Dataset Information

Associations of Body Composition Measurements with Serum Lipid, Glucose and Insulin Profile: A Chinese Twin Study.

ABSTRACT:

Objectives

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.

SUBMITTER: Liao C

PROVIDER: S-EPMC4640552 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Associations of Body Composition Measurements with Serum Lipid, Glucose and Insulin Profile: A Chinese Twin Study.

Liao Chunxiao C Gao Wenjing W Cao Weihua W Lv Jun J Yu Canqing C Wang Shengfeng S Zhou Bin B Pang Zengchang Z Cong Liming L Wang Hua H Wu Xianping X Li Liming L

PloS one 20151110 11

<h4>Objectives</h4>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.<h4>Methods</h4>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 lifest ...[more]

PMID: 26556598

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Project description:Background3D laser-based photonic scanners are increasingly used in health studies to estimate body composition. However, too little is known about whether various 3D body scan measures estimate body composition better than single standard anthropometric measures, and which body scans best estimate it. Furthermore, little is known about differences by sex and age.Methods105 men and 96 women aged between 18 and 90 years were analysed. Bioelectrical Impedance Analysis was used to estimate whole relative fat mass (RFM), visceral adipose tissue (VAT) and skeletal muscle mass index (SMI). An Anthroscan VITUSbodyscan was used to obtain 3D body scans (e.g. volumes, circumferences, lengths). To reduce the number of possible predictors that could predict RFM, VAT and SMI backward elimination was performed. With these selected predictors linear regression on the respective body compositions was performed and the explained variations were compared with models using standard anthropometric measurements (Body Mass Index (BMI), waist circumference (WC) and waist-to-height-ratio (WHtR)).ResultsAmong the models based on standard anthropometric measures, WC performed better than BMI and WHtR in estimating body composition in men and women. The explained variations in models including body scan variables are consistently higher than those from standard anthropometrics models, with an increase in explained variations between 5% (RFM for men) and 10% (SMI for men). Furthermore, the explained variation of body composition was additionally increased when age and lifestyle variables were added. For each of the body composition variables, the number of predictors differed between men and women, but included mostly volumes and circumferences in the central waist/chest/hip area and the thighs.Conclusions3D scan models performed better than standard anthropometric measures models to predict body composition. Therefore, it is an advantage for larger health studies to look at body composition more holistically using 3D full body surface scans.

Dataset Information

Associations of Body Composition Measurements with Serum Lipid, Glucose and Insulin Profile: A Chinese Twin Study.

Objectives

Methods

Results

Conclusions

Publications

Associations of Body Composition Measurements with Serum Lipid, Glucose and Insulin Profile: A Chinese Twin Study.

Similar Datasets

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