Kidney International Reports

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.

Chronic kidney disease is a major global health burden, and its early detection is critical for delaying progression to kidney failure using recently developed targeted therapies. However, current diagnostic screening relies heavily on blood markers that are confounded by muscle mass, and on urine tests that frequently miss structural damage occurring without protein leakage. This creates a critical diagnostic blind spot that hinders timely intervention. Here we show a non-invasive liquid biopsy platform that quantifies a specific protein marker, MUC1, on urinary extracellular vesicles to accurately assess renal parenchymal integrity. By bypassing the systemic metabolic noise of traditional blood tests, our assay provides a remarkably stable, person-specific functional signature. Following extensive validation across diverse cohorts, our longitudinal analysis demonstrated that the discrepancy between this novel urine-based readout and standard blood tests unmasks hidden renal vulnerability, successfully predicting rapid functional decline. By comprehensively evaluating both tubular and glomerular integrity from a single spot urine sample, these findings establish a completely non-invasive, highly scalable prescreening tool that resolves the diagnostic blind spot, enabling broader early detection strategies and ushering in a new era of proactive risk management.

Urinary stone disease (USD) is a major health burden affecting >10% of the UK population at some time. While stone disease is strongly associated with lifestyle, genetic factors also predispose to USD: common genetic variants at multiple loci from genome-wide association studies account for 5% of the estimated 45% heritability of the disorder. We investigated the extent to which rare genetic variation contributes to the unexplained heritability of USD. Among participants of the UK 100,000 genome project, we identified 374 unrelated individuals assigned diagnostic codes indicative of USD. We performed whole genome gene-based variant burden testing and polygenic risk scoring against a control population of 24,930 genetic ancestry matched controls. We observed (and replicated in an independent dataset) exome-wide significant enrichment (P=2.61x10-07) of monoallelic rare, predicted damaging variants in SLC34A3 (previously associated with autosomal recessive hereditary hypophosphataemic rickets with hypercalciuria) present in 19 (5%) cases compared with 1.6% of controls. The risk of USD with a monoallelic SLC34A3 variant (OR=3.75, 95% CI 2.27-5.91) was greater than the top decile of polygenic risk (OR=2.31, 95% CI 1.12-3.51). Addition of the SLC34A3 variant binary to a linear model including polygenic score increased the estimated variance explained, increasing the liability adjusted pseudo-R2 from 5.1% to 14.2%. We also observed significant association at OR9K2, an olfactory receptor, but this signal was not replicated. In this cohort rare variants in SLC34A3 were the most important genetic risk factor for USD, with levels of pathogenicity intermediate between the fully penetrant rare variants linked with Mendelian disorders and the weaker effects of common variants associated with USD. These findings explain some of the heritability unexplained by prior common variant GWAS.

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.

Rare kidney diseases are not well characterised, despite making a significant contribution to the burden of kidney disease globally. The National Registry of Rare Kidney Diseases (RaDaR) collects longitudinal disease and treatment-related data from people living with rare kidney diseases across the UK, and is the largest rare kidney disease registry in the world. We present the clinical demographics and renal function of 25,880 prevalent patients and evaluate for any potential recruitment bias to RaDaR. RaDaR has automatic linkage with the UK Renal Registry (UKRR, with which all UK patients receiving Kidney Replacement Therapy (KRT) are registered). To assess for recruitment bias to RaDaR, ethnicity and socioeconomic status of 1) prevalent RaDaR patients receiving KRT were compared with patients with eligible rare disease diagnoses receiving KRT in the UKRR 2) patients recruited to RaDaR and all eligible unrecruited patients at two renal centres were compared 3) the age-stratified ethnicity distribution of RaDaR patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD) was compared to the English Census. We found evidence of some disparities in ethnicity and social deprivation in recruitment to RaDaR, however these were not consistent across all comparisons. Predominant rare kidney diseases in adults were ADPKD (29.2%), Vasculitis (15.8%) and IgA nephropathy (15.7%), compared to Idiopathic nephrotic syndrome (43.6%), Vasculitis (10.8%) and Alport Syndrome (5.9%) in children. Compared with either adults recruited to RaDaR or the English population, children recruited to RaDaR were more likely to be of Asian ethnicity and live in more socially deprived areas. Lay SummaryRare kidney diseases make a significant contribution to the number of people living with kidney disease globally: >25% of adults and >50% of children with kidney failure have a rare disease. However, there is a lack of high-quality published data on how these conditions present, and in which patient groups. Patients often face delays in diagnosis and lack of reliable information on their condition once diagnosed. The UK National Registry of Rare Kidney Diseases (RaDaR) was formed in 2010 to address this knowledge gap. It collects long-term data for UK patients with rare kidney conditions, and is the largest rare kidney disease registry in the world. Here, we present information about 25,880 adults and children recruited to RaDaR, including ethnicity, socioeconomic status, and kidney function, and investigate whether there is any bias in recruitment to RaDaR. To our knowledge, this is the largest epidemiological description of rare kidney diseases worldwide.

