Profile of urinary exosomal microRNAs and their contribution to Diabetic Kidney Disease through a predictive classification model.
Nephrology (Carlton) 2022;
27:484-493. [PMID:
35289974 DOI:
10.1111/nep.14039]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/13/2022] [Accepted: 03/08/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND
Diabetic kidney disease (DKD) is a main complication of Type 2 diabetes mellitus (T2DM). Exosomal microRNAs (exomiRs) participate in numerous early events in kidney injury regulating progression to DKD. This study aimed to evaluate the expression of exomiRs-126, 146 and 155 in urinary exosomes of patients with T2D 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 2 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 analyzed 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 to 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 <0.0001). 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 analyzed parameters.
CONCLUSIONS
A platform using a combination of clinical variables and exomiRs could be used to to classify individuals with T2D as risk for developing DKD.
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