Kidney International Reports

Dysfunction of the tubulointerstitial compartment is a key driver of chronic kidney disease (CKD) progression. However, tubular function remains largely unaddressed by routine clinical assessment. Here, we show that the urine ammonium-pH index (uAPI), a composite of urinary ammonium and pH, reflects kidney tubular function and predicts kidney function decline. Using acid/base, dietary, and potassium perturbations, segmental disruption of tubular ammonium handling, mathematical modelling, and data from patients with renal tubular acidosis, we identified defective ammoniagenesis as the main uAPI determinant. The uAPI was suppressed across four kidney disease models and dissociated from GFR. Kidney proteomics and single-nucleus RNA sequencing indicated downregulation of ammoniagenesis in proteinuric and diabetic kidney disease. In type 2 diabetes patients with preserved GFR, a reduced uAPI was associated with faster kidney function decline. In three CKD cohorts, low uAPI predicted CKD progression and significantly improved risk prediction. Together, this positions the uAPI as a scalable, non-invasive measure of kidney tubular function.

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. Results: 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.

Introduction: Autosomal-dominant tubulointerstitial kidney disease (ADTKD) is characterized by chronic kidney disease (CKD) with an average age of end-stage renal disease (ESRD) of approximately 45 years, bland urinary sediment, the absence of proteinuria and autosomal dominant inheritance. While several causative genes have been found, there remain families in whom no molecular diagnosis has been identified (ADTKD-NMD). Methods: We identified BICC1 truncating variants in several families with ADTKD-NMD in the Wake Forest Rare Inherited Kidney Disease Registry and then screened families in our database and other referred families for BICC1 truncating variants. We performed segregation analysis and characterized affected individuals for clinical and histopathologic phenotypes. We analyzed oligomer formation of BICC1 mutants with wild-type BICC1-, ANKS3- and ANKS6 proteins through co-immunoprecipitation and Western blotting, and we tested for posttranscriptional regulation of the BICC1 target mRNA, Dand5, in a Luciferase reporter assay. Results: We found 6 heterozygous truncating mutations in BICC1 segregating with the ADTKD phenotype in 8 independent pedigrees worldwide. Affected individuals developed kidney failure in the 6th to 7th decade of life that was characterized pathologically by tubular atrophy and interstitial fibrosis. The truncated gene products localized to cytoplasmic bodies and demonstrated various degrees of self-association or binding to the known interaction partners, ANKS3 and ANKS6. While the wild-type BICC1 gene product acts as a posttranscriptional repressor of target mRNAs, all truncation variants exhibited increased expression of substrate mRNA. Conclusions: Truncating variants in BICC1 are a novel cause of ADTKD, segregating with the disease phenotype and upregulating BICC1 target gene expression through a dominant-negative- or a gain-of-function mode of action.

Background: Traditional real-world studies comparing SGLT2 and DPP4 inhibitors on renal outcomes rely on propensity score matching, which causes high-dimensional data loss. We used causal machine learning (Causal ML) to unmask heterogeneous treatment effects in diabetic kidney disease (DKD). Methods: Using data from 4,588 patients within the Japanese J-CKD-DB-Ex registry, we implemented a doubly robust (DR) learning framework (Linear DR-learner with XGBoost) to compare SGLT2 and DPP4 inhibitors. Outcomes included the chronic eGFR slope and a composite renal endpoint ([≥] 50% eGFR decline or end-stage kidney disease). Heterogeneity was explored via causal SHAP and decision trees. Results: At the population level, SGLT2 inhibitors modestly slowed chronic eGFR decline (average treatment effect [ATE] = 0.14 [95% CI: -0.86, 1.15] mL/min/1.73m^2/year) and reduced composite endpoint risk by 9% (ATE: -0.09 [-0.11, -0.08]) versus DPP4 inhibitors. However, individual-level counterfactual analysis suggested that for the chronic eGFR slope, non-glinide users with stable pre-treatment trajectories who were also taking ACE inhibitors had a greater benefit from SGLT2 inhibitors (ATE: 2.95 [-0.68, 6.58]). Conversely, glinide users with steep pre-treatment decline had a greater benefit from DPP4 inhibitors (ATE: -8.98 [-16.11, -1.85]). For composite renal events, SGLT2 inhibitors had a 28% absolute risk reduction within the algorithmically identified high-risk subgroup (eGFR [≤] 28.1 mL/min/1.73 m^2 and positive proteinuria; ATE: -0.28 [-0.33, -0.23]). Even non-proteinuric decliners demonstrated a 8% risk reduction with SGLT2 inhibitors (ATE: -0.08 [-0.10, -0.06]). Conclusion: Causal ML advances precision medicine in DKD, shifting from uniform prescribing to individualized, data-driven therapy targeting distinct intrarenal pathways.

