Polycystic kidney disease (PKD) is a common hereditary disease and ranks fourth as a leading cause of end-stage renal failure. The primary goal of this study is to exploit volumetric imaging methods to identify and quantify morphologic measures that are associated with the PKD stage and the rate of PKD progression. 200 subjects, divided into three cohorts (GFR=30-50, 70- 90 ml/min) will be followed for a four-year period. Two participating clinical centers (PCC) will each contribute data on approximately 100 subjects. Each PCC will collect clinical data and specimens, perform renal functional tests, and obtain MR and US images for each subject at specific follow-up intervals. The data will be delivered or electronically transferred to the data coordinating and imaging analysis center (DCIAC) proposed in this application.
Our specific aims are (1) to validate magnetic resonance (MR) volumetric imaging methods for accurate and reliable morphometric assessment of the renal parenchyma and renal/hepatic/pancreatic cysts in PKD patients; (2) to compare morphologic measures obtained from MR imaging data with renal functional measures in PKD patients to determine which morphologic measures are associated with the extent of renal functional impairment and with the rate of disease progression; and structural changes, while considering clinical-effectiveness, cost- effectiveness and patient acceptability. Statistical methods appropriate for growth curve estimation with a heterogeneous population will be developed and applied to characterize the PKD progression for each cohort and for the sample as a whole. The long-term goal of this project is to develop methods that would facilitate shortening the observation period necessary to determine efficacy of treatment interventions in PKD patients.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01DK056961-01
Application #
6045928
Study Section
Special Emphasis Panel (ZDK1-GRB-7 (O1))
Program Officer
Flessner, Michael Francis
Project Start
1999-09-30
Project End
2004-11-30
Budget Start
1999-09-30
Budget End
2000-11-30
Support Year
1
Fiscal Year
1999
Total Cost
Indirect Cost
Name
Washington University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
062761671
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Yu, Alan S L; Shen, Chengli; Landsittel, Douglas P et al. (2018) Baseline total kidney volume and the rate of kidney growth are associated with chronic kidney disease progression in Autosomal Dominant Polycystic Kidney Disease. Kidney Int 93:691-699
Cornec-Le Gall, Emilie; Olson, Rory J; Besse, Whitney et al. (2018) Monoallelic Mutations to DNAJB11 Cause Atypical Autosomal-Dominant Polycystic Kidney Disease. Am J Hum Genet 102:832-844
McKenzie, Katelyn A; El Ters, Mirelle; Torres, Vicente E et al. (2018) Relationship between caffeine intake and autosomal dominant polycystic kidney disease progression: a retrospective analysis using the CRISP cohort. BMC Nephrol 19:378
Shen, Chengli; Landsittel, Douglas; Irazabal, María V et al. (2017) Performance of the CKD-EPI Equation to Estimate GFR in a Longitudinal Study of Autosomal Dominant Polycystic Kidney Disease. Am J Kidney Dis 69:482-484
Kline, Timothy L; Korfiatis, Panagiotis; Edwards, Marie E et al. (2017) Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease. Kidney Int 92:1206-1216
Kim, Youngwoo; Bae, Sonu K; Cheng, Tianming et al. (2016) Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease. Phys Med Biol 61:7864-7880
Porath, Binu; Gainullin, Vladimir G; Cornec-Le Gall, Emilie et al. (2016) Mutations in GANAB, Encoding the Glucosidase II? Subunit, Cause Autosomal-Dominant Polycystic Kidney and Liver Disease. Am J Hum Genet 98:1193-1207
Heyer, Christina M; Sundsbak, Jamie L; Abebe, Kaleab Z et al. (2016) Predicted Mutation Strength of Nontruncating PKD1 Mutations Aids Genotype-Phenotype Correlations in Autosomal Dominant Polycystic Kidney Disease. J Am Soc Nephrol 27:2872-84
Kim, Youngwoo; Ge, Yinghui; Tao, Cheng et al. (2016) Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease. Clin J Am Soc Nephrol 11:576-84
Irazabal, María V; Rangel, Laureano J; Bergstralh, Eric J et al. (2015) Imaging classification of autosomal dominant polycystic kidney disease: a simple model for selecting patients for clinical trials. J Am Soc Nephrol 26:160-72

Showing the most recent 10 out of 25 publications