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AIMS: Predicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information. METHODS: Among non-diabetic participants reaching 1year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a "landmark" model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction. RESULTS: A total of 7527 participants remained non-diabetic at 1year, and 2375 developed diabetes during a median of 4years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72-0.74]) than for the baseline model (0.67 [95% CI 0.66-0.68]). The landmark model improved classification to modest (<20%), moderate (20%-40%), and high (>40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10-0.16) and an integrated discrimination index of 0.01 (95% CI 0.003-0.013). CONCLUSIONS: Using historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels.

Original publication




Journal article


J Diabetes Complications

Publication Date





859 - 863


Diabetes risk prediction, Fasting plasma glucose, Impaired glucose tolerance, Post-prandial glucose, Type 2 diabetes, Blood Glucose, Cardiovascular Diseases, Combined Modality Therapy, Diabetes Mellitus, Type 2, Diabetic Angiopathies, Diabetic Cardiomyopathies, Disease Progression, Female, Follow-Up Studies, Global Health, Glucose Tolerance Test, Health Transition, Healthy Lifestyle, Humans, Incidence, Male, Models, Biological, Practice Guidelines as Topic, Prediabetic State, Proportional Hazards Models, Randomized Controlled Trials as Topic, Risk Factors