Gut microbiome–metabolome signatures of osteosarcopenia in fracture patients in China

Background Osteosarcopenia, defined as the coexistence of low bone mass and sarcopenia, is a disabling musculoskeletal condition, yet its gut microbial and metabolic characteristics in clinical populations remain incompletely understood. Integrative multi-omics approaches may help clarify species–metabolite networks associated with this condition, particularly in fracture patients. Methods In this single-center, prospective cross-sectional study, 69 fracture patients aged ≥50 years were classified into four phenotypes: Normal (n = 18), isolated low bone mass (Bone, n = 18), isolated sarcopenia (Muscle, n = 19), and osteosarcopenia (Both, n = 14). Fecal samples were analyzed using shotgun metagenomics and untargeted metabolomics, yielding paired multi-omics data for 52 participants. Results The Bone group had the highest mean age (66.6 ± 9.46 years), whereas the mean ages of the other groups ranged from 60.6 to 61.8 years (overall p  = 0.029), while sex, BMI, lifestyle factors, and comorbidities did not differ significantly. Neither α-diversity nor overall β-diversity showed marked differences across phenotypes, suggesting that broad community replacement was not observed. A multi-stage, multi-method strategy yielded a 17-species consensus feature set associated with differences among musculoskeletal phenotypes. Taxonomic patterns were consistent with a candidate fiber/short-chain fatty acid (SCFA)-associated module, whereas exploratory microbe–metabolite correlations suggested a candidate lipid/sterol-associated module. The latter included correlations linking Firmicutes bacterium CAG:24053_14 with putatively annotated cholesterol and N-acylethanolamines. Conclusions Osteosarcopenia in fracture patients was associated with unadjusted differences in selected gut microbial taxa and fecal metabolites within a broadly shared microbial community. These findings are hypothesis-generating and require validation in larger independent cohorts before clinical or biomarker application.