Metabolic syndrome or lifestyle-related diseases develop as a result of the interaction between various genetic factors and environmental factors. Based on the health check-up data collected during a longterm follow-up (at least 7 years), we categorized a large sample population (n = 2061 subjects; men = 87%) into 3 groups (case: subjects who developed metabolic syndrome during follow-up; supercontrol: subjects free of lifestyle-related risk components; control: subjects with clinical components similar to those observed in the case subjects before follow-up). A bioinformatics approach was employed to determine the combinational genetic and environmental factors. Two types of prediction datasets were constructed to determine the predictive risk factors to discriminate between (1) case and supercontrol and between (2) case and control groups. By using logistic regression analysis, we found 25 novel risk factor combinations including 66 single nucleotide polymorphisms (SNPs) and 6 environmental factors. Moreover, to search risk factor combinations with high prediction accuracy, we used our Criterion of Detecting Personal Group (CDPG) in this study. We found that the combination of ADIPOR1 (rs1539355) with an environment factor (smoking) was the most significant predictor of metabolic syndrome. Such risk factor combinations, and not genetic risk factors alone, could help to identify the need to modify life style for prevention of metabolic syndrome.
|Number of pages||8|
|Publication status||Published - 27-12-2010|
All Science Journal Classification (ASJC) codes
- Food Science
- Applied Microbiology and Biotechnology