Evidence map›Paper›PMID 41737829›Full record

ArticleFrontiers in molecular biosciences2026

Exploring the gut microbiota-microbial metabolites-targets regulatory network in metabolic dysfunction-associated steatotic liver disease.

Dapeng Yin, Wei Wu, Xiaojuan Wang, Junhua He, Yikun Zhu, Jin Li

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Article in Frontiers in molecular biosciences, 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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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Dapeng YinDepartment of Endocrinology and Metabolism, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Wei WuDepartment of Endocrinology and Metabolism, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Xiaojuan WangDepartment of Endocrinology and Metabolism, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Junhua HeDepartment of Endocrinology and Metabolism, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Yikun ZhuDepartment of Endocrinology and Metabolism, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Jin LiDepartment of Endocrinology and Metabolism, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), which has emerged as a significant global public health concern. The regulatory role of gut microbiota and their metabolites in the pathogenesis of MASLD has attracted significant attention. Methods: Mendelian randomization (MR) analysis was performed to identify gut microbiota that were causally associated with MASLD. Evaluate the metabolites derived from the microbiota and predict their targets. Obtaining overlapping targets between metabolites and MASLD. The core targets were screened by protein-protein interaction (PPI) network, and functional enrichment analysis were performed. Binding interactions between metabolites and targets are validated through molecular docking. Meanwhile, the MASLD animal model was used to verify the expression differences of the core targets. Finally, the regulatory network of gut microbiota-metabolites-targets was constructed. Results: We identified 11 gut microbiota causally linked to MASLD by MR analysis. And 19 kinds of microbial metabolites were obtained. Enrichment analysis revealed that the intersection targets were concentrated cholesterol metabolism, PPAR and HIF-1 signaling pathways. Four core targets were identified, including ACE, HMGCR, PPARA and PPARG. Through molecular docking simulations, we predicted that there is potential stable binding affinity between the core targets and metabolites. Notably, a marked difference expression of key target genes was identified in the MASLD mice. Conclusion: Gut microbiota and microbial metabolites played an important role in the development of MASLD. This involved a multi-target and multi-pathway regulatory network, providing new evidence for the mechanism research and targeted intervention of MASLD.

Indexed as

gut microbiotalipid metabolismMASLDmendelian randomizationmicrobial metabolites

Identifiers

PMID41737829
PMCPMC12926106

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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.