Multi-omics signatures of the human early life exposome

The effects of air and transportation noise pollution-related altered blood gene expression, DNA methylation, and protein abundance levels on gastrointestinal diseases risk

INTRODUCTION

Air pollution and transportation noise pollution has been linked to gastrointestinal (GI) diseases, but their relationship remains unclear.

METHODS

We extracted the significantly modulated genes and CpG sites related to air pollution (PM2.5, PM10, and NOx) and transportation noise pollution (aircraft, railway, and traffic road noise) from previous published studies. Genome-wide methylation analysis and colocalization analysis with these CpG sites and GWAS data of GI diseases were performed to disentangle the relationship between pollution-related blood DNA methylation (DNAm) alterations and GI diseases risk. Summary-based Mendelian randomization (SMR) analysis assessed the impact of pollution-related genes on GI diseases risk across methylation, gene expression, and protein levels. Enrichment analysis investigated the implicated biological pathways and immune cell types.

RESULTS

DNAm at cg00227781 [CD300A] (modulated by NOx exposure) and cg19215199 [ZMIZ1] (modulated by PM2.5 exposure) was significantly linked to increased noninfective enteritis and colitis risk, while cg08500171 [BAT2] (modulated by NOx exposure) is significantly associated with an increased gastroesophageal reflux disease (GERD) risk. Colocalization analysis provides strong evidence supporting a shared causal variant between these associations. Multi-omics levels SMR analysis revealed that pollution-modulated lower DNAm at 5 specific CpG sites were associated with increased expression of 4 genes (IL21R, EVPL, SYNGR1, and WDR46), subsequently increasing the risk of GERD, ulcerative colitis, and gastric ulcer. 7 circulating proteins coded by pollution-modulated genes were observed to be associated with 6 GI diseases risk. Enrichment analysis implicates immune and inflammatory responses, MAPK signaling, and telomere maintenance in these pollution-induced effects.

CONCLUSION

We identified potential links between air and transportation noise pollution-related gene methylation, expression, and protein abundance with GI diseases risk, possibly revealing new therapeutic targets.

Maternal smoking during pregnancy could accelerate aging in the adulthood: evidence from a perspective study in UK Biobank

BACKGROUND

Maternal smoking during pregnancy (MSDP) is significantly linked to the short- or long-term health of offspring. However, little research has examined whether MSDP affect the aging rate of offspring.

METHODS

This study used questionnaires to determine out whether the participants' mothers smoked when they were pregnant. For evaluating aging rate, we used the following several outcome measures: telomere length, frailty index, cognitive function, homeostatic dysregulation score, KDM-age, age-related hospitalization rate, premature death, and life expectancy.

RESULT

After adjusting for covariates, we found that the offspring of the MSDP group had significantly shorter telomere length in adulthood by 0.8 % (β = -0.008,95%CI:-0.009 to -0.006) compared with non-MSDP group. Compared to the non-MSDP group, participants in MSDP group showed higher levels of homeostatic dysregulation (β = 0.015,95%CI: 0.007-0.024) and were frailer (β = 0.008,95%CI:0.007-0.009). The KDM age increased by 0.100 due to MSDP (β = 0.100,95 % CI:0.018-0.181), and the age acceleration of KDM algorithm also increases significantly (β = 0.101, 95%CI:0.020-0.183). Additionally, we found that the risk of aging-related hospitalizations was significantly higher than the non-MSDP group by 10.4 %(HR = 1.104,95%CI:1.066-1.144). Moreover, MSDP group had a 12.2 % increased risk of all-cause premature mortality (HR = 1.122,95%CI:1.064-1.182) and a significant risk of lung cancer-specific premature mortality increased by 55.4 %(HR = 1.554,95%CI:1.346-1.793). In addition, participants in the MSDP group had significantly decreased cognitive function and shorter life expectancies than those in non-MSDP group.

CONCLUSION

Our findings indicated a significant association between MSPD and accelerated aging, elevated hospitalization rates, increased premature mortality rates, and reduced life expectancies in offspring.

Multi-adductomics: Advancing mass spectrometry techniques for comprehensive exposome characterization

Adductomics, an emerging field within the 'omics sciences, focuses on the formation and prevalence of DNA, RNA, and protein adducts induced by endogenous and exogenous agents in biological systems. These modifications often result from exposure to environmental pollutants, dietary components, and xenobiotics, impacting cellular functions and potentially leading to diseases such as cancer. This review highlights advances in mass spectrometry (MS) that enhance the detection of these critical modifications and discusses current and emerging trends in adductomics, including developments in MS instrument use, screening techniques, and the study of various biomolecular modifications from mono-adducts to complex hybrid crosslinks between different types of biomolecules. The review also considers challenges, including the need for specialized MS spectra databases and multi-omics integration, while emphasizing techniques to distinguish between exogenous and endogenous modifications. The future of adductomics possesses significant potential for enhancing our understanding of health in relation to environmental exposures and precision medicine.

Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles

Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.

Seminar: Functional Exposomics and Mechanisms of Toxicity-Insights from Model Systems and NAMs

BACKGROUND

Significant progress has been made over the past decade in measuring the chemical components of the exposome, providing transformative population-scale frameworks in probing the etiologic link between environmental factors and disease phenotypes. While the analytical technologies continue to evolve with reams of data being generated, there is an opportunity to complement exposome-wide association studies (ExWAS) with functional analyses to advance etiologic search at organismal, cellular, and molecular levels.

