The human early-life exposome (HELIX): project rationale and design

Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework

Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.

Could environmental exposure and climate change Be a key factor in the rising incidence of early onset colorectal cancer?

The emergence of early onset colorectal cancer (EOCRC) is believed to result from the complex interplay between external environmental factors and internal molecular processes. This review investigates the potential association between environmental exposure to chemicals and climate change and the increased incidence of EOCRC, focusing on their effects on gut microbiota (GM) dynamics. The manuscript explores the birth cohort effect, suggesting that individuals born after 1950 may be at higher risk of developing EOCRC due to cumulative environmental exposures. Furthermore, we also reviewed the impact of environmental pollution, including particulate matter and endocrine disrupting chemicals (EDCs), as well as global warming, on GM disturbance. Environmental exposures have the potential to disrupt GM composition and diversity, leading to dysbiosis, chronic inflammation, and oxidative stress, which are known risk factors associated with EOCRC. Particulate matter can enter the gastrointestinal tract, modifying GM composition and promoting the proliferation of pathogenic bacteria while diminishing beneficial bacteria. Similarly, EDCs, can induce GM alterations and inflammation, further increasing the risk of EOCRC. Additionally, global warming can influence GM through shifts in gut environmental conditions, affecting the host's immune response and potentially increasing EOCRC risk. To summarize, environmental exposure to chemicals and climate change since 1950 has been implicated as contributing factors to the rising incidence of EOCRC. Disruptions in gut microbiota homeostasis play a crucial role in mediating these associations. Consequently, there is a pressing need for enhanced environmental policies aimed at minimizing exposure to pollutants, safeguarding public health, and mitigating the burden of EOCRC.

Chemical exposome and children health: Identification of dose-response relationships from meta-analyses and epidemiological studies

BACKGROUND

Health impact assessment studies quantifying the impact of the chemical exposome on children's health generally consider a small fraction of the exposome. Synthetizing available dose-response relationships is an essential step to fill this gap. We reviewed the literature for dose-response relationships relating the chemical exposome with children health.

METHOD

We focused on 78 substance-outcome pairs for which the level of evidence had previously been classified as 'likely' or 'very likely'. We searched for dose-response relationships for these pairs from meta-analyses and, if none was available, from single epidemiological studies, from which we conducted meta-analyses whenever possible.

RESULTS

We identified dose-response relationships for 50 of the 78 prioritized substance-outcome pairs (64%). Dose-response relationships stemmed from meta-analyses for 21 pairs, from de novo meta-analyses for 1 pair and single studies for 28 pairs. Dose-response relationships were available for tobacco (fetal and infant death, congenital heart defects, birth outcomes, orofacial clefts, respiratory health), lead (asthma, cognition, delayed puberty onset and iron deficiency anaemia), polychlorobiphenyls (PCBs) (cognition, respiratory infections and birth outcomes), bisphenol A (cognition), hexachlorobenzene (HCB) (respiratory health), Polybrominated diphenyl ethers (neurodevelopment), DDT (hypospadias, cryptorchidism, miscarriage), pesticides (neurodevelopment), methylmercury (cognition), PFAS (immune system, birth weight, behavior, miscarriage), arsenic (cognition, birth weight, death, respiratory health), cadmium (cognition, birth weight), manganese (behavior), sodium (blood pressure) and thallium (birth weight). For 28 of the 78 substance-outcome pairs (36%), no dose-response relationship was available from epidemiological studies in children.

CONCLUSIONS

We identified dose-response relationships for 50 substance-outcome pairs, corresponding to 20 chemicals and 17 health outcomes. These can be used to perform more comprehensive quantitative health impact assessment of the exposome on child health. We also identified 28 substance-outcome pairs corresponding to 'likely' or 'very likely' effects for which research generating dose-response functions in children would be relevant.

A better performing algorithm for identification of implausible growth data from longitudinal pediatric medical records

Tracking trajectories of body size in children provides insight into chronic disease risk. One measure of pediatric body size is body mass index (BMI), a function of height and weight. Errors in measuring height or weight may lead to incorrect assessment of BMI. Yet childhood measures of height and weight extracted from electronic medical records often include values which seem biologically implausible in the context of a growth trajectory. Removing biologically implausible values reduces noise in the data, and thus increases the ease of modeling associations between exposures and childhood BMI trajectories, or between childhood BMI trajectories and subsequent health conditions. We developed open-source algorithms (available on github) for detecting and removing biologically implausible values in pediatric trajectories of height and weight. A Monte Carlo simulation experiment compared the sensitivity, specificity and speed of our algorithms to three published algorithms. The comparator algorithms were selected because they used trajectory information, had open-source code, and had published verification studies. Simulation inputs were derived from longitudinal epidemiological cohorts. Our algorithms had higher specificity, with similar sensitivity and speed, when compared to the three published algorithms. The results suggest that our algorithms should be adopted for cleaning longitudinal pediatric growth data.

Exposure to bisphenol A in European women from 2007 to 2014 using human biomonitoring data - The European Joint Programme HBM4EU

BACKGROUND

Bisphenol A (BPA; or 4,4'-isopropylidenediphenol) is an endocrine disrupting chemical. It was widely used in a variety of plastic-based manufactured products for several years. The European Food Safety Authority (EFSA) recently reduced the Tolerable Daily Intake (TDI) for BPA by 20,000 times due to concerns about immune-toxicity.

OBJECTIVE

We used human biomonitoring (HBM) data to investigate the general level of BPA exposure from 2007 to 2014 of European women aged 18-73 years (n = 4,226) and its determinants.

METHODS

Fifteen studies from 12 countries (Austria, Belgium, Denmark, France, Germany, Greece, Israel, Luxembourg, Slovenia, Spain, Sweden, and the United Kingdom) were included in the BPA Study protocol developed within the European Joint Programme HBM4EU. Seventy variables related to the BPA exposure were collected through a rigorous post-harmonization process. Linear mixed regression models were used to investigate the determinants of total urine BPA in the combined population.

