methionine 0.8±0.3 1.4±0.6 NA L-Ornithine 4.4±5.0 NA 5.3±1.9 L-Phenylalanine 5.9±2.0 6.4±2.3 7.8±2.0 L-Proline 1.1±0.6 NA NA L-Serine 24.5±9.2 25.3±9.7 19.0±7.0 L-Threonine 14.6±6.1 13.3±5.3 NA L-Tryptophan 5.6±2.1 6.3±2.2 NA L-Tyrosine 8.7±4.2 9.5±4.5 15.1±5.4 L-Valine 4.3±1.9 3.4±1.4 5.5±2.0 |
| tr | Amino acids* DFI/LC-MS/MS NMR GC-MS |
| td | Amino acids* |
| td | DFI/LC-MS/MS |
| td | NMR |
| td | GC-MS |
| tr | Glycine 101.0±53.5 106±66 75.7±51.7 |
| td | Glycine |
| td | 101.0±53.5 |
| td | 106±66 |
| td | 75.7±51.7 |
| tr | L-Alanine 21.7±10.6 21.8±10.3 NA |
| td | L-Alanine |
| td | 21.7±10.6 |
| td | 21.8±10.3 |
| td | NA |
| tr | L-Arginine 7.4±3.0 8.6±3.6 NA |
| td | L-Arginine |
| td | 7.4±3.0 |
| td | 8.6±3.6 |
| td | NA |
| tr | L-Asparagine 8.8±3.5 10.1±4.0 9.5±5.5 |
| td | L-Asparagine |
| td | 8.8±3.5 |
| td | 10.1±4.0 |
| td | 9.5±5.5 |
| tr | L-Aspartic acid 10.9±7.7 10.9±5.8 NA |
| td | L-Aspartic acid |
| td | 10.9±7.7 |
| td | 10.9±5.8 |
| td | NA |
| tr | L-Citrulline 0.8±0.4 NA NA |
| td | L-Citrulline |
| td | 0.8±0.4 |
| td | NA |
| td | NA |
| tr | L-Cysteine NA 65.8±24.0 NA |
| td | L-Cysteine |
| td | NA |
| td | 65.8±24.0 |
| td | NA |
| tr | L-Cystine NA 12.5±5.4 NA |
| td | L-Cystine |
| td | NA |
| td | 12.5±5.4 |
| td | NA |
| tr | L-Glutamic acid 6.4±4.9 8.5±3.6 NA |
| td | L-Glutamic acid |
| td | 6.4±4.9 |
| td | 8.5±3.6 |
| td | NA |
| tr | L-Glutamine 39.9±14.3 37.2±14.8 NA |
| td | L-Glutamine |
| td | 39.9±14.3 |
| td | 37.2±14.8 |
| td | NA |
| tr | L-Histidine 45.6±15.6 43.9±19.5 NA |
| td | L-Histidine |
| td | 45.6±15.6 |
| td | 43.9±19.5 |
| td | NA |
| tr | L-Isoleucine 1.3±0.4 1.3±0.5 NA |
| td | L-Isoleucine |
| td | 1.3±0.4 |
| td | 1.3±0.5 |
| td | NA |
| tr | L-Leucine 3.0±1.0 2.8±0.9 NA |
| td | L-Leucine |
| td | 3.0±1.0 |
| td | 2.8±0.9 |
| td | NA |
| tr | L-Lysine 17.9±12.2 17.2±11.5 NA |
| td | L-Lysine |
| td | 17.9±12.2 |
| td | 17.2±11.5 |
| td | NA |
| tr | L-Methionine 0.8±0.3 1.4±0.6 NA |
| td | L-Methionine |
| td | 0.8±0.3 |
| td | 1.4±0.6 |
| td | NA |
| tr | L-Ornithine 4.4±5.0 NA 5.3±1.9 |
| td | L-Ornithine |
| td | 4.4±5.0 |
| td | NA |
| td | 5.3±1.9 |
| tr | L-Phenylalanine 5.9±2.0 6.4±2.3 7.8±2.0 |
| td | L-Phenylalanine |
| td | 5.9±2.0 |
| td | 6.4±2.3 |
| td | 7.8±2.0 |
| tr | L-Proline 1.1±0.6 NA NA |
| td | L-Proline |
| td | 1.1±0.6 |
| td | NA |
| td | NA |
| tr | L-Serine 24.5±9.2 25.3±9.7 19.0±7.0 |
| td | L-Serine |
| td | 24.5±9.2 |
| td | 25.3±9.7 |
| td | 19.0±7.0 |
| tr | L-Threonine 14.6±6.1 13.3±5.3 NA |
| td | L-Threonine |
| td | 14.6±6.1 |
| td | 13.3±5.3 |
| td | NA |
| tr | L-Tryptophan 5.6±2.1 6.3±2.2 NA |
| td | L-Tryptophan |
| td | 5.6±2.1 |
| td | 6.3±2.2 |
| td | NA |
| tr | L-Tyrosine 8.7±4.2 9.5±4.5 15.1±5.4 |
| td | L-Tyrosine |
| td | 8.7±4.2 |
| td | 9.5±4.5 |
| td | 15.1±5.4 |
| tr | L-Valine 4.3±1.9 3.4±1.4 5.5±2.0 |
| td | L-Valine |
| td | 4.3±1.9 |
| td | 3.4±1.4 |
| td | 5.5±2.0 |
| table-wrap-foot | * Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available. |
| footnote | * Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available. |
| label | * |
| p | Concentration (Mean ± SD) is expressed by µM/mM creatinine. NA: not available. |
| sec | Conclusion Using a combination of multiple experimental assays supplemented with an extensive computer–assisted literature survey we were able to identify a total of 2651 metabolites or metabolite species (corresponding to 3079 distinct structures) that can be or have been identified and/or quantified in human urine using today’s technology. This information, which includes both normal and abnormal (disease or exposure-associated) metabolites has been placed into a publicly accessible web-enabled database called the Urine Metabolome Database (UMDB). To assess the validity of the literature data and to further investigate the capabilities of existing metabolomics technologies we conducted a comprehensive, quantitative analysis of human urine from 22 healthy volunteers. A total of 6 different platforms: NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC/UV and HPLC/FD were used in this analysis. From this experimental work we were able to identify a total of 445 and quantify 378 metabolites or metabolite species. This corresponds