IntroductionGenome-wide association studies (GWAS) for kidney function have mainly focused on creatinine-based glomerular filtration rate (eGFRcrea), which is affected by variation in muscle mass. Moreover, the genetic basis of the sexual dimorphism of chronic kidney disease is underexplored. MethodsWe performed a GWA meta-analysis for creatinine clearance (CrCl), a muscle mass-independent kidney function phenotype, in 58,976 individuals of European descent from the Lifelines Cohort Study. ResultsWe identified 16 independent loci with 21 genome-wide significant lead single nucleotide polymorphisms (SNPs) associated with CrCl, two of which had not been reported previously in kidney function GWASs: rs146465192, located near the RP1-249F5.3 gene (effect allele frequency (EAF) = 0.01, P = 3.38 x 10-9) and rs117014836, located near the AGPAT4 gene (EAF = 0.02, P = 5.42 x 10-9). Both SNPs were also associated with eGFRcrea in Lifelines (rs146465192: P = 1.34 x 10-8; rs117014836: P = 3.64 x 10-7), but not in previously published eGFR GWASs. In silico follow-up analyses revealed that rs146465192 was associated with plasma IGF2R ({beta} = -0.519, P = 1.40 x 10-6), while rs117014836 was associated with blood expression levels of AGPAT4 (eQTL P = 6.54 x 10-6). Furthermore, we identified two female-specific CrCl loci (t-statistic P < 0.004): rs8002366 (GPC6) and rs12908437 (IGF1R), associated with GPC6 expression in kidney (eQTL P = 8.38 x 10-10) and IGF1R expression in blood (eQTL P = 2.62 x 10-6), respectively. ConclusionThis first large-scale GWAS of CrCl revealed two new genetic variants among both sexes and two female-specific variants influencing kidney function. Lay summaryKidney function is a complex phenotype influenced by many different factors, including genetics. Earlier genetic studies often used the creatinine-based estimated glomerular filtration rate (eGFRcrea) as the measure of kidney function. However, eGFRcrea is influenced not just by kidney function but also by an individuals muscle mass, which may distort the results. Therefore, in this study we used creatinine clearance (CrCl), a measure of kidney function independent of muscle mass, to look for genes in a European-ancestry population. We identified 16 genetic regions; two of which had not been found before. We also found two additional regions that were only related to CrCl in females. This shows the added value of investigating CrCl and suggests sex-based differences in how genetics affect kidney function.

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.

Abstract Background Alport Syndrome (AS), caused by pathogenic variants in type IV collagen genes COL4A3/4/5, is a leading monogenic cause of Kidney Failure (KF). Clinical course varies widely, and disease specific predictors of progression relevant to clinical care and trial design remain incompletely defined. Methods In this retrospective cohort study of individuals with AS in the UK National Registry of Rare Kidney Diseases, patients were classified as having AS or heterozygous genotypes and followed to assess proteinuria progression, eGFR slope and kidney survival. Proteinuria and eGFR trajectories were analysed using mixed effects regression models; kidney survival using Kaplan Meier analysis. Results Among 1032 participants (median follow up 11.6 years; 47% female), 475 (46%) had AS genotypes (Male XLAS or autosomal recessive AS). eGFR decline accelerated with advancing CKD stage across all genotypes (p<0.001). Proteinuria increased as eGFR declined and occurred earlier in AS genotypes. After reaching proteinuria thresholds of more than 1.0 and 3.0g/g, kidney survival over the subsequent 5 years did not differ significantly between genotypes (logrank p=0.14, p=0.17, respectively), although modest differences emerged over longer follow-up. Across eGFR thresholds (90, 60, and 45mL/min/1.73m2), higher proteinuria was associated with shorter time to KF; for example, at eGFR 45mL/min/1.73m2, median time to KF was 3.0 years (IQR, 1.6-5.4) for above-median vs 6.5 years (5.1-not estimable) for below-median proteinuria (p<0.0001). Almost all patients who reached KF had developed proteinuria of more than 0.3g/g. Conclusion In this national cohort, eGFR decline accelerated with CKD stage and proteinuria was strongly associated with progression to KF across genotypes. The non linearity of eGFR decline may inform its interpretation in clinical practice and use as a trial endpoint. Once comparable proteinuria levels were reached, differences in outcomes by genotype were attenuated, supporting proteinuria as a key prognostic marker and strengthening rationale for its use as a surrogate endpoint in AS clinical trials

