Exposome Studies Research Articles

Advancements in Assays for Micro- and Nanoplastic Detection: Paving the Way for Biomonitoring and Exposomics Studies.

Although plastic pollution and exposure to plastic-related compounds have received worldwide attention, health risks associated with micro- and nanoplastics (MNPs) are largely unknown. Emerging evidence suggests MNPs are present in human biofluids and tissue, including blood, breast milk, stool, lung tissue, and placenta; however, exposure assessment is limited and the extent of human exposure to MNPs is not well known. While there is a critical need to establish robust and scalable biomonitoring strategies to assess human exposure to MNPs and plastic-related chemicals, over 10,000 chemicals have been linked to plastic manufacturing with no existing standardized approaches to account for even a fraction of these exposures. This review provides an overview of the status of methods for measuring MNPs and associated plastic-related chemicals in humans, with a focus on approaches that could be adapted for population-wide biomonitoring and integration with biological response measures to develop hypotheses on potential health effects of plastic exposures. We also examine the exposure risks associated with the widespread use of chemical additives in plastics. Despite advancements in analytical techniques, there remains a pressing need for standardized measurement protocols and untargeted, high-throughput analysis methods to enable comprehensive MNP biomonitoring to identify key MNP exposures in human populations. This review aims to merge insights into the toxicological effects of MNPs and plastic additives with an evaluation of analytical challenges, advocating for enhanced research methods to fully assess, understand, and mitigate the public health implications of MNPs.

P23-12 Use of minimally invasive matrices for characterization of biomarkers in occupational exposome studies

P23-12 Use of minimally invasive matrices for characterization of biomarkers in occupational exposome studies

The influence of early-life exposome on children’s respiratory health in a Latin American country: a request for interregional exposome studies

The influence of early-life exposome on children’s respiratory health in a Latin American country: a request for interregional exposome studies

Data-driven causal structure discovery for agnostic analyses: current state and implications for exposome studies

Data-driven causal structure discovery for agnostic analyses: current state and implications for exposome studies

DNA damage and its association with early-life exposome: Gene-environment analysis in Colombian children under five years old

Environmental exposures and gene-exposure interactions are the major causes of some diseases. Early-life exposome studies are needed to elucidate the role of environmental exposures and their complex interactions with biological mechanisms involved in childhood health. This study aimed to determine the contribution of early-life exposome to DNA damage and the modifying effect of genetic polymorphisms involved in air pollutants metabolism, antioxidant defense, and DNA repair. We conducted a cohort study in 416 Colombian children under five years. Blood samples at baseline were collected to measure DNA damage by the Comet assay and to determine GSTT1, GSTM1, CYP1A1, H2AX, OGG1, and SOD2 genetic polymorphisms. The exposome was estimated using geographic information systems, remote sensing, LUR models, and questionnaires. The association exposome-DNA damage was estimated using the Elastic Net linear regression with log link. Our results suggest that exposure to PM2.5 one year before the blood draw (BBD) (0.83, 95 %CI: 0.76; 0.91), soft drinks consumption (0.94, 0.89; 0.98), and GSTM1 null genotype (0.05, 0.01; 0.36) diminished the DNA damage, whereas exposure to PM2.5 one-week BBD (1.18, 1.06; 1.32), NO2 lag-5 days BBD (1.27, 1.18; 1.36), in-house cockroaches (1.10, 1.00; 1.21) at the recruitment, crowding at home (1.34, 1.08; 1.67) at the recruitment, cereal consumption (1.11, 1.04; 1.19) and H2AX (AG/GG vs. AA) (1.44, 1.11; 1.88) increased the DNA damage. The interactions between H2AX (AG/GG vs. AA) genotypes with crowding and PM2.5 one week BBD, GSTM1 (null vs. present) with humidity at the first year of life, and OGG1 (SC/CC vs. SS) with walkability at the first year of life were significant. The early-life exposome contributes to elucidating the effect of environmental exposures on DNA damage in Colombian children under five years old. The exposome-DNA damage effect appears to be modulated by genetic variants in DNA repair and antioxidant defense enzymes.

The Relationship between Exposome and Microbiome.

