Most cases of autosomal dominant polycystic kidney disease (ADPKD) are caused by mutations in PKD1, which reduce polycystin-1 (PC1) levels below a critical functional threshold. Normalizing PC1 dosage mitigates disease progression; therefore, we sought to develop a CRISPR activation (CRISPRa) strategy to transcriptionally upregulate endogenous PKD1. We systematically screened multiple single-guide RNAs using an EGFP-reporter platform and identified potent candidates targeting the proximal PKD1 promoter in mouse and human cell models. Our results demonstrate that CRISPRa effectively increased endogenous Pkd1 mRNA in the mouse collecting duct-derived Pkd1 RC/- cell model and in the primary renal epithelial cells from PKD mice. In Pkd1 RC/- cells, CRISPRa of Pkd1 increased PC1 protein levels and significantly reduced cell proliferation and in vitro cyst formation in 3D cultures. Mechanistically, Pkd1 activation improved mitochondrial membrane potential, reduced dependency on aerobic glycolysis, and corrected signaling pathways involved in cystogenesis, specifically reducing intracellular cAMP, cMyc, pCreb, and pErk levels, while increasing pYap1 levels. We confirmed the translational potential of this platform by successfully activating PKD1 in primary renal epithelial cells from human kidneys. We observed a heterogeneous response across both normal and ADPKD patient-derived donor lines, with significant upregulation achieved in two of the tested cell preparations. These findings provide a compelling proof-of-concept that CRISPRa-mediated gene augmentation can increase PC1 levels, establishing a foundation for promising gene therapies aimed at successfully suppressing the pathogenic features of ADPKD.

Key PointsIn autosomal dominant polycystic kidney disease, there is a linear relationship between the log-transformed total kidney volume and eGFR.There is a predictable change in the rate of eGFR decline with reduction in kidney growth rate.This framework can be used to determine treatment effect to support accelerated approval of drugs for polycystic kidney disease.BackgroundTotal kidney volume (TKV) is accepted by the US Food and Drug Administration as a surrogate end point that is reasonably likely to predict clinical benefit in autosomal dominant polycystic kidney disease and the most commonly used response biomarker for proof-of-concept intervention trials. However, the magnitude of treatment effect on TKV that would be predictive of a meaningful improvement in a clinical outcome, such as eGFR, is unknown. Inference of this from observational studies has previously been approached by examining interindividual variance in the relationship between TKV and GFR slopes over time.MethodsWe developed a novel approach to modeling the intraindividual relationship between TKV and eGFR. Patients from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease and Halt Progression of Polycystic Kidney Disease Study A dataset were stratified by Mayo Imaging Class (MIC). Linear mixed models were fitted to eGFR with a fixed effect of log(TKV) and random intercepts, and the average slope within each MIC was estimated.ResultsWe found that within each MIC, there is a consistent, linear relationship between log(TKV) and eGFR. The model predicts that within classes 1C-1E, for each 1% point per year reduction in TKV growth rate, the rate of eGFR decline would be reduced by 0.40-0.52 ml/min per 1.73 m2 per year.ConclusionsWe have developed a new model that provides a framework for defining the magnitude of treatment effect on TKV that would support accelerated approval of a drug for autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD), the leading monogenic cause of kidney failure, exhibits heterogeneous clinical progression. This systematic review and meta-analysis synthesize and evaluate current evidence on blood and urine prognostic biomarkers in ADPKD, addressing gaps in understanding their role in predicting progression and guiding clinical trial selection and management. We searched PubMed, Embase, and Cochrane up to April 2025 and screened articles in duplicate. We included longitudinal studies evaluating the blood and urine prognostic biomarkers in patients with ADPKD with at least 10 participants and 1 year of follow-up. We used the Quality in Prognosis Studies tool to assess risk of bias, random effects meta-analyses to pool effect estimates, and the GRADE approach to assess the certainty of evidence. We included 58 studies, with 33 urinary biomarkers and 29 serum/blood biomarkers identified. The most frequently studied biomarkers were urine osmolality, copeptin, proteinuria, Monocyte Chemoattractant Protein-1, and uric acid, whereas the most studied outcomes were estimated Glomerular Filtration Rate and Total Kidney Volume. The urinary biomarkers that showed the largest association with ADPKD were Monocyte Chemoattractant Protein-1, Kidney Injury Molecule-1, albumin, and Beta 2 microglobulin. Serum biomarkers associated with outcomes were primarily copeptin and Fibroblast Growth Factor-23, with β-Hydroxybutyrate and bicarbonate exhibiting lesser association. In conclusion, this systematic review highlights the potential prognostic value of blood and urine biomarkers in ADPKD. It also verified the need for further validation of biomarker use in ADPKD. Not applicable.

