Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

Exposure to UV-B filter octylmethoxycinnamate and human health effects: Focus on endocrine disruptor actions

Human skin is the first line of photoprotection against UV radiation. However, despite having its defence mechanisms, the photoprotection that the skin exerts is not enough. To protect human skin, the inclusion of UV filters in the cosmetic industry has grown significantly as a photoprotection strategy. Octylmethoxycinnamate, also designated by octinoxate, or 2-ethylhexyl-4-methoxycinnamate (CAS number: 5466-77-3) is one of the most widely used UV-B filter in the cosmetic industry. The toxic effects of OMC have alarmed the public, but there is still no consensus in the scientific community about its use. This article aims to provide an overview of the UV filters' photoprotection, emphasizing the OMC and the possible negative effects it may have on the public health. Moreover, the current legislation will be addressed. In summary, the recommendations should be rethought to assess their risk-benefit, since the existing literature warns us to endocrine-disrupting effects of OMC. Further studies should be focus on the toxicity of OMC alone, in mixture and should consider its degradation products, to improve the knowledge of its risk assessment as EDC.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Environmental risk score of multiple pollutants for kidney damage among residents in vulnerable areas by occupational chemical exposure in Korea

This study aimed to develop an environmental risk score (ERS) of multiple pollutants (MP) causing kidney damage (KD) in Korean residents near abandoned metal mines or smelters and evaluate the association between ERS and KD by a history of occupational chemical exposure (OCE). Exposure to MP, consisting of nine metals, four polycyclic aromatic hydrocarbons, and four volatile organic compounds, was measured as urinary metabolites. The study participants were recruited from the Forensic Research via Omics Markers (FROM) study (n = 256). Beta-2-microglobulin (β2-MG), N-acetyl-β-D-glucosaminidase (NAG), and estimated glomerular filtration rate (eGFR) were used as biomarkers of KD. Bayesian kernel machine regression (BKMR) was selected as the optimal ERS model with the best performance and stability of the predicted effect size among the elastic net, adaptive elastic net, weighted quantile sum regression, BKMR, Bayesian additive regression tree, and super learner model. Variable importance was estimated to evaluate the effects of metabolites on KD. When stratified with the history of OCE after adjusting for several confounding factors, the risks for KD were higher in the OCE group than those in the non-OCE group; the odds ratio (OR; 95% CI) for ERS in non-OCE and OCE groups were 2.97 (2.19, 4.02) and 6.43 (2.85, 14.5) for β2-MG, 1.37 (1.01, 1.86) and 4.16 (1.85, 9.39) for NAG, and 4.57 (3.37, 6.19) and 6.44 (2.85, 14.5) for eGFR, respectively. We found that the ERS stratified history of OCE was the most suitable for evaluating the association between MP and KD, and the risks were higher in the OCE group than those in the non-OCE group.

Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany)

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone ( O 3 ), nitrogen dioxide ( NO 2 ), particulate matter ( P M 2.5 , P M 10 ) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (-0.84), April (-0.47), and October (-0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O 3 and NO 2 . Spatially, NO 2 , P M 2.5 , and P M 10 concentrations were significantly higher in urban than rural regions. For O 3 , this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

BACKGROUND

Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales.

OBJECTIVE

To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches.

METHODS

We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches.

RESULTS

We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models.

SIGNIFICANCE

The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

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

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

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

BACKGROUND

The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways.

OBJECTIVES

The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics.

DISCUSSION

State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.

Does air pollution confound associations between environmental noise and cardiovascular outcomes? - A systematic review

BACKGROUND

Exposure to environmental noise is associated with adverse health effects, but there is potential for confounding and interaction with air pollution, particularly where both exposures arise from the same source, such as transport.

OBJECTIVES

To review evidence on confounding and interaction of air pollution in relation to associations between environmental noise and cardiovascular outcomes.

METHODS

Papers were identified from similar reviews published in 2013 and 2015, from the systematic reviews supporting the WHO 2018 noise guidelines, and from a literature search covering the period 2016-2022 using Medline and PubMed databases. Additional papers were identified from colleagues. Study selection was according to PECO inclusion criteria. Studies were evaluated against the WHO checklist for risk of bias.

RESULTS

52 publications, 36 published after 2015, were identified that assessed associations between transportation noise and cardiovascular outcomes, that also considered potential confounding (49 studies) or interaction (23 studies) by air pollution. Most, but not all studies, suggested that the associations between traffic noise and cardiovascular outcomes are independent of air pollution. NO2 or PM2.5 were the most commonly included air pollutants and we observed no clear differences across air pollutants in terms of the potential confounding role. Most papers did not appear to suggest an interaction between noise and air pollution. Eight studies found the largest noise effect estimates occurring within the higher noise and air pollution exposure categories, but were not often statistically significant.

CONCLUSION

Whilst air pollution does not appear to confound associations of noise and cardiovascular health, more studies on potential interactions are needed. Current methods to assess quality of evidence are not optimal when evaluating evidence on confounding or interaction.

A weighted quantile sum regression with penalized weights and two indices

Background

New statistical methodologies were developed in the last decade to face the challenges of estimating the effects of exposure to multiple chemicals. Weighted Quantile Sum (WQS) regression is a recent statistical method that allows estimating a mixture effect associated with a specific health effect and identifying the components that characterize the mixture effect.

Objectives

In this study, we propose an extension of WQS regression that estimates two mixture effects of chemicals on a health outcome in the same model through the inclusion of two indices, one in the positive direction and one in the negative direction, with the introduction of a penalization term.

Methods

To evaluate the performance of this new model we performed both a simulation study and a real case study where we assessed the effects of nutrients on obesity among adults using the National Health and Nutrition Examination Survey (NHANES) data.

Results

The method showed good performance in estimating both the regression parameter and the weights associated with the single elements when the penalized term was set equal to the magnitude of the Akaike information criterion of the unpenalized WQS regression. The two indices further helped to give a better estimate of the parameters [Positive direction Median Error (PME): 0.022; Negative direction Median Error (NME): -0.044] compared to the standard WQS without the penalization term (PME: -0.227; NME: 0.215). In the case study, WQS with two indices was able to find a significant effect of nutrients on obesity in both directions identifying sodium and magnesium as the main actors in the positive and negative association, respectively.

Discussion

Through this work, we introduced an extension of WQS regression that improved the accuracy of the parameter estimates when considering a mixture of elements that can have both a protective and a harmful effect on the outcome; and the advantage of adding a penalization term when estimating the weights.

The Association between Maternal Urinary Phthalate Concentrations and Blood Pressure in Pregnancy: A Systematic Review and Meta-Analysis

Phthalates are commonly found in a wide range of environments and have been linked to several negative health outcomes. While earlier research indicated a potential connection between phthalate exposure and blood pressure (BP) during pregnancy, the results of these studies remain inconclusive. The objective of this meta-analysis was to elucidate the relationship between phthalate exposure and BP in pregnancy. A comprehensive literature search was carried out with PubMed, EMBASE, and Web of Science, and pertinent studies published up until 5 March 2023 were reviewed. Random-effects models were utilized to consolidate the findings of continuous outcomes, such as diastolic and systolic BP, as well as the binary outcomes of hypertensive disorders of pregnancy (HDP). The present study included a total of 10 studies. First-trimester MBP exposure exhibited a positive association with mean systolic and diastolic BP during both the second and third trimesters (β = 1.05, 95% CI: 0.27, 1.83, I² = 93%; β = 0.40, 95% CI: 0.05, 0.74, I² = 71%, respectively). Second-trimester monobenzyl phthalate (MBzP) exposure was positively associated with systolic and diastolic BP in the third trimester (β = 0.57, 95% CI: 0.01, 1.13, I² = 0; β = 0.70, 95% CI: 0.27, 1.13, I² = 0, respectively). Conversely, first-trimester mono-2-ethylhexyl phthalate (MEHP) exposure demonstrated a negative association with mean systolic and diastolic BP during the second and third trimesters (β = -0.32, 95% CI: -0.60, -0.05, I² = 0; β = -0.32, 95% CI: -0.60, -0.05, I² = 0, respectively). Additionally, monoethyl phthalate (MEP) exposure was found to be associated with an increased risk of HDP (OR = 1.12, 95% CI: 1.02, 1.23, I² = 26%). Our study found that several phthalate metabolites were associated with increased systolic and diastolic BP, as well as the risk of HDP across pregnancies. Nevertheless, given the limited number of studies analyzed, additional research is essential to corroborate these findings and elucidate the molecular mechanisms linking phthalates to BP changes during pregnancy.

Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression

This study aims to investigate the combined effects of per- and polyfluoroalkyl substances (PFAS) on allostatic load, an index of chronic stress that is linked to several chronic diseases, including cardiovascular disease and cancer. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2014, this study examines the relationship between six PFAS variables (PFDE, PFNA, PFOS, PFUA, PFOA, and PFHS) and allostatic load using Bayesian Kernel Machine Regression (BKMR) analysis. The study also investigates the impact of individual and combined PFAS exposure on allostatic load using various exposure-response relationships, such as univariate, bivariate, or multivariate models. The analysis reveals that the combined exposure to PFDE, PFNA, and PFUA had the most significant positive trend with allostatic load when it was modeled as a binary variable, while PFDE, PFOS, and PFNA had the most significant positive trend with allostatic load when modeled as a continuous variable. These findings provide valuable insight into the consequences of cumulative exposure to multiple PFAS on allostatic load, which can help public health practitioners identify the dangers associated with potential combined exposure to select PFAS of interest. In summary, this study highlights the critical role of PFAS exposure in chronic stress-related diseases and emphasizes the need for effective strategies to minimize exposure to these chemicals to reduce the risk of chronic diseases. It underscores the importance of considering the combined effects of PFAS when assessing their impact on human health and offers valuable information for policymakers and regulators to develop strategies to protect public health.

The Association of Combined Per- and Polyfluoroalkyl Substances and Metals with Allostatic Load Using Bayesian Kernel Machine Regression

Background/Objective: This study aimed to investigate the effect of exposure to per- and polyfluoroalkyl substances (PFAS), a class of organic compounds utilized in commercial and industrial applications, on allostatic load (AL), a measure of chronic stress. PFAS, such as perfluorodecanoic acid (PFDE), perfluorononanoic acid (PFNA), perfluorooctane sulfonic acid (PFOS), perfluoroundecanoic acid (PFUA), perfluorooctanoic acid (PFOA), and perfluorohexane sulfonic acid (PFHS), and metals, such as mercury (Hg), barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (TI), tungsten (W), and uranium (U) were investigated. This research was performed to explore the effects of combined exposure to PFAS and metals on AL, which may be a disease mediator. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014 were used to conduct this study on persons aged 20 years and older. A cumulative index of 10 biomarkers from the cardiovascular, inflammatory, and metabolic systems was used to calculate AL out of 10. If the overall index was ≥ 3, an individual was considered to be chronically stressed (in a state of AL). In order to assess the dose-response connections between mixtures and outcomes and to limit the effects of multicollinearity and other potential interaction effects between exposures, Bayesian kernel machine regression (BKMR) was used. Results: The most significant positive trend between mixed PFAS and metal exposure and AL was revealed by combined exposure to cesium, molybdenum, PFHS, PFNA, and mercury (posterior inclusion probabilities, PIP = 1, 1, 0.854, 0.824, and 0.807, respectively). Conclusions: Combined exposure to metals and PFAS increases the likelihood of being in a state of AL.

Joint Associations of Short-Term Exposure to Ambient Air Pollutants with Hospital Admission of Ischemic Stroke

BACKGROUND

Studies have estimated the associations of short-term exposure to ambient air pollution with ischemic stroke. However, the joint associations of ischemic stroke with air pollution as a mixture remain unknown.

METHODS

We employed a time-stratified case-crossover study to investigate 824,808 ischemic stroke patients across China. We calculated daily mean concentrations of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5), maximum 8-h average for O3 (MDA8 O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) across all monitoring stations in the city where the IS patients resided. We conducted conditional logistic regression models to estimate the exposure-response associations.

RESULTS

Results from single-pollutant models showed positive associations of hospital admission for ischemic stroke with PM2.5 (excess risk [ER] = 0.38%, 95% confidence interval [CI]: 0.29% to 0.47%, for 10 μg/m3), MDA8 O3 (ER = 0.29%, 95% CI: 0.18% to 0.40%, for 10 μg/m3), NO2 (ER = 1.15%, 95% CI: 0.92% to 1.39%, for 10 μg/m3), SO2 (ER = 0.82%, 95% CI: 0.53% to 1.11%, for 10 μg/m3) and CO (ER = 3.47%, 95% CI: 2.70% to 4.26%, for 1 mg/m3). The joint associations (ER) with all air pollutants (for interquartile range width increases in each pollutant) estimated by the single-pollutant model was 8.73% and was 4.27% by the multipollutant model. The joint attributable fraction of ischemic stroke attributable to air pollutants based on the multipollutant model was 7%.

CONCLUSIONS

Short-term exposures to PM2.5, MDA8 O3, NO2, SO2, and CO were positively associated with increased risks of hospital admission for ischemic stroke. The joint associations of air pollutants with ischemic stroke might be overestimated using single-pollutant models. See video abstract at, http://links.lww.com/EDE/C8.

Human health risk assessment for contaminated sites: A retrospective review

Soil contamination is a serious global hazard as contaminants can migrate to the human body through the soil, water, air, and food, threatening human health. Human Health Risk Assessment (HHRA) is a commonly used method for estimating the magnitude and probability of adverse health effects in humans that may be exposed to contaminants in contaminated environmental media in the present or future. Such estimations have improved for decades with various risk assessment frameworks and well-established models. However, the existing literature does not provide a comprehensive overview of the methods and models of HHRA that are needed to grasp the current status of HHRA and future research directions. Thus, this paper aims to systematically review the HHRA approaches and models, particularly those related to contaminated sites from peer-reviewed literature and guidelines. The approaches and models focus on methods used in hazard identification, toxicity databases in dose-response assessment, approaches and fate and transport models in exposure assessment, risk characterization, and uncertainty characterization. The features and applicability of the most commonly used HHRA tools are also described. The future research trend for HHRA for contaminated sites is also forecasted. The transition from animal experiments to new methods in risk identification, the integration and update and sharing of existing toxicity databases, the integration of human biomonitoring into the risk assessment process, and the integration of migration and transformation models and risk assessment are the way forward for risk assessment in the future. This review provides readers with an overall understanding of HHRA and a grasp of its developmental direction.

Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior

Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.

A review on the application of the exposome paradigm to unveil the environmental determinants of age-related diseases

Age-related diseases account for almost half of all diseases among adults worldwide, and their incidence is substantially affected by the exposome, which is the sum of all exogenous and endogenous environmental exposures and the human body’s response to these exposures throughout the entire lifespan. Herein, we perform a comprehensive review of the epidemiological literature to determine the key elements of the exposome that affect the development of age-related diseases and the roles of aging hallmarks in this process. We find that most exposure assessments in previous aging studies have used a reductionist approach, whereby the effect of only a single environmental factor or a specific class of environmental factors on the development of age-related diseases has been examined. As such, there is a lack of a holistic and unbiased understanding of the effect of multiple environmental factors on the development of age-related diseases. To address this, we propose several research strategies based on an exposomic framework that could advance our understanding—in particular, from a mechanistic perspective—of how environmental factors affect the development of age-related diseases. We discuss the statistical methods and other methods that have been used in exposome-wide association studies, with a particular focus on multiomics technologies. We also address future challenges and opportunities in the realm of multidisciplinary approaches and genome–exposome epidemiology. Furthermore, we provide perspectives on precise public health services for vulnerable populations, public communications, the integration of risk exposure information, and the bench-to-bedside translation of research on age-related diseases.

