Kidney & blood pressure research最新文献

Introduction: The aim of this was to evaluate the function of vascular biomarkers to predict the need for hemodialysis in critically ill patients with COVID-19.

Methods: This is a prospective study with 58 critically ill patients due to COVID-19 infection. Laboratory tests in general and vascular biomarkers, such as VCAM-1, syndecan-1, angiopoietin-1, and angiopoietin-2, were quantified on intensive care unit (ICU) admission.

Results: There was a 40% death rate. VCAM and Ang-2/Ang-1 ratio on ICU admission were associated with the need for hemodialysis. Vascular biomarkers (VCAM-1, syndecan-1, angiopoietin-2/angiopoietin-1 ratio) were predictors of death and their cutoff values were useful to stratify patients with a worse prognosis. In the multivariate cox regression analysis with adjusted models, VCAM-1 (OR 1.13 [CI 95%: 1.01-1.27]; p = 0.034) and Ang-2/Ang-1 ratio (OR 4.87 [CI 95%: 1.732-13.719]; p = 0.003) were associated with the need for dialysis.

Conclusion: Vascular biomarkers, mostly VCAM-1 and Ang-2/Ang-1 ratio, showed better efficiency to predict the need for hemodialysis in critically ill COVID-19 patients.

Introduction: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.

Methods: ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs.

Results: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes.

Conclusion: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Introduction: The main objective of this study was to identify the best combination of admission day parameters for predicting COVID-19 mortality in hospitalized patients. Furthermore, we sought to compare the predictive capacity of pulmonary parameters to that of renal parameters for mortality from COVID-19.

Methods: In this retrospective study, all patients admitted to a tertiary hospital between September 1st, 2020, and December 31st, 2020, who were clinically symptomatic and tested positive for COVID-19, were included. We gathered extensive data on patient admissions, including laboratory results, comorbidities, chest X-ray (CXR) images, and SpO2 levels, to determine their role in predicting mortality. Experienced radiologists evaluated the CXR images and assigned a score from 0 to 18 based on the severity of COVID-19 pneumonia. Further, we categorized patients into two independent groups based on their renal function using the RIFLE and KDIGO criteria to define the acute kidney injury (AKI) and chronic kidney disease (CKD) groups. The first group ("AKI&CKD") was subdivided into six subgroups: normal renal function (A); CKD grade 2+3a (B); AKI-DROP (C); CKD grade 3b (D); AKI-RISE (E); and grade 4 + 5 CKD (F). The second group was based only on estimated glomerular filtration rate (eGFR) at the admission, and thus it was divided into four grades: grade 1, grade 2+3a, grade 3b, and grade 4 + 5.

Results: The cohort comprised 619 patients. Patients who died during hospitalization had a significantly higher mean radiological score compared to those who survived, with a p value <0.01. Moreover, we observed that the risk for mortality was significantly increased as renal function deteriorated, as evidenced by the AKI&CKD and eGFR groups (p < 0.001 for each group). Regarding mortality prediction, the area under the curve (AUC) for renal parameters (AKI&CKD group, eGFR group, and age) was found to be superior to that of pulmonary parameters (age, radiological score, SpO2, CRP, and D-dimer) with an AUC of 0.8068 versus 0.7667. However, when renal and pulmonary parameters were combined, the AUC increased to 0.8813. Optimal parameter combinations for predicting mortality from COVID-19 were identified for three medical settings: Emergency Medical Service (EMS), the Emergency Department, and the Internal Medicine Floor. The AUC for these settings was 0.7874, 0.8614, and 0.8813, respectively.

Conclusions: Our study demonstrated that selected renal parameters are superior to pulmonary parameters in predicting COVID-19 mortality for patients requiring hospitalization. When combining both renal and pulmonary factors, the predictive ability of mortality significantly improved. Additionally, we identified the optimal combination of factors for mortality prediction in three distinct settings: EMS, Emergency Department, and Internal Medicine Floor.

Introduction: Acute kidney injury (AKI) is a clinical emergency caused by the rapid decline of renal function caused by various etiologies. Growth differentiation factor 11 (GDF11) can promote renal tubular regeneration and improve kidney function in AKI, but the specific mechanism remains unclear. Herein, we investigated the effect and mechanisms of GDF11 in ameliorating AKI induced by ischemia-reperfusion (I/R).

Methods: An animal model of AKI was established by I/R method, and the changes of serum urea nitrogen and creatinine were measured to evaluate the AKI. Enzyme-linked immunosorbent assay (ELISA) was used to measure cytokines, malondialdehyde, superoxide dismutase, nitric oxide synthase, and arginase 1 levels. Flow cytometry was used to count the M1/M2 macrophages. IHC, WB, and q-PCR experiments were used to evaluate the expression of GDF11.

Results: The changes in serum levels of urea nitrogen and creatinine after I/R suggest that an animal model of AKI induced by I/R was successfully established. AKI caused by I/R significantly changed the M1/M2 macrophage polarization balance, with an increase in M2 being significantly higher than M1 as well as increased oxidative stress. Treatment with GDF11 after I/R significantly increased the differentiation of M2 cells and inhibited the differentiation of M1 macrophages, as well as decreased oxidative stress.

Conclusion: GDF11 can promote the repair of AKI caused by I/R by regulating the balance of M1/M2 polarization in macrophages and oxidative stress.

