Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke

Repeated exposure to eucalyptus wood smoke alters pulmonary gene and metabolic profiles in male Long-Evans rats

Exposure to wildfire smoke is associated with both acute and chronic cardiopulmonary illnesses, which are of special concern for wildland firefighters who experience repeated exposure to wood smoke. It is necessary to better understand the underlying pathophysiology by which wood smoke exposure increases pulmonary disease burdens in this population. We hypothesize that wood smoke exposure produces pulmonary dysfunction, lung inflammation, and gene expression profiles associated with future pulmonary complications. Male Long-Evans rats were intermittently exposed to smoldering eucalyptus wood smoke at 2 concentrations, low (11.0 ± 1.89 mg/m3) and high (23.7 ± 0.077 mg/m3), over a 2-week period. Whole-body plethysmography was measured intermittently throughout. Lung tissue and lavage fluid were collected 24 h after the final exposure for transcriptomics and metabolomics. Increasing smoke exposure upregulated neutrophils and select cytokines in the bronchoalveolar lavage fluid. In total, 3446 genes were differentially expressed in the lungs of rats in the high smoke exposure and only 1 gene in the low smoke exposure (Cd151). Genes altered in the high smoke group reflected changes to the Eukaryotic Initiation Factor 2 stress and oxidative stress responses, which mirrored metabolomics analyses. xMWAS-integrated analysis revealed that smoke exposure significantly altered pathways associated with oxidative stress, lung morphogenesis, and tumor proliferation pathways. These results indicate that intermittent, 2-week exposure to eucalyptus wood smoke leads to transcriptomic and metabolic changes in the lung that may predict future lung disease development. Collectively, these findings provide insight into cellular signaling pathways that may contribute to the chronic pulmonary conditions observed in wildland firefighters.

Human Keratinocyte Responses to Woodsmoke Chemicals

Air pollution consists of complex mixtures of chemicals with serious deleterious health effects from acute and chronic exposure. To help understand the mechanisms by which adverse effects occur, the present work examines the responses of cultured human epidermal keratinocytes to specific chemicals commonly found in woodsmoke. Our earlier findings with liquid smoke flavoring (aqueous extract of charred wood) revealed that such extracts stimulated the expression of genes associated with oxidative stress and proinflammatory response, activated the aryl hydrocarbon receptor, thereby inducing cytochrome P4501A1 activity, and induced cross-linked envelope formation, a lethal event ordinarily occurring during terminal differentiation. The present results showed that furfural produced transcriptional responses resembling those of liquid smoke, cyclohexanedione activated the aryl hydrocarbon receptor, and several chemicals induced envelope formation. Of these, syringol permeabilized the cells to the egress of lactate dehydrogenase at a concentration close to that yielding envelope formation, while furfural induced envelope formation without permeabilization detectable in this way. Furfural (but not syringol) stimulated the incorporation of amines into cell proteins in extracts in the absence of transglutaminase activity. Nevertheless, both chemicals substantially increased the amount of cellular protein incorporated into envelopes and greatly altered the envelope protein profile. Moreover, the proportion of keratin in the envelopes was dramatically increased. These findings are consistent with the chemically induced protein cross-linking in the cells. Elucidating mechanisms by which this phenomenon occurs may help understand how smoke chemicals interact with proteins to elicit cellular responses, interpret bioassays of complex pollutant mixtures, and suggest additional sensitive ways to monitor exposures.

Impact of Wildfires on Cardiovascular Health

Wildfire smoke (WFS) is a mixture of respirable particulate matter, environmental gases, and other hazardous pollutants that originate from the unplanned burning of arid vegetation during wildfires. The increasing size and frequency of recent wildfires has escalated public and occupational health concerns regarding WFS inhalation, by either individuals living nearby and downstream an active fire or wildland firefighters and other workers that face unavoidable exposure because of their profession. In this review, we first synthesize current evidence from environmental, controlled, and interventional human exposure studies, to highlight positive associations between WFS inhalation and cardiovascular morbidity and mortality. Motivated by these findings, we discuss preventative measures and suggest interventions to mitigate the cardiovascular impact of wildfires. We then review animal and cell exposure studies to call attention on the pathophysiological processes that support the deterioration of cardiovascular tissues and organs in response to WFS inhalation. Acknowledging the challenges of integrating evidence across independent sources, we contextualize laboratory-scale exposure approaches according to the biological processes that they model and offer suggestions for ensuring relevance to the human condition. Noting that wildfires are significant contributors to ambient air pollution, we compare the biological responses triggered by WFS to those of other harmful pollutants. We also review evidence for how WFS inhalation may trigger mechanisms that have been proposed as mediators of adverse cardiovascular effects upon exposure to air pollution. We finally conclude by highlighting research areas that demand further consideration. Overall, we aspire for this work to serve as a catalyst for regulatory initiatives to mitigate the adverse cardiovascular effects of WFS inhalation in the community and alleviate the occupational risk in wildland firefighters.

The firestorm within: A narrative review of extreme heat and wildfire smoke effects on brain health

Climate change is generating increased heatwaves and wildfires across much of the world. With these escalating environmental changes comes greater impacts on human health leading to increased numbers of people suffering from heat- and wildfire smoke-associated respiratory and cardiovascular impairment. One area of health impact of climate change that has received far less attention is the effects of extreme heat and wildfire smoke exposure on human brain health. As elevated temperatures, and wildfire-associated smoke, are increasingly experienced simultaneously over summer periods, understanding this combined impact is critical to management of human health especially in the elderly, and people with dementia, and other neurological disorders. Both extreme heat and wildfire smoke air pollution (especially particulate matter, PM) induce neuroinflammatory and cerebrovascular effects, oxidative stress, and cognitive impairment, however the combined effect of these impacts are not well understood. In this narrative review, a comprehensive examination of extreme heat and wildfire smoke impact on human brain health is presented, with a focus on how these factors contribute to cognitive impairment, and dementia, one of the leading health issues today. Also discussed is the potential impact of combined heat and wildfire smoke on brain health, and where future efforts should be applied to help advance knowledge in this rapidly growing and critical field of health research.

