Association of serum uric acid with all-cause and cardiovascular mortality in chronic kidney disease stages 3-5.

BACKGROUND AND AIMS: The role of serum uric acid (SUA) in the prognosis of chronic kidney disease (CKD) is inconclusive. To explore the association of SUA level with all-cause and cardiovascular disease (CVD) mortality in patients with CKD. METHODS AND RESULTS: Leveraging data from the National Health and Nutritional Examination Survey (NHANES) and linked national death records up to December 31 2019, we explored the association of SUA with all-cause and CVD mortality using weighted cox proportional hazards regression models and restricted cubic spline (RCS) models in patients with CKD stages 3-5. The study finally included 2644 patients with CKD stages 3-5, with a median SUA level of 6.5 mg/dL. After a median follow-up of 55 months, a total of 763 deaths were recorded, with 279 of them attributed to CVD. In the fully adjusted model, per 1 mg/dL increment in SUA concentration was found to be associated with increased HRs (95% CIs) of 1.07 (1.00, 1.14) for all-cause mortality and 1.11 (1.00, 1.24) for CVD mortality. Compared to Q2 (reference), those in Q4 had adjusted HRs of 1.72 (1.36, 2.17) for all-cause mortality and 2.17 (1.38, 3.41) for CVD mortality, while those in Q1 had adjusted HRs of 1.49 (1.19, 1.85) for all-cause mortality and 1.93 (1.26, 2.98) for CVD mortality. CONCLUSIONS: Both higher and lower SUA levels were associated with increased risks of all-cause and CVD mortality in patients with CKD stages 3-5.

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients Using Machine Learning and Immune Cell Infiltration Analysis.

Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection. COVID-19 datasets (GSE164805 and GSE180594) and aging dataset (GSE69832) were obtained from the Gene Expression Omnibus database. The combined different expression genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Diseases Ontology functional enrichment analysis, Gene Set Enrichment Analysis, machine learning, and immune cell infiltration analysis. GO and KEGG enrichment analyses revealed that the eight DEGs (IL23A, PTGER4, PLCB1, IL1B, CXCR1, C1QB, MX2, ALOX12) were mainly involved in inflammatory mediator regulation of TRP channels, coronavirus disease-COVID-19, and cytokine activity signaling pathways. Three-degree algorithm (LASSO, SVM-RFE, KNN) and correlation analysis showed that the five DEGs up-regulated the immune cells of macrophages M0/M1, memory B cells, gamma delta T cell, dendritic cell resting, and master cell resisting. Our study identified five hallmark genes that can serve as disease-characteristic genes and target immune cells infiltrated in severe COVID-19 patients among the elderly population, which may contribute to the study of pathogenesis and the evaluation of diagnosis and prognosis in aging patients infected with severe COVID-19.

Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren's syndrome based on comprehensive bioinformatics and immunohistochemical analyses.

IgA nephropathy (IgAN) and Sjogren's syndrome (SS) are two autoimmune diseases with undetermined etiology and related to abnormal activation of lymphocytes. This study aims to explore the crucial genes, pathways and immune cells between IgAN and SS. Gene expression profiles of IgAN and SS were obtained from the Gene Expression Omnibus and Nephroseq data. Differentially expressed gene (DEG) and weighted gene co-expression network analyses (WGCNA) were done to identify common genes. Enrichment analysis and protein-protein interaction network were used to explore potential molecular pathways and crosstalk genes between IgAN and SS. The results were further verified by external validation and immunohistochemistry (IHC) analysis. Additionally, immune cell analysis and transcription factor prediction were also conducted. The DEG analysis revealed 28 commonly up-regulated genes, while WGCNA identified 98 interactively positive-correlated module genes between IgAN and SS. The enrichment analysis suggested that these genes were mainly involved in the biological processes of response to virus and antigen processing and presentation. The external validation and IHC analysis identified 5 hub genes (PSMB8, PSMB9, IFI44, ISG15, and CD53). In the immune cell analysis, the effector memory CD8 T and T follicular helper cells were significantly activated, and the corresponding proportions showed positively correlations with the expressions of the 5 hub genes in the two autoimmune diseases. Together, our data identified the crosstalk genes, molecular pathways, and immune cells underlying the IgAN and SS, which provides valuable insights into the intricate mechanisms of these diseases and offers potential intervention targets.

Machine learning for the prediction of delirium in elderly intensive care unit patients.

PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage. METHODS: Patients admitted to ICU for at least 24 h and using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (76,943 ICU stays from 2008 to 2019) were considered. Patients with a positive delirium test in the first 24 h and under 65 years of age were excluded. Two prediction models, machine learning extreme gradient boosting (XGBoost) and logistic regression (LR) model, were developed and validated to predict the onset of delirium. RESULTS: Of the 18,760 patients included in the analysis, 3463(18.5%) were delirium positive. A total of 22 significant predictors were selected by LASSO regression. The XGBoost model demonstrated superior performance over the LR model, with the Area Under the Receiver Operating Characteristic (AUC) values of 0.853 (95% confidence interval [CI] 0.846-0.861) and 0.831 (95% CI 0.815-0.847) in the training and testing datasets, respectively. Moreover, the XGBoost model outperformed the LR model in both calibration and clinical utility. The top five predictors associated with the onset of delirium were sequential organ failure assessment (SOFA), infection, minimum platelets, maximum systolic blood pressure (SBP), and maximum temperature. CONCLUSION: The XGBoost model demonstrated good predictive performance for delirium among elderly ICU patients, thus assisting clinicians in identifying high-risk patients at the early stage and implementing targeted interventions to improve outcome.