OBJECTIVES

Exposomics is a transdisciplinary field aimed at enabling discovery-based analysis of the nongenetic factors that contribute to disease, including numerous environmental chemical stressors. While advances in exposure assessment are enhancing population-based discovery of exposome-wide effects and chemical exposure agents, functional screening and elucidation of biological effects of exposures represent the next logical step toward precision environmental health and medicine. In this work, we focus on the use, strategies, and prospects of alternative approaches and model systems to enhance the current human exposomics framework in biomarker search and causal understanding, spanning from bench-based nonmammalian organisms and cell culture to computational new approach methods (NAMs).

DISCUSSION

We visit the definition of the functional exposome and exposomics and discuss a need to leverage alternative models as opposed to mammalian animals for delineating exposome-wide health effects. Under the "three Rs" principle of reduction, replacement, and refinement, model systems such as roundworms, fruit flies, zebrafish, and induced pluripotent stem cells (iPSCs) are advantageous over mammals (e.g., rodents or higher vertebrates). These models are cost-effective, and cell-specific genetic manipulations in these models are easier and faster, compared to mammalian models. Meanwhile, in silico NAMs enhance hazard identification and risk assessment in humans by bridging the translational gaps between toxicology data and etiologic inference, as represented by in vitro to in vivo extrapolation (IVIVE) and integrated approaches to testing and assessment (IATA) under the adverse outcome pathway (AOP) framework. Together, these alternatives offer a strong toolbox to support functional exposomics to study toxicity and causal mediators underpinning exposure-disease links. https://doi.org/10.1289/EHP13120.

Exposome Profiling of Environmental Pollutants in Seminal Plasma and Novel Associations with Semen Parameters

Indicators of male fertility are in decline globally, but the underlying causes, including the role of environmental exposures, are unclear. This study aimed to examine organic chemical pollutants in seminal plasma, including both known priority environmental chemicals and less studied chemicals, to identify uncharacterized male reproductive environmental toxicants. Semen samples were collected from 100 individuals and assessed for sperm concentration, percent motility, and total motile sperm. Targeted and nontargeted organic pollutant exposures were measured from seminal plasma using gas chromatography, which showed widespread detection of organic pollutants in seminal plasma across all exposure classes. We used principal component pursuit (PCP) on our targeted panel and derived one component (driven by etriadizole) associated with total motile sperm (p < 0.001) and concentration (p = 0.03). This was confirmed by the exposome-wide association models using individual chemicals, where etriadizole was negatively associated with total motile sperm (FDR q = 0.01) and concentration (q = 0.07). Using PCP on 814 nontargeted spectral peaks identified a component that was associated with total motile sperm (p = 0.001). Bayesian kernel machine regression identified one principal driver of this association, which was analytically confirmed to be N-nitrosodiethylamine. These findings are promising and consistent with experimental evidence showing that etridiazole and N-nitrosodiethylamine may be reproductive toxicants.

Exploring the Exposome Spectrum: Unveiling Endogenous and Exogenous Factors in Non-Communicable Chronic Diseases

The exposome encompasses all endogenous and exogenous exposure individuals encounter throughout their lives, including biological, chemical, physical, psychological, relational, and socioeconomic factors. It examines the duration and intensity of these types of exposure and their complex interactions over time. This interdisciplinary approach involves various scientific disciplines, particularly toxicology, to understand the long-term effects of toxic exposure on health. Factors like air pollution, racial background, and socioeconomic status significantly contribute to diseases such as metabolic, cardiovascular, neurodegenerative diseases, infertility, and cancer. Advanced analytical methods measure contaminants in biofluids, food, air, water, and soil, but often overlook the cumulative risk of multiple chemicals. An exposome analysis necessitates sophisticated tools and methodologies to understand health interactions and integrate findings into precision medicine for better disease diagnosis and treatment. Chronic exposure to environmental and biological stimuli can lead to persistent low-grade inflammation, which is a key factor in chronic non-communicable diseases (NCDs), such as obesity, cardiometabolic disorders, cancer, respiratory diseases, autoimmune conditions, and depression. These NCDs are influenced by smoking, unhealthy diets, physical inactivity, and alcohol abuse, all shaped by genetic, environmental, and social factors. Dietary patterns, especially ultra-processed foods, can exacerbate inflammation and alter gut microbiota. This study investigates the exposome's role in the prevention, development, and progression of NCDs, focusing on endogenous and exogenous factors.

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.

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.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Toward cleaner air and better health: Current state, challenges, and priorities

The most up-to-date estimate of the global burden of disease indicates that ambient air pollution, including fine particulate matter and ozone, contributes to an estimated 5.2 million deaths each year. In this review, we highlight the challenges in estimating population exposure to air pollution and attributable health risks, particularly in low- and middle-income countries and among vulnerable populations. To protect public health, the evidence so far confirms urgent needs to prioritize interdisciplinary research on air pollution exposure and risk assessment and to develop evidence-based intervention policies and risk communication strategies. Here, we synthesize the emerging evidence supporting the monitoring and evaluation of the progress in implementation of the Global Air Quality Guidelines prepared by the World Health Organization.

Epigenome-wide association study of fluoride exposure during early adolescence and DNA methylation among U.S. children

Exposure to fluoride in early childhood has been associated with altered cognition, intelligence, attention, and neurobehavior. Fluoride-related neurodevelopment effects have been shown to vary by sex and very little is known about the mechanistic processes involved. There is limited research on how fluoride exposure impacts the epigenome, potentially leading to changes in DNA methylation of specific genes regulating key developmental processes. In the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS), urine samples were analyzed using a microdiffusion method to determine childhood urinary fluoride adjusted for specific gravity (CUFsg) concentrations. Whole blood DNA methylation was assessed using the Infinium MethylationEPIC BeadChip 850 k Array. In a cross-sectional analysis, we interrogated epigenome-wide DNA methylation at 775,141 CpG loci across the methylome in relation to CUFsg concentrations in 272 early adolescents at age 12 years. Among all participants, higher concentrations of CUF were associated with differential methylation of one CpG (p < 6 × 10-8) located in the gene body of GBF1 (cg25435255). Among females, higher concentrations of CUFsg were associated with differential methylation of 7 CpGs; only three CpGs were differentially methylated among males with no overlap of significant CpGs observed among females. Secondary analyses revealed several differentially methylated regions (DMRs) and CpG loci mapping to genes with key roles in psychiatric outcomes, social interaction, and cognition, as well as immunologic and metabolic phenotypes. While fluoride exposure may impact the epigenome during early adolescence, the functional consequences of these changes are unclear warranting further investigation.

