Early prediction of gestational diabetes mellitus using machine learning-integrated metabolomic and clinical features
Participants with GDM and NGT show great difference in the levels of many metabolites. The ML model according to the metabolites in the first trimester and clinical feature demonstrates high accuracy for early GDM prediction. The result of this research highlighted the potential of metabolites in the prediction of GDM in the early stage of pregnancy.
Authors
- Lan Gao (ORCID: https://orcid.org/0000-0002-7034-6379)
- Yingbei Lin
- Bidong Fu
- Qun Ji (ORCID: https://orcid.org/0009-0002-1104-8846)
- Fei Wang (ORCID: https://orcid.org/0000-0002-5676-2181)
- Xiaofang Chen (ORCID: https://orcid.org/0009-0003-1521-4431)
- Haiwei Liu (ORCID: https://orcid.org/0000-0001-8666-4462)
Institutions
- Hainan General Hospital (CN)
- Hainan Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-11-13
- DOI
- https://doi.org/10.3389/fendo.2025.1687146
- Primary Topic
- Gestational Diabetes Research and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00