Kidney Research and Clinical Practice最新文献

Background: The clinical significance of immunoglobulin M (IgM) deposition in the glomeruli of children with immunoglobulin A vasculitis (IgAV) nephritis remains unclear. This study aimed to analyze the clinical and pathological characteristics and prognoses of this population.

Methods: Patients were divided into three groups according to histopathological IgM deposition intensity: grade A (204 cases); grade B (101 cases); and grade C + D (54 cases). The clinicopathological characteristics and follow-up information of the three groups of patients were collected and compared.

Results: This study included 359 children with IgAV nephritis and found that 44.9% of them had IgM deposition in the kidney glomerulus. Children with IgM deposition and IgAV nephritis have relatively severe clinicopathological features. A total of 39 children (10.9%) had entered the end-stage kidney disease stage. Kaplan-Meier analysis showed that cumulative renal survival was significantly lower in children with higher glomerular IgM deposition (log-rank test chi-square = 55.341, p < 0.001). Multivariable Cox regression analysis found that IgM deposition (grade C + D: hazard ratio [HR], 2.04; 95% confidence interval [CI], 1.67-3.93; p = 0.04; grade B: HR, 2.59; 95% CI, 1.08-4.23; p = 0.03) and S1 (HR, 1.76; 95% CI, 0.42-2.98; p = 0.03) were independent risk factors for poor prognoses in children with IgAV nephritis. The receiver operating characteristic curve indicated that IgM deposition presented significant predictive capability.

Conclusion: There are differences in the clinicopathological features of IgAV nephritis with different degrees of mesangial IgM deposition. IgM deposition and S1 are independent risk factors for poor prognoses of IgAV nephritis in children.

Background: Alport syndrome (AS) is a highly prevalent inherited kidney disease. Early diagnosis and intervention are crucial for improved kidney outcomes. This study evaluated awareness among Korean clinicians about AS and assessed the understanding of AS patients and caregivers.

Methods: An online survey targeting registered members of the Korean Society of Nephrology, the Korean Society of Pediatric Nephrology, AS patients, and their caregivers was conducted from January to April 2023.

Results: Out of 103 respondents, most had treated fewer than 10 AS patients. For certain kidney diseases, such as chronic kidney disease of unknown origin and focal segmental glomerulosclerosis, half or fewer considered AS as a potential diagnosis. Only half preferred immediate confirmation tests for suspected AS. Genetic testing was available at half of the medical centers, and fewer than half of the adult nephrologists considered genetic testing to be essential. While all the surveyed nephrologists would prescribe renin-angiotensin system blockade, the majority hesitated to initiate treatment. Vigilant genetic testing for donor candidates was not a common practice. While 80% of patients and 50% of caregivers understood the nature and prognosis of AS, they regretted the delayed diagnoses, insufficient explanations, and the absence of support groups.

Conclusion: Not rarely, AS patients may have been unrecognized as AS. Despite the noteworthy advancement of AS, the recent guidelines have not been widely adopted in clinical practice in Korea. Considering the challenges in Korea, there is an urgent need for locally tailored clinical practice recommendations and a dedicated registry to optimize patient outcomes.

Nephrology research plays an important role in advancing our understanding of kidney disease and improving patient outcomes. However, the complexity of nephrology data and the application of advanced statistical methods present significant challenges. This review highlights key statistical considerations in nephrology research, focusing on common errors such as violations of statistical assumptions, multicollinearity, missing data, overfitting, and the integration of machine learning tools. It emphasizes the importance of applying appropriate statistical approaches to ensure the reliability of study findings. Additionally, the review underscores the need for transparency and reproducibility in nephrology research, particularly the importance of open access to data, code, and study protocols. By utilizing tools like R, RStudio, Git, and GitHub, researchers can integrate their code, results, and data into a transparent workflow, enhancing the reproducibility of their research. This review also presents a practical checklist for promoting reproducible research practices, which can help improve the quality, transparency, and reliability of nephrology studies. This review aims to contribute to the advancement of nephrology research and, ultimately, to support the long-term goal of improving patient care and outcomes.

Background: A race-free glomerular filtration rate (GFR) estimation equation has recently been developed. However, the performance of the new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations needs to be evaluated in Asian populations.

Methods: We performed a cross-sectional study at a single center in South Korea. The measured GFR (mGFR) was determined based on systemic inulin clearance. The GFR was estimated using the five CKD-EPI equations: 2009 CKD-EPIcr, 2012 CKD-EPIcr-cys, 2012 CKD-EPIcys, 2021 CKD-EPIcr, and 2021 CKD-EPIcr-cys. The performances of five estimated GFR (eGFR) equations were assessed by bias, precision, and accuracy (percentage of estimates within 30% of mGFR).