High-Efficiency Effect-Directed Analysis Leveraging Five High Level Advancements: A Critical Review

Organic contaminants are ubiquitous in the environment, with mounting evidence unequivocally connecting them to aquatic toxicity, illness, and increased mortality, underscoring their substantial impacts on ecological security and environmental health. The intricate composition of sample mixtures and uncertain physicochemical features of potential toxic substances pose challenges to identify key toxicants in environmental samples. Effect-directed analysis (EDA), establishing a connection between key toxicants found in environmental samples and associated hazards, enables the identification of toxicants that can streamline research efforts and inform management action. Nevertheless, the advancement of EDA is constrained by the following factors: inadequate extraction and fractionation of environmental samples, limited bioassay endpoints and unknown linkage to higher order impacts, limited coverage of chemical analysis (i.e., high-resolution mass spectrometry, HRMS), and lacking effective linkage between bioassays and chemical analysis. This review proposes five key advancements to enhance the efficiency of EDA in addressing these challenges: (1) multiple adsorbents for comprehensive coverage of chemical extraction, (2) high-resolution microfractionation and multidimensional fractionation for refined fractionation, (3) robust in vivo/vitro bioassays and omics, (4) high-performance configurations for HRMS analysis, and (5) chemical-, data-, and knowledge-driven approaches for streamlined toxicant identification and validation. We envision that future EDA will integrate big data and artificial intelligence based on the development of quantitative omics, cutting-edge multidimensional microfractionation, and ultraperformance MS to identify environmental hazard factors, serving for broader environmental governance.

Genotype × environment interactions in gene regulation and complex traits

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

Metabolic profiling of smoking, associations with type 2 diabetes and interaction with genetic susceptibility

BACKGROUND

Smokers are at increased risk of type 2 diabetes (T2D), but the underlying mechanisms are unclear. We investigated if the smoking-T2D association is mediated by alterations in the metabolome and assessed potential interaction with genetic susceptibility to diabetes or insulin resistance.

METHODS

In UK Biobank (n = 93,722), cross-sectional analyses identified 208 metabolites associated with smoking, of which 131 were confirmed in Mendelian Randomization analyses, including glycoprotein acetyls, fatty acids, and lipids. Elastic net regression was applied to create a smoking-related metabolic signature. We estimated hazard ratios (HR) of incident T2D in relation to baseline smoking/metabolic signature and calculated the proportion of the smoking-T2D association mediated by the signature. Additive interaction between the signature and genetic risk scores for T2D (GRS-T2D) and insulin resistance (GRS-IR) on incidence of T2D was assessed as relative excess risk due to interaction (RERI).

FINDINGS

The HR of T2D was 1·73 (95% confidence interval (CI) 1·54 - 1·94) for current versus never smoking, and 38·3% of the excess risk was mediated by the metabolic signature. The metabolic signature and its mediation role were replicated in TwinGene. The metabolic signature was associated with T2D (HR: 1·61, CI 1·46 - 1·77 for values above vs. below median), with evidence of interaction with GRS-T2D (RERI: 0·81, CI: 0·23 - 1·38) and GRS-IR (RERI 0·47, CI: 0·02 - 0·92).

INTERPRETATION

The increased risk of T2D in smokers may be mediated through effects on the metabolome, and the influence of such metabolic alterations on diabetes risk may be amplified in individuals with genetic susceptibility to T2D or insulin resistance.

A blueprint for a new commercial driving epidemiology: An emerging paradigm grounded in integrative exposome and network epistemologies

Excess health and safety risks of commercial drivers are largely determined by, embedded in, or operate as complex, dynamic, and randomly determined systems with interacting parts. Yet, prevailing epidemiology is entrenched in narrow, deterministic, and static exposure-response frameworks along with ensuing inadequate data and limiting methods, thereby perpetuating an incomplete understanding of commercial drivers' health and safety risks. This paper is grounded in our ongoing research that conceptualizes health and safety challenges of working people as multilayered "wholes" of interacting work and nonwork factors, exemplified by complex-systems epistemologies. Building upon and expanding these assumptions, herein we: (a) discuss how insights from integrative exposome and network-science-based frameworks can enhance our understanding of commercial drivers' chronic disease and injury burden; (b) introduce the "working life exposome of commercial driving" (WLE-CD)-an array of multifactorial and interdependent work and nonwork exposures and associated biological responses that concurrently or sequentially impact commercial drivers' health and safety during and beyond their work tenure; (c) conceptualize commercial drivers' health and safety risks as multilayered networks centered on the WLE-CD and network relational patterns and topological properties-that is, arrangement, connections, and relationships among network components-that largely govern risk dynamics; and (d) elucidate how integrative exposome and network-science-based innovations can contribute to a more comprehensive understanding of commercial drivers' chronic disease and injury risk dynamics. Development, validation, and proliferation of this emerging discourse can move commercial driving epidemiology to the frontier of science with implications for policy, action, other working populations, and population health at large.

