Chemical mixture that targets the epidermal growth factor pathway impairs human trophoblast cell functions.

Pregnant women are exposed to complex chemical mixtures, many of which reach the placenta. Some of these chemicals interfere with epidermal growth factor receptor (EGFR) activation, a receptor tyrosine kinase that modulates several placenta cell functions. We hypothesized that a mixture of chemicals (Chem-Mix) known to reduce EGFR activation (polychlorinated biphenyl (PCB)-126, PCB-153, atrazine, trans-nonachlor, niclosamide, and bisphenol S) would interfere with EGFR-mediated trophoblast cell functions. To test this, we determined the chemicals' EGFR binding ability, EGFR and downstream effectors activation, and trophoblast functions (proliferation, invasion, and endovascular differentiation) known to be regulated by EGFR in extravillous trophoblasts (EVTs). The Chem-Mix competed with EGF for EGFR binding, however only PCB-153, niclosamide, trans-nonachlor, and BPS competed for binding as single chemicals. The effects of the Chem-Mix on EGFR phosphorylation were tested by exposing the placental EVT cell line, HTR-8/SVneo to control (0.1% DMSO), Chem-Mix (1, 10, or 100 ng/ml), EGF (30 ng/ml), or Chem-Mix + EGF. The Chem-Mix - but not the individual chemicals - reduced EGF-mediated EGFR phosphorylation in a dose dependent manner, while no effect was observed in its downstream effectors (AKT and STAT3). None of the individual chemicals affected EVT cell invasion, but the Chem-Mix reduced EVT cell invasion independent of EGF. In support of previous studies that have explored chemicals targeting a specific pathway (estrogen/androgen receptor), current findings indicate that exposure to a chemical mixture that targets the EGFR pathway can result in a greater impact compared to individual chemicals in the context of placental cell functions.

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model.

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.

Human Biomonitoring of Environmental Chemicals among Elderly in Wuhan, China: Prioritizing Risks Using EPA's ToxCast Database.

In line with the "healthy aging" principle, we aim to assess the exposure map and health risks of environmental chemicals in the elderly. Blood samples from 918 elderly individuals in Wuhan, China, were analyzed using the combined gas/liquid-mass spectrometry technology to detect levels of 118 environmental chemicals. Cluster analysis identified exposure profiles, while risk indexes and bioanalytical equivalence percentages were calculated using EPA's ToxCast database. The detection rates for 87 compounds exceeded 70%. DEHP, DiBP, naphthalene, phenanthrene, DnBP, pyrene, anthracene, permethrin, fluoranthene, and PFOS showed the highest concentrations. Fat-soluble pollutants varied across lifestyles. In cluster 2, which was characterized by higher concentrations of fat-soluble substances, the proportion of smokers or drinkers was higher than that of nonsmokers or nondrinkers. Pesticides emerged as the most active environmental chemicals in peroxisome proliferator-activated receptor gamma antagonist, thyroid hormone receptor (TR) antagonist, TR agonist, and androgen receptor (AR) agonist activity assays. Additionally, PAEs and polycyclic aromatic hydrocarbons played significant roles as active contaminants for the corresponding targets of AR antagonists and estrogen receptor alpha. We proposed a list of priority pollutants linked to endocrine-disrupting toxic effects in the elderly, which may provide the groundwork for further research into environmental etiology.

Are We Justified in Modeling Human Exposure to Chlorinated Paraffin Mixtures Using the Average Properties of Congeners and Homologues?

Concern over human exposure to chlorinated paraffin (CP) mixtures keeps increasing. The absence of a comprehensive understanding of how human exposure varies with the physicochemical properties of CP constituents has hindered the ability to determine at what level of aggregation exposure to CPs should be assessed. We answer this question by comparing exposure predicted with either a "complex" method that utilizes isomer-specific properties or "simplified" methods that rely on median properties of congener, homologue, or short-/medium-/long-chain CP groups. Our results demonstrate the wide range of physicochemical properties across CP mixtures and their dependence on molecular structures. Assuming unit emissions in the environment, these variances translate into an extensive disparity in whole-body concentrations predicted for different isomers, spanning ∼11 orders of magnitude. CPs with 13-19 carbons and 6-10 chlorines exhibit the highest human exposure potential, primarily owing to moderate to high hydrophobicity and slow environmental degradation and biotransformation. Far-field exposure is dominant for most CP constituents. Our study underscores that using average properties of congener, homologue, or S/M/LCCP groups yields results that are consistent with those derived from isomer-based modeling, thus offering an efficient and practical framework for future risk assessments and human exposure studies of CPs and other complex chemical mixtures.

