The Prediction of Major Outcomes of Type 1 Diabetes: a 12-Year Prospective Evaluation of Three Separate Definitions of the Metabolic Syndrome and Their Components and Estimated Glucose Disposal Rate

Abstract

The metabolic syndrome has been shown to confer an increased risk of cardiovascular disease in both the general and type 2 diabetic populations, but few studies have assessed the metabolic syndrome in type 1 diabetic patients. In a type 1 diabetic cohort, we assessed the prevalence and value of the metabolic syndrome in improving the prediction of major complication outcomes compared with its components and a surrogate measure of insulin resistance, estimated glucose disposal rate (eGDR). A total of 514 (78%) subjects participating in the Pittsburgh Epidemiology of Diabetes Complications Study with complete 12-year follow-up clinical data were classified by baseline metabolic syndrome status according to three definitions: those of the National Cholesterol Education Program Adult Treatment Panel III (modified by the American Heart Association), the International Diabetes Federation (IDF), and the World Health Organization (WHO). The complication outcomes included coronary artery disease, renal failure, diabetes-related death, and the aggregate of these three major outcomes of diabetes (MOD). Metabolic syndrome prevalence ranged from 8% (IDF) to 21% (WHO). All definitions showed reasonable specificity (> or = 83%) for each outcome, while the WHO definition had the highest sensitivity for all outcomes except renal failure, for which eGDR was most sensitive. However, the components of each definition predicted better than the overall syndrome. Microalbuminuria was clearly the strongest predictor of all individual measures, yielding hazard ratios of 9 and 6 for mortality and MOD, respectively. Though the three metabolic syndrome classifications predict major complication outcomes in type 1 diabetes, their individual components predict better. Of the variables studied, including HbA1, microalbuminuria appears to be the best single predictor of MOD.