Toward Greater Implementation of the Exposome Research Paradigm within Environmental Epidemiology

Linked Exposures Across Databases: an exposure common data elements aggregation framework to facilitate clinical exposure review

Understanding the health outcomes of military exposures is of critical importance for Veterans, their health care team, and national leaders. Approximately 43% of Veterans report military exposure concerns to their VA providers. Understanding the causal influences of environmental exposures on health is a complex exposure science task and often requires interpreting multiple data sources; particularly when exposure pathways and multi-exposure interactions are ill-defined, as is the case for complex and emerging military service exposures. Thus, there is a need to standardize clinically meaningful exposure metrics from different data sources to guide clinicians and researchers with a consistent model for investigating and communicating exposure risk profiles. The Linked Exposures Across Databases (LEAD) framework provides a unifying model for characterizing exposures from different exposure databases with a focus on providing clinically relevant exposure metrics. Application of LEAD is demonstrated through comparison of different military exposure data sources: Veteran Military Occupational and Environmental Exposure Assessment Tool (VMOAT), Individual Longitudinal Exposure Record (ILER) database, and a military incident report database, the Explosive Ordnance Disposal Information Management System (EODIMS). This cohesive method for evaluating military exposures leverages established information with new sources of data and has the potential to influence how military exposure data is integrated into exposure health care and investigational models.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

BACKGROUND

The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways.

OBJECTIVES

The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics.

DISCUSSION

State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.

Community-level exposomics: a population-centered approach to address public health concerns

Environmental factors affecting health and vulnerability far outweigh genetics in accounting for disparities in health status and longevity in US communities. The concept of the exposome, the totality of exposure from conception onwards, provides a paradigm for researchers to investigate the complex role of the environment on the health of individuals. We propose a complementary framework, community-level exposomics, for population-level exposome assessment. The goal is to bring the exposome paradigm to research and practice on the health of populations, defined by various axes including geographic, social, and occupational. This framework includes the integration of community-level measures of the built, natural and social environments, environmental pollution-derived from conventional and community science approaches, internal markers of exposure that can be measured at the population-level and early responses associated with health status that can be tracked using population-based monitoring. Primary challenges to the implementation of the proposed framework include needed advancements in population-level measurement, lack of existing models with the capability to produce interpretable and actionable evidence and the ethical considerations of labeling geographically-bound populations by exposomic profiles. To address these challenges, we propose a set of recommendations that begin with greater engagement with and empowerment of affected communities and targeted investment in community-based solutions. Applications to urban settings and disaster epidemiology are discussed as examples for implementation.

Preconception exposure to environmental antibiotics among childbearing couples in Anhui and health risk assessment: A multicenter population-based representative study

Only few studies have assessed the health effects due to preconception exposure to antibiotics among childbearing couples. This study investigated the status of preconception exposure to antibiotics among childbearing couples in Anhui, associated with health risks, and influencing factors. Overall, 1500 childbearing couples were randomly selected from the Reproductive Health of Childbearing Couples - Anhui Cohort (RHCC-AC). The urinary levels of 40 antibiotics and 2 metabolites were determined, and specific gravity (SG) adjusted concentrations of antibiotics were measured to assess health risks. Generalized linear models were used to assess the associations of urinary SG-adjusted concentration of antibiotics with demographic parameters and diet frequency. The total detection rates of all antibiotics were 98.9 % and 99.3 % in wives and husbands, respectively. The detection rates of veterinary antibiotics (VAs) and preferred as VAs (PVAs) were above 90 %. Among eight antibiotics, sulfonamides (95.1 %) and fluoroquinolones (87.6 %) had the highest detection rates in couples. Approximately four-fifths of couples were simultaneously exposed to at least three different antibiotics, and more than half of them were exposed to low concentrations of antibiotics. 8.9 % and 9.2 % of wives and husbands had hazard index value of antibiotics exposure greater than 1. Antibiotic concentrations were associated with residence, sampling season, and diet frequency. In Anhui, nearly 98 % of childbearing couples have environmental exposure to antibiotics, and VAs and PVAs are the primary antibiotics. More than 8 % of couples had health risks due to antibiotic exposure. Several potential determinants of urinary antibiotics deserve more attention in future research.

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.

Oral and craniofacial research in the Generation R study: an executive summary

OBJECTIVES

Oral conditions are of high prevalence and chronic character within the general population. Identifying the risk factors and determinants of oral disease is important, not only to reduce the burden of oral diseases, but also to improve (equal access to) oral health care systems, and to develop effective oral health promotion programs. Longitudinal population-based (birth-)cohort studies are very suitable to study risk factors on common oral diseases and have the potential to emphasize the importance of a healthy start for oral health. In this paper, we provide an overview of the comprehensive oral and craniofacial dataset that has been collected in the Generation R study: a population-based prospective birth cohort in the Netherlands that was designed to identify causes of health from fetal life until adulthood.

METHODS

Within the multidisciplinary context of the Generation R study, oral and craniofacial data has been collected from the age of 3 years onwards, and continued at the age of six, nine, and thirteen. Data collection is continuing in 17-year-old participants.

