Kidney volume and functional outcomes in autosomal dominant polycystic kidney disease

Urine Osmolality Is a Potential Marker of Longer-Term Efficacy of Tolvaptan in Autosomal Dominant Polycystic Kidney Disease: A Post Hoc Analysis

Key Points

Post hoc analyses of the TEMPO 3:4 trial suggest that short-term reductions in urine osmolality with tolvaptan predict effects on total kidney volume and eGFR. Change in urine osmolality has potential as a biomarker of treatment response and may facilitate trial design and clinical decision making.

Background

Total kidney volume (TKV) and eGFR are measures of progression and treatment response in autosomal dominant polycystic kidney disease, but utility is limited by the long follow-up required for change assessment. In an analysis of data from the 3-year TEMPO 3:4 trial, we evaluated relationships among a short-term indicator of drug activity (change in urine osmolality [Uosm]) and longer-term outcomes to evaluate Uosm as a potential marker of efficacy.

Methods

Linear regression modeling and single-point analyses assessed relationships among change in Uosm to week 3, change in TKV to month 12, and change in eGFR to month 36 in participants treated with tolvaptan (n =961) or placebo (n =483). Multivariate models evaluated the proportion of the tolvaptan treatment effect on eGFR attributable to change in Uosm.

Results

Change in TKV to month 12 and Uosm to week 3 each correlated with change in eGFR to month 36, regardless of treatment assignment. A greater decrease in Uosm from baseline to week 3 was indicative of a slower decrease in eGFR to month 36 (slope estimate of −0.01, P < 0.00001). The effect of tolvaptan on Uosm accounted for 68.8% of the treatment effect on change in eGFR to month 36. Simulations of TEMPO 3:4 under the null hypothesis (i.e ., replacement of all values for change in Uosm from baseline to week 3 with values from the placebo arm only) yielded a type 1 error rate indicating an acceptable risk of falsely concluding treatment efficacy on the basis of change in Uosm as a trial end point.

Conclusions

Change in Uosm is a potential biomarker for long-term treatment outcome with tolvaptan and might expedite clinical trials and treatment decision making for drugs with similar mechanisms of action.

Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting

Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was 2.1 % ± 2 % compared to 1.1 % ± 1 % ( p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or 1.4 % ± 1 % ( p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( p = 0.05 ) and PKD2 mutation ( p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.

The utility of serum neutrophil gelatinase-associated lipocalin level on predicting autosomal dominant polycystic kidney disease progression

INTRODUCTION

We focused on neutrophil gelatinase-associated lipocalin (NGAL) and autosomal dominant polycystic kidney disease (ADPKD) progression.

METHODS

ADPKD patients with an estimated glomerular filtration rate (eGFR) ≥ 30 mL/min/1.73 m2 were included. Serum NGAL level and NGAL to eGFR ratio (NGR), height-adjusted total kidney volume (hTKV) were assessed initially. Patients were followed-up for 5 years.

RESULTS

Sixty one patients were enrolled and initial eGFR was 73.6 (48.9-101.5) ml/min/1.73m2. EGFR declined by 3.7 mL/min/1.73m2 per year. Thirty four patients (55.7%) exhibited rapid progression. Rapid progression group had lower serum NGAL levels (p < 0.001) and higher hTKV (p < 0.001). Lower serum NGAL level was a risk factor for rapid progression (p < 0.001). NGR was not associated with rapid progression. Serum NGAL level was predictive in for rapid progression ROC analysis (cut-off <10.62 ng/mL).

CONCLUSION

Relatively lower serum NGAL levels can predict worse outcomes in ADPKD and can provide risk stratification in patients with ADPKD.

The screening, diagnosis, and management of patients with autosomal dominant polycystic kidney disease: A national consensus statement from Taiwan

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited cause of end-stage kidney disease (ESKD) worldwide. Guidelines for the diagnosis and management of ADPKD in Taiwan remains unavailable. In this consensus statement, we summarize updated information on clinical features of international and domestic patients with ADPKD, followed by suggestions for optimal diagnosis and care in Taiwan. Specifically, counselling for at-risk minors and reproductive issues can be important, including ethical dilemmas surrounding prenatal diagnosis and pre-implantation genetic diagnosis. Studies reveal that ADPKD typically remains asymptomatic until the fourth decade of life, with symptoms resulting from cystic expansion with visceral compression, or rupture. The diagnosis can be made based on a detailed family history, followed by imaging studies (ultrasound, computed tomography, or magnetic resonance imaging). Genetic testing is reserved for atypical cases mostly. Common tools for prognosis prediction include total kidney volume, Mayo classification and PROPKD/genetic score. Screening and management of complications such as hypertension, proteinuria, urological infections, intracranial aneurysms, are also crucial for improving outcome. We suggest that the optimal management strategies of patients with ADPKD include general medical care, dietary recommendations and ADPKD-specific treatments. Key points include rigorous blood pressure control, dietary sodium restriction and Tolvaptan use, whereas the evidence for somatostatin analogues and mammalian target of rapamycin (mTOR) inhibitors remains limited. In summary, we outline an individualized care plan emphasizing careful monitoring of disease progression and highlight the need for shared decision-making among these patients.

Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD

BACKGROUND

The Mayo Imaging Classification was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease. This study aimed to validate its ability to predict kidney outcomes in a large multicenter autosomal dominant polycystic kidney disease cohort.

METHODS

Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 eGFR values during ≥1-year follow-up. Mayo HtTKV class stability, kidney growth rates, and eGFR decline rates were calculated. The observed eGFR decline was compared with predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for nonlinear eGFR decline. Kaplan-Meier survival analysis and Cox regression models were used to assess time to kidney failure using Mayo HtTKV class as a predictor variable.

RESULTS

We analyzed 618 patients with a mean age of 47±11 years and mean eGFR of 64±25 ml/min per 1.73 m 2 at baseline. Most patients (82%) remained in their baseline Mayo HtTKV class. During a mean follow-up of 5.1±2.2 years, the mean total kidney volume growth rates and eGFR decline were 5.33%±3.90%/yr and -3.31±2.53 ml/min per 1.73 m 2 per year, respectively. Kidney growth and eGFR decline showed considerable overlap between the classes. The observed annual eGFR decline was not significantly different from the predicted values for classes 1A, 1B, 1C, and 1D but significantly slower for class 1E. This was also observed in patients aged younger than 40 years and older than 60 years and those with PKD2 mutations. A polynomial model allowing nonlinear eGFR decline provided more accurate slope predictions. Ninety-seven patients (16%) developed kidney failure during follow-up. The classification predicted the development of kidney failure, although the sensitivity and positive predictive values were limited.

CONCLUSIONS

The Mayo Imaging Classification demonstrated acceptable stability and generally predicted kidney failure and eGFR decline rate. However, there was marked interindividual variability in the rate of disease progression within each class.

Autosomal dominant polycystic kidney disease (ADPKD) with multiple complications: Management challenges

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary polycystic kidney disease characterized by renal enlargement, resulting in renal failure. In Indonesia, the exact prevalence of ADPKD is unknown due to limited reports on the disease. The aim of this study was to report a case of a patient with ADPKD with multiple complications. A 54-year-old male presented to the emergency room of Dr. Soetomo Academic General Hospital, Surabaya, Indonesia, with a chief complaint of dark-red-colored urine for one week. There was a progressive abdominal enlargement over the past five years, which had become more tense and rigid for the past one month. The patient had a history of fatigue and hypertension with routine follow-up. Physical examination on admission showed normal vital signs, and the abdominal assessment revealed a palpable hard mass approximately 4 cm in size in the right upper abdomen. Laboratory test indicated anemia, leukocytosis, lymphopenia, proteinuria, hematuria, leukocyturia, and elevated serum creatinine and urea levels. Abdominal imaging using ultrasonography, computed tomography (CT) scan, and magnetic resonance imaging (MRI) revealed bilateral kidney and liver enlargement containing multiple cysts, suggesting polycystic kidney and liver disease. There was a ruptured cyst in the middle of the left kidney pole with minimal ascites found in the CT scan. The MRI exhibited the presence of multiple cysts in both kidneys, partially filled with blood. The patient was diagnosed with ADPKD, gross hematuria, acute or chronic kidney disease (CKD), urinary tract infection (UTI), normochromic-normocytic anemia, and metabolic acidosis. Dietary control with high-calorie, high-protein, and low-salt diet; fluid balance; and other symptomatic medications were initiated. It is critical to be aware of risk factors associated with the rapid progression of ADPKD in order to be able to provide a favorable impact on the disease prevention and management.

