Correlation between inflammatory cytokines and the likelihood of developing multiple types of digestive system cancers: A Mendelian randomization study.

OBJECTIVE: Inflammatory cytokines have been linked to digestive system cancers, yet their exact causal connection remains uncertain. Consequently, we conducted a Mendelian randomization (MR) analysis to gauge how inflammatory cytokines are linked to the risk of five prevalent digestive system cancers (DSCs). METHODS: We collected genetic variation data for 41 inflammatory cytokines from genome-wide association studies (GWAS), and the results data for five common diseases from the Finnish database. Our primary analytical approach involved employing the inverse-variance weighted, residual sum (IVW) method, complemented by the MR-Egger method, the weighted median method, simple mode analysis, and weighted mode analysis as supplementary analytical techniques. Furthermore, we conducted multiple sensitivity analyses. RESULTS: Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), macrophage colony-stimulating factor (M-CSF), and interleukin (IL)-18 showed a negative association with the risk of hepatocellular carcinoma. Conversely, TRAIL was inversely linked to the risk of gastric cancer, while IL-16 exhibited a positive correlation with gastric cancer risk. Stem cell factor (SCF) acted as a protective factor against pancreatic cancer. For colorectal cancer, IL-7, IL-9, IL-13, and vascular endothelial growth factor (VEGF) were identified as risk factors. Notably, our results did not indicate a significant correlation between inflammatory cytokines and the risk of esophageal cancer. CONCLUSION: Our research unveils potential connections between 41 inflammatory cytokines and the risk of five common DSCs through a MR analysis. These associations offer valuable insights that could aid in the development of diagnostic biomarkers and the identification of novel therapeutic targets for DSCs.

The pivotal role of dysregulated autophagy in the progression of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathologic syndrome characterized by excessive fat deposition in hepatocytes and a major cause of end-stage liver disease. Autophagy is a metabolic pathway responsible for degrading cytoplasmic products and damaged organelles, playing a pivotal role in maintaining the homeostasis and functionality of hepatocytes. Recent studies have shown that pharmacological intervention to activate or restore autophagy provides benefits for liver function recovery by promoting the clearance of lipid droplets (LDs) in hepatocytes, decreasing the production of pro-inflammatory factors, and inhibiting activated hepatic stellate cells (HSCs), thus improving liver fibrosis and slowing down the progression of NAFLD. This article summarizes the physiological process of autophagy, elucidates the close relationship between NAFLD and autophagy, and discusses the effects of drugs on autophagy and signaling pathways from the perspectives of hepatocytes, kupffer cells (KCs), and HSCs to provide assistance in the clinical management of NAFLD.

Development and validation of a predictive nomogram for the risk of MAFLD in postmenopausal women.

Background and aim: Metabolic-associated fatty liver disease (MAFLD) has gradually become one of the main health concerns regarding liver diseases. Postmenopausal women represent a high-risk group for MAFLD; therefore, it is of great importance to identify and intervene with patients at risk at an early stage. This study established a predictive nomogram model of MAFLD in postmenopausal women and to enhance the clinical utility of the new model, the researchers limited variables to simple clinical and laboratory indicators that are readily obtainable. Methods: Data of 942 postmenopausal women from January 2023 to October 2023 were retrospectively collected and divided into two groups according to the collection time: the training group (676 cases) and the validation group (226 cases). Significant indicators independently related to MAFLD were identified through univariate logistic regression and stepwise regression, and the MAFLD prediction nomogram was established. The C-index and calibration curve were used to quantify the nomogram performance, and the model was evaluated by measuring the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: Of 37 variables, 11 predictors were identified, including occupation (worker), body mass index, waist-to-hip ratio, number of abortions, anxiety, hypertension, hyperlipidemia, diabetes, hyperuricemia, and diet (meat and processed meat). The C-index of the training group predicting the related risk factors was 0.827 (95% confidence interval [CI] 0.794-0.860). The C-index of the validation group was 0.787 (95% CI 0.728-0.846). Calibration curves 1 and 2 (BS1000 times) were close to the diagonal, showing a good agreement between the predicted probability and the actual incidence in the two groups. The AUC of the training group was 0.827, the sensitivity was 0.784, and the specificity was 0.735. The AUC of the validation group was 0.787, the sensitivity was 0.674, and the specificity was 0.772. The DCA curve showed that the nomogram had a good net benefit in predicting MAFLD in postmenopausal women. Conclusions: A predictive nomogram for MAFLD in postmenopausal women was established and verified, which can assist clinicians in evaluating the risk of MAFLD at an early stage.