BACKGROUND AND HYPOTHESIS:A decline in renal blood flow (RBF) precedes estimated glomerular filtration rate (eGFR) decline among patients with autosomal dominant polycystic kidney disease (ADPKD) and inversely associates with disease severity and progression. However, it is unknown whether RBF independently predicts progression to kidney failure in patients with ADPKD. METHODS:379 participants with early-stage ADPKD (age 36.6 ± 8.5 years [mean ± SD]; eGFR: 92.9 ± 17.1 mL/min/1.73m2) who participated in the HALT PKD Study A with baseline RBF (phase contrast MRI) data were included in the primary analysis. Kaplan Meier survival analysis and multivariate Cox proportional hazard models were used to determine the association of baseline RBF as a continuous and categorical variable with kidney failure risk (during HALT and subsequently linked to the US Renal Data System) over a median (IQR) follow-up period of 13.1 (7.5, 14.5) years (primary outcome). All-cause mortality was also considered as a competing risk (Fine and Gray method). The associations of RBF with eGFR slope and ∆ height-adjusted total kidney volume (htTKV) were evaluated with linear and logistic regression models. RESULTS:The kidney failure incidence rate was the highest among those with the lowest RBF tertile (1.90 [1.31, 2.78] vs. 0.34 [0.14, 0.83] events/100 person-years in tertile 3). In the fully adjusted model, the lowest RBF tertile was associated with higher risk of progression to kidney failure (Hazard Ratio [HR (95% CI)] 3.19 [1.05, 9.67] vs. highest tertile). The association of RBF with kidney failure risk was slightly attenuated in adjusted models. All associations remained similar when considering death as a competing risk (HR: 0.82 [95% CI 0.67, 1.00] per 100 ml/min/1.73m2 higher RBF). Higher RBF was associated with slower eGFR decline and lower odds of rapid annual ∆htTKV. CONCLUSIONS:Lower baseline RBF independently associates with greater risk of progression to kidney failure in early-stage patients with ADPKD.

Background:Autosomal dominant polycystic kidney disease (ADPKD) is a common inherited disorder marked by numerous renal cysts that impair kidney function, with about half of affected individuals progressing to kidney failure by midlife. Patients exhibit reduced circulating apelin, a ligand of the apelin receptor, known to regulate cardiovascular function including hypertension. We tested whether diminished apelin signaling contributes to cystogenesis and if exogenous apelin receptor activation can improve disease outcomes. Methods:Plasma samples from age- and sex-matched healthy controls and ADPKD participants were analyzed for circulating apelin peptides. To assess direct cystic effects, primary ADPKD renal epithelial cells were grown as 3D collagen-embedded cysts and treated with apelin agonists. Male and female Pkd1 RC/RC ; Pkd2 +/- (PKD) mice were treated for 27 days with apelin agonists, vehicle, or the standard of care drug, Mozavaptan. Kidney and heart weight ratios, BUN, renal cAMP, and kidney transcriptional profiles were evaluated. Results:Circulating apelin peptides were significantly reduced in ADPKD patients despite normal kidney function (eGFR, BUN, and creatinine). In vitro , both apelin and the small molecule apelin receptor agonist Azelaprag inhibited cyst growth. Apelin and Mozavaptan reduced kidney weight, cystic index, blood urea nitrogen and renal cAMP in PKD mice, whereas Azelaprag did not. Apelin downregulated expression of genes associated with cyst progression, including Lcn2 (Ngal) , Postn, and Havcr1 (Kim-1) . Mozavaptan, but not apelin, induced diuresis and reduced urinary concentration. Conclusion:Apelin receptor activation by exogenous apelin inhibited cAMP synthesis and cyst growth and improved kidney function in an orthologous mouse model of ADPKD. We propose that the apelin receptor may be a potential therapeutic target in ADPKD.

