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

Author:

Al Ghadban Yasmina1,Du Yuheng2,Charnock‐Jones D. Stephen345ORCID,Garmire Lana X.2,Smith Gordon C. S.345ORCID,Sovio Ulla345ORCID

Affiliation:

1. Nuffield Department of Women's and Reproductive Health University of Oxford Oxford UK

2. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor Michigan USA

3. Department of Obstetrics and Gynaecology University of Cambridge Cambridge UK

4. NIHR Cambridge Biomedical Research Centre Cambridge UK

5. Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience University of Cambridge Cambridge UK

Abstract

AbstractObjectivesTo 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.DesignCase–cohort design within a prospective cohort study.SettingCambridge, UK.Population or sampleA total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.MethodsAn 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 measuressPTB and sETB.ResultsWe identified 47 metabolites, mostly lipids, as important predictors of sPTB by 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).ConclusionsWe 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.

Funder

Medical Research Council

National Institute of Child Health and Human Development

NIHR Cambridge Biomedical Research Centre

U.S. National Library of Medicine

Publisher

Wiley

Subject

Obstetrics and Gynecology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3