Abstract
Background Coronary artery disease (CAD) is influenced by oxidative stress, which is a critical yet often overlooked factor in disease progression. While traditional biomarkers such as cholesterol levels and blood pressure are commonly used, they do not fully capture oxidative damage. Malondialdehyde (MDA), a byproduct of lipid peroxidation, offers additional insights into oxidative stress and CAD severity. Unlike conventional markers, such as low-density lipoprotein (LDL) cholesterol, which primarily reflects lipid levels, and high-sensitivity C-reactive protein (hs-CRP), which indicates inflammation, MDA directly measures oxidative damage. This makes MDA a potentially valuable complement to these traditional biomarkers, providing a more nuanced understanding of CAD risk. Despite its potential, the role of MDA in clinical assessments remains underexplored. This study aims to address this gap by evaluating MDA's effectiveness as a complementary biomarker, enhancing the assessment of CAD risk and progression beyond what is provided by existing markers. Objective This study aims to assess serum MDA levels in relation to CAD severity to explore its potential as a non-invasive biomarker for disease progression and cardiovascular outcomes. Methodology This cross-sectional study was conducted at the Department of Cardiology, Mardan Medical Complex Teaching Hospital, Pakistan, from June 2023 to May 2024. Patients were divided into different groups with varying severity of CAD. The one-way ANOVA was used to assess differences among groups, and Pearson's correlation coefficient explored relationships between MDA and all study variables. Simple linear regression analyzed associations between MDA levels, patient groups, and other variables, controlling for covariates. MDA's potential as a predictive biomarker was assessed through ROC curve analysis, with statistical significance set at a p-value < 0.05. Results A total of 133 patients were included in the study, categorized based on CAD severity into mild (n=71), moderate (n=39), and severe (n=23) groups. Serum MDA levels significantly increased with the severity of CAD. Specifically, MDA levels were 116.61 ± 41.95 in the mild group, 253.45 ± 180.29 in the moderate group, and peaked at 459.91 ± 149.80 in the severe group. The differences in MDA levels among these groups were statistically significant (p < 0.01), supporting the association between higher MDA levels and increased CAD severity. Factors such as BMI, heart rate, blood pressure, and smoking status also significantly influenced MDA levels. Receiver operating characteristic (ROC) curve analysis demonstrated high diagnostic accuracy of MDA for assessing CAD severity, with area under the curve (AUC) values of 0.81 for moderate and 0.94 for severe CAD. Comorbid conditions such as diabetes mellitus were associated with elevated MDA levels. Conclusion Elevated serum MDA serves as a reliable, non-invasive biomarker for predicting CAD severity, with potential applications in clinical risk assessment and management strategies. By identifying patients with elevated oxidative stress early, clinicians can implement timely interventions, potentially slowing disease progression and improving outcomes.