Predictors Of Kidney Function Research Articles

Association between albumin-corrected anion gap and kidney function in individuals with hypertension – NHANES 2009–2016 cycle

Objectives Long-term uncontrolled hypertension increases the risk of kidney decompensation. This study aimed to explore the connection between albumin-corrected anion gap (ACAG) and kidney function in hypertensive patients. Methods This study utilized data from 1988 participants diagnosed with hypertension sourced from the NHANES database. Binary logistic regression analysis and subgroup analysis were utilized to investigate the relationship between ACAG and kidney function. The study employed restricted cubic spline (RCS) to assess the non-linear associations between ACAG and eGFR, as well as ACAG and ACR. Furthermore, mediation and moderation effect analyses were carried out, with blood pressure serving as the mediator and moderator, ACAG as the independent variable, and eGFR and ACR as the dependent variables. Finally, the study developed ACAG-based models for predicting kidney function decline. Results Higher ACAG is identified as an independent risk factor for eGFR < 60 mL/minute/1.73 m2 and ACR ≥ 30 mg/g. Results from RCS indicate a non-linear relationship between ACAG and eGFR, as well as between ACAG and ACR. The mediation effect analysis revealed that DBP mediated the relationship between ACAG and eGFR. Analysis on moderation effect demonstrated that SBP played a significant role in moderating the interaction between ACAG and ACR. Moreover, the models based on ACAG showed strong performance. Conclusions The levels of ACAG are found to be independently associated with both eGFR and ACR. Additionally, ACAG shows promise as a new and dependable biomarker for predicting the decline in kidney function in hypertensive patients.

Integrating aging biomarkers and immune function to predict kidney health: insights from the future of families and child wellbeing study.

Biomarkers of biological aging predict health outcomes more accurately than chronological age. This study examines the relationship between aging biomarkers, immune function, and kidney health using the Future of Families and Child Wellbeing Study Biomarker Dataset. Using Wave 5 (year 9) and Wave 6 (year 15), we examined biomarker data from a total of 4898 individuals. The panel of aging biomarkers, comprised of epigenetic clocks (GrimAge, Horvath), immune function markers (CD8 + T cells, plasmablasts), and metabolic indicators (GDF-15, leptin), was evaluated in depth. We used principal component analysis (PCA) and K-means clustering for subtype identification. A random forest regressor was employed to predict kidney function using Cystatin C levels, and the importance of features was assessed. Three clusters with unique biological age and immune function profiles were identified. Cluster 1 had younger biological age markers. In Cluster 2, both GrimAge and GDF-15 levels were significantly increased, indicating an elevated risk for age-related diseases. According to predictive modeling, GrimAge, Pack Years, and immune function markers had the strongest influence on Cystatin C levels (R2 = 0.856). The incorporation of immune aging markers enhanced the predictive power, emphasizing the importance of immunosenescence in renal health. Aging biomarkers and immune function significantly impact kidney health prediction. The study results call for the utilization of extensive biomarker tests for individualized elderly care and early recognition of kidney deterioration. Clinical practice can be improved by incorporating biological age assessments for the prevention and management of age-related diseases.

Associations Between CYP3A5 (c.6986A>G) Gene Polymorphism and Kidney Impairment in Hypertensive Adults Without Cystatin C Elevation.

This study aimed to explore the potential role of CYP3A5 (c. 6986A>G) gene polymorphism in predicting kidney function impairment in patients with hypertension who did not have elevated serum cystatin C. We recruited a group of patients with hypertension who did not have elevated cystatin C and analyzed the CYP3A5 (c. 6986A>G) gene polymorphism. Chi-square tests were used to compare the clinical characteristics and genotypic distribution between the two groups. Logistic regression analysis was used to explore the association between CYP3A5 (c.6986A>G) gene polymorphism and renal function impairment in hypertension with non-elevated cystatin. In patients with hypertension who participated in the study, there was a significant association between CYP3A5 (c. 6986A>G) gene polymorphism and kidney function impairment (p < 0.05). Patients with the CYP3A5 (c. 6986A>G) mutation display a greater risk of kidney function impairment. CYP3A5 (c. 6986A>G) gene AA homozygote polymorphism significantly increases risk of kidney function impairment in patients with hypertension with normal cystatin C. However, further studies are needed to validate this association and to further understand the mechanism of CYP3A5 (c. 6986A>G) gene polymorphism in kidney function impairment in patients with hypertension.