Autosomal Dominant Polycystic Kidney Disease is caused by loss-of-function mutations in PKD1 or PKD2 genes, leading to reduced polycystin protein levels. Increasing PKD1 expression via CRISPR activation (CRISPRa) represents a promising therapeutic strategy; however, delivery of large CRISPRa plasmids into renal epithelial cells, and particularly primary cells, remains inefficient due to size-related barriers. We aimed to enable Pkd1 transactivation by miniaturizing CRISPRa plasmids into 6 kb vectors using a one-pot method to enhance cellular uptake in mouse kidney epithelial cells. Using type IIS restriction enzymes, we excised the mammalian expression cassette from full-length large 9–11 kB plasmids. The excised cassette was engineered to have complimentary overhangs. Thermocycling with T4 DNA ligase promoted circularization of the excised cassette (forming 6kB mini-CRISPRa vectors), and T5 exonuclease digestion removed residual backbone fragments. These mini vectors substantially enhanced nucleofection efficiency from 16.10

OBJECTIVE:Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention. MATERIALS AND METHODS:We introduce Similarity Measurement for Acute Kidney Injury Risk Tracking (SMART), a new patient similarity estimation framework with 3 key enhancements: (1) overlap weighting to adjust similarity scores; (2) distance measure optimization; and (3) feature type weight optimization. These enhancements were evaluated using internal and external validation datasets from 2 tertiary academic hospitals to predict AKI risk across varying group sizes of similar patients. RESULTS:The study analyzed data from 8637 patients in the reference patient pool and 8542 patients in each of the internal and external test sets. Each enhancement was independently evaluated while controlling for other variables to determine its impact on prediction performance. SMART consistently outperformed 3 baseline models on both the internal and external test sets (P<.05) and demonstrated improved performance in certain subpopulations with unique health profiles compared to a traditional machine learning approach. DISCUSSION:SMART improves the identification of high-quality similar patient groups, enhancing the accuracy of personalized AKI prediction across various group sizes. By accurately identifying clinically relevant similar patients, clinicians can tailor treatments more effectively, advancing personalized care.

Abstract Background Current understanding of acute kidney injury (AKI) risk factors remains largely descriptive, offering limited precision into how specific biomarker values or physiologic thresholds influence susceptibility. We aimed to synthesize knowledge from machine learning models trained across multiple health systems to identify generalizable, value-specific risk drivers and biomarker interactions contributing to AKI risk. Methods We analyzed electronic health records (EHRs) from 785,497 adult inpatients between 2010 and 2019 across nine U.S. academic medical centers within PCORnet. Interpretable gradient boosting machine models were independently developed at each health system to quantify predictor-outcome associations. Meta-regression was applied to integrate these site-level results, characterize nonlinear value-risk relationships, and identify bivariate interactions between predictors. Result Meta-analysis revealed consistent, value-specific risk drivers across health systems. An increase in glucose from 100 mg/dL to 140 mg/dL was associated with a 1.46-fold higher risk of AKI. Chloride and anion gap also demonstrated elevated AKI risk with risk increases overlapping portions of their reference ranges, with anion gap showing a 1.14-fold increase across 4–12 mmol/L and chloride a 1.28-fold increase across 96–100 mEq/L. Electrolytes including potassium, calcium, and sodium showed quadratic associations with AKI risk. Bivariate meta-regression identified interactions between key predictors, highlighting pathways that jointly modulate AKI risk. Conclusion This cross-system meta-analysis synthesizes machine learning-derived evidence into clinically interpretable knowledge, revealing how specific biomarker ranges and interactions modulate AKI risk. By moving beyond surface-level associations to quantitative, generalizable physiologic thresholds, these findings provide actionable insights to enhance risk stratification and personalized prevention in hospital care. Highlights Cross-system meta-analysis uncovered generalizable, value-specific AKI risk drivers Glucose, chloride, and anion gap within reference ranges linked to higher AKI risk Key predictor interactions suggest coordinated pathways jointly modulating AKI risk

