UMass Chan Medical School Faculty Publications

Title

Racial Differences in the Performance of Existing Risk Prediction Models for Incident Type 2 Diabetes: The CARDIA Study

UMMS Affiliation

Department of Qualitative Health Sciences

Publication Date

2016-2

Document Type

Article

Disciplines

Clinical Epidemiology | Diagnosis | Nutritional and Metabolic Diseases

Abstract

OBJECTIVE: In 2010, the American Diabetes Association (ADA) added hemoglobin A1c (A1C) to the guidelines for diagnosing type 2 diabetes. However, existing models for predicting diabetes risk were developed prior to the widespread adoption of A1C. Thus, it remains unknown how well existing diabetes risk prediction models predict incident diabetes defined according to the ADA 2010 guidelines. Accordingly, we examined the performance of an existing diabetes prediction model applied to a cohort of African American (AA) and white adults from the Coronary Artery Risk Development Study in Young Adults (CARDIA).

RESEARCH DESIGN AND METHODS: We evaluated the performance of the Atherosclerosis Risk in Communities (ARIC) diabetes risk prediction model among 2,456 participants in CARDIA free of diabetes at the 2005-2006 exam and followed for 5 years. We evaluated model discrimination, calibration, and integrated discrimination improvement with incident diabetes defined by ADA 2010 guidelines before and after adding baseline A1C to the prediction model.

RESULTS: In the overall cohort, re-estimating the ARIC model in the CARDIA cohort resulted in good discrimination for the prediction of 5-year diabetes risk (area under the curve [AUC] 0.841). Adding baseline A1C as a predictor improved discrimination (AUC 0.841 vs. 0.863, P = 0.03). In race-stratified analyses, model discrimination was significantly higher in whites than AA (AUC AA 0.816 vs. whites 0.902; P = 0.008).

CONCLUSIONS: Addition of A1C to the ARIC diabetes risk prediction model improved performance overall and in racial subgroups. However, for all models examined, discrimination was better in whites than AA. Additional studies are needed to further improve diabetes risk prediction among AA. long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

Keywords

UMCCTS funding

DOI of Published Version

10.2337/dc15-0509

Source

Diabetes Care. 2016 Feb;39(2):285-91. doi: 10.2337/dc15-0509. Epub 2015 Dec 1. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

Diabetes care

PubMed ID

26628420

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