Open AccessArticle
Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy
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Yaqi Zhang, Karl G. Sylvester, Bo Jin, Ronald J. Wong, James Schilling, C. James Chou, Zhi Han, Ruben Y. Luo, Lu Tian, Subhashini Ladella, Lihong Mo, Ivana Marić, Yair J. Blumenfeld, Gary L. Darmstadt, Gary M. Shaw, David K. Stevenson, John C. Whitin, Harvey J. Cohen, Doff B. McElhinney and Xuefeng B. Ling
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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
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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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