Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Independent and joint effects of neighborhood-level environmental and socioeconomic exposures on body mass index in early childhood: The environmental influences on child health outcomes (ECHO) cohort

Past studies support the hypothesis that the prenatal period influences childhood growth. However, few studies explore the joint effects of exposures that occur simultaneously during pregnancy. To explore the feasibility of using mixtures methods with neighborhood-level environmental exposures, we assessed the effects of multiple prenatal exposures on body mass index (BMI) from birth to age 24 months. We used data from two cohorts: Healthy Start (n = 977) and Maternal and Developmental Risks from Environmental and Social Stressors (MADRES; n = 303). BMI was measured at delivery and 6, 12, and 24 months and standardized as z-scores. We included variables for air pollutants, built and natural environments, food access, and neighborhood socioeconomic status (SES). We used two complementary statistical approaches: single-exposure linear regression and quantile-based g-computation. Models were fit separately for each cohort and time point and were adjusted for relevant covariates. Single-exposure models identified negative associations between NO2 and distance to parks and positive associations between low neighborhood SES and BMI z-scores for Healthy Start participants; for MADRES participants, we observed negative associations between O3 and distance to parks and BMI z-scores. G-computations models produced comparable results for each cohort: higher exposures were generally associated with lower BMI, although results were not significant. Results from the g-computation models, which do not require a priori knowledge of the direction of associations, indicated that the direction of associations between mixture components and BMI varied by cohort and time point. Our study highlights challenges in assessing mixtures effects at the neighborhood level and in harmonizing exposure data across cohorts. For example, geospatial data of neighborhood-level exposures may not fully capture the qualities that might influence health behavior. Studies aiming to harmonize geospatial data from different geographical regions should consider contextual factors when operationalizing exposure variables.

The Role of Neighborhood Air Pollution in Disparate Racial and Ethnic Asthma Acute Care Use

Rationale: The share of Black or Latinx residents in a census tract remains associated with asthma-related emergency department (ED) visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to the within-city heterogeneity of long-term air pollution exposure remains unclear. Objectives: To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity. Methods: An administrative database was used to define census tract population-based incidence rates of asthma-related ED visits. We estimate the associations between census tract incidence rates and 1) average fine and coarse particulate matter, nitrogen dioxide (NO2), and sulfur dioxide (SO2), and 2) racial and ethnic composition using generalized linear models controlling for socioeconomic and housing covariates. We also examine for the attenuation of incidence risk ratios (IRRs) associated with race/ethnicity when controlling for air pollution exposure. Measurements and Main Results: Fine and coarse particulate matter and SO2 are all associated with census tract-level incidence rates of asthma-related ED visits, and multipollutant models show evidence of independent risk associated with coarse particulate matter and SO2. The association between census tract incidence rate and Black resident share (IRR, 1.51 [credible interval (CI), 1.48-1.54]) is attenuated by 24% when accounting for air pollution (IRR, 1.39 [CI, 1.35-1.42]), and the association with Latinx resident share (IRR, 1.11 [CI, 1.09-1.13]) is attenuated by 32% (IRR, 1.08 [CI, 1.06-1.10]). Conclusions: Neighborhood-level rates of asthma acute care use are associated with local air pollution. Controlling for air pollution attenuates associations with census tract racial/ethnic composition, suggesting that intracity variability in air pollution could contribute to neighborhood-to-neighborhood asthma morbidity disparities.

Combined Exposures and Mixtures Research: An Enduring NIEHS Priority

BACKGROUND

The National Institute of Environmental Health Sciences (NIEHS) continues to prioritize research to better understand the health effects resulting from exposure to mixtures of chemical and nonchemical stressors. Mixtures research activities over the last decade were informed by expert input during the development and deliberations of the 2011 NIEHS Workshop "Advancing Research on Mixtures: New Perspectives and Approaches for Predicting Adverse Human Health Effects." NIEHS mixtures research efforts since then have focused on key themes including a) prioritizing mixtures for study, b) translating mixtures data from in vitro and in vivo studies, c) developing cross-disciplinary collaborations, d) informing component-based and whole-mixture assessment approaches, e) developing sufficient similarity methods to compare across complex mixtures, f) using systems-based approaches to evaluate mixtures, and g) focusing on management and integration of mixtures-related data.

OBJECTIVES

We aimed to describe NIEHS driven research on mixtures and combined exposures over the last decade and present areas for future attention.

RESULTS

Intramural and extramural mixtures research projects have incorporated a diverse array of chemicals (e.g., polycyclic aromatic hydrocarbons, botanicals, personal care products, wildfire emissions) and nonchemical stressors (e.g., socioeconomic factors, social adversity) and have focused on many diseases (e.g., breast cancer, atherosclerosis, immune disruption). We have made significant progress in certain areas, such as developing statistical methods for evaluating multiple chemical associations in epidemiology and building translational mixtures projects that include both in vitro and in vivo models.