Background: Whether benchmark performance reflects robust clinical reasoning rather than surface-level pattern recognition remains uncertain. We evaluated the robustness of state-of-the-art large language models (LLMs) on nephrology board renewal questions using "None of the other answers" (NOTA) substitution. Methods: From 210 Japanese Society of Nephrology board renewal questions (2014-2023), two nephrologists independently reviewed all items. Questions in which NOTA became the sole correct answer after replacement were included, yielding 145 validated questions. GPT-5, GPT-4o, Gemini 2.5 Pro, and Gemini 2.0 Flash were evaluated via application programming interfaces under default settings. The primary endpoint was accuracy, and paired differences were assessed using the exact two-sided McNemar test. Results: Accuracy was significantly lower after NOTA substitution for all models: GPT-4o, 66.21% to 19.31% (drop, 46.90 percentage points [pp]); GPT-5, 87.59% to 73.10% (14.48 pp); Gemini 2.0 Flash, 58.62% to 31.03% (27.59 pp); and Gemini 2.5 Pro, 86.90% to 55.86% (31.03 pp); all P < .001. GPT-5 showed the smallest decline and the highest accuracy in both versions. Conclusions: All evaluated LLMs showed a significant robustness gap after NOTA replacement. Newer models may be more robust, but multiple-choice accuracy remains an incomplete measure of clinical reasoning robustness.

Background IgA vasculitis (IgAV) is the most common systemic vasculitis in children, and its prognosis is largely determined by renal involvement (IgAV nephritis). No routine blood test identifies IgAV or stratifies the risk of nephritis, although aberrant O-glycosylation of the IgA1 hinge region is central to its pathogenesis. We developed a mass-spectrometry assay to profile IgA1 hinge O-glycoforms and define signatures of disease activity and renal involvement. Methods IgA was affinity-purified from 5 uL of plasma from 91 children (27 with acute IgAV, 26 in remission, and 38 age-matched healthy controls; 24 with and 29 without nephritis), trypsin-digested, and hinge-region O-glycopeptides were quantified by LC-MS. Sixty-nine glycoforms were normalized to a total-IgA1 tryptic peptide. Duplicate measurements showed good analytical repeatability, with a median coefficient of variation of 6%. Groups were compared using Mann-Whitney and Kruskal-Wallis tests (Benjamini-Hochberg FDR). Discrimination was assessed by ROC analysis and cross-validated logistic regression panels. Results Acute IgAV showed broad remodeling of the hinge glycoform profile (35 glycoforms differed with excellent discrimination (AUC 0.93-0.95) for the best ones), with an increase in low-sialylated, agalactosylated species and a decrease in complex sialylated species. The profile was normalized in remission (no glycoform differed from the controls). Two distinct renal patterns emerged: disease-associated glycoforms already altered without nephritis and renal-specific glycoforms altered only in nephritis (H2N2S1, H3N3S5, H3N4S4, and H4N4S3). A four-marker panel discriminated nephritis among IgAV children with a cross-validated AUC of 0.86 (IC95 % 0.75-0.94). Conclusions A single mass-spectrometry assay, from a small blood volume, captures an IgAV-associated IgA1 hinge O-glycoform signature that normalizes in remission, together with a distinct renal involvement associated signature. These findings identify candidate IgA1 O-glycoform signatures associated with IgAV activity and documented renal involvement. Prospective longitudinal studies are required to determine whether the renal-associated panel can predict subsequent nephritis.

