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 vs >= 60 years), BMI (<25 kg/m(2) vs >= 25 kg/m(2)), and waist circumference (men <102 cm vs >= 102 cm; women <88 cm vs >= 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) <0.0001) in all but one model. Calibration was good for most models, and consistent across countries (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 <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups. 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.
Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models / Kengne, Ap; Beulens, Jwj; Peelen, Lm; Moons, Kgm; van der Schouw, Yt; Schulze, Mb; Spijkerman, Amw; Griffin, Sj; Grobbee, De; Palla, L; Tormo, Mj; Arriola, L; Barengo, Nc; Barricarte, A; Boeing, H; Bonet, C; Clavel-Chapelon, F; Dartois, L; Fagherazzi, G; Franks, Pw; Huerta, Jm; Kaaks, R; Key, Tj; Khaw, Kt; Li, Kr; Muhlenbruch, K; Nilsson, Pm; Overvad, K; Overvad, Tf; Palli, D; Panico, S; Quiros, Jr; Rolandsson, O; Roswall, N; Sacerdote, C; Sanchez, Mj; Slimani, N; Tagliabue, G; Tjonneland, A; Tumino, R; van der, A DL; Forouhi, Ng; Sharp, Sj; Langenberg, C; Riboli, E; Wareham, Nj. - In: THE LANCET DIABETES & ENDOCRINOLOGY. - ISSN 2213-8587. - 2:1(2014), pp. 19-29. [10.1016/S2213-8587(13)70103-7]
Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models
Palla L;
2014
Abstract
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 vs >= 60 years), BMI (<25 kg/m(2) vs >= 25 kg/m(2)), and waist circumference (men <102 cm vs >= 102 cm; women <88 cm vs >= 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) <0.0001) in all but one model. Calibration was good for most models, and consistent across countries (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 <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups. 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
