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. 2020 Aug 25:16:2017-2024.
doi: 10.2147/NDT.S271990. eCollection 2020.

Identification of Potential Metabolite Markers for Middle-Aged Patients with Post-Stroke Depression Using Urine Metabolomics

Jing Xie #  1 , Yu Han #  2 , Yueling Hong #  3 , Wen-Wen Li  4 , Qilin Pei  3 , Xueyi Zhou  4 , Bingbing Zhang  5 , Ying Wang  6
Affiliations

Identification of Potential Metabolite Markers for Middle-Aged Patients with Post-Stroke Depression Using Urine Metabolomics

Jing Xie et al. Neuropsychiatr Dis Treat. .

Abstract

Background: Post-stroke depression (PSD) is one of the most common complications in stroke survivors. But, there are still no objective methods to diagnose PSD. This study aims to identify potential biomarkers for diagnosing PSD in middle-aged stroke survivors.

Methods: Middle-aged subjects aged 30 to 59 years (92 PSD patients and 89 stroke survivors without depression) were included in this study. Urinary metabolites were detected by gas chromatography-mass spectrometry (GC-MS). Differential urinary metabolites and potential biomarkers were screened by applying statistical analysis.

Results: The different urinary metabolic phenotypes between PSD patients and stroke survivors without depression were identified. A total of 12 differential urinary metabolites were accurately identified by using orthogonal partial least-squares-discriminant analysis. After analyzing those 12 differential urinary metabolites by step-wise logistic regression analysis, only seven metabolites (palmitic acid, hydroxylamine, myristic acid, glyceric acid, lactic acid, tyrosine and azelaic acid) were finally selected as potential biomarkers for diagnosing PSD in middle-aged stroke survivors. A panel consisting of these potential biomarkers could effectively diagnose middle-aged PSD patients.

Conclusion: Urinary metabolic profiles were different between middle-aged PSD patients and stroke survivors without depression. Our results would be helpful in future for developing an objective method to diagnose PSD in middle-aged stroke survivors.

Keywords: biomarkers; metabolites; post-stroke depression.

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Conflict of interest statement

The authors declare no financial or other conflicts of interest for this work.

Figures

Figure 1
Figure 1
The scores plot of the OPLS-DA model: (A) OPLS-DA model was constructed using middle-aged PSD patients (red circle) and stroke survivor without depression (green circle); (B) 599-items permutation test showed that the model was valid and stable.
Figure 2
Figure 2
T-predicted scatter plot of the OPLS-DA model. The model could correctly predict the middle-aged PSD patients (red circle) and stroke survivor without depression (green circle) in the testing set.
Figure 3
Figure 3
Column bar graphs of seven potential biomarkers for PSD in middle-aged stroke survivors.
Figure 4
Figure 4
ROC curve analysis of the panel consisting of seven potential biomarkers.
Figure 5
Figure 5
Metabolic pathway analyses of the differential urinary metabolites.
See this image and copyright information in PMC

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