Initial eGFR Changes Predict Response to Tolvaptan in ADPKD

Key Points

This post hoc analysis of the Tolvaptan Efficacy and Safety in Management of Autosomal Dominant Polycystic Kidney Disease and Its Outcomes 3:4 study investigated the long-term predictive potential of initial changes in eGFR. Initial eGFR change from baseline to week 3 proved to be a significant and independent indicator of the long-term effects of tolvaptan. No correlation was found between the initial change in eGFR and the annual rate of percent growth in total kidney volume.

Background

Tolvaptan, the only pharmaceutical treatment available for autosomal dominant polycystic kidney disease (ADPKD), reduced the rates of total kidney volume (TKV) increase and kidney function decline in patients with ADPKD in the global phase 3 Tolvaptan Efficacy and Safety in Management of Autosomal Dominant Polycystic Kidney Disease and Its Outcomes (TEMPO) 3:4 study. Since tolvaptan initiation is associated with an initial decline in the eGFR, this post hoc analysis of the TEMPO 3:4 study investigated whether initial changes in eGFR from baseline to week 3 after tolvaptan administration can predict its longer-term effects on eGFR and TKV in patients with ADPKD.

Methods

eGFR was estimated using the CKD Epidemiology Collaboration equation at baseline and up to month 36. TKV was estimated using standardized kidney magnetic resonance imaging at baseline and after 12, 24, and 36 months of tolvaptan treatment. The effect of tolvaptan on kidney function and kidney volume was evaluated by measuring changes in eGFR from week 3 and TKV from baseline up to 36 months. All 961 patients randomized to receive tolvaptan in TEMPO 3:4 were included in this analysis.

Results

Initial change in eGFR from baseline to week 3 was a significant and independent predictor of the mean rate of change in eGFR per year. By contrast, there was no association between initial change in eGFR and the rate of percent growth in TKV per year.

Conclusions

Changes in eGFR after 3 weeks of treatment are likely due to the pharmacologic effect of tolvaptan, and these initial changes are predictive of the long-term effects of tolvaptan treatment.

Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning

RATIONALE AND OBJECTIVES

Following autosomal dominant polycystic kidney disease (ADPKD) progression by measuring organ volumes requires low measurement variability. The objective of this study is to reduce organ volume measurement variability on MRI of ADPKD patients by utilizing all pulse sequences to obtain multiple measurements which allows outlier analysis to find errors and averaging to reduce variability.

MATERIALS AND METHODS

In order to make measurements on multiple pulse sequences practical, a 3D multi-modality multi-class segmentation model based on nnU-net was trained/validated using T1, T2, SSFP, DWI and CT from 413 subjects. Reproducibility was assessed with test-re-test methodology on ADPKD subjects (n = 19) scanned twice within a 3-week interval correcting outliers and averaging the measurements across all sequences. Absolute percent differences in organ volumes were compared to paired students t-test.

RESULTS

Dice similarlity coefficient > 97%, Jaccard Index > 0.94, mean surface distance < 1 mm and mean Hausdorff Distance < 2 cm for all three organs and all five sequences were found on internal (n = 25), external (n = 37) and test-re-test reproducibility assessment (38 scans in 19 subjects). When averaging volumes measured from five MRI sequences, the model automatically segmented kidneys with test-re-test reproducibility (percent absolute difference between exam 1 and exam 2) of 1.3% which was better than all five expert observers. It reliably stratified ADPKD into Mayo Imaging Classification (area under the curve=100%) compared to radiologist.

CONCLUSION

3D deep learning measures organ volumes on five MRI sequences leveraging the power of outlier analysis and averaging to achieve 1.3% total kidney test-re-test reproducibility.

Magnetic resonance imaging based kidney volume assessment for risk stratification in pediatric autosomal dominant polycystic kidney disease

Introduction

In the pediatric context, most children with autosomal dominant polycystic kidney disease (ADPKD) maintain a normal glomerular filtration rate (GFR) despite underlying structural kidney damage, highlighting the critical need for early intervention and predictive markers. Due to the inverse relationship between kidney volume and kidney function, risk assessments have been presented on the basis of kidney volume. The aim of this study was to use magnetic resonance imaging (MRI)-based kidney volume assessment for risk stratification in pediatric ADPKD and to investigate clinical and genetic differences among risk groups.

Methods

This multicenter, cross-sectional, and case-control study included 75 genetically confirmed pediatric ADPKD patients (5-18 years) and 27 controls. Kidney function was assessed by eGFR calculated from serum creatinine and cystatin C using the CKiD-U25 equation. Blood pressure was assessed by both office and 24-hour ambulatory measurements. Kidney volume was calculated from MRI using the stereological method. Total kidney volume was adjusted for the height (htTKV). Patients were stratified from A to E classes according to the Leuven Imaging Classification (LIC) using MRI-derived htTKV.

Results

Median (Q1-Q3) age of the patients was 6.0 (2.0-10.0) years, 56% were male. There were no differences in sex, age, height-SDS, or GFR between the patient and control groups. Of the patients, 89% had PKD1 and 11% had PKD2 mutations. Non-missense mutations were 73% in PKD1 and 75% in PKD2. Twenty patients (27%) had hypertension based on ABPM. Median htTKV of the patients was significantly higher than controls (141 vs. 117 ml/m, p = 0.0003). LIC stratification revealed Classes A (38.7%), B (28%), C (24%), and D + E (9.3%). All children in class D + E and 94% in class C had PKD1 variants. Class D + E patients had significantly higher blood pressure values and hypertension compared to other classes (p > 0.05 for all).

Discussion

This study distinguishes itself by using MRI-based measurements of kidney volume to stratify pediatric ADPKD patients into specific risk groups. It is important to note that PKD1 mutation and elevated blood pressure were higher in the high-risk groups stratified by age and kidney volume. Our results need to be confirmed in further studies.

An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD

Introduction

Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV).

Methods

An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed.

Results

The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan.

Conclusion

Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.

DKK3 as a potential novel biomarker in patients with autosomal polycystic kidney disease

Backgound

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease, and leads to a steady loss of kidney function in adulthood. The variable course of the disease makes it necessary to identify the patients with rapid disease progression who will benefit the most from targeted therapies and interventions. Currently, magnetic resonance imaging-based volumetry of the kidney is the most commonly used tool for this purpose. Biomarkers that can be easily and quantitatively determined, which allow a prediction of the loss of kidney function, have not yet been established in clinical practice. The glycoprotein Dickkopf 3 (DKK3) which is secreted in the renal tubular epithelium upon stress and contributes to tubulointerstitial fibrosis via the Wnt signaling pathway, was recently described as a biomarker for estimating risk of kidney function loss, but has not been investigated for ADPKD. This study aimed to obtain a first insight into whether DKK3 may indeed improve outcome prediction in ADPKD in the future.

Methods

In 184 ADPKD patients from the AD(H)PKD registry and 47 healthy controls, the urinary DKK3 (uDKK3) levels were determined using ELISA. Multiple linear regression was used to examine the potential of these values in outcome prediction.

Results

ADPKD patients showed significantly higher uDKK3 values compared with the controls (mean 1970 ± 5287 vs 112 ± 134.7 pg/mg creatinine). Furthermore, there was a steady increase in uDKK3 with an increase in the Mayo class (A/B 1262 ± 2315 vs class D/E 3104 ± 7627 pg/mg creatinine), the best-established biomarker of progression in ADPKD. uDKK3 also correlated with estimated glomerular filtration rate (eGFR). Patients with PKD1 mutations show higher uDKK3 levels compared with PKD2 patients (PKD1: 2304 ± 5119; PKD2: 506.6 ± 526.8 pg/mg creatinine). Univariate linear regression showed uDKK3 as a significant predictor of future eGFR slope estimation. In multiple linear regression this effect was not significant in models also containing height-adjusted total kidney volume and/or eGFR. However, adding both copeptin levels and the interaction term between copeptin and uDKK3 to the model resulted in a significant predictive value of all these three variables and the highest R2 of all models examined (∼0.5).

