Matthew J Gurka1, Christa L Lilly2, M Norman Oliver3, Mark D DeBoer4. 1. Department of Biostatistics, School of Public Health, West Virginia University, Morgantown, West Virginia. Electronic address: mgurka@hsc.wvu.edu. 2. Department of Biostatistics, School of Public Health, West Virginia University, Morgantown, West Virginia. 3. Department of Family Medicine, School of Medicine, University of Virginia, Charlottesville, Virginia. 4. Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, Virginia.
Abstract
OBJECTIVE: The metabolic syndrome (MetS) is typically diagnosed based on abnormalities in specific clustered clinical measures that are associated with increased risk for coronary heart disease (CHD) and Type 2 diabetes mellitus (T2DM). However, current MetS criteria result in racial/ethnic discrepancies. Our goals were to use confirmatory factor analysis (CFA) to delineate differential contributions to MetS by sub-group, and if contributions were discovered, develop sex and racial/ethnic-specific equations to calculate MetS severity. RESEARCH DESIGN AND METHODS: Using data on adults from the National Health and Nutrition Examination Survey 1999-2010, we performed a CFA of a single MetS factor that allowed differential loadings across groups, resulting in a sex and race/ethnicity-specific continuous MetS severity score. RESULTS: Loadings to the single MetS factor differed by sub-group for each MetS component (p<0.001), with lower factor loadings among non-Hispanic-blacks for triglycerides and among Hispanics for waist circumference. Systolic blood pressure exhibited low factor loadings among all groups. MetS severity scores were correlated with biomarkers of future disease (high-sensitivity C-reactive-protein, uric acid, insulin resistance). Non-Hispanic-black-males with diabetics had a low prevalence of MetS but high MetS severity scores that were not significantly different from other racial/ethnic groups. CONCLUSIONS: This analysis among adults uniquely demonstrated differences between sexes and racial/ethnic groups regarding contributions of traditional MetS components to an assumed single factor. The resulting equations provide a clinically-accessible and interpretable continuous measure of MetS for potential use in identifying adults at higher risk for MetS-related diseases and following changes within individuals over time. These equations hold potential to be a powerful new outcome for use in MetS-focused research and interventions.
OBJECTIVE: The metabolic syndrome (MetS) is typically diagnosed based on abnormalities in specific clustered clinical measures that are associated with increased risk for coronary heart disease (CHD) and Type 2 diabetes mellitus (T2DM). However, current MetS criteria result in racial/ethnic discrepancies. Our goals were to use confirmatory factor analysis (CFA) to delineate differential contributions to MetS by sub-group, and if contributions were discovered, develop sex and racial/ethnic-specific equations to calculate MetS severity. RESEARCH DESIGN AND METHODS: Using data on adults from the National Health and Nutrition Examination Survey 1999-2010, we performed a CFA of a single MetS factor that allowed differential loadings across groups, resulting in a sex and race/ethnicity-specific continuous MetS severity score. RESULTS: Loadings to the single MetS factor differed by sub-group for each MetS component (p<0.001), with lower factor loadings among non-Hispanic-blacks for triglycerides and among Hispanics for waist circumference. Systolic blood pressure exhibited low factor loadings among all groups. MetS severity scores were correlated with biomarkers of future disease (high-sensitivity C-reactive-protein, uric acid, insulin resistance). Non-Hispanic-black-males with diabetics had a low prevalence of MetS but high MetS severity scores that were not significantly different from other racial/ethnic groups. CONCLUSIONS: This analysis among adults uniquely demonstrated differences between sexes and racial/ethnic groups regarding contributions of traditional MetS components to an assumed single factor. The resulting equations provide a clinically-accessible and interpretable continuous measure of MetS for potential use in identifying adults at higher risk for MetS-related diseases and following changes within individuals over time. These equations hold potential to be a powerful new outcome for use in MetS-focused research and interventions.
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