Kidney Calculi, Ureteral Calculi
Conditions
Brief 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.
Interventions
None listed
Sponsors
Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Inclusion criteria for patients with kidney stones * Age\>=18 years; * Diagnosed with kidney stones by ultrasound, CT or urography; * Willing to provide stool samples and serum samples for study; * No history of antimicrobial use in the past 3 months; * Signed and dated informed consent indicating that the patient or his/her legal representative is fully informed of the study-related information and agrees to participate. * Inclusion criteria for the healthy control group * Age\>=18 years; * No history of kidney stones and family history; * Imaging examination (such as abdominal ultrasound) showed no kidney stones; * Willing to provide stool samples and serum samples for research; * No history of antibiotic use in the past 3 months; * Signed and dated informed consent indicating that the volunteer is fully informed about the study-related information and agrees to participate.
Exclusion criteria
* Use of antimicrobials or probiotics within the past 3 months; * Presence of active urinary tract infection; * Presence of other serious systemic diseases, such as hepatic or renal insufficiency, cardiac or pulmonary diseases, malignant tumors, and immunodeficiency states; * Congenital urinary tract abnormalities; * Previous history of kidney transplantation or urinary diversion surgery; * Pregnant or lactating women; * Presence of chronic intestinal diseases, such as inflammatory bowel disease, irritable bowel syndrome, etc.; * Inability to provide samples or complete follow-up according to the research protocol; * Participation in other clinical studies within the past 3 months; * Other conditions deemed unsuitable for participation in this study by the researcher.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Gut microbiota differences | 3 months postoperatively | 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) |
| Serum indole-3-carboxylic acid (ICA) concentration and Serum kynurenine (Kyn) concentration | 3 months postoperatively | Comparison of serum ICA (indole-3-carboxylic acid) and Kyn (kynurenine) levels between the case and control groups. Unit of Measure: ng/mL. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Kynurenine to indole-3-carboxylic acid ratio (Kyn/ICA) | 3 months postoperatively | 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). |
| Spearman correlation coefficient between Lactobacillus salivarius abundance and serum ICA concentration | 3 months postoperatively | Unit of Measure: Correlation coefficient (ρ-value, unitless). Method: Spearman rank correlation analysis. |
| Gut microbiota α-diversity | 3 months postoperatively | Unit of Measure: Diversity index (e.g., Shannon index, unitless). Method: 16S rRNA gene sequencing; multivariate linear regression |
| Gut microbiota β-diversity | 3 months postoperatively | Unit of Measure: Dissimilarity index (e.g., Bray-Curtis, unitless). Method: 16S rRNA gene sequencing; PERMANOVA. |
| Relative abundance of Lactobacillus salivarius | 3 months postoperatively | Unit of Measure: Relative abundance (unitless proportion). Method: 16S rRNA gene sequencing; multivariate linear regression. |
| Kidney stone recurrence status | 3 months postoperatively | Unit of Measure: Binary outcome (Recurrence: Yes/No). Method: Ultrasound/CT detection (≥2mm stones); logistic regression. |
| Mean ambient temperature | 3 months postoperatively | Unit of Measure: °C. Method: Portable environmental recorder. |
| Mean ambient relative humidity | 3 months postoperatively | Unit of Measure: %. Method: Portable environmental recorder. |
Contacts
CONTACTZhu Ruixuan
PRINCIPAL_INVESTIGATORShao Yi
Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
Outcome results
None listed