Gregory, Adriana V

Background: Total kidney volume (TKV), a prognostic biomarker in ADPKD, is used to stratify high risk individuals. eGFR measures may carry significant error in ADPKD compared to measured GFR(mGFR). This retrospective study investigates the association between height adjusted TKV (htTKV) and eGFR derived from race-free CKD-EPI equations utilizing creatinine and cystatin C. Methods: eGFR was calculated using 3 race-free CKD-EPI formulas: creatinine-based (CKD-EPI creatinine), cystatin C (CKD-EPI cys), and creatinine-cystatin C (CKD-EPI creatinine-cystatin C) equations on 697 Mayo Clinic ADPKD patients. Correlations between eGFR and TKV measures were estimated and stratified by MIC class risk groups and CKD. Linear regression models were computed to predict ln(htTKV) from the 3 eGFR data sets. The eGFR measures were compared to iothalamate-based mGFR in a subset of patients (n=50). Results: A correlation between eGFR measures and htTKV (Pearson correlation r of -0.53 to -0.54), varied by MIC stratification. Pearson correlation was -0.47 to -0.48 for MIC 1A and 1B, -0.67 to -0.71 for MIC 1C, and -0.67 to -0.68 for MIC 1D and 1E. All equations were significantly associated with htTKV levels, with similar adjusted beta values (95% CI) of -0.15 (-0.17 to -0.13), -0.14 (-0.16 to -0.12), -0.15 (-0.16 to -0.13), for CKD-EPI creatinine, CKD-EPI cystatin C, and CKD-EPI creatinine-cystatin C, respectively. Compared to mGFR, the CKD-EPI creatinine-cystatin C equation was the least biased (mean -0.86 (95% CI: -3.09 to 1.37)) versus CKD-EPI creatinine (2.31 (-0.73 to 5.34)) or CKD-EPI cystatin C (-3.54 (-5.86 to -1.21)), and most precise, with 1-P30 of 16% (95% CI 7.2% to 29.1%), compared to 18% (8.6% to 31.4%) for CKD-EPI creatinine, or 20% (10.0% to 33.7%) for CKD-EPI cystatin C. Conclusion: A moderate correlation between eGFR and htTKV associated with MIC stratification. Associations between htTKV and all 3 CKD-EPI equations were similar. CKDEPIcrcys tended to be the least biased and most precise equation when compared to mGFR and may be a reasonable alternative.

Key Points In a cohort of children, 79% of families with kidney cysts had a monogenic diagnosis. Resolved cases were more likely to have a family history of kidney cysts and unresolved cases more often had unilateral cysts. Broad genetic testing revealed genetic diversity and informed prognosis and clinical management in childhood kidney cysts. Background Pediatric kidney cysts may indicate an underlying genetic disorder, yet the full spectrum of causes remains incompletely defined. With advances in genetic testing enabling broader evaluation, this study assessed the diagnostic utility of comprehensive genetic testing in a broad pediatric cohort with kidney cysts and characterized the underlying etiological diversity. Methods This observational cohort study included patients younger than 18 years enrolled between January 2020 and June 2024 at a single tertiary center. Genetic testing used targeted multigene or custom curated exome/genome sequencing panels, with segregation analysis when available. Eligible participants had at least two cysts without family history, one cyst with positive family history, or enlarged echogenic kidneys on prenatal ultrasound; those with congenital anomalies of the kidney and urinary tract associated with cysts were excluded. Clinical presentation was categorized as symptomatic, incidental, prenatal, or family screening. Primary outcomes were diagnostic yield and distribution of pathogenic variants. Results Among 109 patients (median age 7.6 years, 53% female), genetic testing identified a definitive diagnosis in 81 of 100 tested patients (81%) from 72 families (79%; 14 disorders). PKD1 variants were most common (45%), while PKD2 accounted for 7%. Other causes included HNF1B or 17q12 deletions (13%), minor autosomal dominant polycystic kidney disease genes (11%; GANAB, NEK8, IFT140 ), monoallelic PKHD1 (3%), and biallelic PKHD1 (8%), while syndromic ciliopathy genes accounted for 5%. A positive family history of cystic kidney disease was more common among patients with a genetic diagnosis (54% versus 11%; P = 0.008). Patients without an identified genetic diagnosis more often had unilateral cysts (26% versus 4%; P = 0.53). Diagnosis by clinical symptoms was the most genetically diverse category. Conclusions Comprehensive genetic testing in pediatric kidney cysts achieved high diagnostic yield in a selected cohort and identified diverse causes beyond major autosomal dominant polycystic kidney disease/autosomal recessive polycystic kidney disease genes, supporting early evaluation to improve diagnostic accuracy, inform prognosis, and guide management, even in the absence of family history.

