REVIEW article
Front. Med.
Sec. Hepatobiliary Diseases
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1667391
This article is part of the Research TopicGut microbiome-driven Pathogenesis and Intervention Strategies in Liver DiseasesView all 6 articles
Metabolomics in Autoimmune Hepatitis: Progress and Perspectives
Provisionally accepted
- 1Naval Medical University, Shanghai, China
- 2Foshan Fosun Chancheng Hospital, Foshan, China
- 3Shanghai University of Traditional Chinese Medicine, Shanghai, China
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This review summarizes recent advances in applying metabolomics to autoimmune hepatitis (AIH). AIH is a chronic liver disease characterized by immune-mediated hepatocellular injury, with complex pathogenesis involving genetic, immunological, and environmental factors. Metabolomics, a system-wide approach analyzing small molecule metabolites, offers potential in early diagnosis, prognosis, and therapeutic evaluation of AIH. Current studies identify alterations in amino acid, lipid, carbohydrate, and bile acid metabolism, as well as changes in the gut microbiome and specific metabolite markers that distinguish AIH from other liver diseases. Techniques such as liquid chromatography-mass spectrometry (LC-MS), and bioinformatics facilitate biomarker discovery and enhance understanding of disease mechanisms. Despite challenges such as standardization and data integration, metabolomics holds promise for developing personalized treatment strategies and advancing disease management. Future prospects include combining multi-omics approaches, large-scale cohort studies, and artificial intelligence (AI)-based data analysis to deepen insights into AIH pathology and improve clinical outcomes.
Keywords: Autoimmune Hepatitis, Metabolomics, microbiome, artificial intelligence, diagnosis
Received: 16 Jul 2025; Accepted: 21 Oct 2025.
Copyright: © 2025 Huang, Mo and Wang. 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) or licensor 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.
* Correspondence: Xiaoning Wang, wxntcm@126.com
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