TY - JOUR
T1 - Determination of combinational genetic and environmental risk factors of lifestyle-related disease by using health check-up data obtained from long-term follow-up
AU - Ushida, Yasunori
AU - Kato, Ryuji
AU - Tanimura, Daisuke
AU - Izawa, Hideo
AU - Yasui, Kenji
AU - Takase, Tomokazu
AU - Yoshida, Yasuko
AU - Kawase, Mitsuo
AU - Yoshida, Tsutomu
AU - Murohara, Toyoaki
AU - Honda, Hiroyuki
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Health check-up
KW - Lifestyle-related disease
KW - Metabolic syndrome
KW - Risk factor combination
KW - Single nucleotide polymorphism
UR - http://www.scopus.com/inward/record.url?scp=78650399686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650399686&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:78650399686
VL - 88
SP - 562
EP - 569
JO - Seibutsu-kogaku Kaishi
JF - Seibutsu-kogaku Kaishi
SN - 0919-3758
IS - 11
ER -