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BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) is perceived as an emerging environmental endocrine disruptor, which have been linked to children neurodevelopment. However, the potential mechanisms are not clear. Brain-derived neurotrophic factor (BDNF) is a vital protein in neurodevelopment, and the associations between PFAS exposure and BDNF require exploration. OBJECTIVE: We aimed to explore the relationships between PFAS exposure and the levels of BDNF in cord serum. METHODS: A total of 1,189 mother-infant dyads from the Sheyang Mini Birth Cohort Study (SMBCS) were enrolled. The levels of 12 PFAS and BDNF were measured in cord serum. We utilized generalized linear models (GLMs), quantile-based g-computation (QGC) models, and Bayesian Kernel Machine Regression (BKMR) models to explore the relationships between single and mixed PFAS exposure and BDNF concentration. Additionally, the potential sex differences were explored by sex-stratified analysis. RESULTS: Median concentrations of the included 10 PFAS ranged from 0.04 to 3.97 µg/L. In the single chemical models, four PFAS congeners, namely perfluorononanoic acid (PFNA), perfluorooctane sulfonic acid (PFOS), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), were negatively associated with BDNF levels in cord serum among females only (ß: -0.116 to -0.062, p < 0.05). In the BKMR models of total mother-infant dyads and female fetuses, the significant negative relationships between PFAS mixtures and BDNF were observed, and PFUnDA was identified as an important contributor (Posterior inclusion probability, PIP = 0.8584 for the total subjects; PIP = 0.8488 for the females). PFOS was another important driver based on the mixture approaches. CONCLUSIONS: We found that PFNA, PFOS, PFDA, and PFUnDA were associated with decreased BDNF concentration in the females, although the causal inference might be limited. PFAS mixtures were also negatively linked with BDNF levels in the total mother-infant pairs and female fetuses. The adverse effect of PFAS exposure on fetal BDNF levels might be sex-specific.

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BACKGROUND: People are exposed to metals in various ways during their daily lives. However, the association between metal exposure and gallstones remains unclear. OBJECTIVES: To investigate the relationship between serum elemental concentrations and the risk of gallstones. METHODS: Participants (n = 4204) were drawn from the Henan Rural Cohort. Gallstone diagnosis was based on abdominal ultrasound reports during follow-up. Baseline serum elemental concentrations were measured using inductively coupled plasma mass spectrometry. The relationship between serum elemental levels and gallstones was evaluated using robust Poisson regression, restricted cubic spline (RCS), quantile g-computation (Qgcomp), grouped weighted quantile sum (GWQS) and Bayesian kernel machine regression (BKMR). RESULTS: 121 individuals were diagnosed with gallstone (incidence rate of 2.88 %). In robust Poisson regression, after adjusting for confounding factors, the highest quartile of arsenic concentration compared to the lowest quartile had a 1.90 times higher relative risk (RR) [95 % confidence interval (CI): 1.05, 3.44]. Conversely, the highest quartile of zinc concentration compared to the lowest quartile had a 0.50 times lower RR (95 % CI: 0.28, 0.89). RCS showed an approximately "S"-shaped nonlinear relationship between serum arsenic levels and gallstones, with increasing arsenic concentration leading to a higher risk of gallstones; however, the risk plateaued when arsenic concentration exceeded 0.62 µg/L. Both the Qgcomp and GWQS indicated that arsenic plays a significant role in increasing the risk of gallstones, whereas zinc plays a significant role in reducing the risk of gallstones. BKMR showed that raising arsenic exposure from the 25th to the 75th percentile increased the risk of gallstones, while raising serum zinc concentration reduced it. CONCLUSIONS: Higher serum arsenic concentration increases the risk of gallstones, whereas higher zinc concentration may reduce the risk. Effective prevention of gallstones may require further reduction of arsenic exposure and appropriate increases in zinc intake.