RESULTS

Total BPA was quantified in 85-100 % of women in 14 out of 15 contributing studies. Only the Austrian PBAT study (Western Europe), which had a limit of quantification 2.5 to 25-fold higher than the other studies (LOQ=2.5 µg/L), found total BPA in less than 5 % of the urine samples analyzed. The geometric mean (GM) of total urine BPA ranged from 0.77 to 2.47 µg/L among the contributing studies. The lowest GM of total BPA was observed in France (Western Europe) from the ELFE subset (GM=0.77 µg/L (0.98 µg/g creatinine), n = 1741), and the highest levels were found in Belgium (Western Europe) and Greece (Southern Europe), from DEMOCOPHES (GM=2.47 µg/L (2.26 µg/g creatinine), n = 129) and HELIX-RHEA (GM=2.47 µg/L (2.44 µg/g creatinine), n = 194) subsets, respectively. One hundred percent of women in 14 out of 15 data collections in this study exceeded the health-based human biomonitoring guidance value for the general population (HBM-GVGenPop) of 0.0115 µg total BPA/L urine derived from the updated EFSA's BPA TDI. Variables related to the measurement of total urine BPA and those related to the main socio-demographic characteristics (age, height, weight, education, smoking status) were collected in almost all studies, while several variables related to BPA exposure factors were not gathered in most of the original studies (consumption of beverages contained in plastic bottles, consumption of canned food or beverages, consumption of food in contact with plastic packaging, use of plastic film or plastic containers for food, having a plastic floor covering in the house, use of thermal paper…). No clear determinants of total urine BPA concentrations among European women were found. A broader range of data planned for collection in the original questionnaires of the contributing studies would have resulted in a more thorough investigation of the determinants of BPA exposure in European women.

CONCLUSION

This study highlights the urgent need for action to further reduce exposure to BPA to protect the population, as is already the case in the European Union. The study also underscores the importance of pre-harmonizing HBM design and data for producing comparable data and interpretable results at a European-wide level, and to increase HBM uptake by regulatory agencies.

Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease

BACKGROUND

Precision Health aims to revolutionize disease prevention by leveraging information across multiple omic datasets (multi-omics). However, existing methods generally do not consider personalized environmental risk factors (e.g., environmental pollutants).

OBJECTIVE

To develop and apply a precision health framework which combines multiomic integration (including early, intermediate, and late integration, representing sequential stages at which omics layers are combined for modeling) with mediation approaches (including high-dimensional mediation to identify biomarkers, mediation with latent factors to identify pathways, and integrated/quasi-mediation to identify high-risk subpopulations) to identify novel biomarkers of prenatal mercury induced metabolic dysfunction-associated fatty liver disease (MAFLD), elucidate molecular pathways linking prenatal mercury with MAFLD in children, and identify high-risk children based on integrated exposure and multiomics data.

METHODS

This prospective cohort study used data from 420 mother-child pairs from the Human Early Life Exposome (HELIX) project. Mercury concentrations were determined in maternal or cord blood from pregnancy. Cytokeratin 18 (CK-18; a MAFLD biomarker) and five omics layers (DNA Methylation, gene transcription, microRNA, proteins, and metabolites) were measured in blood in childhood (age 6-10 years).

RESULTS

Each standard deviation increase in prenatal mercury was associated with a 0.11 [95% confidence interval: 0.02-0.21] standard deviation increase in CK-18. High dimensional mediation analysis identified 10 biomarkers linking prenatal mercury and CK-18, including six CpG sites and four transcripts. Mediation with latent factors identified molecular pathways linking mercury and MAFLD, including altered cytokine signaling and hepatic stellate cell activation. Integrated/quasi-mediation identified high risk subgroups of children based on unique combinations of exposure levels, omics profiles (driven by epigenetic markers), and MAFLD.

CONCLUSIONS

Prenatal mercury exposure is associated with elevated liver enzymes in childhood, likely through alterations in DNA methylation and gene expression. Our analytic framework can be applied across many different fields and serve as a resource to help guide future precision health investigations.

The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform

The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research. Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: (1) environmental data integration and harmonization system, (2) clinical and omics data integration, (3) advanced analytical algorithm development, and (4) visualization interface development and targeted population-based cohort recruitment. The CHiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.

Early life exposure to mercury and relationships with telomere length and mitochondrial DNA content in European children

BACKGROUND

Telomere length (TL) and mitochondrial function expressed as mitochondrial DNA copy number (mtDNAcn) are biomarkers of aging and oxidative stress and inflammation, respectively. Methylmercury (MeHg), a common pollutant in fish, induces oxidative stress. We hypothesized that elevated oxidative stress from exposure to MeHg decreases mtDNAcn and shortens TL.

METHODS

Study participants are 6-11-year-old children from the HELIX multi-center birth cohort study, comprising six European countries. Prenatal and postnatal total mercury (THg) concentrations were measured in blood samples, TL and mtDNAcn were determined in child DNA. Covariates and confounders were obtained by questionnaires. Robust regression models were run, considering sociodemographic and lifestyle covariates, as well as fish consumption. Sex, ethnicity, and fish consumption interaction models were also run.

RESULTS

We found longer TL with higher pre- and postnatal THg blood concentrations, even at low-level THg exposure according to the RfD proposed by the US EPA. The prenatal association showed a significant linear relationship with a 3.46 % increase in TL for each unit increased THg. The postnatal association followed an inverted U-shaped marginal non-linear relationship with 1.38 % an increase in TL for each unit increased THg until reaching a cut-point at 0.96 μg/L blood THg, from which TL attrition was observed. Higher pre- and postnatal blood THg concentrations were consistently related to longer TL among cohorts and no modification effect of fish consumption nor children's sex was observed. No association between THg exposure and mtDNAcn was found.

DISCUSSION

We found evidence that THg is associated with TL but the associations seem to be time- and concentration-dependent. Further studies are needed to clarify the mechanism behind the telomere changes of THg and related health effects.

Machine learning-based health environmental-clinical risk scores in European children

BACKGROUND

Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data.

METHODS

In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions.

RESULTS

ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health.

CONCLUSIONS

Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

Prenatal Exposure to Chemical Mixtures and Metabolic Syndrome Risk in Children

Importance

Prenatal exposure to ubiquitous endocrine-disrupting chemicals (EDCs) may increase the risk of metabolic syndrome (MetS) in children, but few studies have studied chemical mixtures or explored underlying protein and metabolic signatures.

Objective

To investigate associations of prenatal exposure to EDC mixtures with MetS risk score in children and identify associated proteins and metabolites.

Design, Setting, and Participants

This population-based, birth cohort study used data collected between April 1, 2003, and February 26, 2016, from the Human Early Life Exposome cohort based in France, Greece, Lithuania, Norway, Spain, and the UK. Eligible participants included mother-child pairs with measured prenatal EDC exposures and complete data on childhood MetS risk factors, proteins, and metabolites. Data were analyzed between October 2022 and July 2023.