Background Long-horizon kidney trajectory prediction in type 2 diabetes mellitus (T2DM) is usually reported as a point estimate or event risk, although clinical decision-making also depends on whether an individual prediction is reliable. We developed an uncertainty-aware model for 48-month estimated glomerular filtration rate (eGFR) decline and tested whether conformal interval width provides a clinically structured, patient-level signal of prediction reliability. Methods We performed a secondary prognostic modeling analysis of Action to Control Cardiovascular Risk in Diabetes (ACCORD) participants with baseline and 48-month eGFR (n=6,853). The outcome was annualized eGFR change, calculated as 48-month minus baseline eGFR divided by four years. The primary baseline feature set excluded serum creatinine since eGFR is creatinine-derived, and also excluded urine biomarkers. Random forest, gradient boosting, penalized linear models, and XGBoost were compared using fixed training, calibration, and test partitions. Split and locally adaptive conformal intervals were evaluated by empirical coverage and interval width. Interval-width analyses were repeated after conditioning on baseline eGFR. Results The best primary model was random forest (R2=0.382, MAE=3.271 mL/min/1.73m2). Split 90% conformal intervals achieved empirical coverage of 0.917. Locally adaptive 90% intervals achieved empirical coverage of 0.909 with mean width 13.759 mL/min/1.73m2. In unadjusted analyses, wider intervals were associated with larger errors and more rapid decline. After interval-width quintiles were assigned within baseline-eGFR strata, wider intervals remained associated with realized prediction error (annual adjusted increase, 0.151 mL/min/1.73m2 per quintile). Beyond baseline eGFR, wider intervals were associated with younger age, female sex, higher HbA1c, higher triglycerides, and higher systolic blood pressure. Conclusions Baseline clinical variables predicted 48-month eGFR decline with good long-horizon performance in ACCORD, even after excluding serum creatinine and urine biomarkers from the primary model. Conformal prediction provided calibrated patient-specific intervals, and interval width behaved as an informative reliability phenotype rather than a random modeling artifact. These findings support a novel uncertainty-aware framing of kidney trajectory prediction in which rapid and uncertain decline can be identified from baseline clinical data.

Intracranial aneurysms (IA) and their rupture (subarachnoid haemorrhage, SAH) are a rare but serious complication of autosomal dominant polycystic kidney disease (ADPKD). Both genetic and environmental risk factors contribute to the pathogenesis of IA and SAH, but specific information on risk factors in the ADPKD population are limited and international clinical guidelines mainly recommend screening for ADPKD patients with a family history of IA or SAH. We assessed the associations of monogenic variants, polygenic risk, and clinical factors with the diagnosis of IA or SAH in 2,200 adult ADPKD patients from three cohorts: the Irish Kidney Gene Project (IKGP, n=475), FinnGen (n=826), and Genomics England (GEL, n=899). Polygenic risk scores (PRS) for IA, hypertension, and smoking intensity were derived from previously published GWAS summary statistics and additionally conditioned using mtCOJO to account for their genetic correlation. IA or SAH was diagnosed in 158 of 2,200 patients (7.2%; mean age at diagnosis 51.1 years). Female sex (HR 2.21, 95% CI 1.50-3.25, p=6.52x10^-5) and smoking (HR 2.06, 95% CI 1.43-2.96, p=9.49x10^-5) were associated with IA or SAH in univariate Cox proportional hazards models, while diabetes was protective (HR 0.39, 95% CI 0.22-0.70, p=1.37x10^-3). Monogenic variant status was not found to associate with increased risk of IA or SAH. Polygenic score for IA was associated with increased risk of IA or SAH, with a 1 standard deviation increase in PRS associated with a pooled hazard ratio (HR) of 1.30 (95% CI 1.09-1.57, p=4.66x10^-3), and the association of the conditioned IA PRS remained in multivariate Cox proportional hazards models accounting for clinical factors and monogenic variants (HR 1.26, 95% CI 1.03-1.55, p=0.02). The addition of polygenic scores added significant prognostic information for IA or SAH beyond clinical risk factors in FinnGen (p=3.61x10^-3) and GEL (p=8.84x10^-3) but not in IKGP, as assessed by the likelihood ratio test. Overall, our study shows that the strongest risk factors for IA or SAH in ADPKD patients are smoking history, female sex, and polygenic risk for IA. Hypertension was not associated with IA (HR 0.50, 95% CI 0.24-1.00, p=0.051), likely due to the high prevalence of hypertension in this population across cohorts (69.6-85.3%). While family history may be considered a proxy of genetic risk, this study suggests that family history itself may be of limited utility in discriminating individuals with ADPKD at risk of IA or SAH. Keywords: Intracranial Aneurysms, subarachnoid haemorrhage, Autosomal Dominant Polycystic Kidney Disease, Polygenic Risk Scores, Hypertension, Family History

