Brosnahan, Godela

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.

Significance Statement Accurate prediction of risk for disease progression is crucial for clinical management of autosomal dominant polycystic kidney disease (ADPKD). The Mayo imaging classification of ADPKD uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk. Because the current Mayo classification applies only to patients with typical diffuse cystic disease (class 1) and poorly predicts eGFR decline for the remaining 5%–10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. The authors report an expanded imaging classification model in which use of a recalculated htTKV value that excludes prominent exophytic cysts improved prediction for eGFR trajectory. Using a recalculated htTKV may allow inclusion of class 2 patients in the Mayo classification of ADPKD and reclassification of class 1 patients with prominent exophytic cysts. Background The Mayo Clinic imaging classification of autosomal dominant polycystic kidney disease (ADPKD) uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk for disease progression. However, this classification applies only to patients with typical diffuse cystic disease (class 1). Because htTKV poorly predicts eGFR decline for the 5%–10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. Methods Of 558 adults with ADPKD in the HALT-A study, we identified 25 patients of class 2A with prominent exophytic cysts (class 2Ae) and 43 patients of class 1 with prominent exophytic cysts; we recalculated their htTKVs to exclude exophytic cysts. Using original and recalculated htTKVs in association with imaging classification in logistic and mixed linear models, we compared predictions for developing CKD stage 3 and for eGFR trajectory. Results Using recalculated htTKVs increased specificity for developing CKD stage 3 in all participants from 82.6% to 84.2% after adjustment for baseline age, eGFR, BMI, sex, and race. The predicted proportion of class 2Ae patients developing CKD stage 3 using a cutoff of 0.5 for predicting case status was better calibrated to the observed value of 13.0% with recalculated htTKVs (45.5%) versus original htTKVs (63.6%). Using recalculated htTKVs reduced the mean paired difference between predicted and observed eGFR from 17.6 (using original htTKVs) to 4.0 ml/min per 1.73 m 2 for class 2Ae, and from −1.7 (using original htTKVs) to 0.1 ml/min per 1.73 m 2 for class 1. Conclusions Use of a recalculated htTKV measure that excludes prominent exophytic cysts facilitates inclusion of class 2 patients and reclassification of class 1 patients in the Mayo classification model.

Background Higher serum intact fibroblast growth factor 23 (iFGF23) was associated with disease progression in participants with autosomal dominant polycystic kidney disease (ADPKD) in the HALT-PKD Studies. PKD mutation is also an important determinant of progression. We hypothesized that serum levels of iFGF23 and vitamin D metabolites (1,25-dihydroxyvitamin D [1,25(OH) 2 D] and 25-hydroxyvitamin D [25[OH]D]) differ according to ADPKD mutation and differentially predict clinical end points according to genotype (significant interaction between genotype and mineral metabolites). Methods A total of 864 individuals with ADPKD who participated in the HALT-PKD Study A or B and had measurements of mineral metabolites (1,25[OH] 2 D, 25[OH]D, iFGF23) were categorized by PKD mutation (PKD1 truncating, PKD1 nontruncating, PKD2, or no mutation detected [NMD]). The association of the interactions of genotype × iFGF23, genotype × 1,25(OH)2D, and genotype × 25(OH)D with ( 1 ) annualized change in eGFR; ( 2 ) mean annualized percentage change in height-corrected total kidney volume (Study A only); and ( 3 ) time to a composite of 50% reduction in eGFR, ESKD, or death were evaluated using linear regression and Cox proportional hazards regression. Results Median (interquartile range) iFGF23 differed (PKD1 truncating, 55.8 [40.7–76.8]; PKD1 nontruncating, 49.9 [37.7–71.0]; PKD2, 49.0 [33.8–70.5]; NMD, 50.3 [39.7–67.4] pg/ml; P =0.03) and mean±SD 1,25(OH) 2 D differed (PKD1 truncating, 32.8±12.8; PKD1 nontruncating, 33.4±12.5; PKD2, 34.1±13.1; NMD, 38.0±14.6 pg/ml; P =0.02) according to PKD genotype. There was a significant interaction between iFGF23 and genotype ( P =0.02) for the composite end point in fully adjusted models, but no significant interaction between 1,25(OH) 2 D or 25(OH)D and genotype for clinical end points. Conclusions ADPKD genotype interacts significantly with FGF23 to influence clinical end points. Whereas the worst outcomes were in individuals with a PKD1-truncating or -nontruncating mutation and the highest iFGF23 tertile, risk of the composite end point differed according to iFGF23 the most in the PKD1-nontruncating and PKD2 groups.