OBJECTIVE - Individuals at high risk for chronic cardiometabolic disease (cardiovascular disease [CVD], type 2 diabetes, and chronic kidney disease [CKD]) share many risk factors and would benefit from early intervention. We developed a nonlaboratory-based risk-assessment tool for identification of people at high cardiometabolic disease risk. RESEARCH DESIGN AND METHODS - Data of three population-based cohorts from different regions of the Netherlands were merged. Participants were 2,840 men and 3,940 women, white, aged 28-85 years, free from CVD, type 2 diabetes, and CKD diagnosis at baseline. The outcome was developing cardiometabolic disease during 7 years follow-up. RESULTS - Age, BMI, waist circumference, antihypertensive treatment, smoking, family history of myocardial infarction or stroke, and family history of diabetes were significant predictors, whereas former smoking, history of gestational diabetes, and use of lipid-lowering medication were not. The models showed acceptable calibration (Hosmer and Lemeshow statistics, P > 0.05) and discrimination (area under the receiver operating characteristic [ROC] curve 0.82 [95% CI 0.81-0.83] for women and 0.80 [0.78-0.82] for men). Discrimination of individual outcomes was lowest for diabetes (area under the ROC curve 0.70 for men and 0.73 for women) and highest for CVD mortality (0.83 for men and 0.85 for women). CONCLUSIONS - We demonstrate that a single risk stratification tool can identify people at high risk for future CVD, type 2 diabetes, and/or CKD. The present risk-assessment tool can be used for referring the highest risk individuals to health care for further (multivariable) risk assessment and may as such serve as an important part of prevention programs targeting chronic cardiometabolic disease. © 2012 by the American Diabetes Association.