ABSTRACT Importance: The prevalence of chronic kidney disease (CKD) is increasing, with aging being a major contributor. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are well established therapies for CKD; however, adults aged 80 years or older have been underrepresented in previous large-scale trials. Consequently, evidence regarding effects of SGLT2i therapy in this cohort remains limited. Objective: To evaluate the association of SGLT2i initiation with mortality, kidney outcomes and cardiovascular outcomes among patients over 80 years of age and CKD. Design, Setting, Participants: This retrospective cohort study used data from TriNetX Research Network, a multicenter electronic health record database. To ensure comparable standard of care and SGLT2i eligibility, the period for the occurrence of the index event was restricted to January 1, 2021, until January 1, 2025. Propensity score matching was performed to balance comorbidities, laboratory parameters, concomitant medications, and frailty-associated factors between groups. Exposures: Initiation of SGLT2i therapy vs. non-use Main Outcomes and Measures: Outcome analysis focused on all-cause mortality, major adverse kidney events (MAKE) and major adverse cardiovascular events (MACE). Cox proportional hazards models were used to estimate hazard ratios with 95% confidence intervals; following propensity score matching, results were considered as adjusted hazard ratios (aHR). Results: After propensity score matching, 5,038 patients were included in each group. During two years of follow-up, SGLT2i initiation was associated with lower all-cause mortality (aHR 0.818; 95% CI 0.746 - 0.896, p<0.0001) and fewer MAKE events (aHR 0.779; 95% CI 0.0.715 - 0.850, p<0.0001). No significant difference in MACE was observed (aHR 1.007; 95%CI 0.938 - 1.082, p=0.84). Results were generally consistent across subgroups. Risk of acute kidney injury was higher in the SGLT2i group, while incident dialysis and end-stage renal disease were significantly reduced. Acute myocardial infarction and acute heart failure were increased in the SGLT2i group. Conclusion and Relevance: Regarding survival and kidney endpoints, even the oldest CKD patients seem to benefit from SGLT2i treatment, which was associated with reduced mortality as well as improved long-term renal outcomes. MACE showed no significant differences, while specific cardiac events were increased, reflecting possible safety concerns requiring further investigation in this specific age group.

Acute kidney injury (AKI) is a frequent complication within intensive care units, with its sudden onset often missed. This is especially important because a timely window for intervention is required as delayed detection leads to progressively worse outcomes. Existing machine learning and deep learning models have contributed to closing this gap, but their complexity, requiring hundreds to thousands of features, and lack of generalisation pose a limitation that prevents them from being integrated into clinical workflows across different electronic health-record ecosystems. This study presents a 37-feature XGBoost model trained on the MIMIC-IV dataset with 5.4% positive cases, with hyperparameters optimised via Optuna and probabilities calibrated using isotonic regression, designed for transportability across clinical settings. Validation was conducted internally using a temporal patient-level split simulating prospective deployment, training on 2008-2016 data and testing on 2017-2022 data"External validation was performed on the eICU Collaborative Research Database, a multi-centre dataset spanning 208 US hospitals, using the trained model without retraining. SHAP TreeExplainer was used to provide feature-level explainability for individual predictions. Internal testing yielded an AUROC score of 0.794 for predicting AKI onset within a 12-24 hour window. External validation produced a 0.750 AUROC without retraining. Equitable discrimination was observed across gender, age, chronic kidney disease presence, race, and AKI stages on both datasets, with a 95% internal CI of 0.789-0.799 confirming the model's estimate stability. These results suggest that clinically useful prediction systems are achievable with substantially fewer features than current models require.