Conclusion

uDKK3 shows a clear correlation with the Mayo classification in patients with ADPKD. uDKK3 levels correlated with kidney function, which could indicate that uDKK3 also predicts a disproportionate loss of renal function in this collective. Interestingly, we found an interaction between copeptin and uDKK3 in our prediction models and the best model containing both variables and their interaction term resulted in a fairly good explanation of variance in eGFR slope compared with previous models. Considering the limited number of patients in these analyses, future studies will be required to confirm the results. Nonetheless, uDKK3 appears to be an attractive candidate to improve outcome prediction of ADPKD in the future.

The safety and efficacy of tolvaptan in the treatment of patients with autosomal dominant polycystic kidney disease: A systematic review and meta-analysis

BACKGROUND

The irreversible progression of autosomal dominant polycystic kidney disease (ADPKD) to end-stage renal disease (ESRD) is delayed by tolvaptan. Therefore, we aim to systematically estimate and evaluate the efficacy and safety of tolvaptan in the treatment of ADPKD.

METHODS

Two reviewers independently searched all published randomized controlled trials studies in PubMed, EMBASE, Web of Science and Cochrane databases, extracted data, assessed bias risk and rated the quality of evidence. Data were analyzed by the RevMan software.

RESULTS

We identified 8 trials including 2135 patients. Both of the decline of estimated glomerular filtration rate (eGFR) [MD=1.89, 95% CI (0.74, 3.04), P=0.001] and total kidney volume (TKV) [MD=-3.32, 95% CI (-4.57, -2.07), P<0.001] were delayed in tolvaptan group compared with placebo group in ADPKD patients. The use of tolvaptan delayed TKV progression in the different-month subgroups [MD=-69.99, 95% CI (-91.05, -48.94), P<0.001]. Tolvaptan reduced renal pain [RR=0.66, 95% CI (0.54, 0.81), P<0.001] and hematuria events [RR=0.55, 95% CI (0.41, 0.74), P<0.001] in ADPKD patients. However, the prevalence of thirst [RR=2.75, 95% CI (2.34, 3.24), P<0.001] and nocturia events [RR=3.01, 95% CI (1.27, 7.11), P=0.01] were increased in tolvaptan group. There is no significant difference of hypertension events [RR=0.92, 95% CI (0.82, 1.03), P=0.13] in tolvaptan group compared placebo group.

CONCLUSIONS

This meta-analysis suggests that tolvaptan may improve clinical progression in patients with ADPKD without significantly increasing the risk of adverse reactions.

Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates.

PURPOSE

To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements.

STUDY TYPE

Retrospective training, prospective testing.

SUBJECTS

Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility.

FIELD STRENGTH/SEQUENCE

T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T.

ASSESSMENT

2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded.

STATISTICAL TESTS

Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value < 0.05 was considered statistically significant.

RESULTS

In 17 ADPKD subjects, model-assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model-assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model-assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier.

DATA CONCLUSION

Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model-assisted segmentations and can improve TKV measurement reproducibility more than 2-fold in ADPKD.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Development and Validation of a Nomogram for Renal Survival Prediction in Patients with Autosomal Dominant Polycystic Kidney Disease

Introduction

Due to the wide variation in the prognosis of autosomal dominant polycystic kidney disease (ADPKD), prediction of risk of renal survival in ADPKD patients is a tough challenge. We aimed to establish a nomogram for the prediction of renal survival in ADPKD patients.

Methods

We conducted a retrospective observational cohort study in 263 patients with ADPKD. The patients were randomly assigned to a training set (N = 198) and a validation set (N = 65), and demographic and statistical data at baseline were collected. The total kidney volume was measured using stereology. A clinical prediction nomogram was developed based on multivariate Cox regression results. The performance and clinical utility of the nomogram were assessed by calibration curves, the concordance index (C-index), and decision curve analysis (DCA). The nomogram was compared with the height-adjusted total kidney volume (htTKV) model by receiver operating characteristic curve analysis and DCA.

Results

The five independent factors used to construct the nomogram for prognosis prediction were age, htTKV, estimated glomerular filtration rate, hypertension, and hemoglobin. The calibration curve of predicted probabilities against observed renal survival indicated excellent concordance. The model showed very good discrimination with a C-index of 0.91 (0.83-0.99) and an area under the curve of 0.94, which were significantly higher than those of the htTKV model. Similarly, DCA demonstrated that the nomogram had a better net benefit than the htTKV model.

Conclusion

The risk prediction nomogram, incorporating easily assessable clinical parameters, was effective for the prediction of renal survival in ADPKD patients. It can be a useful clinical adjunct for clinicians to evaluate the prognosis of ADPKD patients and provide individualized decision-making.

A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice

BACKGROUND

Diagnosis and staging of diabetic kidney disease (DKD) via the serial assessment of routine laboratory indices lacks the granularity required to resolve the heterogeneous disease mechanisms driving progression in the individual patient. A systems nephrology approach may help resolve mechanisms underlying this clinically apparent heterogeneity, paving a way for targeted treatment of DKD.

SUMMARY

Given the limited access to kidney tissue in routine clinical care of patients with DKD, data derived from renal tissue in preclinical model systems, including animal and in vitro models, can play a central role in the development of a targeted systems-based approach to DKD. Multi-centre prospective cohort studies, including the Kidney Precision Medicine Project (KPMP) and the European Nephrectomy Biobank (ENBiBA) project, will improve access to human diabetic kidney tissue for research purposes. Integration of diverse data domains from such initiatives including clinical phenotypic data, renal and retinal imaging biomarkers, histopathological and ultrastructural data, and an array of molecular omics (transcriptomics, proteomics, etc.) alongside multi-dimensional data from preclinical modelling offers exciting opportunities to unravel individual-level mechanisms underlying progressive DKD. The application of machine and deep learning approaches may particularly enhance insights derived from imaging and histopathological/ultrastructural data domains.

KEY MESSAGES

Integration of data from multiple model systems (in vitro, animal models, and patients) and from diverse domains (clinical phenotypic, imaging, histopathological/ultrastructural, and molecular omics) offers potential to create a precision medicine approach to DKD care wherein the right treatments are offered to the right patients at the right time.

Abdominal Imaging in ADPKD: Beyond Total Kidney Volume

In the context of autosomal dominant polycystic kidney disease (ADPKD), measurement of the total kidney volume (TKV) is crucial. It acts as a marker for tracking disease progression, and evaluating the effectiveness of treatment strategies. The TKV has also been recognized as an enrichment biomarker and a possible surrogate endpoint in clinical trials. Several imaging modalities and methods are available to calculate the TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess the cyst burden, which can be crucial to assessing the disease state and helping to identify rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond these measurements, the quantification and characterization of non-cystic kidney tissue shows potential for stratifying ADPKD patients early on, monitoring disease progression, and possibly predicting renal function loss. A broad spectrum of radiological imaging techniques are available to characterize the kidney tissue, showing promise when it comes to non-invasively picking up the early signs of ADPKD progression. Radiomics have been used to extract textural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging biomarkers, focusing on the quantification methods, potential, and necessary steps toward a successful translation to clinical practice.

Function follows form: the quest for the best prognostic imaging biomarker in ADPKD

Total kidney volume represents the most solid prognostic biomarker for autosomal dominant polycystic kidney disease, because it mirrors cyst growth that precedes kidney function decline. Considerable variability of glomerular filtration rate trajectories, however, remains unexplained by total kidney volume, and its calculation is time-consuming. Using deep learning algorithms, Gregory et al. determined total kidney volume and other, novel, imaging-based biomarkers. They achieved automation and improved prognostic accuracy for long-term kidney function loss, yet the study leaves some open questions and room for further improvement.

