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
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.
Study Type
OBSERVATIONAL
Enrollment
270
Gut microbiota differences
Comparison of gut microbiota composition between the case and control groups, particularly the relative abundance of Lactobacillus salivarius. Unit of Measure: Relative abundance (unitless proportion)
Time frame: 3 months postoperatively
Serum indole-3-carboxylic acid (ICA) concentration and Serum kynurenine (Kyn) concentration
Comparison of serum ICA (indole-3-carboxylic acid) and Kyn (kynurenine) levels between the case and control groups. Unit of Measure: ng/mL.
Time frame: 3 months postoperatively
Kynurenine to indole-3-carboxylic acid ratio (Kyn/ICA)
Evaluation of differences in tryptophan metabolism-related metabolites across groups, with a focus on changes in the Kyn/ICA ratio. Unit of Measure: Ratio (unitless).
Time frame: 3 months postoperatively
Spearman correlation coefficient between Lactobacillus salivarius abundance and serum ICA concentration
Unit of Measure: Correlation coefficient (ρ-value, unitless). Method: Spearman rank correlation analysis.
Time frame: 3 months postoperatively
Gut microbiota α-diversity
Unit of Measure: Diversity index (e.g., Shannon index, unitless). Method: 16S rRNA gene sequencing; multivariate linear regression
Time frame: 3 months postoperatively
Gut microbiota β-diversity
Unit of Measure: Dissimilarity index (e.g., Bray-Curtis, unitless). Method: 16S rRNA gene sequencing; PERMANOVA.
Time frame: 3 months postoperatively
Relative abundance of Lactobacillus salivarius
Unit of Measure: Relative abundance (unitless proportion). Method: 16S rRNA gene sequencing; multivariate linear regression.
Time frame: 3 months postoperatively
Kidney stone recurrence status
Unit of Measure: Binary outcome (Recurrence: Yes/No). Method: Ultrasound/CT detection (≥2mm stones); logistic regression.
Time frame: 3 months postoperatively
Mean ambient temperature
Unit of Measure: °C. Method: Portable environmental recorder.
Time frame: 3 months postoperatively
Mean ambient relative humidity
Unit of Measure: %. Method: Portable environmental recorder.
Time frame: 3 months postoperatively
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.