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Background: Exposure to environmental metals has been increasingly associated with systemic inflammation, which is implicated in the pathogenesis of various chronic diseases, including those with neurodegenerative aspects. However, the complexity of exposure and response relationships, particularly for mixtures of metals, has not been fully elucidated. Objective: This study aims to assess the individual and combined effects of lead, cadmium, and mercury exposure on systemic inflammation as measured by C-reactive protein (CRP) levels, using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018. Methods: We employed Bayesian Kernel Machine Regression (BKMR) to analyze the NHANES 2017-2018 data, allowing for the evaluation of non-linear exposure-response functions and interactions between metals. Posterior Inclusion Probabilities (PIP) were calculated to determine the significance of each metal's contribution to CRP levels. Results: The PIP results highlighted mercury's significant contribution to CRP levels (PIP = 1.000), followed by cadmium (PIP = 0.6456) and lead (PIP = 0.3528). Group PIP values confirmed the importance of considering the metals as a collective group in relation to CRP levels. Our BKMR analysis revealed non-linear relationships between metal exposures and CRP levels. Univariate analysis showed a flat relationship between lead and CRP, with cadmium having a positive relationship. Mercury exhibited a U-shaped association, indicating both low and high exposures as potential risk factors for increased inflammation. Bivariate analysis confirmed this relationship when contaminants were combined with lead and cadmium. Analysis of single-variable effects suggested that cadmium and lead are associated with higher values of the h function, a flexible function that takes multiple metals and combines them in a way that captures the complex and potentially nonlinear relationship between the metals and CRP. The overall exposure effect of all metals on CRP revealed that exposures below the 50th percentile exposure level are associated with an increase in CRP levels, while exposures above the 60th percentile are linked to a decrease in CRP levels. Conclusions: Our findings suggest that exposure to environmental metals, particularly mercury, is associated with systemic inflammation. These results highlight the need for public health strategies that address the cumulative effects of metal exposure and reinforce the importance of using advanced statistical methods to understand the health impact of environmental contaminants. Future research should focus on the mechanistic pathways of metal-induced inflammation and longitudinal studies to ascertain the long-term effects of these exposures.

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BACKGROUND: To determine the relationship between mixed exposure to three types of endocrine-disrupting chemicals (EDCs), namely phenyl hydroxides, polycyclic aromatic hydrocarbons (PAHs), and phthalates (PAEs), and risk of arthritis. METHODS: Participants were selected from National Health and Nutrition Examination Survey (NHANES). The relationships between the urinary concentrations of phenyl hydroxides, PAHs, and PAEs and the risk of arthritis were analyzed by generalized linear regression model. The mixed exposure to these EDCs and the risk of arthritis was analyzed by weighted quantile sums (WQSs) and Bayesian kernel machine regression (BKMR) model. RESULTS: Our analysis showed that participants with urinary benzophenone-3 and methylparaben concentrations in the highest quartile (Q4) had an increased risk of arthritis compared with those in Q1. For each one-unit increase in the natural logarithm-converted urinary concentrations of 1-hydroxynapthalene and 2-hydroxynapthalene, the risk of arthritis increased by 5% and 8%, respectively. Chemical mixing index coefficients were significantly associated with risk of arthritis in both WQS positive- and negative-constraint models. In the BKMR model, there was a significant positive correlation between mixed exposure and the risk of arthritis. CONCLUSION: Mixed exposure to phenyl hydroxides, PAHs, and PAEs increased the risk of arthritis, with exposure to PAHs being the key factor.