Non-HLA donor-recipient (D-R) genetic mismatches contribute to kidney allograft injury and long-term graft loss, but their clinical use is limited by the unavailability of donor DNA after transplantation. We tested whether non-invasively obtained, recipient-derived samples could be used to infer donor genotype and D-R mismatches. Genomic DNA (g-DNA) of 11 unselected kidney transplant recipients and donors underwent whole-exome sequencing (100x). Additional customized probes were added for intronic coverage (300x) of 55 targeted non-HLA genes of reported clinical relevance. Variants identified from sequencing results were compared with plasma cell-free DNA (cfDNA), urine cell-pellet DNA (U-DNA) obtained from the same recipients. Genome-wide-, exonic-, or non-synonymous exonic- mismatches in transmembrane or secreted proteins, and mismatches within target genes were benchmarked using donor g-DNA to generate mismatch scores for each D-R pair. Within each of these genomic scales of mismatch, U-DNA identified D-R mismatches significantly better than the corresponding cfDNA (P<0.001 for each comparison). U-DNA also identified gene-level mismatches in the LIMS1 gene, and correctly inferred established donor-origin risk alleles, including SHROOM3 and APOL1. Our findings demonstrate proof-of-concept that U-DNA in tandem with recipient genome, can non-invasively infer relevant non-HLA loci/mismatches circumventing the need for the donor genomic DNA.

BackgroundUnexplained kidney failure (uKF) affects 15% of individuals requiring kidney replacement therapy. Absence of a diagnosis creates uncertainty around recurrence after transplantation, familial risk, and participation in therapeutic trials. Whole genome sequencing (WGS) was used to identify genetic variants contributing to uKF. Methods218 patients who presented with uKF < 50 years old were recruited to the UKs 100,000 Genomes Project. Candidate variants in 183 genes were reviewed for pathogenicity by a multidisciplinary team. Gene-based association testing, structural variant analyses, and assessment of high-risk APOL1 genotypes were performed. Polygenic risk scores (PRS) were calculated for chronic kidney disease (CKD), and various glomerulonephritides. HLA associations in those with APOL1 high-risk genotype were also investigated. ResultsA positive genetic diagnosis was made in 17% (38/218) of patients. The median age of uKF onset was 36 years. Fewer genetic diagnoses were found in those aged [&ge;] 36 years compared to younger individuals, both with (11% vs. 35%, P=0.03) and without (5% vs. 19%, P=0.05) a family history. Three patients [&ge;] 36 years without a family history had pathogenic variants in type IV collagen genes. High-risk APOL1 genotypes were enriched in patients with recent African ancestry (52% vs 8.4%, P=5.97x10-8). Dividing the uKF cohort by subsequent identification of monogenic diagnosis, High-risk APOL1 genotype, or neither, we found that the SSNS PRS was higher in those with High-risk APOL1 (P=0.048), driven by differences at HLA-DQB1*03:19 (P=0.001). ConclusionsThese findings estimate the likelihood of a genetic diagnosis using WGS in uKF patients, showing fewer diagnoses in older patients without a family history. APOL1 contributes significantly to uKF in those with recent African ancestry, potentially interacting with HLA-DQB1. The lack of PRS signal for CKD suggests distinct biology between uKF and more common causes of CKD.

