Several models for estimating risk of incident diabetes in US adults are available. The authors aimed to determine the discriminative ability and calibration of published diabetes risk prediction models in a contemporary multiethnic cohort. Participants in the Multi-Ethnic Study of Atherosclerosis without diabetes at baseline (2000–2002; n = 5,329) were followed for a median of 4.75 years. The predicted risk of diabetes was calculated using published models from the Framingham Offspring Study, the Atherosclerosis Risk in Communities (ARIC) Study, and the San Antonio Heart Study. The mean age of participants was 61.6 years (standard deviation, 10.2); 29.3% were obese, 53.1% had hypertension, 34.9% had a family history of diabetes, 27.5% had high triglyceride levels, 33.8% had low high density lipoprotein cholesterol levels, and 15.3% had impaired fasting glucose. There were 446 incident cases of diabetes (fasting glucose level ≥126 mg/dL or initiation of antidiabetes medication use) diagnosed during follow-up. C statistics were 0.78, 0.84, and 0.83 for the Framingham, ARIC, and San Antonio risk prediction models, respectively. There were significant differences between observed and predicted diabetes risks (Hosmer-Lemeshow goodness-of-fit chi-squared test for each model: P < 0.001). The recalibrated and best-fit models achieved sufficient goodness of fit (each P > 0.10). The Framingham, ARIC, and San Antonio models maintained high discriminative ability but required recalibration in a modern, multiethnic US cohort
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