Kidney Research and Clinical Practice最新文献

Background: Mycophenolate mofetil (MMF) is usually prescribed with a reduced fixed dose in Asian kidney transplant recipients (KTRs). However, the clinical efficacy and safety of the fixed dose have not yet been investigated via therapeutic drug monitoring. We evaluated whether reduced fixed-dose MMF is an optimal dosing strategy to achieve the therapeutic target of mycophenolic acid (MPA) exposure in Korean KTRs.

Methods: This open-label, prospective study enrolled 50 de novo KTRs prescribed with tacrolimus, corticosteroid, and fixed-dose MMF (1.0-1.5 g/day). The trough level (C0) and area under the curve (AUC0-12 hr) of MPA were measured at 1 and 24 weeks after kidney transplantation (KT). The relationship of body weight (BW)-adjusted MMF dose with MPA C0 and MPA AUC0-12 hr was assessed using linear regression analysis.

Results: The initial fixed dose of MMF of 1.44 ± 0.16 g/day was adjusted in 24 patients (48.0%) and then reduced to a mean dose of 1.19 ± 0.31 g/day at 24 weeks after KT. Most patients (≥84.0%) attained the minimum required MPA C0 of 1.0 μg/mL and MPA AUC0-12 hr of 30 μg × hr/mL at 1 and 24 weeks. The BW-adjusted MMF dose demonstrated significant positive correlations with MPA C0 and MPA AUC0-12 hr at 1 and 24 weeks after KT. Moreover, 14 patients (28.0%) reported MPA-related adverse events that were predictable based on MPA AUC0-12 hr (cutoff level, 46.4 μg × hr/mL).

Conclusion: The current reduced fixed-dose MMF strategy can help achieve the therapeutic target of MPA exposure in tacrolimus-treated Korean KTRs during the early posttransplant period.

Background: Hypertension is one of the most important complications of chronic kidney disease (CKD) as it exacerbates disease progression in children. The aim of this study is to identify characteristics and factors associated with hypertension in children with CKD.

Methods: This is a cross-sectional study using baseline data from the 10-year ongoing cohort study named KNOW-PedCKD (Korean Cohort Study for Outcome in Patients with Pediatric Chronic Kidney Disease). We enrolled finally 378 patients aged <18 years at seven major pediatric nephrology centers in Republic of Korea. Blood pressure was measured and samples and clinical data were collected during the patients' annual hospital visits.

Results: We found that 30.7% of the patients had hypertension (n = 116); specifically, 16.4% (n = 62) had systolic hypertension, and 22.8% (n = 86) had diastolic hypertension. Multiple logistic regression analysis indicated that older age (odds ratio [OR], 1.13; p < 0.001), female sex (OR, 2.32; p = 0.002), a high left ventricular mass index (OR, 1.05; p < 0.001), and a high urine protein/creatinine ratio (OR, 1.12; p = 0.02) were significant associated factors for systolic or diastolic hypertension.

Conclusion: This study analyzed the associated factors for hypertension in children with CKD. Hypertension is associated with various factors, including age, sex, heart status, and proteinuria. Therefore, clinicians should consider these factors during patient evaluations to improve health outcomes.

Background: Membranous nephropathy (MN) is a specific autoimmune disease affecting kidneys. It is characterized by the accumulation of immune complexes in the glomerular basement membrane. Renal biopsy is currently the standard procedure to confirm the diagnosis, although the presence of autoantibodies against the phospholipase A2 receptor (PLA2R) can also help diagnose. In this study, we aimed to investigate the potential of urinary exosomes as noninvasive markers for diagnosing MN.

Methods: Exosomes were extracted from urine samples of five patients with MN and four healthy controls. The concentration of PLA2R was measured in both urine and isolated exosomes using enzyme-linked immunosorbent assay techniques. The measurements were adjusted based on the urine creatinine (UCr) level of each participant.

Results: The levels of PLA2R/UCr were investigated in urine and urine-derived exosomes from patients and controls. Results of the analysis revealed significantly higher expression of PLA2R/UCr in patients compared to the control group (p < 0.05). Furthermore, the expression level of PLA2R/UCr was higher in urine-derived exosomes than in urine samples. Additionally, a positive correlation was observed between the expression levels of PLA2R/UCr and the urine protein-to-creatinine ratio, with urine-derived exosomes exhibiting a stronger correlation than urine samples.

