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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Cell. Infect. Microbiol.</journal-id>
<journal-title>Frontiers in Cellular and Infection Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Cell. Infect. Microbiol.</abbrev-journal-title>
<issn pub-type="epub">2235-2988</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fcimb.2025.1635638</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Cellular and Infection Microbiology</subject>
<subj-group>
<subject>Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Gut microbiota and metabolomics in metabolic dysfunction-associated fatty liver disease: interaction, mechanism, and therapeutic value</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Wang</surname>
<given-names>Luyu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3094948/overview"/>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Wang</surname>
<given-names>Hongtao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2369998/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Wu</surname>
<given-names>Jian</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1180624/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/funding-acquisition/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Ji</surname>
<given-names>Changyi</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/software/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Ying</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1480125/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Gu</surname>
<given-names>Mengmeng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3010053/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Miaomiao</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/supervision/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Yang</surname>
<given-names>Hongwei</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Anhui Province Key Laboratory of Immunology in Chronic Diseases, Research Center of Laboratory, School of Laboratory, Bengbu Medical University</institution>, <addr-line>Bengbu</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University</institution>, <addr-line>Suzhou, Jiangsu</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Suzhou Key Laboratory of Intelligent Critical Illness Biomarkers Translational Reserach</institution>, <addr-line>Suzhou, Jiangsu</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Infection Management, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University</institution>, <addr-line>Suzhou, Jiangsu</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Department of Clinical Laboratory, Suzhou BOE Hospital</institution>, <addr-line>Suzhou, Jiangsu</addr-line>,&#xa0;<country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Ren-Lei Ji, Harvard Medical School, United States</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Gang Chen, Zhenjiang stomatological Hospital, China</p>
<p>Liqing Wei, Wuhan University, China</p>
<p>Mu-xing Li, Peking University Third Hospital, China</p>
<p>Dinesh Mohan Swamikkannu, Vishnu Institute of Pharmaceutical Education and Research, India</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Hongwei Yang, <email xlink:href="mailto:yanghongwei0114@163.com">yanghongwei0114@163.com</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>23</day>
<month>07</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>15</volume>
<elocation-id>1635638</elocation-id>
<history>
<date date-type="received">
<day>26</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>07</day>
<month>07</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Wang, Wang, Wu, Ji, Wang, Gu, Li and Yang</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Wang, Wang, Wu, Ji, Wang, Gu, Li and Yang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>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-&#x3ba;B 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&#x2014;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 &#x201c;artificial microbial ecosystems&#x201d; represent a cutting-edge direction. Future efforts must establish a &#x201c;multi-omics profiling&#x2013;AI-powered dynamic modeling&#x2013;clinical validation&#x201d; closed-loop framework to precisely halt MAFLD progression to cirrhosis and hepatocellular carcinoma by deciphering patient-specific mechanisms.</p>
</abstract>
<kwd-group>
<kwd>metabolic dysfunction-associated fatty liver disease (MAFLD)</kwd>
<kwd>gut microbiota</kwd>
<kwd>metabolomics</kwd>
<kwd>gut-liver axis</kwd>
<kwd>precision medicine</kwd>
</kwd-group>
<contract-num rid="cn001">82272396</contract-num>
<contract-sponsor id="cn001">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content>
</contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="115"/>
<page-count count="13"/>
<word-count count="5848"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Intestinal Microbiome</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Metabolic dysfunction-associated fatty liver disease (MAFLD), previously termed non-alcoholic fatty liver disease (NAFLD), represents the most prevalent chronic liver disease globally, affecting approximately 32.4% of the population (<xref ref-type="bibr" rid="B80">Riazi et&#xa0;al., 2022</xref>). It is closely associated with obesity, insulin resistance, and type 2 diabetes (<xref ref-type="bibr" rid="B38">Hu et&#xa0;al., 2020</xref>). International consensus recommends the nomenclature shift to MAFLD to emphasize its underlying metabolic dysregulation (<xref ref-type="bibr" rid="B51">Lazarus et&#xa0;al., 2024</xref>). MAFLD progression encompasses hepatic steatosis, inflammation, and fibrosis (<xref ref-type="bibr" rid="B50">Koliaki et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B14">Chen and Vitetta, 2020</xref>). Recent research highlights the pivotal role of the gut-liver axis: gut dysbiosis, characterized by an elevated Firmicutes/Bacteroidetes ratio (<xref ref-type="bibr" rid="B42">Jasirwan et&#xa0;al., 2021</xref>), modulates hepatic inflammation and metabolism through microbial metabolites (<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B6">Aron-Wisnewsky et&#xa0;al., 2020</xref>). Specifically, microbiota-derived secondary bile acids regulate lipid metabolism via the FXR signaling pathway (<xref ref-type="bibr" rid="B39">Huang and Kong, 2021</xref>), short-chain fatty acids (SCFAs) influence energy balance (<xref ref-type="bibr" rid="B48">Khan et&#xa0;al., 2021</xref>), and lipopolysaccharide (LPS) activates the hepatic TLR4 pathway, driving inflammation and fibrosis (<xref ref-type="bibr" rid="B26">Di Vincenzo et&#xa0;al., 2024</xref>). Gut barrier dysfunction and subsequent bacterial translocation exacerbate these processes (<xref ref-type="bibr" rid="B64">Mart&#xed;n-Mateos and Albillos, 2021</xref>). Diagnostic approaches have undergone significant innovation: while liver biopsy remains the gold standard (<xref ref-type="bibr" rid="B102">Wei et&#xa0;al., 2024</xref>), non-invasive strategies have evolved from traditional biomarkers (e.g., TG/HDL-C ratio (<xref ref-type="bibr" rid="B99">Wang et&#xa0;al., 2024</xref>), serum Biglycan (<xref ref-type="bibr" rid="B12">Cengiz et&#xa0;al., 2021</xref>), and BARD score (<xref ref-type="bibr" rid="B98">Vilar-Gomez and Chalasani, 2018</xref>)) towards a new era of multi-omics integration. <xref ref-type="bibr" rid="B69">Nychas et&#xa0;al. (2025)</xref> identified nine cross-ethnicity conserved microbial signatures (e.g., enrichment of pathobionts and depletion of protective bacteria) across seven global cohorts (n=1,892), achieving an AUC of 0.95 for distinguishing MAFLD with high inter-ethnic specificity (<xref ref-type="bibr" rid="B69">Nychas et&#xa0;al., 2025</xref>). The Xu team pioneered a plasma metabolomics-clinical parameter combined model, demonstrating superior predictive efficacy for severe liver outcomes compared to traditional tools like FIB-4 and NFS (<xref ref-type="bibr" rid="B109">Xu et&#xa0;al., 2025</xref>). Therapeutically, probiotics and symbiotic show potential through microbiota modulation (<xref ref-type="bibr" rid="B59">Liu et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B11">Carpi et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B83">Rong et&#xa0;al., 2023a</xref>). However, addressing individual heterogeneity and mechanistic complexity necessitates precision strategies driven by multi-omics approaches.</p>
