Chronic kidney disease (CKD) is one of the most deadly diseases faced by patients and is a major global health and socioeconomic burden.CKD increases cardiovascular morbidity and premature mortality and decreases quality of life. Hypertension (HTN) and type 2 diabetes mellitus (T2DM), which are reaching epidemic levels, are major risk factors for CKD. CKD diagnosis and progression is based on estimated GFR (eGFR) and urinary albumin excretion. However, eGFR only has a predictive value in advanced disease and there is risk of progressive CKD in non-albuminuric individuals. Thus, there is an urgent need for new approaches for early detection of the most “at risk” individuals and identification of CKD signatures to aid in designing novel drugs and preventive measures that could ameliorate progression of CKD. Our overarching goal is to identify metabolites that predict kidney cell phenotypes during CKD and how crosstalk of these metabolites with the proteome drive CKD progression. We will integrate metabolomics and proteomic information from animal models of CKD with human CKD patient biopsies to identify common signatures in the tubulointerstitium that correlate with human pathophysiology. Here we provide quantitative proteomic and metabolomic datasets, as well as plasma and urine electrolyte measurements on five CKD mouse models.

The KidDO project is funded by the ODIN platform. ODIN is sponsored by the Novo Nordisk Foundation (grant number NNF20SA0061466).