Currently, exposome studies include a raft of different monitoring tools, including remote sensors, smartphones, omics analyses, distributed lag models, etc. The similarity in structure between the exposome and the microbiota plus their functions led us to pose three pertinent questions from this viewpoint, looking at the actual relationship between the exposome and the microbiota. In terms of the exposome, a bistable equilibrium between health and disease depends on constantly dealing with an ever-changing totality of exposures that together shape an individual from conception to death. Regarding scientific knowledge, the exposome is still lagging in certain areas, like the importance of microorganisms in the equation. The human microbiome is defined as an aggregate assemblage of gut commensals that are hosted by our surfaces related to the external environment. Commensals' resistance to a variety of environmental exposures, such as antibiotic administration, confirms that a layer of these organisms is protected within the host. The exposome is a conceptual framework defined as the environmental component of the science-inspired systems ideology that shifts from a specificity-based medical approach to reasoning in terms of complexity. A parallel concept in population health research and precision public health is the human flourishing index, which aims to account for the numerous environmental factors that affect individual and population well-being beyond ambient pollution.

Early-life external exposome in children 2–5 years old in Colombia

Exposome studies are advancing in high-income countries to understand how multiple environmental exposures impact health. However, there is a significant research gap in low- and middle-income and tropical countries. We aimed to describe the spatiotemporal variation of the external exposome, its correlation structure between and within exposure groups, and its dimensionality. A one-year follow-up cohort study of 506 children under 5 in two cities in Colombia was conducted to evaluate asthma, acute respiratory infections, and DNA damage. We examined 48 environmental exposures during pregnancy and 168 during childhood in eight exposure groups, including atmospheric pollutants, natural spaces, meteorology, built environment, traffic, indoor exposure, and socioeconomic capital. The exposome was estimated using geographic information systems, remote sensing, spatiotemporal modeling, and questionnaires. The median age of children at study entry was 3.7 years (interquartile range: 2.9–4.3). Air pollution and natural spaces exposure decreased from pregnancy to childhood, while socioeconomic capital increased. The highest median correlations within exposure groups were observed in meteorology (r = 0.85), traffic (r = 0.83), and atmospheric pollutants (r = 0.64). Important correlations between variables from different exposure groups were found, such as atmospheric pollutants and meteorology (r = 0.76), natural spaces (r = −0.34), and the built environment (r = 0.53). Twenty principal components explained 70%, and 57 explained 95% of the total variance in the childhood exposome. Our findings show that there is an important spatiotemporal variation in the exposome of children under 5. This is the first characterization of the external exposome in urban areas of Latin America and highlights its complexity, but also the need to better characterize and understand the exposome in order to optimize its analysis and applications in local interventions aimed at improving the health conditions and well-being of the child population and contributing to environmental health decision-making.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design withmultiple factors orphenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization,and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

A data-centric perspective on exposomics data analysis

Abstract Exposomics represents a systematic approach to investigate the etiology of diseases by formally integrating individuals’ entire environmental exposures and associated biological responses into the traditional genotype-phenotype framework. The field is largely enabled by various omics technologies which offer practical means to comprehensively measure key components in exposomics. The bottleneck in exposomics has gradually shifted from data collection to data analysis. Effective and easy-to-use bioinformatics tools and computational workflows are urgently needed to help obtain robust associations and to derive actionable insights from the observational, heterogenous, and multi-omics datasets collected in exposomics studies. This data-centric perspective starts with an overview of the main components and common analysis workflows in exposomics. We then introduce six computational approaches that have proven effective in addressing some key analytical challenges, including linear modeling with covariate adjustment, dimensionality reduction for covariance detection, neural networks for identification of complex interactions, network visual analytics for organizing and interpreting multi-omics results, Mendelian randomization for causal inference, and cause-effect validation by coupling effect-directed analysis with dose-response assessment. Finally, we present a series of well-designed web-based tools, and briefly discuss how they can be used for exposomics data analysis.

A long and winding road: culture change on data sharing in exposomics

Abstract Data sharing requires cooperation from data generators (eg, epidemiologists, lab investigators) and data users (eg, epidemiologists, biostatisticians, computer scientists). Data generation and data use in human exposome studies require significant but different skill sets and are separated temporally in many cases. Sharing will require maintaining a history of data generation and a system to address the concerns of data generators around credit for conducting rigorous work (eg, authorship). Sharing also requires addressing the needs of data users to facilitate harmonization, searchability and QA/QC of data. We present these issues from the perspectives of data generators and data users and include the special case of real-world data (eg, electronic health records). We conclude with recommendations to address how to better promote data sharing in exposomics through authorship, cost recovery and addressing ethical issues.