<p>This study aims to systematically elucidate the role of the gut-microbiota-metabolite-liver axis in MAFLD pathogenesis through integrated multi-omics analysis, providing a theoretical foundation for early diagnosis, risk stratification, and precision intervention strategies. We searched the pubmed, spring link and science direct databases for the past year, and found a total of 1947 articles, including 470 PubMed articles, 659 spring link articles, and 818 science direct articles, and finally we selected 140 relevant articles for research (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flowchart of literature selection process in this study.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-15-1635638-g001.tif">
<alt-text content-type="machine-generated">Flowchart depicting a systematic review process. Initial identification involved 1,947 records from PubMed (470), Springer Link (659), and Science Direct (818). After removing 570 duplicates, 1,377 records were screened. Subsequently, 647 reports were excluded due to reasons including meeting summaries, book chapters, abstracts, other factors, and non-English literature. Of 730 reports sought, 196 were not retrieved. The remaining 534 reports were assessed for eligibility, with 394 excluded based on objectives, unclear diagnostic methods, and undesirable outcomes. Finally, 140 studies were included.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s2">
<label>2</label>
<title>Gut microbiota and metabolic dysfunction-associated fatty liver disease</title>
<sec id="s2_1">
<label>2.1</label>
<title>Characteristics of gut microbiota in patients with MAFLD</title>
<p>The development and progression of MAFLD are closely linked to gut dysbiosis. Alterations in the gut microbiota exhibit taxonomic-level specificity and dynamic changes across disease stages (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Gut microbiota alterations in MAFLD patients.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Taxonomic level</th>
<th valign="middle" align="left">Biomarker name</th>
<th valign="middle" align="left">Change in MAFLD/MASH</th>
<th valign="middle" align="left">Disease stage association</th>
<th valign="middle" align="left">Function/mechanism</th>
<th valign="middle" align="left">References</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" rowspan="4" align="left">Phylum Level</td>
<td valign="middle" align="left">
<italic>Bacteroidetes</italic>
</td>
<td valign="middle" align="left">&#x2193; Decreased abundance</td>
<td valign="middle" align="left">MAFLD, MASH</td>
<td valign="middle" align="left">Maintains gut barrier integrity; reduction promotes energy absorption &amp; inflammation</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B42">Jasirwan et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B44">Jennison and Byrne, 2021</xref>; <xref ref-type="bibr" rid="B31">Forlano et&#xa0;al., 2022</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Firmicutes</italic>
</td>
<td valign="middle" align="left">&#x2191; Increased abundance</td>
<td valign="middle" align="left">MAFLD, MASH</td>
<td valign="middle" align="left">Enhances energy harvest, pro-inflammatory</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B42">Jasirwan et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B44">Jennison and Byrne, 2021</xref>; <xref ref-type="bibr" rid="B31">Forlano et&#xa0;al., 2022</xref>),</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Proteobacteria</italic>
</td>
<td valign="middle" align="left">&#x2191; Significantly increased</td>
<td valign="middle" align="left">MAFLD, Fibrosis</td>
<td valign="middle" align="left">Pro-inflammatory (e.g., endotoxin release)</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B63">Loomba et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B31">Forlano et&#xa0;al., 2022</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Actinobacteria</italic>
</td>
<td valign="middle" align="left">&#x2191;Increased abundance</td>
<td valign="middle" align="left">MAFLD</td>
<td valign="middle" align="left">Associated with dysbiosis</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B63">Loomba et&#xa0;al., 2019</xref>)</td>
</tr>
<tr>
<td valign="top" rowspan="3" align="left">Family Level</td>
<td valign="middle" align="left">
<italic>Enterobacteriaceae</italic>
</td>
<td valign="middle" align="left">&#x2191;Elevated relative abundance</td>
<td valign="middle" align="left">MAFLD, MASH</td>
<td valign="middle" align="left">Pro-inflammatory (LPS biosynthesis)</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B14">Chen and Vitetta, 2020</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Rikenellaceae</italic>
</td>
<td valign="middle" align="left">&#x2193; Reduced</td>
<td valign="middle" align="left">Early MAFLD</td>
<td valign="middle" align="left">Loss of anti-inflammatory metabolic functions</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B14">Chen and Vitetta, 2020</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Ruminococcaceae</italic>
</td>
<td valign="middle" align="left">Variable (&#x2193; in adults, &#x2191; in children)</td>
<td valign="middle" align="left">MAFLD &#x2192; Fibrosis</td>
<td valign="middle" align="left">Contradictory fibrosis associations; context-dependent compensatory role</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B23">Del Chierico et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B52">Lee et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B70">Oh et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B55">Li et&#xa0;al., 2021</xref>)</td>
</tr>
<tr>
<td valign="top" rowspan="6" align="left">Genus Level</td>
<td valign="middle" align="left">
<italic>Escherichia</italic>
</td>
<td valign="middle" align="left">&#x2191; Expansion</td>
<td valign="middle" align="left">MAFLD, MASH</td>
<td valign="middle" align="left">Pro-inflammatory, promotes endotoxemia</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B6">Aron-Wisnewsky et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B61">Long et&#xa0;al., 2024</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Dorea</italic>
</td>
<td valign="middle" align="left">&#x2191; Increased</td>
<td valign="middle" align="left">MAFLD</td>
<td valign="middle" align="left">Pro-inflammatory, disrupts gut barrier</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B6">Aron-Wisnewsky et&#xa0;al., 2020</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Faecalibacterium</italic>
</td>
<td valign="middle" align="left">&#x2193; Significantly reduced</td>
<td valign="middle" align="left">MAFLD, MASH</td>
<td valign="middle" align="left">Reduced butyrate production, impaired anti-inflammatory function</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B6">Aron-Wisnewsky et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B61">Long et&#xa0;al., 2024</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Coprococcus</italic>
</td>
<td valign="middle" align="left">&#x2193; Decreased</td>
<td valign="middle" align="left">MAFLD</td>
<td valign="middle" align="left">Insufficient SCFA production</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B6">Aron-Wisnewsky et&#xa0;al., 2020</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Prevotella</italic>
</td>
<td valign="middle" align="left">&#x2193; Reduced</td>
<td valign="middle" align="left">MAFLD</td>
<td valign="middle" align="left">Diminished anti-inflammatory metabolite generation</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B6">Aron-Wisnewsky et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B61">Long et&#xa0;al., 2024</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">
<italic>Eubacterium rectale</italic>
</td>
<td valign="middle" align="left">&#x2191; (moderate MAFLD); &#x2193; (fibrosis)</td>
<td valign="middle" align="left">MAFLD &#x2192; Fibrosis</td>
<td valign="middle" align="left">Dual role: compensatory adaptation &#x2192; profibrotic transition</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B14">Chen and Vitetta, 2020</xref>)</td>
</tr>
<tr>
<td valign="top" rowspan="3" align="left">Functional Features</td>
<td valign="middle" align="left">SCFA synthesis (e.g., butyrate)</td>
