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The Interplay Between GLP-1-Based Therapies, the Gut Microbiome, and MASLD/MASH in Type 2 Diabetes Mellitus: A
Boris Dinkov1,2,3, Diana Pendicheva-Duhlenska1,2
1Department of Pharmacology and Toxicology, Medical University, 5800 Pleven, Bulgaria.
Abstract:
GLP-1-based drugs are approved for the treatment of type 2 diabetes mellitus (T2DM) and obesity. Metabolic dysfunction-associated steatotic liver disease (MASLD) affects more than 60% of patients with T2DM, and the gut microbiome plays a critical role in its pathogenesis. The gut-liver axis represents a key mechanistic link between dysbiosis and hepatic steatosis. A narrative literature review was conducted using PubMed, Scopus, and ClinicalTrials.gov (2015-2026). Search terms included "GLP-1 receptor agonist," "microbiome," "MASLD," "MASH," "NAFLD," "NASH," "liraglutide," "semaglutide," "tirzepatide," "dulaglutide," and "exenatide." Of 363 identified articles, 330 were excluded due to duplication or non-relevant study design; 33 studies (18 preclinical, 15 clinical) were included. In preclinical models, liraglutide demonstrated normalization of the Firmicutes/Bacteroidetes ratio and increased Bifidobacterium and Lactobacillus spp., while tirzepatide significantly reduced hepatic steatosis and increased Akkermansia abundance in diabetic mice. Semaglutide improved gut barrier integrity, increased Alloprevotella and Alistipes, and ameliorated MASLD in murine models. In clinical studies, tirzepatide achieved MASH resolution in 44-62% of patients in the phase 2 SYNERGY-NASH trial. In August 2025, the FDA approved semaglutide for MASH with fibrosis based on the Phase 3 ESSENCE trial. A recent longitudinal study in T2DM patients showed that baseline microbiome composition predicted glycemic response to semaglutide, without significant changes in microbiome diversity. In conclusion, GLP-1-based therapies demonstrate consistent preclinical associations with gut microbiome modulation and reduction in hepatic steatosis. Baseline microbiome composition has been suggested as a potential predictor of treatment response, supporting a personalized approach to MASLD management and warranting future clinical studies.
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