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

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.