Novel urinary metabolite signature for diagnosing postpartum depression

Lin Lin, Xiao-mei Chen, Rong-hua Liu Department of Obstetrics and Gynecology, Linyi People’s Hospital, Shandong, People’s Republic of China Background: Postpartum depression (PPD) could affect ~10% of women and impair the quality of mother–infant interactions. Current...

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Autores principales:	Lin L, Chen X, Liu R
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2017
Materias:	postpartum depression PPD gas chromatography-mass spectroscopy GC-MS biomarker metabolomics Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/363f6cf3ee2f4362af16038c439d7e1c
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spelling	oai:doaj.org-article:363f6cf3ee2f4362af16038c439d7e1c2021-12-02T00:10:45ZNovel urinary metabolite signature for diagnosing postpartum depression1178-2021https://doaj.org/article/363f6cf3ee2f4362af16038c439d7e1c2017-05-01T00:00:00Zhttps://www.dovepress.com/novel-urinary-metabolite-signature-for-diagnosing-postpartum-depressio-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Lin Lin, Xiao-mei Chen, Rong-hua Liu Department of Obstetrics and Gynecology, Linyi People’s Hospital, Shandong, People’s Republic of China Background: Postpartum depression (PPD) could affect ~10% of women and impair the quality of mother–infant interactions. Currently, there are no objective methods to diagnose PPD. Therefore, this study was conducted to identify potential biomarkers for diagnosing PPD.Materials and methods: Morning urine samples of PPD subjects, postpartum women without depression (PPWD) and healthy controls (HCs) were collected. The gas chromatography-mass spectroscopy (GC-MS)-based urinary metabolomic approach was performed to characterize the urinary metabolic profiling. The orthogonal partial least-squares-discriminant analysis (OPLS-DA) was used to identify the differential metabolites. The logistic regression analysis and Bayesian information criterion rule were further used to identify the potential biomarker panel. The receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker panel.Results: Totally, 73 PPD subjects, 73 PPWD and 74 HCs were included, and 68 metabolites were identified using GC-MS. The OPLS-DA model showed that there were 22 differential metabolites (14 upregulated and 8 downregulated) responsible for separating PPD subjects from HCs and PPWD. Meanwhile, a panel of five potential biomarkers – formate, succinate, 1-methylhistidine, a-glucose and dimethylamine – was identified. This panel could effectively distinguish PPD subjects from HCs and PPWD with an area under the curve (AUC) curve of 0.948 in the training set and 0.944 in the testing set.Conclusion: These results demonstrated that the potential biomarker panel could aid in the future development of an objective diagnostic method for PPD. Keywords: postpartum depression, gas chromatography-mass spectroscopy, biomarker, metabolomicsLin LChen XLiu RDove Medical Pressarticlepostpartum depressionPPDgas chromatography-mass spectroscopyGC-MSbiomarkermetabolomicsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 13, Pp 1263-1270 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	postpartum depression PPD gas chromatography-mass spectroscopy GC-MS biomarker metabolomics Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	postpartum depression PPD gas chromatography-mass spectroscopy GC-MS biomarker metabolomics Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Lin L Chen X Liu R Novel urinary metabolite signature for diagnosing postpartum depression
description	Lin Lin, Xiao-mei Chen, Rong-hua Liu Department of Obstetrics and Gynecology, Linyi People’s Hospital, Shandong, People’s Republic of China Background: Postpartum depression (PPD) could affect ~10% of women and impair the quality of mother–infant interactions. Currently, there are no objective methods to diagnose PPD. Therefore, this study was conducted to identify potential biomarkers for diagnosing PPD.Materials and methods: Morning urine samples of PPD subjects, postpartum women without depression (PPWD) and healthy controls (HCs) were collected. The gas chromatography-mass spectroscopy (GC-MS)-based urinary metabolomic approach was performed to characterize the urinary metabolic profiling. The orthogonal partial least-squares-discriminant analysis (OPLS-DA) was used to identify the differential metabolites. The logistic regression analysis and Bayesian information criterion rule were further used to identify the potential biomarker panel. The receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker panel.Results: Totally, 73 PPD subjects, 73 PPWD and 74 HCs were included, and 68 metabolites were identified using GC-MS. The OPLS-DA model showed that there were 22 differential metabolites (14 upregulated and 8 downregulated) responsible for separating PPD subjects from HCs and PPWD. Meanwhile, a panel of five potential biomarkers – formate, succinate, 1-methylhistidine, a-glucose and dimethylamine – was identified. This panel could effectively distinguish PPD subjects from HCs and PPWD with an area under the curve (AUC) curve of 0.948 in the training set and 0.944 in the testing set.Conclusion: These results demonstrated that the potential biomarker panel could aid in the future development of an objective diagnostic method for PPD. Keywords: postpartum depression, gas chromatography-mass spectroscopy, biomarker, metabolomics
format	article
author	Lin L Chen X Liu R
author_facet	Lin L Chen X Liu R
author_sort	Lin L
title	Novel urinary metabolite signature for diagnosing postpartum depression
title_short	Novel urinary metabolite signature for diagnosing postpartum depression
title_full	Novel urinary metabolite signature for diagnosing postpartum depression
title_fullStr	Novel urinary metabolite signature for diagnosing postpartum depression
title_full_unstemmed	Novel urinary metabolite signature for diagnosing postpartum depression
title_sort	novel urinary metabolite signature for diagnosing postpartum depression
publisher	Dove Medical Press
publishDate	2017
url	https://doaj.org/article/363f6cf3ee2f4362af16038c439d7e1c
work_keys_str_mv	AT linl novelurinarymetabolitesignaturefordiagnosingpostpartumdepression AT chenx novelurinarymetabolitesignaturefordiagnosingpostpartumdepression AT liur novelurinarymetabolitesignaturefordiagnosingpostpartumdepression
_version_	1718403855655370752

Novel urinary metabolite signature for diagnosing postpartum depression

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