Background:
In 2010, the American Diabetes Association (ADA) updated diagnostic criteria for diabetes to include hemoglobin A1c (A1c). However, the appropriateness of these criteria in African Americans (AAs) is unclear as A1c may not reflect glycemic control as accurately in AAs as in whites. Moreover, existing diabetes risk prediction models have been developed in populations composed primarily of whites. Objectives were to (1) examine the predictive power of existing diabetes risk prediction models in the Jackson Heart Study (JHS), a prospective cohort of 5,301 AA adults and (2) explore the impact of incorporating A1c into these models.
Methods:
We selected 3 widely-used diabetes risk prediction models and examined their ability to predict 5-year diabetes risk among 3,185 JHS participants free of diabetes at baseline and who returned for the 5 year follow-up visit. Incident diabetes was identified at follow-up based on current antidiabetic medications, fasting glucose ≥126 mg/dl or A1c ≥6.5%. We evaluated model performance using model discrimination (C-statistic) and reclassification (net reclassification index (NRI) and integrated discrimination improvement (IDI)). For each of the 3 models, model performance in JHS was evaluated using (1) covariates identified in the original published model and (2) published covariates plus A1c.
Results:
Of 3,185 participants (mean age 53.7; 64.0% female), 9.8% (n=311) developed diabetes over 5 years of follow-up. Each diabetes prediction model suffered a drop in predictive power when applied to JHS using ADA 2010 criteria (Table 1). The performance of all 3 models improved significantly with the addition of A1c, as evidenced by the increase in C-statistic and improvement in reclassification.
Conclusion:
Despite evidence that A1c may not accurately reflect glycemic control in AAs as well as in whites, adding A1c to existing diabetes risk prediction models developed in primarily white populations significantly improved 5-year predictive power of all 3 models among AAs in the JHS.
Effect of model updating strategies on the performance of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa
