Bayesian inference of chemical exposures from NHANES urine biomonitoring data

The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations

Since the initial release of the Chemical and Products Database (CPDat) in 2018, the United States Environmental Protection Agency has added a considerable amount of chemical exposure-related information to the database and has expanded its schema to accommodate new types of data. This data descriptor provides information regarding the structure and types of data contained within CPDat (both existing and new), new controlled vocabularies implemented to harmonize terminology across the different data types, application of a rigorous data curation and quality assurance tracking system, and various methods of accessing CPDat.

Biomonitoring-Based Risk Assessment of Pyrethroid Exposure in the U.S. Population: Application of High-Throughput and Physiologically Based Kinetic Models

Pyrethroid insecticides have been extensively utilized in agriculture and residential areas in the United States. This study evaluated the exposure risk by age using available biomonitoring data. We analyzed pyrethroid metabolite concentrations in urine using the National Health and Nutrition Examination Survey (NHANES) data. Reverse dosimetry was conducted with a high-throughput model and a physiologically based kinetic (PBK) model integrated with a Bayesian inference framework. We further derived Benchmark Dose (BMD) values and systemic points of departure in rats using Bayesian BMD and PBK models. Margins of exposure (MOE) were calculated to assess neurotoxic risk based on estimated daily oral intake and dose metrics in plasma and brain. Results from both models indicated that young children have higher pyrethroid exposure compared to other age groups. All estimated risk values were within acceptable levels of acute neurotoxic effect. Additionally, MOEs calculated from oral doses were lower than those derived from internal doses, highlighting that traditional external exposure assessments tend to overestimate risk compared to advanced internal dose-based techniques. In conclusion, combining high-throughput and PBK approaches enhances the understanding of human health risks associated with pyrethroid exposures, demonstrating their potential for future applications in exposure tracking and health risk assessment.

Assessing the impact of volatile organic compounds on cardiovascular health: Insights from the National Health and Nutrition Examination Survey 2011-2020

Volatile organic compounds (VOCs) are environmental pollutants that may negatively impact cardiovascular health. This study investigates the association between VOC mixtures, measured through urinary VOC metabolites (VOCMs), and cardiovascular health using Life's Essential 8 (LE8) scores. Data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 were analyzed for 2967 adults aged 20-79 years. Multiple statistical methods, including correlation analysis, variance inflation factor (VIF) analysis, quantile g-computation (q-gcomp), and Bayesian kernel machine regression (BKMR), were applied to assess the association between VOCMs and LE8 scores. Sensitivity analyses were conducted with different random seeds and subsampling techniques to confirm robustness. Correlation and VIF analyses revealed strong collinearity among VOCMs, highlighting the need for advanced models. Survey-weighted regression indicated that lower VOC exposure was associated with better cardiovascular health. Q-gcomp identified both positive and negative associations between individual VOCMs and LE8 scores, with some unexpected positive associations. BKMR highlighted the complex mixture effects of VOCMs on cardiovascular health. Sensitivity analyses confirmed the consistency of these findings. This study underscores the intricate relationship between VOC exposure and cardiovascular health and the necessity of advanced statistical methods for mixture analysis. Despite some unexpected findings, the results suggest that VOC exposure, as reflected by urinary VOCMs, is associated with adverse cardiovascular health outcomes. Further research is needed to clarify the biological mechanisms and implications of these associations.

The environmental neuroactive chemicals list of prioritized substances for human biomonitoring and neurotoxicity testing: A database and high-throughput toxicokinetics approach

There is a diversity of chemicals to which humans are potentially exposed. Few of these chemicals have linked human biomonitoring data, and most have very limited neurotoxicity testing. Of particular concern are environmental exposures impacting children, who constitute a population of heightened susceptibility due to rapid neural growth and plasticity, yet lack biomonitoring data compared to other age/population subgroups. This study set out to develop a prioritized list of neuroactive substances, titled the Environmental NeuRoactIve CHemicals (ENRICH) list, to be used as a defined screening library in the evaluation of human biological samples, with emphasis on early childhood-relevant environmental exposures. In silico database mining approaches were used to prioritize chemicals based upon likelihood of neuroactivity, human exposure, and feasible detection in biological samples. Evidence of neuroactivity was compiled across in vitro high-throughput screening, animal testing, and/or human epidemiological findings. Chemicals were considered for their likelihood of human exposure and detection presence in biological samples (including metabolites), with additional evidence indicating presence within child-relevant products. The resulting list of 1827 chemicals were ranked using a Chemical Prioritization Index. Manual inclusion/exclusion criteria were employed for the top-ranking chemical candidates to ensure that chemicals were within the study's scope (i.e., environmentally relevant) and, for the purposes of biomonitoring, had properties amenable to mass spectrometry methods. These elements were combined to produce the ENRICH list of 250 top-ranking chemicals, spanning pesticides and those used in home maintenance, personal care, cleaning products, vehicles, arts and crafts, and consumer electronics, among other sources. Chemicals were additionally evaluated for high-throughput toxicokinetics to predict how much of a chemical and/or its metabolite(s) may reach urine, as an example biological matrix for practical use in biomonitoring efforts. This novel study couples databases and in silico-based predictions to prioritize chemicals in the environment with potential neurological impacts for future study.

