Some challenges of studies aiming to relate the Exposome to human health

Longitudinal associations of an exposome score with serum metabolites from childhood to adolescence

Environmental and lifestyle factors, including air pollution, impaired diet, and low physical activity, have been associated with cardiometabolic risk factors in childhood and adolescence. However, environmental and lifestyle exposures do not exert their physiological effects in isolation. This study investigated associations between an exposome score to measure the impact of multiple exposures, including diet, physical activity, sleep duration, air pollution, and socioeconomic status, and serum metabolites measured using LC-MS and NMR, compared to the individual components of the score. A general population of 504 children aged 6-9 years at baseline was followed up for eight years. Data were analysed with linear mixed-effects models using the R software. The exposome score was associated with 31 metabolites, of which 12 metabolites were not associated with any individual exposure category. These findings highlight the value of a composite score to predict metabolic changes associated with multiple environmental and lifestyle exposures since childhood.

Environmental health science research: opportunities and challenges for some developing countries in Africa

Due to ongoing developmental projects, there is a need for regular monitoring of the impact of pollutants on the environment. This review documented the challenges and opportunities in the field of environmental health sciences in some African countries. A systematic review was used to investigate opportunities and challenges in the field of environmental health science in Africa by examining published work with a specific focus on Africa. The reports showed that funding and infrastructure as the major problems. The study also highlighted recruiting study participants, retention, and compensation as a bane in the field in Africa. The absence of modern equipment also hinders research. The review, however, noted research collaboration from the region including studies on emerging pollutants such as pharmaceuticals, per and polyfluoroalkyl substances (PFAS), and microplastic (MPs) as great opportunities. The study concluded that collaboration with other continents, exchange programs and improved governmental interventions may help.

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

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

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.

Environmental asbestos exposure and nonmalignant pleural findings: a retrospective evaluation of a five-year chest CT repository

This registry-based case-control study aimed to assess the association between asbestos deposits in the birthplace and/or residence and nonmalignant pleural findings, namely pleural plaques (PPs) and pleural thickening (PT), on chest CT scans. In total, 39,472 CT scans obtained over five years in a tertiary referral hospital in Ankara, Turkey, were evaluated. Cases involving patients with PP (n = 537), PT (n = 263), PP&PT (n = 69), and controls (n = 543) from the same study base without those conditions were included. Each case group was compared to controls using unconditional logistic regression. The presence of asbestos deposits in the district of birthplace (adjusted OR = 2.13, 95% CI: 1.35-3.37) and both birthplace and residence (aOR = 4.32, 95% CI: 2.26-8.27) was significantly related to the PPs. As the importance of environmental asbestos exposure in Turkey continues, future prospective studies could contribute to developing screening strategies.

Performance of approaches relying on multidimensional intermediary data to decipher causal relationships between the exposome and health: A simulation study under various causal structures

Challenges in the assessment of the health effects of the exposome, defined as encompassing all environmental exposures from the prenatal period onwards, include a possibly high rate of false positive signals. It might be overcome using data dimension reduction techniques. Data from the biological layers lying between the exposome and its possible health consequences, such as the methylome, may help reducing exposome dimension. We aimed to quantify the performances of approaches relying on the incorporation of an intermediary biological layer to relate the exposome and health, and compare them with agnostic approaches ignoring the intermediary layer. We performed a Monte-Carlo simulation, in which we generated realistic exposome and intermediary layer data by sampling with replacement real data from the Helix exposome project. We generated a Gaussian outcome assuming linear relationships between the three data layers, in 2381 scenarios under five different causal structures, including mediation and reverse causality. We tested 3 agnostic methods considering only the exposome and the health outcome: ExWAS (for Exposome-Wide Association study), DSA, LASSO; and 3 methods relying on an intermediary layer: two implementations of our new oriented Meet-in-the-Middle (oMITM) design, using ExWAS and DSA, and a mediation analysis using ExWAS. Methods' performances were assessed through their sensitivity and FDP (False-Discovery Proportion). The oMITM-based methods generally had lower FDP than the other approaches, possibly at a cost in terms of sensitivity; FDP was in particular lower under a structure of reverse causality and in some mediation scenarios. The oMITM-DSA implementation showed better performances than oMITM-ExWAS, especially in terms of FDP. Among the agnostic approaches, DSA showed the highest performance. Integrating information from intermediary biological layers can help lowering FDP in studies of the exposome health effects; in particular, oMITM seems less sensitive to reverse causality than agnostic exposome-health association studies.