Key PointsHigher body mass index increased risk of progression to ESKD in patients with early-stage autosomal dominant polycystic kidney disease.Higher body mass index did not increase the risk of progression to ESKD in patients with late-stage autosomal dominant polycystic kidney disease.BackgroundPrior research has linked higher body mass index (BMI) and greater visceral adiposity with more rapid progression of early-stage autosomal dominant polycystic kidney disease (ADPKD). We now evaluate the association between overweight and obesity in patients with early- and late-stage ADPKD with progression to ESKD.MethodsParticipants with early-stage ADPKD (study A; N=556; eGFR: 91 +/- 17 ml/min per 1.73 m2) and late-stage ADPKD (study B; N=483; eGFR: 48 +/- 12 ml/min per 1.73 m2) who participated in the Halt Progression of Polycystic Kidney Disease (HALT) polycystic kidney disease trials were categorized by BMI as normal weight (18.5-24.9 kg/m2; ref; n=357), overweight (25.0-29.9 kg/m2; n=384), or obese (>= 30 kg/m2; n=298). Kaplan-Meier survival analysis and multivariate Cox proportional hazard models were used to determine the association of baseline BMI as a continuous and categorical variable with risk of ESKD (according to the United States Renal Data System) over a median (interquartile range) follow-up period of 12.2 (7.5-13.3; study A) and 7.3 (5.1-11.7; study B) years (primary outcome). All-cause mortality (National Death Index) was also considered as a competing risk (Fine and Gray method).ResultsThe number of ESKD events was greater with overweight (n=24) and obesity (n=23) in HALT study A versus normal weight (n=12) but not in HALT study B (normal weight: n=89, overweight: n=102, obese: n=92). In fully adjusted models, higher BMI was associated with risk of progression to ESKD in study A (hazard ratio [HR (95% confidence interval)], 1.09 [1.03 to 1.15] per unit higher BMI) but not in study B (HR, 0.98 [0.96 to 1.00]). Obesity was associated with increased risk of ESKD (HR, 2.71 [1.22 to 6.02] versus normal weight) in study A only. Results were similar when considering death as a competing risk.ConclusionsHigher BMI, particularly obesity, increased the risk of progression to ESKD in patients with early-stage ADPKD but not in those with late-stage ADPKD.

BACKGROUND:Acute Kidney Injury (AKI) can adversely affect multiple organ systems, including the heart, brain, and immune system. Stage 1 AKI (AKI-1), although mild in clinical presentation, constitutes a substantial subset of AKI patients with heterogeneous outcomes, warranting further investigation into its subphenotypes. METHODS:We performed clustering analysis on seven-day serum creatinine (SCr) trajectories preceding AKI-1 onset in 53,565 AKI-1 patients (aged 18-89 years; 55.57% male) across eight academic hospitals. Each AKI-1 patient was matched to a non-AKI counterpart to evaluate how different AKI-1 subphenotypes influence clinical indicators and outcomes. RESULTS:Three distinct AKI-1 subphenotypes are identified. Patients in Subphenotype C (n = 5,378; 10.0%) exhibit a higher proportion of abnormal values across clinical indicators compared to those in Subphenotypes A (n = 27,049; 50.5%) and B (n = 21,138; 39.5%). Subphenotype C is associated with significantly higher odds ratios (ORs) for in-hospital, 30-day, and one-year all-cause mortality relative to Subphenotypes A and B. Conversely, Subphenotype B exhibits a higher susceptibility to developing chronic kidney disease (CKD) within one year after discharge following AKI-1, compared to both Subphenotypes A and C, after adjustment for baseline SCr levels. All AKI-1 subphenotypes are associated with significantly elevated risks of all-cause mortality and the need for dialysis or renal replacement therapy (RRT) compared to their respective non-AKI counterparts. CONCLUSIONS:This study reveals substantial heterogeneity in clinical indicators and outcomes within AKI-1. Future research focusing on these subphenotypes may pave the way for more personalized and targeted interventions for patients with AKI-1.

