Characterization of arteriosclerosis based on computer-aided measurements of intra-arterial thickness.

Purpose: Our purpose is to develop a computer vision approach to quantify intra-arterial thickness on digital pathology images of kidney biopsies as a computational biomarker of arteriosclerosis. Approach: The severity of the arteriosclerosis was scored (0 to 3) in 753 arteries from 33 trichrome-stained whole slide images (WSIs) of kidney biopsies, and the outer contours of the media, intima, and lumen were manually delineated by a renal pathologist. We then developed a multi-class deep learning (DL) framework for segmenting the different intra-arterial compartments (training dataset: 648 arteries from 24 WSIs; testing dataset: 105 arteries from 9 WSIs). Subsequently, we employed radial sampling and made measurements of media and intima thickness as a function of spatially encoded polar coordinates throughout the artery. Pathomic features were extracted from the measurements to collectively describe the arterial wall characteristics. The technique was first validated through numerical analysis of simulated arteries, with systematic deformations applied to study their effect on arterial thickness measurements. We then compared these computationally derived measurements with the pathologists' grading of arteriosclerosis. Results: Numerical validation shows that our measurement technique adeptly captured the decreasing smoothness in the intima and media thickness as the deformation increases in the simulated arteries. Intra-arterial DL segmentations of media, intima, and lumen achieved Dice scores of 0.84, 0.78, and 0.86, respectively. Several significant associations were identified between arteriosclerosis grade and pathomic features using our technique (e.g., intima-media ratio average [ τ = 0.52 , p < 0.0001 ]) through Kendall's tau analysis. Conclusions: We developed a computer vision approach to computationally characterize intra-arterial morphology on digital pathology images and demonstrate its feasibility as a potential computational biomarker of arteriosclerosis.

Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms.

Introduction: Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide valuable insights into the cellular states of kidney cells. However, the annotation of cell types often requires extensive domain expertise and time-consuming manual curation, limiting scalability and generalizability. To facilitate this process, we tested the performance of five supervised classification methods for automatic cell type annotation. Results: We analyzed publicly available sc/snRNA-seq datasets from five expert-annotated studies, comprising 62,120 cells from 79 kidney biopsy samples. Datasets were integrated by harmonizing cell type annotations across studies. Five different supervised machine learning algorithms (support vector machines, random forests, multilayer perceptrons, k-nearest neighbors, and extreme gradient boosting) were applied to automatically annotate cell types using four training datasets and one testing dataset. Performance metrics, including accuracy (F1 score) and rejection rates, were evaluated. All five machine learning algorithms demonstrated high accuracies, with a median F1 score of 0.94 and a median rejection rate of 1.8 %. The algorithms performed equally well across different datasets and successfully rejected cell types that were not present in the training data. However, F1 scores were lower when models trained primarily on scRNA-seq data were tested on snRNA-seq data. Conclusions: Despite limitations including the number of biopsy samples, our findings demonstrate that machine learning algorithms can accurately annotate a wide range of adult kidney cell types in scRNA-seq/snRNA-seq data. This approach has the potential to standardize cell type annotation and facilitate further research on cellular mechanisms underlying kidney disease.

Proteinuria as an Endpoint in Clinical Trials of Focal Segmental Glomerulosclerosis.

Focal Segmental Glomerulosclerosis (FSGS) is a characteristic histopathological lesion that is indicative of underlying glomerular dysfunction. It is not a single disease entity but rather a heterogeneous disorder that is an important cause of nephrotic syndrome and kidney failure in children and adults. The aim of this Kidney Health Initiative project was to evaluate potential endpoints for clinical trials in FSGS. This paper focuses on the data supporting proteinuria as a surrogate endpoint. Available data support the use of complete remission of proteinuria in patients with heavy proteinuria as a surrogate endpoint for progression to kidney failure. While substantial treatment effects on proteinuria that are short of a complete remission may also predict the effect of a treatment on progression to kidney failure, further work is needed to determine how such an endpoint should be defined. Fortunately, efforts are underway to bring together patient-level data from randomized controlled trials, observational studies, and registries to address this issue.

Increased risk of kidney failure in patients with genetic kidney disorders.

