Analysing metabolomics to improve the prediction of pregnancy-related disorders

  • 17 August 2021

Using analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) to detect the presence of certain metabolites – the molecules produced during cell processes – could improve how we predict certain pregnancy-related disorders. A recent study, part funded by the NIHR Bristol BRC, discovered that profiling in this way could lead to improved prediction of pregnancy-related disorders and create tailored and more effective antenatal care.

Currently, prediction methods for the risk of common pregnancy-related disorders such as gestational diabetes, gestational hypertension and pre-eclampsia is reliant on classifying women using risk factors such as maternal smoking, age, ethnicity, and body mass index (BMI). In many cases women who don’t exhibit any of these risk factors can still experience complications in pregnancy.

Therefore, developing better tools for early prediction of risk of pregnancy related disorders is integral to improving earlier and more accurate identification of those high-risk categories of women not picked up by current screening processes.

The research team conducted a series of studies that found both MS and NMR showed supporting evidence for the use of blood-derived metabolomics to improve prediction of these common pregnancy-disorders. MS is more sensitive than NMR – NMR is less detailed and only detects larger metabolites such as lipids and therefore benefits from being more cost effective and shows greater clinical translation potential.

Although these studies show promising results, the authors stress that these tools should be considered an additive measure to the current risk factor prediction, as opposed to the replacement of existing screening processes.

The data for this study came from the Born in Bradford cohort and the Pregnancy Outcome Prediction study based in Cambridge. The next steps for this research would be to validate the findings in larger, independent cohorts of women and examine their accuracy when measured earlier on in pregnancy.

This research formed part of Dr. Nancy McBride’s PhD, she said:

“We are so grateful to the women from the Born in Bradford cohort and the Pregnancy Outcome Prediction study.

Their participation means we can undertake this work which can hopefully improve antenatal care for women – by improving identification of those at risk of pregnancy-related disorders, so we can target treatments to those who will benefit most. We look forward to further validating these models and evaluating their clinical use”


Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation by Nancy McBride, Paul Yousefi, Sara L. White, Lucilla Poston, Diane Farrar, Naveed Sattar, Scott M. Nelson, John Wright, Dan Mason, Matthew Suderman, Caroline Relton & Deborah A. Lawlor in BMC Medicine

Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation. By Nancy McBride, Paul Yousefi, Ull Sovio, Kurt Taylor, Yassaman Vafai, Tiffany Yang, Bo Hou Matthew Suderman Caroline Relton, Gordon C S Smith & Deborah A. Lawlor in Metabolite.