TY - JOUR
T1 - Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct)
T2 - A validation of existing models
AU - Kengne, Andre Pascal
AU - Beulens, Joline W.J.
AU - Peelen, Linda M.
AU - Moons, Karel G.M.
AU - van der Schouw, Yvonne T.
AU - Schulze, Matthias B.
AU - Spijkerman, Annemieke M.W.
AU - Griffin, Simon J.
AU - Grobbee, Diederick E.
AU - Palla, Luigi
AU - Tormo, Maria Jose
AU - Arriola, Larraitz
AU - Barengo, Noël C.
AU - Barricarte, Aurelio
AU - Boeing, Heiner
AU - Bonet, Catalina
AU - Clavel-Chapelon, Françoise
AU - Dartois, Laureen
AU - Fagherazzi, Guy
AU - Franks, Paul W.
AU - Huerta, José María
AU - Kaaks, Rudolf
AU - Key, Timothy J.
AU - Khaw, Kay Tee
AU - Li, Kuanrong
AU - Mühlenbruch, Kristin
AU - Nilsson, Peter M.
AU - Overvad, Kim
AU - Overvad, Thure F.
AU - Palli, Domenico
AU - Panico, Salvatore
AU - Quirós, J. Ramón
AU - Rolandsson, Olov
AU - Roswall, Nina
AU - Sacerdote, Carlotta
AU - Sánchez, María José
AU - Slimani, Nadia
AU - Tagliabue, Giovanna
AU - Tjønneland, Anne
AU - Tumino, Rosario
AU - van der A, Daphne L.
AU - Forouhi, Nita G.
AU - Sharp, Stephen J.
AU - Langenberg, Claudia
AU - Riboli, Elio
AU - Wareham, Nicholas J.
N1 - Funding Information:
The InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). JWJB and YTvdS received additional funding for verification of diabetes cases from the NL Agency ( grant IGE05012 ) and an Incentive Grant from the Board of the University Medical Center Utrecht. AMWS and DLvdA received funding from the Dutch Ministry of Public Health, Welfare and Sports; Netherlands Cancer Registry; LK Research Funds; Dutch Prevention Funds; Zorg Onderzoek Nederland; World Cancer Research Fund; and Statistics Netherlands. MJT received funding from the Health Research Fund of the Spanish Ministry of Health and the CIBER en Epidemiología y Salud Pública, and Murcia Regional Government (number 6236). PWF and PN received funding from the Swedish Research Council. PWF also received funding from Novo Nordisk, Swedish Diabetes Association, and Swedish Heart-Lung Foundation. RK and KL received funding from German Cancer Aid and the Federal Ministry of Education and Research. TK and KTK received funding from Cancer Research UK. KTK also received funding from Medical Research Council UK. KO and AT received funding from the Danish Cancer Society. OR received funding from the county of Västerbotten. JRQG received funding from Asturias Regional Government. RT received funding from AIRE-ONLUS Ragusa, AVIS-Ragusa, and the Sicilian Regional Government. We thank all EPIC participants and staff for their contribution to the study.
PY - 2014/1
Y1 - 2014/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84890159890&partnerID=8YFLogxK
U2 - 10.1016/S2213-8587(13)70103-7
DO - 10.1016/S2213-8587(13)70103-7
M3 - Article
C2 - 24622666
AN - SCOPUS:84890159890
SN - 2213-8587
VL - 2
SP - 19
EP - 29
JO - The Lancet Diabetes and Endocrinology
JF - The Lancet Diabetes and Endocrinology
IS - 1
ER -