Urinary biomarkers for diagnosing poststroke depression in patients with type 2 diabetes mellitus
| Autor: | Mei-Ling Wang, Zi-Ru Li, Min Li, Yan-Bo Jia, Lei Shi, Run-Xiu Zhu, Yong-Li Yun, Li-Jun Yu, Zi-Hong Liang |
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| Rok vydání: | 2019 |
| Předmět: |
Pharmacology
medicine.medical_specialty endocrine system diseases Receiver operating characteristic business.industry Urinary system Area under the curve nutritional and metabolic diseases Type 2 Diabetes Mellitus 030209 endocrinology & metabolism 030204 cardiovascular system & hematology Logistic regression Urinary biomarkers 03 medical and health sciences 0302 clinical medicine Internal medicine Internal Medicine medicine Post-stroke depression In patient business |
| Zdroj: | Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. 12:1379-1386 |
| ISSN: | 1178-7007 |
| DOI: | 10.2147/dmso.s215187 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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