INTRODUCTION RNA editing has been implicated in endogenous double-stranded RNA (dsRNA) sensing and inflammatory disease, but its prevalence, genetic regulation, and consequences in diseased human kidney tissue have not been systematically characterized. Because ADAR enzymes edit multiple neighboring adenosines often in the same transcript, analyzing these sites holistically (as "clusters") may reveal effects missed by single-site analysis. METHODS We profiled both single-site and cluster A-to-I RNA editing in the kidneys of 215 participants from the Nephrotic Syndrome Study Network with focal segmental glomerulosclerosis or minimal change disease who had microdissected glomerular and/or tubulointerstitial RNA-seq and blood genome sequencing. We tested single-site and cluster editing association with estimated glomerular filtration rate, proteinuria, and an interferon stimulated gene expression score. To discover the genetic determinants of editing, we conducted mapping of both single-site, cis-editing QTL and cluster-level editing QTLs (cledQTLs). We then tested cledQTLs for colocalization with kidney eQTLs and kidney-relevant GWAS. RESULTS Greater cluster mean editing in tubulointerstitium was associated with lower interferon-stimulated gene activity (P = 4.81 x 10-9), lower UPCR (P = 0.01) and higher eGFR (P = 8.52 x 10-5). Genetic mapping identified 290 glomerular and 473 tubulointerstitial single-site edQTLs, as well as 21 glomerular and 51 tubulointerstitial cledQTLs. We identified 10 colocalized signals between cledQTL and GWAS and 14 between cledQTL and eQTL. Nine of 51 tubulointerstitial cledQTL clusters were individually associated with eGFR in NEPTUNE. CONCLUSION These results identify A-to-I RNA editing as a measurable and partly genetically regulated molecular phenotype in proteinuric kidney disease and nominate clustered editing of tubulointerstitial transcripts as a putative contributor to attenuated immune activity and higher kidney function. Cluster-level analysis identified additional genetically regulated editing patterns and colocalized signals not detected at individual sites, highlighting the added value of analyzing nearby editing sites as clusters.

Background: Fibrosis, characterised by excessive accumulation of collagen type I (COL1), is a common feature of chronic diseases, including liver diseases (LDs), chronic kidney disease (CKD) and heart failure (HF). COL1 degradation products can be detected in urine by proteomics/ peptidomics analyses and may serve as non-invasive biomarkers of fibrosis. We aimed to identify a common molecular signature of fibrosis across these diseases that may ultimately guide interventions to slow disease progression and prevent organ damage. Methods: Using capillary electrophoresis coupled to mass spectrometry (CE-MS), naturally occurring COL1 degradation products (peptides) in the urine of patients with fibrotic disease, LDs (n=127), CKD (n=263) or HF (n=187), were investigated and compared with the same number of matched controls. Disease-associated COL1 peptides were identified separately for each condition, and peptides showing consistent associations across the three diseases were selected to define a common fibrosis signature. A support vector machine model based on the selected peptides was developed and validated in independent cohorts of patients with LDs (n=110), CKD (n=93), HF (n=32) and controls (n=643). Results: We identified a common fibrotic signature consisting of 50 COL1 degradation products, mainly downregulated in fibrosis. A model based on these peptides achieved a strong performance, with an area under the receiver operating characteristic curve (AUC) of 0.935 (95% confidence interval (CI) 0.917-0.953, p<0.0001) in an external validation cohort comprising pooled disease groups (LDs, CKD, and HF) and controls. Performance was maintained in LDs, CKD and HF, with AUCs of 0.917 (95% CI 0.890-0.944, p<0.0001), 0.951 (95% CI 0.931-0.971, p<0.0001) and 0.950 (95% CI 0.903-0.997, p<0.0001), respectively. The model scores were significantly associated with fibrosis stage in LDs (p=0.0097) and with interstitial fibrosis and tubular atrophy in CKD (p=0.045). Conclusion: A model of urinary COL1 peptides captures a shared collagen degradation signature across organs and diseases, enabling the non-invasive assessment of fibrosis irrespective of its origin. As these peptides exclusively reflect collagen degradation, the findings suggest impaired collagen degradation as a driver in fibrosis. Future clinical studies are warranted to evaluate the utility of this model for early fibrosis detection and earlier implementation of anti-fibrotic interventions.

