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Apoa1/Hdl-C Ratio As a Predictor for Coronary Artery Disease in Patients With Type 2 Diabetes: A Matched Case-Control Study Publisher Pubmed



Ghaemi F1 ; Rabizadeh S1 ; Yadegar A1 ; Mohammadi F1 ; Asadigandomani H1 ; Bafrani MA1 ; Reyhan SK1 ; Esteghamati A1 ; Nakhjavani M1
Authors
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Authors Affiliations
  1. 1. Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran

Source: BMC Cardiovascular Disorders Published:2024


Abstract

Introduction: This study investigated the possible relationship between the Apo lipoprotein A1 /high-density lipoprotein cholesterol (ApoA1/HDL-C) ratio and coronary artery disease (CAD) in patients with type 2 diabetes (T2D). Methods: This was a matched case-control study of 482 patients with T2D in two groups of CAD and (n = 241) non-CAD (n = 241). The patients were classified into four quartiles according to the ApoA1/HDL-C ratio, and multivariate logistic regression analysis was performed to assess the relationship between ApoA1/HDL-C and CAD. ROC analysis was also conducted. Results: This study showed that the ApoA1/HDL-C ratio has an independent association with CAD in individuals with T2D. The CAD group exhibited a significantly higher ApoA1/HDL-C ratio than those without CAD (p-value = 0.004). Moreover, the risk of CAD increased significantly across the ApoA1/HDL-C ratio quartiles, with the highest odds in the fourth quartile. The second quartile showed an odds ratio (OR) of 2.03 (p-value = 0.048) compared to the first. Moving to the third quartile, the OR increased to 2.23 (p-value = 0.023). The highest OR was noted in the fourth, reaching 3.41 (p-value = 0.001). Employing a cut-off value of 2.66 and an area under the curve (AUC) of 0.885, the ApoA1/HDL-C ratio predicts CAD among patients with T2D with a sensitivity of 75% and a specificity of 91% (p-value < 0.001). Conclusion: The current study revealed an independent association between ApoA1/HDL-C ratio and CAD in patients with T2D. This ratio can be a promising tool for predicting CAD during the follow-up of patients with T2D, aiding in identifying those at higher risk for CAD. © The Author(s) 2024.
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