Who inhabits the built environment? A microbiological point of view on the principal bacteria colonizing our urban areas

Modern lifestyle greatly influences human well-being. Indeed, nowadays people are centered in the cities and this trend is growing with the ever-increasing population. The main habitat for modern humans is defined as the built environment (BE). The modulation of life quality in the BE is primarily mediated by a biodiversity of microbes. They derive from different sources, such as soil, water, air, pets, and humans. Humans are the main source and vector of bacterial diversity in the BE leaving a characteristic microbial fingerprint on the surfaces and spaces. This review, focusing on articles published from the early 2000s, delves into bacterial populations present in indoor and outdoor urban environments, exploring the characteristics of primary bacterial niches in the BE and their native habitats. It elucidates bacterial interconnections within this context and among themselves, shedding light on pathways for adaptation and survival across diverse environmental conditions. Given the limitations of culture-based methods, emphasis is placed on culture-independent approaches, particularly high-throughput techniques to elucidate the genetic and -omic features of BE bacteria. By elucidating these microbiota profiles, the review aims to contribute to understanding the implications for human health and the assessment of urban environmental quality in modern cities.

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.

Challenges in IBD Research 2024: Environmental Triggers

Environmental factors play an important role in inflammatory bowel diseases (IBD; Crohn's disease, [CD], ulcerative colitis [UC]). As part of the Crohn's & Colitis Challenges 2024 agenda, the Environmental Triggers workgroup summarized the progress made in the field of environmental impact on IBD since the last Challenges cycle in this document. The workgroup identified 4 unmet gaps in this content area pertaining to 4 broad categories: (1) Epidemiology; (2) Exposomics and environmental measurement; (3) Biologic mechanisms; and (4) Interventions and Implementation. Within epidemiology, the biggest unmet gaps were in the study of environmental factors in understudied populations including racial and ethnic minority groups and in populations witnessing rapid rise in disease incidence globally. The workgroup also identified a lack of robust knowledge of how environmental factors may impact difference stages of the disease and for different disease-related end points. Leveraging existing cohorts and targeted new prospective studies were felt to be an important need for the field. The workgroup identified the limitations of traditional questionnaire-based assessment of environmental exposure and placed high priority on the identification of measurable biomarkers that can quantify cross-sectional and longitudinal environmental exposure. This would, in turn, allow for identifying the biologic mechanisms of influence of environmental factors on IBD and understand the heterogeneity in effect of such influences. Finally, the working group emphasized the importance of generating high-quality data on effective environmental modification on an individual and societal level, and the importance of scalable and sustainable methods to deliver such changes.

Epigenetics of the non-coding RNA nc886 across blood, adipose tissue and skeletal muscle in offspring exposed to diabetes in pregnancy

BACKGROUND

Diabetes in pregnancy is associated with increased risk of long-term metabolic disease in the offspring, potentially mediated by in utero epigenetic variation. Previously, we identified multiple differentially methylated single CpG sites in offspring of women with gestational diabetes mellitus (GDM), but whether stretches of differentially methylated regions (DMRs) can also be identified in adolescent GDM offspring is unknown. Here, we investigate which DNA regions in adolescent offspring are differentially methylated in blood by exposure to diabetes in pregnancy. The secondary aim was to characterize the RNA expression of the identified DMR, which contained the nc886 non-coding RNA.

METHODS

To identify DMRs, we employed the bump hunter method in samples from young (9-16 yr, n = 92) offspring of women with GDM (O-GDM) and control offspring (n = 94). Validation by pyrosequencing was performed in an adult offspring cohort (age 28-33 years) consisting of O-GDM (n = 82), offspring exposed to maternal type 1 diabetes (O-T1D, n = 67) and control offspring (O-BP, n = 57). RNA-expression was measured using RT-qPCR in subcutaneous adipose tissue and skeletal muscle.

RESULTS

One significant DMR represented by 10 CpGs with a bimodal methylation pattern was identified, located in the nc886/VTRNA2-1 non-coding RNA gene. Low methylation status across all CpGs of the nc886 in the young offspring was associated with maternal GDM. While low methylation degree in adult offspring in blood, adipose tissue, and skeletal muscle was not associated with maternal GDM, adipose tissue nc886 expression was increased in O-GDM compared to O-BP, but not in O-T1D. In addition, adipose tissue nc886 expression levels were positively associated with maternal pre-pregnancy BMI (p = 0.006), but not with the offspring's own adiposity.

CONCLUSIONS

Our results highlight that nc886 is a metastable epiallele, whose methylation in young offspring is negatively correlated with maternal obesity and GDM status. The physiological effect of nc886 may be more important in adipose tissue than in skeletal muscle. Further research should aim to investigate how nc886 regulation in adipose tissue by exposure to GDM may contribute to development of metabolic disease.

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.

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study

BACKGROUND

Lower socioeconomic position (SEP) associates with adverse pregnancy and perinatal outcomes and with less favourable metabolic profile in nonpregnant adults. Socioeconomic differences in pregnancy metabolic profile are unknown. We investigated association between a composite measure of SEP and pregnancy metabolic profile in White European (WE) and South Asian (SA) women.

METHODS

We included 3,905 WE and 4,404 SA pregnant women from a population-based UK cohort. Latent class analysis was applied to nineteen individual, household, and area-based SEP indicators (collected by questionnaires or linkage to residential address) to derive a composite SEP latent variable. Targeted nuclear magnetic resonance spectroscopy was used to determine 148 metabolic traits from mid-pregnancy serum samples. Associations between SEP and metabolic traits were examined using linear regressions adjusted for gestational age and weighted by latent class probabilities.

