Predictors Of Kidney Function Research Articles

TOPOGRAPHIC ANATOMY OF KIDNEYS AND CHANGES IN THE SIZE AND POSITION OF A SINGLE KIDNEY

The article examines the influence of macroscopic and microscopic changes in the kidney on function and the development of pathological processes. Kidney volume has been shown to be the optimal parameter for predicting kidney function. It has been established that weight gain often leads to nephroptosis. The variability of the renal vessels in terms of number, sources of departure from the main vessels, method of branching, spatial relationships of the branches was noted.

A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease.

The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from -7.08 ml/min/1.73 m2/year to -4.27 ml/min/1.73 m2/year in high-risk patients (P = .0003), -2.65 to -1.04 in intermediate risk, and -3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P < .001). Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

Circulating tumor necrosis factor-related biomarkers predict kidney function decline in Japanese patients with diabetes: An observational cohort study

AimsTumor necrosis factor (TNF) receptors (TNFRs: TNFR1 and, TNFR2) are reportedly associated with chronic kidney disease (CKD) progression chiefly in Caucasian patients with diabetes. We assessed the prognostic value of TNF-related biomarkers for CKD progression in Japanese patients with diabetes. MethodsWe estimated TNF-related biomarkers using an enzyme-linked immunosorbent assay in 640 patients with diabetes. Cox proportional hazards analysis was performed to estimate hazard ratios (HRs) per one standard deviation (SD) increase in a log-transformed biomarker. The kidney and the composite outcome were defined as a 30% reduction in estimated glomerular filtration rate (eGFR) from baseline, and kidney outcome plus death before kidney outcome, respectively. ResultsDuring the median follow-up of 5.4 years, 75 (11.7%) patients reached the kidney outcome and 37 (5.8%) died before reaching the kidney outcome. Each SD increase in baseline circulating TNFR1, TNFR2, and ephrin type-A receptor 2 (EphA2) was associated with a higher risk of the kidney outcome independently from baseline eGFR and urine albumin-to-creatinine ratio. However, circulating osteoprotegerin was associated with the composite outcome only. ConclusionsElevated TNFR1, TNFR2, and EphA2 were associated with both kidney and composite outcomes in Japanese patients with diabetes.

Plasma Cystatin C Predicts Kidney Function Recovery in Patients Requiring Continuous Kidney Replacement Therapy (CKRT) for AKI

Plasma Cystatin C Predicts Kidney Function Recovery in Patients Requiring Continuous Kidney Replacement Therapy (CKRT) for AKI

Urinary Fetuin-A Fragments Predict Progressive Estimated Glomerular Filtration Rate Decline in Two Independent Type 2 Diabetes Cohorts of Different Ethnicities

Introduction: There is a great clinical need for novel markers to predict kidney function decline in patients with type 2 diabetes. We explored the potential of posttranslationally modified fetuin-A fragments in urine (uPTM-FetA) as such a marker. Methods: We included patients with type 2 diabetes from two independent, nonoverlapping prospective cohort studies. A cut-off for uPTM-FetA, measured via ELISA method, was determined using the Youden index in the primary cohort of patients with type 2 diabetes from Taiwan. Kidney endpoint was defined as an estimated glomerular filtration rate (eGFR) decline ≥30% from baseline, reaching of an eGFR <15 mL/min/1.73 m², or a need of renal replacement therapy. Prospective associations were assessed in Cox regression models. All analyses were replicated in a cohort of patients with type 2 diabetes from the Netherlands. Results: In total, 294 patients with type 2 diabetes (age 61 ± 10 years, 55% male, eGFR 88 ± 16 mL/min/1.73 m²) were included in the primary cohort. During a follow-up of median 4.6 years, 42 participants (14%) experienced the kidney endpoint. Using the defined cut-off, a high uPTM-FetA was associated with a higher risk of renal function decline (P_log-rank < 0.0001). This association was similar in subgroups depending on albuminuria. This association remained, independent of age, sex, baseline eGFR, albuminuria, HbA1c, and other potential confounders (HR: 9.94; 95% CI: 2.96–33.40; p < 0.001 in the final model). Analyses in the validation cohort (376 patients with type 2 diabetes, age 64 ± 11 years, 66% male, eGFR 76 ± 24 mL/min/1.73 m²) using the same cut-off yielded similar results. Conclusion: uPTM-FetA was independently associated with kidney function decline in patients with type 2 diabetes validated in a 2-cohort study. The significant additive predictive power of this biomarker from conventional risk factors suggests its clinical use for renal function progression in patients with type 2 diabetes.