Conclusion: Studies have indicated that measuring exosomal PLA2R/UCr levels in urine could be a noninvasive method for diagnosing MN. Using urine-derived exosomes could also reduce the burden of performing a biopsy on patients and facilitate follow-up treatment, such as monitoring for future recurrence.

Background: Coronavirus disease 2019 (COVID-19) has led to severe pneumonia and mortality worldwide, however, clinical outcomes in end-stage renal disease patients remain unclear. This study evaluates the prognostic value of chest computed tomography (CT) findings in predicting COVID-19-related outcomes in prevalent hemodialysis patients.

Methods: We retrospectively analyzed 326 prevalent hemodialysis patients diagnosed with COVID-19 who underwent chest CT scans. Characteristics assessed included pleural effusion, lung involvement volume, nodular consolidation, patchy infiltration, and ground-glass opacity. Artificial intelligence (AI)-assisted CT analysis quantified lung involvement. The primary endpoint was in-hospital mortality. Clinical data were collected, and logistic regression analysis assessed the association between CT findings and mortality.

Results: The mean age of the patients was 66.7 ± 12.6 years, 61.0% were male, and 58.6% were diabetic. Chest CT showed that 18.1% had lung involvement >10%, 32.5% had pleural effusion, 68.7% had nodular consolidation, 57.1% had patchy infiltration, and 58.0% had ground-glass opacity. Seventy patients (21.5%) died. Multivariate logistic regression analysis identified lung involvement >2.7% (odds ratio [OR], 16.70; 95% confidence interval [CI], 4.35-65.63), pleural effusion (OR, 3.28; 95% CI, 1.15-9.35), nodular consolidation (OR, 4.08; 95% CI, 1.12-14.82), and patchy infiltration (OR, 3.75; 95% CI, 1.17-12.03) as significant mortality risk factors.

Conclusion: Chest CT findings, including lung involvement >2.7% and the presence of pleural effusion, nodular consolidation, and patchy infiltrates, significantly indicated mortality in COVID-19 pneumonia among prevalent hemodialysis patients. AI-assisted CT analysis proved useful in assessing lung involvement extent, showing that even minimal lung involvement can be associated with increased mortality.

Background: This study addresses the gap in knowledge regarding the long-term mortality implications of postoperative acute kidney injury (PO-AKI) utilizing advanced machine learning techniques to predict outcomes more accurately than traditional statistical models.

Methods: A retrospective cohort study was conducted using data from seven institutions between March 2009 and December 2019. Machine learning models were developed to predict all-cause mortality of PO-AKI patients using 23 preoperative variables and one postoperative variable. Model performance was compared to a traditional statistical approach with Cox regression analysis. The concordance index was used as a predictive performance metric to compare prediction capabilities among different models.

Results: Among 199,403 patients, 2,105 developed PO-AKI. During a median follow-up of 144 months (interquartile range, 99.61-170.71 months), 472 in-hospital deaths occurred. Subjects with PO-AKI had a significantly lower survival rate than those without PO-AKI (p < 0.001). For predicting mortality, the XGBoost with an accelerated failure time model had the highest concordance index (0.7521), followed by random survival forest (0.7371), multivariable Cox regression model (0.7318), survival support vector machine (0.7304), and gradient boosting (0.7277).

Conclusion: XGBoost with an accelerated failure time model was developed in this study to predict long-term mortality associated with PO-AKI. Its performance was superior to conventional models. The application of machine learning techniques may offer a promising approach to predict mortality following PO-AKI more accurately, providing a basis for developing targeted interventions and clinical guidelines to improve patient outcomes.

