Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models
Year: 2013
Type: article
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 (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.
Authors: 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, 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
Institutions: South African Medical Research Council, University of Cape Town, The George Institute for Global Health, Utrecht University, University Medical Center Utrecht, The George Institute for Global Health, German Institute of Human Nutrition, National Institute for Public Health and the Environment, MRC Epidemiology Unit, Medical Research Council, Comunidad Autónoma de la Región de Murcia, University of Helsinki, Institut Català d'Oncologia, Inserm, Centre de recherche en Epidémiologie et Santé des Populations, Lund University, German Cancer Research Center, University of Oxford, University of Cambridge, Aarhus University, Aalborg University Hospital, University of Naples Federico II, Gobierno del Principado de Asturias, Umeå University, Danish Cancer Society, Andalusian School of Public Health, Centre international de recherche sur le cancer, Fondazione IRCCS Istituto Nazionale dei Tumori, Imperial College London
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $7,280
Funders: World Health Organization, Kræftens Bekæmpelse, Cancer Research UK, National Institute for Health and Care Research, European Commission, Agence Nationale de la Recherche, Bundesministerium für Bildung und Forschung, Ministerie van Volksgezondheid, Welzijn en Sport, Hjärt-Lungfonden, Novo Nordisk, Vetenskapsrådet, Deutsche Krebshilfe, Associazione Iblea per la Ricerca Epidemiologica, Consejería de Salud y Familias, Junta de Andalucía, Medical Research Council, World Cancer Research Fund, Agentschap NL
OpenAlex ID: W2114439920