Literature DB >> 17766366

Genetic risk for metabolic syndrome: examination of candidate gene polymorphisms related to lipid metabolism in Japanese people.

Y Yamada1, S Ichihara, K Kato, T Yoshida, K Yokoi, H Matsuo, S Watanabe, N Metoki, H Yoshida, K Satoh, Y Aoyagi, A Yasunaga, H Park, M Tanaka, W Lee, Y Nozawa.   

Abstract

BACKGROUND: The aetiology of metabolic syndrome is complex, being determined by the interplay of both genetic and environmental factors. The aim of this study was to identify genetic polymorphisms that confer susceptibility to metabolic syndrome, to allow prediction of genetic risk for this condition.
METHODS: The study population comprised 2417 unrelated Japanese subjects (1522 with metabolic syndrome and 895 controls). The genotypes for 44 polymorphisms of 31 candidate genes related to lipid metabolism were determined using a combination of PCR and sequence-specific oligonucleotide probes with suspension array technology.
RESULTS: The chi(2) test and subsequent multivariate logistic regression analysis with adjustment for age, sex and smoking status found that the-3A-->G and 553G-->T (Gly185Cys) polymorphisms of APOA5, the 2052T-->C (Val653Val) and 1866C-->T (Asn591Asn) polymorphisms of LDLR, the 13989A-->G (Ile118Val) polymorphism of CYP3A4 and the 1014T-->A polymorphism of C1QTNF5 were significantly (false discovery rate <0.05) associated with the prevalence of metabolic syndrome, with the variant alleles of APOA5 and C1QTNF5 representing risk factors for and those of LDLR and CYP3A4 being protective against this condition. Serum levels of triglycerides and high-density lipoprotein (HDL) cholesterol differed significantly (p<0.05) among APOA5 genotypes; the serum level of HDL cholesterol differed among LDLR genotypes; and the fasting plasma glucose level and body mass index differed between CYP3A4 and C1QTNF5 genotypes, respectively.
CONCLUSIONS: APOA5, LDLR, CYP3A4 and C1QTNF5 are susceptibility loci for metabolic syndrome in Japanese people. Genotypes for these polymorphisms may prove informative for prediction of genetic risk for metabolic syndrome.

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Year:  2007        PMID: 17766366     DOI: 10.1136/jmg.2007.052415

Source DB:  PubMed          Journal:  J Med Genet        ISSN: 0022-2593            Impact factor:   6.318


  27 in total

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