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

NCT ID: NCT07043374

Last Updated: 2026-01-21

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

270 participants

Study Classification

OBSERVATIONAL

Study Start Date

2026-09-01

Study Completion Date

2027-12-31

Brief Summary

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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

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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

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Kidney Calculi Ureteral Calculi

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Kidney Stone Group

Patients diagnosed with kidney stones via imaging examinations (ultrasound, CT, or urography) were recruited from both outpatient clinics and inpatient departments of the Urology Department at our hospital.

No interventions assigned to this group

Healthy Control Group

1. Recruit healthy volunteers confirmed by imaging examinations to be free of kidney stones from our hospital's Physical Examination Center.
2. Recruit patients with no history of kidney diseases from other departments of our hospital (e.g., Department of Orthopedics, Department of General Surgery).
3. Recruit healthy volunteers through community outreach, confirmed by ultrasound examination to be free of kidney stones.

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* 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.

* 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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

OTHER

Sponsor Role lead

Responsible Party

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Yi Shao

Director

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Shao Yi

Role: PRINCIPAL_INVESTIGATOR

Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

Central Contacts

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Zhu Ruixuan

Role: CONTACT

+86 13780820139

Other Identifiers

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IIT2025-059

Identifier Type: -

Identifier Source: org_study_id

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