Instability of Variable-selection Algorithms Used to Identify True Predictors of an Outcome in Intermediate-dimension Epidemiologic Studies

BACKGROUND

Machine-learning algorithms are increasingly used in epidemiology to identify true predictors of a health outcome when many potential predictors are measured. However, these algorithms can provide different outputs when repeatedly applied to the same dataset, which can compromise research reproducibility. We aimed to illustrate that commonly used algorithms are unstable and, using the example of Least Absolute Shrinkage and Selection Operator (LASSO), that stabilization method choice is crucial.

METHODS

In a simulation study, we tested the stability and performance of widely used machine-learning algorithms (LASSO, Elastic-Net, and Deletion-Substitution-Addition [DSA]). We then assessed the effectiveness of six methods to stabilize LASSO and their impact on performance. We assumed that a linear combination of factors drawn from a simulated set of 173 quantitative variables assessed in 1,301 subjects influenced to varying extents a continuous health outcome. We assessed model stability, sensitivity, and false discovery proportion.

RESULTS

All tested algorithms were unstable. For LASSO, stabilization methods improved stability without ensuring perfect stability, a finding confirmed by application to an exposome study. Stabilization methods also affected performance. Specifically, stabilization based on hyperparameter optimization, frequently implemented in epidemiology, increased the false discovery proportion dramatically when predictors explained a low share of outcome variability. In contrast, stabilization based on stability selection procedure often decreased the false discovery proportion, while sometimes simultaneously lowering sensitivity.

CONCLUSIONS

Machine-learning methods instability should concern epidemiologists relying on them for variable selection, as stabilizing a model can impact its performance. For LASSO, stabilization methods based on stability selection procedure (rather than addressing prediction stability) should be preferred to identify true predictors.

Association between the pregnancy exposome and fetal growth

BACKGROUND

Several environmental contaminants were shown to possibly influence fetal growth, generally from single exposure family studies, which are prone to publication bias and confounding by co-exposures. The exposome paradigm offers perspectives to avoid selective reporting of findings and to control for confounding by co-exposures. We aimed to characterize associations of fetal growth with the pregnancy chemical and external exposomes.

METHODS

Within the Human Early-Life Exposome project, 131 prenatal exposures were assessed using biomarkers and environmental models in 1287 mother-child pairs from six European cohorts. We investigated their associations with fetal growth using a deletion-substitution-addition (DSA) algorithm considering all exposures simultaneously, and an exposome-wide association study (ExWAS) considering each exposure independently. We corrected for exposure measurement error and tested for exposure-exposure and sex-exposure interactions.

RESULTS

The DSA model identified lead blood level, which was associated with a 97 g birth weight decrease for each doubling in lead concentration. No exposure passed the multiple testing-corrected significance threshold of ExWAS; without multiple testing correction, this model was in favour of negative associations of lead, fine particulate matter concentration and absorbance with birth weight, and of a positive sex-specific association of parabens with birth weight in boys. No two-way interaction between exposure variables was identified.

CONCLUSIONS

This first large-scale exposome study of fetal growth simultaneously considered >100 environmental exposures. Compared with single exposure studies, our approach allowed making all tests (usually reported in successive publications) explicit. Lead exposure is still a health concern in Europe and parabens health effects warrant further investigation.

Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study

Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.