Metabolomics revealed disruptions in amino acid and antioxidant biochemistry in Daphnia magna exposed to industrial effluents associated with plastic and polymer production.

Industrial wastewater effluents are a major source of chemicals in aquatic environments, and many of these chemicals may negatively impact aquatic life. In this study, the crustacean Daphnia magna, a common model organism in ecotoxicity studies, was exposed for 48 h to nine different industrial effluent samples from manufacturing facilities associated with the production of plastics, polymers, and coating products at a range of dilutions: 10, 25, 50, 100% (undiluted). A targeted metabolomic-based approach using liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to quantify polar metabolites from individual daphnids that survived the 48 h exposure. Multivariate analyses and metabolite changes revealed metabolic perturbations across all effluent samples studied, with non-monotonic responses and both up and downregulation relative to the unexposed control. Pathway analyses indicated the disruption of similar and distinct pathways, mostly connected to protein synthesis, amino acid metabolism, and antioxidant processes. Overall, we observed disruptions in Daphnia biochemistry that were similar across the effluent samples, but with unique features for each effluent sample. Additionally, non-monotonic heightened responses suggested additive and/or synergistic interactions between the chemicals within the industrial effluents. These findings demonstrate that targeted metabolomic approaches are a powerful tool for the biomonitoring of aquatic ecosystems in the context of complex mixtures, such as industrial wastewater effluents.

EDC mixtures during pregnancy and body fat at 7 years of age in a Swedish cohort, the SELMA study.

BACKGROUND: Some endocrine disrupting chemicals (EDC), are "obesogens" and have been associated with overweight and obesity in children. Daily exposure to different classes of EDCs demands for research with mixtures approach. OBJECTIVES: This study evaluates the association, considering sex-specific effects, between prenatal exposure to EDC mixture and children's body fat at seven years of age. METHODS: A total of 26 EDCs were assessed in prenatal urine and serum samples from first trimester in pregnancy from 737 mother-child pairs participating in the Swedish Environmental Longitudinal, Mother and child, Asthma and allergy (SELMA) study. An indicator for children's "overall body fat" was calculated, using principal component analysis (PCA), based on BMI, percent body fat, waist, and skinfolds measured at seven years of age. Weighted quantile sum (WQS) regression was used to assess associations between EDC mixture and children's body fat. RESULTS: Principal component (PC1) represented 83.6 % of the variance, suitable as indicator for children's "overall body fat", with positive loadings of 0.40-0.42 for each body fat measure. A significant interaction term, WQS*sex, confirmed associations in the opposite direction for boys and girls. Higher prenatal exposure to EDC mixture was borderline significant with more "overall body fat" for boys (Mean ß = 0.20; 95 % CI: -0.13, 0.53) and less for girls (Mean ß = -0.23; 95 % CI: -0.58, 0.13). Also, higher prenatal exposure to EDC mixture was borderline significant with more percent body fat (standardized score) for boys (Mean ß = 0.09; 95 % CI: -0.04, 0.21) and less for girls (Mean ß = -0.10 (-0.26, 0.05). The chemicals of concern included bisphenols, phthalates, PFAS, PAH, and pesticides with different patterns for boys and girls. DISCUSSION: Borderline significant associations were found between prenatal exposure to a mixture of EDCs and children's body fat. The associations in opposite directions suggests that prenatal exposure to EDCs may present sex-specific effects on children's body fat.

Gestational exposure to organochlorine compounds and metals and infant birth weight: effect modification by maternal hardships.

