Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Mar 13;40(6):e71687. doi: 10.1096/fj.202503607RR

The Gut‐Liver Axis in Metabolic Dysfunction‐Associated Steatotic Liver Disease: From Mechanistic Insights to Precision Therapeutics

Shaoliang Zhu 1, Mengjie Zou 2, Qijun Wu 1, Yan Zou 1, Tingting Tan 1, Zhangnan Huang 1, Zheng Gong 3, Honglin Luo 4,✉, Xiaofeng Dong 1,✉
PMCID: PMC12986715  PMID: 41824007

ABSTRACT

Metabolic dysfunction‐associated steatotic liver disease (MASLD) has become the most prevalent chronic liver condition globally, shifting the diagnostic paradigm toward an affirmative, metabolism‐focused framework. The gut‐liver axis is a central pathophysiological pathway. This review aims to synthesize revolutionary advances from 2023 to 2025 in understanding and treating MASLD by focusing on the gut microbiome's role. This comprehensive review analyzes cutting‐edge research published between 2023 and 2025. We examined evidence from landmark clinical trials, developments in next‐generation probiotics, the integration of artificial intelligence (AI) with multiomics for diagnostics, and studies clarifying the interplay between host genetics and the microbiome in MASLD pathogenesis. Causal links between gut dysbiosis and MASLD pathology are now firmly established. Fecal microbiota transplantation (FMT) effectively prevents hepatic encephalopathy recurrence, and next‐generation probiotics like Akkermansia muciniphila have entered MASLD‐specific trials. AI‐driven diagnostic tools have achieved regulatory qualification from the European Medicines Agency. Furthermore, host genetics, particularly PNPLA3 variants, are shown to not only predispose to MASLD but also shape specific microbial communities that functionally contribute to disease progression. The field is rapidly advancing from correlative observations to causal evidence, enabling the development of microbiome‐based biomarkers and personalized therapies. The future of MASLD management lies in precision strategies, such as bacteriophage therapy and functionally defined probiotics, which integrate metabolic, microbial, and genetic factors into individualized care, heralding a new therapeutic era.

Keywords: fecal microbiota transplantation, gut‐liver axis, MASLD, microbiome, precision medicine

Description: This review synthesizes groundbreaking advances in the gut‐liver axis for MASLD management. It details mechanistic pathways from transkingdom dysbiosis to liver pathology, integrating AI‐driven diagnostics and host genetics (PNPLA3). The framework transitions from broad microbial restoration to precision medicine, showcasing targeted therapies like fecal microbiota transplantation and next‐generation probiotics. By combining multiomic data with artificial intelligence, this integrated approach provides personalized risk stratification and precision therapeutics for metabolic health.

graphic file with name FSB2-40-e71687-g002.jpg

1. Introduction: A Paradigm Shift in Hepatic Steatosis

The landscape of fatty liver disease has undergone a fundamental transformation with the 2023 adoption of “Metabolic Dysfunction‐Associated Steatotic Liver Disease” (MASLD) nomenclature, as defined in the multisociety Delphi consensus statement [1, 2]. This transition represents more than semantic refinement; it embodies a conceptual evolution from diagnosis by exclusion to affirmative identification based on cardiometabolic dysfunction [3]. With MASLD affecting approximately 30%–38% of the global population and projected to become the leading indication for liver transplantation, understanding its pathophysiology has become paramount [4].

The establishment of MASLD as a systemic metabolic disorder naturally elevates the gut‐liver axis to a central position in disease pathogenesis [5]. The anatomical and functional connection between the intestine and liver via the portal venous system positions the liver as the primary recipient of gut‐derived nutrients, metabolites, and microbial products [6]. This intimate relationship transforms the gut microbiota from a passive bystander to an active participant in hepatic metabolic regulation, making the gut‐liver axis a critical therapeutic target.

Recent advances have illuminated the complexity of this bidirectional communication network, revealing sophisticated mechanisms involving transkingdom interactions, epigenetic modifications, and host genetic susceptibility [7]. The integration of multiomics technologies with artificial intelligence has begun to unravel causal relationships that were previously obscured by correlative observations [8]. Simultaneously, precision therapeutic approaches targeting specific microbial populations or metabolic pathways are emerging from clinical trials, promising individualized treatment strategies [9].

This comprehensive review synthesizes the most recent advances in gut‐liver axis research, from mechanistic discoveries to clinical applications, highlighting the transformation of MASLD management through microbiome‐centered precision medicine.

2. Mechanistic Foundations: The Multifaceted Gut‐Liver Crosstalk

2.1. Transkingdom Dysbiosis: Beyond Bacterial Imbalance

Traditional microbiome research in MASLD has predominantly focused on bacterial compositional changes, characterized by reduced diversity and shifts toward proinflammatory species [10]. However, groundbreaking 2025 research has revealed a more complex phenomenon termed “transkingdom dysbiosis,” encompassing simultaneous alterations in bacterial, viral, and fungal communities [11].

