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Glycosylated hemoglobin A1c predicts coronary artery disease in non‐diabetic patients
Author(s) -
Kayali Yildiz,
Ozder Aclan
Publication year - 2021
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.23612
Subject(s) - medicine , stenosis , cardiology , coronary artery disease , diabetes mellitus , logistic regression , artery , risk factor , endocrinology
Background In primary care, there is a need for simple and cost‐effective tool that will allow the determination of the risk of coronary artery disease (CAD). We aimed to research the value of glycosylated hemoglobin (HbA1c) in the prediction of coronary artery disease. Methods Patients admitted to the outpatient clinic of the Cardiology for angiography were retrospectively screened. Patients with diabetes or with HbA1c of 6.5 or above were excluded. Comparative HbA1c data were obtained according to the stenosis groups. Logistic regression analysis was used to investigate the risk factors affecting stenosis positivity. Results Of the study group, 120 patients were without any stenosis in any coronary artery, 56 patients were with >50% stenosis in one coronary artery, and 71 patients were with >50% stenosis in more than one coronary artery. There was a statistically significant difference between HbA1c measurements according to the degree of stenosis ( P  = .001 and P  < .01, respectively). The odd ratio for HbA1c was 6.260 (95% CI: 3,160‐12,401). According to the stenosis positivity, the cutoff point for HbA1c was found to be 5.6 and above. In the regression analysis, HbA1c was an independent risk factor for CAD. One unit increase in HbA1c level increases the risk of stenosis up to 12.4‐fold (95% CI: 5,990‐25,767). Conclusion The study showed HbA1c can be used as an independent marker in determining the probability and severity of coronary artery disease in non‐diabetic individuals and as a useful marker in primary care predicting CAD.

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