Inter-epidemic Rift Valley fever virus infection incidence and risks for zoonotic spillover in northern Tanzania

Rift Valley fever virus (RVFV) is a mosquito-borne pathogen that has caused epidemics involving people and animals across Africa and the Arabian Peninsula. A number of studies have found evidence for the circulation of RVFV among livestock between these epidemics but the population-level incidence of infection during this inter-epidemic period (IEP) is rarely reported. General force of infection (FOI) models were applied to age-adjusted cross-sectional serological data to reconstruct the annual FOI and population-level incidence of RVFV infection among cattle, goats, and sheep in northern Tanzania from 2009 through 2015, a period without reported Rift Valley fever (RVF) cases in people or animals. To evaluate the potential for zoonotic RVFV spillover during this period, the relationship between village-level livestock RVFV FOI and human RVFV seropositivity was quantified using multi-level logistic regression. The predicted average annual incidence was 72 (95% Credible Interval [CrI] 63, 81) RVFV infections per 10,000 animals and 96 (95% CrI 81, 113), 79 (95% CrI 62, 98), and 39 (95% CrI 28, 52) per 10,000 cattle, sheep, and goats, respectively. There was variation in transmission intensity between study villages, with the highest estimated village-level FOI 2.49% (95% CrI 1.89, 3.23) and the lowest 0.12% (95% CrI 0.02, 0.43). The human RVFV seroprevalence was 8.2% (95% Confidence Interval 6.2, 10.9). Human seropositivity was strongly associated with the village-level FOI in livestock, with the odds of seropositivity in an individual person increasing by around 1.2 times (95% CrI 1.1, 1.3) for each additional annual RVFV seroconversion per 1,000 animals. A history of raw milk consumption was also positively associated with human seropositivity. RVFV has circulated at apparently low levels among livestock in northern Tanzania in the period since the last reported epidemic. Although our data do not allow us to confirm human RVFV infections during the IEP, a strong association between human seropositivity and the FOI in cattle, goats, and sheep supports the hypothesis that RVFV circulation among livestock during the IEP poses a risk for undetected zoonotic spillover in northern Tanzania. We provide further evidence for the likely role of raw milk consumption in RVFV transmission from animals to people.

Association of urinary or blood heavy metals and mortality from all causes, cardiovascular disease, and cancer in the general population: a systematic review and meta-analysis of cohort studies

Amounting epidemiological evidence has shown detrimental effects of heavy metals on a wide range of diseases. However, the effect of heavy metal exposure on mortality in the general population remains unclear. The primary objective of this study was to clarify the associations between heavy metals and mortality from all causes, cardiovascular disease (CVD), and cancer based on prospective studies. We comprehensively searched Pubmed, Embase, and Web of Science electronic databases to identify studies published from their inception until 1 March 2022. Investigators identified inclusion criteria, extracted study characteristics, and assessed the methodological quality of included studies according to standardized guidelines. Meta-analysis was conducted if the effect estimates of the same outcome were reported in at least three studies. Finally, 42 original studies were identified. The results of meta-analysis showed that cadmium and lead exposure was significantly associated with mortality from all causes, CVD, and cancer in the general population. Moderate evidence suggested there was a link between arsenic exposure and mortality. The adverse effects of mercury and other heavy metals on mortality were inconclusive. Epidemiological evidence for the joint effect of heavy metal exposure on mortality was still indeterminate. In summary, our study provided compelling evidence that exposure to cadmium, lead, and arsenic were associated with mortality from all causes, CVD, and cancer, while the evidence on other heavy metals, for example mercury, was insignificant or indeterminate. Nevertheless, further prospective studies are warranted to explore the joint effects of multiple metal exposure on mortality.

Outdoor Air Pollution and Pregnancy Loss: a Review of Recent Literature

Purpose of Review

This review summarizes recent literature about the impacts of outdoor air pollution on pregnancy loss (spontaneous abortion/miscarriage and stillbirth), identifies challenges and opportunities, and provides recommendations for actions.

Recent Findings

Both short- and long-term exposures to ubiquitous air pollutants, including fine particulate matter < 2.5 and < 10 μm, may increase pregnancy loss risk. Windows of susceptibility include the entire gestational period, especially early pregnancy, and the week before event. Vulnerable subpopulations were not consistently explored, but some evidence suggests that pregnant parents from more disadvantaged populations may be more impacted even at the same exposure level.

Summary

Given environmental conditions conductive to high air pollution exposures become more prevalent as the climate shifts, air pollution’s impacts on pregnancy is expected to become a growing public health concern. While awaiting larger preconception studies to further understand causal impacts, multi-disciplinary efforts to minimize exposures among pregnant women are warranted.

Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.

Do urinary metals associate with the homeostasis of inflammatory mediators? Results from the perspective of inflammatory signaling in middle-aged and older adults

OBJECTIVE

We aimed to investigate whether urinary metal mixtures are associated with the homeostasis of inflammatory mediators in middle-aged and older adults.

METHODS

A four-visit repeated-measures study was conducted with 98 middle-aged and older adults from five communities in Beijing, China. Only one person was lost to follow-up at the third visit. Ultimately, 391 observations were included in the analysis. The urinary concentrations of 10 metals were measured at each visit using inductively coupled plasma mass spectrometry (ICP-MS) with a limit of detection (LOD) ranging from 0.002 to 0.173 µg/L, and the detection rates were all above 84%. Similarly, 14 serum inflammatory mediators were measured using a Beckman Coulter analyzer and the Bio-Plex MAGPIX system. A linear mixed model (LMM), LMM with least absolute shrinkage and selection operator regularization (LMMLASSO), and Bayesian kernel machine regression (BKMR) were adopted to explore the effects of urinary metal mixtures on inflammatory mediators.

RESULTS

In LMM, a two-fold increase in urinary cesium (Cs) and chromium (Cr) was statistically associated with -35.22% (95% confidence interval [CI]: -53.17, -10.40) changes in interleukin 6 (IL-6) and -11.13% (95 %CI: -20.67, -0.44) in IL-8. Urinary copper (Cu) and selenium (Se) was statistically associated with IL-6 (88.10%, 95%CI: 34.92, 162.24) and tumor necrosis factor-alpha (TNF-α) (22.32%, 95%CI: 3.28, 44.12), respectively. Similar results were observed for the LMMLASSO and BKMR. Furthermore, Cr, Cs, Cu, and Se were significantly associated with other inflammatory regulatory network mediators. For example, urinary Cs was statistically associated with endothelin-1, and Cr was statistically associated with endothelin-1 and intercellular adhesion molecule 1 (ICAM-1). Finally, the interaction effects of Cu with various metals on inflammatory mediators were observed.

CONCLUSION

Our findings suggest that Cr, Cs, Cu, and Se may disrupt the homeostasis of inflammatory mediators, providing insight into the potential pathophysiological mechanisms of metal mixtures and chronic diseases.

A permutation-based approach to inference for weighted sum regression with correlated chemical mixtures

There is a growing demand for methods to determine the effects that chemical mixtures have on human health. One statistical challenge is identifying true "bad actors" from a mixture of highly correlated predictors, a setting in which standard approaches such as linear regression become highly variable. Weighted Quantile Sum regression has been proposed to address this problem, through a two-step process where mixture component weights are estimated using bootstrap aggregation in a training dataset and inference on the overall mixture effect occurs in a held-out test set. Weighted Quantile Sum regression is popular in applied papers, but the reliance on data splitting is suboptimal, and analysts who use the same data for both steps risk inflating the Type I error rate. We therefore propose a modification of Weighted Quantile Sum regression that uses a permutation test for inference, which allows for weight estimation using the entire dataset and preserves Type I error. To minimize computational burden, we propose replacing the bootstrap with L1 or L2 penalization and describe how to choose the appropriate penalty given expert knowledge about a mixture of interest. We apply our method to a national pregnancy cohort study of prenatal phthalate exposure and child health outcomes.

