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Article

Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation

1
MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
2
NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol BS8 2BN, UK
3
Department of Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
4
NIHR Cambridge Biomedical Research Centre, Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge CB2 0QQ, UK
5
Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford BD9 6DA, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Guangju Zhai
Metabolites 2021, 11(8), 530; https://doi.org/10.3390/metabo11080530
Received: 16 June 2021 / Revised: 23 July 2021 / Accepted: 30 July 2021 / Published: 10 August 2021
(This article belongs to the Special Issue Metabolomics of Complex Traits II)
Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26–28 weeks’ gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models’ area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA. View Full-Text
Keywords: prediction; pregnancy; metabolomics; metabolites; mass spectrometry prediction; pregnancy; metabolomics; metabolites; mass spectrometry
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MDPI and ACS Style

McBride, N.; Yousefi, P.; Sovio, U.; Taylor, K.; Vafai, Y.; Yang, T.; Hou, B.; Suderman, M.; Relton, C.; Smith, G.C.S.; Lawlor, D.A. Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation. Metabolites 2021, 11, 530. https://doi.org/10.3390/metabo11080530

AMA Style

McBride N, Yousefi P, Sovio U, Taylor K, Vafai Y, Yang T, Hou B, Suderman M, Relton C, Smith GCS, Lawlor DA. Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation. Metabolites. 2021; 11(8):530. https://doi.org/10.3390/metabo11080530

Chicago/Turabian Style

McBride, Nancy, Paul Yousefi, Ulla Sovio, Kurt Taylor, Yassaman Vafai, Tiffany Yang, Bo Hou, Matthew Suderman, Caroline Relton, Gordon C.S. Smith, and Deborah A. Lawlor. 2021. "Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation" Metabolites 11, no. 8: 530. https://doi.org/10.3390/metabo11080530

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