Background: Exposure to heavy metals in the environment is widespread, while the relationship between combined exposure to heavy metals and dyslipidemia is unclear.Methods: A cross-sectional study was performed, and 3544 participants aged 30 years or older were included in the analyses. Heavy metal concentrations in plasma were based on inductively coupled plasma‒mass spectrometry. The relationship between heavy metals and dyslipidemia was estimated by logistic regression. BKMR was used to evaluate metal mixtures and their potential interactions.Results: In logistic regression analysis, participants in the fourth quartile of Fe and Zn (Fe > 1352.38 µg/L; Zn > 4401.42 µg/L) had a relatively higher risk of dyslipidemia (Fe, OR = 1.13, 95% CI: 0.92,1.38; Zn, OR = 1.30, 95% CI: 1.03,1.64). After sex stratification, females in the third quartile of plasma Zn (1062.05-4401.42 µg/L) had a higher relative risk of dyslipidemia (OR = 1.75, 95% CI: 1.28, 2.38). In BKMR analysis, metal mixtures were negatively associated with dyslipidemia in females when all metal concentrations were above the 50th percentile. In the total population (estimated from 0.030 to 0.031), As was positively associated with dyslipidemia when other metals were controlled at the 25th, 50th, or 75th percentile, respectively, and As was below the 75th percentile. In females (estimated from − 0.037 to -0.031), Zn was negatively associated with dyslipidemia when it was above the 50th percentile.Conclusion: This study indicated that As was positively associated with dyslipidemia and that Zn may be negatively associated with dyslipidemia in females. Combined metal exposure was negatively associated with dyslipidemia in females. Females with low plasma Zn levels are more likely to develop dyslipidemia and should receive more clinical attention in this population.

Perfluorobutanesulfonic acid (PFBS), a short-chain alternative to perfluorooctanesulfonic acid (PFOS), is widely used in various products and is increasingly present in environmental media and human bodies. Recent epidemiological findings have raised concerns about its potential adverse health effects, although the specific toxic mechanism remains unclear. This study aimed to investigate the metabolic toxicity of gestational PFBS exposure in maternal rats. Pregnant Sprague Dawley (SD) rats were randomly assigned to three groups and administered either 3% starch gel (control), 5, or 50 mg/kg bw·d PFBS. Oral glucose tolerance tests (OGTT) and lipid profiles were measured, and integrated omics analysis (transcriptomics and non-targeted metabolomics) was employed to identify changes in genes and metabolites and their relationships with metabolic phenotypes. The results revealed that rats exposed to 50 mg/kg bw·d PFBS exhibited a significant decrease in 1-h glucose levels and the area under the curve (AUC) of OGTT compared with the starch group. Transcriptomics analysis indicated significant alterations in gene expression related to cytochrome P450 exogenous metabolism, glutathione metabolism, bile acid secretion, tumor pathways, and retinol metabolism. Differentially expressed metabolites (DEMs) were enriched in pathways such as pyruvate metabolism, the glucagon signaling pathway, central carbon metabolism in cancer, and the citric acid cycle. Co-enrichment analysis and pairwise correlation analysis among genes, metabolites, and outcomes identified several differentially expressed genes (DEGs), including Gstm1, Kit, Adcy1, Gck, Ppp1r3c, Ppp1r3d, and DEMs such as fumaric acid, L-lactic acid, 4-hydroxynonenal, and acetylvalerenolic acid. These DEGs and DEMs may play a role in the modulation of glucolipid metabolic pathways. In conclusion, our results suggest that gestational exposure to PFBS may induce molecular perturbations in glucose homeostasis. These findings provide insights into the potential mechanisms contributing to the heightened risk of abnormal glucose tolerance associated with PFBS exposure.