BACKGROUND: Gestational exposure to toxic environmental chemicals and maternal social hardships are individually associated with impaired fetal growth, but it is unclear whether the effects of environmental chemical exposure on infant birth weight are modified by maternal hardships. METHODS: We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1982 pregnant females enrolled between 2008 and 2011. We quantified eleven environmental chemical concentrations from two chemical classes - six organochlorine compounds (OCs) and five metals - that were detected in ≥ 70% of blood samples collected during the first trimester. We examined fetal growth using birth weight adjusted for gestational age and assessed nine maternal hardships by questionnaire. Each maternal hardship variable was dichotomized to indicate whether the females experienced the hardship. In our analysis, we used elastic net to select the environmental chemicals, maternal hardships, and 2-way interactions between maternal hardships and environmental chemicals that were most predictive of birth weight. Next, we obtained effect estimates using multiple linear regression, and plotted the relationships by hardship status for visual interpretation. RESULTS: Elastic net selected trans-nonachlor, lead, low educational status, racially minoritized background, and low supplemental folic acid intake. All were inversely associated with birth weight. Elastic net also selected interaction terms. Among those with increasing environmental chemical exposures and reported hardships, we observed stronger negative associations and a few positive associations. For example, every two-fold increase in lead concentrations was more strongly associated with reduced infant birth weight among participants with low educational status (ß = -100 g (g); 95% confidence interval (CI): -215, 16), than those with higher educational status (ß = -34 g; 95% CI: -63, -3). In contrast, every two-fold increase in mercury concentrations was associated with slightly higher birth weight among participants with low educational status (ß = 23 g; 95% CI: -25, 71) compared to those with higher educational status (ß = -9 g; 95% CI: -24, 6). CONCLUSIONS: Our findings suggest that maternal hardships can modify the associations of gestational exposure to some OCs and metals with infant birth weight.

Exposures to pesticides and risk of cancer: Evaluation of recent epidemiological evidence in humans and paths forward.

Knowledge of the role in cancer etiology of environmental exposures as pesticides is a prerequisite for primary prevention. We review 63 epidemiological studies on exposure to pesticides and cancer risk in humans published from 2017 to 2021, with emphasis on new findings, methodological approaches, and gaps in the existing literature. While much of the recent evidence suggests causal relationships between pesticide exposure and cancer, the strongest evidence exists for acute myeloid leukemia (AML) and colorectal cancer (CRC), diseases in which the observed associations were consistent across several studies, including high-quality prospective studies and those using biomarkers for exposure assessment, with some observing dose-response relationships. Though high-quality studies have been published since the IARC monograph on organophosphate insecticides in 2017, there are still gaps in the literature on carcinogenic evidence in humans for a large number of pesticides. To further knowledge, we suggest leveraging new techniques and methods to increase sensitivity and precision of exposure assessment, incorporate multi-omics data, and investigate more thoroughly exposure to chemical mixtures. There is also a strong need for better and larger population-based cohort studies that include younger and nonoccupationally exposed individuals, particularly during developmental periods of susceptibility. Though the existing evidence has limitations, as always in science, there is sufficient evidence to implement policies and regulatory action that limit pesticide exposure in humans and, hence, further prevent a significant burden of cancers.

A latent functional approach for modeling the effects of multidimensional exposures on disease risk.

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.

Toward Advancing Precision Environmental Health: Developing a Customized Exposure Burden Score to PFAS Mixtures to Enable Equitable Comparisons Across Population Subgroups, Using Mixture Item Response Theory.

Quantifying a person's cumulative exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures is important for risk assessment, biomonitoring, and reporting of results to participants. However, different people may be exposed to different sets of PFASs due to heterogeneity in the exposure sources and patterns. Applying a single measurement model for the entire population (e.g., by summing concentrations of all PFAS analytes) assumes that each PFAS analyte is equally informative to PFAS exposure burden for all individuals. This assumption may not hold if PFAS exposure sources systematically differ within the population. However, the sociodemographic, dietary, and behavioral characteristics that underlie systematic exposure differences may not be known, or may be due to a combination of these factors. Therefore, we used mixture item response theory, an unsupervised psychometrics and data science method, to develop a customized PFAS exposure burden scoring algorithm. This scoring algorithm ensures that PFAS burden scores can be equitably compared across population subgroups. We applied our methods to PFAS biomonitoring data from the United States National Health and Nutrition Examination Survey (2013-2018). Using mixture item response theory, we found that participants with higher household incomes had higher PFAS burden scores. Asian Americans had significantly higher PFAS burden compared with non-Hispanic Whites and other race/ethnicity groups. However, some disparities were masked when using summed PFAS concentrations as the exposure metric. This work demonstrates that our summary PFAS burden metric, accounting for sources of exposure variation, may be a more fair and informative estimate of PFAS exposure.

