You Zerui, Wang Chengyu, Lan Xiaofeng, Li Weicheng, Shang Dewei, Zhang Fan, Ye Yanxiang, Liu Haiyan, Zhou Yanling, Ning Yuping
The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
Prog Neuropsychopharmacol Biol Psychiatry. 2024 Jan 10;128:110849. doi: 10.1016/j.pnpbp.2023.110849. Epub 2023 Sep 1.
Approximately one-third of major depressive disorder (MDD) patients do not respond to standard antidepressants and develop treatment-resistant depression (TRD). We aimed to reveal metabolic differences and discover promising potential biomarkers in TRD.
Our study recruited 108 participants including healthy controls (n = 40) and patients with TRD (n = 35) and first-episode drug-naive MDD (DN-MDD) (n = 33). Plasma samples were presented to ultra performance liquid chromatography-tandem mass spectrometry. Then, a machine-learning algorithm was conducted to facilitate the selection of potential biomarkers.
TRD appeared to be a distinct metabolic disorder from DN-MDD and healthy controls (HCs). Compared to HCs, 199 and 176 differentially expressed metabolites were identified in TRD and DN-MDD, respectively. Of all the metabolites that were identified, spermine, spermidine, and carnosine were considered the most promising biomarkers for diagnosing TRD and DN-MDD patients, with the resulting area under the ROC curve of 0.99, 0.99, and 0.93, respectively. Metabolic pathway analysis yielded compelling evidence of marked changes or imbalances in both polyamine metabolism and energy metabolism, which could potentially represent the primary altered pathways associated with MDD. Additionally, L-glutamine, Beta-alanine, and spermine were correlated with HAMD score.
A more disordered metabolism structure is found in TRD than in DN-MDD and HCs. Future investigations should prioritize the comprehensive analysis of potential roles played by these differential metabolites and disturbances in polyamine pathways in the pathophysiology of TRD and depression.
约三分之一的重度抑郁症(MDD)患者对标准抗抑郁药无反应,并发展为难治性抑郁症(TRD)。我们旨在揭示TRD中的代谢差异,并发现有前景的潜在生物标志物。
我们的研究招募了108名参与者,包括健康对照者(n = 40)、TRD患者(n = 35)和首发未用药的MDD患者(DN-MDD)(n = 33)。血浆样本采用超高效液相色谱-串联质谱法进行分析。然后,运用机器学习算法来辅助筛选潜在的生物标志物。
TRD似乎是一种与DN-MDD和健康对照者(HCs)不同的代谢紊乱疾病。与HCs相比,在TRD和DN-MDD中分别鉴定出199种和176种差异表达的代谢物。在所有鉴定出的代谢物中,精胺、亚精胺和肌肽被认为是诊断TRD和DN-MDD患者最有前景的生物标志物,其ROC曲线下面积分别为0.99、0.99和0.93。代谢途径分析提供了令人信服的证据,表明多胺代谢和能量代谢均发生了显著变化或失衡,这可能是与MDD相关的主要改变途径。此外,L-谷氨酰胺、β-丙氨酸和精胺与汉密尔顿抑郁量表(HAMD)评分相关。
与DN-MDD和HCs相比,TRD中发现了更紊乱的代谢结构。未来的研究应优先全面分析这些差异代谢物和多胺途径紊乱在TRD和抑郁症病理生理学中所起的潜在作用。