ArticleMetabolites2023
Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy.
Article in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Integrating urine metabolomic biomarkers and machine learning algorithms to predict preeclampsia.European journal of medical research · 2025Article
- Transfer learning-enhanced CNN model for integrative ultrasound and biomarker-based diagnosis of polycystic ovarian disease.Scientific reports · 2025Article
- Heme oxygenase/carbon monoxide system affects the placenta and preeclampsia.Medical gas research · 2025Review
- Identification of metabolism-related subtypes and feature genes of pre-eclampsia.Scientific reports · 2025Article
- Post-natal gestational age assessment using targeted metabolites of neonatal heel prick and umbilical cord blood: A GARBH-Ini cohort study from North India.Journal of global health · 2024Article
- Potential urinary biomarkers in preeclampsia: a narrative review.Molecular biology reports · 2024Review
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 2 countries.
Funding
Abstract
Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.
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Registered trials
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