Long-term trajectory of kidney function in autosomal-dominant polycystic kidney disease

Abstract

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by cyst and kidney growth, which is hypothesized to cause loss of functioning renal mass and eventually end-stage kidney disease. However, the time course of decline in glomerular filtration rate (GFR) is poorly defined. The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study is a 14-year observational cohort study of 241 adults with ADPKD. As an estimate of the rate of kidney growth, participants were stratified into 5 subclasses based on baseline age and magnetic resonance imaging measurements of total kidney volume (TKV) according to the method of Irazabal. GFR trajectories spanning over four decades of life were reconstructed and fitted using mixed polynomial models, which were validated using data from the HALT-PKD study. GFR trajectories were nonlinear, with a period of relative stability in most participants, followed by accelerating decline. The shape and slope of these trajectories were strongly associated with baseline Irazabal class. Patients with PKD1 mutations had a steeper GFR decline than patients with PKD2 mutations or with no detected mutation, largely mediated by the effect of genotype on Irazabal class. Thus, GFR decline in ADPKD is nonlinear, and its trajectory throughout adulthood can be predicted from a single measurement of kidney volume. These models can be used for clinical prognostication, clinical trial design, and patient selection for clinical interventions. Our findings support a causal link between growth in kidney volume and GFR decline, adding support for the use of TKV as a surrogate endpoint in clinical trials.

Keywords: ADPKD; chronic kidney disease.

Fig. 1.

Kidney growth in the CRISP cohort, stratified by Irazabal class. Based on baseline age and htTKV, patients were assigned to one of 5 Irazabal classes (A-E, color-coded as shown). htTKV values determined over the course of the study are plotted, with each line representing an individual patient.

Fig. 2.

Trajectories of measured GFR fitted to patients in each Irazabal class (A-E) in the CRISP study. Individual patient data are represented by gray dots connected by straight lines in different colors. The red curved lines represent the best fit to a polynomial mixed model.

Fig. 3.

Summary plot of GFR trajectories from the CRISP cohort, divided by Irazabal class, without imposing an arbitrary model. Data points represent mean (± S.E.) GFR for each 5-year range of age.

Fig. 4.

Validation of the polynomial trajectory model in patients from the HALT study. Individual patient data from HALT Study A are represented by straight lines in different colors. The predicted eGFR trajectory for each Irazabal class, using the model developed with the CRISP cohort, is depicted by the superimposed red curved lines.

Fig. 5.

Effect of gene type and test of mediation model. A. Trajectories of GFR estimated from modeling the entire CRISP cohort, using gene type as a predictor variable (Model 1 in Table 3). B. Diagram of unmediated model. C. Diagram of mediated model. X, causal variable; Y, outcome variable; M, mediator variable. Path a represents the association between X and M, and path b the association between M and Y. Path c represents the association between X and Y (total effect), and path c’ represents the association between X and Y after controlling for M (direct effect). The effect mediated by Irazabal class (c-c’), is called the indirect effect and is equal to the product of the coefficients, ab.

Fig. 6.

Trajectories of GFR estimated from modeling the entire CRISP cohort, using both gene type and Irazabal class as predictor variables (Model 2 in Table 3).