ReviewWorld journal of microbiology & biotechnology2026
From metabolites to membrane vesicles: Unifying gut microbial signals in obesity, t2dm, and MASLD.
Review in World journal of microbiology & biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Obesity, Type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD) represent interconnected global health problems that can be caused by dietary factors, life-style changes and alterations in the gut microbiota composition. While numerous reports highlight the connections between microbial taxa and the host diseases, the underlying molecular mechanisms behind the impact of metabolites on the disease development remain poorly defined. In this review, the connection between four interrelated pathways through which metabolites from the gut microbiota affect metabolic disease are highlighted. They include (i) reprogramming of host metabolism through histone deacetylase (HDAC) inhibition and remodeling of the chromatin structure; (ii) mitochondrial dysfunction and disturbance in redox balance; (iii) hijacking of receptors and pathway biased crosstalk (FFAR2/3, GPR109A, FXR, TGR5, AhR, TLR4) and (iv) disruption of intestinal barrier and induction of metabolic endotoxemia. Such axes form a feedback network through which the inflammation and insulin resistance spread over the entire gut-adipose-liver-pancreas-muscle axis. The effect of the metabolites is very context-dependent since it relies on the concentration threshold, receptor bias, disease state and interaction between the host genotype and enterotype. The translational applications include composite metabolite biomarkers, enterotype-based therapeutic approaches, bacterial extracellular vesicles and machine learning approaches to develop multi-omics data analysis resulting in generation of a digital twin model.
Indexed as
Identifiers
42690471What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.