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OBJECTIVE@#This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength.@*METHODS@#We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.@*RESULTS@#In the multimetal linear regression, Cu (β = -2.119), As (β = -1.318), Sr (β = -2.480), Ba (β = 0.781), Fe (β = 1.130) and Mn (β = -0.404) were significantly correlated with grip strength ( P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95% confidence interval: -1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn ( P interactions of 0.003 and 0.018, respectively).@*CONCLUSION@#In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.
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Estudios Transversales , Teorema de Bayes , China/epidemiología , Metales/toxicidad , Arsénico , EstroncioRESUMEN
Background Sleep is a crucial physiological activity for the human body, and research has shown that air pollution can affect sleep quality. However, the association between polycyclic aromatic hydrocarbons (PAHs) exposure, neurotoxic compounds in air pollutants, and sleep quality remains uncertain. Objective To evaluate the association of PAHs exposure with sleep quality, and to provide evidence for improving sleep quality. Methods This study used a cross-sectional design. We selected 632 workers from a coking plant of a large state-owned enterprise as the exposure group, and 477 workers from the energy and power plant of the same enterprise as the control group. All workers worked in three shifts. A questionnaire survey was conducted to collect basic information including gender, years of service, age, educational level, smoking, alcohol consumption, consumption of fried foods, cooking frequency, types of cooking fuels. Worker's post-shift morning midstream urine was sampled to determine the concentrations of eight PAHs metabolites (OH-PAHs) using gas chromatography-tandem mass spectrometry (GC-MS). Worker's sleep quality was assessed using Pittsburgh Sleep Quality Index (PSQI). A higher PSQI score indicated a lower sleep quality. Associations of urinary OH-PAHs levels with sleep quality in the workers were analyzed using linear regression, Bayesian kernel-machine regression (BKMR), and quantile g-computation. Results The median (P25, P75) concentration of total OH-PAHs in the exposure group [88.84 (46.27, 151.96) μg·L−1] was higher than that in the control group [54.33 (24.86, 97.97) μg·L−1]. Additionally, the PSQI score (\begin{document}$ \overline{x}\pm {s} $\end{document}) in the exposure group (5.16±3.84) was higher than that in the control group (4.60±3.17). The multiple linear regression revealed that an increase in the sum of the concentrations of eight OH-PAHs after natural logarithmic transformation (lnΣ8OH-PAHs) was associated with an increase of 0.3646 (95%CI: 0.1337, 0.5955) in PSQI score, and an increase in lnΣlow-ring OH-PAHs was associated with an increase of 0.2954 (95%CI: 0.0941, 0.4967) in PSQI score. The BKMR analysis demonstrated that PSQI score was gradually increased as the increasing of lnΣ8OH-PAHs concentration. The quantile g-computation analysis indicated that a quantile increase in lnΣ8OH-PAHs concentration was associated with an increase of 0.4062% (95%CI: 0.1176%, 0.6949%) in PSQI score. Conclusion Compared to the controls, the coking workers show a higher concentration of urinary OH-PAHs and report worse sleep quality. The concentration of OH-PAHs is significantly negatively associated with sleep quality.
