Quantifying clinical and genetic factors influencing rate and severity of autosomal dominant tubulointerstitial kidney disease progression

Abstract

Autosomal dominant tubulointerstitial kidney disease (ADTKD), caused by mutations in UMOD and MUC1 genes, leads to tubular damage and fibrosis, ultimately resulting in kidney failure (KF). This study investigated clinical and genetic factors influencing the rate and severity of ADTKD progression by developing quantitative models. An estimated glomerular filtration rate (eGFR) of 10 mL/min/1.73 m² was used to define KF, corresponding to dialysis initiation. Natural history data from the Wake Forest University School of Medicine study were used to develop the models for UMOD (n = 371) and MUC1 (n = 233) disease types (age ≥ 18 years). Longitudinal change in eGFR and time-to-KF were quantified using nonlinear mixed-effects and parametric time-to-event modeling approaches, respectively, in Monolix (version 2024R1). Sigmoid I_max functions with steepness parameters varying before and after inflection points best captured eGFR decline. Patients with UMOD and MUC1 disease variants exhibited a similar initial shallow steepness ( $\approx$ 1), but after inflection, each declined rapidly. MUC1 patients progressed faster than UMOD during the post-inflection phase (γ₂ = 10.23 vs. 6.34). eGFR at first clinic visit (eGFR_FCV) and age at first clinic visit (AFCV) significantly affected between-subject variability in eGFR decline. A Weibull hazard function best described the time to KF. In UMOD, males reached Te (the age at which approximately 36.8% of individuals remain free from KF) 4 years earlier than females on average (β_Te_Male = -0.07), indicating faster progression in males. Older AFCV was associated with slower progression to KF (β_Te_AFCV = 0.59 for UMOD and 0.81 for MUC1). These models may help enable quantitative data-driven subgroup analysis in the future, optimizing inclusion/exclusion criteria for ADTKD clinical trials.

Keywords: Baseline characteristics; Disease progression model; Rare kidney disease; eGFR.

Fig. 1

Data formatting process and summary of the data

Fig. 2

Representative Simulation Results Visualizing the Influence of Significant Covariates in the Final Models, quantifying the Longitudinal Decline in estimated Glomerular Filtration Rate (eGFR) for ADTKD-UMOD and ADTKD-MUC1. Subjects were created from minimum to maximum values for each of the continuous covariates, such as eGFR_FCV (eGFR collected at first clinical visit) and AFCV (age at first clinical visit). Each subject was simulated with 10 replicates. Mean eGFR was calculated and plotted with standard errors. Each panel represents the predicted effects of the covariates on the longitudinal decline of eGFR: for UMOD, effects of eGFR_FCV (A), AFCV (C), and for MUC1, eGFR_FCV (B) and AFCV (D)

Fig. 3

Representative Evaluation Plots of the Final Longitudinal Models Quantifying the Decline in estimated Glomerular Filtration Rate (eGFR) for ADTKD-UMOD and ADTKD-MUC1. The figure represents the visual predictive check plots for A. UMOD and B. MUC1. VPCs show the median (red dashed curves) and 10th and 90th percentiles (lower and upper blue dashed curves, respectively) of the predicted profiles. The shaded areas indicate the 90% confidence intervals of each of the percentile curves

Fig. 4

Visual Predictive Check Plots for Time-to-Event Model Predicting Age of Kidney Failure (KF), Stratified by Significant Covariates of ADTKD-UMOD. Kaplan–Meier curves for UMOD are shown in Indian red (females) and forest green (males) solid lines, representing observed data. Dots (navy blue, forest green) indicate right-censored data points. The shaded gray area represents the 90% confidence interval of the model prediction, while the solid lines (navy blue, forest green), age at first clinical visit (AFCV) represent the model-predicted median. The plots are stratified by sex and AFCV in 10-year increments

Fig. 5

Visual Predictive Check Plots for Time-to-Event Model Predicting Age of Kidney Failure (KF), Stratified by a Significant Covariate of ADTKD-MUC1. Kaplan–Meier curves for MUC1 are shown in solid lines, representing observed data. Dots indicate right-censored data points. The shaded gray area represents the 90% confidence interval of the model prediction, while the solid lines, age at first clinical visit (AFCV) represent the model-predicted median. The plots are stratified by AFCV in 10-year increments