Identification of Potential Metabolite Markers for Middle-Aged Patients with Post-Stroke Depression Using Urine Metabolomics - Xie J (Neuropsychiatric Disease and Treatment, 2020)
Jing Xie,1,* Yu Han,2,* Yueling Hong,3,* Wen-wen Li,4 Qilin Pei,3 Xueyi Zhou,4 Bingbing Zhang,5 Ying Wang6 1Chongqing Emergency Medical Center, Department of Endocrinology and Nephrology, The Fourth People’s Hospital of Chongqing, Central Hospital of Chongqing University, Chongqing 400014, People’s Republic of China; 2Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People’s Republic of China; 3Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, People’s Republic of China; 4Department of Pathology, Faculty of Basic Medicine, Chongqing Medical University, Chongqing 400016, People’s Republic of China; 5Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, People’s Republic of China; 6Department of Rehabilitation, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying Wang; Bingbing Zhang Email wangyingtgf@163.com; zbb@cqu.edu.cnBackground: 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: post-stroke depression, biomarkers, metabolites