A Method for Identifying Prevalent Chemical Combinations in the U.S. Population

The silent threat and countermeasures: Navigating the mixture risk of endocrine-disrupting chemicals on pregnancy loss in China

Currently, many countries and regions worldwide face the challenge of declining population growth due to persistently low rates of female reproduction. Since 2017, China's birth rate has hit historic lows and continued to decline, with the death rate now equaling the birth rate. Concerns have emerged regarding the potential impact of environmental contaminants on reproductive health, including pregnancy loss. Endocrine-disrupting chemicals (EDCs) like phthalate esters (PAEs), bisphenol A (BPA), triclosan (TCS), and perfluoroalkyl substances (PFASs) have raised attention due to their adverse effects on biological systems. While China's 14th Five-Year Plan (2021-2025) for national economic and social development included the treatment of emerging pollutants, including EDCs, there are currently no national appraisal standards or regulatory frameworks for EDCs and their mixtures. Addressing the risk of EDC mixtures is an urgent matter that needs consideration from China's perspective in the near future. In this Perspective, we delve into the link between EDC mixture exposure and pregnancy loss in China. Our focus areas include establishing a comprehensive national plan targeting reproductive-aged women across diverse urban and rural areas, understanding common EDC combinations in women and their surrounding environment, exploring the relationship between EDCs and pregnancy loss via epidemiology, and reconsidering the safety of EDCs, particularly in mixtures and low-dose scenarios. We envision that this study could aid in creating preventive strategies and interventions to alleviate potential risks induced by EDC exposure during pregnancy in China.

Screening for frequently detected quaternary ammonium mixture systems in waters based on frequent itemset mining and prediction of their toxicity

Screening and prioritizing research on frequently detected mixture systems in the environment is of great significance, as conducting toxicity testing on all mixtures is impractical. Therefore, the frequent itemset mining (FIM) was introduced and applied in this paper to identify variables that commonly co-occur in a dataset. Based on the dataset of the quaternary ammonium compounds (QACs) in the water environment, the four frequent QAC mixture systems with detection rate ≥ 35 % were found, including [BDMM]+Cl--[BTMM]+Cl- (M1), [BDMM]+Cl--[BHMM]+Cl- (M2), [BTMM]+Cl- -[BHMM]+Cl- (M3), and [BDMM]+Cl--[BTMM]+Cl--[BHMM]+Cl- (M4). [BDMM]+Cl-, [BTMM]+Cl-, and [BHMM]+Cl- are benzyl dodecyl dimethyl ammonium chloride, benzyl tetradecyl dimethyl ammonium chloride, and benzyl hexadecyl dimethyl ammonium chloride, respectively. Then, the toxicity of the representative mixture rays and components for the four frequently detected mixture systems was tested using Vibrio qinghaiensis sp.-Q67 (Q67) as a luminescent indicator organism at 0.25 and 12 h. The toxicity of the mixtures was predicted using concentration addition (CA) and independent action (IA) models. It was shown that both the components and the representative mixture rays for the four frequently detected mixture systems exhibited obvious acute and chronic toxicity to Q67, and their median effective concentrations (EC50) were below 7 mg/L. Both CA and IA models predicted the toxicity of the four mixture systems well. However, the CA model had a better predictive ability for the toxicity of the M3 and M4 mixtures than IA at 12 h.

Genetic and epigenetic modulations in toxicity: The two-sided roles of heavy metals and polycyclic aromatic hydrocarbons from the environment

This study emphasizes the importance of considering the metabolic and toxicity mechanisms of environmental concern chemicals in real-life exposure scenarios. Furthermore, environmental chemicals may require metabolic activation to become toxic, and competition for binding sites on receptors can affect the severity of toxicity. The multicomplex process of chemical toxicity is reflected in the activation of multiple pathways during toxicity of which AhR activation is major. Real-life exposure to a mixture of concern chemicals is common, and the composition of these chemicals determines the severity of toxicity. Nutritional essential elements can mitigate the toxicity of toxic heavy metals, while the types and ratio of composition of PAH can either increase or decrease toxicity. The epigenetic mechanisms of heavy metals and PAH toxicity involves either down-regulation or up-regulation of some non-coding RNAs (ncRNAs) whereas specific small RNAs (sRNAs) may have dual role depending on the tissue and circumstance of expression. Similarly, decrease DNA methylation and histone modification are major players in heavy metals and PAH mediated toxicity and FLT1 hypermethylation is a major process in PAH induced carcinogenesis. Overall, this review provides the understanding of the metabolism of environmental concern chemicals, emphasizing the importance of considering mixed compositions and real-life exposure scenarios in assessing their potential effects on human health and diseases development as well as the dual mechanism of toxicity via genetic or epigenetic axis.

Modeling PAH Mixture Interactions in a Human In Vitro Organotypic Respiratory Model

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air-liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples.

Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS

Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.

Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data

BACKGROUND

Xenobiotic metabolites are widely present in human urine and can indicate recent exposure to environmental chemicals. Proper inference of which chemicals contribute to these metabolites can inform human exposure and risk. Furthermore, longitudinal biomonitoring studies provide insight into how chemical exposures change over time.

OBJECTIVES

We constructed an exposure landscape for as many human-exposure relevant chemicals over as large a time span as possible to characterize exposure trends across demographic groups and chemical types.

METHODS

We analyzed urine data of nine 2-y cohorts (1999-2016) from the National Health and Nutrition Examination Survey (NHANES). Chemical daily intake rates (in milligrams per kilogram bodyweight per day) were inferred, using the R package bayesmarker, from metabolite concentrations in each cohort individually to identify exposure trends. Trends for metabolites and parents were clustered to find chemicals with similar exposure patterns. Exposure variation by age, gender, and body mass index were also assessed.

RESULTS

Intake rates for 179 parent chemicals were inferred from 151 metabolites (96 measured in five or more cohorts). Seventeen metabolites and 44 parent chemicals exhibited fold-changes ≥ 10 between any two cohorts (deltamethrin, di-n -octyl phthalate, and di-isononyl phthalate had the greatest exposure increases). Di-2-ethylhexyl phthalate intake began decreasing in 2007, whereas both di-isobutyl and di-isononyl phthalate began increasing shortly before. Intake for four parabens was markedly higher in females, especially reproductive-age females, compared with males and children. Cadmium and arsenobetaine exhibited higher exposure for individuals > 65 years of age and lower for individuals < 20 years of age.

DISCUSSION

With appropriate analysis, NHANES indicates trends in chemical exposures over the past two decades. Decreases in exposure are observable as the result of regulatory action, with some being accompanied by increases in replacement chemicals. Age- and gender-specific variations in exposure were observed for multiple chemicals. Continued estimation of demographic-specific exposures is needed to both monitor and identify potential vulnerable populations. https://doi.org/10.1289/EHP12188.

