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A confirmatory factor analysis of the metabolic syndrome in adolescents: an examination of sex and racial/ethnic differences

Matthew J Gurka1*, Christa L Ice1, Shumei S Sun2 and Mark D DeBoer3

Author Affiliations

1 Department of Biostatistics, School of Public Health, West Virginia University, Morgantown, West Virginia, USA

2 Department of Biostatistics, School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA

3 Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, Virginia, USA

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Cardiovascular Diabetology 2012, 11:128  doi:10.1186/1475-2840-11-128

Published: 13 October 2012



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.

Research Design and 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.


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.


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.

Metabolic syndrome; Factor analysis, Statistical; Insulin resistance; Pediatrics; Adolescents; Epidemiology; Clinical studies; Obesity; Risk factors