Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

Screening for endocrine-disrupting chemicals in the serum and cerebrospinal fluid of patients with autoimmune encephalitis

Humans are exposed to a variety of endocrine-disrupting chemicals (EDCs), such as organophosphate esters (OPEs) and phthalate ester (PAE) metabolites. Although EDCs can cross the blood-cerebrospinal fluid barrier (BCSFB) to the brain, their effects on autoimmune encephalitis (AE) remain unclear. Therefore, the association between EDC exposure and AE were determined. The study recruited 106 patients with AE and 119 patients without AE. Using paired serum-CSF samples, we quantified 8 categories of 17 EDCs. The penetration of some EDCs was evaluated through two indices: calculated the blood-brain barrier (BBB) index and the paired serum-CSF concentration ratio. Further, we investigated the association of EDC exposure with AE by the conditional logistic regression analysis. Antioxidants, PAE metabolites, and OPEs were identified as the most dominant EDC in the ∑8 categories of EDCs. The median EDC ratios (REDC = EDCCSF/EDCSerum) ranged from 0.040 % for methyl paraben (MeP) to 3.808 % for ethyl paraben (EtP). AE patients are more likely to have BBB disruption. The associations between AE and some EDCs were found. For example, serum EtP and butyl paraben (BuP) levels [Odds Ratio (OR) = 1.69; 95 % confidence interval (CI): 1.25, 2.27; OR = 1.51; 95 % CI: 1.14, 2.00), and CSF MeP levels (OR =1.54; 95 % CI: 1.30, 1.84) were both associated with an increased risk of AE. We identified that exposure to certain environmental EDCs may be a risk factor for the development of AE. Our findings support an evidence base for the effects of specific chemicals may impair neural functions.

Exposome-wide association study of thyroid function using U.S. National Health and Nutrition Examination Survey data

Previous epidemiologic studies examining thyroid function and chemical exposures have typically focused on a single or a limited number of chemical classes, often neglecting the effects of chemical mixtures. This study addressed this gap by exploring the associations between exposure to hundreds of chemicals and thyroid function using an exposome-wide association study (ExWAS) approach and National Health and Nutrition Examination Survey (NHANES) data. We analyzed data from three NHANES cycles (2007-2008, 2009-2010, and 2011-2012), which include measures of thyroid function (free and total triiodothyronine [T3], free and total thyroxine [T4], thyroid-stimulating hormone [TSH]) and chemical biomarker concentrations from 9,082 participants. For adolescents (aged 12-19 years) and adults (aged ≥20 years), we employed multiple regression by accounting for survey weights to identify biomarkers associated with thyroid function test levels and used Bayesian group weighted quantile sum (BGWQS) regression to assess the effects of chemical mixtures on these measurements. After adjusting for multiple comparisons, we found in single exposure scenarios that 44 and 67 biomarkers were associated with at least one thyroid function measure in adolescents and adults, respectively (adjusted p-value <0.05). In scenarios involving mixed chemical exposures, groups such as pesticides, sodium/iodide symporter (NIS) inhibitors, and metals were associated with alterations in thyroid hormones or TSH across both age groups. Volatile organic compounds were specifically linked to lower T4 levels in adolescents, whereas phenols and parabens were associated with lower TSH levels exclusively in adults. Although limited by the cross-sectional data, this study identified chemical biomarkers linked to thyroid function.

Global research trends on the human exposome: a bibliometric analysis (2005-2024)

Exposome represents one of the most pressing issues in the environmental science research field. However, a comprehensive summary of worldwide human exposome research is lacking. We aimed to explore the bibliometric characteristics of scientific publications on the human exposome. A bibliometric analysis of human exposome publications from 2005 to December 2024 was conducted using the Web of Science in accordance with PRISMA guidelines. Trends/hotspots were investigated with keyword frequency, co-occurrence, and thematic map. Sex disparities in terms of publications and citations were examined. From 2005 to 2024, 931 publications were published in 363 journals and written by 4529 authors from 72 countries. The number of publications tripled during the last 5 years. Publications written by females (51% as first authors and 34% as last authors) were cited fewer times (13,674) than publications written by males (22,361). Human exposome studies mainly focused on air pollution, metabolomics, chemicals (e.g., per- and polyfluoroalkyl substances (PFAS), endocrine-disrupting chemicals, pesticides), early-life exposure, biomarkers, microbiome, omics, cancer, and reproductive disorders. Social and built environment factors, occupational exposure, multi-exposure, digital exposure (e.g., screen use), climate change, and late-life exposure received less attention. Our results uncovered high-impact countries, institutions, journals, references, authors, and key human exposome research trends/hotspots. The use of digital exposome technologies (e.g., sensors, and wearables) and data science (e.g., artificial intelligence) has blossomed to overcome challenges and could provide valuable knowledge toward precision prevention. Exposome risk scores represent a promising research avenue.