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Club cell secretory protein (CC16) is considered a biological marker indicating lung epithelial and lung permeability. The joint effect of polycyclic aromatic hydrocarbons (PAHs) exposure on CC16 levels and the association between CC16 levels and long-term lung function changes lacks epidemiological evidence. To investigate the effect of PAHs exposure on plasma CC16 levels and the association between CC16 levels and long-term lung function changes, this study enrolled 307 coke oven workers in 2014, measured their baseline concentrations of urinary PAHs metabolites and plasma CC16, with follow-up after nine years. Bayesian kernel machine regression (BKMR) was employed to analyze the effect of mixed PAHs metabolites. The dose-effect association between baseline CC16 levels and lung function during 2014-2023 was explored using restricted cubic spline (RCS) models, and stratified analysis investigated the effect modification of PAHs exposure and smoking status on this association. The median age of the participants was 40 years, with 93.81 % male. The results showed that plasma CC16 levels decreased by 2.02 ng/mL (95 % CI: -3.77, -0.27) among all participants and FVC (% predicted) decreased by 2.87 % (95 % CI: -5.59, -0.14) in the low CC16 group with each unit increase in log-transformed 2-OHNAP. The BKMR model revealed a negative association between PAHs metabolites and both plasma CC16 levels and FVC (% predicted). Plasma CC16 decreased by 1.05 units when all PAHs metabolites at P65 compared to those at P50. After 9 years of follow-up, baseline CC16 levels were significantly associated with follow-up FVC (% predicted), FEV1 (% predicted), and small airway dysfunction risk. Furthermore, high PAHs exposure and smoking enhanced the association between CC16 and lung function. In conclusion, PAHs exposure decreases CC16 levels, and coking workers with low baseline CC16 levels may experience more severe future lung function decline.

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Properly assessing the effects of environmental chemical exposures on disease risk remains a challenging problem in environmental epidemiology. Various analytic approaches have been proposed, but there are few papers that have compared the performance of different statistical methods on a single dataset. In this paper, we compare different regression-based approaches for estimating interactions between chemical mixture components using data from a case-control study on non-Hodgkin's lymphoma. An analytic challenge is the high percentage of exposures that are below the limit of detection (LOD). Using imputation for LOD, we compare different Bayesian shrinkage prior approaches including an approach that incorporates the hierarchical principle where interactions are only included when main effects exist. Further, we develop an approach where main and interactive effects are represented by a series of distinct latent functions. We also fit the Bayesian kernel machine regression to these data. All of these approaches show little evidence of an interaction among the chemical mixtures when measurements below the LOD were imputed. The imputation approach makes very strong assumptions about the relationship between exposure and disease risk for measurements below the LOD. As an alternative, we show the results of an analysis where we model the exposure relationship with two parameters per mixture component; one characterizing the effect of being below the LOD and the other being a linear effect above the LOD. In this later analysis, we identify numerous strong interactions that were not identified in the analyses with imputation. This case study demonstrated the importance of developing new approaches for mixtures when the proportions of exposure measurements below the LOD are high.

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This study aimed to explore the isomer-specific, sex-specific, and joint associations of PFAS and red blood cell indices. We used data of 1,238 adults from the Isomers of C8 Health Project in China. Associations of PFAS isomers and red blood cell indices were explored using multiple linear regression models, Bayesian Kernel Machine Regression models and subgroup analysis across sex. We found that serum concentration of linear (n-) and branched (Br-) isomers of perfluorooctane sulfonate (PFOS) and perfluorohexanesulfonic acid (PFHxS) were significantly associated with red blood cell indices in single-pollutant models, with stronger associations observed for n-PFHxS than Br-PFHxS, in women than in men. For instance, the estimated percentage change in hemoglobin concentration for n-PFHxS (3.65%; 95% CI: 2.95%, 4.34%) was larger than that for Br-PFHxS (0.96%; 95% CI: 0.52%, 1.40%). The estimated percentage change in red blood cell count for n-PFHxS in women (2.55%; 95% CI: 1.81%, 3.28%) was significantly higher than that in men (0.12%; 95% CI: -1.04%, 1.29%) (Pinter < 0.001). Similarly, sex-specific positive association of PFAS mixture and outcomes was observed. Therefore, the structure, susceptive population, and joint effect of PFAS isomers should be taken into consideration when evaluating the health risk of chemicals.

