Sleep and psychiatric disorders: Bidirectional interactions and shared neurobiological mechanisms

Abstract

Sleep is critical for emotional regulation, memory, and cognitive performance. Sleep disturbances, including insomnia, hypersomnia, and circadian misalignment, are highly prevalent and clinically significant across various psychiatric disorders. Once considered secondary, sleep problems are now recognized as active contributors to the onset, course, and relapse of mental illness. This narrative review synthesizes current evidence on the bidirectional interactions between sleep and major psychiatric conditions such as major depressive disorder, bipolar disorder, anxiety disorders, posttraumatic stress disorder, schizophrenia, attention deficit and hyperactivity disorder, and substance use disorders. We highlight convergent neurobiological mechanisms, including dysregulation of circadian systems, neurotransmitter networks (GABA, serotonin, dopamine, orexin), affective circuitry (prefrontal-amygdala interactions), and stress-immune pathways. Findings consistently show that sleep problems are transdiagnostic features, impacting diagnostic presentation, prognostic trajectories, and underlying pathology. For instance, chronic insomnia increases depression risk, sleep loss can precipitate manic episodes, and distinct sleep architecture anomalies are linked to schizophrenia. Sleep disturbances also predict worse outcomes in substance use disorders, including increased craving and relapse risk. Sleep is a tractable factor in mental health, offering a potent intervention leverage point. Routine, structured sleep assessment should be integrated into psychiatric care, emphasizing first-line behavioral and chronobiological strategies like Cognitive Behavioral Therapy for Insomnia (CBT-I) and light/rhythm therapies. Directly addressing sleep significantly improves psychiatric outcomes, reducing symptoms of depression and anxiety, decreasing suicidal ideation, and lowering relapse risk in bipolar disorder and psychoses. Future research should prioritize causal designs, mechanistic neuroimaging, biomarker identification, and responsible integration of objective measurement technologies and artificial intelligence for early warning systems and personalized treatment protocols.

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