PKD1 Mutation Is a Biomarker for Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

Autosomal dominant polycystic kidney disease (ADPKD) occurs in 1 in 500-4000 people worldwide. Genetic mutation is a biomarker for predicting renal dysfunction in patients with ADPKD. In this study, we performed a genetic analysis of Japanese patients with ADPKD to investigate the prognostic utility of genetic mutations in predicting renal function outcomes.

METHODS

Patients clinically diagnosed with ADPKD underwent a panel genetic test for germline mutations in PKD1 and PKD2. This study was conducted with the approval of the Ethics Committee of Juntendo University (no. 2019107).

RESULTS

Of 436 patients, 366 (83.9%) had genetic mutations. Notably, patients with PKD1 mutation had a significantly decreased ΔeGFR/year compared to patients with PKD2 mutation, indicating a progression of renal dysfunction (-3.50 vs. -2.04 mL/min/1.73 m²/year, p = 0.066). Furthermore, PKD1 truncated mutations had a significantly decreased ΔeGFR/year compared to PKD1 non-truncated mutations in the population aged over 65 years (-6.56 vs. -2.16 mL/min/1.73 m²/year, p = 0.049). Multivariate analysis showed that PKD1 mutation was a more significant risk factor than PKD2 mutation (odds ratio, 1.81; 95% confidence interval, 1.11-3.16; p = 0.020).

CONCLUSIONS

The analysis of germline mutations can predict renal prognosis in Japanese patients with ADPKD, and PKD1 mutation is a biomarker of ADPKD.

Combining robust urine biomarkers to assess chronic kidney disease progression

BACKGROUND

Urinary biomarkers may improve the prediction of chronic kidney disease (CKD) progression. Yet, data reporting the applicability of most commercial biomarker assays to the detection of their target analyte in urine together with an evaluation of their predictive performance are scarce.

METHODS

30 commercial assays (ELISA) were tested for their ability to quantify the target analyte in urine using strict (FDA-approved) validation criteria. In an exploratory analysis, LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression analysis was used to identify potentially complementary biomarkers predicting fast CKD progression, determined as the ⁵¹CrEDTA clearance-based measured glomerular filtration rate (mGFR) decline (>10% per year) in a subsample of 229 CKD patients (mean age, 61 years; 66% men; baseline mGFR, 38 mL/min) from the NephroTest prospective cohort.

FINDINGS

Among the 30 assays, directed against 24 candidate biomarkers, encompassing different pathophysiological mechanisms of CKD progression, 16 assays fulfilled the FDA-approved criteria. LASSO logistic regressions identified a combination of five biomarkers including CCL2, EGF, KIM1, NGAL, and TGF-α that improved the prediction of fast mGFR decline compared to the kidney failure risk equation variables alone: age, gender, mGFR, and albuminuria. Mean area under the curves (AUC) estimated from 100 re-samples was higher in the model with than without these biomarkers, 0.722 (95% confidence interval 0.652-0.795) vs. 0.682 (0.614-0.748), respectively. Fully-adjusted odds-ratios (95% confidence interval) for fast progression were 1.87 (1.22, 2.98), 1.86 (1.23, 2.89), 0.43 (0.25, 0.70), 1.10 (0.71, 1.83), 0.55 (0.33, 0.89), and 2.99 (1.89, 5.01) for albumin, CCL2, EGF, KIM1, NGAL, and TGF-α, respectively.

INTERPRETATION

This study provides a rigorous validation of multiple assays for relevant urinary biomarkers of CKD progression which combination may improve the prediction of CKD progression.

FUNDING

This work was supported by Institut National de la Santé et de la Recherche Médicale, Université de Paris, Assistance Publique Hôpitaux de Paris, Agence Nationale de la Recherche, MSDAVENIR, Pharma Research and Early Development Roche Laboratories (Basel, Switzerland), and Institut Roche de Recherche et Médecine Translationnelle (Paris, France).

QSM Throughout the Body

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

An update on treatments for autosomal dominant polycystic kidney disease

ABSTRACT

Autosomal dominant polycystic kidney disease (ADPKD) is less common than primary hypertension or diabetes but should be considered as a possible cause of end-stage renal disease, especially in young patients without comorbidities. Because of ADPKD's nonspecific symptoms, the diagnosis, treatment, and pertinent patient education may be delayed. This article describes ADPKD and its management, including tolvaptan, a new treatment with the potential to reduce or delay morbidity. However, only a subset of patients qualifies for this expensive treatment.

How to Estimate Kidney Growth in Patients with Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is a disease characterized by a progressive kidney growth due to the development of cysts that lead to gradual destruction of the surrounding parenchyma. In the first stage, the estimated GFR will remain stable despite the reduction of the renal parenchyma because of an increase in glomerular hyperfiltration. The total kidney volume (TKV) measured with computed tomography or magnetic resonance imaging is related to the future GFR decline. Thus, TKV has become an early marker to be analyzed in all patients with ADPKD. In addition, in recent years, it has been pointed out that kidney growth rate estimated with a single TKV measurement can be a clear prognostic marker for future glomerular filtration decline. However, there is no consensus on how to measure kidney volume growth in ADPKD, so each author has used different models that, not having the same meaning, have been handled as if they produced similar values. This may lead to erroneous estimates of kidney growth rate with the consequent prognostic error. The Mayo Clinic classification is now the most widely accepted prognostic model in clinical practice to predict patients who will deteriorate faster and to decide what patients should be treated with tolvaptan. However, some aspects of this model have not been discussed in depth. Our aim in this review was to present the models that can be used to estimate kidney volume growth rate in ADPKD, to facilitate their applicability in daily clinical practice.

Biomarkers in Polycystic Kidney Disease: Are We There?

This article describes the use of prognostic, predictive, and response biomarkers that have been developed for autosomal dominant polycystic kidney disease and their use in clinical care or drug development. We focus on biochemical markers that can be assayed in patients' blood and urine and their association with the outcome of decreased glomerular filtration rate. There have been several studies on prognostic biomarkers. The most promising ones have been markers of tubular injury, inflammation, metabolism, or the vasopressin-urinary concentration axis. So far, none have been shown to be superior to kidney volume-based biomarkers. Several biomarkers are additive to kidney volume and genotype in prognostic models, but there have been few direct comparisons between the biochemical markers to identify the best ones. Moreover, there is a lack of uniformity in the statistical tools used to assess and compare biomarkers. There have been few reports of predictive and response biomarkers, and none are suitable surrogate endpoints. The U.S. Food and Drug Administration's Biomarker Qualification Program provides a regulatory pathway to approve biomarkers for use across multiple drug-development programs.

Risk Severity Model for Pediatric Autosomal Dominant Polycystic Kidney Disease Using 3D Ultrasound Volumetry

BACKGROUND

Height-adjusted total kidney volume (htTKV) measured by imaging defined as Mayo Imaging Class (MIC) is a validated prognostic measure for autosomal dominant polycystic kidney disease (ADPKD) in adults to predict and stratify disease progression. However, no stratification tool is currently available in pediatric ADPKD. Because magnetic resonance imaging and computed tomography in children are difficult, we propose a novel 3D ultrasound-based pediatric Leuven Imaging Classification to complement the MIC.

METHODS

A prospective study cohort of 74 patients with genotyped ADPKD (37 female) was followed longitudinally with ultrasound, including 3D ultrasound, and they underwent in total 247 3D ultrasound assessments, with patients' median age (interquartile range [IQR]) at diagnosis of 3 (IQR, 0-9) years and at first 3D ultrasound evaluation of 10 (IQR, 5-14) years. First, data matching was done to the published MIC classification, followed by subsequent optimization of parameters and model type.

RESULTS

PKD1 was confirmed in 70 patients (95%), PKD2 in three (4%), and glucosidase IIα unit only once (1%). Over these 247 evaluations, the median height was 143 (IQR, 122-166) cm and total kidney volume was 236 (IQR, 144-344) ml, leading to an htTKV of 161 (IQR, 117-208) ml/m. Applying the adult Mayo classification in children younger than 15 years strongly underestimated ADPKD severity, even with correction for height. We therefore optimized the model with our pediatric data and eventually validated it with data of young patients from Mayo Clinic and the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease used to establish the MIC.

