Metabolic Syndrome

T he concept of a “metabolic syndrome ” conferring increased risk of incident type 2 diabetes and cardiovascular disease (CVD) has been around for more than a couple of decades. Recent publication of clinical definitions has transformed the metabolic syndrome from a physiological curiosity to a major focus of research and of clinical and public health interest (1,2). The metabolic syndrome is generally considered to be the co-occurrence of obesity (particularly central obesity); elevated glucose, triglyceride, and blood pressure levels; and/or low HDL cholesterol levels, and in many cases their co-occurrence signifies underlying insulin resistance (3). The metabolic syndrome has been most widely promoted as a means to identify patients for lifestyle interventions to reduce risk factor levels and, theoretically, incident disease, particularly CVD. However, various tools already exist for the identification of apparently healthy people at elevated risk of diabetes (4–6) or CVD (7). The degree to which a clinical diagnosis of the metabolic syndrome complements or improves upon risk prediction using existing methods has not been defined. In this issue of Diabetes Care, Stern et al. (8) address this question using the experience of Mexican American and nonHispanic white participants in the San Antonio Heart Study (SAHS). After a baseline examination and 7 years of follow-up, 195 subjects subsequently developed type 2 diabetes out of 1,709 subjects without baseline diabetes (defined by diabetic hyperglycemia or self-reported use of diabetes medications) and 156 experienced a CVD event out of 2,570 subjects without baseline CVD (defined as selfreported physician diagnosis of heart attack, revascularization procedure, stroke, or CVD death by death certificate). Clinical history and metabolic risk factors measured at baseline were used to categorize subjects with the metabolic syndrome by National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP-III) criteria (or World Health Organization [WHO] criteria in a secondary analysis available in an online appendix) and to calculate the probability of developing diabetes (using the Diabetes Predicting Model) or CVD (with the Framingham Risk Score). The Diabetes Predicting Model is one of the more extensively validated diabetes clinical prediction rules currently in the literature (4,9), and the present work also examines diabetes outcomes in the Mexico City Diabetes Study, providing further useful validation data for the Diabetes Predicting Model. The Framingham Risk Score is a widely available, extensively validated prediction rule for defining the probability of a coronary heart disease event in diverse populations (10). Stern et al. then used the metabolic syndrome or the prediction rules to calculate sensitivities (the probability that a set of risk factors correctly identify a subject with a subsequent event, i.e., the true-positive rate), falsepositive rates, and areas under the receiver operating characteristic curve (aROC; the probability that a set of risk factors correctly discriminate subjects developing an outcome from those without an outcome, where 0.5 is chance discrimination and 1.0 is perfect discrimination). They found that in the SAHS population, the prevalence of the NCEP ATP-III– defined metabolic syndrome was 27– 36%, depending on ethnicity and whether baseline diabetes or CVD were excluded. The true-positive rates of the metabolic syndrome were 66% for type 2 diabetes and 67% for CVD, and the falsepositive rates were 28% for type 2 diabetes and 34% for CVD. Then, to compare the performance of the metabolic syndrome with the prediction rules, they fixed the true-positive rate for diabetes at 66% and found that the false-positive rate of the Diabetes Predicting Model was 19%. When the false-positive rate was fixed at 28%, the true-positive rate for diabetes was 76%. When they fixed the true-positive rate for CVD at 67%, they found that the false-positive rate of the Framingham Risk Score was 20%. When the false-positive rate was fixed at 34%, the true-positive rate for CVD was 81%. The aROC of the Diabetes Predicting Model was 0.819, and considering both the Diabetes Predicting Model and the metabolic syndrome together only improved the aROC to 0.824. The aROC of the Framingham Risk Score was 0.816, and considering both the Framingham Risk Score and the metabolic syndrome together gave a similar aROC (0.811). What these data mean is that the Diabetes Predicting Model more accurately identifies subjects at future risk of type 2 diabetes and the Framingham Risk Score more accurately identifies subjects at future risk of CVD than diagnosis of the NCEP ATPIII–defined metabolic syndrome. Further assigning a diagnosis of the metabolic syndrome would not tell the clinician anything more about future disease risk than they would have already known on the basis of information from the prediction rules. Stern et al. conclude that the metabolic syndrome is inferior to established rules for the prediction of either type 2 diabetes or CVD. These data contribute valuable quantitative information that might temper the gathering enthusiasm for widespread use of the metabolic syndrome as a focus of screening for metabolic risk modification (11,12), and in many ways, the SAHS results are not surprising. As Stern et al. discuss, the metabolic syndrome would be expected a priori to be inferior to the Diabetes Predicting Model for prediction of diabetes because the metabolic syndrome does not consider a family history of diabetes, one of the most potent known diabetes risk factors. Likewise, the metabolic syndrome would be expected to be inferior to the Framingham Risk Score for prediction of CVD because the metabolic syndrome does not consider age, sex, smoking, or total cholesterol levels, all potent CVD risk factors. Further, because of collinearity in regression models containing metabolic syndrome and either of the prediction rules (here collinearity means, in effect, that the same variable E D I T O R I A L ( S E E S T E R N E T A L . , P . 2 6 7 6 )