Comparative toxicology of syringe exchange and postmortem blood samples in the District of Columbia: Trends and affinity analysis

This cross-sectional analysis aimed to understand the similarities and differences between drugs detected in syringes collected from syringe service providers in the District of Columbia and fatal overdose deaths captured by the State Unintentional Drug Overdose Reporting System. Substance exposures for these fatal and non-fatal drug use outcomes have not been previously compared. Substance distributions were examined and a paired significance test was used to compare changes over time. Affinity analysis was employed to reveal substance co-occurrences. Between September 2020 and September 2022, 1,118 postmortem blood samples (PBSs) and 3,646 syringes exchange samples (SESs) were processed, with fatal overdoses increasing 24.1%. Polysubstance use was more commonly found in postmortem blood (82.5%) than in syringe samples (48.6%). Of samples containing opioids, 94.8% of blood samples and 86.3% of syringes contained fentanyl, fentanyl analogs or fentanyl precursors/metabolites. PBSs had double the frequency of co-occurring stimulants and opioids (43.9%) as SESs (21.8%). Major changes in occurrence frequency over time were found for opioid and stimulant exposure in both groups, especially in the increased occurrence of fluorofentanyl (>400%), methamphetamine (>90%) and xylazine (>60%), while the incidence of fentanyl, heroin and metabolite morphine declined. These results indicate that while fatal use and syringe exchange populations have distinct substance exposures, which may contribute to differences in mortality rate, their substance distributions have similar change magnitudes. This study highlights the utility of using multiple data sources to provide a comprehensive description of drug use patterns and discusses the limitations in reporting data from each source.

Wrangling Whole Mixtures Risk Assessment: Recent Advances in Determining Sufficient Similarity

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine "thresholds of similarity". Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput toxicokinetics and mixture toxicity modeling

Modern society continues to pollute the environment with larger quantities of chemicals that have also become more structurally and functionally diverse. Risk assessment of chemicals can hardly keep up with the sheer numbers that lead to complex mixtures of increasing chemical diversity including new chemicals, substitution products on top of still abundant legacy compounds. Fortunately, over the last years computational tools have helped us to identify and prioritize chemicals of concern. These include toxicokinetic models to predict exposure to chemicals as well as new approach methodologies such as in-vitro bioassays to address toxicodynamic effects. Combined, they allow for a prediction of mixtures and their respective effects and help overcome the lack of data we face for many chemicals. In this study we propose a high-throughput approach using experimental and predicted exposure, toxicokinetic and toxicodynamic data to simulate mixtures, to which a virtual population is exposed to and predict their mixture effects. The general workflow is adaptable for any type of toxicity, but we demonstrated its applicability with a case study on neurotoxicity. If no experimental data for neurotoxicity were available, we used baseline toxicity predictions as a surrogate. Baseline toxicity is the minimal toxicity any chemical has and might underestimate the true contribution to the mixture effect but many neurotoxicants are not by orders of magnitude more potent than baseline toxicity. Therefore, including baseline-toxic effects in mixture simulations yields a more realistic picture than excluding them in mixture simulations. This workflow did not only correctly identify and prioritize known chemicals of concern like benzothiazoles, organochlorine pesticides and plasticizers but we were also able to identify new potential neurotoxicants that we recommend to include in future biomonitoring studies and if found in humans, to also include in neurotoxicity screening.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics

BACKGROUND

Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking.

OBJECTIVE

This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk.

METHODS

The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources.

RESULTS

A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators.

SIGNIFICANCE

Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk.

IMPACT STATEMENT

Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics

BACKGROUND

Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking.

OBJECTIVE

This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk.

METHODS

The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources.

RESULTS

A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators.

SIGNIFICANCE

Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk.

IMPACT STATEMENT

Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

Principal Component Pursuit for Pattern Identification in Environmental Mixtures

BACKGROUND

Environmental health researchers often aim to identify sources or behaviors that give rise to potentially harmful environmental exposures.

OBJECTIVE

We adapted principal component pursuit (PCP)-a robust and well-established technique for dimensionality reduction in computer vision and signal processing-to identify patterns in environmental mixtures. PCP decomposes the exposure mixture into a low-rank matrix containing consistent patterns of exposure across pollutants and a sparse matrix isolating unique or extreme exposure events.

METHODS

We adapted PCP to accommodate nonnegative data, missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions < LOD and three noise structures. We applied PCP-LOD to evaluate its performance in comparison with principal component analysis (PCA). We next applied principal component pursuit with limit of detection (PCP-LOD) to an exposure mixture of 21 persistent organic pollutants (POPs) measured in 1,000 U.S. adults from the 2001-2002 National Health and Nutrition Examination Survey (NHANES). We applied singular value decomposition to the estimated low-rank matrix to characterize the patterns.

RESULTS

PCP-LOD recovered the true number of patterns through cross-validation for all simulations; based on an a priori specified criterion, PCA recovered the true number of patterns in 32% of simulations. PCP-LOD achieved lower relative predictive error than PCA for all simulated data sets with up to 50% of the data < LOD . When 75% of values were < LOD , PCP-LOD outperformed PCA only when noise was low. In the POP mixture, PCP-LOD identified a rank-three underlying structure and separated 6% of values as extreme events. One pattern represented comprehensive exposure to all POPs. The other patterns grouped chemicals based on known structure and toxicity.

DISCUSSION

PCP-LOD serves as a useful tool to express multidimensional exposures as consistent patterns that, if found to be related to adverse health, are amenable to targeted public health messaging. https://doi.org/10.1289/EHP10479.

Leveraging Multiple Data Streams for Prioritization of Mixtures for Hazard Characterization

There is a growing need to establish alternative approaches for mixture safety assessment of polycyclic aromatic hydrocarbons (PAHs). Due to limitations with current component-based approaches, and the lack of established methods for using whole mixtures, a promising alternative is to use sufficiently similar mixtures; although, an established framework is lacking. In this study, several approaches are explored to form sufficiently similar mixtures. Multiple data streams including environmental concentrations and empirically and predicted toxicity data for cancer and non-cancer endpoints were used to prioritize chemical components for mixture formations. Air samplers were analyzed for unsubstituted and alkylated PAHs. A synthetic mixture of identified PAHs was created (Creosote-Fire Mix). Existing toxicity values and chemical concentrations were incorporated to identify hazardous components in the Creosote-Fire Mix. Sufficiently similar mixtures of the Creosote-Fire Mix were formed based on (1) relative abundance; (2) toxicity values; and (3) a combination approach incorporating toxicity and abundance. Hazard characterization of these mixtures was performed using high-throughput screening in primary normal human bronchial epithelium (NHBE) and zebrafish. Differences in chemical composition and potency were observed between mixture formation approaches. The toxicity-based approach (Tox Mix) was the most potent mixture in both models. The combination approach (Weighted-Tox Mix) was determined to be the ideal approach due its ability to prioritize chemicals with high exposure and hazard potential.

