Kidney360

BackgroundCystine stones, a rare but recurrent type of kidney stones, primarily result from cystinuria, an inherited disorder caused by mutations in the genes SLC3A1 and SLC7A9, which encode renal cystine transporters. These mutations impair cystine reabsorption, resulting in elevated urinary cystine concentrations and stone formation. Current diagnostic methods are limited, particularly for detecting molecular-level dysfunctions. We aimed to develop a nonradioactive method for functional assessment of cystine transporters and mutation-specific pathologies. MethodsWe designed an innovative diagnostic approach combining a selenocystine-based fluorescence assay with structural predictions using AlphaFold to rapidly and accurately assess cystine transporter function and the molecular impacts of genetic mutations. ResultsOur assay demonstrated comparable transport efficiencies of cystine and selenocystine by the SLC3A1/SLC7A9 complex, and effectively differentiated mild, moderate, and severe functional impairments associated with known clinical mutations, including A354T and P482L. Structural modeling further provided mechanistic insights into mutation-induced dysfunctions. ConclusionThis integrated approach--combining a sensitive selenocystine fluorescence assay with AI-powered structural analysis--enables rapid, precise diagnosis of cystinuria variants and delivers mechanistic insights for personalized therapeutic strategies.

Sodium-glucose co-transporter 2 (SGLT2) inhibitors are recommended for use in adults with chronic kidney disease (CKD) and are widely prescribed. SGLT2 inhibition markedly increases urine glucose excretion, which could interfere with laboratory assays. We assessed whether assays for several key urine tubular biomarkers (alpha-1 microglobulin [1M], dickkopf-3 [DKK-3], epidermal growth factor [EGF], interleukin-18 [IL-18], kidney injury molecule-1 [KIM-1], monocyte chemoattractant protein-1 [MCP-1], neutrophil gelatinase-associated lipocalin [NGAL], uromodulin [UMOD], and human cartilage glycoprotein-40 [YKL-40]) are affected by glycosuria using urine samples from participants with CKD. Each urine sample was divided into three aliquots, with one serving as control and the other two being spiked with glucose to reach effective concentrations of 28 mmol/l and 111 mmol/l. There was large positive mean bias [95% CI] observed at 28 mmol/l glucose concentration for IL-18 (0.10 [0.01, 0.23]) and YKL-40 (0.40 [0.32, 0.49]). The limits of agreement (LOA) for both biomarkers were wide, spanning >1 unit difference in log-transformed biomarker values. The rest of the biomarkers had narrow LOA. Modest negative mean bias at 28 mmol/l glucose concentration was observed for DKK-3 (-0.02 [-0.04, 0]), KIM-1 (-0.04 [-0.06, -0.02]), and UMOD (-0.08 [-0.11, -0.06]), with similar values observed at 111 mmol/l glucose concentration. There was no evidence of any bias in measurements of 1M, EGF, MCP-1, and NGAL. Glycosuria substantially interferes with IL-18 and YKL-40 measurements, without importantly affecting 1M, DKK-3, EGF, KIM-1, MCP-1, NGAL or UMOD.

BackgroundDistinguishing patients with the inherited salt-losing tubulopathies (SLT), Gitelman or Bartter syndrome (GS or BS) from wildtype (WT) patients who purge is difficult. We decided to identify clinical/biochemical characteristics which correctly classify SLT. Methods66 patients with possible SLT were recruited to a prospective observational cohort study at the UCL Renal Tubular Clinic (London). 31 datapoints were recorded on each patient. All patients were genotyped for pathogenic mutations in genes which cause SLT; 39 patients had pathogenic variants in genes causing SLT. We obtained similar datasets from cohorts in Taipei and Kobe; the combined dataset comprised 419 patients, 291 had genetically confirmed SLT. London and Taipei datasets were combined to train machine learning (ML) algorithms. These were then tested on the Kobe dataset to determine the best biochemical predictors of genetic confirmation of SLT. ResultsSingle biochemical variables (e.g. plasma renin) were significantly, but inconsistently different between SLT and WT, in the London and combined cohorts. A decision table algorithm using serum bicarbonate and urinary sodium excretion (FENa) achieved a classification accuracy of 74%. A simpler algorithm based on the FECl achieved a classification accuracy of 61%. This was superior to all of the single biochemical variables identified previously.