to 873 unique structures (identified) and 806 unique structures (quantified). A total of 53 compounds or compound species are being reported here for the first time as being normal constituents of human urine, while 77 compounds or compound species are being robustly quantified in human urine for the first time. All of the metabolites that we experimentally identified and/or quantified have been added to the UMDB. Based on the information in the UMDB, our experimentally acquired data corresponds to 16% of the human urine metabolome (or 28% if we include probable or putative metabolites). Considering the level of coverage, the diversity of chemical species and the ease with which analyses can be performed, we have determined that NMR spectroscopy appears to be the method of choice for global or untargeted metabolomic analysis of urine. On the other hand, the kit-based combined DFI/LC-MS/MS methods appear to be optimal for a targeted metabolomic approach. Using a multi-pronged GC-MS approach for urine metabolomics appears to be very promising in terms of coverage, but is not ideal for high-throughput analyses. All methods used in this study appear to be quite complementary with relatively little compound overlap. This strongly suggests that if sufficient time and resources are available, multiple methods should be used in urine metabolomic studies. If additional resources had been available, we would have liked to assess other technologies (GCxGC-MS, FT-MS, isotope labeled-LC-MS) and to compare the level of metabolite coverage and chemical diversity attainable with these methods. However, this study is not intended to be the “final” word on urine or urine metabolome. Rather, it should be viewed as a starting point for future studies and future improvements in this field. Indeed, our primary objective for undertaking these studies and compiling this data was to help advance the fields of quantitative metabolomics, especially with regard to clinically important biofluids such as urine. Experimentally, our data should serve as a useful benchmark from which to compare other technologies and to assess coming methodological improvements in human urine characterization. From a clinical standpoint, we think the information contained in the human urine metabolome database (UMDB) should provide metabolomic researchers as well as clinicians and clinical chemists with a convenient, centralized resource from which to learn more about human urine and its unique chemical constituents. |
| title | Conclusion |
| p | Using a combination of multiple experimental assays supplemented with an extensive computer–assisted literature survey we were able to identify a total of 2651 metabolites or metabolite species (corresponding to 3079 distinct structures) that can be or have been identified and/or quantified in human urine using today’s technology. This information, which includes both normal and abnormal (disease or exposure-associated) metabolites has been placed into a publicly accessible web-enabled database called the Urine Metabolome Database (UMDB). To assess the validity of the literature data and to further investigate the capabilities of existing metabolomics technologies we conducted a comprehensive, quantitative analysis of human urine from 22 healthy volunteers. A total of 6 different platforms: NMR, GC-MS, DFI/LC-MS/MS, ICP-MS, HPLC/UV and HPLC/FD were used in this analysis. From this experimental work we were able to identify a total of 445 and quantify 378 metabolites or metabolite species. This corresponds to 873 unique structures (identified) and 806 unique structures (quantified). A total of 53 compounds or compound species are being reported here for the first time as being normal constituents of human urine, while 77 compounds or compound species are being robustly quantified in human urine for the first time. All of the metabolites that we experimentally identified and/or quantified have been added to the UMDB. Based on the information in the UMDB, our experimentally acquired data corresponds to 16% of the human urine metabolome (or 28% if we include probable or putative metabolites). |