Multiomics analyses have identified the enrichment of oral‐typical bacteria, particularly Streptococcus and Veillonella species, in the gut of MASLD patients, accompanied by corresponding expansions of bacteriophages targeting these bacterial hosts [12]. This parallel expansion suggests the establishment of stable, pathological microbial ecosystems rather than random fluctuations, indicating active colonization and niche formation within the diseased gut environment.

The concept of transkingdom dysbiosis has profound implications for therapeutic strategies. Unlike simple bacterial replacement approaches, effective interventions must consider the entire microbial ecosystem, including viral predators and fungal communities that may stabilize pathological states [13]. This understanding has informed the design of next‐generation therapeutic approaches that target multiple microbial kingdoms simultaneously.

2.2. From Correlation to Causation: Establishing Mechanistic Pathways

One of the most significant advances in recent MASLD research has been the establishment of causal relationships between specific gut microbes and liver pathology. Mediation analysis in pediatric cohorts has revealed that the relationship between Ruminococcus torques abundance and liver stiffness is significantly mediated by deoxycholic acid production, establishing a specific bacterium‐metabolite‐pathology axis [14].

Definitive experimental proof of causality emerged from sophisticated germ‐free mouse studies demonstrating that identical pathogenic diets induced severe fibrosing MASH in conventional mice but failed to cause fibrosis in germ‐free animals [15]. This experimental paradigm unequivocally establishes the gut microbiota as a necessary mediator of diet‐induced liver fibrosis, resolving the long‐standing “chicken‐and‐egg” dilemma regarding dysbiosis and liver disease (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

Causal Pathways in the Gut‐Liver Axis of MASLD. (A) Healthy gut‐liver homeostasis: characterized by intact tight junctions, a robust mucus layer, and balanced beneficial microbiota such as Faecalibacterium and Akkermansia, leading to physiological translocation of short‐chain fatty acids via the portal vein to maintain metabolic health in hepatocytes. (B) MASLD Pathogenesis: Compromised intestinal barrier allows for elevated translocation of pathobionts, bacterial DNA, and pathogen‐associated molecular patterns. These triggers induce immune activation via TLR4 signaling in Kupffer cells, promoting proinflammatory cytokine release, fibrogenesis in stellate cells, and progression from steatosis to steatohepatitis.

2.3. Host Genetics as Microbiome Architects

The discovery that MASLD genetic risk variants, particularly PNPLA3 rs738409, not only predispose to liver disease but also shape gut microbiota composition represents a paradigm shift in understanding gene–environment interactions [16]. Individuals carrying PNPLA3 risk alleles exhibit distinct microbial signatures characterized by enrichment in Gemmiger and Oscillospira species, coupled with functional upregulation of de novo fatty acid biosynthesis pathways [17].

This genetic‐microbial convergence creates a feedforward loop where genetic susceptibility to hepatic fat accumulation is amplified by a microbiome functionally skewed toward producing lipogenic substrates [18]. The implications extend beyond pathogenesis to therapeutic targeting, suggesting that genetically susceptible individuals may require microbiome interventions that specifically address upregulated lipogenic pathways.

2.4. Epigenetic Regulation by Microbial Metabolites

The gut microbiota exerts a profound influence on host gene expression through epigenetic modifications, with microbial metabolites serving as key regulatory molecules [19]. Short‐chain fatty acids, particularly butyrate, function as histone deacetylase inhibitors, directly modulating chromatin structure and gene transcription in hepatocytes [20]. The depletion of butyrate‐producing bacteria in MASLD patients therefore represents not merely a metabolite deficiency but the loss of critical anti‐inflammatory and metabolic regulatory signals.

Emerging research has identified additional microbial metabolites with epigenetic activity, including secondary bile acids that influence FXR‐mediated transcriptional programs and tryptophan metabolites that regulate AhR signaling pathways [21]. This expanding understanding of metabolite‐mediated epigenetic regulation provides new targets for therapeutic intervention and biomarker development.

This shift from viewing microbes as simple metabolic by‐products to epigenetic regulators highlights a new layer of control in the gut‐liver axis. However, the primary challenge remains the high interindividual variability in metabolite production, which necessitates longitudinal profiling to distinguish transient fluctuations from stable pathological markers.

2.5. Integrative Perspectives on Mechanistic Causality

The transition from observational associations to mechanistic causality represents a fundamental paradigm shift in MASLD research. By establishing the gut microbiota as a requisite mediator of hepatic fibrosis through germ‐free models and mediation analyses, the field has progressed beyond descriptive catalogs of dysbiosis toward functional interactomes [14, 22].