<td valign="middle" align="left">&#x2193; Suppressed</td>
<td valign="middle" align="left">MAFLD &#x2192; MASH</td>
<td valign="middle" align="left">Gut barrier disruption, promotes inflammation</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">LPS biosynthesis</td>
<td valign="middle" align="left">&#x2191; Activated</td>
<td valign="middle" align="left">MASH, Fibrosis</td>
<td valign="middle" align="left">Drives endotoxemia &amp; oxidative stress</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Tryptophan metabolism</td>
<td valign="middle" align="left">&#x2191; Aberrantly activated</td>
<td valign="middle" align="left">MASH</td>
<td valign="middle" align="left">Promotes pro-inflammatory mediator production</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>)</td>
</tr>
<tr>
<td valign="top" rowspan="2" align="left">Diagnostic Markers</td>
<td valign="middle" align="left">16-genus combination</td>
<td valign="middle" align="left">Stage-specific alterations</td>
<td valign="middle" align="left">Advanced Fibrosis</td>
<td valign="middle" align="left">Non-invasive model (e.g., <italic>Ruminococcus</italic> + <italic>Streptococcus</italic>)</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B63">Loomba et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B70">Oh&#xa0;et&#xa0;al., 2020</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Microbial &#x3b1;-diversity</td>
<td valign="middle" align="left">&#x2193; Decreases with hepatic fat accumulation</td>
<td valign="middle" align="left">MAFLD progression</td>
<td valign="middle" align="left">Correlates with disease severity</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B61">Long et&#xa0;al., 2024</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>MAFLD, metabolic dysfunction-associated fatty liver disease; MASH, metabolic dysfunction-associated steatohepatitis.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>A common hallmark of dysbiosis is an increased abundance of <italic>Proteobacteria</italic> and <italic>Actinobacteria</italic> phyla, along with an elevated <italic>Firmicutes</italic>/<italic>Bacteroidetes</italic> ratio (<xref ref-type="bibr" rid="B63">Loomba et&#xa0;al., 2019</xref>). At the phylum level, MAFLD patients typically show reduced abundance of <italic>Bacteroidetes</italic> and increased abundance of <italic>Firmicutes</italic> and <italic>Proteobacteria</italic> (<xref ref-type="bibr" rid="B31">Forlano et&#xa0;al., 2022</xref>), forming a characteristic &#x201c;Firmicutes/Bacteroidetes imbalance.&#x201d; In healthy individuals, <italic>Firmicutes</italic> and <italic>Bacteroidetes</italic> dominate, while <italic>Proteobacteria</italic> and others are relatively scarce (<xref ref-type="bibr" rid="B44">Jennison and Byrne, 2021</xref>). Disruption of this homeostasis may drive MAFLD progression by promoting energy harvest and inflammatory responses. At the family level, MAFLD patients exhibit an increased relative abundance of <italic>Enterobacteriaceae</italic> and a decrease in <italic>Rikenellaceae</italic> and <italic>Ruminococcaceae</italic>, which possess anti-inflammatory metabolic functions (<xref ref-type="bibr" rid="B14">Chen and Vitetta, 2020</xref>). Notably, <italic>Ruminococcaceae</italic> abundance shows a positive association with significant fibrosis, suggesting its dynamic changes correlate with disease severity (<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>). However, investigations into <italic>Ruminococcaceae</italic> abundance in MAFLD/MASH patients reveal inconsistent trends across populations. <xref ref-type="bibr" rid="B23">Del Chierico et&#xa0;al. (2017)</xref> observed a significant increase in <italic>Ruminococcaceae</italic> in children/adolescents with MAFLD or MASH compared to healthy controls. Conversely, a meta-analysis by <xref ref-type="bibr" rid="B55">Li et&#xa0;al. (2021)</xref> involving 1,265 subjects (including 577 MAFLD patients from 8 countries) found reduced <italic>Ruminococcaceae</italic> in MAFLD patients. <xref ref-type="bibr" rid="B52">Lee et&#xa0;al. (2020)</xref> further highlighted this discrepancy, reporting a negative association between <italic>Ruminococcaceae</italic> abundance and significant fibrosis in non-obese patients&#x2014;a finding contradictory to Boursier et&#xa0;al (<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>). These collective data indicate that <italic>Ruminococcaceae</italic> abundance varies significantly depending on patient cohorts and metabolic subgroups. Furthermore, alterations at the genus level are more complex: pro-inflammatory genera such as <italic>Escherichia</italic> and <italic>Dorea</italic> expand, while butyrate-producing genera like <italic>Faecalibacterium</italic>, <italic>Coprococcus</italic>, and <italic>Prevotella</italic> are significantly reduced (<xref ref-type="bibr" rid="B6">Aron-Wisnewsky et&#xa0;al., 2020</xref>). The abundance change of <italic>Eubacterium rectale</italic> is particularly unique&#x2014;it increases in moderate-to-severe MAFLD but decreases sharply when fibrosis develops, suggesting a dual role in compensatory adaptation and profibrotic processes across different pathological stages (<xref ref-type="bibr" rid="B14">Chen and Vitetta, 2020</xref>).</p>
<p>As the disease progresses to metabolic dysfunction-associated steatohepatitis (MASH) and fibrosis, functional remodeling of the microbiota intensifies. Metagenomic analysis reveals abnormal activation of tryptophan/phenylalanine metabolism and lipopolysaccharide (LPS) biosynthesis pathways in MASH-associated microbiota, while pathways for cellulose degradation and short-chain fatty acid (SCFA) synthesis (e.g., butyrate) are suppressed (<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>). This metabolic shift amplifies endotoxemia and oxidative stress via the gut-liver axis, further worsening insulin resistance. Non-invasive diagnostic techniques based on microbial signatures are rapidly advancing; for instance, a 16-genus marker model including <italic>Ruminococcus</italic> and <italic>Streptococcus</italic> significantly improves diagnostic accuracy for advanced fibrosis (<xref ref-type="bibr" rid="B63">Loomba et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B70">Oh et&#xa0;al., 2020</xref>). Methodologically, targeted 16S rRNA sequencing is commonly used for bacterial community analysis, while 18S rRNA or internal transcribed spacer (ITS) sequencing can profile fungal communities; metagenomic sequencing (mNGS) and probe-capture techniques enhance the detection of low-abundance species. Studies indicate that gut microbial &#x3b1;-diversity in MAFLD patients decreases with increasing hepatic fat accumulation, and meta-analyses reveal a core dysbiotic signature characterized by increased <italic>Escherichia</italic> and <italic>Prevotella</italic>, alongside decreased <italic>Faecalibacterium</italic> and <italic>Ruminococcaceae</italic> (<xref ref-type="bibr" rid="B61">Long et&#xa0;al., 2024</xref>). These findings suggest that hierarchical disruptions in microbial composition and function are not only biomarkers for MAFLD but also key pathological drivers of disease progression.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Pathological mechanisms of gut microbiota in patients with MAFLD</title>
<p>MAFLD is characterized by excessive hepatic triglyceride accumulation (<xref ref-type="bibr" rid="B84">Rong et&#xa0;al., 2023b</xref>). Its pathological progression is closely linked to gut-liver axis dysfunction driven by gut dysbiosis. The gut-liver axis forms a bidirectional regulatory network via the portal circulation, bile acid metabolism, and immune signaling (<xref ref-type="bibr" rid="B2">Albillos et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B104">Wu et&#xa0;al., 2022</xref>; <xref ref-type="bibr" rid="B107">Xiang et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B92">Siddiqui et&#xa0;al., 2025</xref>). Dysregulation of microbial metabolites, gut barrier impairment, and bile acid signaling imbalance constitute three core mechanisms driving hepatic lipid metabolism abnormalities, inflammation activation, and fibrosis (<xref ref-type="bibr" rid="B8">Blesl and Stadlbauer, 2021</xref>) (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Mechanisms of the gut-liver axis and bile acid metabolism in MAFLD. Bile acids (e.g., CA, cholic acid) derived from hepatic cholesterol (CHO) metabolism are synthesized via CYP7A1 (cholesterol 7&#x3b1;-hydroxylase). They enter intestinal circulation and activate the Farnesoid X Receptor (FXR), inducing fibroblast growth factor 15/19 (FGF15/19). This suppresses hepatic CYP7A1 via portal feedback, completing the enterohepatic loop. The axis interacts with gut microbiota metabolites and influences intestinal barrier integrity. Dysregulation of this pathway (highlighted in MAFLD-condition) links gut-liver crosstalk, microbial metabolites, and barrier dysfunction to disease progression.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-15-1635638-g002.tif">