| p | Considering the level of coverage, the diversity of chemical species and the ease with which analyses can be performed, we have determined that NMR spectroscopy appears to be the method of choice for global or untargeted metabolomic analysis of urine. On the other hand, the kit-based combined DFI/LC-MS/MS methods appear to be optimal for a targeted metabolomic approach. Using a multi-pronged GC-MS approach for urine metabolomics appears to be very promising in terms of coverage, but is not ideal for high-throughput analyses. All methods used in this study appear to be quite complementary with relatively little compound overlap. This strongly suggests that if sufficient time and resources are available, multiple methods should be used in urine metabolomic studies. |
| p | If additional resources had been available, we would have liked to assess other technologies (GCxGC-MS, FT-MS, isotope labeled-LC-MS) and to compare the level of metabolite coverage and chemical diversity attainable with these methods. However, this study is not intended to be the “final” word on urine or urine metabolome. Rather, it should be viewed as a starting point for future studies and future improvements in this field. Indeed, our primary objective for undertaking these studies and compiling this data was to help advance the fields of quantitative metabolomics, especially with regard to clinically important biofluids such as urine. Experimentally, our data should serve as a useful benchmark from which to compare other technologies and to assess coming methodological improvements in human urine characterization. From a clinical standpoint, we think the information contained in the human urine metabolome database (UMDB) should provide metabolomic researchers as well as clinicians and clinical chemists with a convenient, centralized resource from which to learn more about human urine and its unique chemical constituents. |
| sec | Methods Ethics Statement All samples were collected in accordance with the ethical guidelines mandated by the University of Alberta as approved by the University’s Health Research Ethics Board. All individuals were over 18 years of age. All were approached using approved ethical guidelines and those who agreed to participate in this study were required to sign consent forms. All participants provided written consent. Collection of Urine Samples Human urine samples (first pass, morning) were collected from 22 healthy adult volunteers (14 male, 8 female) in 120 mL sterile urine specimen cups. The average age of the volunteers was 30 (range 19–65) for females, and 32 (range 21–67) for males. Upon receipt (typically within 1 hour of collection), all samples were immediately treated with sodium azide to a final concentration of 2.5 mM. After centrifugation at 4000 rpm for 10 min to remove particulate matter, the urine samples were stored in 2 mL aliquots in falcon tube at −20°C until further use. Prior to each analysis, the samples were thawed at room temperature for 30 minutes and filtered for a second time via centrifugation. NMR Compound Identification and Quantification All 1H-NMR spectra were collected on a 500 MHz Inova (Varian Inc., Palo Alto, CA) spectrometer using the first transient of the tnnoesy-presaturation pulse sequence. The resulting 1H-NMR spectra were processed and analyzed using the Chenomx NMR Suite Professional software package version 7.0 (Chenomx Inc., Edmonton, AB), as previously described [17]. Additional NMR spectra for 39 compounds were added to the Chenomx Spectral Reference Library using the company’s recommended spectral acquisition and formatting protocols. Further details on the NMR sample preparation, NMR data acquisition and the customized spectral library are provided in Method S1. GC-MS Compound Identification and Quantification Twenty-two urine samples were extracted separately to obtain separate pools of polar, organic acid, bile acid (3 of the 22 urine samples were chosen for analysis and aliquots from these provided an additional “pooled normal” sample) and volatile metabolites using different protocols. The polar metabolites were extracted with cold HPLC grade methanol and double-distilled water after pretreatment with urease, followed by derivatization with MSTFA (N-Methyl-N-trifluoroacetamide) with 1% TMCS (trimethylchlorosilane). For organic acids, the