Citation: Zhang, Y.; Sylvester, K.G.; Jin, B.; Wong, R.J.; Schilling, J.; Chou, C.J.; Han, Z.; Luo, R.Y.; Tian, L.; Ladella, S.; et al. Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy. Metabolites 2023, 13, 715. https://doi.org/10.3390/ metabo13060715 Academic Editors: Susanne Aufreiter and Bo Li Received: 21 April 2023 Revised: 21 May 2023 Accepted: 25 May 2023 Published: 31 May 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). metabolites H OH OH Article Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy Yaqi Zhang 1,2 , Karl G. Sylvester 2 , Bo Jin 3 , Ronald J. Wong 4 , James Schilling 3 , C. James Chou 2 , Zhi Han 2 , Ruben Y. Luo 5 , Lu Tian 6 , Subhashini Ladella 7 , Lihong Mo 8 , Ivana Mari´ c 4 , Yair J. Blumenfeld 9 , Gary L. Darmstadt 4 , Gary M. Shaw 4 , David K. Stevenson 4 , John C. Whitin 4 , Harvey J. Cohen 4 , Doff B. McElhinney 10 and Xuefeng B. Ling 2, * 1 College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China; yaqizhang@gpnu.edu.cn 2 Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; karls@stanford.edu (K.G.S.); cjchou@stanford.edu (C.J.C.); zhihan01@stanford.edu (Z.H.) 3 mProbe Inc., Palo Alto, CA 94303, USA; bo.jin@mprobe.com (B.J.); james.s@mprobe.com (J.S.) 4 Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; rjwong@stanford.edu (R.J.W.); ivanam@stanford.edu (I.M.); gdarmsta@stanford.edu (G.L.D.); gmshaw@stanford.edu (G.M.S.); dks750@stanford.edu (D.K.S.); cuke@stanford.edu (J.C.W.); punko@stanford.edu (H.J.C.) 5 Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; rubenluo@stanford.edu 6 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA; lutian@stanford.edu 7 Community Medical Centers, UCSF Fresno, Fresno, CA 93722, USA; sladella@communitymedical.org 8 UC Davis Health, Sacramento, CA 95817, USA 9 Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA; yairb@stanford.edu 10 Departments of Cardiothoracic Surgery and Pediatrics (Cardiology), Stanford University School of Medicine, Stanford, CA 94305, USA; doff@stanford.edu * Correspondence: bxling@stanford.edu; Tel.: +1-650-427-9198 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 in- terventions, 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. Keywords: early pregnancy; preeclampsia risk prediction; biomarker; urinary metabolite; LC-MS/MS 1. Introduction Preeclampsia (PE) is a severe hypertensive disorder that can contribute to the mortality and morbidity of pregnant women [1]. PE can cause problems in the liver, kidneys, brain, and blood coagulation system of pregnant women and can also lead to adverse pregnancy outcomes such as poor fetal growth and premature birth [2]. Early treatment with low-dose aspirin can effectively reduce the risk of developing PE [3]. Usually, PE develops after the 20th week of gestation. Therefore, accurate PE prediction before the 20th week could help Metabolites 2023, 13, 715. https://doi.org/10.3390/metabo13060715 https://www.mdpi.com/journal/metabolites