Progressive chronic kidney disease (CKD) is a major source of public health spending. Current non-invasive tests estimate CKD but provide a minimal understanding of cell- or compartment-specific injury. The gold standard for CKD diagnosis is a kidney biopsy, which affords risks and is impractical to repeat multiple times. Hence, repeatable, non-invasive tests to estimate pathologic kidney injury for diagnoses, prognosis and follow-up of CKD represent a knowledge gap. We hypothesized that urinary shedding of specific cells is proportional to injury of those cells on biopsy, and that tracking cell-specific urine mRNA will correlate with ongoing injury. Informed by apriori biopsy and urine single cell RNA studies, we developed a targeted 10-gene urine mRNA assay to estimate kidney injury non-invasively (Nephro-Dx). In a pilot study of 48 patients with diverse kidney pathology on biopsy and 20 controls, we confirmed our assays utility in differentiating any kidney disease from controls. Within biopsied cases, we confirmed correlations of cell-specific urinary gene expression with corresponding compartment injury on biopsy using a validated quantitative digital pathology platform. We show that the gene signatures including individual genes associate with subsequent loss of kidney function within our cases providing an advantage over existing non-invasive tests. Our parsimonious set of gene signatures in Nephro-Dx shows advantages in early diagnosis, monitoring, and prognosis to impact this public health problem.

IntroductionEnd stage kidney disease (ESKD) affects an estimated 5500 persons living in the United States without legal residency documentation. One likely, but underappreciated cause of ESKD in the Hispanic migrant population, is chronic kidney disease of unknown etiology (CKDu). CKDu is an interstitial nephritis that disproportionately affects young adult agricultural workers in Central America who lack traditional risk factors for kidney disease. In underserved populations, such as those at risk for CKDu, substantial barriers to optimal kidney care translate to poorer health outcomes and widening health disparities. Without funding for non-emergent healthcare, this underserved population, often only have access to hemodialysis (HD) once a life-threatening condition occurs. Despite the presence of a migrant population from CKDu endemic countries and anecdotes of its presence, CKDu has very rarely been directly investigated or documented in the US. We undertook this study to establish the existence of CKDu in the United States and to characterize CKDu as a cause of ESKD in patients accessing emergent HD. MethodsIn a retrospective cross-sectional study among patients receiving emergent HD in Texas, we analyzed medical record data from a large, county hospital. We ascertained cause of ESKD and underlying hypertension and diabetes and compared these proportions to data on patients on maintenance HD from the US Renal Data System (USRDS). Undocumented immigrants are largely excluded from the USRDS, as with many health statistics databases in the US. We identified patients whose clinicians had indicated CKDu as a diagnosis and classified others as having suspected CKDu or possible CKDu based on clinically informed criteria. ResultsWe identified 346 patients with ESKD requiring emergent HD (2012-2015), who were younger than patients in the USRDS (median age 52 yrs vs. 61 yrs, p <0.001), had more comorbid diabetes (60% vs. 47%, p <0.001), and more often had an unknown cause of ESKD (16% vs. 4%, p<0.001). Patients requiring emergent HD also had less frequent arteriovenous access (12% vs. 82%, p<0.001). ESKD attributed to diabetes and/or hypertension accounted for only 67% of emergent HD patients, compared to 81% of USRDS patients (p<0.001). 14% of the patients on emergent HD died during the study period. Four patients had been clinically diagnosed with CKDu, while we classified 14 with suspected CKDu and 40 with possible CKDu, for a total of 58 patients (17%) with potentially CKDu-related ESKD. ConclusionOur analysis suggests that up to 17% of patients in this population utilizing emergent HD had CKDu-related ESKD, suggesting that CKDu is likely underdiagnosed in the US. Further, patients receiving emergent HD were younger but were at higher risk of infection or complication than patients receiving scheduled, maintenance HD. Understanding CKDu and improving access to scheduled dialysis for migrants without legal residency documentation should be prioritized to reduce stress on the healthcare system and improve health among vulnerable populations in the US.

