Kidney360

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.

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.

Recent initiatives by the U.S. Food and Drug Administration and the National Institutes of Health to reduce animal testing in drug development have highlighted the need for in vitro platforms that better recapitulate human biology for preclinical safety assessment. Drug-induced nephrotoxicity remains a major cause of drug attrition, underscoring the need for human-relevant kidney models. To address this, a pump-free human patient-derived proximal tubule microphysiological system was developed by integrating human renal proximal tubular epithelial cells (hRPTECs), isolated from non-tumorous nephrectomy cortex, with a porous membrane-based microfluidic device. Expanded hRPTECs were cultured for 10 days under static conditions or rocker-driven shear stress approximating physiological proximal tubular flow. Shear stress increased epithelial density, enhanced proximal tubule marker expression (Na+/K+-ATPase and aquaporin-1), and improved Zonula occludens-1 and occludin localization. Bulk RNA sequencing demonstrated transcriptomic changes associated with enhanced apical maturation and epithelial signature. In cisplatin-induced injury assays, shear-conditioned epithelia exhibited reduced cell density and increased {gamma}H2AX staining, indicating greater sensitivity to nephrotoxicity. These findings demonstrate that rocker-driven shear stress promotes epithelial maturation in patient-derived hRPTECs. The pump-free human patient-derived proximal tubule microphysiological system offers a practical, scalable, and physiologically relevant platform for modeling flow-dependent proximal tubule biology and assessing human-relevant nephrotoxicity.

Chronic kidney disease (CKD) affects nearly 850 million individuals globally; the prevalence of undiagnosed CKD is 60%. Taking advantage of the relationship between CKD and cardiovascular disease, we developed a deep learning (DL) model to detect CKD from parasternal long-axis (PLAX) videos using 325,377 PLAX videos from 62,818 patients at Cedars-Sinai Medical Center (CSMC). We externally validated our model in two independent cohorts of 2,224 patients at Stanford Healthcare (SHC) and 41,611 patients at Kaiser-Permanente Northern California (KPNC). In a held-out test cohort at CSMC, our model detected any stage of CKD with an area under the curve (AUC) of 0.756 [95% confidence interval 0.749 - 0.763], with consistently strong performance in KPNC (AUC 0.718 [0.714 - 0.723]) and SHC (AUC 0.719 [0.704 - 0.735]). Our DL echo model detected CKD with robust performance at two external clinical sites, offering an avenue for noninvasive screening and improved detection rates.

ImportanceObesity-related glomerulopathy (ORG), part of the cardio-renal spectrum, has an early reversible stage of hyperfiltration. Early identification in the obese adolescent population provides an opportunity to reverse the damage. ObjectiveAge-appropriate formulae for estimated glomerular filtration rate (eGFR), are standardized to ideal body surface area (BSA) and provide assessment of renal function in mL/min/1.73 m2 units, may underestimate prevalence of early ORG. We investigated whether adjusting eGFR to actual BSA more readily identifies early ORG. DesignCross sectional cohort study. Data were collected between 2011-2015 and analysis was performed between January-November 2020. SettingElectronic health records clinical data base from 12 academic health centers and community health centers in the New York metropolitan area. Participants22,417 women and girls ages 12-21 years for whom data of body measurements and renal function were available. Main Outcome and measuresThe hypothesis was generated using previously collected health record data. eGFR was calculated in two ways: BSA-standardized eGFR according to KDIGO recommended formula; and Absolute eGFR adjusted to individual BSA. Hyperfiltration was defined above a threshold of 135mL/min/1.73 m2 or 135 mL/min, respectively. The prevalence of hyperfiltration according to each formula was assessed in parallel to 24-hour urine creatinine. Results22,417 female adolescents mean age 17 with high prevalence of underrepresented populations (32.6% African American, 12.8% Caucasians and 40.4% Hispanic) were evaluated. Serum creatinine values and hyperfiltration rates according to BSA-standardized eGFR were similar,13.4-15.3%, across Body Mass Index (BMI) groups. Prevalence of hyperfiltration determined by Absolute eGFR differed across groups: Underweight - 2.3%; Normal 6.1%; Overweight - 17.4%; Obese - 31.4%. This trend paralleled the rise in 24-hour urine creatinine across BMI groups. Conclusions and relevanceAbsolute eGFR more readily identifies early ORG compared to currently used formulae, which are adjusted to an archaic value of a BSA, not representative of current population BMI measures. The high proportion of underrepresented populations in this study accounts for the higher-than-expected obesity rates and should raise awareness for missed opportunities for screening, early diagnosis, and intervention particularly in young Black adults. Key pointsO_ST_ABSQuestionC_ST_ABSDo the currently recommended formulae estimating GFR reliably predict hyperfiltration due to Obesity Related Glomerulopathy (ORG)? FindingsRenal function in relation to BMI was evaluated in a cohort of 22,417 adolescents from the New York metropolitan. Serum creatinine values and BSA-standardized eGFR (mL/min/1.73m2) were similar across BMI groups, and as a result, hyperfiltration rates were also similar. However, Absolute eGFR (mL/min) adjusted to individual BSA, created a positive trend across BMI groups similar to urine creatinine. MeaningAbsolute eGFR better reflects the prevalence of hyperfiltration due to Obesity Related Glomerulopathy providing an opportunity for early intervention and damage reversal.