Long-term exposure to ambient air pollutants and age-related macular degeneration in middle-aged and older adults

Absract In developed countries, age-related macular degeneration (AMD) is a leading cause of irreversible blindness in adults. The key pathways of AMD are suggested to be excessive oxidative stress and inflammation in the central retina. Because air pollution has been found capable of inducing oxidative stress and inflammation, it may play a role in development of AMD. This study investigated the association between ambient air pollution and AMD in 15,115 middle-aged and older adults (≥40 years) from Korean National Health and Nutrition Examination Survey 2008-2012. After controlling for important confounders, ambient NO₂ and CO in current-to-5 prior years and PM₁₀ in 2-to-5 prior years were significantly associated with higher prevalence of early AMD, while O₃ in current-to-5 prior years was significantly associated with lower prevalence of early AMD. When modeled air pollution within administrative division units, its ORs with an IQR increase in NO₂, CO, and O₃ at current year were 1.24 (95% CI: 1.05-1.46), 1.22 (95% CI: 1.09-1.38), and 0.80 (95% CI: 0.70-0.92), respectively. Overall, results from air pollution at local/town units were consistent with those at administrative division units. Long-term exposures to ambient air pollution may play a role in the risk of AMD in middle-aged and older adults.

Associations between prenatal multiple metal exposure and preterm birth: Comparison of four statistical models

BACKGROUND

Exposure to some heavy metals has been demonstrated to be related to the risk of preterm birth (PTB). However, the effects of multi-metal mixture are seldom assessed. Thus, we aimed to investigate the associations of maternal exposure to metal mixture with PTB, and to identify the main contributors to PTB from the mixture.

METHODS

The population in the nested case-control study was from a prospective cohort enrolled in Wuhan, China between 2012 and 2014. Eighteen metals were measured in maternal urine collected before delivery. Logistic regression, elastic net regularization (ENET), weighted quantile sum regression (WQSR), and Bayesian kernel machine regression (BKMR) were used to estimate the overall effect and identify important mixture components that drive the associations with PTB.

RESULTS

Logistic regression found naturally log-transformed concentrations of 13 metals were positively associated with PTB after adjusting for the covariates, and only V, Zn, and Cr remained the significantly positive associations when additionally adjusting for the 13 metals together. ENET identified 11 important metals for PTB, and V (β = 0.23) had the strongest association. WQSR determined the positive combined effect of metal mixture on PTB (OR: 1.44, 95%CI: 1.32, 1.57), and selected Cr and V (weighted 0.41 and 0.32, respectively) as the most weighted metals. BKMR analysis confirmed the overall mixture was positively associated with PTB, and the independent effect of V was the most significant. Besides, BKMR showed the non-linear relationships of V and Cu with PTB, and the potential interaction between Zn and Cu.

CONCLUSION

Applying different statistical models, the study found that exposure to the metal mixture was associated with a higher risk of PTB, and V was identified as the most important risk factor among co-exposed metals for PTB.

Individual and joint effects of metal exposure on metabolic syndrome among Chinese adults

Growing evidence suggests that metal exposure contributes to metabolic syndrome (MetS), but little is known about the effects of combined exposure to metal mixtures. This cross-sectional study included 3748 adults who were recruited from the Medical Physical Examination Center of Tongji Hospital, Wuhan, China. The levels of 21 metal(loid)s in urine were measured by inductively coupled plasma mass spectrometry. MetS was diagnosed according to National Cholesterol Education Program's Adult Treatment Panel III recommendations. Multivariate logistic regression model was uesd to explore the effects of single-metal and multi-metal exposures. The elastic net (ENET) regularization with an environmental risk score (ERS) was performed to estimate the joint effects of exposure to metal mixtures. A total of 636 participants (17%) were diagnosed with MetS. In single metal models, MetS was positively associated with zinc (Zn) and negatively associated with nickel (Ni). In multiple metal models, the associations remained significant after adjusting for the other metals. In the joint association analysis, the ENET models selected Zn as the strongest predictor of MetS. Compared to the lowest quartile, the highest quartile of ERS was associated with an elevated risk of MetS (OR = 3.72; 95% CI: 2.77, 5.91; P-trend < 0.001). Overall, we identified that the combined effect of multiple metals was related to an increased MetS risk, with Zn being the major contributor. These findings need further validation in prospective studies.

Machine learning approaches to characterize the obesogenic urban exposome

BACKGROUND

Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously.

OBJECTIVES

We aimed to explore what environmental factors of the urban exposome are related to body mass index (BMI), and evaluated the consistency of findings across multiple statistical approaches.

METHODS

A cross-sectional analysis was conducted using baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study. BMI was obtained from self-reported height and weight. Geocoded exposures linked to individual home addresses (using 6-digit postcode) of 86 environmental factors were estimated, including air pollution, traffic noise, green-space, built environmental and neighborhood socio-demographic characteristics. Exposure-obesity associations were identified using the following approaches: sparse group Partial Least Squares, Bayesian Model Averaging, penalized regression using the Minimax Concave Penalty, Generalized Additive Model-based boosting Random Forest, Extreme Gradient Boosting, and Multiple Linear Regression, as the most conventional approach. The models were adjusted for individual socio-demographic variables. Environmental factors were ranked according to variable importance scores attributed by each approach and median ranks were calculated across these scores to identify the most consistent associations.

RESULTS

The most consistent environmental factors associated with BMI were the average neighborhood value of the homes, oxidative potential of particulate matter air pollution (OP), healthy food outlets in the neighborhood (5 km buffer), low-income neighborhoods, and one-person households in the neighborhood. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households and smaller amount of healthy food retailers. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households, smaller amounts of healthy food retailers and higher OP levels. Across the approaches, we observed consistent patterns of results based on model's capacity to incorporate linear or nonlinear associations.

DISCUSSION

The pluralistic analysis on environmental obesogens strengthens the existing evidence on the role of neighborhood socioeconomic position, urbanicity and air pollution.

The impacts of pollution and its associated spatial spillover effects on ill-health in China

While the adverse health effects of air pollution and its associated spatial spillovers have been extensively explored, there are a paucity of studies examining and comparing the effects of air pollution, water pollution, and their associated spatial spillover consequences for health. This study aims to evaluate and compare the impacts of water pollution, air pollution, and their associated spillover effects on ill-health. This study combined individual-level health data acquired from three waves of the China Health and Retirement Longitudinal Study (CHARLS) for 25,504 residents from 28 Chinese provinces with provincial-level pollution data for 2011, 2013 and 2015. We used Moran's I statistic to examine the existence and direction of the spatial spillover effects of pollution. The Spatial Durbin Model was then employed to assess the impacts of pollution and its associated spatial spillover effects on ill-health. A province's ill-health score increased by 6.649 for every 1 ton per capita per annum increase in the average amount of soot/dust discharged by its adjacent provinces. For every 1 ton per capita per annum increase in wastewater discharged, a province's ill-health score increased by 0.004. Targeted actions through the construction of cooperative action with adjacent provinces are suggested by our study to improve the efficiency of policy interventions.

Davis C, Rudolph KE, Keyes KM, Hasin DS, Cerdá M. Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning

BACKGROUND

Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research.

METHODS

Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time.

RESULTS

PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing.

CONCLUSIONS

Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.

