Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Author:

Zhang Yaqi12,Sylvester Karl G.2,Jin Bo3,Wong Ronald J.4ORCID,Schilling James3,Chou C. James2,Han Zhi2,Luo Ruben Y.5ORCID,Tian Lu6,Ladella Subhashini7,Mo Lihong8ORCID,Marić Ivana4,Blumenfeld Yair J.9,Darmstadt Gary L.4ORCID,Shaw Gary M.4,Stevenson David K.4,Whitin John C.4,Cohen Harvey J.4,McElhinney Doff B.10,Ling Xuefeng B.2

Affiliation:

1. College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China

2. Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA

3. mProbe Inc., Palo Alto, CA 94303, USA

4. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA

5. Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA

6. Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA

7. Community Medical Centers, UCSF Fresno, Fresno, CA 93722, USA

8. UC Davis Health, Sacramento, CA 95817, USA

9. Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA

10. Departments of Cardiothoracic Surgery and Pediatrics (Cardiology), Stanford University School of Medicine, Stanford, CA 94305, USA

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.

Funder

March of Dimes

the Hess Research Fund

the Roberts Research Fund

the Ballinger Family Prematurity Research Fund

Publisher

MDPI AG

Subject

Molecular Biology,Biochemistry,Endocrinology, Diabetes and Metabolism

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