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

TitleRacial Differences in the Performance of Existing Risk Prediction Models for Incident Type 2 Diabetes: The CARDIA Study.
Publication TypeJournal Article
Year of Publication2016
AuthorsLacy ME, Wellenius GA, Carnethon MR, Loucks EB, Carson AP, Luo X, Kiefe CI, Gjelsvik A, Gunderson EP, Eaton CB
Secondary AuthorsWu W-C
JournalDiabetes Care
Volume39
Issue2
Pagination285-91
Date Published2016 Feb
ISSN1935-5548
KeywordsAdult, African Americans, Area Under Curve, Atherosclerosis, Cohort Studies, Diabetes Mellitus, Type 2, European Continental Ancestry Group, Female, Glycated Hemoglobin A, Humans, Male, Middle Aged, Risk Assessment, Risk Factors, Young Adult
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.

DOI10.2337/dc15-0509
Alternate JournalDiabetes Care
PubMed ID26628420
PubMed Central IDPMC4722943
Grant ListHHSN268201300026C / HL / NHLBI NIH HHS / United States
AG0005 / AG / NIA NIH HHS / United States
UL1 TR000161 / TR / NCATS NIH HHS / United States
F31 DK105791 / DK / NIDDK NIH HHS / United States
P30 DK079626 / DK / NIDDK NIH HHS / United States
UL1 TR001453 / TR / NCATS NIH HHS / United States
HHSN268201300025C / HL / NHLBI NIH HHS / United States
F31-DK-105791 / DK / NIDDK NIH HHS / United States
HHSN268201300027C / HL / NHLBI NIH HHS / United States
HHSN268200900041C / HL / NHLBI NIH HHS / United States
HHSN268201300028C / HL / NHLBI NIH HHS / United States
K01 DK095928 / DK / NIDDK NIH HHS / United States
HHSN268201300029C / HL / NHLBI NIH HHS / United States
K01-DK-095928 / DK / NIDDK NIH HHS / United States