A modular mechanistic framework for estimating exposure to SVOCs: Next steps for modeling emission and partitioning of plasticizers and PFAS

Estimates of human exposure to semi-volatile organic compounds (SVOCs) such as phthalates, phthalate alternatives, and some per- and polyfluoroalkyl substances (PFAS) are required for the risk-based evaluation of chemicals. Recently, a modular mechanistic modeling framework to rapidly predict SVOC emission and partitioning in indoor environments has been presented, in which several mechanistically consistent source emission categories (SECs) were identified. However, not all SECs have well-developed emission models. In addition, data on model parameters are missing even for frequently studied SVOCs. These knowledge gaps impede the comprehensive prediction of the fate of SVOCs indoors. In this paper, sets of high-priority phthalates, phthalate alternatives, and PFAS were identified based on chemical occurrence indoors and additional selection criteria. These high-priority chemicals served as the basis for exploring model parameter availability for existing indoor SVOC emission and partitioning models. The results reveal that additional experimental and modeling work is needed to fully understand the behavior of SVOCs indoors and to predict exposures with greater confidence and lower uncertainty. Modeling approaches to fill some of the identified gaps are proposed. The prioritized sets of chemicals and proposed new modeling approaches will help guide future research. The inclusion of polar phases in the framework will further expand its applicability and scope. IMPACT STATEMENT: This paper compiles data on high-priority chemicals commonly found indoors and information on the availability of applicable models and model parameters to predict emission, partitioning, and subsequent exposure to these chemicals. Modeling approaches for a selection of the missing SECs (source emission categories) are proposed, to illustrate the path forward. The comprehensive data set helps inform researchers, exposure assessors, and policy makers to better understand the state of the science regarding modeling of indoor exposure to semi-volatile organic compounds (SVOCs) and per- and polyfluoroalkyl substances (PFAS).

Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.

Integrating Selection and Risk Assessment of Chemical Mixtures: A Novel Approach Applied to a Breast Milk Survey

BACKGROUND

One of the main challenges of modern risk assessment is to account for combined exposure to the multitude of various substances present in food and the environment.

OBJECTIVE

The present work proposes a methodological approach to perform chemical risk assessment of contaminant mixtures across regulatory silos regarding an extensive range of substances and to do so when comprehensive relevant data concerning the specific effects and modes of action of the mixture components are not available.

METHODS

We developed a complete step-by-step approach using statistical methods to prioritize substances involved in combined exposure, and we used a component-based approach to cumulate the risk using dose additivity. The most relevant toxicological end point and the associated reference point were selected from the literature to construct a toxicological threshold for each substance.

DISCUSSION

By applying the proposed method to contaminants in breast milk, we observed that among the 19 substances comprising the selected mixture, ∑DDT, ∑PCBi, and arsenic were main joint contributors to the risk of neurodevelopmental and thyroid effects for infants. In addition, ∑PCCD/F contributed to the thyroid effect and ∑aldrin-dieldrin to the neurodevelopmental effect. Our case study on contaminants in breast milk demonstrated the importance of crossing regulatory silos when studying mixtures and the importance of identifying risk drivers to regulate the risk related to environmental contamination. Applying this method to another set of data, such as human biomonitoring or in ecotoxicology, will reinforce its relevance for risk assessment. https://doi.org/10.1289/EHP8262.

Application of Pattern Mining Methods to Assess Exposures to Multiple Airborne Chemical Agents in Two Large Occupational Exposure Databases from France

Surveys of the French working population estimate that approximately 15% of all workers may be exposed to at least three different chemical agents, but the most prevalent coexposure situations and their associated health risks remain relatively understudied. To characterize occupational coexposure situations in France, we extracted personal measurement data from COLCHIC and SCOLA, two large administrative occupation exposure databases. We selected 118 chemical agents that had ≥100 measurements with detected concentrations over the period 2010-2019, including 31 carcinogens (IARC groups 1, 2A, and 2B). We grouped measurements by work situations (WS, combination of sector, occupation, task, and year). We characterized the mixtures across WS using frequent itemset mining and association rules mining. The 275,213 measurements extracted came from 32,670 WS and encompassing 4692 unique mixtures. Workers in 32% of all WS were exposed to ≥2 agents (median 3 agents/WS) and 13% of all WS contained ≥2 carcinogens (median 2 carcinogens/WS). The most frequent coexposures were ethylbenzene-xylene (1550 WS), quartz-cristobalite (1417 WS), and toluene-xylene (1305 WS). Prevalent combinations of carcinogens also included hexavalent chromium-lead (368 WS) and benzene-ethylbenzene (314 WS). Wood dust (6% of WS exposed to at least one other agent) and asbestos (8%) had the least amount of WS coexposed with other agents. Tasks with the highest proportions of coexposure to carcinogens include electric arc welding (37% of WS with coexposure), polymerization and distillation (34%), and construction drilling and excavating (34%). Overall, the coexposure to multiple chemical agents, including carcinogens, was highly prevalent in the databases, and should be taken into account when assessing exposure risks in the workplace.

Mixture predicted no-effect concentrations derived by independent action model vs concentration addition model based on different species sensitivity distribution models

In the hazard assessment of mixtures, the mixture predicted no-effect concentration (mPNEC) is always derived by the concentration addition (CA) model (mPNEC_CA) to assess the risk of mixtures combined with exposure assessment. However, the independent action (IA) model, which is also widely used as the CA model in the prediction and evaluation of mixture toxicity, is always used to calculate the population fraction showing a predefined effect, not mPNEC, and this limits the application of IA model in the mixture risk assessment. In this study, we explored the process of mPNEC derived by the IA method (mPNEC_IA) based on the species sensitivity distribution (SSD) and compared mPNEC_IA with mPNEC_CA. Taking two common pesticides, dimethoate (DIM) and dichlorvos (DIC), exposed in the actual water environment as an example, their SSD models were constructed separately using nine distribution functions after toxicity data screening and quality testing. For both DIC and DIM, all different nine models had passed the Kolmogorov-Smirnov test. Then, the PNECs of two pesticides were derived based on SSD models. Finally, mPNEC_IA with different concentration ratios was derived and compared to mPNEC_CA based on 81 combinations of nine SSD models. Most mPNEC values derived by IA model were more conservative than those by CA. It is worth noting that the mPNEC_IA is more conservative than mPNEC_CA for the commonly used log-logit distribution (function 7), log-normal distribution (8), and log-Weibull distribution (9). This study provides a new direction for the application of IA in the risk assessment and enriches the framework of mixture risk assessment.