Crucially, these findings reveal that microbial metabolites exert control over host gene expression via specific epigenetic signatures, effectively bridging environmental triggers with genetic susceptibility [20, 23]. However, the primary challenge remains the substantial interindividual variability in microbial metabolite production [24]. Future research must prioritize longitudinal multiomic profiling to distinguish transient fluctuations from stable pathological markers, ensuring that therapeutic targets are functionally validated across diverse patient cohorts [25].

3. Diagnostic Revolution: AI‐Driven Multimodal Approaches

3.1. Regulatory Milestone: AI Standardization of Histopathology

The European Medicines Agency's 2025 qualification of AIM‐NASH, an artificial intelligence tool for standardizing liver biopsy interpretation, represents a transformative regulatory milestone. This AI system, trained on over 100 000 expert annotations, provides consistent quantification of MASH histological features, addressing the long‐standing problem of interobserver variability that has plagued clinical trials [26].

The qualification of AI‐based histopathological analysis signals broader acceptance of machine learning approaches in regulatory frameworks, paving the way for more sophisticated diagnostic applications. This development is particularly significant as it improves the reliability of the diagnostic gold standard itself, potentially increasing the statistical power of clinical trials and accelerating drug development [27].

3.2. Microbiome‐Based Biomarker Validation

Large‐scale validation studies have confirmed specific microbial signatures as robust biomarkers for MASLD diagnosis and staging. A comprehensive meta‐analysis of over 400 pediatric subjects validated the depletion of Faecalibacterium prausnitzii and enrichment of Prevotella copri as consistent markers of MASLD severity [28]. Machine learning algorithms integrating multiple microbial species achieved area under the curve values of 0.89 for discriminating MASH from simple steatosis [29].

Functional validation of fecal zonulin as a marker of intestinal permeability has provided mixed results, with elevated levels (> 107 ng/mL) correlating with moderate‐to‐severe steatosis but showing limited utility as a standalone diagnostic marker [30]. These findings highlight the complexity of gut barrier dysfunction in MASLD and suggest that comprehensive panels may be more effective than individual biomarkers.

3.3. The Missing Link: Integrated Multimodal AI Models

Despite parallel advances in serum‐based biomarkers and microbiome signatures, a critical gap exists in the development of integrated multimodal diagnostic models [31]. Current research streams remain largely siloed, with sophisticated AI models built separately for clinical parameters, gut microbiome data, and circulating biomarkers. The development of unified models that integrate established panels like NIS4 (incorporating miR‐34a‐5p, α2‐macroglobulin, YKL‐40, and HbA1c) with microbiome signatures represents the next major frontier in MASLD diagnostics [32] (Figure 2).

FIGURE 2.

FIGURE 2

Open in a new tab

Evolution of MASLD Diagnostic Methodologies. This timeline depicts the transition from traditional liver biopsy to current noninvasive standards like serum biomarkers and elastography. The future landscape focuses on AI‐driven precision diagnostics, where unified multimodal AI models integrate genomics, validated microbiome signatures, and metabolomics to generate personalized risk scores and prognostic trajectories. Regulatory milestones, such as the EMA qualification of AI‐standardized histopathology, are highlighted to demonstrate increasing diagnostic precision.

Recent breakthroughs in single‐nuclei transcriptomics, integrated with proteomics and HiCap bulk data, have provided a high‐resolution map of the human liver, identifying specific cellular niches and signaling networks influenced by gut‐derived metabolites [29, 33]. This granular understanding allows for the identification of subtype‐specific biomarkers that were previously obscured in bulk tissue analyses.

3.4. The Paradigm Shift Toward Multimodal AI Diagnostics

The convergence of AI with multimodal data streams is redefining the MASLD diagnostic landscape, transitioning from subjective histological grading to objective, predictive modeling [26, 34]. Particularly in digital pathology and imaging‐based pipelines, where reproducibility and external validation can be formally audited. This evolution addresses long‐standing challenges of interobserver variability and provides a framework for dynamic risk stratification [35]. Nevertheless, the ‘black‐box’ nature of deep learning algorithms and the lack of standardized data acquisition protocols across clinical centers remain significant obstacles. Establishing transparent, cross‐validated AI frameworks is essential for the regulatory acceptance and clinical implementation of these precision diagnostic tools.

4. Therapeutic Frontiers: From Ecosystem Replacement to Precision Editing

4.1. Fecal Microbiota Transplantation: Clinical Translation and Innovation

The maturation of fecal microbiota transplantation (FMT) from an experimental intervention to a clinical reality is underscored by several landmark trials in 2025. The phase II THEMATIC trial (NCT03796598) provided the most robust evidence to date for microbiome modulation in liver disease, demonstrating FMT's efficacy in preventing hepatic encephalopathy (HE) recurrence. With HE recurrence rates of 9% in FMT‐treated patients compared to 40% in placebo controls, this study establishes FMT as a viable standard‐of‐care intervention for refractory HE [36].

