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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Autor*in:	Takafumi Yamauchi [verfasserIn] Daisuke Ochi [verfasserIn] Naomi Matsukawa [verfasserIn] Daisuke Saigusa [verfasserIn] Mami Ishikuro [verfasserIn] Taku Obara [verfasserIn] Yoshiki Tsunemoto [verfasserIn] Satsuki Kumatani [verfasserIn] Riu Yamashita [verfasserIn] Osamu Tanabe [verfasserIn] Naoko Minegishi [verfasserIn] Seizo Koshiba [verfasserIn] Hirohito Metoki [verfasserIn] Shinichi Kuriyama [verfasserIn] Nobuo Yaegashi [verfasserIn] Masayuki Yamamoto [verfasserIn] Masao Nagasaki [verfasserIn] Satoshi Hiyama [verfasserIn] Junichi Sugawara [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: Scientific Reports - Nature Portfolio, 2011, 11(2021), 1, Seite 12
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/s41598-021-97342-z

Katalog-ID:	DOAJ052258483

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allfields	10.1038/s41598-021-97342-z doi (DE-627)DOAJ052258483 (DE-599)DOAJ29fb6bbfa97f4ccc98f20c973417a67f DE-627 ger DE-627 rakwb eng Takafumi Yamauchi verfasserin aut Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Medicine R Science Q Daisuke Ochi verfasserin aut Naomi Matsukawa verfasserin aut Daisuke Saigusa verfasserin aut Mami Ishikuro verfasserin aut Taku Obara verfasserin aut Yoshiki Tsunemoto verfasserin aut Satsuki Kumatani verfasserin aut Riu Yamashita verfasserin aut Osamu Tanabe verfasserin aut Naoko Minegishi verfasserin aut Seizo Koshiba verfasserin aut Hirohito Metoki verfasserin aut Shinichi Kuriyama verfasserin aut Nobuo Yaegashi verfasserin aut Masayuki Yamamoto verfasserin aut Masao Nagasaki verfasserin aut Satoshi Hiyama verfasserin aut Junichi Sugawara verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 12 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:12 https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f kostenfrei https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 12
spelling	10.1038/s41598-021-97342-z doi (DE-627)DOAJ052258483 (DE-599)DOAJ29fb6bbfa97f4ccc98f20c973417a67f DE-627 ger DE-627 rakwb eng Takafumi Yamauchi verfasserin aut Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Medicine R Science Q Daisuke Ochi verfasserin aut Naomi Matsukawa verfasserin aut Daisuke Saigusa verfasserin aut Mami Ishikuro verfasserin aut Taku Obara verfasserin aut Yoshiki Tsunemoto verfasserin aut Satsuki Kumatani verfasserin aut Riu Yamashita verfasserin aut Osamu Tanabe verfasserin aut Naoko Minegishi verfasserin aut Seizo Koshiba verfasserin aut Hirohito Metoki verfasserin aut Shinichi Kuriyama verfasserin aut Nobuo Yaegashi verfasserin aut Masayuki Yamamoto verfasserin aut Masao Nagasaki verfasserin aut Satoshi Hiyama verfasserin aut Junichi Sugawara verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 12 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:12 https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f kostenfrei https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 12
allfields_unstemmed	10.1038/s41598-021-97342-z doi (DE-627)DOAJ052258483 (DE-599)DOAJ29fb6bbfa97f4ccc98f20c973417a67f DE-627 ger DE-627 rakwb eng Takafumi Yamauchi verfasserin aut Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Medicine R Science Q Daisuke Ochi verfasserin aut Naomi Matsukawa verfasserin aut Daisuke Saigusa verfasserin aut Mami Ishikuro verfasserin aut Taku Obara verfasserin aut Yoshiki Tsunemoto verfasserin aut Satsuki Kumatani verfasserin aut Riu Yamashita verfasserin aut Osamu Tanabe verfasserin aut Naoko Minegishi verfasserin aut Seizo Koshiba verfasserin aut Hirohito Metoki verfasserin aut Shinichi Kuriyama verfasserin aut Nobuo Yaegashi verfasserin aut Masayuki Yamamoto verfasserin aut Masao Nagasaki verfasserin aut Satoshi Hiyama verfasserin aut Junichi Sugawara verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 12 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:12 https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f kostenfrei https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 12