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Background Welders' exposure to welding fumes with multiple metals leads to decreased pulmonary function. Previous studies have focused on single metal exposure, while giving little attention to the impact of metal mixtures. Objective To assess the association between metal levels in urine and blood of welders and pulmonary function indicators, and to identify key metals for occupational health risk assessment. Methods Questionnaire surveys, lung function tests, urine and blood sampling were conducted among welders and control workers in a shipyard in Shanghai. Inductively coupled plasma mass spectrometry (ICP-MS) was used to detect the concentrations of 12 metals such as vanadium, chromium, and manganese in urine and blood. Spearman correlation was applied to analyze the correlations between the metals in urine and blood. Multiple linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) were used to analyze the relationships between mixed metal exposure and pulmonary function parameters, such as forced vital capacity (FVC), forced vital capacity as a percentage of predicted value (FVC%), forced expiratory volume in the first second (FEV1), forced expiratory volume in the first second as a percentage of predicted value (FEV1%), and forced expiratory volume in the first second/forced vital capacity (FEV1/FVC). Results This study enrolled 445 subjects, including 322 welders (72.36%) and 123 controls (27.64%). The mean age of the 445 participants was (37.64±8.80) years, and 87.19% participants were male. The welders had significantly higher levels of urinary cadmium (0.88 vs 0.58 μg·L−1), blood chromium (5.86 vs 5.06 μg·L−1), and blood manganese (24.24 vs 21.38 μg·L−1) than the controls (P<0.05). The Spearman correlation coefficients between the metals in urine and blood ranged from −0.46 to 0.68. After adjustment for confounders, the multiple linear regression indicted that the urine molybdenum of the welders was negatively correlated with FVC and FEV1. There were also negative correlations between the molybdenum in blood and FVC, FVC%, FEV1, and FEV1%, and between the copper in blood and FEV1/FVC. The WQS model showed that FEV1 and FVC decreased by 0.112 L and 0.353 L with each quartile increase of metal mixture concentrations in urine and blood among the welders respectively, and the leading contributors were copper, zinc, vanadium, and antimony. The BKMR model showed a negative overall effect of metal mixtures in urine and blood among the welders on FVC, FVC%, FEV1, and FEV1%, and the univariate exposure response-relationship between the molybdenum concentration in urine or blood and FVC, FVC%, FEV1, or FEV1% had an approximately linear decreasing trend. Meanwhile, there may be an interaction of cadmium with manganese, nickel, or vanadium, and an interaction of vanadium with iron, molybdenum, zinc, or copper, when different metals in urine among the welders interacted with FEV1%. Conclusion Exposure to multiple metals in welders leads to a decline in lung function, with molybdenum, antimony, copper, and zinc as the leading contributors.
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Background The human body is usually exposed to a variety of heavy metals at the same time, and different types and concentrations of heavy metals may have complex interactions during their absorption and metabolism in the human body. Seminal fructose is an important energy source for sperm movement. A large number of studies have shown that metal exposure may impair semen quality, and seminal fructose is an important factor affecting male reproduction, so it is necessary to investigate the relationship between mixed heavy metal exposure and seminal fructose to explore the mechanism of semen quality damage caused by metal exposure. Objective To understand the status of common heavy metal exposure in men of childbearing age in Puyang City, Henan Province, and to study the relationship between mixed exposure to heavy metals and seminal fructose, as well as potential interactions among heavy metals. Methods Volunteers were recruited from the Puyang Maternal and Child Health Hospital Reproductive Center for a cross-sectional survey on general demographic characteristics, smoking, alcohol consumption, and other information. Semen samples were collected to detect 12 metals such as vanadium (V), manganese (Mn), cobalt (Co), nickel (Ni), zinc (Zn), selenium (Se), silver (Ag), cadmium (Cd), barium (Ba), thallium (Tl), iron (Fe), and lead (Pb) in seminal plasma and seminal fructose. After correcting for selected confounding factors, a Bayesian kernel machine regression (BKMR) model was used to evaluate the impact of seminal plasma heavy metal mixed exposure and its interactions on seminal fructose. Results A total of 825 adult males were enrolled. The concentrations