Artificial intelligence and machine learning are transforming healthcare by improving clinical risk predictions and diagnostic precision. However, their performance can be compromised by data drifts due to changes in patient populations and evolving clinical practices. This study investigated performance drift in models predicting Acute Kidney Injury (AKI) using electronic health records from 249,749 inpatient encounters over ten years, analyzing performance across both the overall population and nine subgroups with unique health profiles. To mitigate the performance drift, we implemented two model updating strategies: an Overall Population Update (OPU) and a Specific Subgroup Update (SSU). Our results demonstrated significant reductions in drift, with OPU increasing the average area-under-the-precision-recall-curve (AUPRC) by 0.14 in the overall population and 0.11 across subgroups, and SSU improving the average AUPRC by 0.10 among subgroups. These findings highlight the importance of continuous model surveillance and adaptive updates to maintain reliable predictive performance in dynamic clinical environments.

KEY POINTS:TraceOrg is a web-based tool that automatically labels kidney, liver, and cysts, reporting volumes and Mayo Imaging Classification. External validation showed high performance and good generalizability on Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease, Polycystic Kidney Disease-Research Resource Consortium, and other external datasets. Training on multiple pulse sequences enables TraceOrg to process images from a wide variety of protocols. BACKGROUND:Kidney, liver, and cyst volumes are important for diagnosis, classification, and management of autosomal dominant polycystic kidney disease (ADPKD) but challenging to measure accurately and reproducibly. Here, we develop a web-based deep learning platform to automatically and robustly measure kidneys, liver, and cyst volumes in ADPKD. METHODS:Magnetic resonance imaging (MRI) and computed tomography scans from patients with ADPKD ( n =611) and participants without ADPKD ( n =109) were used to train a 3D hybrid model combining U-Net and transformer elements for segmenting kidneys, liver, and cysts. The model is implemented as a web-based calculator at www.traceorg.com , providing segmentation labels, volumes, and Mayo Clinic Image Classification. Automatic browser anonymization of digital imaging and communications in medicine images ensures privacy. Internal validation was conducted on 70 MRIs for kidney and liver segmentations and 46 MRIs for cyst segmentations, and performance was compared with five open access segmentation models (TotalSegmentator, MRAnnotator, Kim, Woznicki, and Gregory-Kline). External validation was performed on one single-center dataset ( n =58), one multicenter dataset ( n =73), Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease 2 (CRISP, n =30), and Polycystic Kidney Disease-Research Resource Consortium (PKD-RRC, n =115) MRIs with T2-weighted and T1-weighted images. RESULTS:After training on 720 participants (mean age=48±15, eGFR=74±32 ml/min per 1.73 m 2 and height-adjusted total kidney volume=826±772 ml/m), TraceOrg internal validation performance achieved high mean Dice scores of 0.97 (kidneys), 0.97 (liver), 0.93 (kidney cysts), and 0.82 (liver cysts) outperforming existing models for ADPKD. External validation showed strong performance with Dice scores of 0.92-0.94 (kidney), 0.87-0.96 (liver), 0.85 (kidney cysts), and 0.76-0.90 (liver cysts) for the single-center dataset and 0.95 (kidney) and 0.81 (kidney cysts) for the multicenter dataset. Compared with CRISP volumes measured by stereology, the mean absolute percent difference was 5.3% (kidneys, n =30), 11% (kidney cysts, n =30), and 5.5% (liver, n =22). Compared with PKD-RRC ( n =115), the mean absolute percent difference in total kidney volume was 4.9%. CONCLUSIONS:TraceOrg, a publicly available web-based tool, automatically measured kidney, liver, and cyst volumes from abdominal MRI in ADPKD with high accuracy compared with manual segmentations. PODCAST:This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2026_02_03_ASN0000000904.mp3.