CONCLUSIONS

We proposed a five-level Leuven Imaging Classification ADPKD pediatric model as a novel classification tool on the basis of patients' age and 3D ultrasound-htTKV for reliable discrimination of childhood ADPKD severity.

OVERTURE: A Worldwide, Prospective, Observational Study of Disease Characteristics in Patients With ADPKD

Introduction

The course of autosomal dominant polycystic kidney disease (ADPKD) varies greatly among affected individuals, necessitating natural history studies to characterize the determinants and effects of disease progression. Therefore, we conducted an observational, longitudinal study (OVERTURE; NCT01430494) of patients with ADPKD.

Methods

This prospective study enrolled a large international population (N = 3409) encompassing a broad spectrum of ages (12-78 years), chronic kidney disease (CKD) stages (G1-G5), and Mayo imaging classifications (1A-1E). Outcomes included kidney function, complications, quality of life, health care resource utilization, and work productivity.

Results

Most subjects (84.4%) completed ≥12 months of follow-up. Consistent with earlier findings, each additional l/m of height-adjusted total kidney volume (htTKV) on magnetic resonance imaging (MRI) was associated with worse outcomes, including lower estimated glomerular filtration rate (eGFR) (regression coefficient 17.02, 95% confidence interval [CI] 15.94-18.11) and greater likelihood of hypertension (odds ratio [OR] 1.25, 95% CI 1.17-1.34), kidney pain (OR 1.22, 95% CI 1.11-1.33), and hematuria (OR 1.35, 95% CI 1.21-1.51). Greater baseline htTKV was also associated with worse patient-reported health-related quality of life (e.g., ADPKD Impact Scale physical score, regression coefficient 1.02, 95% CI 0.65-1.39), decreased work productivity (e.g., work days missed, regression coefficient 0.55, 95% CI 0.18-0.92), and increased health care resource utilization (e.g., hospitalizations, OR 1.48, 95% CI 1.33-1.64) during follow-up.

Conclusion

Although limited by a maximum 3-year duration of follow-up, this observational study characterized the burden of ADPKD in a broad population and indicated the predictive value of kidney volume for outcomes other than kidney function.

Relationship between disease awareness and severity of kidney disease in autosomal dominant polycystic kidney disease patients

INTRODUCTION

Polycystic kidney disease (PKD) is responsible for 5%-10% of end-stage renal disease. We examined the relationship between renal and extrarenal findings, disease severity, and the level of consciousness of PKD patients.

METHODS

Patients were asked to answer the questionnaire about PKD. Disease severity was determined according to estimated glomerular filtration rate, and disease awareness was assessed by adapting the Disease Perception Scale to PKD. Awareness of patients was evaluated comparatively with chronic kidney disease stage, age, region, and symptoms.

RESULTS

One out of five patients does not know that this disease is inherited. Mean awareness scores of the patients decreased significantly with increasing age. Awareness scores were significantly higher in patients with flank pain, hematuria, and urinary tract stones.

CONCLUSION

Although PKD is the most common hereditary kidney disease, the rate of patients' knowledge on this subject is low. Increased awareness might lead to better treatment in those patients.

Combining Metformin and Drug-Loaded Kidney-Targeting Micelles for Polycystic Kidney Disease

Introduction

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease that leads to eventual renal failure. Metformin (MET), an AMP-activated protein kinase (AMPK) activator already approved for type 2 diabetes, is currently investigated for ADPKD treatment. However, despite high tolerability, MET showed varying therapeutic efficacy in preclinical ADPKD studies. Thus, newer strategies have combined MET with other ADPKD small molecule drug candidates, thereby targeting multiple ADPKD-associated signaling pathways to enhance therapeutic outcomes through potential drug synergy. Unfortunately, the off-target side effects caused by these additional drug candidates pose a major hurdle. To address this, our group has previously developed kidney-targeting peptide amphiphile micelles (KMs), which displayed significant kidney accumulation in vivo, for delivering drugs to the site of the disease.

Methods

To mitigate the adverse effects of ADPKD drugs and evaluate their therapeutic potential in combination with MET, herein, we loaded KMs with ADPKD drug candidates including salsalate, octreotide, bardoxolone methyl, rapamycin, tolvaptan, and pioglitazone, and tested their in vitro therapeutic efficacy when combined with free MET. Specifically, after determining the 40% inhibitory concentration for each drug (IC40), the size, morphology, and surface charge of drug-loaded KMs were characterized. Next, drug-loaded KMs were applied in combination with MET to treat renal proximal tubule cells derived from Pkd1flox/-:TSLargeT mice in 2D proliferation and 3D cyst model.

Results

MET combined with all drug-loaded KMs demonstrated significantly enhanced efficacy as compared to free drugs in inhibiting cell proliferation and cyst growth. Notably, synergistic effects were found for MET and KMs loaded with either salsalate or rapamycin as determined by Bliss synergy scores.

Conclusion

Together, we show drug synergy using drug-loaded nanoparticles and free MET for the first time and present a novel nanomedicine-based combinatorial therapeutic approach for ADPKD with enhanced efficacy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-022-00753-9.

Utility of ultrasonography for predicting indications for tolvaptan in patients with autosomal dominant polycystic kidney disease

PURPOSE

Tolvaptan is the first approved treatment for autosomal dominant polycystic kidney disease (ADPKD) that targets a mechanism directly contributing to the development and growth of renal cysts. We investigated the ability of ultrasonography to predict total kidney volume (TKV) of 750 mL or more, which is an indication for tolvaptan therapy in patients with ADPKD.

METHODS

A total of 46 patients with ADPKD were evaluated. The most statistically appropriate measurement based on ultrasonography for predicting TKV determined by computed tomography (CT) was assessed.

RESULTS

TKV determined by CT was 796.8 (508.8-1,560.3) mL. The median length, anteroposterior distance, and mediolateral distance determined using ultrasonography were 15.7 cm, 7.6 cm, and 7.6 cm in the left kidney, and 13.4 cm, 6.9 cm, and 7.2 cm in the right kidney, respectively. Multivariate regression analysis showed that total kidney length (left and right) [variance inflation factor (VIF), 9.349] and total mediolateral distance (left and right) (VIF, 3.988) were independently associated with TKV. The correlation (r) between the logarithm of TKV determined by CT and total mediolateral distance determined using ultrasonography was 0.915 (p < 0.001). The linear regression equation was log (total kidney volume) = 1.833 + 0.075 × total mediolateral distance (left and right) based on ultrasonography. The area under the receiver operating characteristic curve for total mediolateral distance determined using ultrasonography to predict TKV of 750 mL or more was 0.989. Using the total mediolateral distance cut-off value of 14.2 cm, the sensitivity and specificity were 96.0% and 100.0%, respectively.

CONCLUSION

Total mediolateral distance determined using ultrasonography can predict TKV in patients with ADPKD.

Cyst Fraction as a Biomarker in Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disease. Patients at high risk of severe disease progression should be identified early in order to intervene with supportive and therapeutic measures. However, the glomerular filtration rate (GFR) may remain within normal limits for decades until decline begins, making it a late indicator of rapid progression. Kidney volumetry is frequently used in clinical practice to allow for an assessment of disease severity. Due to limited prognostic accuracy, additional imaging markers are of high interest to improve outcome prediction in ADPKD, but data from clinical cohorts are still limited. In this study, we examined cyst fraction as one of these parameters in a cohort of 142 ADPKD patients. A subset of 61 patients received MRIs in two consecutive years to assess longitudinal changes. All MRIs were analyzed by segmentation and volumetry of the kidneys followed by determination of cyst fraction. As expected, both total kidney volume (TKV) and cyst fraction correlated with estimated GFR (eGFR), but cyst fraction showed a higher R² in a univariate linear regression. Besides, only cyst fraction remained statistically significant in a multiple linear regression including both htTKV and cyst fraction to predict eGFR. Consequently, this study underlines the potential of cyst fraction in ADPKD and encourages prospective clinical trials examining its predictive value in combination with other biomarkers to predict future eGFR decline.

