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Novel urinary metabolite signature for diagnosing postpartum depression

Authors Lin L, Chen X, Liu R

Received 20 February 2017

Accepted for publication 18 April 2017

Published 10 May 2017 Volume 2017:13 Pages 1263—1270

DOI https://doi.org/10.2147/NDT.S135190

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Prof. Dr. Roumen Kirov

Peer reviewer comments 2

Editor who approved publication: Professor Wai Kwong Tang


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

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