"Air That Once Was Breath" Part 2: Wildfire Smoke and Airway Disease - "Climate Change, Allergy and Immunology" Special IAAI Article Collection: Collegium Internationale Allergologicum Update 2023

BACKGROUND

Population growth and climate change have led to more frequent and larger wildfires, increasing the exposure of individuals to wildfire smoke. Notably, asthma exacerbations and allergic airway sensitization are prominent outcomes of such exposure.

SUMMARY

Key research questions relate to determining the precise impact on individuals with asthma, including the severity, duration, and long-term consequences of exacerbations. Identifying specific risk factors contributing to vulnerability, such as age, genetics, comorbidities, or environmental factors, is crucial. Additionally, reliable biomarkers for predicting severe exacerbations need exploration. Understanding the long-term health effects of repeated wildfire smoke exposures in individuals with asthma and addressing healthcare disparities are important research areas.

KEY MESSAGES

This review discusses the need for comprehensive research efforts to better grasp wildfire smoke-induced respiratory health, particularly in vulnerable populations such as farmworkers, firefighters, pregnant women, children, the elderly, and marginalized communities. Effective mitigation would require addressing the current limitations we face by supporting research aimed at a better understanding of wildfire smoke-induced airway disease.

Monitoring redox stress in human airway epithelial cells exposed to woodsmoke at an air-liquid interface

Wildland fires contribute significantly to the ambient air pollution burden worldwide, causing a range of adverse health effects in exposed populations. The toxicity of woodsmoke, a complex mixture of gases, volatile organic compounds, and particulate matter, is commonly studied in vitro using isolated exposures of conventionally cultured lung cells to either resuspended particulate matter or organic solvent extracts of smoke, leading to incomplete toxicity evaluations. This study aimed to improve our understanding of the effects of woodsmoke inhalation by building an advanced in vitro exposure system that emulates human exposure of the airway epithelium. We report the development and characterization of an innovative system that permits live-cell monitoring of the intracellular redox status of differentiated primary human bronchial epithelial cells cultured at an air-liquid interface (pHBEC-ALI) as they are exposed to unfractionated woodsmoke generated in a tube furnace in real time. pHBEC-ALI exposed to freshly generated woodsmoke showed oxidative changes that were dose-dependent and reversible, and not attributable to carbon monoxide exposure. These findings show the utility of this novel system for studying the molecular initiating events underlying woodsmoke-induced toxicity in a physiologically relevant in vitro model, and its potential to provide biological plausibility for risk assessment and public health measures.

"Air That Once Was Breath" Part 1: Wildfire-Smoke-Induced Mechanisms of Airway Inflammation - "Climate Change, Allergy and Immunology" Special IAAI Article Collection: Collegium Internationale Allergologicum Update 2023

BACKGROUND

Wildfires are a global concern due to their wide-ranging environmental, economic, and public health impacts. Climate change contributes to an increase in the frequency and intensity of wildfires making smoke exposure a more significant and recurring health concern for individuals with airway diseases. Some of the most prominent effects of wildfire smoke exposure are asthma exacerbations and allergic airway sensitization. Likely due to the delayed recognition of its health impacts in comparison with cigarette smoke and industrial or traffic-related air pollution, research on the composition, the mechanisms of toxicity, and the cellular/molecular pathways involved is poor or non-existent.

SUMMARY

This review discusses potential underlying pathological mechanisms of wildfire-smoke-related allergic airway disease and asthma. We focused on major gaps in understanding the role of wildfire smoke composition in the development of airway disease and the known and potential mechanisms involving cellular and molecular players of oxidative injury at the epithelial barrier in airway inflammation. We examine how PM2.5, VOCs, O3, endotoxin, microbes, and toxic gases may affect oxidative stress and inflammation in the respiratory mucosal barrier. We discuss the role of AhR in mediating smoke's effects in alarmin release and IL-17A production and how glucocorticoid responsiveness may be impaired by IL-17A-induced signaling and epigenetic changes leading to steroid-resistant severe airway inflammation.

KEY MESSAGE

Effective mitigation of wildfire-smoke-related respiratory health effects would require comprehensive research efforts aimed at a better understanding of the immune regulatory effects of wildfire smoke in respiratory health and disease.

Telomere length as a modifier in the relationship between phthalate metabolites exposure and glucose homeostasis

Given the rising concern over the potential impact of environmental factors on metabolic heath, we conducted a cross-sectional analysis among 645 adults aged 20 and older in the National Health and Nutrition Examination Survey (NHANES), examining the association between nine phthalate metabolites (Mono-n-butyl phthalate (MBP), Mono-ethyl phthalate (MEP), Mono-(2-ethyl)-hexyl phthalate (MEHP), Mono-benzyl phthalate (MBzP), Mono-n-methyl phthalate (MnMP), Mono-(3-carboxy propyl) phthalate (MCPP), Mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), Mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), Mono-isobutyl phthalate (MiBP)) and six glucose homeostasis indices (fasting glucose, fasting insulin, hemoglobin A1C (HbA1C), homeostatic model assessment of insulin resistance (HOMA-IR), single Point Insulin Sensitivity Estimator (SPISE), and HOMA-β). Latent Class Analysis identified three phthalate metabolites exposure patterns: high MEP-low MEOHP (n = 282), high MBzP-low MEHHP (n = 214), and high MEHHP, MEOHP (n = 149). The high MBzP-low MEHHP and high MEHHP, MEOHP, versus the high MEP-low MEOHP, exposure groups showed significantly higher levels of fasting insulin (β = 0.126, 95% CI: 0.023-0.228), SPISE (β = 0.091, 95% CI: 0.018-0.164), and HOMA-IR (β = 0.091, 95% CI: 0.018-0.164). In the shorter telomere length group, high MEHHP, MEOHP exposure showed an increase in SPISE levels (β = 0.153, 95% CI: 0.037-0.269), while in the overweight/obese subgroup, high MEHHP, MEOHP exposure was significantly positively associated with HOMA-IR (β = 0.392, 95% CI: 0.150-0.735). Bayesian kernel machine regression analyses showed positive associations between higher combined phthalate exposure and increased glucose homeostasis indices (fasting glucose, HbA1C, fasting insulin, SPISE, and HOMA-IR). The quantile of g-calculation analysis also supported the positive associations with HbA1C, HOMA-IR, and fasting insulin. Our findings indicate that phthalate exposure was positively associated with glucose homeostasis indices, which strengthen the call for proactive measures to reduce phthalate exposure and mitigate potential risks to glucose metabolism.