Guidance Document on Scientific criteria for grouping chemicals into assessment groups for human risk assessment of combined exposure to multiple chemicals

This guidance document provides harmonised and flexible methodologies to apply scientific criteria and prioritisation methods for grouping chemicals into assessment groups for human risk assessment of combined exposure to multiple chemicals. In the context of EFSA's risk assessments, the problem formulation step defines the chemicals to be assessed in the terms of reference usually through regulatory criteria often set by risk managers based on legislative requirements. Scientific criteria such as hazard-driven criteria can be used to group these chemicals into assessment groups. In this guidance document, a framework is proposed to apply hazard-driven criteria for grouping of chemicals into assessment groups using mechanistic information on toxicity as the gold standard where available (i.e. common mode of action or adverse outcome pathway) through a structured weight of evidence approach. However, when such mechanistic data are not available, grouping may be performed using a common adverse outcome. Toxicokinetic data can also be useful for grouping, particularly when metabolism information is available for a class of compounds and common toxicologically relevant metabolites are shared. In addition, prioritisation methods provide means to identify low-priority chemicals and reduce the number of chemicals in an assessment group. Prioritisation methods include combined risk-based approaches, risk-based approaches for single chemicals and exposure-driven approaches. Case studies have been provided to illustrate the practical application of hazard-driven criteria and the use of prioritisation methods for grouping of chemicals in assessment groups. Recommendations for future work are discussed.

Combined chemical exposure using exposure loads on human biomonitoring data of the 4th Flemish Environment and Health Study (FLEHS-4)

To improve our understanding of internal exposure to multiple chemicals, the concept exposure load (EL) was used on human biomonitoring (HBM) data of the 4th FLEHS (Flemish Environment and Health Study; 2016-2020). The investigated chemicals were per- and polyfluoroalkyl substances (PFASs), bisphenols, phthalates and alternative plasticizers, flame retardants, pesticides, toxic metals, organochlorine compounds and polycyclic aromatic hydrocarbons (PAHs). The EL calculates "the number of chemicals to which individuals are internally exposed above a predefined threshold". In this study, the 50th and 90th percentile of each of the 45 chemicals were applied as thresholds for the EL calculations for 387 study participants. Around 20% of the participants were exposed to >27 chemicals above the P50 and to >6 chemicals above the P90 level. This shows that participants can be internally exposed to multiple chemicals in relatively high concentrations. When the chemical composition of the EL was considered, the variability between individuals was driven by some chemicals more than others. The variability of the chemical profiles at high exposure loads (EL-P90) was somewhat dominated by e.g. organochlorine chemicals, PFASs, phthalates, PAHs, organophosphate flame retardants, bisphenols (A & F), pesticides, metals, but to a lesser extent by brominated flame retardants, the organophosphorus flame retardants TCIPP & TBOEP, naphthalene and benzene, bisphenols S, B & Z, the pesticide 2,4-D, the phthalate DEP and alternative plasticizer DINCH. Associations between the EL and exposure determinants suggested determinants formerly associated with fat soluble chemicals, PFASs, bisphenols, and PAHs. This information adds to the knowledge needed to reduce the exposure by policymakers and citizens. However, a more in depth study is necessary to explore in detail the causes for the higher EL in some individuals. Some limitations in the EL concept are that a binary number is used for exposure above or below a threshold, while toxicity and residence time in the body are not accounted for and the sequence of exposure in different life stages is unknown. However, EL is a first useful step to get more insight in multiple chemical exposure in higher exposed subpopulations (relative to the rest of the sampled population).

Data Mining Approaches for Assessing Chemical Coexposures Using Consumer Product Purchase Data

The use of consumer products presents a potential for chemical exposures to humans. Toxicity testing and exposure models are routinely employed to estimate risks from their use; however, a key challenge is the sparseness of information concerning who uses products and which products are used contemporaneously. Our goal was to demonstrate a method to infer use patterns by way of purchase data. We examined purchase patterns for three types of personal care products (cosmetics, hair care, and skin care) and two household care products (household cleaners and laundry supplies) using data from 60,000 households collected over a one-year period in 2012. The market basket analysis methodology frequent itemset mining (FIM) was used to identify co-occurring sets of product purchases for all households and demographic groups based on income, education, race/ethnicity, and family composition. Our methodology captured robust co-occurrence patterns for personal and household products, globally and for different demographic groups. FIM identified cosmetic co-occurrence patterns captured in prior surveys of cosmetic use, as well as a trend of increased diversity of cosmetic purchases as children mature to teenage years. We propose that consumer product purchase data can be mined to inform person-oriented use patterns for high-throughput chemical screening applications, for aggregate and combined chemical risk evaluations.

Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures

BACKGROUND

Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge.

OBJECTIVES

We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products.

METHODS

We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 households to identify chemical combinations resulting from co-use of consumer products.

RESULTS

We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Last, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways.

DISCUSSION

Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. https://doi.org/10.1289/EHP8610.

Parental preconception exposure to phenol and phthalate mixtures and the risk of preterm birth

BACKGROUND

Parental preconception exposure to select phenols and phthalates was previously associated with increased risk of preterm birth in single chemical analyses. However, the joint effect of phenol and phthalate mixtures on preterm birth is unknown.

METHODS

We included 384 female and 211 male (203 couples) participants seeking infertility treatment in the Environment and Reproductive Health (EARTH) Study who gave birth to 384 singleton infants between 2005 and 2018. Mean preconception urinary concentrations of bisphenol A (BPA), parabens, and eleven phthalate biomarkers, including di(2-ethylhexyl) phthalate (DEHP) metabolites, were examined. We used principal component analysis (PCA) with log-Poisson regression and Probit Bayesian Kernel Machine Regression (BKMR) with hierarchical variable selection to examine maternal and paternal phenol and phthalate mixtures in relation to preterm birth. Couple-based BKMR model was fit to assess couples' joint mixtures in relation to preterm birth.

RESULTS

PCA identified the same four factors for maternal and paternal preconception mixtures. Each unit increase in PCA scores of maternal (adjusted Risk Ratio (aRR): 1.36, 95%CI: 1.00, 1.84) and paternal (aRR: 1.47, 95%CI: 0.90, 2.42) preconception DEHP-BPA factor was positively associated with preterm birth. Maternal and paternal BKMR models consistently presented the DEHP-BPA factor with the highest group Posterior Inclusion Probability (PIP). BKMR models further showed that maternal preconception BPA and mono(2-ethyl-5-hydroxyhexyl) phthalate, and paternal preconception mono(2-ethylhexyl) phthalate were positively associated with preterm birth when the remaining mixture components were held at their median concentrations. Couple-based BKMR models showed a similar relative contribution of paternal (PIP: 61%) and maternal (PIP: 77%) preconception mixtures on preterm birth. We found a positive joint effect on preterm birth across increasing quantiles of couples' total mixture concentrations.