Bayesian Profile Regression to Deal With Multiple Highly Correlated Exposures and a Censored Survival Outcome. First Application in Ionizing Radiation Epidemiology

As multifactorial and chronic diseases, cancers are among these pathologies for which the exposome concept is essential to gain more insight into the associated etiology and, ultimately, lead to better primary prevention strategies for public health. Indeed, cancers result from the combined influence of many genetic, environmental and behavioral stressors that may occur simultaneously and interact. It is thus important to properly account for multifactorial exposure patterns when estimating specific cancer risks at individual or population level. Nevertheless, the risk factors, especially environmental, are still too often considered in isolation in epidemiological studies. Moreover, major statistical difficulties occur when exposures to several factors are highly correlated due, for instance, to common sources shared by several pollutants. Suitable statistical methods must then be used to deal with these multicollinearity issues. In this work, we focused on the specific problem of estimating a disease risk from highly correlated environmental exposure covariates and a censored survival outcome. We extended Bayesian profile regression mixture (PRM) models to this context by assuming an instantaneous excess hazard ratio disease sub-model. The proposed hierarchical model incorporates an underlying truncated Dirichlet process mixture as an attribution sub-model. A specific adaptive Metropolis-Within-Gibbs algorithm-including label switching moves-was implemented to infer the model. This allows simultaneously clustering individuals with similar risks and similar exposure characteristics and estimating the associated risk for each group. Our Bayesian PRM model was applied to the estimation of the risk of death by lung cancer in a cohort of French uranium miners who were chronically and occupationally exposed to multiple and correlated sources of ionizing radiation. Several groups of uranium miners with high risk and low risk of death by lung cancer were identified and characterized by specific exposure profiles. Interestingly, our case study illustrates a limit of MCMC algorithms to fit full Bayesian PRM models even if the updating schemes for the cluster labels incorporate label-switching moves. Then, although this paper shows that Bayesian PRM models are promising tools for exposome research, it also opens new avenues for methodological research in this class of probabilistic models.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Relying on repeated biospecimens to reduce the effects of classical-type exposure measurement error in studies linking the exposome to health

The exposome calls for assessing numerous exposures, typically using biomarkers with varying amounts of measurement error, which can be assumed to be of classical type. We evaluated the impact of classical-type measurement error on the performance of exposome-health studies, and the efficiency of two measurement error correction methods relying on the collection of repeated biospecimens: within-subject biospecimens pooling and regression calibration. In a simulation study, we generated 237 exposures from a realistic correlation matrix, with various amounts of classical-type measurement error, and a continuous health outcome linearly influenced by exposures. Measurement error decreased the sensitivity to identify exposures influencing health from a value of 75% down to 46%, increased false discovery proportion from 26% to 49% and increased attenuation bias in the slope of true predictors from 45% to 66%. Assuming that repeated biospecimens were available, within-subject pooling and regression calibration improved sensitivity (which increased to 63%), false discovery proportion (down to 37%) and bias (down to 49%) compared to an error-prone study with a single biospecimen per subject. Performances were poorer for the exposures with the largest amount of measurement error, and increased with the number of available biospecimens. Relying on repeated biospecimens only for the exposures with the largest amount of measurement error provided similar performance improvement. Exposome studies relying on spot exposure biospecimens suffer from decreased performances if some biomarkers suffer from measurement error due to their temporal variability; performances can be improved by collecting repeated biospecimens per subject, in particular for non persistent chemicals.

Characterizing the Neurodevelopmental Pesticide Exposome in a Children's Agricultural Cohort

The exposome provides a conceptual model for identifying and characterizing lifetime environmental exposures and resultant health effects. In this study, we applied key exposome concepts to look specifically at the neurodevelopmental pesticide exposome, which focuses on exposures to pesticides that have the potential to cause an adverse neurodevelopmental impact. Using household dust samples from a children's agricultural cohort located in the Yakima Valley of Washington state, we identified 87 individual pesticides using liquid chromatography-tandem mass spectrometry. A total of 47 of these have evidence of neurotoxicity included in the Environmental Protection Agency (EPA) (re)registration materials. We used a mixed effects model to model trends in pesticide exposure. Over the two study years (2005 and 2011), we demonstrate a significant decrease in the neurodevelopmental pesticide exposome across the cohort, but particularly among farmworker households. Additional analysis with a non-parametric binomial analysis that weighted the levels of potentially neurotoxic pesticides detected in household dust by their reference doses revealed that the decrease in potentially neurotoxic pesticides was largely a result of decreases in some of the most potent neurotoxicants. Overall, this study provides evidence that the neurodevelopmental pesticide exposome framework is a useful tool in assessing the effectiveness of specific interventions in reducing exposure as well as setting priorities for future targeted actions.