in M (P25, P75) of V, Mn, Co, Ni, Zn, Se, Ag, Cd, Ba, Tl, Fe, and Pb in seminal plasma were 0.39 (0.28, 0.54), 12.31 (8.92, 17.52), 0.26 (0.18, 0.38), 5.15 (3.32, 8.64), 182159.80 (121847.80, 199144.50), 13.61 (10.55, 17.68), 0.03 (0.02, 0.04), 0.34 (0.27, 0.46), 8.64 (5.94, 13.43), 0.06 (0.05, 0.08), 168.74 (114.17, 259.45), and 1.69 (1.15, 2.36) μg·L−1 respectively. The Spearman correlation results indicated that there was a negative correlation between V, Mn, Co, Zn, Se, Ba, Tl, or Fe in seminal plasma and seminal fructose (P<0.05), and the values of r (95%CI) were −0.044 (−0.087, −0.001), −0.129 (−0.171, −0.087), −0.055 (−0.099, −0.012), −0.099 (−0.143, −0.056), −0.053 (−0.097, −0.010), −0.068 (−0.111, −0.025), −0.095 (−0.138, −0.052), and −0.082 (−0.125, −0.039), respectively. The results of multiple linear regression indicated that there was a negative correlation between the exposure level of Cd, Mn, Zn, Ag, Ba, Tl, or Fe in seminal plasma and seminal fructose (P<0.05), the values of associated β (95%CI) were −0.551 (−0.956, −0.147), −0.315 (−0.419, −0.212), −0.187 (−0.272, −0.103), −0.161 (−0.301, −0.021), −0.188 (−0.314, −0.062), −1.159 (−2.170, −0.147), and −0.153 (−0.230, −0.076), respectively. The BKMR model analysis showed that seminal fructose level decreased with the increase of plasma metal mixed exposure concentration. Compared with all metal exposure at P50, the seminal fructose level decreased by 0.2374 units when all metal exposure was at P75. Seminal plasma Zn [posterior inclusion probabilities (PIPs)=1.0000] had the strongest effect on seminal fructose, followed by Mn (PIPs=0.5872), Se (PIPs=0.5656), and Ba (PIPs=0.5398). The univariate exposure-response curve showed a negative approximate linear correlations between Ba or Mn and seminal fructose, a positive linear correlation between Se and seminal fructose, and an approximate inverted U-shaped association between Zn and seminal fructose. No significant interaction between studied metals was found. Conclusion Mixed metal exposure may lead to decrease of seminal fructose, in which Zn, Mn, Se, and Ba may play an important role. Mn and Zn exposure may reduce the level of seminal fructose, Se may increase the level of seminal fructose, and there may be a threshold effect between Zn exposure and seminal fructose level. No interaction between different metals on seminal fructose is found.
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Background Parabens, a widely used class of preservatives, are suspected to be potential obesogens as emerging endocrine disrupting chemicals with reproductive and developmental toxicity. Objective To analyze five urinary parabens (PBs) and estimate the associations of exposure to PBs with adiposity measures in 10-year-old school-age children. Methods A total of 471 school-age children aged 10 years from the Sheyang Mini Birth Cohort were enrolled in this study. A questionnaire survey was conducted to collect socio-demographic information, physical activity, and dietary intake. Weight, height, and waist circumference of children were measured, and age- and sex-adjusted body mass index (BMI-Z score) was calculated. Spot urine samples were collected during the follow-up visits. Urinary concentrations of five PBs including methyl-paraben (MeP), ethyl-paraben (EtP), propyl-paraben (PrP), butyl-paraben (BuP), and benzyl-paraben (BzP) were detected by gas chromatography-tandem mass spectrometry (GC-MS/MS). Generalized linear models (GLMs) and Bayesian kernel machine regression (BKMR) models were applied to estimate associations of individual/overall urinary PBs concentrations with BMI Z-score and waist circumference. Results The positive rates of selected five urinary PBs were in the range from 78.98% to 98.94%. The urinary PBs concentrations (geometric mean) were in the range of 0.31-5.43 μg·L−1. The children's BMI Z-score and waist circumference (mean ± standard deviation) were (0.56±1.40) and (67.62±10.07) cm respectively. The GLMs results showed that the urinary BzP concentration was negatively associated with waist circumference (b=−0.08, 95%CI: −0.14, −0.02; P=0.01). In sex-stratified analysis, the urinary concentration of BzP was negatively associated with BMI-Z score (b=−0.59, 95%CI: −0.88, −0.30; P<0.001) and waist circumference (b=−0.80, 95%CI: −1.23, −0.37; P<0.001) in boys, but not in girls. The BKMR results also found significant negative correlations of urinary BzP concentrations with BMI-Z score and waist circumference, which were consistent with the GLM results. Conclusion The selected 10-year-old children are extensively exposed to PBs in the study area. Furthermore, childhood PBs exposure may have potential impacts on childhood adiposity measures with sex-specific effects.
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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.