Exposures

Nine metals, 3 organochlorine pesticides, 5 polychlorinated biphenyls, 2 polybrominated diphenyl ethers (PBDEs), 5 perfluoroalkyl substances (PFAS), 10 phthalate metabolites, 3 phenols, 4 parabens, and 4 organophosphate pesticide metabolites measured in urine and blood samples collected during pregnancy.

Main Outcomes and Measures

At 6 to 11 years of age, a composite MetS risk score was constructed using z scores of waist circumference, systolic and diastolic blood pressures, triglycerides, high-density lipoprotein cholesterol, and insulin levels. Childhood levels of 44 urinary metabolites, 177 serum metabolites, and 35 plasma proteins were quantified using targeted methods. Associations were assessed using bayesian weighted quantile sum regressions applied to mixtures for each chemical group.

Results

The study included 1134 mothers (mean [SD] age at birth, 30.7 [4.9] years) and their children (mean [SD] age, 7.8 [1.5] years; 617 male children [54.4%] and 517 female children [45.6%]; mean [SD] MetS risk score, -0.1 [2.3]). MetS score increased per 1-quartile increase of the mixture for metals (β = 0.44; 95% credible interval [CrI], 0.30 to 0.59), organochlorine pesticides (β = 0.22; 95% CrI, 0.15 to 0.29), PBDEs (β = 0.17; 95% CrI, 0.06 to 0.27), and PFAS (β = 0.19; 95% CrI, 0.14 to 0.24). High-molecular weight phthalate mixtures (β = -0.07; 95% CrI, -0.10 to -0.04) and low-molecular weight phthalate mixtures (β = -0.13; 95% CrI, -0.18 to -0.08) were associated with a decreased MetS score. Most EDC mixtures were associated with elevated proinflammatory proteins, amino acids, and altered glycerophospholipids, which in turn were associated with increased MetS score.

Conclusions and Relevance

This cohort study suggests that prenatal exposure to EDC mixtures may be associated with adverse metabolic health in children. Given the pervasive nature of EDCs and the increase in MetS, these findings hold substantial public health implications.

Systematic investigation of organochlorine pesticides and polychlorinated biphenyls blood levels in Greek children from the Rhea birth cohort suggests historical exposure to DDT and through diet to DDE

The blood levels of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) have been thoroughly investigated in Greek children from the Rhea birth cohort study. This investigation aimed to assess exposure levels, explore their possible relationship with children's age and sex, and indicate potential sources of exposure. Exposure patterns and common sources of PCBs and OCPs were analyzed using bivariate and multivariate statistics. A total of 947 blood samples from study participants were analyzed for OCP and PCB exposure, with 375 samples collected at 4 years old, 239 at 6.5 years old, and 333 at 11 years old. Elevated levels of DDE were observed in 6.5-year-old children compared to corresponding levels in other European countries. Higher levels of DDE were found in 4-year-old children, with the lowest concentrations in the 11-year-old group. The DDT/DDE ratio was consistently less than 1 among all the examined subjects. These results indicate exposure to DDT and DDE both in utero and through breastfeeding and dietary intake. For the entire cohort population, the highest concentration was determined for PCB 28, followed by PCBs 138, 153, and 180. The sum of the six indicator PCBs implied low exposure levels for the majority of the cohort population. Spearman correlations revealed strong associations between PCBs and OCPs, while principal component analysis identified two different groupings of exposure. DDE exhibited a correlation with a series of PCBs (153, 156, 163, 180), indicating a combined OCP-PCB source, and an anticorrelation with others (52, 28, 101), implying a separate and competing source.

Estimating the dynamic early life exposure to PFOA and PFOS of the HELIX children: Emerging profiles via prenatal exposure, breastfeeding, and diet

In utero and children's exposure to per- and polyfluoroalkyl substances (PFAS) is a major concern in health risk assessment as early life exposures are suspected to induce adverse health effects. Our work aims to estimate children's exposure (from birth to 12 years old) to PFOA and PFOS, using a Physiologically-Based Pharmacokinetic (PBPK) modelling approach. A model for PFAS was updated to simulate the internal PFAS exposures during the in utero life and childhood, and including individual characteristics and exposure scenarios (e.g., duration of breastfeeding, weight at birth, etc.). Our approach was applied to the HELIX cohort, involving 1,239 mother-child pairs with measured PFOA and PFOS plasma concentrations at two sampling times: maternal and child plasma concentrations (6 to 12 y.o). Our model predicted an increase in plasma concentrations during fetal development and childhood until 2 y.o when the maximum concentrations were reached. Higher plasma concentrations of PFOA than PFOS were predicted until 2 y.o, and then PFOS concentrations gradually became higher than PFOA concentrations. From 2 to 8 y.o, mean concentrations decreased from 3.1 to 1.88 µg/L or ng/mL (PFOA) and from 4.77 to 3.56 µg/L (PFOS). The concentration-time profiles vary with the age and were mostly influenced by in utero exposure (on the first 4 months after birth), breastfeeding (from 5 months to 2 (PFOA) or 5 (PFOS) y.o of the children), and food intake (after 3 (PFOA) or 6 (PFOS) y.o of the children). Similar measured biomarker levels can correspond to large differences in the simulated internal exposures, highlighting the importance to investigate the children's exposure over the early life to improve exposure classification. Our approach demonstrates the possibility to simulate individual internal exposures using PBPK models when measured biomarkers are scarce, helping risk assessors in gaining insight into internal exposure during critical windows, such as early life.

