AST-120 alleviates renal ischemia-reperfusion injury by inhibiting HK2-mediated glycolysis.

OBJECTIVE: Renal ischemia/reperfusion injury (IRI) is a major cause of acute kidney injury (AKI), which is associated with high incidence and mortality. AST-120 is an oral carbonaceous adsorbent that can alleviate kidney damage. This study aimed to explore the effects of AST-120 on renal IRI and the molecular mechanism. METHODS: A renal IRI mouse model was established and administrated AST-120, and differentially expressed genes were screened using RNA sequencing. Renal function and pathology were analyzed in mice. Hypoxia/reoxygenation (H/R) cell model was generated, and glycolysis was evaluated by detecting lactate levels and Seahorse analysis. Histone lactylation was analyzed by western blotting, and its relationship with hexokinase 2 (HK2) was assessed using chromatin immunoprecipitation. RESULTS: The results showed that HK2 expression was increased after IRI, and AST-120 decreased HK2 expression. Knockout of HK2 attenuated renal IRI and inhibits glycolysis. AST-120 inhibited renal IRI in the presence of HK2 rather than HK2 absence. In proximal tubular cells, knockdown of HK2 suppressed glycolysis and H3K18 lactylation caused by H/R. H3K18 lactylation was enriched in HK2 promoter and upregulated HK2 levels. Rescue experiments revealed that lactate reversed IRI that suppressed by HK2 knockdown. CONCLUSIONS: In conclusion, AST-120 alleviates renal IRI via suppressing HK2-mediated glycolysis, which suppresses H3K18 lactylation and further reduces HK2 levels. This study proposes a novel mechanism by which AST-120 alleviates IRI.

A prediction model for in-hospital mortality in intensive care unit patients with metastatic cancer.

Aim: To identify predictors for in-hospital mortality in patients with metastatic cancer in intensive care units (ICUs) and established a prediction model for in-hospital mortality in those patients. Methods: In this cohort study, the data of 2,462 patients with metastatic cancer in ICUs were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to identify the predictors for in-hospital mortality in metastatic cancer patients. Participants were randomly divided into the training set (n = 1,723) and the testing set (n = 739). Patients with metastatic cancer in ICUs from MIMIC-IV were used as the validation set (n = 1,726). The prediction model was constructed in the training set. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were employed for measuring the predictive performance of the model. The predictive performance of the model was validated in the testing set and external validation was performed in the validation set. Results: In total, 656 (26.65%) metastatic cancer patients were dead in hospital. Age, respiratory failure, the sequential organ failure assessment (SOFA) score, the Simplified Acute Physiology Score II (SAPS II) score, glucose, red cell distribution width (RDW) and lactate were predictors for the in-hospital mortality in patients with metastatic cancer in ICUs. The equation of the prediction model was ln(P/(1 + P)) = -5.9830 + 0.0174 × age + 1.3686 × respiratory failure + 0.0537 × SAPS II + 0.0312 × SOFA + 0.1278 × lactate - 0.0026 × glucose + 0.0772 × RDW. The AUCs of the prediction model was 0.797 (95% CI,0.776-0.825) in the training set, 0.778 (95% CI, 0.740-0.817) in the testing set and 0.811 (95% CI, 0.789-0.833) in the validation set. The predictive values of the model in lymphoma, myeloma, brain/spinal cord, lung, liver, peritoneum/pleura, enteroncus and other cancer populations were also assessed. Conclusion: The prediction model for in-hospital mortality in ICU patients with metastatic cancer exhibited good predictive ability, which might help identify patients with high risk of in-hospital death and provide timely interventions to those patients.