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BACKGROUND: Although the relationship between oxidative stress and insulin resistance (IR) has been established, the associations of the composite dietary antioxidant index (CDAI) and its components with the surrogate index of insulin resistance (IR), triglyceride-glucose index (TyG), is still not clear. METHODS: This study analyzed the cross-sectional data of the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018. Multivariate linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were used to analyze the associations of the CDAI and its components with the TyG. In addition, subgroup analysis and several sensitivity analyses were conducted. RESULTS: A total of 14,673 participants with complete data were included, with a median age of 50 years and 7,257 women (49%). Multivariate linear regression showed that after full adjustment, the CDAI was significantly negatively associated with the TyG [ß: -0.005, 95% CI: (-0.008, -0.002), p = 0.002]. The model in which six nutrients were mutually corrected showed that vitamin E (per-SD increase) was most strongly associated with the TyG [ß: -0.062, 95% CI: (-0.074, -0.050), p < 0.0001]. In the WQS model, the WQS index of the antioxidant diet was negatively associated with the TyG (ß: -0.060; P < 0.0001). Similar effects were observed in the BKMR analysis. Notably, in the WQS and BKMR models, vitamin E became the most influential component. In addition, in the subgroup analysis, the association between the CDAI and the TyG in overweight or obese and diabetic populations was significantly weaker. CONCLUSION: Antioxidant diets, especially vitamin E, are significantly negatively correlated with TyG. This study emphasizes the important value of supplementing vitamin E to improve IR. However, patients with poor weight management and diabetes seem to benefit less from antioxidant diets.

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BACKGROUND: Uterine leiomyomata (UL; fibroids) are hormone-dependent neoplasms that can cause significant gynecologic morbidity. Studies have documented associations between concentrations of persistent endocrine-disrupting chemicals (EDCs) and UL incidence; however, few have assessed the effects of EDC mixtures on UL. METHODS: In the Study of Environment, Lifestyle, and Fibroids, a prospective cohort study, participants attended study visits at baseline and approximately every 20 months for up to 10 years; at each visit, they completed questionnaires, provided blood samples, and underwent standardized ultrasound examinations. In baseline plasma samples (n = 1155), we quantified concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and organochlorine pesticides using high-resolution mass spectrometry. We selected nine EDCs detected in >60 % of samples (4 PCBs, 4 PBDEs, and 2,2-bis(4-chlorophenyl)-1,1-dichloroethene (p,p'-DDE)) and conducted probit Bayesian kernel machine regression with hierarchical variable selection to estimate effects of the EDC mixture and individual EDCs on UL incidence, adjusting for potential confounders. RESULTS: During 10 years of follow-up, 32 % of participants developed ultrasound-detected UL. The EDC mixture was not appreciably associated with the probit of UL (ß comparing all EDCs at their 75th vs. 50th percentile:= - 0.01, 95 % credible interval [CrI]: -0.11, 0.10). However, individual EDC concentrations were associated with UL in opposing directions: PCB138/158 was positively associated with UL (ß for 25th-to-75th-percentile increase when all other chemicals were set to their 50th percentile = 0.18, 95 % CrI: -0.09, 0.44), whereas PBDE99 and p,p'-DDE were inversely associated with UL (ß = -0.06, 95 % CrI: -0.21, 0.10 and ß = -0.12, 95 % CrI: -0.34, 0.10, respectively). There was little evidence of interaction between EDCs. CONCLUSION: In this prospective ultrasound study, a mixture of persistent EDCs was not appreciably associated with incident UL during 10 years of follow-up, but individual EDCs were associated with UL in opposite directions.

