Multi-habitat microbiome profiling identifies habitat-dependent alterations and complementary discriminatory information in urolithiasis.

Background: Urolithiasis has been associated with microbial alterations in individual anatomical niches; however, whether microbial signatures across urinary, intestinal, and oral habitats represent shared, site-specific, or complementary disease-associated information remains unclear. This study aimed to characterize multi-habitat microbiome alterations associated with urolithiasis and to evaluate whether integrated multi-site profiling captures internally cross-validated disease-associated microbial information. Methods: Salivary, clean-catch midstream urinary, and fecal samples were collected from 80 stone formers (SF) and 40 healthy controls (HC) and profiled using 16S rRNA gene sequencing. After quality control, the final analytical dataset comprised 101 urinary, 117 fecal, and 120 salivary samples, with 98 participants contributing complete three-habitat profiles. Habitat-specific alpha- and beta-diversity, taxonomic alterations, and exploratory inferred-network and predicted-functional profiles were evaluated. Random forest models were assessed using repeated nested stratified cross-validation to examine internal discriminatory information from single- and multi-habitat microbial features. Results: Urolithiasis was associated with statistically detectable but modest differences in microbial community structure across all three habitats, with small PERMANOVA effect sizes and significant dispersion differences. Fecal samples from SF showed significantly reduced richness, including lower Sobs, Chao1, and ACE indices than HC after false discovery rate correction (all q = 0.022), whereas urinary and salivary alpha-diversity did not show broad loss. Taxonomic alterations were habitat dependent: saliva yielded the broadest covariate-robust genus-level candidate set, feces showed fewer stable HC-enriched genera alongside reduced richness, and urinary candidate associations were identified but require prospective contamination-controlled validation because of the low-biomass nature of urine and the absence of negative controls. In matched participants, the combined multi-habitat microbiome model achieved an area under the receiver operating characteristic curve of 0.865 (95% CI, 0.839-0.886), exceeding the limited clinical-only model based on age, sex, and body mass index (AUC, 0.738; delta-AUC, 0.128; 95% CI, 0.026-0.229). Improvement over the best single-habitat microbiome model was not statistically conclusive. External contextual analyses provided partial urinary community-level support in KiSMi and an inverse but FDR-non-significant NHANES oral-richness association after extensive covariate adjustment, supporting harmonized prospective validation. Conclusions: These findings identify habitat-dependent microbiome alterations in urolithiasis and show that integrated multi-habitat profiling captures disease-associated microbial information beyond a limited clinical baseline. The combination of reduced fecal richness, broad salivary covariate-robust candidates, biologically proximal urinary candidates, and external contextual signals supports simultaneous multi-site profiling as a valuable framework for future mechanistic and translational studies. Prospective studies with rigorous low-biomass controls, comprehensive exposure metadata, direct functional measurements, and prespecified independent validation are warranted.