Integrating urine metabolomic biomarkers and machine learning algorithms to predict preeclampsia
This study identified four important urinary biomarkers and constructed an XGBoost-based predictive model for PE early detection. These findings provide a noninvasive approach for early PE screening in clinic and insights into its metabolic pathophysiology.
Authors
- Hai Zhang (ORCID: https://orcid.org/0000-0002-7183-9570)
- Qingshan Chen (ORCID: https://orcid.org/0009-0004-1655-2210)
- Hongjuan Xie
- Yong Qian (ORCID: https://orcid.org/0000-0003-2133-6200)
- M. Feng (ORCID: https://orcid.org/0000-0002-9717-8491)
Institutions
- Standard Bio (Norway) (NO)
- Shanghai First Maternity and Infant Hospital (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1186/s40001-025-03337-1
- Citations
- 1
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 2.06
Funders
- Shanghai Municipal Health Commission
- Wu Jieping Medical Foundation