Extracellular Vesicle Surface Markers as a Diagnostic Tool in Transient Ischemic Attacks

Background and Purpose: Extracellular vesicles (EVs) are promising biomarkers for cerebral ischemic diseases, but not systematically tested in patients with transient ischemic attacks (TIAs). We aimed at (1) investigating the profile of EV-surface antigens in patients with symptoms suspicious for TIA; (2) developing and validating a predictive model for TIA diagnosis based on a specific EV-surface antigen profile. Methods: We analyzed 40 subjects with symptoms suspicious for TIA and 20 healthy controls from a training cohort. An independent cohort of 28 subjects served as external validation. Patients were stratified according to likelihood of having a real ischemic event using the Precise Diagnostic Score, defined as: unlikely (score 0-1), possible-probable (score 2-3), or very likely (score 4-8). Serum vesicles were quantified by nanoparticle tracking analysis and EV-surface antigen profile characterized by multiplex flow cytometry. Results: EV concentration increased in patients with very likely or possible-probable TIA (P<0.05) compared with controls. Nanoparticle concentration was directly correlated with the Precise Diagnostic score (R=0.712; P<0.001). After EV immuno-capturing, CD8, CD2, CD62P, melanoma-associated chondroitin sulfate proteoglycan, CD42a, CD44, CD326, CD142, CD31, and CD14 were identified as discriminants between groups. Receiver operating characteristic curve analysis confirmed a reliable diagnostic performance for each of these markers taken individually and for a compound marker derived from their linear combinations (area under the curve, 0.851). Finally, a random forest model combining the expression levels of selected markers achieved an accuracy of 96% and 78.9% for discriminating patients with a very likely TIA, in the training and external validation cohort, respectively. Conclusions: The EV-surface antigen profile appears to be different in patients with transient symptoms adjudicated to be very likely caused by brain ischemia compared with patients whose symptoms were less likely to due to brain ischemia. We propose an algorithm based on an EV-surface-antigen specific signature that might aid in the recognition of TIA.