Circulating extracellular vesicle-based biomarkers: advances, clinical implications and challenges in coronary artery disease

first_pagesettingsOrder Article Reprints Open AccessReview Circulating Extracellular Vesicle-Based Biomarkers: Advances, Clinical Implications and Challenges in Coronary Artery Disease by Valeria Carcia 1,†,Alessandro Vincenzo De Salve 2,†ORCID,Chiara Nonno 1 andMaria Felice Brizzi 1,*ORCID 1 Department of Medical Sciences, University of Turin, 10126 Turin, Italy 2 Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, 10126 Turin, Italy * Author to whom correspondence should be addressed. † These authors contributed equally to this work. Int. J. Transl. Med. 2025, 5(3), 39; https://doi.org/10.3390/ijtm5030039 Submission received: 25 June 2025 / Revised: 31 July 2025 / Accepted: 17 August 2025 / Published: 22 August 2025 Downloadkeyboard_arrow_down Browse Figure Review Reports Versions Notes Abstract Coronary artery disease (CAD) is a leading cause of death worldwide, encompassing a broad spectrum of pathological conditions ranging from chronic to acute coronary syndromes. It underlies complex biological mechanisms, among which an emerging role is played by extracellular vesicles (EVs). EVs are non-replicable cell-derived particles enclosed by lipid bilayers acting as mediators of cellular interactions. In the past two decades, there has been a growing interest in EVs as potential diagnostic, prognostic and therapeutic tools in cardiovascular disease. We reviewed the most recent studies on circulating EVs in CAD with a particular focus on their role in biomarker discovery. Our aim was to evaluate the feasibility of translating these findings into routine clinical practice. To this end, we underlie the development and application of integrated indicators, referred to as “Bioscores”, which combine clinical, laboratory, and molecular data to enhance diagnostic and prognostic accuracy. We briefly discuss the opportunity and pitfalls related to the emerging use of Machine Learning (ML) algorithms. Moreover, we highlight that further investigation of mechanistic pathways is required beyond the initially predicted associations generated by in silico studies. Finally, we analyzed the key limitations, challenges, and unmet needs in the field, including small and unrepresentative sample sizes, a lack of external validation, overlapping and often contradictory effects on targeted pathways, difficulties in standardizing EV isolation and characterization methods, as well as concerns regarding affordability and clinical reliability.