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Background: The global burden of chronic diseases has been increasing, with evidence suggesting that diet and exposure to environmental pollutants, such as per- and polyfluoroalkyl substances (PFAS) and heavy metals, may contribute to their development. The Dietary Inflammatory Index (DII) assesses the inflammatory potential of an individual's diet. However, the complex interplay between PFAS, heavy metals, and DII remains largely unexplored. Objective: The goal of this cross-sectional study was to investigate the associations between diet operationalized as the DII with individual and combined lead, cadmium, mercury, perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS) exposures using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018. Methods: Descriptive statistics, a correlational analysis, and linear regression were initially used to assess the relationship between the variables of interest. We subsequently employed Bayesian kernel Machine regression (BKMR) to analyze the data to assess the non-linear, non-additive, exposure-response relationships and interactions between PFAS and metals with the DII. Results: The multi-variable linear regression revealed significant associations between the DII and cadmium and mercury. Our BKMR analysis revealed a complex relationship between PFAS, metal exposures, and the DII. In our univariate exposure-response function plot, cadmium and mercury exhibited a positive and negative linear relationship, respectively, which indicated a positive and negative relationship across the spectrum of exposures with the DII. In addition, the bivariate exposure-response function between two exposures in a mixture revealed that cadmium had a robust positive relationship with the DII for different quantiles of lead, mercury, PFOA, and PFOS, indicating that increasing levels of cadmium are associated with the DII. Mercury's bivariate plot demonstrated a negative relationship across all quantiles for all pollutants. Furthermore, the posterior inclusion probability (PIP) results highlighted the consistent importance of cadmium and mercury with the inflammatory potential of an individual's diet, operationalized as the DII in our study, with both showing a PIP of 1.000. This was followed by PFOS with a PIP of 0.8524, PFOA at 0.5924, and lead, which had the lowest impact among the five environmental pollutants, with a PIP of 0.5596. Conclusion: Our study suggests that exposures to environmental metals and PFAS, particularly mercury and cadmium, are associated with DII. These findings also provide evidence of the intricate relationships between PFAS, heavy metals, and the DII. The findings underscore the importance of considering the cumulative effects of multi-pollutant exposures. Future research should focus on elucidating the mechanistic pathways and dose-response relationships underlying these associations in a study that examines causality, which will enable a deeper understanding of the dietary risks associated with environmental pollutants.

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Many studies have indicated that individual exposure to phthalates (PAEs) or polycyclic aromatic hydrocarbons (PAHs) affects pregnancy outcomes. However, combined exposure to PAEs and PAHs presents a more realistic situation, and research on the combined effects of PAEs and PAHs on gestational age and newborn size is still limited. This study aimed to assess the effects of combined exposure to PAEs and PAHs on neonatal gestational age and birth size. Levels of 9 PAE and 10 PAH metabolites were measured from the urine samples of 1030 women during early pregnancy from the Zunyi Birth Cohort in China. Various statistical models, including linear regression, restricted cubic spline, Bayesian kernel machine regression, and quantile g-computation, were used to study the individual effects, dose-response relationships, and combined effects, respectively. The results of this prospective study revealed that each ten-fold increase in the concentration of monoethyl phthalate (MEP), 2-hydroxynaphthalene (2-OHNap), 2-hydroxyphenanthrene (2-OHPhe), and 1-hydroxypyrene (1-OHPyr) decreased gestational age by 1.033 days (95 % CI: -1.748, -0.319), 0.647 days (95 % CI: -1.076, -0.219), 0.845 days (95 % CI: -1.430, -0.260), and 0.888 days (95 % CI: -1.398, -0.378), respectively. Moreover, when the concentrations of MEP, 2-OHNap, 2-OHPhe, and 1-OHPyr exceeded 0.528, 0.039, 0.012, and 0.002 µg/g Cr, respectively, gestational age decreased in a dose-response manner. Upon analyzing the selected PAE and PAH metabolites as a mixture, we found that they were significantly negatively associated with gestational age, birth weight, and the ponderal index, with 1-OHPyr being the most important contributor. These findings highlight the adverse effects of single and combined exposure to PAEs and PAHs on gestational age. Therefore, future longitudinal cohort studies with larger sample sizes should be conducted across different geographic regions and ethnic groups to confirm the impact of combined exposure to PAEs and PAHs on birth outcomes.

