Metabolic signatures of population exposure to metal mixtures: A metabolome-wide association study.

Numerous studies have explored the health impacts of individual metal exposures, yet the effects of metal mixtures on human endogenous metabolism remain largely unexplored. We aimed to assess the serum metabolic signatures of people exposed to metal mixtures. Serum and urine samples were collected from 186 workers at a steel factory in Anhui, China, in September 2019. Inductively coupled plasma mass spectrometry was used to analyze the concentrations of 23 metal elements. The serum metabolome was determined by liquid chromatography-mass spectrometry (LC-MS). A metabolome-wide association study (MWAS) was performed across the metal exposures and metabolism using quantile g-computation modeling. Pathway enrichment analysis was performed using MetaboAnalyst. We identified 226 metabolites associated with metal mixtures, primarily involving lipid metabolism (glycerophospholipids, sphingolipids), amino acid metabolism (arginine and proline, alanine, aspartate and glutamate metabolism) and caffeine metabolic pathways. Exposure to metal mixtures is mainly associated with alterations in lipid metabolism and amino acid metabolism, particularly in the glycerophospholipid and arginine and proline metabolism pathways.

MTBE exposure may increase the risk of insulin resistance in male gas station workers.

Insulin resistance is closely related to many metabolic diseases and has become a serious public health problem worldwide. So, it is crucial to find its environmental pathogenic factors. Methyl tert-butyl ether (MTBE), a widely used unleaded gasoline additive, has been proven to affect glycolipid metabolism. However, results from population studies are lacking. For this purpose, the potential relationships between MTBE exposure and the triglyceride glucose (TyG) index, a useful surrogate marker of insulin resistance, were evaluated using a small-scale occupational population. In this study, 201 participants including occupational and non-occupational MTBE exposure workers were recruited from the Occupational Disease Prevention and Control Hospital of Huaibei, and their health examination information and blood samples with informed consent were collected. The internal exposure levels were assessed by detecting blood MTBE using solid-phase-micro-extraction gas chromatography-mass spectrometry. Then the adjusted linear regression model was used to assess the relationship between MTBE exposure and fasting plasma glucose (FPG), or TyG index. Then, receiver-operating-characteristic (ROC) curves were performed to calculate the optimal cut-off points. Multivariable and hierarchical logistic regression models were used to analyze the impact of MTBE exposure on the risk of insulin resistance. Obvious correlations were observed between blood MTBE levels with TyG index (p = 0.016) and FPG (p = 0.001). Further analysis showed that using the mean of the TyG index (8.77) as a cutoff value had a good effect on reflecting the risk of insulin resistance. Multivariable logistic regression analysis also indicated that MTBE exposure was an independent risk factor for a high TyG index (OR = 1.088, p = 0.038), which indicated that MTBE exposure might be a new environmental pathogenic factor leading to insulin resistance, and MTBE exposure might increase the risk of insulin resistance by independently elevating the TyG index in male gas station workers.

Mediation effect of serum zinc on insulin secretion inhibited by methyl tert-butyl ether in gas station workers.

Methyl tert-butyl ether (MTBE), a type of gasoline additive, has been found to affect insulin function and glucose homeostasis in animal experiments, but there is still no epidemiological evidence. Zinc (Zn) is a key regulatory element of insulin secretion and function, and Zn homeostasis can be disrupted by MTBE exposure through inducing oxidative stress. Therefore, we suspected that Zn might be involved and play an important role in the process of insulin secretion inhibited by MTBE exposure. In this study, we recruited 201 male subjects including occupational and non-occupational MTBE exposure from Anhui Province, China in 2019. Serum insulin and functional analog fibroblast growth factor 1 (FGF1) and blood MTBE were detected by Elisa and headspace solid-phase microextraction and gas chromatography-high-resolution mass spectrometry. According to MTBE internal exposure level, the workers were divided into low- and high-exposed groups and found that the serum insulin level in the high-exposed group was significantly lower than that in the low-exposed group (p = 0.003) while fasting plasma glucose (FPG) level increased obviously in the high-exposed group compared to the low-exposed group (p = 0.001). Further analysis showed that MTBE exposure level was positively correlated with FPG level, but negatively correlated with serum insulin level, which suggested that the FPG level increase might be related to the decrease of serum insulin level induced by MTBE exposure. The results of further mediation effect analysis showed that changes in serum zinc levels played a major intermediary role in the process of insulin secretion inhibition and blood glucose elevation caused by MTBE exposure. In addition, a significant negative correlation was found between MTBE exposure and serum Zn level, which might play a strong mediating effect on the inhibition of insulin secretion induced by MTBE exposure. In conclusion, our study provided evidence that MTBE could inhibit insulin secretion and interfere with Zn metabolism in gas station workers for the first time, and found that Zn might play an important mediation effect during the process of inhibiting insulin secretion and interfering with glucose metabolism induced by MTBE exposure.