Bisphenol A exposure affects specific gut taxa and drives microbiota dynamics in childhood obesity

Cumulative xenobiotic exposure has an environmental and human health impact which is currently assessed under the One Health approach. Bisphenol A (BPA) exposure and its potential link with childhood obesity that has parallelly increased during the last decades deserve special attention. It stands during prenatal or early life and could trigger comorbidities and non-communicable diseases along life. Accumulation in the nature of synthetic chemicals supports the "environmental obesogen" hypothesis, such as BPA. This estrogen-mimicking xenobiotic has shown endocrine disruptive and obesogenic effects accompanied by gut microbiota misbalance that is not yet well elucidated. This study aimed to investigate specific microbiota taxa isolated and selected by direct BPA exposure and reveal its role on the overall children microbiota community and dynamics, driving toward specific obesity dysbiosis. A total of 333 BPA-resistant isolated species obtained through culturing after several exposure conditions were evaluated for their role and interplay with the global microbial community. The selected BPA-cultured taxa biomarkers showed a significant impact on alpha diversity. Specifically, Clostridium and Romboutsia were positively associated promoting the richness of microbiota communities, while Intestinibacter, Escherichia-Shigella, Bifidobacterium, and Lactobacillus were negatively associated. Microbial community dynamics and networks analyses showed differences according to the study groups. The normal-weight children group exhibited a more enriched, structured, and connected taxa network compared to overweight and obese groups, which could represent a more resilient community to xenobiotic substances. In this sense, subnetwork analysis generated with the BPA-cultured genera showed a correlation between taxa connectivity and more diverse potential enzymatic BPA degradation capacities.IMPORTANCEOur findings indicate how gut microbiota taxa with the capacity to grow in BPA were differentially represented within differential body mass index children study groups and how these taxa affected the overall dynamics toward patterns of diversity generally recognized in dysbiosis. Community network and subnetwork analyses corroborated the better connectedness and stability profiles for normal-weight group compared to the overweight and obese groups.

[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.

Building towards an adolescent neural urbanome: Expanding environmental measures using linked external data (LED) in the ABCD study

Many recent studies have demonstrated that environmental contexts, both social and physical, have an important impact on child and adolescent neural and behavioral development. The adoption of geospatial methods, such as in the Adolescent Brain Cognitive Development (ABCD) Study, has facilitated the exploration of many environmental contexts surrounding participants' residential locations without creating additional burdens for research participants (i.e., youth and families) in neuroscience studies. However, as the number of linked databases increases, developing a framework that considers the various domains related to child and adolescent environments external to their home becomes crucial. Such a framework needs to identify structural contextual factors that may yield inequalities in children's built and natural environments; these differences may, in turn, result in downstream negative effects on children from historically minoritized groups. In this paper, we develop such a framework - which we describe as the "adolescent neural urbanome" - and use it to categorize newly geocoded information incorporated into the ABCD Study by the Linked External Data (LED) Environment & Policy Working Group. We also highlight important relationships between the linked measures and describe possible applications of the Adolescent Neural Urbanome. Finally, we provide a number of recommendations and considerations regarding the responsible use and communication of these data, highlighting the potential harm to historically minoritized groups through their misuse.

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.

Genome-Wide Association Study of Blood Mercury in European Pregnant Women and Children

Mercury has high industrial utility and is present in many products, and environmental contamination and occupational exposure are widespread. There are numerous biological systems involved in the absorption, metabolism, and excretion of Hg, and it is possible that some systems may be impacted by genetic variation. If so, genotype may affect tissue concentrations of Hg and subsequent toxic effects. Genome-wide association testing was performed on blood Hg samples from pregnant women of the Avon Longitudinal Study of Parents and Children (n = 2893) and children of the Human Early Life Exposome (n = 1042). Directly-genotyped single-nucleotide polymorphisms (SNPs) were imputed to the Haplotype Reference Consortium r1.1 panel of whole genotypes and modelled againstlog-transformed Hg. Heritability was estimated using linkage disequilibrium score regression. The heritability of Hg was estimated as 24.0% (95% CI: 16.9% to 46.4%) in pregnant women, but could not be determined in children. There were 16 SNPs associated with Hg in pregnant women above a suggestive p-value threshold (p < 1 × 10-5), and 21 for children. However, no SNP passed this threshold in both studies, and none were genome-wide significant (p < 5 × 10-8). SNP-Hg associations were highly discordant between women and children, and this may reflect differences in metabolism, a gene-age interaction, or dose-response effects. Several suggestive variants had plausible links to Hg metabolism, such as rs146099921 in metal transporter SLC39A14, and two variants (rs28618224, rs7154700) in potassium voltage-gated channel genes. The findings would benefit from external validation, as suggestive results may contain both true associations and false positives.

A roadmap to advance exposomics through federation of data

The scale of the human exposome, which covers all environmental exposures encountered from conception to death, presents major challenges in managing, sharing, and integrating a myriad of relevant data types and available data sets for the benefit of exposomics research and public health. By addressing these challenges, the exposomics research community will be able to greatly expand on its ability to aggregate study data for new discoveries, construct and update novel exposomics data sets for building artificial intelligence and machine learning-based models, rapidly survey emerging issues, and advance the application of data-driven science. The diversity of the field, which spans multiple subfields of science disciplines and different environmental contexts, necessitates adopting data federation approaches to bridge between numerous geographically and administratively separated data resources that have varying usage, privacy, access, analysis, and discoverability capabilities and constraints. This paper presents use cases, challenges, opportunities, and recommendations for the exposomics community to establish and mature a federated exposomics data ecosystem.

A Long Way from Steubenville: Environmental Epidemiology in a Rapidly Changing World

This commentary focuses on research that has long been at the core of environmental epidemiology: studies of the health effects of air pollution. It highlights publications in the American Journal of Epidemiology going back more than 50 years that have contributed to the debate about the validity of this research and its meaning for public policy. Technological advances have greatly expanded the toolbox of environmental epidemiologists in terms of measuring and analyzing complex exposures in large populations. Yet, discussions about biases in estimating air pollution health effects have always been and remain intense. Epidemiologists have brought new methodologies and concepts to this research, alleviating some but not all concerns. Here, the focus is on seminal epidemiologic work that established valid links between air pollution exposures and health outcomes and generated data for environmental policies and prevention. With this commentary, I hope to inspire epidemiologists to address many more of the burning environmental health questions-wildfires included-with a similar scientific doggedness. The rapidly changing conditions of our planet are challenging us to innovate and offer solutions, albeit perhaps a little bit faster this time around.