RESEARCH OUTCOMES

In total, the cohort population comprised 9749 children at birth, and 7405 eligible participants at the age of seventeen. Based on questionnaires, the dataset contains information on oral hygiene, dental visits, oral habits, oral health-related quality of life, orthodontic treatment, and obstructive sleep apnea. Based on direct measurements, the dataset contains information on dental caries, developmental defects of enamel, objective orthodontic treatment need, dental development, craniofacial characteristics, mandibular cortical thickness, and 3D facial measurements.

CONCLUSIONS

Several research lines have been set up using the oral and craniofacial data linked with the extensive data collection that exists within the Generation R study.

CLINICAL RELEVANCE

Being embedded in a multidisciplinary and longitudinal birth cohort study allows researchers to study several determinants of oral and craniofacial health, and to provide answers and insight into unknown etiologies and oral health problems in the general population.

Reducing the complexity of high-dimensional environmental data: An analytical framework using LASSO with considerations of confounding for statistical inference

PURPOSE

Frameworks for selecting exposures in high-dimensional environmental datasets, while considering confounding, are lacking. We present a two-step approach for exposure selection with subsequent confounder adjustment for statistical inference.

METHODS

We measured cognitive ability in 338 children using the Woodcock-Muñoz General Intellectual Ability (GIA) score, and potential associated features across several environmental domains. Initially, 111 variables theoretically associated with GIA score were introduced into a Least Absolute Shrinkage and Selection Operator (LASSO) in a 50% feature selection subsample. Effect estimates for selected features were subsequently modeled in linear regressions in a 50% inference (hold out) subsample, first adjusting for sex and age and later for covariates selected via directed acyclic graphs (DAGs). All models were adjusted for clustering by school.

RESULTS

Of the 15 LASSO selected variables, eleven were not associated with GIA score following our inference modeling approach. Four variables were associated with GIA scores, including: serum ferritin adjusted for inflammation (inversely), mother's IQ (positively), father's education (positively), and hours per day the child works on homework (positively). Serum ferritin was not in the expected direction.

CONCLUSIONS

Our two-step approach moves high-dimensional feature selection a step further by incorporating DAG-based confounder adjustment for statistical inference.

Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior

Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.

Silicone wristbands as personal passive sampling devices: Current knowledge, recommendations for use, and future directions

Personal chemical exposure assessment is necessary to determine the frequency and magnitude of individual chemical exposures, especially since chemicals present in everyday environments may lead to adverse health outcomes. In the last decade, silicone wristbands have emerged as a new chemical exposure assessment tool and have since been utilized for assessing personal exposure to a wide range of chemicals in a variety of populations. Silicone wristbands can be powerful tools for quantifying personal exposure to chemical mixtures in a single sample, associating exposure with health outcomes, and potentially overcoming some of the challenges associated with quantifying the chemical exposome. However, as their popularity grows, it is crucial that they are used in the appropriate context and within the limits of the technology. This review serves as a guide for researchers interested in utilizing silicone wristbands as a personal exposure assessment tool. Along with briefly discussing the passive sampling theory behind silicone wristbands, this review performs an in-depth comparison of wristbands to other common exposure assessment tools, including biomarkers of exposure measured in biospecimens, and evaluates their utility in exposure assessments and epidemiological studies. Finally, this review includes recommendations for utilizing silicone wristbands to evaluate personal chemical exposure and provides suggestions on what research is needed to recognize silicone wristbands as a premier chemical exposure assessment tool.

Next-generation biomonitoring of the early-life chemical exposome in neonatal and infant development

Exposure to synthetic and natural chemicals is a major environmental risk factor in the etiology of many chronic diseases. Investigating complex co-exposures is necessary for a holistic assessment in exposome-wide association studies. In this work, a sensitive liquid chromatography-tandem mass spectrometry approach was developed and validated. The assay enables the analysis of more than 80 highly-diverse xenobiotics in urine, serum/plasma, and breast milk; with detection limits generally in the pg-ng mL-1 range. In plasma of extremely-premature infants, 27 xenobiotics are identified; including contamination with plasticizers, perfluorinated alkylated substances and parabens. In breast milk samples collected longitudinally over the first 211 days post-partum, 29 analytes are detected, including pyrrolizidine- and tropane alkaloids which have not been identified in this matrix before. A preliminary estimation of daily toxicant intake via breast milk is conducted. In conclusion, we observe significant early-life co-exposure to multiple toxicants, and demonstrate the method's applicability for large-scale exposomics-type cohort studies.

