ArticleScientific reports2022
Prediction of gestational age using urinary metabolites in term and preterm pregnancies.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Multi-Omics for Mothers and Infants (MOMI) Consortium: a global initiative to study adverse pregnancy outcomes.Journal of global health · 2026Article
- Review
- Association between gestational weight gain and adverse pregnancy outcomes: cohort analysis from South Asia and Sub-Saharan Africa.BMJ public health · 2025Article
- Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling.BMC pregnancy and childbirth · 2024Article
- Longitudinal urine metabolic profiling and gestational age prediction in human pregnancy.Briefings in bioinformatics · 2024Article
- Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.Clinics in perinatology · 2024Review
- Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis.EClinicalMedicine · 2024Article
- Maternal Vaccination to Prevent Adverse Pregnancy Outcomes: An Underutilized Molecular Immunological Intervention?Journal of molecular biology · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
50 authors.
Funding
Abstract
Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.
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Registered trials
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