New advances in exposomics-analysis methods and research paradigms based on chromatography-mass spectrometry

The occurrence and development of human diseases are influenced by both genetic and environmental factors. Research models that describe disease occurrence only from the perspective of genetics present certain limitations. In recent years, effects of environment factors on the occurrence and development of diseases have attracted extensive attentions. Exposomics focuses on the measurement of all exposure factors in an individual's life and how these factors are related to disease development. Exposomics provides new ideas to promote studies on the relationship between human health and environmental factors. Environmental exposures are characterized with different physical and chemical properties, as well as very low concentrations in vivo, which contribute great challenges in the comprehensive measurement of chemical residues in the human body. Chromatography-mass spectrometry-based technologies combine the high-efficiency separation ability of chromatography with the high resolution and sensitive detection characteristics of mass spectrometry; the combination of these techniques can achieve the high-coverage, high-throughput, and sensitive detection of environmental exposures, thus providing a powerful tool for measuring chemical exposures. Exposomics-analysis methods based on chromatography-mass spectrometry mainly include targeted quantitative analysis, suspect screening, and non-targeted screening. To explore the relationship between environmental exposure and the occurrence and development of diseases, researchers have developed research paradigms, including exposome wide association study, mixed-exposure study, exposomics and multi-omics (genome, transcriptome, proteome, metabolome)-association study, and so on. The emergence of these methods has brought about unprecedented developments in exposomics studies. In this manuscript, analytical methods based on chromatography-mass spectrometry, exposomics research paradigms, and their relevant prospects are reviewed.

IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra

The majority of tandem mass spectrometry (MS/MS) spectra in untargeted metabolomics and exposomics studies lack any annotation. Our deep learning framework, Integrated Data Science Laboratory for Metabolomics and Exposomics—Mass INTerpreter (IDSL_MINT) can translate MS/MS spectra into molecular fingerprint descriptors. IDSL_MINT allows users to leverage the power of the transformer model for mass spectrometry data, similar to the large language models. Models are trained on user-provided reference MS/MS libraries via any customizable molecular fingerprint descriptors. IDSL_MINT was benchmarked using the LipidMaps database and improved the annotation rate of a test study for MS/MS spectra that were not originally annotated using existing mass spectral libraries. IDSL_MINT may improve the overall annotation rates in untargeted metabolomics and exposomics studies. The IDSL_MINT framework and tutorials are available in the GitHub repository at https://github.com/idslme/IDSL_MINT.Scientific contributionStructural annotation of MS/MS spectra from untargeted metabolomics and exposomics datasets is a major bottleneck in gaining new biological insights. Machine learning models to convert spectra into molecular fingerprints can help in the annotation process. Here, we present IDSL_MINT, a new, easy-to-use and customizable deep-learning framework to train and utilize new models to predict molecular fingerprints from spectra for the compound annotation workflows.

The contribution of environmental exposures to the burden of disease – what can redox biology contribute to exposome studies?

The contribution of environmental exposures to the burden of disease – what can redox biology contribute to exposome studies?

Per- and polyfluoroalkyl substances and cardiometabolic diseases: A review.

Per- and polyfluoroalkyl substances (PFAS) are a group of persistent and widespread environmental pollutants that represent a high concern for human health. They have been shown to be associated with several important physiological processes such as lipid metabolism and the immune system. Consequently, PFAS are suspected to play a role in cardiometabolic disease development. However, the evidence regarding associations between PFAS and overt cardiovascular disease and type 2 diabetes remains limited and inconsistent. To address this, we conducted a review of the epidemiological evidence. A deeper understanding of potential underlying molecular mechanisms may help to explain inconsistencies in epidemiological findings. Thus, to gain more mechanistic insight, we also summarized evidence from omics and laboratory studies into an adverse outcome pathway framework. Our observations indicate the potential for associations of PFAS with multiple molecular pathways that could have opposite associations with disease risk, which could be further modified by mixture composition, lifestyle factors or genetic polymorphisms. This identifies the need for exposome studies considering mixture effects, the use of multi-omics data to gain insight in relevant pathways and the integration of epidemiological and laboratory studies to enhance mechanistic understanding and causal inference. Improved comprehension is essential for environmental health risk assessments.

Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches.

Introduction: The positive identification of xenobiotics and their metabolites in human biosamples is an integral aspect of exposomics research, yet challenges in compound annotation and identification continue to limit the feasibility of comprehensive identification of total chemical exposure. Nonetheless, the adoption of in silico tools such as metabolite prediction software, QSAR-ready structural conversion workflows, and molecular standards databases can aid in identifying novel compounds in untargeted mass spectral investigations, permitting the assessment of a more expansive pool of compounds for human health hazard. This strategy is particularly applicable when it comes to flame retardant chemicals. The population is ubiquitously exposed to flame retardants, and evidence implicates some of these compounds as developmental neurotoxicants, endocrine disruptors, reproductive toxicants, immunotoxicants, and carcinogens. However, many flame retardants are poorly characterized, have not been linked to a definitive mode of toxic action, and are known to share metabolic breakdown products which may themselves harbor toxicity. As U.S. regulatory bodies begin to pursue a subclass- based risk assessment of organohalogen flame retardants, little consideration has been paid to the role of potentially toxic metabolites, or to expanding the identification of parent flame retardants and their metabolic breakdown products in human biosamples to better inform the human health hazards imposed by these compounds. Methods: The purpose of this study is to utilize publicly available in silico tools to 1) characterize the structural and metabolic fates of proposed flame retardant classes, 2) predict first pass metabolites, 3) ascertain whether metabolic products segregate among parent flame retardant classification patterns, and 4) assess the existing coverage in of these compounds in mass spectral database. Results: We found that flame retardant classes as currently defined by the National Academies of Science, Engineering and Medicine (NASEM) are structurally diverse, with highly variable predicted pharmacokinetic properties and metabolic fates among member compounds. The vast majority of flame retardants (96%) and their predicted metabolites (99%) are not present in spectral databases, posing a challenge for identifying these compounds in human biosamples. However, we also demonstrate the utility of publicly available in silico methods in generating a fit for purpose synthetic spectral library for flame retardants and their metabolites that have yet to be identified in human biosamples. Discussion: In conclusion, exposomics studies making use of fit-for-purpose synthetic spectral databases will better resolve internal exposure and windows of vulnerability associated with complex exposures to flame retardant chemicals and perturbed neurodevelopmental, reproductive, and other associated apical human health impacts.

Geographic uncertainties in external exposome studies: A multi-scale approach to reduce exposure misclassification

BackgroundMany studies on environment-health associations have emphasized that the selected buffer size (i.e., the scale of the geographic context when exposures are assigned at people's address location) may affect estimated effect sizes. However, there is limited methodological progress in addressing these buffer size-related uncertainties. AimWe aimed to 1) develop a statistical multi-scale approach to address buffer-related scale effects in cohort studies, and 2) investigate how environment-health associations differ between our multi-scale approach and ad hoc selected buffer sizes. MethodsWe used lacunarity analyses to determine the largest meaningful buffer size for multiple high-resolution exposure surfaces (i.e., fine particulate matter [PM2.5], noise, and the normalized difference vegetation index [NDVI]). Exposures were linked to 7.7 million Dutch adults at their home addresses. We assigned exposure estimates based on buffers with fine-grained distance increments until the lacunarity-based upper limit was reached. Bayesian Cox model averaging addressed geographic uncertainties in the estimated exposure effect sizes within the exposure-specific upper buffer limits on mortality. Z-tests assessed statistical differences between averaged effect sizes and those obtained through pre-selected 100, 300, 1200, and 1500 m buffers. ResultsThe estimated lacunarity curves suggested exposure-specific upper buffer size limits; the largest was for NDVI (960 m), followed by noise (910 m) and PM2.5 (450 m). We recorded 845,229 deaths over eight years of follow-up. Our multi-scale approach indicated that higher values of NDVI were health-protectively associated with mortality risk (hazard ratio [HR]: 0.917, 95 % confidence interval [CI]: 0.886–0.948). Increased noise exposure was associated with an increased risk of mortality (HR: 1.003, 95 % CI: 1.002–1.003), while PM2.5 showed null associations (HR:0.998, 95 % CI: 0.997–1.000). Effect sizes of NDVI and noise differed significantly across the averaged and prespecified buffers (p < 0.05). ConclusionsGeographic uncertainties in residential-based exposure assessments may obscure environment-health associations or risk spurious ones. Our multi-scale approach produced more consistent effect estimates and mitigated contextual uncertainties.

