Urinary Metabolite Signatures for Predicting Elderly Stroke Survivors with Depression
Authors Chen J, Lv Y, Li X, Xiong J, Liang H, Xie L, Wan C, Chen Y, Wang H, Liu P, Zheng H
Received 30 December 2020
Accepted for publication 7 March 2021
Published 25 March 2021 Volume 2021:17 Pages 925—933
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Jun Chen
Jin Chen,1,* Yan-ni Lv,2,* Xiao-bing Li,1,* Jia-jun Xiong,1,* Hui-ting Liang,1 Liang Xie,3 Chen-yi Wan,1 Yun-qing Chen,1 Han-sen Wang,3 Pan Liu,3 He-qing zheng3
1Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China; 2Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China; 3Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jin Chen
Department of Neurology, The First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Road, Nanchang, Jiangxi, 330006, People’s Republic of China
Email [email protected]
Background: Post-stroke depression (PSD) is a major complication in stroke survivors, especially in elderly stroke survivors. But there are still no objective methods to diagnose depression in elderly stroke survivors. Thus, this study was conducted to identify potential biomarkers for diagnosing elderly PSD subjects.
Methods: Elderly (60 years or older) stroke survivors with depression were assigned into the PSD group, and elderly stroke survivors without depression and elderly healthy controls (HCs) were assigned into the non-depressed group. Urinary metabolite signatures obtained from gas chromatography-mass spectrometry (GC-MS)-based metabolomic platform were collected. Both univariate and multivariate statistical analysis were used to find the differential urinary metabolites between the two groups.
Results: The 78 elderly HCs, 122 elderly stroke survivors without depression and 124 elderly PSD subjects were included. A set of 13 differential urinary metabolites responsible for distinguishing PSD subjects from non-depressed subjects were found. The Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylalanine metabolism and Galactose metabolism were found to be significantly changed in elderly PSD subjects. The phenylalanine was significantly negatively correlated with age and depressive symptoms. Meanwhile, a biomarker panel consisting of 3-hydroxyphenylacetic acid, tyrosine, phenylalanine, sucrose, palmitic acid, glyceric acid, azelaic acid and α-aminobutyric acid was identified.
Conclusion: These results provided candidate molecules for developing objective methods to diagnose depression in elderly stroke survivors, suggested that taking supplements of phenylalanine might be an effective method to prevent depression in elderly stroke survivors, and would be helpful for future revealing the pathophysiological mechanism of PSD.
Keywords: post-stroke depression, metabolomics, biomarker
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