Kidney Research and Clinical Practice最新文献

Background: Sepsis-associated acute kidney injury (SA-AKI) is a prominent sepsis complication, often resulting in adverse clinical outcomes. Hyperbaric oxygen therapy (HBOT), known for its anti-inflammatory characteristics, antioxidant effects, and ability to deliver high oxygen tension to hypo-perfused tissues, offers potential benefits for SA-AKI. This study investigated whether HBOT improved renal injury in sepsis and elucidated its underlying mechanisms.

Methods: A lipopolysaccharide (LPS)-induced endotoxemia model was established using 8-week-old C57BL/6 mice. Thirty minutes post-LPS administration, a group of mice underwent HBOT at a 2.5 atmospheric pressure absolute with 100% oxygen for 60 minutes. After 24 hours, all mice were euthanized for measurements.

Results: Our results demonstrated that HBOT effectively mitigated renal tubular cell apoptosis. Additionally, HBOT significantly reduced phosphorylated p53 proteins and cytochrome C levels, suggesting that HBOT may attenuate renal apoptosis by impeding p53 activation and cytochrome C release. Notably, HBOT preserved manganese-dependent levels of superoxide dismutase, an antioxidant enzyme, compared to the LPS group. Furthermore, transforming growth factor beta (TGF-β)/Smad4 and alpha smooth muscle actin expressions were significantly reduced in the LPS + HBOT group.

Conclusion: An early single session of HBOT exhibited renoprotective effects in LPS-induced endotoxemia mice models by suppressing p53 activation and cytochrome C levels to mitigate apoptosis. The observed TGF-β decrease, downstream Smad expression reduction, and antioxidant capacity preservation following HBOT may contribute to these effects.

The worldwide prevalence of chronic kidney disease (CKD) is high and growing, making CKD a leading cause of mortality. Skeletal muscle wasting, sometimes called sarcopenia or protein-energy wasting, is a frequent, serious consequence of CKD that reduces muscle strength and function, diminishes the quality of life of patients, and raises their risk of comorbidities and death. Muscle atrophy results from a disturbance in muscle protein balance that results from some combination of an increased rate of protein degradation, a decreased rate of protein synthesis, and dysfunctional muscle regeneration. Development of therapeutic strategies to ameliorate muscle loss, or maintain muscle mass, is challenging because of the multifactorial nature of the signals that alter protein homeostasis. This review discusses the cellular signals and mechanisms that negatively alter protein turnover in skeletal muscle during CKD.

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.