© 2016 by the American Diabetes Association. OBJECTIVE: Cardiovascular disease (CVD) is the major cause of morbidity and mortality in diabetes; yet, heterogeneity in CVD risk has been suggested in diabetes, providing a compelling rationale for improving diabetes risk stratification. We hypothesized that N-terminal prohormone brain natriuretic peptide (NTproBNP) and high-sensitivity troponin T may enhance CVD risk stratification beyond commonly used markers of risk and that CVD risk is heterogeneous in diabetes. RESEARCH DESIGN AND METHODS: Among 8,402 participants without prevalent CVD at visit 4 (1996-1998) of the Atherosclerosis Risk in Communities (ARIC) study there were 1,510 subjects with diabetes (mean age 63 years, 52% women, 31% African American, and 60% hypertensive). RESULTS: Over a median follow-up of 13.1 years, there were 540 incident fatal/nonfatal CVD events (coronary heart disease, heart failure, and stroke). Both troponin T ≥14 ng/L (hazard ratio [HR] 1.96 [95% CI 1.57-2.46]) and NTproBNP >125 pg/mL (1.61 [1.29-1.99]) were independent predictors of incident CVD events at multivariable Cox proportional hazard models. Addition of circulating cardiac biomarkers to traditional risk factors, abnormal electrocardiogram (ECG), and conventional markers of diabetes complications including retinopathy, nephropathy, and peripheral arterial disease significantly improved CVD risk prediction (net reclassification index 0.16 [95% CI 0.07-0.22]). Compared with individuals without diabetes, subjects with diabetes had 1.6-fold higher adjusted risk of incident CVD. However, participants with diabetes with normal cardiac biomarkers and no conventional complications/abnormal ECG (n = 725 [48%]) were at low risk (HR 1.12 [95% CI 0.95-1.31]), while those with abnormal cardiac biomarkers, alone (n = 186 [12%]) or in combination with conventional complications/abnormal ECG (n = 243 [16%]), were at greater risk (1.99 [1.59-2.50] and 2.80 [2.34-3.35], respectively). CONCLUSIONS: Abnormal levels of NTproBNP and troponin T may help to distinguish individuals with high diabetes risk from those with low diabetes risk, providing incremental risk prediction beyond commonly used markers of risk.