Gu JX, Liu CQ, Chen GX, Yao T, Sun ZX, Wang Y. Post-stroke depression update 2025: Mechanisms, prediction, and management. World J Psychiatry 2026; 16(6): 115996 [DOI: 10.5498/wjp.v16.i6.115996]
Corresponding Author of This Article
Yong Wang, Researcher, Henan Key Laboratory of Rare Diseases, Center of Endocrinology and Metabolism, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, Henan Province, China. wangyong0327ybu@163.com
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Psychology, Multidisciplinary
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review-article
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Gu JX, Liu CQ, Chen GX, Yao T, Sun ZX, Wang Y. Post-stroke depression update 2025: Mechanisms, prediction, and management. World J Psychiatry 2026; 16(6): 115996 [DOI: 10.5498/wjp.v16.i6.115996]
World J Psychiatry. Jun 19, 2026; 16(6): 115996 Published online Jun 19, 2026. doi: 10.5498/wjp.v16.i6.115996
Post-stroke depression update 2025: Mechanisms, prediction, and management
Jia-Xi Gu, Chao-Qiang Liu, Guang-Xi Chen, Tao Yao, Zhao-Xia Sun, Yong Wang
Jia-Xi Gu, Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun 130033, Jilin Province, China
Chao-Qiang Liu, Guang-Xi Chen, Yong Wang, Department of Neurology, Wuhan Third Hospital & Tongren Hospital of Wuhan University, Wuhan 430060, Hubei Province, China
Tao Yao, Department of General Practice, Wuhan Third Hospital & Tongren Hospital of Wuhan University, Wuhan 430060, Hubei Province, China
Zhao-Xia Sun, Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
Yong Wang, Henan Key Laboratory of Rare Diseases, Center of Endocrinology and Metabolism, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, Henan Province, China
Author contributions: Gu JX designed the overall concept, outline, and manuscript design; performed data analysis and software utilization; created the figures and graphical concepts; led the writing and contributed to the review of the literature; Liu CQ and Chen GX contributed to the discussion, edited the manuscript, and reviewed the literature; Yao T supervised the project and provided critical revision; Wang Y conceived the study, supervised the entire project, and approved the final version to be published; Sun ZX guided the entire revision of the manuscript, and Gu JX and Wang Y collaboratively completed the revision work. All authors approved the final version to be published.
AI contribution statement: AI tools (DeepL and DeepSeek) were used solely for linguistic refinement and formatting assistance. The study design, methodology, data analysis, and interpretation of results were conducted solely by the human authors. AI tools played no role in these intellectual and scientific aspects of the research. Only Figure 4 was created using the AI-powered design tool “nano Banana” to generate medical/biological vector illustration elements in a style consistent with platforms like Figdraw and BioRender. All other figures and images in the manuscript are original creations produced by the authors without AI assistance.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Corresponding author: Yong Wang, Researcher, Henan Key Laboratory of Rare Diseases, Center of Endocrinology and Metabolism, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, Henan Province, China. wangyong0327ybu@163.com
Received: October 31, 2025 Revised: January 9, 2026 Accepted: February 2, 2026 Published online: June 19, 2026 Processing time: 209 Days and 23.9 Hours
Abstract
Post-stroke depression (PSD) has a stable prevalence of 30%. Beyond stroke severity, lesion location and prior depression, vitamin D deficiency, hyperhomocysteinaemia and insulin resistance are now recognised as independent risk factors. Mechanistically, phenotypic switching of microglia and astrocytes drives neuro-inflammation via NF-κB, JAK-STAT3 and NLRP3 inflammasome signalling. Gut-brain axis studies show reduced Faecalibacterium, increased Enterococcus and decreased short-chain fatty acids. Multi-omics evidence also indicates down-regulation of BDNF/TrkB, glutamate excitotoxicity, hypothalamic-pituitary-adrenal-axis dysregulation and altered miR-146a/34a expression. Predictively, machine-learning models integrating magnetic resonance imaging lesion-network patterns, clinical variables and inflammatory markers achieve area under the curve > 0.90, although external validation is still required. Therapeutically, selective serotonin reuptake inhibitors (sertraline, escitalopram) remain first-line, and evidence for agomelatine, vitamin D and tumor necrosis factor-α inhibitors is increasing. Traditional Chinese medicine (TCM) formulations such as Chaihu Shugan San, Danzhi Xiaoyao San and Shugan Jieyu capsule attenuate neuro-inflammation and restore synaptic plasticity by modulating JAK-STAT3-GSK3β/ PTEN/Akt, Nrf2/HO-1 and NMDAR/BDNF pathways. Additionally, acupuncture, repetitive transcranial magnetic stimulation, exercise and music therapy have proven safe and effective. Looking ahead, PSD research should integrate multi-omics, artificial intelligence and individualised TCM to establish a full-chain paradigm for risk prediction and precision stratification.
Core Tip: Post-stroke depression risk is refined by emerging biomarkers like vitamin D deficiency, hyperhomocysteinemia, and insulin resistance, alongside traditional factors. Multi-omics and artificial intelligence-driven models integrating clinical, neuroimaging, and inflammatory data show high predictive potential for precision stratification. Mechanistically, microglial/astrocytic polarization-driven neuroinflammation and gut-brain axis dysregulation are key therapeutic targets. Beyond first-line selective serotonin reuptake inhibitors, agomelatine, vitamin D supplementation, anti-inflammatory strategies, and evidence-based traditional Chinese medicine formulations that modulate neuro-immune crosstalk offer promising adjunctive treatments. A “predict-mechanism-intervene” paradigm is advocated to translate these advances into personalized management and improve long-term outcomes.