Pregnancy exposome and child psychomotor development in three European birth cohorts

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

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

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

Influence of the Urban Exposome on Birth Weight

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

The early-life exposome: Description and patterns in six European countries

Characterization of the "exposome", the set of all environmental factors that one is exposed to from conception onwards, has been advocated to better understand the role of environmental factors on chronic diseases. Here, we aimed to describe the early-life exposome. Specifically, we focused on the correlations between multiple environmental exposures, their patterns and their variability across European regions and across time (pregnancy and childhood periods). We relied on the Human Early-Life Exposome (HELIX) project, in which 87 environmental exposures during pregnancy and 122 during the childhood period (grouped in 19 exposure groups) were assessed in 1301 pregnant mothers and their children at 6-11 years in 6 European birth cohorts. Some correlations between exposures in the same exposure group reached high values above 0.8. The median correlation within exposure groups was >0.3 for many exposure groups, reaching 0.69 for water disinfection by products in pregnancy and 0.67 for the meteorological group in childhood. Median correlations between different exposure groups rarely reached 0.3. Some correlations were driven by cohort-level associations (e.g. air pollution and chemicals). Ten principal components explained 45% and 39% of the total variance in the pregnancy and childhood exposome, respectively, while 65 and 90 components were required to explain 95% of the exposome variability. Correlations between maternal (pregnancy) and childhood exposures were high (>0.6) for most exposures modeled at the residential address (e.g. air pollution), but were much lower and even close to zero for some chemical exposures. In conclusion, the early life exposome was high dimensional, meaning that it cannot easily be measured by or reduced to fewer components. Correlations between exposures from different exposure groups were much lower than within exposure groups, which have important implications for co-exposure confounding in multiple exposure studies. Also, we observed the early life exposome to be variable over time and to vary by cohort, so measurements at one time point or one place will not capture its complexities.

The early-life exposome: Description and patterns in six European countries

Characterization of the "exposome", the set of all environmental factors that one is exposed to from conception onwards, has been advocated to better understand the role of environmental factors on chronic diseases. Here, we aimed to describe the early-life exposome. Specifically, we focused on the correlations between multiple environmental exposures, their patterns and their variability across European regions and across time (pregnancy and childhood periods). We relied on the Human Early-Life Exposome (HELIX) project, in which 87 environmental exposures during pregnancy and 122 during the childhood period (grouped in 19 exposure groups) were assessed in 1301 pregnant mothers and their children at 6-11 years in 6 European birth cohorts. Some correlations between exposures in the same exposure group reached high values above 0.8. The median correlation within exposure groups was >0.3 for many exposure groups, reaching 0.69 for water disinfection by products in pregnancy and 0.67 for the meteorological group in childhood. Median correlations between different exposure groups rarely reached 0.3. Some correlations were driven by cohort-level associations (e.g. air pollution and chemicals). Ten principal components explained 45% and 39% of the total variance in the pregnancy and childhood exposome, respectively, while 65 and 90 components were required to explain 95% of the exposome variability. Correlations between maternal (pregnancy) and childhood exposures were high (>0.6) for most exposures modeled at the residential address (e.g. air pollution), but were much lower and even close to zero for some chemical exposures. In conclusion, the early life exposome was high dimensional, meaning that it cannot easily be measured by or reduced to fewer components. Correlations between exposures from different exposure groups were much lower than within exposure groups, which have important implications for co-exposure confounding in multiple exposure studies. Also, we observed the early life exposome to be variable over time and to vary by cohort, so measurements at one time point or one place will not capture its complexities.

The human exposome unraveling the impact of environment on health: promise or reality?