Accurate, reproducible interpretation of kidney allograft biopsies is critical for diagnosis of graft injury to guide prognosis and management. The international Banff classification is a consensus diagnostic system based on semiquantitative histological lesion scoring on either extent or severity of kidney transplant biopsies. However, pathologist scoring is limited by substantial interobserver variability, constrained scalability, and the inherent nature of the scoring system itself. Here we present BanffNET, a weakly supervised, probabilistic deep learning framework that combines self-supervised feature extraction with a novel Bayesian multiple-instance learning framework to predict (continuously) the full spectrum of Banff lesion scores directly from whole-slide images (WSIs). Using lesion-specific aggregation functions tailored to localized (modeling lesion severity) and diffuse pathologies (modeling lesion extent), BanffNET generates interpretable, patch-level probability maps and calibrated slide-level scores. BanffNET's performance was assessed relative to consensus, biological correlates of rejection and clinical outcome, demonstrating superior consistency, transportability and generalization. Trained on 7,249 WSIs from three cohorts, BanffNET demonstrates consistent performance on 11,028 WSIs across five external test sets, performing on par or exceeding expert consensus across lesions. BanffNET scores align more closely than pathologist Banff scores with molecular profiles of rejection, offering a transparent, biologically grounded framework for computational pathology with relevance beyond transplantation.

Introduction: B cell depletion with rituximab leads to complete or partial remission (CR/PR) in only ~60% of patients with primary membranous nephropathy (PMN). Adding belimumab to rituximab may result in greater depletion of memory B cells, limit the re-emergence of autoreactive B cells, and improve clinical responses. Methods: REBOOT Part A (NCT03949855) is a single arm, open-label, pharmacokinetic study where all participants had proteinuria [&ge;] 4g/day and detectable serum anti-phospholipase A2 receptor (anti-PLA2R) antibodies. Participants received belimumab 200 mg subcutaneously weekly for 52 weeks and rituximab 1000 mg intravenously at weeks 4 and 6. Assessments included belimumab exposure at week 4 and CR/PR at week 104. Results: Seventeen participants started belimumab. Belimumab exposure was not significantly reduced in those with high (> 9 g/day) proteinuria at week 4. Among all treated participants, 59% (10/17) achieved CR/PR at week 104, while in per protocol analyses, 91% (10/11) achieved CR/PR at week 104. All participants in per protocol analyses had normal serum albumin and undetectable serum anti-PLA2R by week 104. Circulating memory B cells increased before rituximab and were depleted by rituximab. B cell re-constitution occurred after week 52 with primarily naive and transitional B cells. Belimumab with rituximab was well-tolerated, with one participant discontinuing belimumab due to infection. Conclusion: In this study, a high proportion of participants receiving belimumab with rituximab achieved CR/PR. Thus, a multi-targeted approach to B cell depletion may improve immunologic and clinical outcomes in PMN and is being studied in a larger, randomized, placebo-controlled clinical trial.