The PROMISE trial (NCT06461208) further advanced the field through the innovative use of oral, encapsulated, freeze‐dried FMT, effectively addressing practical barriers to widespread implementation [37]. By eliminating the necessity for endoscopic delivery, oral formulations significantly enhance patient acceptance and clinical feasibility, potentially transitioning FMT from a specialized hospital procedure to a routine therapeutic option.

The SYNCH trial (NCT05821010) represents a sophisticated evolution in FMT design, employing a “FMT+” approach that combines vegan donor conditioning with recipient supplementation using next‐generation probiotics. This dual‐strategy tests critical hypotheses regarding the optimization of donor microbiota through dietary modulation and the enhancement of engraftment efficacy through targeted supplementation, marking a significant milestone in precision gut‐liver medicine [38].

4.2. Next‐Generation Probiotics: From Promise to Clinical Reality

The transition to MASLD‐specific trials is exemplified by the study of pasteurized A. muciniphila (e.g., NCT02636618), which demonstrated safety and significant reductions in liver injury markers (ALT, AST) and systemic inflammation, confirming its role in reinforcing the gut barrier and improving metabolic profiles in overweight individuals.

Complementing direct supplementation approaches, innovative strategies for cultivating endogenous beneficial microbes have emerged. Research demonstrating that berberine alleviates MASH primarily by increasing endogenous A. muciniphila abundance through MUC2 upregulation illustrates the potential for indirect microbial cultivation [39]. This approach offers a complementary strategy to direct probiotic supplementation, potentially providing more sustainable microbiome modifications.

4.3. Evidence‐Based Synbiotic Formulations

Recent network meta‐analyses have provided quantitative evidence for the superiority of synbiotic formulations over probiotics alone [40]. Analysis of 37 randomized controlled trials involving 1921 patients demonstrated that synbiotics achieved superior reductions in liver stiffness measurement compared to probiotics alone, suggesting genuine synergistic effects when prebiotics and probiotics are combined [41].

The completion of the MAFLD‐RCT trial evaluating SLP07, a proprietary synbiotic containing specific Bifidobacterium and Lactobacillus strains combined with omega‐3 fatty acids and vitamin E, represents progress in rationally designed multicomponent formulations [42]. While results await publication, the trial's design exemplifies the evolution toward evidence‐based, mechanistically informed therapeutic development (Figure 3).

FIGURE 3.

FIGURE 3

Open in a new tab

Therapeutic Landscape of Gut‐Liver Axis Interventions. (A) Established interventions: includes high‐evidence strategies such as dietary modifications and approved probiotics or synbiotics that fortify the gut barrier. (B) Advanced Interventions in Clinical Trials: Features fecal microbiota transplantation delivered via oral capsules and next‐generation probiotics like Akkermansia muciniphila focused on metabolic regulation. (C) Emerging Precision Approaches: Highlights future frontiers including bacteriophage therapy for targeted pathobiont elimination and AI‐guided personalized interventions tailored to individual microbial profiles.

4.4. Precision Therapeutics: The Promise of Targeted Interventions

The conceptual framework for bacteriophage therapy in MASLD has been well‐established, offering the potential for precision microbiome editing without collateral damage to beneficial microbes [43]. While MASLD‐specific applications remain in early development, the broader acceleration of phage therapy research, supported by major European Union funding initiatives, is creating the necessary infrastructure for future liver disease applications.

The development of functionally defined therapeutic interventions represents another frontier in precision medicine. Rather than relying on taxonomic classifications, future therapeutics may target specific microbial functions, such as endotoxin production or short‐chain fatty acid synthesis, regardless of the bacterial species responsible [44].

4.5. Future Directions: From Ecosystem Restoration to Precision Editing

Therapeutic strategies for MASLD are evolving from broad microbial restoration toward precision ecosystem editing. The clinical success of conditioned FMT and next‐generation probiotics, such as pasteurized A. muciniphila , underscores the potential of targeted microbiome modulation [45]. Despite these advances, ensuring stable engraftment and assessing the long‐term safety of live biotherapeutic products in metabolically compromised hosts remain critical priorities [38]. The next generation of therapeutics will likely employ bacteriophages and functionally defined microbial consortia to achieve surgical‐grade precision in microbiome modification [46]. This approach aims to eliminate specific pathobionts while preserving the integrity of the beneficial commensal community, thereby minimizing off‐target metabolic consequences.

5. Integration and Future Perspectives: Toward Personalized Gut‐Liver Medicine

5.1. Multiomics Integration: The Path to Precision

The convergence of genomics, transcriptomics, metabolomics, and microbiomics data through artificial intelligence represents the next evolutionary step in MASLD management [47]. Future diagnostic and therapeutic approaches will likely integrate an individual's genetic predisposition (PNPLA3 status), microbiome composition and function, metabolic profile, and environmental factors to provide personalized risk assessment and treatment recommendations.

