A New Methodology for Predicting the Onset of Chronic Kidney Disease, Its Subtypes, and Disease Trajectories

Chronic Kidney Disease (CKD) is among the main health hazards in the world, closely associated with high blood pressure and diabetes. We are in a unique position to study CKD as we have deep metabolomics data, genome wide data and rich medical data available for 211,639 individuals (icl. 5000 CKD cases). We are focusing on defining specific CKD subtypes, disease formation trajectories and prediction algorithms using novel approaches and machine learning. The main goal is to predict CKD risk and trajectory for each individual utilizing the CKD subtype, severity type, genomic background, lifestyle choices, medicines used etc. For this we are building a CKD development landscape for all disease scenarios and assign each individual to a location on that landscape to predict future events - but more importantly discover intervention solutions for slower disease progression and better medical outcomes. Our analytical methods will be packaged to an open source computational tool.