Kidney stone disease is characterized by hypercalciuria and intestinal hyperabsorption of calcium, leading to the formation of calcium crystals in the kidney. Claudin-2 is a tight junction protein that forms paracellular cation pores, and mutations in its gene are associated with kidney stone disease. We have recently shown that mice deficient in Cldn2 are hypercalciuric due to both decreased renal reabsorption and increased intestinal absorption of calcium and develop medullary mineral deposits reminiscent of kidney stone formers. Therefore, we hypothesized that intestinal claudin-2 is important for calcium secretion and that loss of claudin-2 results in increased net intestinal calcium absorption, thereby contributing to kidney stone disease. To test this, we generated intestine-specific Cldn2 knockout mice using a villin-Cre promoter. Female mice showed Cldn2 deletion only in the intestine; however, male mice showed partial deletion of Cldn2 in kidneys. Ileal and colonic calcium permeability were significantly reduced in knockout animals of both sexes. Knockout animals developed transient hypercalciuria (more severe in males than females) at weaning, which was normalized by 4 wk of age. In metabolic balance studies, there was no change in net calcium absorption and in whole body calcium balance in knockout mice of either sex on normal or high-calcium diet, with the exception that males were in slightly positive calcium balance on normal-calcium diet. Our results show that claudin-2 contributes to intestinal permeability to calcium but does not play a significant role in net intestinal calcium absorption or secretion.NEW & NOTEWORTHY Global claudin-2 knockout mice have hypercalciuria due to both intestinal overabsorption of calcium and a renal calcium leak. Here, we generated intestine-specific claudin-2 knockout mice. Ileal and colonic calcium permeability were reduced, but surprisingly these animals exhibited only transient hypercalciuria for 1 wk after weaning. Thus, claudin-2 contributes to intestinal permeability to calcium but does not play a significant role in intestinal calcium absorption or secretion.

Deposits of hydroxyapatite called Randall's plaques are found in the renal papilla of calcium oxalate kidney stone formers and likely serve as the nidus for stone formation, but their pathogenesis is unknown. Claudin-2 is a paracellular ion channel that mediates calcium reabsorption in the renal proximal tubule. To investigate the role of renal claudin-2, we generated kidney tubule-specific claudin-2 conditional KO mice (KS-Cldn2 KO). KS-Cldn2 KO mice exhibited transient hypercalciuria in early life. Normalization of urine calcium was accompanied by a compensatory increase in expression and function of renal tubule calcium transporters, including in the thick ascending limb. Despite normocalciuria, KS-Cldn2 KO mice developed papillary hydroxyapatite deposits, beginning at 6 months of age, that resembled Randall's plaques and tubule plugs. Bulk chemical tissue analysis and laser ablation-inductively coupled plasma mass spectrometry revealed a gradient of intrarenal calcium concentration along the corticomedullary axis in normal mice that was accentuated in KS-Cldn2 KO mice. Our findings provide evidence for the "vas washdown" hypothesis for Randall's plaque formation and identify the corticomedullary calcium gradient as a potential target for therapies to prevent kidney stone disease.

KEY POINTS:Data were collected from a cohort of 759 patients with autosomal dominant polycystic kidney disease that were followed for a median of 10.2 years. Prognostic models were developed to predict kidney failure using routinely obtained clinical information and externally validated. Risk predictions had good discrimination and calibration over a time horizon of 15 years. BACKGROUND:Autosomal dominant polycystic kidney disease (ADPKD) is a common cause of kidney failure. Progression is highly variable, and accurate prognostic information is needed to guide early treatment decisions. The objective of this study was to develop a multivariable predictive model for progression to kidney failure. METHODS:We developed prognostic models using Cox regression for the outcome of kidney failure, defined as eGFR <15 ml/min per 1.73 m 2 , dialysis, or kidney transplantation. The development dataset consisted of participants in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) and Halt Progression of Polycystic Kidney Disease study A (HALT-A) studies. The validation dataset consisted of patients with ADPKD in the Mayo clinical registry aged 15-49 years, with eGFR ≥60 ml/min per 1.73 m 2 . Predictor variables in the base model were age, sex, creatinine or eGFR, ADPKD genotype, and Mayo Imaging Class. Clinical and laboratory data were evaluated in the full model. RESULTS:The development cohort included 759 patients with baseline eGFR of 91±29 ml/min per 1.73 m 2 (mean±SD), of whom 16% reached end point after median (interquartile range) follow-up of 10.2 (5.5-16.7) years. The validation cohort included 535 patients with baseline eGFR of 90±31 ml/min per 1.73 m 2 (mean±SD), of whom 11% reached end point after median (interquartile range) follow-up of 5.5 (1.5-13.7) years. The full model, including age, sex, serum creatinine, Mayo Imaging Class, total carbon dioxide, hemoglobin, diastolic BP, and body mass index, had a C-index of 0.81 (95% confidence interval, 0.72 to 0.90) in the validation cohort and 0.75 (95% confidence interval, 0.62 to 0.88) when ADPKD genotype was also included. Risk predictions from base and full models were well-calibrated out to 15 years. CONCLUSIONS:In persons with early ADPKD and preserved eGFR, a model using routinely obtained clinical information quantified risk of kidney failure over 15 years.