The development of such integrated approaches requires sophisticated computational frameworks capable of handling multidimensional datasets and identifying complex interaction patterns [48]. Early applications may focus on stratifying patients for clinical trials or predicting treatment responses to specific interventions.

5.2. Personalized Nutrition: From Population to Individual

The ultimate goal of gut‐liver axis research is the development of personalized nutrition strategies that optimize individual microbiome‐liver interactions [49]. The Dietary Index of Gut Microbiota concept, which demonstrated that 60% of its protective effect against MASLD is mediated through BMI reduction and 16% through inflammation reduction, provides a framework for mechanistically informed dietary recommendations [50].

Future personalized nutrition approaches may incorporate real‐time microbiome monitoring, metabolomic profiling, and genetic testing to provide dynamic dietary guidance that adapts to changing metabolic states and microbiome composition [51] (Figure 4).

FIGURE 4.

FIGURE 4

Open in a new tab

Integrated Precision Medicine Framework for MASLD Management. This conceptual framework illustrates a dynamic clinical cycle starting with multiomic data acquisition, including host genetics like PNPLA3 variants and microbiome profiling. An AI‐driven analysis platform processes these data to identify subtype clusters and provide precision stratification. The resulting personalized risk scores inform targeted therapeutic interventions, ranging from personalized diets to microbiome‐targeted therapies, with continuous feedback loops for outcome monitoring.

5.3. Challenges and Research Priorities

Despite remarkable progress, several critical challenges remain. The establishment of causality versus correlation requires continued longitudinal studies with sophisticated statistical modeling [52]. Standardization of microbiome analysis protocols, biomarker validation across diverse populations, and long‐term safety assessment of microbiome interventions represent ongoing priorities [53].

The development of regulatory frameworks for complex microbiome‐based therapeutics, particularly those involving live organisms or ecosystem modifications, requires continued collaboration between researchers, clinicians, and regulatory agencies [54]. The EMA's qualification of AI‐based histopathological tools provides a precedent for evidence‐based acceptance of innovative diagnostic approaches.

6. Conclusions: A New Era of Gut‐Liver Medicine

The research landscape of MASLD and the gut‐liver axis has undergone profound transformation, evolving from descriptive studies of microbial associations to mechanistic understanding of causal pathways and evidence‐based therapeutic interventions. The establishment of MASLD as a metabolic disorder has appropriately positioned the gut microbiota as a central therapeutic target, leading to innovative treatment approaches that extend far beyond traditional probiotics.

The transition from correlation to causation, exemplified by germ‐free mouse studies and mediation analyses, has provided the scientific foundation for targeted interventions. Simultaneously, the regulatory acceptance of AI‐based diagnostic tools and the clinical validation of sophisticated therapeutic approaches like conditioned FMT represent the practical translation of research advances.

As we stand at the threshold of personalized gut‐liver medicine, the integration of multiomics data with artificial intelligence promises to revolutionize both diagnosis and treatment of MASLD. The future lies not in one‐size‐fits‐all approaches but in precision interventions tailored to individual genetic, microbial, and metabolic profiles.

The journey from observational studies to precision therapeutics exemplifies the power of interdisciplinary research combining microbiology, hepatology, genetics, and computational biology. As this field continues to mature, the promise of truly personalized, microbiome‐guided therapy for MASLD is becoming an achievable reality, offering hope for the millions of patients affected by this increasingly prevalent condition.

Author Contributions

S.L.Z., H.L., Z.G. and X.D. designed the study. M.Z. and Q.J.W. analyzed the data, generated charts, and wrote the manuscript. Y.Z., T.T.T. and Z.H. helped to collect data and assemble references. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by Guangxi Natural Science Fundation, 2024GXNSFAA010247. Guangxi Youth Science Fun Project, 2024GXNSFBA010227 Research Foudation for Advanced Talents of The people’s and Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, QYY‐GCRC‐202301.

Disclosure

Causal relationships between gut microbiota alterations and MASLD pathology are now firmly established, moving the field beyond simple correlation. Microbiome‐targeted therapies, including fecal microbiota transplantation (FMT) and next‐generation probiotics, are demonstrating clinical efficacy in recent landmark trials. Artificial intelligence (AI) is revolutionizing diagnostics by integrating multiomics data, with AI‐based tools achieving regulatory qualification for clinical use. Host genetics, specifically variants like PNPLA3, directly influence MASLD risk by shaping the composition of disease‐promoting gut microbial communities. The therapeutic paradigm is shifting from broad microbial modulation toward precision medicine that integrates genetic and metabolic factors for personalized treatment.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

We express our gratitude to all the participants who were involved in this study. Furthermore, we thank the anonymous reviewers for their review comments and suggestions.

