Liang Zi-Hong, Jia Yan-Bo, Li Zi-Ru, Li Min, Wang Mei-Ling, Yun Yong-Li, Yu Li-Jun, Shi Lei, Zhu Run-Xiu
Department of Neurology, Inner Mongolia Autonomous Region People's Hospital, Huhhot, Inner Mongolia, People's Republic of China.
Department of Orthopaedics, The Second Affiliated Hospital of Inner Mongolia Medical University, Huhhot, Inner Mongolia, People's Republic of China.
Diabetes Metab Syndr Obes. 2019 Aug 13;12:1379-1386. doi: 10.2147/DMSO.S215187. eCollection 2019.
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.
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.
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.
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.
抑郁症会严重影响2型糖尿病（T2DM）患者中风后的生活质量。然而，目前仍没有客观的方法来诊断T2DM合并中风后抑郁症（PSD）的患者。因此，我们开展了本研究来解决这一问题。
采用基于气相色谱 - 质谱联用（GC-MS）的代谢组学分析方法，对83例中风后无抑郁的T2DM患者和101例T2DM合并PSD患者的尿液代谢物进行分析。进行正交偏最小二乘判别分析，以探索T2DM合并PSD患者的代谢差异。进行逻辑回归分析，以确定诊断T2DM合并PSD患者的最佳简化生物标志物组合。采用受试者工作特征曲线分析来评估该生物标志物组合的诊断性能。
共发现23种差异代谢物（T2DM合并PSD患者中7种减少，16种增加）。确定了一个由假尿苷、苹果酸、次黄嘌呤、3,4 - 二羟基丁酸、果糖和肌醇组成的组合。该组合能够有效区分中风后T2DM合并PSD患者与无抑郁的T2DM患者。训练集曲线下面积为0.965，验证集为0.909。同时，我们发现T2DM合并PSD患者的半乳糖代谢受到显著影响。
我们的研究结果有助于未来开发一种客观诊断T2DM合并PSD患者的方法，并为研究抑郁症的发病机制提供新思路。