ketoacids were converted first to methoxime derivatives, followed by derivatization with BSTFA (N,O-Bis(trimethylsilyl)trifluoroacetamide) after two successive extractions by ethyl acetate and diethyl ether. The bile acids were eluted with methanol through a SPE column (Bond Elute C18), followed by two different derivatization steps. First the bile acid extracts were esterified using 2% sulfuric acid in methanol, then after phase separation, the esterified bile acids were converted to the corresponding methyl ester-trimethylsilyl ether derivatives using MSTFA with 1% TMCS. Each set of extracted/derivatized metabolites (polar metabolites, organic acids, bile acids) was separated and analyzed using an Agilent 5890 Series II GC-MS operating in electron impact (EI) ionization mode. The volatile compound extraction and analysis by GC-MS was far different from the other protocols. The pooled urine samples were acidified using HCl and transferred to a SPME (solid phase microextraction) vial (75 µm Carboxen/PDMS from Supelco), and then introducing the SPME fiber assembly into the GC-MS injector port according to procedures described elsewhere [99], [100]. The fibers were conditioned prior to use according to the manufacturer’s instructions by inserting them into the GC injector port. Further details on the extraction, derivatization, separation and GC-MS data analysis for the 4 separate groups of urine metabolites are provided in Method S2. Direct Flow Injection/LC-MS/MS Compound Identification and Quantification To assess the performance of direct flow injection DFI-MS/MS methods in urine metabolomics and to determine the concentration ranges of a number of metabolites not measurable by other methods, we used the commercially available Absolute-IDQ p180 Kit (BIOCRATES Life Sciences AG - Austria). The kit, in combination with an ABI 4000 Q-Trap (Applied Biosystems/MDS Sciex) mass spectrometer, can be used for the targeted identification and quantification of 187 different metabolites or metabolite species including amino acids, biogenic amines, creatinine, acylcarnitines, glycerophospholipids, sphingolipids and hexoses. This method involves derivatization and extraction of analytes from the biofluid of interest, along with selective mass spectrometric detection and quantification via multiple reactions monitoring (MRM). Isotope-labeled internal standards are integrated into the kit plate filter to facilitate metabolite quantification. Metabolite concentrations were expressed as ratios relative to creatinine to correct for dilution, assuming a constant rate creatinine excretion for each urine sample (see Method S3 for additional information). Trace Element Analysis Using ICP-MS Before trace elemental analysis by ICP-MS was performed, 22 urine samples were processed as described previously [101]. The concentrations of trace elements were determined on a Perkin-Elmer Sciex Elan 6000 quadrupole ICP-MS operating in a dual detector mode. Blank subtraction was applied after internal standard correction (see Method S4 for additional information). The accuracy of the ICP-MS analytical protocol was periodically evaluated via the analysis of certified reference standard materials (whole rock powders) BE-N and DR-N available from the SARM laboratory at the CRPG (Centre de Recherches Pétrographiques et Géologiques). Characterization of Isoflavones from Urine We processed 22 urine samples as described previously [102], [103]. The isoflavones were isolated and concentrated by solid-phase extraction (Bond Elut C18 column). The elutes were hydrolyzed enzymatically as the urinary isoflavones occur predominantly as glucuronate and sulfate conjugates. The analysis were performed on an Agilent 1100 HPLC system using NovaPak C18 reversed-phase column connected to Agilent G1315B diode array detector with signals scanned between 190 and 400 nm (see Method S5 for additional information). Characterization of Thiols from Urine To extract urinary thiols, we derivatized all 22 urine samples as described previously [104]. A mixture of reagent was used for the reduction and derivatization (with bromobimane) of thiols. The derivatized thiols were injected immediately into a hypersil-ODS HPLC Column connected to Agilent fluorometer operating at an excitation wavelength of 485 nm and emission wavelength of 510 nm (see Method S6 for additional information). |
