Association between ACE I/D genetic polymorphism and the severity of coronary artery disease in Vietnamese patients with acute myocardial infarction

Front Cardiovasc Med. 2023 May 3:10:1091612. doi: 10.3389/fcvm.2023.1091612. eCollection 2023.

Abstract

Background: The severity of coronary artery disease is a prognostic factor for major adverse cardiovascular events in patients diagnosed with acute myocardial infarction. ACE I/D polymorphism is one of the genetic factors that may affect the severity of coronary artery disease. This study aimed to investigate the association between ACE I/D genotypes and the severity of coronary artery disease in patients with acute myocardial infarction.

Materials and methods: A single-center, prospective, observational study was conducted at the Department of Cardiology and Department of Interventional Cardiology, Cho Ray Hospital, Ho Chi Minh City, Vietnam from January 2020 to June 2021. All participants diagnosed with acute myocardial infarction underwent contrast-enhanced coronary angiography. The severity of coronary artery disease was determined by Gensini score. ACE I/D genotypes were identified in all subjects by using the polymerase chain reaction method.

Results: A total of 522 patients diagnosed with first acute myocardial infarction were recruited. The patients' median Gensini score was 34.3. The II, ID, and DD genotype rates of ACE I/D polymorphism were 48.9%, 36.4%, and 14.7%, respectively. After adjusting for confounding factors, multivariable linear regression analysis showed that the ACE DD genotype was independently associated with a higher Gensini score compared with the II or ID genotypes.

Conclusion: The DD genotype of the ACE I/D polymorphism was associated with the severity of coronary artery disease in Vietnamese patients diagnosed with first acute myocardial infarction.

Keywords: ACE I/D; Gensini score; Vietnamese; acute myocardial infarction; coronary artery disease; genetic polymorphism.

Grants and funding

This study was supported partially by the University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam. Duy Cong Tran was funded by Vingroup JSC and supported by the Masters and PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2022.TS027.