NDLI: Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling

Content Provider	Springer Nature : BioMed Central
Author	Zhang, Yaqi Sylvester, Karl G. Wong, Ronald J. Blumenfeld, Yair J. Hwa, Kuo Yuan Chou, C. James Thyparambil, Sheeno Liao, Weili Han, Zhi Schilling, James Jin, Bo Marić, Ivana Aghaeepour, Nima Angst, Martin S. Gaudilliere, Brice Winn, Virginia D. Shaw, Gary M. Tian, Lu Luo, Ruben Y. Darmstadt, Gary L. Cohen, Harvey J. Stevenson, David K. McElhinney, Doff B. Ling, Xuefeng B.
Abstract	Background Preterm birth (PTB) is a serious health problem. PTB complications is the main cause of death in infants under five years of age worldwide. The ability to accurately predict risk for PTB during early pregnancy would allow early monitoring and interventions to provide personalized care, and hence improve outcomes for the mother and infant. Objective This study aims to predict the risks of early preterm (< 35 weeks of gestation) or very early preterm (≤ 26 weeks of gestation) deliveries by using high-resolution maternal urinary metabolomic profiling in early pregnancy. Design A retrospective cohort study was conducted by two independent preterm and term cohorts using high-density weekly urine sampling. Maternal urine was collected serially at gestational weeks 8 to 24. Global metabolomics approaches were used to profile urine samples with high-resolution mass spectrometry. The significant features associated with preterm outcomes were selected by Gini Importance. Metabolite biomarker identification was performed by liquid chromatography tandem mass spectrometry (LCMS-MS). XGBoost models were developed to predict early or very early preterm delivery risk. Setting and participants The urine samples included 329 samples from 30 subjects at Stanford University, CA for model development, and 156 samples from 24 subjects at the University of Alabama, Birmingham, AL for validation. Results 12 metabolites associated with PTB were selected and identified for modelling among 7,913 metabolic features in serial-collected urine samples of pregnant women. The model to predict early PTB was developed using a set of 12 metabolites that resulted in the area under the receiver operating characteristic (AUROCs) of 0.995 (95% CI: [0.992, 0.995]) and 0.964 (95% CI: [0.937, 0.964]), and sensitivities of 100% and 97.4% during development and validation testing, respectively. Using the same metabolites, the very early PTB prediction model achieved AUROCs of 0.950 (95% CI: [0.878, 0.950]) and 0.830 (95% CI: [0.687, 0.826]), and sensitivities of 95.0% and 60.0% during development and validation, respectively. Conclusion Models for predicting risk of early or very early preterm deliveries were developed and tested using metabolic profiling during the 1st and 2nd trimesters of pregnancy. With patient validation studies, risk prediction models may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights of preterm birth.
Related Links	https://bmcpregnancychildbirth.biomedcentral.com/counter/pdf/10.1186/s12884-024-06974-2.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14712393
DOI	10.1186/s12884-024-06974-2
Journal	BMC Pregnancy and Childbirth
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-11-25
Access Restriction	Open
Subject Keyword	Reproductive Medicine Maternal and Child Health Gynecology Early pregnancy Preterm risk prediction Spontaneous preterm birth Biomarker Urinary metabolite LC-MS/MS
Content Type	Text
Resource Type	Article
Subject	Obstetrics and Gynecology
Journal Impact Factor	2.8/2023
5-Year Journal Impact Factor	3.4/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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