Disclosure: The authors declared no conflict of interest.
Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants
Version of Record online: 13 JUN 2013
Copyright © 2013 The Obesity Society
Volume 21, Issue 12, pages E745–E754, December 2013
How to Cite
Karns, R., Succop, P., Zhang, G., Sun, G., Indugula, S. R., Havas-Augustin, D., Novokmet, N., Durakovic, Z., Milanovic, S. M., Missoni, S., Vuletic, S., Chakraborty, R., Rudan, P. and Deka, R. (2013), Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants. Obesity, 21: E745–E754. doi: 10.1002/oby.20445
Funding agencies: The study was supported by grants from the National Institutes of Health, USA (R01 DK069845, R01 DK069845-4S, and P30 ES006096) and from the Croatian Ministry of Science, Education and Sports (196-1962766-2751, 196-1962766-2747, and 196-0342282-0291). R.K. was supported by a training grant fellowship from the National Institutes of Environmental Health Sciences, USA (T32 ES010957).
- Issue online: 3 DEC 2013
- Version of Record online: 13 JUN 2013
- Accepted manuscript online: 20 MAR 2013 02:23AM EST
- Manuscript Accepted: 19 FEB 2013
- Manuscript Received: 24 FEB 2012
- National Institutes of Health, USA. Grant Numbers: R01 DK069845, R01 DK069845-4S, P30 ES006096
- Croatian Ministry of Science, Education and Sports. Grant Numbers: 196-1962766-2751, 196-1962766-2747, 196-0342282-0291
- National Institutes of Environmental Health Sciences, USA. Grant Number: T32 ES010957
To provide a quantitative map of relationships between metabolic traits, genome-wide association studies (GWAS) variants, metabolic syndrome (MetS), and metabolic diseases through factor analysis and structural equation modeling (SEM).
Design and Methods
Cross-sectional data were collected on 1,300 individuals from an eastern Adriatic Croatian island, including 14 anthropometric and biochemical traits, and diagnoses of type 2 diabetes, coronary heart disease, gout, kidney disease, and stroke. MetS was defined based on Adult Treatment Panel III criteria. Forty widely replicated GWAS variants were genotyped. Correlated quantitative traits were reduced through factor analysis; relationships between factors, genetic variants, MetS, and metabolic diseases were determined through SEM.
MetS was associated with obesity (P < 0.0001), dyslipidemia (P < 0.0001), glycated hemoglobin (HbA1c; P = 0.0013), hypertension (P < 0.0001), and hyperuricemia (P < 0.0001). Of metabolic diseases, MetS was associated with gout (P = 0.024), coronary heart disease was associated with HbA1c (P < 0.0001), and type 2 diabetes was associated with HbA1c (P < 0.0001) and obesity (P = 0.008). Eleven GWAS variants predicted metabolic variables, MetS, and metabolic diseases. Notably, rs7100623 in HHEX/IDE was associated with HbA1c (β = 0.03; P < 0.0001) and type 2 diabetes (β = 0.326; P = 0.0002), underscoring substantial impact on glucose control.
Although MetS was associated with obesity, dyslipidemia, glucose control, hypertension, and hyperuricemia, limited ability of MetS to indicate metabolic disease risk is suggested.