Gut Microbiome-Brain-Liver Crosstalk in Metabolic Dysfunction: Mechanisms and Therapeutic Opportunities

Authors

  • Junseo Lim Faculty of Science, University of Melbourne, Melbourne, Australia
  • Alvin Charlie Faculty of Science, University of Melbourne, Melbourne, Australia
  • Seungmin Seok Faculty of Science, University of Melbourne, Melbourne, Australia
  • Axel Craig Faculty of Science, University of Melbourne, Melbourne, Australia

Keywords:

Gut Microbiome, Metabolic Dysfunction, Inter-organ Crosstalk, Vagus Nerve Signaling, Therapeutic Interventions

Abstract

The human body operates as a highly integrated network of organ systems, wherein the gut, brain, and liver form a critical tripartite axis responsible for maintaining metabolic homeostasis. Disruption in any single component of this system frequently cascades into systemic metabolic dysfunction, precipitating conditions such as obesity, type 2 diabetes mellitus, and non-alcoholic fatty liver disease. This paper provides a comprehensive academic analysis of the gut microbiome-brain-liver crosstalk, elucidating the complex biochemical, neural, and immunological mechanisms that underpin inter-organ communication. By exploring the production and systemic circulation of microbe-derived metabolites, including short-chain fatty acids, secondary bile acids, and lipopolysaccharides, we delineate how the intestinal bioreactor influences both hepatic lipid metabolism and central neuroendocrine regulation. Furthermore, we examine the critical role of the vagus nerve and the enteric nervous system in facilitating rapid bidirectional signaling between the intestinal lumen and the central nervous system. Advanced analytical methodologies, such as multi-omics integration and germ-free animal models, are discussed in the context of mapping these complex communication networks. Finally, we explore emerging therapeutic opportunities that target this tripartite axis, evaluating the clinical potential of microbial interventions, targeted pharmacotherapies, and precision nutrition. Ultimately, this analysis underscores the necessity of a paradigm shift from organ-centric treatments toward holistic, network-based interventions in the management of systemic metabolic diseases.

References

1. Lu, Y., Shi, T., Liu, W., Shang, D., Liu, X., Wang, Q., ... & Duan, X. (2025). Supramonomer‐Based Glyco‐Cationic Nanoparticles With GSH Depletion‐Enhanced Drug/Gene Co‐Delivery for Combination Therapy. Medicine Bulletin, 1(2), 171-181.

2. Guo, Hanzhong, et al. "Real-time one-step diffusion-based expressive portrait videos generation." arXiv preprint arXiv:2412.13479 (2024).

3. Xu, X., Bowtell, J., Young, W. R., Fong, D. T., & Williams, G. K. (2026). Increased common corticospinal input during eyes-closed unilateral stance in people with chronic ankle instability. Scientific Reports.

4. Xiao, Y., Xiao‐yue, Z., Yue, W., Ruo‐tao, L., Xiang‐jie, L., Xing‐yuan, W., ... & Zhen‐yi, J. (2022). Use of computed tomography for the diagnosis of surgical sarcopenia: Review of recent research advances. Nutrition in Clinical Practice, 37(3), 583-593.

5. Peng, Q., Zheng, C., & Chen, C. (2024). A dual-augmentor framework for domain generalization in 3d human pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2240-2249).

6. Xie, C., Zhu, D., Wang, Z., Zhang, H., & Wei, Z. (2026). Explainable AI for Mental Health Detection from Social Media: A Comparative Study of Traditional Machine Learning and a Large Language Model. Available at SSRN 6429778.

7. Banerjee, S., Kwaan, M. R., Wu, Y., Ren, Y., & Xirasagar, S. (2023). Ostomy Surgery for Patients with Large Bowel Obstruction in the Modern Era: a Nationwide Inpatient Sample Study. Journal of Gastrointestinal Surgery, 27(3), 585-589.

