An integrated multi-omics signature of kidney fibrosis for CKD precision medicine
Reference number | |
Coordinator | Högskolan i Skövde - Högskolan i Skövde Inst f biovetenskap |
Funding from Vinnova | SEK 3 000 000 |
Project duration | November 2022 - December 2025 |
Status | Ongoing |
Venture | European partnership for Personalised Medicine |
Call | ERA PerMed Joint Transnational Call 2022: Personalised Prevention |
Purpose and goal
Chronic kidney disease (CKD) is a serious disease that affects around 10% of Europe´s population. Renal fibrosis is characteristic during CKD progression but can also be caused by many other diseases. The degree of renal fibrosis can be determined using a kidney biopsy, but it is an invasive procedure that cannot be generalized. A non-invasive marker reflecting renal fibrosis would greatly improve the detection of progressive CKD with direct clinical interest. The goal of the project is to identify a signature of markers to predict progression of CKD.
Expected effects and result
Expected results from KidneySign is a clinical decision support system for early identification of CKD progression based on easily measurable molecular signatures. From material available in patient cohorts, biobanks as well as a KidneySign prospective clinical trial, kidney biopsy, urine, serum and plasma will be analyzed with advanced data analysis and correlated with CKD progression in patients. This will result in the identification of new markers that can be used in different combinations to measure progression of CKD without performing invasive interventions.
Planned approach and implementation
Urine and plasma peptidome assays, including classifiers jointly developed by KidneySign partners, have shown promising results but need further validation. I KidneySign, we will use translational large-scale data to develop and validate an innovative multimodal protein-based signature of biomarkers from different body fluids that can predict in situ fibrosis in the kidney and predict the risk of CKD progression. Both statistical and AI-based approaches will be used to analyze and combine data from complex cohorts.