CONCLUSION

In this prospective cohort of subfertile couples, maternal BPA and DEHP, and paternal DEHP exposure before conception were positively associated with preterm birth. Both parental windows jointly contributed to the outcome. These results suggest that preterm birth may be a couple-based pregnancy outcome.

Exposure Load: Using biomonitoring data to quantify multi-chemical exposure burden in a population

People are often concurrently exposed to numerous chemicals. Here we sought to leverage existing large biomonitoring datasets to improve our understanding of multi-chemical exposures in a population. Using nationally-representative data from the 2012-2015 Canadian Health Measures Survey (CHMS), we developed Exposure Load, a metric that counts the number of chemicals measured in people above a defined concentration threshold. We calculated Exposure Loads based on five concentration thresholds: the analytical limit of detection (LOD) and the 50th, 75th, 90th and 95th percentiles. Our analysis considered 44 analyte biomarkers representing 26 chemicals from the 2012-2015 CHMS; complete biomarker data were available for 1858 participants aged 12-79 years following multiple imputation of results that were missing due to sample loss. Chemicals may have one or more biomarkers, and for the purposes of Exposure Load calculation, participants were considered to be exposed to a chemical if at least one biomarker was above the threshold. Distributions of Exposure Loads are reported for the total population, as well as by age group, sex and smoking status. Canadians had an Exposure Load between 9 and 21 (out of 26) when considering LOD as the threshold, with the majority between 13 and 18. At higher thresholds, such as the 95th percentile, the majority of Canadians had an Exposure Load between 0 and 3, although some people had an Exposure Load of up to 15, indicating high exposures to multiple chemicals. Adolescents aged 12-19 years had significantly lower Exposure Loads than adults aged 40-79 years at all thresholds and adults aged 20-39 years at the 50th and 75th percentiles. Smokers had significantly higher Exposure Loads than nonsmokers at all thresholds except the LOD, which was expected given that tobacco smoke is a known source of certain chemicals included in our analysis. No differences in Exposure Loads were observed between males and females at any threshold. These findings broadly suggest that Canadians are concurrently exposed to many chemicals at lower concentrations and to fewer chemicals at high concentrations. They should assist in identifying vulnerable subpopulations disproportionately exposed to numerous chemicals at high concentrations. Future work will use Exposure Loads to identify prevalent chemical combinations and their link with adverse health outcomes in the Canadian population. The Exposure Load concept can be applied to other large datasets, through collaborative efforts in human biomonitoring networks, in order to further improve our understanding of multiple chemical exposures in different populations.

Phthalate exposures and one-year change in body mass index across the menopausal transition

BACKGROUND

The menopausal transition is a hormonally sensitive period associated with changes in body weight. Phthalates are ubiquitous endocrine disrupting chemicals that could disrupt weight homeostasis, but it is unknown whether this occurs during the menopausal transition.

OBJECTIVES

Our objectives were to (1) determine if phthalate exposure in pre- and perimenopausal women was associated with one-year change in body mass index (BMI), and (2) determine if these associations differed across the menopausal transition.

METHODS

We addressed our objectives using data from 524 participants enrolled in the Midlife Women's Health Study. We calculated change in BMI from baseline to first follow-up visit approximately one year later. Phthalate exposures were approximated by measuring urinary metabolites in pools of two-to-four spot urine samples collected across a four-week period at baseline. We molar-converted and summed mono-(2-ethylhexyl) phthalate (mEHP), mono (2-ethyl-5-hydroxyhexyl) phthalate (mEHHP), mono (2-ethyl-5-oxohexyl) phthalate (mEOHP), and mono (2-ethyl-5-carboxypentyl) phthalate (mECPP) to approximate exposure to di-(2-ethylhexyl) phthalate (∑DEHP); ∑DEHP, mono (3-carboxypropyl) phthalate (mCPP), and monobenzyl phthalate (mBzP) to approximate exposure to plasticizer phthalates (∑Plastics); and monoethyl phthalate (mEP), monobutyl phthalate (mBP), and monoisobutyl phthalate (miBP) to approximate exposure to phthalates from personal care products (∑PCP). We used multivariable linear regression models to evaluate associations of specific gravity-adjusted ln-transformed phthalate metabolites or sums with one-year BMI change, and also considered whether associations differed depending on each woman's menopausal status change from baseline to first follow-up.

RESULTS

At baseline, most women were premenopausal (67.8%), non-Hispanic white (67.9%), and college educated (65.8%). Overall, urinary phthalate metabolites or sums were not associated with one-year BMI change. Stratified analysis identified positive associations between ∑DEHP (and three of its metabolites: MEHP, MEHHP, and MEOHP) and one-year BMI change among women who transitioned from peri-to post-menopause from baseline to first follow-up. For example, in these women, with each doubling of ∑DEHP, BMI increased by 0.65 kg/m² (95%CI: 0.17, 1.13) from baseline to first follow-up. Personal care product-associated phthalate metabolites (mBP and mEP) were negatively associated with one-year BMI change among women who remained perimenopausal from baseline to first follow-up, while miBP and mEP were positively associated with one-year BMI change among women who transitioned from peri-to post-menopause.

CONCLUSION

We found the strongest associations between some phthalates and one-year BMI change in women who transitioned from peri-to post-menopause from baseline to first follow-up. This supports previous evidence that the menopausal transition is a hormonally sensitive period in women's lives. To establish whether phthalate exposure contributes to body weight changes associated with the menopausal transition, substantially more research is needed to corroborate our findings.

Assessing the relation of chemical and non-chemical stressors with risk-taking related behavior and adaptive individual attributes among adolescents living near the New Bedford Harbor Superfund site

BACKGROUND

Early life exposure to neurotoxicants and non-chemical psychosocial stressors can impede development of prefrontal cortical functions that promote behavioral regulation and thereby may predispose to adolescent risk-taking related behaviors (e.g., substance use or high-risk sexual activity). This is particularly concerning for communities exposed to multiple stressors.

METHODS

This study examined the relation of exposure to mixtures of chemical stressors, non-chemical psychosocial stressors, and other risk factors with neuropsychological correlates of risk-taking. Specifically, we assessed psychometric measures of both adverse behavioral regulation and adaptive attributes among adolescents (age ∼ 15 years) in the New Bedford Cohort (NBC), a sociodemographically diverse cohort of 788 children born 1993-1998 to mothers residing near the New Bedford Harbor Superfund site. The NBC includes biomarkers of prenatal exposure to organochlorines and metals; sociodemographic, parental and home characteristics; and periodic neurodevelopmental assessments. We modelled exposure mixtures using multi-dimensional smooths within generalized additive models.

