The exposome in practice: Design of the EXPOsOMICS project

Personal environmental exposure to plasticizers and organophosphate flame retardants using silicone wristbands and urine: Patterns, comparisons, and correlations

Plasticizers (PLs) and organophosphate flame retardants (OPFRs) are ubiquitous in the environment due to their widespread use and potential for leaching from consumer products. Environmental exposure is a critical aspect of the human exposome, revealing complex interactions between environmental contaminants and potential health effects. Silicone wristbands (SWBs) have emerged as a novel and non-invasive sampling device for assessing personal external exposure. In this study, SWBs were used as a proxy to estimate personal dermal adsorption (EDdermal) to PLs and OPFRs in Belgian participants for one week; four morning urine samples were also collected and analyzed for estimated daily intake (EDI). The results of the SWBs samples showed that all the participants were exposed to these chemicals, and the exposure was found to be highest for the legacy and alternative plasticizers (LP and AP), followed by the legacy and emerging OPFRs (LOPFR and EOPFR). In urine samples, the highest levels were observed for metabolites of diethyl phthalate (DEP), di-isobutyl phthalate (DiBP) and di-n-butyl phthalate (DnBP) among LPs and di(2-ethylhexyl) terephthalate (DEHT) for APs. Outliers among the participants indicated that there were other sources of exposure that were not identified. Results showed a significant correlation between EDdermal and EDI for DiBP, tris (2-butoxyethyl) phosphate (TBOEP) and triphenyl phosphate (TPhP). These correlations indicated their suitability for predicting exposure via SWB monitoring for total chemical exposure. The results of this pilot study advance our understanding of SWB sampling and its relevance for predicting aggregate environmental chemical exposures, while highlighting the potential of SWBs as low-cost, non-invasive personal samplers for future research. This innovative approach has the potential to advance the assessment of environmental exposures and their impact on public health.

Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.

Exposure to PM2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV

BACKGROUND

Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer.

OBJECTIVE

To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study.

METHODS

A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC.

RESULTS

We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5.

CONCLUSION

Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.

[Application of multiomics mass spectrometry in the research of chemical exposome]

Environmental factors, such as environmental pollutants, behaviors, and lifestyles, are the leading causes of chronic noncommunicable diseases. Estimates indicate that approximately 50% of all deaths worldwide can be attributed to environmental factors. The exposome is defined as the totality of human environmental (i.e., all nongenetic) exposures from conception, including general external exposure (e.g., climate, education, and urban environment), specific external exposure (e.g., pollution, physical activity, and diet), and internal exposure (e.g., metabolic factors, oxidative stress, inflammation, and protein modification). As a new paradigm, this concept aims to comprehensively understand the link between human health and environmental factors. Therefore, a comprehensive measurement of the exposome, including accurate and reliable measurements of exposure to the external environment and a wide range of biological responses to the internal environment, is of great significance. The measurement of the general external exposome depends on advances in environmental sensors, personal-sensing technologies, and geographical information systems. The determination of exogenous chemicals to which individuals are exposed and endogenous chemicals that are produced or modified by external stressors relies on improvements in methodology and the development of instrumental approaches, including colorimetric, chromatographic, spectral, and mass-spectrometric methods. This article reviews the research strategies for chemical exposomes and summarizes existing exposome-measurement methods, focusing on mass spectrometry (MS)-based methods. The top-down and bottom-up approaches are commonly used in exposome studies. The bottom-up approach focuses on the identification of chemicals in the external environment (e.g., soil, water, diet, and air), whereas the top-down approach focuses on the evaluation of endogenous chemicals and biological processes in biological samples (e.g., blood, urine, and serum). Low- and high-resolution MS (LRMS and HRMS, respectively) have become the most popular methods for the direct measurement of exogenous and endogenous chemicals owing to their superior sensitivity, specificity, and dynamic range. LRMS has been widely applied in the targeted analysis of expected chemicals, whereas HRMS is a promising technique for the suspect and unknown screening of unexpected chemicals. The development of MS-based multiomics, including proteomics, metabolomics, epigenomics, and spatial omics, provides new opportunities to understand the effects of environmental exposure on human health. Metabolomics involves the sum of all low-molecular-weight metabolites in a living system. Nontargeted metabolomics can measure both endogenous and exogenous chemicals, which would directly link exposure to biological effects, internal dose, and disease pathobiology, whereas proteomics could play an important role in predicting potential adverse health outcomes and uncovering molecular mechanisms. MS imaging (MSI) is an emerging technique that provides unlabeled in-depth measurements of endogenous and exogenous molecules directly from tissue and cell sections without changing their spatial information. MSI-based spatial omics, which has been widely applied in biomarker discovery for clinical diagnosis, as well as drug and pollutant monitoring, is expected to become an effective method for exposome measurement. Integrating these response measurements from metabolomics, proteomics, spatial omics, and epigenomics will enable the generation of new hypotheses to discover the etiology of diseases caused by chemical exposure. Finally, we highlight the major challenges in achieving chemical exposome measurements.

Blurred lines: Crossing the boundaries between the chemical exposome and the metabolome

The aetiology of every human disease lies in a combination of genetic and environmental factors, each contributing in varying proportions. While genomics investigates the former, a comparable holistic paradigm was proposed for environmental exposures in 2005, marking the onset of exposome research. Since then, the exposome definition has broadened to include a wide array of physical, chemical, and psychosocial factors that interact with the human body and potentially alter the epigenome, the transcriptome, the proteome, and the metabolome. The chemical exposome, deeply intertwined with the metabolome, includes all small molecules originating from diet as well as pharmaceuticals, personal care and consumer products, or pollutants in air and water. The set of techniques to interrogate these exposures, primarily mass spectrometry and nuclear magnetic resonance spectroscopy, are also extensively used in metabolomics. Recent advances in untargeted metabolomics using high resolution mass spectrometry have paved the way for the development of methods able to provide in depth characterisation of both the internal chemical exposome and the endogenous metabolome simultaneously. Herein we review the available tools, databases, and workflows currently available for such work, and discuss how these can bridge the gap between the study of the metabolome and the exposome.

Exposome and basal cell carcinoma: a multicenter case-control study

BACKGROUND

While ultraviolet radiation (UVR) present in sunlight is recognized as the main etiological agent of skin cancer, the most frequent form of which is basal cell carcinoma (BCC), other exposome factors like pollution, diet, and lifestyle may also contribute. This study aimed to investigate the association of BCC and exposome-related factors in the Spanish population.

