Metabolic patterns of sweat-extracellular vesicles during exercise and recovery states using clinical grade patches

Metabolite-based sensors are attractive and highly valued for monitoring physiological parameters during rest and/or during physical activities. Owing to their molecular composition consisting of nucleic acids, proteins, and metabolites, extracellular vesicles (EVs) have become acknowledged as a novel tool for disease diagnosis. However, the evidence for sweat related EVs delivering information of physical and recovery states remains to be addressed. Taking advantage of our recently published methodology allowing the enrichment and isolation of sweat EVs from clinical patches, we investigated the metabolic load of sweat EVs in healthy participants exposed to exercise test or recovery condition. -Ten healthy volunteers (-three females and -seven males) were recruited to participate in this study. During exercise test and recovery condition, clinical patches were attached to participants' skin, on their back. Following exercise test or recovery condition, the patches were carefully removed and proceed for sweat EVs isolation. To explore the metabolic composition of sweat EVs, a targeted global metabolomics profiling of 41 metabolites was performed. Our results identified seventeen metabolites in sweat EVs. These are associated with amino acids, glutamate, glutathione, fatty acids, creatine, and glycolysis pathways. Furthermore, when comparing the metabolites' levels in sweat EVs isolated during exercise to the metabolite levels in sweat EVs collected after recovery, our findings revealed a distinct metabolic profiling of sweat EVs. Furthermore, the level of these metabolites, mainly myristate, may reflect an inverse correlation with blood glucose, heart rate, and respiratory rate levels. Our data demonstrated that sweat EVs can be purified using routinely used clinical patches during physical activity, setting the foundations for larger-scale clinical cohort work. Furthermore, the metabolites identified in sweat EVs also offer a realistic means to identify relevant sport performance biomarkers. This study thus provides proof-of-concept towards a novel methodology that will focus on the use of sweat EVs and their metabolic composition as a non-invasive approach for developing the next-generation of sport wearable sensors.