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Published on: January 16, 2015
Urine and Plasma Extracellular Vesicle Analysis for Monitoring Renal Recovery after Acute Kidney Injury
Adele Tanzi1, Cristina Grange1, Miriam Rosina2,3
1Department of Medical Science; University of Torino, Italy.
Background:
Maladaptive repair processes following AKI can promote the progression of chronic kidney disease, highlighting the need for biomarkers capable of monitoring post-AKI renal function trajectory. EVs, nanosized lipid vesicles secreted by cells into biofluids, are considered valuable biomarker sources, as their cargo may reflect the originating cells and their pathophysiological condition.
Methods:
Urinary and plasma EV surface markers were analyzed in AKI patients compared with age- and sex-matched controls using multiplex flow cytometry. Patients were stratified according to renal function changes at 90-day follow-up, distinguishing between those with an improved or not improved kidney disease.
Results:
In AKI patients at the hospital discharge, urinary EV analysis showed a downregulation of the tubular progenitor markers CD133 and CD24, with upregulation of mesenchymal/inflammatory markers CD29, SSEA-4 and HLA-DR, in comparison with controls. Plasma EV analysis showed concomitant modulation in AKI patients of markers of endothelial dysfunction and platelet activation. Considering the kidney function at 90 days of follow-up, urinary EV SSEA-4, a marker of cell dedifferentiation, was higher in AKI patients with improved function and correlated with renal recovery. Conversely, plasma EV CD42a, a marker of platelet activation, was higher in patients with persistent renal impairment and negatively correlated with renal recovery. Furthermore, the combined uSSEA-4/pCD42a index provided higher correlation and regression performance compared with either marker alone.
Conclusions:
These findings suggest that EV surface profiling could represent a minimally invasive strategy to distinguish adaptive from maladaptive repair after AKI, supporting the potential use of integrated urinary and plasma EV biomarkers to predict kidney disease progression.
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