Key Points Automated cyst segmentation in autosomal dominant polycystic kidney disease could transform care by enabling precise monitoring of cyst progression with minimal manual effort. This technology can facilitate more consistent assessments of cyst burden across patients. Translating these segmentation models from magnetic resonance imaging to computed tomography is crucial for providing comprehensive, modality-independent evaluations. Background Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, magnetic resonance (MR) imaging-based advanced biomarkers like total cyst number and cyst parenchyma surface area have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study was to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in computed tomography (CT) images. Methods Abdominal CTs of patients with ADPKD were gathered retrospectively between 2001 and 2018. A three-dimensional deep learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the noncystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in noncontrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Results Two-hundred CT scans from 148 patients (mean age, 51.2±14.1 years; 48% male) were used for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model versus reader: TCN kidney/liver r =0.96/0.97 and CPSA kidney r =0.98, inter-reader: TCN kidney/liver r =0.96/0.98 and CPSA kidney r =0.99). Conclusions Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.

Key Points Loss-of-function ALG8 and ALG9 variants were enriched in polycystic kidney/liver groups and International Classification of Diseases–coded cystic individuals in population cohorts. The ALG8 and ALG9 kidney phenotypes were usually mild to moderate, and lower eGFR or kidney failure was rare. ALG8 pathogenic variants sometimes resulted in severe polycystic liver disease. Background Autosomal dominant polycystic kidney disease (ADPKD) is a common, inherited nephropathy often resulting in kidney failure. It is genetically heterogeneous; along with the major genes, PKD1 and PKD2 , at least eight others have been suggested. ALG8 pathogenic variants have been associated with autosomal dominant polycystic liver disease and implicated in ADPKD, while ALG9 has been suggested as an ADPKD gene, but details of the phenotypes and penetrance are unclear. Methods We screened >3900 families with cystic kidneys and/or livers using global approaches to detect ALG8 or ALG9 pathogenic variants. In addition, population cohorts with sequence data (Genomics England 100K Genomics Project, UK Biobank, and Mayo Clinic Biobank [MCBB]) were screened for ALG8 / ALG9 pathogenic variants. Results Multicenter screening of individuals with polycystic kidney and/or liver disease identified 51 (1.3%) ALG8 (7 multiplex) and 23 (0.6%) ALG9 (5 multiplex) families—frequencies that were approximately 10× and approximately 24× greater than nonpolycystic kidney disease controls. Analysis of individuals with polycystic kidney disease phenotypes in 100K Genomics Project, UK Biobank, and MCBB identified nine ALG8 (0.39%) and nine ALG9 (0.39%) families, an enriched frequency over controls. Two individuals had PKD1 and ALG8 pathogenic changes. Eighty-nine percent of individuals with ALG8 mutations with imaging in the entire MCBB had kidney cysts (50%, >10 cysts), with greater median kidney and liver cyst numbers than controls. For ALG9, 78% had kidney cysts (27%, >10 cysts). Individuals with ALG8 mutations typically had mild cystic kidneys with limited enlargement. Liver cysts were common (71%), with enlarged livers (>2L) found in 11 of 62 patients, although surgical intervention was rare. The ALG9 kidney phenotype was also of mild cystic kidneys, but enlarged livers were rare; for both genes, CKD or kidney failure were rare. Conclusions ALG8 and ALG9 are defined as cystic kidney/liver genes but with limited penetrance for lower eGFR.

