Objective
The goal of the study is the identification of epigenetic, genetic, renal, genomic, and biomarker profiles that differentiates patients with rapid kidney function decline from those with slower rate of kidney function decline. This information will help to identify patients who are at risk of developing end stage kidney disease and identify novel therapeutic targets.
Background Rationale
According to the 2013 survey the total cost of diabetes have risen to $245 billion; this figure represents a 41% increase compared to the 2007 data. Complications remain the major driver of cost. Recent reports from the FinnDiane study indicate that renal disease likely explains the excess mortality associated with DM1. The application aims to understand nephropathy development in patients with type1 diabetes and to identify biomarkers that predict kidney function decline and potential therapeutic targets.
Description of Project
Recent reports indicate that in patients with type1 diabetes the presence of kidney disease explains almost all excess mortality. Despite its key importance our understanding of diabetic kidney disease is still limited.
Transformative research in diabetic nephropathy; TRIDENT is a prospective, observational, cohort study of patients with a clinical diagnosis of diabetes who are undergoing clinically indicated kidney biopsy. High through-put genomic analysis associated with genetic and biomarker testing will serve to identify key potential therapeutic targets for diabetic kidney disease by comparing patients with rapid and slow progression patterns. Furthermore, Trident will enable to test the concept of precision medicine in the context of diabetic kidney disease by the identification of subjects, who show significant enrichment for specific interventions.
Anticipated Outcome
Trident is a unique academic industry partnership aiming to understand diabetic kidney disease development. Trident will study kidney, blood and urine samples from patients with type1 diabetes and use state of the art genetic, genomic and epigenetic methods coupled with machine learning to identify key markers and pathways for progressive kidney disease.
Relevance to T1D
The current proposal will enroll only subjects with established type1 diabetes. There has been very studies on patients with type 1 diabetes, most recommendations come from observations from patients with type2 diabetes. Here we will analyze changes that are specific for type1 diabetes, in addition we will perform direct comparison to subjects with type 2 diabetes.