Multi-omics Study of Peritoneal Dialysis Effluent to Explore Biomarkers of Peritoneal Fibrosis

NCT ID: NCT07208513

Last Updated: 2025-10-06

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 55 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-10-20
• Study Completion Date: 2026-09-30

Brief Summary

Biomarkers of peritoneal fibrosis in patients with peritoneal dialysis were investigated by transcriptomics of exfoliated cells and metabolomics of exfoliated cells in peritoneal dialysis

Detailed Description

This study is a cross-sectional study with no clinical intervention and follow-up.

Patients who met the inclusion criteria were enrolled in the study, and demographic indicators and clinical hematological indicators were collected within 2 weeks, clinical assessment of peritoneal function and other indicators were collected within 4 weeks, abdominal diarrhea effusion exfoliated cells and supernatant were collected within 4 weeks, and some patients were collected for fibrosis assessment by wall peritoneal samples. After the clinical sample was tested, correlation analysis was performed to explore the biomarkers of peritoneal fibrosis.

1. Collection of clinical indicators Relevant information such as demographic indicators, primary renal disease, comorbidities, complications, abdominal dialysis regimen, dialysis age, urine output, ultrafiltration volume, peritonitis history, and concomitant medication were recorded.

After the patients were enrolled in the group, they completed a physical examination (weight, blood pressure, BCM measurement, etc.) within 2 weeks, and collected clinical laboratory indicators including whole blood analysis, hsCRP, NT-proBNP, TNI, blood biochemistry (liver and kidney function, electrolytes, blood glucose, HbA1C, blood lipids, calcium, phosphorus, iPTH, iron, total iron binding capacity, ferritin), mGFR, exudate electrolyte, exudate albumin concentration, etc., exudate CA125, and peritoneal CT peritoneal thickness within 3 months.

2. Clinical assessment of peritoneal function Standard peritoneal balance test was performed to evaluate the peritoneal ultrafiltration function (net ultrafiltration volume after 4 hours of 2.5% glucose dialysis solution) and solute transport rate (D/PCR).

3. Peritoneal dialysis effusion collection and exfoliated cell collection 2L of abdominal translate was collected overnight, cell sediment was collected by centrifugation (1500 rpm, 10 min), RNA was extracted by RNA extraction kit for transcriptome sequencing, and the supernatant of the permeate was cryopreserved to -70 oC for metabolomics determination.

3.1 Exfoliated cell RNA-sequencing

1. RNA extraction quality inspection: Extract total RNA from exfoliated cells using MJzol Animal RNA Isolation Kit and according to standard operating procedures, and purify them using RNAClean XP Kit and RNase-Free DNase Set. RNA integrity was detected by the Agilent 2100 Bioanalyzer, and total RNA volume and purity were determined using the Qubit 2.0 Fluorometer and NanoDrop ND-2000 spectrophotometer.

2. Sequencing library construction: The mRNA sequencing library is constructed by separating and fragmenting the purified total RNA, fragmenting the first strand cDNA synthesis, the second strand cDNA synthesis, ending repair, adding A at the 3' end, junction, and enrichment. Library concentrations were detected using the Qubit 2.0 Fluorometer, and library fragment distribution was detected with the Agilent 4200 TapeStation.

3. On-machine sequencing: Sequencing is carried out according to the effective concentration of the library and the demand for data output. The sequencing platform uses Illumina NovaSeq6000, and the sequencing mode adopts PE150 (Pair-end 150 bp), that is, double-ended sequencing measures 150 bp at each end.

3.2 Metabolomics analysis of permeate fluid

1. Sample collection and processing: The collected permeable solution is stored in a refrigerator at 4°C. After thawing the permeate stored in the ultra-low temperature refrigerator at room temperature, 100μ was added to 300μL of methanol for vortexing for 3 minutes, then left for 5 minutes, then centrifuged at low temperature and high speed for 10 minutes, and finally the supernatant was injected.