RESULTS

Five SEP sub-groups were identified and labelled 'Highest SEP' (48% WE and 52% SA), 'High-Medium SEP' (77% and 23%), 'Medium SEP' (56% and 44%) 'Low-Medium SEP' (21% and 79%), and 'Lowest SEP' (52% and 48%). Lower SEP was associated with more adverse levels of 113 metabolic traits, including lower high-density lipoprotein (HDL) and higher triglycerides and very low-density lipoprotein (VLDL) traits. For example, mean standardized difference (95%CI) in concentration of small VLDL particles (vs. Highest SEP) was 0.12 standard deviation (SD) units (0.05 to 0.20) for 'Medium SEP' and 0.25SD (0.18 to 0.32) for 'Lowest SEP'. There was statistical evidence of ethnic differences in associations of SEP with 31 traits, primarily characterised by stronger associations in WE women e.g., mean difference in HDL cholesterol in WE and SA women respectively (vs. Highest-SEP) was -0.30SD (-0.41 to -0.20) and -0.16SD (-0.27 to -0.05) for 'Medium SEP', and -0.62SD (-0.72 to -0.52) and -0.29SD (-0.40 to -0.20) for 'Lowest SEP'.

CONCLUSIONS

We found widespread socioeconomic differences in metabolic traits in pregnant WE and SA women residing in the UK. Further research is needed to understand whether the socioeconomic differences we observe here reflect pre-conception differences or differences in the metabolic pregnancy response. If replicated, it would be important to explore if these differences contribute to socioeconomic differences in pregnancy outcomes.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Inverse association between maternal serum concentrations of trace elements and risk of spontaneous preterm birth: a nested case-control study in China

Few studies have evaluated the joint effect of trace elements on spontaneous preterm birth (SPTB). This study aimed to examine the relationships between the individual or mixed maternal serum concentrations of Fe, Cu, Zn, Se, Sr and Mo during pregnancy, and risk of SPTB. Inductively coupled plasma MS was employed to determine maternal serum concentrations of the six trace elements in 192 cases with SPTB and 282 controls with full-term delivery. Multivariate logistic regression, weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR) were used to evaluate the individual and joint effects of trace elements on SPTB. The median concentrations of Sr and Mo were significantly higher in controls than in SPTB group (P < 0·05). In multivariate logistic regression analysis, compared with the lowest quartile levels of individual trace elements, the third- and fourth-quartile Sr or Mo concentrations were significantly associated with reduced risk of SPTB with adjusted OR (aOR) of 0·432 (95 CI < 0·05). In multivariate logistic regression analysis, compared with the lowest quartile levels of individual trace elements, the third- and fourth-quartile Sr or Mo concentrations were significantly associated with reduced risk of SPTB with adjusted aOR of 0·432 (95 % CI 0·247, 0·756), 0·386 (95 % CI 0·213, 0·701), 0·512 (95 % CI 0·297, 0·883) and 0·559 (95 % CI 0·321, 0·972), respectively. WQSR revealed the inverse combined effect of the trace elements mixture on SPTB (aOR = 0·368, 95 % CI 0·228, 0·593). BKMR analysis confirmed the overall mixture of the trace elements was inversely associated with the risk of SPTB, and the independent effect of Sr and Mo was significant. Our findings suggest that the risk of SPTB decreased with concentrations of the six trace elements, with Sr and Mo being the major contributors.

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

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

Challenges in the Metabolomics-Based Biomarker Validation Pipeline

As end-products of the intersection between the genome and environmental influences, metabolites represent a promising approach to the discovery of novel biomarkers for diseases. However, many potential biomarker candidates identified by metabolomics studies fail to progress beyond analytical validation for routine implementation in clinics. Awareness of the challenges present can facilitate the development and advancement of innovative strategies that allow improved and more efficient applications of metabolite-based markers in clinical settings. This minireview provides a comprehensive summary of the pre-analytical factors, required analytical validation studies, and kit development challenges that must be resolved before the successful translation of novel metabolite biomarkers originating from research. We discuss the necessity for strict protocols for sample collection, storage, and the regulatory requirements to be fulfilled for a bioanalytical method to be considered as analytically validated. We focus especially on the blood as a biological matrix and liquid chromatography coupled with tandem mass spectrometry as the analytical platform for biomarker validation. Furthermore, we examine the challenges of developing a commercially viable metabolomics kit for distribution. To bridge the gap between the research lab and clinical implementation and utility of relevant metabolites, the understanding of the translational challenges for a biomarker panel is crucial for more efficient development of metabolomics-based precision medicine.

Perspective of Dose-Response: New Chapter With New "Exposome" and "-omics"

This is an editorial from the new editor-in-chief, EIC, who starts his role in March 2024. Due to the remarkable advance of various research fields in terms of its concepts and technology, the emerging concepts of "Exposome" and associated "Exposomics" have been introduced into the toxicology research. Therefore, the mission of "Dose-Response" will remain adhered to the original goals but will incorporate with these new concepts as the next chapter's principle and strategies. Accordingly, although it remains with special interest in the biological response to low-dose (level) ranges of stresses. The types and contents of interested manuscripts will be extended with more diverse, newer concepts from a wide range of disciplines. We wish this journal can open a door for various discipline scientists and researchers to share their state-of-the-art discoveries to shed new insight to understand the impact of environmental and toxicological medicine and develop more specific and effective intervention strategies.

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.

[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.

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.