Occupational exposure to potentially toxic elements alters gene expression profiles in formal and informal Brazilian workers

Chemical elements, such as toxic metals, have previously demonstrated their ability to alter gene expression in humans and other species. In this study, microarray analysis was used to compare the gene expression profiles of different occupational exposure populations: a) informal workers who perform soldering of jewelry inside their houses (n = 22) in São Paulo (SP) State; and b) formal workers from a steel company (n = 10) in Rio de Janeiro (RJ) state, Brazil. Control participants were recruited from the same neighborhoods without occupational chemical exposure (n = 19 in SP and n = 8 in RJ). A total of 68 blood samples were collected and RNA was extracted and hybridized using an Agilent microarray platform. Data pre-processing, statistical and pathway analysis were performed using GeneSpring software. Different expression was detected by fold-change analysis resulting in 16 up- and 33 down-regulated genes in informal workers compared to the control group. Pathway analysis revealed genes enriched in MAPK, Toll-like receptor, and NF-kappa B signaling pathways, involved in inflammatory and immune responses. In formal workers, 20 up- and 50 down-regulated genes were found related to antimicrobial peptides, defensins, neutrophil degranulation, Fc-gamma receptor-dependent phagocytosis, and pathways associated with atherosclerosis development, which is one of the main factors involved in the progression of cardiovascular diseases. The gene IFI27 was the only one commonly differentially expressed between informal and formal workers and is known to be associated with various types of cancer. In conclusion, differences in gene expression related to occupational exposure are mainly associated with inflammation and immune response. Previous research has identified a link between inflammation and immune responses and the development of chronic diseases, suggesting that prolonged occupational exposures to potentially toxic elements in Brazilian metal workers could lead to negative health outcomes. Further analysis should be carried out to investigate its direct effects and to validate causal associations.

Microbiota analysis for risk assessment of xenobiotic exposure and the impact on dysbiosis: identifying potential next-generation probiotics

On-going projects of the team are currently dealing with microbiota, xenobiotics, endocrine-disrupting chemicals (EDCs), obesity, inflammation and probiotics. The combination of diet, lifestyle and the exposure to dietary xenobiotics categorised into microbiota-disrupting chemicals (MDCs) could determine obesogenic-related dysbiosis. This modification of the microbiota diversity impacts on individual health-disease balance, inducing altered phenotypes. Specific, complementary, and combined prevention and treatments are needed to face these altered microbial patterns and the specific misbalances triggered. In this sense, searching for next-generation probiotics (NGP) by microbiota culturing, and focusing on their demonstrated, extensive scope and well-defined functions could contribute to counteracting and repairing the effects of obesogens. Therefore, EU-FORA project contributes to present a perspective through compiling information and key strategies for directed taxa searching and culturing of NGP that could be administered for preventing obesity and endocrine-related dysbiosis by (i) observing the differential abundance of specific microbiota taxa in obesity-related patients and analysing their functional roles, (ii) developing microbiota-directed strategies for culturing these taxa groups, and (iii) design and applying the successful compiled criteria from recent NGP clinical studies. New isolated or cultivable microorganisms from healthy gut microbiota specifically related to xenobiotic obesogens' neutralisation effects might be used as an NGP single strain or in consortia, both presenting functions and the ability to palliate metabolic-related disorders. Identification of holistic approaches for searching and using potential NGP, key aspects, the bias, gaps and proposals of solutions were also considered in this workplan.

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.

Environmental exposures in early-life and general health in childhood

BACKGROUND

Early-life environmental exposures are suspected to be involved in the development of chronic diseases later in life. Most studies conducted so far considered single or few exposures and single-health parameter. Our study aimed to identify a childhood general health score and assess its association with a wide range of pre- and post-natal environmental exposures.

METHODS

The analysis is based on 870 children (6-12 years) from six European birth cohorts participating in the Human Early-Life Exposome project. A total of 53 prenatal and 105 childhood environmental factors were considered, including lifestyle, social, urban and chemical exposures. We built a general health score by averaging three sub-scores (cardiometabolic, respiratory/allergy and mental) built from 15 health parameters. By construct, a child with a low score has a low general health status. Penalized multivariable regression through Least Absolute Shrinkage and Selection Operator (LASSO) was fitted in order to identify exposures associated with the general health score.

FINDINGS

The results of LASSO show that a lower general health score was associated with maternal passive and active smoking during pregnancy and postnatal exposure to methylparaben, copper, indoor air pollutants, high intake of caffeinated drinks and few contacts with friends and family. Higher child's general health score was associated with prenatal exposure to a bluespace near residency and postnatal exposures to pets, cobalt, high intakes of vegetables and more physical activity. Against our hypotheses, postnatal exposure to organochlorine compounds and perfluorooctanoate were associated with a higher child's general health score.

CONCLUSION

By using a general health score summarizing the child cardiometabolic, respiratory/allergy and mental health, this study reinforced previously suspected environmental factors associated with various child health parameters (e.g. tobacco, air pollutants) and identified new factors (e.g. pets, bluespace) warranting further investigations.

Prenatal environmental exposures associated with sex differences in childhood obesity and neurodevelopment

BACKGROUND

Obesity and neurodevelopmental delay are complex traits that often co-occur and differ between boys and girls. Prenatal exposures are believed to influence children's obesity, but it is unknown whether exposures of pregnant mothers can confer a different risk of obesity between sexes, and whether they can affect neurodevelopment.

METHODS

We analyzed data from 1044 children from the HELIX project, comprising 93 exposures during pregnancy, and clinical, neuropsychological, and methylation data during childhood (5-11 years). Using exposome-wide interaction analyses, we identified prenatal exposures with the highest sexual dimorphism in obesity risk, which were used to create a multiexposure profile. We applied causal random forest to classify individuals into two environments: E1 and E0. E1 consists of a combination of exposure levels where girls have significantly less risk of obesity than boys, as compared to E0, which consists of the remaining combination of exposure levels. We investigated whether the association between sex and neurodevelopmental delay also differed between E0 and E1. We used methylation data to perform an epigenome-wide association study between the environments to see the effect of belonging to E1 or E0 at the molecular level.

RESULTS

We observed that E1 was defined by the combination of low dairy consumption, non-smokers' cotinine levels in blood, low facility richness, and the presence of green spaces during pregnancy (OR_interaction = 0.070, P = 2.59 × 10^-5). E1 was also associated with a lower risk of neurodevelopmental delay in girls, based on neuropsychological tests of non-verbal intelligence (OR_interaction = 0.42, P = 0.047) and working memory (OR_interaction = 0.31, P = 0.02). In line with this, several neurodevelopmental functions were enriched in significant differentially methylated probes between E1 and E0.

CONCLUSIONS

The risk of obesity can be different for boys and girls in certain prenatal environments. We identified an environment combining four exposure levels that protect girls from obesity and neurodevelopment delay. The combination of single exposures into multiexposure profiles using causal inference can help determine populations at risk.

