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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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Autores principales: Zhang, Yaqi, Sylvester, Karl G., Jin, Bo, Wong, Ronald J., Schilling, James, Chou, C. James, Han, Zhi, Luo, Ruben Y., Tian, Lu, Ladella, Subhashini, Mo, Lihong, Marić, Ivana, Blumenfeld, Yair J., Darmstadt, Gary L., Shaw, Gary M., Stevenson, David K., Whitin, John C., Cohen, Harvey J., McElhinney, Doff B., Ling, Xuefeng B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301596/
https://www.ncbi.nlm.nih.gov/pubmed/37367874
http://dx.doi.org/10.3390/metabo13060715
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author Zhang, Yaqi
Sylvester, Karl G.
Jin, Bo
Wong, Ronald J.
Schilling, James
Chou, C. James
Han, Zhi
Luo, Ruben Y.
Tian, Lu
Ladella, Subhashini
Mo, Lihong
Marić, Ivana
Blumenfeld, Yair J.
Darmstadt, Gary L.
Shaw, Gary M.
Stevenson, David K.
Whitin, John C.
Cohen, Harvey J.
McElhinney, Doff B.
Ling, Xuefeng B.
author_facet Zhang, Yaqi
Sylvester, Karl G.
Jin, Bo
Wong, Ronald J.
Schilling, James
Chou, C. James
Han, Zhi
Luo, Ruben Y.
Tian, Lu
Ladella, Subhashini
Mo, Lihong
Marić, Ivana
Blumenfeld, Yair J.
Darmstadt, Gary L.
Shaw, Gary M.
Stevenson, David K.
Whitin, John C.
Cohen, Harvey J.
McElhinney, Doff B.
Ling, Xuefeng B.
author_sort Zhang, Yaqi
collection PubMed
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.
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spelling pubmed-103015962023-06-29 Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy Zhang, Yaqi Sylvester, Karl G. Jin, Bo Wong, Ronald J. Schilling, James Chou, C. James Han, Zhi Luo, Ruben Y. Tian, Lu Ladella, Subhashini Mo, Lihong Marić, Ivana Blumenfeld, Yair J. Darmstadt, Gary L. Shaw, Gary M. Stevenson, David K. Whitin, John C. Cohen, Harvey J. McElhinney, Doff B. Ling, Xuefeng B. Metabolites Article 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. MDPI 2023-05-31 /pmc/articles/PMC10301596/ /pubmed/37367874 http://dx.doi.org/10.3390/metabo13060715 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Zhang, Yaqi
Sylvester, Karl G.
Jin, Bo
Wong, Ronald J.
Schilling, James
Chou, C. James
Han, Zhi
Luo, Ruben Y.
Tian, Lu
Ladella, Subhashini
Mo, Lihong
Marić, Ivana
Blumenfeld, Yair J.
Darmstadt, Gary L.
Shaw, Gary M.
Stevenson, David K.
Whitin, John C.
Cohen, Harvey J.
McElhinney, Doff B.
Ling, Xuefeng B.
Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301596/
https://www.ncbi.nlm.nih.gov/pubmed/37367874
http://dx.doi.org/10.3390/metabo13060715
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