<alt-text content-type="machine-generated">Illustration showing the bile acid circulation related to metabolic associated fatty liver disease (MAFLD). On the left is a human silhouette with highlighted liver and gastrointestinal areas. The liver section details regulation pathways involving FXR, FGF15, CYP7A1, and SMPD3. The intestinal barrier is depicted, showing interactions with gut microbiota, including CA, ACA, and CM. Key terms such as portal circulation, bile acids, RXR, and enterocyte are noted. Various molecular interactions and metabolic processes are visually connected.</alt-text>
</graphic>
</fig>
<sec id="s2_2_1">
<label>2.2.1</label>
<title>Dysregulation of microbial metabolites</title>
<p>Gut microbiota ferment dietary fibers to generate short-chain fatty acids (SCFAs), uch as butyrate and propionate. These activate the hepatocyte GPR43 receptor, inhibit histone deacetylases (HDACs), upregulate PPAR&#x3b1; to promote fatty acid oxidation, and enhance leptin signaling to suppress SREBP-1 and cholesterol synthesis gene expression, thereby reducing hepatic lipid accumulation (<xref ref-type="bibr" rid="B54">Li et&#xa0;al., 2024</xref>). Tryptophan metabolites derived from gut microbiota (e.g., indole derivatives) delay hepatic stellate cell (HSC) activation by activating the aryl hydrocarbon receptor (AhR) (<xref ref-type="bibr" rid="B97">Venkatesh et&#xa0;al., 2014</xref>). Additionally, microbiota convert primary bile acids to secondary bile acids via 7&#x3b1;-dehydroxylation, activating the farnesoid X receptor (FXR) and TGR5 receptor to regulate lipid metabolism (<xref ref-type="bibr" rid="B39">Huang and Kong, 2021</xref>). However, MAFLD patients often exhibit downregulated FXR expression (<xref ref-type="bibr" rid="B62">Long et&#xa0;al., 2024</xref>) and reduced secondary/primary bile acid ratios (<xref ref-type="bibr" rid="B108">Xie et&#xa0;al., 2022</xref>), weakening negative feedback on lipid synthesis and exacerbating steatosis (<xref ref-type="bibr" rid="B113">Zhang et&#xa0;al., 2006</xref>).</p>
</sec>
<sec id="s2_2_2">
<label>2.2.2</label>
<title>Gut barrier impairment</title>
<p>Reduced expression of the tight junction protein ZO-1 facilitates translocation of lipopolysaccharide (LPS) and CpG DNA (<xref ref-type="bibr" rid="B34">Giorgio et&#xa0;al., 2014</xref>). LPS activates the TLR4 receptor on Kupffer cells, triggering the release of pro-inflammatory factors (e.g., NF-&#x3ba;B, JNK/AP1) (<xref ref-type="bibr" rid="B93">Stephens and von der Weid, 2020</xref>) and disrupting intestinal epithelial junctions, forming a &#x201c;gut leak-LPS leakage-inflammation&#x201d; vicious cycle (<xref ref-type="bibr" rid="B106">Wu et&#xa0;al., 2019</xref>). Translocated CpG DNA induces insulin resistance via hepatocyte TLR9 (<xref ref-type="bibr" rid="B94">Tripathi et&#xa0;al., 2018</xref>), while pathobiont-derived toxic metabolites directly damage hepatocytes (<xref ref-type="bibr" rid="B38">Hu et&#xa0;al., 2020</xref>). Clinical studies confirm that intestinal permeability positively correlates with hepatic steatosis in MAFLD, and blood microbial translocation markers are elevated (<xref ref-type="bibr" rid="B20">Cui et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B24">De Munck et&#xa0;al., 2020</xref>).</p>
</sec>
<sec id="s2_2_3">
<label>2.2.3</label>
<title>Bile acid signaling imbalance</title>
<p>Chenodeoxycholic acid (CDCA) activates TGR5 to promote HSC collagen synthesis (<xref ref-type="bibr" rid="B86">Saga et&#xa0;al., 2018</xref>), while deoxycholic acid (DCA) induces hepatocyte apoptosis via the NF-&#x3ba;B/miR-21/PDCD4 pathway (<xref ref-type="bibr" rid="B82">Rodrigues et&#xa0;al., 2015</xref>). Dysbiosis-induced reduction of secondary bile acids and increased DCA/CDCA ratio (<xref ref-type="bibr" rid="B108">Xie et&#xa0;al., 2022</xref>) not only impair FXR-mediated suppression of lipogenesis but also exacerbate inflammation by disrupting gut immune homeostasis (<xref ref-type="bibr" rid="B10">Cai et&#xa0;al., 2022</xref>). TLR signaling plays a central role: TLR4 amplifies inflammation through MyD88-dependent (activating NF-&#x3ba;B, JNK/AP1) and TRIF-dependent pathways (<xref ref-type="bibr" rid="B34">Giorgio et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B93">Stephens and von der Weid, 2020</xref>). Hepatic lipid accumulation enhances TLR4 sensitivity to LPS, creating a &#x201c;lipid accumulation &#x2192; inflammation &#x2192; metabolic dysregulation&#x201d; vicious cycle (<xref ref-type="bibr" rid="B41">Huang et&#xa0;al., 2012</xref>). TLR9 regulates HSC function by recognizing CpG DNA, driving collagen deposition during chronic injury (<xref ref-type="bibr" rid="B86">Saga et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B20">Cui et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B24">De Munck et&#xa0;al., 2020</xref>). Animal studies show TLR4 knockout alleviates liver injury (<xref ref-type="bibr" rid="B38">Hu et&#xa0;al., 2020</xref>), and clinical research confirms TLR4 mRNA levels in MAFLD liver tissue correlate with inflammation/fibrosis severity (<xref ref-type="bibr" rid="B91">Sharifnia et&#xa0;al., 2015</xref>).</p>
<p>Collectively, these findings demonstrate that MAFLD pathogenesis involves a network of microbiota-derived metabolites, gut barrier dysfunction, and TLR-mediated immune responses. Metabolic imbalance and amplified inflammation create a positive feedback loop that accelerates disease progression, while the dual roles of TLR signaling and bile acid dysregulation further exacerbate hepatic fibrosis. Targeting gut barrier repair, modulating microbiota composition to restore protective metabolites, and precision intervention in key TLR signaling pathways represent promising strategies for treating MAFLD pathology.</p>
</sec>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Application of gut microbiota in the treatment of metabolic dysfunction-associated fatty liver disease</title>
<p>Recent studies have revealed the multifaceted mechanisms by which gut microbiota and their metabolic regulation contribute to treating MAFLD. <italic>Lactobacillus</italic> and <italic>Bifidobacterium</italic> significantly reduce serum cholesterol levels by modulating host metabolic pathways, likely through inhibiting intestinal cholesterol absorption and promoting bile acid excretion (<xref ref-type="bibr" rid="B105">Wu and Chiou, 2021</xref>). Further research indicates functional differentiation in liver farnesoid X receptor (FXR) subtypes during lipid metabolism regulation. FXR&#x3b1;2 exhibits stronger triglyceride (TG)-inhibiting capacity than FXR&#x3b1;1 via specific binding to DNA motifs, suggesting that targeted selective activation of FXR subtypes may become a novel therapeutic strategy for MAFLD (<xref ref-type="bibr" rid="B79">Ramos Pittol et&#xa0;al., 2020</xref>).</p>
<p>In probiotic combination interventions, a mixture of six probiotics (including <italic>Lactobacillus</italic> and <italic>Bifidobacterium</italic>) significantly increased the abundance of beneficial bacteria such as <italic>Agathobaculum</italic>, <italic>Blautia</italic>, and <italic>Ruminococcus</italic> in the gut while reducing hepatic free fatty acids (FFA) and body mass index (BMI), demonstrating the synergistic role of microbiota in ameliorating metabolic disorders (<xref ref-type="bibr" rid="B30">Fang et&#xa0;al., 2022</xref>). Exercise intervention reshapes gut microbiota structure, such as reducing <italic>Parabacteroides</italic> and <italic>Flavobacterium</italic>, to enhance hepatic fatty acid oxidation capacity. Independent of weight loss, exercise suppresses the NF-&#x3ba;B inflammatory pathway, thereby reducing intrahepatic lipid accumulation (<xref ref-type="bibr" rid="B73">Ortiz-Alvarez et&#xa0;al., 2020</xref>).</p>
