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Lancet Diabetes Endocrinol. 2014 Jan;2(1):19-29. doi: 10.1016/S2213-8587(13)70103-7. Epub 2013 Oct 8.

Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models.

Author information

1
University Medical Center Utrecht, Utrecht, Netherlands; University of Cape Town and South African Medical Research Council, Cape Town, South Africa; The George Institute for Global Health, Sydney, NSW, Australia.
2
University Medical Center Utrecht, Utrecht, Netherlands. Electronic address: j.beulens@umcutrecht.nl.
3
University Medical Center Utrecht, Utrecht, Netherlands.
4
German Institute of Nutrition, Potsdam-Rehbruecke, Germany.
5
National Institute for Public Health and the Environment, Bilthoven, Netherlands.
6
Medical Research Council Epidemiology Unit, Cambridge, UK.
7
Murcia Regional Health Council, Murcia, Spain.
8
Public Health Division of Gipuzkoa, San Sebastian, Spain.
9
Hjelt Institute, University of Helsinki, Helsinki, Finland.
10
Navarre Public Health Institute, Pamplona, Spain.
11
Catalan Institute of Oncology, Barcelona, Spain.
12
Inserm, Centre for Research in Epidemiology and Population Health, U1018, Villejuif, France.
13
Lund University, Malmö, Sweden.
14
German Cancer Research Centre, Heidelberg, Germany.
15
University of Oxford, Oxford, UK.
16
University of Cambridge, Cambridge, UK.
17
Department of Public Health, Aarhus University, Aarhus, Denmark.
18
Aalborg University Hospital, Aalborg, Denmark.
19
Cancer Research and Prevention Institute, Florence, Italy.
20
Federico II University, Naples, Italy.
21
Public Health Directorate, Asturias, Spain.
22
Umeå University, Umeå, Sweden.
23
Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark.
24
Center for Cancer Prevention, Turin, Italy.
25
Andalusian School of Public Health, Granada, Spain.
26
International Agency for Research on Cancer, Lyon, France.
27
Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy.
28
Cancer Registry and Histopathology Unit, Azienda Sanitaria Provinciale 7, Ragusa, Italy.
29
Imperial College London, London, UK.

Erratum in

  • Lancet Diabetes Endocrinol. 2014 Apr;2(4):e11.

Abstract

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.

Comment in

PMID:
24622666
DOI:
10.1016/S2213-8587(13)70103-7
[Indexed for MEDLINE]
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