Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by traditional linear methods. This study explores the association between urinary VOC metabolites and CKD risk using a combination of epidemiological and interpretable machine learning approaches. Data from the National Health and Nutrition Examination Survey (2011-March 2020 pre-pandemic) were analyzed to examine 15 urinary VOC metabolites. Analytical methods included multivariable logistic regression, LASSO regression, and five machine learning models: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Significant associations were observed for metabolites including CEMA (N-Acetyl-S-(2-carboxyethyl)-L-cysteine) (OR = 1.66, 95% CI: 1.17-2.37), DHBMA (N-Acetyl-S-(3,4-dihydroxybutyl)-L-cysteine) (OR = 1.95, 95% CI: 1.38-2.76), HMPMA (N-Acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine) (OR = 2.18, 95% CI: 1.53-3.10), and PGA (Phenylglyoxylic acid) (OR = 1.66, 95% CI: 1.22-2.27). The XGBoost model demonstrated strong predictive performance, with SHAP analysis highlighting DHBMA as a key predictor. Inverse associations were observed for AAMA (N-Acetyl-S-(2-carbamoylethyl)-L-cysteine) and CYMA (N-Acetyl-S-(2-cyanoethyl)-L-cysteine) in their highest quartiles. This integrated approach identified significant associations between specific urinary VOC metabolites and CKD risk, particularly DHBMA. These findings underscore the role of environmental VOC exposure in CKD pathogenesis and may inform targeted prevention strategies.

Interleukin-33 (IL-33) and high mobility group box 1 (HMGB1) have been reported to play crucial and distinct roles in experimental autoimmune encephalomyelitis (EAE). However, little is known about their interaction in the progression of EAE. In this study, the dynamic expression and release of IL-33 and HMGB1 in different stages of EAE in vivo, and their interaction in vitro were explored. We found that HMGB1 was dominant in pre-onset stage of EAE, while IL-33 was dominant in peak stage. Moreover, both blockade of extracellular HMGB1 in the central nervous system (CNS) and conditional knockout of HMGB1 in astrocytes decreased IL-33 release. HMGB1 promoted the release of IL-33, while IL-33 reduced the release of HMGB1 from primary astrocytes in vitro. Taken together, IL-33 and HMGB1 in the CNS jointly participate in the EAE progression and the inhibitory effect of IL-33 on HMGB1 may be involved in the self-limiting of EAE.

Background: Primary Sjögren’s syndrome (pSS) is a chronic systemic autoimmune disorder. However, current diagnostic methods remain limited, necessitating the exploration of non-invasive diagnostic markers with higher specificity. Methods: This study integrated two GEO expression datasets to identify differentially expressed genes (DEGs) specific to pSS (distinct from SLE) and applied LASSO, XGBoost, RF, and SVM-RFE algorithms to screen candidate genes. Correlation and interaction network analyses were performed, followed by construction and validation of a diagnostic nomogram. The model’s differential diagnostic ability was validated in IgG4-RD, RA, SLE, and SSc cohorts. Additionally, candidate genes and the diagnostic model were experimentally validated using RT-qPCR in clinical samples. Results: Three candidate genes ( CD48 , CD69 , and TIGIT ) were identified, showing significant upregulation in pSS (individual AUC > 0.80). The combined diagnostic model achieved an AUC of 0.924, with AUC > 0.90 in validation sets, efficiently distinguishing pSS from IgG4-RD, RA, SLE, and SSc. RT-qPCR confirmed their high expression in pSS, with the model yielding AUC 0.875 (accuracy/precision > 0.85). Notably, combining these candidate genes with erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) yielded an AUC of 0.876 and a specificity of 83.3%, outperforming conventional markers such as ANA, anti-SSA, and anti-SSB antibodies. Conclusions: CD48 , CD69 , and TIGIT were identified as potential diagnostic markers for pSS. The combined model significantly enhanced diagnostic accuracy and effectively differentiated pSS from other autoimmune conditions. Integration with ESR/CRP substantially improved specificity compared to conventional serological markers.