Prenatal exposure to neurotoxic metals and micronutrients and neurodevelopmental outcomes in early school age children from Poland

Exposure to environmental factors, such as neurotoxic metals and micronutrients, during critical periods of development can contribute to long-term consequences in offspring's health, including neurodevelopmental outcomes. The aim of this study was to evaluate the association between simultaneous prenatal exposure to metals [lead (Pb), cadmium (Cd), mercury (Hg)] and micronutrients [selenium (Se), zinc (Zn), copper (Cu)] and neurodevelopmental outcomes in school-age children from the Polish Mother and Child Cohort (REPRO_PL). Metals and micronutrients concentrations were measured in cord blood (Pb, Cd, Se, Zn, Cu) and in maternal hair (Hg) collected during the 3rd trimester of pregnancy. Behavioral and emotional problems, as well as children's cognitive and psychomotor development, were assessed in 436 school-age children using the Strengths and Difficulties Questionnaire (SDQ, filled in by the mothers) and the Polish adaptation of the Intelligence and Development Scales (IDS, administered by trained psychologists). Multivariate regression models were applied after imputation of missing values, using two approaches: (i) a joint analysis taking into account all metals and micronutrients simultaneously, and (ii) an ExWAS study (single-exposure model). In the SDQ, Hyperactivity/Inattention problems and Total difficulties were associated with higher Hg concentrations in maternal hair (0.18, 95% CI: 0.05; 0.3; and 0.14, 95% CI: 0.01; 0.3, respectively), whereas Emotional symptoms were inversely associated with Se and Zn levels in cord blood (-0.13, 95% CI: -0.3; 0.004; and -0.10, 95% CI: -0.2; 0.02, respectively). In the IDS, cord blood Pb levels were found to be negatively associated with Fluid and Crystallized IQ (-0.12, 95% CI: -0.3; 0.02; and -0.14, 95% CI: -0.3; 0.007, respectively) as well as Mathematical skills (-0.15, 95% CI: -0.3; 0.01). The current research has been able to simultaneously assess the exposure to various interacting chemicals during the prenatal period. We demonstrate that prenatal co-exposures to Pb, Hg, Zn and Se have long-term influences on the neuropsychological outcome of school-age children.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

Modeling environment through a general exposome factor in two independent adolescent cohorts

Exposures to perinatal, familial, social, and physical environmental stimuli can have substantial effects on human development. We aimed to generate a single measure that capture's the complex network structure of the environment (ie, exposome) using multi-level data (participant's report, parent report, and geocoded measures) of environmental exposures (primarily from the psychosocial environment) in two independent adolescent cohorts: The Adolescent Brain Cognitive Development Study (ABCD Study, N = 11 235; mean age, 10.9 years; 47.7% females) and an age- and sex-matched sample from the Philadelphia Neurodevelopmental Cohort (PNC, N = 4993). We conducted a series of data-driven iterative factor analyses and bifactor modeling in the ABCD Study, reducing dimensionality from 348 variables tapping to environment to six orthogonal exposome subfactors and a general (adverse) exposome factor. The general exposome factor was associated with overall psychopathology (B = 0.28, 95% CI, 0.26-0.3) and key health-related outcomes: obesity (odds ratio [OR] , 1.4; 95% CI, 1.3-1.5) and advanced pubertal development (OR, 1.3; 95% CI, 1.2-1.5). A similar approach in PNC reduced dimensionality of environment from 29 variables to 4 exposome subfactors and a general exposome factor. PNC analyses yielded consistent associations of the general exposome factor with psychopathology (B = 0.15; 95% CI, 0.13-0.17), obesity (OR, 1.4; 95% CI, 1.3-1.6), and advanced pubertal development (OR, 1.3; 95% CI, 1-1.6). In both cohorts, inclusion of exposome factors greatly increased variance explained in overall psychopathology compared with models relying solely on demographics and parental education (from <4% to >38% in ABCD; from <4% to >18.5% in PNC). Findings suggest that a general exposome factor capturing multi-level environmental exposures can be derived and can consistently explain variance in youth's mental and general health.

Investigating Health Context: Using Geospatial Big Data Ecosystem (Preprint)

Background

Objective

Methods

Results

Conclusions

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Estimating monthly concentrations of ambient key air pollutants in Japan during 2010-2015 for a national-scale birth cohort

Exposure to ambient air pollution is associated with maternal and child health. Some air pollutants exhibit similar behavior in the atmosphere, and some interact with each other; thus, comprehensive assessments of individual air pollutants are required. In this study, we developed national-scale monthly models for six air pollutants (NO, NO₂, SO₂, O₃, PM_2.5, and suspended particulate matter (SPM)) to obtain accurate estimates of pollutant concentrations at 1 km × 1 km resolution from 2010 through 2015 for application to the Japan Environment and Children's Study (JECS), which is a large-scale birth cohort study. We developed our models in the land use regression framework using random forests in conjunction with kriging. We evaluated the model performance via 5-fold location-based cross-validation. We successfully predicted monthly NO (r² = 0.65), NO₂ (r² = 0.84), O₃ (r² = 0.86), PM_2.5 (r² = 0.79), and SPM (r² = 0.64) concentrations. For SO₂, a satisfactory model could not be developed (r² = 0.45) because of the low SO₂ concentrations in Japan. The performance of our models is comparable to those reported in previous studies at similar temporal and spatial scales. The model predictions in conjunction with the JECS will reveal the critical windows of prenatal and infancy exposure to ambient air pollutants, thus contributing to the development of environmental policies on air pollution.