DISCUSSION

Moving forward, additional work is needed to improve mixtures data integration, elucidate interactions between chemical and nonchemical stressors, and resolve the geospatial and temporal nature of mixture exposures. Continued mixtures research will be critical to informing cumulative impact assessments and addressing complex challenges, such as environmental justice and climate change. https://doi.org/10.1289/EHP14340.

Power Analysis of Exposure Mixture Studies via Monte Carlo Simulations

Estimating sample size and statistical power is an essential part of a good epidemiological study design. Closed-form formulas exist for simple hypothesis tests but not for advanced statistical methods designed for exposure mixture studies. Estimating power with Monte Carlo simulations is flexible and applicable to these methods. However, it is not straightforward to code a simulation for non-experienced programmers and is often hard for a researcher to manually specify multivariate associations among exposure mixtures to set up a simulation. To simplify this process, we present the R package mpower for power analysis of observational studies of environmental exposure mixtures involving recently-developed mixtures analysis methods. The components within mpower are also versatile enough to accommodate any mixtures methods that will developed in the future. The package allows users to simulate realistic exposure data and mixed-typed covariates based on public data set such as the National Health and Nutrition Examination Survey or other existing data set from prior studies. Users can generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This paper presents tutorials and examples of power analysis using mpower.

Estimating effects of longitudinal and cumulative exposure to PFAS mixtures on early adolescent body composition

Few methods have been used to characterize repeatedly measured biomarkers of chemical mixtures. We applied latent profile analysis (LPA) to serum concentrations of 4 perfluoroalkyl and polyfluoroalkyl substances (PFAS) measured at 4 time points from gestation to age 12 years. We evaluated the relationships between profiles and z scores of height, body mass index, fat mass index, and lean body mass index at age 12 years (n = 218). We compared LPA findings with an alternative approach for cumulative PFAS mixtures using g-computation to estimate the effect of simultaneously increasing the area under the receiver operating characteristic curve (AUC) for all PFAS. We identified 2 profiles: a higher PFAS profile (35% of sample) and a lower PFAS profile (relative to each other), based on their average PFAS concentrations at all time points. The higher PFAS profile had generally lower z scores for all outcomes, with somewhat larger effects for males, though all 95% CIs crossed the null. For example, the higher PFAS profile was associated with a 0.50-unit lower (β = -0.50; 95% CI, -1.07 to 0.08) BMI z score among males but not among females (β = 0.04; 95% CI, -0.45 to 0.54). We observed similar patterns with AUCs. We found that a higher childhood PFAS profile and higher cumulative PFAS mixtures may be associated with altered growth in early adolescence. This article is part of a Special Collection on Environmental Epidemiology.

Contributions of Synthetic Chemicals to Autoimmune Disease Development and Occurrence

PURPOSE OF REVIEW

Exposure to many synthetic chemicals has been linked to a variety of adverse human health effects, including autoimmune diseases. In this scoping review, we summarize recent evidence detailing the effects of synthetic environmental chemicals on autoimmune diseases and highlight current research gaps and recommendations for future studies.

RECENT FINDINGS

We identified 68 recent publications related to environmental chemical exposures and autoimmune diseases. Most studies evaluated exposure to persistent environmental chemicals and autoimmune conditions including rheumatoid arthritis (RA), systemic lupus (SLE), systemic sclerosis (SSc), and ulcerative colitis (UC) and Crohn's disease. Results of recent original research studies were mixed, and available data for some exposure-outcome associations were particularly limited. PFAS and autoimmune inflammatory bowel diseases (UC and CD) and pesticides and RA appeared to be the most frequently studied exposure-outcome associations among recent publications, despite a historical research focus on solvents. Recent studies have provided additional evidence for the associations of exposure to synthetic chemicals with certain autoimmune conditions. However, impacts on other autoimmune outcomes, particularly less prevalent conditions, remain unclear. Owing to the ubiquitous nature of many of these exposures and their potential impacts on autoimmune risk, additional studies are needed to better evaluate these relationships, particularly for understudied autoimmune conditions. Future research should include larger longitudinal studies and studies among more diverse populations to elucidate the temporal relationships between exposure-outcome pairs and to identify potential population subgroups that may be more adversely impacted by immune modulation caused by exposure to these chemicals.

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.

