Finding the Missing Biology in Atherosclerosis: Identification of Novel Cardiac Biomarkers Utilising the BioHEART Study

Abstract

Background: Coronary artery disease (CAD) remains the most significant cause of morbidity and mortality worldwide, and risk-factor based scoring systems remain concerningly inaccurate at the level of the individual. Improvement in risk prediction and subsequent identification of CAD in the early stages would facilitate the commencement of preventative therapies that would prevent heart attacks and save lives, particularly for patients who do not have standard modifiable cardiovascular risk factors (SMuRFs).

Aims: To identify novel biomarkers which correlate with atherosclerosis burden and improve risk prediction for CAD in both general and SMuRFless populations.

Methods & Results: Patients from the BioHEART biobank had CT coronary angiograms analysed and scored for disease. Correlating biological samples were analysed to determine the associations with candidate markers by various approaches. Mass cytometry analysis revealed different immune cell profiles in patients with atherosclerosis, indicating a systemic response to coronary atherosclerosis. Global assays of coagulation demonstrated hypercoagulable profiles which were associated with different cardiac risk factors for each sex and disease severity in males. Serum soluble lectin-like oxidised low-density lipoprotein receptor-1 was found to be associated with atherosclerosis severity and demonstrated increased predictive ability for the SMuRFless cohort. Tumour necrosis factor-related apoptosis-inducing ligand, interleukin-18 and osteoprotegerin were demonstrated to associate with atherosclerosis severity scores, however this correlation was mitigated by adjustment for cardiac risk factors.

Conclusion: The analysis has identified several biomarkers which have prognostic potential for both the general and SMuRFless populations. Future research will confirm these discoveries in larger populations with associated outcome data and combine them into multi-marker biomarker panels to improve atherosclerosis prediction.