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Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (ß: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (ß:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

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Erythrogram, despite its prevalent use in assessing red blood cell (RBC) disorders and can be utilized to evaluate various diseases, still lacks evidence supporting the effects of per- and polyfluoroalkyl substances (PFASs) and organophosphate esters (OPEs) on it. A cross-sectional study involving 467 adults from Shijiazhuang, China was conducted to assess the associations between 12 PFASs and 11 OPEs and the erythrogram (8 indicators related to RBC). Three models, including multiple linear regression (MLR), sparse partial least squares regression, and Bayesian kernel machine regression (BKMR) were employed to evaluate both the individual and joint effects of PFASs and OPEs on the erythrogram. Perfluorohexane sulfonic acid (PFHxS) showed the strongest association with HGB (3.68%, 95% CI: 2.29%, 5.10%) when doubling among PFASs in MLR models. BKMR indicated that PFASs were more strongly associated with the erythrogram than OPEs, as evidenced by higher group posterior inclusion probabilities (PIPs) for PFASs. Within hemoglobin and hematocrit, PFHxS emerged as the most significant component (conditional PIP = 1.0 for both). Collectively, our study emphasizes the joint effect of PFASs and OPEs on the erythrogram and identified PFASs, particularly PFHxS, as the pivotal contributors to the erythrogram. Nonetheless, further investigations are warranted to elucidate the underlying mechanisms.

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Synthetic steroid hormones are an emerging class of environmental pollutants, but their influence on pubertal timing remains unclear. This case-control study explored the association between synthetic steroid hormone exposure and precocious puberty. Using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), synthetic steroid hormones were detected in urine samples from 229 Chinese girls, aged 6-9 years. Puberty status was assessed using Tanner staging by professional pediatricians. We conducted the least absolute shrinkage and selection operator (LASSO) regression combined with logistic regression. Besides, we evaluated the joint effects of steroid hormone mixture and identified the main contributor using the Weighted quantile sum (WQS) model and Bayesian kernel machine regression (BKMR) model. The logistic regression model reflected an inverse individual association between precocious puberty and halcinonide [OR (95 %CI): 0.20 (0.07, 0.46)], and budesonide [OR (95 %CI): 0.77 (0.62, 0.95)]. In the joint effects utilizing the WQS model, precocious puberty showed a marginal association with steroid hormone mixture, but was not significant [OR (95 %CI): 0.88 (0.75, 1.04)]. Prednisolone (0.31), fluorometholone acetate (0.24), and dexamethasone acetate (0.12) had the highest weight. Consistently, mixture exposure was not associated with precocious puberty in the BKMR model. In conclusion, precocious puberty was associated with halcinonide and budesonide exposure, but not steroid hormone mixture among girls. It highlighted the management of the residual synthetic steroid hormones in the environment and provided a direction for the prevention of precocious puberty.

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OBJECTIVE: Maternal environmental metal exposure is common, but long-term prospective epidemiological evidence of its impact on children's intellectual development is still insufficient. METHODS: Data on maternal plasma metal levels and child intelligence were obtained for 211 3-6-year-old children from Guangxi Zhuang Birth Cohort. ICP-MS was employed to detect 17 metals, including 7 essential metals (Mn, Fe, Co, Ni, Cu, Zn, Mo) and 10 non-essential metals (As, Rb, Sr, Cd, Sb, Cs, Ba, W, Pb, U), in maternal plasma samples obtained before 13 weeks of gestation during the initial maternity checkup. Child intelligence was assessed using the Wechsler Intelligence Scale for Children-Fourth Edition. The GLM, RCS and mixture models were used to assess the associations of maternal plasma metal levels with child intelligence quotient (IQ) scores. RESULTS: The GLM analysis revealed that U had a significant adverse effect on child IQ scores in high-dose exposure groups (-9.236 [-18.644, -4.936], p = 0.006) after adjusting for covariates, while Sb showed a linear adverse effect on children's intelligence in the adjusted model (-4.028 [-7.432, -0.626], p = 0.021). BKMR modeling indicated that overall IQ scores decreased as concentrations of non-essential metals mixtures increased after adjusting for essential metal mixtures, consistent with findings from the WQS (ß [95% CI], -8.463 [-14.449, -2.476], p = 0.007) and Qgcomp models (-7.003 [-12.928, -1.078], p = 0.022). Among the non-essential metals, U had the highest negative weight at 37.96%, followed by Pb (23.35%) and Sb (16.91%). Furthermore, potential interactions were observed between metals (Pb and U) and Sb in the study findings. CONCLUSION: Reducing exposure to non-essential metal mixtures, especially U, Sb and Pb, during early pregnancy and ensuring adequate intake of specific essential metal elements could be a critical intervention in addressing childhood intellectual impairment.