RESULTS

Children of younger mothers with lower IQ who were exposed prenatally to higher polychlorinated biphenyls and lead had poorer anger control. This pattern was not apparent for children of older mothers with higher IQs. Direction of associations between increased hyperactivity and prenatal levels of organochlorine mixtures differed by maternal age and depression symptoms. Higher cord blood Pb levels, in conjunction with poorer HOME scores, were associated with poorer self-esteem when mothers had fewer depression symptoms.

CONCLUSIONS

Analyses suggest that prenatal chemical exposures and non-chemical factors interact to contribute to neuropsychological correlates of risk-taking behaviors in adolescence. By simultaneously considering multiple factors associated with adverse behavioral regulation, we identified potential high-risk combinations that reflect both chemical and psychosocial stressors amenable to intervention.

BNNmix: A new approach for predicting the mixture toxicity of multiple components based on the back-propagation neural network

The chemical mixtures in various environmental media not only have concentration diversity but also mixture-ratio diversity. It is impossible to experimentally determine the toxicities of all mixtures; therefore, it is necessary to develop effective methods based on models to predict mixture toxicity. In this study, a new approach (BNNmix) based on the back-propagation neural network (BPNN) was developed and used to predict the toxicities of seven-component mixtures (consisting of two substituted phenols, two pesticides, two ionic liquids, and one heavy metal) on Caenorhabditis elegans. We found that the combined toxicities of various mixtures used in the experiments were neither global concentration-additive nor global response-additive, which implied that it was impossible to accurately predict the toxicities of such mixtures by using common models such as concentration addition (CA) and response addition (independent action, IA). Using the BNNmix approach to estimate or predict the toxicities of the mixtures under test, it was found that the predictive toxicities of various mixtures with different mixture ratios and concentrations were almost in accordance with those observed experimentally. Unlike the CA and IA models, the BNNmix approach can predict not only the toxicities of mixtures having toxicological interactions but also those with global concentration or response additivities.

Accounting for the uncertainty due to chemicals below the detection limit in mixture analysis

Simultaneous exposure to a mixture of chemicals over a lifetime may increase an individual's risk of disease to a greater extent than individual exposures. Researchers have used weighted quantile sum (WQS) regression to estimate the effect of multiple exposures in a manner that identifies the important (etiologically relevant) components in the mixture. However, complications arise when an experimental apparatus detects concentrations for each chemical with a different detection limit. Current strategies to account for values below the detection limit (BDL) in WQS include single imputation or placing the BDL values into the first quantile of the weighted index (BDLQ1), which do not fully capture the uncertainty in the data when estimating mixture effects. In response, we integrated WQS regression into the multiple imputation framework (MI-WQS). In a simulation study, we compared the BDLQ1 approach to MI-WQS when using either a Bayesian imputation or bootstrapping imputation approach over a range of BDL values. We examined the ability of each method to estimate the mixture's overall effect and to identify important chemicals. The results showed that as the number of BDL values increased, the accuracy, precision, model fit, and power declined for all imputation approaches. When chemical values were missing at 10%, 33%, or 50%, the MI approaches generally performed better than single imputation and BDLQ1. In the extreme case of 80% of all the chemical values were missing, the BDLQ1 approach was superior in some examined metrics.

Setting safer exposure limits for toxic substance combinations

Toxic stimuli (stressors) exposure limits are typically based on single toxic stimuli experiments, but are presently used for both toxic stimuli in isolation and in combination with other toxic stimuli (simultaneous co-exposure or exposures separated in time). In the combination case, typically less of each constituent of the combination is required to cause damage compared to the amount determined from single stressor experiments. Thus, exposure limits based on single toxic stimulus experiments are inadequate for setting limits for stressor combinations. This article presents a recommended simplified approach to improving regulatory exposure limits for toxic stimuli combinations, and a more expansive and expensive alternative to the recommended simplified approach. The recommended approach will partially compensate for the enhanced adverse effects of toxic stimuli combinations relative to adverse effects of toxic stimuli in isolation. The approach covers myriad categories of toxic stimuli reflective of real-life exposures due to lifestyle, iatrogenic, biotoxin, occupational/environmental, and psychosocial/socioeconomic conditions. The proposed approach 1) assumes that all potential toxic stimuli to which an individual might be exposed have the same mechanisms/modes of action on biological mechanisms, and are, thus, indistinguishable by the impacted organism; 2) normalizes the myriad stimuli by converting the doses of toxic stimuli exposures to the respective toxicity reference values (TRV) fractions; 3) sums all the TRVs fractions from these toxic stimuli exposures; and 4) divides all the single substance TRVs by the sum of fractions. While it is an additive approach conceptually, it differs from other additive approaches in the breadth of its inter-category coverage, in order to reflect true inter-category real-life simulation. The newly posited approach does not account for hormetic, antagonistic, or synergistic effects of toxic stimuli in combination. It does not adjust for 1) low-dose toxicants with adverse effects that have been under-reported, or 2) exposure limits like the Occupational Safety and Health Administration - Permissible Exposure Limits (OSHA PELs) that are orders of magnitude above levels shown by published single toxic stimuli studies to have caused adverse effects. Practical considerations for the application of this approach are presented.

High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA's Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (µM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

Occupational Co-exposures to Multiple Chemical Agents from Workplace Measurements by the US Occupational Safety and Health Administration

OBJECTIVES

The occupational environment represents an important source of exposures to multiplehazards for workers' health. Although it is recognized that mixtures of agents may have differenteffects on health compared to their individual effects, studies generally focus on the assessment ofindividual exposures. Our objective was to identify occupational co-exposures occurring in the United States using the multi-industry occupational exposure databank of the Occupational Safety and Health Administration (OSHA).

METHODS

Using OSHA's Integrated Management Information System (IMIS), measurement data from workplace inspections occurring from 1979 to 2015 were examined. We defined a workplace situation (WS) by grouping measurements that occurred within a company, within the same occupation (i.e. job title) within 1 year. All agents present in each WS were listed and the resulting databank was analyzed with the Spectrosome approach, a methodology inspired by network science, to determine global patterns of co-exposures. The presence of an agent in a WS was defined either as detected, or measured above 20% of a relevant occupational exposure limit (OEL).

RESULTS

Among the 334 648 detected exposure measurements of 105 distinct agents collected from 14 513 US companies, we identified 125 551 WSs, with 31% involving co-exposure. Fifty-eight agents were detected with others in >50% of WSs, 29 with a proportion >80%. Two clusters were highlighted, one for solvents and one for metals. Toluene, xylene, acetone, hexone, 2-butanone, and N-butyl acetate formed the basis of the solvent cluster. The main agents of the metal cluster were zinc, iron, lead, copper, manganese, nickel, cadmium, and chromium. 68 556 WS were included in the analyses based on levels of exposure above 20% of their OEL, with 12.4% of co-exposure. In this analysis, while the metal cluster remained, only the combinations of toluene with xylene or 2-butanone were frequently observed among solvents. An online web application allows the examination of industry specific patterns.

