title: Factor analysis of metabolic syndrome components in the Coronary Artery Risk Development in Young Adults (CARDIA) study: examination of factors by race-sex groups and across time.
abstract: OBJECTIVE
  This study tests hypotheses of one-, two-, three-, and four-factor models of metabolic syndrome (MetS) components and assesses the consistency and fit of the factor models 10 years later using confirmatory factor analysis in a large biracial sample of men and women.
  
  
  METHODS
  With the use of data from the baseline and year-10 exams of the Coronary Artery Risk Development in Young Adults Study, confirmatory factor analysis was performed overall and for race- and sex-specific groups for one-, two-, three-, and four-factor MetS models in 3403 white and black men and women at baseline and in 2532 white and black men and women 10 years later. Metabolic risk variables used in the factor analysis were insulin resistance (HOMA-IR), fasting glucose, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, systolic and diastolic blood pressure, waist circumference, waist-hip ratio, triceps skinfolds, and uric acid.
  
  
  RESULTS
  Three- and four-factor models of MetS achieved excellent fits of the data, ranging from 0.92 to 0.96 for race- and sex-specific models and from the baseline to year-10 exams.
  
  
  CONCLUSIONS
  The results suggest that MetS factors are consistent across time and race-sex groups. When investigating the MetS, it is necessary to evaluate race-sex groups.
authors.affiliation: Department of Community Health, Saint Louis University School of Public Health, St Louis, MO 63104-1314, USA. tfergus2@slu.edu
authors.forename: T Freeman
authors.initials: TF
authors.lastname: Ferguson
authors.forename: Ellen
authors.initials: E
authors.lastname: Funkhouser
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Roseman
mesh_terms: D000293:Adolescent; D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data*; D001786:Blood Glucose / Q000032:analysis; D015992:Body Mass Index; D002784:Cholesterol / Q000097:blood; D003327:Coronary Disease / Q000097:blood / Q000453:epidemiology* / Q000208:ethnology; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000453:epidemiology* / Q000208:ethnology; D009765:Obesity / Q000097:blood / Q000453:epidemiology / Q000208:ethnology; D018570:Risk Assessment; D014280:Triglycerides / Q000097:blood; D014527:Uric Acid / Q000097:blood; D044465:White People / Q000706:statistics & numerical data*; D055815:Young Adult
chemical_list: D001786:Blood Glucose; D014280:Triglycerides; D014527:Uric Acid; D002784:Cholesterol