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BACKGROUND: With increased applications of rare earth elements (REEs) across various industries, evaluating the relationship between REEs exposure and potential health effects has become a public concern. In vivo experiments have established that REEs impact renal function. However, relevant epidemiological evidence on this relationship remains scarce. The objective of this study is to examine the impact of exposure to REEs on renal function. METHODS: In this cross-sectional study, 1052 participants were recruited from Guangxi, China. We measured urinary concentrations of 12 REEs using an inductively coupled plasma-mass spectrometer (ICP-MS). Multiple linear regression models were developed to explore the relationship between a single REEs exposure and the estimated glomerular filtration rate (eGFR), a marker of renal function. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were used to examine the combined effects of REE co-exposure on eGFR. RESULTS: In the multiple linear regression analysis, increasing the concentrations of lanthanum (La, ß: 8.22, 95% CI: 5.67-10.77), cerium (Ce, ß:6.61, 95% CI: 3.80-9.43), praseodymium (Pr, ß: 8.46, 95% CI: 5.85-11.07), neodymium (Nd, ß:8.75, 95% CI: 6.10-11.41), and dysprosium (Dy, ß:7.38, 95% CI: 4.85-9.91) significantly increased the eGFR. In the WQS regression model, the WQS index was significantly associated with eGFR (ß: 4.03, 95% CI: 2.46-5.60), with Pr having the strongest correlation with eGFR. Similar results were obtained in the BKMR model. Additionally, interactions between Pr and La, and Pr and Nd were observed. CONCLUSIONS: Co-exposure to REEs is positively associated with elevated eGFR. Pr is likely to have the most significant influence on increased eGFRs and this might be exacerbated when interacting with La and Nd. Mixed exposure to low doses of REEs had a protective effect on renal function, which can provide some evidence for the exposure threshold of REEs in the environment. TRIAL REGISTRATION: The study has been approved by the Guangxi Medical University Medical Ethics Committee (#20170206-1), and all participants provided written informed consent.

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Background: This study investigates the individual and cumulative effects of 12 aldehydes concentrations on cardiovascular disease (CVD). Methods: A total of 1529 individuals from the 2013-2014 National Health and Nutrition Examination Survey were enrolled. We assessed serum concentrations of 12 aldehydes, including benzaldehyde, butyraldehyde, crotonaldehyde, decanaldehyde, heptanaldehyde, hexanaldehyde, isopentanaldehyde, nonanaldehyde, octanaldehyde, o-tolualdehyde, pentanaldehyde, and propanaldehyde. CVD patients were identified based on self-reported disease history from questionnaires. The Bayesian kernel machine regression was used to evaluate the cumulative effect of 12 aldehyde concentrations on CVD. Both weighted and unweighted logistic regression were used to assess the association of serum aldehyde concentrations with CVD, presenting effect sizes as odds ratio (OR) with 95% confidence interval (CI). Additionally, a restricted cubic spline analysis was also conducted to explore the relationship between benzaldehyde and CVD. Results: Among the participants, 111 (7.3%) were identified as having CVD. Isopentanaldehyde concentrations were notably higher in CVD patients compared to those without CVD. Bayesian kernel machine regression indicated no cumulative effect of aldehydes on CVD. Unweighted logistic regression revealed a positive association between benzaldehyde and CVD when adjusting for age and sex (OR = 1.12, 95% CI = 1.03-1.21). This association persisted after adjusting for age, sex, race, education, hypertension, diabetes, alcohol consumption, and smoking, with an OR of 1.12 (95% CI = 1.02-1.22). The restricted cubic spline showed a linear association between benzaldehyde and CVD. In the weighted logistic model, the association between benzaldehyde and CVD remains significant (OR = 1.17, 95% CI = 1.06-1.29). However, no significant association was found between other aldehydes and CVD. Conclusions: Our study reveals the potential contributing role of benzaldehyde to CVD. Future studies should further validate these findings in diverse populations and elucidate the underlying biological mechanisms.