| title | Methods |
| sec | Ethics Statement All samples were collected in accordance with the ethical guidelines mandated by the University of Alberta as approved by the University’s Health Research Ethics Board. All individuals were over 18 years of age. All were approached using approved ethical guidelines and those who agreed to participate in this study were required to sign consent forms. All participants provided written consent. |
| title | Ethics Statement |
| p | All samples were collected in accordance with the ethical guidelines mandated by the University of Alberta as approved by the University’s Health Research Ethics Board. All individuals were over 18 years of age. All were approached using approved ethical guidelines and those who agreed to participate in this study were required to sign consent forms. All participants provided written consent. |
| sec | Collection of Urine Samples Human urine samples (first pass, morning) were collected from 22 healthy adult volunteers (14 male, 8 female) in 120 mL sterile urine specimen cups. The average age of the volunteers was 30 (range 19–65) for females, and 32 (range 21–67) for males. Upon receipt (typically within 1 hour of collection), all samples were immediately treated with sodium azide to a final concentration of 2.5 mM. After centrifugation at 4000 rpm for 10 min to remove particulate matter, the urine samples were stored in 2 mL aliquots in falcon tube at −20°C until further use. Prior to each analysis, the samples were thawed at room temperature for 30 minutes and filtered for a second time via centrifugation. |
| title | Collection of Urine Samples |
| p | Human urine samples (first pass, morning) were collected from 22 healthy adult volunteers (14 male, 8 female) in 120 mL sterile urine specimen cups. The average age of the volunteers was 30 (range 19–65) for females, and 32 (range 21–67) for males. Upon receipt (typically within 1 hour of collection), all samples were immediately treated with sodium azide to a final concentration of 2.5 mM. After centrifugation at 4000 rpm for 10 min to remove particulate matter, the urine samples were stored in 2 mL aliquots in falcon tube at −20°C until further use. Prior to each analysis, the samples were thawed at room temperature for 30 minutes and filtered for a second time via centrifugation. |
| sec | NMR Compound Identification and Quantification All 1H-NMR spectra were collected on a 500 MHz Inova (Varian Inc., Palo Alto, CA) spectrometer using the first transient of the tnnoesy-presaturation pulse sequence. The resulting 1H-NMR spectra were processed and analyzed using the Chenomx NMR Suite Professional software package version 7.0 (Chenomx Inc., Edmonton, AB), as previously described [17]. Additional NMR spectra for 39 compounds were added to the Chenomx Spectral Reference Library using the company’s recommended spectral acquisition and formatting protocols. Further details on the NMR sample preparation, NMR data acquisition and the customized spectral library are provided in Method S1. |
| title | NMR Compound Identification and Quantification |
| p | All 1H-NMR spectra were collected on a 500 MHz Inova (Varian Inc., Palo Alto, CA) spectrometer using the first transient of the tnnoesy-presaturation pulse sequence. The resulting 1H-NMR spectra were processed and analyzed using the Chenomx NMR Suite Professional software package version 7.0 (Chenomx Inc., Edmonton, AB), as previously described [17]. Additional NMR spectra for 39 compounds were added to the Chenomx Spectral Reference Library using the company’s recommended spectral acquisition and formatting protocols. Further details on the NMR sample preparation, NMR data acquisition and the customized spectral library are provided in Method S1. |