Introduction: Most chronic kidney disease (CKD) patients experience cardiovascular issues before commencing renal replacement therapy. An accuracy prediction model is helpful for physicians to assess cardiovascular prognoses in each individual and to provide insights on how to outline individualized lines of therapy.

Method: This study enrolled 1,138 participants with non-dialysis-dependent chronic kidney disease (NDD-CKD). Following a proportion of 7:3, patients were randomly assigned to training and validation cohorts. The relevant predictors of cardiovascular events were screened using the least absolute shrinkage and selection operator (Lasso) regression. The area under the receiver operating characteristic curve (AUC) and the calibration curve with 1,000 bootstrap resamples were used to assess the nomogram's performance. Tests on the discrimination of the prediction model used Kaplan-Meier (KM) curve.

Results: After screening all the predictors by lasso regression, the five remaining ones (albumin, estimated glomerular filtration rate, etiology of CKD, cardiovascular disease history, and age) were used to construct the prediction model. The AUCs of 1 year, 2 years, and 3 years were 0.81 (95% CI = 0.75-0.87), 0.80 (95% CI = 0.75-0.86), and 0.80 (95% CI = 0.73-0.86), respectively. The calibration curve and the KM curve showed good prediction features, and the external validation also had a good prediction performance (AUCs of 1, 2, and 3 years were 0.77, 0.84, and 0.82, respectively).

Conclusion: We successfully developed a novel nomogram that has decent prediction performance and can be used for assessing the probability of cardiovascular events in patients with NDD-CKD, displaying valuable potential for clinical application.

Introduction: The optimal dialysate bicarbonate concentration (DBIC) for hemodialysis (HD) remains controversial. Herein, we analyzed the effect of dialysate bicarbonate levels on mortality in HD patients.

Methods: Patients undergoing maintenance HD were recruited from the HD unit of the Daping Hospital. Patients were categorized into quartiles according to their DBIC level (quartile 1: <31.25 mmol/L, n = 77; quartile 2: 31.25-32.31 mmol/L, n = 76; quartile 3: 32.31-33.6 mmol/L; n = 81; quartile 4: ≥33.6 mmol/L, n = 79). Demographic and clinical data were collected. Survival curves were estimated using the Kaplan-Meier method. A Cox proportional hazards regression model was used to estimate the association between DBIC and all-cause mortality.

Results: We included 313 patients undergoing maintenance HD with a mean DBIC of 32.16 ± 1.59 mmol/L (range, 27.20-34.72 mmol/L). The patients in quartile 4 were more likely to have higher pre- and post-HD serum bicarbonate concentrations than those in other quartiles. The mortality rate was lowest in quartile 2 (10.53%). The survival time was significantly lower in the quartile 4 group than in the other quartiles (p = 0. 008, log-rank test). After full adjustment, the hazard ratio (per 3 mmol/L higher DBIC) for all-cause mortality was 4.29 (95% confidence interval, 2.11-8.47) in all patients, whereas no significant association was observed between DBIC and initial hospitalization.

Conclusions: Our data indicate that DBIC is positively associated with all-cause mortality. A DBIC concentration of 31-32 mmol/L may benefit patient outcomes. This study provides an evidence-based medical basis for optimal dialysis prescription in the future.

Introduction: Chronic kidney disease (CKD) is a global health problem with increasing incidence which is closely associated with cardiac dysfunction. In CKD, uremic toxins accumulate as kidney function declines. Additionally, high salt intake is a growing health issue worldwide which can exacerbate kidney disease. In this study, we investigated the effect of reducing plasma levels of protein-bound uremic toxins in a rat model of CKD, challenged with high salt intake and compared the effects to those of conventional treatment using an angiotensin-converting enzyme inhibitor (ACEI).

Methods: In rats, the right kidney and 2/3 of the left kidney were surgically removed (5/6 nephrectomy). Animals were fed a normal-salt diet and randomized to either no treatment (control) or chronic treatment with either the oral absorbent AST-120 to reduce plasma levels of protein-bound uremic toxins or the ACEI enalapril to inhibit angiotensin II signaling for 5 weeks. Following treatment, kidney function was measured before and after a week of high salt intake. Cardiac output and markers of oxidative stress were measured at the end of the study period.

Results: Treatment with AST-120 resulted in decreased levels of the uremic toxin indoxyl sulfate, improved cardiac output (mL/min: AST-120 44.9 ± 5.4 compared to control 26.6 ± 2.0; p < 0.05), and decreased urinary oxidative stress. ACEI reduced oxidative stress in kidney tissue and improved the glomerular filtration rate in response to high salt intake (mL/min: ACEI 1.5 ± 0.1; compared to control 1.1 ± 0.1; p < 0.05). Both interventions improved intrarenal oxygen availability (mm Hg: AST-120 42.8 ± 0.8; ACEI 43.2 ± 1.9; compared to control 33.4 ± 1.3; p < 0.05).

Conclusion: AST-120 administered to reduce plasma levels of uremic toxins, such as indoxyl sulfate, has potential beneficial effects on both cardiac and kidney function. Targeting uremic toxins and angiotensin II signaling simultaneously could be an efficient strategy to target both cardiac and kidney dysfunction in CKD, to further slow progression of disease in patients with CKD.