Patterns of Kidney Function Decline in Autosomal Dominant Polycystic Kidney Disease: A Post Hoc Analysis From the HALT-PKD Trials

Abstract

Background: Previous clinical studies of autosomal dominant polycystic kidney disease (ADPKD) reported that loss of kidney function usually follows a steep and relentless course. A detailed examination of individual patterns of decline in estimated glomerular filtration rate (eGFR) has not been performed.

Study design: Longitudinal post hoc analysis of data collected during the Halt Progression of Polycystic Kidney Disease (HALT-PKD) trials.

Setting & participants: 494 HALT-PKD Study A participants (younger; preserved eGFR) and 435 Study B participants (older; reduced eGFR) who had more than 3 years of follow-up and 7 or more eGFR assessments.

Measurements: Longitudinal eGFR assessments using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) creatinine equation.

Predictors: Demographic, clinical, laboratory, and imaging features of participants.

Outcomes: Probability of linear and nonlinear decline patterns or of stable eGFR calculated for each participant from a Bayesian model of individual eGFR trajectories.

Results: Most (62.5% in Study A and 81% in Study B) participants had a linear decline in eGFR during up to 8 years of follow-up. A proportion (22% in Study A and 13% in Study B) of progressors had a nonlinear pattern. 15.5% of participants in Study A and 6% in Study B had a prolonged (≥4.5 years) period of stable eGFRs. These individuals (Study A) had significantly smaller total kidney volumes, higher renal blood flows, lower urinary albumin excretion, and lower body mass index at baseline and study end. In Study B, participants with reduced but stable eGFRs were older than the progressors. Two-thirds of nonprogressors in both studies had PKD1 mutations, with enrichment for weak nontruncating mutations.

Limitations: Relatively short follow-up of a clinical trial population.

Conclusions: Although many individuals with ADPKD have a linear decline in eGFR, prolonged intervals of stable GFRs occur in a substantial fraction. Lower body mass index was associated with more stable kidney function in early ADPKD.

Keywords: Autosomal dominant polycystic kidney disease (ADPKD); Bayesian models; eGFR slope; eGFR trajectory; end-stage renal disease (ESRD); estimated glomerular filtration rate (eGFR); kidney disease progression; mutation analysis; total kidney volume.

Figure 1. Progressive linear patterns in Study A (top) and B (bottom)

Examples of individual eGFR trajectories with low probabilities of nonlinearity (Pr.nlin) and zero probability of nonprogression (Pr.nprog). The X-axis shows years from baseline visit until the end of the HALT PKD trial, and the Y-axis shows eGFR in mL/min/1.73 m². The dots are actual eGFR data derived from centralized serum creatinine measurements, the black line is the estimated trajectory (the average of 3,000 Monte Carlo simulated curves), with 95% Bayesian confidence intervals in grey. The vertical dotted line signifies the end of study participation of that individual. The top figure is from a Study A participant with a truncating PKD1 mutation, the bottom figure from a Study B participant, also with a truncating PKD1 mutation. Figure courtesy of L. Lee.

Figure 2. Progressive nonlinear patterns in Study A (top) and B (bottom)

Shown are 5 individual eGFR trajectories as examples of a nonlinear pattern, with high probabilities of nonlinearity (Pr.nlin) and very low probabilities of nonprogression (Pr.nprog). The setup of the 5 trajectories is the same as in figure 1. Figure 2 demonstrates the high variability in the shapes of nonlinear progression. The top left figure is from a Study A participant with a nontruncating PKD1 mutation, the top right figure from a Study A participant with a truncating PKD2 mutation. The bottom left figure is from a Study B participant with a truncating PKD2 mutation, the middle figure from a Study B participant with a truncating PKD1 mutation, and the bottom right figure from a Study B participant with no mutation detected. Figure courtesy of L. Lee.

Figure 3. Nonprogressive patterns in Study A (top) and B (bottom)

These 5 examples of individual eGFR trajectories have high probability of nonprogression (Pr.nprog > 0.5, often > 0.9). The upper row left figure shows an example of a linear (probability of nonlinearity very low) nonprogressive eGFR trajectory from a Study A participant with a truncating PKD2 mutation; the upper right figure shows a nonlinear (probability of nonlinearity > 0.5) nonprogressive pattern from a Study A participant with a truncating PKD1 mutation. The bottom row left and middle figures show 2 examples of stable (Pr.nprog > 0.9) eGFR in Study B participants with low eGFR at baseline, the left figure from a participant with a truncating PKD1 mutation and the middle figure from a subject with no mutation detected. The bottom right figure is an example of nonlinear (Pr.nlin > 0.5) nonprogression (Pr.nprog > 0.5): an initial rapid eGFR decline is followed by an extended (> 4.5 years) period of nonprogression; the trajectory is from a Study B participant with a truncating PKD1 mutation. Setup is the same as in figure 1. Figure courtesy of L. Lee.