<p>For targeted microbial therapies, specific probiotic strains like <italic>Lactobacillus rhamnosus</italic> GG (LGG) inhibit intestinal NF-&#x3ba;B signaling to reduce systemic inflammation, while their metabolites activate the FGF21-adiponectin axis to promote lipid metabolism (<xref ref-type="bibr" rid="B59">Liu et&#xa0;al., 2020</xref>) and stimulate butyrate-producing bacteria proliferation to repair the gut barrier (<xref ref-type="bibr" rid="B115">Zhao et&#xa0;al., 2019</xref>). <italic>Lactococcus lactis</italic> subsp. <italic>cremoris</italic> outperforms LGG in ameliorating high-fat-induced metabolic dysregulation, evidenced by reduced serum cholesterol, attenuated hepatic steatosis, and restored glucose tolerance (<xref ref-type="bibr" rid="B66">Naudin et&#xa0;al., 2020</xref>). The multi-strain probiotic VSL#3 alleviates liver inflammation by suppressing the NF-&#x3ba;B pathway and downregulating key lipogenesis genes (SREBP-1c and <italic>FAS</italic>) (<xref ref-type="bibr" rid="B43">Jena et&#xa0;al., 2020</xref>). Prebiotics and synbiotics not only enhance fatty acid &#x3b2;-oxidation by upregulating PPAR-&#x3b1;/CPT-1 but also inhibit colonization of pro-inflammatory bacteria such as <italic>Enterobacteriaceae</italic>, thereby improving insulin resistance and liver injury (<xref ref-type="bibr" rid="B4">Alves et&#xa0;al., 2017</xref>). These findings highlight the potential of precision intervention strategies based on microbiota-host interactions in MAFLD management.</p>
<p>Current clinical research on fecal microbiota transplantation (FMT) for MAFLD remains exploratory. Three key trials reveal its potential and limitations: <xref ref-type="bibr" rid="B19">Craven et&#xa0;al. (2020)</xref> found that allogeneic FMT significantly improved intestinal permeability in MAFLD patients, though without improving HOMA-IR or MRI-PDFF. <xref ref-type="bibr" rid="B103">Witjes et&#xa0;al. (2020)</xref> demonstrated that FMT from healthy donors upregulated hepatic <italic>ARHGAP18</italic> (a cytoskeleton regulator) and serine dehydratase (<italic>SDS</italic>) expression in patients with MASH while reducing serum GGT and ALT. <xref ref-type="bibr" rid="B110">Xue et&#xa0;al. (2022)</xref> reported decreased CAP values post-FMT alongside proliferation of butyrate-producing bacteria, activation of the FXR/TGR5 axis, and inhibition of fatty acid synthase (<italic>FASN</italic>). These results suggest FMT may mitigate liver injury by repairing the gut barrier, regulating host gene expression, and modulating metabolic pathways. However, heterogeneous efficacy, long-term safety concerns, and insufficient mechanistic validation (<xref ref-type="bibr" rid="B77">Qiu et&#xa0;al., 2024</xref>) require resolution through standardized donor screening and optimized trial designs.</p>
<p>Emerging gut-liver axis strategies indicate that symbiotic supplementation enriches butyrate-producing microbiota, elevates short-chain fatty acid (SCFA) levels, improves insulin resistance, inhibits hepatic lipogenic enzymes, and alleviates inflammation/oxidative stress via FXR/TGR5 signaling (<xref ref-type="bibr" rid="B27">Eslamparast et&#xa0;al., 2014</xref>). This underscores the potential of microbiota modulation to reshape gut-liver metabolic crosstalk, offering a microbe-centric paradigm for MAFLD.</p>
<p>The field of microbiota-targeted therapy is evolving from single-strain supplementation toward systematic ecological modulation. Future advances demand prioritizing functional gene clusters over individual species, establishing real-time monitoring of dynamic microbiota-host interactions, and leveraging synthetic biology to design therapeutic artificial microbial ecosystems. We prioritize butyrate synthesis (e.g., but/buk gene clusters) (<xref ref-type="bibr" rid="B46">Kalkan et&#xa0;al., 2025</xref>)and bile acid metabolism (e.g., bai/bsh genes) (<xref ref-type="bibr" rid="B56">Li et&#xa0;al., 2023</xref>) as core therapeutic targets due to their direct regulation of intestinal barrier integrity, host immunity, and metabolic homeostasis; concurrently, short-chain fatty acid transporters and antimicrobial peptide synthesis gene clusters will be incorporated to enhance microbial colonization resistance. Clinical efficacy will be evaluated via a multidimensional strategy: metagenomic tracking of functional gene abundance, metabolomic quantification (GC-MS/LC-MS) of butyrate and bile acid metabolites, host-response analysis of serum inflammatory markers and intestinal barrier indicators, and systematic correlation with clinical symptom scores to validate therapeutic mechanisms and translational potential. Ultimately, by redefining the microbiome as a programmable biological network, precise strategies for MAFLD prevention and treatment can be achieved.</p>
</sec>
</sec>
<sec id="s3">
<label>3</label>
<title>Metabolomics and metabolic dysfunction-associated fatty liver disease</title>
<sec id="s3_1">
<label>3.1</label>
<title>Metabolomic signatures in MAFLD patients</title>
<p>Metabolomic studies reveal significant metabolic dysregulations in patients with MAFLD, involving multiple pathways such as lipid, amino acid, bile acid, and energy metabolism. These alterations are closely linked to disease progression. MAFLD patients commonly exhibit hepatic lipid deposition, characterized by elevated free fatty acid (FFA) levels (<xref ref-type="bibr" rid="B36">Guo et&#xa0;al., 2022</xref>), increased triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio (a non-invasive diagnostic marker) (<xref ref-type="bibr" rid="B29">Fan et&#xa0;al., 2019</xref>), and phospholipid imbalance (e.g., decreased phosphatidylcholine/phosphatidylethanolamine (PC/PE) ratio) (<xref ref-type="bibr" rid="B76">Peng et&#xa0;al., 2021</xref>). Impaired hepatic mitochondrial &#x3b2;-oxidation leads to long-chain fatty acid accumulation, exacerbating lipotoxicity (<xref ref-type="bibr" rid="B50">Koliaki et&#xa0;al., 2015</xref>).</p>
<p>Dysregulated branched-chain amino acid (BCAA) metabolism is a hallmark feature, with elevated blood levels of BCAAs (e.g., leucine, isoleucine) and their metabolites correlating with insulin resistance and hepatic steatosis (<xref ref-type="bibr" rid="B60">Lo et&#xa0;al., 2022</xref>). Concurrently, increased aromatic amino acids (e.g., phenylalanine, tyrosine) and glutamate may promote inflammation and fibrosis via mTOR pathway activation (<xref ref-type="bibr" rid="B87">Samuel et&#xa0;al., 2004</xref>). Gut microbiota dysbiosis (e.g., elevated <italic>Firmicutes</italic>/<italic>Bacteroidetes</italic> ratio) (<xref ref-type="bibr" rid="B42">Jasirwan et&#xa0;al., 2021</xref>) (<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>) disrupts the gut-liver axis through bile acid metabolism, resulting in increased secondary bile acids (e.g., deoxycholic acid) and reduced primary bile acids (e.g., taurocholic acid). This impairs farnesoid X receptor (FXR) signaling, worsening lipid dysregulation and inflammation (<xref ref-type="bibr" rid="B100">Wang et&#xa0;al., 2025</xref>).</p>
<p>While mitochondrial adaptive responses persist in simple steatosis (e.g., compensatory enhanced fatty acid oxidation), progression to MASH reduces oxidative phosphorylation efficiency. Accumulation of tricarboxylic acid (TCA) cycle intermediates (e.g., citrate, succinate) and elevated reactive oxygen species (ROS) production drive cellular damage and fibrosis (<xref ref-type="bibr" rid="B50">Koliaki et&#xa0;al., 2015</xref>).</p>
<p>Metabolomics has identified multiple potential biomarkers (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>), including serum BCAAs, 2-aminoadipic acid (2-AAA), and specific lipid profiles (e.g., Lys phosphatidylcholines), which correlate significantly with hepatic fat content, inflammation, and fibrosis severity (<xref ref-type="bibr" rid="B25">Di Mauro et&#xa0;al., 2021</xref>). Integrating these with machine learning models (e.g., laboratory parameter-based MAFLD screening) (<xref ref-type="bibr" rid="B111">Yip et&#xa0;al., 2017</xref>) or traditional scoring systems (e.g., BARD score) (<xref ref-type="bibr" rid="B81">Rigor et&#xa0;al., 2022</xref>) enhances diagnostic and staging accuracy.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Metabolic dysfunction-associated biomarkers.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Indicator name</th>