Epidemic-specific association of maternal exposure to per- and polyfluoroalkyl substances (PFAS) and their components with maternal glucose metabolism: A cross-sectional analysis in a birth cohort from Hong Kong

Per- and polyfluoroalkyl substances (PFAS) are persistent chemicals that have been linked to increased risk of gestational diabetes mellitus (GDM) and may affect glucose metabolisms during pregnancy. We examined the associations between maternal PFAS exposure and maternal glucose metabolisms and GDM risk among 1601 mothers who joined the Hyperglycaemia-and-Adverse-Pregnancy-Outcome (HAPO) Study in Hong Kong in 2001-2006. All mothers underwent a 75 g-oral-glucose-tolerance test at 24-32 weeks of gestation. We measured serum concentrations of six PFAS biomarkers using high-performance liquid-chromatography-coupled-with-tandem-mass-spectrometry (LC-MS-MS). We fitted conventional and advanced models (quantile-g-computation [qgcomp] and Bayesian-kernel machine regression [BKMR]) to assess the associations of individual and a mixture of PFAS with glycaemic traits. Subgroup analyses were performed based on the enrollment period by the severe-acute-respiratory-syndrome (SARS) epidemic periods in Hong Kong between March 2003 and May 2004. PFOS and PFOA were the main components of PFAS mixture among 1601 pregnant women in the Hong Kong HAPO study, with significantly higher median PFOS concentrations (19.09 ng/mL), compared to Chinese pregnant women (9.40 ng/mL) and US women (5.27 ng/mL). Maternal exposure to PFAS mixture was associated with higher HbA1c in the qgcomp (β = 0.04, 95 % CI: 0.01-0.06) model. We did not observe significant associations of PFAS mixture with fasting plasma glucose (PG), 1-h and 2-h PG in either model, except for 2-h PG in the qgcmop model (β = 0.074, 95 % CI: 0.01-0.15). PFOS was the primary contributor to the overall positive effects on HbA1c. Epidemic-specific analyses showed specific associations between PFAS exposure and the odds of GDM in the pre-SARS epidemic period. The median concentration of PFOS was highest during the peri-SARS epidemic (21.2 [14.5-43.6] ng/mL) compared with the pre-SARS (12.3 [9.2-19.9] ng/mL) and post-SARS (20.3 [14.2-46.3] ng/mL) epidemic periods. Potential interactions and exposure-response relationships between PFOA and PFNA with elevated HbA1c were observed in the peri-SARS period in BKMR model. Maternal exposure to PFAS mixture was associated with altered glucose metabolism during pregnancy. SARS epidemic-specific associations call for further studies on its long-term adverse health effects, especially potential modified associations by lifestyle changes during the COVID-19 pandemic.

Statistical methods for chemical mixtures: a roadmap for practitioners

Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. With many existing statistical methods and emerging approaches, it is important for practitioners to understand when each method is best suited for their inferential goals. In this study, we conduct a review and comparison of 11 analytical methods available for use in mixtures research, through extensive simulation studies for continuous and binary outcomes. These methods fall in three different classes: identifying important components of a mixture, identifying interactions and creating a summary score for risk stratification and prediction. We carry out an illustrative data analysis in the PROTECT birth cohort from Puerto Rico. Most importantly we develop an integrated package "CompMix" that provides a platform for mixtures analysis where the practitioner can implement a pipeline for several types of mixtures analysis. Our simulation results suggest that the choice of methods depends on the goal of analysis and there is no clear winner across the board. For selection of important toxicants in the mixture and for identifying interactions, Elastic net by Zou et al. (Enet), Lasso for Hierarchical Interactions by Bien et al (HierNet), Selection of nonlinear interactions by a forward stepwise algorithm by Narisetty et al. (SNIF) have the most stable performance across simulation settings. Additionally, the predictive performance of the Super Learner ensembling method by Van de Laan et al. and HierNet are found to be superior to the rest of the methods. For overall summary or a cumulative measure, we find that using the Super Learner to combine multiple Environmental Risk Scores can lead to improved risk stratification properties. We have developed an R package "CompMix: A comprehensive toolkit for environmental mixtures analysis", allowing users to implement a variety of tasks under different settings and compare the findings. In summary, our study offers guidelines for selecting appropriate statistical methods for addressing specific scientific questions related to mixtures research. We identify critical gaps where new and better methods are needed.

Associations of maternal exposure to multiple plasma trace elements with the prevalence of fetal congenital heart defects: A nested case-control study

BACKGROUND

Scanty knowledge prevails regarding the combined impact of multiple plasma trace elements and main contributors on the prevalence of congenital heart defects (CHDs) in offspring. Thus, we performed a nested case-control analysis in a neonates cohort to investigate this important public health issue.

METHODS

We selected 164 pairs of cases and non-malformed controls from live births registered in the parent cohort (n = 11,578) at the same hospital. Plasma levels of 14 trace elements were determined by inductively coupled plasma-mass spectrometry. The odds ratios (ORs) of exposure were compared between cases and controls. Bayesian Kernel Machine Regression (BKMR) and Quantile g-Computation (QgC) models were employed to assess the cumulative effect of exposure to trace elements.

RESULTS

We found positive associations and linear dose-response relationships between plasma Pb and Sn and CHD. BKMR models indicated that the overall effect of the trace element mixture was associated with CHDs below the 45th percentile or above the 50th percentile, and the combined effect was primarily attributed to Sn and Pb. The QgC model indicated significantly increased odds of CHD with simultaneous exposure to all studied trace elements (OR: 2.19, 95%CI: 1.44-3.33).