Results: The median mGFR and interquartile range (IQR) was 53.5 (32.4-80.0) mL/min/1.73 m2. The mGFR better correlated with 2009 CKD-EPIcr (ρ = 0.628) and 2021 CKD-EPIcr-cys (ρ = 0.806) than with 2021 CKD-EPIcr (ρ = 0.623) and 2012 CKD-EPIcr-cys (ρ = 0.801). The median bias of 2009 CKD-EPIcr and 2012 CKD-EPIcr-cys were lower than those of 2021 CKD-EPI equations (2009 CKD-EPIcr, 2.24 [IQR, -8.83 to 17.39] vs. 2021 CKD-EPIcr, 5.40 [IQR, -6.04 to 20.40]; 2012 CKD-EPIcr-cys, 6.74 [IQR, -2.81 to 20.80] vs. 2021 CKD-EPIcr-cys, 10.54 [IQR, 0.30-24.37]; all in mL/min/1.73 m2). The percentage of eGFR values within 30% of mGFR was higher in 2009 CKD-EPIcr and 2012 CKD-EPIcr-cys equations than 2021 CKD-EPI equations. The CKD prevalence in 2009 CKD-EPIcr, 2021 CKD-EPIcr, 2012 CKD-EPIcr-cys, and 2021 CKD-EPIcr-cys was 54.8%, 51.0%, 47.7%, and 44.8%, respectively.

Conclusion: Our study demonstrated better performance of the original CKD-EPIcr and CKD-EPIcr-cys equations than the 2021 new CKD-EPI equations. We do not recommend the adoption of the new CKD-EPI equations in Korea.

Background: Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development.

Methods: At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist.

Results: Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3.

Conclusion: This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients' diet/exercise in the future.

Background: Immunoglobulin A nephropathy (IgAN) is a major cause of end-stage kidney disease (ESKD). The International IgA Nephropathy Prediction Tool (IIgAN-PT) predicts IgAN prognosis, but improvement in the prediction performance using machine learning (ML)-based methods is needed.

Methods: We analyzed 4,425 biopsy-confirmed patients with IgAN and ≥6 months of follow-up from nine tertiary university hospitals in Korea. The study population was divided into development and validation cohorts. Using the collected 87 clinicodemographic and pathological variables, ML-based prediction models for ESKD or estimated glomerular filtration rate decline (50% reduction or <15 mL/min/1.73 m2 ) were constructed: 1) the conventional CatBoost model, 2) the optimized CatBoost model with Cox proportional hazards, 3) the deep Cox proportional hazards model, and 4) the deep Cox mixture model. The area under the curve (AUC) and calibration plots were used to investigate the discriminative and calibration performance of the models, which were then compared with those of the IIgAN-PT full model.

Results: The full model showed excellent performance (AUC [95% confidence interval] for 5-year outcome, 0.896 [0.853-0.940]), with acceptable calibration results. The ML-based models showed good performance in predicting adverse kidney outcomes and revealed acceptable discrimination performance in the external validation (AUC [95% confidence interval] for the 5-year outcome: 1) 0.829 [0.791-0.866]; 2) 0.847 [0.804-0.890]; 3) 0.823 [0.784-0.862]; and 4) 0.832 [0.794-0.870]), although the models showed underestimation in calibration analysis of the external validation cohort. With the validation data, the overall performance of the IIgAN-PT was non-inferior to that of the ML-based model.

Conclusions: Our ML-based models showed good performance in predicting adverse kidney outcomes in patients with IgAN but they did not outperform the IIgAN-PT.

Background: The association between abnormal left ventricular geometry (LVG) patterns and the presence of coronary artery calcification is unclear in patients with CKD.

Methods: A total of 2,038 patients with pre-dialysis CKD at stages 1 to 5 were categorized by LVG patterns, which were echocardiographically determined by the presence or absence of left ventricular hypertrophy (LVH) and relative wall thickness (RWT): normal, concentric remodeling, eccentric LVH, and concentric LVH. The study outcome was the presence of heavy coronary artery calcification, which is defined as coronary artery calcium score >1,000 Agatston units.

Results: Logistic regression analyses demonstrated that concentric remodeling (adjusted odds ratio [OR], 2.53; 95% confidence interval [95% CI], 1.32-4.85) and concentric LVH (adjusted OR, 2.89; 95% CI, 1.49-5.62), but not eccentric LVH (adjusted OR, 1.58; 95% CI, 0.71-3.51), were significantly associated with the risk of heavy coronary artery calcification. The presence of LVH alone was not significantly associated with the risk of heavy coronary artery calcification (adjusted OR, 1.65; 95% CI, 0.97-2.81), while the increase in RWT independently increased the risk of heavy coronary artery calcification (adjusted OR, 2.423; 95% CI, 1.48-4.00).

