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. 2019 Feb 5;11(3):1008-1018.
doi: 10.18632/aging.101805.

Urinary metabolite signature in bipolar disorder patients during depressive episode

Jian-Jun Chen #  1   2 , Jing Xie #  3 , Li Zeng  2   4 , Chan-Juan Zhou  2 , Peng Zheng  5   2 , Peng Xie  5   2
Affiliations

Urinary metabolite signature in bipolar disorder patients during depressive episode

Jian-Jun Chen et al. Aging (Albany NY). .

Abstract

The first few episodes of bipolar disorder (BD) are highly likely to be depressive. This phenomenon causes many BD patients to be misdiagnosed as having major depression. Therefore, it is very important to correctly diagnose BD patients during depressive episode. Here, we conducted this study to identify potential biomarkers for young and middle-aged BD patients during depressive episode. Both gas chromatography-mass spectroscopy (GC-MS) and nuclear magnetic resonance (NMR) spectroscopy were used to profile the urine samples from the recruited subjects. In total, 13 differential metabolites responsible for the discrimination between healthy controls (HCs) and patients were identified. Most differential metabolites had a close relationship with energy homeostasis. Meanwhile, a panel consisting of five differential metabolites was identified. This panel could effectively distinguish the patients from HCs with an AUC of 0.998 in the training set and 0.974 in the testing set. Our findings on one hand could be helpful in developing an objective diagnostic method for young and middle-aged BD patients during depressive episode; on the other hand could provide critical insight into the pathological mechanism of BD and the biological mechanisms responsible for the transformation of different episodes.

Keywords: biomarker; bipolar disorder; metabolites; metabolomics.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no financial or other conflicts of interest.

Figures

Figure 1
Figure 1
Metabolomic analysis of urine samples from the recruited subjects. (A) OPLS-DA model; (B) T-predicted scatter plot; (C) 300-iteration permutation test.
Figure 2
Figure 2. Heatmap of the 13 identified differential metabolites.
Figure 3
Figure 3
Metabolomic analysis of urine samples from the recruited subjects. (A) PLS-DA model; (B) T-predicted scatter plot; (C) 300-iteration permutation test.
Figure 4
Figure 4. Pearson correlation coefficient of the 13 identified differential metabolites.
Figure 5
Figure 5. Metabolite-metabolite interaction network of the 13 identified differential metabolites.
Figure 6
Figure 6. Diagnostic performance of the simplified biomarker panel.
Figure 7
Figure 7. Metabolic phenotypes homogeneity between non-medicated and medicated patients.
See this image and copyright information in PMC

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