Interdisciplinary fetal-neonatal neurology training applies neural exposome perspectives to neurology principles and practice

An interdisciplinary fetal-neonatal neurology (FNN) program over the first 1,000 days teaches perspectives of the neural exposome that are applicable across the life span. This curriculum strengthens neonatal neurocritical care, pediatric, and adult neurology training objectives. Teaching at maternal-pediatric hospital centers optimally merges reproductive, pregnancy, and pediatric approaches to healthcare. Phenotype-genotype expressions of health or disease pathways represent a dynamic neural exposome over developmental time. The science of uncertainty applied to FNN training re-enforces the importance of shared clinical decisions that minimize bias and reduce cognitive errors. Trainees select mentoring committee participants that will maximize their learning experiences. Standardized questions and oral presentations monitor educational progress. Master or doctoral defense preparation and competitive research funding can be goals for specific individuals. FNN principles applied to practice offer an understanding of gene-environment interactions that recognizes the effects of reproductive health on the maternal-placental-fetal triad, neonate, child, and adult. Pre-conception and prenatal adversities potentially diminish life-course brain health. Endogenous and exogenous toxic stressor interplay (TSI) alters the neural exposome through maladaptive developmental neuroplasticity. Developmental disorders and epilepsy are primarily expressed during the first 1,000 days. Communicable and noncommunicable illnesses continue to interact with the neural exposome to express diverse neurologic disorders across the lifespan, particularly during the critical/sensitive time periods of adolescence and reproductive senescence. Anomalous or destructive fetal neuropathologic lesions change clinical expressions across this developmental-aging continuum. An integrated understanding of reproductive, pregnancy, placental, neonatal, childhood, and adult exposome effects offers a life-course perspective of the neural exposome. Exosome research promises improved disease monitoring and drug delivery starting during pregnancy. Developmental origins of health and disease principles applied to FNN practice anticipate neurologic diagnoses with interventions that can benefit successive generations. Addressing health care disparities in the Global South and high-income country medical deserts require constructive dialogue among stakeholders to achieve medical equity. Population health policies require a brain capital strategy that reduces the global burden of neurologic diseases by applying FNN principles and practice. This integrative neurologic care approach will prolong survival with an improved quality of life for persons across the lifespan confronted with neurological disorders.

Contrasted life trajectories: reconstituting the main population exposomes in French Guiana

In French Guiana, life expectancy is between 2 and 3 years below that of France, reflecting differences in mortality rates that are largely sensitive to primary healthcare and thus preventable. However, because poverty affects half of the population in French Guiana, global measurements of life expectancy presumably conflate at least two distinct situations: persons who have similar life expectancies as in mainland France and persons living in precariousness who have far greater mortality rates than their wealthier counterparts. We thus aimed to synthesize what is known about statistical regularities regarding exposures and sketch typical French Guiana exposomes in relation to health outcomes. We conducted a narrative review on common exposures in French Guiana and made comparisons between French Guiana and mainland France, between rich and poor in French Guiana, and between urban and rural areas within French Guiana. The most striking fact this panorama shows is that being a fetus or a young child in French Guiana is fraught with multiple threats. In French Guiana, poverty and poor pregnancy follow-up; renouncing healthcare; wide variety of infectious diseases; very high prevalence of food insecurity; psychosocial stress; micronutrient deficiencies; obesity and metabolic problems; and frequent exposure to lead and mercury in rural areas constitute a stunningly challenging exposome for a new human being to develop into. A substantial part of the population's health is hence affected by poverty and its sources of nutrition.

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.

Centennial-scale source shift in potentially toxic metal(loid)s in Yangtze River

Estuarine sedimentation is an important historical record of potentially toxic metal (PTM) emissions from human activities that can be used to improve environmental management. However, the contribution of different human activities to PTM deposition has not been accurately estimated, and their coupled relationship with riverine organic matter is typically not considered. In this study, we reconstruct the century-scale PTM depositional history of sediment cores from the Yangtze Grand Delta. Eight potential metal sources (PMSs) were identified using positive matrix factorization, and the results of lagged correlation determined the PMSs associated with the riverine discharge of the Yangtze River. Riverine PTMs were predominantly composed of Cr (79.0%), Ni (77.3%), and Pb (64.1%) but were deprived in Cu (34.9%). Glomalin-related soil protein (GRSP), which is a typical terrestrial refractory carbon, has a strong affinity for Cu, and contributed to 2.82-22.6% Cu deposition. The change in the PMS is mainly related to power generation, whereas the GRSP-bound PTM is mainly related to road construction and transportation. We advocate for responsible management of human activities in river catchments, particularly on coal-based power generation and road transportation, to maintain ecological security and promote the overall achievement of the Sustainable Development Goals.

Lifestyle differences between co-twins are associated with decreased similarity in their internal and external exposome profiles

Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.

Linking Prenatal Environmental Exposures to Lifetime Health with Epigenome-Wide Association Studies: State-of-the-Science Review and Future Recommendations

BACKGROUND

The prenatal environment influences lifetime health; epigenetic mechanisms likely predominate. In 2016, the first international consortium paper on cigarette smoking during pregnancy and offspring DNA methylation identified extensive, reproducible exposure signals. This finding raised expectations for epigenome-wide association studies (EWAS) of other exposures.

OBJECTIVE

We review the current state-of-the-science for DNA methylation associations across prenatal exposures in humans and provide future recommendations.

METHODS

We reviewed 134 prenatal environmental EWAS of DNA methylation in newborns, focusing on 51 epidemiological studies with meta-analysis or replication testing. Exposures spanned cigarette smoking, alcohol consumption, air pollution, dietary factors, psychosocial stress, metals, other chemicals, and other exogenous factors. Of the reproducible DNA methylation signatures, we examined implementation as exposure biomarkers.