Exposome-wide association study of environmental chemical exposures and epigenetic aging in the national health and nutrition examination survey

Epigenetic clocks can serve as pivotal biomarkers linking environmental exposures with biological aging. However, research on the influence of environmental exposures on epigenetic aging has largely been limited to a small number of chemicals and specific populations. We harnessed data from the National Health and Nutrition Examination Survey 1999-2000 and 2001-2002 cycles to examine exposome-wide associations between environmental exposures and epigenetic aging. A total of 8 epigenetic aging biomarkers were obtained from whole blood in 2,346 participants ranging from 50-84 years of age. A total of 64 environmental exposures including phthalates, metals, pesticides, dioxins, and polychlorinated biphenyls (PCBs) were measured in blood and urine. Associations between log2-transformed/standardized exposure measures and epigenetic age acceleration (EAA) were assessed using survey-weighted generalized linear regression. A 1 standard deviation (SD) increase in log2 serum cadmium levels was associated with higher GrimAge acceleration (beta = 1.23 years, p = 3.63e-06), higher GrimAge2 acceleration (beta = 1.27 years, p = 1.62e-05), and higher DunedinPoAm (beta = 0.02, p = 2.34e-05). A 1 SD increase in log2 serum cotinine levels was associated with higher GrimAge2 acceleration (beta = 1.40 years, p = 6.53e-04) and higher DunedinPoAm (beta = 0.03, p = 6.31e-04). Associations between cadmium and EAA across several clocks persisted in sensitivity models adjusted for serum cotinine levels, and other associations involving lead, dioxins, and PCBs were identified. Several environmental exposures are associated with epigenetic aging in a nationally representative US adult population, with particularly strong associations related to cadmium and cotinine across several epigenetic clocks.

Mapping the exposome of mental health: exposome-wide association study of mental health outcomes among UK Biobank participants

BACKGROUND

Dissecting the exposome linked to mental health outcomes can help identify potentially modifiable targets to improve mental well-being. However, the multiplicity of exposures and the complexity of mental health phenotypes pose a challenge that requires data-driven approaches.

METHODS

Guided by our previous systematic approach, we conducted hypothesis-free exposome-wide analyses to identify factors associated with 7 psychiatric diagnostic domains and 19 symptom dimensions in 157,298 participants from the UK Biobank Mental Health Survey. After quality control, 294 environmental, lifestyle, behavioral, and economic variables were included. An Exposome-Wide Association Study was conducted per outcome in two equally split datasets. Variables associated with each outcome were then tested in a multivariable model.

RESULTS

Across all diagnostic domains and symptom dimensions, the top three exposures were childhood adversities and traumatic events. Cannabis use was associated with common psychiatric disorders (depressive, anxiety, psychotic, and bipolar manic disorders), with ORs ranging from 1.10 to 1.79 in the multivariable models. Additionally, differential associations were identified between specific outcomes-such as neurodevelopmental disorders, eating disorders, and self-harm behaviors-and exposures, including early life experiences (being adopted), lifestyle (time spent using computers), and dietary habits (vegetarian diet).

CONCLUSIONS

This comprehensive mapping of the exposome revealed that several factors, particularly in the domains of those previously well-studied were shared across mental health phenotypes, providing further support for transdiagnostic pathoetiology. Our findings also showed that distinct relations might exist. Continued exposome research through multimodal mechanistic studies guided by the transdiagnostic mental health framework is required to better inform public health policies.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Exposome-wide association study of cognition among older adults in the National Health and Nutrition Examination Survey

