Plasma metabolic disturbances during pregnancy and postpartum in women with depression

Zhiqian Yu; Naomi Matsukawa; Daisuke Saigusa; Ikuko N. Motoike; Chiaki Ono; Yasunobu Okamura; Tomomi Onuma; Yuta Takahashi; Mai Sakai; Hisaaki Kudo; Taku Obara; Keiko Murakami; Matusyuki Shirota; Saya Kikuchi; Natsuko Kobayashi; Yoshie Kikuchi; Junichi Sugawara; Naoko Minegishi; Soichi Ogishima; Kengo Kinoshita; Masayuki Yamamoto; Nobuo Yaegashi; Shinichi Kuriyama; Seizo Koshiba; Hiroaki Tomita

doi:10.1016/j.isci.2022.105666

Plasma metabolic disturbances during pregnancy and postpartum in women with depression

Zhiqian Yu, Naomi Matsukawa, Daisuke Saigusa, Ikuko N. Motoike, Chiaki Ono, Yasunobu Okamura, Tomomi Onuma, Yuta Takahashi, Mai Sakai, Hisaaki Kudo, Taku Obara, Keiko Murakami, Matusyuki Shirota, Saya Kikuchi, Natsuko Kobayashi, Yoshie Kikuchi, Junichi Sugawara, Naoko Minegishi, Soichi Ogishima, Kengo KinoshitaMasayuki Yamamoto, Nobuo Yaegashi, Shinichi Kuriyama, Seizo Koshiba, Hiroaki Tomita

研究成果: Article › 査読

抄録

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.

本文言語	English
論文番号	105666
ジャーナル	iScience
巻	25
号	12
DOI	https://doi.org/10.1016/j.isci.2022.105666
出版ステータス	Published - 2022 12月 22

ASJC Scopus subject areas

一般

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10.1016/j.isci.2022.105666

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Yu, Z., Matsukawa, N., Saigusa, D., Motoike, I. N., Ono, C., Okamura, Y., Onuma, T., Takahashi, Y., Sakai, M., Kudo, H., Obara, T., Murakami, K., Shirota, M., Kikuchi, S., Kobayashi, N., Kikuchi, Y., Sugawara, J., Minegishi, N., Ogishima, S., ... Tomita, H. (2022). Plasma metabolic disturbances during pregnancy and postpartum in women with depression. iScience, 25(12), [105666]. https://doi.org/10.1016/j.isci.2022.105666

Yu, Z, Matsukawa, N, Saigusa, D, Motoike, IN, Ono, C, Okamura, Y, Onuma, T, Takahashi, Y, Sakai, M , Kudo, H , Obara, T , Murakami, K, Shirota, M, Kikuchi, S , Kobayashi, N, Kikuchi, Y, Sugawara, J, Minegishi, N, Ogishima, S , Kinoshita, K , Yamamoto, M , Yaegashi, N , Kuriyama, S , Koshiba, S & Tomita, H 2022, 'Plasma metabolic disturbances during pregnancy and postpartum in women with depression', iScience, vol. 25, no. 12, 105666. https://doi.org/10.1016/j.isci.2022.105666

@article{1582d4b2d6e04425a3e44d1026cc4aa0,

title = "Plasma metabolic disturbances during pregnancy and postpartum in women with depression",

abstract = "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.",

keywords = "Biological sciences, Metabolomics, Physiology",

author = "Zhiqian Yu and Naomi Matsukawa and Daisuke Saigusa and Motoike, {Ikuko N.} and Chiaki Ono and Yasunobu Okamura and Tomomi Onuma and Yuta Takahashi and Mai Sakai and Hisaaki Kudo and Taku Obara and Keiko Murakami and Matusyuki Shirota and Saya Kikuchi and Natsuko Kobayashi and Yoshie Kikuchi and Junichi Sugawara and Naoko Minegishi and Soichi Ogishima and Kengo Kinoshita and Masayuki Yamamoto and Nobuo Yaegashi and Shinichi Kuriyama and Seizo Koshiba and Hiroaki Tomita",

note = "Funding Information: This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas (No. 24116007 and 20K07962 ) from the Ministry of Education, Culture, Sports, Science and Technology of Japan and the Strategic Research Program for Brain Sciences from Japan Agency for Medical Research and Development ( 20dm0107099h0005 , JP19dm0107099 , JP18ek0109183 , JP17km0105001 , JP21tm0124005 , JP16km0405001 , JP16km0405203 , and JP22zf0127001 ). Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = dec,

day = "22",

doi = "10.1016/j.isci.2022.105666",

language = "English",

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T1 - Plasma metabolic disturbances during pregnancy and postpartum in women with depression

AU - Yu, Zhiqian

AU - Matsukawa, Naomi

AU - Saigusa, Daisuke

AU - Motoike, Ikuko N.

AU - Ono, Chiaki

AU - Okamura, Yasunobu

AU - Onuma, Tomomi

AU - Takahashi, Yuta

AU - Sakai, Mai

AU - Kudo, Hisaaki

AU - Obara, Taku

AU - Murakami, Keiko

AU - Shirota, Matusyuki

AU - Kikuchi, Saya

AU - Kobayashi, Natsuko

AU - Kikuchi, Yoshie

AU - Sugawara, Junichi

AU - Minegishi, Naoko

AU - Ogishima, Soichi

AU - Kinoshita, Kengo

AU - Yamamoto, Masayuki

AU - Yaegashi, Nobuo

AU - Kuriyama, Shinichi

AU - Koshiba, Seizo

AU - Tomita, Hiroaki

N1 - Funding Information: This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas (No. 24116007 and 20K07962 ) from the Ministry of Education, Culture, Sports, Science and Technology of Japan and the Strategic Research Program for Brain Sciences from Japan Agency for Medical Research and Development ( 20dm0107099h0005 , JP19dm0107099 , JP18ek0109183 , JP17km0105001 , JP21tm0124005 , JP16km0405001 , JP16km0405203 , and JP22zf0127001 ). Publisher Copyright: © 2022 The Author(s)

PY - 2022/12/22

Y1 - 2022/12/22

N2 - 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.

AB - 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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KW - Metabolomics

KW - Physiology

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DO - 10.1016/j.isci.2022.105666

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SN - 2589-0042

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JO - iScience

JF - iScience

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M1 - 105666

ER -

Plasma metabolic disturbances during pregnancy and postpartum in women with depression

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