γ-Gammaglutamyl transferase predicts all-cause mortality within three-year intervals in patients undergoing peritoneal dialysis.

OBJECTIVES: Peritoneal dialysis (PD) serves as a vital renal replacement therapy for patients with end-stage kidney disease (ESKD). γ-Gamma-glutamyl transferase (γ-GGT) is a recognized predictor of oxidative stress and mortality. This study aimed to assess the prognostic significance of γ-GGT in predicting all-cause and cardiovascular mortality among PD patients. METHODS: A retrospective study was conducted, enrolling 640 PD patients from a single center. The one-year, three-year, and five-year mortality rates for all causes and cardiovascular causes were evaluated. Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed. RESULTS: Within five years of initiating PD, the observed all-cause mortality rates at one, three, and five years were 11.72%, 16.09%, and 23.44%, while cardiovascular mortality rates were 2.97%, 7.34%, and 11.09%, respectively. Lower γ-GGT levels were associated with decreased all-cause mortality during one-, three-, and five-year follow-ups, along with reduced cardiovascular mortality in the first and third years, as indicated by Kaplan-Meier analysis on median γ-GGT groupings. Multivariate Cox regression analysis showed significantly decreased hazard ratios (HRs) for one- to five-year all-cause mortality and cardiovascular mortality in the lower γ-GGT group compared to higher groups. However, when sex differences were eliminated using separate tertile groupings for males and females, only the one- and three-year all-cause mortality rates demonstrated significantly reduced hazard ratios (HRs) in the lower γ-GGT groups. CONCLUSION: This retrospective study suggests that γ-GGT levels have prognostic significance in predicting one- and three-year all-cause mortality among PD patients when accounting for sex differences.

Machine-learning algorithm-based prediction of a diagnostic model based on oxidative stress-related genes involved in immune infiltration in diabetic nephropathy patients.

Diabetic nephropathy (DN) is the most prevalent microvascular consequence of diabetes and has recently risen to the position of the world's second biggest cause of end-stage renal diseases. Growing studies suggest that oxidative stress (OS) responses are connected to the advancement of DN. This study aimed to developed a novel diagnostic model based on OS-related genes. The differentially expressed oxidative stress-related genes (DE-OSRGs) experiments required two human gene expression datasets, which were given by the GEO database (GSE30528 and GSE96804, respectively). The potential diagnostic genes were identified using the SVM-RFE assays and the LASSO regression model. CIBERSORT was used to determine the compositional patterns of the 22 different kinds of immune cell fraction seen in DN. These estimates were based on the combined cohorts. DN serum samples and normal samples were both subjected to RT-PCR in order to investigate the degree to which certain genes were expressed. In this study, we were able to locate 774 DE-OSRGs in DN. The three marker genes (DUSP1, PRDX6 and S100A8) were discovered via machine learning on two different machines. The high diagnostic value was validated by ROC tests, which focused on distinguishing DN samples from normal samples. The results of the CIBERSORT study suggested that DUSP1, PRDX6, and S100A8 may be associated to the alterations that occur in the immunological microenvironment of DN patients. Besides, the results of RT-PCR indicated that the expression of DUSP1, PRDX6, and S100A8 was much lower in DN serum samples compared normal serum samples. The diagnostic value of the proposed model was likewise verified in our cohort, with an area under the curve of 9.946. Overall, DUSP1, PRDX6, and S100A8 were identified to be the three diagnostic characteristic genes of DN. It's possible that combining these genes will be effective in diagnosing DN and determining the extent of immune cell infiltration.

Serum platelet distribution width predicts cardiovascular and all-cause mortality in patients undergoing peritoneal dialysis.

BACKGROUND: Platelet distribution width (PDW) is a predictor for all-cause mortality in patients with cardiovascular diseases (CVD). This study aimed to evaluate the prognostic implication of PDW in predicting cardiovascular and all-cause mortality in patients undergoing peritoneal dialysis (PD). METHODS: In total, 762 PD patients from a single center were recruited retrospectively from 2005 to 2017 and followed up until 2021. The primary and secondary outcomes were cardiovascular and all-cause mortality, respectively. Survival analysis was conducted using Kaplan-Meier estimates and Cox regression analysis. RESULTS: During a median of 52.2 months of follow-up, 135 (17.7%) cases of CVD and 253 (33.2%) cases of all-cause mortality were reported. After multivariate adjustment, high levels of PDW were associated with an increased risk of death from CVD (HR: 1.583; 95% CI: 1.109-2.258; P = 0.011) and all-cause mortality (HR: 1.313; 95% CI: 1.006-1.758; P = 0.045). Subgroup analysis indicated a stronger association between PDW and all-cause mortality among female participants (P-value for interaction = 0.033). Higher levels of PDW predicted an increased risk of all-cause mortality in female patients (HR: 1.986; 95% CI,1.261-3.127). CONCLUSION: High levels of PDW are independently associated with cardiovascular and all-cause mortality in the PD population, and differences by sex exist in the association of PDW with all-cause mortality.