Project Summary Forty million Americans live with kidney disease and this number is projected to increase with rising rates of CKD comorbid conditions, including diabetes, obesity, and hypertension, superimposed on an aging patient population. A tremendous financial burden is imparted by dialysis therapy and kidney transplantation for end- stage CKD. At present, however, there are limited tools in existence to predict the progression of AKI and CKD, and development of therapies has been disappointingly restricted. The overall objective of this application is to establish the Mount Sinai Kidney Precision Medicine Project (KPMP) recruitment site in support of the larger consortium’s tissue interrogation and phenotyping activities. There is a tremendous need to utilize human kidney tissue as a research tool for the identification of AKI and CKD disease markers to elucidate molecular pathways that contribute to kidney disease development and progression. We have proposed three Specific Aims: Aim 1 will establish a robust system of patient centered oversight to recruit diverse patients into a kidney biopsy cohort while maintaining the highest standards of safety, quality and ethical research conduct. In Aim 2 we will recruit and retain a spectrum of patients with CKD in response to KPMP priorities. This includes leveraging existing institutional risk stratification tools and resources to identify and recall patients at risk for CKD progression due to diabetes, hypertension, prior COVID-19 infection and apolipoprotein L1 associated disease. Aim 3 will recruit and retain patients with AKI as well as those at high risk for AKI identified by a machine learning algorithm. These Aims overseen by a stakeholder board and executed by an experienced multidisciplinary team, will integrate with the KPMP consortium to accomplish its transformative aims.
|Effective start/end date||15/09/22 → 30/06/23|
- National Institute of Diabetes and Digestive and Kidney Diseases: $374,867.00
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.