Advancements of Xenobiotic Toxicity Screening for the Advancement of Human Health

"The TOXI Chemical Exposures and Impact on Health" session at the 2020 Fall ACS meeting presented analytical and biological approaches, advancing our understanding of legacy and emerging chemical pollutants and their impact on human health.

Interdisciplinary data science to advance environmental health research and improve birth outcomes

Rates of preterm birth and low birthweight continue to rise in the United States and pose a significant public health problem. Although a variety of environmental exposures are known to contribute to these and other adverse birth outcomes, there has been a limited success in developing policies to prevent these outcomes. A better characterization of the complexities between multiple exposures and their biological responses can provide the evidence needed to inform public health policy and strengthen preventative population-level interventions. In order to achieve this, we encourage the establishment of an interdisciplinary data science framework that integrates epidemiology, toxicology and bioinformatics with biomarker-based research to better define how population-level exposures contribute to these adverse birth outcomes. The proposed interdisciplinary research framework would 1) facilitate data-driven analyses using existing data from health registries and environmental monitoring programs; 2) develop novel algorithms with the ability to predict which exposures are driving, in this case, adverse birth outcomes in the context of simultaneous exposures; and 3) refine biomarker-based research, ultimately leading to new policies and interventions to reduce the incidence of adverse birth outcomes.

Exposure to heavy metals from point pollution sources and risk of incident type 2 diabetes among women: a prospective cohort analysis

Heavy metal exposures may contribute to diabetes risk but prospective studies are uncommon. We analyzed the Australian Longitudinal Study on Women's Health (three cohorts aged 18-23, 45-50, or 70-75 at baseline in 1996, N = 34,191) merged with emissions data for 10 heavy metals (As, Be, Co, Cr, Cu, Hg, Mn, Ni, Pb, Zn) from the National Pollutant Inventory. Over 20-year follow-up, 2,584 women (7.6%) reported incident diabetes. Cox proportional hazards regression models showed that women aged 45-50 at baseline had higher diabetes risk in association with exposure to total air emissions, total water emissions, all individual metals air emissions, and six individual water emissions. After correction for false discovery rate, nine of 11 air emissions and five water emissions remained significant. Associations were not observed for land-based emissions, or for younger or older cohorts. Emissions were dominated by mining, electricity generation and other metals-related industrial processes.

Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics

Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.

Unravelling the chemical exposome in cohort studies: routes explored and steps to become comprehensive

Environmental factors contribute to the risk for adverse health outcomes against a background of genetic predisposition. Among these factors, chemical exposures may substantially contribute to disease risk and adverse outcomes. In fact, epidemiological cohort studies have established associations between exposure against individual chemicals and adverse health effects. Yet, in daily life individuals are exposed to complex mixtures in varying compositions. To capture the totality of environmental exposures the concept of the exposome has been developed. Here, we undertake an overview of major exposome projects, which pioneered the field of exposomics and explored the links between chemical exposure and health outcomes using cohort studies. We seek to reflect their achievements with regard to (i) capturing a comprehensive picture of the environmental chemical exposome, (ii) aggregating internal exposures using chemical and bioanalytical means of detection, and (iii) identifying associations that provide novel options for risk assessment and intervention. Various complementary approaches can be distinguished in addressing relevant exposure routes and it emerges that individual exposure histories may not easily be grouped. The number of chemicals for which human exposure can be detected is substantial and highlights the reality of mixture exposures. Yet, to a large extent it depends on targeted chemical analysis with the specific challenges to capture all relevant exposure routes and assess the chemical concentrations occurring in humans. The currently used approaches imply prior knowledge or hypotheses about relevant exposures. Typically, the number of chemicals considered in exposome projects is counted in dozens-in contrast to the several thousands of chemicals for which occurrence have been reported in human serum and urine. Furthermore, health outcomes are often still compared to single chemicals only. Moreover, explicit consideration of mixture effects and the interrelations between different outcomes to support causal relationships and identify risk drivers in complex mixtures remain underdeveloped and call for specifically designed exposome-cohort studies.

Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

PURPOSE OF REVIEW

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

RECENT FINDINGS

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.

Advancing health equity and access using telemedicine: a geospatial assessment

INTRODUCTION

Health disparity affects both urban and rural residents, with evidence showing that rural residents have significantly lower health status than urban residents. Health equity is the commitment to reducing disparities in health and in its determinants, including social determinants.

OBJECTIVE

This article evaluates the reach and context of a virtual urgent care (VUC) program on health equity and accessibility with a focus on the rural underserved population.

MATERIALS AND METHODS

We studied a total of 5343 patient activation records and 2195 unique encounters collected from a VUC during the first 4 quarters of operation. Zip codes served as the analysis unit and geospatial analysis and informatics quantified the results.

RESULTS

The reach and context were assessed using a mean accumulated score based on 11 health equity and accessibility determinants calculated for each zip code. Results were compared among VUC users, North Carolina (NC), rural NC, and urban NC averages.