Clinical characteristics and risk factors for kidney failure in patients with autosomal dominant polycystic kidney disease: A retrospective study

Autosomal dominant polycystic kidney disease (ADPKD) is a hereditary and progressive renal disease. By the age of 65 years, 45% to 70% of patients with ADPKD reach end-stage renal disease (ESRD). Although there are various treatments for this condition, no standard therapy exists to delay the progression of ADPKD. Hence, understanding the factors that affect disease progression may be helpful for the treatment of ADPKD. The medical records of 288 patients with ADPKD at Keimyung University Dongsan Medical Center between January 1989 and August 2018 were analyzed retrospectively. Furthermore, we inspected the risk factors involved in the progression of ADPKD and the kidney survival rates of patients using the Cox proportional hazards model and Kaplan-Meier survival analysis. The mean age at the time of diagnosis was 43.1 ± 14.1 years, and there were 146 males (50.7%). In total, 197 patients (68.4%) had hypertension and 11 patients (3.8%) had cerebral aneurysm. Stroke occurred in 35 patients (12.1%), including 11 cases of cerebral hemorrhage and 24 cases of cerebral infarction. Twenty-eight patients (9.7%) died during the follow-up period (117.1 ± 102.1 months). Infection (42.9%) was the most common cause of mortality, followed by sudden cardiac death (25.0%). Overall, 132 patients (45.8%) progressed to ESRD and 104 patients (36.1%) required renal replacement therapy (RRT). The mean duration from diagnosis to RRT was 110.8 ± 93.9 months. Age at diagnosis after 30 years (odd's ratio [OR], 2.737; 95% confidence interval [CI], 1.320-5.675; P = .007), baseline serum creatinine levels (OR, 1.326; 95% CI, 1.259-1.396; P < .001), and cyst infection (OR, 2.065; 95% CI, 1.242-3.433; P = .005) were the independent risk factors for kidney failure in multivariable analysis. To delay the advance of ADPKD to ESRD, early diagnosis and close observation for the onset of cyst infection are crucial.

Identification of Serum Metabolites for Predicting Chronic Kidney Disease Progression according to Chronic Kidney Disease Cause

Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

Identification of osteopontin as a urinary biomarker for autosomal dominant polycystic kidney disease progression

BACKGROUND

Autosomal dominant polycystic kidney disease (ADPKD), one of the most common human monogenic diseases, is characterized by the presence of numerous fluid-filled renal cysts and is a leading cause of end-stage renal disease (ESRD). Urinary biomarkers may be useful for predicting the variable course of ADPKD progression from cyst growth to ESRD.

METHODS

To identify candidate urinary biomarkers of ADPKD progression, we used CRISPR/Cas9 genome editing to generate porcine fibroblasts with mono- and biallelic ADPKD gene knockout (PKD2+/- and PKD2-/-, respectively). We then performed RNA-sequencing analysis on these cells.

RESULTS

Levels of osteopontin (OPN), which is expressed by renal epithelial tubular cells and excreted into urine, were reduced in PKD2-/- cells but not in PKD2+/- cells. OPN levels were also reduced in the renal cyst cells of ADPKD patients. Next, we investigated whether OPN excretion was decreased in patients with ADPKD via enzyme-linked immunosorbent assay. OPN levels excreted into renal cyst cell culture media and urine from ADPKD patients were decreased. To investigate whether OPN can predict the rate of ADPKD progression, we compared urinary excretion of OPN in ADPKD patients with slow progression and those with rapid progression. Those with rapid progression had an estimated glomerular filtration rate of >60 mL/min/1.73 m2 . Urinary OPN excretion levels were lower in rapid progressors than in slow progressors.

CONCLUSION

These findings suggest that OPN is a useful urinary biomarker for predicting ADPKD progression.

A deep learning model for CT-based kidney volume determination in dogs and normal reference definition

Kidney volume is associated with renal function and the severity of renal diseases, thus accurate assessment of the kidney is important. Although the voxel count method is reported to be more accurate than several methods, its laborious and time-consuming process is considered as a main limitation. In need of a new technology that is fast and as accurate as the manual voxel count method, the aim of this study was to develop the first deep learning model for automatic kidney detection and volume estimation from computed tomography (CT) images of dogs. A total of 182,974 image slices from 386 CT scans of 211 dogs were used to develop this deep learning model. Owing to the variance of kidney size and location in dogs compared to humans, several processing methods and an architecture based on UNEt Transformers which is known to show promising results for various medical image segmentation tasks including this study. Combined loss function and data augmentation were applied to elevate the performance of the model. The Dice similarity coefficient (DSC) which shows the similarity between manual segmentation and automated segmentation by deep-learning model was 0.915 ± 0.054 (mean ± SD) with post-processing. Kidney volume agreement analysis assessing the similarity between the kidney volume estimated by manual voxel count method and the deep-learning model was r = 0.960 (p < 0.001), 0.95 from Lin's concordance correlation coefficient (CCC), and 0.975 from the intraclass correlation coefficient (ICC). Kidney volume was positively correlated with body weight (BW), and insignificantly correlated with body conditions score (BCS), age, and sex. The correlations between BW, BCS, and kidney volume were as follows: kidney volume = 3.701 × BW + 11.962 (R 2 = 0.74, p < 0.001) and kidney volume = 19.823 × BW/BCS index + 10.705 (R 2 = 0.72, p < 0.001). The deep learning model developed in this study is useful for the automatic estimation of kidney volume. Furthermore, a reference range established in this study for CT-based normal kidney volume considering BW and BCS can be helpful in assessment of kidney in dogs.

Persistence of tolvaptan medication for autosomal dominant polycystic kidney disease: A retrospective cohort study using Shizuoka Kokuho Database

Autosomal dominant polycystic kidney disease (ADPKD) is a rare hereditary disease leading to end-stage renal failure in approximately half of patients by seventy years of age. It is important to continuously take tolvaptan to control disease progression. However, adherence to tolvaptan in a real-world setting, rather than randomized controlled trials (RCTs), has not been sufficiently reported. We aimed to investigate tolvaptan persistence among patients with ADPKD using a large claims database. Using the Shizuoka Kokuho Database, we identified patients diagnosed with ADPKD who were prescribed tolvaptan from March 2014-September 2018 in Japan. The persistence rate of tolvaptan medication was estimated by Kaplan-Meier analysis, and patient background factors associated with treatment discontinuation were exploratively evaluated with log-rank tests. We identified 1714 eligible patients with ADPKD, and among them, 25 patients used tolvaptan medication. We followed up these patients, whose median treatment duration was 21 months. The persistence rates at 12, 24, and 36 months were estimated to be 70.8% (95% confidence interval: 48.2-93.4), 46.5% (23.2-66.9), and 38.7% (16.4-60.8), respectively. In the exploratory analysis, there were no factors that were obviously associated with tolvaptan discontinuation. The persistence rate of tolvaptan in patients with ADPKD in a real-world setting may be lower than that in previous RCTs. Our innovative method, particularly in Japan, to analyze adherence using large claims data should change the way clinical epidemiological research and health policies of rare diseases are designed in the future.

The STAGED-PKD 2-Stage Adaptive Study With a Patient Enrichment Strategy and Treatment Effect Modeling for Improved Study Design Efficiency in Patients With ADPKD

Rationale & Objective

Venglustat, a glucosylceramide synthase inhibitor, inhibits cyst growth and reduces kidney failure in mouse models of autosomal dominant polycystic kidney disease (ADPKD). STAGED-PKD aims to determine the safety and efficacy of venglustat and was designed using patient enrichment for progression to end-stage kidney disease and modeling from prior ADPKD trials.

Study Design

STAGED-PKD is a 2-stage, international, double-blind, randomized, placebo-controlled trial in adults with ADPKD (Mayo Class 1C-1E) and estimated glomerular filtration rate (eGFR) 45-<90 mL/min/1.73 m² at risk of rapidly progressive disease. Enrichment for rapidly progressing patients was identified based on retrospective analysis of total kidney volume (TKV) and eGFR slope from the combined Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease and HALT Progression of Polycystic Kidney Disease A studies.

Setting & Participants

Target enrollment in stages 1 and 2 was 240 and 320 patients, respectively.