METHODS

BCC cases (n = 119) and controls (n = 127) with no history of skin cancer were recruited between April 2020 and August 2022 by 13 dermatologists throughout Spain in this prospective multicenter case-control study.

RESULTS

The BCC group had a higher proportion of outdoor workers, more years of UVR exposure, and a greater consumption of drugs (statins, ASA, hydrochlorothiazide, ACE inhibitors and omeprazole), P < 0.05. Avoidance of sun exposure was the most used photoprotection measure in both groups. The use of hats or caps was higher in the BCC group (P = 0.01). The solar protection factor (SPF) used 15 years previously was higher in the control group (P = 0.04). The control group had a higher daily screen time (P < 0.001), and practiced more relaxation activities (P = 0.03). Higher linolenic acid intake and lower coffee consumption were the only dietary variables associated with BCC (P < 0.05). Statistical significance for all the aforementioned variables was maintained in the multivariate analysis (P < 0.05).

CONCLUSIONS

The study found a significant association between BCC and multiple exposome-related factors in addition to chronic sun exposure in the Spanish population. Primary prevention strategies should target specific populations, such as outdoor workers, promoting sun-safe behaviors and stress-reducing activities, and also adequate skin photoprotection in patients on certain medications associated with increased BCC risk.

A population-based urinary and plasma metabolomics study of environmental exposure to cadmium

BACKGROUND

The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies.

METHOD

We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure.

RESULT

Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle).

CONCLUSION

These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

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.

Exposome and unhealthy aging: environmental drivers from air pollution to occupational exposures

The aging population worldwide is facing a significant increase in age-related non-communicable diseases, including cardiovascular and brain pathologies. This comprehensive review paper delves into the impact of the exposome, which encompasses the totality of environmental exposures, on unhealthy aging. It explores how environmental factors contribute to the acceleration of aging processes, increase biological age, and facilitate the development and progression of a wide range of age-associated diseases. The impact of environmental factors on cognitive health and the development of chronic age-related diseases affecting the cardiovascular system and central nervous system is discussed, with a specific focus on Alzheimer's disease, Parkinson's disease, stroke, small vessel disease, and vascular cognitive impairment (VCI). Aging is a major risk factor for these diseases. Their pathogenesis involves cellular and molecular mechanisms of aging such as increased oxidative stress, impaired mitochondrial function, DNA damage, and inflammation and is influenced by environmental factors. Environmental toxicants, including ambient particulate matter, pesticides, heavy metals, and organic solvents, have been identified as significant contributors to cardiovascular and brain aging disorders. These toxicants can inflict both macro- and microvascular damage and many of them can also cross the blood-brain barrier, inducing neurotoxic effects, neuroinflammation, and neuronal dysfunction. In conclusion, environmental factors play a critical role in modulating cardiovascular and brain aging. A deeper understanding of how environmental toxicants exacerbate aging processes and contribute to the pathogenesis of neurodegenerative diseases, VCI, and dementia is crucial for the development of preventive strategies and interventions to promote cardiovascular, cerebrovascular, and brain health. By mitigating exposure to harmful environmental factors and promoting healthy aging, we can strive to reduce the burden of age-related cardiovascular and brain pathologies in the aging population.

The Influence of the Exposome in the Cutaneous Squamous Cell Carcinoma, a Multicenter Case-Control Study

INTRODUCTION

The concept of exposome refers to the total of harmful and beneficial environmental exposures that can help predict the organism's biological responses over time. Ultraviolet radiation (UVR) from sun exposure has been recognized as the main etiological agent of skin cancer, and squamous cell carcinoma (SCC) is one most commonly associated with chronic exposure. However, in recent years, evidence suggests that lifestyle, environmental pollution, and contaminants in water and food can have an influence.

OBJECTIVES

To study the relationship between SCC and sun exposure, pollution, stress, and lifestyle in a Spanish cohort.

MATERIALS AND METHOD

A multicenter case-control study was carried out in which 13 dermatologists from different regions of Spain recruited cases and controls between April 2020 and August 2022. The group of cases were patients diagnosed with SCC and, as a control group, people who attended Dermatology consultations as companions with no history of skin cancer.

RESULTS

A total of 62 patients with SCC and 126 controls were included (62.9% males, median age 76.46 (10.1) and 33.3%, median age 55.7 (15), respectively). The SCC group had experienced more outside work than the controls (75% vs. 22.4%, p < 0.001), less recreational exposure (sunbathing, p = 0.05, and outdoor sports, p = 0.01), and a lower annual income (p = 0.01), with an increase in tobacco exposure (p < 0.001), without differences in other carcinogens, such as ionizing radiation or chemical exposure. The control group had a higher daily screentime use (p < 0.001) and practiced more relaxation activities (p = 0.03). A higher linolenic acid intake and lower coffee consumption were the only dietary variables associated with SCC (p < 0.05). Some chronic medications (anxiolytics, antidepressants, beta-blockers, statins, hydrochlorothiazide, ACE inhibitors, metformin, and omeprazole) were also statistically associated with SCC. Statistical significance for all aforementioned variables was maintained in the multivariate analysis (p < 0.05).

CONCLUSIONS

The study found a significant association between SCC and multiple exposome-related factors in addition to chronic sun exposure in the Spanish population. Primary prevention strategies should target specific populations, such as outdoor workers promoting sun-safe behaviors and stress-reducing activities, in addition to adequate skin photoprotection in patients under certain medications associated with SCC.

The built environment and cardiovascular disease: an umbrella review and meta-meta-analysis

AIMS

To provide a comprehensive overview of the current evidence on objectively measured neighbourhood built environment exposures in relation to cardiovascular disease (CVD) events in adults.

METHODS AND RESULTS

We searched seven databases for systematic reviews on associations between objectively measured long-term built environmental exposures, covering at least one domain (i.e. outdoor air pollution, food environment, physical activity environment like greenspace and walkability, urbanization, light pollution, residential noise, and ambient temperature), and CVD events in adults. Two authors extracted summary data and assessed the risk of bias independently. Robustness of evidence was rated based on statistical heterogeneity, small-study effect, and excess significance bias. Meta-meta-analyses were conducted to combine the meta-analysis results from reviews with comparable exposure and outcome within each domain. From the 3304 initial hits, 51 systematic reviews were included, covering 5 domains and including 179 pooled estimates. There was strong evidence of the associations between increased air pollutants (especially PM2.5 exposure) and increased residential noise with greater risk of CVD. Highly suggestive evidence was found for an association between increased ambient temperature and greater risk of CVD. Systematic reviews on physical activity environment, food environment, light pollution, and urbanization in relation to CVD were scarce or lacking.