Key Points Robotic ultrasound performed favorably compared with magnetic resonance imaging for evaluating total kidney volume. Collagen evaluation by two novel methods of picrosirius red imaging were more informative than the standard method by brightfield imaging. Findings can improve research by increasing speed and access to total kidney volume determination and sensitivity of collagen assessment. Background 3D imaging and histology are critical tools for assessing polycystic kidney disease (PKD) in patients and animal models. Magnetic resonance (MR) imaging provides micron resolution but is time consuming and expensive, and access to equipment and expertise is limited. Robotic ultrasound (US) imaging has lower spatial resolution but is faster, more cost-effective, and accessible. Similarly, picrosirius red (PSR) staining and brightfield microscopy are commonly used to assess fibrosis; however, alternative methods have been shown in non-kidney tissues to provide greater sensitivity and more detailed structural characterization. Methods In this study, we evaluated the utility of robotic US and alternative methods of quantifying PSR staining for PKD research. We compared longitudinal total kidney volume measurements using US and MR imaging. We additionally compared PSR imaging and quantification using standard brightfield microscopy with that by circularly polarized light with hue analysis and fluorescence imaging analyzed using curvelet transform fiber extraction software for automatic detection of individual collagen fibers. Results Increased total kidney volume was detected by US in Pkd1 RC/RC versus wild-type (WT) at time points spanning from early to established disease. US interobserver variability was greater but allowed scanning in 2–5 minutes/mouse, whereas MR imaging required 20–30 minutes/mouse. While no change in fibrotic index was detected in this cohort of relatively mild disease using brightfield microscopy, polarized light showed fibers skewed thinner in Pkd1 RC/RC versus WT. Fluorescence imaging showed a higher density of collagen fibers in Pkd1 RC/RC versus WT, and fibers were thinner and curvier with no change in length. In addition, fiber density was higher in both glomeruli and tubules in Pkd1 RC/RC , and glomeruli had a higher fiber density than tubules in Pkd1 RC/RC and trended higher in WT. Conclusions These studies show robotic US is a rigorous imaging tool for preclinical PKD research. In addition, they demonstrate the increased sensitivity of polarized and fluorescence analysis of PSR-stained collagen.

Significance Statement Segmentation of multiple structures in cross-sectional imaging is time-consuming and impractical to perform manually, especially if the end goal is clinical implementation. In this study, we developed, validated, and demonstrated the capability of a deep learning algorithm to segment individual medullary pyramids in a rapid, accurate, and reproducible manner. The results demonstrate that cortex volume, medullary volume, number of pyramids, and mean pyramid volume is associated with patient clinical characteristics and microstructural findings and provide insights into the mechanisms that may lead to CKD. Background The kidney is a lobulated organ, but little is known regarding the clinical importance of the number and size of individual kidney lobes. Methods After applying a previously validated algorithm to segment the cortex and medulla, a deep-learning algorithm was developed and validated to segment and count individual medullary pyramids on contrast-enhanced computed tomography images of living kidney donors before donation. The association of cortex volume, medullary volume, number of pyramids, and mean pyramid volume with concurrent clinical characteristics (kidney function and CKD risk factors), kidney biopsy morphology (nephron number, glomerular volume, and nephrosclerosis), and short- and long-term GFR <60 or <45 ml/min per 1.73 m 2 was assessed. Results Among 2876 living kidney donors, 1132 had short-term follow-up at a median of 3.8 months and 638 had long-term follow-up at a median of 10.0 years. Larger cortex volume was associated with younger age, male sex, larger body size, higher GFR, albuminuria, more nephrons, larger glomeruli, less nephrosclerosis, and lower risk of low GFR at follow-up. Larger pyramids were associated with older age, female sex, larger body size, higher GFR, more nephrons, larger glomerular volume, more nephrosclerosis, and higher risk of low GFR at follow-up. More pyramids were associated with younger age, male sex, greater height, no hypertension, higher GFR, lower uric acid, more nephrons, less nephrosclerosis, and a lower risk of low GFR at follow-up. Conclusions Cortex volume and medullary pyramid volume and count reflect underlying variation in nephron number and nephron size as well as merging of pyramids because of age-related nephrosclerosis, with loss of detectable cortical columns separating pyramids.