Discerning Emittable from Extractable Chemicals Identified in Consumer Products by Suspect Screening GCxGC-TOFMS

Characterization of chemicals in household products is important for understanding this potential source of chemical exposure. Increasingly, suspect screening and nontargeted analysis techniques are used to characterize as many chemical signatures as possible. Solids such as household products are most conveniently prepared using solvent extraction, revealing what chemicals are contained within the product matrix but providing no information about the potential of those chemicals to leave the matrix and cause actual exposure. In this work, the profile and relative abundances of "extractable" chemical signatures found after solvent extraction are compared to those "emittable" to the headspace for 81 household products analyzed by two-dimensional gas chromatography time-of-flight mass spectrometry. This study retrospectively fuses data collected in separate efforts over 3.8 years and 13 analytical batches. Management of the data is made possible by recent developments in processing systems for complex data such as Highlight. Compounds were generically classified as aromatic heteroatom, aromatic hydrocarbon, glycol, hydrocarbon, long chain heteroatom, nonaromatic heteroatom, and unknown/unclassified. Class-based retention time and abundance trends were observed. Liquid extraction resulted in the greatest number of features and the highest relative abundances, while low temperature emission conditions produced the smallest number of features and lowest relative abundances.

Pesticide exposure and asthma morbidity in children residing in urban, multi-family housing

BACKGROUND

Children are potentially more susceptible to the adverse effects of pesticides due to more sensitive organ systems and lower capacity to metabolize and eliminate chemicals compared to adults. The health risks are particularly concerning children with asthma, living in low-income neighborhoods in multi-family housing because of their impaired respiratory health, and factors associated with low-income, multi-family environments.

OBJECTIVE

To assess the association between pesticide exposure and asthma morbidity among children 7-12 years residing in low-income, multi-family housing.

METHODS

The concentrations of seven urinary pesticide biomarkers: 3,5,6-trichloro-2-pyridinol (TCPy), 2-isopropyl-4-methyl-6-hydroxypyrimidine, para-nitrophenol (PNP), 3-phenoxybenzoic acid (3-PBA), 4-fluoro-3-phenoxybenzoic acid, trans-3-(2,2-dichlorovinyl)-2,2-dimethyl-cyclopropane-1-carboxylic acid, and 2,4-dichlorophenoxyacetic acid (2,4-D) were measured. Children (n = 162) were followed for one year with three measures of pesticides biomarkers. Associations between individual biomarkers and asthma attack, asthma related health care utilization, and fraction of exhaled nitric oxide (FeNO), adjusting for demographic and household factors were examined with Generalized Estimating Equations (GEE). Weighted Quantile Sum (WQS) regression was used to examine the effect of pesticide mixture on asthma attacks and asthma-related health care utilization (HCU).

RESULTS

In adjusted GEE models, positive non-significant associations were found between PNP and HCU (adjusted Odds Ratio(aOR):2.05 95% CI:0.76-5.52) and null associations for 3-PBA and HCU (aOR:1.07 95% CI: 0.88-1.29). Higher concentrations of PNP and 2,4-D were associated with significantly lower FeNO levels (PNP: -17.4%; 2,4-D:-19.74%). The mixture was positively associated with HCU in unadjusted (OR: 1.56 97.5% CI: 1.08-2.27) but not significant in adjusted models (aOR: 1.40 97.5% CI: .86-2.29). The non-specific pyrethroid biomarker 3-PBA at baseline contributed the greatest weight to the index (45%).

SIGNIFICANCE

There were non-significant associations between pesticide biomarkers and respiratory outcomes in children with asthma. There was a suggestive association between urinary pesticide biomarkers and HCU. Further studies with larger sample sizes could help to confirm these findings.