CONCLUSIONS

This study is the first to report an association between elevated levels of mixed trace elements in maternal plasma with an increased prevalence of fetal CHDs, particularly in the case of Pb and Sn. Findings from this study provide further evidence of the important of heavy metal pollution to human health, and can help stakeholders prioritize policies and develop interventions to target the leading contributors to human exposure.

Exploring the association between two groups of metals with potentially opposing renal effects and renal function in middle-aged and older adults: Evidence from an explainable machine learning method

BACKGROUND

Machine learning models have promising applications in capturing the complex relationship between mixtures of exposures and outcomes.

OBJECTIVE

Our study aimed at introducing an explainable machine learning (EML) model to assess the association between metal mixtures with potentially opposing renal effects and renal function in middle-aged and older adults.

METHODS

This study extracted data from two cycle years of the National Health and Nutrition Examination Survey (NHANES). Participants aged 45 years or older with complete data on six metals (lead, cadmium, manganese, mercury, and selenium) and related covariates were enrolled. The EML model was developed by the optimized machine learning model together with Shapley Additive exPlanations (SHAP) to assess the chronic kidney disease (CKD) risk with metal mixtures. The results from EML were further compared in detail with multiple logistic regression (MLR) and Bayesian kernel machine regression (BKMR).

RESULTS

After adjusting for included covariates, MLR pointed out the lead and arsenic were generally positively associated with CKD, but manganese had a negative association. In the BKMR analysis, each metal was found to have a non-linear association with the risk of CKD, and interactions can exist between metals, especially for arsenic and lead. The EML ranked the feature importance: lead, manganese, arsenic and selenium were close behind in importance after gender, age or BMI for participants with CKD. Strong interactions between mercury and lead, manganese and cadmium and arsenic and manganese were identified by partial dependence plot (PDP) of SHAP and bivariate exposure-response effect plots of BKMR. The EML model determined the "trigger point" at which the risk of CKD abruptly changed.

CONCLUSION

Co-exposure to metals with different nephrotoxicity could have different joint association with renal function, and EML can be a powerful method for studying complex exposure mixtures.

Association of postnatal exposure to mixture of bisphenol A, Di-n-butyl phthalate and Di-(2-ethylhexyl) phthalate with Children's IQ at 5 Years of age: Mothers and Children's environmental health (MOCEH) study

Early childhood is important for neurodevelopment, and exposure to endocrine disruptors such as bisphenol A (BPA) and phthalates in this period may cause neurodevelopmental disorders and delays. The present study examined the association between exposure to mixtures of BPA and three metabolites of phthalates in early childhood and IQ at 5 years of age. The Mother and Children's Environmental Health (MOCEH) study is a prospective birth cohort study conducted in Korea with 1751 pregnant women enrolled from 2006 to 2010. After excluding those without relevant data, 47 children were included in the final analysis. We measured children's urinary concentrations of metabolites of endocrine-disrupting chemicals (Bisphenol A, mono-(2-ethyl-5-oxohexyl) phthalate, mono-(2-ethyl-5-hydroxyhexyl) phthalate and mono-(2-ethyl-5-butyl) phthalate) at ages of 24 and 36 months. We evaluated the children's IQ with the Korean Wechsler Intelligence Test at the age of 5 years. After adjusting for potential confounders, a multiple linear regression was conducted to examine the associations between individual endocrine-disrupting chemicals and the IQ of the children. Weighted Quantile Sum (WQS) regression and quantile-based g-computation were used to assess the association between IQ at age 5 and exposure to mixtures of BPA and phthalates. In the single-chemical analyses, mono-(2-ethyl-5-butyl) phthalate exposure at 36 months was adversely associated with children's IQ (β = -4.93, 95% confidence interval (CI): -9.22, -0.64). In the WQS regression and quantile-based g-computation analyses, exposure to the mixture of BPA and phthalates was associated with lower IQ [β = -9.13 (P-value = 0.05) and β = -9.18 (P-value = 0.05), respectively]. The largest contributor to the overall association was exposure to mono-(2-ethyl-5-butyl) phthalate at 36 months. In the present study, postnatal exposure to mixtures of BPA and three metabolites of phthalates was associated with decreased IQ of children at age 5.

Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment

A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD.

