ABSTRACT
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.
ABSTRACT
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.