Abstract
BACKGROUND A treatment option for autosomal dominant polycystic kidney disease (ADPKD) has highlighted the need to identify rapidly progressive patients. Kidney size/age and genotype have predictive power for renal outcomes, but their relative and additive value, plus associated trajectories of disease progression, are not well defined.METHODS The value of genotypic and/or kidney imaging data (Mayo Imaging Class; MIC) to predict the time to functional (end-stage kidney disease [ESKD] or decline in estimated glomerular filtration rate [eGFR]) or structural (increase in height-adjusted total kidney volume [htTKV]) outcomes were evaluated in a Mayo Clinic PKD1/PKD2 population, and eGFR and htTKV trajectories from 20–65 years of age were modeled and independently validated in similarly defined CRISP and HALT PKD patients.RESULTS Both genotypic and imaging groups strongly predicted ESKD and eGFR endpoints, with genotype improving the imaging predictions and vice versa; a multivariate model had strong discriminatory power (C-index = 0.845). However, imaging but not genotypic groups predicted htTKV growth, although more severe genotypic and imaging groups had larger kidneys at a young age. The trajectory of eGFR decline was linear from baseline in the most severe genotypic and imaging groups, but it was curvilinear in milder groups. Imaging class trajectories differentiated htTKV growth rates; severe classes had rapid early growth and large kidneys, but growth later slowed.CONCLUSION The value of imaging, genotypic, and combined data to identify rapidly progressive patients was demonstrated, and reference values for clinical trials were provided. Our data indicate that differences in kidney growth rates before adulthood significantly define patients with severe disease.FUNDING NIDDK grants: Mayo DK058816 and DK090728; CRISP DK056943, DK056956, DK056957, and DK056961; and HALT PKD DK062410, DK062408, DK062402, DK082230, DK062411, and DK062401.
Authors
Sravanthi Lavu, Lisa E. Vaughan, Sarah R. Senum, Timothy L. Kline, Arlene B. Chapman, Ronald D. Perrone, Michal Mrug, William E. Braun, Theodore I. Steinman, Frederic F. Rahbari-Oskoui, Godela M. Brosnahan, Kyongtae T. Bae, Douglas Landsittel, Fouad T. Chebib, Alan S.L. Yu, Vicente E. Torres, the HALT PKD and CRISP Study Investigators, Peter C. Harris
Figure 1
Flowchart showing the selection and details of the Analysis and Validation cohorts.