Contributor Information

Honglin Luo, Email: hlluo@gxams.org.cn.

Xiaofeng Dong, Email: gandanyingcai@163.com.

Data Availability Statement

No new data.

References

  • 1. Rinella M. E., Lazarus J. V., Ratziu V., et al., “A Multisociety Delphi Consensus Statement on New Fatty Liver Disease Nomenclature,” Hepatology 78 (2023): 1966–1986. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Cusi K., Abdelmalek M. F., Apovian C. M., et al., “Metabolic Dysfunction–Associated Steatotic Liver Disease (MASLD) in People With Diabetes: The Need for Screening and Early Intervention. A Consensus Report of the American Diabetes Association,” Diabetes Care 48 (2025): 1057–1082. [DOI] [PubMed] [Google Scholar]
  • 3. Putri S., Ciminata G., Lewsey J., Jani B., McMeekin N., and Geue C., “The Conceptualisation of Cardiometabolic Disease Policy Model in the UK,” BMC Health Services Research 24 (2024): 1060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Younossi Z. M., Kalligeros M., and Henry L., “Epidemiology of Metabolic Dysfunction‐Associated Steatotic Liver Disease,” Clinical and Molecular Hepatology 31 (2025): S32–s50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Yang Z., Zhao J., Xie K., Tang C., Gan C., and Gao J., “MASLD Development: From Molecular Pathogenesis Toward Therapeutic Strategies,” Chinese Medical Journal 138 (2025): 1807–1824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Hsu C. L. and Schnabl B., “The Gut‐Liver Axis and Gut Microbiota in Health and Liver Disease,” Nature Reviews. Microbiology 21 (2023): 719–733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Carabotti M., Scirocco A., Maselli M. A., and Severi C., “The Gut‐Brain Axis: Interactions Between Enteric Microbiota, Central and Enteric Nervous Systems,” Annals of Gastroenterology 28 (2015): 203–209. [PMC free article] [PubMed] [Google Scholar]
  • 8. Chakraborty S., Sharma G., Karmakar S., and Banerjee S., “Multi‐OMICS Approaches in Cancer Biology: New Era in Cancer Therapy,” Biochimica et Biophysica Acta (BBA) ‐ Molecular Basis of Disease 1870 (2024): 167120. [DOI] [PubMed] [Google Scholar]
  • 9. Ponce Alencastro J. A., Salinas Lucero D. A., Solis R. P., Herrera Giron C. G., Estrella López A. S., and Anda Suárez P. X., “Molecular Mechanisms and Emerging Precision Therapeutics in the Gut Microbiota‐Cardiovascular Axis,” Cureus 17 (2025): e83022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Qian W., He L., Fu C., Zeng T., Wang H., and Li H., “Metabolic Dysfunction‐Associated Steatotic Liver Disease (MASLD): Emerging Insights Into Gut Microbiota Interactions and Therapeutic Perspectives,” Exploration of Digestive Diseases 4 (2025): 100579. [Google Scholar]
  • 11. Vanevery H., Franzosa E., Nguyen L., and Huttenhower C., “Microbiome Epidemiology and Association Studies in Human Health,” Nature Reviews Genetics 24 (2022): 1–16. [DOI] [PubMed] [Google Scholar]
  • 12. Kim H., Nelson P., Nzabarushimana E., et al., “Multi‐Omic Analysis Reveals Transkingdom Gut Dysbiosis in Metabolic Dysfunction‐Associated Steatotic Liver Disease,” Nature Metabolism 7 (2025): 1476–1492. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Gagliardi A., Totino V., Cacciotti F., et al., “Rebuilding the Gut Microbiota Ecosystem,” International Journal of Environmental Research and Public Health 15 (2018): 1679. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Du L., Zhang K., Liang L., et al., “Multi‐Omics Analyses of the Gut Microbiota and Metabolites in Children With Metabolic Dysfunction‐Associated Steatotic Liver Disease,” mSystems 10 (2025): e0114824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Wang S. and Friedman S. L., “Found in Translation‐Fibrosis in Metabolic Dysfunction‐Associated Steatohepatitis (MASH),” Science Translational Medicine 15 (2023): eadi0759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Ha S., Wong V. W., Zhang X., and Yu J., “Interplay Between Gut Microbiome, Host Genetic and Epigenetic Modifications in MASLD and MASLD‐Related Hepatocellular Carcinoma,” Gut 74 (2024): 141–152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Pirola C. J., Salatino A., Quintanilla M. F., Castaño G. O., Garaycoechea M., and Sookoian S., “The Influence of Host Genetics on Liver Microbiome Composition in Patients With NAFLD,” eBioMedicine 76 (2022): 103858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Bergentall M., Tremaroli V., Sun C., et al., “Gut Microbiota Mediates SREBP‐1c‐Driven Hepatic Lipogenesis and Steatosis in Response to Zero‐Fat High‐Sucrose Diet,” Molecular Metabolism 97 (2025): 102162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Zhang Q., Liu Y., Li Y., et al., “Implications of Gut Microbiota‐Mediated Epigenetic Modifications in Intestinal Diseases,” Gut Microbes 17 (2025): 2508426. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Davie J. R., “Inhibition of Histone Deacetylase Activity by Butyrate,” Journal of Nutrition 133 (2003): 2485S–2493S. [DOI] [PubMed] [Google Scholar]