BackgroundRenal intratubular casts are frequently observed in the distal nephron segments of the kidney and have long been regarded as a sign of renal disease. However, the composition and pathological significance of intratubular casts have remained understudied. MethodsWe leveraged Hematoxylin and Eosin (H&E) staining to identify intratubular casts along with concurrent Co-detection by indexing (CODEX) multiplexed spatial protein imaging on human kidney biopsy sections from the Kidney Precision Medicine Project (KPMP). We also conducted immunoblotting of Prominin-1 (PROM1) in urine and assessed its levels from publicly available urinary proteomics datasets of the KPMP consortium. ResultsWe analyzed 424 intratubular casts across 33 individuals with kidney disease or healthy controls. We identified PROM1 and IGFBP7 as major constituents of casts (positive staining in 90.1% and 35.6%, respectively). Staining for UMOD, an established cast component, was present in 86.1%. These components exhibited distinct alterations depending on the disease state. Intratubular casts were predominantly detected in the distal nephron segments, and their presence was associated with a marked loss of NCC and AQP2 expression in the cast-containing tubular epithelium, suggesting underlying injury. The loss of these membrane transporters correlated with protein components within casts, and the presence of intra-cast PROM1 showed the strongest association, with an odds ratio of 30.8 (95% confidence interval: 13.4-71.0). Urinary PROM1 secretion was confirmed by immunoblotting and was increased in patients with acute kidney injury (AKI) compared to healthy controls (p = 0.01). ConclusionsWe identified PROM1, a dedifferentiation and injury marker expressed in epithelial cells, as a novel major constituent of intratubular casts. Our studies suggest that protein composition signature within casts varies with disease state and is associated with tubular injury in distal nephron segments. Our study also suggests that urinary PROM1 may serve as a biomarker for AKI. Key Points{checkmark} Utilized CODEX multiplex protein imaging to elucidate the intratubular cast components and the associated tubular alterations. {checkmark}Identified PROM1, a dedifferentiation marker, as a major constituent of intratubular casts. {checkmark}Protein components within casts were altered by disease state and were associated with the injury of the surrounding tubular epithelium.

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is a genetic kidney disease with high phenotypic variability. Insights into ADPKD progression could lead to earlier detection and management prior to end stage kidney disease (ESKD). We sought to identify patients with rapid decline (RD) in kidney function and to determine clinical factors associated with RD using a data-driven approach. MethodsA retrospective cohort study was performed among patients with incident ADPKD (1/1/2002-12/31/2018). Latent class mixed models were used to identify RD patients using rapidly declining eGFR trajectories over time. Predictors of RD were selected based on agreements among feature selection methods, including logistic, regularized, and random forest modeling. The final model was built on the selected predictors and clinically relevant covariates. ResultsAmong 1,744 patients with incident ADPKD, 125 (7%) were identified as RD. Feature selection included 42 clinical measurements for adaptation with multiple imputations; mean (SD) eGFR was 85.2 (47.3) and 72.9 (34.4) in the RD and non-RD groups, respectively. Multiple imputed datasets identified variables as important features to distinguish RD and non-RD groups with the final prediction model determined as a balance between area under the curve (AUC) and clinical relevance which included 6 predictors: age, sex, hypertension, cerebrovascular disease, hemoglobin, and proteinuria. Results showed 72%-sensitivity, 70%-specificity, 70%-accuracy, and 0.77-AUC in identifying RD. 5-year ESKD rates were 38% and 7% among RD and non-RD groups, respectively. ConclusionUsing real-world routine clinical data among patients with incident ADPKD, we observed that six variables highly predicted RD in kidney function.

IntroductionAcute kidney injury (AKI) is associated with significant morbidity and mortality. The diagnosis is currently based on urine output and serum creatinine and there is a lack of biomarkers that directly reflect tubular damage. Here, we establish flow cytometric quantification of renal epithelial cells as a potential biomarker for quantifying the severity of tubular kidney damage and for predicting AKI outcome. MethodsA total of 84 patients with AKI were included in this study, divided into an exploratory cohort (n=21) and confirmatory cohort (n=63), as well as 25 controls. Urine of patients was collected and processed within 72 hours after AKI onset. Different urinary tubular epithelial cell (TEC) populations were identified and quantified by flow cytometry (FACS). Urinary cell counts were analyzed regarding AKI severity defined by KDIGO stage as well as renal recovery, length of hospital stay and occurrence of MAKE-30 events. ResultsUrinary TEC counts correlated with stages of AKI based on KDIGO classification and were significantly enriched in patients with AKI compared to healthy donors and inpatient controls in both cohorts. Furthermore, both proximal and distal TEC (pTEC, dTEC) counts performed well in identification of patients with AKI regardless of stage. Urinary amounts of pTEC and dTEC showed a strong correlation, with predominance of dTEC. Higher numbers of TEC were associated with extended length of hospital stay, while elevated pTEC counts were associated with the occurrence of MAKE-30 events. Follow-up measurements showed decreasing amounts of urinary TEC after AKI recovery over several days. ConclusionThe amount of urinary TEC directly reflects severity of tissue damage in human AKI. Our protocol furthermore provides a basis for a deeper phenotypic analysis of urinary TEC populations.

