Genetic and Environmental Contributions to Phenotypic Components of Metabolic Syndrome: A Population‐based Twin Study |
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| Authors: | Shanchun Zhang Xin Liu Yunxian Yu Xiumei Hong Katherine K Christoffel Binyan Wang Hui‐Ju Tsai Zhiping Li Xue Liu Genfu Tang Houxun Xing Wendy J Brickman Donald Zimmerman Xiping Xu Xiaobin Wang |
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| Institution: | 1. Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Children's Memorial Hospital and Children's Memorial Research Center, Chicago, Illinois, USA;2. Institute for Biomedicine, Anhui Medical University, Hefei, China;3. Division of Endocrinology, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Children's Memorial Hospital, Chicago, Illinois, USA;4. Center for Population Genetics, University of Illinois at Chicago School of Public Health, Chicago, Illinois, USA |
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| Abstract: | The increasing prevalence of metabolic syndrome (MS) poses a serious public‐health problem worldwide. Effective prevention and intervention require improved understanding of the factors that contribute to MS. We analyzed data on a large twin cohort to estimate genetic and environmental contributions to MS and to major MS components and their intercorrelations: waist circumference (WC), systolic (SBP) and diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TGs), and high‐density lipoprotein–cholesterol (HDL‐C). We applied structural equation modeling to determine genetic and environmental structure of MS and its major components, using 1,617 adult female twin pairs recruited from rural China. The heritability estimate for MS was 0.42 (95% confidence interval (CI): 0.00–0.83) in this sample with low MS prevalence (4.4%). For MS components, heritability estimates were statistically significant and ranged from 0.13 to 0.64 highest for WC, followed by TG, SBP, DBP, HDL‐C, and FPG. HDL‐C was mainly influenced by common environmental factors (0.62, 95% CI: 0.58–0.62), whereas the other five MS components were largely influenced by unique environmental factors (0.32–0.44). Bivariate Cholesky decomposition analysis indicated that the clinical clustering of MS components may be explained by shared genetic and/or environmental factors. Our study underscores the importance of examining MS components as intercorrelated traits, and to carefully consider environmental and genetic factors in studying MS etiology. |
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