Prenatal exposure to fine particulate matter chemical constituents and the risk of stillbirth and the mediating role of pregnancy complications: A cohort study

Evidence on the association of fine particulate matter (PM2.5) exposure with stillbirth is limited and inconsistent, which is largely attributed to differences in PM2.5 constituents. Studies have found that the hazards of certain PM2.5 constituents to the fetus are comparable to or even higher than total PM2.5 mass. However, few studies have linked PM2.5 constituents to stillbirth. Moreover, the mediating role of pregnancy complications in PM2.5-related stillbirth remains unclear. To our knowledge, this study was the first to explore the individual and mixed associations of PM2.5 and its constituents with stillbirth in China. After matching the concentrations of PM2.5 and its constituents (sulfate [SO42-], nitrate [NO3-], ammonium [NH4+], organic matter [OM], and black carbon [BC]) for participants according to their geographical location, there were 170,507 participants included in this study. We found that stillbirth was associated with exposure to PM2.5 and its constituents in the year before pregnancy and during the entire pregnancy, and the associations in trimester 1 were strongest. The risk of stillbirth increased sharply when PM2.5 and its constituents during pregnancy exceeded the median concentrations. Moreover, stillbirth was associated with exposure to the mixtures of SO42-, NO3-, NH4+, OM, and BC before and during pregnancy (trimesters 1 and 2). Meanwhile, two-pollutant models also suggested stillbirth was associated with PM2.5 and its constituents in the year before and during pregnancy. The associations of PM2.5 and its constituents with stillbirth were stronger in mothers with advanced age and without cesarean delivery history. Additionally, hypertensive disorders in pregnancy, gestational diabetes, and placental abruption mediated the association of PM2.5 with stillbirth. Therefore, enhanced protection against PM2.5 for pregnant women before and during pregnancy and targeted interventions for pregnancy complications and anthropogenic sources of PM2.5 constituents are important to reduce stillbirth risk.

Prenatal Metal Exposure Alters the Placental Proteome in a Sex-Dependent Manner in Extremely Low Gestational Age Newborns: Links to Gestational Age

Prenatal exposure to toxic metals is associated with altered placental function and adverse infant and child health outcomes. Adverse outcomes include those that are observed at the time of birth, such as low birthweight, as well as those that arise later in life, such as neurological impairment. It is often the case that these adverse outcomes show sex-specific responses in relation to toxicant exposures. While the precise molecular mechanisms linking in utero toxic metal exposures with later-in-life health are unknown, placental inflammation is posited to play a critical role. Here, we sought to understand whether in utero metal exposure is associated with alterations in the expression of the placental proteome by identifying metal associated proteins (MAPs). Within the Extremely Low Gestational Age Newborns (ELGAN) cohort (n = 230), placental and umbilical cord tissue samples were collected at birth. Arsenic (As), cadmium (Cd), lead (Pb), selenium (Se), and manganese (Mn) concentrations were measured in umbilical cord tissue samples via ICP-MS/MS. Protein expression was examined in placental samples using an LC-MS/MS-based, global, untargeted proteomics analysis measuring more than 3400 proteins. MAPs were then evaluated for associations with pregnancy and neonatal outcomes, including placental weight and gestational age. We hypothesized that metal levels would be positively associated with the altered expression of inflammation/immune-associated pathways and that sex-specific patterns of metal-associated placental protein expression would be observed. Sex-specific analyses identified 89 unique MAPs expressed in female placentas and 41 unique MAPs expressed in male placentas. Notably, many of the female-associated MAPs are known to be involved in immune-related processes, while the male-associated MAPs are associated with intracellular transport and cell localization. Further, several MAPs were significantly associated with gestational age in males and females and placental weight in males. These data highlight the linkage between prenatal metal exposure and an altered placental proteome, with implications for altering the trajectory of fetal development.

Wrangling Whole Mixtures Risk Assessment: Recent Advances in Determining Sufficient Similarity

Human health risk assessments for complex mixtures can address real-world exposures and protect public health. While risk assessors typically prefer whole mixture approaches over component-based approaches, data from the precise exposure of interest are often unavailable and surrogate data from a sufficiently similar mixture(s) are required. This review describes recent advances in determining sufficient similarity of whole, complex mixtures spanning the comparison of chemical features, bioactivity profiles, and statistical evaluation to determine "thresholds of similarity". Case studies, including water disinfection byproducts, botanical ingredients, and wildfire emissions, are used to highlight tools and methods. Limitations to application of sufficient similarity in risk-based decision making are reviewed and recommendations presented for developing best practice guidelines.

Vast ecosystem disturbance in a warming climate may jeopardize our climate goal of reducing CO2: a case study for megafires in the Australian 'black summer'

A warming climate is one of the most important driving forces of intensified wildfires globally. The unprecedented wildfires broke out in the Australian 'Black Summer' (November 2019-February 2020), which released massive heat, gases, and particles into the atmosphere. The total carbon dioxide (CO₂) emissions from wildfires were estimated at ∼963 million tons by using a top-down approach based on direct satellite measurements of CO₂ and fire radiative power. The fire emissions have led to an approximately 50-80 folds increase in total CO₂ emission in Australia compared with the similar seasons of 2014-2019. The excess CO₂ from wildfires has offset almost half of the global anthropogenic CO₂ emission reductions due to the Corona Virus Disease 2019 in 2020. When the wildfires were intense in December 2019, they caused a 1.48 watts per square meter additional positive radiative forcing above the monthly average in Australia and the vicinity. Our findings demonstrate that vast ecosystem disturbance in a warming climate can strongly influence the global carbon cycle and hamper our climate goal of reducing CO₂.