In kidney transplantation, obtaining early information about the risk of graft failure helps physicians and patients anticipate a potential return to dialysis or retransplantation. Clinical prediction models are commonly used to obtain such risk estimation, but their performance needs to be continuously evaluated in various contexts. We propose an external validation study of the Kidney Transplant Failure Score in a pooled sample of 3,144 patients transplanted between 2010 and 2015 in France, Belgium, Norway and Canada. This score is used at the first transplantation anniversary to predict the probability of graft failure over the following seven years. The target population was defined as adult recipients of a kidney from a neurologically deceased donor without graft failure in the first year post-transplantation. Graft failure was defined as a return to dialysis. Around 10% of patients returned to dialysis, and 12.6% died during the seven-year follow-up. The KTFS authors fitted a Cox model and then adjusted its coefficients to maximize the discrimination, yielding the KTFS final version. We evaluated the performance of the initial and final versions of the KTFS, as well as the performance of another model we developed to consider death as a competing event. All KTFS versions yielded similarly good discrimination (area under the time-dependant receiver operating curve around from 0.79 [0.76-0.82] to 0.80 [0.77-0.84]), while the discrimination-optimized one presented important miscalibration. Clinical utility, assessed through net benefit, was also the lowest for the discrimination-optimized version. Our results warn against using the current KTFS version and recommend using either the initial coefficients or the competing risk-based ones instead. Lay summaryFrench nephrologists have used the Kidney Transplant Failure Score (KTFS) for nearly fifteen years to predict kidney graft failure eight years after the transplantation. Because predictive performance decreases over time, we first verified that the score could still predict correctly in France and also in other countries. Then, we compared the different KTFS formulas to find that the one currently used is suboptimal and should be avoided. Our findings show that the KTFS is still a reliable source of information for both kidney recipients and nephrologists when using its first version.

Shroom Family Member 3 (SHROOM3) encodes an actin-binding protein that impacts kidney development. Genome-wide association studies (GWAS) identified CKD-associated common variants around SHROOM3 and Shroom3 knock-out mice develop glomerular abnormalities. We sought to evaluate the impact of genetically predicted SHROOM3 expression on kidney traits and the circulating proteome, and validate findings in a mouse model. Genetic instruments for SHROOM3 expression in distinct kidney compartments (glomerular n=240, tubulointerstitial n=311) were constructed using single cell sequencing data from NephQTL2. Using two-sample Mendelian randomization, we evaluated the effects of glomerular and tubulointerstitial SHROOM3 expression on kidney traits and the concentration of 1,463 plasma proteins in the UK Biobank and CKDGen Consortium. Genetically predicted tubulointerstitial SHROOM3 expression colocalized with the genetic signals for eGFR and albuminuria. A 34% reduction in genetically predicted tubulointerstitial SHROOM3 expression was associated with a 0.3% increase in cross-sectional eGFR (P = 6.8x10-4), a 1.5% increase in albuminuria (P = 0.01), and a 2.2% reduction in plasma COL18A1 concentration (P = 1.2x10-5). In contrast, genetically predicted glomerular SHROOM3 expression showed neither colocalization nor significant Mendelian randomization results. Using immunofluorescence, heterozygous Shroom3 knockout mice had a concordant reduction of Col18a1 in their kidneys, primarily around the tubules. Thus, reduced tubulointerstitial SHROOM3 expression, but not glomerular, is associated with increased cross-sectional eGFR, increased uACR, and reduced plasma COL18A1 and Shroom3 knockout leads to reduced kidney Col18a1, agnostically linking SHROOM3 and COL18A1 in CKD pathogenesis. Lay SummaryThe SHROOM3 gene is consistently linked to kidney disease, but we have yet to fully understand why. We looked at how genetic changes affecting SHROOM3 impact different regions of the kidney. We found that less SHROOM3 in kidney tubules led to increased kidney filtration, but also increased leakage of protein into the urine. After looking at over 1000 proteins, we identified a new link between SHROOM3 and a collagen protein called COL18A1. We then confirmed the link between SHROOM3 and COL18A1 by imaging the kidneys of mice designed to have less SHROOM3. Our results suggest an interaction of SHROOM3 and COL18A1 leads to increased pressure on the kidney filtration system.

