Background: The prevalence of premature coronary artery disease (PCAD) has increased in recent years, particularly among younger populations. Differences in the risk factor profiles of PCAD and late coronary artery disease (LCAD) remain insufficiently understood. Therefore, this study aimed to compare the demographic, clinical, anthropometric, and laboratory risk factors associated with PCAD and LCAD.
Methods: This case-control study was conducted among 206 patients with angiography-confirmed CAD who were referred to Fatemeh-Zahra Heart Center during 2021-2022. Participants were divided into two groups: 103 patients with PCAD and 103 patients with LCAD. Demographic, clinical, anthropometric, and laboratory data were collected using a researcher-designed checklist based on patients’ medical records and clinical evaluations. Data were analyzed using IBM SPSS Statistics(version 26). Independent-samples t test, Mann-Whitney U test, chi-square test, and logistic regression analysis were used for statistical analysis.
Results: Patients with PCAD had significantly higher body mass index (BMI), waist circumference (WC), hip circumference (HC), triglyceride levels, low-density lipoprotein (LDL) levels, and waist-to-hip ratio (WHR), whereas creatinine, blood urea nitrogen (BUN), aspartate aminotransferase (AST), and vitamin D levels were significantly higher among patients with LCAD. Multivariable logistic regression analysis demonstrated that higher educational level, housewife status, alcohol consumption, family history of PCAD, and elevated WC were independently associated with increased odds of PCAD (p < 0.05). In contrast, cigarette smoking was more strongly associated with LCAD (p = 0.002).
Conclusions: The findings of this study suggest that the risk factor profiles of PCAD and LCAD differ considerably. Anthropometric indices and familial predisposition appear to play a more prominent role in PCAD, whereas smoking and renal dysfunction were more common among patients with LCAD. Identification of these distinct risk factor patterns may contribute to improved prevention, early detection, and management strategies for CAD, particularly among younger populations.