  • 21. Zheng X., Cai X., and Hao H., “Emerging Targetome and Signalome Landscape of Gut Microbial Metabolites,” Cell Metabolism 34 (2022): 35–58. [DOI] [PubMed] [Google Scholar]
  • 22. Zhang X., Lau H. C.‐H., Ha S., et al., “Intestinal TM6SF2 Protects Against Metabolic Dysfunction‐Associated Steatohepatitis Through the Gut–Liver Axis,” Nature Metabolism 7 (2025): 102–119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Tang R., Liu R., Zha H., Cheng Y., Ling Z., and Li L., “Gut Microbiota Induced Epigenetic Modifications in the Non‐Alcoholic Fatty Liver Disease Pathogenesis,” Engineering in Life Sciences 24 (2024): 2300016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Raverdy V., Tavaglione F., Chatelain E., et al., “Data‐Driven Cluster Analysis Identifies Distinct Types of Metabolic Dysfunction‐Associated Steatotic Liver Disease,” Nature Medicine 30 (2024): 3624–3633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Kruger K., Myeonghyun Y., van der Wielen N., et al., “Evaluation of Inter‐ and Intra‐Variability in Gut Health Markers in Healthy Adults Using an Optimised Faecal Sampling and Processing Method,” Scientific Reports 14 (2024): 24580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Pulaski H., Harrison S. A., Mehta S. S., et al., “Clinical Validation of an AI‐Based Pathology Tool for Scoring of Metabolic Dysfunction‐Associated Steatohepatitis,” Nature Medicine 31 (2025): 315–322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Chopra H., Annu, Shin D. K., et al., “Revolutionizing Clinical Trials: The Role of AI in Accelerating Medical Breakthroughs,” International Journal of Surgery 109 (2023): 4211–4220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Marcos‐Fernández R., Riestra S., Alonso‐Arias R., Ruiz L., Sánchez B., and Margolles A., “Immunomagnetic Capture of Faecalibacterium Prausnitzii Selectively Modifies the Fecal Microbiota and Its Immunomodulatory Profile,” Microbiology Spectrum 11 (2023): e01817‐22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Cavalli M., Diamanti K., Pan G., et al., “A Multi‐Omics Approach to Liver Diseases: Integration of Single Nuclei Transcriptomics With Proteomics and HiCap Bulk Data in Human Liver,” OMICS 24 (2020): 180–194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Ciurea N.‐A., Pantea C. M., Grama P., Kosovski I.‐B., and Bataga S., “Fecal Zonulin as a Non‐Invasive Marker of Intestinal Permeability: Findings From a Prospective Cohort Study,” Medicina (Kaunas, Lithuania) 61 (2025): 1527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Khan I., Panaiotov S., Attia K. A., et al., “Integrated Analysis of Blood Microbiome and Metabolites Reveals Key Biomarkers and Functional Pathways in Myocardial Infarction,” Journal of Translational Medicine 23 (2025): 797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Pal P., Palui R., and Ray S., “Heterogeneity of Non‐Alcoholic Fatty Liver Disease: Implications for Clinical Practice and Research Activity,” World Journal of Hepatology 13 (2021): 1584–1610. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Watson B. R., Paul B., Rahman R. U., et al., “Spatial Transcriptomics of Healthy and Fibrotic Human Liver at Single‐Cell Resolution,” Nature Communications 16 (2025): 319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Eskandar K., “Artificial Intelligence in Hepatology: A Comprehensive Scoping Review of Clinical Applications, Challenges, and Future Directions,” Iliver 4 (2025): 100205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Pavlides M., Birks J., Fryer E., et al., “Interobserver Variability in Histologic Evaluation of Liver Fibrosis Using Categorical and Quantitative Scores,” American Journal of Clinical Pathology 147 (2017): 364–369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Bajaj J. S., Fagan A., Gavis E. A., et al., “Microbiota Transplant for Hepatic Encephalopathy in Cirrhosis: The THEMATIC Trial,” Journal of Hepatology 83 (2025): 81–91. [DOI] [PubMed] [Google Scholar]
  • 37. Cheung S., “A PROspective Faecal MIcrobiota tranSplantation Trial to Improve outcomEs in Patients With Cirrhosis (PROMISE). ClinicalTrials.gov identifier: NCT06461208. Updated 2023,” accessed January 20, 2026, https://clinicaltrials.gov/study/NCT06461208.