BackgroundThe heterogeneous course of IgA nephropathy limits risk stratification based on static markers. We sought to identify clinical trajectory subgroups using unsupervised deep learning and validate their association with long-term renal outcomes. MethodsWe analyzed 873 biopsy-proven cases from the nationwide Japan IgA Nephropathy Prospective Cohort Study (J-IGACS). A long short-term memory autoencoder was used to generate low-dimensional representations of hematuria, proteinuria, and estimated glomerular filtration rate (eGFR) over the first 12 months after renal biopsy. We applied k-means clustering to these representations. The primary outcome was a 30% decline in eGFR from baseline. ResultsThree trajectory clusters were identified. Cluster 1 (n=284) showed rapid resolution of hematuria and proteinuria with stable eGFR and favorable prognosis. Cluster 2 (n=215) exhibited persistent severe hematuria, modest proteinuria reduction, and mild eGFR decline. Cluster 3 (n=374) presented with the lowest baseline eGFR and showed further decline within the first 12 months after biopsy, with incomplete proteinuria resolution despite milder hematuria. Clusters 2 and 3 had worse outcomes than Cluster 1. In Cox models adjusted for age, mean arterial pressure, and Oxford classification, cluster membership was independently associated with the primary outcome (hazard ratio 2.12; 95% CI 1.35-3.34 for Clusters 2 and 3 versus 1). ConclusionsAn unsupervised deep learning approach applied to trajectories of hematuria, proteinuria, and eGFR within the first year after renal biopsy identified three patient subgroups with distinct long-term renal risks. Trajectory-based classification may complement established baseline predictors and support more dynamic risk stratification in IgA nephropathy. Key PointsO_LIDeep learning on clinical trajectories revealed the heterogeneity of IgA nephropathy, identifying three distinct patient subgroups. C_LIO_LIThese subgroups, reflecting a spectrum of progression patterns and treatment responses, had distinct long-term renal outcomes. C_LIO_LIThis approach may provide a dynamic framework to understand clinical course, moving beyond static, single-point risk assessment. C_LI

Introduction Acute interstitial nephritis is an important differential diagnosis in patients with deteriorating kidney function. Diagnosis currently requires kidney biopsy, an invasive procedure associated with risks. We hypothesized that urinary T cells may serve as a non-invasive biomarker for acute interstitial nephritis. Methods A total of 320 patients undergoing clinically indicated kidney biopsy were enrolled in a discovery cohort at Charite Berlin (n = 80), an internal validation cohort at Charite (n = 100), and an external validation cohort at The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou (n = 140). Urinary immune cells were assessed by flow cytometry. Renal T cell infiltration was evaluated by immunofluorescence in kidney biopsy specimens from the discovery and internal validation cohorts, including 16 patients with acute interstitial nephritis and 9 patients without acute interstitial nephritis. Additionally, CXCL9 was measured by ELISA in 102 urine samples from these cohorts. Results Across all cohorts, 27 patients (8.4%) were diagnosed with acute interstitial nephritis. In the discovery cohort, multiple urinary T cell subsets were increased in acute interstitial nephritis, with activated CD4+ effector memory T cells expressing CD38 and HLA-DR showing the strongest diagnostic performance. This marker outperformed urinary monocytes, eosinophils, and CXCL9 and was validated in both independent cohorts. Across all cohorts, the area under the receiver operating characteristic curve was 0.84 and increased to 0.91 after exclusion of 8 patients receiving corticosteroids. A cutoff of 211 activated CD4+ effector memory T cells per 100 mL urine yielded a sensitivity of 78% and a specificity of 81%. Urinary activated CD4+ effector memory T cell counts correlated with renal CD4+ and CD4+ CD38+ T cell infiltration in acute interstitial nephritis. Conclusions Urinary activated CD4+ effector memory T cells expressing CD38 and HLA-DR represent a promising non-invasive biomarker for the diagnosis of acute interstitial nephritis.

BackgroundAcute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. MethodsElectronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. ResultsOut of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality. ConclusionsUncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.

One of the biggest challenges in treating chronic kidney disease (CKD) is that 80 - 90% of people with this disease are undiagnosed, and thus do not access healthcare promptly. The problem arises because early stage CKD has no overt symptoms and the current policy is to perform diagnostic tests (e.g. glomerular filtration rate and urinary albumin to creatinine ratio) only when accompanied by risk factors such as old age, hypertension and diabetes. Genetic testing may be useful to identify those most likely to have CKD and who therefore may benefit from screening. This work describes the development of an algorithm termed RICK (for RIsk for Chronic Kidney disease) that employs a polygenic risk score for CKD plus clinical risk factors to identify people at risk. In data from the UK biobank, those in the top decile of RICK have a 4.4-fold increased risk of CKD, and about 34% of all those with CKD are included in this decile. Using RICK to selectively test those in the general population with highest risk may help in early identification of CKD and thereby facilitate early access to renal healthcare. Lay SummaryOne of the biggest challenges in renal health is that 80 - 90% of people with Chronic Kidney Disease (CKD) are undiagnosed, and thus do not access healthcare promptly. The problem arises because early stage CKD has no overt symptoms and the current policy is to perform diagnostic tests (e.g. glomerular filtration rate and urinary albumin to creatinine ratio) only when accompanied by risk factors such as old age, hypertension and diabetes. This work describes the development of an algorithm termed RICK (for RIsk for Chronic Kidney disease) that employs a genetic test for CKD plus clinical risk factors to identify people at risk and who therefore may benefit from screening. Those in the top ten percentile of RICK have a 15-fold increased risk of stage 3 CKD. Diagnostic testing of the top decile would capture about 43% of the undiagnosed stage 3 CKD cases. Thus, using RICK to selectively test those with highest risk could have an immense impact on renal health by facilitating early identification of CKD and thereby enabling access to healthcare.