Prenatal Lead Exposure Is Associated with Reduced Abundance of Beneficial Gut Microbial Cliques in Late Childhood: An Investigation Using Microbial Co-Occurrence Analysis (MiCA)

Many analytical methods used in gut microbiome research focus on either single bacterial taxa or the whole microbiome, ignoring multibacteria relationships (microbial cliques). We present a novel analytical approach to identify microbial cliques within the gut microbiome of children at 9-11 years associated with prenatal lead (Pb) exposure. Data came from a subset of participants (n = 123) in the Programming Research in Obesity, Growth, Environment and Social Stressors cohort. Pb concentrations were measured in maternal whole blood from the second and third trimesters of pregnancy. Stool samples collected at 9-11 years old underwent metagenomic sequencing to assess the gut microbiome. Using a novel analytical approach, Microbial Co-occurrence Analysis (MiCA), we paired a machine learning algorithm with randomization-based inference to first identify microbial cliques that were predictive of prenatal Pb exposure and then estimate the association between prenatal Pb exposure and microbial clique abundance. With second-trimester Pb exposure, we identified a two-taxa microbial clique that included Bifidobacterium adolescentis and Ruminococcus callidus and a three-taxa clique that also included Prevotella clara. Increasing second-trimester Pb exposure was associated with significantly increased odds of having the two-taxa microbial clique below the median relative abundance (odds ratio (OR) = 1.03, 95% confidence interval (CI) [1.01-1.05]). Using a novel combination of machine learning and causal inference, MiCA identified a significant association between second-trimester Pb exposure and the reduced abundance of a probiotic microbial clique within the gut microbiome in late childhood.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 μg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Systematic evidence mapping informs a class-based approach to assessing personal care products and pubertal timing

BACKGROUND

Personal care products (PCPs) contain many different compounds and are a source of exposure to endocrine disrupting chemicals (EDCs), including phthalates and phenols. Early-life exposure to EDCs commonly found in PCPs has been linked to earlier onset of puberty.

OBJECTIVE

To characterize the human and animal evidence on the association between puberty-related outcomes and exposure to PCPs and their chemical constituents and, if there is sufficient evidence, identify groups of chemicals and outcomes to support a systematic review for a class-based hazard or risk assessment.

METHODS

We followed the OHAT systematic review framework to characterize the human and animal evidence on the association between puberty-related health outcomes and exposure to PCPs and their chemical constituents.

RESULTS

Ninety-eight human and 299 animal studies that evaluated a total of 96 different chemicals were identified and mapped by key concepts including chemical class, data stream, and puberty-related health outcome. Among these studies, phthalates and phenols were the most well-studied chemical classes. Most of the phthalate and phenol studies examined secondary sex characteristics and changes in estradiol and testosterone levels. Studies evaluating PCP use and other chemical classes (e.g., parabens) had less data.

CONCLUSIONS

This systematic evidence map identified and mapped the published research evaluating the association between exposure to PCPs and their chemical constituents and puberty-related health outcomes. The resulting interactive visualization allows researchers to make evidence-based decisions on the available research by enabling them to search, sort, and filter the literature base of puberty-related studies by key concepts. This map can be used by researchers and regulators to prioritize and target future research and funding to reduce uncertainties and address data gaps. It also provides information to inform a class-based hazard or risk assessment on the association between phthalate and phenol exposures and puberty-related health outcomes.

In Utero Exposure to Metals and Birth Outcomes in an Artisanal and Small-Scale Gold Mining Birth Cohort in Madre de Dios, Peru

BACKGROUND

Few birth cohorts in South America evaluate the joint effect of minerals and toxic metals on neonatal health. In Madre de Dios, Peru, mercury exposure is prevalent owing to artisanal gold mining, yet its effect on neonatal health is unknown.

OBJECTIVES

We aimed to determine whether toxic metals are associated with lower birth weight and shorter gestational age independently of antenatal care and other maternal well-being factors.

METHODS

Data are from the COhorte de NAcimiento de MAdre de Dios (CONAMAD) birth cohort, which enrolled pregnant women in Madre de Dios prior to their third trimester and obtained maternal and cord blood samples at birth. We use structural equation models (SEMs) to construct latent variables for the maternal metals environment (ME) and the fetal environment (FE) using concentrations of calcium, iron, selenium, zinc, magnesium, mercury, lead, and arsenic measured in maternal and cord blood, respectively. We then assessed the relationship between the latent variables ME and FE, toxic metals, prenatal visits, hypertension, and their effect on gestational age and birth weight.

RESULTS

Among 198 mothers successfully enrolled and followed at birth, 29% had blood mercury levels that exceeded the U.S. Centers for Disease Control and Prevention threshold of 5.8 μ g / L and 2 mothers surpassed the former 5 - μ g / dL threshold for blood lead. The current threshold value is 3.5 μ g / dL . Minerals and toxic metals loaded onto ME and FE latent variables. ME was associated with FE (β = 0.24; 95% CI: 0.05, 0.45). FE was associated with longer gestational age (β = 2.31; 95% CI: - 0.3 , 4.51) and heavier birth weight. Mercury exposure was not directly associated with health outcomes. A 1% increase in maternal blood lead shortened gestational age by 0.05 d (β = - 0.75 ; 95% CI: - 1.51 , - 0.13 ), which at the 5 - μ g / dL threshold resulted in a loss of 3.6 gestational days and 76.5 g in birth weight for newborns. Prenatal care visits were associated with improved birth outcomes, with a doubling of visits from 6 to 12 associated with 5.5 more gestational days (95% CI: 1.6, 9.4) and 319 g of birth weight (95% CI: 287.6, 350.7).