CONCLUSION

Air pollutants, increased noise levels, temperature, and greenspace were associated with CVD outcomes. Standardizing design and exposure assessments may foster the synthesis of evidence. Other crucial research gaps concern the lack of prospective study designs and lack of evidence from low-to-middle-income countries (LMICs).

REGISTRATION

PROSPERO: CRD42021246580.

The contribution of the exposome to the burden of cardiovascular disease

Large epidemiological and health impact assessment studies at the global scale, such as the Global Burden of Disease project, indicate that chronic non-communicable diseases, such as atherosclerosis and diabetes mellitus, caused almost two-thirds of the annual global deaths in 2020. By 2030, 77% of all deaths are expected to be caused by non-communicable diseases. Although this increase is mainly due to the ageing of the general population in Western societies, other reasons include the increasing effects of soil, water, air and noise pollution on health, together with the effects of other environmental risk factors such as climate change, unhealthy city designs (including lack of green spaces), unhealthy lifestyle habits and psychosocial stress. The exposome concept was established in 2005 as a new strategy to study the effect of the environment on health. The exposome describes the harmful biochemical and metabolic changes that occur in our body owing to the totality of different environmental exposures throughout the life course, which ultimately lead to adverse health effects and premature deaths. In this Review, we describe the exposome concept with a focus on environmental physical and chemical exposures and their effects on the burden of cardiovascular disease. We discuss selected exposome studies and highlight the relevance of the exposome concept for future health research as well as preventive medicine. We also discuss the challenges and limitations of exposome studies.

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.

Characterisation of the risk associated with chronic lifetime exposure to mixture of chemical hazards: case study of trace elements

Risk assessment methodology, mostly commonly used, faces the complexity of the environment. Populations are exposed to multiple sources of chemicals throughout life and the chemical mixtures they are exposed change during time (lifestyle factors, regulatory decisions, etc). The risk assessment needs to consider these dynamics and the evolution of the body with age, in order to refine the exposure assessment to chemicals and to predict the health impact of these exposures. This review highlights the latest methodologies developed to improve risk assessment, especially cor heavy metals. The methodologies aim to better describe the chemical toxicokinetic and toxicodynamic as well as the exposure assessment. Human Biomonitoring (HBM) data give great opportunities to link biomarkers of exposure with an adverse effect. Physiologically-Based Toxicokinetic (PBTK) models are more and more used to simulate the evolution of biomarkers in organisms, considering the external exposures and the physiological evolutions. PBTK models may also be used to determine the routes of exposure or to predict the impacts of schemes of exposure. The major limit is the integration of several chemicals in mixture with common adverse effects and the interactions between them.

State of the science on outdoor air pollution exposure and liver cancer risk

Background

There is emerging evidence that air pollution exposure increases the risk of developing liver cancer. To date, there have been four epidemiologic studies conducted in the United States, Taiwan, and Europe showing generally consistent positive associations between ambient exposure to air pollutants, including particulate matter <2.5 μm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2), and liver cancer risk. There are several research gaps and thus valuable opportunities for future work to continue building on this expanding body of literature. The objectives of this paper are to narratively synthesize existing epidemiologic literature on the association between air pollution exposure and liver cancer incidence and describe future research directions to advance the science of understanding the role of air pollution exposure in liver cancer development.

Future research directions

include 1) accounting for potential confounding by established risk factors for the predominant histological subtype, hepatocellular carcinoma (HCC); 2) examination of incident primary liver cancer outcomes with consideration of potential differential associations according to histology; 3) air pollution exposure assessments considering early-life and/or historical exposures, residential histories, residual confounding from other sources of air pollution (e.g., tobacco smoking), and integration of geospatial ambient exposure modeling with novel biomarker technologies; 4) examination of air pollution mixtures experienced in the exposome; 5) consideration of increased opportunities for exposure to outdoor air pollution due to climate change (e.g., wildfires); and 6) consideration of modifying factors for air pollution exposure, such as socioeconomic status, that may contribute to disparities in liver cancer incidence.

Conclusions

In light of mounting evidence demonstrating that higher levels of air pollution exposure increase the risk for developing liver cancer, methodological considerations primarily concerning residual confounding and improved exposure assessment are warranted to robustly demonstrate an independent association for air pollution as a hepatocarcinogen.

Building effective intervention models utilizing big data to prevent the obesity epidemic

INTRODUCTION

The exposome consists of factors an individual is exposed to across the life course. The exposome is dynamic, meaning the factors are constantly changing, affecting each other and individuals in different ways. Our exposome dataset includes social determinants of health as well as policy, climate, environment, and economic factors that could impact obesity development. The objective was to translate spatial exposure to these factors with the presence of obesity into actionable population-based constructs that could be further explored.

METHODS

Our dataset was constructed from a combination of public-use datasets and the Center of Disease Control's Compressed Mortality File. Spatial Statistics using Queens First Order Analysis was performed to identify hot- and cold-spots of obesity prevalence; followed by Graph Analysis, Relational Analysis, and Exploratory Factor Analysis to model the multifactorial spatial connections.

RESULTS

Areas of high and low presence of obesity had different factors associated with obesity. Factors associated with obesity in areas of high obesity propensity were: poverty / unemployment; workload, comorbid conditions (diabetes, CVD) and physical activity. Conversely, factors associated in areas where obesity was rare were: smoking, lower education, poorer mental health, lower elevations, and heat.

DISCUSSION

The spatial methods described within the paper are scalable to large numbers of variables without issues of multiple comparisons lowering resolution. These types of spatial structural methods provide insights into novel variable associations or factor interactions that can then be studied further at the population or policy levels.

