Dyslipidemia is a critical factor in the development of atherosclerosis and consequent cardiovascular disease. Numerous pieces of evidence demonstrate the association between air pollution and abnormal blood lipids. Although the results of epidemiological studies on the link between air pollution and blood lipids are unsettled due to different research methods and conditions, most of them corroborate the harmful effects of air pollution on blood lipids. Mechanism studies have revealed that air pollution may affect blood lipids via oxidative stress, inflammation, insulin resistance, mitochondrial dysfunction, and hypothalamic hormone and epigenetic changes. Moreover, there is a risk of metabolic diseases associated with air pollution, including fatty liver disease, diabetes mellitus, and obesity, which are often accompanied by dyslipidemia. Therefore, it is biologically plausible that air pollution affects blood lipids. The overall evidence supports that air pollution has a deleterious effect on blood lipid health. However, further research into susceptibility, indoor air pollution, and gaseous pollutants is required, and the issue of assessing the effects of mixtures of air pollutants remains an obstacle for the future.

Nanomaterials are widely employed in everyday life, including food and engineering. Food additives on a nanoscale can enter the body via the digestive tract. The human gut microbiota is a dynamically balanced ecosystem composed of a multitude of microorganisms that play a crucial role in maintaining the proper physiological function of the digestive tract and the body’s endocrine coordination. While the antibacterial capabilities of nanomaterials have received much interest in recent years, their impacts on gut microbiota ought to be cautioned about and explored. Nanomaterials exhibit good antibacterial capabilities in vitro. Animal studies have revealed that oral exposure to nanomaterials inhibits probiotic reproduction, stimulates the inflammatory response of the gut immune system, increases opportunistic infections, and changes the composition and structure of the gut microbiota. This article provides an overview of the impacts of nanomaterials, particularly titanium dioxide nanoparticles (TiO₂ NPs), on the gut microbiota. It advances nanomaterial safety research and offers a scientific foundation for the prevention, control, and treatment of illnesses associated with gut microbiota abnormalities.

The genotoxicity of nanomaterials has attracted great attention in recent years. As a possible occupational carcinogen, the genotoxic effects and underlying mechanisms of titanium dioxide nanoparticles (TiO₂ NPs) have been of particular concern. In this study, the effect of TiO₂ NPs (0, 25, 50 and 100 µg/mL) on DNA damage and the role of oxidative stress were investigated using human bronchial epithelial cells (BEAS-2B) as an in vitro model. After detailed characterization, the cytotoxicity of TiO₂ NPs was detected. Through transmission electron microscopy (TEM), we found that TiO₂ NPs entered the cytoplasm but did not penetrate deep into the nucleus of cells. The intracellular levels of reactive oxygen species (ROS) significantly increased in a dose-dependent manner and the ratios of GSH/GSSG also significantly decreased. The results of the normal comet assay were negative, while the Fpg-modified comet assay that specifically detected DNA oxidative damage was positive. Meanwhile, N-acetyl-L-cysteine (NAC) intervention inhibited the oxidative stress and genotoxicity induced by TiO₂ NPs. Therefore, it was suggested that TiO₂ NPs could induce cytotoxicity, oxidative stress and DNA oxidative damage in BEAS-2B cells. DNA oxidative damage may be a more sensitive genetic endpoint to detect the genotoxicity of TiO₂ NPs.

Pneumoconiosis remains one of the most serious global occupational diseases. However, effective treatments are lacking, and early detection is crucial for disease prevention. This study aimed to explore serum biomarkers of occupational coal workers’ pneumoconiosis (CWP) by high-throughput metabolomics, combining with machine learning strategy for precision screening. A case–control study was conducted in Beijing, China, involving 150 pneumoconiosis patients with different stages and 120 healthy controls. Metabolomics found a total of 68 differential metabolites between the CWP group and the control group. Then, potential biomarkers of CWP were screened from these differential metabolites by three machine learning methods. The four most important differential metabolites were identified as benzamide, terazosin, propylparaben and N-methyl-2-pyrrolidone. However, after adjusting for the influence of confounding factors, including age, smoking, drinking and chronic diseases, only one metabolite, propylparaben, was significantly correlated with CWP. The more severe CWP was, the higher the content of propylparaben in serum. Moreover, the receiver operating characteristic curve (ROC) of propylparaben showed good sensitivity and specificity as a biomarker of CWP. Therefore, it was demonstrated that the serum metabolite profiles in CWP patients changed significantly and that the serum metabolites represented by propylparaben were good biomarkers of CWP.

As a serious occupational pulmonary fibrosis disease, pneumoconiosis still lacks effective biomarkers. Previous studies suggest that pneumoconiosis may affect the body’s lipid metabolism. The purpose of this study was to explore lipidomics profiles and lipid metabolite biomarkers in the serum of coal workers’ pneumoconiosis (CWP) by a population case-control study. A total of 150 CWP cases and 120 healthy controls from Beijing, China were included. Blood lipids were detected in serum biochemistry. Lipidomics was performed in serum samples for high-throughput detection of lipophilic metabolites. Serum high density lipoprotein cholesterol (HDL-C) decreased significantly in CWP cases. Lipidomics data found 131 differential lipid metabolites between the CWP case and control groups. Further, the top eight most important differential lipid metabolites were screened. They all belonged to differential metabolites of CWP at different stages. However, adjusting for potential confounding factors, only three of them were significantly related to CWP, including acylhexosylceramide (AHEXCER 43:5), diacylglycerol (DG 34:8) and dimethyl-phosphatidylethanolamine (DMPE 36:0|DMPE 18:0_18:0), of which good sensitivity and specificity were proven. The present study demonstrated that lipidomics profiles could change significantly in the serum of CWP patients and that the lipid metabolites represented by AHEXCER, DG and DMPE may be good biomarkers of CWP.