title: 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).
  
  
  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.
authors.affiliation: Division of Epidemiology and Biostatistics, Department of Environmental Health, University of Cincinnati, Cincinnati, Ohio, USA.
authors.forename: Rebekah
authors.initials: R
authors.lastname: Karns
authors.forename: Paul
authors.initials: P
authors.lastname: Succop
authors.forename: Ge
authors.initials: G
authors.lastname: Zhang
authors.forename: Guangyun
authors.initials: G
authors.lastname: Sun
authors.forename: Subba R
authors.initials: SR
authors.lastname: Indugula
authors.forename: Dubravka
authors.initials: D
authors.lastname: Havas-Augustin
authors.forename: Natalija
authors.initials: N
authors.lastname: Novokmet
authors.forename: Zijad
authors.initials: Z
authors.lastname: Durakovic
authors.forename: Sanja Music
authors.initials: SM
authors.lastname: Milanovic
authors.forename: Sasa
authors.initials: S
authors.lastname: Missoni
authors.forename: Silvije
authors.initials: S
authors.lastname: Vuletic
authors.forename: Ranajit
authors.initials: R
authors.lastname: Chakraborty
authors.forename: Pavao
authors.initials: P
authors.lastname: Rudan
authors.forename: Ranjan
authors.initials: R
authors.lastname: Deka
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001794:Blood Pressure; D015897:Comorbidity; D003430:Cross-Sectional Studies; D050171:Dyslipidemias / Q000453:epidemiology; D005260:Female; D055106:Genome-Wide Association Study; D005838:Genotype; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D033461:Hyperuricemia / Q000453:epidemiology; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology* / Q000235:genetics*; D008875:Middle Aged; D015999:Multivariate Analysis; D009765:Obesity / Q000453:epidemiology; D010641:Phenotype; D012044:Regression Analysis; D012307:Risk Factors; D055815:Young Adult
chemical_list: D006442:Glycated Hemoglobin