Associations between human internal chemical exposure to Persistent Organic Pollutants (POPs) and In Vitro Fertilization (IVF) outcomes: Systematic review and evidence map of human epidemiological evidence

The impact of environmental chemicals like persistent organic pollutants (POPs) on reproductive health is still poorly understood, despite the high societal and economical costs. The aim of the present study was to systematically review and evaluate the human evidence on the associations between internal levels of POPs and in vitro Fertilization (IVF) outcomes among women. We applied a protocol based on the National Toxicology Program Office of Health Assessment and Translation's guidelines for the study search, selection and quality assessment. Fifteen studies were finally retained in the present work. The results showed that main families of POPs are still pervasive in follicular fluid and serum of women undergoing IVF treatments. Globally, we found inconsistent findings across studies for specific exposure-outcome dyads, suggesting that adverse effects of POPs on IVF outcomes cannot be ruled out. Specifically, there is evidence that POPs, notably some polychlorinated biphenyls and organochlorine pesticides, may impair embryo quality and pregnancy rates. Most studies have been performed in small cohorts (n<50) and focused on PCBs and OCPs, whereas major research gaps remain for emerging compounds (e.g. perfluoroalkylated substances) and the most clinically relevant outcome, live birth rate. The overall evidence presented 'serious' or 'very serious' risk of bias, mainly due to the lack of consideration of relevant confounding variables, low sample size or underreporting of methods. Globally, we judged the level of evidence being "low". Given the high economical and societal costs associated to infertility and IVF, further well-designed research is urged to fill the highlighted gaps.

Considering environmental exposures to per- and polyfluoroalkyl substances (PFAS) as risk factors for hypertensive disorders of pregnancy

Hypertensive disorders of pregnancy (HDP), including preeclampsia and gestational hypertension, lead to significant maternal morbidity and in some cases, maternal mortality. Environmental toxicants, especially those that disrupt normal placental and endothelial function, are emerging as potential risk factors for HDP. Per- and polyfluoroalkyl substances (PFAS) are a large group of ubiquitous chemicals found in consumer products, the environment, and increasingly in drinking water. PFAS have been associated with a multitude of adverse health effects, including dyslipidemia, hypertension, and more recently, HDP. In this review, we present epidemiological and mechanistic evidence for the link between PFAS and HDP and recommend next steps for research and prevention efforts. To date, epidemiological studies have assessed associations between only ten of the thousands of PFAS and HDP. Positive associations between six PFAS (PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonic acid; PFHxS, perfluorohexane sulfonic acid; PFHpA, perfluoroheptanoic acid; PFBS, perfluorobutanesulfonic acid; and PFNA, perfluoronanoic acid) and risk for HDP have been reported in some, but not all, studies. PFAS disrupt placental and immune function, cause oxidative stress, and disrupt lipid metabolism. These physiological disruptions may be mechanisms through which PFAS can lead to HDP. Overall, limited epidemiological evidence and plausible mechanisms support PFAS as risk factors for HDP. More research is needed in diverse, well-powered cohorts that assess exposures to as many PFAS as possible. Such research should consider not only individual PFAS but also the totality of exposures to PFAS and other environmental chemicals. Pregnant women may be a group that is vulnerable to PFAS exposure, and as such HDP risk should be considered by policymakers setting PFAS exposure limits. In the interim, medical and public health professionals in regions with PFAS contamination could provide short-term solutions in the form of patient-level prevention, increased monitoring, and early intervention for HDP.

Prenatal exposure to endocrine disrupting chemical mixtures and infant birth weight: A Bayesian analysis using kernel machine regression

BACKGROUND

Pregnant women are regularly exposed to a multitude of endocrine disrupting chemicals (EDCs). EDC exposures, both individually and as mixtures, may affect fetal growth. The relationship of EDC mixtures with infant birth weight, however, remains poorly understood. We examined the relations between prenatal exposure to EDC mixtures and infant birth weight.

METHODS

We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1857 pregnant women enrolled between 2008 and 2011. We quantified twenty-one chemical concentrations from five EDC classes, including organochlorine compounds (OCs), metals, perfluoroalkyl substances (PFAS), phenols and phthalate metabolites that were detected in >70% of urine or blood samples collected during the first trimester. In our primary analysis, we used Bayesian kernel machine regression (BKMR) models to assess variable importance, explore EDC mixture effects, and identify any interactions among EDCs. Our secondary analysis used traditional linear regression to compare the results with those of BKMR and to quantify the changes in mean birth weight in relation to prenatal EDC exposures.

RESULTS

We found evidence that mixtures of OCs and metals were associated with monotonic decreases in mean birth weight across the whole range of exposure. trans-Nonachlor from the OC mixture and lead (Pb) from the metal mixture had the greatest impact on birth weight. Our linear regression analysis corroborated the BKMR results and found that a 2-fold increase in trans-nonachlor and Pb concentrations reduced mean birth weight by -38 g (95% confidence interval (CI): -67, -10) and -39 g (95% CI: -69, -9), respectively. A sex-specific association for OC mixture was observed among female infants. PFAS, phenols and phthalates were not associated with birth weight. No interactions were observed among the EDCs.

CONCLUSIONS

Using BKMR, we observed that both OC and metal mixtures were associated with decreased birth weight in the MIREC Study. trans-Nonachlor from the OC mixture and Pb from the metal mixture contributed most to the adverse effects.

Identifying environmental exposure profiles associated with timing of menarche: A two-step machine learning approach to examine multiple environmental exposures

BACKGROUND

Variation in the timing of menarche has been linked with adverse health outcomes in later life. There is evidence that exposure to hormonally active agents (or endocrine disrupting chemicals; EDCs) during childhood may play a role in accelerating or delaying menarche. The goal of this study was to generate hypotheses on the relationship between exposure to multiple EDCs and timing of menarche by applying a two-stage machine learning approach.

METHODS

We used data from the National Health and Nutrition Examination Survey (NHANES) for years 2005-2008. Data were analyzed for 229 female participants 12-16 years of age who had blood and urine biomarker measures of 41 environmental exposures, all with >70% above limit of detection, in seven classes of chemicals. We modeled risk for earlier menarche (<12 years of age vs older) with exposure biomarkers. We applied a two-stage approach consisting of a random forest (RF) to identify important exposure combinations associated with timing of menarche followed by multivariable modified Poisson regression to quantify associations between exposure profiles ("combinations") and timing of menarche.

RESULTS

RF identified urinary concentrations of monoethylhexyl phthalate (MEHP) as the most important feature in partitioning girls into homogenous subgroups followed by bisphenol A (BPA) and 2,4-dichlorophenol (2,4-DCP). In this first stage, we identified 11 distinct exposure biomarker profiles, containing five different classes of EDCs associated with earlier menarche. MEHP appeared in all 11 exposure biomarker profiles and phenols appeared in five. Using these profiles in the second-stage of analysis, we found a relationship between lower MEHP and earlier menarche (MEHP ≤ 2.36 ng/mL vs >2.36 ng/mL: adjusted PR = 1.36, 95% CI: 1.02, 1.80). Combinations of lower MEHP with benzophenone-3, 2,4-DCP, and BPA had similar associations with earlier menarche, though slightly weaker in those smaller subgroups. For girls not having lower MEHP, exposure profiles included other biomarkers (BPA, enterodiol, monobenzyl phthalate, triclosan, and 1-hydroxypyrene); these showed largely null associations in the second-stage analysis. Adjustment for covariates did not materially change the estimates or CIs of these models. We observed weak or null effect estimates for some exposure biomarker profiles and relevant profiles consisted of no more than two EDCs, possibly due to small sample sizes in subgroups.

CONCLUSION

A two-stage approach incorporating machine learning was able to identify interpretable combinations of biomarkers in relation to timing of menarche; these should be further explored in prospective studies. Machine learning methods can serve as a valuable tool to identify patterns within data and generate hypotheses that can be investigated within future, targeted analyses.

Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study

Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.

