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Circadian disruption is a known driver of multisystem pathophysiology, yet how it interfaces with the gut microbiome and systemic metabolism across the life course, and how these interactions are shaped by biological sex, remains poorly defined. We performed longitudinal multi-omics characterization of Bmal1 -deficient mice in both sexes across three developmental stages. Ablation of Bmal1 induced core circadian disruption phenotypes, including reduced lifespan, decreased body weight, impaired glucose tolerance, and loss of locomotor rhythmicity, and drove sex- and age-dependent metabolic reprogramming, characterized by divergent insulin sensitivity and systemic lipid profiles. Loss of Bmal1 resulted in pronounced, sex- and age-dependent reorganization of gut microbial community structure and rhythmicity, with specific genera (e.g., norank_f_Muribaculaceae , norank_f_norank_o_Clostridia_UCG-014 , Bifidobacterium , Allobaculum ) exhibiting altered diurnal oscillations and divergent ecological trajectories across aging. Concurrent plasma metabolomic profiling unveiled sex‑specific metabolic variations and time‑of‑day‑dependent differences in circulating metabolites, suggesting coordinated disruption of microbiome-metabolome dynamics. Targeted quantification further revealed sex‑ and age‑specific alterations in plasma short‑chain fatty acids (SCFAs). Tissue‑specific gene expression analysis in the jejunum, colon, and liver revealed sexually dimorphic and age‑dependent transcriptional reprogramming of clock, barrier, inflammatory, and lipid‑metabolism genes, further underscoring the multi‑organ impact of Bmal1 deficiency. Integrated multi-omics correlation analysis demonstrated that associations between gut microbiome, plasma metabolites, and host glycolipid phenotypes are jointly shaped by sex and developmental age. Our findings identify aging and biological sex as pivotal, non-redundant ecological modifiers of circadian-associated host-microbiome interactions, underscoring the necessity of incorporating these variables into mechanistic models and translational strategies for circadian-related disorders.
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Yu et al. (2026) studied this question.
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