Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy
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 exa...
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MDPI AG
2023-05-01
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Online Access: | https://www.mdpi.com/2218-1989/13/6/715 |
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author | 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 Xuefeng B. Ling |
author_facet | 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 Xuefeng B. Ling |
author_sort | Yaqi Zhang |
collection | DOAJ |
description | 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. |
first_indexed | 2024-03-11T02:09:57Z |
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id | doaj.art-12f704f046f849cbb2610c1b43b80fd1 |
institution | Directory Open Access Journal |
issn | 2218-1989 |
language | English |
last_indexed | 2024-03-11T02:09:57Z |
publishDate | 2023-05-01 |
publisher | MDPI AG |
record_format | Article |
series | Metabolites |
spelling | doaj.art-12f704f046f849cbb2610c1b43b80fd12023-11-18T11:34:42ZengMDPI AGMetabolites2218-19892023-05-0113671510.3390/metabo13060715Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early PregnancyYaqi Zhang0Karl G. Sylvester1Bo Jin2Ronald J. Wong3James Schilling4C. James Chou5Zhi Han6Ruben Y. Luo7Lu Tian8Subhashini Ladella9Lihong Mo10Ivana Marić11Yair J. Blumenfeld12Gary L. Darmstadt13Gary M. Shaw14David K. Stevenson15John C. Whitin16Harvey J. Cohen17Doff B. McElhinney18Xuefeng B. Ling19College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaDepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USAmProbe Inc., Palo Alto, CA 94303, USADepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USAmProbe Inc., Palo Alto, CA 94303, USADepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USACommunity Medical Centers, UCSF Fresno, Fresno, CA 93722, USAUC Davis Health, Sacramento, CA 95817, USADepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USADepartments of Cardiothoracic Surgery and Pediatrics (Cardiology), Stanford University School of Medicine, Stanford, CA 94305, USADepartment of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USAPreeclampsia (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.https://www.mdpi.com/2218-1989/13/6/715early pregnancypreeclampsia risk predictionbiomarkerurinary metaboliteLC-MS/MS |
spellingShingle | 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 Xuefeng B. Ling Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy Metabolites early pregnancy preeclampsia risk prediction biomarker urinary metabolite LC-MS/MS |
title | Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy |
title_full | Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy |
title_fullStr | Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy |
title_full_unstemmed | Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy |
title_short | Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy |
title_sort | development of a urine metabolomics biomarker based prediction model for preeclampsia during early pregnancy |
topic | early pregnancy preeclampsia risk prediction biomarker urinary metabolite LC-MS/MS |
url | https://www.mdpi.com/2218-1989/13/6/715 |
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