Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data - A case-cohort study

Aug 5, 2026

Objectives: Identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods with sequential maternal serum samples, and predict spontaneous early term birth (sETB) using these metabolites. Design: Case–cohort study within a prospective cohort. Setting: Cambridge, UK. Methods: Untargeted metabolomics of maternal serum at 12, 20, 28, and 36 weeks; six supervised models and weighted Cox modeling at 28 weeks with feature selection, then cross-gestational prediction. Results: 47 metabolites (mostly lipids) predicted sPTB; a 4-metabolite model showed optimism-corrected AUC 0.703 at 28 weeks. Conclusions: Maternal serum metabolites predictive of sPTB were identified and internally validated, including a novel lysolipid biomarker; external validation is needed.

LanguageEnglish

CategoryResearch & Report

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Reading Time23 min

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Introduction

Background and clinical importance

Metabolomics and prior prediction work

Machine learning challenges and study rationale

Accepted: 2 November 2023 DOI: 10. 1111/1471-0528 .17723 R E S E A R C H A R T I C L E Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case–cohort study Yasmina Al Ghadban1 | Yuheng Du2 | D. Stephen Charnock-Jones3,4,5 | Lana X. Garmire2 | Gordon C. S. Smith3,4,5 | Ulla Sovio3,4,5 1Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA 3Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK 4NIHR Cambridge Biomedical Research Centre, Cambridge, UK 5Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK Correspondence Ulla Sovio, Department of Obstetrics and Gynaecology, University of Cambridge, Box 223 The Rosie Hospital, Cambridge CB2 0SW, UK. [Email:](Email: us253@medschl.cam.ac.uk)[ us253@medschl.cam.ac.uk](Email: us253@medschl.cam.ac.uk) Funding information Cambridge Reproduction; Medical Research Council, Grant/Award Number: G1100221; National Institute of Child Health and Human Development, Grant/Award Number: R01HD084633; NIHR Cambridge Biomedical Research Centre, Grant/Award Number: BRC-1215-20014; U. S. National Library of Medicine, Grant/Award Number: R01LM012373 and R01LM12907 Abstract Objectives: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites. Design: Case–cohort design within a prospective cohort study. Setting: Cambridge, UK. Population or sample: A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB. Methods: An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB. Main outcome measures: sPTB and sETB. Results: We identified 47 metabolites, mostly lipids, as important predictors of sPTBby two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544–0.667) and 20-week samples (0.657, 95% CI 0.597–0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606–0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548–0.670), 20 weeks (0.630, 95% CI 0 .569–0.690) and 28 weeks (0 .660, 95% CI 0.599–0.722), and of sETB at 36 weeks (0 .739, 95% CI 0 .618–0. 860). Conclusions: We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required. K E Y WO R D S metabolomics, prediction, preterm, risk, spontaneous This article includes Author Insights, a video abstract available at: [https://drive.google.com/file/d/1escFXSBGikAjByXWYWFkeyM7nPL6JWtO/view?usp=sharing](https://drive.google.com/file/d/1escFXSBGikAjByXWYWFkeyM7nPL6JWtO/view?usp=sharing). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the ori

FAQs

What was the primary objective of this study?

To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods, and to predict spontaneous early term birth (sETB) using these metabolites.

How were metabolites measured and at what gestational time points?

Maternal serum was analyzed using untargeted metabolomics at 12, 20, 28, and 36 weeks of gestation.

Which model performance and biomarker results were reported?

The best 4-predictor model had an optimism-corrected AUC of 0.703 at 28 weeks, and the lysolipid 1-palmitoleoyl-GPE (16:1) was the strongest predictor across multiple gestational ages and also for sETB at 36 weeks.

Aug 5, 2026

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