Gut microbiota and metabolomics in metabolic dysfunction-associated fatty liver disease: interaction, mechanism, and therapeutic value
The global epidemic of Metabolic dysfunction-associated fatty liver disease (MAFLD) urgently demands breakthroughs in precision medicine strategies. Its pathogenesis centers on the cascade dysregulation of the gut microbiota-metabolite-liver axis: microbial dysbiosis drives hepatic lipid accumulatio...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2025.1635638/full |
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| Summary: | The global epidemic of Metabolic dysfunction-associated fatty liver disease (MAFLD) urgently demands breakthroughs in precision medicine strategies. Its pathogenesis centers on the cascade dysregulation of the gut microbiota-metabolite-liver axis: microbial dysbiosis drives hepatic lipid accumulation and fibrosis by suppressing short-chain fatty acid synthesis, activating the TLR4/NF-κB inflammatory pathway, and disrupting bile acid signaling. Metabolomics further reveals characteristic disturbances including free fatty acid accumulation, aberrantly elevated branched-chain amino acids (independently predictive of hepatic steatosis), and mitochondrial dysfunction, providing a molecular basis for disease stratification. The field of precision diagnosis is undergoing transformative innovation—multi-omics integration combined with AI-driven analysis of liver enzymes and metabolic biomarkers enables non-invasive, ultra-high-accuracy staging of fibrosis. Therapeutic strategies are shifting towards personalization: microbial interventions require matching to patient-specific microbial ecology, drug selection necessitates efficacy and safety prediction, and synthetically engineered “artificial microbial ecosystems” represent a cutting-edge direction. Future efforts must establish a “multi-omics profiling–AI-powered dynamic modeling–clinical validation” closed-loop framework to precisely halt MAFLD progression to cirrhosis and hepatocellular carcinoma by deciphering patient-specific mechanisms. |
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| ISSN: | 2235-2988 |