Risk assessment of chemicals and their mixtures are hindered by scarcity and inconsistencies between different environmental exposure limits.

In chemical risk assessment, measured or modelled environmental concentrations are compared to environmental exposure limits (EELs), such as Predicted No Effect Concentrations (PNECs) or hazardous concentrations for 5% of species (HC05s) derived from species sensitivity distributions (SSDs). However, for many chemicals the EELs include large uncertainties or, in the worst case, the necessary data for their estimation are completely missing. This makes the assessment of chemical risks and any subsequent implementation of management strategies challenging. In this study we analyzed the uncertainty of EELs and its impact on chemical risk assessment. First, we compared three individual EEL datasets, two primarily based on experimental data and one based on computational predictions. The comparison demonstrates large disagreements between EEL data sources, with experimentally derived EELs differing by more than seven orders of magnitude. In a case-study, based on the predicted emissions of 2005 chemicals, we showed that these uncertainties lead to significantly different risk assessment outcomes, including large differences in the magnitude of the total risk, risk driver identification, and the ranking of use categories as risk contributors. We also show that the large data-gaps in EEL datasets cannot be covered by commonly used computational approaches (QSARs). We conclude that an expanded framework for interpreting risk characterization outcomes is needed. We also argue that the large data-gaps present in ecotoxicological data need to be addressed in order to achieve the European zero pollution vision as the growing emphasis on ambient exposures will further increase the demand for accurate and well-established EELs.

Environmental metabolomics uncovers oxidative stress, amino acid dysregulation, and energy impairment in Daphnia magna with exposure to industrial effluents.

Anthropogenic activities are regarded as point sources of pollution entering freshwater bodies worldwide. With over 350,000 chemicals used in manufacturing, wastewater treatment and industrial effluents are comprised of complex mixtures of organic and inorganic pollutants of known and unknown origins. Consequently, their combined toxicity and mode of action are not well understood in aquatic organisms such as Daphnia magna. In this study, effluent samples from wastewater treatment and industrial sectors were used to examine molecular-level perturbations to the polar metabolic profile of D. magna. To determine if the industrial sector and/or the effluent chemistries played a role in the observed biochemical responses, Daphnia were acutely (48 h) exposed to undiluted (100%) and diluted (10, 25, and 50%) effluent samples. Endogenous metabolites were extracted from single daphnids and analyzed using targeted mass spectrometry-based metabolomics. The metabolic profile of Daphnia exposed to effluent samples resulted in significant separation compared to the unexposed controls. Linear regression analysis determined that no single pollutant detected in the effluents was significantly correlated with the responses of metabolites. Significant perturbations were uncovered across many classes of metabolites (amino acids, nucleosides, nucleotides, polyamines, and their derivatives) which serve as intermediates in keystone biochemical processes. The combined metabolic responses are consistent with oxidative stress, disruptions to energy metabolism, and protein dysregulation which were identified through biochemical pathway analysis. These results provide insight into the molecular processes driving stress responses in D. magna. Overall, we determined that the metabolic profile of Daphnia could not be predicted by the chemical composition of environmentally relevant mixtures. The findings of this study demonstrate the advantage of metabolomics in conjunction with chemical analyses to assess the interactions of industrial effluents. This work further demonstrates the ability of environmental metabolomics to characterize molecular-level perturbations in aquatic organisms exposed to complex chemical mixtures directly.

Food, Beverage, and Feedstock Processing Facility Wastewater: a Unique and Underappreciated Source of Contaminants to U.S. Streams.

Process wastewaters from food, beverage, and feedstock facilities, although regulated, are an under-investigated environmental contaminant source. Food process wastewaters (FPWWs) from 23 facilities in 17 U.S. states were sampled and documented for a plethora of chemical and microbial contaminants. Of the 576 analyzed organics, 184 (32%) were detected at least once, with concentrations as large as 143 µg L-1 (6:2 fluorotelomer sulfonic acid), and as many as 47 were detected in a single FPWW sample. Cumulative per/polyfluoroalkyl substance concentrations up to 185 µg L-1 and large pesticide transformation product concentrations (e.g., methomyl oxime, 40 µg L-1; clothianidin TMG, 2.02 µg L-1) were observed. Despite 48% of FPWW undergoing disinfection treatment prior to discharge, bacteria resistant to third-generation antibiotics were found in each facility type, and multiple bacterial groups were detected in all samples, including total coliforms. The exposure-activity ratios and toxicity quotients exceeded 1.0 in 13 and 22% of samples, respectively, indicating potential biological effects and toxicity to vertebrates and invertebrates associated with the discharge of FPWW. Organic contaminant profiles of FPWW differed from previously reported contaminant profiles of municipal effluents and urban storm water, indicating that FPWW is another important source of chemical and microbial contaminant mixtures discharged into receiving surface waters.