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.

Background and hypothesis Autosomal dominant polycystic kidney disease (ADPKD) affects over 12 million people worldwide including an estimated 30,000-70,000 in the United Kingdom (UK). Tolvaptan is the only disease-modifying therapy approved for rapidly progressing disease. Despite national guidance, prescribing rates were hypothesised to vary by kidney centre. Treatment may not always align with guidelines: some patients eligible for tolvaptan may not be initiated, while other patients initiated on tolvaptan may not meet eligibility criteria. This may have important consequences for healthcare costs and health-related quality of life. Methods The National Registry of Rare Kidney Diseases (RaDaR) collects longitudinal data from UK NHS kidney centres. This retrospective cohort study used routinely collected data (2016-2023) to examine tolvaptan prescribing across kidney centres. Kidney centre-level initiation patterns were described, assessed using mixed-effects logistic regression and visualised with funnel plots. Cost-effectiveness analyses combined observed prescribing practices under likely negotiated commercial discounts to estimate costs and quality-adjusted life year (QALY) consequences of prescribing at the national level. Results Our study included 3,609 people with ADPKD from 72 kidney centres. Patients eligible for tolvaptan who were not initiated accounted for 34.8% (292/839). Across centres, five (6.9%) initiated tolvaptan significantly more than expected among eligible participants, while one centre (1.4%) initiated significantly less. Nationally, this could result in up to {pound}53.7 million in lost savings (assuming a 60% medication price reduction) and result in up to 1,245 lost QALYs. Patients initiated on tolvaptan who were not eligible accounted for 26.1% (103/395). Only one centre had significantly fewer eligible patients than expected among initiated patients. Nationally, this could cost up to {pound}15.9 million (assuming a 60% medication price reduction). Conclusions There is evidence of variation in tolvaptan prescribing in the UK. A substantial proportion of patients eligible for tolvaptan were not initiated at the cohort-level, with evidence of variation between centres suggesting differences in treatment decision-making. A substantial proportion of patients initiated on tolvaptan were not eligible at the cohort-level, but there was limited evidence of variation between centres. Together, these findings raise questions regarding the potential consistency of clinical decision-making, equitable access to a sole disease-modifying therapy in a rare disease, alignment with national guidance, and effective use of healthcare resources.

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.

Monoallelic pathogenic or likely pathogenic variants in COL4A3 and COL4A4 occur in approximately 1 in 106 individuals, yet whether these paralogous genes confer equivalent pathogenicity for the same variant classes has not been tested at population scale. Using whole-genome sequencing data from the UK Biobank (UKB; n = 500,000), with replication in the All of Us Research Program (n = 414,000), we performed per-variant association testing, gene-based collapsing analyses and phenome-wide association studies (PheWAS) across haematuria, proteinuria and chronic kidney disease. We identified 64 COL4A3 and 92 COL4A4 rare variants significantly associated with haematuria or proteinuria, generating a quantitative allelic series for clinical variant interpretation. Glycine substitutions within collagenous domains conferred similar risks in both genes. In contrast, truncating and non-collagenous domain (NC1) missense variants were strongly associated with haematuria and proteinuria in COL4A4 carriers but showed substantially attenuated or absent associations in COL4A3 carriers despite comparable carrier frequencies and predicted pathogenicity scores. These findings were independently replicated in All of Us. Genome-wide association analysis identified the COL4A3/COL4A4 locus as the dominant genetic determinant of haematuria, with the signal attributable to the aggregate effects of rare coding variants and no evidence of independent common variant or trans-acting modifier effects. These findings demonstrate substantial gene-specific differences in tolerance to truncating and NC1 variants between COL4A3 and COL4A4, challenging assumptions of equivalent pathogenicity across paralogous collagen IV genes. Gene identity and not variant class alone, should inform risk stratification, variant interpretation and genetic counselling in individuals carrying collagen IV risk genotypes.