KidneySign

Affecting around 10% of the population, chronic kidney disease (CKD) is a concerning disease of European societies, which ultimate stage (end-stage kidney disease (ESKD)) is irreversible and fatal if untreated. The progression rate of CKD cannot be precisely assessed from estimated glomerular filtration rate and urinary albumin excretion, which are the classical diagnostic and risk markers. Kidney fibrosis is the main feature of CKD progression shared by many causal diseases. Fibrosis can be estimated from kidney biopsy, an invasive procedure that cannot be generalised. A non-invasive marker reflecting kidney fibrosis would greatly improve the detection of progressive CKD with direct clinical interest. Urinary and plasma peptidome analyses, including classifiers collectively developed by KidneySign partners, have shown promising results that could benefit from further validation and/or additional molecular dimensions. In KidneySign, we aim to use translational Big Data to develop and validate an innovative multimodal protein-based (proteomics, peptidomics) signature from different body fluids (urine, serum/plasma) robustly associated with in situ fibrosis in the kidney, and predictive of the risk of CKD progression. KidneySign will result in a clinical decision support system that identifies patients at greater risk of CKD progression based on molecular signatures, and recommend individually optimised nephroprotective therapies, which is the hallmark of precision medicine. From existing patient cohorts and biobanks and a KidneySign prospective clinical study, kidney biopsy, urine, serum and plasma will be evaluated by standard, high throughput and Deep Learning approaches and associated with CKD progression endpoints. Ethical and societal aspects will be considered at all project stages, including patients and medical professionals perceptions, and coordinated with other ERA-PerMed projects to facilitate responsible implementation.