Chronic kidney disease (CKD) affects more than 10% of North Americans. Two thirds of CKD cases are due to hypertension and/or diabetes. Our group and others have observed CKD progression subtypes; some patients have long periods of non-progression while others have a rapid progression of the disease. CKD can be asymptomatic until an advanced stage, and thus a large proportion of patients with CKD are unaware of their condition. Untreated CKD can lead to end-stage renal disease, leading to permanent dialysis or kidney transplantation. CKD is usually diagnosed and monitored by estimating kidney function with the estimated glomerular filtration rate (eGFR). Since eGFR measurements is a routine test frequently performed in all patients, medical records usually contain multiple eGFR values. We believe that investigating the longitudinal data contained in genotyped electronic medical record (EMR) linked cohorts could identify risk factors for CKD in general and for CKD subtypes that show rapid progression in particular. The identification of such risk factors (both genetic and non-genetic risk factors such as demographics, temporal patterns of laboratory values, hemodynamic parameters and medication usage) could help to provide personalized, stratified healthcare to hypertensive and diabetic CKD patients according to genetic variants and eGFR profiling by alerting both patients and providers to the risk of rapid CKD progression. For my project, I will use the genome-wide genotyping data and EMR data of 24,196 diabetic and/or hypertensive patients from the eMERGE network and genome-wide genotyping data of 33,678 control samples from the eMERGE network. I will validate my results in 16,000 samples of the BioMe biobank, Mount Sinai's EMR linked biobank. The longitudinal phenotypes in both cohorts include information such as age, family history, anthropometric data, prescriptions, diagnoses codes, vital signs and laboratory results.
|Effective start/end date||1/04/15 → 9/01/17|
- Institute of Genetics: $70,264.00