IMPACT STATEMENT

Pesticide exposure among children in the urban environment is ubiquitous and there is a dearth of information on the impact of low-level chronic exposure in vulnerable populations. This study suggested that pesticide exposure at concentrations below the national average may not affect asthma morbidity in children. However, different biomarkers of pesticides showed different effects, but the mixture suggested increasing pesticide exposure results in asthma related HCU. The results may show that children with asthma may be at risk for negative health outcomes due to pesticides and the need to further examine this relationship.

Human biomonitoring and toxicokinetics as key building blocks for next generation risk assessment

Human health risk assessment is historically built upon animal testing, often following Organisation for Economic Co-operation and Development (OECD) test guidelines and exposure assessments. Using combinations of human relevant in vitro models, chemical analysis and computational (in silico) approaches bring advantages compared to animal studies. These include a greater focus on the human species and on molecular mechanisms and kinetics, identification of Adverse Outcome Pathways and downstream Key Events as well as the possibility of addressing susceptible populations and additional endpoints. Much of the advancement and progress made in the Next Generation Risk Assessment (NGRA) have been primarily focused on new approach methodologies (NAMs) and physiologically based kinetic (PBK) modelling without incorporating human biomonitoring (HBM). The integration of toxicokinetics (TK) and PBK modelling is an essential component of NGRA. PBK models are essential for describing in quantitative terms the TK processes with a focus on the effective dose at the expected target site. Furthermore, the need for PBK models is amplified by the increasing scientific and regulatory interest in aggregate and cumulative exposure as well as interactions of chemicals in mixtures. Since incorporating HBM data strengthens approaches and reduces uncertainties in risk assessment, here we elaborate on the integrated use of TK, PBK modelling and HBM in chemical risk assessment highlighting opportunities as well as challenges and limitations. Examples are provided where HBM and TK/PBK modelling can be used in both exposure assessment and hazard characterization shifting from external exposure and animal dose/response assays to animal-free, internal exposure-based NGRA.

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.

Characterizing the Contribution of Indoor Residential Phthalate and Phthalate Alternative Dust Concentrations to Internal Dose in the US General Population: An Updated Systematic Review and Meta-Analysis

Diet is the primary exposure pathway for phthalates, but relative contributions of other exposure sources are not well characterized. This study quantifies the relative contribution of indoor residential dust phthalate and phthalate alternative concentrations to total internal dose estimated from the National Health and Nutrition Examination Survey (NHANES) urinary metabolite concentrations. Specifically, median phthalate and phthalate alternative concentrations measured in residential dust were determined by updating a pre-existing systematic review and meta-analysis published in 2015 and the attributable internal dose was estimated using intake and reverse dosimetry models. Employing a predetermined search strategy, 12 studies published between January 2000 and April 2022 from Web of Science and PubMed measuring phthalates and phthalate alternatives in residential dust were identified. From the data extracted, it was estimated that dust contributed more significantly to the internal dose of low-molecular weight chemicals such as DEP and BBP when compared to high-molecular weight chemicals such as DEHTP. Additionally, findings showed that the chemical profile of residential dust is changing temporally with more phthalate alternatives being detected in the indoor environment. Future studies should seek to characterize the contribution of dust to an overall phthalate and phthalate alternative intake for individuals who have higher than normal exposures.

Impact of occupational pesticide exposure on the human gut microbiome

The rising use of pesticides in modern agriculture has led to a shift in disease burden in which exposure to these chemicals plays an increasingly important role. The human gut microbiome, which is partially responsible for the biotransformation of xenobiotics, is also known to promote biotransformation of environmental pollutants. Understanding the effects of occupational pesticide exposure on the gut microbiome can thus provide valuable insights into the mechanisms underlying the impact of pesticide exposure on health. Here we investigate the impact of occupational pesticide exposure on human gut microbiome composition in 7198 participants from the Dutch Microbiome Project of the Lifelines Study. We used job-exposure matrices in combination with occupational codes to retrieve categorical and cumulative estimates of occupational exposures to general pesticides, herbicides, insecticides and fungicides. Approximately 4% of our cohort was occupationally exposed to at least one class of pesticides, with predominant exposure to multiple pesticide classes. Most participants reported long-term employment, suggesting a cumulative profile of exposure. We demonstrate that contact with insecticides, fungicides and a general "all pesticides" class was consistently associated with changes in the gut microbiome, showing significant associations with decreased alpha diversity and a differing beta diversity. We also report changes in the abundance of 39 different bacterial taxa upon exposure to the different pesticide classes included in this study. Together, the extent of statistically relevant associations between gut microbial changes and pesticide exposure in our findings highlights the impact of these compounds on the human gut microbiome.

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.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

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.

Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.