Development and Validation of a Diabetes Risk Prediction Model With Individualized Preventive Intervention Effects
Authors' conclusion
This is the first study to develop and validate a diabetes risk prediction model with individualized preventive intervention effects that may improve clinical decision-making and diabetes prevention.
Study context
- Population
- Racially diverse populations at risk of T2D; Diabetes Prevention Program (DPP) participants and Multi-Ethnic Study of Atherosclerosis (MESA) participants with prediabetes
- Comparison
- Diabetes risk model with individualized preventive intervention effects
- Outcome
- Discrimination, calibration and net benefit of the model's predictions for incident diabetes
- Follow-up
- 3 years
Methodological detailsinternal validation, external validation, Cox proportional hazards regression
- Internal validation
- External validation
- Cox proportional hazards regression
Performance
As reported in the abstract
C-statistic
DPP
0.71
mean · 95% CI 0.68–0.74
MESA
0.86
mean · 95% CI 0.83–0.88
Further findings
Optimal preventive intervention (lowest 3-year risk) = lifestyle
Optimal preventive intervention (lowest 3-year risk) = lifestyle
- Metformin optimal for the remaining participants
Model performance by race/ethnicity
- Model performance was similar across race/ethnicity groups
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Development and Validation of a Diabetes Risk Prediction Model With Individualized Preventive Intervention Effects. J Clin Endocrinol Metab. Published 23 April 2025. doi:10.
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