Sex-specific associations of plasma metals and metal mixtures with glucose metabolism: An occupational population-based study in China

Studies with multi-pollutant approach on the relationships between multiple metals and fasting plasma glucose (FPG) are limited. Few studies are available on the potential sex-specific associations between metal exposures and glucose metabolism. We explored the associations between 22 plasma metals and FPG level among the 769 participants from the manganese-exposed workers healthy cohort in China. We applied a sparse partial least squares (sPLS) regression followed by ordinary least-squares regression to evaluate multi-pollutant association. Bayesian kernel machine regression (BKMR) model was used to deal with metal mixtures and evaluate their joint effects on FPG level. In the sPLS model, negative associations on FPG levels were observed for plasma iron (belta = -0.066), cobalt (belta = -0.075), barium (belta = -0.109), and positive associations for strontium (belta = 0.082), and selenium (belta = 0.057) in men, which overlapped with the results among the overall participants. Among women, plasma copper (belta = 0.112) and antimony (belta = 0.137) were positively associated with elevated FPG level. Plasma magnesium was negatively associated with FPG level in both sexes (belta = -0.071 in men and belta = -0.144 in women). The results of overlapped for plasma magnesium was selected as the significant contributor to decreasing FPG level in the multi-pollutant, single-metal, and multi-metal models. BKMR model showed a significantly negative over-all effect of six metal mixtures (magnesium, iron, cobalt, selenium, strontium and barium) on FPG level among the overall participants from all the metals fixed at 50th percentile. In summary, our findings underline the probable role of metals in glucose homeostasis with potential sex-dependent heterogeneities, and suggest more researches are needed to explore the sex-specific associations of metal exposures with risk of diabetes.

Cross-Sectional Estimation of Endogenous Biomarker Associations with Prenatal Phenols, Phthalates, Metals, and Polycyclic Aromatic Hydrocarbons in Single-Pollutant and Mixtures Analysis Approaches

BACKGROUND

Humans are exposed to mixtures of toxicants that can impact several biological pathways. We investigated the associations between multiple classes of toxicants and an extensive panel of biomarkers indicative of lipid metabolism, inflammation, oxidative stress, and angiogenesis.

METHODS

We conducted a cross-sectional study of 173 participants (median 26 wk gestation) from the LIFECODES birth cohort. We measured exposure analytes of multiple toxicant classes [metals, phthalates, phenols, and polycyclic aromatic hydrocarbons (PAHs)] in urine samples. We also measured endogenous biomarkers (eicosanoids, cytokines, angiogenic markers, and oxidative stress markers) in either plasma or urine. We estimated pair-wise associations between exposure analytes and endogenous biomarkers using multiple linear regression after adjusting for covariates. We used adaptive elastic net regression, hierarchical Bayesian kernel machine regression, and sparse-group LASSO regression to evaluate toxicant mixtures associated with individual endogenous biomarkers.

RESULTS

After false-discovery adjustment (q<0.2), single-pollutant models yielded 19 endogenous biomarker signals associated with phthalates, 13 with phenols, 17 with PAHs, and 18 with trace metals. Notably, adaptive elastic net revealed that phthalate metabolites were selected for several positive signals with the cyclooxygenase (n=7), cytochrome p450 (n=7), and lipoxygenase (n=8) pathways. Conversely, the toxicant classes that exhibited the greatest number of negative signals overall in adaptive elastic net were phenols (n=20) and metals (n=21).

DISCUSSION

This study characterizes cross-sectional endogenous biomarker signatures associated with individual and mixtures of prenatal toxicant exposures. These results can help inform the prioritization of specific pairs or clusters of endogenous biomarkers and exposure analytes for investigating health outcomes. https://doi.org/10.1289/EHP7396.

Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

PURPOSE OF REVIEW

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

RECENT FINDINGS

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.

Co-exposure of serum calcium, selenium and vanadium is nonlinearly associated with increased risk of type 2 diabetes mellitus in a Chinese population

BACKGROUND

Metals play an important role in type 2 diabetes mellitus (T2DM). This study aimed to explore the association of T2DM risk with single metal exposure and multi-metal co-exposure.

METHODS

A case-control study with 223 T2DM patients and 302 controls was conducted. Serum concentrations of 19 metals were determined by inductively coupled plasma mass spectrometry (ICP-MS). Those metals with greater effects were screened out and co-exposure effects of metals were assessed by least absolute shrinkage and selection operator (LASSO) regression.

RESULTS

Serum calcium (Ca), selenium (Se) and vanadium (V) were found with greater effects. Higher levels of Ca and Se were associated with increased T2DM risk (OR = 2.23, 95%CI: 1.38-3.62, P_trend = 0.002; OR = 3.16, 95%CI: 1.82-5.50, P_trend < 0.001), but higher V level was associated with decreased T2DM risk (OR = 0.58, 95%CI: 0.34-0.97, P_trend < 0.001). Serum Ca and V concentrations were nonlinearly associated with T2DM risk (P_overall < 0.001, P_nonliearity < 0.001); however, Se concentration was linearly associated with T2DM risk (P_overall < 0.001, P_nonliearity = 0.389). High co-exposure score of serum Ca, Se and V was associated with increased T2DM risk (OR = 3.50, 95%CI: 2.08-5.89, P_trend < 0.001) as a non-linear relationship (P_overall < 0.001, P_nonliearity = 0.003).

CONCLUSIONS

This study suggest that higher levels of serum Ca and Se were associated with increased T2DM risk, but higher serum V level was associated with decreased T2DM risk. Moreover, co-exposure of serum Ca, Se and V was nonlinearly associated with T2DM risk, and high co-exposure score was positively associated with T2DM risk.

INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH

Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.

Early-life exposure to multiple persistent organic pollutants and metals and birth weight: Pooled analysis in four Flemish birth cohorts

BACKGROUND AND AIMS

Prenatal chemical exposure has frequently been associated with reduced fetal growth although results have been inconsistent. Most studies associate single pollutant exposure to this health outcome, even though this does not reflect real life situations as humans are exposed to many pollutants during their life time. The objective of this study is to investigate the association between prenatal exposure to a mixture of persistent environmental chemicals and birth weight using multipollutant models.

METHODS

We combined exposure biomarker data measured in cord blood samples of 1579 women from four Flemish birth cohorts collected over a 10 years' time period. The common set of available and detectable exposure measures in these cohorts are three polychlorinated biphenyls (PCB) congeners (138, 153 and 180), hexachlorobenzene (HCB), dichlorodiphenyldichloroethylene (p,p'-DDE) and the metals cadmium and lead. Multiple linear regression (MLR), Bayesian Information Criterion (BIC), penalized regression using minimax concave penalty (MCP) and Bayesian Adaptive Sampling (BAS) were applied to assess the influence of multiple pollutants in a single analysis on birth weight, adjusted for a priori selected covariates.

RESULTS

In the pooled dataset, a median (P25-P75) birth weight and gestational age of 3420 (3140-3700) grams and 39 (39-40) weeks was observed respectively. The median contaminant levels in cord blood were: 15.8, 26.5, 18.0, 16.9 and 91.5 ng/g lipid for PCB 138, PCB 153, PCB 180, HCB and p,p'-DDE, respectively, 0.075 µg/L for cadmium and 9.7 µg/L for lead. According to the applied statistical methods for multipollutant assessment, p,p'-DDE and PCB 180 were most consistently associated with birth weight. In addition, PCB 153 was selected when applying MCP and BAS. An inverse association with birth weight was found for the PCB congeners, while an increased birth weight was observed for elevated levels of p,p'-DDE.

CONCLUSIONS

Assessing the health risk of combinations of exposure biomarkers reflects better real-world situations and thereby allows more effective risk assessment. Our results add to the existing evidence based on detrimental effects of PCBs on birth weight and indicate a possible increase in birth weight due to p,p'-DDE (while correcting for PCBs).