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.

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.

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.

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

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.

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.

IntroductionOrganoids are miniature organs produced by newly emerging technologies. Kidney organoids originated from human inducible pluripotent stem cells (iPSCs) were developed to recapitulate renal diseases. However, producing iPSC kidney organoids from multiple individuals at the same time and in a uniform condition is still impossible. Here, we report adult renal tubular organoids, "tubuloids", established from primary renal epithelial cells from multiple human individuals in a uniform manner. MethodsKidneys obtained from patients due to the surgery for malignancy were minced into small pieces, and primary renal epithelial tubule cells are cultured. 4 patients had normal kidney function and 4 had mild chronic kidney disease (CKD). Growth factors were added to the primary cultured cells at the same time and Matrigel was added to these 8 lines. ResultsPrimary cultured renal epithelial cells from normal kidneys showed a large number of fine, swollen epithelial appearance. On the other hand, primary cultured kidney epithelial cells from mild CKD kidneys were smaller and slightly elongated than those of normal kidneys. The growth speed was faster in normal kidney cells than in mild CKD cells. At the beginning of the three-dimensionalization (day 0), normal renal tubuloids grew faster than mild CKD tubuloids. The difference in size between normal tubuloids and mild CKD ones became less noticeable on day 5. Both types of tubuloids reached almost same size on day 10. All 8 strains are of different human origin, and uniform tubuloids could be produced at the same time and in a uniform protocol. ConclusionIn terms of pathological models, the differences between mouse models and humans cannot be ignored, and there is a great need for a more human-like model of human pathology from both medical and research perspectives. Our renal tubular organoids can be produced in a uniform manner at the same time. It is expected to be used as a new type of convenient human pathological model.

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

BackgroundMicrovascular dysfunction is a key contributor to the development and progression of chronic kidney disease (CKD), yet direct and reproducible assessment of microvascular function in clinical CKD populations remains limited. Laser Doppler flowmetry (LDF) provides a noninvasive, dynamic assessment of skin microvascular blood flow and may serve as a surrogate measure of systemic microvascular health. However, the extent to which LDF-derived measures relate to kidney function, proteinuria, and kidney histopathology in CKD remains unclear. MethodsWe assessed cutaneous microvascular function in 150 participants with CKD (estimated glomerular filtration rate [eGFR] <90 mL/min/1.73 m{superscript 2}) using a standardized forearm LDF protocol. Baseline perfusion was recorded at [~]30{degrees}C, followed by local heating to 44 {degrees}C to induce hyperemia. The percentage change in perfusion unit (PU) was calculated and used to define microvascular functional reserve. Associations between LDF-derived measures with eGFR and urine protein-to-creatinine ratio (uPCR) were assessed using multivariable linear regression adjusted for demographic and clinical covariates. Unsupervised k-means clustering was performed to identify microvascular phenotypes based on resting PU and microvascular function reserve. Associations of LDF measures with glomerulosclerosis (GS) and interstitial fibrosis and tubular atrophy (IFTA) were evaluated in a subset of participants (n = 20) who underwent clinically indicated kidney biopsies. ResultsAmong 150 CKD participants, the mean (SD) age was 64 (14) years, 46% were female, 38% had diabetes, and 83% had hypertension. The mean eGFR was 42 (21) mL/min/1.73 m{superscript 2} and median uPCR was 0.21 (interquartile range (IQR) 0.11 to 1.20) mg/mg. Higher baseline PU ({beta} = -12; 95% CI, -24 to -1) and reduced percentage change in PU ({beta} = 7; 95% CI, 2 to 13) was associated with lower eGFR, independent of covariates. Baseline PU or percentage change in PU were not associated with uPCR. Unsupervised clustering identified four distinct microvascular phenotypes characterized by graded differences in resting perfusion and microvascular function reserve. Among participants with biopsy data, higher baseline PU and lower percentage change in PU were associated with greater severity of GS and IFTA. ConclusionIn persons with CKD, elevated resting perfusion and impaired microvascular functional reserve were associated with lower eGFR. These findings suggest that LDF-derived measures capture clinically relevant alterations in systemic microvascular function and may serve as a noninvasive biomarker of kidney function and underlying histopathologic injury in CKD.