Cognitive impairment among older adults is a growing public health challenge and environmental chemicals may be modifiable risk factors. A wide array of chemicals has not yet been tested for association with cognition in an environment-wide association framework. In the US National Health and Nutrition Examination Survey (NHANES) 1999-2000 and 2011-2014 cross-sectional cycles, cognition was assessed using the Digit Symbol Substitution Test (DSST, scores 0-117) among participants aged 60 years and older. Concentrations of environmental chemicals measured in blood or urine were log2 transformed and standardized. Chemicals with at least 50% of measures above the lower limit of detection were included (nchemicals = 147, nclasses=14). We tested for associations between chemical concentrations and cognition using parallel survey-weighted multivariable linear regression models adjusted for age, sex, race/ethnicity, education, smoking status, fish consumption, cycle year, urinary creatinine, and cotinine. Participants with at least one chemical measurement (n = 4982) were mean age 69.8 years, 55.0% female, 78.2% non-Hispanic White, and 77.0% at least high school educated. The mean DSST score was 50.4 (standard deviation (SD)=17.4). In adjusted analyses, 5 of 147 exposures were associated with DSST at P-value <.01. Notably, a SD increase in log2-scaled cotinine concentration was associated with 2.71 points lower DSST score (95% CI -3.69, -1.73). A SD increase in log2-scaled urinary tungsten concentration was associated with 1.34 points lower DSST score (95% CI -2.11, -0.56). Exposure to environmental chemicals, particularly metals and tobacco smoke, may be modifiable factors for cognition among older adults.

Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024)

BACKGROUND

Although agricultural health has gained importance, to date, much of the existing research relies on traditional epidemiological approaches that often face limitations related to sample size, geographic scope, temporal coverage, and the range of health events examined. To address these challenges, a complementary approach involves leveraging and reusing data beyond its original purpose. Administrative health databases (AHDs) are increasingly reused in population-based research and digital public health, especially for populations such as farmers, who face distinct environmental risks.

OBJECTIVE

We aimed to explore the reuse of AHDs in addressing health issues within farming populations by summarizing the current landscape of AHD-based research and identifying key areas of interest, research gaps, and unmet needs.

METHODS

We conducted a scoping review and bibliometric analysis using PubMed and Web of Science. Building upon previous reviews of AHD-based public health research, we conducted a comprehensive literature search using 72 terms related to the farming population and AHDs. To identify research hot spots, directions, and gaps, we used keyword frequency, co-occurrence, and thematic mapping. We also explored the bibliometric profile of the farming exposome by mapping keyword co-occurrences between environmental factors and health outcomes.

RESULTS

Between 1975 and April 2024, 296 publications across 118 journals, predominantly from high-income countries, were identified. Nearly one-third of these publications were associated with well-established cohorts, such as Agriculture and Cancer and Agricultural Health Study. The most frequently used AHDs included disease registers (158/296, 53.4%), electronic health records (124/296, 41.9%), insurance claims (106/296, 35.8%), population registers (95/296, 32.1%), and hospital discharge databases (41/296, 13.9%). Fifty (16.9%) of 296 studies involved >1 million participants. Although a broad range of exposure proxies were used, most studies (254/296, 85.8%) relied on broad proxies, which failed to capture the specifics of farming tasks. Research on the farming exposome remains underexplored, with a predominant focus on the specific external exposome, particularly pesticide exposure. A limited range of health events have been examined, primarily cancer, mortality, and injuries.

CONCLUSIONS

The increasing use of AHDs holds major potential to advance public health research within farming populations. However, substantial research gaps persist, particularly in low-income regions and among underrepresented farming subgroups, such as women, children, and contingent workers. Emerging issues, including exposure to per- and polyfluoroalkyl substances, biological agents, microbiome, microplastics, and climate change, warrant further research. Major gaps also persist in understanding various health conditions, including cardiovascular, reproductive, ocular, sleep-related, age-related, and autoimmune diseases. Addressing these overlooked areas is essential for comprehending the health risks faced by farming communities and guiding public health policies. Within this context, promoting AHD-based research, in conjunction with other digital data sources (eg, mobile health, social health data, and wearables) and artificial intelligence approaches, represents a promising avenue for future exploration.

Environmental Mixtures Analysis (E-MIX) Workflow and Methods Repository

Human exposure to complex, changing, and variably correlated mixtures of environmental chemicals has presented analytical challenges to epidemiologists and human health researchers. There have been a wide variety of recent advances in statistical methods for analyzing mixtures data, with most of these methods having open-source software for implementation. However, there is no one-size-fits-all method for analyzing mixtures data given the considerable heterogeneity in scientific focus and study design. For example, some methods focus on predicting the overall health effect of a mixture and others seek to disentangle main effects and pairwise interactions. Some methods are only appropriate for cross-sectional designs, while other methods can accommodate longitudinally measured exposures or outcomes. This article focuses on greatly simplifying the daunting task of identifying which methods are most suitable for a particular study design, data type, and scientific focus. With this goal in mind, we present an organized workflow for statistical analysis considerations in environmental mixtures data. This systematic strategy builds on epidemiological and statistical principles, considering specific nuances for the mixtures' context. We also describe an accompanying online methods repository in development to increase awareness of and inform application of existing methods and new methods as they are developed and identify gaps in existing methods warranting further development.