8. Chu, Y. B., Li, J., Jia, P., Cui, J., Zhang, R., Kang, X., ... & Zhang, S. (2021). Irf1-and Egr1-activated transcription plays a key role in macrophage polarization: A multiomics sequencing study with partial validation. International Immunopharmacology, 99, 108072.

9. Wei, D., Lin, X., & Bresalier, R. S. (2025). Reframing cancer cachexia: the vagal brain–liver axis as a novel neuro-metabolic target. Signal Transduction and Targeted Therapy, 10(1), 370.

10. Mi, L., Wang, W., Tu, W., He, Q., Kong, R., Fang, X., ... & Liu, Y. (2025, March). Empower vision applications with LoRA LMM. In Proceedings of the Twentieth European Conference on Computer Systems (pp. 261-277).

11. Xu, X., Bowtell, J., Young, W. R., Fong, D. T., & Williams, G. K. (2025). Distinct patterns in neuromuscular adaptation to repeated perturbations in chronic ankle instability. Journal of NeuroEngineering and Rehabilitation.

12. Cai, Lie, et al. "Deep learning model for breast shear wave elastography to improve breast cancer diagnosis (INSPiRED 006): An international, multicenter analysis." Journal of Clinical Oncology 43.32 (2025): 3482-3493.

13. Song, S., Liu, Z., Wang, Y., & Gong, B. (2025). Human organoids and their application in tumor models, disease modeling, and tissue engineering. Medicine Bulletin, 1(1), 17-36.

14. Kwaan, M. R., Ren, Y., Wu, Y., & Xirasagar, S. (2021). Colonic stent use by indication and patient outcomes: a nationwide inpatient sample study. Journal of Surgical Research, 265, 168-179.

15. Gao, Z., Guo, M., Chen, K., Jiang, M. L., Zhang, L. M., Di Cesare Mannelli, L., & Zhang, C. J. (2025). Oligodendrocyte Precursor Cells in Demyelination Repair: Mechanisms, Crosstalk, and Therapeutic Frontiers. Medicine Bulletin, 1(2), 104-120.

16. Yang, Y., Li, G., Zhong, Y., Xu, Q., Chen, B. J., Lin, Y. T., ... & Cai, J. J. (2023). Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks. Nucleic acids research, 51(13), 6578-6592.

17. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.

18. Li, Y., Liao, X., Tang, S., Wang, Q., Lin, H., Yu, X., ... & Zhong, T. (2025). Potential Therapeutic Targets for Androgenetic Alopecia (AGA) in Obese Individuals as Revealed by a Gut Microbiome Analysis: A Mendelian Randomization Study. Nutrients, 17(11), 1892.

19. Che, X., Wang, H., Wang, H., & Jiang, Y. Z. (2025). Intratumoral Nerve Phased Development: A Promising Therapeutic Target. Medicine Bulletin, 1(2), 154-170.

20. Yang, D., Wang, X., Gao, Y., Liu, S., Ren, B., Yue, Y., & Yang, Y. (2025, October). Opengs-fusion: Open-vocabulary dense mapping with hybrid 3D Gaussian splatting for refined object-level understanding. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 21135-21142). IEEE.

21. Yu, X., Zhang, Y., Yao, Y., Cho, W. C., & Shang, A. (2025). Biofilm-mediated immune dysregulation in chronic pulmonary diseases: mechanisms and clinical implications. Frontiers in Microbiology, 16, 1736384.

22. Xie, C., Zhu, D., Wang, Z., Zhang, H., & Wei, Z. (2026). Compliance-Aware Discharge Agent for Auditable ICU Discharge Planning: A Pilot Feasibility Study Using Structured eICU Records. Available at SSRN 6429758.

23. Tao, X., Chen, D., Liang, R., Zhang, X., Yu, X., Chung, S., ... & Xiao, Y. (2021). A comparative study on knowledge towards COVID-19 prevention among undergraduate students in Macao and Zhuhai, China. PeerJ, 9, e11833.

Downloads

Published

2026-05-25