title: Genetic and environmental dissections of sub-phenotypes of metabolic syndrome in the Chinese population: a twin-based heritability study.
abstract: OBJECTIVE
  We perform a comprehensive heritability study on multiple phenotypes related to metabolic syndrome using Chinese twins to assess the genetic and environmental effects in determining the variation and covariation of the phenotypes in the Chinese population.
  
  
  METHODS
  The studied sample contains 654 twins collected in the Qingdao municipality. A total of 10 phenotypes covering anthropometric measurements, plasma glucose levels, lipids, blood pressures etc. were examined. Univariate and bivariate structural equation models were fitted for assessing the genetic and environmental contributions.
  
  
  RESULTS
  The AE model combining additive genetic (A) and unique environmental (E) factors produced the best fit for all phenotypes except for triglyceride. Modest to high heritability estimates were obtained in univariate analysis ranging from 0.5 for total cholesterol to 0.78 for weight. The bivariate model estimated high genetic correlations between systolic and diastolic blood pressures, between total cholesterol and low density lipoprotein cholesterol, modest genetic correlations between BMI and blood pressures. No significant common environmental correlation was found between any pair of the phenotypes.
  
  
  CONCLUSIONS
  Our results showed significant genetic contributions to the sub-phenotypes of metabolic syndrome. Although pleiotropic genetic control may exist for some physiologically similar phenotypes, our results do not support a common genetic mechanism among the phenotypes covered in our study.
authors.affiliation: Qingdao Center for Disease Control and Prevention, Qingdao University Medical College, Qingdao China.
authors.forename: Haiping
authors.initials: H
authors.lastname: Duan
authors.forename: Zengchang
authors.initials: Z
authors.lastname: Pang
authors.forename: Dongfeng
authors.initials: D
authors.lastname: Zhang
authors.forename: Shuxia
authors.initials: S
authors.lastname: Li
authors.forename: Torben A
authors.initials: TA
authors.lastname: Kruse
authors.forename: Kirsten Ohm
authors.initials: KO
authors.lastname: Kyvik
authors.forename: Kaare
authors.initials: K
authors.lastname: Christensen
authors.forename: Qihua
authors.initials: Q
authors.lastname: Tan
mesh_terms: D000328:Adult; D001794:Blood Pressure / Q000235:genetics*; D015992:Body Mass Index; D001835:Body Weight / Q000235:genetics*; D002681:China; D002784:Cholesterol / Q000097:blood / Q000235:genetics; D004777:Environment; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008055:Lipids / Q000097:blood / Q000235:genetics*; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000235:genetics*; D008875:Middle Aged; D008954:Models, Biological; D010641:Phenotype; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood / Q000235:genetics; D014427:Twins / Q000235:genetics*; D055815:Young Adult
chemical_list: D008055:Lipids; D014280:Triglycerides; D002784:Cholesterol