CONCLUSIONS

The study concluded that patients facing inequities from rural areas were enabled better healthcare access by utilizing the VUC. Through geospatial analysis, recommendations are outlined to help improve healthcare access to rural underserved populations.

What about the environment? Leveraging multi-omic datasets to characterize the environment's role in human health

The environment plays an important role in mediating human health. In this session we consider research addressing ways to overcome the challenges associated with studying the multifaceted and ever-changing environment. Environmental health research has a need for technological and methodological advances which will further our knowledge of how exposures precipitate complex phenotypes and exacerbate disease.

Perceived Risks, Benefits, and Interest in Participating in Environmental Health Studies That Share Personal Exposure Data: A U.S. Survey of Prospective Participants

Little is known about the willingness of prospective study participants to share environmental health data. To fill this gap, we conducted a hypothetical vignette survey among 1,575 women who have volunteered to be contacted about breast cancer studies. Eighty-three percent were interested in participating in the environmental studies, with little difference whether data were restricted to the research team, shared with approved researchers, or publicly accessible. However, participants somewhat preferred controlled access for children's data. Respondents were more interested in studies with environmental rather than biological samples and more interested when researchers would return personal results, a practice of increasing importance. They were more reluctant to share location or to participate if studies involved electronic medical records. Many expressed concerns about privacy, particularly security breaches, but reidentification risks were mentioned infrequently, indicating that this topic should be discussed during informed consent.

Integrating Intersectionality Into the Exposome Paradigm: A Novel Approach to Racial Inequities in Uterine Fibroids

Intersectionality is a critical theoretical framework that emphasizes the influence of intersecting systems of oppression on the lived experiences of people marginalized by inequity. Although applications of intersectionality are increasing in public health, this framework is absent in environmental health, which has instead focused on the exposome, a paradigm that considers the totality of an individual's environmental exposures across the life course.Despite advancements in the biological complexity of exposome models, they continue to fall short in addressing health inequities. Therefore, we highlight the need for integrating intersectionality into the exposome. We introduce key concepts and tools for environmental health scientists interested in operationalizing intersectionality in exposome studies and discuss examples of this innovative approach from our work on racial inequities in uterine fibroids.Our case studies illustrate how interlocking systems of racism and sexism may affect Black women's exposure to environmental chemicals, their epigenetic regulation of uterine fibroids, and their clinical care. Because health relies on biological and social-structural determinants and varies across different intersectional positions, our proposed framework may be a promising approach for understanding environmental health inequities and furthering social justice.

Advances in Comprehensive Exposure Assessment: Opportunities for the US Military

OBJECTIVE

Review advances in exposure assessment offered by the exposome concept and new -omics and sensor technologies.

METHODS

Narrative review of advances, including current efforts and potential future applications by the US military.

RESULTS

Exposure assessment methods from both bottom-up and top-down exposomics approaches are advancing at a rapid pace, and the US military is engaged in developing both approaches. Top-down approaches employ various -omics technologies to identify biomarkers of internal exposure and biological effect. Bottom-up approaches use new sensor technology to better measure external dose. Key challenges of both approaches are largely centered around how to integrate, analyze, and interpret large datasets that are multidimensional and disparate.

CONCLUSIONS

Advances in -omics and sensor technologies may dramatically enhance exposure assessment and improve our ability to characterize health risks related to occupational and environmental exposures, including for the US military.

Exposomal research in the context of birth cohorts: What have they taught us?

OBJECTIVE

To review birth cohorts with an exposomal approach and their key outcomes and challenges. Exposome encompasses all human environmental exposures from conception onward. The impact of environmental exposures is greatest in critical stages of life, including fetal and early childhood. Birth cohorts provide a good study setting to assess exposome in the sensitive periods of life. Here, we review birth cohorts with an exposomal approach and their key outcomes and challenges.

DATA SOURCES

MEDLINE was searched for birth cohorts that have used an exposomal approach.

STUDY SELECTIONS

Relevant studies in English language were selected and reviewed.

RESULTS

The outcomes of birth cohorts with an exposomal approach improve our understanding of the association between environmental exposures and childhood diseases. For example, results from The Canadian Healthy Infant Longitudinal Development Study suggest an association between first trimester exposure to traffic-related air pollution and increased risk of allergic sensitization at 12 months of age (P = .6). In a smaller Canadian birth cohort study, it was found that regular use of air fresheners (adjusted P = .04) and presence of mold in the residence (adjusted P < .001) were associated with early childhood wheezing and cough. The application of emerging molecular omics technologies and new analytical tools has facilitated the comprehensive assessment of exposome in birth cohorts.

CONCLUSION

Birth cohort studies with an exposomal approach improve our understanding of the origin of childhood diseases by examining a complex network of environmental exposures during pregnancy and years beyond birth. International collaboration is required to develop large birth cohorts for better and more extensive assessment of exposome with standardized protocols and new statistical frameworks.

