Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants

Abstract

Objective: 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. Results: MetS was associated with obesity (P < 0.0001), dyslipidemia (P < 0.0001), glycated hemoglobin (HbA1c; P ¼ 0.0013), hypertension (P < 0.0001), and hyperuricemia (P < 0.0001). Of metabolic diseases, MetS was associated with gout (P ¼ 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 ¼ 0.008). Eleven GWAS variants predicted metabolic variables, MetS, and metabolic diseases. Notably, rs7100623 in HHEX/IDE was associated with HbA1c (b ¼ 0.03; P < 0.0001) and type 2 diabetes (b ¼ 0.326; P ¼ 0.0002), underscoring substantial impact on glucose control. Conclusions: Although MetS was associated with obesity, dyslipidemia, glucose control, hypertension, and hyperuricemia, limited ability of MetS to indicate metabolic disease risk is suggested.Obesity (2013) 21, E745-E754.

Association of DNA Methylation at CPT1A Locus with Metabolic Syndrome in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study

“…Both genetic and environmental factors play a role in the pathogenesis of MetS. Genetic studies of MetS have often shown that genetic predisposition is attributable to the individual traits rather than the syndrome as a whole and that MetS is a clinical rather than biological phenomenon [ 3 , 4 ]. However, genome-wide association studies (GWAS) for the individual components of MetS have reported the same loci as being associated with more than one MetS-related trait.…”

Factor Analysis of Metabolic Syndrome Components in an Iranian Non-Diabetic Adult Population: A Population-Based Study from the North of Iran

“…Choi et al found 4 major factors of cardiovascular risk, including impaired glucose tolerance, dyslipidemia, hypertension and obesity, among non-diabetic elderly Korean individuals ( 22 ). In another study, through structural equation of metabolic traits, several indicators of abdominal obesity, body mass index, and also lipid and glucose observed variables were entered in the factor analysis; the 5-factor model was extracted and in fact, obesity and some measures of abdominal obesity were extracted as 2 separate factors ( 23 ). In contrast, Esteghamati et al found a single factor model in diabetic and non-diabetic population when TG and HDL were replaced by TG to HDL ratio, as an observed variable ( 14 ).…”

Multi-trait multi-locus SEM model discriminates SNPs of different effects

“…Once predominantly used in genetics, econometric, and sociology, SEM applications have gradually shifted to the field of molecular biology [28]. For example, SEM has been used to explore alterations in gene networks in diseases [29,30], to provide a quantitative map of relationships between traits and disease [31], and to infer gene regulatory networks involving several hundred genes and eQTLs [32,33].…”