Daphnia as a Sentinel Species for Environmental Health Protection: A Perspective on Biomonitoring and Bioremediation of Chemical Pollution.

Despite available technology and the knowledge that chemical pollution damages human and ecosystem health, chemical pollution remains rampant, ineffectively monitored, rarely prevented, and only occasionally mitigated. We present a framework that helps address current major challenges in the monitoring and assessment of chemical pollution by broadening the use of the sentinel species Daphnia as a diagnostic agent of water pollution. And where prevention has failed, we propose the application of Daphnia as a bioremediation agent to help reduce hazards from chemical mixtures in the environment. By applying "omics" technologies to Daphnia exposed to real-world ambient chemical mixtures, we show improvements at detecting bioactive components of chemical mixtures, determining the potential effects of untested chemicals within mixtures, and identifying targets of toxicity. We also show that using Daphnia strains that naturally adapted to chemical pollution as removal agents of ambient chemical mixtures can sustainably improve environmental health protection. Expanding the use of Daphnia beyond its current applications in regulatory toxicology has the potential to improve both the assessment and the remediation of environmental pollution.

Watershed-Scale Risk to Aquatic Organisms from Complex Chemical Mixtures in the Shenandoah River.

River waters contain complex chemical mixtures derived from natural and anthropogenic sources. Aquatic organisms are exposed to the entire chemical composition of the water, resulting in potential effects at the organismal through ecosystem level. This study applied a holistic approach to assess landscape, hydrological, chemical, and biological variables. On-site mobile laboratory experiments were conducted to evaluate biological effects of exposure to chemical mixtures in the Shenandoah River Watershed. A suite of 534 inorganic and organic constituents were analyzed, of which 273 were detected. A watershed-scale accumulated wastewater model was developed to predict environmental concentrations of chemicals derived from wastewater treatment plants (WWTPs) to assess potential aquatic organism exposure for all stream reaches in the watershed. Measured and modeled concentrations generally were within a factor of 2. Ecotoxicological effects from exposure to individual components of the chemical mixture were evaluated using risk quotients (RQs) based on measured or predicted environmental concentrations and no effect concentrations or chronic toxicity threshold values. Seventy-two percent of the compounds had RQ values <0.1, indicating limited risk from individual chemicals. However, when individual RQs were aggregated into a risk index, most stream reaches receiving WWTP effluent posed potential risk to aquatic organisms from exposure to complex chemical mixtures.

Validated single urinary assay designed for exposomic multi-class biomarkers of common environmental exposures.

Epidemiological studies often call for analytical methods that use a small biospecimen volume to quantify trace level exposures to environmental chemical mixtures. Currently, as many as 150 polar metabolites of environmental chemicals have been found in urine. Therefore, we developed a multi-class method for quantitation of biomarkers in urine. A single sample preparation followed by three LC injections was optimized in a proof-of-approach for a multi-class method. The assay was validated to quantify 50 biomarkers of exposure in urine, belonging to 7 chemical classes and 16 sub-classes. The classes represent metabolites of 12 personal care and consumer product chemicals (PCPs), 5 polycyclic aromatic hydrocarbons (PAHs), 5 organophosphate flame retardants (OPFRs), 18 pesticides, 5 volatile organic compounds (VOCs), 4 tobacco alkaloids, and 1 drug of abuse. Human urine (0.2 mL) was spiked with isotope-labeled internal standards, enzymatically deconjugated, extracted by solid-phase extraction, and analyzed using high-performance liquid chromatography-tandem mass spectrometry. The methanol eluate from the cleanup was split in half and the first half analyzed for PCPs, PAH, and OPFR on a Betasil C18 column; and pesticides and VOC on a Hypersil Gold AQ column. The second half was analyzed for tobacco smoke metabolites and a drug of abuse on a Synergi Polar RP column. Limits of detection ranged from 0.01 to 1.0 ng/mL of urine, with the majority ≤0.5 ng/mL (42/50). Analytical precision, estimated as relative standard deviation of intra- and inter-batch uncertainty, variabilities, was <20%. Extraction recoveries ranged from 83 to 109%. Results from the optimized multi-class method were qualified in formal international proficiency testing programs. Further method customization options were explored and method expansion was demonstrated by inclusion of up to 101 analytes of endo- and exogenous chemicals. This exposome-scale assay is being used for population studies with savings of assay costs and biospecimens, providing both quantitative results and the discovery of unexpected exposures.