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

Introduction: Pediatric renal vein thrombosis is a rare but well-recognized form of venous thromboembolism. While long-term renal outcomes of neonatal cases are well-described, they are relatively unknown in cases affecting older children. Moreover, the ability of anticoagulation treatment to prevent these outcomes remains unknown. The objective of this study was to assess adverse long-term renal outcomes and determine if anticoagulation reduced their likelihood. Methods: Administrative data analysis utilizing the Pediatric Health Information System database. Renal vein thrombosis occurring in patients under 18 years were assessed. Cases involving tumor thrombus were excluded to focus the analysis only on thrombotic disease. Demographics, co-morbid conditions, anticoagulant therapies, and renal outcomes were assessed over a 9-year period. In sub-analyses, neonatal ([&le;]28 days) and non-neonatal renal vein thromboses were assessed to determine how their characteristics may differ. Results: 383 eligible renal vein thrombosis cases with 796 patient-years of follow-up were identified for analysis. 48.8% of the cases occurred in neonates. 25.3% of the cases occurred in children with pre-existing complex chronic conditions and 9.1% were associated with the onset of nephrotic syndrome. Mortality followed 15.1% of the cases, but causality cannot be assigned from administrative data. Most (80.2%) of the cases were treated with anticoagulation. Acute kidney injury occurred in 24% of cases, chronic kidney disease developed in 22.2%, hypertension in 26.9%, and proteinuria in 3.1%. Anticoagulation did not have a discernable effect on the likelihood of these long-term renal outcomes. Conclusion: Acute kidney injury, chronic kidney disease, and hypertension are prevalent in survivors of childhood renal vein thrombosis. Anticoagulation does not appear to reduce the incidence of long-term renal outcomes, but the low percentage of non-anticoagulated patients suggests treatment bias. Long-term kidney health surveillance is warranted in pediatric renal vein thrombosis survivors.

Background: The dietary approaches to stop hypertension (DASH) diet is often recommended to patients with chronic kidney disease, although evidence regarding its efficacy in this population is limited. Our study tested the hypothesis that increased adherence to the dietary approaches to stop hypertension (DASH) diet score would be associated with longer time to kidney function decline among Veterans. Methods: We conducted a retrospective cohort study of 251,921 Veterans enrolled in the Million Veteran Program (MVP). The DASH diet score was calculated from the food frequency questionnaire and categorized into tertiles. The primary outcome was a composite of: Kidney event or death, where a kidney event was defined as a sustained 40% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease (ESKD). Cox regression models compared the hazard for both outcomes by DASH score tertiles. We examined modification by ancestry, sex and other clinical characteristics Results: The median age was 67 years and 90% of Veterans were men. There were 59,269 (23.5%) who experienced the primary composite outcome, during the maximum follow-up of 10 years (median 6.1 years). Crude incidence rates for the kidney event and death outcome were 43.3, 40.4, and 36.8 per 1000 person-years of DASH score by tertiles. DASH score was associated with a lower hazard ratio (HR) for the primary composite outcome; third vs first tertile 0.81 (95% Confidence Interval (CI) 0.80 - 0.83) and second vs first tertile HR 0.90 [95% CI 0.88 - 0.92]. In subgroup analysis for individuals of African ancestry, Admixed American, and Females, only the third tertile of the DASH score was associated with a statistically significant reduction in composite outcome. Conclusion: Beneficial associations of the DASH diet were observed across subgroups. Future research is needed to understand gene and environmental factors that influence the observed subgroup differences

Background: Adult congenital heart disease (ACHD) patients are prone to HLA-antibody formation from multiple surgeries, transfusions, and prosthetic surgical material. Females with ACHD may accrue additional, non-surgical alloantigen exposure. Whether sex modifies the impact of allosensitization on heart transplant (HT) access and outcomes in ACHD remains unknown. Methods: We retrospectively analyzed the OPTN/UNOS registry of adults with ACHD listed for first-time HT (2018-2025). Sensitization was defined by calculated panel reactive antibodies (cPRA) at listing. We tested the sex x sensitization (highly sensitized, cPRA >50%) interaction on transplant access using Fine-Gray competing-risks regression, treating transplantation as the event of interest and death or removal from the waitlist as competing events, and on post-transplant survival using multivariable Cox proportional-hazards regression, both adjusted for age at listing, mechanical support at listing, and the number of distinct prior cardiac surgery categories. Results: Among 856 candidates (38% female), females were more often highly sensitized than males (23% vs 14%; age-adjusted OR 1.81, 95% CI 1.26-2.61), even after adjusting for surgical burden. Sensitization reduced transplant access in females (84% to 71%; median wait 60 to 110 days, p < 0.001) but not males (79% vs 79%, median wait 88 vs 98 days). In adjusted Fine-Gray models, the subdistribution hazard for transplant was reduced in sensitized females (sHR 0.54, 95% CI 0.41-0.72) with no effect in males (sHR 0.96, 95% CI 0.73-1.26), and the sex x sensitization interaction was significant (interaction sHR 0.64, 95% CI 0.44-0.94, p = 0.02). Post-transplant mortality was numerically higher in sensitized than non-sensitized candidates in both sexes and the sex x sensitization interaction on 1-year mortality was not significant. The sex-asymmetric effect persisted and was more pronounced in the multiorgan candidates. Conclusions: Allosensitization is not a sex-neutral barrier to transplant in HT candidates with ACHD. Females are more sensitized and have reduced transplant access without differences in 1-year mortality. The female excess in sensitization is not accounted for by surgical burden, and the exposures responsible remain to be defined. These findings warrant a sex-aware listing strategy and further studies.