Associations between multiple heavy metals exposure and neural damage biomarkers in welders: A cross-sectional study.

BACKGROUND: Both occupational and environmental exposure to heavy metals are associated with various neurodegenerative diseases. However, limited evidence is available on the potential effects of exposure to metallic mixtures and neural damage. OBJECTIVES: This study aimed to evaluate the association between metal mixtures in urine and neural damage biomarkers in welders. METHODS: In this cross-sectional study, a total of 186 workers were recruited from steel mills. Twenty-three metals in urine were measured by inductively coupled plasma mass spectrometry. Serum neural damage biomarkers, including neurofilament light chain (NfL), sphingosine-1-phosphate (S1P), prolactin (PRL), and dopamine (DA) were detected using enzyme-linked immunosorbent assay kits. Multivariable linear regression, Bayesian kernel machine regression (BKMR), and Quantile g-computation (QG-C) were employed to estimate the association between metals exposure and neural damage biomarkers. RESULTS: Inverted u-shaped associations of nickel with NfL, S1P, and DA were observed in the BKMR model. A non-linear relationship was also found between Fe and PRL. Urinary cobalt was positively associated with serum PRL and had the strongest positive weights in the QG-C model. Urinary lead was associated with higher serum S1P levels. We also found the interaction among nickel, zinc, arsenic, strontium, iron, and lead with the neural damage biomarkers. CONCLUSION: This study provides new evidence of a direct association between metal mixture exposure and the serum biomarkers of neural damage. Several metals Ni, Co, Pb, Sr, As and Fe, may have adverse effects on the nervous system, while Zn may have neuroprotective effects.

Associations of multiple metals with lung function in welders by four statistical models.

BACKGROUND: Exposure to heavy metals has been related to decreased lung function in workers. However, due to limitations in statistical methods for mixtures, previous studies mainly focused on single or several toxic metals, with few studies involving metal exposome and lung function. OBJECTIVES: The study aimed to evaluate the effects of co-exposure to the metal mixtures on multiple parameters of pulmonary function tests and to identify the elements that play an essential role in elastic-net regression (ENET), multivariate linear regression, bayesian kernel machine regression (BKMR), and quantile g-computation (QG-C) models. METHODS: We have recruited 186 welders from Anhui (China) in 2019. And their end-of-shift urine and lung function measure data were collected with informed consent. The urinary concentrations of 23 metals were measured by inductively coupled urinary mass spectrometry. The lung function measures including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) were also detected as outcome indicators. Four statistical methods, ENET, multivariate linear regression, BKMR, and QG-C models were used to evaluate the associations of element mixtures on lung function comprehensively. RESULTS: Lead and cadmium were negatively associated with FVC and FEV1, nickel and chromium were inversely associated with PEF, and strontium showed significant positive effects in linear regression models, which were consistent with the results in BKMR and QG-C models. Both BKMR and QG-C models showed a significantly negative overall effect of metal mixtures on lung function parameters (FVC, FEV1, and PEF). Meanwhile, BKMR showed the non-linear relationships of cadmium with FVC. CONCLUSION: Multi-pollutant mixtures of metals were negatively associated with lung function. Lead, cadmium, nickel, and strontium might be crucial elements. Our findings highlight a need to prioritize workers' environmental health, and guide future research into the toxic mechanisms of metal-mediated lung function injury.

[Investigation about prevention behavior for dust workers in machinery, ceramic, and metallurgy industry].

OBJECTIVE: The purposes of this thesis were to study the behavior about workers exposed to dust and provide scientific basis for health promotion. METHODS: We designed a questionnaire and carry it on the 746 dust workers in the 3 representative corporations of Machinery, Ceramic, and Metallurgy Industry. All data were input into computer. And a database was established with Excel. SPSS11.5 statistical analysis software was used to analyze the influence on protecting behavioral between the application of qualifications, different jobs, training or protection, and other aspects etc. RESULTS: The rates were 94.4% and 75.3% about the regular physical examination and requirements for protective equipment. The rate of choosing an effective way of protection was generally low (15.4%). There was significant difference for among different educational background workers (P < 0.01). The rates of choosing an effective way of protection (20.3%), the regular physical examination (98.3%) and requirements for protective equipment (86.4%) in the dust workers who participated in the training of dust protection were superior than those who did not participated in the training. There was the significant difference (P < 0.05, P < 0.01). There was the significant difference for the rate of effective way of protection, regular physical examination, and requirements for protective equipment among the different corporations (P < 0.05). CONCLUSIONS: Dust workers' using rate about the choosing an effective way of protection was generally low in Machinery, Ceramic, and Metallurgy Industry. Those who were not educated had a lower using rate about the protection behavior, regular physical examination, and requirements for protective equipment than those educated.