Developing serum proteomics based prediction models of disease progression in ADPKD

Abstract

Abstract Autosomal Dominant Polycystic Kidney Disease is the most common genetic cause of kidney failure. Outcome prediction is essential to guide therapeutic decisions. However, currently available models are of limited accuracy. We aimed to examine the potential of serum proteomics for improved risk stratification. Here we show that 29 proteins are significantly associated with yearly kidney function decline. Functional enrichment on these 29 proteins reveals GO:BP terms related to immune response, lipoproteins and metabolic processes. A comparison to an Immunoglobulin A nephropathy cohort provides information regarding the eGFR-dependency and disease specificity of these proteins. The final outcome prediction model (adjusted R² 0.31) contains six proteins, namely Endothelial Plasminogen Activator Inhibitor (SERPINF1), Glutathione Peroxidase 3 (GPX3), Afamin (AFM), FERM Domain Containing Kindlin-3 (FERMT3), Complement Factor H Related 1 (CFHR1), and Retinoic Acid Receptor Responder 2 (RARRES2), the predictive value of which is independent from the clinical and imaging parameters currently used in clinical care. The validation of these models in different cohorts indicates the accuracy of the models. It will now be important to move towards targeted validation in a prospective study.