RESULTS

Only 19 (14%) of these prenatal EWAS were conducted in cohorts of 1,000 or more individuals, reflecting the still early stage of the field. To date, the largest perinatal EWAS sample size was 6,685 participants. For comparison, the most recent genome-wide association study for birth weight included more than 300,000 individuals. Replication, at some level, was successful with exposures to cigarette smoking, folate, dietary glycemic index, particulate matter with aerodynamic diameter < 10 μ m and < 2.5 μ m , nitrogen dioxide, mercury, cadmium, arsenic, electronic waste, PFAS, and DDT. Reproducible effects of a more limited set of prenatal exposures (smoking, folate) enabled robust methylation biomarker creation.

DISCUSSION

Current evidence demonstrates the scientific premise for reproducible DNA methylation exposure signatures. Better powered EWAS could identify signatures across many exposures and enable comprehensive biomarker development. Whether methylation biomarkers of exposures themselves cause health effects remains unclear. We expect that larger EWAS with enhanced coverage of epigenome and exposome, along with improved single-cell technologies and evolving methods for integrative multi-omics analyses and causal inference, will expand mechanistic understanding of causal links between environmental exposures, the epigenome, and health outcomes throughout the life course. https://doi.org/10.1289/EHP12956.

Acquired Brain Injuries Across the Perinatal Spectrum: Pathophysiology and Emerging Therapies

The development of the central nervous system can be directly disrupted by a variety of acquired factors, including infectious, inflammatory, hypoxic-ischemic, and toxic insults. Influences external to the fetus also impact neurodevelopment, including placental health, maternal comorbidities, adverse experiences, environmental exposures, and social determinants of health. Acquired perinatal brain insults tend to affect the developing brain in a stage-specific manner that reflects the susceptible cell types, developmental processes, and risk factors present at the time of the insult. In this review, we discuss the pathophysiology, neurodevelopmental outcomes, and management of common acquired perinatal brain conditions. In the fetal brain, we divide insults based on trimester, and in the postnatal brain, we focus on common pathologies that have a presentation dependent on gestational age at birth: white matter injury and germinal matrix hemorrhage/intraventricular hemorrhage in preterm infants and hypoxic-ischemic encephalopathy in term infants. Although specific treatments for fetal and newborn brain disorders are currently limited, we emphasize therapies in preclinical or early clinical phases of the development pipeline. The growing number of novel cell type- and stage-specific emerging therapies suggests that in the near future we may have a dramatically improved ability to treat acquired perinatal brain disorders and to mitigate the associated neurodevelopmental consequences.

Cardiometabolic risk estimation using exposome data and machine learning

BACKGROUND

The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention.

OBJECTIVE

Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort.

METHODS

From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method.

RESULTS

The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified.

CONCLUSIONS

We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.

Integrating the social environment with an equity perspective into the exposome paradigm: A new conceptual framework of the Social Exposome

The importance of the social environment and social inequalities in disease etiology is well-known due to the profound research and conceptual framework on social determinants of health. For a long period, in exposome research with its classical orientation towards detrimental health effects of biological, chemical, and physical exposures, this knowledge remained underrepresented. But currently it gains great awareness and calls for innovations in rethinking the role of social environmental health determinants. To fill this gap that exists in terms of the social domain within exposome research, we propose a novel conceptual framework of the Social Exposome, to integrate the social environment in conjunction with the physical environment into the exposome concept. The iterative development process of the Social Exposome was based on a systematic compilation of social exposures in order to achieve a holistic portrayal of the human social environment - including social, psychosocial, socioeconomic, sociodemographic, local, regional, and cultural aspects, at individual and contextual levels. In order to move the Social Exposome beyond a mere compilation of exposures, three core principles are emphasized that underly the interplay of the multitude of exposures: Multidimensionality, Reciprocity, and Timing and continuity. The key focus of the conceptual framework of the Social Exposome is on understanding the underlying mechanisms that translate social exposures into health outcomes. In particular, insights from research on health equity and environmental justice have been incorporated to uncover how social inequalities in health emerge, are maintained, and systematically drive health outcomes. Three transmission pathways are presented: Embodiment, Resilience and Susceptibility or Vulnerability, and Empowerment. The Social Exposome conceptual framework may serve as a strategic map for, both, research and intervention planning, aiming to further explore the impact of the complex social environment and to alter transmission pathways to minimize health risks and health inequalities and to foster equity in health.

The Gut-Brain Axis as a Therapeutic Target in Multiple Sclerosis

The CNS is very susceptible to oxidative stress; the gut microbiota plays an important role as a trigger of oxidative damage that promotes mitochondrial dysfunction, neuroinflammation, and neurodegeneration. In the current review, we discuss recent findings on oxidative-stress-related inflammation mediated by the gut-brain axis in multiple sclerosis (MS). Growing evidence suggests targeting gut microbiota can be a promising strategy for MS management. Intricate interaction between multiple factors leads to increased intra- and inter-individual heterogeneity, frequently painting a different picture in vivo from that obtained under controlled conditions. Following an evidence-based approach, all proposed interventions should be validated in clinical trials with cohorts large enough to reach significance. Our review summarizes existing clinical trials focused on identifying suitable interventions, the suitable combinations, and appropriate timings to target microbiota-related oxidative stress. Most studies assessed relapsing-remitting MS (RRMS); only a few studies with very limited cohorts were carried out in other MS stages (e.g., secondary progressive MS-SPMS). Future trials must consider an extended time frame, perhaps starting with the perinatal period and lasting until the young adult period, aiming to capture as many complex intersystem interactions as possible.