Prenatal exposure to a mixture of organochlorines and metals and internalizing symptoms in childhood and adolescence.

BACKGROUND: Although prenatal chemical exposures influence neurobehavior, joint exposures are not well explored as risk factors for internalizing disorders through adolescence. OBJECTIVE: To evaluate associations of prenatal organochlorine and metal exposures, considered individually and as a mixture, with mid-childhood and adolescent internalizing symptoms. METHODS: Participants were 468 children from a prospective cohort recruited at birth (1993-1998) in New Bedford, Massachusetts. Organochlorines (hexachlorobenzene, p,p'-dichlorodiphenyl dichloroethylene, polychlorinated biphenyls) and metals (lead, manganese) were analyzed in cord blood. Internalizing symptoms (anxiety, depressive, somatic) were assessed via multiple informants on the Conners' Rating Scale (CRS) at 8-years and Behavior Assessment System for Children, Second Edition (BASC-2) at 15-years; higher T-scores indicate greater symptoms. Overall and sex-specific covariate-adjusted associations were evaluated using Bayesian Kernel Machine Regression (BKMR) and five-chemical linear regression models. RESULTS: The cohort was socioeconomically diverse (35% household income <$20,000; 55% maternal ≤ high school education at birth). Most chemical concentrations were consistent with background levels [e.g., median (range) cord blood lead: 1.1 (0-9.4) µg/dL]. BKMR suggested linear associations and no interactions between chemicals. The overall mixture was positively associated with Conners' Parent Rating Scale (CPRS) and BASC-2 Self Report of Personality (SRP) anxiety and depressive symptoms, and negatively with somatic symptoms. Prenatal lead was positively associated with adolescent anxiety symptoms [1.56 (95% CI: 0.50, 2.61) BASC-2 SRP Anxiety score increase per doubling lead]. For CRPS and BASC-2 SRP, a doubling of cord blood manganese was positively associated with internalizing symptoms for girls [e.g., 3.26 (95% CI: 0.27, 6.25) BASC-2 SRP Depression score increase], but not boys. Organochlorine exposures were not adversely associated with internalizing symptoms. DISCUSSION: Low-level prenatal lead exposure was positively associated with adolescent anxiety symptoms, and prenatal manganese exposure was positively associated with internalizing symptoms for girls from mid-childhood through adolescence. In utero neurotoxicant metal exposures may contribute to the emergence of anxiety and depression.

How to investigate human health effects related to exposure to mixtures of per- and polyfluoroalkyl substances: A systematic review of statistical methods.

BACKGROUND: Humans are exposed to several per- and polyfluoroalkyl substances (PFAS) daily; however, most previous studies have focused on individual PFAS. Although attention to effects of exposure to mixtures of PFAS has grown in recent years, there is no consensus on the appropriate statistical methods that can be used to assess their combined effect on human health. OBJECTIVES: We aim to perform a comprehensive review of the statistical methods used in the existing studies which evaluate the association between exposure to mixtures of PFAS and any adverse human health effect. METHODS: The online databases PubMed, Embase and Scopus were searched for eligible studies, published during the last ten years (last search performed on April 08, 2021). Covidence software was used by two different reviewers to perform a title/abstract screening, followed by a full text revision of the selected papers. RESULTS: A total of 3640 papers were identified, and after the screening process, 53 papers were included in the current review. Most of the studies were published between 2019 and 2021 and were conducted mainly in North America and Europe; more than half of the studies (28 out of 53) were conducted on mother and child pairs. WQS (Weighted Quantile Sum) Regression and BKMR (Bayesian Kernel Machine Regression) were used in 36 out of 53 papers to model mixtures' effects. Health outcomes included in the studies are immunotoxicity (n = 8), fetal development (n = 7), neurodevelopment (n = 9), reproductive hormones (n = 6), thyroid hormones (n = 7), outcomes related to metabolic pathways (n = 16). CONCLUSION: Studies on human exposure to PFAS as complex mixtures and health consequences have substantially increased in the last few years. Based on our findings, we propose that addressing risk from PFAS mixtures will likely require combinations of approaches and implementation of constantly evolving statistical methods. Specific guidelines and tools for quality assessment and publication of mixture observational studies are warranted.