Rationale and ObjectiveThe NKF-ASN Task Force recommends accurate kidney function estimation avoiding biases through racial adjustments. We explored the use of multiple kidney function biomarkers and hence estimated glomerular filtration rate (eGFR) equations to improve kidney function calculations in an ethnically diverse patient population. Study designProspective community cohort study. Setting and ParticipantsRural New Mexico clinic with patients > 18 yo. MethodsMarkers of kidney function, IDMS-Creatinine (SCr), chemiluminescence Beta-2 Microglobulin (B2M), Nephelometry-calibrated ELISA Cystatin C (CysC), inflammation, glucose tolerance, demographics, BUN/UACR from the baseline visit of the COMPASS cohort, were analyzed by Kernel-based Virtual Machine learning methods. ResultsAmong 205 participants, the mean age was 50.1, 62% were female, 54.1% Hispanic American and 30.2% Native American. Average kidney function biomarkers were: SCr 0.9 mg/dl, B2M 1.8 mg/L, and CysC 0.7 mg/dl. The highest agreement was observed between SCr and B2M-based eGFR equations [mean difference in eGFRs: (4.48 ml/min/1.73m2], and the lowest agreement between B2M and CysC-based eGFR equations (-24.75 ml/min/1.73m2). There was no pattern of association between the differences in eGFR measures and gender. In the continuous analyses, the absolute eGFR value (p<2 x 10-16) and serum albumin (p =6.4 x 10-5) predicted the difference between B2M- and SCr-based e-GFR. The absolute eGFR value (p<2 x 10-16) and age (p =7.6 x 10-5) predicted the difference between CysC- and SCr-based e-GFR. LimitationsRelatively small sample size, elevated inflammatory state in majority of study participants and no inulin excretion rate measurements. ConclusionB2M should be strongly considered as a kidney function biomarker fulfilling the criteria for the NKF-ASN. B2Ms eGFR equation does not need adjustment for gender or race and showed the highest agreement with SCr-based eGFR equations.

Acute kidney injury (AKI) in COVID-19 patients is associated with high mortality and morbidity. Critically ill COVID-19 patients are at twice the risk of in-hospital mortality compared to non-COVID AKI patients. We know little about the cell-specific mechanism in the kidney that contributes to worse clinical outcomes in these patients. New generation single cell technologies have the potential to provide insights into physiological states and molecular mechanisms in COVID-AKI. One of the key limitations is that these patients are severely ill posing significant risks in procuring additional biopsy tissue. We recently generated single nucleus RNA-sequencing data using COVID-AKI patient biopsy tissue as part of the human kidney atlas. Here we describe this approach in detail and report deeper comparative analysis of snRNAseq of 4 COVID-AKI, 4 reference, and 6 non-COVID-AKI biopsies. We also generated and analyzed urine transcriptomics data to find overlapping COVID-AKI-enriched genes and their corresponding cell types in the kidney from snRNA-seq data. We identified all major and minor cell types and states by using by using less than a few cubic millimeters of leftover tissue after pathological workup in our approach. Differential expression analysis of COVID-AKI biopsies showed pathways enriched in viral response, WNT signaling, kidney development, and cytokines in several nephron epithelial cells. COVID-AKI profiles showed a much higher proportion of altered TAL cells than non-COVID AKI and the reference samples. In addition to kidney injury and fibrosis markers indicating robust remodeling we found that, 17 genes overlap between urine cell COVID-AKI transcriptome and the snRNA-seq data from COVID-AKI biopsies. A key feature was that several of the distal nephron and collecting system cell types express these markers. Some of these markers have been previously observed in COVID-19 studies suggesting a common mechanism of injury and potentially the kidney as one of the sources of soluble factors with a potential role in disease progression. Translational StatementThe manuscript describes innovation, application and discovery that impact clinical care in kidney disease. First, the approach to maximize use of remnant frozen clinical biopsies to inform on clinically relevant molecular features can augment existing pathological workflow for any frozen tissue without much change in the protocol. Second, this approach is transformational in medical crises such as pandemics where mechanistic insights are needed to evaluate organ injury, targets for drug therapy and diagnostic and prognostic markers. Third, the cell type specific and soluble markers identified and validated can be used for diagnoses or prognoses in AKI due to different etiologies and in multiorgan injury.

