Logistic Regression Analysis
Logistic regression model with covariates BMI, age and sex were built with each variable (lipid/metabolite) to search for significant variables that can predict the conditions (i.e., healthy control, mild, moderate, severe COVID-19) of subjects. The p value of the variable estimate in the model was extracted and those with false discovery rate (fdr) smaller than 0.05 were shortlisted. Forest plots of these significant variables were constructed and the boxplots of individual significant variables were generated. A list of 61 lipids and 89 metabolites were found to be significant (fdr < 0.05) in their respective logistic regression models. Forest plots illustrate the magnitude of estimates on signed log x axis, with indicator of significance of the estimate in the model, with ∗∗∗ representing p < 0.001, ∗∗ representing p < 0.01 and ∗ representing p < 0.05. For non-significant species, the estimates were plotted as zeros.