Urine microbiome profiles in patients with different types of kidney stones

Kidney stone disease (KSD) is a multifactorial condition influenced by systemic and extrinsic factors, including diet, genetics, metabolic disorders, and microbial and environmental factors. Although preliminary evidence links the urinary microbiome (UMB) to stone formation, its role in different stone types remains unclear. We analysed UMB in patients with four distinct types of kidney stones (calcium oxalate, struvite, uric acid, and mixed composition). Urine samples from patients with KSD and controls were analysed using 16S rRNA gene sequencing. Patients with KSD exhibited distinct microbiota compositions, with increased abundances of Corynebacterium, Prevotella, and Staphylococcus than controls. The abundance of Dongia and Stenotrophomonas was higher in pure-stone formers than in mixed-stone formers (P < .05). Calcium oxalate stone formers had elevated levels of Pseudomonas and Dongia and reduced levels of Peptoniphilus than controls (P < .05). No significant differences in microbial diversity were observed between groups. Microbial composition correlated with blood and urine parameters, suggesting a potential influence on metabolic health and stone formation. These findings underscore the important role of UMB in KSD and provide valuable insights into its involvement in disease development and new opportunities for microbiome-based therapeutic strategies.

Authors

Institutions

Publication Details

Journal
FEMS Microbiology Letters
Published
2025-01-01
DOI
https://doi.org/10.1093/femsle/fnaf054
Citations
1
Primary Topic
Kidney Stones and Urolithiasis Treatments
Type
article
Field-Weighted Citation Impact
1.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Urine microbiome profiles in patients with different types of kidney stones

A. B. Arun, M Mujeeburahiman, Mangesh Suryavanshi, Muhammed Manzoor et al.
1 citations
FEMS Microbiology Letters
Kidney Stones and Urolithiasis Treatments
1.52
article

Urine microbiome profiles in patients with different types of kidney stones

A. B. Arun, M Mujeeburahiman, Mangesh Suryavanshi, Muhammed Manzoor, P. D. Rekha, Aaron W. Miller, Yogesh S. Shouche
article en
1 citations

Abstract

Kidney stone disease (KSD) is a multifactorial condition influenced by systemic and extrinsic factors, including diet, genetics, metabolic disorders, and microbial and environmental factors. Although preliminary evidence links the urinary microbiome (UMB) to stone formation, its role in different stone types remains unclear. We analysed UMB in patients with four distinct types of kidney stones (calcium oxalate, struvite, uric acid, and mixed composition). Urine samples from patients with KSD and controls were analysed using 16S rRNA gene sequencing. Patients with KSD exhibited distinct microbiota compositions, with increased abundances of Corynebacterium, Prevotella, and Staphylococcus than controls. The abundance of Dongia and Stenotrophomonas was higher in pure-stone formers than in mixed-stone formers (P < .05). Calcium oxalate stone formers had elevated levels of Pseudomonas and Dongia and reduced levels of Peptoniphilus than controls (P < .05). No significant differences in microbial diversity were observed between groups. Microbial composition correlated with blood and urine parameters, suggesting a potential influence on metabolic health and stone formation. These findings underscore the important role of UMB in KSD and provide valuable insights into its involvement in disease development and new opportunities for microbiome-based therapeutic strategies.

FEMS Microbiology LettersVol. 372
Cleveland Clinic (US), Yenepoya University (IN), National Centre for Cell Science (IN), Karnataka Health Promotion Trust (IN), G.S. Science, Arts And Commerce College (IN)
Openalex Percentile: Top 18%
Kidney Stones and Urolithiasis Treatments
1.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.