Background: Lower urine pH has been associated with reduced kidney function and an increased risk of kidney disease; however, its prognostic and pathological significance in biopsy-proven kidney disease remains unclear. A recent study demonstrated that medullary cast formation is independently associated with adverse renal outcomes beyond established predictors such as interstitial fibrosis and tubular atrophy (IFTA), yet its clinical determinants are not fully elucidated. Urine pH reflects the intratubular acid-base microenvironment and may contribute to tubular obstruction through cast formation. In this study, we examined kidney outcomes in patients undergoing native kidney biopsy, and the associations of urine pH with medullary cast formation. Methods: Among 1167 adults who underwent native kidney biopsy between 2011 and 2024, 503 patients with evaluable medullary tissue were included in this retrospective observational cohort study. Urine pH was analyzed in relation to clinical and histological variables and kidney outcomes. The primary outcome was a 40% decline in estimated glomerular filtration rate (eGFR) or initiation of renal replacement therapy. Results: The mean baseline eGFR was 54.3 mL/min/1.73 m2, the mean urine pH was 6.15, and the median urinary protein excretion was 1.1 g/gCr. During a median follow-up of 2.11 years, 113 patients (22.5%) reached the kidney outcome. Kaplan-Meier analysis showed that lower urine pH was associated with a higher risk of kidney outcomes. In Cox proportional hazards models adjusted for proteinuria, baseline eGFR, and IFTA score, urine pH remained independently associated with kidney outcomes (hazard ratio, 0.69; 95% confidence interval, 0.51-0.91). Inclusion of urine pH improved prognostic discrimination beyond established risk factors (Harrell C-index, 0.642 to 0.654). Lower urine pH was also associated with greater medullary cast formation. Conclusion: In patients undergoing native kidney biopsy, lower urine pH was independently associated with adverse kidney outcomes and greater medullary cast formation.

BackgroundThe International Immunoglobulin A nephropathy (IgAN) risk prediction assesses the risk of kidney failure in patients with IgAN. The performance of this risk prediction tool has not been studied in American Indians and Hispanics. We conducted a single-center study to assess the equation performance in this population. MethodsWe calculated the 5-year risk of developing kidney failure with the IgAN risk prediction equation without race and assessed the equation performance using the metrics of calibration, discrimination, and overall prediction error. ResultsThirty-four patients were included, most of whom identified as of Hispanic race/ethnicity (44.1%), or as American Indians (26.5%). At biopsy, the median (IQR) age, serum creatinine, and spot urine protein to creatinine ratio were 38 years (27-45), 2.15 mg/dl (1.51-3.04), and 2.7 g/g (1.5-5.8), respectively. The equation identified patients at high risk of developing kidney failure early with a concordance statistic of 0.79 (95% CI 0.68 - 0.89). The agreement between observed and predicted outcomes at 5 years was marginal, with over-estimation of risk for patients with low observed risk and vice versa. Overall prediction error was suboptimal in this cohort [index of prediction accuracy 0.34 (0.03 - 0.51)]. ConclusionsThe International IgAN risk prediction equation without race accurately identified patients at elevated risk of developing kidney failure. At 5 years, the agreement between the observed and predicted outcomes was sub-optimal, possibly due to advanced kidney disease in this cohort. A diverse development population may improve the risk prediction.