  • 38. Augustijn Q. J. J., Grefhorst A., de Groen P., et al., “Randomised Double‐Blind Placebo‐Controlled Trial Protocol to Evaluate the Therapeutic Efficacy of Lyophilised Faecal Microbiota Capsules Amended With Next‐Generation Beneficial Bacteria in Individuals With Metabolic Dysfunction‐Associated Steatohepatitis,” BMJ Open 15 (2025): e088290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Xu J., Lao Y., Zhang W., et al., “Berberine Alleviates Metabolic Dysfunction‐Associated Steatohepatitis by Enhancing the Abundance of Akkermansia muciniphila ,” Journal of Nutritional Biochemistry 146 (2025): 110069. [DOI] [PubMed] [Google Scholar]
  • 40. Zhang C., Zhang Q., Zhang X., et al., “Effects of Synbiotics Surpass Probiotics Alone in Improving Type 2 Diabetes Mellitus: A Randomized, Double‐Blind, Placebo‐Controlled Trial,” Clinical Nutrition 44 (2025): 248–258. [DOI] [PubMed] [Google Scholar]
  • 41. Pan Y., Yang Y., Wu J., Zhou H., and Yang C., “Efficacy of Probiotics, Prebiotics, and Synbiotics on Liver Enzymes, Lipid Profiles, and Inflammation in Patients With Non‐Alcoholic Fatty Liver Disease: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials,” BMC Gastroenterology 24 (2024): 283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Mantri A., Köhlmoos A., Schelski D. S., et al., “Impact of Synbiotic Intake on Liver Metabolism in Metabolically Healthy Participants and Its Potential Preventive Effect on Metabolic‐Dysfunction‐Associated Fatty Liver Disease (MAFLD): A Randomized, Placebo‐Controlled, Double‐Blinded Clinical Trial,” Nutrients 16 (2024): 1300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Wortelboer K. and Herrema H., “Opportunities and Challenges in Phage Therapy for Cardiometabolic Diseases,” Trends in Endocrinology and Metabolism 35 (2024): 687–696. [DOI] [PubMed] [Google Scholar]
  • 44. Schupack D. A., Mars R. A. T., Voelker D. H., Abeykoon J. P., and Kashyap P. C., “The Promise of the Gut Microbiome as Part of Individualized Treatment Strategies,” Nature Reviews. Gastroenterology & Hepatology 19 (2022): 7–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Pitashny M., Kesten I., Shlon D., Hur D. B., and Bar‐Yoseph H., “The Future of Microbiome Therapeutics,” Drugs 85 (2025): 117–125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Ianiro G., Punčochář M., Karcher N., et al., “Variability of Strain Engraftment and Predictability of Microbiome Composition After Fecal Microbiota Transplantation Across Different Diseases,” Nature Medicine 28 (2022): 1913–1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Wasilewski T., Kamysz W., and Gębicki J., “AI‐Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring,” Biosensors 14 (2024): 356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Chen J., Lin A., Jiang A., et al., “Computational Frameworks Transform Antagonism to Synergy in Optimizing Combination Therapies,” npj Digital Medicine 8 (2025): 44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Cui C., Gao S., Shi J., and Wang K., “Gut‐Liver Axis: The Role of Intestinal Microbiota and Their Metabolites in the Progression of Metabolic Dysfunction‐Associated Steatotic Liver Disease,” Gut Liver 19 (2025): 479–507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Pu Y., Tan Z., Wu Y., et al., “Association of Gut Microbiota Dietary Index With Metabolic Dysfunction‐Associated Steatotic Liver Disease: The Mediating Roles of Inflammation and Body Mass Index,” Frontiers in Nutrition 12 (2025): 1573636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Singar S., Nagpal R., Arjmandi B. H., and Akhavan N. S., “Personalized Nutrition: Tailoring Dietary Recommendations Through Genetic Insights,” Nutrients 16 (2024): 2673. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Caruana E. J., Roman M., Hernández‐Sánchez J., and Solli P., “Longitudinal Studies,” Journal of Thoracic Disease 7 (2015): E537–E540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Metris A., Walker A. W., Showering A., et al., “Assessing the Safety of Microbiome Perturbations,” Microbial Genomics 11 (2025): 001405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Rodriguez J., Cordaillat‐Simmons M., Pot B., and Druart C., “The Regulatory Framework for Microbiome‐Based Therapies: Insights Into European Regulatory Developments,” npj Biofilms and Microbiomes 11 (2025): 53. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

No new data.

Articles from The FASEB Journal are provided here courtesy of Wiley

RESOURCES