Characterizing the adult exposome in men and women from the general population: Results from the EHES-LUX study

Throughout life individuals are exposed to a large array of diverse environmental exposures (exposome). Hair analyses can assess chronic exposure to a large number of chemicals with less intra-variability than urine and blood. This is essential for studies that aim to achieve a global vision of the exposome. We aimed at characterizing the adult exposome by describing 175 environmental exposures and correlation patterns between and within exposure groups. A subsample of participants of the European Health Examination Survey, covering information on exposure to chemical pollutants in hair samples, were included in the present analysis (N = 442). Concentrations of micronutrients, lifestyle, home environment and socioeconomic information completed the exposome description and were obtained through blood analyses and questionnaires. We detected 29 persistent and non-persistent chemical pollutants in more than 70% of hair samples. Compared to women, men had higher concentrations of pesticides, lower concentrations of micronutrients (with the exception of vitamin A), and presented higher alcohol consumption. Across all exposures, a low median absolute correlation was found, 0.05 (5th - 95th centiles = 0.10, 0.20). We observed higher correlations and median correlations within exposure groups than between groups of exposure. The highest median correlation was observed between plasticizers (bisphenol A and S) in both men (0.50) and women (0.31). A 70% and 95% of cumulative variance was explained by 37 and 73 principal components respectively. We found a wide range of chemical exposures in hair samples of men and women. The adult exposome was complex and multidimensional. Future exposome studies should include hair as a matrix for characterizing exposure to multiple environmental chemicals.

Metabolism of benzo[a]pyrene after low-dose subchronic exposure to an industrial mixture of carcinogenic polycyclic aromatic hydrocarbons in rats: a cocktail effect study

Polycyclic aromatic hydrocarbons (PAHs) are interesting environmental pollutants for understanding cocktail effects. High-molecular-weight-PAHs (HMW-PAHs) are classified as probable or possible carcinogens; only benzo[a]pyrene (B[a]P) is a certain carcinogen in humans. Their toxicity depends on their metabolic activation. While 3-hydroxybenzo[a]pyrene (3-OHB[a]P) represents its detoxification pathway, trans-anti-7,8,9,10-tetrahydroxy-7,8,9,10-tetrahydrobenzo[a]pyrene (tetrol-B[a]P) represents the carcinogenicity pathway. The objective was to study the metabolism of B[a]P and HMW-PAHs during chronic low-dose exposure to B[a]P or a PAH mixture. Rats were exposed orally 5 times/week for 10 weeks to low-levels of B[a]P (0.02 and 0.2 mg.kg^-1.d^-1) or to an industrial mixture extracted from coal tar pitch (CTP) adjusted to 0.2 mg.kg^-1.d^-1 B[a]P. Urinary levels of monohydroxy-, diol-, and tetrol-PAH were measured during weeks 1 and 10 by HPLC-fluorescence and GC‒MS/MS. After 1 week, the percentages of B[a]P eliminated as 3-OHB[a]P and tetrol-B[a]P were not different depending on the dose of B[a]P, whereas they were reduced by half in the CTP group. Repeated exposure led to an increase in the percentages of the 2 metabolites for the 0.02-B[a]P group. Moreover, the percentage of B[a]P eliminated as 3-OHB[a]P was equal in the 0.2-B[a]P and CTP groups, whereas it remained halved for tetrol-B[a]P in the CTP group. The percent elimination of HMW-PAH metabolites did not vary between weeks 1 and 10. Thus, dose, duration of exposure and chemical composition of the mixture have a major influence on PAH metabolism that goes beyond a simple additive effect. This work contributes to the reflection on determination of limit values and risk assessments in a context of poly-exposures.

The Future of Precision Medicine in the Cure of Alzheimer's Disease

This decade has seen the beginning of ground-breaking conceptual shifts in the research of Alzheimer's disease (AD), which acknowledges risk elements and the evolving wide spectrum of complicated underlying pathophysiology among the range of diverse neurodegenerative diseases. Significant improvements in diagnosis, treatments, and mitigation of AD are likely to result from the development and application of a comprehensive approach to precision medicine (PM), as is the case with several other diseases. This strategy will probably be based on the achievements made in more sophisticated research areas, including cancer. PM will require the direct integration of neurology, neuroscience, and psychiatry into a paradigm of the healthcare field that turns away from the isolated method. PM is biomarker-guided treatment at a systems level that incorporates findings of the thorough pathophysiology of neurodegenerative disorders as well as methodological developments. Comprehensive examination and categorization of interrelated and convergent disease processes, an explanation of the genomic and epigenetic drivers, a description of the spatial and temporal paths of natural history, biological markers, and risk markers, as well as aspects about the regulation, and the ethical, governmental, and sociocultural repercussions of findings at a subclinical level all require clarification and realistic execution. Advances toward a comprehensive systems-based approach to PM may finally usher in a new era of scientific and technical achievement that will help to end the complications of AD.

Cord blood epigenome-wide meta-analysis in six European-based child cohorts identifies signatures linked to rapid weight growth

BACKGROUND

Rapid postnatal growth may result from exposure in utero or early life to adverse conditions and has been associated with diseases later in life and, in particular, with childhood obesity. DNA methylation, interfacing early-life exposures and subsequent diseases, is a possible mechanism underlying early-life programming.

METHODS

Here, a meta-analysis of Illumina HumanMethylation 450K/EPIC-array associations of cord blood DNA methylation at single CpG sites and CpG genomic regions with rapid weight growth at 1 year of age (defined with reference to WHO growth charts) was conducted in six European-based child cohorts (ALSPAC, ENVIRONAGE, Generation XXI, INMA, Piccolipiù, and RHEA, N = 2003). The association of gestational age acceleration (calculated using the Bohlin epigenetic clock) with rapid weight growth was also explored via meta-analysis. Follow-up analyses of identified DNA methylation signals included prediction of rapid weight growth, mediation of the effect of conventional risk factors on rapid weight growth, integration with transcriptomics and metabolomics, association with overweight in childhood (between 4 and 8 years), and comparison with previous findings.

RESULTS

Forty-seven CpGs were associated with rapid weight growth at suggestive p-value <1e-05 and, among them, three CpGs (cg14459032, cg25953130 annotated to ARID5B, and cg00049440 annotated to KLF9) passed the genome-wide significance level (p-value <1.25e-07). Sixteen differentially methylated regions (DMRs) were identified as associated with rapid weight growth at false discovery rate (FDR)-adjusted/Siddak p-values < 0.01. Gestational age acceleration was associated with decreasing risk of rapid weight growth (p-value = 9.75e-04). Identified DNA methylation signals slightly increased the prediction of rapid weight growth in addition to conventional risk factors. Among the identified signals, three CpGs partially mediated the effect of gestational age on rapid weight growth. Both CpGs (N=3) and DMRs (N=3) were associated with differential expression of transcripts (N=10 and 7, respectively), including long non-coding RNAs. An AURKC DMR was associated with childhood overweight. We observed enrichment of CpGs previously reported associated with birthweight.