BackgroundKidney stones, predominantly composed of calcium oxalate, are a prevalent and recurrent urological condition. Given their high incidence and recurrence rates, understanding their pathogenesis and identifying effective treatment strategies are imperative. MethodsIn this study, we established a calcium oxalate nephrolithiasis model using tree shrews, a primate-like animal species. When compared to commonly used rodent models (rats and mice), the tree shrew model demonstrated superior reproducibility and relevance. And leveraging transcriptome sequencing and comprehensive bioinformatics analysis. Resultswe identified 1,927 differentially expressed genes, including 1,450 upregulated and 476 downregulated genes. Furthermore, we annotated these genes to 41 KEGG enriched pathways and 1,413 GO enrichments, encompassing 1276 Biological Processes, 72 Cellular Components, and Molecular Functions. Notably, we prioritized the top 50 core genes that could potentially underlie the pathogenesis of calcium oxalate nephrolithiasis. ConclusionsOur findings establish the tree shrew as a relevant model for studying kidney stone formation and provide valuable insights into the underlying molecular mechanisms. These insights hold promise for the development of novel therapeutic strategies to address this significant health burden.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression remains challenging. This multicenter study developed and validated POCC, a machine learning model for predicting kidney failure risk at 1, 3, and 5 years post-diagnosis in CAKUT patients. Two versions were created using data from 2,249 children. The general model achieved internal AUCs of 0.93-0.99 and external AUCs of 0.90-0.98 and 0.81- 0.90 in two independent validations at pediatric and general hospitals, respectively. The specialized model, integrating congenital-hereditary features, achieved internal AUCs of 0.93-0.99 and external AUCs of 0.91-0.96 in pediatric hospitals. Deployed online, POCC demonstrated 90.7% accuracy in real-world validation, with the specialized model reaching 100% sensitivity and specificity for 5-year predictions. As the first tool for multi-timepoint risk prediction across diverse CAKUT subphenotypes per patient, POCC has strong potential to support personalized management.

INTRODUCTIONThe role of carotid revascularization for asymptomatic carotid artery stenosis (ACAS) in dialysis-dependent end-stage kidney disease (ESKD) patients remains poorly defined, as these patients have high periprocedural risks and limited long-term survival. This study evaluated the trends in carotid revascularization in this group and studied associated short- and long-term outcomes. METHODSWe analyzed the United States Renal Data System (USRDS) to study dialysis-dependent ESKD patients with ACAS who underwent carotid endarterectomy (CEA) or carotid artery stenosis (CAS) between 2010 and 2019. Primary outcomes included trends in CEA and CAS utilization, 30-day stroke or death rates. Secondary outcomes were in-hospital and one-year stroke or death rates. RESULTSAmong 11,405 ESKD patients on dialysis with ACAS, 4954 underwent carotid revascularization (4098 CEA; 856 CAS). CEA rates reduced by 55% and CAS by 50% between 2010 and 2019. CAS was associated with a higher 30-day composite stroke or death rate (7.13% vs 4.5%; P=0.0014), in-hospital stroke or death rate (3.39% vs 2.22%, P=0.0433), and 1-year stroke or death rate (33.1% vs 25.4%, P<0.001) compared to CEA. No significant improvements in the outcomes over time were observed. CONCLUSIONCarotid revascularization rates for ACAS have declined among dialysis-dependent ESKD patients, yet both CEA and CAS are associated with significant procedure-related stroke and death risk. These support a cautious approach and underscore the need for a more selective and individualized shared decision-making approach in this high-risk population.

BackgroundThe Kidney Precision Medicine Project (KPMP) consortium aims to redefine chronic kidney disease (CKD) by integrating clinical, pathological, and molecular tissue data from kidney biopsies. Here, we demonstrate how biopsy data in CKD can clarify disease etiology and contribute to understandings of disease pathophysiology and clinical prognosis. MethodsThe KPMP is obtaining research kidney biopsies from individuals with CKD (defined as an estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73m2 and/or albuminuria [&ge;]30 mg/g creatinine) and diabetes (enrolled as diabetes and CKD or DKD) or hypertension (enrolled as hypertension and CKD or HCKD). A team of kidney pathologists and nephrologists adjudicated the primary clinico-pathological diagnosis for 258 participants with CKD. We compared pathological features and kidney transcriptional signatures between participants with a primary adjudicated diagnosis of diabetic nephropathy and those with other causes of CKD. We developed a model using clinical and biomarker data that predicted the probability of diabetic nephropathy and tested associations of the signature with CKD progression among Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes (n=229). ResultsAmong 183 participants enrolled as DKD, 102 (56%) had a primary adjudicated clinico-pathologic diagnosis of diabetic nephropathy. Among 75 participants enrolled as HCKD, 42 (56%) had a primary diagnosis of hypertension-associated kidney disease. Those with diabetic nephropathy, compared with other diagnoses, had more severe interstitial fibrosis, tubular atrophy, tubular injury, segmental sclerosis, and severe arteriolar hyalinosis, and single-nucleus and single-cell transcriptional analyses revealed upregulation of immune and inflammatory pathways and downregulation of oxidative phosphorylation. A combination of age, hemoglobin A1c, urine albumin-creatinine ratio, and serum KIM-1 and sTNFR1 predicted a clinico-pathologic diagnosis of diabetic nephropathy in the KPMP (AUC 0.82, 95% CI 0.75-0.89) and was associated with an increased risk of CKD progression among patients with diabetes enrolled in CRIC (HR 1.48 [95% CI 1.27-1.73] per 10% higher predicted probability of diabetic nephropathy). ConclusionIn common presentations of CKD, kidney biopsies may alter a priori impressions, reveal a diversity of diagnosis, structure, and function that is associated with clinical outcomes and can impact therapeutic decisions.

BackgroundUndiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs. MethodsWe collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs from between 2005 and 2018. ResultsUsing 12-lead ECG waveforms, our deep learning algorithm achieved discrimination for CKD of any stage with an AUC of 0.77 (95% CI 0.76-0.77) in a held-out test set and an AUC of 0.71 (0.71-0.71) in the external cohort. Our 12-lead ECG-based model performance was consistent across the severity of CKD, with an AUC of 0.75 (0.0.74-0.77) for mild CKD, AUC of 0.76 (0.75-0.77) for moderate-severe CKD, and an AUC of 0.78 (0.77-0.79) for ESRD. In our internal health system with 1-lead ECG waveform data, our model achieved an AUC of 0.74 (0.74-0.75) in detecting any stage CKD. In the external cohort, our 1-lead ECG-based model achieved an AUC of 0.70 (0.70-0.70). In patients under 60 years old, our model achieved high performance in detecting any stage CKD with both 12-lead (AUC 0.84 [0.84-0.85]) and 1-lead ECG waveform (0.82 [0.81-0.83]). ConclusionsOur deep learning algorithm was able to detect CKD using ECG waveforms, with particularly strong performance in younger patients and patients with more severe stages of CKD. Given the high global burden of undiagnosed CKD, further studies are warranted to evaluate the clinical utility of ECG-based CKD screening.

BackgroundThe heterogeneous phenotype of diabetic nephropathy (DN) from type 2 diabetes complicates appropriate treatment approaches and outcome prediction. Kidney histology helps diagnose DN and predict its outcomes, and an artificial intelligence (AI)- based approach will maximize clinical utility of histopathological evaluation. Herein, we addressed whether AI-based integration of urine proteomics and image features improves DN classification and its outcome prediction, altogether augmenting and advancing pathology practice. MethodsWe studied whole slide images (WSIs) of periodic acid-Schiff-stained kidney biopsies from 56 DN patients with associated urinary proteomics data. We identified urinary proteins differentially expressed in patients who developed end-stage kidney disease (ESKD) within two years of biopsy. Extending our previously published human-AI-loop pipeline, six renal sub-compartments were computationally segmented from each WSI. Hand-engineered image features for glomeruli and tubules, and urinary protein measurements, were used as inputs to deep-learning frameworks to predict ESKD outcome. Differential expression was correlated with digital image features using the Spearman rank sum coefficient. ResultsA total of 45 urinary proteins were differentially detected in progressors, which was most predictive of ESKD (AUC=0.95), while tubular and glomerular features were less predictive (AUC=0.71 and AUC=0.63, respectively). Accordingly, a correlation map between canonical cell-type proteins, such as epidermal growth factor and secreted phosphoprotein 1, and AI-based image features was obtained, which supports previous pathobiological results. Conclusions: Computational method-based integration of urinary and image biomarkers may improve the pathophysiological understanding of DN progression as well as carry clinical implications in histopathological evaluation. Significance StatementThe complex phenotype of diabetic nephropathy from type 2 diabetes complicates diagnosis and prognosis of patients. Kidney histology may help overcome this difficult situation, particularly if it further suggests molecular profiles. This study describes a method using panoptic segmentation and deep learning to interrogate both urinary proteomics and histomorphometric image features to predict whether patients progress to end-stage kidney disease since biopsy date. A subset of urinary proteomics had the most predictive power in identifying progressors, which could annotate significant tubular and glomerular features related to outcomes. This computational method, which aligns molecular profiles and histology, may improve our understanding of pathophysiological progression of diabetic nephropathy as well as carry clinical implications in histopathological evaluation.