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BACKGROUND: Brominated Flame Retardants (BFRs) have attracted widespread concern due to their environmental persistence and potential toxicity. This study aims to examine the association between BFRs exposure and hypertension. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) spanning 2005 to 2016 for the cross-sectional analysis. To evaluate the individual and combined impacts of BFRs exposure on hypertension, we utilized multivariate models, including generalized additive models, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models. RESULTS: 9882 individuals (48% male) aged ≥ 20 were included in the final analysis, of whom 4114 had hypertension. After controlling for potential covariates, higher serum concentrations of PBDE100 (OR: 1.26; 95% CI: 1.01, 1.57) and PBDE153 (OR: 1.50; 95% CI: 1.18, 1.88) were significantly associated with hypertension. A nonlinear relationship between PBDE28 and hypertension was observed (P = 0.03). Moreover, BFRs mixture were positively associated with the prevalence of hypertension in both the WQS (ß:1.09; 95% CI: 1.02, 1.17; P = 0.02) and BKMR models. CONCLUSION: Our study suggested that BFRs exposure is positively associated with hypertension in the general population. To confirm this association and elucidate the mechanisms, further research is required.

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Background: The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. Methods: This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results: Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25th percentile compared to their 50th percentile and a positive association of 0.0285 units when all the metals were at their 75th percentile compared to their 50th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. Conclusions: This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.

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Objective: The effect of environmental pollution on sleep has been widely studied, yet the relationship between exposure to volatile organic compounds (VOCs) and sleep health requires further exploration. We aimed to investigate the single and mixed effect of urinary VOC metabolites on sleep health and identify potential mediators. Methods: Data for this cross-sectional study was collected from the National Health and Nutrition Examination Surveys (NHANES) (2005-2006, 2011-2014). A weighted multivariate logistic regression was established to explore the associations of 16 VOCs with four sleep outcomes. Following the selection of important VOCs through the least absolute shrinkage and selection operator (LASSO) regression, principal component analyses (PCA), weight quantile sum (WQS), and Bayesian kernel machine regression (BKMR) analyses were conducted to explore the associations between exposure to single and mixed VOCs and sleep outcomes, as well as identify the most contributing components. A mediation analysis was performed to explore the potential effect of depression scores. Results: Of the 3,473 participants included in the study, a total of 618 were diagnosed with poor sleep patterns. In logistic regression analyses, 7, 10, 1, and 5 VOCs were significantly positively correlated with poor sleep patterns, abnormal sleep duration, trouble sleeping, and sleep disorders, respectively. The PCA analysis showed that PC1 was substantially linked to a higher risk of poor sleep patterns and its components. The WQS model revealed a positive association between VOC mixture of increased concentrations and poor sleep patterns [OR (95% CI): 1.285 (1.107, 1.493)], abnormal sleep duration [OR (95% CI): 1.154 (1.030, 1.295)], trouble sleeping [OR (95% CI): 1.236 (1.090, 1.403)] and sleep disorders [OR (95% CI): 1.378 (1.118, 1.705)]. The BKMR model found positive associations of the overall VOC exposure with poor sleep patterns, trouble sleeping, and sleep disorders. PCA, WQS, and BKMR models all confirmed the significant role of N-acetyl-S-(N-methylcarbamoyl)-l-cysteine (AMCC) in poor sleep patterns and its components. The depression score was a mediator between the positive VOC mixture index and the four sleep outcomes. Conclusion: Exposure to single and mixed VOCs negatively affected the sleep health of American population, with AMCC playing a significant role. The depression score was shown to mediate the associations of VOC mixtures with poor sleep patterns and its components.

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