The Dietary Approach to Stop Hypertension (DASH) diet is a proven intervention to treat hypertension, yet its mechanism is not clearly known. We investigated the change in protein abundance patterns in urine extracellular vesicles (uEVs) following DASH diet implementation. A pilot study was carried out to compare uEVs isolated using three different methods: a low centrifugation (P20), high centrifugation (P100), and a combination of both (P20 and P100). Uromodulin was removed by size exclusion chromatography and low ionic strength washing. Mass spectrometry analysis identified 1,593 proteins in the combined fraction (P20+P100), 1434 in the P20 fraction and 1229 in the P100 fraction. The combined fraction was chosen for further analysis. Statistical analysis was carried out using R and Limma to identify all proteins that changed before and during 11 days of DASH intervention (p < 0.05) as well as between individual timepoints. Nine hypertensive volunteers were admitted for a 14-day supervised transition from American style diet to DASH diet. First-void urine was collected on days 0, 5, 11 for uEV processing. In total, 1800 proteins were identified across all 27 DASH samples with 22 proteins upregulated and 25 down regulated between day 0 and both days 5 and 11. These included increased abundance of SLC12A3 (NCC) and reduced abundance of Aquaporine 2. These changes could explain the increased urine volume and reduced sodium reabsorption that lead to blood pressure reduction following consumption of the high potassium and low sodium DASH diet. uEVs may serve as a surrogate to a more invasive procedure. Translational StatementMyriad studies have characterized blood pressure reduction following DASH diet implementation, yet its precise mechanism is unclear. Here, we demonstrate for the first time the effect of DASH nutritional changes, on kidney ion channel composition using proteomic analysis of urinary EVs. Using this innovative tool as a substitute for an invasive procedure, we show that the expression of Aquaporine2 increases and NCC decreases in response to DASH which may account for its antihypertensive effect. Our results indicate that urinary EV are a potential biomarker for DASH compliance, and targeting aquaporine2 may be an effective innovative therapeutic strategy for blood pressure reduction.

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

BackgroundSingle-nephron glomerular filtration rate (GFR) represents a nephron-level functional index that may reveal key pathophysiological mechanisms driving progression in patients with diabetic nephropathy. However, its clinical relevance remains incompletely understood. This cross-sectional study assessed single-nephron estimated GFR (eGFR) across different chronic kidney disease (CKD) stages in patients with advanced diabetic nephropathy. MethodsNephron number was estimated as the number of nonglobally sclerotic glomeruli per kidney using computed tomography-derived cortical volume combined with biopsy stereology. Single-nephron eGFR was calculated by dividing eGFR by the nephron number of both kidneys. Patients were stratified according to CKD stage at kidney biopsy. Associations between CKD stages and single-nephron eGFR were evaluated using multivariable linear regression models adjusted for age, sex, urinary protein excretion, and eGFR. ResultsThe study included 105 patients with biopsy-proven diabetic nephropathy and overt proteinuria (median age 59 years, 83% male, HbA1c 6.6%, 57% had nephrotic range proteinuria). The percentage of globally sclerotic glomeruli, mesangial expansion score, and prevalence of nodular lesions increased significantly with advancing CKD stage. Median nephron number declined from 529,178 to 224,458 per kidney, whereas glomerular volume remained constant. Single-nephron eGFR decreased markedly with CKD stage and remained significantly inversely associated with CKD stage after adjustment for clinicopathologic covariates (P for trend <0.001). ConclusionIn overt diabetic nephropathy, single-nephron eGFR decreased with advancing CKD stage, despite relatively preserved glomerular volume. At this stage of disease, structural alterations specific to diabetic nephropathy may impair effective single-nephron filtration capacity.

