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

• Published in: Obesity (Silver Spring, Md.) Vol. 21; no. 12; pp. E745 - E754
• Main Authors: Karns, Rebekah, Succop, Paul, Zhang, Ge, Sun, Guangyun, Indugula, Subba R., Havas‐Augustin, Dubravka, Novokmet, Natalija, Durakovic, Zijad, Milanovic, Sanja Music, Missoni, Sasa, Vuletic, Silvije, Chakraborty, Ranajit, Rudan, Pavao, Deka, Ranjan
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01-12-2013
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Blood Pressure; Cardiovascular disease; Cholesterol; Comorbidity; Cross-Sectional Studies; Diabetes; Dyslipidemias - epidemiology; Epidemiology; Female; Genome-Wide Association Study; Genotype; Glucose; Glycated Hemoglobin A - metabolism; Humans; Hypertension; Hypertension - epidemiology; Hyperuricemia - epidemiology; Islands; Male; Metabolic disorders; Metabolic syndrome; Metabolic Syndrome - epidemiology; Metabolic Syndrome - genetics; Middle Aged; Multivariate Analysis; Obesity; Obesity - epidemiology; Phenotype; Population; Regression Analysis; Risk Factors; Studies; Young Adult
• ISSN: 1930-7381; 1930-739X
• DOI: 10.1002/oby.20445

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 = 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.
Conclusions
Although MetS was associated with obesity, dyslipidemia, glucose control, hypertension, and hyperuricemia, limited ability of MetS to indicate metabolic disease risk is suggested.