A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences.
OBJECTIVE
The metabolic syndrome (MetS) is a cluster of clinical indices that signals increased risk for cardiovascular disease and Type 2 diabetes. The diagnosis of MetS is typically based on cut-off points for various components, e.g. waist circumference and blood pressure. Because current MetS criteria result in racial/ethnic discrepancies, our goal was to use confirmatory factor analysis to delineate differential contributions to MetS by sub-group.


METHODS
Using 1999-2010 data from the National Health and Nutrition Examination Survey (NHANES), we performed a confirmatory factor analysis of a single MetS factor that allowed differential loadings across sex and race/ethnicity, resulting in a continuous MetS risk score that is sex and race/ethnicity-specific.


RESULTS
Loadings to the MetS score differed by racial/ethnic and gender subgroup with respect to triglycerides and HDL-cholesterol. ROC-curve analysis revealed high area-under-the-curve concordance with MetS by traditional criteria (0.96), and with elevations in MetS-associated risk markers, including high-sensitivity C-reactive protein (0.71), uric acid (0.75) and fasting insulin (0.82). Using a cut off for this score derived from ROC-curve analysis, the MetS risk score exhibited increased sensitivity for predicting elevations in ≥2 of these risk markers as compared with traditional pediatric MetS criteria.


CONCLUSIONS
The equations from this sex- and race/ethnicity-specific analysis provide a clinically-accessible and interpretable continuous measure of MetS that can be used to identify children at higher risk for developing adult diseases related to MetS, who could then be targeted for intervention. These equations also provide a powerful new outcome for use in childhood obesity and MetS research.
Department of Biostatistics, School of Public Health, West Virginia University, Morgantown, West Virginia, USA. mgurka@hsc.wvu.edu.
Matthew J
MJ
Gurka
Christa L
CL
Ice
Shumei S
SS
Sun
Mark D
MD
Deboer
D000293:Adolescent; D001741:Black or African American / Q000706:statistics & numerical data*; D000367:Age Factors; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000032:analysis; D016009:Chi-Square Distribution; D002648:Child; D008076:Cholesterol, HDL / Q000097:blood; D003430:Cross-Sectional Studies; D005163:Factor Analysis, Statistical; D006630:Hispanic or Latino / Q000706:statistics & numerical data*; D006801:Humans; D007328:Insulin / Q000097:blood; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis / Q000208:ethnology*; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D014280:Triglycerides / Q000097:blood; D014481:United States / Q000453:epidemiology; D014527:Uric Acid / Q000097:blood; D044465:White People / Q000706:statistics & numerical data*; D055815:Young Adult
D015415:Biomarkers; D008076:Cholesterol, HDL; D007328:Insulin; D014280:Triglycerides; D014527:Uric Acid; D002097:C-Reactive Protein