Development and external validation of a nomogram for the early prediction of acute kidney injury in septic patients: a multicenter retrospective clinical study.

Background and purpose: Acute kidney injury (AKI) is a common serious complication in sepsis patients with a high mortality rate. This study aimed to develop and validate a predictive model for sepsis associated acute kidney injury (SA-AKI). Methods: In our study, we retrospectively constructed a development cohort comprising 733 septic patients admitted to eight Grade-A tertiary hospitals in Shanghai from January 2021 to October 2022. Additionally, we established an external validation cohort consisting of 336 septic patients admitted to our hospital from January 2017 to December 2019. Risk predictors were selected by LASSO regression, and a corresponding nomogram was constructed. We evaluated the model's discrimination, precision and clinical benefit through receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curves (CIC) in both internal and external validation. Results: AKI incidence was 53.2% in the development cohort and 48.2% in the external validation cohort. The model included five independent indicators: chronic kidney disease stages 1 to 3, blood urea nitrogen, procalcitonin, D-dimer and creatine kinase isoenzyme. The AUC of the model in the development and validation cohorts was 0.914 (95% CI, 0.894-0.934) and 0.923 (95% CI, 0.895-0.952), respectively. The calibration plot, DCA, and CIC demonstrated the model's favorable clinical applicability. Conclusion: We developed and validated a robust nomogram model, which might identify patients at risk of SA-AKI and promising for clinical applications.

Mucin 1 Inhibits Ferroptosis and Sensitizes Vitamin E to Alleviate Sepsis-Induced Acute Lung Injury through GSK3ß/Keap1-Nrf2-GPX4 Pathway.

Background: Ferroptosis is a nonapoptotic form of programmed cell death, which may be related to the occurrence and development of sepsis-induced acute respiratory distress syndrome (ARDS)/acute lung injury (ALI). Mucin 1 (MUC1) is a kind of macromolecule transmembrane glycoprotein. Previous studies have shown that MUC1 could relieve ALI in sepsis and predict whether sepsis patients would develop into ARDS. However, the role of MUC1 in the ferroptosis of sepsis-induced ALI/ARDS remains unclear. Materials and Methods: Sera samples from 50 patients with sepsis/septic shock were used to detect iron metabolism-related markers. Western blot and qRT-PCR were conducted to detect the expression levels of ferroptosis-related genes. Enzyme-linked immunosorbent assay (ELISA) was performed to evaluate inflammatory factors. Transmission electron microscopy (TEM) was used to assess morphological changes of cells. Results: The results showed that the iron metabolism-related indicators in sepsis-induced ARDS patients changed significantly, suggesting the iron metabolism disorder. The expression levels of ferroptosis-related genes in lung tissues of sepsis had marked changes, and the lipid peroxidation levels increased, while Ferrostatin-1 (Fer-1) could reverse the above results, which confirmed the occurrence of ferroptosis. In terms of mechanism studies, inhibition of MUC1 dimerization could increase the expression level of Keap1, reduce the phosphorylation level of GSK3ß, inhibit the entry of Nrf2 into the nucleus, further inhibit the expression level of GPX4, enhance the lipid peroxidation level of lung tissues, trigger ferroptosis, and aggravate lung injury. Besides, inhibiting MUC1 reversed the alleviating effect of vitamin E on ALI caused by sepsis, increased the aggregation of inflammatory cells in lung tissues, and aggravated alveolar injury and edema. Conclusions: Our study was the first to explore the changes of iron metabolism indicators in ALI/ARDS of sepsis, clarify the important role of ferroptosis in ALI/ARDS induced by sepsis, and reveal the effects and specific mechanisms of MUC1 in regulating ferroptosis, as well as the sensitization on vitamin E.

Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis.

Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes-LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN-as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.

Protective and predictive role of Mucin1 in sepsis-induced ALI/ARDS.

OBJECTIVE: We aimed to investigate whether inhibition of MUC1 would aggravate sepsis-induced ALI, and explore the predictive value of plasma MUC1 for sepsis patients with or without ARDS. MATERIALS AND METHODS: MUC1 siRNA pre-treatment was used to knockdown MUC1 expression in vitro. GO203 was used to inhibit the homodimerization of MUC1-C in vivo. Expression levels of MUC1, TLR 4 and HIF-1α were detected by Western blot. In addition, plasma MUC1 levels of enrolled patients were detected by ELISA on the day of admission and on the 3rd day. ROC curve was used to determine the predictive value of MUC1 in sepsis patients with ARDS. RESULTS: Our results showed that inhibition of MUC1 could aggravate sepsis-induced acute lung injury and increase the expression of inflammatory cytokines in sera and BALF of sepsis mice. At the same time, we confirmed that inhibition of MUC1 could significantly decrease HIF-1α expression and thereby activate the expression level of TLR4. HIF-1α was a negative regulator of TLR-4. In addition, plasma MUC1 levels of sepsis patients with ARDS were significantly higher than those without ARDS and healthy adults. ROC curve showed that predictive value of plasma MUC1 on sepsis with ARDS on the 3rd day of enrollment was higher than the day of enrollment. CONCLUSION: MUC1 could inhibit the expression of TLR-4 by stabilizing HIF-1α, thereby alleviate sepsis-induced lung injury and protect organ function. At the same time, elevated MUC1 levels in plasma had a good predictive valud on whether patients with sepsis would develop ARDS.