Childhood exposure to non-persistent endocrine disrupting chemicals and multi-omic profiles: A panel study

BACKGROUND

Individuals are exposed to environmental pollutants with endocrine disrupting activity (endocrine disruptors, EDCs) and the early stages of life are particularly susceptible to these exposures. Previous studies have focused on identifying molecular signatures associated with EDCs, but none have used repeated sampling strategy and integrated multiple omics. We aimed to identify multi-omic signatures associated with childhood exposure to non-persistent EDCs.

METHODS

We used data from the HELIX Child Panel Study, which included 156 children aged 6 to 11. Children were followed for one week, in two time periods. Twenty-two non-persistent EDCs (10 phthalate, 7 phenol, and 5 organophosphate pesticide metabolites) were measured in two weekly pools of 15 urine samples each. Multi-omic profiles (methylome, serum and urinary metabolome, proteome) were measured in blood and in a pool urine samples. We developed visit-specific Gaussian Graphical Models based on pairwise partial correlations. The visit-specific networks were then merged to identify reproducible associations. Independent biological evidence was systematically sought to confirm some of these associations and assess their potential health implications.

RESULTS

950 reproducible associations were found among which 23 were direct associations between EDCs and omics. For 9 of them, we were able to find corroborating evidence from previous literature: DEP - serotonin, OXBE - cg27466129, OXBE - dimethylamine, triclosan - leptin, triclosan - serotonin, MBzP - Neu5AC, MEHP - cg20080548, oh-MiNP - kynurenine, oxo-MiNP - 5-oxoproline. We used these associations to explore possible mechanisms between EDCs and health outcomes, and found links to health outcomes for 3 analytes: serotonin and kynurenine in relation to neuro-behavioural development, and leptin in relation to obesity and insulin resistance.

CONCLUSIONS

This multi-omics network analysis at two time points identified biologically relevant molecular signatures related to non-persistent EDC exposure in childhood, suggesting pathways related to neurological and metabolic outcomes.

Modelling socioeconomic position as a driver of the exposome in the first 18 months of life of the NINFEA birth cohort children

BACKGROUND

The exposome drivers are less studied than its consequences but may be crucial in identifying population subgroups with unfavourable exposures.

OBJECTIVES

We used three approaches to study the socioeconomic position (SEP) as a driver of the early-life exposome in Turin children of the NINFEA cohort (Italy).

METHODS

Forty-two environmental exposures, collected at 18 months of age (N = 1989), were classified in 5 groups (lifestyle, diet, meteoclimatic, traffic-related, built environment). We performed cluster analysis to identify subjects sharing similar exposures, and intra-exposome-group Principal Component Analysis (PCA) to reduce the dimensionality. SEP at childbirth was measured through the Equivalised Household Income Indicator. SEP-exposome association was evaluated using: 1) an Exposome Wide Association Study (ExWAS), a one-exposure (SEP) one-outcome (exposome) approach; 2) multinomial regression of cluster membership on SEP; 3) regressions of each intra-exposome-group PC on SEP.

RESULTS

In the ExWAS, medium/low SEP children were more exposed to greenness, pet ownership, passive smoking, TV screen and sugar; less exposed to NO₂, NO_X, PM_25abs, humidity, built environment, traffic load, unhealthy food facilities, fruit, vegetables, eggs, grain products, and childcare than high SEP children. Medium/low SEP children were more likely to belong to a cluster with poor diet, less air pollution, and to live in the suburbs than high SEP children. Medium/low SEP children were more exposed to lifestyle PC1 (unhealthy lifestyle) and diet PC2 (unhealthy diet), and less exposed to PC1s of the built environment (urbanization factors), diet (mixed diet), and traffic (air pollution) than high SEP children.

CONCLUSIONS

The three approaches provided consistent and complementary results, suggesting that children with lower SEP are less exposed to urbanization factors and more exposed to unhealthy lifestyles and diet. The simplest method, the ExWAS, conveys most of the information and is more replicable in other populations. Clustering and PCA may facilitate results interpretation and communication.

A network approach to compute hypervolume under receiver operating characteristic manifold for multi-class biomarkers

Computation of hypervolume under ROC manifold (HUM) is necessary to evaluate biomarkers for their capability to discriminate among multiple disease types or diagnostic groups. However the original definition of HUM involves multiple integration and thus a medical investigation for multi-class receiver operating characteristic (ROC) analysis could suffer from huge computational cost when the formula is implemented naively. We introduce a novel graph-based approach to compute HUM efficiently in this article. The computational method avoids the time-consuming multiple summation when sample size or the number of categories is large. We conduct extensive simulation studies to demonstrate the improvement of our method over existing R packages. We apply our method to two real biomedical data sets to illustrate its application.

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.

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.

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.

Study protocol to explore the social effects of environmental exposure and lifestyle behaviours on pregnancy outcome: an overview of cohort of pregnant women study

INTRODUCTION

A growing number of international studies have highlighted the adverse consequences of lived experience in the first thousand days of pregnancy and early life on the probability of stillbirth, child mortality, inadequate growth and healthy development during both childhood and adulthood. The lived experience of the fetus inside the womb and at the birth is strongly related to both maternal health during pregnancy and maternal exposure to a set of environmental factors known as 'exposome' characteristics, which include environmental exposure, health behaviours, living conditions, neighbourhood characteristics and socioeconomic profile. The aim of our project is to explore the relationships between exposome characteristics and the health status of pregnant women and their newborns. We are particularly interested in studying the relationships between the social inequality of adverse pregnancy outcomes and (1) short-term exposure to atmospheric pollution (MobiFem project) and (2) pregnancy lifestyle (EnviFem project).

METHODS AND ANALYSIS

Ours is a prospective, observational and multisite cohort study of pregnant women, involving one teaching hospital across two sites in the Strasbourg metropolitan area.The research team at University Hospital of Strasbourg (HUS) Health collects data on outcomes and individual characteristics from pregnancy registries, clinical records data and questionnaires administered via email to study participants. Recruitment began in February 2021 and will be complete by December 2021. Participants are recruited from first trimester antenatal ultrasound examinations (conducted on weekdays across both sites); each woman meeting our inclusion criteria enters the cohort at the end of her first trimester. Study participants receive a total of three online questionnaires covering sociodemographic characteristics, travel behaviour patterns and lifestyle. Participants complete these questionnaires at recruitment, during the second and third trimester. The level of personal exposure to air pollution is characterised using a dynamic spatiotemporal trajectory model that describes the main daily movements of pregnant women and the time spent in each place frequented. Univariate, multilevel and Bayesian model will be used to investigate the relationships between exposome characteristics and the health status of pregnant women and their newborns.

