Microbiome-centered therapies for the management of metabolic dysfunction-associated steatotic liver disease
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a significant global health issue, affecting over 30% of the population worldwide due to the rising prevalence of metabolic risk factors such as obesity and type 2 diabetes mellitus. This spectrum of liver disease ranges from isolated steatosis to more severe forms such as steatohepatitis, fibrosis, and cirrhosis. Recent studies highlight the role of gut microbiota in MASLD pathogenesis, showing that dysbiosis significantly impacts metabolic health and the progression of liver disease. This review critically evaluates current microbiome-centered therapies in MASLD management, including prebiotics, probiotics, synbiotics, fecal microbiota transplantation, and emerging therapies such as engineered bacteria and bacteriophage therapy. We explore the scientific rationale, clinical evidence, and potential mechanisms by which these interventions influence MASLD. The gut-liver axis is crucial in MASLD, with notable changes in microbiome composition linked to disease progression. For instance, specific microbial profiles and reduced alpha diversity are associated with MASLD severity. Therapeutic strategies targeting the microbiome could modulate disease progression by improving gut permeability, reducing endotoxin-producing bacteria, and altering bile acid metabolism. Although promising, these therapies require further research to fully understand their mechanisms and optimize their efficacy. This review integrates findings from clinical trials and experimental studies, providing a comprehensive overview of microbiome-centered therapies' potential in managing MASLD. Future research should focus on personalized strategies, utilizing microbiome features, blood metabolites, and customized dietary interventions to enhance the effectiveness of these therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".