Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): A validation of existing models

Background The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.

Methods We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27 779 individuals from eight European countries, of whom 12 403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (= 60 years), BMI (= 25 kg/m(2)), and waist circumference (men = 102 cm; women = 88 cm).

Findings We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0.76 (95% CI 0.72-0.80) to 0.81 (0.77-0.84) overall, from 0.73 (0.70-0.76) to 0.79 (0.74-0.83) in men, and from 0.78 (0.74-0.82) to 0.81 (0.80-0.82) in women. We noted significant heterogeneity in discrimination (p(heterogeneity) 0.05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of

Interpretation Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.