Multi-omics Study of Peritoneal Dialysis Effluent to Explore Biomarkers of Peritoneal Fibrosis
Trial Snapshot
- Phase
- Not Applicable
- Status
- Not yet recruiting
- Sponsor
- Enrollment
- 55
- Locations
- 1
- Primary Endpoint
- (1) RNA-seq transcriptomics of exfoliated cells; (2) LC-MS metabolomics of permeable supernatant.
Study Overview
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.
- 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
Study Design
- Study Type
- Observational
- Observational Model
- Case Control
- Time Perspective
- Cross Sectional
Eligibility Criteria
- Ages
- 18 Years to 75 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Patients on peritoneal dialysis: a. Age 18-75 years; b. Regular abdominal dialysis due to uremia> 3 months; c. Signed informed consent
- •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
- •Patients with normal renal function: a. Age 18-75 years; b. Proposed elective inguinal hernia repair surgery; c. Signed informed consent.
Exclusion Criteria
- •Patients on peritoneal dialysis: a. History of peritonitis in the past 3 months; b. History of abdominal tumors with peritoneal metastases
- •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
Outcomes
Primary Outcomes
(1) RNA-seq transcriptomics of exfoliated cells; (2) LC-MS metabolomics of permeable supernatant.
Time Frame: October 2024 - September 2026
Extract RNA from exfoliated cells in exupine, perform mRNA-seq analysis, collect exudate supernatant at the same time, perform metabolome analysis based on mass spectrometry detection, machine learning analysis of multi-omics data, and explore sensitive biomarkers for predicting peritoneal fibrosis based on the evaluation data of peritoneal samples of some patients.
Secondary Outcomes
No secondary outcomes reported
Investigators
Na Jiang
doctor
Shanghai Jiao Tong University School of Medicine