Alport syndrome, a rare genetic disorder affecting around 1 in 50,000 individuals, primarily presents as microscopic hematuria and chronic kidney disease (CKD) with associated extrarenal complications. The Alport syndrome results from mutations in COL4A3, COL4A4, and COL4A5 genes, disrupting the formation of the α3-α4-α5 chain in the collagen IV network. The etiology involves X chromosome-related, autosomal dominant, autosomal recessive, and digenic inheritance patterns. The disease primarily manifests as kidney involvement, featuring persistent hematuria, proteinuria, and a progressive decline in renal function. Hearing loss, ocular abnormalities, and extrarenal manifestations further contribute to its complexity. Genotype-phenotype correlations are relatively evident, with distinct presentations in X-linked, autosomal recessive, and autosomal dominant cases. Diagnosis relies on urinalysis, histologic examination, and genetic testing with advancements in next-generation sequencing aiding identification. Although no specific treatment exists, early diagnosis improves outcomes, emphasizing the importance of genetic testing for prognosis and familial screening. The purpose of this review is to advance knowledge and enhance understanding of Alport syndrome.

Background: This study investigates angiotensin II (Ang II)'s regulatory mechanism on renal outer medullary potassium channel (ROMK) activity in the distal convoluted tubule (DCT) during low potassium intake, focusing on the Janus kinase 2 (JAK2) pathway activation mediated by the Ang II type 1 receptor (AT1R).

Methods: Utilizing a low potassium diet mouse model, various methods including patch clamping, reverse transcription-quantitative polymerase chain reaction, Western blotting, and immunohistochemical staining were applied to analyze ROMK channel activity and the expression of related proteins.

Results: The findings reveal that Ang II inhibits ROMK activity in the DCT2 membrane through AT1R activation, with the JAK2 pathway playing a central role. Further, inhibiting JAK2 reverses this effect, indicating its potential in hypertension treatment.

Conclusion: This study provides novel insights into the role of Ang II in renal potassium excretion and hypertension pathophysiology.

Background: Cardiovascular disease is an important risk factor for mortality among kidney transplant recipients. In this study, we aimed to investigate the association between cardiovascular risk score at kidney transplantation and long-term outcomes of patients.

Methods: In this prospective, observational cohort study, we enrolled kidney transplant recipients who participated in the Korean Organ Transplantation Registry and underwent transplantation between April 2014 and December 2019. The cardiovascular risk status of kidney transplant recipients was assessed using the Framingham risk score. All-cause mortality, major adverse cardiovascular events, allograft failure, estimated glomerular filtration rates (eGFRs), and composite outcomes were evaluated after kidney transplantation.

Results: Of the 4,682 kidney transplant recipients, 96 died during 30.7 ± 19.1 months of follow-up. The Kaplan-Meier survival analysis results showed that high Framingham risk scores were associated with all-cause mortality, major adverse cardiovascular events, and composite outcomes. According to the multivariable Cox analysis, high Framingham risk scores were associated with an increased risk of mortality (hazard ratio [HR], 3.20; 95% confidence interval [CI], 1.30-7.91), major adverse cardiovascular events (HR, 8.43; 95% CI, 2.41-29.52), and composite outcomes (HR, 2.05; 95% CI, 1.19-3.46). The eGFRs after transplantation were significantly higher among patients in the low Framingham risk score group (p < 0.001). However, Framingham risk scores were not associated with graft loss or rapid decline in eGFRs.

Conclusion: The Framingham risk score is a useful indicator of cardiovascular events, mortality, and kidney function after kidney transplantation.

Donor-derived cell-free DNA (dd-cfDNA) based liquid kidney biopsies have the potential to detect the chances of kidney transplant rejection. Several studies have found that dd-cfDNA can be used to determine the risk of kidney transplant rejection and may correlate with antibody-mediated rejection (ABMR), T cell-mediated rejection (TCMR), and estimated glomerular filtration rate (eGFR). A high concentration of dd-cfDNA in the body fluids may indicate possible transplant rejection since dd-cfDNA is released as a result of apoptotic and necrotic processes initiated by the recipient's immune system. dd-cfDNA assays have advantages over conventional biopsies since they are noninvasive, and therefore, have the potential to provide a safe and reliable biomarker. Different dd-cfDNA levels have been reported above a number of cutoff thresholds: ABMR at 2.45% and TCMR at 1.3%, compared with 0.44% in healthy patients; and eGFR at 2.5%, a decrease of 25% compared with healthy patients. These results indicate the levels of dd-cfDNA that may be used to signal possible kidney rejection. dd-cfDNA assay is a rapid technique, making it particularly useful in emergencies, and further research into its use in the study of kidney rejection should prove beneficial.

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 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 they underestimated the external validation cohort risks. 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.