BACKGROUNDIt is unknown whether the risk of kidney disease progression and failure differs between patients with and without genetic kidney disorders.METHODSThree cohorts were evaluated: the prospective Cure Glomerulonephropathy Network (CureGN) and 2 retrospective cohorts from Columbia University, including 5,727 adults and children with kidney disease from any etiology who underwent whole-genome or exome sequencing. The effects of monogenic kidney disorders and APOL1 kidney-risk genotypes on the risk of kidney failure, estimated glomerular filtration rate (eGFR) decline, and disease remission rates were evaluated along with diagnostic yields and the impact of American College of Medical Genetics secondary findings (ACMG SFs).RESULTSMonogenic kidney disorders were identified in 371 patients (6.5%), high-risk APOL1 genotypes in 318 (5.5%), and ACMG SFs in 100 (5.2%). Family history of kidney disease was the strongest predictor of monogenic disorders. After adjustment for traditional risk factors, monogenic kidney disorders were associated with an increased risk of kidney failure (hazard ratio [HR] = 1.72), higher rate of eGFR decline (-3.06 vs. 0.25 mL/min/1.73 m2/year), and lower risk of complete remission (odds ratioNot achieving CR = 5.25). High-risk APOL1 genotypes were associated with an increased risk of kidney failure (HR = 1.67) and faster eGFR decline (-2.28 vs. 0.25 mL/min/1.73 m2), replicating prior findings. ACMG SFs were not associated with personal or family history of associated diseases, but were predicted to impact care in 70% of cases.CONCLUSIONSMonogenic kidney disorders were associated with an increased risk of kidney failure, faster eGFR decline, and lower rates of complete remission, suggesting opportunities for early identification and intervention based on molecular diagnosis.TRIAL REGISTRATIONNA.FUNDINGNational Institute of Diabetes and Digestive and Kidney Diseases grants U24DK100845 (formerly UM1DK100845), U01DK100846 (formerly UM1DK100846), U01DK100876 (formerly UM1DK100876), U01DK100866 (formerly UM1DK100866), U01DK100867 (formerly UM1DK100867), U24DK100845, DK081943, RC2DK116690, 2U01DK100876, 1R01DK136765, 5R01DK082753, and RC2-DK122397; NephCure Kidney International; Department of Defense Research Awards PR201425, W81XWH-16-1-0451, and W81XWH-22-1-0966; National Center for Advancing Translational Sciences grant UL1TR001873; National Library of Medicine grant R01LM013061; National Human Genome Research Institute grant 2U01HG008680.

Elevated Exposure to Air Pollutants Accelerates Primary Glomerular Disease Progression.

Introduction: Environmental contributors to kidney disease progression remain elusive. We explored how residential air pollution affects disease progression in patients with primary glomerulopathies. Methods: Nephrotic Syndrome Study Network (NEPTUNE) and CureGlomerulonephropathy (CureGN) participants with residential census tract data and ≥2 years of follow-up were included. Using Cox proportional hazards models, the associations per doubling in annual average baseline concentrations of total particulate matter with diameter ≤2.5 µm (PM2.5) and its components, black carbon (BC), and sulfate, with time to ≥40% decline in estimated glomerular filtration rate (eGFR) or kidney failure were estimated. Serum tumour necrosis factor levels and kidney tissue transcriptomic inflammatory pathway activation scores were used as molecular markers of disease progression. Results: PM2.5, BC, and sulfate exposures were comparable in NEPTUNE (n = 228) and CureGN (n = 697). In both cohorts, participants from areas with higher levels of pollutants had lower eGFR, were older and more likely self-reported racial and ethnic minorities. In a fully adjusted model combining both cohorts, kidney disease progression was associated with PM2.5 (adjusted hazard ratio 1.55 [95% confidence interval: 1.00-2.38], P = 0.0489) and BC (adjusted hazard ratio 1.43 [95% confidence interval: 0.98-2.07], P = 0.0608) exposure. Sulfate and PM2.5 exposure were positively correlated with serum tumour necrosis factor (TNF) (P = 0.003) and interleukin-1ß levels (P = 0.03), respectively. Sulfate exposure was also directly associated with transcriptional activation of the TNF and JAK-STAT signaling pathways in kidneys (r = 0.55-0.67, P-value <0.01). Conclusion: Elevated exposure to select air pollutants is associated with increased risk of disease progression and systemic inflammation in patients with primary.