<th valign="middle" align="left">Category</th>
<th valign="middle" align="left">Application</th>
<th valign="middle" align="left">Advantage</th>
<th valign="middle" align="left">Key parameters/features</th>
<th valign="middle" align="left">Related study</th>
<th valign="middle" align="left">Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">TG/HDL-C ratio</td>
<td valign="middle" align="left">Blood biochemical marker</td>
<td valign="middle" align="left">Predict MAFLD</td>
<td valign="middle" align="left">Simple, effective surrogate</td>
<td valign="middle" align="left">Ratio calculation</td>
<td valign="middle" align="left">Fan et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B29">Fan et&#xa0;al., 2019</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Serum BGN (Biglycan)</td>
<td valign="middle" align="left">Serum marker</td>
<td valign="middle" align="left">Diagnose MASH and significant fibrosis</td>
<td valign="middle" align="left">Non-invasive, novel biomarker</td>
<td valign="middle" align="left">High specificity</td>
<td valign="middle" align="left">Cengiz et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B12">Cengiz et&#xa0;al., 2021</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Arachidonic acid oxidation products</td>
<td valign="middle" align="left">Metabolic marker</td>
<td valign="middle" align="left">Diagnose MASH</td>
<td valign="middle" align="left">High specificity, reflects oxidative stress</td>
<td valign="middle" align="left">Multi-metabolite panel</td>
<td valign="middle" align="left">Di Mauro et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B25">Di Mauro et&#xa0;al., 2021</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">Hepascore (GGT, HA, &#x3b1;2m combination)</td>
<td valign="middle" align="left">Blood biochemical composite</td>
<td valign="middle" align="left">Diagnose advanced fibrosis (F3-F4)</td>
<td valign="middle" align="left">High diagnostic performance</td>
<td valign="middle" align="left">Balanced sensitivity/specificity</td>
<td valign="middle" align="left">Boursier et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B9">Boursier et&#xa0;al., 2016</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">miRNA-122 &amp; miRNA-34a</td>
<td valign="middle" align="left">Circulating microRNA</td>
<td valign="middle" align="left">Differentiate MAFLD patients from controls</td>
<td valign="middle" align="left">High AUC (0.93-0.96)</td>
<td valign="middle" align="left">Non-invasive, high accuracy</td>
<td valign="middle" align="left">Hochreuter et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B37">Hochreuter et&#xa0;al., 2022</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">MAFLD Ridge Score</td>
<td valign="middle" align="left">Machine learning model</td>
<td valign="middle" align="left">Exclude MAFLD (epidemiological studies)</td>
<td valign="middle" align="left">Comparable to existing scores</td>
<td valign="middle" align="left">Laboratory-parameter based</td>
<td valign="middle" align="left">Yip et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B111">Yip et&#xa0;al., 2017</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">HSI (Hepatic Steatosis Index)</td>
<td valign="middle" align="left">Clinical scoring system</td>
<td valign="middle" align="left">Screen MAFLD</td>
<td valign="middle" align="left">Simple, effective for steatosis grading</td>
<td valign="middle" align="left">BMI + ALT + gender</td>
<td valign="middle" align="left">Di Mauro et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B25">Di Mauro et&#xa0;al., 2021</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">BARD Score</td>
<td valign="middle" align="left">Clinical scoring system</td>
<td valign="middle" align="left">Diagnose advanced fibrosis</td>
<td valign="middle" align="left">Avoids biopsy, highly applicable</td>
<td valign="middle" align="left">BMI + AST/ALT + diabetes status</td>
<td valign="middle" align="left">Vilar-Gomez et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B98">Vilar-Gomez and Chalasani, 2018</xref>)</td>
</tr>
<tr>
<td valign="middle" align="left">ALT/AST ratio, FIB-4, MAFLD Fibrosis Score</td>
<td valign="middle" align="left">Non-invasive scoring systems</td>
<td valign="middle" align="left">Exclude advanced fibrosis</td>
<td valign="middle" align="left">High reliability, simplified assessment</td>
<td valign="middle" align="left">Multi-parameter evaluation</td>
<td valign="middle" align="left">Vilar-Gomez et&#xa0;al.</td>
<td valign="middle" align="left">(<xref ref-type="bibr" rid="B98">Vilar-Gomez and Chalasani, 2018</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>MAFLD, metabolic dysfunction-associated fatty liver disease; MASH, metabolic dysfunction-associated steatohepatitis; ALT, alanine aminotransferase; AST, glutamic oxaloacetic transaminase; BMI, body mass index; TG/HDL-C, triglyceride/high-density lipoprotein cholesterol.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Thus, metabolomics not only provides molecular insights into MAFLD pathogenesis but also enables novel approaches for non-invasive diagnosis, disease subtyping, and targeted therapies (e.g., FXR agonists, gut microbiota modulation) (<xref ref-type="bibr" rid="B109">Xu et&#xa0;al., 2025</xref>). Future research should integrate multi-omics data to precisely delineate metabolic network dynamics in MAFLD progression.</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Mechanisms of host metabolism in metabolic dysfunction-associated fatty liver disease</title>
<p>Metabolomics systematically analyzes dynamic changes in endogenous metabolites to elucidate the pathological mechanisms of MAFLD. This metabolic disorder, characterized by hepatic lipid accumulation, involves complex pathogenesis encompassing dysregulated lipid, amino acid, and carbohydrate metabolism. Metabolomics thus provides novel insights into these disturbances (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Integrated metabolic network of glucose transport, lipid synthesis, and insulin signaling. Cellular glucose uptake, facilitated by Glucose Transporters (GLUT), fuels glycolysis to generate pyruvate and also regulates the transport of Branched-Chain Amino Acids (BCAAs), creating a fundamental link between carbohydrate and amino acid metabolism. Glucose-derived metabolites, notably acetyl-CoA, activate the Carbohydrate-Responsive Element-Binding Protein (ChREBP), which drives <italic>de novo</italic> lipogenesis by upregulating key enzymes: Acetyl-CoA Carboxylase (ACC) and Fatty Acid Synthase (FAS) for palmitic acid synthesis, and Diacylglycerol Acyltransferase (DGAT) for Triacylglycerol (TAG) assembly. Concurrently, lipid metabolism involves the release of free fatty acids via lipolysis, their cellular transport via specific transporters, and their utilization in pathways like &#x3b2;-oxidation or modulation of membrane fluidity. The Insulin Signaling Pathway is central to coordinating this metabolic network; insulin receptor activation promotes glucose uptake and anabolic processes, but impaired signaling disrupts critical functions including membrane fluidity, receptor efficacy, and overall metabolic homeostasis. This network features significant cross-talk, particularly where BCAA metabolism intersects with glucose flux and lipid synthesis pathways. Additionally, catecholamines (e.g., adrenaline) influence energy balance by activating &#x3b2;3-adrenergic receptors, which modulate lipolysis and energy expenditure, further integrating hormonal control with core metabolic processes.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-15-1635638-g003.tif">
<alt-text content-type="machine-generated">Flowchart illustrating metabolic pathways in a cell, including glucose and amino acid transport, glycolysis, TCA cycle in mitochondria, and fatty acid synthesis. It shows interactions between various components like ACLY, ACC1, FASN, ELOVL6, and SCD, highlighting the roles in lipogenesis and lipolysis processes. Components like ChREBP&#x3b1; and receptors for insulin and catecholamines are also depicted, illustrating pathways leading to Acetyl-CoA, Malonyl-CoA, and ensuing fatty acids such as palmitic acid and oleic acid. The chart implies outcomes like increased insulin signaling and membrane fluidity.</alt-text>
