The core inflammatory factors in patients with major depressive disorder: a network analysis

Yexian Zeng; Bin Sun; Fan Zhang; Zhibo Hu; Weicheng Li; Xiaofeng Lan; Yuping Ning; Yanling Zhou

doi:10.3389/fpsyt.2023.1216583

The core inflammatory factors in patients with major depressive disorder: a network analysis

Front Psychiatry. 2023 Aug 25:14:1216583. doi: 10.3389/fpsyt.2023.1216583. eCollection 2023.

Authors

Yexian Zeng^{1

2

3}, Bin Sun^{1

3}, Fan Zhang^{1

2

3}, Zhibo Hu^{1

2

3}, Weicheng Li^{1

2

3}, Xiaofeng Lan^{1

2

3}, Yuping Ning^{1

2

3

4

5}, Yanling Zhou^{1

2

3}

Affiliations

¹ Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
² Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
³ Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China Guangzhou Medical University, Guangzhou, China.
⁴ Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
⁵ Department of Psychology,The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.

Abstract

Introduction: 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.

Measures: 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.

Results: 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.

Conclusion: 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.

Keywords: cluster analysis; inflammatory factors; major depressive disorder (MDD); network analysis; trait depression.