CONCLUSIONS

We identified frequent co-exposure situations in the IMIS databank. Using the spectrome approach, we revealed global combination patterns and the agents most often implicated. Future work should endeavor to explore the toxicological effects of prevalent combinations of exposures on workers' health to prioritize research and prevention efforts.

Multiple plasma metals, genetic risk and serum C-reactive protein: A metal-metal and gene-metal interaction study

Background

C-reactive protein (CRP) is a well-recognized biomarker of inflammation, which can be used as a predictor of cardiovascular disease. Evidence have suggested exposure to multiple metals/metalloids may affect immune system and give rise to cardiovascular disease. However, it is lack of study to comprehensively evaluate the association of multiple metals and CRP, the interactions between metals, and the gene-metal interaction in relation to CRP levels.

Aims

To explore the associations of multiple plasma metals with serum CRP, and to test the interactions between metals, and gene-metal interactions on the levels of serum CRP.

Methods

We included 2882 participants from the Dongfeng-Tongji cohort, China, and measured 23 plasma metals and serum CRP concentrations. The genetic risk score (GRS) was calculated based on 7 established CRP-associated variants. For metals which were associated with the levels of CRP, we further tested the interactions between metals on CRP, and analyzed the gene-metal interactions on CRP.

Results

The median level for CRP in the total population was 1.17 mg/L. After multivariable adjustment, plasma copper was positively associated with serum CRP (FDR < 0.001), whereas selenium was negatively associated with serum CRP (FDR = 0.01). Moreover, selenium and zinc attenuated the positive association between high plasma copper and CRP (P for interaction < 0.001). Participants with a higher GRS had a higher CRP level, with the increase in ln-transformed CRP per increment of 5 risk alleles were 0.64 for weighted GRS, and 0.54 for unweighted GRS (both P < 0.001). Furthermore, the genetic association with CRP was modified by copper concentration (P for interaction < 0.001).

Conclusions

Our results suggest that serum CRP is positively associated with plasma concentration of copper, and inversely associated with selenium. Plasma zinc, selenium and CRP genetic predisposition would modify the associations between plasma copper and serum CRP.

•

We found that serum CRP was positively associated with plasma copper, and inversely associated with selenium.

•

The positive association of plasma copper with serum CRP appeared to be attenuated with high plasma zinc and selenium.

•

This is the first study that explored the potential gene-metal interactions in relation to CRP levels.

•

These novel findings may provide new insights to personalized prevention and interventions for inflammation.

Assessing Human Exposure to Chemicals in Materials, Products and Articles: The International Risk Management Landscape for Phthalates

Risk-based chemical safety assessments are increasingly being conducted to support chemical management decisions and informed substitution to protect public health. Rapid evaluation and prioritization of large numbers of chemicals used in materials, products, and other indoor articles has become a major focus of chemical risk management strategies. Internationally, although a shared understanding of the value of rapid risk-based evaluations appears to be emerging, implementation strategies and associated management decisions vary from one agency and jurisdiction to another. This paper highlights the international chemical risk management landscape focusing on phthalates as an example, and reviews how phthalate exposure assessments have been performed, resulting at times in different decisions based on the application of scientific information within different policy contexts. In general, the need for efficient and effective risk-based assessment approaches is driving increased needs for high-quality exposure data and validated, mechanistic exposure models. Further development of mechanistic models and related parameters will reduce uncertainties in exposure estimates and support scientific risk-based evaluations of chemical/product combinations for a variety of decisions.

Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes

BACKGROUND

Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches.

OBJECTIVE

We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO.

METHODS

We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006-2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density.

RESULTS

From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses.

CONCLUSION

This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes.

Circulating Multiple Metals and Incident Stroke in Chinese Adults

Background and Purpose- Circulating metals synchronously reflect multiple metal exposures from both natural and anthropogenic sources, which may be linked with the risk of stroke. However, there is a lack of prospective studies investigating the associations of multiple metal exposures with incident stroke. Methods- We performed a nested case-control study within the ongoing Dongfeng-Tongji cohort launched in 2008. A total of 1304 incident stroke cases (1035 ischemic strokes and 269 hemorrhagic strokes) were prospectively identified by December 31, 2016, and matched to incident identity sampled controls according to age (within 1 year), sex, and blood sampling date (within 1 month). We determined the concentrations of 24 plasma metals and assessed the associations of plasma multiple metal concentrations with incident stroke using conditional logistic regression and elastic net model. Results- The average follow-up was 6.1 years. After adjusting for established risk confounders, copper, molybdenum, and titanium were significantly associated with higher risk of ischemic stroke (odds ratios according to per interquartile range increase, 1.29 [95% CI, 1.13-1.46], 1.19 [95% CI, 1.05-1.35], and 1.30 [95% CI, 1.07-1.59]), whereas rubidium and selenium were associated with lower risk of hemorrhagic stroke (odds ratios according to per interquartile range increase, 0.66 [95% CI, 0.50-0.87] and 0.68 [95% CI, 0.51-0.91]). The predictive plasma metal scores based on multiple metal exposures were significantly associated with higher risk of ischemic and hemorrhagic stroke (adjusted odds ratios according to per interquartile range increase, 1.37 [95% CI, 1.20-1.56] and 1.53 [95% CI, 1.16-2.01]). Conclusions- Plasma copper, molybdenum, and titanium were associated with higher risk of ischemic stroke, whereas plasma rubidium and selenium were associated with lower risk of hemorrhagic stroke. These findings may have important public health implications given the ever-increasing burden of stroke worldwide.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Long-Term Combinatorial Exposure to Trichloroethylene and Inorganic Arsenic in Genetically Heterogeneous Mice Results in Renal Tubular Damage and Cancer-Associated Molecular Changes

Trichloroethylene (TCE) and inorganic arsenic (iAs) are environmental contaminants that can target the kidney. Chronic exposure to TCE is associated with increased incidence of renal cell carcinoma, while co-exposure to TCE and iAs likely occurs in exposed human populations, such as those near Superfund sites. In order to better understand the kidney health consequences of TCE and/or iAs exposure, a genetically heterogeneous mouse population derived from FVB/NJ and CAST/EiJ mouse strains and deficient for multidrug resistance genes (Abcb1atm1Bor , Abcb1btm1Bor ) was chronically exposed for 52-weeks to varying concentrations of TCE and iAs. Although no exposure group resulted in primary renal cell tumors, kidneys from exposed mice did have significant increases in histologic and biochemical evidence of renal tubular disease with each toxicant alone and with combined exposure, with males having significantly higher levels of damage. Although no added increase in tubular disease was observed with combination exposure compared to single toxicants, molecular changes in kidneys from mice that had the combined exposure were similar to those previous observed in an embryonic stem cell assay for the P81S TCE-induced renal cell carcinoma mutation in the Von Hippel-Lindau syndrome (VHL) gene. While this model more accurately reflects human exposure conditions, development of primary renal tumors observed in humans following chronic TCE exposure was not reproduced even after inclusion of genetic heterogeneity and co-carcinogenic iAs.

