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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): A validation of existing models
The Lancet Diabetes and Endocrinology, Volume 2, No. 1, Year 2014
Notification
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Description
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/m2 vs ≥25 kg/m2), 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/m2. 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. © 2014 Elsevier Ltd.
Authors & Co-Authors
Kengne, Andre-Pascal Pascal
Netherlands, Utrecht
University Medical Center Utrecht
South Africa, Tygerberg
South African Medical Research Council
Australia, Sydney
George Institute for Global Health
Beulens, J. W.J.
Netherlands, Utrecht
University Medical Center Utrecht
Peelen, Linda Margaretha
Netherlands, Utrecht
University Medical Center Utrecht
Moons, Karel G.M.
Netherlands, Utrecht
University Medical Center Utrecht
van der Schouw, Y. T.
Netherlands, Utrecht
University Medical Center Utrecht
Schulze, Matthias Bernd
Germany, Nuthetal
German Institute of Human Nutrition
Spijkerman, Annemieke M.W.
Netherlands, Bilthoven
Rijksinstituut Voor Volksgezondheid en Milieu
Griffin, Simon J.
United Kingdom, Cambridge
Mrc Epidemiology Unit
Grobbee, Diederick E Egbertus
Netherlands, Utrecht
University Medical Center Utrecht
Palla, Luigi
United Kingdom, Cambridge
Mrc Epidemiology Unit
Tormo, María José
Spain, Murcia
Health Council of Murcia
Arriola, Larraitz
Spain, Donostia-san Sebastian
Public Health Division of Gipuzkoa
Barengo, Noël C.
Finland, Helsinki
Helsingin Yliopisto
Barricarte, Aurelio
Spain, Pamplona
Navarre Public Health Institute
Boeing, Heiner
Germany, Nuthetal
German Institute of Human Nutrition
Bonet, Catalina
Spain, Hospitalet de Llobregat
Institute Catala Oncologia
Clavel-Chapelon, Fraņcoise
France, Villejuif
Centre de Recherche en Épidémiologie et Santé Des Populations
Dartois, Laureen
France, Villejuif
Centre de Recherche en Épidémiologie et Santé Des Populations
Fagherazzi, Guy
France, Villejuif
Centre de Recherche en Épidémiologie et Santé Des Populations
Franks, Paul W.
Sweden, Lund
Lunds Universitet
Huerta, José Mª
Spain, Murcia
Health Council of Murcia
Kaaks, Rudolf J.
Germany, Heidelberg
German Cancer Research Center
Key, Timothy J.
United Kingdom, Oxford
University of Oxford
Khaw, Kay Tee T.
United Kingdom, Cambridge
University of Cambridge
Li, Kuanrong
Germany, Heidelberg
German Cancer Research Center
Mühlenbruch, Kristin
Germany, Nuthetal
German Institute of Human Nutrition
Nilsson, Peter M.
Sweden, Lund
Lunds Universitet
Overvad, Kim
Denmark, Aarhus
Aarhus Universitet
Overvad, Thure F.
Denmark, Aalborg
Aalborg Universitetshospital
Palli, Domenico
Italy
Centro Per lo Studio e la Prevenzione Oncologica
Panico, Salvatore
Italy, Naples
Università Degli Studi Di Napoli Federico Ii
Quirõs, Josè Ramõn
Spain, Oviedo
Public Health Directorate
Rolandsson, Olov
Sweden, Umea
Umeå Universitet
Roswall, Nina
Denmark, Copenhagen
Kræftens Bekæmpelse
Sacerdote, Carlotta
Italy, Torino
Center for Cancer Prevention
Sánchez, Maria José
Spain, Granada
Escuela Andaluza Salud Publica
Slimani, Nadia
France, Lyon
Centre International de Recherche Sur le Cancer
Tagliabue, Giovanna
Italy, Milan
Fondazione Irccs Istituto Nazionale Dei Tumori, Milan
Tjønneland, A. Marie
Australia, Sydney
George Institute for Global Health
Tumino, Rosario
Italy, Ragusa
Cancer Registry and Histopathology Unit
van der A, Daphne L.
Netherlands, Bilthoven
Rijksinstituut Voor Volksgezondheid en Milieu
Forouhi, Nita G.
United Kingdom, Cambridge
Mrc Epidemiology Unit
Sharp, Stephen John
United Kingdom, Cambridge
Mrc Epidemiology Unit
Langenberg, Claudia C.
United Kingdom, Cambridge
Mrc Epidemiology Unit
Riboli, Elio B.
United Kingdom, London
Imperial College London
Wareham, Nicholas J.
United Kingdom, Cambridge
Mrc Epidemiology Unit
Statistics
Citations: 142
Authors: 46
Affiliations: 29
Identifiers
Doi:
10.1016/S2213-8587(13)70103-7
ISSN:
22138587
Research Areas
Genetics And Genomics
Noncommunicable Diseases
Study Design
Cross Sectional Study
Cohort Study
Participants Gender
Male
Female