Ambient air pollution exposed during preantral-antral follicle transition stage was sensitive to associate with clinical pregnancy for women receiving IVF

Maternal exposure to air pollution is associated with poor reproductive outcomes in in vitro fertilization (IVF). However, the susceptible time windows are still not been known clearly. In the present study, we linked the air pollution data with the information of 9001 women receiving 10,467 transfer cycles from August 2014 to August 2019 in The Second Hospital of Hebei Medical University, Shijiazhuang City, China. Maternal exposure was presented as individual average daily concentrations of PM_2.5, PM₁₀, NO₂, SO₂, CO, and O_3, which were predicted by spatiotemporal kriging model based on residential addresses. Exposure windows were divided to five periods according to the process of follicular and embryonic development in IVF. Generalized estimating equation model was used to evaluate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for association between clinical pregnancy and interquartile range increased average daily concentrations of pollutants during each exposure period. The increased PM_2.5 (adjusted OR = 0.95, 95% CI: 0.90, 0.99), PM₁₀ (adjusted OR = 0.93, 95% CI: 0.89, 0.98), NO₂ (adjusted OR = 0.89, 95% CI: 0.85, 0.94), SO₂ (OR = 0.94, 95% CI: 0.90, 0.98), CO (adjusted OR = 0.93, 95% CI: 0.89, 0.97) whereas decreased O₃ (OR = 1.08, 95% CI: 1.02, 1.14) during the duration from preantral follicles to antral follicles were the strongest association with decreased probability of clinical pregnancy among the five periods. Especially, women aged 20-29 years old were more susceptible in preantral-antral follicle transition stage. Women aged 36-47 years old were more vulnerable during post-oocyte retrieve period. Our results suggested air pollution exposure during preantral-antral follicle transition stage was a note-worthy challenge to conceive among females receiving IVF.

Performance of variable and function selection methods for estimating the nonlinear health effects of correlated chemical mixtures: A simulation study

Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure-response relationships. Nonmonotonic relationships are increasingly recognized (eg, for endocrine-disrupting chemicals); however, the impact of nonmonotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression (BKMR), Bayesian additive regression trees (BART), Bayesian structured additive regression with spike-slab priors (BSTARSS), generalized additive models with double penalty (GAMDP) and thin plate shrinkage smoothers (GAMTS), multivariate adaptive regression splines (MARS), and lasso penalized regression. We simulated realistic exposure data based on pregnancy exposure to 17 phthalates and phenols in the US National Health and Nutrition Examination Survey using a multivariate copula. We simulated data sets of size N = 250 and compared methods across 32 scenarios, varying by model size and sparsity, signal-to-noise ratio, correlation structure, and exposure-response relationship shapes. We compared methods in terms of their sensitivity, specificity, and estimation accuracy. In most scenarios, BKMR, BSTARSS, GAMDP, and GAMTS achieved moderate to high sensitivity (0.52-0.98) and specificity (0.21-0.99). BART and MARS achieved high specificity (≥0.90), but low sensitivity in low signal-to-noise ratio scenarios (0.20-0.51). Lasso was highly sensitive (0.71-0.99), except for quadratic relationships (≤0.27). Penalized regression methods that assume linearity, such as lasso, may not be suitable for studies of environmental chemicals hypothesized to have nonmonotonic relationships with outcomes. Instead, BKMR, BSTARSS, GAMDP, and GAMTS are attractive methods for flexibly estimating the shapes of exposure-response relationships and selecting among correlated exposures.

Non-linear association of serum molybdenum and linear association of serum zinc with nonalcoholic fatty liver disease: Multiple-exposure and Mendelian randomization approach

The homeostasis imbalance of metals is closely associated with nonalcoholic fatty liver disease (NAFLD). A total of 1594 and 566 Chinese Han men were enrolled in cross-sectional and longitudinal analyses, respectively. We measured the serum concentrations of 22 metals by ICP-MS. The traditional and the LASSO regression methods were used to construct multiple-metals models, respectively. We performed Mendelian randomization (MR) analysis to confirm the causal relationship between NAFLD and metals using three NAFLD-related SNPs as instrumental variable. After adjustment in the six-metal model, only depressed molybdenum and elevated zinc were associated with a higher NAFLD risk, in both cross-sectional and longitudinal analyses. In the twelve-metal model, similar results were still observed. Moreover, dose-response relationships were non-linear for molybdenum and positively linear for zinc with NAFLD risk. In MR analysis, no causal associations were found from NAFLD to molybdenum and zinc. Our results support that serum molybdenum levels were non-linearly associated with NAFLD risk in Chinese men, whereas serum zinc levels showed a positively linear association. Moreover, MR analysis indicated the changes in serum molybdenum and zinc levels might be not caused by NAFLD, further confirmed our findings in cross-sectional and longitudinal analyses.

Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

Evaluating associations between early pregnancy trace elements mixture and 2nd trimester gestational glucose levels: A comparison of three statistical approaches

OBJECTIVE

Studies have shown that individual trace element levels might be associated with abnormal glycemic status, with implications for diabetes. Few studies have considered these trace elements as a mixture and their impact on gestational glucose levels. Comparing three statistical approaches, we assessed the associations between essential trace elements mixture and gestational glucose levels.

METHODS

We used data from 1720 women enrolled in the Eunice Kennedy Shriver National Institute of Child Health and Human Development's Fetal Growth Study, for whom trace element concentrations (zinc, selenium, copper, molybdenum) were measured by inductively coupled plasma mass spectrometry (ICP-MS) using plasma collected during the 1st trimester. Non-fasting glucose levels were measured during the gestational diabetes mellitus (GDM) screening test in the 2nd trimester. We applied (1) Bayesian Kernel Machine Regression (BKMR); (2) adaptive Least Absolute Shrinkage and Selection Operator (LASSO) in a mutually adjusted linear regression model; and (3) generalized additive models (GAMs) to evaluate the joint associations between trace elements mixture and glucose levels adjusting for potential confounders.

RESULTS

Using BKMR, we observed a mean 2.7 mg/dL higher glucose level for each interquartile increase of plasma copper (95% credible interval: 0.9, 4.5). The positive association between plasma copper and glucose levels was more pronounced at higher quartiles of zinc. Similar associations were detected using adaptive LASSO and GAM. In addition, results from adaptive LASSO and GAM suggested a super-additive interaction between molybdenum and selenium (both p-values = 0.04).

CONCLUSION

Employing different statistical methods, we found consistent evidence of higher gestational glucose levels associated with higher copper and potential synergism between zinc and copper on glucose levels.

Plasma metals and serum bilirubin levels in workers from manganese-exposed workers healthy cohort (MEWHC)

Few studies specifically address the possible associations between multiple-metal exposures and liver damage among the occupational population. This study aimed to explore the cross-sectional relationships of plasma metals with liver function parameters. For 571 on-the-spot workers in the manganese-exposed workers healthy cohort (MEWHC), we determined liver function parameters: total bilirubin (TBILI), direct bilirubin (DBILI), indirect bilirubin (IBILI), alanine transaminase (ALT) and aspartate transaminase (AST). Total concentrations of 22 plasma metals were measured by ICP-MS. The LASSO (least absolute shrinkage and selection operator) penalized regression model was applied for selecting plasma metals independently associated with liver function parameters. Multiple linear regression analyses and restricted cubic spline (RCS) were utilized for identifying the exposure-response relationship of plasma metals with liver function parameters. After adjusting for covariates and selected metals, a 1-SD increase in log-10 transformed levels of iron was associated with increases in the levels of TBILI, DBILI and IBILI by 20.3%, 12.1% and 23.7%, respectively; similar increases in molybdenum for decreases in levels of TBILI, DBILI and IBILI by 6.1%, 2.6% and 8.3%, respectively. The effect of a 1-SD increase in plasma copper corresponded decreases of 3.2%, 3.4% and 5.0% in TBILI, AST and ALT levels, respectively. The spline analyses further clarified the non-linear relationships between plasma iron and bilirubin whilst negative linear relationships for plasma molybdenum and bilirubin. Plasma iron was positively whilst plasma molybdenum was negatively associated with increased serum bilirubin levels. Further studies are needed to validate these associations and uncover the underlying mechanisms.

