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Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models
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2014 (English)In: Lancet Diabetes & Endocrinology, ISSN 2213-8587, Vol. 2, no 1, 19-29 p.Article in journal (Refereed) Published
Abstract [en]

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 (p(heterogeneity) <0.0001) in all but one model. Calibration was good for most models, and consistent across countries (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 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.

Place, publisher, year, edition, pages
2014. Vol. 2, no 1, 19-29 p.
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Endocrinology and Diabetes
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URN: urn:nbn:se:umu:diva-90878DOI: 10.1016/S2213-8587(13)70103-7ISI: 000336720400018OAI: oai:DiVA.org:umu-90878DiVA: diva2:732255
Available from: 2014-07-03 Created: 2014-07-01 Last updated: 2014-07-03Bibliographically approved

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