Translational insights into extracellular vesicles in peritoneal dialysis.

Abstract

Peritoneal dialysis (PD) is a cornerstone kidney replacement therapy for patients with end-stage kidney disease; however, chronic exposure to bioincompatible dialysis solutions progressively damages the peritoneal membrane, leading to mesothelial-to-mesenchymal transition, fibrosis, and ultimately ultrafiltration failure. Currently, peritoneal membrane dysfunction is detected only at advanced stages through the peritoneal equilibration test, underscoring the need for earlier and more sensitive biomarkers. Extracellular vesicles (EVs) isolated from peritoneal dialysis effluent (PDE) have emerged as promising candidates, given their capacity to carry proteins, lipids, and nucleic acids that reflect local and systemic cellular activity. To date, 12 studies have successfully isolated and characterized PDE-derived EVs, identifying canonical EV markers, such as CD9, CD63, CD81, TSG101, and HSP70, alongside disease-relevant molecules, including galectin-3 binding protein, aquaporin-1, glycoprotein 96, and integrin-linked kinase. These EVs are enriched in signaling components associated with inflammation, angiogenesis, and fibrosis, particularly through TGF-β/p38 and NF-κB pathways. Moreover, specific EV-associated microRNAs (e.g., miR-125a-5p, miR-132-3p, miR-296-3p, miR-432-5p) overlap with molecular signatures observed in kidney and cardiometabolic disorders, suggesting broader systemic relevance. As an original contribution to this review, and to address cross-study comparability, we applied EVqualityMS, a mass spectrometry-based quality assessment tool, to benchmark EV enrichment and contaminant profiles and calculate quality indices across publicly available PDE-EV proteomics datasets. Despite methodological heterogeneity and limited sample sizes, PDE-derived EVs represent a powerful "liquid biopsy" of the peritoneal environment. Their integration into PD monitoring holds promise for the early detection of membrane injury and for supporting a predictive, biomarker-guided, and personalized approach to PD management.