Prenatal exposure to chemical mixtures and working memory among adolescents.

Working memory is the ability to keep information in one's mind and mentally manipulate it. Decrements in working memory play a key role in many behavioral and psychiatric disorders, therefore identifying modifiable environmental risk factors for such decrements is important for mitigating these disorders. There is some evidence that prenatal exposure to individual chemicals may adversely impact working memory among children, but few studies have explored the association of co-exposure to multiple chemicals with this outcome in adolescence, a time when working memory skills undergo substantial development. We investigated the association of organochlorines (DDE, HCB, PCBs) and metals (lead, manganese) measured in cord serum and cord blood, respectively, with working memory measured with the Wide Range Assessment of Memory and Learning, 2nd Edition among 373 adolescents living near a Superfund site in New Bedford, Massachusetts. We used Bayesian Kernel Machine Regression (BKMR) and linear regression analyses and assessed effect modification by sex and prenatal social disadvantage. In BKMR models, we observed an adverse joint association of the chemical mixture with Verbal, but not Symbolic, Working Memory. In co-exposure and covariate-adjusted linear regression models, a twofold increase in cord blood manganese was associated with lower working memory scaled scores, with a stronger association with Verbal Working Memory (difference = -0.75; 95% CI: -1.29, -0.20 points) compared to Symbolic Working Memory (difference = -0.44; 95% CI: -1.00, 0.12 points). There was little evidence of effect modification by sex and some evidence associating organochlorine pesticides with poorer working memory scores among those with greater prenatal social disadvantage. This study provided evidence of an adverse joint association of a chemical mixture with a verbal working memory task among adolescents, as well as an adverse association of prenatal manganese exposure with working memory.

Perfluoroalkyl substance mixtures and cardio-metabolic outcomes in highly exposed male workers in the Veneto Region: A mixture-based approach.

BACKGROUND: Perfluoroalkyl substances (PFAS) have been consistently associated with cardio-metabolic traits. Occupational exposures to multiple PFAS with health outcomes have been poorly investigated. The aim of the present study was to examine these associations among former workers involved in PFAS production. METHODS: We considered 232 male ex-employees who had worked in a factory (Trissino, Veneto Region, Italy), which produced PFAS and other chemicals during 1968-2018. Out of twelve serum PFAS, only four (PFOA, PFOS, PFHxS, and PFNA) were quantifiable in at least 50% of samples. Non-fasting serum total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured. The associations between serum PFAS mixture and considered outcomes were assessed through linear regression mixed models and Weighted Quantile Sum (WQS) regression, adjusting for potential confounders. RESULTS: PFOA was detected at the highest level, with a median concentration (in ng/mL) of 80.8 (min-max: 0.35-13,033), followed by PFOS (median: 8.55, min-max: 0.35-343), PFHxS (median: 6.8, min-max: 0.35-597) and PFNA (median: 0.8, min-max: 0.35-5). We observed that each A quartile increase in the WQS index was positively associated with the levels of TC (ß: 8.41, 95% IC: 0.78-16.0), LDL-C (ß: 8.02, 95% IC: 1-15.0) and SBP (ß: 3.21, 95% IC: 0.82-5.60). No association of serum PFAS concentration on HDL cholesterol and DBP emerged. WQS analyses revealed a major contribution of PFNA and PFHxS for the cholesterol levels, although PFOA reported the highest concentration. PFOA and PFOS emerged as chemicals of concern regarding the association with SBP. CONCLUSIONS: The results showed a clear association between serum PFAS levels and markers of cardiovascular risk and support the importance of clinical surveillance of cardiovascular risk factors in population with a high exposure to PFAS, especially in the occupational setting.