Combined effects of multiple prenatal exposure to pollutants on birth weight: The Mothers and Children's Environmental Health (MOCEH) study

BACKGROUND

Increasing evidence shows that prenatal environmental exposures is a risk factor for restricted intrauterine growth. However, only a few studies have examined the effects of multiple environmental exposures on fetal growth.

OBJECTIVE

To investigate the effects of prenatal exposure on multiple environmental pollutants (heavy metals, bisphenol, phthalates, and air pollutants) on birth weight.

METHODS

The Mothers and Children's Environmental Health study is a prospective birth cohort comprising a total of 719 mother-child pairs, including 466 pairs undergoing early pregnancy exposure and 542 pairs of late pregnancy exposure. The concentrations of three heavy metals (mercury, lead, and cadmium) in the maternal blood samples were measured. The concentrations of three phthalate metabolites [mono(2-ethyl-5-hydroxyhexyl) phthalate, mono(2-ethyl-5-oxohexyl) phthalate, and mono-n-butyl phthalate] and bisphenol A in maternal urine samples were measured. Daily exposure to ambient particulate matter (PM10) and nitrogen dioxide (NO2) exposure was estimated based on residence and averaged by gestational age. To assess the combined effect of multiple pollutants, principal components analysis (PCA) and supervised principal components analysis (SCPA) were conducted.

RESULTS

Based on PCA, the components representing PM10 and NO2 exposure during early pregnancy were significantly associated with birth weight of -32.68 g (95% CI: -64.45 g to -0.91 g) per unit increase of the corresponding component. In SCPA model, the components representing NO2 exposure during early pregnancy and the combined exposure to mercury and lead during late pregnancy were negatively associated with birth weight of -46.63 g (95% CI: -90.65 g to -2.62 g) and -55.32 g (95% CI: -99.01 g to -11.64 g), respectively, per unit increase of the corresponding component.

CONCLUSION

Based on our multi-pollutant model, PM10 and NO2 exposure in early pregnancy and the combined effect of Pb and Hg in late pregnancy were associated with reduced birth weight. Our results suggest that exposure to various pollutants during pregnancy has a significant cumulative effect on birth weight, even if each pollutant is at a level below the concentration required for direct effect.

Analyzing Metabolomics Data for Environmental Health and Exposome Research

The exposome is the cumulative measure of environmental influences and associated biological responses across the life span, with critical relevance for understanding how exposures can impact human health. Metabolomics analysis of biological samples offers unique advantages for examining the exposome. Simultaneous analysis of external exposures, biological responses, and host susceptibility at a systems level can help establish links between external exposures and health outcomes. As metabolomics technologies continue to evolve for the study of the exposome, metabolomics ultimately will help provide valuable insights for exposure risk assessment, and disease prevention and management. Here, we discuss recent advances in metabolomics, and describe data processing protocols that can enable analysis of the exposome. This chapter focuses on using liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics for analysis of the exposome, including (1) preprocessing of untargeted metabolomics data, (2) identification of exposure chemicals and their metabolites, and (3) methods to establish associations between exposures and diseases.

Critical assessment and integration of separate lines of evidence for risk assessment of chemical mixtures

Humans are exposed to multiple chemicals on a daily basis instead of to just a single chemical, yet the majority of existing toxicity data comes from single-chemical exposure. Multiple factors must be considered such as the route, concentration, duration, and the timing of exposure when determining toxicity to the organism. The need for adequate model systems (in vivo, in vitro, in silico and mathematical) is paramount for better understanding of chemical mixture toxicity. Currently, shortcomings plague each model system as investigators struggle to find the appropriate balance of rigor, reproducibility and appropriateness in mixture toxicity studies. Significant questions exist when comparing single-to mixture-chemical toxicity concerning additivity, synergism, potentiation, or antagonism. Dose/concentration relevance is a major consideration and should be subthreshold for better accuracy in toxicity assessment. Previous work was limited by the technology and methodology of the time, but recent advances have resulted in significant progress in the study of mixture toxicology. Novel technologies have added insight to data obtained from in vivo studies for predictive toxicity testing. These include new in vitro models: omics-related tools, organs-on-a-chip and 3D cell culture, and in silico methods. Taken together, all these modern methodologies improve the understanding of the multiple toxicity pathways associated with adverse outcomes (e.g., adverse outcome pathways), thus allowing investigators to better predict risks linked to exposure to chemical mixtures. As technology and knowledge advance, our ability to harness and integrate separate streams of evidence regarding outcomes associated with chemical mixture exposure improves. As many national and international organizations are currently stressing, studies on chemical mixture toxicity are of primary importance.

Unexplained Variance in Hydration Study

With the collection of water-intake data, the National Health and Nutrition Examination Survey (NHANES) is becoming an increasingly popular resource for large-scale inquiry into human hydration. However, are we leveraging this resource properly? We sought to identify the opportunities and limitations inherent in hydration-related inquiry within a commonly studied database of hydration and nutrition. We also sought to critically review models published from this dataset. We reproduced two models published from the NHANES dataset, assessing the goodness of fit through conventional means (proportion of variance, R²). We also assessed model sensitivity to parameter configuration. Models published from the NHANES dataset typically yielded a very low goodness of fit R² < 0.15. A reconfiguration of variables did not substantially improve model fit, and the goodness of fit of models published from the NHANES dataset may be low. Database-driven inquiry into human hydration requires the complete reporting of model diagnostics in order to fully contextualize findings. There are several emergent opportunities to potentially increase the proportion of explained variance in the NHANES dataset, including novel biomarkers, capturing situational variables (meteorology, for example), and consensus practices for adjustment of co-variates.

High plasma concentrations of organic pollutants negatively impact survival in amyotrophic lateral sclerosis

OBJECTIVE

To determine whether persistent organic pollutants (POP) affect amyotrophic lateral sclerosis (ALS) survival.

METHODS

ALS participants seen at the University of Michigan (Ann Arbor, MI, USA) provided plasma samples for measurement of POPs. ALS disease and clinical features were collected prospectively from the medical records. Survival models used a composite summary measure of exposure due to multiple POPs (environmental risk score or ERS).

RESULTS

167 participants (40.7% female, n=68) with ALS were recruited, of which 119 died during the study period. Median diagnostic age was 60.9 years (IQR 52.7-68.2), median time from symptom onset to diagnosis was 1.01 years (IQR 0.67-1.67), bulbar onset 28.7%, cervical onset 33.5% and lumbar onset 37.7%. Participants in the highest quartile of ERS (representing highest composite exposure), adjusting for age at diagnosis, sex and other covariates had a 2.07 times greater hazards rate of mortality (p=0.018, 95% CI 1.13 to 3.80) compared with those in the lowest quartile. Pollutants with the largest contribution to the ERS were polybrominated diphenyl ethers 154 (HR 1.53, 95% CI 0.90 to 2.61), polychlorinated biphenyls (PCB) 118 (HR 1.50, 95% CI 0.95 to 2.39), PCB 138 (HR 1.69, 95% CI 0.99 to 2.90), PCB 151 (HR 1.46, 95% CI 1.01 to 2.10), PCB 175 (HR 1.53, 95% CI 0.98 to 2.40) and p,p'-DDE (HR 1.39, 95% CI 1.07 to 1.81).

CONCLUSIONS

Higher concentrations of POPs in plasma are associated with reduced ALS survival, independent of age, gender, segment of onset and other covariates. This study helps characterise and quantify the combined effects of POPs on ALS and supports the concept that environmental exposures play a role in disease pathogenesis.