Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD
Our findings highlight distinct metabolic signatures in central carbon metabolism associated with IBD subtypes. The identified metabolite panels, combined with machine learning models, offer promising non-invasive tools for differentiating UC and CD from healthy individuals.
Authors
- Yanbo Yu (ORCID: https://orcid.org/0000-0003-2995-3270)
- Xiaohan Zhao (ORCID: https://orcid.org/0000-0002-3467-9467)
- Zhoutong Sun (ORCID: https://orcid.org/0000-0002-9923-0951)
- Xinrui Guo
- Yue Wang (ORCID: https://orcid.org/0000-0002-0561-9289)
- Feng Li (ORCID: https://orcid.org/0000-0001-5868-7456)
- Xiaolin Yin
- Huipeng Zhang (ORCID: https://orcid.org/0009-0002-5328-2489)
- Guan-Wei Bi
- M Lei (ORCID: https://orcid.org/0009-0000-7005-5910)
Institutions
- Shandong University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Qilu Hospital of Shandong University (CN)
Publication Details
- Journal
- Frontiers in Molecular Biosciences
- Published
- 2025-08-11
- DOI
- https://doi.org/10.3389/fmolb.2025.1615047
- Citations
- 1
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.81