Impact of chronic kidney disease and end-stage renal disease on the mid-term adverse outcomes in diabetic patients with cardiovascular diseases.

The evidence for the impact of renal dysfunction in patients with diabetes mellitus (DM) and first cardiovascular diseases on mid-term adverse outcomes remain scarce. This study included the data of patients with DM having first atherosclerotic cardiovascular disease (ASCVD) or congestive heart failure (CHF) from the Taipei Medical University Clinical Research Database. A Cox proportional hazards regression model was used to assess the impact of chronic kidney disease (CKD) or end-stage renal disease (ESRD) on the 1-year mortality and recurrent ASCVD/CHF outcomes. We enrolled 21,320 patients with DM hospitalized for ASCVD or CHF; of them, 18,185, 2639, and 496 were assigned to the non-CKD, CKD, and ESRD groups, respectively. After propensity score matching, compared with the non-CKD group, the CKD and ESRD groups had higher mid-term all-cause mortality (adjusted hazard ratio 1.72 [95% confidence interval 1.48-1.99] and 2.77 [2.05-3.73], respectively), cardiovascular death (1.84 [1.44-2.35] and 1.87 [1.08-3.24], respectively), and recurrent hospitalization for ASCVD (1.44 [1.24-1.68] and 2.33 [1.69-3.23], respectively) and CHF (2.08 [1.75-2.47] and 1.50 [1.04-2.17], respectively). The advancing age was associated with mortality in CKD/ESRD groups. In CKD group, male sex was associated with all-cause mortality and recurrent ASCVD risk; the diuretics usage was associated with mortality and recurrent CHF risks. Our findings suggest that CKD and ESRD are significant risk factors for mid-term adverse outcomes in patients with DM and established cardiovascular diseases. Additionally, old age, male sex and diuretics usage requires attention. Further good quality studies are needed in the future.

Pressure induces peritoneal fibrosis and inflammation through CD44 signaling.

Peritoneal dialysis (PD) is a widely used sustainable kidney replacement therapy. Prolonged use of PD fluids is associated with mesothelial-mesenchymal transition, peritoneal fibrosis, and eventual ultrafiltration (UF) failure. However, the impact of pressure on the peritoneum remains unclear. In the present study, we hypothesized increased pressure is a potential contributing factor to peritoneal fibrosis and investigated the possible mechanisms. In vitro experiments found that pressurization led to a mesenchymal phenotype, the expression of fibrotic markers and inflammatory factors in human mesothelial MeT-5A cells. Pressure also increased cell proliferation and augmented cell migration potential in MeT-5A cells. The mouse PD model and human peritoneum equilibrium test (PET) data both showed a positive association between higher pressure and increased small solute transport, along with decreased net UF. Mechanistically, we found that significant upregulation of CD44 in mesothelial cells upon pressurization. Notably, the treatment of CD44 neutralizing antibodies prevented pressure-induced phenotypic changes in mesothelial cells, while a CD44 inhibitor oligo-fucoidan ameliorated pressure-induced peritoneal thickening, fibrosis, and inflammation in PD mice. To conclude, intraperitoneal pressure results in peritoneal fibrosis in PD via CD44-mediated mesothelial changes and inflammation. CD44 blockage can be utilized as a novel preventive approach for PD-related peritoneal fibrosis and UF failure.

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.