Objectives To validate two computational fluid dynamics (CFD) models derived from computed tomography angiography (CTA) for estimating trans-stenotic pressure gradients, using invasive intra-arterial pressure measurements as the reference standard in patients with renal artery stenosis (RAS). Background We assessed whether non-invasive assessment of the pressure gradient using CFD could be a reliable alternative to intra-arterial measurements for identifying hemodynamically significant RAS. Methods We performed intra-arterial measurements at rest and during dopamine-induced hyperemia to assess the trans-stenotic pressure gradient in 28 patients with RAS. A pre-intervention CTA scan was used to simulate the pressure gradient with a CFD model using a strategy based on Murray's law (CFD-Mu) and cortical volume (CFD-C). The agreement between the simulated and measured pressure gradients was assessed using intraclass correlation coefficients (ICC), Bland-Altman analysis and diagnostic agreement on the presence of a hemodynamically significant stenosis. Results In 20 patients, successful measurements and simulations were obtained. The ICC between measured pressure gradient and the CFD pressure gradient was 0.78 and 0.94 during baseline and 0.86 and 0.72 during hyperemia, for CFD-Mu and CFD-C, respectively. The sensitivity of CFD-Mu and CFD-C was 70% for both models at rest and 100% compared to the hyperemic measurements, whereas the specificity was 90% and 70% at rest and 79% and 72% during hyperemia, respectively. Conclusions The results support the use of individualized CFD simulations for hemodynamic assessment of RAS using CTA as input. The CFD models demonstrated high accuracy for the identification of a hemodynamically significant stenosis.

Objective. Privacy regulation constrains sharing line-level electronic health records (EHR) across institutions. One alternative is to aggregate counts into a cube, a table of counts for every combination of categorical variables, with cells below a threshold suppressed. This study asked whether common analyses on the cube reproduce conclusions from line-level data, and whether suppression prevents recovery of the small cells it is meant to hide. Materials and Methods. A Bayesian count-inference pipeline was built that reconstructs suppressed counts and doubles as a reconstruction attack. Applied to 285 pediatric kidney-transplant patients at Boston Children's Hospital, statistical fidelity (Jensen-Shannon divergence, Cramer's V, and R2) and analytical utility (marginal distributions, subgroup graft rejection odds ratios, and logistic-regression classification) were evaluated. Conditional Tabular GAN (CTGAN) synthetic data served as a comparator. Results. Statistical analyses on the cube recapitulated results from line-level data. Across 106 demographic-by-medication subgroups, a bootstrap mean of 3.5 subgroups showed a significant graft-rejection association. The cube's odds-ratio sign changes reversed no significant associations, versus 2.3 for CTGAN. The same reconstruction also defeated suppression: in a 10-variable cube, 76.6% of suppressed cube cells were recovered exactly (14,554 of 18,994), including 85.5% of single-patient cells. Discussion. The cube reproduced common kidney-transplant analyses, but the same reconstruction also recovered suppressed cells; fidelity and privacy risk are thus two faces of one reconstruction rather than independent properties. Conclusions. The cube is a useful surrogate for these kidney-transplant analyses only when paired with a stronger privacy mechanism. This study demonstrated reconstructability of suppressed counts, not re-identification.