PMC:3492653 / 1607-5133 JSONTXT

{"project":"2_test","denotations":[{"id":"23166528-19863607-44839273","span":{"begin":494,"end":495},"obj":"19863607"},{"id":"23166528-15900041-44839274","span":{"begin":968,"end":969},"obj":"15900041"},{"id":"23166528-19114700-44839275","span":{"begin":1264,"end":1265},"obj":"19114700"},{"id":"23166528-16132794-44839275","span":{"begin":1264,"end":1265},"obj":"16132794"},{"id":"23166528-19207713-44839275","span":{"begin":1264,"end":1265},"obj":"19207713"},{"id":"23166528-7969357-44839275","span":{"begin":1264,"end":1265},"obj":"7969357"},{"id":"23166528-20645399-44839276","span":{"begin":1475,"end":1477},"obj":"20645399"},{"id":"23166528-21558169-44839277","span":{"begin":1479,"end":1481},"obj":"21558169"},{"id":"23166528-17476472-44839278","span":{"begin":1776,"end":1778},"obj":"17476472"},{"id":"23166528-17293876-44839279","span":{"begin":1780,"end":1782},"obj":"17293876"},{"id":"23166528-17460697-44839280","span":{"begin":1984,"end":1986},"obj":"17460697"},{"id":"23166528-19114701-44839281","span":{"begin":2208,"end":2210},"obj":"19114701"},{"id":"23166528-20554262-44839282","span":{"begin":2414,"end":2416},"obj":"20554262"},{"id":"23166528-17443022-44839283","span":{"begin":2673,"end":2675},"obj":"17443022"},{"id":"23166528-10891514-44839284","span":{"begin":2878,"end":2880},"obj":"10891514"},{"id":"23166528-17618283-44839284","span":{"begin":2878,"end":2880},"obj":"17618283"},{"id":"23166528-20530476-44839284","span":{"begin":2878,"end":2880},"obj":"20530476"},{"id":"23166528-18372901-44839284","span":{"begin":2878,"end":2880},"obj":"18372901"},{"id":"23166528-19114701-44839285","span":{"begin":3000,"end":3002},"obj":"19114701"},{"id":"23166528-20554262-44839286","span":{"begin":3004,"end":3006},"obj":"20554262"},{"id":"23166528-19020323-44839287","span":{"begin":3193,"end":3195},"obj":"19020323"},{"id":"23166528-17020404-44839287","span":{"begin":3193,"end":3195},"obj":"17020404"},{"id":"23166528-17785532-44839287","span":{"begin":3193,"end":3195},"obj":"17785532"}],"text":"Introduction\nColorectal cancer (CRC), also called colon cancer or large bowel cancer, includes cancerous growths in the colon, rectum, and appendix [1]. With 655,000 deaths worldwide per year, it is the fourth most common form of cancer in the United States (US) and the third leading cause of cancer-related deaths in the western world [1, 2]. In Korea, CRC is one of the most commonly diagnosed cancers, and its incidence is now dramatically increasing with the westernization of lifestyles [3]. According to statistics for Korea, the incidence of CRC was 9.8 per 100,000 men and 10.4 per 100,000 women from 1999-2001 [4]. These incidence rates for CRC increased to 18.2 per 100,000 men and 13.7 per 100,000 women in 2003 [5].\nGiven the high incidence of CRC and its significant cost to society, the ability to accurately predict the possibility of developing the disease using identifiable risk factors may help both physicians and patients prevent its occurrence [6]. Numerous studies have identified risk factors related to CRC, such as age, sex, family history of CRC, smoking, physical activity, aspirin/nonsteroidal anti-inflammatory drug (NSAID) use, vegetable intake, body mass index (BMI), alcohol consumption, and hormone replacement therapy by women [7-10].\nRecently, there have been a number of studies that developed a risk score or a prediction model of certain diseases, such as coronary heart disease (CHD) and cancers, using these identified risk factors [11, 12]. However, these risk scores or prediction models have excluded genetic risk factors. Genetic polymorphisms contributing to certain disease incidences, such as CHD, could be one type of emerging risk factor under investigation in studies generally focused on a priori selected candidate genes [13, 14]. Advances in genome technologies have made it possible to genotype and evaluate many single-nucleotide polymorphisms (SNPs) throughout the human genome to identify novel disease susceptibility genes [15].\nA CRC prediction model has been developed in a previous study that estimates the probability of developing CRC, given a specific age, risk factor profile, and time period in white men and women aged 50 years and older [16]. However, genetic polymorphisms have not been included in the study. Another prediction model, developed among middle-aged Japanese men, included conventional risk factors without genetic risk factors [17]. A recent study developed a prediction of CHD risk, aggregating information from multiple SNPs into a single genetic risk score (GRS) and determined an improvement in the prediction of incident CHD in the Atherosclerosis Risk in Communities (ARIC) study [18].\nFrom recent studies, several SNPs that may play an important role in triggering CRC have been introduced by a genome-wide association study (GWAS) among whites, Japanese, and Chinese but not Koreans [19-22]. Moreover, prediction models that were developed recently were performed among whites and the Japanese population [16, 17]. Previous studies have shown that combining multiple loci with modest effects into a global GRS might improve the identification of persons who are at risk for common complex diseases [23-25]. Therefore, in this study, we intended to describe a GRS by aggregation of multiple SNPs contributing to CRC through a GWAS. Furthermore, we aimed to develop a prediction model consisting of conventional risk factors as well as a genetic risk factor, such as GRS, in Koreans in the Korean Cancer Prevention Study-II (KCPS-II)."}