A recent study has revealed promising advancements in identifying and quantifying the burden of atherosclerosis, a condition that often progresses silently before leading to serious health issues such as myocardial infarction or stroke. An international team of researchers, primarily from Boston and Munich, presented their findings at the ASHG Conference held in Boston, detailing their efforts to define plasma proteomic signatures that could serve as reliable biomarkers for this condition.
The research focused on the analysis of plasma proteomes using the Olink Explore 3072 platform, which encompasses data from 44,788 participants of the UK Biobank. The scientists employed CatBoost, a machine learning technique, to analyze the data and derive four distinct proteomic signatures. These signatures were categorized based on biologically relevant protein sets: the complete proteome (WholeProteome; n=2,920), proteins linked to genetic predisposition (Genetic; n=402), proteins involved in atherogenesis (Mechanistic; n=680), and those enriched in arterial tissue (Arterial; n=248).
Significant Findings and Implications
According to the researchers, these proteomic signatures effectively differentiated individuals with established atherosclerotic disease from matched controls, achieving a receiver operating characteristic area under the curve (ROC-AUC) of up to 0.91 (95% CI: 0.89–0.93). Notably, among 41,200 individuals without baseline atherosclerosis, all four signatures were significantly associated with future major adverse cardiovascular events (MACE) over a median follow-up period of 13.7 years. The analysis indicated that an increase in the WholeProteome signature by one standard deviation correlated with a hazard ratio of 1.70 (95% CI: 1.64–1.77), enhancing risk discrimination substantially (ΔC-index: +0.036; p < 0.001). The levels of these proteomic signatures were found to correlate with the extent of affected vascular beds and plaque burden as measured by carotid ultrasound. Furthermore, they successfully predicted future cardiovascular events in independent cohorts, including KORA S4 (n=1,361) and KORA-Age1 (n=796). Longitudinal analyses utilizing linear mixed-effects models demonstrated that signature trajectories exhibited steeper annual increases among individuals with higher baseline risk factors or those who experienced subsequent MACE events. This reflects the signatures' ability to capture genetically influenced atherosclerotic burden, as indicated by consistent associations with polygenic risk scores for coronary artery disease.
Future Directions in Cardiovascular Disease Prevention
These findings underscore the potential of plasma proteomic signatures as effective tools for capturing atherosclerotic burden and enhancing cardiovascular risk prediction in asymptomatic individuals. The researchers emphasized that this approach may provide a scalable and accessible alternative to traditional imaging techniques for identifying subclinical atherosclerosis. Such advancements could significantly bolster prevention strategies for cardiovascular diseases, ultimately improving patient outcomes.
The study marks a significant step forward in understanding atherosclerosis and its implications for cardiovascular health, highlighting the importance of innovative technological applications in biomedical research. The insights gained from this research may pave the way for new diagnostic and preventive measures in the management of cardiovascular disease.
