#AI reads Urine# Urinary biochemical ecology reveals microbiome-metabolite interactions and metabolic markers of recurrent urinary tract infection

This study focused on postmenopausal women and explored the associations between recurrent urinary tract infections (rUTIs), the urinary microbiome, and the metabolome by integrating metagenomic and metabolomic data. The researchers divided 75 participants into three groups: those with no history of urinary tract infections (UTIs), those with a history of recurrent UTIs but no active infection at the time of the study, and those with a history of recurrent UTIs and an active infection at the time of the study. Using targeted metabolomics technology, they detected more than 600 metabolites in urine and combined this with metagenomic analysis to examine the composition of the urinary microbiome.

The results showed that pathogenic bacteria (such as Escherichia coli) and beneficial bacteria (such as Lactobacillus) each have unique metabolite association networks. Active UTIs are associated with specific lipid metabolic signatures, and 11 types of lipids can relatively accurately distinguish the infection status. Additionally, deoxycholic acid was identified as a prognostic indicator for UTI recurrence, and metabolites related to urinary tract health (such as tetradecanedioic acid and dodecanedioic acid) were also recognized. These findings provide a basis for the precise diagnosis, prognostic assessment, and development of new treatment strategies for recurrent UTIs, but the generalizability of the study results still needs to be verified in larger-scale and more diverse populations.

NPJ Biofilms Microbiomes. 2025 Nov 24;11(1):216. doi: 10.1038/s41522-025-00844-1.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/