| sec | GC-MS Compound Identification and Quantification Twenty-two urine samples were extracted separately to obtain separate pools of polar, organic acid, bile acid (3 of the 22 urine samples were chosen for analysis and aliquots from these provided an additional “pooled normal” sample) and volatile metabolites using different protocols. The polar metabolites were extracted with cold HPLC grade methanol and double-distilled water after pretreatment with urease, followed by derivatization with MSTFA (N-Methyl-N-trifluoroacetamide) with 1% TMCS (trimethylchlorosilane). For organic acids, the ketoacids were converted first to methoxime derivatives, followed by derivatization with BSTFA (N,O-Bis(trimethylsilyl)trifluoroacetamide) after two successive extractions by ethyl acetate and diethyl ether. The bile acids were eluted with methanol through a SPE column (Bond Elute C18), followed by two different derivatization steps. First the bile acid extracts were esterified using 2% sulfuric acid in methanol, then after phase separation, the esterified bile acids were converted to the corresponding methyl ester-trimethylsilyl ether derivatives using MSTFA with 1% TMCS. Each set of extracted/derivatized metabolites (polar metabolites, organic acids, bile acids) was separated and analyzed using an Agilent 5890 Series II GC-MS operating in electron impact (EI) ionization mode. The volatile compound extraction and analysis by GC-MS was far different from the other protocols. The pooled urine samples were acidified using HCl and transferred to a SPME (solid phase microextraction) vial (75 µm Carboxen/PDMS from Supelco), and then introducing the SPME fiber assembly into the GC-MS injector port according to procedures described elsewhere [99], [100]. The fibers were conditioned prior to use according to the manufacturer’s instructions by inserting them into the GC injector port. Further details on the extraction, derivatization, separation and GC-MS data analysis for the 4 separate groups of urine metabolites are provided in Method S2. |
| title | GC-MS Compound Identification and Quantification |
| p | Twenty-two urine samples were extracted separately to obtain separate pools of polar, organic acid, bile acid (3 of the 22 urine samples were chosen for analysis and aliquots from these provided an additional “pooled normal” sample) and volatile metabolites using different protocols. The polar metabolites were extracted with cold HPLC grade methanol and double-distilled water after pretreatment with urease, followed by derivatization with MSTFA (N-Methyl-N-trifluoroacetamide) with 1% TMCS (trimethylchlorosilane). For organic acids, the ketoacids were converted first to methoxime derivatives, followed by derivatization with BSTFA (N,O-Bis(trimethylsilyl)trifluoroacetamide) after two successive extractions by ethyl acetate and diethyl ether. The bile acids were eluted with methanol through a SPE column (Bond Elute C18), followed by two different derivatization steps. First the bile acid extracts were esterified using 2% sulfuric acid in methanol, then after phase separation, the esterified bile acids were converted to the corresponding methyl ester-trimethylsilyl ether derivatives using MSTFA with 1% TMCS. Each set of extracted/derivatized metabolites (polar metabolites, organic acids, bile acids) was separated and analyzed using an Agilent 5890 Series II GC-MS operating in electron impact (EI) ionization mode. |
| p | The volatile compound extraction and analysis by GC-MS was far different from the other protocols. The pooled urine samples were acidified using HCl and transferred to a SPME (solid phase microextraction) vial (75 µm Carboxen/PDMS from Supelco), and then introducing the SPME fiber assembly into the GC-MS injector port according to procedures described elsewhere [99], [100]. The fibers were conditioned prior to use according to the manufacturer’s instructions by inserting them into the GC injector port. Further details on the extraction, derivatization, separation and GC-MS data analysis for the 4 separate groups of urine metabolites are provided in Method S2. |
