TY - JOUR
T1 - Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct)
T2 - A validation of existing models
AU - Kengne, A.P.
AU - Beulens, J.W.J.
AU - Peelen, L.M.
AU - Moons, K.G.M.
AU - van der Schouw, Y.T.
AU - Schulze, M.B.
AU - Spijkerman, A.M.
AU - Griffin, S.J.
AU - Grobbee, D.E.
AU - Palla, L.
AU - Tormo, M.J.
AU - Arriola, L.
AU - Barengo, N.C.
AU - Barricarte, A.
AU - Boeing, H.
AU - Bonet, C.
AU - Clavel Chapelon, F.
AU - Dartois, L.
AU - Fagherazzi, G.
AU - Franks, P.W.
AU - Huerta, J.M.
AU - Kaaks, R.
AU - Key, T.J.
AU - Khaw, K.T.
AU - Li, K.
PY - 2014/1/1
Y1 - 2014/1/1
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), BMI (= 25 kg/m(2)), and waist circumference (men = 102 cm; women = 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) 0.05) 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 ofInterpretation 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.
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), BMI (= 25 kg/m(2)), and waist circumference (men = 102 cm; women = 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) 0.05) 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 ofInterpretation 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.
KW - LIFE-STYLE INTERVENTIONS
KW - IDENTIFYING INDIVIDUALS
KW - EXTERNAL VALIDATION
KW - FOLLOW-UP
KW - PREVENTION
KW - MELLITUS
KW - COHORT
KW - TOOL
KW - METAANALYSIS
KW - VALIDITY
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
SN - 2213-8587
VL - 2
SP - 19
EP - 29
JO - The Lancet Diabetes & Endocrinology
JF - The Lancet Diabetes & Endocrinology
IS - 1
ER -