Zeng Yexian, Sun Bin, Zhang Fan, Hu Zhibo, Li Weicheng, Lan Xiaofeng, Ning Yuping, Zhou Yanling
Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
Front Psychiatry. 2023 Aug 25;14:1216583. doi: 10.3389/fpsyt.2023.1216583. eCollection 2023.
The symptoms of major depressive disorder (MDD) vary widely. Psycho-neuro-inflammation has shown that MDD's inflammatory factors can accelerate or slow disease progression. This network analysis study examined the complex interactions between depressed symptoms and inflammatory factors in MDD prevention and treatment.
We gathered participants' inflammatory factor levels, used the Hamilton Depression Scale (HAMD-17), and network analysis was used to analyzed the data. Network analysis revealed the core inflammatory (nodes) and their interactions (edges). Stability and accuracy tests assessed these centrality measures' network robustness. Cluster analysis was used to group persons with similar dimension depressive symptoms and examine their networks.
Interleukin-1β (IL-1β) is the core inflammatory factor in the overall sample, and IL-1β-interleukin-4 (IL-4) is the strongest correlation. Network precision and stability passed. Network analysis showed significant differences between Cluster 1 (with more severe anxiety/somatization and sleep disruption) and Cluster 3 (with more severe retardation and cognitive disorders), as well as between Cluster 2 (with more severe anxiety/somatization, sleep disruption and body weight) and Cluster 3. IL-1β is the core inflammatory factor in Cluster 1 and Cluster 2, while tumor necrosis factor alpha (TNF-α) in Cluster 3.
IL-1β is the central inflammatory factor in the network, and there is heterogeneity in the core inflammatory factor of MDD with specific depressive dimension symptoms as the main manifestation. In conclusion, inflammatory factors and their links should be prioritized in future theoretical models of MDD and may provide new research targets for MDD intervention and treatment.
重度抑郁症(MDD)的症状差异很大。心理神经炎症表明,MDD的炎症因子可加速或减缓疾病进展。这项网络分析研究探讨了MDD预防和治疗中抑郁症状与炎症因子之间的复杂相互作用。
我们收集了参与者的炎症因子水平,使用汉密尔顿抑郁量表(HAMD-17),并采用网络分析对数据进行分析。网络分析揭示了核心炎症因子(节点)及其相互作用(边)。稳定性和准确性测试评估了这些中心性指标的网络稳健性。聚类分析用于将具有相似维度抑郁症状的人分组并检查他们的网络。
白细胞介素-1β(IL-1β)是总体样本中的核心炎症因子,且IL-1β与白细胞介素-4(IL-4)的相关性最强。网络精度和稳定性通过测试。网络分析显示,第1组(焦虑/躯体化和睡眠障碍更严重)和第3组(迟缓及认知障碍更严重)之间,以及第2组(焦虑/躯体化、睡眠障碍和体重更严重)和第3组之间存在显著差异。IL-1β是第1组和第2组中的核心炎症因子,而第3组中的核心炎症因子是肿瘤坏死因子-α(TNF-α)。
IL-1β是网络中的核心炎症因子,以特定抑郁维度症状为主要表现的MDD核心炎症因子存在异质性。总之,在未来MDD的理论模型中应优先考虑炎症因子及其联系,这可能为MDD的干预和治疗提供新的研究靶点。