Urinary biomarkers for diagnosing poststroke depression in patients with type 2 diabetes mellitus
Authors Liang ZH, Jia YB, Li ZR, Li M, Wang ML, Yun YL, Yu LJ, Shi L, Zhu RX
Received 9 May 2019
Accepted for publication 4 July 2019
Published 13 August 2019 Volume 2019:12 Pages 1379—1386
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Colin Mak
Peer reviewer comments 2
Editor who approved publication: Dr Antonio Brunetti
Zi-Hong Liang,1 Yan-Bo Jia,2 Zi-Ru Li,1 Min Li,1 Mei-Ling Wang,1 Yong-Li Yun,1 Li-Jun Yu,1 Lei Shi,1 Run-Xiu Zhu1
1Department of Neurology, Inner Mongolia Autonomous Region People’s Hospital, Huhhot, Inner Mongolia, People’s Republic of China; 2Department of Orthopaedics, The Second Affiliated Hospital of Inner Mongolia Medical University, Huhhot, Inner Mongolia, People’s Republic of China
Background: Depression can seriously affect the quality of life of type 2 diabetes mellitus (T2DM) patients after stroke. However, there were still no objective methods to diagnose T2DM patients with poststroke depression (PSD). Therefore, we conducted this study to deal with this problem.
Methods: Gas chromatography-mass spectroscopy (GC-MS)-based metabolomics profiling method was used to profile the urinary metabolites from 83 nondepressed T2DM patients after stroke and 101 T2DM patients with PSD. The orthogonal partial least-squares discriminant analysis was conducted to explore the metabolic differences in T2DM patients with PSD. The logistic regression analysis was performed to identify the optimal and simplified biomarker panel for diagnosing T2DM patients with PSD. The receiver operating characteristic curve analysis was used to assess the diagnostic performance of this biomarker panel.
Results: In total, 23 differential metabolites (7 decreased and 16 increased in T2DM patients with PSD) were found. A panel consisting of pseudouridine, malic acid, hypoxanthine, 3,4-dihydroxybutyric acid, fructose and inositol was identified. This panel could effectively separate T2DM patients with PSD from nondepressed T2DM patients after stroke. The area under the curve was 0.965 in the training set and 0.909 in the validation set. Meanwhile, we found that the galactose metabolism was significantly affected in T2DM patients with PSD.
Conclusion: Our results could be helpful for future development of an objective method to diagnose T2DM patients with PSD and provide novel ideas to study the pathogenesis of depression.
Keywords: type 2 diabetes mellitus, post-stroke depression, metabolite, metabolomics
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