DISCUSSION

Maternal lead, even at low exposures, was associated with shorter gestation and lower birth weight. Studies that focus only on harmful exposures or nutrition may mischaracterize the dynamic maternal ME and FE. SEMs provide a framework to evaluate these complex relationships during pregnancy and reduce overcontrolling that can occur with linear regression. https://doi.org/10.1289/EHP10557.

Cytokines as mediators of the associations of prenatal exposure to phenols, parabens, and phthalates with internalizing behaviours at age 3 in boys: A mixture exposure and mediation approach

Childhood internalizing disorders refer to inwardly focused negative behaviours such as anxiety, depression, and somatic complains. Interactions between psychosocial, genetic, and environmental risk factors adversely impact neurodevelopment and can contribute to internalizing disorders. While prenatal exposure to single endocrine disruptors (EDs) is associated with internalizing behaviours in infants, the associations with prenatal exposure to EDs in mixture remain poorly addressed. In addition, the biological mediators of EDs in mixture effects on internalizing behaviours remain unexplored. EDs do not only interfere with endocrine function, but also with immune function and inflammatory processes. Based on this body of evidence, we hypothetised that inflammation at birth is a plausible biological pathway through which prenatal exposure to EDs in mixture could operate to influence offspring internalizing behaviours. Based on the EDEN birth cohort, we investigated whether exposure to a mixture of EDs increased the odds of internalizing disorders in 459 boy infants at age 3, and whether the pro-inflammatory cytokines IL-1β, IL-6, and TNF-α measured at birth were mediators of this effect. To determine both the joint and individual associations of prenatal exposure to EDs with infant internalizing behaviours and the possible mediating role of cytokines, we used the counterfactual hierarchical Bayesian Kernel Machine Regression (BKMR) regression-causal mediation analysis. We show that prenatal exposure to a complex mixture of EDs has limited effects on internalizing behaviours in boys at age 3. We also show that IL-1β, IL-6, and TNF-α are unlikely mediators or suppressors of ED mixture effects on internalizing behaviours in boys at age 3. Further studies on larger cohorts are warranted to refine the deleterious effects of EDs in mixtures on internalizing behaviours and identify possible mediating pathways.

Prenatal Pb exposure is associated with reduced abundance of beneficial gut microbial cliques in late childhood: an investigation using Microbial Co-occurrence Analysis (MiCA)

Background

Many analytical methods used in gut microbiome research focus on either single bacterial taxa or the whole microbiome, ignoring multi-bacteria relationships (microbial cliques). We present a novel analytical approach to identify multiple bacterial taxa within the gut microbiome of children at 9-11 years associated with prenatal Pb exposure.

Methods

Data came from a subset of participants (n=123) in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort. Pb concentrations were measured in maternal whole blood from the second and third trimesters of pregnancy. Stool samples collected at 9-11 years old underwent metagenomic sequencing to assess the gut microbiome. Using a novel analytical approach, Microbial Co-occurrence Analysis (MiCA), we paired a machine-learning algorithm with randomization-based inference to first identify microbial cliques that were predictive of prenatal Pb exposure and then estimate the association between prenatal Pb exposure and microbial clique abundance.

Results

With second-trimester Pb exposure, we identified a 2-taxa microbial clique that included Bifidobacterium adolescentis and Ruminococcus callidus, and a 3-taxa clique that added Prevotella clara. Increasing second-trimester Pb exposure was associated with significantly increased odds of having the 2-taxa microbial clique below the 50th percentile relative abundance (OR=1.03,95%CI[1.01-1.05]). In an analysis of Pb concentration at or above vs. below the United States and Mexico guidelines for child Pb exposure, odds of the 2-taxa clique in low abundance were 3.36(95%CI[1.32-8.51]) and 6.11(95%CI[1.87-19.93]), respectively. Trends were similar with the 3-taxa clique but not statistically significant.

Discussion

Using a novel combination of machine-learning and causal-inference, MiCA identified a significant association between second-trimester Pb exposure and reduced abundance of a probiotic microbial clique within the gut microbiome in late childhood. Pb exposure levels at the guidelines for child Pb poisoning in the United States, and Mexico are not sufficient to protect against the potential loss of probiotic benefits.

