Rationale & objective: Body-mass index (BMI) is an independent predictor of kidney disease progression in individuals with autosomal dominant polycystic kidney disease (ADPKD). Adipocytes do not simply act as a fat reservoir but are active endocrine organs. We hypothesized that greater visceral abdominal adiposity would associate with more rapid kidney growth in ADPKD and influence the efficacy of tolvaptan.
Study design: A retrospective cohort study.
Setting & participants: 1053 patients enrolled in the TEMPO 3:4 tolvaptan trial with ADPKD and high risk of rapid disease progression.
Predictor: Estimates of visceral adiposity extracted from coronal plane MRIs using deep learning.
Outcome: Annual change in total kidney volume (TKV) and effect of tolvaptan on kidney growth.
Analytical approach: Multinomial logistic regression and linear mixed models.
Results: In fully adjusted models, the highest tertile of visceral adiposity was associated with greater odds of annual change in TKV of ≥7% vs. <5% (OR: 4.78 [3.03, 7.47]). The association was stronger in females than males (interaction p<0.01). In linear mixed models with an outcome of % change in TKV per year, tolvaptan efficacy (% change in TKV) was reduced with higher visceral adiposity (three-way interaction of treatment*time*visceral adiposity p=0.002). Visceral adiposity significantly improved classification performance of predicting rapid annual % change in TKV for individuals with a normal BMI (De-Long's test Z-score: -2.03; p=0.04). Greater visceral adiposity was not associated with estimated glomerular filtration rate (eGFR) slope in the overall cohort; however, visceral adiposity was associated with more rapid decline in eGFR slope (below the median) in females (fully adjusted OR 1.06 [1.01, 1.11] per 10 unit increase in visceral adiposity) but not males (0.98 [0.95, 1.02]).
Limitations: Retrospective; rapid progressors; computational demand of deep learning.
Conclusions: Visceral adiposity that can be quantified by MRI in the coronal plane using a deep learning segmentation model, independently associates with more rapid kidney growth, and improves classification of rapid progression in individuals with a normal BMI. Tolvaptan efficacy decreases with increasing visceral adiposity.
Keywords: ADPKD; adiposity; machine learning; obesity; polycystic kidney disease.
Copyright © 2024. Published by Elsevier Inc.