Prolonged smoldering Douglas fir smoke inhalation augments respiratory resistances, stiffens the aorta, and curbs ejection fraction in hypercholesterolemic mice

While mounting evidence suggests that wildland fire smoke (WFS) inhalation may increase the burden of cardiopulmonary disease, the occupational risk of repeated exposure during wildland firefighting remains unknown. To address this concern, we evaluated the cardiopulmonary function in mice following a cumulative exposure to lab-scale WFS equivalent to a mid-length wildland firefighter (WLFF) career. Dosimetry analysis indicated that 80 exposure hours at a particulate concentration of 22 mg/m3 yield in mice the same cumulative deposited mass per unit of lung surface area as 3600 h of wildland firefighting. To satisfy this condition, male Apoe-/- mice were whole-body exposed to either air or smoldering Douglas fir smoke (DFS) for 2 h/day, 5 days/week, over 8 consecutive weeks. Particulate size in DFS fell within the respirable range for both mice and humans, with a count median diameter of 110 ± 20 nm. Expiratory breath hold in mice exposed to DFS significantly reduced their minute volume (DFS: 27 ± 4; Air: 122 ± 8 mL/min). By the end of the exposure time frame, mice in the DFS group exhibited a thicker (DFS: 109 ± 3; Air: 98 ± 3 μm) and less distensible (DFS: 23 ± 1; Air: 28 ± 1 MPa-1) aorta with reduced diastolic blood augmentation capacity (DFS: 53 ± 2; Air: 63 ± 2 kPa). Cardiac magnetic resonance imaging further revealed larger end-systolic volume (DFS: 14.6 ± 1.1; Air: 9.9 ± 0.9 μL) and reduced ejection-fraction (DFS: 64.7 ± 1.0; Air: 75.3 ± 0.9 %) in mice exposed to DFS. Consistent with increased airway epithelium thickness (DFS: 10.4 ± 0.8; Air: 7.6 ± 0.3 μm), airway Newtonian resistance was larger following DFS exposure (DFS: 0.23 ± 0.03; Air: 0.20 ± 0.03 cmH2O-s/mL). Furthermore, parenchyma mean linear intercept (DFS: 36.3 ± 0.8; Air: 33.3 ± 0.8 μm) and tissue thickness (DFS: 10.1 ± 0.5; Air: 7.4 ± 0.7 μm) were larger in DFS mice. Collectively, mice exposed to DFS manifested early signs of cardiopulmonary dysfunction aligned with self-reported events in mid-career WLFFs.

Metal mixtures modeling identifies birth weight-associated gene networks in the placentas of children born extremely preterm

Prenatal exposure to toxic metals is linked to numerous adverse birth and later-in-life outcomes. These outcomes are tied to disrupted biological processes in fetal-derived tissues including the placenta and umbilical cord yet the precise pathways are understudied in these target tissues. We set out to examine the relationship between metal concentrations in umbilical cord and altered gene expression networks in placental tissue. These novel relationships were investigated in a subset of the Extremely Low Gestational Age Newborn (ELGAN) cohort (n = 226). Prenatal exposure to 11 metals/metalloids was measured using inductively coupled plasma tandem-mass spectrometry (ICP-MS/MS) in cord tissue, ensuring passage through the placental barrier. RNA-sequencing was used to quantify >37,000 mRNA transcripts. Differentially expressed genes (DEGs) were identified with respect to each metal. Weighted gene co-expression analysis identified gene networks modulated by metals. Two innovative mixtures modeling techniques, namely principal components analysis and quantile-based g-computation, were employed to identify genes/gene networks associated with multi-metal exposure. Individually, lead was associated with the strongest genomic response of 191 DEGs. Joint lead and cadmium exposure was related to 657 DEGs, including DNA Methyl Transferase 1 (DNMT1). These genes were enriched for the Eukaryotic Initiation Factor 2 (EIF2) pathway. Four gene networks, each containing genes within a Nuclear Factor kappa-light-chain-enhancer of Activated B Cells (NF-kB)-mediated network, were significantly increased in average expression level in relation to increases in all metal concentrations. All four of these metal mixture-associated gene networks were negatively correlated with important predictors of neonatal health including birth weight, placenta weight, and fetal growth. Bringing together novel methodologies from epidemiological mixtures analyses and toxicogenomics, applied to a unique cohort of extremely preterm children, the present study highlighted critical genes and pathways in the placenta dysregulated by prenatal metal mixtures. These represent potential mechanisms underlying the developmental origins of metal-induced disease.

Computational Approach to Link Chemicals in Anthropogenic Smoke Particulate Matter with Toxicity

A weighted chemical coexpression network analysis (WCCNA) was utilized to identify chemicals co-modulated to variable burning of anthropogenic materials and to link chemicals to biological responses (lung toxicity and mutagenicity). Polyaromatic hydrocarbons (PAHs) were co-modulated with increased concentrations in flaming smoke particulate matter (PM) from the burning of plastic-containing materials and showed significant association with increased neutrophil influx, cytokine levels, and mutagenicity. Inorganic elements were co-modulated with increased concentrations in flaming plywood and cardboard smoke PM and showed significant association with increased protein and albumin levels. This study shows the potential for using a computational network analysis to identify and prioritize hazardous chemical components within complex environmental mixtures and provides guidance on key chemical tracers required for intervention research to protect public health from the exposure.

Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring

The prevalence of wildfires continues to grow globally with exposures resulting in increased disease risk. Characterizing these health risks remains difficult due to the wide landscape of exposures that can result from different burn conditions and fuel types. This study tested the hypothesis that biomass smoke exposures from variable fuels and combustion conditions group together based on similar transcriptional response profiles, informing which wildfire-relevant exposures may be considered as a group for health risk evaluations. Mice (female CD-1) were exposed via oropharyngeal aspiration to equal mass biomass smoke condensates produced from flaming or smoldering burns of eucalyptus, peat, pine, pine needles, or red oak species. Lung transcriptomic signatures were used to calculate transcriptomic similarity scores across exposures, which informed exposure groupings. Exposures from flaming peat, flaming eucalyptus, and smoldering eucalyptus induced the greatest responses, with flaming peat grouping with the pro-inflammatory agent lipopolysaccharide. Smoldering red oak and smoldering peat induced the least transcriptomic response. Groupings paralleled pulmonary toxicity markers, though they were better substantiated by higher data dimensionality and resolution provided through -omic-based evaluation. Interestingly, groupings based on smoke chemistry signatures differed from transcriptomic/toxicity-based groupings. Wildfire-relevant exposure groupings yield insights into risk assessment strategies to ultimately protect public health.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics

BACKGROUND

Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking.

OBJECTIVE

This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk.

METHODS

The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources.

RESULTS

A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators.

SIGNIFICANCE

Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk.

IMPACT STATEMENT

Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics

BACKGROUND

Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking.

OBJECTIVE

This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk.

METHODS

The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources.

RESULTS

A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators.

SIGNIFICANCE

Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk.

IMPACT STATEMENT

Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.

Perfluoroalkyl substances (PFASs) as risk factors for breast cancer: a case-control study in Chinese population

BACKGROUND

Perfluoroalkyl substances (PFASs) are a large family of synthetic chemicals, some of which are mammary toxicants and endocrine disruptors. Recent studies have implicated exposure to PFASs as a risk factor for breast cancer in Europe and America. Little is known about the role of PFASs with respect to breast cancer in the Chinese population.

METHODS

Participants who were initially diagnosed with breast cancer at Tianjin Medical University Cancer Institute and Hospital between 2012 and 2016 were recruited as cases. The controls were randomly selected from the participants with available blood samples in the Chinese National Breast Cancer Screening Program (CNBCSP) cohort. Ultimately, we enrolled 373 breast cancer patients and 657 controls. Plasma PFASs were measured by an ultra-performance liquid chromatography (UPLC) system coupled to a 5500 Q-Trap triple quadrupole mass spectrometer. A logistic regression model with least absolute shrinkage and selection operator (LASSO) regularization was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to assess the relationships between PFASs and breast cancer. The three most predictive variables in the LASSO model were selected from 17 PFASs, which was based on the optimal penalty coefficient (λ = 0.0218) identified with the minimum criterion. Additionally, Bayesian kernel machine regression (BKMR) and quantile g-computation models were applied to evaluate the associations between separate and mixed exposure to PFASs and breast cancer.

RESULTS

Perfluorooctanesulfonic acid (PFOS) exhibited the highest concentration in both the cases and controls. Perfluorooctanoic acid (PFOA) and perfluoro-n-decanoic acid (PFDA) were positively associated with breast cancer, and perfluoro-n-tridecanoic acid (PFTrDA) was negatively associated with breast cancer according to both the continuous-PFASs and the quartile-PFASs logistic regression models. Of note, PFOA was associated with the occurrence of estrogen receptor (ER)-, progesterone receptor (PR)-, and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (OR_ER+  = 1.47, 95% CI: 1.19, 1.80; OR_PR+  = 1.36, 95% CI: 1.09, 1.69; OR_HER2 = 1.62, 95% CI: 1.19, 2.21).

CONCLUSIONS

Overall, we observed that PFASs were associated with breast cancer in Chinese women. Prospective cohort studies and mechanistic experiments are warranted to elucidate whether these associations are causal.

Integrative exposomic, transcriptomic, epigenomic analyses of human placental samples links understudied chemicals to preeclampsia

BACKGROUND

Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology.

OBJECTIVES

We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures.

METHODS

A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia.

RESULTS

A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes.

CONCLUSIONS

These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

Wildfires and extracellular vesicles: Exosomal MicroRNAs as mediators of cross-tissue cardiopulmonary responses to biomass smoke

INTRODUCTION

Wildfires are a threat to public health world-wide that are growing in intensity and prevalence. The biological mechanisms that elicit wildfire-associated toxicity remain largely unknown. The potential involvement of cross-tissue communication via extracellular vesicles (EVs) is a new mechanism that has yet to be evaluated.

METHODS

Female CD-1 mice were exposed to smoke condensate samples collected from the following biomass burn scenarios: flaming peat; smoldering peat; flaming red oak; and smoldering red oak, representing lab-based simulations of wildfire scenarios. Lung tissue, bronchoalveolar lavage fluid (BALF) samples, peripheral blood, and heart tissues were collected 4 and 24 h post-exposure. Exosome-enriched EVs were isolated from plasma, physically characterized, and profiled for microRNA (miRNA) expression. Pathway-level responses in the lung and heart were evaluated through RNA sequencing and pathway analyses.

RESULTS

Markers of cardiopulmonary tissue injury and inflammation from BALF samples were significantly altered in response to exposures, with the greatest changes occurring from flaming biomass conditions. Plasma EV miRNAs relevant to cardiovascular disease showed exposure-induced expression alterations, including miR-150, miR-183, miR-223-3p, miR-30b, and miR-378a. Lung and heart mRNAs were identified with differential expression enriched for hypoxia and cell stress-related pathways. Flaming red oak exposure induced the greatest transcriptional response in the heart, a large portion of which were predicted as regulated by plasma EV miRNAs, including miRNAs known to regulate hypoxia-induced cardiovascular injury. Many of these miRNAs had published evidence supporting their transfer across tissues. A follow-up analysis of miR-30b showed that it was increased in expression in the heart of exposed mice in the absence of changes to its precursor molecular, pri-miR-30b, suggesting potential transfer from external sources (e.g., plasma).

DISCUSSION

This study posits a potential mechanism through which wildfire exposures induce cardiopulmonary responses, highlighting the role of circulating plasma EVs in intercellular and systems-level communication between tissues.