Face-to-face with scorching wildfire: potential toxicant exposure and the health risks of smoke for wildland firefighters at the wildland-urban interface

As wildfire risks have elevated due to climate change, the health risks that toxicants from fire smoke pose to wildland firefighters have been exacerbated. Recently, the International Agency for Research on Cancer (IARC) has reclassified wildland firefighters' occupational exposure as carcinogenic to humans (Group 1). Wildfire smoke contributes to an increased risk of cancer and cardiovascular disease, yet wildland firefighters have inadequate respiratory protection. The economic cost of wildland fires has risen concurrently, as illustrated by the appropriation of $45 billion for wildfire management over FYs 2011-2020 by the U.S. Congress. Occupational epidemiological studies of wildland firefighters are crucial for minimizing health risks; however, they must account for the mixture of exposures in wildfire smoke. This review focuses on four aspects of wildland firefighters' health risks at the wildland-urban interface: 1) economic costs and health impact, 2) respiratory protection, 3) multipollutant mixtures, and 4) proactive management of wildfires.

Evaluating Chemical Mixtures in Epidemiological Studies to Inform Regulatory Decisions

BACKGROUND

Epidemiological studies are increasingly going beyond the evaluation of health effects of individual chemicals to consider chemical mixtures. To our knowledge, the advantages and disadvantages of addressing chemical mixtures for informing regulatory decisions-as opposed to obtaining a more comprehensive understanding of etiology-has not been carefully considered.

OBJECTIVES

We offer a framework for the study of chemical mixtures in epidemiological research intended to inform regulatory decisions. We identify a) the different ways mixtures originate (product source, pollution source, shared mode of action, or shared effect on health outcome), b) the use of indicator chemicals to address mixtures, and c) the requirements for epidemiological studies to be informative for regulatory purposes.

DISCUSSION

The principal advantage of considering mixtures is to obtain a more complete understanding of the role of the chemical environment as a determinant of health. Incorporating other exposures may improve the assessment of the net effect of the chemicals of interest. However, the increased complexity and potential loss of generalizability may limit the value of studies of mixtures, especially for mixtures based on mode of action or shared health outcomes. Our recommended strategy is to successively assess the marginal contribution of individual chemicals, joint effects with other specific chemicals, and hypothesis-driven evaluation of mixtures rather than applying hypothesis-free data exploration methods. Although more ambitious statistical approaches to mixtures may, in time, be helpful for guiding regulation, the authors believe conventional methods for assessing individual and combined effects of chemicals remain preferable. https://doi.org/10.1289/EHP11899.

Chemical Element Mixtures and Kidney Function in Mining and Non-Mining Settings in Northern Colombia

The exposure to chemical mixtures is a problem of concern in developing countries and it is well known that the kidney is the major target organ for toxic elements. This cross-sectional study aimed to estimate the individual and composite mixture effect of a large number of chemical elements on kidney function in gold-mining and surrounding non-mining populations in northeast Colombia. We measured concentrations of 36 chemical elements in hair as indicators of chronic exposure from 199 adult participants. We estimated the effect of exposure to mixtures of chemical elements on estimated glomerular filtration rate (eGFR) using weighted quantile sum regression (WQS). The WQS index of the mixture was associated with reduced eGFR (Coefficient -2.42; 95%CI: -4.69, -0.16) being Be, Cd, Pb, As, and Mn, the principal contributors of the toxic mixture. Mining activities and Hg concentration were not associated with decreased kidney function. Our results suggest that complex mixtures of chemical elements, mainly heavy metals, act as nephrotoxic in these populations and therefore the analysis of chemical element mixtures is a better approach to identify environmental and occupational chemical risks for kidney damage.

Single and Combined Associations of Plasma and Urine Essential Trace Elements (Zn, Cu, Se, and Mn) with Cardiovascular Risk Factors in a Mediterranean Population

Trace elements are micronutrients that are required in very small quantities through diet but are crucial for the prevention of acute and chronic diseases. Despite the fact that initial studies demonstrated inverse associations between some of the most important essential trace elements (Zn, Cu, Se, and Mn) and cardiovascular disease, several recent studies have reported a direct association with cardiovascular risk factors due to the fact that these elements can act as both antioxidants and pro-oxidants, depending on several factors. This study aims to investigate the association between plasma and urine concentrations of trace elements and cardiovascular risk factors in a general population from the Mediterranean region, including 484 men and women aged 18−80 years and considering trace elements individually and as joint exposure. Zn, Cu, Se, and Mn were determined in plasma and urine using an inductively coupled plasma mass spectrometer (ICP-MS). Single and combined analysis of trace elements with plasma lipid, blood pressure, diabetes, and anthropometric variables was undertaken. Principal component analysis, quantile-based g-computation, and calculation of trace element risk scores (TERS) were used for the combined analyses. Models were adjusted for covariates. In single trace element models, we found statistically significant associations between plasma Se and increased total cholesterol and systolic blood pressure; plasma Cu and increased triglycerides and body mass index; and urine Zn and increased glucose. Moreover, in the joint exposure analysis using quantile g-computation and TERS, the combined plasma levels of Zn, Cu, Se (directly), and Mn (inversely) were strongly associated with hypercholesterolemia (OR: 2.03; 95%CI: 1.37−2.99; p < 0.001 per quartile increase in the g-computation approach). The analysis of urine mixtures revealed a significant relationship with both fasting glucose and diabetes (OR: 1.91; 95%CI: 1.01−3.04; p = 0.046). In conclusion, in this Mediterranean population, the combined effect of higher plasma trace element levels (primarily Se, Cu, and Zn) was directly associated with elevated plasma lipids, whereas the mixture effect in urine was primarily associated with plasma glucose. Both parameters are relevant cardiovascular risk factors, and increased trace element exposures should be considered with caution.