Evaluation of Biomarker-Based Glomerular Filtration Rate Estimating Equations in Glomerular Disease.

Introduction: Glomerular filtration rate (GFR) is typically estimated with equations that use biomarkers such as serum creatinine and/or cystatin-C. The impact of these different biomarkers on GFR estimates in glomerular disease patients is unclear. In this study, we compared the different GFR estimating equations in the Cure Glomerulonephropathy (CureGN) cohort of children and adults with glomerular disease. Methods: All available cystatin-C measurements from CureGN study participants were matched to same-day serum creatinine measurements to estimate GFR. To explore the strength of agreement between eGFR values obtained from the "Under 25" (U25) and Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equations, we used intraclass correlation coefficients. Multivariable linear mixed effects models were used to determine which factors were independently associated with differences in eGFR values. Results: A total of 928 cystatin-C measurements were matched to same-day serum creatinine measurements from N = 332 CureGN study participants (58% male, 69% White/Caucasian, 20% Black/African American). Among 628 measurements collected while study participants were under 25 years old, there was moderate agreement (0.731) in serum creatinine versus cystatin-C U25 equations. Models showed that higher eGFR values were associated with larger differences between the two equations (p < 0.001). Among 253 measurements collected while study participants were at least 18 years old, there was excellent agreement (0.891-0.978) among CKD-Epi equations using serum creatinine alone, cystatin-C alone, or the combination of both. Younger age was associated with larger differences between CKD-Epi equations (p = 0.06 to p = 0.016). Conclusion: Excellent agreement between CKD-Epi equations indicates continued use of serum creatinine alone for GFR estimation could be appropriate for adults. In contrast, only moderate agreement between U25 equations indicates a need for more frequent measurement of cystatin-C among children and young adults, especially as eGFR increases.

Design of a User-Centered Electronic Health Tool for Glomerular Disease Management.

Introduction: Patients with primary glomerular disease (GN) have unique management needs. We describe the design of a user-centered, patient-facing electronic health (eHealth) tool to support GN management. Methods: We surveyed patients and GN expert nephrologists on disease management tasks, educational needs, and barriers and facilitators of eHealth tool use. Results were summarized and presented to patients, nephrologists, engineers, and a behavioral and implementation science expert in stakeholder meetings to jointly design an eHealth tool. Key themes from the meetings are described using rapid qualitative analysis. Results: Sixty-six patients with minimal change disease, focal segmental glomerulosclerosis, IgA nephropathy, and membranous nephropathy responded to the survey, as well as 25 nephrologists from the NIH-funded Cure Glomerulonephropathy study network. Overall, patients performed fewer management tasks and acknowledged fewer informational needs than recommended by nephrologists. Patients were more knowledgeable about eHealth tools than nephrologists. Nine patient stakeholders reflected on the survey findings and noted a lack of awareness of key recommended management tasks and receiving little guidance from nephrologists on using eHealth. Key themes and concepts from the stakeholder meetings about eHealth tool development included the need for customizable design, trustworthy sources, seamless integration with other apps and clinical workflow, and reliable data tracking. The final design of our eHealth tool, the UrApp System, has 5 core features: "Profile" generates personalized data tracking, educational information, facilitation with provider discussions and inputting other preferences; "Data Tracking" displays patient health data with the ability to communicate important trends to patients and nephrologists; "Resources" provides trusted education information in a personalized manner; "Calendar" displays key events and generate reminders; and "Journal" facilitates information documentation using written or audio notes. Conclusion: Our theory- and evidenced-based, stakeholder-engaged design process created designs for an eHealth tool to support the unique needs of patients with GN, optimized for effectiveness and implementation.

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS.

Background: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis. Methods: Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset. Results: N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA. Conclusions: Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.

Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.

Glomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.

Association of COVID-19 Versus COVID-19 Vaccination With Kidney Function and Disease Activity in Primary Glomerular Disease: A Report of the Cure Glomerulonephropathy Study.