How can data visualization support interdisciplinary research? LuxTIME: studying historical exposomics in Belval.

The Luxembourg Time Machine (LuxTIME) is an interdisciplinary project that studies the historical exposome during the industrialization of the Minett region, located in the south of Luxembourg. Exposome research encompasses all external and internal non-genetic factors influencing the health of the population, such as air pollution, green spaces, noise, work conditions, physical activity, and diet. Due to the wide scope of the interdisciplinary project, the historical study of the exposome in Belval involved the collection of quantitative and qualitative data from the National Archive of Luxembourg, various local archives (e.g., the communes of Esch-sur-Alzette and Sanem), the National Library, the Library of National Statistics STATEC, the National Geoportal of Luxembourg, scientific data from other research centers, and information from newspapers and journals digitized in eluxemburgensia. The data collection and the resulting inventory were performed to create a proof of concept to critically test the potential of a multi-layered research design for the study of the historical exposome in Belval. The guiding navigation tool throughout the project was data visualization. It has facilitated the exploration of the data collected (or just the data) and the metadata. It has also been a valuable tool for mapping knowledge and defining the scope of the project. Furthermore, different data visualization techniques have helped us to reflect on the process of knowledge sharing, to understand how the relevance of certain topics changed throughout the project and why, and to learn about the publication process in different journals and the experience of the participants. Data visualization is used not only as a means to an end but also to embrace the idea of sandcastles using a speculative and process-oriented approach to advance knowledge within all research fields involved. LuxTIME has proven to be an ideal case study to explore the possibilities offered by different data visualization concepts and techniques resulting in a data visualization toolbox that could be evaluated and extended in other interdisciplinary projects.

A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia

BackgroundExposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods. ObjectsWe aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data. MethodsPre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA. ResultsThe prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02–1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13–1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA. ConclusionsHeavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.

Blood miRNA levels associated with ADHD traits in children across six European birth cohorts

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a prevalent and highly heritable neurodevelopmental disorder of major societal concern. Diagnosis can be challenging and there are large knowledge gaps regarding its etiology, though studies suggest an interplay of genetic and environmental factors involving epigenetic mechanisms. MicroRNAs (miRNAs) show promise as biomarkers of human pathology and novel therapies, and here we aimed to identify blood miRNAs associated with traits of ADHD as possible biomarker candidates and further explore their biological relevance.MethodsOur study population consisted of 1126 children (aged 5–12 years, 46% female) from the Human Early Life Exposome study, a study spanning six ongoing population-based European birth cohorts. Expression profiles of miRNAs in whole blood samples were quantified by microarray and tested for association with ADHD-related measures of behavior and neuropsychological functions from questionnaires (Conner’s Rating Scale and Child Behavior Checklist) and computer-based tests (the N-back task and Attention Network Test).ResultsWe identified 29 miRNAs significantly associated (false discovery rate < .05) with the Conner’s questionnaire-rated trait hyperactivity, 15 of which have been linked to ADHD in previous studies. Investigation into their biological relevance revealed involvement in several pathways related to neurodevelopment and function, as well as being linked with other neurodevelopmental or psychiatric disorders known to overlap with ADHD both in symptomology, genetic risk, and co-occurrence, such as autism spectrum disorder or schizophrenia. An additional three miRNAs were significantly associated with Conner’s-rated inattention. No associations were found with questionnaire-rated total ADHD index or with computer-based tests.ConclusionsThe large overlap of our hyperactivity-associated miRNAs with previous studies on ADHD is intriguing and warrant further investigation. Though this study should be considered explorative and preliminary, these findings contribute towards identifying a set of miRNAs for use as blood-based biomarkers to aid in earlier and easier ADHD diagnosis.

Identification of key predictors of severe annoyance caused by railway induced vibrations using machine learning: an external exposome study

Identification of key predictors of severe annoyance caused by railway induced vibrations using machine learning: an external exposome study