CONCLUSIONS

Our findings provide evidence of the association between cord blood DNA methylation and rapid weight growth and suggest links with prenatal exposures and association with childhood obesity providing opportunities for early prevention.

Epigenome-wide analysis of maternal exposure to green space during gestation and cord blood DNA methylation in the ENVIRONAGE cohort

BACKGROUND

DNA methylation programming is sensitive to prenatal life environmental influences, but the impact of maternal exposure to green space on newborns DNA methylation has not been studied yet.

METHODS

We conducted a meta-epigenome-wide association study (EWAS) of maternal exposure to green space during gestation with cord blood DNA methylation in two subsets of the ENVIRONAGE cohort (N = 538). Cord blood DNA methylation was measured by Illumina HumanMethylation 450K in one subset (N = 189) and EPICarray in another (N = 349). High (vegetation height>3 m (m)), low (vegetation height<3 m) and total (including both) high-resolution green space exposures during pregnancy were estimated within 100 m and 1000 m distance around maternal residence. In each subset, we sought cytosine-phosphate-guanine (CpG) sites via linear mixed models adjusted on newborns' sex, ethnicity, gestational age, season at delivery, sampling day, maternal parity, age, smoking, education, and estimated blood cell proportions. EWASs results were meta-analysed via fixed-effects meta-analyses. Differentially methylated regions (DMRs) were identified via ENmix-combp and DMRcate algorithms. Sensitivity analyses were additionally adjusted on PM_2.5, distance to major roads, urbanicity and neighborhood income. In the 450K subset, cord blood expression of differentially methylated genes was measured by Agilent microarrays and associated with green space.

RESULTS

147 DMRs were identified, 85 of which were still significant upon adjustment for PM_2.5, distance to major roads, urbanicity and neighborhood income, including HLA-DRB5, RPTOR, KCNQ1DN, A1BG-AS1, HTR2A, ZNF274, COL11A1 and PRSS36 DMRs. One CpG reached genome-wide significance, while 54 CpGs were suggestive significant (p-values<1e-05). Among them, a CpG, hypermethylated with 100 m buffer total green space, was annotated to PAQR9, whose expression decreased with 1000 m buffer low green space (p-value = 1.45e-05).

CONCLUSIONS

Our results demonstrate that maternal exposure to green space during pregnancy is associated with cord blood DNA methylation, mainly at loci organized in regions, in genes playing important roles in neurological development (e.g., HTR2A).

A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases

Age-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.

The exposome in rheumatoid arthritis

The exposome integrates the variety and accumulation of exposures (external and internal) to which an individual is submitted to from conception to death. Exposome may therefore be a useful tool for understanding the diversity of these factors and their role in the pathophysiology of rheumatoid arthritis (RA). Life is perceived as a continuum of cumulative changes, with key periods of disruption (e.g. birth, adolescence, pregnancy, prolonged treatment). The combination of these changes and the external signals that cause them constitute an individual's exposome, which is constantly changing and expanding throughout life. Thus, measuring the exposome requires specific tools and approaches as well as a global perspective. RA, a complex, heterogeneous, pro-inflammatory autoimmune disease with a genetic component and for which a large number of environmental factors have already been incriminated is an appropriate field of application for the exposome. The aim of this review is to define the exposome concept, outline the different analytic tools available for its study and finally apply them to the field of RA.

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Climate change, human health, and the exposome: Utilizing OMIC technologies to navigate an era of uncertainty

Climate change is an anthropogenic phenomenon that is alarming scientists and non-scientists alike. The emission of greenhouse gases is causing the temperature of the earth to rise and this increase is accompanied by a multitude of climate change-induced environmental exposures with potential health impacts. Tracking human exposure has been a major research interest of scientists worldwide. This has led to the development of exposome studies that examine internal and external individual exposures over their lifetime and correlate them to health. The monitoring of health has also benefited from significant technological advances in the field of "omics" technologies that analyze physiological changes on the nucleic acid, protein, and metabolism levels, among others. In this review, we discuss various climate change-induced environmental exposures and their potential health implications. We also highlight the potential integration of the technological advancements in the fields of exposome tracking, climate monitoring, and omics technologies shedding light on important questions that need to be answered.

Interrelationships and determinants of aging biomarkers in cord blood

BACKGROUND

Increasing evidence supports the concept of prenatal programming as an early factor in the aging process. DNA methylation age (DNAm age), global genome-wide DNA methylation (global methylation), telomere length (TL), and mitochondrial DNA content (mtDNA content) have independently been shown to be markers of aging, but their interrelationship and determinants at birth remain uncertain.

METHODS

We assessed the inter-correlation between the aging biomarkers DNAm age, global methylation, TL and mtDNA content using Pearson's correlation in 190 cord blood samples of the ENVIRONAGE birth cohort. TL and mtDNA content was measured via qPCR, while the DNA methylome was determined using the human 450K methylation Illumina microarray. Subsequently, DNAm age was calculated according to Horvath's epigenetic clock, and mean global, promoter, gene-body, and intergenic DNA methylation were determined. Path analysis, a form of structural equation modeling, was performed to disentangle the complex causal relationships among the aging biomarkers and their potential determinants.

RESULTS

DNAm age was inversely correlated with global methylation (r = -0.64, p < 0.001) and mtDNA content (r = - 0.16, p = 0.027). Cord blood TL was correlated with mtDNA content (r = 0.26, p < 0.001) but not with global methylation or DNAm age. Path analysis showed the strongest effect for global methylation on DNAm age with a decrease of 0.64 standard deviations (SD) in DNAm age for each SD (0.01%) increase in global methylation (p < 0.001). Among the applied covariates, newborn sex and season of delivery were the strongest determinants of aging biomarkers.

CONCLUSIONS

We provide insight into molecular aging signatures at the start of life, including their interrelations and determinants, showing that cord blood DNAm age is inversely associated with global methylation and mtDNA content but not with newborn telomere length. Our findings demonstrate that cord blood TL and DNAm age relate to different pathways/mechanisms of biological aging and can be influenced by environmental factors already at the start of life. These findings are relevant for understanding fetal programming and for the early prevention of noncommunicable diseases.

