European Journal of Epidemiology

, Volume 27, Issue 1, pp 47–52 | Cite as

External validation of the KORA S4/F4 prediction models for the risk of developing type 2 diabetes in older adults: the PREVEND study

  • Ali Abbasi
  • Eva Corpeleijn
  • Linda M. Peelen
  • Ron T. Gansevoort
  • Paul E. de Jong
  • Rijk O. B. Gans
  • Wolfgang Rathmann
  • Bernd Kowall
  • Christine Meisinger
  • Hans L. Hillege
  • Ronald P. Stolk
  • Gerjan Navis
  • Joline W. J. Beulens
  • Stephan J. L. Bakker
DIABETES MELLITUS

Abstract

Recently, prediction models for type 2 diabetes mellitus (T2DM) in older adults (aged ≥55 year) were developed in the KORA S4/F4 study, Augsburg, Germany. We aimed to externally validate the KORA models in a Dutch population. We used data on both older adults (n = 2,050; aged ≥55 year) and total non-diabetic population (n = 6,317; aged 28–75 year) for this validation. We assessed performance of base model (model 1: age, sex, BMI, smoking, parental diabetes and hypertension) and two clinical models: model 1 plus fasting glucose (model 2); and model 2 plus uric acid (model 3). For 7-year risk of T2DM, we calculated C-statistic, Hosmer–Lemeshow χ2-statistic, and integrated discrimination improvement (IDI) as measures of discrimination, calibration and reclassification, respectively. After a median follow-up of 7.7 years, 199 (9.7%) and 374 (5.9%) incident cases of T2DM were ascertained in the older and total population, respectively. In the older adults, C-statistic was 0.66 for model 1. This was improved for model 2 and model 3 (C-statistic = 0.81) with significant IDI. In the total population, these respective C-statistics were 0.77, 0.85 and 0.85. All models showed poor calibration (P < 0.001). After adjustment for the intercept and slope of each model, we observed good calibration for most models in both older and total populations. We validated the KORA clinical models for prediction of T2DM in an older Dutch population, with discrimination similar to the development cohort. However, the models need to be corrected for intercept and slope to acquire good calibration for application in a different setting.

Keywords

Type 2 diabetes Prediction model External validation Update Older adults 

Abbreviations

ARICA

Atherosclerosis risk in communities

BMI

Body mass index

DESIR

Data from the epidemiological study on the insulin resistance syndrome

FINDRISC

Finnish diabetes risk score

HbA1c

Glycosylated hemoglobin

IDI

Integrated discrimination improvement

KORA

Cooperative health research in the region of Augsburg

PREVEND

Prevention of renal and vascular end stage disease

Supplementary material

10654_2011_9648_MOESM1_ESM.doc (73 kb)
Supplementary material 1 (DOC 73 kb)

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Ali Abbasi
    • 1
    • 2
    • 3
  • Eva Corpeleijn
    • 1
  • Linda M. Peelen
    • 3
  • Ron T. Gansevoort
    • 2
  • Paul E. de Jong
    • 2
  • Rijk O. B. Gans
    • 2
  • Wolfgang Rathmann
    • 4
  • Bernd Kowall
    • 4
  • Christine Meisinger
    • 5
  • Hans L. Hillege
    • 1
  • Ronald P. Stolk
    • 1
  • Gerjan Navis
    • 2
  • Joline W. J. Beulens
    • 3
  • Stephan J. L. Bakker
    • 2
  1. 1.Department of EpidemiologyUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
  2. 2.Department of Internal MedicineUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
  3. 3.Julius Center for Health Sciences and Primary CareUniversity Medical Center UtrechtUtrechtThe Netherlands
  4. 4.Institute of Biometrics and Epidemiology, German Diabetes CenterLeibniz Center for Diabetes Research at Heinrich Heine UniversityDüsseldorfGermany
  5. 5.Helmholtz Zentrum München, German Research Center of Environmental HealthInstitute of Epidemiology IINeuherbergGermany

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