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Racial Differences in the Performance of Existing Risk Prediction Models for Incident Type 2 Diabetes: The CARDIA Study
University of Massachusetts Medical School Faculty Publications
  • Mary E. Lacy, Brown University
  • Gregory A. Wellenius, Brown University
  • Mercedes R. Carnethon, Northwestern University
  • Eric B. Loucks, Brown University
  • April P. Carson, University of Alabama at Birmingham
  • Xi Luo, Brown University
  • Catarina I. Kiefe, University of Massachusetts Medical School
  • Annie Gjelsvik, Brown University
  • Erica P. Gunderson, Kaiser Permanente
  • Charles B. Eaton, Brown University
  • Wen-Chih Wu, Brown University
UMMS Affiliation
Department of Qualitative Health Sciences
Date
2-1-2016
Document Type
Article
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.
Rights and Permissions
Citation: 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
Keywords
  • UMCCTS funding
PubMed ID
26628420
Citation Information
Mary E. Lacy, Gregory A. Wellenius, Mercedes R. Carnethon, Eric B. Loucks, et al.. "Racial Differences in the Performance of Existing Risk Prediction Models for Incident Type 2 Diabetes: The CARDIA Study" Vol. 39 Iss. 2 (2016) ISSN: 0149-5992 (Linking)
Available at: http://works.bepress.com/catarina_kiefe/243/