Path Analysis of Metabolic Syndrome in Youth

Tulane University Of Louisiana, New Orleans LA

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Abstract

DESCRIPTION (provided by applicant): The metabolic syndrome, a concurrence of obesity, insulin resistance, hypertension and dyslipidemia, is associated with the risk for developing cardiovascular disease and type 2 diabetes. Factor analysis has generally been used in cross-sectional studies to describe features of the syndrome determined by the underlying latent factors. However, it is not clear whether the different dimensions of the metabolic syndrome change together over time and if the relationships among the various parameters change from childhood to adulthood. Path analysis (structure equation modeling), which can combine factor analysis and multiple regression models, can be an invaluable approach to study such questions. This proposal is aimed at examining longitudinally from childhood to adulthood the complex interrelationships among the long-term trends of the metabolic syndrome components and the latent factors by the path analysis. The Bogalusa Heart Study, a long-term biracial (black-white) epidemiologic study of cardiovascular risk factors from birth through 44 years of age, offers an unparalleled database to use for these analyses. The study cohort includes 1794 subjects who were screened at least 4 times beginning in childhood, with 12,346 observations. Four criterion metabolic syndrome risk variables included in the analyses are body mass index, homeostasis model assessment index of insulin resistance, systolic blood pressure and triglycerides/HDL cholesterol ratio. The complex pathways among the four metabolic syndrome components and the latent factors will be elucidated by the path analysis separately in childhood, adolescence and adulthood by race and sex. In addition, serial data will be used to develop a random effects model for each metabolic syndrome component to calculate the area under the curve (AUC, a measure of long-term burden) incremental AUC (total AUC - baseline AUC, a measure of linear and nonlinear long-term trends) and linear slopes for each subject. These AUCs will be analyzed to examine the clustering patterns and the complex pathways in terms of observed/expected ratio, intra-class correlation, factor analysis and path analysis by race and sex. The findings from the proposed analyses will extend our knowledge of the longitudinal evolution of the metabolic syndrome in blacks and whites, and males and females from childhood. Importantly, the analyses will aid the development of rational preventive strategies beginning in early life.