Association of CCR2 and AGT gene variants with lipid profiles and cardiovascular risk factors in coronary artery disease patients

Abstract Introduction Coronary artery disease (CAD) is a major global health issue, particularly affecting young individuals in low- and middle-income countries like South-east Asians, notably Indians. Inflammation, mediated by chemokine like C–C motif chemokine ligand 2 (CCL2) and the renin-angiotensin system (RAS), plays a crucial role in atherosclerosis and CAD pathogenesis. The study aims to explore the association between genetic variants of C–C chemokine receptor type 2 (CCR2) and angiotensinogen (AGT) with CAD in the Indian population, elucidating their roles in disease progression. Materials and methods A total of 120 CAD patients from the Cardiology Division were enrolled in this study. We performed single nucleotide polymorphism (SNP) analysis using the polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) method. Results The study explored the association between CCR2 and AGT gene variants with CAD and its risk factors in the Indian population. Significant findings include the impact of CCR2 genotypes on low-density lipoprotein (LDL) cholesterol and triglyceride levels, while AGT genotypes showed no significant associations with various parameters except for posterior descending artery/posterolateral ventricular (PDA/PLV) plaquing/stenosis. Multinomial logistic regression analysis highlighted the influence of systolic blood pressure (SBP) and triglycerides on outcomes related to CCR2 genotypes. For AGT genotypes, a potential association with diabetes mellitus (DM) and PDA/PLV plaquing was observed. Conclusion This study found no significant associations between CCR2 and AGT gene variants and CAD-related factors such as age, blood pressure, or cholesterol levels. Despite these findings, it highlights the potential role of genetic markers in CAD, emphasizing the need for further research with larger populations. Understanding these genetic factors could improve risk prediction and personalized treatment for coronary disease. Continued research is essential to unravel the complex genetic contributions to CAD.