The Effect of Mixture of Heavy Metals on Obesity in Individuals ≥50 Years of Age

Little is known about the association between a mixture of heavy metals and obesity among individuals ≥50 years of age with comorbidities. Thus, we identified the associations of serum cadmium (Cd), lead (Pb), and mercury (Hg) with obesity using linear regression models; weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted as secondary analyses. Of the 6434 subjects included in the analysis, 13.8% had obesity and 44.6% had abdominal obesity. In the logistic regression model, serum Hg was associated with obesity and abdominal obesity, and significant trends were observed for these heavy metal tertiles (p < 0.001). Serum Hg levels were also associated with body mass index (BMI) and waist circumference (WC). The WQS index was significantly associated with both obesity (OR = 1.43, 95% CI: 1.40-1.46) and abdominal obesity (β = 1.51, 95% CI: 1.48-1.54). The qgcomp index also found a significant association between heavy metals and both obesity (OR = 1.35, 95% CI: 1.12-1.63) and abdominal obesity (OR = 1.34, 95% CI: 1.12-1.60). Serum Hg was the most heavily weighed heavy metal in these models. In BKMR analysis, the overall effect of the mixture was significantly associated with obesity, BMI, and WC. Serum Hg showed positive trends and was observed as the most important factor associated with obesity, BMI, and WC. Our findings were largely robust to secondary analyses that used three novel mixture modeling approaches: WQS, qpcomp, and BKMR. Given increasing exposure to heavy metals, well-characterized cohorts of individuals aged ≥50 years are required to determine the mixed effects of heavy metals on obesity and related diseases.

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS

Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

The effects of chemical mixtures on lipid profiles in the Korean adult population: threshold and molecular mechanisms for dyslipidemia involved

A scarcity of research assesses the effects of exposure to a combination of chemicals on lipid profiles as well as molecular mechanisms related to dyslipidemia. A cross-sectional study of 3692 adults aims to identify the association between chemical mixtures, including blood and urine 26 chemicals, and lipid profiles among Korean adults (aged ≥ 18) using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). In silico toxicogenomic data-mining, we assessed molecular mechanisms linked with dyslipidemia, including genes, miRNAs, pathways, biological processes, and diseases. In the linear regression models, heavy metals, volatile organic compound metabolites, and phthalate metabolites were found to be related to HDL-C, triglycerides, LDL-C, total lipids, and total cholesterol, and significant trends were observed for these chemical quartiles (p < 0.01). The WQS index was significantly linked with HDL-C, triglycerides, LDL-C, total cholesterol, and total lipids. The qgcomp index also found a significant association between chemicals and HDL-C, triglycerides, and total lipids. In BKMR analysis, the overall effect of the chemical mixture was significantly associated with HDL-C, triglycerides, total cholesterol, and total lipids. We found that mixed chemicals interacted with the PPARA gene and were linked with dyslipidemia. Several pathways ("SREBF and miR33 in cholesterol," "estrogen receptor pathway and lipid homeostasis," and "regulation of PGC-1α"), "negative regulation of hepatocyte apoptotic process," "negative regulation of sequestering of triglycerides," "regulation of hepatocyte apoptotic process," and "negative regulation of cholesterol storage," and "abdominal obesity metabolic syndrome" were identified as key molecular mechanisms that may be affected by mixed chemicals and implicated in the development of dyslipidemia. The highest interaction and expression of miRNAs involved in the process of dyslipidemia were also described. Especially, the cutoff levels for chemical exposure levels related to lipid profiles were also provided.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.

Analysis of the novel NCWELL database highlights two decades of co-occurrence of toxic metals in North Carolina private well water: Public health and environmental justice implications

Private well users are particularly vulnerable to metal exposure as they are not protected by the Safe Drinking Water Act. In North Carolina (NC), approximately 2.4 million individuals rely on private well water. In the present study, we constructed the NCWELL database: a comprehensive database of 117,960 geocoded well water tests over twenty-years in NC inclusive of 28 metals/metalloids. The NCWELL database was analyzed to identify areas of concern for single and co-occurring toxic metal contamination of private wells in NC. County-level population-at-risk rankings were calculated by combining toxic metal levels and the proportion of residents relying on well water. Additionally, k-means analysis was used to identify counties with critical co-occurrence of toxic metals. In the NCWELL database, inorganic arsenic (iAs) and lead (Pb) were detected above the EPA standards of 10 and 15 ppb in over 2500 and over 3000 tests, respectively. Shockingly, iAs was observed at levels up to 806 ppb and Pb at levels up to 105,440 ppb. Manganese (Mn) was detected above the EPA lifetime Health Advisory Limit in 4.9% and above the secondary Maximum Contaminant Level in 24.3% of all well water tests in NC, with a maximum concentration of 46,300 ppb reported. Mixtures-based analysis identified four distinct clusters of counties, one demonstrating high iAs and Mn and another with high Pb. Over the twenty-year period, metal levels remained high, indicative of sustained contamination in areas of concern. This study provides a novel database for researchers and concerned citizens in NC, demonstrates a methodology for identifying priority geographic regions for single and multiple contaminants, and has environmental justice implications in NC where metal exposure via private well water remains a serious public health concern.

Mixtures modeling identifies vitamin B1 and B3 intakes associated with depression

BACKGROUND

We aim to identify the association between a mixture of vitamin B1, B2, and B3 intakes and depression.

METHODS

Daily intake of vitamins was measured by a one-day 24 h recall. Multivariate logistic regression, weighted quantile sum (WQS), quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were used.

RESULTS

Of 9,848 adults included in the final analysis, 4.38% had depression. In the logistic regression model, daily vitamin B1 and B3 intakes were associated with depression, and significant trends were observed for these vitamin intake tertiles (p < 0.001). The WQS index was significantly associated with depression (OR = 0.24, 95% CI: 0.23-0.24). The gqcomp index also found a significant association between a mixture of vitamin B1 and B3 intake and depression (OR = 0.67, 95% CI: 0.44-0.98). Vitamin B1 intake was the most heavily weighed vitamin intake in this model. In BKMR analysis, the overall effects of vitamin B1 and B3 intake mixture were negatively associated with depression. Vitamin B1 and B3 intake showed negative trends and was observed as the most important factor associated with depression. The cutoff levels for B vitamin intake levels related to depression were reported.