Considering the innovative nature of the approach to human exposome, we present the state of the art of studies on exposome, and discuss current challenges and perspectives in this area. Several reading and discussion activities were conducted by the Expossoma e Saúde do Trabalhador (eXsat – Group Exposome and Worker's Health), with systematization of the literature in the area published between January 2005 and January 2017, available in the databases PubMed and Web of Science. This comment brings a thematic analysis to encourage the dissemination of the exposome approach for studies in the Public Health area.

Variability of urinary concentrations of non-persistent chemicals in pregnant women and school-aged children

BACKGROUND

Exposome studies are challenged by exposure misclassification for non-persistent chemicals, whose temporal variability contributes to bias in dose-response functions.

OBJECTIVES

We evaluated the variability of urinary concentrations of 24 non-persistent chemicals: 10 phthalate metabolites, 7 phenols, 6 organophosphate (OP) pesticide metabolites, and cotinine, between weeks from different pregnancy trimesters in pregnant women, and between days and between seasons in children.

METHODS

154 pregnant women and 152 children from six European countries were enrolled in 2014-2015. Pregnant women provided three urine samples over a day (morning, midday, and night), for one week in the 2nd and 3rd pregnancy trimesters. Children provided two urines a day (morning and night), over two one-week periods, six months apart. We pooled all samples for a given subject that were collected within a week. In children, we also made four daily pools (combining morning and night voids) during the last four days of the first follow-up week. Pools were analyzed for all 24 metabolites of interest. We calculated intraclass-correlation coefficients (ICC) and estimated the number of pools needed to obtain an ICC above 0.80.

RESULTS

All phthalate metabolites and phenols were detected in >90% of pools whereas certain OP pesticide metabolites and cotinine were detected in <43% of pools. We observed fair (ICC = 0.40-0.59) to good (0.60-0.74) between-day reliability of the pools of two samples in children for all chemicals. Reliability was poor (<0.40) to fair between trimesters in pregnant women and between seasons in children. For most chemicals, three daily pools of two urines each (for weekly exposure windows) and four weekly pools of 15-20 urines each would be necessary to obtain an ICC above 0.80.

CONCLUSIONS

This quantification of the variability of biomarker measurements of many non-persistent chemicals during several time windows shows that for many of these compounds a few dozen samples are required to accurately assess exposure over periods encompassing several trimesters or months.

Patterns of cleaning product exposures using a novel clustering approach for data with correlated variables

PURPOSE

Clustering methods may be useful in epidemiology to better characterize exposures and account for their multidimensional aspects. In this context, application of clustering models allowing for highly dependent variables is of particular interest. We aimed to characterize patterns of domestic exposure to cleaning products using a novel clustering model allowing for highly dependent variables.

METHODS

To identify domestic cleaning patterns in a large population of French women, we used a mixture model of dependency blocks. This novel approach specifically models within-class dependencies, and is an alternative to the latent class model, which assumes conditional independence. Analyses were conducted in 19,398 participants of the E3N study (women aged 61-88 years) who completed a questionnaire regarding household cleaning habits.

RESULTS

Seven classes were identified, which differed with the frequency of cleaning tasks (e.g., dusting/sweeping/hoovering) and use of specific products (e.g., bleach, sprays). The model also grouped the variables into conditionally independent blocks, providing a summary of the main dependencies among the variables.

CONCLUSIONS

The mixture model of dependency blocks, a useful alternative to the latent class model, may have broader application in epidemiology, in particular, in the context of exposome research and growing need for data-reduction methods.

Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies

Supplemental Digital Content is available in the text.

For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high.

A Systematic Comparison of Linear Regression-Based Statistical Methods to Assess Exposome-Health Associations

BACKGROUND

The exposome constitutes a promising framework to improve understanding of the effects of environmental exposures on health by explicitly considering multiple testing and avoiding selective reporting. However, exposome studies are challenged by the simultaneous consideration of many correlated exposures.

OBJECTIVES

We compared the performances of linear regression-based statistical methods in assessing exposome-health associations.

