BMC Cardiovascular Disorders (Jun 2024)

ApoA1/HDL-C ratio as a predictor for coronary artery disease in patients with type 2 diabetes: a matched case-control study

  • Farzaneh Ghaemi,
  • Soghra Rabizadeh,
  • Amirhossein Yadegar,
  • Fatemeh Mohammadi,
  • Hassan Asadigandomani,
  • Melika Arab Bafrani,
  • Sahar Karimpour Reyhan,
  • Alireza Esteghamati,
  • Manouchehr Nakhjavani

DOI
https://doi.org/10.1186/s12872-024-03986-w
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 9

Abstract

Read online

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.

Keywords