LIMITATIONS

A 24-hour recall and cross-sectional design were used.

CONCLUSIONS

Given the rising prevalence of depressive symptoms in Korea, an increase in daily intake of vitamin B1 and/or B3 through regular diets may help to reduce the risk of depression. Therefore, there is an ongoing need to investigate these associations between B vitamin supplementation and depression, either separately or jointly, in well-characterized cohorts of depression population.

Woodsmoke particle exposure prior to SARS-CoV-2 infection alters antiviral response gene expression in human nasal epithelial cells in a sex-dependent manner

Inhalational exposure to particulate matter (PM) derived from natural or anthropogenic sources alters gene expression in the airways and increases susceptibility to respiratory viral infection. Woodsmoke-derived ambient PM from wildfire events during 2020 was associated with higher COVID-19 case rates in the western United States. We hypothesized that exposure to suspensions of woodsmoke particles (WSPs) or diesel exhaust particles (DEPs) prior to SARS-CoV-2 infection would alter host immune gene expression at the transcript level. Primary human nasal epithelial cells (hNECs) from both sexes were exposed to WSPs or DEPs (22 μg/cm2) for 2 h, followed by infection with SARS-CoV-2 at a multiplicity of infection of 0.5. Forty-six genes related to SARS-CoV-2 entry and host response were assessed. Particle exposure alone minimally affected gene expression, whereas SARS-CoV-2 infection alone induced a robust transcriptional response in hNECs, upregulating type I and III interferons, interferon-stimulated genes, and chemokines by 72 h postinfection (p.i.). This upregulation was higher overall in cells from male donors. However, exposure to WSPs prior to infection dampened expression of antiviral, interferon, and chemokine mRNAs. Sex stratification of these results revealed that WSP exposure downregulated gene expression in cells from females more so than males. We next hypothesized that hNECs exposed to particles would have increased apical viral loads compared with unexposed cells. Although apical viral load was correlated to expression of host response genes, viral titer did not differ between groups. These data indicate that WSPs alter epithelial immune responses in a sex-dependent manner, potentially suppressing host defense to SARS-CoV-2 infection.

Environmental factors associated with gastric carcinogenesis

PURPOSE OF REVIEW

Gastric cancer is one of the leading causes of cancer-related deaths globally. Several modifiable environmental factors have been linked to gastric carcinogenesis but in many cases, evidence is either weak or fragmented. In this review, we interrogate the latest evidence implicating environmental factors in the development of gastric cancer.

RECENT FINDINGS

We are writing this review at a time when technological advancements are enabling scientists to effectively conduct large epidemiological studies with better tools for exposure estimations. We have highlighted risk factors that do not yet have enough evidence to be included as definite carcinogens in the International Agency for Research on Cancer monographs but have the potential for inclusion in the near future. Considered in our review are the links between gastric cancer and exposure to biomass smoke, particulate matter, occupational hazards and water contamination.

SUMMARY

This review illustrates the need for vigilance as evidence linking gastric cancer to various environmental factors is mounting. Many of these factors are modifiable, allowing for preventive strategies that could further decrease the global burden of gastric cancer.

Mixtures modeling identifies heavy metals and pyrethroid insecticide metabolites associated with obesity

We aim to examine the association between chemical mixtures and obesity. Blood and urinary levels of tween-six chemicals were measured in adults who participated in the KoNEHS. We identified the associations of chemicals with obesity using linear regression models. Weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted as secondary analyses. Of the 3,692 participants included in the analysis, 18.0% had obesity. In the logistic regression model, mercury (Hg), lead (Pb), and 3PBA levels were associated with obesity, and significant trends were observed for these chemical tertiles (p < 0.001). Hg, Pb, and 3PBA levels were also associated with BMI. The WQS index was significantly associated with both obesity (OR = 2.15, 95% CI: 2.11-2.20) and BMI (β = 0.39, 95% CI: 0.37-0.51). The qgcomp index also found a significant association between chemicals and both obesity (OR = 1.70, 95% CI: 1.56-1.85) and BMI (β = 0.40, 95% CI: 0.39-0.41). Hg, Pb, and 3PBA were the most heavily weighed chemicals in these models. In BKMR analysis, the overall effect of the mixture was significantly associated with obesity. Hg, Pb, and 3PBA showed positive trends and were observed as the most important factors associated with obesity. Given increasing exposure to chemicals, there is a need to investigate the associations between chemical exposures, either separately or together, and incident obesity risk factors in well-characterized cohorts of different populations, and to identify potential approaches to chemical exposure prevention.

The association of wildfire air pollution with COVID-19 incidence in New South Wales, Australia

The 2020 COVID-19 outbreak in New South Wales (NSW), Australia, followed an unprecedented wildfire season that exposed large populations to wildfire smoke. Wildfires release particulate matter (PM), toxic gases and organic and non-organic chemicals that may be associated with increased incidence of COVID-19. This study estimated the association of wildfire smoke exposure with the incidence of COVID-19 in NSW. A Bayesian mixed-effect regression was used to estimate the association of either the average PM₁₀ level or the proportion of wildfire burned area as proxies of wildfire smoke exposure with COVID-19 incidence in NSW, adjusting for sociodemographic risk factors. The analysis followed an ecological design using the 129 NSW Local Government Areas (LGA) as the ecological units. A random effects model and a model including the LGA spatial distribution (spatial model) were compared. A higher proportional wildfire burned area was associated with higher COVID-19 incidence in both the random effects and spatial models after adjustment for sociodemographic factors (posterior mean = 1.32 (99% credible interval: 1.05-1.67) and 1.31 (99% credible interval: 1.03-1.65), respectively). No evidence of an association between the average PM₁₀ level and the COVID-19 incidence was found. LGAs in the greater Sydney and Hunter regions had the highest increase in the risk of COVID-19. This study identified wildfire smoke exposures were associated with increased risk of COVID-19 in NSW. Research on individual responses to specific wildfire airborne particles and pollutants needs to be conducted to further identify the causal links between SARS-Cov-2 infection and wildfire smoke. The identification of LGAs with the highest risk of COVID-19 associated with wildfire smoke exposure can be useful for public health prevention and or mitigation strategies.

