Background

Increasing data on early biological changes from chemical exposures requires new interpretation tools to support decision-making.

Objectives

To test the possibility of applying a quantitative approach using human data linking chemical exposures and upstream biological perturbations to overt downstream outcomes.

Methods

Using polychlorinated biphenyl (PCB) exposures and maternal thyroid hormone (TH) perturbations as a case study, we model three relationships: (1) prenatal PCB exposures and TH changes, using free T(4) (FT(4)); (2) prenatal TH and childhood neurodevelopmental outcomes; and (3) prenatal PCB exposures and childhood neurodevelopmental outcomes (IQ). We surveyed the epidemiological literature; extracted relevant quantitative data; and developed models for each relationship, applying meta-analysis where appropriate.

Results

For relationship 1, a meta-analysis of 3 studies gives a coefficient of -0.27 pg/mL FT(4) per ln(sum of PCBs) (95% confidence interval [CI] -0.82 to 0.27). For relationship 2, regression coefficients from three studies of maternal FT(4) levels and cognitive scores ranged between 0.99 IQ points/(pg/mL FT(4)) (95% CI -0.31 to 2.2) and 7.6 points/(pg/mL FT(4)) (95% CI 1.2 to 16.3). For relationship 3, a meta-analysis of five studies produces a coefficient of -1.98 IQ points (95% CI -4.46 to 0.50) per unit increase in ln(sum of PCBs). Combining relationships 1 and 2 yields an estimate of -2.0 to -0.27 points of IQ per unit increase in ln(sum of PCBs).

Conclusions

Combining analysis of chemical exposures and early biological perturbations (PCBs and FT(4)) with analysis of early biological perturbations and downstream overt effects (FT(4) and IQ) yields estimates within the range of studies of exposures and overt effects (PCBs and IQ). This is an example approach using upstream biological perturbations for effect prediction.

There is a growing need for quantitative approaches to extrapolate relationships between chemical exposures and early biological perturbations from animals to humans given increasing use of biological assays to evaluate toxicity pathways. We have developed such an approach using polychlorinated biphenyls (PCBs) and thyroid hormone (TH) disruption as a case study. We reviewed and identified experimental animal literature from which we developed a low-dose, linear model of PCB body burdens and decrements in free thyroxine (FT(4)) and total thyroxine (TT(4)), accounting for 33 PCB congeners; extrapolated the dose-response from animals to humans; and compared the animal dose-response to the dose-response of PCB body burdens and TH changes from eleven human epidemiological studies. We estimated a range of potencies for PCB congeners (over 4 orders of magnitude), with the strongest for PCB 126. Our approach to developing toxic equivalency models produced relative potencies similar to the toxicity equivalency factors (TEFs) from the World Health Organization (WHO). We generally found that the dose-response extrapolated from the animal studies tends to under-predict the dose-response estimated from human epidemiological studies. A quantitative approach to evaluating the relationship between chemical exposures and TH perturbations, based on animal data can be used to assess human health consequences of thyroid toxicity and inform decision-making.

BACKGROUND:Some studies have linked long-term exposure to traffic related air pollutants (TRAP) with adverse cardiovascular health outcomes; however, previous studies have not linked highly variable concentrations of TRAP measured at street-level within neighborhoods to cardiovascular health outcomes. METHODS:Long-term pollutant concentrations for nitrogen dioxide [NO2], nitric oxide [NO], and black carbon [BC] were obtained by street-level mobile monitoring on 30 m road segments and linked to residential addresses of 41,869 adults living in Oakland during 2010 to 2015. We fit Cox proportional hazard models to estimate the relationship between air pollution exposures and time to first cardiovascular event. Secondary analyses examined effect modification by diabetes and age. RESULTS:Long-term pollutant concentrations [mean, (standard deviation; SD)] for NO2, NO and BC were 9.9 ppb (SD 3.8), 4.9 ppb (SD 3.8), and 0.36 μg/m3 (0.17) respectively. A one SD increase in NO2, NO and BC, was associated with a change in risk of a cardiovascular event of 3% (95% confidence interval [CI] -6% to 12%), 3% (95% CI -5% to 12%), and - 1% (95% CI -8% to 7%), respectively. Among the elderly (≥65 yrs), we found an increased risk of a cardiovascular event of 12% for NO2 (95% CI: 2%, 24%), 12% for NO (95% CI: 3%, 22%), and 7% for BC (95% CI: -3%, 17%) per one SD increase. We found no effect modification by diabetes. CONCLUSIONS:Street-level differences in long-term exposure to TRAP were associated with higher risk of cardiovascular events among the elderly, indicating that within-neighborhood differences in TRAP are important to cardiovascular health. Associations among the general population were consistent with results found in previous studies, though not statistically significant.

Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km² West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R ²=0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.

Background

In February 2021, over one hundred scientists and policy experts participated in a web-based Workshop to discuss the ways that divergent evaluations of evidence and scientific uncertainties are used to delay timely protection of human health and the environment from exposures to hazardous agents. The Workshop arose from a previous workshop organized by the European Environment Agency (EEA) in 2008 and which also drew on case studies from the EEA reports on 'Late Lessons from Early Warnings' (2001, 2013). These reports documented dozens of hazardous agents including many chemicals, for which risk reduction measures were delayed for decades after scientists and others had issued early and later warnings about the harm likely to be caused by those agents.

Results

Workshop participants used recent case studies including Perfluorooctanoic acid (PFOA), Extremely Low Frequency - Electrical Magnetic Fields (ELF-EMF fields), glyphosate, and Bisphenol A (BPA) to explore myriad reasons for divergent outcomes of evaluations, which has led to delayed and inadequate protection of the public's health. Strategies to overcome these barriers must, therefore, at a minimum include approaches that 1) Make better use of existing data and information, 2) Ensure timeliness, 3) Increase transparency, consistency and minimize bias in evidence evaluations, and 4) Minimize the influence of financial conflicts of interest.

Conclusion

The recommendations should enhance the production of "actionable evidence," that is, reliable evaluations of the scientific evidence to support timely actions to protect health and environments from exposures to hazardous agents. The recommendations are applicable to policy and regulatory settings at the local, state, federal and international levels.

Background

Benzene, an established cause of acute myeloid leukemia (AML), may also cause one or more lymphoid malignancies in humans. Previously, we identified genes and pathways associated with exposure to high (> 10 ppm) levels of benzene through transcriptomic analyses of blood cells from a small number of occupationally exposed workers.

Objectives

The goals of this study were to identify potential biomarkers of benzene exposure and/or early effects and to elucidate mechanisms relevant to risk of hematotoxicity, leukemia, and lymphoid malignancy in occupationally exposed individuals, many of whom were exposed to benzene levels < 1 ppm, the current U.S. occupational standard.

Methods

We analyzed global gene expression in the peripheral blood mononuclear cells of 125 workers exposed to benzene levels ranging from < 1 ppm to > 10 ppm. Study design and analysis with a mixed-effects model minimized potential confounding and experimental variability.

Results

We observed highly significant widespread perturbation of gene expression at all exposure levels. The AML pathway was among the pathways most significantly associated with benzene exposure. Immune response pathways were associated with most exposure levels, potentially providing biological plausibility for an association between lymphoma and benzene exposure. We identified a 16-gene expression signature associated with all levels of benzene exposure.

Conclusions

Our findings suggest that chronic benzene exposure, even at levels below the current U.S. occupational standard, perturbs many genes, biological processes, and pathways. These findings expand our understanding of the mechanisms by which benzene may induce hematotoxicity, leukemia, and lymphoma and reveal relevant potential biomarkers associated with a range of exposures.