NCT07043374

Impact of Humid-Heat on Gut-Tryptophan-Stone Pathway

A Mechanistic Study on How Humid and Hot Environment Promotes Urinary Tract Stone Formation Through Influencing Gut Microbiota and Tryptophan Metabolism

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 270 (estimated)
• Sponsor: Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

Investigating the differences in gut microbiota composition and tryptophan metabolite levels between kidney stone patients and healthy individuals, with special focus on: 1. Comparing the gut microbiota composition between kidney stone patients and healthy controls, with emphasis on analyzing the relative abundance of Lactobacillus salivarius 2. Comparing the differences in tryptophan metabolite levels such as indole-3-carboxylic acid (ICA) and kynurenine (Kyn) in serum between the two groups 3. Exploring the correlation between gut microbiota composition and tryptophan metabolite levels 4. Analyzing the influence of different environmental conditions (seasons, temperature and humidity) on gut microbiota and metabolite levels

Detailed description

1\. Objectives To investigate differences in gut microbiota composition and tryptophan metabolite levels between kidney stone patients and healthy individuals, specifically focusing on: 1. Comparing gut microbiota composition between stone patients and healthy controls, with emphasis on the relative abundance of Lactobacillus salivarius. 2. Comparing serum levels of tryptophan metabolites-indole-3-carboxylic acid (ICA) and kynurenine (Kyn)-between groups. 3. Exploring correlations between gut microbiota composition and tryptophan metabolite levels. 4. Analyzing the impact of environmental conditions (season, temperature/humidity) on gut microbiota and metabolite levels. 2\. Trial Design This prospective case-control study compares gut microbiota composition and serum metabolite levels between kidney stone patients (case group) and stone-free healthy volunteers (control group), while exploring associations with environmental factors. 3\. Participants Case Group: Patients diagnosed with kidney stones. Control Group: Healthy volunteers without kidney stones. 4\. Group Allocation Case Group: Kidney stone patients. Control Group: Stone-free healthy volunteers. Participants are assigned based on clinical status (no randomization). Stratified analyses will consider: Environmental exposure (temperature/humidity data). Seasonal factors (summer vs. non-summer). Gut microbiota composition (L. salivarius abundance via 16S rRNA sequencing). Serum metabolite levels (ICA, Kyn). 5\. Endpoints Primary Endpoints: Gut microbiota differences (α/β diversity, L. salivarius abundance). Serum ICA and Kyn level differences. Secondary Endpoints: Tryptophan pathway metabolite changes (Trp, IAA, Kyn/Trp ratio, ICA/Trp ratio). Microbiota-metabolite correlations. Environmental impact analysis. 6\. Observational Parameters Primary Parameters: Gut microbiota structure (α/β diversity, L. salivarius abundance). Serum ICA/Kyn concentrations (ng/ml). Secondary Parameters: Tryptophan pathway metabolites (Trp, IAA, ratios). Environmental factors (temperature, humidity, season). Demographics (gender, age, BMI). Stone history (type, frequency, seasonality). Comorbidities (hypertension, diabetes, intestinal diseases). 7\. Randomization Not applicable (case-control design). Participants are assigned based on clinical diagnosis. 8\. Blinding No blinding during enrollment. Laboratory personnel are blinded to group allocation during 16S rRNA sequencing and metabolomic analyses. Samples are coded, and statisticians design analysis plans before data unblinding. 9\. Sample Size Calculation Accounting for 10% attrition and multiple analyses, final recruitment targets: 200 cases and 100 controls (expected completions: 180 cases, 90 controls). 10\. Statistical Analysis Descriptive Statistics: Mean±SD for continuous variables; frequencies for categorical variables. Group Comparisons: t-test/Mann-Whitney U (continuous); χ²/Fisher's exact test (categorical). Correlations: Spearman/partial correlation analysis. Multivariate Analysis: Linear/logistic regression adjusting for confounders. Microbiome Analysis: QIIME2 (α/β diversity, LEfSe, ANCOM). Metabolomics: MetaboAnalyst (pathway enrichment). Software: R 4.3.0; P\<0.05 deemed significant. 11\. Follow-up Plan Screening Period (-7 days): Informed consent. Demographics, medical history, physical exam, vital signs (blood pressure, pulse, temperature, respiration). Case group: Collect routine renal function tests, electrolytes, and imaging data. Sample Collection Phase: Case Group: Fecal sample (5g) for 16S rRNA sequencing. Venous blood (10ml) for LC-MS metabolomics. Residual surgical stones (if available). Control Group: Fecal sample (5g) and venous blood (10ml). All samples collected in a single visit. Follow-up via phone for health status confirmation.

Conditions

• Kidney Calculi
• Ureteral Calculi

Timeline

• Start date: 2026-09-01
• Primary completion: 2026-11-20
• Completion: 2027-12-31
• First posted: 2025-06-29
• Last updated: 2026-01-21