Cargando…
Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation
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 fa...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399752/ https://www.ncbi.nlm.nih.gov/pubmed/34436471 http://dx.doi.org/10.3390/metabo11080530 |
_version_ | 1783745153124532224 |
---|---|
author | McBride, Nancy Yousefi, Paul Sovio, Ulla Taylor, Kurt Vafai, Yassaman Yang, Tiffany Hou, Bo Suderman, Matthew Relton, Caroline Smith, Gordon C. S. Lawlor, Deborah A. |
author_facet | McBride, Nancy Yousefi, Paul Sovio, Ulla Taylor, Kurt Vafai, Yassaman Yang, Tiffany Hou, Bo Suderman, Matthew Relton, Caroline Smith, Gordon C. S. Lawlor, Deborah A. |
author_sort | McBride, Nancy |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8399752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83997522021-08-29 Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation McBride, Nancy Yousefi, Paul Sovio, Ulla Taylor, Kurt Vafai, Yassaman Yang, Tiffany Hou, Bo Suderman, Matthew Relton, Caroline Smith, Gordon C. S. Lawlor, Deborah A. Metabolites Article 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. MDPI 2021-08-10 /pmc/articles/PMC8399752/ /pubmed/34436471 http://dx.doi.org/10.3390/metabo11080530 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article McBride, Nancy Yousefi, Paul Sovio, Ulla Taylor, Kurt Vafai, Yassaman Yang, Tiffany Hou, Bo Suderman, Matthew Relton, Caroline Smith, Gordon C. S. Lawlor, Deborah A. Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation |
title | Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation |
title_full | Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation |
title_fullStr | Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation |
title_full_unstemmed | Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation |
title_short | Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation |
title_sort | do mass spectrometry-derived metabolomics improve the prediction of pregnancy-related disorders? findings from a uk birth cohort with independent validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399752/ https://www.ncbi.nlm.nih.gov/pubmed/34436471 http://dx.doi.org/10.3390/metabo11080530 |
work_keys_str_mv | AT mcbridenancy domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT yousefipaul domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT sovioulla domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT taylorkurt domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT vafaiyassaman domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT yangtiffany domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT houbo domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT sudermanmatthew domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT reltoncaroline domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT smithgordoncs domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation AT lawlordeboraha domassspectrometryderivedmetabolomicsimprovethepredictionofpregnancyrelateddisordersfindingsfromaukbirthcohortwithindependentvalidation |