Association Between Peritoneal Glucose Absorption, Lipid Metabolism, and Cardiovascular Disease Risk in Nondiabetic Patients on Peritoneal Dialysis.

BACKGROUND: Excessive sugar intake increases the energy metabolic burden and the risk of cardiovascular disease (CVD). Patients on peritoneal dialysis absorb much more glucose than the World Health Organization recommends, but the link to CVD is unclear. OBJECTIVE: To identify the association between peritoneal glucose absorption, lipid metabolism, and CVD. METHODS: We applied generalized additive mixed effects and mixed effects Cox proportional hazard models to evaluate the impact of peritoneal glucose absorption on lipid profiles and CVD risk. We performed subgroup analyses by using protein intake (normalized protein nitrogen appearance [nPNA] and normalized protein catabolic rate [nPCR] were used to assess protein intake) and high-sensitivity C-reactive protein (hs-CRP). RESULTS: After multivariable adjustment, peritoneal glucose absorption per 10 g/d increase was associated with an increase in cholesterol of 0.145 (95% confidence interval [CI]: 0.086-0.204) mmol/L. No link with the total risk of CVD was observed; however, protein intake and hs-CRP levels affected the relationship between glucose absorption and CVD risk. Patients with values for nPNA and nPCR <1.0 g/(kg·d) were associated with a lower risk of CVD (hazard ratio [HR] 95% CI: 0.68 (0.46-0.98)) with glucose absorption per 10 g/d increase. While patients with hs-CRP levels ≥3 mg/d or values for nPNA or nPCR ≥1.0 g/(kg·d) were associated with a higher risk of CVD (HR 95% CI: 1.32 (1.07-1.63); 1.31 (1.02-1.68)) for glucose absorption per 10 g/d increase. CONCLUSIONS: Our study found a positive correlation between peritoneal glucose absorption and lipid profiles. Increased glucose absorption was associated with a lower risk of CVD in lower protein intake patients and a higher risk of CVD in higher hs-CRP or protein intake levels in patients on peritoneal dialysis.

Infection of domestic pigs with a genotype II potent strain of ASFV causes cytokine storm and lymphocyte mass reduction.

The whole-genome sequence of an African swine fever virus (ASFV) strain (HuB/HH/2019) isolated from Hubei, China, was highly similar to that of the Georgia 2007/1 strain ASFV. After infection with strong strains, domestic pigs show typical symptoms of infection, including fever, depression, reddening of the skin, hemorrhagic swelling of various tissues, and dysfunction. The earliest detoxification occurred in pharyngeal swabs at 4 days post-infection. The viral load in the blood was extremely high, and ASFV was detected in multiple tissues, with the highest viral loads in the spleen and lungs. An imbalance between pro- and anti-inflammatory factors in the serum leads to an excessive inflammatory response in the body. Immune factor expression is suppressed without effectively eliciting an immune defense. Antibodies against p30 were not detected in acutely dead domestic pigs. Sequencing of the peripheral blood mononuclear cell transcriptome revealed elevated transcription of genes associated with immunity, defense, and stress. The massive reduction in lymphocyte counts in the blood collapses the body's immune system. An excessive inflammatory response with a massive reduction in the lymphocyte count may be an important cause of mortality in domestic pigs. These two reasons have inspired researchers to reduce excessive inflammatory responses and stimulate effective immune responses for future vaccine development.

Development and validation of prediction model for technique failure in peritoneal dialysis patients: An observational study.

AIM: This study aimed to establish a prediction model in peritoneal dialysis patients to estimate the risk of technique failure and guide clinical practice. METHODS: Clinical and laboratory data of 424 adult peritoneal dialysis patients were retrospectively collected. The risk prediction models were built using univariate Cox regression, best subsets approach and LASSO Cox regression. Final nomogram was constructed based on the best model selected by the area under the curve. RESULTS: After comparing three models, the nomogram was built using the LASSO Cox regression model. This model included variables consisting of hypertension and peritonitis, serum creatinine, low-density lipoprotein, fibrinogen and thrombin time, and low red blood cell count, serum albumin, triglyceride and prothrombin activity. The predictive model constructed performed well using receiver operating characteristic curve and area under the curve value, C-index and calibration curve. CONCLUSION: This study developed and verified a new prediction instrument for the risk of technique failure among peritoneal dialysis patients.