RATIONALE & OBJECTIVE: Patients with glomerular disease (GN) may be at increased risk of severe COVID-19, yet concerns over vaccines causing disease relapse may lead to vaccine hesitancy. We examined the associations of COVID-19 with longitudinal kidney function and proteinuria and compared these with similar associations with COVID-19 vaccination. STUDY DESIGN: Observational cohort study from July 1, 2021, to January 1, 2023. SETTING & PARTICIPANTS: A prospective observational study network of 71 centers from North America and Europe (CureGN) with children and adults with primary minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, or IgA nephropathy. EXPOSURE: COVID-19 and COVID-19 vaccination. OUTCOME: Repeated measure of estimated glomerular filtration rate (eGFR); recurrent time-to-event outcome of GN disease worsening as defined by doubling of the urinary protein-creatinine ratio (UPCR) to at least 1.5g/g or increase in dipstick urine protein by 2 ordinal levels to 3+(300mg/dL) or above. ANALYTICAL APPROACH: Interrupted time series analysis for eGFR. Prognostic matched sequential stratification recurrent event analysis for GN disease worsening. RESULTS: Among 2,055 participants, 722 (35%) reported COVID-19 infection; of these, 92 (13%) were hospitalized, and 3 died (<1%). The eGFR slope before COVID-19 infection was-1.40mL/min/1.73m2 (± 0.29 SD); within 6 months after COVID-19 infection, the eGFR slope was-4.26mL/min/1.73m2 (± 3.02 SD), which was not significantly different (P=0.34). COVID-19 was associated with increased risk of worsening GN disease activity (HR, 1.35 [95% CI, 1.01-1.80]). Vaccination was not associated with a change in eGFR (-1.34mL/min/1.73m2±0.15 SD vs-2.16mL/min/1.73m2±1.74 SD; P=0.6) or subsequent GN disease worsening (HR 1.02 [95% CI, 0.79-1.33]) in this cohort. LIMITATIONS: Infrequent or short follow-up. CONCLUSIONS: Among patients with primary GN, COVID-19 infection was severe for 1 in 8 cases and was associated with subsequent worsening of GN disease activity, as defined by proteinuria. By contrast, vaccination against COVID-19 was not associated with change in disease activity or kidney function decline. These results support COVID-19 vaccination for patients with GN. PLAIN-LANGUAGE SUMMARY: In this cohort study of 2,055 patients with minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, or IgA nephropathy, COVID-19 resulted in hospitalization or death for 1 in 8 cases and was associated with a 35% increase in risk for worsening proteinuria. By contrast, vaccination did not appear to adversely affect kidney function or proteinuria. Our data support vaccination for COVID-19 in patients with glomerular disease.

Scaffold protein SH3BP2 signalosome is pivotal for immune activation in nephrotic syndrome.

Despite clinical use of immunosuppressive agents, the immunopathogenesis of minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) remains unclear. Src homology 3-binding protein 2 (SH3BP2), a scaffold protein, forms an immune signaling complex (signalosome) with 17 other proteins, including phospholipase Cγ2 (PLCγ2) and Rho-guanine nucleotide exchange factor VAV2 (VAV2). Bioinformatic analysis of human glomerular transcriptome (Nephrotic Syndrome Study Network cohort) revealed upregulated SH3BP2 in MCD and FSGS. The SH3BP2 signalosome score and downstream MyD88, TRIF, and NFATc1 were significantly upregulated in MCD and FSGS. Immune pathway activation scores for Toll-like receptors, cytokine-cytokine receptor, and NOD-like receptors were increased in FSGS. Lower SH3BP2 signalosome score was associated with MCD, higher estimated glomerular filtration rate, and remission. Further work using Sh3bp2KI/KI transgenic mice with a gain-in-function mutation showed ~6-fold and ~25-fold increases in albuminuria at 4 and 12 weeks, respectively. Decreased serum albumin and unchanged serum creatinine were observed at 12 weeks. Sh3bp2KI/KI kidney morphology appeared normal except for increased mesangial cellularity and patchy foot process fusion without electron-dense deposits. SH3BP2 co-immunoprecipitated with PLCγ2 and VAV2 in human podocytes, underscoring the importance of SH3BP2 in immune activation. SH3BP2 and its binding partners may determine the immune activation pathways resulting in podocyte injury leading to loss of the glomerular filtration barrier.

CureGN-Diabetes Study: Rationale, Design, and Methods of a Prospective Observational Study of Glomerular Disease Patients with Diabetes.

