The first two authors contributed equally to this work.
Genetic and Environmental Contributions to Phenotypic Components of Metabolic Syndrome: A Population-based Twin Study
Article first published online: 6 SEP 2012
2009 North American Association for the Study of Obesity (NAASO)
Volume 17, Issue 8, pages 1581–1587, August 2009
How to Cite
Zhang, S., Liu, X., Yu, Y., Hong, X., Christoffel, K. K., Wang, B., Tsai, H.-J., Li, Z., Liu, X., Tang, G., Xing, H., Brickman, W. J., Zimmerman, D., Xu, X. and Wang, X. (2009), Genetic and Environmental Contributions to Phenotypic Components of Metabolic Syndrome: A Population-based Twin Study. Obesity, 17: 1581–1587. doi: 10.1038/oby.2009.125
- Issue published online: 6 SEP 2012
- Article first published online: 6 SEP 2012
- Received 16 July 2008; accepted 25 March 2009
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.