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Metabolomic prediction of severe maternal and newborn complications in preeclampsia
Journal article   Open access   Peer reviewed

Metabolomic prediction of severe maternal and newborn complications in preeclampsia

Jay Idler, Onur Turkoglu, Ali Yilmaz, NAdia Ashrafi, Marta Szymanska, Ilyas Ustun, Kara Patek, Amy Whitten, Stewart F. Graham and Ray O. Bahado-Singh
Metabolics , v 20, 56
18 May 2024
DOI: https://doi.org/10.1007/s11306-024-02123-0
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1007/s11306-024-02123-0View
Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2024CC BY V4.0,  Open

Abstract

Introduction Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management. Objectives Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications. Methods Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography–Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE. Results Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660–0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712–0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674–0.982) in early-onset PreE and 0.911 (0.828–0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes. Conclusion Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.

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3 citations in Web of Science
2 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#5 Gender Equality

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Collaboration types
Domestic collaboration
Web of Science research areas
Endocrinology & Metabolism
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