Combined exposure to multiple metals on cardiovascular disease in NHANES under five statistical models.

BACKGROUND: It is well-documented that heavy metals are associated with cardiovascular disease (CVD). However, there is few studies exploring effect of metal mixture on CVD. Therefore, the primary objective of present study was to investigate the joint effect of heavy metals on CVD and to identify the most influential metals in the mixture. METHODS: Original data for study subjects were obtained from the National Health and Nutrition Examination Survey. In this study, adults with complete data on 12 kinds of urinary metals (antimony, arsenic, barium, cadmium, cobalt, cesium, molybdenum, mercury, lead, thallium, tungsten, and uranium), cardiovascular disease, and core covariates were enrolled. We applied five different statistical strategies to examine the CVD risk with metal exposure, including multivariate logistic regression, adaptive elastic net combined with Environmental Risk Score, Quantile g-computation, Weighted Quantile Sum regression, and Bayesian kernel machine regression. RESULTS: Higher levels of cadmium, tungsten, cobalt, and antimony were significantly associated with Increased risk of CVD when covariates were adjusted for multivariate logistic regression. The results from multi-pollutant strategies all indicated that metal mixture was positively associated with the risk of CVD. Based on the results of multiple statistical strategies, it was determined that cadmium, tungsten, cobalt, and antimony exhibited the strongest positive correlations, whereas barium, lead, molybdenum, and thallium were most associated with negative correlations. CONCLUSION: Overall, our study demonstrates that exposure to heavy metal mixture is linked to a higher risk of CVD. Meanwhile, this association may be driven primarily by cadmium, tungsten, cobalt, and antimony. Further prospective studies are warranted to validate or refute our primary findings as well as to identify other important heavy metals linked with CVD.

[Efficiency and Mechanism of Purifying High Iron-Manganese from Ground Water in the Cold Villages and Towns Based on The Coupling of Rice Husk and Iron-Manganese Oxidizing Bacteria].

Aiming at the special geo-climatic conditions and typical problems constraining the development of villages and towns, making villages and towns have the following characteristics:green and low-carbon, energy saving and environmental friendly, the coupling process of adsorption by carbonized rice husk particles and biological oxidation using Bacillus megaterium bacteria was developed for purifying high iron-manganese from groundwater in the cold villages and towns. The quick start-up method of biological filter at low temperature was studied. Based on the contact oxidation and biological method, iron removal efficiency and reaction rate in different filtration layers under different filtration conditions were comparatively analyzed, and iron removal mechanism of biological filter was determined. Filter materials and the solid samples in backwash water at different stages were characterized and the manganese removal mechanism of biological filter was analyzed through SEM, FTIR, XPS, Raman spectra and EPR. The results showed that the active biological membrane reached mature and stable only after 15 d with the operation mode of bacteria liquid cycle and low filtration rate in the filter column. The effluent concentration met the requirements of drinking water quality (GB 5749-2006) in the stable operation process. Fe, Mn and bacteria were lower than 0.3 mg·L-1, 0.1 mg·L-1, 100 CFU·mL-1, respectively. Iron removal mechanism relied mainly on the physical and chemical effects, supplemented by biological function. During the biofilm culturing stage and initial stable operation stage of filter column, manganese removal relied mainly on the biological effect. The physical chemistry was preferred at later stable operation stage. The study provides technical support for the applications of treating collectively Fe2+ and Mn2+ in groundwater.