Value of Histopathologic Assessment for Nephrosclerosis in Predicting Kidney Function Decline

Value of Histopathologic Assessment for Nephrosclerosis in Predicting Kidney Function Decline

Cystatin C and Kidney Function Recovery in Patients Requiring Continuous Kidney Replacement Therapy for Acute Kidney Injury.

Plasma cystatin C is a reliable marker to estimate kidney function; however, it is unknown whether this remains true in patients receiving continuous kidney replacement therapy (CKRT). Herein, we tested the hypothesis that lower concentrations of plasma cystatin C during the first three days of CKRT would predict kidney function recovery. We performed a retrospective observational study of 72 patients from a 126-patient, single-center CKRT study. We studied two a priori defined cohorts of patients without advanced CKD who had acute kidney injury requiring CKRT (AKI-CKRT): 1) with early kidney function recovery defined as liberation from KRT within seven days of CKRT initiation versus 2) with delayed kidney function recovery defined as receipt of KRT for >21 days or death while on KRT. Subsequent analysis included patients with advanced CKD and intermediate kidney function recovery (liberation between 8 and 21 days). Cystatin C was then measured on stored plasma, urine, and dialysis effluent collected prior to CKRT initiation and on days 1, 2, and 3 of CKRT. Plasma cystatin C was significantly lower in patients with early kidney function recovery in comparison to patients with delayed kidney function recovery on days 1 (1.79 vs. 2.39mg/L), 2 (1.91 vs. 2.38mg/L) and 3 (2.04 vs. 2.67mg/L) of CKRT. Sieving coefficient and CKRT clearance of cystatin C were similar for patients with early and delayed kidney function recovery. The lowest plasma cystatin C concentration on days 1-3 of CKRT predicted early kidney function recovery with an area under the receiver operating curve of 0.77 (P = 0.002), positive likelihood ratio of 5.60 for plasma cystatin C <1.30mg/L, and negative likelihood ratio of 0.17 for plasma cystatin C ≥1.88mg/L. Lower plasma cystatin C concentrations during the first three days of CKRT are associated with early kidney function recovery.

The role of artificial intelligence measured preoperative kidney volume in predicting kidney function loss in elderly kidney donors: A multicenter cohort study.

The increasing use of kidneys from elderly donors raises concerns due to age-related nephron loss. Combined with nephrectomy, this loss of nephrons markedly increases the risk of developing chronic kidney disease (CKD). This study aimed to investigate the prognostic value of preoperative kidney cortex volume in predicting the loss of kidney function in elderly donors, by developing an artificial intelligence (AI)-based model for precise kidney volume measurement and applying it to living kidney donors. A multicenter retrospective cohort study using data from living donors who underwent donor nephrectomy between January 2010 and December 2020 was conducted. An AI segmentation model was developed and validated to measure kidney cortex volume from pre-donation computer tomographic (CT) images. The association between measured preoperative kidney volumes and post-nephrectomy renal function was analyzed through a generalized additive model. A total of 1074 living kidney donors were included in the study. Validation of the developed kidney cortex volume model showed a Dice similarity coefficient of 0.97 and a Hausdorff distance of 0.76mm. The measured cortex volumes exhibited an age-related decrease, which correlated with declining kidney function. Elderly donors showed greater decreases in estimated glomerular filtration rates (eGFR) post-donation compared to young donors (P=0.041). Larger preoperative remnant kidney cortex volume was associated with significantly less decline of eGFR post-donation than those with smaller preoperative remnant kidney cortex volume (P<0.001). This study highlights the critical role of preoperative kidney cortex volume in the donor assessment process, particularly for elderly donors. The fully automated model for measuring kidney cortex volume provides a valuable tool for predicting post-donation renal function and holds promise for enhancing donor evaluation and safety.

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.