Prognostic value of multiple biomarkers in American Indians free of clinically overt cardiovascular disease (from the Strong Heart Study).
Several biomarkers have been documented, singly or jointly, to improve risk prediction, but the extent to which they improve prediction-model performance in populations with high prevalences of obesity and diabetes has not been specifically examined. The aim of this study was to evaluate the ability of various biomarkers to improve prediction-model performance for death and major cardiovascular disease (CVD) events in a high-risk population. The relations of 6 biomarkers with outcomes were examined in 823 American Indians free of prevalent CVD or renal insufficiency, as were their contributions to risk prediction. In single-marker models adjusting for standard clinical and laboratory risk factors, 4 of 6 biomarkers significantly predicted mortality and major CVD events. In multimarker models, these 4 biomarkers-urinary albumin/creatinine ratio (UACR), glycosylated hemoglobin, B-type natriuretic peptide, and fibrinogen-significantly predicted mortality, while 2-UACR and fibrinogen-significantly predicted CVD. On the basis of its robust association in participants with diabetes, UACR was the strongest predictor of mortality and CVD, individually improving model discrimination or classification in the entire cohort. Singly, all remaining biomarkers also improved risk classification for mortality and enhanced average sensitivity for mortality and CVD. The addition of > or =1 biomarker to the single marker UACR further improved discrimination or average sensitivity for these outcomes. In conclusion, biomarkers derived from diabetic cohorts, and novel biomarkers evaluated primarily in lower risk populations, improve risk prediction in cohorts with prevalent obesity and diabetes. Risk stratification of these populations with multimarker models could enhance selection for aggressive medical or surgical approaches to prevention.