Associations of exposure to metal and metal mixtures with thyroid hormones: Results from the NHANES 2007-2012

BACKGROUND

Disrupted thyroid homeostasis plays a role in neurocognitive dysfunction and metabolic disorders. Since individuals are exposed to multiple metals simultaneously, it is important to assess the effects of metal mixtures on thyroid hormone status. This study aimed to investigate the associations of metal mixtures and individual metals with thyroid hormone levels.

METHODS

Data included 2399 men and 1988 women from the 2007-2012 National Health and Nutrition Examination Survey (2007-2012). Thyroid hormones measured included total triiodothyronine (T3), total thyroxine (T4), free forms of T3 (FT3) and T4 (FT4), and thyroid stimulating hormone (TSH). We included twelve metals (arsenic, barium, cobalt, cesium, molybdenum, antimony, thallium, tungsten, and uranium from urine; cadmium, lead, and mercury from blood) in traditional linear regression models controlling for 12 metals simultaneously and in quantile-based g-computation (QGC) to assess the relative contribution of each metal as well as the overall association with thyroid hormones as a metal mixture.

RESULTS

There were associations of the total metal mixture with thyroid hormones for T3 (beta: -0.023, 95% CI: -0.04, -0.01, in women), T4 (beta: -0.03, 95% CI: -0.05, -0.01, in men; beta: -0.026, 95% CI: -0.04, -0.01, in women), and the T3:T4 ratio (beta: 0.026, 95% CI: 0.01, 0.05, in men). Arsenic had negative contributions to T3 and T4. Cadmium had a positive contribution to T4 but negative contributions to T3 and T3:T4. Lead had a positive contribution to T3 and T3:T4, but a negative contribution to T4.

CONCLUSION

Multiple metals as a mixture were associated with thyroid hormone levels. Arsenic, cadmium, and lead were individually associated with multiple thyroid hormones. Examination of associations of metal mixtures and individual metals with thyroid hormones can contribute to an understanding of thyroid hormone homeostasis and provide evidence for developing intervention and guidance for health promotion.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

Exposure to Metal Mixtures in Association with Cardiovascular Risk Factors and Outcomes: A Scoping Review

Since the National Institute of Environmental Health Sciences (NIEHS) declared conducting combined exposure research as a priority area, literature on chemical mixtures has grown dramatically. However, a systematic evaluation of the current literature investigating the impacts of metal mixtures on cardiovascular disease (CVD) risk factors and outcomes has thus far not been performed. This scoping review aims to summarize published epidemiology literature on the cardiotoxicity of exposure to multiple metals. We performed systematic searches of MEDLINE (PubMed), Scopus, and Web of Science to identify peer-reviewed studies employing statistical mixture analysis methods to evaluate the impact of metal mixtures on CVD risk factors and outcomes among nonoccupationally exposed populations. The search was limited to papers published on or after 1998, when the first dedicated funding for mixtures research was granted by NIEHS, through 1 October 2021. Twenty-nine original research studies were identified for review. A notable increase in relevant mixtures publications was observed starting in 2019. The majority of eligible studies were conducted in the United States (n = 10) and China (n = 9). Sample sizes ranged from 127 to 10,818. Many of the included studies were cross-sectional in design. Four primary focus areas included: (i) blood pressure and/or diagnosis of hypertension (n = 15), (ii) risk of preeclampsia (n = 3), (iii) dyslipidemia and/or serum lipid markers (n = 5), and (iv) CVD outcomes, including stroke incidence or coronary heart disease (n = 8). The most frequently investigated metals included cadmium, lead, arsenic, and cobalt, which were typically measured in blood (n = 15). The most commonly utilized multipollutant analysis approaches were Bayesian kernel machine regression (BKMR), weighted quantile sum regression (WQSR), and principal component analysis (PCA). To our knowledge, this is the first scoping review to assess exposure to metal mixtures in relation to CVD risk factors and outcomes. Recommendations for future studies evaluating the associations of exposure to metal mixtures with risk of CVDs and related risk factors include extending environmental mixtures epidemiologic studies to populations with wider metals exposure ranges, including other CVD risk factors or outcomes outside hypertension or dyslipidemia, using repeated measurement of metals to detect windows of susceptibility, and further examining the impacts of potential effect modifiers and confounding factors, such as fish and seafood intake.