Commentary - (2025) Volume 10, Issue 1
Received: 23-Jan-2025, Manuscript No. jdcm-25-168163;
Editor assigned: 25-Jan-2025, Pre QC No. P-168163;
Reviewed: 08-Feb-2025, QC No. Q-168163;
Revised: 13-Feb-2025, Manuscript No. R-168163;
Published:
20-Feb-2025
, DOI: 10.37421/2475-3211.2025.10.294
Citation: Frewe, Genica. "The Role of miRNA Networks in Beta Cel Function and Insulin Treatment Outcomes in Diabetes." J Diabetic
Complications Med 10 (2025): 294.
Copyright: © 2025 Frewe G. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
During pancreatic development, specific miRNAs are dynamically expressed to regulate lineage commitment and differentiation. For example, miR-375, one of the most extensively studied miRNAs in beta cells, plays a pivotal role in pancreatic islet development and beta cell mass maintenance. Knockout studies in mice have shown that the absence of miR-375 results in a significant reduction in beta cell mass and impaired glucose homeostasis. miR-7 is another key miRNA involved in pancreatic development. It regulates transcription factors such as Pax6 and NeuroD1, essential for endocrine pancreas differentiation. Dysregulation of these miRNAs during development can lead to beta cell dysfunction and predisposition to diabetes. In mature beta cells, miRNAs are crucial in maintaining insulin synthesis and secretion in response to glucose. miR-375 continues to play a vital role in adult beta cells by targeting genes such as myotrophin (Mtpn), which modulates insulin exocytosis. Overexpression or suppression of miR-375 directly influences insulin secretion, demonstrating its role as a key regulatory hub in glucose-stimulated insulin release. Another example is miR-124a, which targets multiple components of the insulin secretion machinery including synapsin-1A and Rab3a. Altered expression of miR-124a in diabetic conditions has been associated with defective insulin release. Similarly, miR-9 and miR-34a have been implicated in regulating the vesicle transport and exocytosis processes that are essential for proper insulin granule release [2,3].
In diabetes, particularly type 2 diabetes (T2D), chronic metabolic stress, glucotoxicity, and lipotoxicity lead to beta cell apoptosis. Several miRNAs are involved in modulating the beta cellâ??s response to these stressors. For instance, miR-21, which is generally considered a protective miRNA, is upregulated in response to inflammatory cytokines and may help beta cells resist apoptosis. On the other hand, miR-34a is associated with pro-apoptotic signaling and has been found to be elevated in the islets of diabetic models. miR-146a and miR-29 family members have also been linked to inflammatory signaling pathways in beta cells, particularly in autoimmune destruction as seen in Type 1 diabetes (T1D). These miRNAs regulate key components of the NF-κB signaling pathway and cytokine responses, thus influencing beta cell survival under immunologic assault. The inter-individual variability in insulin therapy response among diabetic patients is a major challenge in clinical management. Identifying biomarkers that can predict treatment efficacy is a step toward personalized medicine. Circulating miRNAs, detectable in plasma or serum, have emerged as promising non-invasive biomarkers.
Studies have identified altered levels of miR-375, miR-122, miR-126, and miR-29a in patients before and after insulin therapy. For instance, patients with poor glycemic control despite insulin treatment often exhibit persistent elevation of miR-29a and miR-34a, which are associated with insulin resistance and beta cell dysfunction. Conversely, successful insulin therapy has been linked to normalization of miR-126 levels, a miRNA involved in endothelial function and glucose metabolism. Moreover, machine learning approaches using miRNA expression profiles are being developed to predict which patients will respond favorably to insulin therapy. These predictive models could guide early intervention strategies and reduce the trial-and-error approach currently prevalent in diabetes care.
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