ETHICS AND DISSEMINATION

Our research was approved by the Commission de Protection des Personnes (CPP) Ile de France VI (Paris) on 9 December 2020 (File reference No. 20.09.15.41703 ID RCB: 2020-A02580-39 and No. 20 080-42137 IDRCB 2020-A02581-38). The Agence Nationale de Sécurité du Médicament was informed of it on 15 December 2020. Findings from the study will be disseminated through publications and international conferences and through presentation at meetings with local stakeholders, researchers and policy-makers.

TRIAL REGISTRATION NUMBERS

NCT04705272, NCT04725734.

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.

Association of Prenatal Exposure to Endocrine-Disrupting Chemicals With Liver Injury in Children

Importance

Prenatal exposures to endocrine-disrupting chemicals (EDCs) may increase the risk for liver injury in children; however, human evidence is scarce, and previous studies have not considered potential EDC-mixture effects. Furthermore, the association between prenatal EDC exposure and hepatocellular apoptosis in children has not been studied previously.

Objective

To investigate associations of prenatal exposure to EDC mixtures with liver injury risk and hepatocellular apoptosis in childhood.

Design, Setting, and Participants

This prospective cohort study used data collected from April 1, 2003, to February 26, 2016, from mother-child pairs from the Human Early-Life Exposome project, a collaborative network of 6 ongoing, population-based prospective birth cohort studies from 6 European countries (France, Greece, Lithuania, Norway, Spain, and the UK). Data were analyzed from April 1, 2021, to January 31, 2022.

Exposures

Three organochlorine pesticides, 5 polychlorinated biphenyls, 2 polybrominated diphenyl ethers (PBDEs), 3 phenols, 4 parabens, 10 phthalates, 4 organophosphate pesticides, 5 perfluoroalkyl substances, and 9 metals.

Main Outcomes and Measures

Child serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltransferase (GGT), and CK-18 were measured at 6 to 11 years of age. Risk for liver injury was defined as having ALT, AST, and/or GGT levels above the 90th percentile. Associations of liver injury or cytokeratin 18 (CK-18) levels with each chemical group among the 45 EDCs measured in maternal blood or urine samples collected in pregnancy were estimated using 2 complimentary exposure-mixture methods: bayesian weighted quantile sum (BWQS) and bayesian kernel machine regression.

Results

The study included 1108 mothers (mean [SD] age at birth, 31.0 [4.7] years) and their singleton children (mean [SD] age at liver assessment, 8.2 [1.6] years; 598 [54.0%] boys). Results of the BWQS method indicated increased odds of liver injury per exposure-mixture quartile increase for organochlorine pesticides (odds ratio [OR], 1.44 [95% credible interval (CrI), 1.21-1.71]), PBDEs (OR, 1.57 [95% CrI, 1.34-1.84]), perfluoroalkyl substances (OR, 1.73 [95% CrI, 1.45-2.09]), and metals (OR, 2.21 [95% CrI, 1.65-3.02]). Decreased odds of liver injury were associated with high-molecular-weight phthalates (OR, 0.74 [95% CrI, 0.60-0.91]) and phenols (OR, 0.66 [95% CrI, 0.54-0.78]). Higher CK-18 levels were associated with a 1-quartile increase in polychlorinated biphenyls (β, 5.84 [95% CrI, 1.69-10.08] IU/L) and PBDEs (β, 6.46 [95% CrI, 3.09-9.92] IU/L). Bayesian kernel machine regression showed associations in a similar direction as BWQS for all EDCs and a nonlinear association between phenols and CK-18 levels.

Conclusions and Relevance

With a combination of 2 state-of-the-art exposure-mixture approaches, consistent evidence suggests that prenatal exposures to EDCs are associated with higher risk for liver injury and CK-18 levels and constitute a potential risk factor for pediatric nonalcoholic fatty liver disease.

Addressing Exposome: An Innovative Approach to Environmental Determinants in Pediatric Respiratory Health

Developmental age is particularly vulnerable to impacts of environmental exposures. Until recent years, the field of environment and child health has predominantly relied on the study of single exposure-health effect relationships. The exposome is an emerging concept in epidemiology, encompassing the totality of the exposures experienced by an individual throughout life and their changes over time. This innovative approach provides a risk profile instead of individual predictors. Exposome research may contribute to better understand the complex relationships between environmental exposures and childhood respiratory health, in order to implement prevention strategies and mitigate adverse health outcomes across the life span. Indeed, an accurate assessment of the exposome needs several measurements as well as different technologies. High-throughput "omics" technologies may be promising tools to integrate a wide range of exposures. However, analyzing large and complex datasets requires the development of advanced statistical tools. This narrative review summarizes the current knowledge on exposome-based approaches in pediatric respiratory health. Further, it explores practical implementation, associated evidence gaps, research limitations and future research perspectives.

The early-life exposome modulates the effect of polymorphic inversions on DNA methylation

Polymorphic genomic inversions are chromosomal variants with intrinsic variability that play important roles in evolution, environmental adaptation, and complex traits. We investigated the DNA methylation patterns of three common human inversions, at 8p23.1, 16p11.2, and 17q21.31 in 1,009 blood samples from children from the Human Early Life Exposome (HELIX) project and in 39 prenatal heart tissue samples. We found inversion-state specific methylation patterns within and nearby flanking each inversion region in both datasets. Additionally, numerous inversion-exposure interactions on methylation levels were identified from early-life exposome data comprising 64 exposures. For instance, children homozygous at inv-8p23.1 and higher meat intake were more susceptible to TDH hypermethylation (P = 3.8 × 10⁻²²); being the inversion, exposure, and gene known risk factors for adult obesity. Inv-8p23.1 associated hypermethylation of GATA4 was also detected across numerous exposures. Our data suggests that the pleiotropic influence of inversions during development and lifetime could be substantially mediated by allele-specific methylation patterns which can be modulated by the exposome.

Analysis of the relationship between presence of common DNA sequence inversions and DNA methylation patterns suggests a role for environmental exposures (such as food intake) in mediating inversion state-specific methylation patterns.

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.

