Abstract: SA-OR53
Proteomic Characterisation of CKD Progression
Session Information
- CKD: Cutting Edge of Mechanisms
November 06, 2021 | Location: Simulive, Virtual Only
Abstract Time: 04:30 PM - 06:00 PM
Category: CKD (Non-Dialysis)
- 2103 CKD (Non-Dialysis): Mechanisms
Authors
- Ramirez Medina, Carlos Raul, The University of Manchester Faculty of Biology Medicine and Health, Manchester, Manchester, United Kingdom
- Geifman, Nophar, The University of Manchester Faculty of Biology Medicine and Health, Manchester, Manchester, United Kingdom
- Kalra, Philip A., The University of Manchester Faculty of Biology Medicine and Health, Manchester, Manchester, United Kingdom
- Whetton, Anthony, The University of Manchester Faculty of Biology Medicine and Health, Manchester, Manchester, United Kingdom
- Ali, Ibrahim, National Health Service England, Salford, Salford, United Kingdom
Background
Delaying or halting progression of chronic kidney disease (CKD) to established renal failure is a major goal of global health research. The mechanism of CKD progression among different CKD entities involves pro-fibrotic, pro-inflammatory and vascular pathways but current treatments are non-specific, with heterogeneity in terms of response and outcome. In depth phenotypic and proteomic data can help investigate differences between those CKD patients with rapid disease progression and those who remain stable after diagnosis.
Methods
Using eGFR slope analysis, 414 patients with a broad range of kidney disease aetiologies were divided into fast progressors (δGFR > -3 ml/min/yr; n=170) and stable patients (δGFR > 0 ml/min/yr; n=244); these composed our discovery cohort. Plasma samples were obtained, and interrogated for novel proteomic signals with SWATH-MS which enabled a digitised proteomic profile to be generated. For hypothesis testing, the t-test was used to identify differentially expressed proteins between our patient groups (p<0.05, after multiple testing corrections was considered statistically significant). Statistical analysis and machine learning approaches for discovery (Random Forest and Boruta Feature Selection) were performed using the computing environment R and additional software packages were obtained via the Bioconductor project.
Results
A SWATH map (on 414 patients with 943 proteins quantified) was generated and investigated in tandem with available clinical data in order to identify potential progression biomarkers. After differential expression analysis and supervised machine learning algorithms for feature selection, we identified a set of proteins that differentiate between our patient groups (AUC= 0.77). Baseline creatinine was not an accurate predictor of CKD progression (AUC=0.51). Functional enrichment analysis revealed platelet degranulation to be statistically important, suggesting a possible role for platelet function in then pathogenesis of CKD.
Conclusion
The in-depth proteomic characterisation of this large-scale CKD cohort is a step forward in generating mechanism based hypotheses that might then lend themselves to future drug targeting. Candidate proteomic biomarkers will be validated in samples from selected patients in other large CKD cohorts such as NURTuRE using a targeted mass spectrometric analysis.