| sec | Direct Flow Injection/LC-MS/MS Compound Identification and Quantification To assess the performance of direct flow injection DFI-MS/MS methods in urine metabolomics and to determine the concentration ranges of a number of metabolites not measurable by other methods, we used the commercially available Absolute-IDQ p180 Kit (BIOCRATES Life Sciences AG - Austria). The kit, in combination with an ABI 4000 Q-Trap (Applied Biosystems/MDS Sciex) mass spectrometer, can be used for the targeted identification and quantification of 187 different metabolites or metabolite species including amino acids, biogenic amines, creatinine, acylcarnitines, glycerophospholipids, sphingolipids and hexoses. This method involves derivatization and extraction of analytes from the biofluid of interest, along with selective mass spectrometric detection and quantification via multiple reactions monitoring (MRM). Isotope-labeled internal standards are integrated into the kit plate filter to facilitate metabolite quantification. Metabolite concentrations were expressed as ratios relative to creatinine to correct for dilution, assuming a constant rate creatinine excretion for each urine sample (see Method S3 for additional information). |
| title | Direct Flow Injection/LC-MS/MS Compound Identification and Quantification |
| p | To assess the performance of direct flow injection DFI-MS/MS methods in urine metabolomics and to determine the concentration ranges of a number of metabolites not measurable by other methods, we used the commercially available Absolute-IDQ p180 Kit (BIOCRATES Life Sciences AG - Austria). The kit, in combination with an ABI 4000 Q-Trap (Applied Biosystems/MDS Sciex) mass spectrometer, can be used for the targeted identification and quantification of 187 different metabolites or metabolite species including amino acids, biogenic amines, creatinine, acylcarnitines, glycerophospholipids, sphingolipids and hexoses. This method involves derivatization and extraction of analytes from the biofluid of interest, along with selective mass spectrometric detection and quantification via multiple reactions monitoring (MRM). Isotope-labeled internal standards are integrated into the kit plate filter to facilitate metabolite quantification. Metabolite concentrations were expressed as ratios relative to creatinine to correct for dilution, assuming a constant rate creatinine excretion for each urine sample (see Method S3 for additional information). |
| sec | Trace Element Analysis Using ICP-MS Before trace elemental analysis by ICP-MS was performed, 22 urine samples were processed as described previously [101]. The concentrations of trace elements were determined on a Perkin-Elmer Sciex Elan 6000 quadrupole ICP-MS operating in a dual detector mode. Blank subtraction was applied after internal standard correction (see Method S4 for additional information). The accuracy of the ICP-MS analytical protocol was periodically evaluated via the analysis of certified reference standard materials (whole rock powders) BE-N and DR-N available from the SARM laboratory at the CRPG (Centre de Recherches Pétrographiques et Géologiques). |
| title | Trace Element Analysis Using ICP-MS |
| p | Before trace elemental analysis by ICP-MS was performed, 22 urine samples were processed as described previously [101]. The concentrations of trace elements were determined on a Perkin-Elmer Sciex Elan 6000 quadrupole ICP-MS operating in a dual detector mode. Blank subtraction was applied after internal standard correction (see Method S4 for additional information). The accuracy of the ICP-MS analytical protocol was periodically evaluated via the analysis of certified reference standard materials (whole rock powders) BE-N and DR-N available from the SARM laboratory at the CRPG (Centre de Recherches Pétrographiques et Géologiques). |
| sec | Characterization of Isoflavones from Urine We processed 22 urine samples as described previously [102], [103]. The isoflavones were isolated and concentrated by solid-phase extraction (Bond Elut C18 column). The elutes were hydrolyzed enzymatically as the urinary isoflavones occur predominantly as glucuronate and sulfate conjugates. The analysis were performed on an Agilent 1100 HPLC system using NovaPak C18 reversed-phase column connected to Agilent G1315B diode array detector with signals scanned between 190 and 400 nm (see Method S5 for additional information). |
| title | Characterization of Isoflavones from Urine |
| p | We processed 22 urine samples as described previously [102], [103]. The isoflavones were isolated and concentrated by solid-phase extraction (Bond Elut C18 column). The elutes were hydrolyzed enzymatically as the urinary isoflavones occur predominantly as glucuronate and sulfate conjugates. The analysis were performed on an Agilent 1100 HPLC system using NovaPak C18 reversed-phase column connected to Agilent G1315B diode array detector with signals scanned between 190 and 400 nm (see Method S5 for additional information). |
