Considerable debate is occurring over the clinical utility of the metabolic syndrome concept, despite a large volume of published literature concerning its associations with cardiovascular disease (CVD). A number of different definitions for the metabolic syndrome have been proposed, each derived primarily from expert opinion rather than from quantitative optimization of syndrome components and cutpoints. Prospective data are lacking comparing the accuracy of those definitions in the classification and prediction of incident CVD, examining whether the syndrome confers any additional risk information above its individual components and above clinical risk tools such as the Framingham Risk Score, and examining whether the metabolic syndrome imparts the same CVD risk information among gender and race-ethnic subgroups. To address these limitations, we propose to pool existing data from 4 large, population-based, NHLBI-supported, longitudinal studies: The Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Framingham Cohort Study, and the Framingham Offspring Study. This pooled database will allow for the first quantitative determination of the optimal metabolic syndrome definition and assessment of its accuracy as a CVD risk predictor. This R21 application has 3 specific aims: 1.) To determine a novel definition of the metabolic syndrome that is optimal in terms of its sensitivity and specificity for predicting incident CVD events and all-cause mortality, including determining a.) The most appropriate cut points for each of the components of the metabolic syndrome, and b.) The appropriate number and combination of components for designation of the metabolic syndrome, 2.) To compare the ability of various risk stratification schemes to predict incident CVD events and all-cause mortality, including: a.) the Adult Treatment Panel (ATP) III metabolic syndrome definition, b.) The International Diabetes Federation (IDF) metabolic syndrome definition, c.) The optimal definition of the metabolic syndrome as identified in Aim 1, d.) The individual metabolic syndrome components and e.) The Framingham Risk Score equation and 3.) To examine the performance of the ATP III, IDF, and optimal metabolic syndrome definitions in predicting incident CVD events and all-cause mortality among subgroups defined by: a.) gender, b.) race-ethnicity, and c.) presence or absence of major risk factors including diabetes and hypertension. Through the pooling of these large databases and the aims above, we will be using statistical methodologies that consider both the strength of the association between the metabolic syndrome and CVD events, as well as the accuracy of the metabolic syndrome to classify individuals who subsequently develop CVD, within the framework of prospective data. This will provide the first truly comprehensive assessment of the syndrome's definition and predictive abilities. This study will provide the experts engaged in the debate on the clinical utility of the metabolic syndrome with information critical to making an evidence-based, data-driven decision.
Between 25% and 40% of adults has been shown to have the metabolic syndrome, based on current syndrome definitions. The data concerning the cardiovascular disease risk prediction capabilities of the metabolic syndrome provided by the current application will provide critical information to making an evidence- based decision regarding the clinical utility of the metabolic syndrome. Therefore, given the large proportion of the population affected by the syndrome, this application could have a significant impact on the practice of cardiovascular disease prevention.
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