Unravelling the calcification – inflammation axis to predict the risk of heart disease

Cardiovascular disease remains the greatest health burden for Australia and has an enormous social and economic impact across the globe. Despite common perceptions, it is not all solved. There is an urgent need for the development of an improved clinical pathway for management of coronary artery disease (CAD) patients who are suffering heart attacks, despite having no standard modifiable risk factors, and the need for novel biomarkers for early detection of CAD.

Professor Gemma Figtree and the BioHEART team have been successful in establishing a cardiology BioBank for biomarker discovery, which has grown into a large multi-centre study and is continuing to expand nationally. Together, the team has created an extremely powerful, secure, and user-friendly platform to discover and validate markers of early atherosclerosis and cardiovascular disease risk.

Using the state-of-the-art platform, Professor Figtree and her team have profiled many thousands of human samples providing multiple large datasets that are now being used for the identification and development of biosignatures for CAD. When we integrate this with genomic data on the same individuals, it provides us with the ability to make causal inference about the specific metabolic pathways and to select the optimal biomarkers.

The team collected atheromas from human carotid arteries and used imaging mass cytometry (IMC) to analyze them, helping the team to understand the complexity and variation in atherosclerotic plaques based on cell marker expression. Specifically, they identified key cell types and their distribution within the plaques, with T cells being more concentrated in the shoulder region, while macrophages were more widespread throughout the lesions. The team also made significant progress in identifying multiomics biomarkers for CAD. By analysing blood samples from a large cohort, they discovered specific cell subsets related to CAD, particularly T-regulatory cells expressing certain markers.

These findings could guide the development of new therapeutic targets. Additionally, the team identified a metabolite, DMGV, strongly associated with CAD, which may have clinical applications. Lipid profiles and machine learning were used to predict cardiovascular disease risk, showing promise in early CAD detection, especially in low to intermediate risk groups.

With the generosity and ongoing support of Heart Research Australia, The BioHEART team are a step closer to unravelling new mechanisms and finding new biomarkers for coronary artery disease. They were able to identify previously unrecognised molecules exist in the blood that can be powerful biomarkers to inform us regarding the burden and activity of silent coronary artery disease. The team are working collaboratively with many researchers across Australia and overseas to test these novel therapeutic targets with the hopes of translating these findings back to the clinics.