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

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.

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Publication Details

Journal
BMC Pregnancy and Childbirth
Published
2024-11-25
DOI
https://doi.org/10.1186/s12884-024-06974-2
Citations
5
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
1.17

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article

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

Gary L. Darmstadt, Zhi Han, Xuefeng B. Ling, Doff B. McElhinney et al.
5 citations
BMC Pregnancy and Childbirth
Metabolomics and Mass Spectrometry Studies
1.17
article

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

Gary L. Darmstadt, Zhi Han, Xuefeng B. Ling, Doff B. McElhinney, Sheeno Thyparambil, Martin S. Angst, Lü Tian, Ruben Yiqi Luo, Yair J. Blumenfeld, Kuo‐Yuan Hwa, Nima Aghaeepour, Gary M. Shaw, Brice Gaudillière, Chen-Rui Chou, Virginia D. Winn, Ronald J. Wong, Yaqi Zhang, Karl G. Sylvester, Ivana Marić, David K. Stevenson, Harvey J. Cohen, Bo Jin, Wei‐Li Liao
article en
5 citations

Abstract

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.

BMC Pregnancy and ChildbirthVol. 24(1)
National Taipei University of Technology (TW), Guangdong Polytechnic Normal University (CN), Stanford Medicine (US), Stanford University (US)
March of Dimes Foundation, School of Medicine, Stanford University
Openalex Percentile: Top 21%
Metabolomics and Mass Spectrometry Studies
1.17
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