Raman spectroscopy of urinary extracellular vesicles to stratify patients with chronic kidney disease in type 2 diabetes

In this study, we verified the hypothesis that Raman signature of urinary extracellular vesicles (UEVs) can be used to stratify patients with diabetes at various stages of chronic kidney disease (CKD). Patients with type 2 diabetes diagnosed with different stages of CKD and healthy subjects were enrolled in the study. UEVs were isolated using low-vacuum filtration followed by ultracentrifugation. Correlation analysis, multiple linear regression and principal component analysis were used to find differences between spectral fingerprints of UEVs derived from both groups of patients. Electron microscopy and nanoparticle tracking analysis were applied to characterize the size and morphology of UEVs. We observed significant correlations between selected Raman bands measured for UEVs and clinical parameters. We found significant differences in the area under the specific bands originating mainly from proteins and lipids between the study groups. Based on the tryptophan and amide III bands, we were able to predict the estimated glomerular filtration rate (eGFR). Principal component analysis, partial least squares regression (PLSR) and correlation analysis of the UEV Raman spectra supported the results obtained from the direct analysis of Raman spectra. Our analysis revealed that PLSR and a regression model including tryptophan and amide III bands allows to estimate the value of eGFR. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This study verified the hypothesis that the Raman signature of urinary extracellular vesicles (UEVs) can be used to classify patients with diabetes at different stages of chronic kidney disease (CKD). The study found significant correlations between Raman bands measured for UEVs and clinical parameters, and significant differences in specific bands mainly derived from proteins and lipids between different study groups. Regression models including tryptophan and amide III bands were able to estimate the values of estimated glomerular filtration rate (eGFR).