Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

Abstract The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predic...

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Autores principales:	Takafumi Yamauchi, Daisuke Ochi, Naomi Matsukawa, Daisuke Saigusa, Mami Ishikuro, Taku Obara, Yoshiki Tsunemoto, Satsuki Kumatani, Riu Yamashita, Osamu Tanabe, Naoko Minegishi, Seizo Koshiba, Hirohito Metoki, Shinichi Kuriyama, Nobuo Yaegashi, Masayuki Yamamoto, Masao Nagasaki, Satoshi Hiyama, Junichi Sugawara
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f
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spelling	oai:doaj.org-article:29fb6bbfa97f4ccc98f20c973417a67f2021-12-02T17:41:18ZMachine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis10.1038/s41598-021-97342-z2045-2322https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97342-zhttps://doaj.org/toc/2045-2322Abstract The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.Takafumi YamauchiDaisuke OchiNaomi MatsukawaDaisuke SaigusaMami IshikuroTaku ObaraYoshiki TsunemotoSatsuki KumataniRiu YamashitaOsamu TanabeNaoko MinegishiSeizo KoshibaHirohito MetokiShinichi KuriyamaNobuo YaegashiMasayuki YamamotoMasao NagasakiSatoshi HiyamaJunichi SugawaraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Takafumi Yamauchi Daisuke Ochi Naomi Matsukawa Daisuke Saigusa Mami Ishikuro Taku Obara Yoshiki Tsunemoto Satsuki Kumatani Riu Yamashita Osamu Tanabe Naoko Minegishi Seizo Koshiba Hirohito Metoki Shinichi Kuriyama Nobuo Yaegashi Masayuki Yamamoto Masao Nagasaki Satoshi Hiyama Junichi Sugawara Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
description	Abstract The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.
format	article
author	Takafumi Yamauchi Daisuke Ochi Naomi Matsukawa Daisuke Saigusa Mami Ishikuro Taku Obara Yoshiki Tsunemoto Satsuki Kumatani Riu Yamashita Osamu Tanabe Naoko Minegishi Seizo Koshiba Hirohito Metoki Shinichi Kuriyama Nobuo Yaegashi Masayuki Yamamoto Masao Nagasaki Satoshi Hiyama Junichi Sugawara
author_facet	Takafumi Yamauchi Daisuke Ochi Naomi Matsukawa Daisuke Saigusa Mami Ishikuro Taku Obara Yoshiki Tsunemoto Satsuki Kumatani Riu Yamashita Osamu Tanabe Naoko Minegishi Seizo Koshiba Hirohito Metoki Shinichi Kuriyama Nobuo Yaegashi Masayuki Yamamoto Masao Nagasaki Satoshi Hiyama Junichi Sugawara
author_sort	Takafumi Yamauchi
title	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
title_short	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
title_full	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
title_fullStr	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
title_full_unstemmed	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
title_sort	machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f
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Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

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