Air Pollution from Global Health to Individual Risk Factor—Is It Time for Enviropathies in Everyday Clinical Practice?

While the link between cardiovascular and respiratory conditions and air pollution is well-known, recent studies provided a growing body of evidence that polluted air, particularly air with high levels of particulate matter with a diameter smaller than 2.5 micrometers (PM 2.5), can have a range of negative impacts on health, both in terms of mortality and morbidity. It is time to emphasize the role of environmental factors as contributory factors or determinants of both global and individual health levels, and to consider them together as a health priority, as enviropathies (meant as pathologies caused, triggered or worsened by environmental exposure). Bringing attention to harmful air pollution exposure has fostered population studies, which developed accurate quantification of environmental exposure in polluted regions, aiding our understanding of the dose-response relationship between pollutants and diseases. Those efforts have influenced local and global health policy strategies. Now we face the challenge of controlling environmental pollution and limiting individual exposure to prevent or avoid serious health risks. Is it time for enviropathies in everyday clinical practice?

Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

Cord blood metabolites and rapid postnatal growth as multiple mediators in the prenatal propensity to childhood overweight

BACKGROUND

The mechanisms underlying childhood overweight and obesity are poorly known. Here, we investigated the direct and indirect effects of different prenatal exposures on offspring rapid postnatal growth and overweight in childhood, mediated through cord blood metabolites. Additionally, rapid postnatal growth was considered a potential mediator on childhood overweight, alone and sequentially to each metabolite.

METHODS

Within four European birth-cohorts (N = 375 mother-child dyads), information on seven prenatal exposures (maternal education, pre-pregnancy BMI, weight gain and tobacco smoke during pregnancy, age at delivery, parity, and child gestational age), selected as obesogenic according to a-priori knowledge, was collected. Cord blood levels of 31 metabolites, associated with rapid postnatal growth and/or childhood overweight in a previous study, were measured via liquid-chromatography-quadrupole-time-of-flight-mass-spectrometry. Rapid growth at 12 months and childhood overweight (including obesity) between four and eight years were defined with reference to WHO growth charts. Single mediation analysis was performed using the imputation approach and multiple mediation analysis using the extended-imputation approach.

RESULTS

Single mediation suggested that the effect of maternal education, pregnancy weight gain, parity, and gestational age on rapid postnatal growth but not on childhood overweight was partly mediated by seven metabolites, including cholestenone, decenoylcarnitine(C10:1), phosphatidylcholine(C34:3), progesterone and three unidentified metabolites; and the effect of gestational age on childhood overweight was mainly mediated by rapid postnatal growth. Multiple mediation suggested that the effect of gestational age on childhood overweight was mainly mediated by rapid postnatal growth and that the mediating role of the metabolites was marginal.

CONCLUSION

Our findings provide evidence of the involvement of in utero metabolism in the propensity to rapid postnatal growth and of rapid postnatal growth in the propensity to childhood overweight. We did not find evidence supporting a mediating role of the studied metabolites alone between the studied prenatal exposures and the propensity to childhood overweight.

A novel human biomonitoring study by semiconductor gas sensors in Exposomics: investigation of health risk in contaminated sites

Two areas in central-southern Italy Land of Fires in Campania and Valley of Sacco river in Lazio are known to be contaminated sites, the first due to illegal fly-tipping and toxic fires, and the second due to an intensive industrial exploitation done by no-scruple companies and crooked public administration offices with dramatic consequences for environment and resident people. The work is intended to contribute to Human BioMonitoring (HBM) studies conducted in these areas on healthy young male population by a semiconductor gas sensor array trained by SPME-GC/MS. Human semen, blood and urine were investigated. The fingerprinting of the Volatile Organic Compounds (VOCs) by a gas sensors system allowed to discriminate the different contamination of the two areas and was able to predict the chemical concentration of several VOCs identified by GC/MS.

Reframing the environment in data-intensive health sciences

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research.

Prenatal exposure to PM10 and changes in DNA methylation and telomere length in cord blood

BACKGROUND

Air pollution exposure in pregnancy can cause molecular level alterations that might influence later disease susceptibility.

OBJECTIVES

We investigated DNA methylation (DNAm) and telomere length (TL) in the cord blood in relation to gestational PM₁₀ exposure and explored potential gestational windows of susceptibility.

METHODS

Cord blood epigenome-wide DNAm (N = 384) and TL (N = 500) were measured in children of the Italian birth cohort Piccolipiù, using the Infinium Methylation EPIC BeadChip and qPCR, respectively. PM₁₀ daily exposure levels, based on maternal residential address, were estimated for different gestational periods using models based on satellite data. Epigenome-wide analysis to identify differentially methylated probes (DMPs) and regions (DMRs) was conducted, followed by a pathway analysis and replication analysis in an second Piccolipiù dataset. Distributed lag models (DLMs) using weekly exposures were used to study the association of PM₁₀ exposure across pregnancy with telomere length, as well as with the DMPs that showed robust associations.

RESULTS

Gestational PM₁₀ exposure was associated with the DNA methylation of more than 250 unique DMPs, most of them identified in early gestation, and 1 DMR. Out of 151 DMPs available in the replication dataset, ten DMPs showed robust associations: eight were associated with exposure during early gestation and 2 with exposure during the whole pregnancy. These exposure windows were supported by the DLM analysis. The PM₁₀ exposure between 15th and 20th gestational week seem to be associated with shorter telomeres at birth, while exposure between 24th and 29th was associated with longer telomeres.

DISCUSSION

The early pregnancy period is a potential critical window during which PM₁₀ exposure can influence cord blood DNA methylation. The results from the TL analysis were consistent with previous findings and merit further exploration in future studies. The study underlines the importance of considering gestational windows outside of the predefined trimesters that may not always overlap with biologically relevant windows of exposure.

Impacts of dietary exposure to pesticides on faecal microbiome metabolism in adult twins

BACKGROUND

Dietary habits have a profound influence on the metabolic activity of gut microorganisms and their influence on health. Concerns have been raised as to whether the consumption of foodstuffs contaminated with pesticides can contribute to the development of chronic disease by affecting the gut microbiome. We performed the first pesticide biomonitoring survey of the British population, and subsequently used the results to perform the first pesticide association study on gut microbiome composition and function from the TwinsUK registry.