Association between exposure to chemical mixtures in relation to serum total IgE among adults 19-86 years old

There is a scarcity of studies on the effects of mixed chemicals on total IgE. We aim to assess whether there is a link between chemical mixtures (blood and urine of 26 chemicals including lead, mercury, cadmium, t,t-muconic acid, benzylmercapturic acid , 1-hydroxypyrene, 2-naphthol, 2-hydroxyfluorene, 1-hydroxyphenanthrene, mono-(2-ethyl-5-hydroxyhexyl) phthalate, mono-(2-ethyl-5-oxohexyl) phthalate, mono-n-butyl phthalate, mono-benzyl phthalate, mono-(2-ethyl-5-carboxypentyl) phthalate, mono-carboxyoctyl phthalate, mono-carboxy-isononly phthalate, mono (3-carboxypropyl) phthalate, bisphenol A, bisphenol F, bisphenol S, triclosan, methylparaben, ethylparaben, propylparaben, 3-phenoxybenzoic acid, and cotinine), and total IgE in 3,642 Korean adults aged ≥ 19. The effects of mixed chemical exposure on total IgE were identified using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). The most relevant factors linked with IgE, according to the linear regression models, were blood or urine mercury and urine bisphenol A levels, with significant trends detected for these chemical tertiles (p < 0.01). The WQS index was significantly linked with ln2-transformed levels of serum total IgE (β = 0.30, 95 %CI 0.25-0.32). The qgcomp index also found a significant link between chemicals and ln2-transformed levels of serum total IgE (β = 0.52, 95 %CI 0.21-0.82), and elevated serum total IgE levels (OR = 2.55, 95 %CI 1.14-5.71). In BKMR analysis, the overall effect of the mixture was significantly associated with ln2-transformed levels of serum total IgE. The cutoff levels for exposure levels related to serum total IgE levels/elevated serum total IgE levels were reported. We discovered that whole-body exposure to 26 chemicals was associated with serum total IgE levels after assessing the findings of these four models. More research is needed in the future to gain a better understanding of the impact of mixed chemical exposure on allergic disorders and how to minimize chemical exposure, especially for people under the age of 18.

Chemistry, lung toxicity and mutagenicity of burn pit smoke-related particulate matter

BACKGROUND

Open burning of anthropogenic sources can release hazardous emissions and has been associated with increased prevalence of cardiopulmonary health outcomes. Exposure to smoke emitted from burn pits in military bases has been linked with respiratory illness among military and civilian personnel returning from war zones. Although the composition of the materials being burned is well studied, the resulting chemistry and potential toxicity of the emissions are not.

METHODS

Smoke emission condensates from either flaming or smoldering combustion of five different types of burn pit-related waste: cardboard; plywood; plastic; mixture; and mixture/diesel, were obtained from a laboratory-scale furnace coupled to a multistage cryotrap system. The primary emissions and smoke condensates were analyzed for a standardized suite of chemical species, and the condensates were studied for pulmonary toxicity in female CD-1 mice and mutagenic activity in Salmonella (Ames) mutagenicity assay using the frameshift strain TA98 and the base-substitution strain TA100 with and without metabolic activation (S9 from rat liver).

RESULTS

Most of the particles in the smoke emitted from flaming and smoldering combustion were less than 2.5 µm in diameter. Burning of plastic containing wastes (plastic, mixture, or mixture/diesel) emitted larger amounts of particulate matter (PM) compared to other types of waste. On an equal mass basis, the smoke PM from flaming combustion of plastic containing wastes caused more inflammation and lung injury and was more mutagenic than other samples, and the biological responses were associated with elevated polycyclic aromatic hydrocarbon levels.

CONCLUSIONS

This study suggests that adverse health effects of burn pit smoke exposure vary depending on waste type and combustion temperature; however, burning plastic at high temperature was the most significant contributor to the toxicity outcomes. These findings will provide a better understanding of the complex chemical and combustion temperature factors that determine toxicity of burn pit smoke and its potential health risks at military bases.

Woodsmoke particulates alter expression of antiviral host response genes in human nasal epithelial cells infected with SARS-CoV-2 in a sex-dependent manner

We have previously shown that exposure to particulate air pollution, both from natural and anthropogenic sources, alters gene expression in the airways and increases susceptibility to respiratory viral infection. Additionally, we have shown that woodsmoke particulates (WSP) affect responses to influenza in a sex-dependent manner. In the present study, we used human nasal epithelial cells (hNECs) from both sexes to investigate how particulate exposure could modulate gene expression in the context of SARS-CoV-2 infection. We used diesel exhaust particulate (DEP) as well as WSP derived from eucalyptus or red oak wood. HNECs were exposed to particulates at a concentration of 22 μg/cm ² for 2 h then immediately infected with SARS-CoV-2 at a MOI (multiplicity of infection) of 0.5. Exposure to particulates had no significant effects on viral load recovered from infected cells. Without particulate exposure, hNECs from both sexes displayed a robust upregulation of antiviral host response genes, though the response was greater in males. However, WSP exposure before infection dampened expression of genes related to the antiviral host response by 72 h post infection. Specifically, red oak WSP downregulated IFIT1, IFITM3, IFNB1, MX1, CCL3, CCL5, CXCL11, CXCL10 , and DDX58 , among others. After sex stratification of these results, we found that exposure to WSP prior to SARS-CoV-2 infection downregulated anti-viral gene expression in hNECs from females more so than males. These data indicate that WSP, specifically from red oak, alter virus-induced gene expression in a sex-dependent manner and potentially suppress antiviral host defense responses following SARS-CoV-2 infection.