allfieldsGer	10.1038/s41598-021-97342-z doi (DE-627)DOAJ052258483 (DE-599)DOAJ29fb6bbfa97f4ccc98f20c973417a67f DE-627 ger DE-627 rakwb eng Takafumi Yamauchi verfasserin aut Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Medicine R Science Q Daisuke Ochi verfasserin aut Naomi Matsukawa verfasserin aut Daisuke Saigusa verfasserin aut Mami Ishikuro verfasserin aut Taku Obara verfasserin aut Yoshiki Tsunemoto verfasserin aut Satsuki Kumatani verfasserin aut Riu Yamashita verfasserin aut Osamu Tanabe verfasserin aut Naoko Minegishi verfasserin aut Seizo Koshiba verfasserin aut Hirohito Metoki verfasserin aut Shinichi Kuriyama verfasserin aut Nobuo Yaegashi verfasserin aut Masayuki Yamamoto verfasserin aut Masao Nagasaki verfasserin aut Satoshi Hiyama verfasserin aut Junichi Sugawara verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 12 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:12 https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f kostenfrei https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 12
allfieldsSound	10.1038/s41598-021-97342-z doi (DE-627)DOAJ052258483 (DE-599)DOAJ29fb6bbfa97f4ccc98f20c973417a67f DE-627 ger DE-627 rakwb eng Takafumi Yamauchi verfasserin aut Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Medicine R Science Q Daisuke Ochi verfasserin aut Naomi Matsukawa verfasserin aut Daisuke Saigusa verfasserin aut Mami Ishikuro verfasserin aut Taku Obara verfasserin aut Yoshiki Tsunemoto verfasserin aut Satsuki Kumatani verfasserin aut Riu Yamashita verfasserin aut Osamu Tanabe verfasserin aut Naoko Minegishi verfasserin aut Seizo Koshiba verfasserin aut Hirohito Metoki verfasserin aut Shinichi Kuriyama verfasserin aut Nobuo Yaegashi verfasserin aut Masayuki Yamamoto verfasserin aut Masao Nagasaki verfasserin aut Satoshi Hiyama verfasserin aut Junichi Sugawara verfasserin aut In Scientific Reports Nature Portfolio, 2011 11(2021), 1, Seite 12 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:11 year:2021 number:1 pages:12 https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f kostenfrei https://doi.org/10.1038/s41598-021-97342-z kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 1 12
language	English
source	In Scientific Reports 11(2021), 1, Seite 12 volume:11 year:2021 number:1 pages:12
sourceStr	In Scientific Reports 11(2021), 1, Seite 12 volume:11 year:2021 number:1 pages:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Medicine R Science Q
isfreeaccess_bool	true
container_title	Scientific Reports
authorswithroles_txt_mv	Takafumi Yamauchi @@aut@@ Daisuke Ochi @@aut@@ Naomi Matsukawa @@aut@@ Daisuke Saigusa @@aut@@ Mami Ishikuro @@aut@@ Taku Obara @@aut@@ Yoshiki Tsunemoto @@aut@@ Satsuki Kumatani @@aut@@ Riu Yamashita @@aut@@ Osamu Tanabe @@aut@@ Naoko Minegishi @@aut@@ Seizo Koshiba @@aut@@ Hirohito Metoki @@aut@@ Shinichi Kuriyama @@aut@@ Nobuo Yaegashi @@aut@@ Masayuki Yamamoto @@aut@@ Masao Nagasaki @@aut@@ Satoshi Hiyama @@aut@@ Junichi Sugawara @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	663366712
id	DOAJ052258483
language_de	englisch
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author	Takafumi Yamauchi
spellingShingle	Takafumi Yamauchi misc Medicine misc R misc Science misc Q Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
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topic_title	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
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title	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
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title_full	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
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author_browse	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
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title_sort	machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
title_auth	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis
url	https://doi.org/10.1038/s41598-021-97342-z https://doaj.org/article/29fb6bbfa97f4ccc98f20c973417a67f https://doaj.org/toc/2045-2322
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author2	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
author2Str	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
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Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

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