Interventions

Stage 1 randomizes patients 1:1:1 to venglustat 8 mg or 15 mg once daily or placebo. Stage 2 randomizes patients 1:1 to placebo or venglustat, with the preferred dose based on stage 1 safety data.

Outcomes

Primary endpoints are TKV growth rate over 18 months in stage 1 and eGFR slope over 24 months in stage 2. Secondary endpoints include: annualized rate of change in eGFR from baseline to 18 months (stage 1); annualized rate of change in TKV based on magnetic resonance imaging from baseline to 18 months (stage 2); and safety, tolerability, pain, and fatigue (stages 1 and 2).

Limitations

If stage 1 is unsuccessful, patients enrolled in the trial may develop drug-related adverse events that can have long-lasting effects.

Conclusions

Modeling allows the design and powering of a 2-stage combined study to assess venglustat’s impact on TKV growth and eGFR slope. Stage 1 TKV assessment via a nested approach allows early evaluation of efficacy and increased efficiency of the trial design by reducing patient numbers and trial duration.

Funding

This study was funded by Sanofi.

Trial registration

STAGED-PKD has been registered at ClinicalTrials.gov with study number NCT03523728.

A Systematic Review of Reported Outcomes in ADPKD Studies

Introduction

Methods

Results

Conclusion

Shared pathobiology identifies AMPK as a therapeutic target for obesity and autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common Mendelian kidney disease, affecting approximately one in 1,000 births and accounting for 5% of end-stage kidney disease in developed countries. The pathophysiology of ADPKD is strongly linked to metabolic dysregulation, which may be secondary to defective polycystin function. Overweight and obesity are highly prevalent in patients with ADPKD and constitute an independent risk factor for progression. Recent studies have highlighted reduced AMP-activated protein kinase (AMPK) activity, increased mammalian target of rapamycin (mTOR) signaling, and mitochondrial dysfunction as shared pathobiology between ADPKD and overweight/obesity. Notably, mTOR and AMPK are two diametrically opposed sensors of energy metabolism that regulate cell growth and proliferation. However, treatment with the current generation of mTOR inhibitors is poorly tolerated due to their toxicity, making clinical translation difficult. By contrast, multiple preclinical and clinical studies have shown that pharmacological activation of AMPK provides a promising approach to treat ADPKD. In this narrative review, we summarize the pleiotropic functions of AMPK as a regulator of cellular proliferation, macromolecule metabolism, and mitochondrial biogenesis, and discuss the potential for pharmacological activation of AMPK to treat ADPKD and obesity-related kidney disease.

A Deep Learning Approach for Automated Segmentation of Kidneys and Exophytic Cysts in Individuals with Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

Total kidney volume (TKV) is an important imaging biomarker in autosomal dominant polycystic kidney disease (ADPKD). Manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming.

METHODS

We developed a fully automated segmentation method for TKV using a deep learning network to selectively segment kidney regions while excluding exophytic cysts. We used abdominal T₂ -weighted magnetic resonance images from 210 individuals with ADPKD who were divided into two groups: one group of 157 to train the network and a second group of 53 to test it. With a 3D U-Net architecture using dataset fingerprints, the network was trained by K-fold cross-validation, in that 80% of 157 cases were for training and the remaining 20% were for validation. We used Dice similarity coefficient, intraclass correlation coefficient, and Bland-Altman analysis to assess the performance of the automated segmentation method compared with the manual method.

RESULTS

The automated and manual reference methods exhibited excellent geometric concordance (Dice similarity coefficient: mean±SD, 0.962±0.018) on the test datasets, with kidney volumes ranging from 178.9 to 2776.0 ml (mean±SD, 1058.5±706.8 ml) and exophytic cysts ranging from 113.4 to 2497.6 ml (mean±SD, 549.0±559.1 ml). The intraclass correlation coefficient was 0.9994 (95% confidence interval, 0.9991 to 0.9996; P<0.001) with a minimum bias of -2.424 ml (95% limits of agreement, -49.80 to 44.95).

CONCLUSIONS

We developed a fully automated segmentation method to measure TKV that excludes exophytic cysts and has an accuracy similar to that of a human expert. This technique may be useful in clinical studies that require automated computation of TKV to evaluate progression of ADPKD and response to treatment.

The predictive value of renal parenchymal information for renal function impairment in patients with ADPKD: a multicenter prospective study

OBJECTIVE

Although the guideline indicates that total kidney volume (TKV) is an important detection indicator in patients with autosomal dominant polycystic kidney disease (ADPKD), this study attempted to demonstrate that renal parenchymal information, combined with parenchymal volume and radiomics features, may have more valuable clinical guiding significance.

METHODS

A totals of 340 ADPKD patients with normal renal function were prospectively collected and followed-up for five years, with renal function tests and non-contrast computed tomography (CT) performed every six months. The relationship between renal function impairment and renal parenchymal volume (RPV) as along with radiomics features was explored using a multiple linear regression model and multiple logistic regression. Then, a combined model of RPV with radiomics features was constructed to comprehensively evaluate its predictive value.

RESULTS

Compared with TKV, decreased RPV presented a closer relationship with renal function impairment, namely, with the impairment rate (RPV: 82.3% vs. TVK: 67.1%) and eGFR (RPV: r = 0.614, p < 0.001 vs. TKV: r = -0.452, p < 0.001), and showed higher predictive power (RPV: AUC = 0.752 [95%CI 0.692-0.805], p < 0.001 vs. TKV: AUC = 0.711 [95%CI 0.649-0.768], p < 0.001). Correspondingly, the radiomics analysis that was derived from the renal parenchyma also showed a satisfactory result (AUC = 0.849 [95%Cl 0.797-0.892], p < 0.001). Importantly, the predictive power for renal function impairment was further improved in the combined model of RPV and radiomics features (AUC = 0.902 [95%Cl 0.857-0.937], p < 0.001).

CONCLUSION

Renal parenchyma information may be a sensitive biomarker of renal function impairment in ADPKD, which can provide a new approach for clinically monitoring renal function, and may greatly improve the pre-hospital prevention and treatment effects.

A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

OBJECTIVES

This study is aimed to establish a fusion model of radiomics-based nomogram to predict the renal function of autosomal dominant polycystic kidney disease (ADPKD).

METHODS

One hundred patients with ADPKD were randomly divided into training group (n = 69) and test group (n = 31). The radiomics features were extracted from T1-weighted fat suppression images (FS-T1WI) and T2-weighted fat suppression images (FS-T2WI). Decision tree algorithm was employed to build radiomics model to get radiomics signature. Then multivariate logistic regression analysis was used to establish the radiomics nomogram based on independent clinical factors, conventional MR imaging variables and radiomics signature. The receiver operating characteristic (ROC) analysis and Delong test were used to compare the performance of radiomics model and radiomics nomogram model, and the decision curve to evaluate the clinical application value of radiomics nomogram model in the evaluation of renal function in patients with ADPKD.

RESULTS

Fourteen radiomics features were selected to establish radiomics model. Based on FS-T1WI and FS-T2WI sequences, the radiomics model showed good discrimination ability in training group and test group [training group: (AUC) = 0.7542, test group (AUC) = 0.7417]. The performance of radiomics nomogram model was significantly better than that of radiomics model in all data sets [radiomics model (AUC) = 0.7505, radiomics nomogram model (AUC) = 0.8435, p value = 0.005]. The analysis of calibration curve and decision curve showed that radiomics nomogram model had more clinical application value.

CONCLUSION

radiomics analysis of MRI can be used for the preliminary evaluation and prediction of renal function in patients with ADPKD. The radiomics nomogram model shows better prediction effect in renal function evaluation, and can be used as a non-invasive renal function prediction tool to assist clinical decision-making. Trial registration ChiCTR, ChiCTR2100046739. Registered 27 May 2021-retrospectively registered, http://www.ChiCTR.org.cn/showproj.aspx?proj=125955.

