title: Associations of Body Composition Measurements with Serum Lipid, Glucose and Insulin Profile: A Chinese Twin Study.
abstract: OBJECTIVE
  To quantitate and compare the associations of various body composition measurements with serum metabolites and to what degree genetic or environmental factors affect obesity-metabolite relation.
  
  
  METHODS
  Body mass index (BMI), waist circumference (WC), lean body mass (LBM), percent body fat (PBF), fasting serum high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), glucose, insulin and lifestyle factors were assessed in 903 twins from Chinese National Twin Registry (CNTR). Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated from fasting serum glucose and insulin. Linear regression models and bivariate structural equation models were used to examine the relation of various body composition measurements with serum metabolite levels and genetic/environmental influences on these associations, respectively.
  
  
  RESULTS
  At individual level, adiposity measurements (BMI, WC and PBF) showed significant associations with serum metabolite concentrations in both sexes and the associations still existed in male twins when using within-MZ twin pair comparison analyses. Associations of BMI with TG, insulin and HOMA-IR were significantly stronger in male twins compared to female twins (BMI-by-sex interaction p = 0.043, 0.020 and 0.019, respectively). Comparison of various adiposity measurements with levels of serum metabolites revealed that WC explained the largest fraction of variance in serum LDL-C, TG, TC and glucose concentrations while BMI performed best in explaining variance in serum HDL-C, insulin and HOMA-IR levels. Of these phenotypic correlations, 64-81% were attributed to genetic factors, whereas 19-36% were attributed to unique environmental factors.
  
  
  CONCLUSIONS
  We observed different associations between adiposity and serum metabolite profile and demonstrated that WC and BMI explained the largest fraction of variance in serum lipid profile and insulin resistance, respectively. To a large degree, shared genetic factors contributed to these associations with the remaining explained by twin-specific environmental factors.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Chunxiao
authors.initials: C
authors.lastname: Liao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Wenjing
authors.initials: W
authors.lastname: Gao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Cao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Lv
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Canqing
authors.initials: C
authors.lastname: Yu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Shengfeng
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Zhou
authors.affiliation: Qingdao Center for Diseases Control and Prevention, Qingdao, China.
authors.forename: Zengchang
authors.initials: Z
authors.lastname: Pang
authors.affiliation: Zhejiang Center for Disease Control and Prevention, Hangzhou, China.
authors.forename: Liming
authors.initials: L
authors.lastname: Cong
authors.affiliation: Jiangsu Center for Disease Control and Prevention, Nanjing, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Wang
authors.affiliation: Sichuan Center for Disease Control and Prevention, Chengdu, China.
authors.forename: Xianping
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
authors.forename: Liming
authors.initials: L
authors.lastname: Li
mesh_terms: D050154:Adiposity; D000328:Adult; D000886:Anthropometry; D044466:Asian People; D001786:Blood Glucose / Q000032:analysis*; D001823:Body Composition; D001835:Body Weight; D002681:China / Q000453:epidemiology; D004200:Diseases in Twins / Q000453:epidemiology; D050171:Dyslipidemias / Q000453:epidemiology / Q000235:genetics; D005260:Female; D059647:Gene-Environment Interaction; D056726:Genetic Association Studies; D006801:Humans; D006943:Hyperglycemia / Q000453:epidemiology / Q000235:genetics; D007328:Insulin / Q000097:blood*; D008019:Life Style; D016014:Linear Models; D008055:Lipids / Q000097:blood*; D008074:Lipoproteins / Q000097:blood; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009765:Obesity / Q000453:epidemiology / Q000235:genetics; D012737:Sex Factors; D014429:Twins, Dizygotic; D014430:Twins, Monozygotic
chemical_list: D001786:Blood Glucose; D007328:Insulin; D008055:Lipids; D008074:Lipoproteins