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.

BackgroundCardiorenal syndrome (CRS)--renal injury during heart failure (HF)--is linked to higher morbidity. Whether circulating extracellular vesicles (EVs) and their RNA cargo directly impact its pathogenesis remains unclear. MethodsUsing a microfluidic kidney chip model (KC), we investigated transcriptional effects of circulating EVs from patients with CRS on renal epithelial/endothelial cells. We used small RNA-seq on circulating EVs and regression to prioritize subsets of EV miRNAs associated with serum creatinine, a biomarker of renal function. In silico pathway analysis, human genetics, and interrogation of expression of miRNA target genes in the KC model and in a separate cohort of individuals post-renal transplant with microarray-based gene expression was performed for validation. ResultsRenal epithelial and endothelial cells in the KC model exhibited uptake of EVs. EVs from patients with CRS led to higher expression of renal injury markers (IL18, NGAL, KIM1) a greater cystatin C secretion relative to non-CRS EVs. Small RNA-seq and regression identified 15 miRNAs related to creatinine, targeting 1143 gene targets specifying pathways relevant to renal injury, including TGF-b and AMPK signaling. We observed directionally consistent changes in expression of TGF-b pathway members (BMP6, FST, TIMP3) in KC model exposed to CRS EVs, as well as in renal tissue after transplant rejection. Mendelian randomization suggested a role for FST in renal function. ConclusionEVs from patients with CRS directly elicit adverse transcriptional and phenotypic responses in a KC model by regulating biologically relevant pathways, suggesting a novel role for EVs in CRS. Trial RegistrationClinicalTrials.gov NCT 03345446. FundingAHA (SFRN16SFRN31280008), NHLBI (1R35HL150807-01) and NCATS (UH3 TR002878).

ObjectiveTo explore the frequency of occurrence of extra-renal manifestations associated with monogenic kidney stone diseases. MethodsA literature review was conducted to identify genes that are well-established monogenic causes of nephrolithiasis. The Online Mendelian Inheritance in Man (OMIM) and Human Protein Atlas (HPA) databases were used to identify associated diseases and their properties. Disease phenotypes were ascertained using OMIM clinical synopses and sorted into 24 different phenotype categories as classified in OMIM. Disease phenotypes caused by the same gene were merged into a single gene-associated phenotype (GAP) unit such that one GAP encompasses all related disease phenotypes for a specific gene. We measured the total number of GAPs involving each phenotype category and determined the median phenotype category. Phenotype categories were classified as overrepresented or underrepresented if the number of GAPs involving them was higher or lower than the median, respectively. A chi-square test was conducted to determine whether the number of GAPs affecting a given category significantly deviated from the median. ResultsFifty-five genes were identified as monogenic causes of nephrolithiasis. All GAPs comprised at least one extra-renal phenotype category. The median phenotype category was part of 10 (18%) unique GAPs. A total of 6 significantly overrepresented (growth, skeletal, neurologic, abdomen/gastrointestinal, muscle, metabolic features) and 5 significantly underrepresented (mouth, voice, neck, immunology, neoplasia) phenotype categories were identified among our group of monogenic kidney stone diseases (p<0.05) with impaired growth being the most common manifestation. ConclusionMonogenic nephrolithiasis is a multi-system disorder. Recognizing the extra-renal manifestations associated with monogenic causes of kidney stones is critical for earlier diagnosis and optimal prognosis in patients.