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: A scoping review

Health effects of endocrine disrupting chemicals (EDCs) are challenging to detect in the general population. Omics technologies become increasingly common to identify early biological changes before the apparition of clinical symptoms, to explore toxic mechanisms and to increase biological plausibility of epidemiological associations. This scoping review systematically summarises the application of omics in epidemiological studies assessing EDCs-associated biological effects to identify potential gaps and priorities for future research. Ninety-eight human studies (2004-2021) were identified through database searches (PubMed, Scopus) and citation chaining and focused on phthalates (34 studies), phenols (19) and PFASs (17), while PAHs (12) and recently-used pesticides (3) were less studied. The sample sizes ranged from 10 to 12,476 (median = 159), involving non-pregnant adults (38), pregnant women (11), children/adolescents (15) or both latter populations studied together (23). Several studies included occupational workers (10) and/or highly exposed groups (11) focusing on PAHs, PFASs and pesticides, while studies on phenols and phthalates were performed in the general population only. Analysed omics layers included metabolic profiles (30, including 14 targeted analyses), miRNA (13), gene expression (11), DNA methylation (8), microbiome (5) and proteins (3). Twenty-one studies implemented targeted multi-assays focusing on clinical routine blood lipid traits, oxidative stress or hormones. Overall, DNA methylation and gene expression associations with EDCs did not overlap across studies, while some EDC-associated metabolite groups, such as carnitines, nucleotides and amino acids in untargeted metabolomic studies, and oxidative stress markers in targeted studies, were consistent across studies. Studies had common limitations such as small sample sizes, cross-sectional designs and single sampling for exposure biomonitoring. In conclusion, there is a growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a need for larger longitudinal studies, wider coverage of exposures and biomarkers, replication studies and standardisation of research methods and reporting.

The contribution to policies of an exposome-based approach to childhood obesity

Childhood obesity is an increasingly severe public health problem, with a prospective impact on health. We propose an exposome approach to identify actionable risk factors for this condition. Our assumption is that relationships between external exposures and outcomes such as rapid growth, overweight, or obesity in children can be better understood through a "meet-in-the-middle" model. This is based on a combination of external and internal exposome-based approaches, that is, the study of multiple exposures (in our case, dietary patterns) and molecular pathways (metabolomics and epigenetics). This may strengthen causal reasoning by identifying intermediate markers that are associated with both exposures and outcomes. Our biomarker-based studies in the STOP consortium suggest (in several ways, including mediation analysis) that branched-chain amino acids (BCAAs) could be mediators of the effect of dietary risk factors on childhood overweight/obesity. This is consistent with intervention and animal studies showing that higher intake of BCAAs has a positive impact on body composition, glycemia, and satiety. Concerning food, of particular concern is the trend of increasing intake of ultra-processed food (UPF), including among children. Several mechanisms have been proposed to explain the impact of UPF on obesity and overweight, including nutrient intake (particularly proteins), changes in appetite, or the role of additives. Research from the Avon Longitudinal Study of Parents and Children cohort has shown a relationship between UPF intake and trajectories in childhood adiposity, while UPF was related to lower blood levels of BCAAs. We suggest that an exposome-based approach can help strengthening causal reasoning and support policies. Intake of UPF in children should be restricted to prevent obesity.

Adverse Maternal Environments Perturb Hepatic DNA Methylome and Transcriptome Prior to the Adult-Onset Non-Alcoholic Fatty Liver Disease in Mouse Offspring

Exposure to adverse early-life environments (AME) increases the incidence of developing adult-onset non-alcoholic fatty liver disease (NAFLD). DNA methylation has been postulated to link AME and late-onset diseases. This study aimed to investigate whether and to what extent the hepatic DNA methylome was perturbed prior to the development of NAFLD in offspring exposed to AME in mice. AME constituted maternal Western diet and late-gestational stress. Male offspring livers at birth (d0) and weaning (d21) were used for evaluating the DNA methylome and transcriptome using the reduced representation of bisulfite sequencing and RNA-seq, respectively. We found AME caused 5879 differentially methylated regions (DMRs) and zero differentially expressed genes (DEGs) at d0 and 2970 and 123, respectively, at d21. The majority of the DMRs were distal to gene transcription start sites and did not correlate with DEGs. The DEGs at d21 were significantly enriched in GO biological processes characteristic of liver metabolic functions. In conclusion, AME drove changes in the hepatic DNA methylome, which preceded perturbations in the hepatic metabolic transcriptome, which preceded the onset of NAFLD. We speculate that subtle impacts on dynamic enhancers lead to long-range regulatory changes that manifest over time as gene network alternations and increase the incidence of NAFLD later in life.

The Rise of Gastrointestinal Cancers as a Global Phenomenon: Unhealthy Behavior or Progress?

The overall burden of cancer is rapidly increasing worldwide, reflecting not only population growth and aging, but also the prevalence and spread of risk factors. Gastrointestinal (GI) cancers, including stomach, liver, esophageal, pancreatic, and colorectal cancers, represent more than a quarter of all cancers. While smoking and alcohol use are the risk factors most commonly associated with cancer development, a growing consensus also includes dietary habits as relevant risk factors for GI cancers. Current evidence suggests that socioeconomic development results in several lifestyle modifications, including shifts in dietary habits from local traditional diets to less-healthy Western diets. Moreover, recent data indicate that increased production and consumption of processed foods underlies the current pandemics of obesity and related metabolic disorders, which are directly or indirectly associated with the emergence of various chronic noncommunicable conditions and GI cancers. However, environmental changes are not restricted to dietary patterns, and unhealthy behavioral features should be analyzed with a holistic view of lifestyle. In this review, we discussed the epidemiological aspects, gut dysbiosis, and cellular and molecular characteristics of GI cancers and explored the impact of unhealthy behaviors, diet, and physical activity on developing GI cancers in the context of progressive societal changes.

Promises and challenges in pharmacoepigenetics

Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.