Glomerular diseases (GDs) represent the third leading cause of end-stage kidney disease (ESKD) in the US Diabetes was excluded from the CureGN Study, an NIH/NIDDK-sponsored observational cohort study of four leading primary GDs: IgA nephropathy (IgAN), membranous nephropathy (MN), focal segmental glomerulosclerosis (FSGS), and minimal change disease (MCD). CureGN-Diabetes, an ancillary study to CureGN, seeks to understand how diabetes influences the diagnosis, treatment, and outcomes of GD. It is a multicenter, prospective cohort study, targeting an enrollment of 300 adults with prevalent type 1 or type 2 diabetes and MCD, FSGS, MN, or IgAN, with first kidney biopsy obtained within 5 years of enrollment in 80% (20% allowed if biopsy after 2010). CureGN and Transformative Research in DiabEtic NephropaThy (TRIDENT) provide comparator cohorts. Retrospective and prospective clinical data and patient-reported outcomes are obtained. Blood and urine specimens are collected at study visits annually. Kidney biopsy reports and digital images are obtained, and standardized pathologic evaluations performed. Light microscopy images are uploaded to the NIH pathology repository. Outcomes include relapse and remission rates, changes in proteinuria and estimated glomerular filtration rate, infections, cardiovascular events, malignancy, ESKD, and death. Multiple analytical approaches will be used leveraging the baseline and longitudinal data to compare disease presentation and progression across subgroups of interest. With 300 patients and an average of 3 years of follow-up, the study has 80% power to detect a HR of 1.4-1.8 for time to complete remission of proteinuria, a rate ratio for hospitalizations of 1.18-1.56 and difference in eGFR slope of 6.0-8.6 mL/min/year between two groups of 300 participants each. CureGN-Diabetes will enhance our understanding of diabetes as a modifying factor of the pathology and outcomes of GDs and support studies to identify disease mechanisms and improve patient outcomes in this understudied patient population.

Developing an Edema Clinician-Reported Outcome Measure for Nephrotic Syndrome.

Introduction: Edema is a common manifestation of proteinuric kidney diseases, but there is no consensus approach for reliably evaluating edema. The objective of this study was to develop an edema clinician-reported outcome measure for use in patients with nephrotic syndrome. Methods: A literature review was conducted to assess existing clinician-rated measures of edema. Clinical experts were recruited from internal medicine, nephrology, and pediatric nephrology practices to participate in concept elicitation using semi-structured interviews and cognitive debriefing. Qualitative analysis methods were used to collate expert input and inform measurement development. In addition, training and assessment modules were developed using an iterative process that also utilized expert input and cognitive debriefing to ensure interrater reliability. Results: While several clinician-rated measures of edema have been proposed, our literature review did not identify any studies to support the reliability or validity of these measures. Fourteen clinician experts participated in the concept elicitation interviews, and twelve participated in cognitive debriefing. A clinician-reported outcome measure for edema was developed. The measure assesses edema severity in multiple individual body parts. An online training module and assessment tool were generated and refined using additional clinician input and investigative team expertise. Conclusion: The Edema ClinRO (V1) measure is developed specifically to measure edema in nephrotic syndrome. The tool assesses edema across multiple body parts, and it includes a training module to ensure standardized administration across raters. Future examination of this measure is ongoing to establish its reliability and validity.

The Significance of Hematuria in Podocytopathies.

