Bidirectional relationship between sleep disturbance and depression: Mechanisms, clinical implications, and future directions

doi:10.5498/wjp.v16.i8.118042

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World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Aug 19, 2026; 16(8): 118042
Published online Aug 19, 2026. doi: 10.5498/wjp.v16.i8.118042

Bidirectional relationship between sleep disturbance and depression: Mechanisms, clinical implications, and future directions

Muhammad Sanan, Sun Yao, Nicha Wareesawetsuwan, Pei-Fen Zhang, Xue-Qin Song

Muhammad Sanan, Sun Yao, Pei-Fen Zhang, Xue-Qin Song, Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China

Nicha Wareesawetsuwan, Department of Medicine Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02108, United States

Co-corresponding authors: Pei-Fen Zhang and Xue-Qin Song.

Author contributions: Sanan M and Yao S conceived the review topic and performed the literature search; Sanan M drafted the initial manuscript; Yao S contributed to literature organization and revision of the manuscript; Wareesawetsuwan N critically reviewed the literature, contributed to interpretation of key findings, and substantially revised the manuscript for important intellectual content; Zhang PF and Song XQ provided senior supervision, contributed to conceptual refinement, and critically reviewed and revised the manuscript; all authors read and approved the final version of the manuscript. Zhang PF and Song XQ are co-corresponding authors and contributed equally to the supervision of this work.

AI contribution statement: ChatGPT was used for limited language polishing and rephrasing assistance during preparation of the revised manuscript and point-by-point response. Grammarly and DeepL were not used. The entirety of the Main Text was not AI-generated. The scientific content, literature interpretation, structure, arguments, references, and conclusions were developed, checked, and approved by the authors. AI assistance was limited to improving language clarity, grammar, readability, and expression of selected text. ChatGPT was used only for language polishing and writing assistance. It was not used for translation, data analysis, statistical analysis, reference selection, or generation of scientific conclusions. No AI tool participated in the study design, literature selection strategy, scientific judgment, analysis, interpretation of results, or formulation of conclusions. These were performed entirely by the authors. No images, figures, tables, or graphical materials in the manuscript were generated by AI. All authors have reviewed and approved the final version of the manuscript and take full responsibility for the accuracy, integrity, originality, and scientific content of the work.

Supported by the National Key Research and Development Program of China, No. 2023YFC2506204 (to Song XQ); and the Henan Provincial Science and Technology Research Project, No. 232102311069 (to Zhang PF).

Conflict-of-interest statement: The authors declare that they have no competing interests.

Corresponding author: Xue-Qin Song, MD, PhD, Professor, Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe East Road, Zhengzhou 450000, Henan Province, China. fccsongxq@zzu.edu.cn

Received: December 23, 2025
Revised: March 7, 2026
Accepted: May 18, 2026
Published online: August 19, 2026
Processing time: 209 Days and 6 Hours

Abstract

Sleep disturbance is one of the most common and clinically important features of depressive disorders. Although it has traditionally been viewed as a secondary symptom of depression, growing evidence indicates that sleep disturbance plays an active role in both the development and persistence of depressive illness. Longitudinal and experimental studies demonstrate that insomnia, poor sleep quality, and circadian rhythm disruption increase vulnerability to depression, are associated with greater symptom severity, and elevate the risk of relapse. Conversely, depressive disorders disrupt sleep regulation through characteristic changes in sleep architecture, circadian misalignment, and maladaptive sleep related behaviors, creating a self reinforcing cycle between sleep disturbance and mood dysregulation. Shared mechanisms underlying this bidirectional relationship include alterations in monoaminergic neurotransmission, hypothalamic pituitary adrenal axis activation, circadian rhythm disruption, inflammatory processes, and impaired emotional regulation. Clinical evidence further suggests that interventions targeting sleep, particularly cognitive behavioral therapy for insomnia and chronotherapeutic approaches, can reduce depressive symptoms and improve treatment outcomes when integrated into standard depression care. This narrative review synthesizes current evidence on the bidirectional relationship between sleep disturbance and depression and highlights its implications for clinical practice and future research.

Keywords: Depression; Sleep disturbance; Insomnia; Circadian rhythm; Cognitive behavioral therapy for insomnia

Core Tip: Sleep disturbance and depression interact in a bidirectional and self-reinforcing manner rather than a simple cause-effect relationship. Accumulating evidence shows that insomnia, poor sleep quality, and circadian rhythm disruption not only result from depression but also actively contribute to its onset, persistence, and relapse through shared neurobiological, emotional, and behavioral mechanisms. Recognizing sleep disturbance as a core and modifiable component of depressive disorders supports routine sleep assessment and the integration of targeted sleep interventions, such as cognitive behavioral therapy for insomnia, into standard depression care to improve outcomes and reduce relapse risk.