METHODS

Dietary exposure of 186 common insecticide, herbicide, or fungicide residues and the faecal microbiome in 65 twin pairs in the UK was investigated. We evaluated if dietary habits, geographic location, or the rural/urban environment, are associated with the excretion of pesticide residues. The composition and metabolic activity of faecal microbiota was evaluated using shotgun metagenomics and metabolomics respectively. We performed a targeted urine metabolomics analysis in order to evaluate whether pesticide urinary excretion was also associated with physiological changes.

RESULTS

Pyrethroid and/or organophosphorus insecticide residues were found in all urine samples, while the herbicide glyphosate was found in 53% of individuals. Food frequency questionnaires showed that residues from organophosphates were higher with increased consumption of fruit and vegetables. A total of 34 associations between pesticide residue concentrations and faecal metabolite concentrations were detected. Glyphosate excretion was positively associated with an overall increased bacterial species richness, as well as to fatty acid metabolites and phosphate levels. The insecticide metabolite Br2CA, reflecting deltamethrin exposure, was positively associated with the phytoestrogens enterodiol and enterolactone, and negatively associated with some N-methyl amino acids. Urine metabolomics performed on a subset of samples did not reveal associations with the excretion of pesticide residues.

CONCLUSIONS

The consumption of conventionally grown fruit and vegetables leads to higher ingestion of pesticides with unknown long-term health consequences. Our results highlight the need for future dietary intervention studies to understand effects of pesticide exposure on the gut microbiome and possible health consequences.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Defining the Exposome Using Popular Education and Concept Mapping With Communities in Atlanta, Georgia

Introduction

The exposome concept provides a framework to better incorporate the environment into the study of health and disease and has been defined by academics to encompass all lifetime exposures including toxicants, diet, and lifestyle choices. However, initial applications of the exposome concept have been less apt at measuring social determinants of health, focusing primarily on conventional environmental exposures and lifestyle choices that do not reflect the complex lived experience of many communities. To bring community voice into the exposome concept, the HERCULES Exposome Research Center and its Stakeholder Advisory Board co-developed the Exposome Roadshow. We present and discuss the resulting community-exposome definition to inform and improve exposome research.

Materials and Methods

Four communities from distinct areas across metro-Atlanta participated in separate 2-day Exposome Roadshow workshops with concept mapping. Aligned with a popular education approach in which community knowledge is used to work collectively for change, concept mapping provided a systematic method to collect and visualize community members' knowledge and create a shared understanding to take action. Community members brainstormed, sorted, and rated their responses to the prompt: "What in your environment is affecting your and your community's health?" Responses were analyzed and visually depicted by concept maps consisting of separate but interrelated clusters of ideas. Community members discussed and validated the maps, selecting a final map illustrating their community's exposome.

Results

A total of 118 community members completed concept mapping. On average communities identified 7 clusters to define their exposome. The resulting concept maps offer a community definition of the exposome. Five major themes arose across all four communities: conventional environmental concerns, built environment, social relationships, crime and safety, and individual health and behaviors.

Discussion

The resulting community-exposome definition demonstrates the importance of expanding the scope of exposures beyond traditional environmental influences to include the lived experience of individuals and communities. While newer exposome definitions align more closely with this community definition, traditional exposome methods do not routinely include these factors. To truly capture the totality of lifetime exposures and improve human health, researchers should incorporate community perspectives into exposome research.

The Exposome and Toxicology: A Win-Win Collaboration

The development of the exposome concept has been one of the hallmarks of environmental and health research for the last decade. The exposome encompasses the life course environmental exposures including lifestyle factors from the prenatal period onwards. It has inspired many research programs and is expected to influence environmental and health research, practices, and policies. Yet, the links bridging toxicology and the exposome concept have not been well developed. In this review, we describe how the exposome framework can interface with and influence the field of toxicology, as well as how the field of toxicology can help advance the exposome field by providing the needed mechanistic understanding of the exposome impacts on health. Indeed, exposome-informed toxicology is expected to emphasize several orientations including (1) developing approaches integrating multiple stressors, in particular chemical mixtures, as well as the interaction of chemicals with other stressors, (2) using mechanistic frameworks such as the adverse outcome pathways to link the different stressors with toxicity outcomes, (3) characterizing the mechanistic basis of long-term effects by distinguishing different patterns of exposures and further exploring the environment-DNA interface through genetic and epigenetic studies, and (4) improving the links between environmental and human health, in particular through a stronger connection between alterations in our ecosystems and human toxicology. The exposome concept provides the linkage between the complex environment and contemporary mechanistic toxicology. What toxicology can bring to exposome characterization is a needed framework for mechanistic understanding and regulatory outcomes in risk assessment.

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS²) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(−) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.

Impact of Acute and Chronic Psychosocial Stress on Vascular Inflammation

Significance: Atherosclerosis and its complications, such as acute coronary syndromes, are the leading causes of death worldwide. A wide range of inflammatory processes substantially contribute to the initiation and progression of cardiovascular disease (CVD). In addition, epidemiological studies strongly associate both chronic stress and acute psychosocial stress with the occurrence of CVDs. Recent Advances: Extensive research during recent decades has not only identified major pathways in cardiovascular inflammation but also revealed a link between psychosocial factors and the immune system in the context of atherosclerosis. Both chronic and acute psychosocial stress drive systemic inflammation via neuroimmune interactions and promote atherosclerosis progression. Critical Issues: The associations human epidemiological studies found between psychosocial stress and cardiovascular inflammation have been substantiated by additional experimental studies in mice and humans. However, we do not yet fully understand the mechanisms through which psychosocial stress drives cardiovascular inflammation; consequently, specific treatment, although urgently needed, is lacking. Future Directions: Psychosocial factors are increasingly acknowledged as risk factors for CVD and are currently treated via behavioral interventions. Additional mechanistic insights might provide novel pharmacological treatment options to reduce stress-related morbidity and mortality. Antioxid. Redox Signal. 35, 1531-1550.

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.

Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.

Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.

Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.

