Friskchen1091

Association between long-term exposure to multiple metals and obesity remains inconclusive, and prospective evidence on the region along the Yangtze River was limited. Thus, our study aimed to examine the association of multiple metal exposure and obesity. We measured baseline urine levels of 22 metals of 982 adults living along the Yangtze River, incidence of obesity was calculated from body mass index (BMI) and waist circumference (WC) measured at follow-up survey. Cox proportional hazards models were used to examine the hazard ratios (HR) and 95% confidence interval (CI) for the association between urinary metals and obesity, and the mixing effect of metals on obesity was estimated by using quantile g-computation. In multiple-metal models, arsenic was significantly associated with BMI/obesity, with the HR in the highest quartiles of 0.33 (95% CI 0.16, 0.69; p-trend = 0.004). The HRs for WC/obesity of arsenic and molybdenum were 0.49 (95% CI 0.32, 0.75 for the fourth vs. first quartile; p-trend = 0.002) and 1.83 (95% CI 1.25, 2.70; p-trend = 0.001), respectively. Quantile g-computation mixtures approach showed a significantly negative joint effect of multiple metals on WC/obesity, with the HR of 0.26 (95% CI 0.14, 0.47; p less then 0.001) when increasing all seventeen metals by one quartile. Our study suggests that all seventeen metal mixed exposure may be negatively associated with obesity. Further cohort studies are needed to confirm these findings and clarify the underlying biological mechanisms.Research on the relationship between short-term exposure to fine particulate matter (PM2.5) and urinary metabolites of polycyclic aromatic hydrocarbons (PAHs) is sparse in the nonoccupationally exposed populations. A quasi-experimental observation of haze events nested within a randomized crossover trial of alternative 1-week real or sham indoor air filtration was conducted to evaluate the associations of urinary monohydroxy-PAHs (OH-PAHs) with short-term exposure to PM2.5 and PM2.5-bound PAHs. The study was conducted among 57 healthy college students in Beijing, China. PM2.5-bound PAHs and urinary OH-PAHs were quantified using gas chromatography coupled with a triple-quadrupole tandem mass spectrometer. Linear mixed-effect models were applied to evaluate the association of urinary OH-PAHs with time-weighted personal PM2.5 and PM2.5-bound PAHs, controlling for potentially confounding variables. The results demonstrated that air filtration could markedly reduce external exposure to PM2.5 and PM2.5-bound parent, nitrated, and oxygenated PAHs. In the intervention trial, the urinary concentrations of 2-hydroxyfluorene (2-OH-FLU) and 9-hydroxyphenanthrene (9-OH-PHE) were elevated significantly by 16.5% (95% CI, 2.1%, 33.1%) and 37.9% (95% CI, 8.4%, 75.4%), respectively, in association with a doubling increase in personal PM2.5 exposure. Urinary 9-OH-PHE was also significantly positively associated with the increase in the sum of PM2.5-bound parent PAHs. ABT-737 in vitro Furthermore, the levels of urinary OH-PAHs such as 2-OH-FLU and 9-OH-PHE in the haze events were elevated by 31.1% (95% CI, 8.7%, 53.4%) and 73.5% (95% CI, 16.0%, 131.0%), respectively, in association with a doubling increase in personal PM2.5 exposure. The findings indicated that urinary 2-OH-FLU and 9-OH-PHE could serve as potential internal exposure biomarkers for assessing short-term PM2.5 exposure in nonoccupational populations.Current assessments of human exposure to flame retardants (FRs) via dust ingestion rely on measurements of FR concentrations in dust samples collected at specific points in time and space. Such exposure assessments are rendered further uncertain by the possibility of within-room and within-building spatial and temporal variability, differences in dust particle size fraction analysed, as well as differences in dust sampling approach. A meta-analysis of peer-reviewed data was undertaken to evaluate the impact of these factors on reported concentrations of brominated flame retardants (BFRs) and organophosphate esters (OPEs) in dust and subsequent human exposure estimates. Except for a few cases, concentrations of FRs in elevated surface dust (ESD) exceeded significantly those in floor dust (FD). The implications of this for exposure assessment are not entirely clear. However, they imply that analysing FD only will underestimate exposure for adults who likely rarely ingest floor dust, while analysing ESD only wounded period such dust spends in the dust bag. Temporal variability in FR concentrations is apparent during month-to-month or seasonal monitoring, with such variability likely due more to changes in room contents rather than seasonal temperature variation.The current estimations of the burden of disease (BD) of PM2.5 exposure is still potentially biased by two factors ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM2.5 exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM2.5 exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework. We applied the developed framework to Tainan City in Taiwan as a use case estimated the risks by using 2006-2016 emergency department visit data, air quality monitoring data, and land-use characteristics and further estimated the BD caused by daily PM2.5 exposure in 2013. Our results found that the risks of CVD and RD in highly urbanized areas and death in rural areas could reach 1.20-1.57 times higher than average. Furthermore, we performed a sensitivity analysis to assess the uncertainty of BD estimations from utilizing different data sources, and the results showed that the uncertainty of the BD estimations could be contributed by different PM2.5 exposure data (20-32%) and risk values (0-86%), especially for highly urbanized areas. In conclusion, our approach for estimating BD based on local databases has the potential to be generalized to the developing and overpopulated countries and to support local air quality and health management plans.