BACKGROUND: Hematuria is frequently present in podocytopathies, but its significance and prognostic value is not well described in these proteinuric kidney diseases. This study describes the prevalence and association between hematuria and kidney-related outcomes in these disorders. METHODS: Hematuria was assessed at the initial urinalysis in participants with the following podocytopathies, membranous nephropathy, minimal change disease, and focal segmental glomerulosclerosis, in the Nephrotic Syndrome Study Network (NEPTUNE) and Cure Glomerulonephropathy (CureGN) cohorts with >24 months of follow-up. Multivariable Cox proportional hazards models were fit for time to composite outcome (end-stage kidney disease or 40% decline in estimated glomerular filtration rate (eGFR) and eGFR <60 ml/min/1.73 m2) and proteinuria remission (UPCR <0.3 mg/mg). RESULTS: Among the 1,516 adults and children in the study, 528 (35%) participants had focal segmental glomerulosclerosis, 499 (33%) had minimal change disease, and 489 (32%) had membranous nephropathy. Median (IQR) time from biopsy until the initial study urinalysis was 260 days (49, 750), and 498 (33%) participants were positive for hematuria. Participants with hematuria compared to those without, were older (37 [16, 55] vs 33 years [12, 55]), more likely to have an underlying diagnosis of membranous nephropathy (44% vs 27%), had shorter time since biopsy (139 [27, 477] vs 325 [89, 878] days) and higher UPCR (3.8 [1.4, 8.0] vs 0.9 [0.1, 3.1]g/g). After adjusting for diagnosis, age, sex, UPCR, eGFR, time since biopsy, and study cohort, hematuria was associated with a higher riskof reaching the composite outcome (HR 1.31 [1.04, 1.65], p-value 0.02) and lower rate of reaching proteinuria remission (HR 0.80 [0.65-0.98], p-value 0.03). CONCLUSIONS: Hematuria is prevalent among participants with the three podocytopathies and is significantly and independently associated with worse kidney-related outcomes, including both progressive loss of kidney function remission of proteinuria.

Reinke's Edema: New Insights into Voice Analysis, a Retrospective Study.

PURPOSE: Reinke's edema (RE) is a pathological condition involving increased volume of the vocal folds and resulting in significant impact on speech, fundamental frequency, and vocal range. Literature reports few studies which analyze vocal features according to the severity of RE. The aims of this study were to investigate the aerodynamics, acoustic characteristics, and sound spectrograms of a group of RE patients and to assess whether there was any correlation with their endoscopic grading. METHODS: A total of 98 patients were included in the study, 49 patients with RE and 49 healthy volunteers (HV). Multidimensional Voice Program was used to perform objective voice assessment. Maximum phonation time (MPT) and Voice Handicap Index (VHI) questionnaire were collected. The spectrograms of the vowel /a/ and of the word /aiuole/, which contains the five Italian vowels, of each patient were analyzed according to the classification of Yanaghiara modified by Ricci Maccarini and De Colle. Laryngological assessment was used to record vocal folds morphology according to Yonekawa's classification. Univariate analysis was used to compare group outcomes. Bivariate analysis was used to compare endoscopic grading and voice analysis results. RESULTS: Univariate analysis of the HV and RE groups revealed statistically significant differences (P < 0.05) for the following parameters: jitter%, shimmer%, harmonic-to-noise ratio (NHR), voice turbulence index (VTI), MPT, VHI except for soft phonation index. Spearman's rank correlation showed a positive correlation between vocal parameters such as jitter%, shimmer%, NHR, VTI, and RE gradings. A negative correlation was found between MPT and RE gradings. Bivariate analysis indicated a strong positive correlation between RE grading and the spectrogram classification performed both with the vowel / a / (Rho 0.86; P = 0.0001) and with the word / aiuole / (Rho 0.81; P = 0.0001). CONCLUSION: The present study demonstrates that patients with RE have different voice characteristics compared to HV. In particular, the voice analysis highlighted acoustic parameters that correlated to differing degrees of RE. In addition, spectrogram analysis should be considered for acoustic assessments before and after medical and surgical therapy and also in forensic medicine.

Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

An integrated organoid omics map extends modeling potential of kidney disease.

Kidney organoids are a promising model to study kidney disease, but their use is constrained by limited knowledge of their functional protein expression profile. Here, we define the organoid proteome and transcriptome trajectories over culture duration and upon exposure to TNFα, a cytokine stressor. Older organoids increase deposition of extracellular matrix but decrease expression of glomerular proteins. Single cell transcriptome integration reveals that most proteome changes localize to podocytes, tubular and stromal cells. TNFα treatment of organoids results in 322 differentially expressed proteins, including cytokines and complement components. Transcript expression of these 322 proteins is significantly higher in individuals with poorer clinical outcomes in proteinuric kidney disease. Key TNFα-associated protein (C3 and VCAM1) expression is increased in both human tubular and organoid kidney cell populations, highlighting the potential for organoids to advance biomarker development. By integrating kidney organoid omic layers, incorporating a disease-relevant cytokine stressor and comparing with human data, we provide crucial evidence for the functional relevance of the kidney organoid model to human kidney disease.

An atlas of healthy and injured cell states and niches in the human kidney.

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.