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Updated: May 17, 2025

The 4-vessel Sampling Approach to Integrative Studies of Human Placental Physiology In Vivo

The 4-vessel Sampling Approach to Integrative Studies of Human Placental Physiology In Vivo

Published on: August 2, 2017

Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and

Sarah Miller¹, Deirdre Lyell, Ivana Maric

¹Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, Massachusetts; the Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, the Department of Pediatrics, the Metabolic Health Center, the Division of Pediatric Surgery, Department of General Surgery, the Department of Genetics, the Department of Anesthesiology, Peri-operative, and Pain Medicine, and the Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, and the Department of Physiology and Membrane Biology, University of California, Davis, Davis, California; and the Division of Maternal Fetal Medicine, University of Utah Health, Salt Lake City, Utah.

Obstetrics and Gynecology

|May 15, 2025

Summary

This summary is machine-generated.

Metabolomic and lipidomic profiling showed similar predictive performance to clinical risk factors for predicting placenta accreta spectrum (PAS) using machine learning. These advanced analyses did not outperform traditional clinical characteristics in identifying PAS risk.

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Area of Science:

Obstetrics and Gynecology
Biomarker Discovery
Computational Biology

Background:

Placenta accreta spectrum (PAS) is a high-risk obstetric complication.
Accurate prediction of PAS is crucial for improving maternal outcomes.
Current prediction methods rely heavily on clinical characteristics.

Purpose of the Study:

To investigate the utility of metabolomic and lipidomic profiling in predicting PAS.
To identify potential plasma biomarkers for PAS.
To compare the predictive performance of machine learning models integrating clinical data with omics profiles.

Main Methods:

A multicenter case-control study involving patients with PAS and controls.
Plasma samples from the third trimester were analyzed using untargeted metabolomics and targeted lipidomics.
Machine learning models (elastic net) were trained using clinical features, omics data, and integrated datasets.

Main Results:

While univariate analysis identified numerous associated lipids and metabolites, these did not remain significant after adjustment.
Machine learning models using only clinical characteristics, lipidomics, or metabolomics showed comparable predictive performance (AUCs ranging from 0.685 to 0.71).
Integration of metabolomic and lipidomic data with clinical features did not significantly improve PAS prediction accuracy.

Conclusions:

Metabolomic and lipidomic profiling, when analyzed with machine learning, did not offer superior predictive value for PAS compared to clinical risk factors alone.
Further research may be needed to identify specific omics biomarkers or refine analytical approaches for PAS prediction.