| sec | Characterization of Thiols from Urine To extract urinary thiols, we derivatized all 22 urine samples as described previously [104]. A mixture of reagent was used for the reduction and derivatization (with bromobimane) of thiols. The derivatized thiols were injected immediately into a hypersil-ODS HPLC Column connected to Agilent fluorometer operating at an excitation wavelength of 485 nm and emission wavelength of 510 nm (see Method S6 for additional information). |
| title | Characterization of Thiols from Urine |
| p | To extract urinary thiols, we derivatized all 22 urine samples as described previously [104]. A mixture of reagent was used for the reduction and derivatization (with bromobimane) of thiols. The derivatized thiols were injected immediately into a hypersil-ODS HPLC Column connected to Agilent fluorometer operating at an excitation wavelength of 485 nm and emission wavelength of 510 nm (see Method S6 for additional information). |
| sec | Supporting Information Method S1 NMR Compound Identification and Quantification. (DOC) Click here for additional data file. Method S2 GC-MS Compound Identification and Quantification. (DOC) Click here for additional data file. Method S3 Direct Flow Injection/LC-MS/MS Compound Identification and Quantification. (DOC) Click here for additional data file. Method S4 Trace Element Analysis Using ICP-MS. (DOC) Click here for additional data file. Method S5 Characterization of Isoflavones from Urine. (DOC) Click here for additional data file. Method S6 Characterization of Thiols from Urine. (DOC) Click here for additional data file. |
| title | Supporting Information |
| label | Method S1 |
| caption | NMR Compound Identification and Quantification. (DOC) |
| p | NMR Compound Identification and Quantification. |
| p | (DOC) |
| caption | Click here for additional data file. |
| p | Click here for additional data file. |
| label | Method S2 |
| caption | GC-MS Compound Identification and Quantification. (DOC) |
| p | GC-MS Compound Identification and Quantification. |
| p | (DOC) |
| caption | Click here for additional data file. |
| p | Click here for additional data file. |
| label | Method S3 |
| caption | Direct Flow Injection/LC-MS/MS Compound Identification and Quantification. (DOC) |
| p | Direct Flow Injection/LC-MS/MS Compound Identification and Quantification. |
| p | (DOC) |
| caption | Click here for additional data file. |
| p | Click here for additional data file. |
| label | Method S4 |
| caption | Trace Element Analysis Using ICP-MS. (DOC) |
| p | Trace Element Analysis Using ICP-MS. |
| p | (DOC) |
| caption | Click here for additional data file. |
| p | Click here for additional data file. |
| label | Method S5 |
| caption | Characterization of Isoflavones from Urine. (DOC) |
| p | Characterization of Isoflavones from Urine. |
| p | (DOC) |
| caption | Click here for additional data file. |
| p | Click here for additional data file. |
| label | Method S6 |
| caption | Characterization of Thiols from Urine. (DOC) |
| p | Characterization of Thiols from Urine. |
| p | (DOC) |
| caption | Click here for additional data file. |
| p | Click here for additional data file. |
| back | The authors wish to thank Kruti Chaudhary and Hetal Chaudhary for adding concentration values from the literature to the UMDB, Rolando Perez-Pineiro for the synthesis of HPHPA and Beomsoo Han for converting all figures to high resolution images. |
| ack | The authors wish to thank Kruti Chaudhary and Hetal Chaudhary for adding concentration values from the literature to the UMDB, Rolando Perez-Pineiro for the synthesis of HPHPA and Beomsoo Han for converting all figures to high resolution images. |
| p | The authors wish to thank Kruti Chaudhary and Hetal Chaudhary for adding concentration values from the literature to the UMDB, Rolando Perez-Pineiro for the synthesis of HPHPA and Beomsoo Han for converting all figures to high resolution images. |
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