Conclusion: Abnormal LVG patterns, such as concentric remodeling and concentric LVH, but not eccentric LVH, are significantly associated with the risk of heavy coronary artery calcification in patients with CKD. It is expected that the determination of LVG patterns may facilitate risk stratification in relation to the coronary evaluation strategy.

Background: Since the glomerular filtration rate (GFR) naturally declines with age, age-adjusted chronic kidney disease diagnostic criteria have been proposed. This study aimed to investigate the prognostic impact of estimated GFR (eGFR) on mortality and progression to end-stage kidney disease (ESKD) in normoalbuminuric older adults with type 2 diabetes mellitus.

Methods: We categorized patients aged ≥65 years without albuminuria who visited our diabetes center by their baseline eGFR levels. Primary outcomes were composite events encompassing all-cause mortality and ESKD.

Results: Among 1,997 participants, 8%, 71%, 16%, and 5% had an eGFR of ≥90, 60-89, 45-59, and 15-44 mL/min/1.73 m2, respectively. Adjusted hazard ratios for composite outcomes were 1.30 (95% confidence interval, 1.01-1.76) for those with an eGFR of 45-59 mL/min/1.73 m2, compared to those with an eGFR of 60-90 mL/min/1.73 m2. Subgroup analyses revealed consistently increased risk associated with eGFR 45-59 mL/min/1.73 m2 across individuals with body mass index <25 kg/m2 and those with urine albumin-to-creatinine ratio <10 µg/mgCr.

Conclusion: This study indicated that an eGFR of 45-59 mL/min/1.73 m2 possesses an elevated risk of composite events, which suggests that the current traditional eGFR criteria could be applicable to older patients with diabetes mellitus.

Background: A paucity of literature exists on the development of predictive tools for the decline of kidney function in pediatric chronic kidney disease (CKD). The objective of this study is to develop and internally validate a tool for the short-term prediction of a kidney function decline in pediatric patients with CKD.

Methods: A total of 539 patients participating in the KNOW-PedCKD (KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease) were evaluated for 48 variables related to sociodemographic characteristics, laboratory data, and treatment use. These variables were assessed as potential predictors of a kidney function decline in pediatric patients with CKD using a range of machine learning algorithms.

Results: The models demonstrated strong predictive performances in identifying kidney function decline, defined as an estimated glomerular filtration rate (eGFR) decline of ≥20%, which includes progression to kidney replacement therapy or death. The random forest and XGBoost models demonstrated the best performance in predicting eGFR outcomes at 1 year compared with 2 and 3 years, respectively. The spot urine protein-to-creatinine ratio was the most influential variable in the prediction model, followed by baseline eGFR and serum albumin, chloride, and hemoglobin levels.

Conclusion: A tool for predicting kidney function decline in children with CKD over a short period of time was developed using potential predictors and machine learning methods in a large Korean pediatric CKD cohort.

Background: Receptor for advanced glycation end products (RAGE) has been implicated in the pathogenesis of numerous inflammatory conditions including sepsis. We investigated the possible therapeutic role of soluble RAGE (sRAGE) in septic acute kidney injury (AKI) models.

Methods: sRAGE level was measured in healthy controls and patients with septic AKI. C57/BL6 mice with cecal ligation and puncture (CLP) were injected with sRAGE (CLP + sRAGE) 1 hour before the operation. NRK-52E cells were treated with lipopolysaccharide (LPS, 1 μg/mL) and sRAGE (1 μg/mL) or RAGE small interfering RNA. RAGE-associated signaling molecule and apoptosis-related protein (ARP) expression levels were analyzed.

Results: Serum sRAGE level was significantly higher in septic AKI patients than in healthy controls, and higher sRAGE level was associated with better survival rates. Blood urea nitrogen and creatinine levels were significantly higher in CLP mice than controls, and these increases were significantly abrogated in CLP + sRAGE mice. Renal MyD88 and phospho-ERK, -p38, and -JNK proteins and ARP expression levels in the CLP group were also significantly increased compared to controls, and these changes were significantly ameliorated by sRAGE treatment in CLP mice. In vitro, RAGE-associated activation of mitogen-activated protein kinase and ARP expression in LPS-stimulated cells were significantly ameliorated by sRAGE. Furthermore, the increases in nuclear factor kappa B nuclear translocation and intercellular adhesion molecule 1 protein expression by LPS were significantly attenuated by sRAGE in these cells.

Conclusion: These findings suggest that RAGE plays an important role in septic AKI, and its inhibition by sRAGE may be a potential therapeutic target for AKI in severe sepsis.