The relationship between heavy metals and metabolic syndrome using machine learning.

Background: Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets using machine learning (ML) method. Methods: The data used in this study are from the national health and nutrition examination survey 2003-2018. According to the demographic information and heavy metal exposure level of participants, a total of 22 variables were included. Lasso was used to screen out the key variables, and 9 commonly used ML models were selected to establish the associations with the 5-fold cross validation method. Finally, we choose the SHapley Additive exPlanations (SHAP) method to explain the prediction results of Adaboost model. Results: 11,667 eligible individuals were randomly divided into two groups to train and verify the prediction model. Through lasso, characteristic variables were selected from 24 variables as predictors. The AUC (area under curve) of the models selected in this study were all greater than 0.7, and AdaBoost was the best model. The AUC value of AdaBoost was 0.807, the accuracy was 0.720, and the sensitivity was 0.792. It is noteworthy that higher levels of cadmium, body mass index, cesium, being female, and increasing age were associated with an increased probability of MetS. Conversely, lower levels of cobalt and molybdenum were linked to a decrease in the estimated probability of MetS. Conclusion: Our study highlights the AdaBoost model proved to be highly effective, precise, and resilient in detecting a correlation between exposure to heavy metals and MetS. Through the use of interpretable methods, we identified cadmium, molybdenum, cobalt, cesium, uranium, and barium as prominent contributors within the predictive model.

Inhibition of microRNA-297 alleviates THLE-2 cell injury induced by hypoxia/reoxygenation by inhibiting NLRP3 inflammasome activation via sirtuin 3.

It has been acknowledged that microRNAs (miRNAs/miRs) assume a critical role in hypoxia/reoxygenation (H/R) - induced hepatocyte injury. Therefore, cell experiments were performed in this study to investigate the mechanism of miR-297 in H/R-induced hepatocyte injury with the involvement of sirtuin 3 (SIRT3) and NOD-like receptor pyrin domain containing 3 (NLRP3). Initially, transformed human liver epithelial-2 (THLE-2) cells were utilized for H/R challenge. After miR-297 antagomir and NLRP3 adenovirus vector delivery, THLE-2 cell proliferation and apoptosis were measured by MTT, EdU, and TUNEL assays, respectively. Enzyme-linked immunosorbent assay was conducted to evaluate the levels of apoptosis-related indicators (Bax and Bcl-2) and inflammation-related indicators (interleukin 6 (IL-6) and IL-10), Western blot analysis to detect NLRP3, and cleaved caspase-1 expression. The binding relation between miR-297 and SIRT3 was examined using dual-luciferase assay. The results showed that miR-297 antagomir repressed the apoptosis and inflammation induced by H/R treatment in THLE-2 cells. Mechanistically, miR-297 antagomir diminished the extent of IκBα and nuclear factor-kappa B (NF-κB) phosphorylation and NLRP3 activation in H/R-induced THLE-2 cells by targeting SIRT3. Furthermore, NLRP3 overexpression normalized the promoting effects of miR-297 antagomir on proliferation and its inhibitory effects on apoptosis and inflammation in H/R-induced THLE-2 cells. In summary, our results elucidated that miR-297 antagomir repressed H/R-induced THLE-2 cell injury via SIRT3 promotion and NLRP3 inactivation.