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Usage Information

Metabolic dysfunction–associated steatotic liver disease and the gut microbiome: pathogenic insights and therapeutic innovations
Bernd Schnabl, Christopher J. Damman, Rotonya M. Carr
Bernd Schnabl, Christopher J. Damman, Rotonya M. Carr
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Review Series

Metabolic dysfunction–associated steatotic liver disease and the gut microbiome: pathogenic insights and therapeutic innovations

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Abstract

Metabolic dysfunction–associated steatotic liver disease (MASLD) is a major cause of liver disease worldwide, and our understanding of its pathogenesis continues to evolve. MASLD progresses from steatosis to steatohepatitis, fibrosis, and cirrhosis, and this Review explores how the gut microbiome and their metabolites contribute to MASLD pathogenesis. We explore the complexity and importance of the intestinal barrier function and how disruptions of the intestinal barrier and dysbiosis work in concert to promote the onset and progression of MASLD. The Review focuses on specific bacterial, viral, and fungal communities that impact the trajectory of MASLD and how specific metabolites (including ethanol, bile acids, short chain fatty acids, and other metabolites) contribute to disease pathogenesis. Finally, we underscore how knowledge of the interaction between gut microbes and the intestinal barrier may be leveraged for MASLD microbial-based therapeutics. Here, we include a discussion of the therapeutic potential of prebiotics, probiotics, postbiotics, and microbial-derived metabolites.

Authors

Bernd Schnabl, Christopher J. Damman, Rotonya M. Carr

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Usage data is cumulative from June 2025 through June 2026.

Usage JCI PMC
Text version 6,994 2,223
PDF 1,487 468
Figure 1,639 0
Table 178 0
Citation downloads 383 0
Totals 10,681 2,691
Total Views 13,372

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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ISSN: 0021-9738 (print), 1558-8238 (online)

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