Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models.
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 p...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
2014
|
| _version_ | 1797063748723146752 |
|---|---|
| author | Kengne, A Beulens, J Peelen, L Moons, K van der Schouw, Y Schulze, M Spijkerman, A Griffin, S Grobbee, D Palla, L Tormo, M Arriola, L Barengo, N Barricarte, A Boeing, H Bonet, C Clavel-Chapelon, F Dartois, L Fagherazzi, G Franks, P Huerta, J Kaaks, R Key, T Khaw, K Li, K |
| author_facet | Kengne, A Beulens, J Peelen, L Moons, K van der Schouw, Y Schulze, M Spijkerman, A Griffin, S Grobbee, D Palla, L Tormo, M Arriola, L Barengo, N Barricarte, A Boeing, H Bonet, C Clavel-Chapelon, F Dartois, L Fagherazzi, G Franks, P Huerta, J Kaaks, R Key, T Khaw, K Li, K |
| author_sort | Kengne, A |
| collection | OXFORD |
| 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/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. |
| first_indexed | 2024-03-06T21:04:21Z |
| format | Journal article |
| id | oxford-uuid:3bf22dec-7382-4a9d-a000-dd59558d2b07 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:04:21Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | oxford-uuid:3bf22dec-7382-4a9d-a000-dd59558d2b072022-03-26T14:10:38ZNon-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3bf22dec-7382-4a9d-a000-dd59558d2b07EnglishSymplectic Elements at Oxford2014Kengne, ABeulens, JPeelen, LMoons, Kvan der Schouw, YSchulze, MSpijkerman, AGriffin, SGrobbee, DPalla, LTormo, MArriola, LBarengo, NBarricarte, ABoeing, HBonet, CClavel-Chapelon, FDartois, LFagherazzi, GFranks, PHuerta, JKaaks, RKey, TKhaw, KLi, KBACKGROUND: 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. |
| spellingShingle | Kengne, A Beulens, J Peelen, L Moons, K van der Schouw, Y Schulze, M Spijkerman, A Griffin, S Grobbee, D Palla, L Tormo, M Arriola, L Barengo, N Barricarte, A Boeing, H Bonet, C Clavel-Chapelon, F Dartois, L Fagherazzi, G Franks, P Huerta, J Kaaks, R Key, T Khaw, K Li, K Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. |
| title | Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. |
| title_full | Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. |
| title_fullStr | Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. |
| title_full_unstemmed | Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. |
| title_short | Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. |
| title_sort | non invasive risk scores for prediction of type 2 diabetes epic interact a validation of existing models |
| work_keys_str_mv | AT kengnea noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT beulensj noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT peelenl noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT moonsk noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT vanderschouwy noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT schulzem noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT spijkermana noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT griffins noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT grobbeed noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT pallal noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT tormom noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT arriolal noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT barengon noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT barricartea noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT boeingh noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT bonetc noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT clavelchapelonf noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT dartoisl noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT fagherazzig noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT franksp noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT huertaj noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT kaaksr noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT keyt noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT khawk noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels AT lik noninvasiveriskscoresforpredictionoftype2diabetesepicinteractavalidationofexistingmodels |