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Diabet Med. 2010 Oct;27(10):1116-23. doi: 10.1111/j.1464-5491.2010.03065.x.

Prediction models for incident type 2 diabetes mellitus
in the older population: KORA S4/F4 cohort study.

Author information

1
Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf. rathmann@ddz.uni-duesseldorf.de

Abstract

BACKGROUND:

The aim was to derive Type 2 diabetes prediction models for the older population and to check to what degree addition of 2-h glucose measurements (oral glucose tolerance test) and biomarkers improves the predictive power of risk scores which are based on non-biochemical as well as conventional clinical parameters.

METHODS:

Oral glucose tolerance tests were carried out in a population-based sample of 1353 subjects, aged 55-74 years (62% response) in Augsburg (Southern Germany) from 1999 to 2001. The cohort was reinvestigated in 2006-2008. Of those individuals without diabetes at baseline, 887 (74%) participated in the follow-up. Ninety-three (10.5%) validated diabetes cases occurred during the follow-up. In logistic regression analyses for model 1, variables were selected from personal characteristics and additional variables were selected from routinely measurable blood parameters (model 2) and from 2-h glucose, adiponectin, insulin and homeostasis model assessment of insulin resistance (HOMA-IR) (model 3).

RESULTS:

Age, sex, BMI, parental diabetes, smoking and hypertension were selected for model 1. Model 2 additionally included fasting glucose, HbA(1c) and uric acid. The same variables plus 2-h glucose were selected for model 3. The area under the receiver operating characteristic curve significantly increased from 0.763 (model 1) to 0.844 (model 2) and 0.886 (model 3) (P<0.01). Biomarkers such as adiponectin and insulin did not improve the predictive abilities of models 2 and 3. Cross-validation and bootstrap-corrected model performance indicated high internal validity.

CONCLUSIONS:

This longitudinal study in an older population provides models to predict the future risk of Type 2 diabetes. The OGTT, but not biomarkers, improved discrimination of incident diabetes.

[Indexed for MEDLINE]

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