Profile of urinary exosomal microRNAs and their contribution to diabetic kidney disease through a predictive classification model

Abstract Aim Evaluate the expression of exomiRs‐126, ‐146, and ‐155 in urinary exosomes of patients with T2DM and diabetic kidney disease to establish a predictive classification model with exomiRs and clinical variables in order to determine their contribution to DKD. Methods The study group included 92 subjects: 64 patients diagnosed with T2DM subclassified into two groups with albuminuria (T2DM with albuminuria, n = 30) and without albuminuria (TD2M, n = 34) as well as 28 healthy, non‐diabetic participants. Exosomes were isolated from urine and identified by TEM and flow cytometry. Profile expression of exomiRs‐126, ‐146 and ‐155 was evaluated by RT‐qPCR. Data were analysed by permutational multivariate analysis of variance (PERMANOVA), similarity percentage (SIMPER), principal coordinate analysis (PCO), and canonical analysis of principal coordinates (CAP). Results T2DM patients with and without albuminuria showed higher levels of miR‐155 and miR‐146 compared with controls. In addition, T2DM patients with albuminuria presented a significant increase in miR‐126 contrasted to controls and patients without albuminuria. PCO analysis explained 34.6% of the total variability of the data (PERMANOVA; p < .0001). Subsequently, SIMPER analysis showed that miR‐146, miR‐155, and miR‐126 together, with some clinical parameters, contributed to 50% of the between‐group significance. Finally, the CAP analysis developed showed a correct classification of 89.01% with the analysed parameters. Conclusion A platform using a combination of clinical variables and exomiRs could be used to classify individuals with T2D as risk for developing DKD.