A Shared Nephroprotective Mechanism for Renin-Angiotensin-System Inhibitors, Sodium-Glucose Co-Transporter 2 Inhibitors, and Vasopressin Receptor Antagonists: Immunology Meets Hemodynamics

A major paradigm in nephrology states that the loss of filtration function over a long time is driven by a persistent hyperfiltration state of surviving nephrons. This hyperfiltration may derive from circulating immunological factors. However, some clue about the hemodynamic effects of these factors derives from the effects of so-called nephroprotective drugs. Thirty years after the introduction of Renin-Angiotensin-system inhibitors (RASi) into clinical practice, two new families of nephroprotective drugs have been identified: the sodium-glucose cotransporter 2 inhibitors (SGLT2i) and the vasopressin receptor antagonists (VRA). Even though the molecular targets of the three-drug classes are very different, they share the reduction in the glomerular filtration rate (GFR) at the beginning of the therapy, which is usually considered an adverse effect. Therefore, we hypothesize that acute GFR decline is a prerequisite to obtaining nephroprotection with all these drugs. In this study, we reanalyze evidence that RASi, SGLT2i, and VRA reduce the eGFR at the onset of therapy. Afterward, we evaluate whether the extent of eGFR reduction correlates with their long-term efficacy. The results suggest that the extent of initial eGFR decline predicts the nephroprotective efficacy in the long run. Therefore, we propose that RASi, SGLT2i, and VRA delay kidney disease progression by controlling maladaptive glomerular hyperfiltration resulting from circulating immunological factors. Further studies are needed to verify their combined effects.

Asymptomatic Pyuria as a Prognostic Biomarker in Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

Autosomal dominant polycystic kidney disease (ADPKD) has phenotypic variability only partially explained by established biomarkers that do not readily assess pathologically important factors of inflammation and kidney fibrosis. We evaluated asymptomatic pyuria (AP), a surrogate marker of inflammation, as a biomarker for disease progression.

METHODS

We performed a retrospective cohort study of adult patients with ADPKD. Patients were divided into AP and no pyuria (NP) groups. We evaluated the effect of pyuria on kidney function and kidney volume. Longitudinal models evaluating kidney function and kidney volume rate of change with respect to incidences of AP were created.

RESULTS

There were 687 included patients (347 AP, 340 NP). The AP group had more women (65% versus 49%). Median ages at kidney failure were 86 and 80 years in the NP and AP groups (log rank, P=0.49), respectively, for patients in Mayo Imaging Class (MIC) 1A-1B as compared with 59 and 55 years for patients in MIC 1C-1D-1E (log rank, P=0.02), respectively. Compared with the NP group, the rate of kidney function (ml/min per 1.73 m² per year) decline shifted significantly after detection of AP in the models, including all patients (-1.48; P<0.001), patients in MIC 1A-1B (-1.79; P<0.001), patients in MIC 1C-1D-1E (-1.18; P<0.001), and patients with PKD1 (-1.04; P<0.001). Models evaluating kidney volume rate of growth showed no change after incidence of AP as compared with the NP group.

CONCLUSIONS

AP is associated with kidney failure and faster kidney function decline irrespective of the ADPKD gene, cystic burden, and cystic growth. These results support AP as an enriching prognostic biomarker for the rate of disease progression.

Management of autosomal dominant polycystic kidney disease in the era of disease-modifying treatment options

Autosomal dominant polycystic kidney disease (ADPKD) is the reported etiology in 10% of end-stage kidney disease (ESKD) patients and has an estimated prevalence of 12.5 million cases worldwide across all ethnicities. There have been major advancements over the last two decades in understanding the pathogenesis and development of disease-modifying treatment options for ADPKD, culminating in regulatory approval of tolvaptan for ADPKD patients at risk of rapid progression to kidney failure. This review highlights the genetic mutations associated with ADPKD, defines patients at risk of rapid progression to ESKD, and focuses on the management of ADPKD in the era of disease-modifying agents.

Autosomal Dominant Polycystic Kidney Disease: From Pathophysiology of Cystogenesis to Advances in the Treatment

Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic renal disease, with an estimated prevalence between 1:1000 and 1:2500. It is mostly caused by mutations of the PKD1 and PKD2 genes encoding polycystin 1 (PC1) and polycystin 2 (PC2) that regulate cellular processes such as fluid transport, differentiation, proliferation, apoptosis and cell adhesion. Reduction of calcium ions and induction of cyclic adenosine monophosphate (sAMP) promote cyst enlargement by transepithelial fluid secretion and cell proliferation. Abnormal activation of MAPK/ERK pathway, dysregulated signaling of heterotrimeric G proteins, mTOR, phosphoinositide 3-kinase, AMPK, JAK/STAT activator of transcription and nuclear factor kB (NF-kB) are involved in cystogenesis. Another feature of cystic tissue is increased extracellular production and recruitment of inflammatory cells and abnormal connections among cells. Moreover, metabolic alterations in cystic cells including defective glucose metabolism, impaired beta-oxidation and abnormal mitochondrial activity were shown to be associated with cyst expansion. Although tolvaptan has been recently approved as a drug that slows ADPKD progression, some patients do not tolerate tolvaptan because of frequent aquaretic. The advances in the knowledge of multiple molecular pathways involved in cystogenesis led to the development of animal and cellular studies, followed by the development of several ongoing randomized controlled trials with promising drugs. Our review is aimed at pathophysiological mechanisms in cystogenesis in connection with the most promising drugs in animal and clinical studies.

Deep Learning-Enabled Technologies for Bioimage Analysis

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

Pooled Data Analysis of the Long-Term Treatment Effects of Tolvaptan in ADPKD

Introduction

Methods

Results

Conclusion

Long-Term Outcomes of Longitudinal Efficacy Study With Tolvaptan in ADPKD

Introduction

The effects of long-term and uninterrupted tolvaptan treatment on autosomal dominant polycystic kidney disease (ADPKD) are unclear. Therefore, a more than 3-year continuous treatment study was performed.

Methods

From the Kyorin University cohort, 299 patients were surveyed and 179 patients were indicated for tolvaptan having a total kidney volume (TKV) ≥750 ml, TKV slope ≥5%/yr, and estimated glomerular filtration rate (eGFR) ≥15 ml/min per 1.73 m². Among 179 patients, 118 patients consented to the study.

Results

Retrospective pretreatment and prospective on-treatment periods had a median of 1.8 and 4.0 years, respectively. During the 5 treatment-years, the log₁₀(TKV) slope/yr decreased from the pretreatment period (P < 0.0001) and the estimated height-adjusted TKV growth rate α (eHTKV-α, %/yr) decreased from baseline (P < 0.0001). The decline in eGFR improved in female patients (P < 0.0001), but not in males (P = 0.6321). Furthermore, during the 5 treatment-years, eGFR remained significantly better in the group with a percent decrease in eHTKV-α from baseline to the first treatment-year ≥ the median (2.94%) than in the group with a decrease <2.94%. The free-water clearance was higher in males than in females irrespective of treatment.

Conclusion

The TKV growth rate decreased in 4 years with tolvaptan in both sexes. The insignificant effects of tolvaptan on the eGFR slope in males were likely due to androgen stimulation of cystogenesis and analytical difficulty of longitudinal changes in nonlinear trajectories of eGFR. The larger decrease in eHTKV-α in the first year was related to a better renal prognosis. The vasopressin-mediated water reabsorption was activated more in females than males irrespective of tolvaptan administration.

Weight loss and cystic disease progression in autosomal dominant polycystic kidney disease

Progression of autosomal dominant polycystic kidney disease (ADPKD) is modified by metabolic defects and obesity. Indeed, reduced food intake slows cyst growth in preclinical rodent studies. Here, we demonstrate the feasibility of daily caloric restriction (DCR) and intermittent fasting (IMF) in a cohort of overweight or obese patients with ADPKD. Clinically significant weight loss occurred with both DCR and IMF; however, weight loss was greater and adherence and tolerability were better with DCR. Further, slowed kidney growth correlated with body weight and visceral adiposity loss independent of dietary regimen. Similarly, we compared the therapeutic efficacy of DCR, IMF, and time restricted feeding (TRF) using an orthologous ADPKD mouse model. Only ADPKD animals on DCR lost significant weight and showed slowed cyst growth compared to ad libitum, IMF, or TRF feeding. Collectively, this supports therapeutic feasibility of caloric restriction in ADPKD, with potential efficacy benefits driven by weight loss.