An exploratory study of gut microbiota and serum metabolite profiles in type 2 diabetes patients with sleep disorders

Abstract

To characterize gut microbiota composition and serum metabolites in T2DM patients with and without sleep disorders. This single center cross sectional study enrolled 40 T2DM patients with sleep disorders (Group I), 40 T2DM patients without sleep disorders (Group N) and 30 age and sex matched healthy controls (Group ZC). Fecal and serum samples were collected for 16S rRNA gene sequencing and untargeted liquid chromatography–mass spectrometry metabolomics, respectively. Gut microbiota composition, serum metabolites and metabolic pathways, and microbe–metabolite correlations were analyzed. Compared with T2DM patients without sleep disorders, T2DM patients with sleep disorders showed reduced gut microbial diversity and evenness, as indicated by lower Shannon, Simpson, and Pielou's evenness indices. Beta diversity analysis showed a small but statistically significant difference in microbial structure between Groups I and N. At the genus level, nominal differences were observed between the two groups, including higher relative abundances of TM7x, Achromobacter , and Pseudomonas and lower relative abundances of Eubacterium_eligens_group, Lachnospiraceae_UCG-001 , and Negativibacillus in Group I. Metabolomic results revealed 17 nominally differential metabolites between Groups I and N, involving caffeine metabolism and porphyrin and chlorophyll metabolism. Spearman analysis revealed that stercobilin abundance positively correlated with Lachnospiraceae_UCG-001 ( R = 0.381, P < 0.001, Q = 0.095), Lachnospiraceae_ND3007 ( R = 0.354, P = 0.002, Q = 0.121), and Odoribacter ( R = 0.280, P = 0.01, Q = 0.301). T2DM patients with sleep disorders showed modest differences in gut microbial community structure and exploratory changes in serum metabolite profiles. Stercobilin and caffeine-related metabolic features may represent exploratory characteristics associated with sleep disorders in T2DM. These findings suggest potential microbiota-metabolite associations and provide a basis for future longitudinal and mechanistic studies.