</graphic>
</fig>
<p>The core etiology of MAFLD stems from disrupted hepatic lipid metabolism, primarily characterized by excessive triglyceride (TG) accumulation. This steatosis develops when lipid metabolic capacity becomes overwhelmed due to an imbalance between lipogenesis and degradation pathways, resulting in abnormal lipid deposition within hepatocytes (<xref ref-type="bibr" rid="B17">Chen et&#xa0;al., 2019</xref>). Excessive hepatic free fatty acid (FFA) accumulation serves as a key driver of this process, originating from three interconnected sources: adipose tissue lipolysis increasing circulating FFA levels (closely linked to insulin resistance [IR] and hepatic inflammation) (<xref ref-type="bibr" rid="B35">Griffin et&#xa0;al., 1999</xref>); hyperactive <italic>de novo</italic> lipogenesis (DNL) where skeletal muscle IR-induced hyperglycemia and hyperinsulinemia activate transcription factors ChREBP and SREBP1c, upregulating lipogenic enzymes that convert excess glucose into FFA (<xref ref-type="bibr" rid="B88">Samuel and Shulman, 2018</xref>); and dietary lipids entering the liver through bile acid receptor-mediated absorption (e.g., via FXR), further exacerbating FFA burden (<xref ref-type="bibr" rid="B16">Cheng et&#xa0;al., 2024</xref>).FFA overaccumulation not only impairs mitochondrial &#x3b2;-oxidation (e.g., through CPT1 downregulation (<xref ref-type="bibr" rid="B90">Serviddio et&#xa0;al., 2011</xref>)) but also promotes oxidative stress and hepatic fibrosis (<xref ref-type="bibr" rid="B78">Ramanathan et&#xa0;al., 2022</xref>). Beyond FFA dysregulation, other lipid abnormalities contribute to MAFLD progression. An imbalanced phosphatidylcholine-to-phosphatidylethanolamine (PC/PE) ratio disrupts membrane integrity, with decreased ratios distinguishing simple steatosis from MASH and liver injury (<xref ref-type="bibr" rid="B76">Peng et&#xa0;al., 2021</xref>). Mitochondrial adaptive responses (e.g., PPAR&#x3b1; and CPT1 upregulation) may initially enhance fatty acid oxidation, but these compensatory mechanisms progressively fail amid evolving IR and hormonal changes like leptin dysregulation (<xref ref-type="bibr" rid="B7">Begriche et&#xa0;al., 2013</xref>).</p>
<p>Amino acid metabolic disturbances critically influence MAFLD pathogenesis, particularly the branched-chain amino acid (BCAA) and aromatic amino acid (AAA) imbalance. BCAA dysregulation activates the mammalian target of rapamycin (mTOR) pathway, exacerbating IR and hepatocyte steatosis (<xref ref-type="bibr" rid="B60">Lo et&#xa0;al., 2022</xref>). BCAT2 knockout models demonstrate that BCAA accumulation induces energy metabolism disorders while paradoxically conferring obesity resistance, revealing its dual metabolic roles (<xref ref-type="bibr" rid="B5">Ananieva et&#xa0;al., 2017</xref>). Clinical studies confirm significant positive correlations between serum BCAA levels and intrahepatic triglyceride content (IHTC), ALT, AST, and GGT in MAFLD patients. Critically, the BCAA-IHTC association persists after adjusting for obesity and IR, indicating BCAA&#x2019;s direct steatogenic role (<xref ref-type="bibr" rid="B67">Ni et&#xa0;al., 2023</xref>) (<xref ref-type="bibr" rid="B95">van den Berg et&#xa0;al., 2019</xref>). <italic>In vitro</italic> validation shows valine upregulates lipogenic transcription factors (e.g., SREBP-1c), promoting lipid synthesis while inhibiting fatty acid oxidation to increase hepatocellular TG (<xref ref-type="bibr" rid="B67">Ni et&#xa0;al., 2023</xref>). Notably, obese MAFLD patients exhibit higher BCAA elevations than non-obese counterparts, with valine and isoleucine accumulation directly correlating with hepatic fat content (<xref ref-type="bibr" rid="B58">Lischka et&#xa0;al., 2020</xref>). High BCAA intake also correlates with liver injury severity in obese MAFLD patients, highlighting diet-metabolism interactions (<xref ref-type="bibr" rid="B32">Galarregui et&#xa0;al., 2021</xref>).</p>
<p>AAA metabolic abnormalities associate closely with hepatic inflammation and fibrosis in MASH, potentially through pro-inflammatory pathway activation (e.g., NF-&#x3ba;B) (<xref ref-type="bibr" rid="B45">Kalhan et&#xa0;al., 2011</xref>). Excessive glutamine breakdown causes ammonia accumulation, impairing hepatocyte function. Recent evidence reveals ammonia promotes SREBP-1 maturation and lipogenesis by activating SCAP/Insig complex dissociation, elucidating its molecular role in MAFLD/MASH (<xref ref-type="bibr" rid="B15">Cheng et&#xa0;al., 2022</xref>). This process interfaces with gut microbiota metabolism, as elevated serum BCAA correlates with dysbiosis and IR (<xref ref-type="bibr" rid="B75">Pedersen et&#xa0;al., 2016</xref>), positioning the &#x201c;gut microbiota-amino acid-liver&#x201d; axis as central to MAFLD. Collectively, amino acid dysregulation orchestrates MAFLD pathology by modulating lipid synthesis, inflammation, and energy metabolism.</p>
<p>Carbohydrate metabolism dysregulation represents another hallmark of MAFLD, manifesting through coordinated glycolysis and gluconeogenesis dysfunction. Elevated blood lactate and pyruvate in MAFLD patients indicate disordered hepatic glucose metabolism and mitochondrial impairment (<xref ref-type="bibr" rid="B50">Koliaki et&#xa0;al., 2015</xref>). Dietary patterns critically drive this imbalance: high-glycemic-index (GI) diets induce postprandial hyperglycemia, stimulating hepatic DNL and lipid accumulation (<xref ref-type="bibr" rid="B74">Parker and Kim, 2019</xref>). Excessive monosaccharide/disaccharide intake (e.g., fructose, sucrose, glucose) promotes MAFLD progression primarily through ChREBP activation (<xref ref-type="bibr" rid="B47">Katz et&#xa0;al., 2021</xref>). As a central lipogenic transcription factor, ChREBP directly binds promoters of DNL enzymes like fatty acid synthase (FASN) and acetyl-CoA carboxylase (ACC) (<xref ref-type="bibr" rid="B72">Ortega-Prieto and Postic, 2019</xref>). High-carbohydrate diets enhance ChREBP nuclear translocation and synergism with SREBP-1c, driving postprandial metabolic reprogramming (<xref ref-type="bibr" rid="B57">Linden et&#xa0;al., 2018</xref>). In 30%-sucrose-fed mouse models, aberrant ChREBP activation increases hepatic lipid droplets and inflammatory signaling&#x2014;phenotypes partially reversed by ChREBP inhibition (<xref ref-type="bibr" rid="B22">Daniel et&#xa0;al., 2021</xref>). ChREBP also mediates fructose-induced gluconeogenesis dysregulation via insulin-independent mechanisms, indicating its unique role in metabolic compensation (<xref ref-type="bibr" rid="B49">Kim et&#xa0;al., 2016</xref>).</p>
<p>Investigation of these pathological mechanisms reveals that MAFLD&#x2019;s metabolic disturbances involve multidimensional crosstalk. Insulin resistance acts as the central hub, coordinating synergistic dysregulation across lipid, amino acid, and carbohydrate metabolism to promote concurrent hepatocellular injury, inflammation, and fibrogenesis&#x2014;ultimately driving progression from steatosis to MASH.</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Therapeutic applications of metabolomics in metabolic dysfunction-associated fatty liver disease</title>
<p>Metabolomics research provides a systemic perspective for elucidating the pathogenesis of MAFLD and developing clinical interventions, driving a paradigm shift from single-pathway targeting toward systemic network modulation. At the foundational intervention level, scientific dietary management remains central: low-fat, high-fiber diets alleviate intrahepatic lipid deposition by optimizing metabolic profiles, while &#x3c9;-3 polyunsaturated fatty acid (EPA/DHA)-rich regimens significantly reduce hepatic triglycerides, enhance insulin sensitivity, and suppress inflammation (<xref ref-type="bibr" rid="B89">Scorletti and Byrne, 2018</xref>). The Mediterranean diet, rich in olive oil, nuts, and deep-sea fish, demonstrates efficacy by modulating lipid metabolism, reducing liver enzymes such as ALT and AST, and attenuating hepatic inflammation (<xref ref-type="bibr" rid="B33">Gantenbein and Kanaka-Gantenbein, 2021</xref>). More recently, the ketogenic diet&#x2014;characterized by very low carbohydrate and high fat intake&#x2014;has been shown to improve MAFLD through enhanced lipid oxidation and reduced hepatic lipogenesis (<xref ref-type="bibr" rid="B101">Watanabe et&#xa0;al., 2020</xref>). Exercise functions as a synergistic metabolic modulator, improving glucose-lipid metabolism and reducing intrahepatic fat content (<xref ref-type="bibr" rid="B96">Vanweert et&#xa0;al., 2021</xref>), with combined resistance and aerobic training yielding superior outcomes in both non-obese and obese MAFLD patients (<xref ref-type="bibr" rid="B114">Zhang et&#xa0;al., 2022</xref>).</p>