Comprehensive Blood Metabolome and Exposome Analysis, Annotation, and Interpretation in E-Waste Workers

Background: Electronic and electrical waste (e-waste) production has emerged to be of global environmental public health concern. E-waste workers, who are frequently exposed to hazardous chemicals through occupational activities, face considerable health risks. Methods: To investigate the metabolic and exposomic changes in these workers, we analyzed whole blood samples from 100 male e-waste workers and 49 controls from the GEOHealth II project (2017-2018 in Accra, Ghana) using LC-MS/MS. A specialized computational workflow was established for exposomics data analysis, incorporating two curated reference libraries for metabolome and exposome profiling. Two feature detection algorithms, asari and centWave, were applied. Results: In comparison to centWave, asari showed better sensitivity in detecting MS features, particularly at trace levels. Principal component analysis demonstrated distinct metabolic profiles between e-waste workers and controls, revealing significant disruptions in key metabolic pathways, including steroid hormone biosynthesis, drug metabolism, bile acid biosynthesis, vitamin metabolism, and prostaglandin biosynthesis. Correlation analyses linked metal exposures to alterations in hundreds to thousands of metabolic features. Functional enrichment analysis highlighted significant perturbations in pathways related to liver function, vitamin metabolism, linoleate metabolism, and dynorphin signaling, with the latter being observed for the first time in e-waste workers. Conclusions: This study provides new insights into the biological impact of prolonged metal exposure in e-waste workers.

Association of urban environments with Atherosclerotic cardiovascular disease: A prospective cohort study in the UK Biobank

Urban environments and cardiovascular health are closely linked, yet only a few specific exposures have been explored in isolation and mostly adopting cross-sectional design. The influence of socioeconomic status and genetic predisposition also remains unclear. Hence, leveraging the UK Biobank data (n = 206,681), we conducted a prospective analysis of 213 urban environmental variables and their association with atherosclerotic cardiovascular disease (ASCVD). The ExWAS and regularized Cox models analyses highlighted air pollution, industrial sites, and complex street networks as primary environmental risk factors. Instead, land-use density of leisure, public services, infrastructure and residential, and drinking water hardness showed a negative association with ASCVD risk. By integrating sparse canonical correlation and mediation analyses, we found distinct urban environment patterns through diverse pathways influence ASCVD. The environment characterized by pollution and complex streets impact ASCVD through adverse mental health (mediation proportion:30.7 %, 95 % CI:22.4 %-44.0 %), while highly-developed community and high-water hardness environment via cardiometabolic status (22.6 %, 95 % CI:19.7 %-26.0 %). Further, we found low socioeconomic status amplifies disadvantaged urban environment effects on ASCVD, yet there were no similar findings for ASCVD genetic predisposition. This research deepened our understanding of city-cardiovascular health links and the role of socioeconomic status, with implications for urban planning and public health interventions.

ASIA Syndrome: State-of-the-Art and Future Perspectives

The expression "Autoimmune/inflammatory syndrome induced by adjuvants (ASIA)" was coined by Shoenfeld and colleagues in 2011. It defines a group of immune-mediated disorders that arise in people, with a genetic predisposition, following exposure to adjuvant agents. This syndrome has been reported after contact with silicone implants, medications, infections, metals, vaccines, and other substances. It typically occurs in individuals with a genetic predisposition, particularly involving genes, such as HLA-DRB1 (major histocompatibility complex, class II, DR beta 1) and PTPN22 (protein tyrosine phosphatase non-receptor type 22). Some stimuli lead to an overactivation of the immune system, prompt the production of autoantibodies, and finally cause autoimmune disorders. This narrative review aims to provide an overview of the ASIA syndrome with a special focus on the role of adjuvants in different vaccines, especially after the COVID-19 pandemic, and insights into development of new treatments.

Reconceptualizing and Defining Exposomics within Environmental Health: Expanding the Scope of Health Research

BACKGROUND

Exposomics, the study of the exposome, is flourishing, but the field is not well defined. The term "exposome" refers to all environmental influences and associated biological responses throughout the lifespan. However, this definition is very similar to that of the term "environment"-the external elements and conditions that surround and affect the life and development of an organism. Consequently, the exposome seems to be nothing more than a synonym for the environment, and exposomics a synonym for environmental research. As a result, some have rebranded their "standard" environmental health research with the neologistic exposome term, whereas others ignore or seek to abandon the seemingly redundant concept of the exposome.

