Distinct Gut Microbiota Profiles Reflect Severity in Chronic Insomnia Disorder

INTRODUCTION: Severe insomnia symptoms increase the risk of persistent sleep disorders, underscoring the need for timely identification to optimize therapeutic interventions. Given the established association between chronic insomnia disorder (CID) and gut microbiota, this study aimed to evaluate the utility of gut microbiota characteristics for stratifying CID severity. METHODS: A total of 65 patients with CID were categorized into two groups based on Pittsburgh Sleep Quality Index (PSQI) scores: S-CID (more severe poor sleep quality; median age: 36, IQR: 30-47; M/F: 12/22) and M-CID (milder poor sleep quality; median age: 33, IQR: 25-43; M/F: 11/20). Thirty healthy controls (HC; median age: 32, IQR: 26-48; M/F: 8/22) were also included. All participants underwent polysomnography and clinical assessments. Fecal samples were collected and analyzed via 16S rRNA gene sequencing. We compared microbial structure across severity groups, identified key bacterial genera using LASSO regression and the Boruta algorithm, and examined their correlations with sleep parameters via Spearman analysis. Functional pathway predictions were performed with PICRUSt2. A random forest model was constructed to evaluate severity-stratified discriminative capacity. RESULTS: Significant alterations in gut microbial diversity and composition were observed in S-CID patients compared to HC, whereas M-CID patients showed less pronounced differences. Seven key bacterial genera were identified and consistently correlated with sleep parameters. Functional perturbations in glutamate/butanoate metabolism and branched-chain amino acid degradation pathways differed by severity. The random forest model demonstrated moderate efficacy (AUC = 0.711-0.730) in distinguishing S-CID patients based on microbial signatures. CONCLUSION: This study reveals distinct gut microbial signatures associated with varying severity levels of CID, providing insights that may support the development of microbiota-based diagnostic and therapeutic interventions.