The exposome in practice: an exploratory panel study of biomarkers of air pollutant exposure in Chinese people aged 60-69 years (China BAPE Study)

The exposome overhauls conventional environmental health impact research paradigms and provides a novel methodological framework that comprehensively addresses the complex, highly dynamic interplays of exogenous exposures, endogenous exposures, and modifiable factors in humans. Holistic assessments of the adverse health effects and systematic elucidation of the mechanisms underlying environmental exposures are major scientific challenges with widespread societal implications. However, to date, few studies have comprehensively and simultaneously measured airborne pollutant exposures and explored the associated biomarkers in susceptible healthy elderly subjects, potentially resulting in the suboptimal assessment and management of health risks. To demonstrate the exposome paradigm, we describe the rationale and design of a comprehensive biomarker and biomonitoring panel study to systematically explore the association between individual airborne exposure and adverse health outcomes. We used a combination of personal monitoring for airborne pollutants, extensive human biomonitoring, advanced omics analysis, confounding information, and statistical methods. We established an exploratory panel study of Biomarkers of Air Pollutant Exposure in Chinese people aged 60-69 years (China BAPE), which included 76 healthy residents from a representative community in Jinan City, Shandong Province. During the period between September 2018 and January 2019, we conducted prospective longitudinal monitoring with a 3-day assessment every month. This project: (1) leveraged advanced tools for personal airborne exposure monitoring (external exposures); (2) comprehensively characterized biological samples for exogenous and endogenous compounds (e.g., targeted and untargeted monitoring) and multi-omics scale measurements to explore potential biomarkers and putative toxicity pathways; and (3) systematically evaluated the relationships between personal exposure to air pollutants, and novel biomarkers of exposures and effects using exposome-wide association study approaches. These findings will contribute to our understanding of the mechanisms underlying the adverse health impacts of air pollution exposures and identify potential adverse clinical outcomes that can facilitate the development of effective prevention and targeted intervention techniques.

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.

[Chemical exposome and non-targeted approaches]

The technological advances in high-resolution mass spectrometry (HRMS), associated with the development of bioinformatics tools, allows the simultaneous detection of tens of thousands of chemical signals in biological matrices, including exogenous (i.e. xenobiotics) and endogenous molecules. These novel approaches based on HRMS, called "non-targeted" approaches, provide a unique opportunity to capture exposures to a wide range of chemicals (i.e. the internal chemical exposome) in populations, and to better understand the links between chemical exposures and the occurrence of chronic diseases.

Software Application Profile: exposomeShiny—a toolbox for exposome data analysis

Motivation

Studying the role of the exposome in human health and its impact on different omic layers requires advanced statistical methods. Many of these methods are implemented in different R and Bioconductor packages, but their use may require strong expertise in R, in writing pipelines and in using new R classes which may not be familiar to non-advanced users. ExposomeShiny provides a bridge between researchers and most of the state-of-the-art exposome analysis methodologies, without the need of advanced programming skills.

Implementation

ExposomeShiny is a standalone web application implemented in R. It is available as source files and can be installed in any server or computer avoiding problems with data confidentiality. It is executed in RStudio which opens a browser window with the web application.

General features

The presented implementation allows the conduct of: (i) data pre-processing: normalization and missing imputation (including limit of detection); (ii) descriptive analysis; (iii) exposome principal component analysis (PCA) and hierarchical clustering; (iv) exposome-wide association studies (ExWAS) and variable selection ExWAS; (v) omic data integration by single association and multi-omic analyses; and (vi) post-exposome data analyses to gain biological insight for the exposures, genes or using the Comparative Toxicogenomics Database (CTD) and pathway analysis.

Availability

The exposomeShiny source code is freely available on Github at [https://github.com/isglobal-brge/exposomeShiny], Git tag v1.4. The software is also available as a Docker image [https://hub.docker.com/r/brgelab/exposome-shiny], tag v1.4. A user guide with information about the analysis methodologies as well as information on how to use exposomeShiny is freely hosted at [https://isglobal-brge.github.io/exposome_bookdown/].

Applications of Adductomics in Chemically Induced Adverse Outcomes and Major Emphasis on DNA Adductomics: A Pathbreaking Tool in Biomedical Research

Adductomics novel and emerging discipline in the toxicological research emphasizes on adducts formed by reactive chemical agents with biological molecules in living organisms. Development in analytical methods propelled the application and utility of adductomics in interdisciplinary sciences. This review endeavors to add a new dimension where comprehensive insights into diverse applications of adductomics in addressing some of society's pressing challenges are provided. Also focuses on diverse applications of adductomics include: forecasting risk of chronic diseases triggered by reactive agents and predicting carcinogenesis induced by tobacco smoking; assessing chemical agents' toxicity and supplementing genotoxicity studies; designing personalized medication and precision treatment in cancer chemotherapy; appraising environmental quality or extent of pollution using biological systems; crafting tools and techniques for diagnosis of diseases and detecting food contaminants; furnishing exposure profile of the individual to electrophiles; and assisting regulatory agencies in risk assessment of reactive chemical agents. Characterizing adducts that are present in extremely low concentrations is an exigent task and more over absence of dedicated database to identify adducts is further exacerbating the problem of adduct diagnosis. In addition, there is scope of improvement in sample preparation methods and data processing software and algorithms for accurate assessment of adducts.

Paternal body mass index and offspring DNA methylation: findings from the PACE consortium

BACKGROUND

Accumulating evidence links paternal adiposity in the periconceptional period to offspring health outcomes. DNA methylation has been proposed as a mediating mechanism, but very few studies have explored this possibility in humans.

METHODS

In the Pregnancy And Childhood Epigenetics (PACE) consortium, we conducted a meta-analysis of coordinated epigenome-wide association studies (EWAS) of paternal prenatal body mass index (BMI) (with and without adjustment for maternal BMI) in relation to DNA methylation in offspring blood at birth (13 data sets; total n = 4894) and in childhood (6 data sets; total n = 1982).

RESULTS

We found little evidence of an association at either time point: at all CpGs, the false-discovery-rate-adjusted P-values were >0.05. In secondary sex-stratified analyses, we found just four CpGs for which there was robust evidence of an association in female offspring. To compare our findings to those of other studies, we conducted a systematic review, which identified seven studies, including five candidate gene studies showing associations between paternal BMI/obesity and offspring or sperm DNA methylation at imprinted regions. However, in our own study, we found very little evidence of enrichment for imprinted genes.

CONCLUSION

Our findings do not support the hypothesis that paternal BMI around the time of pregnancy is associated with offspring-blood DNA methylation, even at imprinted regions.

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery

BACKGROUND

Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown.

OBJECTIVES

We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics.

DISCUSSION

The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome-the totality of the biologically active exposures relevant to disease development-through coupling biochemical receptor-binding assays with affinity purification-mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships.

CONCLUSIONS

Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327.