FRI619 Kidney Magnetic Resonance Imaging Reveals Altered Tissue Architecture In Young Persons With Type 1 And 2 Diabetes

Abstract Disclosure: S. Poon: None. K. Nash: None. Y. Choi: None. L. Li: None. P. Prasad: None. D. Raalte: Other; Self; consulting relationships with Boehringer Ingelheim, Eli Lilly, Merck and Sanofi, and receives research operating funding from AstraZeneca, Boehringer Ingelheim-Eli Lilly Diabetes Alliance, MSD. A. Karihaloo: Employee; Self; Novo Nordisk. A. Sawyer: None. P. Bjornstad: Advisory Board Member; Self; AstraZeneca, Bayer, Lilly, Boehringer Ingelheim, Novo Nordisk, and XORTX. Other; Self; PB reports serving or having served as a consultant for AstraZeneca, Bayer, Bristol-Myers Squibb, Boehringer Ingelheim, Eli Lilly, LG Chemistry, Sanofi, Novo Nordisk, and Horizon Pharma. Background: Apparent diffusion coefficient (ADC) by magnetic resonance imaging (MRI) provides information about the microstructure and cellular organization of the kidney, and has been linked with kidney function decline in adults with chronic kidney disease. However, it is unclear whether alterations in tissue architecture or cellular density are also present in young persons with type 1 and 2 diabetes. Accordingly, the objective of this analysis was to determine early changes in ADC among young persons with and without diabetes. Materials and Method: Kidney MRIs were performed in 37 lean controls (age: 18±6 years, 54% female, eGFR: 106±19 ml/min/1.73m2), 28 participants with youth-onset T1D (age: 20±4 years, 50% female, diabetes duration: 9±6 years, HbA1c: 8.3±1.3%, eGFR: 110±19 ml/min/1.73m2) and 30 participants with youth-onset T2D (age: 17±4 years, 62% female, diabetes duration: 2±2 years, HbA1c: 7.4±2.2%, eGFR: 127±24 ml/min/1.73m2). All scans were performed on 3T whole-body scanners. A single reader at The NorthShore University HealthSystem analyzed all images while blinded to participants’ clinical information. Lower values of cortical ADC on diffusion-weighted MRI may indicate increased cellular density and are often used to estimate fibrosis. Estimated GFR (eGFR) were calculated by Full Age Spectrum serum creatinine equation. We performed multivariable linear regression models adjusted for age and sex. Results: Compared to lean controls (age and sex-adjusted means ± standard errors (SE): (1722±22 × 10−6 mm2/s), young persons with T1D (1636±28 × 10−6 mm2/s, p=0.02) and T2D (1612±27 × 10−6 mm2/s, p=0.002) exhibited lower cortical ADC. Despite younger age and shorter diabetes duration, young persons with T2D had numerically lower cortical ADC compared to those with T1D, although the difference did not reach statistical significance. Higher age and male sex were associated with lower cortical ADC (β±SE: −7.5±2.8 x 10−6 mm2/s per 1 year of age, p=0.009; β±SE: −71.1±28.5 x 10−6 mm2/s per 1 year for male sex, p=0.008) in multivariable models. Lower eGFR was associated with lower cortical ADC (β±SE: 2.3±0.8 x 10−6 mm2/s per 1 ml/min/1.73m2 increment eGFR, p=0.005). HbA1c and urine albumin-to-creatinine ratio did not associate significantly with cortical ADC in multivariable models. Conclusion: Young persons with diabetes exhibit lower cortical ADC compared to healthy lean controls, suggesting alternations in microstructure and cellular density and potentially early signs of fibrosis. Additionally, despite shorter duration of disease and younger age, participants with T2D had the lowest cortical ADC values among the 3 groups. Further research is needed to investigate the role of cortical ADC in predicting kidney function decline in young individuals with diabetes, as well as molecular determinants of the alterations in microstructure and cellular density. Presentation: Friday, June 16, 2023

Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses

BackgroundThe von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL. ObjectiveWe sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function. Design, setting, and participantsWe identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software. Outcome measurements and statistical analysisSerum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1. Results and limitationsPreoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2–5) tumors and five (IQR: 0–13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m2 (IQR: 58–89). Preoperative split renal function derived from MAG-3 studies and MRI split renal volume were significantly correlated (r = 0.848, p < 0.001). On the multivariable analysis, total preoperative parenchymal volume, solitary kidney, and preoperative eGFR were significant independent predictors of 12-mo eGFR. When only considering patients with two kidneys undergoing partial nephrectomy, preoperative parenchymal volume and eGFR remained significant predictors of 12-mo eGFR. ConclusionsA parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application. Patient summaryPrior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy.

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

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

Glomerular filtration rate in patients with type 2 diabetes mellitus: is serum isthmin-1 level a possible link?

IntroductionIsthmin-1 (Ism-1) is a novel adipokine. However, little is known regarding the association between Ism-1 and type 2 diabetes mellitus (T2DM). This study aimed to investigate the relationship between serum...

#6060 A NOVEL CT-BASED RADIOMICS APPROACH FOR KIDNEY FUNCTION EVALUATION IN ADPKD

Abstract Background and Aims Clinical management of autosomal dominant polycystic kidney disease (ADPKD) might take advantage of the use of new tools to predict risk of progression towards end stage kidney disease (ESKD). The aim of this study is to develop and validate a model based on radiomic features to predict kidney function among patients with ADPKD obtained from CT scans performed for the determination of total kidney volume (TKV). Method We retrospectively selected a cohort of 58 patients with ADPKD who underwent CT scan from February 2020 to March 2021, including 30 patients with eGFR ≥ 60 mL/min/1.73 m2 and 28 with eGFR &amp;lt; 60 mL/min/1.73 m2 at baseline. An expert radiologist generated a region of interest (ROI) segmentation for cystic kidney compounds, obtaining 58 ROIs from which we extracted 217 radiomic features using a dedicated software. We built three different logistic regression models to predict kidney function based on different predictors: height-adjusted TKV (ht-TKV), a selected radiomic feature (F_cm_merged.clust.tend), and both. Area under the curve (AUC) of the receiver operating characteristic (ROC) and accuracy were employed to evaluate models’ performance in discriminating between the two eGFR groups. Internal 3-fold cross-validation (CV) was performed. Results The ht-TKV, radiomic and combined models presented respectively an AUC (95% confidence interval) of 0.79 (0.67, 0.91), 0.83 (0.72, 0.93), 0.84 (0.74, 0.94), confirmed by the CV. Mean (standard deviation) values of the accuracy over CV iterations were 0.67 (0.10), 0.77 (0.09), 0.77 (0.09) for the three models. A model combining ht-TKV with a radiomic feature based on CT images from polycystic kidneys resulted effective in the prediction of baseline kidney function in our cohort. Furthermore, a logistic regression model based on a different radiomic feature (F_cm.2.5Dmerged.info.corr.2) selected among a subcohort of 29 ADPKD patients with a clinical follow-up, predicted rapid progression with a AUC of 0.81, a sensitivity of 100% and a specificity of 53%. Conclusion This is among the first studies which aimed to investigate, in a clinical setting, radiomics potential ability in discriminating eGFR at baseline and to explore as well whether a reliable radiomic feature could be taken into account in predicting faster rapid kidney function impairment over time. Further studies should implement a model extension to predict kidney function slope in order to confirm the role of radiomics in ADPKD management.

Identification of a New RNA and Protein Integrated Biomarker Panel Associated with Kidney Function Impairment in DKD: Translational Implications.