Patterns and Variability of Endocrine-disrupting Chemicals During Pregnancy: Implications for Understanding the Exposome of Normal Pregnancy

BACKGROUND

The exposome is a novel research paradigm offering promise for understanding the complexity of human exposures, including endocrine-disrupting chemicals (EDCs) and pregnancy outcomes. The physiologically active state of pregnancy requires understanding temporal changes in EDCs to better inform the application of the exposome research paradigm and serve as the impetus for study.

METHODS

We randomly selected 50 healthy pregnant women with uncomplicated pregnancies from a pregnancy cohort who had available serum/urine samples in each trimester for measuring 144 persistent and 48 nonpersistent EDCs. We used unsupervised machine-learning techniques capable of handling hierarchical clustering of exposures to identify EDC patterns across pregnancy, and linear mixed-effects modeling with false-discovery rate correction to identify those that change over pregnancy trimesters. We estimated the percent variation in chemical concentrations accounted for by time (pregnancy trimester) using Akaike Information Criterion-based R methods.

RESULTS

Four chemical clusters comprising 80 compounds, of which six consistently increased, 63 consistently decreased, and 11 reflected inconsistent patterns over pregnancy. Overall, concentrations tended to decrease over pregnancy for persistent EDCs; a reverse pattern was seen for many nonpersistent chemicals. Explained variance was highest for five persistent chemicals: polybrominated diphenyl ethers #191 (51%) and #126 (47%), hexachlorobenzene (46%), p,p'-dichloro-diphenyl-dichloroethylene (46%), and o,p'-dichloro-diphenyl-dichloroethane (36%).

CONCLUSIONS

Concentrations of many EDCs are not stable across pregnancy and reflect varying patterns depending on their persistency underscoring the importance of timed biospecimen collection. Analytic techniques are available for assessing temporal patterns of EDCs during pregnancy apart from physiologic changes.

What Is New about the Exposome? Exploring Scientific Change in Contemporary Epidemiology

In this commentary, I discuss the scientific changes brought by the exposome, asking what is new about this approach and line of research. I place the exposome in a historical perspective, by analyzing the conditions under which the exposome has been conceived, developed and established in the context of contemporary epidemiological research. I argue that the exposome has been developed by transferring approaches, methods and conceptualizations from other lines of research in the life and health sciences. I thus discuss the conceptual and methodological innovations of the exposome as a result of the merging and adaptation of these elements for new uses and purposes. On this basis, I argue that the novelty of the exposome should be seen in incremental rather than revolutionary terms and, in this sense, the exposome shares significant elements with other projects and repertoires in postgenomics. I conclude by discussing the consequences of this analysis for the potential limitations and future development of exposome research.

Pregnancy exposome and child psychomotor development in three European birth cohorts

Characterization of the exposome, the totality of all environmental factors that one is exposed to from conception onwards, has been recommended to better evaluate the role of environmental influences on developmental programming and life-course vulnerability to major chronic diseases. In the framework of the Health and Environment-wide Associations based on Large population Surveys (HEALS) project we considered the pregnancy exposome exploiting two databases (PHIME and REPRO_PL) that include birth cohorts from three EU countries (Croatia, Slovenia and Poland). The databases contained information on several chemical exposures, socio-demographic, lifestyle and health related factors from conception to child birth, and neuropsychological scores assessed by the Bayley Scales of Infant and Toddler Development in the first two years of life. Our main goal was to assess consistency of environmental influences on neurodevelopment, if any, across European countries differing for geographical, socio-demographic characteristics and levels of chemical exposures to metals such as lead (Pb), mercury (Hg), cadmium (Cd) and trace elements, including micronutrients such as zinc (Zn) and selenium (Se). To this aim, we first selected variables common to the different databases, then applied univariate and multivariate regression analyses to identify factors linked to neurodevelopment, and finally performed meta-analysis to detect potential heterogeneity among cohorts and pooled estimates. Significant differences in exposure levels among the three sub-cohorts were observed as for Hg and Se; exposure levels under study were relatively low and within the range described in existing EU biomonitoring studies. The univariate analyses did not show any common pattern of association as only in the Polish cohort chemical exposure had an impact on neuropsychological outcome. In the meta-analysis, some consistent trends were evident, relative to the adverse influence of Pb on children's language and cognition and the positive influence of Se on language abilities. The effects of the neurotoxic metal Hg positively influenced the motor scores in the Polish cohorts, while it decreased the motor scores in the Slovenia and Croatian sub-cohorts. The only socio-demographic factor consistently associated to the outcome among cohorts was child's sex, with females performing better than males on cognitive and language scores. These findings point to the need of harmonizing existing cohorts or creating prospective study designs that facilitate comparisons in the exposome over time, places and kind of environmental exposures.

