Integrating CCL2 and TNF-α into the Framingham Risk Score for cardiovascular risk prediction: a cross-sectional study in a Malaysian cohort.

Abstract

Introduction: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, significantly contributing to increased healthcare costs and deteriorated health. In Malaysia, CVDs account for 20.79% of deaths in government hospitals. Key risk factors include high blood sugar levels, elevated blood pressure, and increased cholesterol levels. Atherosclerosis frequently serves as the underlying condition for coronary heart disease (CHD), with CCL2 and TNF-α playing a crucial role in recruiting immune cells to inflammation sites. Early diagnosis of CVDs risk is important for preventing severe complications. This crosssectional study aims to investigate the relationship between biomarker CCL2 and TNF-α expression levels and Framingham Risk Score (FRS) categories in a Malaysian cohort.

Materials and methods: A total of 333 patients from the Family Medicine Specialist Clinic at Hospital Sultan Abdul Aziz Shah were recruited between March 2022 and February 2023. Blood samples were taken after a 12-hour fasting period, and levels of fasting blood sugar (FBS), triglycerides (TG), total cholesterol (TC), HDL cholesterol, and LDL cholesterol were measured. 150 plasma samples were randomly selected for cytokine analysis of CCL2 and TNF-α using the Human Magnetic Luminex Assay. Patients' cardiovascular risk was assessed using the FRS calculator. The Kruskal-Wallis test was used to analyze the relationship between cytokine levels and FRS categories, followed by a post hoc test with Bonferroni correction. A logistic regression model was implemented to assess the independent effects of these variables.

Results: The results demonstrated a significant association between the level of chemokines CCL2 and proinflammatory TNF-α, and FRS categories (low-risk, moderate-risk, and high-risk). CCL2 levels were notably higher in the high-risk group, as were TNF-α levels, with both biomarkers showing increasing trends with higher risk categories, (p<0.001, effect size=0.32) and (p<0.001, effect size-0.29), respectively. Multiple logistic regression analysis showed that dyslipidaemia, FBS, and TNF-α remained significant after adjusting for other variables. Specifically, dyslipidaemia had lower odds of being in the high-risk group (AOR: 0.04), while FBS (AOR: 3.19) and TNF-α (AOR: 1.18).

Conclusion: This study highlights the potential of CCL2 and TNF-α as biomarkers for CVDs risk assessment. Integrating these biomarkers into CVDs risk prediction models may enhance the precision of identifying individuals at elevated risk. However, the study's cross-sectional design and small sample size for cytokine analysis constrain the findings. Future research should explore the long-term predictive value of these cytokines in larger, longitudinal cohorts and explore more advanced techniques for improving CHD risk prediction models.