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Background Arsenic, cobalt, barium, and other individual metal exposure have been confirmed to be associated with the incidence of kidney stones. However, there are few studies on the association between mixed metal exposure and kidney stones, especially in occupational groups. Objective To investigate the association between mixed metal exposure and kidney stones in an occupational population from a metal smelting plant. Methods A questionnaire survey was conducted to collect sociodemographic characteristics, medical history, and lifestyle information of 1158 mixed metal-exposed workers in a metal smelting plant in Guangdong Province from July 2021 to January 2022. Midstream morning urine samples were collected from the workers, the concentrations of 18 metals including lithium, vanadium, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, selenium, strontium, molybdenum, cadmium, cesium, barium, tungsten, titanium, and lead were measured by inductively coupled plasma mass spectrometry, and the urinary mercury levels were measured by cold atomic absorption spectroscopy. Based on predetermined inclusion criteria, a total of 919 mixed metal-exposed workers were included in the study, including 117 workers in the kidney stone group and 802 workers in the non-kidney stone group. With a detection rate of urinary metals greater than 80% as entry criterion, 16 eligible metals were finally included for further analysis. Parametric or non-parametric methods were used to compare the differences between continuous or categorical variables of the non-kidney stone group and the kidney stone group. Logistic regression models were constructed to explore the association between individual metal exposures and kidney stones. Weighted quantile sum (WQS) regression models were used to evaluate the association between mixed metal exposure and kidney stones, as well as the weights of each metal on kidney stones. Then Bayesian kernel machine regression (BKMR) models were used to explore the overall effect of mixed metal exposure on renal calculi and the potential interactions between metals. Results We found that there were significant differences in sex, age, length of service, and body mass Index (BMI) between the non-kidney stone group and the kidney stone group (P<0.05). The urinary concentrations of molybdenum and barium in the kidney stone group were higher than those in the non-kidney stone group, and the differences were statistically significant (P<0.05). The logistic regression models demonstrated that urinary cobalt, arsenic, molybdenum, and barium were positively correlated with the risk of kidney stones (Ptrend<0.05). The WQS regression models showed that the mixed exposure to vanadium, cobalt, arsenic, molybdenum, and barium was positively associated with the risk of kidney stones (P<0.05). Among them, molybdenum, arsenic, and barium accounted for 0.391, 0.337, and 0.154, respectively. The BKMR results revealed a positive association between metal mixture exposure and the risk of kidney stones (P<0.05). When other metals were fixed at the 25th, 50th, or 75th percentile, arsenic, molybdenum, cobalt, and barium exhibited significant positive effects on the risk of kidney stones (P<0.05), while vanadium showed a significant negative effect (P<0.05). The interaction analysis demonstrated interactions between barium and cobalt, as well as between vanadium and cobalt (P<0.05). Conclusion In the occupational population of this smelter, occupational mixed metal exposure could increase the risk of kidney stones, and the main metals are molybdenum, arsenic, barium, and cobalt.
