Abstract: SA-PO440
Metabolomic Analysis of Peritoneal Dialysis Effluent across Different Peritoneal Transport Types
Session Information
- Home Dialysis - 2
October 26, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Dialysis
- 802 Dialysis: Home Dialysis and Peritoneal Dialysis
Authors
- Wang, Xiaoling, Renal Research Institute, New York, New York, United States
- Absar, Hasib, Renal Research Institute, New York, New York, United States
- Grobe, Nadja, Renal Research Institute, New York, New York, United States
- Kakembo, Mark, Renal Research Institute, New York, New York, United States
- Wang, Xin, Renal Research Institute, New York, New York, United States
- Ferreira Dias, Gabriela, Renal Research Institute, New York, New York, United States
- Tisdale, Lela, Renal Research Institute, New York, New York, United States
- Zhu, Fansan, Renal Research Institute, New York, New York, United States
- Rosales M., Laura, Renal Research Institute, New York, New York, United States
- Wang, Lin-Chun, Renal Research Institute, New York, New York, United States
- Han, Maggie, Renal Research Institute, New York, New York, United States
- Kotanko, Peter, Renal Research Institute, New York, New York, United States
Background
The efficiency of peritoneal dialysis (PD) depends on the peritoneal transport rate, which varies among patients. High transporter characteristics are associated with PD technique failure and increased mortality. Understanding the biochemical changes in different transport types over time is crucial for optimizing PD.
Methods
Sixteen PD patients participated in this study. All patients completed a 2-hour PD session using 2-liter 2.5% dextrose dialysis fluid. Additionally, six patients underwent a separate 4-hour PD session with the same solution on another day. PD effluent (PDE) was collected at the end of each PD session, with serum collected midway. Patients’ transport types were determined using dialysate-to-serum creatinine ratio and D/D0 of glucose at 2 hours and were classified as high/high-average (H/HA) and low/low-average (L/LA). Untargeted metabolomics of PDE was conducted using semi-quantitative liquid chromatography mass spectrometry.
Results
A total of 1,987 features (predicted formula and retention time) met quantification criteria. We first selected features with intensities differing between the 2-hour and 4-hour PD sessions within the same patient (paired t-test threshold of p<0.05) for 6 patients. Subsequently, the remaining 474 significantly different features were compared between the H/HA and L/LA groups (unpaired t-test; Figure 1A). We observed significant differences (p<0.05) in 102 features between the two transport types. Among these,15 metabolites were identified (Figure 1B).
Conclusion
Our analysis revealed 150 metabolites in PDE. Notably, 15 metabolites showed potential as biomarkers indicating transport status and/or membrane function under the same prescription regimen. Further validation and exploration of these findings are warranted to understand their clinical applications.
Figure 1: Feature and metabolite identification. (A) Metabolite identification workflow. (B) 15 metabolites showed differences between transport types H/HA and L/LA.
Funding
- Commercial Support – Renal Research Institute