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors

PURPOSE OF REVIEW

The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health?

RECENT FINDINGS

There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study, chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources-consumer (near-field), dietary, far-field industrial, and far-field pesticide-to the general population. The balanced accuracies of these source-based exposure pathway models range from 73 to 81%, with the error rate for identifying positive chemicals ranging from 17 to 36%. We then used exposure pathways to organize predictions from 13 different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R² of ∼0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479 926 chemicals, mostly with minimal exposure information. This approach identifies 1880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 μg/kg BW/day for 474572 compounds.

Co-exposure to metals and polycyclic aromatic hydrocarbons, microRNA expression, and early health damage in coke oven workers

BACKGROUND

All humans are now co-exposed to multiple toxic chemicals, among which metals and polycyclic aromatic hydrocarbons (PAHs) are of special concern as they are often present at high levels in various human environments. They can also induce similar early health damage, such as genetic damage, oxidative stress, and heart rate variability (HRV). Exposure to metals, PAHs, and their combined pollutants can alter microRNA (miRNA) expression patterns.

OBJECTIVES

To explore the associations of metal-PAH co-exposure with miRNA expression, and of the associated miRNAs with early health damage.

METHODS

We enrolled 360 healthy male coke oven workers and quantified their exposure levels of metals and PAHs by urinary metals, urinary monohydroxy-PAHs (OH-PAHs), and plasma benzo[a]pyrene-r-7,t-8,t-9,c-10-tetrahydotetrol-albumin (BPDE-Alb) adducts, respectively. We selected and measured ten miRNAs: let-7b-5p, miR-126-3p, miR-142-5p, miR-150-5p, miR-16-5p, miR-24-3p, miR-27a-3p, miR-28-5p, miR-320b, and miR-451a. For miRNAs influenced by the effect modification of metals or PAHs and/or metal-PAH interactions, we further evaluated their associations with biomarkers for genetic damage, oxidative stress, and HRV.

RESULTS

After adjusting for PAHs and other metals, miRNA expression was found to be negatively associated with aluminum, antimony, lead, and titanium, and positively associated with molybdenum and tin (p < 0.05). Antimony showed modifying effects on the PAH-miRNA associations, while OH-PAHs and BPDE-Alb adducts modified the associations of metals with miRNAs (p for modifying effect < 0.05). Furthermore, miRNA expression was influenced by the antagonistic interactions between antimony and OH-PAHs, and by the synergistical interactions between metals and BPDE-Alb adducts (p_interaction < 0.05). Let-7b-5p, miR-126-3p, miR-16-5p, and miR-320b were additionally found to be associated with increased genetic damage in the present study [false discovery rate (FDR)-adjusted p < 0.05].

CONCLUSIONS

Associations of metal-PAH co-exposure with miRNA expression, and of associated miRNAs with early health damage, suggested potential mechanistic connections between the complex metal-PAH interactions and their deleterious effects that are worthy of further investigation.

Biomarkers of exposure to SVOCs in children and their demographic associations: The TESIE Study

Semi-volatile organic compounds (SVOCs) are used extensively in consumer and personal care products; electronics; furniture; and building materials and are detected in most indoor environments. As a result, human exposure to mixtures of SVOCs is wide-spread. However, very few studies have measured biomarkers of exposure to multiple SVOC classes, and exposure determinants have not been thoroughly explored, particularly for young children. In this study, we investigated biomarkers of exposure to SVOCs among children (age 3-6 years), who may experience higher exposures and be more susceptible to adverse health outcomes than other age groups. We enrolled 203 participants in the Toddlers Exposure to SVOCs in Indoor Environments (TESIE) study (181 provided urine samples and 90 provided serum samples).We quantified 44 biomarkers of exposure to phthalates, organophosphate esters (OPEs), parabens, phenols, antibacterial agents and per- and polyfluoroalkyl substances (PFASs); we detected 29 of the 44 biomarkers in >95% of samples, and many biomarkers were detected at higher median concentrations than those previously reported in the U.S. general population. Demographic characteristics were associated with differences in concentrations. In general, non-Hispanic white race and higher maternal education were associated with lower concentrations, even after adjusting for other potential confounding variables. Our results suggest that outdoor temperature at the time of biospecimen collection may be a particularly important and under-evaluated predictor of biomarker concentrations; statistically significant relationships were observed between 10 biomarkers and outdoor temperature at the time of collection. A complex correlation structure was also observed among the biomarkers assessed. By and large, statistically significant correlations between biomarkers of exposure to phthalates, parabens, phenols, and OPEs were positive. Conversely, although PFASs were positively correlated with one another, they tended to be negatively correlated with other biomarkers where significant associations were observed. Taken together, our results provide evidence that the assessments of SVOC-associated health impacts should focus on chemical mixtures.

Profiles and Predictors of Environmental Chemical Mixture Exposure among Pregnant Women: The Health Outcomes and Measures of the Environment Study

Pregnant women are exposed to numerous environmental chemicals, but there is limited understanding of chemical mixture exposure profiles and predictors. In a prospective cohort of 389 pregnant women from Cincinnati, OH, we used biomarkers to estimate exposure to 41 phenols, phthalates, metals, organophosphate/pyrethroid/organochlorine pesticides, polychlorinated biphenyls, polybrominated diphenyl ethers, perfluoroalkyl substances, and environmental tobacco smoke. Using pairwise correlations, k-means clustering, and principal component analysis (PCA), we identified several profiles of chemical exposure. Chemicals within structurally, commercially, or industrially related chemical classes (e.g., phthalates) were moderate to strongly correlated compared to unrelated chemicals (e.g., pyrethroid pesticides and environmental tobacco smoke). Using k-means clustering and PCA, we identified 3 clusters of women ( N = 106, 158, and 125) and 6 PC scores, respectively, that characterized profiles of cumulative chemical exposure. The first two PC scores significantly varied by cluster, indicating that some of these profiles could be identified using both methods. Cluster membership and PCA scores were associated with race, marital status, consumption of fresh fruits and vegetables, and parity. Future work could use clusters and PCA scores to characterize environmental chemical mixture exposures in other cohorts of pregnant women and predict potential health effects of environmental chemical mixture exposure.