OBJECTIVES

We argue that exposomics needs to sharpen its mission focus to counteract this apparent redundancy. Exposomics should be defined as a research program in environmental health aimed at enabling a comprehensive and discovery-driven approach to identifying environmental determinants of human health. Similar to the aim of the Human Genome Project, exposomics aims to analyze the complete complexity of exposures and their corresponding biological responses. Exposomics' primary premise is that the existence of undiscovered, potentially interconnected, nongenetic (environmental) risk factors for health necessitates a comprehensive discovery-driven analysis approach.

DISCUSSION

We argue that exposomics researchers should adopt our reconceptualization of exposomics and focus on the productiveness and integrity of their research program: its purpose and principles. We suggest that exposomics researchers should coordinate the writing of reviews that assess the program's productiveness and integrity, as well as provide a platform for exposomics researchers to define their vision for the field. https://doi.org/10.1289/EHP14509.

Exposures and conditions prior to age 16 are associated with dementia status among adults in the United States Health and Retirement Study

Background

Dementia susceptibility likely begins years before symptoms. Early life has not been comprehensively tested for dementia associations.

Method

In the US Health and Retirement Study (normal baseline cognition; n=16,509; 2008-2018 waves), 31 exposures before age 16 were retrospectively assessed with ten-year incident cognitive status (dementia, impaired, normal). Using parallel logistic models, each exposure was tested with incident cognition, adjusting for sex, baseline age, follow-up, race/ethnicity, personal/parental education.

Result

14.5% had incident impairment and 5.3% had dementia. Depression was associated with 1.71 (95%CI:1.28,2.26) times higher odds of incident impairment, relative to normal cognition. Headaches/migraines were associated with 1.63 (95%CI:1.18,2.22) times higher odds of incident impairment. Learning problems were associated with 1.75 (95%CI:1.05,2.79) times higher odds of incident impairment. Childhood self-rated health of fair (1.86, 95%CI:1.27,2.64) and poor (3.39, 95%CI:1.91,5.82) were associated with higher incident dementia odds, relative to excellent.

Conclusion

Early life factors may be important for impairment or dementia, extending the relevant risk window.

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

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

Exposome-wide association study of cognition among older adults in the National Health and Nutrition Examination Survey

Cognitive impairment among older adults is a growing public health challenge and environmental chemicals may be modifiable risk factors. A wide array of chemicals has not yet been tested for association with cognition in an environment-wide association framework. In the US National Health and Nutrition Examination Survey (NHANES) 1999-2000 and 2011-2014 cross-sectional cycles, cognition was assessed using the Digit Symbol Substitution Test (DSST, scores 0-117) among participants aged 60 years and older. Concentrations of environmental chemicals measured in blood or urine were log2 transformed and standardized. Chemicals with at least 50% of measures above the lower limit of detection were included (nchemicals=147, nclasses=14). We tested for associations between chemical concentrations and cognition using parallel survey-weighted multivariable linear regression models adjusted for age, sex, race/ethnicity, education, smoking status, fish consumption, cycle year, urinary creatinine, and cotinine. Participants with at least one chemical measurement (n=4,982) were mean age 69.8 years, 55.0% female, 78.2% non-Hispanic White, and 77.0% at least high school educated. The mean DSST score was 50.4 (standard deviation (SD)=17.4). In adjusted analyses, 5 of 147 exposures were associated with DSST at p-value<0.01. Notably, a SD increase in log2-scaled cotinine concentration was associated with 2.71 points lower DSST score (95% CI -3.69, -1.73). A SD increase in log2-scaled urinary tungsten concentration was associated with 1.34 points lower DSST score (95% CI -2.11, -0.56). Exposure to environmental chemicals, particularly heavy metals and tobacco smoke, may be modifiable factors for cognition among older adults.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Exposure to heavy metals in utero and autism spectrum disorder at age 3: a meta-analysis of two longitudinal cohorts of siblings of children with autism

BACKGROUND

Autism spectrum disorder (ASD) is a prevalent and heterogeneous neurodevelopmental disorder. Risk is attributed to genetic and prenatal environmental factors, though the environmental agents are incompletely characterized.