Diabetic kidney disease (DKD) is a complication that strongly increases the risk of end-stage kidney disease and cardiovascular events. The identification of novel, highly sensitive, and specific early biomarkers to identify DKD patients and predict kidney function decline is a pivotal aim of translational medicine. In a previous study, after a high-throughput approach, we identified in 69 diabetic patients 5 serum mitochondrial RNAs (MT-ATP6, MT-ATP8, MT-COX3, MT-ND1, and MT-RNR1) progressively downregulated with increasing eGFR stages. Here, we analyzed the protein serum concentrations of three well-validated biomarkers: TNFRI, TNFRII, and KIM-1. The protein biomarkers were gradually upregulated from G1 to G2 and G3 patients. All protein biomarkers correlated with creatinine, eGFR, and BUN. Performing multilogistic analyses, we found that, with respect to single protein biomarkers, the combination between (I) TNFRI or KIM-1 with each RNA transcript and (II) TNFRII with MT-ATP8, MT-ATP6, MT-COX-3, and MT-ND1 determined an outstanding improvement of the diagnostic performance of G3 versus G2 patient identification, reaching values in most cases above 0.9 or even equal to 1. The improvement of AUC values was also evaluated in normoalbuminuric or microalbuminuric patients considered separately. This study proposes a novel, promising multikind marker panel associated with kidney impairment in DKD.

Magnetic Resonance Imaging to Evaluate Kidney Structure, Function, and Pathology: Moving Toward Clinical Application

Recent advances in multiparametric Magnetic Resonance Imaging (MRI) allow multiple quantitative measures to assess kidney morphology, tissue microstructure, oxygenation, kidney blood flow and perfusion to be collected in a single scan session. Animal and clinical studies have investigated the relationship between the different MRI measures and biological processes, although their interpretation can be complex due to variations in study design and generally small participant numbers. However, emerging themes include the apparent diffusion coefficient (ADC) derived from diffusion weighted imaging, T1 and T2 mapping parameters and cortical perfusion being consistently associated with kidney damage and predicting kidney function decline. Blood Oxygen Level-Dependent (BOLD) MRI has shown inconsistent associations with kidney damage markers but has been predictive of kidney function decline in several studies. Therefore, multiparametric MRI of the kidneys has the potential to address the limitations of existing diagnostic methods to provide a non-invasive, non-contrast and radiation-free method to assess whole kidney structure and function. Barriers to be overcome to facilitate widespread clinical application include improved understanding of biological factors that impact MRI measures, development of a larger evidence base for clinical utility, standardization of MRI protocols, automation of data analysis, determining optimal combination of MRI measures, and health economic evaluation.

Canagliflozin Attenuates PromarkerD Diabetic Kidney Disease Risk Prediction Scores.

PromarkerD is a biomarker-based blood test that predicts kidney function decline in people with type 2 diabetes (T2D) who may otherwise be missed by current standard of care tests. This study examined the association between canagliflozin and change in PromarkerD score (Δ score) over a three-year period in T2D participants in the CANagliflozin cardioVascular Assessment Study (CANVAS). PromarkerD scores were measured at baseline and Year 3 in 2008 participants with preserved kidney function (baseline eGFR ≥60 mL/min/1.73 m2). Generalized estimating equations were used to assess the effect of canagliflozin versus placebo on PromarkerD scores. At baseline, the participants (mean age 62 years, 32% females) had a median PromarkerD score of 3.9%, with 67% of participants categorized as low risk, 14% as moderate risk, and 19% as high risk for kidney function decline. After accounting for the known acute drop in eGFR following canagliflozin initiation, there was a significant treatment-by-time interaction (p < 0.001), whereby participants on canagliflozin had decreased mean PromarkerD scores from baseline to Year 3 (Δ score: -1.0% [95% CI: -1.9%, -0.1%]; p = 0.039), while the scores of those on placebo increased over the three-year period (Δ score: 6.4% [4.9%, 7.8%]; p < 0.001). When stratified into PromarkerD risk categories, participants with high risk scores at baseline who were randomized to canagliflozin had significantly lower scores at Year 3 (Δ score: -5.6% [-8.6%, -2.5%]; p < 0.001), while those on placebo retained high scores (Δ score: 4.5% [0.3%, 8.8%]; p = 0.035). This post hoc analysis of data from CANVAS showed that canagliflozin significantly lowered PromarkerD risk scores, with the effect greatest in those T2D participants who were classified at study entry as at high risk of a subsequent decline in kidney function.