<p>Pharmacological strategies for MAFLD and metabolic dysfunction-associated steatohepatitis (MASH) exhibit multi-tiered advances. Classic insulin sensitizers like metformin improve underlying metabolic abnormalities by regulating glucose-lipid metabolism, though evidence for histological improvement such as fibrosis reversal in MASH remains limited (<xref ref-type="bibr" rid="B85">Ruan et&#xa0;al., 2023</xref>). Conversely, the PPAR&#x3b3; agonist pioglitazone significantly reduces hepatic steatosis, lobular inflammation, and hepatocyte ballooning in non-diabetic MASH patients while delaying diabetes progression (<xref ref-type="bibr" rid="B21">Cusi et&#xa0;al., 2016</xref>). Among emerging targeted agents, the bile acid-fatty acid conjugate Aramchol inhibits SCD1 to reduce lipid synthesis, with its Phase III ARMOR trial (NCT04104321) for F2-F3 fibrosis MASH patients currently evaluating efficacy (<xref ref-type="bibr" rid="B3">Alkhouri et&#xa0;al., 2021</xref>). The FXR agonist Obet cholic acid (OCA), a selective bile acid modulator, significantly improved MASH-related fibrosis (&#x2265;1-stage improvement without worsening) at 25 mg/day in Phase III trials, though approximately 20% of patients discontinued treatment due to pruritus (<xref ref-type="bibr" rid="B18">Chiang and Ferrell, 2022</xref>). Notably, the GLP-1 receptor agonist semaglutide demonstrated substantial advantages in a Phase II trial where 0.4 mg daily treatment for 72 weeks achieved histological resolution without worsening fibrosis in 320 MASH patients, positioning it as the most promising metabolic-regulating therapy to date (<xref ref-type="bibr" rid="B112">Zhang et&#xa0;al., 2025</xref>). For severely obese patients, foregut bariatric surgery is recommended by international guidelines as an effective intervention (<xref ref-type="bibr" rid="B28">European Association for the Study of the Liver (EASL) et&#xa0;al., 2016</xref>), significantly improving BMI, fibrosis scores, and histological features (<xref ref-type="bibr" rid="B68">Nickel et&#xa0;al., 2018</xref>), while statins serve as adjunctive therapy for dyslipidemia comorbidities but remain contraindicated in decompensated cirrhosis (<xref ref-type="bibr" rid="B13">Chalasani et&#xa0;al., 2018</xref>).</p>
<p>In summary, the current therapeutic framework integrates foundational lifestyle interventions, precision medications targeting the gut-liver axis such as OCA and semaglutide, and surgical approaches, highlighting the necessity for metabolomics-driven individualized treatment selection. Semaglutide demonstrates superior histological resolution and safety profiles, whereas OCA improves fibrosis but faces limitations due to side effects. Future exploration of combination strategies&#x2014;particularly GLP-1 and FXR agonist synergism&#x2014;is warranted to cooperatively regulate multiple pathological pathways and optimize therapeutic outcomes.</p>
</sec>
</sec>
<sec id="s4">
<label>4</label>
<title>Summary and outlook</title>
<p>Despite progress in elucidating gut-liver axis mechanisms and metabolomic features of MAFLD, significant challenges persist. Heterogeneity in microbiota research constitutes a primary obstacle, with current conclusions largely derived from small-sample cross-sectional studies vulnerable to technical variations like sensitivity differences between 16S rRNA and metagenomic sequencing, and population-specific metabolic contexts such as obese versus non-obese subtypes. This compromises reproducibility and generalizability, exemplified by inconsistent Ruminococcaceae abundance patterns&#x2014;elevated in pediatric MAFLD yet reduced in adult meta-analyses, with paradoxical fibrosis correlations&#x2014;highlighting context-dependent microbiota-host interactions.</p>
<p>Metabolomic platform variability similarly hinders translation due to unstandardized detection techniques and analytical pipelines, while cross-regulatory metabolic pathways diminish single-metabolite biomarker specificity. Although machine learning models integrating lipid profiles and amino acid signatures improve diagnostics, clinical adoption remains limited by technical discrepancies and metabolic network dynamism.</p>
<p>The translational gap is particularly pronounced: While probiotics, FXR agonists, and fecal microbiota transplantation demonstrate efficacy in animal models, human trials show marked heterogeneity. Long-term safety and efficacy of emerging therapies require large-scale validation, and lifestyle interventions lack clarity on long-term fibrotic impacts. Bariatric surgery demands precise patient stratification due to strict indications. Limitations in multimodal data and machine learning exacerbate challenges&#x2014;inconsistent diagnostic data acquisition across centers, limited model generalizability without external validation, and clinician skepticism regarding &#x201c;black-box&#x201d; interpretability impede real-world adoption (<xref ref-type="bibr" rid="B65">Meng et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B40">Huang et&#xa0;al., 2025</xref>). These issues collectively necessitate a paradigm shift toward multi-omics-driven dynamic network intervention.</p>
<p>Future breakthroughs depend on integrating three synergistic strategies: Cross-omics dynamic network deconvolution will establish causal mechanisms linking strain function to host phenotypes, resolving paradoxes like Ruminococcaceae variability. AI-driven precision management systems will enable full-cycle care&#x2014;ML models like the NAFLD Ridge Score (AUROC=0.88 (<xref ref-type="bibr" rid="B1">Aggarwal and Alkhouri, 2021</xref>)) integrating clinical and multi-omics features for dynamic risk stratification; deep learning fusing liver enzymes, radiomics, and cell death markers for high-accuracy fibrosis staging (<xref ref-type="bibr" rid="B71">Okanoue et&#xa0;al., 2021</xref>); and SVM algorithms predicting treatment responses to optimize probiotic dosing or FMT donor selection (<xref ref-type="bibr" rid="B53">Lewinska et&#xa0;al., 2021</xref>). Finally, adaptive clinical trials will stratify patients by baseline microbial, metabolic, and genetic profiles to validate targeted therapies, incorporating real-time metabolomic monitoring for efficacy assessment. Only by embedding microbiomes and metabolomes within a systems medicine framework can we bridge the gap from mechanistic exploration to clinical precision in NAFLD, ultimately alleviating the global burden of cirrhosis and hepatocellular carcinoma.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="author-contributions">
<title>Author contributions</title>
<p>LW: Writing &#x2013; original draft, Software, Conceptualization. HW: Conceptualization, Investigation, Writing &#x2013; review &amp; editing. JW: Funding acquisition, Methodology, Writing &#x2013; review &amp; editing. CJ: Data curation, Software, Writing &#x2013; original draft. YW: Data curation, Methodology, Writing &#x2013; review &amp; editing. MG: Writing &#x2013; original draft, Software, Data curation. ML: Supervision, Writing &#x2013; original draft, Data curation. HY: Supervision, Investigation, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s6" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the National Natural Science Foundation of China (No. 82272396), Suzhou Medical and Health Science and Technology Innovation Project (No. SKY2022057 and SKY2023205) and Gusu Health Project of Suzhou, China (GSWS2023004).</p>
</sec>
<sec id="s7" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s8" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="s9" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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