Emotion recognition reflects the psychological and physiological status of humans. Numerous studies have investigated the neural mechanisms of emotion recognition based on electroencephalography (EEG) features. In the previous study, emotion target was presented under a static or irregular background, which made the response highly time-locked. As an oscillatory component of EEG, steady-state visual evoked potential (SSVEP) has distinctive frequency and phase properties, which provides more stable information than the other components of EEG. This study combined the emotion target with SSVEP to explore neural mechanisms of visual neurons under flickering background. Three basic emotions (delightfulness, sadness and, anger) were presented in 216 frequency-intensity conditions. Participants were asked to recognize the emotions and make judgments. The degree of alpha entrainment (valued as normalized Shannon entropy), SSVEP amplitude and recognition accuracy were calculated as response features. The results indicated that: SSVEP amplitude and recognition accuracy positively correlated with each other in frequency domain (7-15 Hz); alpha entrainment, and recognition accuracy had similar linear variation in intensity domain (level 1-4), and had a threshold around intensity 3; the three basic emotions had no clear relationship with each other in recognition. This study provided a new sight for neuroscience and would be an important reference to clinical psychology.

Human lifespan is determined by genetic and environmental factors. Potential longevity genes are neither specific nor reproducible, and longevity-related genes are constantly confused with age-related genes. To distinguish specific age- and longevity-related genes, we analyzed a Gene Expression Omnibus (GEO) dataset established by the Leiden Longevity Study. The individuals were classified into longevity (mean age, 93.4 ± 3.0 years), longevity offspring (60.8 ± 6.1) and control (61.9 ± 6.9) groups. The series matrix files were downloaded, and average expression values were calculated. Differentially expressed genes (DEGs) between longevity and control groups and those between longevity and their offspring were identified by GEO2R online. A total of 507 longevity- and 755 age-related DEGs were visualized using a Venn diagram. Weighted gene co-expression network analysis (WGCNA) was performed on the longevity- and age-related DEGs. Age-related color modules and genes were identified. However, no longevity-related modules or genes were found. The green module, with 46 age-related DEGs, was the most biologically significant to age and aging. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein–protein interaction pathway analyses were conducted on these 46 DEGs, which are mainly enriched in B cell activation and receptor signaling pathways. CR2, VPREB3, MS4A1 and CCR6 were considered the most crucial candidate genes for aging.

Background: Laboratory evidence has recently shown that exposure to organophosphate flame retardants (OPFRs) can cause adverse liver outcomes, which lacks further validation. Objective: This investigated the correlation and toxicological mechanism between OPFRs exposure and hepatic steatosis or fibrosis. Method: To explore the association of OPFRs exposure and hepatic steatosis or liver fibrosis, we conducted the population analysis using the data of urinary OPFRs monitoring and liver vibration-controlled transient elastography examinations from the National Health and Nutrition Examination Survey during 2017–2018. Network toxicology and transcriptomics analyses were used to explore the potential toxicological mechanisms of OPFRs-associated hepatic steatosis. Results: Univariate analysis suggested that urinary bis(1,3-dichloro-2-propyl) phosphate level was negatively correlated with the risk of hepatic steatosis. Nonlinear relationships were observed between urinary diphenyl phosphate levels and the risk of hepatic steatosis in restricted cubic splines analysis. Network toxicology and transcriptomics analysis suggested that OPFRs might directly interact with several metabolism-related proteins, including monoglyceride lipase, fatty acid amide hydrolase, cannabinoid receptor 1 (CNR1), CNR2, and phosphatidylinositol-5-phosphate 4-kinase type 2 gamma (PIP4K2C). These interactions could disrupt lipid metabolic processes by altering lipid metabolism (e.g. PPAR signaling pathway and insulin signaling pathway), energy metabolism (e.g. adipocytokine signaling pathway and HIF-1 signaling pathway), and apoptosis pathway (e.g. p53 signaling pathway), thereby contributing to hepatic steatosis. Conclusion: Our results indicate that OPFRs exposure was associated with adverse pathological changes in the liver.