Development and Validation of a Prediction Model for Future Estimated Glomerular Filtration Rate in People With Type 2 Diabetes and Chronic Kidney Disease

Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking. To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts. This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of ≥30 mL/min/1.73 m2) were included. Data were analyzed between June 30, 2021, and January 31, 2023. Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A1c [mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated. Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R2 ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5. In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting kidney function decline up to 5 years after baseline. The results and prediction model are publicly available in an accompanying web-based application, which may open the way for improved prediction of individual eGFR trajectories and disease progression.

CKD.Net: A novel deep learning hybrid model for effective, real-time, automated screening tool towards prediction of multi stages of CKD along with eGFR and creatinine

Clinical tests have long been considered appropriate in diagnosing chronic kidney disease (CKD) because of their noninvasiveness, simplicity, and cost. Timely detection and management of CKD are the most effective methods to address the expanding global burden induced by CKD. We adopted an S-MTL (Supervised Multi-task Learning) approach and combined SimpleRNN (Simple Recurrent Neural Network) and MLP (Multi-Layer Perception) to develop a hybrid model-CKD.Net to predict five CKD stages. This hybrid neural network architecture was trained on massive clinical datasets with heterogeneous 27 features to predict kidney function. We employed various data augmentation strategies to balance the five CKD stage datasets and meticulously utilized the hyperparameter to minimize the loss and validation loss to reduce overfitting and hence increase model generalization. Performance comparisons of CKD.Net were evaluated using Accuracy, Precision, Recall, and F1-score while comparing the performance with that of generic SimpleRNN and MLP models. CKD.Net demonstrated superior classification accuracy ranging from 99.2 to 99.8 percent in predicting the five classes. Furthermore, CKD.Net was utilized to predict eGFR (estimated glomerular filtration rate) and creatinine by evaluating the confidence level using Pearson correlation values. Subsequently, key risk factors of CKD were identified, and their clinical significance was discussed. CKD.Net web application was developed to automate the prediction of CKD disease. To the best of our knowledge, the CKD.Net model is the first essential step toward predicting multi-stages of kidney disease as an effective, real-time, automated screening tool. CKD.Net allows noninvasive measurement of kidney function, which is a crucial objective of artificial intelligence powered by functional automation in clinical practice.

IgM deposition is a risk factor for delayed remission and early relapse of the pediatric minimal change disease.

Minimal change disease (MCD) is the most common pathological subtype of pediatric idiopathic nephrotic syndrome (INS). It has been suggested that IgM deposition might predict kidney function deterioration in the course of MCD. However, the specific role of IgM deposition in the prognosis of MCD is still controversial. This study aims to investigate the clinical significance of IgM deposition on delayed remission and early relapse in a pediatric population. This study enrolled 283 children diagnosed with MCD by renal biopsy in a single center from 2010 to 2022. These cases were divided into two groups according to the histopathological deposition of IgM. Patients' demographics, clinical parameters, and follow-up data were collected and analyzed. The primary and secondary outcomes were defined as the time to the first remission and the first relapse. The IgM-positive group had a weaker response to steroids (steroid-sensitive: 23.5% vs. 40.8%; steroid-dependent: 74.0% vs. 51.0%; steroid-resistant: 18.4% vs. 8.2%, P = 0.001), and showed more recurrent cases (47.2% vs. 34.4%, P = 0.047) compared with the IgM-negative group. The Kaplan-Meier analysis showed that the IgM-positive group had a lower cumulative rate of the first remission (Log-rank, P < 0.001) and a higher rate of the first relapse (Log-rank, P = 0.034) than the IgM-negative group. Multivariate Cox analysis showed that IgM deposition was independently associated with the delayed first remission (hazard ratio [HR] = 0.604, 95% confidence interval [CI] = 0.465-0.785, P < 0.001) and the early first relapse (HR = 1.593, 95% CI = 1.033-2.456, P = 0.035). IgM deposition was associated with a weaker steroid response. MCD children with IgM deposition were prone to delayed first remission and early first relapse.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease.