Key PointsTwenty-seven percent of patients with autosomal dominant polycystic kidney disease discontinued tolvaptan in a real-world cohort in the midwestern United States.Most patients maintained tolvaptan on lower doses than trials, and a minority tolerated above the 45 mg (am)/15 mg (pm) starting dosage.Adverse effects, specifically aquaretic side effects, strongly influenced tolvaptan tolerability, dosage titration, and discontinuation.BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is the most prevalent genetic kidney disease leading to kidney failure. Tolvaptan, a vasopressin V2 receptor antagonist, is the only medication approved by the US Food and Drug Administration for slowing kidney growth in individuals with rapidly progressive ADPKD, but its long-term tolerability and effective implementation has yet to be studied, particularly in real-world clinical settings within the United States.MethodsThis retrospective cohort study examined adults with ADPKD treated with tolvaptan at the University of Kansas Medical Center and the University of Iowa Hospitals & Clinics from May 2018 to April 2023. Data on demographics, clinical characteristics, tolvaptan dosage, and treatment duration were collected from electronic health records for an average follow-up duration of 28.2 months (interquartile range: 8.5-47.1 months). The study focused on examining tolvaptan dosage trends, treatment discontinuation reasons, and the impact of aquaretic side effects on dosage and adherence.ResultsOf 134 patients, 27% stopped tolvaptan during the observational period, with 10.4% of the cohort withdrawing from treatment due to intolerance of aquaretic side effects. Most patients maintained a lower tolvaptan dosage (<= 45/15 mg) than in clinical trials, with two thirds of individuals who underwent dosage adjustment undergoing net decrease in dosage. Adverse effects significantly influenced and dosage decisions, presenting a potential early barrier for adherence, particularly in female patients.ConclusionsThe study highlights real-world challenges in the use of tolvaptan for ADPKD, particularly for side effects leading to high discontinuation rates and dosage adjustments. These findings underscore the need for standardized and improved management strategies to enhance tolerability and adherence, offering insights for future research and practice in the treatment of ADPKD with tolvaptan.

Somatic mutations in non-malignant tissues are selected for because they confer increased clonal fitness. However, it is uncertain whether these clones can benefit organ health. Here, ultra-deep targeted sequencing of 150 liver samples from 30 chronic liver disease patients revealed recurrent somatic mutations. PKD1 mutations were observed in 30% of patients, whereas they were only detected in 1.3% of hepatocellular carcinomas (HCCs). To interrogate tumor suppressor functionality, we perturbed PKD1 in two HCC cell lines and six in vivo models, in some cases showing that PKD1 loss protected against HCC, but in most cases showing no impact. However, Pkd1 haploinsufficiency accelerated regeneration after partial hepatectomy. We tested Pkd1 in fatty liver disease, showing that Pkd1 loss was protective against steatosis and glucose intolerance. Mechanistically, Pkd1 loss selectively increased mTOR signaling without SREBP-1c activation. In summary, PKD1 mutations exert adaptive functionality on the organ level without increasing transformation risk.