Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings f
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RESEARCH ARTICLE
Open Access
Do nuclear magnetic resonance (NMR)based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation Nancy McBride1,2,3* , Paul Yousefi1,3, Sara L. White4, Lucilla Poston4, Diane Farrar5, Naveed Sattar2,6,7, Scott M. Nelson2,6,7, John Wright5, Dan Mason5, Matthew Suderman1,3, Caroline Relton1,3 and Deborah A. Lawlor1,2,3
Abstract Background: Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders. Methods: We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonancederived metabolites (N = 156 quantified metabolites, collected at 24–28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women. (Continued on next page)
* Correspondence: [email protected] 1 MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK 2 NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
McBride et al. BMC
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