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Plasma metabolic disturbances during pregnancy and postpartum in women with depression

Examining plasma metabolic profiling during pregnancy and postpartum could help clinicians understand the risk factors for postpartum depression (PPD) development. This analysis targeted paired plasma metabolites in mid-late gestational and 1 month postpartum periods in women with (n = 209) or witho...

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Autores principales: Yu, Zhiqian, Matsukawa, Naomi, Saigusa, Daisuke, Motoike, Ikuko N., Ono, Chiaki, Okamura, Yasunobu, Onuma, Tomomi, Takahashi, Yuta, Sakai, Mai, Kudo, Hisaaki, Obara, Taku, Murakami, Keiko, Shirota, Matusyuki, Kikuchi, Saya, Kobayashi, Natsuko, Kikuchi, Yoshie, Sugawara, Junichi, Minegishi, Naoko, Ogishima, Soichi, Kinoshita, Kengo, Yamamoto, Masayuki, Yaegashi, Nobuo, Kuriyama, Shinichi, Koshiba, Seizo, Tomita, Hiroaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732390/
https://www.ncbi.nlm.nih.gov/pubmed/36505921
http://dx.doi.org/10.1016/j.isci.2022.105666
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author Yu, Zhiqian
Matsukawa, Naomi
Saigusa, Daisuke
Motoike, Ikuko N.
Ono, Chiaki
Okamura, Yasunobu
Onuma, Tomomi
Takahashi, Yuta
Sakai, Mai
Kudo, Hisaaki
Obara, Taku
Murakami, Keiko
Shirota, Matusyuki
Kikuchi, Saya
Kobayashi, Natsuko
Kikuchi, Yoshie
Sugawara, Junichi
Minegishi, Naoko
Ogishima, Soichi
Kinoshita, Kengo
Yamamoto, Masayuki
Yaegashi, Nobuo
Kuriyama, Shinichi
Koshiba, Seizo
Tomita, Hiroaki
author_facet Yu, Zhiqian
Matsukawa, Naomi
Saigusa, Daisuke
Motoike, Ikuko N.
Ono, Chiaki
Okamura, Yasunobu
Onuma, Tomomi
Takahashi, Yuta
Sakai, Mai
Kudo, Hisaaki
Obara, Taku
Murakami, Keiko
Shirota, Matusyuki
Kikuchi, Saya
Kobayashi, Natsuko
Kikuchi, Yoshie
Sugawara, Junichi
Minegishi, Naoko
Ogishima, Soichi
Kinoshita, Kengo
Yamamoto, Masayuki
Yaegashi, Nobuo
Kuriyama, Shinichi
Koshiba, Seizo
Tomita, Hiroaki
author_sort Yu, Zhiqian
collection PubMed
description Examining plasma metabolic profiling during pregnancy and postpartum could help clinicians understand the risk factors for postpartum depression (PPD) development. This analysis targeted paired plasma metabolites in mid-late gestational and 1 month postpartum periods in women with (n = 209) or without (n = 222) PPD. Gas chromatogram-mass spectrometry was used to analyze plasma metabolites at these two time points. Among the 170 objected plasma metabolites, principal component analysis distinguished pregnancy and postpartum metabolites but failed to discriminate women with and without PPD. Compared to women without PPD, those with PPD exhibited 37 metabolites with disparate changes during pregnancy and the 1-month postpartum period and an enriched citrate cycle. Machine learning and multivariate statistical analysis identified two or three compounds that could be potential biomarkers for PPD prediction during pregnancy. Our findings suggest metabolic disturbances in women with depression and may help to elucidate metabolic processes associated with PPD development.
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spelling pubmed-97323902022-12-10 Plasma metabolic disturbances during pregnancy and postpartum in women with depression Yu, Zhiqian Matsukawa, Naomi Saigusa, Daisuke Motoike, Ikuko N. Ono, Chiaki Okamura, Yasunobu Onuma, Tomomi Takahashi, Yuta Sakai, Mai Kudo, Hisaaki Obara, Taku Murakami, Keiko Shirota, Matusyuki Kikuchi, Saya Kobayashi, Natsuko Kikuchi, Yoshie Sugawara, Junichi Minegishi, Naoko Ogishima, Soichi Kinoshita, Kengo Yamamoto, Masayuki Yaegashi, Nobuo Kuriyama, Shinichi Koshiba, Seizo Tomita, Hiroaki iScience Article Examining plasma metabolic profiling during pregnancy and postpartum could help clinicians understand the risk factors for postpartum depression (PPD) development. This analysis targeted paired plasma metabolites in mid-late gestational and 1 month postpartum periods in women with (n = 209) or without (n = 222) PPD. Gas chromatogram-mass spectrometry was used to analyze plasma metabolites at these two time points. Among the 170 objected plasma metabolites, principal component analysis distinguished pregnancy and postpartum metabolites but failed to discriminate women with and without PPD. Compared to women without PPD, those with PPD exhibited 37 metabolites with disparate changes during pregnancy and the 1-month postpartum period and an enriched citrate cycle. Machine learning and multivariate statistical analysis identified two or three compounds that could be potential biomarkers for PPD prediction during pregnancy. Our findings suggest metabolic disturbances in women with depression and may help to elucidate metabolic processes associated with PPD development. Elsevier 2022-11-24 /pmc/articles/PMC9732390/ /pubmed/36505921 http://dx.doi.org/10.1016/j.isci.2022.105666 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yu, Zhiqian
Matsukawa, Naomi
Saigusa, Daisuke
Motoike, Ikuko N.
Ono, Chiaki
Okamura, Yasunobu
Onuma, Tomomi
Takahashi, Yuta
Sakai, Mai
Kudo, Hisaaki
Obara, Taku
Murakami, Keiko
Shirota, Matusyuki
Kikuchi, Saya
Kobayashi, Natsuko
Kikuchi, Yoshie
Sugawara, Junichi
Minegishi, Naoko
Ogishima, Soichi
Kinoshita, Kengo
Yamamoto, Masayuki
Yaegashi, Nobuo
Kuriyama, Shinichi
Koshiba, Seizo
Tomita, Hiroaki
Plasma metabolic disturbances during pregnancy and postpartum in women with depression
title Plasma metabolic disturbances during pregnancy and postpartum in women with depression
title_full Plasma metabolic disturbances during pregnancy and postpartum in women with depression
title_fullStr Plasma metabolic disturbances during pregnancy and postpartum in women with depression
title_full_unstemmed Plasma metabolic disturbances during pregnancy and postpartum in women with depression
title_short Plasma metabolic disturbances during pregnancy and postpartum in women with depression
title_sort plasma metabolic disturbances during pregnancy and postpartum in women with depression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732390/
https://www.ncbi.nlm.nih.gov/pubmed/36505921
http://dx.doi.org/10.1016/j.isci.2022.105666
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