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor‐1 and ‐2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule‐1, beta‐2‐microglobulin and neutrophil gelatinase‐associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

Dickkopf 3-A New Indicator for the Deterioration of Allograft Function After Kidney Transplantation.

Evidence of tubular atrophy and interstitial fibrosis is prognostically unfavorable and associated with a premature graft loss after kidney transplantation. Recently, Dickkopf 3 (DKK3), a profibrotic glycoprotein released by stressed tubular epithelial cells, has been identified to cause IF/TA by regulating the Wnt/β-catenin signaling and seems to engage a T-cell response. The aim of our study was to determine if a correlation between DKK3 and graft function exists and if DKK3 could be a new indicator to identify patients at risk for a deterioration in graft function. Patients, transplanted between 2016 and 2018, were analyzed with regard to DKK3 in the urine and graft function (creatinine, eGFR, albuminuria). Multivariable analyzes were used including known factors influencing graft function (PRA, donor age) to stress robustness of DKK3. The 3 and 12 month DKK3 values were significant predictors for subsequent graft function up to 36 months. An increase of DKK3 from month 3 to 12 of ≥ 25% showed a higher risk of an impaired graft function, with, e.g., a reduction in eGFR of about 9–10 ml/min in contrast to patients without intensified DKK3 increase. Induction therapy has an influence on DKK3 as patients induced with a T-cell depleting therapy showed a trend toward lower DKK3 values. In summary, our study is the first investigation of DKK3 in kidney transplant recipients and was able to show that DKK3 could forecast graft function. It is recommended to investigate the potential of DKK3 as a predictor of kidney function after transplantation in further studies.

Progranulin and Its Receptor Predict Kidney Function Decline in Patients With Type 2 Diabetes.

Progranulin (PGRN), a growth factor, is abundantly expressed in a broad range of tissues and cell types with pleiotropic functions including inflammation, neurodegeneration, and facilitating lysosome acidification. PGRN binds to TNF receptors (TNFR) and inhibits downstream inflammatory signaling pathways. TNFR is a well-known predictor of glomerular filtration rate (GFR) decline in a variety of diseases. Therefore, we measured circulating PGRN in addition to TNFR using an enzyme-linked immunosorbent assay and explored whether it predicted renal prognosis in 201 Japanese patients with type 2 diabetes. During a median follow-up of 7.6 years, 21 participants reached primary renal endpoint, which involves a decline of at least 57% in eGFR from baseline, or the onset of end-stage renal disease. Univariate Cox regression analysis revealed that classical renal measures (GFR and albuminuria), two TNF-related biomarkers (PGRN and TNFR), and BMI were associated with this outcome. Multivariate analysis demonstrated that high levels of PGRN [HR 2.50 (95%CI 2.47–2.52)] or TNFR1 [HR 5.38 (95%CI 5.26–5.50)] were associated with this outcome after adjusting for relevant covariates. The high levels of PGRN as well as TNFR1 were associated with a risk of primary renal outcome in patients with type 2 diabetes after adjusting for established risk factors.

Comparison of CT volumetry versus nuclear renography for predicting remaining kidney function after uninephrectomy in living kidney donors

Computed tomography (CT) and nuclear renography are used to determine kidney procurement in living kidney donors (LKDs). The present study investigated which modality better predicts kidney function after donation. This study included 835 LKDs and they were divided into two subgroups based on whether the left–right dominance of kidney volume was concordant with kidney function (concordant group) or not (discordant group). The predictive value for post-donation kidney function between the two imaging modalities was compared at 1 month, 6 months, and > 1 year in total cohort, concordant, and discordant groups. Split kidney function (SKF) measured by both modalities showed significant correlation with each other at baseline. SKFs of remaining kidney measured using both modalities before donation showed significant correlation with eGFR (estimated glomerular filtration rate) after donation in the total cohort group and two subgroups, respectively. CT volumetry was superior to nuclear renography for predicting post-donation kidney function in the total cohort group and both subgroups. In the discordant subgroup, a higher tendency of kidney function recovery was observed when kidney procurement was determined based on CT volumetry. In conclusion, CT volumetry is preferred when determining procurement strategy especially when discordance is found between the two imaging modalities.