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First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study

Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the meta...

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Autores principales: Austdal, Marie, Tangerås, Line H., Skråstad, Ragnhild B., Salvesen, Kjell Å., Austgulen, Rigmor, Iversen, Ann-Charlotte, Bathen, Tone F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4613265/
https://www.ncbi.nlm.nih.gov/pubmed/26370975
http://dx.doi.org/10.3390/ijms160921520
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author Austdal, Marie
Tangerås, Line H.
Skråstad, Ragnhild B.
Salvesen, Kjell Å.
Austgulen, Rigmor
Iversen, Ann-Charlotte
Bathen, Tone F.
author_facet Austdal, Marie
Tangerås, Line H.
Skråstad, Ragnhild B.
Salvesen, Kjell Å.
Austgulen, Rigmor
Iversen, Ann-Charlotte
Bathen, Tone F.
author_sort Austdal, Marie
collection PubMed
description Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.
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spelling pubmed-46132652015-10-26 First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study Austdal, Marie Tangerås, Line H. Skråstad, Ragnhild B. Salvesen, Kjell Å. Austgulen, Rigmor Iversen, Ann-Charlotte Bathen, Tone F. Int J Mol Sci Article Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia. MDPI 2015-09-08 /pmc/articles/PMC4613265/ /pubmed/26370975 http://dx.doi.org/10.3390/ijms160921520 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Austdal, Marie
Tangerås, Line H.
Skråstad, Ragnhild B.
Salvesen, Kjell Å.
Austgulen, Rigmor
Iversen, Ann-Charlotte
Bathen, Tone F.
First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study
title First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study
title_full First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study
title_fullStr First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study
title_full_unstemmed First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study
title_short First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study
title_sort first trimester urine and serum metabolomics for prediction of preeclampsia and gestational hypertension: a prospective screening study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4613265/
https://www.ncbi.nlm.nih.gov/pubmed/26370975
http://dx.doi.org/10.3390/ijms160921520
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