2. QC quality control and batch correction of sample data: Use unsupervised Principal Component Analysis (PCA) to establish a model for each group of samples, and then display the score graph, and the results of the quality control samples are close, indicating that the detection repeatability is good. The most commonly used feature correction for sample standardization is the median metabolite content and the upper and lower quartiles can basically reach a level after standardized correction.

3. Statistics of metabolite content after extraction: the composition and structure differences of each group can be directly compared by stacked column charts; Sample clustering analysis can also be used to construct clusters of samples to investigate the similarity between different samples

4. Unsupervised PCA analysis: The sample grouping information is not considered in the PCA plot, each point corresponds to a sample, and the distance between the two points is approximately the difference in the composition and structure of the metabolites of the two samples. If the two groups of point clouds are significantly distributed in different regions, it indicates that there are significant differences in the composition structure of the two groups of metabolites.

5. Supervised least squares discriminant analysis: It also includes partial least squares discriminant analysis, PLS-DA metabolite importance map, and orthogonal partial least squares discriminant analysis, which are used to divide metabolites into different groups.

6. Univariate analysis: to understand whether there are changes in metabolites in each group and whether the differences between these metabolites are significant, and also to know the degree of this change, and to evaluate how much impact the changes in metabolites will have on the organism.

7. Machine learning: Machine learning belongs to the category of discriminant analysis, which is an extension of discriminant analysis in machine learning. Support vector machines, random forests, and neural networks all fall under the category of machine learning. It is used to analyze and compare the degree of difference between metabolites in each group.

8. Metabolic pathway analysis: The degree of influence of the target metabolites on the metabolic pathway can be calculated (measured by Impact), and the interaction intensity and direction of action between different substances in each pathway can be compared to determine whether they have changed or produced new active products. In addition, it can intuitively reflect the upstream and downstream relationships and modes of action of metabolites, and metabolite-related genes and metabolites with significant differences between groups can be found.

(4) Evaluation of parietal peritoneal fibrosis Some patients with abdominal dialysis (n=5) collected a piece of parietal peritoneum (about 2cm2cm) during kidney transplantation, rinsed and cut in PBS, half of the samples were fixed in 4oC 4% paraformaldehyde, and half of the samples were frozen at -70oC.

A hemodialysis control group (n=5) and a normal renal function control group (n=5) were set up, and a wall peritoneum (about 2cm2cm) was collected from patients in the hemodialysis control group during kidney transplantation, and a piece of peritoneum (about 2cm*2cm) was collected from patients with normal renal function during inguinal hernia repair.

Masson staining evaluated peritoneal thickness and submesothelial fibrous layer thickness, and FN and Coll I immunohistochemical staining evaluated peritoneal extracellular matrix protein deposition.

Conditions

Peritoneal Dialysis; Peritoneal Fibrosis

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: CROSS_SECTIONAL

Eligibility Criteria

Inclusion Criteria

1. Patients on peritoneal dialysis: a. Age 18-75 years; b. Regular abdominal dialysis due to uremia> 3 months; c. Signed informed consent

2. Hemodialysis patients: a. Age 18-75 years; b. Due to regular hemodialysis due to uremia> 3 months old, allogeneic kidney transplantation is planned; c. Signed informed consent

3. Patients with normal renal function: a. Age 18-75 years; b. Proposed elective inguinal hernia repair surgery; c. Signed informed consent.

Exclusion Criteria

1. Patients on peritoneal dialysis: a. History of peritonitis in the past 3 months; b. History of abdominal tumors with peritoneal metastases

2. Hemodialysis patients: a. Previous abdominal dialysis history; b. History of abdominal tumors with peritoneal metastases Patients with normal renal function: a. History of chronic kidney disease; b. History of abdominal tumors with peritoneal metastases

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Shanghai Jiao Tong University School of Medicine

OTHER

Sponsor Role: lead

Responsible Party

Na Jiang

doctor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Renji Hospital

Shanghai, Shanghai Municipality, China

Countries

China

Central Contacts

Na Jiang, doctor

Role: CONTACT

jiangjiang198311@163.com

13585579332

chenhong feng, doctor

Role: CONTACT

15167930075

Other Identifiers

Multi-omics study

Identifier Type: -

Identifier Source: org_study_id