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Published on: January 26, 2024
Metabolomic prediction of severe maternal and newborn complications in preeclampsia
Jay Idler1,2, Onur Turkoglu3, Ali Yilmaz4
1Drexel College of Medicine, Philadelphia, PA, USA. jayidler@gmail.com.
Insights
Prenatal metabolomic analysis accurately predicts maternal and newborn complications in preeclampsia (PreE). This approach offers superior prediction compared to traditional methods, aiding in better pregnancy management.
Area of Science:
- Obstetrics and Gynecology
- Metabolomics
- Biochemistry
Background:
- Preeclampsia (PreE) is a significant cause of maternal and newborn complications.
- Effective prenatal prediction is crucial for improved pregnancy management.
Purpose of the Study:
- To utilize metabolomic analysis for prenatal prediction of maternal and newborn complications in early and late preeclampsia.
- To investigate the underlying pathogenesis of these complications.
Main Methods:
- Serum samples from 76 preeclampsia cases and 40 controls were analyzed using Liquid Chromatography-Mass Spectrometry and NMR spectroscopy.
- Logistic regression and metabolite set enrichment analysis (MSEA) were employed for prediction and pathway identification.
Main Results:
- Significant alterations in 43 metabolites for maternal complications and 162 for newborn outcomes were observed.
- Prediction models achieved high AUC values (0.806-0.911) for both early and late-onset preeclampsia.
- Lipid metabolism dysregulation was strongly associated with adverse outcomes.
Conclusions:
- Prenatal metabolomic markers provide robust prediction of adverse outcomes in preeclampsia.
- This study establishes the predictive capability and metabolomic basis of adverse outcomes in preeclampsia.
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.
