AIMS: We developed a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in persons with diabetes, using demographic and clinical information, and a panel of traditional and nontraditional biomarkers. MATERIALS AND METHODS: We included 654 persons in the ARIC Study, a prospective cohort study, with diagnosed diabetes (visit 2, 1990-92). Models included self-reported variables (Model 1), clinical measurements (Model 2), and HbA1c (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. RESULTS: Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (P < 0.05 for differences). Addition of three traditional and nontraditional biomarkers (beta-2 microglobulin, creatinine-based estimated glomerular filtration rate [eGFR], and cystatin C-based eGFR) to model 3 significantly improved discrimination (C-statistic = 0.716, P = 0.003) and accuracy of 10-year risk prediction for major complications in persons with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18%-68% to 12%-87%). CONCLUSIONS: These biomarkers, particularly those of kidney filtration, may help distinguish between persons at low versus high risk of long-term major complications.
AD - Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.AIMS: We developed a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in persons with diabetes, using demographic and clinical information, and a panel of traditional and nontraditional biomarkers. MATERIALS AND METHODS: We included 654 persons in the ARIC Study, a prospective cohort study, with diagnosed diabetes (visit 2, 1990-92). Models included self-reported variables (Model 1), clinical measurements (Model 2), and HbA1c (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. RESULTS: Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (P < 0.05 for differences). Addition of three traditional and nontraditional biomarkers (beta-2 microglobulin, creatinine-based estimated glomerular filtration rate [eGFR], and cystatin C-based eGFR) to model 3 significantly improved discrimination (C-statistic = 0.716, P = 0.003) and accuracy of 10-year risk prediction for major complications in persons with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18%-68% to 12%-87%). CONCLUSIONS: These biomarkers, particularly those of kidney filtration, may help distinguish between persons at low versus high risk of long-term major complications.
PY - 2016 SN - 1463-1326 (Electronic)