An exposomic framework to uncover environmental drivers of aging

The exposome, the environmental complement of the genome, is an omics level characterization of an individual's exposures. There is growing interest in uncovering the role of the environment in human health using an exposomic framework that provides a systematic and unbiased analysis of the non-genetic drivers of health and disease. Many environmental toxicants are associated with molecular hallmarks of aging. An exposomic framework has potential to advance understanding of these associations and how modifications to the environment can promote healthy aging in the population. However, few studies have used this framework to study biological aging. We provide an overview of approaches and challenges in using an exposomic framework to investigate environmental drivers of aging. While capturing exposures over a life course is a daunting and expensive task, the use of historical data can be a practical way to approach this research.

Associations between the Maternal Exposome and Metabolome during Pregnancy

BACKGROUND

Maternal exposure to environmental chemicals during pregnancy can influence various maternal and offspring health parameters. Modification of maternal metabolism by environmental exposure may be an important pathway for these impacts. However, there is limited evidence regarding exposure to a wide array of chemicals and the metabolome during pregnancy.

OBJECTIVES

We investigated the relationship between the urinary exposome and metabolome during pregnancy.

METHODS

Urine samples were collected in the first and third trimesters from 1,024 pregnant women recruited in prenatal clinics in Jiangsu Province, China. The exposome was analyzed using the first trimester sample with ultra-high performance liquid chromatography-high resolution accurate mass spectrometry (UHPLC-HRMS) and inductively coupled plasma mass spectrometry. The metabolome was analyzed using the third trimester sample with UHPLC-HRMS. We evaluated associations between each of 106 exposures in the first trimester with 139 metabolites in the third trimester.

RESULTS

We identified 1,245 significant associations (p<3.39×10-6, Bonferroni correction) between chemical exposures and maternal metabolism during pregnancy. Among elements, the largest number of the significant metabolic associations were observed for magnesium, and among organic compounds, for 4-tert-octylphenol. We used exposome-metabolome associations to explore mechanisms underlying published associations between prenatal chemical exposures and offspring health outcomes. This integration of the literature with our results suggests that reported associations between 10 analytes and birth weight, gestational age, fat deposition, neurobehavioral development, immunological disorders, and hypertension may be partially mediated by metabolites associated with these exposures.

DISCUSSION

This high-dimensional analysis of the urinary exposome and metabolome identified many associations between chemical exposures and maternal metabolism during pregnancy. Integration of these associations with the literature on health outcomes of exposure suggests that environmental modulation of the maternal metabolome may play a role in the association between prenatal exposure on pregnancy and child health outcomes. https://doi.org/10.1289/EHP9745.

What to Expect When Expecting in Lab: A Review of Unique Risks and Resources for Pregnant Researchers in the Chemical Laboratory

Pregnancy presents a unique risk to chemical researchers due to their occupational exposures to chemical, equipment, and physical hazards in chemical research laboratories across science, engineering, and technology disciplines. Understanding “risk” as a function of hazard, exposure, and vulnerability, this review aims to critically examine the state of the science for the risks and associated recommendations (or lack thereof) for pregnant researchers in chemical laboratories (labs). Commonly encountered hazards for pregnant lab workers include chemical hazards (organic solvents, heavy metals, engineered nanomaterials, and endocrine disruptors), radiation hazards (ionizing radiation producing equipment and materials and nonionizing radiation producing equipment), and other hazards related to the lab environment (excessive noise, excessive heat, psychosocial stress, strenuous physical work, and/or abnormal working hours). Lab relevant doses and routes of exposure in the chemical lab environment along with literature and governmental recommendations or resources for exposure mitigation are critically assessed. The specific windows of vulnerability based on stage of pregnancy are described for each hazard, if available. Finally, policy gaps for further scientific research are detailed to enhance future guidance to protect pregnant lab workers.

Exposome and Skin: Part 1. Bibliometric Analysis and Review of the Impact of Exposome Approaches on Dermatology

Introduction

Most skin disorders, such as atopic dermatitis, psoriasis, skin cancer or age-related skin issues, are the result of a complex interaction between genetic and environmental factors over time. As an external organ, the skin provides the opportunity to study the link between exposure to the environment and several specific biological responses using an exposome approach. The aim of this review was to identify the state of the art of exposome approaches and elucidate the impact of the new era of exposomics on dermatology.

Methods

Two parallel and independent bibliometric analyses were conducted based on documents extracted from the Core Collection and the Science Citation Index Expanded (SCI-Expanded) databases from the Clarivate Analytics’ Web of Science (WOS) platform by using the following search terms “exposome” and “skin exposome”. In both searches, we used the topic field that includes title, abstract, author keywords and keywords plus terms and the following filters: “English language” and all documents published up to 30 September 2021. We further analysed and interpreted documents extracted in plain text format.

Results

Based on the defined searches, 910 documents were identified as being related to “exposome” and 45 as being related to “skin exposome”. Environmental sciences and toxicology were the most impacted research areas, and aging, cancer and respiratory allergies were the most documented diseases while, surprisingly, dermatology was much less impacted. Krutmann et al. were the pioneers in implementing this new concept in dermatology with publication of “The skin aging exposome” in 2017 (J Dermatol Sci. 2017;85:152–61). After this tipping point, the number of publications in dermatology evaluating the impact of exposome factors in many skin disorders has steadily increased.

Conclusions

Exposome studies are rapidly attracting interest in dermatology. The results of these studies will undoubtedly improve our understanding of why and under which circumstances some individuals develop skin disorders and help design tailored prevention strategies for patients suffering from these disorders.

Sample management: a primary critical starting point for successful omics studies

Biological samples collected from cohort studies are widely utilized in molecular genetic studies and are typically stored long term for future applications, such as omics analyses. The extent of sample availability is determined by proper sample handling, and it is of primary importance for successful omics studies. However, questions on whether samples in long-term storage are properly available for omics experiments has been raised, because the quality and availability of such samples remain unknown until their actual utilization. In that perspective, several guidelines for proper sample management have been suggested. In addition, several researchers assessed how improper management damages sample using mock sample and suggested a set of requirements for sample handling. In this review, we present several considerations for sample handling eligible for omics studies. Focusing on birth cohorts, we describe the types of samples collected from which omics data were generated. This review ultimately aims to provide proper guidelines for sample handling for successful human omics studies.

The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester

BACKGROUND

Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort.

METHODS

A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites.

RESULTS

Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO and the exposure estimates of PM_2.5 and NO_2, and positively associated with O₃.

CONCLUSIONS

This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.

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/].

Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview

Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.