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The value of genotypic and imaging information to predict functional and structural outcomes in ADPKD
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
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
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Clinical Research and Public Health Genetics Nephrology

The value of genotypic and imaging information to predict functional and structural outcomes in ADPKD

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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

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Figure 5

Trajectory analysis of htTKV increase for the genotypic and imaging groups.

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Trajectory analysis of htTKV increase for the genotypic and imaging grou...
(A–D) Trajectory plots of htTKV for the 4 genotypic groups in the Analysis (A) and the Validation (B) cohorts and for the 5 MICs in the Analysis (C) and Validation (D) cohorts. Fitted average htTKV trajectories from the polynomial model determined from the Analysis Cohort are plotted for each genotypic (A) and imaging group (C), with the same trajectory plotted on the corresponding data from the Validation Cohort (B and D). (E and F) The summary of these plots for the genotypic (E) and MIC (F) groups are also shown. The slope at the average age for each genotypic group is: 5.82, 5.08, 7.25, and 5.47 %/y for PKD1T, PKD1NT1, PKD1NT2, and PKD2, respectively (E), and for the MICs: 8.33, 6.96, 5.54, 4.46, 2.10 %/y for MIC-1E, -1D, -1C, -1B and -1A, respectively (F). However, because of the curvilinear trajectories for many groups, the rate of decline varies over time (Table 7).

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