Plasma proteomics improves prediction of coronary plaque progression.

Abstract

Aims: Coronary computed tomography angiography (CCTA) offers detailed imaging of plaque burden and composition, with plaque progression being a key determinant of future cardiovascular events. As repeated CCTA scans are burdensome and costly, there is a need for non-invasive identification of plaque progression. This study evaluated whether combining proteomics with traditional risk factors can detect patients at risk for accelerated plaque progression.

Methods and results: This long-term follow-up study included 97 participants who underwent two CCTA scans and plasma proteomics analysis using the Olink platform. Accelerated plaque progression was defined as rates above the median for percent atheroma volume (PAV), percent noncalcified plaque volume (NCPV), and percent calcified plaque volume (CPV). High-risk plaque (HRP) was identified by positive remodeling or low-density plaque at baseline and/or follow-up. Significant proteins associated with PAV, NCPV, CPV, and HRP development were incorporated into predictive models. The mean baseline age was 58.0±7.4 years, with 63 (65%) male, and a median follow-up of 8.5±0.6 years. The area under the curve (AUC) for accelerated PAV progression increased from 0.830 with traditional risk factors and baseline plaque volume to 0.909 with the protein panel (p=0.023). For NCPV progression, AUC improved from 0.685 to 0.825 (p=0.008), while no improvement was observed for CPV progression. For HRP development, AUC increased from 0.791 to 0.860 with the protein panel (p=0.036).

Conclusion: Integrating proteomics with traditional risk factors enhances the prediction of accelerated plaque progression and high-risk plaque development, potentially improving risk stratification and treatment decisions without the need for repeated CCTAs.