METHODS

In Early Autism Risk Longitudinal Investigation (EARLI) and Markers of Autism Risk in Babies Learning Early Signs (MARBLES), two pregnancy cohorts of siblings of children with ASD, urinary metals concentrations during two pregnancy time periods (< 28 weeks and ≥ 28 weeks of gestation) were measured using inductively coupled plasma mass spectrometry. At age three, clinicians assessed ASD with DSM-5 criteria. In an exposure-wide association framework, using multivariable log binomial regression, we examined each metal for association with ASD status, adjusting for gestational age at urine sampling, child sex, age at pregnancy, race/ethnicity and education. We meta-analyzed across the two cohorts.

RESULTS

In EARLI (n = 170) 17% of children were diagnosed with ASD, and 44% were classified as having non-neurotypical development (Non-TD). In MARBLES (n = 231), 21% were diagnosed with ASD, and 14% classified as Non-TD. During the first and second trimester period (< 28 weeks), having cadmium concentration over the level of detection was associated with 1.69 (1.08, 2.64) times higher risk of ASD, and 1.29 (0.95, 1.75)times higher risk of Non-TD. A doubling of first and second trimester cesium concentration was marginally associated with 1.89 (0.94, 3.80) times higher risk of ASD, and a doubling of third trimester cesium with 1.69 (0.97, 2.95) times higher risk of ASD.

CONCLUSION

Exposure in utero to elevated levels of cadmium and cesium, as measured in urine collected during pregnancy, was associated with increased risk of developing ASD.

Environmental chemical-wide associations with immune biomarkers in US adults: A cross-sectional analysis

Environmental chemical exposures influence immune system functions, and humans are exposed to a wide range of chemicals, termed the chemical "exposome". A comprehensive, discovery analysis of the associations of multiple chemical families with immune biomarkers is needed. In this study, we tested the associations between environmental chemical concentrations and immune biomarkers. We analyzed the United States cross-sectional National Health and Nutrition Examination Survey (NHANES, 1999-2018). Chemical biomarker concentrations were measured in blood or urine (196 chemicals, 17 chemical families). Immune biomarkers included counts of lymphocytes, neutrophils, monocytes, basophils, eosinophils, red blood cells, white blood cells, and mean corpuscular volume. We conducted separate survey-weighted, multivariable linear regressions of each log2-transformed chemical and immune measure, adjusted for relevant covariates. We accounted for multiple comparisons using a false discovery rate (FDR). Among 45,528 adult participants, the mean age was 45.7 years, 51.4% were female, and 69.3% were Non-Hispanic White. 71 (36.2%) chemicals were associated with at least one of the eight immune biomarkers. The most chemical associations (FDR<0.05) were observed with mean corpuscular volume (36 chemicals) and red blood cell counts (35 chemicals). For example, a doubling in the concentration of cotinine was associated with 0.16 fL (95% CI: 0.15, 0.17; FDR<0.001) increased mean corpuscular volume, and a doubling in the concentration of blood lead was associated with 61,736 increased red blood cells per μL (95% CI: 54,335, 69,138; FDR<0.001). A wide variety of chemicals, such as metals and smoking-related compounds, were highly associated with immune system biomarkers. This environmental chemical-wide association study identified chemicals from multiple families for further toxicological, immunologic, and epidemiological investigation.

The Need for an Alternative Health Claim Process for Foods Based on Both Nutrient and Contaminant Profiles

Most authorized health claims on foods have been established on the basis of single dietary components, mainly micronutrients, such as vitamins, minerals, and possibly bioactives. Failure to sufficiently define and characterize the nutritional profile of a food product is one of the main reasons for rejection or incomplete status for thousands of health claim applications, whereas the food's contaminant profile is simply not accounted for. The objective of this work was to highlight the accumulating scientific evidence supporting a reform of the health claim evaluation process for foods toward more holistic approaches. This would entail the characterization of multiple nutrient-contaminant pairs and contaminant mixture profiles at contaminant levels currently considered "safe," including their interactions that would impact human health outcome(s) in a net positive or negative direction. The notion of a stable nutritional profile in food commodities has been challenged by studies reporting a variable food contaminant content and a declining content of proteins/micronutrients in crops due to anthropogenic greenhouse gas emissions. A holistic approach in the health claim process for foods would entail the incorporation of cumulative risk assessment and/or risk-benefit protocols that effectively combine health risks and benefits associated with multiple nutritional and contaminant attributes of the food/diet under evaluation.