Serum Uric Acid and Cardiovascular or All-Cause Mortality in Peritoneal Dialysis Patients: A Systematic Review and Meta-Analysis

Causal factors of cardiovascular disease in end-stage renal disease with maintenance hemodialysis: a longitudinal and Mendelian randomization study

Background

The risk factors of cardiovascular disease (CVD) in end-stage renal disease (ESRD) with hemodialysis remain not fully understood. In this study, we developed and validated a clinical-longitudinal model for predicting CVD in patients with hemodialysis, and employed Mendelian randomization to evaluate the causal 6study included 468 hemodialysis patients, and biochemical parameters were evaluated every three months. A generalized linear mixed (GLM) predictive model was applied to longitudinal clinical data. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the model. Kaplan-Meier curves were applied to verify the effect of selected risk factors on the probability of CVD. Genome-wide association study (GWAS) data for CVD (n = 218,792,101,866 cases), end-stage renal disease (ESRD, n = 16,405, 326 cases), diabetes (n = 202,046, 9,889 cases), creatinine (n = 7,810), and uric acid (UA, n = 109,029) were obtained from the large-open GWAS project. The inverse-variance weighted MR was used as the main analysis to estimate the causal associations, and several sensitivity analyses were performed to assess pleiotropy and exclude variants with potential pleiotropic effects.

Results

The AUCs of the GLM model was 0.93 (with accuracy rates of 93.9% and 93.1% for the training set and validation set, sensitivity of 0.95 and 0.94, specificity of 0.87 and 0.86). The final clinical-longitudinal model consisted of 5 risk factors, including age, diabetes, ipth, creatinine, and UA. Furthermore, the predicted CVD response also allowed for significant (p < 0.05) discrimination between the Kaplan-Meier curves of each age, diabetes, ipth, and creatinine subclassification. MR analysis indicated that diabetes had a causal role in risk of CVD (β = 0.088, p < 0.0001) and ESRD (β = 0.26, p = 0.007). In turn, ESRD was found to have a causal role in risk of diabetes (β = 0.027, p = 0.013). Additionally, creatinine exhibited a causal role in the risk of ESRD (β = 4.42, p = 0.01).

Conclusions

The results showed that old age, diabetes, and low level of ipth, creatinine, and UA were important risk factors for CVD in hemodialysis patients, and diabetes played an important bridging role in the link between ESRD and CVD.

Construction and validation of a predictive model for the risk of peritoneal dialysis-associated peritonitis after peritoneal dialysis catheterization

Aim

To construct and validate a risk prediction model for the development of peritoneal dialysis-associated peritonitis (PDAP) in patients undergoing peritoneal dialysis (PD).

Methods

This retrospective analysis included patients undergoing PD at the Department of Nephrology, the First Affiliated Hospital of Anhui University of Chinese Medicine, between January 2016 and January 2021. Baseline data were collected. The primary study endpoint was PDAP occurrence. Patients were divided into a training cohort (n = 264) and a validation cohort (n = 112) for model building and validation. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to optimize the screening variables. Predictive models were developed using multifactorial logistic regression analysis with column line plots. Receiver operating characteristic (ROC) curves, calibration curves, and Hosmer-Lemeshow goodness-of-fit tests were used to verify and evaluate the discrimination and calibration of the prediction models. Decision curve analysis (DCA) was used to assess the clinical validity of the prediction models.

Results

Five potential predictors of PDAP after PD catheterization were screened using LASSO regression analysis, including neutrophil-to-lymphocyte ratio (NLR), serum ALBumin (ALB), uric acid (UA), high sensitivity C-reactive protein (hsCRP), and diabetes mellitus (DM). Predictive models were developed by multi-factor logistic regression analysis and plotted in columns. The area under the ROC curve (AUC) values were 0.891 (95% confidence interval [CI]: 0.829-0.844) and 0.882 (95% CI: 0.722-0.957) for the training and validation cohorts, respectively. The Hosmer-Lemeshow test showed a good fit (p = 0.829 for the training cohort; p = 0.602 for the validation cohort). The DCA curves indicated that the threshold probabilities for the training and validation cohorts were 4-64% and 3-90%, respectively, predicting a good net gain for the clinical model.

Conclusion

NLR, ALB, UA, hsCRP, and DM are independent predictors of PDAP after PD catheterization. The column line graph model constructed based on the abovementioned factors has good discriminatory and calibrating ability and helps to predict the risk of PDAP after PD catheterization.

Serum uric acid level and all-cause and cardiovascular mortality in peritoneal dialysis patients: A systematic review and dose-response meta-analysis of cohort studies

BACKGROUND

The association between serum uric acid (SUA) and all-cause and cardiovascular disease (CVD) mortality in peritoneal dialysis (PD) patients is controversial. Therefore, we aimed to determine the relationship between SUA and all-cause and CVD mortality in PD patients.

METHOD

Web of Science, EMBASE, PubMed and the Cochrane Library databases were searched from their inception to 7 April 2021. Effect estimates were presented as hazard ratios (HRs) with 95% confidence intervals (95% CIs) and pooled using random effects model.

RESULT

Thirteen cohort studies with 22418 patients were included in this systematic review, of which 9 were included in the meta-analysis. Before switching the reference group, pooled result for the highest SUA category was significantly greater than the median for all-cause mortality (HR = 2.41, 95% CI: 1.37-4.26). After switching the reference group, the highest SUA category did not demonstrate an increased all-cause (HR = 1.40, 95% CI: 0.95-2.05) or CVD (HR = 1.30, 95% CI: 0.72-2.34) mortality compared with the lowest SUA category. Dose-response analysis suggested a nonlinear association between SUA and all-cause mortality risk (Pnonlinearity = 0.002).

CONCLUSION

This meta-analysis didn't find the relationship between SUA levels and all-cause and CVD mortality risk in PD patients. More rigorously designed studies are warranted in the future.