Diabetes Metab J.  2023 Sep;47(5):668-681. 10.4093/dmj.2022.0342.

Glimepiride Compared to Liraglutide Increases Plasma Levels of miR-206, miR-182-5p, and miR-766-3p in Type 2 Diabetes Mellitus: A Randomized Controlled Trial

• Scherbak NN
• Kruse R
• Nyström T
• Jendle J

• KMID: 2546125
• DOI: http://doi.org/10.4093/dmj.2022.0342

Abstract

Background
Diabetes is a chronic disease with several long-term complications. Several glucose-lowering drugs are used to treat type 2 diabetes mellitus (T2DM), e.g., glimepiride and liraglutide, in which both having different modes of action. Circulating microRNAs (miRNAs) are suggested as potential biomarkers that are associated with the disease development and the effects of the treatment. In the current study we evaluated the effect of glimepiride, liraglutide on the expression of the circulating miRNAs.
Methods
The present study is a post hoc trial from a previously randomized control trial comparing liraglutide versus glimepiride both in combination with metformin in subjects with T2DM, and subclinical heart failure. miRNAs were determined in the subjects’ serum samples with next generation sequencing. Expression patterns of the circulating miRNAs were analyzed using bioinformatic univariate and multivariate analyses (clinical trial registration: NCT01425580).
Results
Univariate analyses show that treatment with glimepiride altered expression of three miRNAs in patient serum, miR-206, miR-182-5p, and miR-766-3p. Both miR-182-5p and miR-766-3p were also picked up among the top contributing miRNAs with penalized regularised logistic regressions (Lasso). The highest-ranked miRNAs with respect to Lasso coefficients were miR-3960, miR-31-5p, miR-3613-3p, and miR-378a-3p. Liraglutide treatment did not significantly influence levels of circulating miRNAs.
Conclusion
Present study indicates that glucose-lowering drugs differently affect the expression of circulating miRNAs in serum in individuals with T2DM. More studies are required to investigate possible mechanisms by which glimepiride is affecting the expression of circulating miRNAs.

Keyword

Circulating microRNA; Diabetes mellitus, type 2; Glimepiride; Liraglutide

Figure

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  Fig. 1. Distribution of variation in microRNA (miRNA) expression in samples with respect to treatments. Unsupervised dimensionality reduction of miRNA expressions with (A) principal component analysis (PCA) and with (B) uniform manifold approximation and projection (UMAP), with first four dimensions shown, and (C) Gaussian finite mixture model-based clustering of miRNA expressions into four clusters. Figures are overlaid with colour representing the treatment groups (before treatment with liraglutide [L0] or glimepiride [G0] and after treatment with liraglutide [L1] or glimepiride [G1]).

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  Fig. 2. Univariate differential expression in microRNAs (miRNAs) before and after glimepiride treatment. (A) Volcano plot of miRNA log fold changes (FCs) and statistical significance show three differentially expressed miRNAs (red dots) further depicted in (B, C, D) box-violin plots with patient paired individual points overlaid. Statistical analysis performed with DESeq fitting patient paired binomial generalized linear models and Wald test for significance testing. Biological and statistical significance was set at a FC >1.5 and a Benjamini-Hochberg adjusted P<0.05, respectively. NS, not significant.

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  Fig. 3. Machine learning multivariate classification of microRNA (miRNA) expression before and after glimepiride treatment. (A) Posterior distributions of receiver operating characteristic (ROC) and area under the curve (AUC) from penalized regularised logistic regressions (Lasso), Random forests, XGBoosted trees, and Neural network (performed on both miRNAs and principal component analysis [PCA]-derived principal components). (B) Show the ROC-curve and (C) show the averaged iterated variable coefficients of the top 40 contributing miRNAs for the Lasso models. LR, penalized regularised logistic regressions (Lasso); NN, Neural network; NN_PCA, Neural network on PCA-derived principal components; RF, random forest; XG, XGBoosted trees; ST, stack of LR, XG and RF.

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  Fig. 4. The three microRNAs (miRNAs) differentially expressed with glimepiride treatment; miR-206, miR-182-5p, and miR-766-3p, and their mRNA-associated targets with a focus on targets belonging to (A) the gene ontologies/WikiPathways “Cellular response to insulin stimulus” (GO:0032869), “Response to insulin” (GO:0032868), “Insulin signalling” (WP481), and “Insulin receptor signalling pathway” (GO:0008286), and (B) targets belonging to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway “advanced glycation endproduct/receptor for advanced glycation end product (AGE-RAGE) signalling pathway in diabetic complications” (hsa04933). Node color of miRNAs reflect their fold change and edges reflects miRTarBase prediction towards gene products (grey nodes).

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