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

Andre Pascal Kengne; Joline WJ Beulens; Linda M Peelen; Karel GM Moons; Yvonne T van der Schouw; Matthias B Schulze; Annemieke MW Spijkerman; Simon J Griffin; Diederick E Grobbee; Luigi Palla ORCID logo; +36 more... Maria-Jose Tormo; Larraitz Arriola; Noël C Barengo; Aurelio Barricarte; Heiner Boeing; Catalina Bonet; Françoise Clavel-Chapelon; Laureen Dartois; Guy Fagherazzi; Paul W Franks; José María Huerta; Rudolf Kaaks; Timothy J Key; Kay Tee Khaw; Kuanrong Li; Kristin Mühlenbruch; Peter M Nilsson; Kim Overvad; Thure F Overvad; Domenico Palli; Salvatore Panico; J Ramón Quirós; Olov Rolandsson; Nina Roswall; Carlotta Sacerdote; María-José Sánchez; Nadia Slimani; Giovanna Tagliabue; Anne Tjønneland; Rosario Tumino; Daphne L van der A; Nita G Forouhi; Stephen J Sharp; Claudia Langenberg; Elio Riboli; Nicholas J Wareham; (2013) Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. The lancet Diabetes & endocrinology, 2 (1). pp. 19-29. ISSN 2213-8587 DOI: 10.1016/S2213-8587(13)70103-7
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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 (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>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. FUNDING: The European Union.


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