Associations between circadian rhythm disruption, specific gut microbiota, and DNA methylation in major depressive disorder: a pilot study

Major depressive disorder (MDD) is a debilitating condition characterized by disturbances in circadian rhythms, gut microbial composition, and epigenetic patterns, yet the integrative relationships among these systems remain poorly understood. We conducted a cross-sectional study involving 49 first-episode, drug-naïve MDD patients and 35 healthy controls (HCs). Circadian rhythms were evaluated using the Munich Chronotype Questionnaire, with two key metrics—corrected mid-sleep time on free days and social jetlag—selected for analysis. Fecal samples were subjected to shotgun metagenomic sequencing, and peripheral blood DNA methylation was characterized via reduced-representation bisulfite sequencing (RRBS). Multi-omics integration was performed using weighted gene co-expression network analysis (WGCNA) and correlation analyses. While gut microbial alpha diversity was comparable, beta diversity uncovered significantly distinct gut microbial communities between MDD patients and HCs. WGCNA detected microbial modules significantly correlated with circadian parameters. RRBS analysis identified 9,749 differentially methylated genes (DMGs) in MDD, which were annotated as enriched in neural-related pathways based on gene ontology and KEGG analyses. These enrichments provide hypothesis‑generating evidence for potential systemic associations, but do not establish that peripheral blood methylation directly reflects brain methylation status. Crucially, integrative analysis identified 9 key microbial operational taxonomic units that were simultaneously linked to circadian rhythm measures and the methylation rates of 898 neurodevelopment- and synaptic function-related DMGs. Our findings identify a preliminary cross-sectional associative network among circadian rhythm disturbance, specific gut microbial variation and peripheral DNA methylation signatures in MDD, providing a hypothesis for subsequent mechanistic exploration rather than confirming causal pathogenic mechanisms. These findings suggests a potential integrative framework for understanding MDD pathophysiology and providing a preliminary exploratory multi-omic network hypothesis for understanding MDD pathological correlates and guiding further mechanistic verification.