METHODS

In a simulation study, we generated 237 exposure covariates with a realistic correlation structure and with a health outcome linearly related to 0 to 25 of these covariates. Statistical methods were compared primarily in terms of false discovery proportion (FDP) and sensitivity.

RESULTS

On average over all simulation settings, the elastic net and sparse partial least-squares regression showed a sensitivity of 76% and an FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm revealed a sensitivity of 81% and an FDP of 34%. The environment-wide association study (EWAS) underperformed these methods in terms of FDP (average FDP, 86%) despite a higher sensitivity. Performances decreased considerably when assuming an exposome exposure matrix with high levels of correlation between covariates.

CONCLUSIONS

Correlation between exposures is a challenge for exposome research, and the statistical methods investigated in this study were limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. Although GUESS and DSA provided a marginally better balance between sensitivity and FDP, they did not outperform the other multivariate methods across all scenarios and properties examined, and computational complexity and flexibility should also be considered when choosing between these methods. Citation: Agier L, Portengen L, Chadeau-Hyam M, Basagaña X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen MJ, Vineis P, Vrijheid M, Slama R, Vermeulen R. 2016. A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ Health Perspect 124:1848-1856; http://dx.doi.org/10.1289/EHP172.

The exposome concept: a challenge and a potential driver for environmental health research

The exposome concept was defined in 2005 as encompassing all environmental exposures from conception onwards, as a new strategy to evidence environmental disease risk factors. Although very appealing, the exposome concept is challenging in many respects. In terms of assessment, several hundreds of time-varying exposures need to be considered, but increasing the number of exposures assessed should not be done at the cost of increased exposure misclassification. Accurately assessing the exposome currently requires numerous measurements, which rely on different technologies; resulting in an expensive set of protocols. In the future, high-throughput ‘omics technologies may be a promising technique to integrate a wide range of exposures from a small numbers of biological matrices. Assessing the association between many exposures and health raises statistical challenges. Due to the correlation structure of the exposome, existing statistical methods cannot fully and efficiently untangle the exposures truly affecting the health outcome from correlated exposures. Other statistical challenges relate to accounting for exposure misclassification or identifying synergistic effects between exposures. On-going exposome projects are trying to overcome technical and statistical challenges. From a public health perspective, a better understanding of the environmental risk factors should open the way to improved prevention strategies.

Environmental pollutants and child health-A review of recent concerns

In recent years, many new studies have evaluated associations between environmental pollutants and child health. This review aims to provide a broad summary of this literature, comparing the state of epidemiological evidence for the effects of a wide range of environmental contaminants (air pollutants, heavy metals, organochlorine compounds, perfluoroalkyl substances, polybrominated diphenyl ethers, pesticides, phthalates and bisphenol A) on child health outcomes. The review addresses effects on foetal growth and prematurity, neurodevelopment, respiratory and immune health, and childhood growth and obesity. Findings of recent prospective studies and meta-analyses have corroborated previous good evidence, often at lower exposure levels, for effects on foetal growth of air pollution and polychlorinated biphenyls (PCBs), for neurotoxic effects of lead, methylmercury, PCBs and organophosphate pesticides, and for respiratory health effects of air pollution. Moderate evidence has emerged for a potential role of environmental pollutants in attention deficit hyperactivity disorder and autism (lead, PCBs, air pollution), respiratory and immune health (dichlorodiphenyldichloroethylene - DDE - and PCBs), and obesity (DDE). In addition, there is now moderate evidence that certain chemicals of relatively recent concern may be associated with adverse child health outcomes, specifically perfluorooctanoate and foetal growth, and polybrominated diphenyl ethers and neurodevelopment. For other chemicals of recent concern, such as phthalates and bisphenol A, the literature is characterised by large inconsistencies preventing strong conclusions. In conclusion, since most of the recent literature evaluates common exposures in the general population, and not particularly high exposure situations, this accumulating body of evidence suggests that the unborn and young child require more protection than is currently provided. Large, coordinated research efforts are needed to improve understanding of long-term effects of complex chemical mixtures.