Machine learning-based early screening of mild cognitive impairment using nutrition-related biomarkers and functional indicators
TMAO-related metabolites consistently contributed positive SHAP effects, suggesting biologically relevant links between dietary metabolism and early cognitive decline. This interpretable ML framework offers a feasible, sensitive, and biologically informed approach for early MCI screening and supports the integration of nutritional biomarkers into cognitive health surveillance.
Authors
- Yiwen Wu (ORCID: https://orcid.org/0000-0002-8652-116X)
- Anke Zhang (ORCID: https://orcid.org/0000-0002-0007-7724)
- Ling Yuan (ORCID: https://orcid.org/0000-0002-3623-113X)
- Zhaoxin Wang (ORCID: https://orcid.org/0009-0000-3226-8974)
- Liqiang Zheng (ORCID: https://orcid.org/0000-0003-0422-9585)
- Yao Zhang (ORCID: https://orcid.org/0000-0001-8878-891X)
- He Bai
- Mengyao He
Institutions
- Shanghai Jiao Tong University (CN)
- XinHua Hospital (CN)
- International Peace Maternity & Child Health Hospital (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Hainan Medical University (CN)
- China Medical University (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-12-05
- DOI
- https://doi.org/10.3389/fnagi.2025.1641690
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China