Analyzing Metabolomics Data for Environmental Health and Exposome Research

The exposome is the cumulative measure of environmental influences and associated biological responses across the life span, with critical relevance for understanding how exposures can impact human health. Metabolomics analysis of biological samples offers unique advantages for examining the exposome. Simultaneous analysis of external exposures, biological responses, and host susceptibility at a systems level can help establish links between external exposures and health outcomes. As metabolomics technologies continue to evolve for the study of the exposome, metabolomics ultimately will help provide valuable insights for exposure risk assessment, and disease prevention and management. Here, we discuss recent advances in metabolomics, and describe data processing protocols that can enable analysis of the exposome. This chapter focuses on using liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics for analysis of the exposome, including (1) preprocessing of untargeted metabolomics data, (2) identification of exposure chemicals and their metabolites, and (3) methods to establish associations between exposures and diseases.

Toward achieving precision health

Health care systems primarily focus on patients after they present with disease, not before. The emerging field of precision health encourages disease prevention and earlier detection by monitoring health and disease based on an individual's risk. Active participation in health care can be encouraged with continuous health-monitoring devices, providing a higher-resolution picture of human health and disease. However, the development of monitoring technologies must prioritize the collection of actionable data and long-term user engagement.

Towards Mass Spectrometry-Based Chemical Exposome: Current Approaches, Challenges, and Future Directions

The proposal of the “exposome” concept represents a shift of the research paradigm in studying exposure-disease relationships from an isolated and partial way to a systematic and agnostic approach. Nevertheless, exposome implementation is facing a variety of challenges including measurement techniques and data analysis. Here we focus on the chemical exposome, which refers to the mixtures of chemical pollutants people are exposed to from embryo onwards. We review the current chemical exposome measurement approaches with a focus on those based on the mass spectrometry. We further explore the strategies in implementing the concept of chemical exposome and discuss the available chemical exposome studies. Early progresses in the chemical exposome research are outlined, and major challenges are highlighted. In conclusion, efforts towards chemical exposome have only uncovered the tip of the iceberg, and further advancement in measurement techniques, computational tools, high-throughput data analysis, and standardization may allow more exciting discoveries concerning the role of exposome in human health and disease.

Dispositional negativity, cognition, and anxiety disorders: An integrative translational neuroscience framework

When extreme, anxiety can become debilitating. Anxiety disorders, which often first emerge early in development, are common and challenging to treat, yet the underlying mechanisms have only recently begun to come into focus. Here, we review new insights into the nature and biological bases of dispositional negativity, a fundamental dimension of childhood temperament and adult personality and a prominent risk factor for the development of pediatric and adult anxiety disorders. Converging lines of epidemiological, neurobiological, and mechanistic evidence suggest that dispositional negativity increases the likelihood of psychopathology via specific neurocognitive mechanisms, including attentional biases to threat and deficits in executive control. Collectively, these observations provide an integrative translational framework for understanding the development and maintenance of anxiety disorders in adults and youth and set the stage for developing improved intervention strategies.

Influence of the Urban Exposome on Birth Weight

BACKGROUND

The exposome is defined as the totality of environmental exposures from conception onwards. It calls for providing a holistic view of environmental exposures and their effects on human health by evaluating multiple environmental exposures simultaneously during critical periods of life.

OBJECTIVE

We evaluated the association of the urban exposome with birth weight.

METHODS

We estimated exposure to the urban exposome, including the built environment, air pollution, road traffic noise, meteorology, natural space, and road traffic (corresponding to 24 environmental indicators and 60 exposures) for nearly 32,000 pregnant women from six European birth cohorts. To evaluate associations with either continuous birth weight or term low birth weight (TLBW) risk, we primarily relied on the Deletion-Substitution-Addition (DSA) algorithm, which is an extension of the stepwise variable selection method. Second, we used an exposure-by-exposure exposome-wide association studies (ExWAS) method accounting for multiple hypotheses testing to report associations not adjusted for coexposures.

RESULTS

The most consistent statistically significant associations were observed between increasing green space exposure estimated as Normalized Difference Vegetation Index (NDVI) and increased birth weight and decreased TLBW risk. Furthermore, we observed statistically significant associations among presence of public bus line, land use Shannon's Evenness Index, and traffic density and birth weight in our DSA analysis.

CONCLUSION

This investigation is the first large urban exposome study of birth weight that tests many environmental urban exposures. It confirmed previously reported associations for NDVI and generated new hypotheses for a number of built-environment exposures. https://doi.org/10.1289/EHP3971.

Earth Observation: Investigating Noncommunicable Diseases from Space

The United Nations has called on all nations to take immediate actions to fight noncommunicable diseases (NCDs), which have become an increasingly significant burden to public health systems around the world. NCDs tend to be more common in developed countries but are also becoming of growing concern in low- and middle-income countries. Earth observation (EO) technologies have been used in many infectious disease studies but have been less commonly employed in NCD studies. This review discusses the roles that EO data and technologies can play in NCD research, including ( a) integrating natural and built environment factors into NCD research, ( b) explaining individual-environment interactions, ( c) scaling up local studies and interventions, ( d) providing repeated measurements for longitudinal studies including cohorts, and ( e) advancing methodologies in NCD research. Such extensions hold great potential for overcoming the challenges of inaccurate and infrequent measurements of environmental exposure at the level of both the individual and the population, which is of great importance to NCD research, practice, and policy.

