Abstract
Introduction: Diabetes mellitus (DM) is the strongest risk factor for incident myocardial infarction (MI). Glycated hemoglobin (A1c) is used for diagnosing DM, but racial/ethnic specific A1c diagnostic thresholds do not exist. This study aims to determine whether A1c thresholds for predicting major adverse cardiac events (MACE) differ among racial-ethnic groups in a large, diverse integrated healthcare system. Methods: This is a retrospective cohort study of Kaiser Permanente Northern California members age 30 to 75 without cardiovascular disease for whom A1c during 2014, race/ethnicity data, and 5-year follow-up post A1c were available. Multivariable logistic regression was used to evaluate the odds of MACE (MI, stroke, cardiovascular death) after adjusting for demographic and clinical covariates including age, sex, race, A1c, BMI, smoking, DM meds. Univariate logistic regression with Youden’s index was used to determine A1c thresholds for MACE prediction stratified by race/ ethnicity. Receiver operating characteristic curves were plotted for each equation and a Youden’s index was calculated to determine the race-ethnic specific threshold for prediction of MACE. Results: A1c was associated with an increased odds of MACE (aOR 1.024, 95%CI: 1.022, 1.025). Compared to Whites, South Asian ethnicity was associated with increased odds of MACE (aOR 1.641, 95%CI: 1.456, 1.843). Hispanic (aOR 0.748, 95%CI: 0.702, 0.796) and East Asian ethnicities (aOR 0.804, 95% CI: 0.739, 0.874) were associated with lower odds of MACE. A Youden’s index range of A1c values between 6.0 to 7.3% predicted MACE, with the lowest values seen in Whites (6.0%) and South Asians (6.1%). A1c thresholds for Filipinos, East Asians, Blacks, and Hispanics were 7.3, 6.6, 6.5 and 6.5%, respectively. Conclusions: Our study shows South Asians have a higher odds of MACE compared to Whites. A1c values in a pre-diabetic range predicted MACE in Whites and South Asians, whereas thresholds for MACE prediction were in a diabetic range for other groups. Further studies will help identify whether these trends can be replicated in other geographic areas and influence risk prediction tools.
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