“Trust your gut”: exploring the connection between gut microbiome dysbiosis and the advancement of Metabolic Associated Steatosis Liver Disease (MASLD)/Metabolic Associated Steatohepatitis (MASH): a systematic review of animal and human studies
Bibliographic record
Abstract
Metabolic Associated Steatosis Liver Disease (MASLD) and its advanced form, Metabolic Associated Steatohepatitis (MASH), represent growing global health concerns closely linked to obesity, type 2 diabetes mellitus (T2DM), and metabolic syndrome. The gut microbiome has emerged as a key modulator in MASLD pathogenesis through the gut-liver axis, influencing hepatic fat accumulation, inflammation, and fibrosis via microbial metabolites and immune responses. Dysbiosis-characterized by altered microbial diversity and composition-contributes to hepatic lipid dysregulation, systemic inflammation, and impaired bile acid signaling. Metabolites such as short-chain fatty acids (SCFAs), trimethylamine-N-oxide (TMAO), and ethanol play critical roles in disease progression. Recent innovations in precision medicine, including microbiome profiling, metabolomics, and genomics, offer promising diagnostic and therapeutic strategies. Targeted probiotics, fecal microbiota transplantation (FMT), and personalized dietary interventions are under investigation for modulating the gut microbiome. This systematic review, conducted in accordance with PRISMA 2020 guidelines, is the first to comprehensively integrate both animal and human studies on MASLD/MASH-related gut microbiome alterations. It uniquely synthesizes microbial taxa, functional metabolites, and region-specific patterns-including data from underrepresented MENA populations. Eligible studies from PubMed, Scopus, and Web of Science evaluated microbial composition, metabolite profiles, and associations with steatosis, inflammation, and fibrosis. The findings underscore the diagnostic and therapeutic potential of microbiome modulation and emphasize the need for longitudinal, mechanistically driven studies. This systematic review is the first to integrate both animal and human studies on MASLD/MASH-related gut microbiome alterations. Unlike previous reviews, it uniquely emphasizes microbial taxa, functional metabolites, and region-specific patterns, including underrepresented MENA populations. By synthesizing findings from diverse cohorts, this review highlights diagnostic and therapeutic opportunities while identifying persistent gaps in longitudinal data, regional representation, and multi-omics integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".