Associations between environmental quality and adult asthma prevalence in medical claims data

As of 2014, approximately 7.4% of U.S. adults had current asthma. The etiology of asthma is complex, involving genetics, behavior, and environmental factors. To explore the association between cumulative environmental quality and asthma prevalence in U.S. adults, we linked the U.S. Environmental Protection Agency's Environmental Quality Index (EQI) to the MarketScan® Commercial Claims and Encounters Database. The EQI is a summary measure of five environmental domains (air, water, land, built, sociodemographic). We defined asthma as having at least 2 claims during the study period, 2003-2013. We used a Bayesian approach with non-informative priors, implementing mixed-effects regression modeling with a Poisson link function. Fixed effects variables were EQI, sex, race, and age. Random effects were counties. We modeled quintiles of the EQI comparing higher quintiles (worse quality) to lowest quintile (best quality) to estimate prevalence ratios (PR) and credible intervals (CIs). We estimated associations using the cumulative EQI and domain-specific EQIs; we assessed U.S. overall (non-stratified) as well as stratified by rural-urban continuum codes (RUCC) to assess rural/urban heterogeneity. Among the 71,577,118 U.S. adults with medical claims who could be geocoded to county of residence, 1,147,564 (1.6%) met the asthma definition. Worse environmental quality was associated with increased asthma prevalence using the non-RUCC-stratified cumulative EQI, comparing the worst to best EQI quintile (PR:1.27; 95% CI: 1.21, 1.34). Patterns varied among different EQI domains, as well as by rural/urban status. Poor environmental quality may increase asthma prevalence, but domain-specific drivers may operate differently depending on rural/urban status.

The Curious Case of Cholangiocarcinoma: Opportunities for Environmental Health Scientists to Learn about a Complex Disease

Deaths from complex, noncommunicable diseases such as cancer are predicted to continue to increase worldwide, with low- and middle-income countries bearing the brunt of the burden. This problem requires a concentrated global effort to avoid devastating losses of life as well as economic losses. Cholangiocarcinoma (CCA) is a readily studied model of complex, noncommunicable disease, but it receives little attention outside of the scientific community in Southeast Asia. Here, we bring attention to the opportunity to study CCA as a model to understand the role of multiple, complex factors in cancer. These factors include allostatic load, individual genetic susceptibility, and environmental exposures such as chemicals, diet, socioeconomic factors, and psychosocial stress. The study of CCA offers a unique opportunity to make novel observations that could advance progress in prevention and intervention approaches for prevalent diseases that involve complex, multifactorial interactions.

Toward Capturing the Exposome: Exposure Biomarker Variability and Coexposure Patterns in the Shared Environment

Many factors affect the variation in the exposome. We examined the influence of shared household and partner's sex in relation to the variation in 128 endocrine disrupting chemical (EDC) exposures among couples. In a cohort comprising of 501 couples trying for pregnancy, we measured 128 (13 chemical classes) persistent and nonpersistent EDCs and estimated 1) sex-specific differences; 2) variance explained by shared household; and 3) Spearman's rank correlation coefficients ( r_s) for females, males, and couples' exposures. Sex was correlated with 8 EDCs including per- and polyfluoroalkyl substances (PFASs) ( p < 0.05). Shared household explained 43% and 41% of the total variance for PFASs and blood metals, respectively, but less than 20% for the remaining 11 EDC classes. Coexposure patterns of the exposome were similar between females and males, with within-class r_s higher for persistent than for nonpersistent chemicals. Median r_ss of polybrominated compounds and urine metalloids were 0.45 and 0.09, respectively, for females (0.41 and 0.08 for males; 0.21 and 0.04 for couples). Our findings suggest that individual, rather than shared environment, could be a major factor influencing the covariation of the exposome. Understanding the correlations of exposures has important analytical and sampling implications for exposomics research.

Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression

There is substantial interest in assessing how exposure to environmental mixtures, such as chemical mixtures, affect child health. Researchers are also interested in identifying critical time windows of susceptibility to these complex mixtures. A recently developed method, called lagged kernel machine regression (LKMR), simultaneously accounts for these research questions by estimating effects of time-varying mixture exposures, and identifying their critical exposure windows. However, LKMR inference using Markov chain Monte Carlo methods (MCMC-LKMR) is computationally burdensome and time intensive for large datasets, limiting its applicability. Therefore, we develop a mean field variational Bayesian inference procedure for lagged kernel machine regression (MFVB-LKMR). The procedure achieves computational efficiency and reasonable accuracy as compared with the corresponding MCMC estimation method. Updating parameters using MFVB may only take minutes, while the equivalent MCMC method may take many hours or several days. We apply MFVB-LKMR to PROGRESS, a prospective cohort study in Mexico. Results from a subset of PROGRESS using MFVB-LKMR provide evidence of significant positive association between second trimester cobalt levels and z-scored birthweight. This positive association is heightened by cesium exposure. MFVB-LKMR is a promising approach for computationally efficient analysis of environmental health datasets, to identify critical windows of exposure to complex mixtures.