OBJECTIVESPrimary care for chronic kidney disease (CKD) stage 1-2 has recently been shown to slow CKD progression. We describe an approach to detect early decline in glomerular filtration rate (GFR) above 60 milliliters per minute (mL/min), before CKD stage 3. METHODSWe re-examined a standard reference that found low tubular secretion of creatinine (TScr) at GFRs above 80 mL/min and suggested "...observation of subtle changes in serum creatinine levels". We explain why that method extends down to 60 mL/min and summarize why estimated GFR (eGFR) is unreliable above 60 mL/min. RESULTSFour patient cases show how serum creatinine (sCr) referenced to an individuals historical maximum suggests increased risk, triggering investigation to separate benign processes that alter sCr from decline in GFR of prechronic kidney disease (preCKD). CONCLUSIONSAt GFRs above 60 mL/min, serial creatinine is more reliable than GFR estimating equations and appears practical for race-free clinical monitoring and early intervention. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/24313678v9_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1d7e4d6org.highwire.dtl.DTLVardef@f0b51dorg.highwire.dtl.DTLVardef@102a01forg.highwire.dtl.DTLVardef@15e7597_HPS_FORMAT_FIGEXP M_FIG C_FIG STRENGTHS OF THIS STUDYO_LIIncludes serial creatinine proof-of-concept cases that show preCKD C_LIO_LIReviews race-free properties that favor serial creatinine for early kidney disease C_LIO_LIAddresses mathematical limitations of GFR estimating equations C_LIO_LIRe-analyzes a 40-year-old standard reference that still influences research, showing its overlooked importance for CKD stage 1-2 C_LI

BackgroundChronic kidney disease (CKD) affects over 10% of the global population. A genetic diagnosis can be identified in about 30% of pediatric and 10-30% of adults, informing treatment, prognosis, and family-based risk assessment. However, access to renal genetics services remains limited across many healthcare systems. ObjectivesTo characterize the clinical and genetic landscape of CKD in patients referred for genetic evaluation within a Canadian single-centre nephrology-genetics program, and to evaluate the diagnostic yield and clinical utility of an integrated renal genetics clinic. MethodsWe conducted a retrospective study of 206 probands referred for suspected hereditary kidney disease to the McGill University Health Centre Renal Genetics Clinic between 2019 and 2024. Genetic testing was performed in accredited laboratories, predominantly through comprehensive multi-gene panels or phenotype-directed exome sequencing. All reported variants were classified according to the ACMG/AMP criteria, and variants of uncertain significance were reevaluated post hoc using standardized quantitative and evidence-tier frameworks to determine whether they trended toward "likely pathogenic" ("hot") or "likely benign" ("cold"), without implying formal reclassification. ResultsA molecular diagnosis was established in 34.5% of probands (71/206), implicating pathogenic or likely pathogenic variants across 35 genes representing diverse monogenic kidney disease etiologies. The highest diagnostic yields were observed in cystic nephropathy (51.9%), tubulopathy (38.5%), and glomerulopathy (35.6%). Genetic results affected clinical management in 23.9% of diagnosed cases, leading to changes in treatment for 16.9%, modification of transplant management in 5.6%, informed living donor risk assessment in 14.1%, and facilitated cascade testing in 66.2% of families. CKD of unknown etiology comprised 28% of the cohort, with a genetic diagnosis reached in 25.9% of these cases. Variants of uncertain significance (VUS) were reported in 39.3% of probands, with higher overall variant burden and lower diagnostic yields among individuals of non-European ancestry. Post hoc internal reassessment stratified 67.7% of VUS as mid or lower confidence ("cold") and 32.2% as higher confidence ("hot") or likely pathogenic. ConclusionsIn a diverse urban population, integration of a dedicated renal genetics service within nephrology care achieved high diagnostic yield, substantially influenced management, and facilitated family risk assessment. Structured referral pathways and multidisciplinary variant interpretation optimize the clinical utility of genetic testing in CKD.