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Background Occupational exposure to lead, cadmium, or arsenic is a potential risk factor for blood pressure elevation. Current studies mainly focus on the relationship between a single metal and blood pressure. However, mixed metal exposure often exists in the actual working environment, and the interactive effects of polymetallic interactions on blood pressure and the dose-effect relationship remain unclear yet. Objective To explore the influence proportion of occupational exposure to lead, cadmium, or arsenic on blood pressure and their interactive effects. Methods From January to December 2021, workers from a smelter in southern China were selected. Demographic characteristics, height, weight, and blood pressure of workers were collected through questionnaire and physical examination. At the same time, their urine samples were collected and the levels of urinary lead, urinary cadmium, and urinary arsenic were detected by inductively coupled plasma mass spectrometry, and corrected by urinary creatinine (Cr). Linear regression and logistic regression were used to analyze the relationship between urinary lead, cadmium, and arsenic and blood pressure. Weighted quantile sum (WQS) regression was applied to evaluate the dose-effect relationship between urinary lead, cadmium, and arsenic exposures and blood pressure and the effect weight of each metal on blood pressure. Generalized linear regression and additive/multiplicative scaling were used to identify interactive effects of the three metals on blood pressure. Results A total of 1075 workers were included in this study, with a mean age of (44.68±5.11) years and mean working seniority of (24.66±5.23) years. There were 891 males (88.9%) and 184 were females (17.1%); 24.7% workers were drinkers and 45.7% workers were smokers; 302 workers (28.1%) reported hypertension and 37 of them were taking antihypertensive drugs. The P50 (P25, P75) levels of urinary lead, urinary cadmium, and urinary arsenic were 6.11 (3.71, 11.08), 3.88 (2.68, 5.44), and 26.04 (19.99, 35.11) μg·g−1, respectively. After adjusting for gender, age, working seniority, body mass index, smoking, drinking, and the usage of antihypertensive drugs, systolic and diastolic blood pressure increased by 0.772 and 0.418 mmHg respectively for 10% increase in lead, cadmium, and arsenic mixed exposure. Urinary cadmium, among the three single exposures, had the greatest effect on systolic and diastolic blood pressure, weight (w)=0.523 and 0.551 respectively. The interaction of urinary lead and urinary cadmium was positively correlated with the occurrence of hypertension, multiplicative interaction OR (ORint)=1.88 (95%CI: 1.09, 3.63), attributable proportion due to interaction (AP)=1.19 (95%CI: 0.40, 8.18). Conclusion This study shows that mixed exposure to lead, cadmium, and arsenic has a positive relationship with blood pressure, in which cadmium plays a major role. Co-exposure to lead and cadmium has a positive interactive effect on hypertension development and systolic blood pressure elevation.
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Background As a complex organic pollutant, polycyclic aromatic hydrocarbons (PAHs) exposure shares the common exposure characteristics of multiple hydroxyl metabolites. Most studies have analyzed independent effect of each PAHs metabolite and have adjusted for the potential confounding effects induced by other metabolites concomitantly, without considering possible interactions among them. Proper statistical methods are needed to study their toxic effects. Objective To explore the applicability of logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) in evaluating the correlation between mixed exposures to exogenous chemicals and health outcomes, compare the advantages and limitations of the three models, and propose analytical strategies for evaluating the health effects of mixed chemical exposure for application in the analysis of the association between PAHs exposure and cognition. Methods Urine samples were collected of workers from a coke oven plant and a water treatment plant in Shanxi Province, who participated in their routine employee healthexamination. Mono-hydroxylated PAHs were detected by high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS), cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). A cut-off value of MoCA less than 26 was considered mild cognitive impairment (MCI). According to a predetermined inclusion and exclusion criteria, 1 051 cases were included in the final data analysis. Logistic regression, WQS regression, and BKMR were used to analyze the relationship between PAHs metabolites and MCI. Results The prevalence rate of reporting MCI among the 1 051 workers was 21.7% (228/1 051). The concentration of 2-hydroxynathalene (2-OHNAP) was the highest among the 11 PAHs metabolites with a median concentration of 0.30 μg·L−1, followed by 9-hydroxyphenanthrene (9-OHPHE) (0.26 μg·L−1). There were significant differences between the two groups in 2-OHNAP, 1-hydroxynaphthalene (1-OHNAP), 2-hydroxyfluorene (2-OHFLU), 9-OHPHE, 1-hydroxyphenanthrene (1-OHPHE), and 1-hydroxypyrene (1-OHPYR) (all Ps<0.05). In the logistic regression, 2-OHNAP and 2-OHPHE were associated with MCI, and the OR (95%CI) for reporting MCI was 1.28 (1.01-1.67) and 1.27 (1.00-1.72) for each 10-fold increase in 2-OHNAP and 2-OHPHE concentrations, respectively. In the WQS regression analysis, the WQS index was positively correlated with the prevalence rate of reporting MCI (OR=1.37, 95%CI: 1.10-1.72). In the BKMR analysis, compared with the median exposure levels of all chemicals, the overall effect was statistically significant when all PAHs metabolites concentrations were at or above their 30th percentile; when all exposures were at the 75th percentile, the risk of reporting MCI increased by 6%. Conclusion Based on the results of these three models, 2-OHNAP and 2-OHPHE are the most important factors related to cognitive. It is recommended to use a combination of traditional logistic regression and either WQS or BKMR to study the association between PAHs and MCI.