Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

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.

Multiple xenoestrogen air pollutants and breast cancer risk: Statistical approaches to investigate combined exposures effect

Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5222 cases and 5222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants' residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03-1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03-1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution.

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.

Endocrine disrupting chemical mixture exposure and risk of papillary thyroid cancer in U.S. military personnel: A nested case-control study

Single-pollutant methods to evaluate associations between endocrine disrupting chemicals (EDCs) and thyroid cancer risk may not reflect realistic human exposures. Therefore, we evaluated associations between exposure to a mixture of 18 EDCs, including polychlorinated biphenyls (PCBs), brominated flame retardants, and organochlorine pesticides, and risk of papillary thyroid cancer (PTC), the most common thyroid cancer histological subtype. We conducted a nested case-control study among U.S. military servicemembers of 652 histologically-confirmed PTC cases diagnosed between 2000 and 2013 and 652 controls, matched on birth year, sex, race/ethnicity, military component (active duty/reserve), and serum sample timing. We estimated mixture odds ratios (OR), 95% confidence intervals (95% CI), and standard errors (SE) for associations between pre-diagnostic serum EDC mixture concentrations, overall PTC risk, and risk of histological subtypes of PTC (classical, follicular), adjusted for body mass index and military branch, using quantile g-computation. Additionally, we identified relative contributions of individual mixture components to PTC risk, represented by positive and negative weights (w). A one-quartile increase in the serum mixture concentration was associated with a non-statistically significant increase in overall PTC risk (OR = 1.19; 95% CI = 0.91, 1.56; SE = 0.14). Stratified by histological subtype and race (White, Black), a one-quartile increase in the mixture was associated with increased classical PTC risk among those of White race (OR = 1.59; 95% CI = 1.06, 2.40; SE = 0.21), but not of Black race (OR = 0.95; 95% CI = 0.34, 2.68; SE = 0.53). PCBs 180, 199, and 118 had the greatest positive weights driving this association among those of White race (w = 0.312, 0.255, and 0.119, respectively). Findings suggest that exposure to an EDC mixture may be associated with increased classical PTC risk. These findings warrant further investigation in other study populations to better understand PTC risk by histological subtype and race.

Modeling PAH Mixture Interactions in a Human In Vitro Organotypic Respiratory Model

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air-liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples.

A high-throughput analytical method for complex contaminant mixtures in biosolids

Biosolids are rich in organic matter and other nutrients that contribute to environmental and agricultural sustainability by improving soil textural and biological properties and enhancing plant growth when applied to agricultural crops. Land application of biosolids encourages resource recovery and circumvents drawbacks associated with landfilling or incineration. However, biosolids contain numerous chemicals at trace levels, and quantitative analysis of such mixtures in this complex matrix is crucial for understanding and managing application risks. There are currently few analytical methods available that are capable of extracting and quantifying a large range of the emerging contaminants found in biosolids. In this study, a simplified, rapid, and robust method of analysis was developed and validated for a high-priority organic contaminant mixture of 44 endocrine disrupting compounds known to occur in biosolids. Analytes consisted of chemicals from many classes with a wide range of physiochemical properties (e.g., log Kow values from -1.4 to 8.9). The biosolids extraction and cleanup protocol was validated for 42 of the targeted compounds. The UPLC-MS2 parameters were validated for all 44 organic contaminants targeted for study. From the two batches of biosolids tested using this analytical method, most of the targeted contaminants (86%) were detected with 100% frequency at concentrations ranging from 0.036 to 10,226 μg/kg dw. Performance results highlighted that internal standards alone could not negate biosolids matrix effects; thus, internal standards and the standard addition method were used for residue quantification. This was the first study to detect and quantify 6PPD-q in biosolids, and the first to quantify lidocaine and 11 other chemicals in biosolids using a single analytical method. This method may be expanded for analysis of additional chemicals in biosolids and comparable matrices.

Evaluating the accuracy of satellite-based methods to estimate residential proximity to agricultural crops

BACKGROUND

Epidemiologic investigations increasingly employ remote sensing data to estimate residential proximity to agriculture as a means of approximating individual-level pesticide exposure. Few studies have examined the accuracy of these methods and the implications for exposure misclassification.

OBJECTIVES

Compare metrics of residential proximity to agricultural land between a groundtruth approach and commonly-used satellite-based estimates.

METHODS

We inspected 349 fields and identified crops in current production within a 0.5 km radius of 40 residences in Idaho. We calculated the distance from each home to the nearest agricultural field and the total acreage of agricultural fields within a 0.5 km buffer. We compared these groundtruth estimates to satellite-derived estimates from three widely used datasets: CropScape, the National Land Cover Database (NLCD), and Landsat imagery (using Normalized Difference Vegetation Index thresholds).

RESULTS

We found poor to moderate agreement between the classification of individuals living within 0.5 km of an agricultural field between the groundtruth method and the comparison datasets (53.1-77.6%). All satellite-derived estimates overestimated the acreage of agricultural land within 0.5 km of each home (average = 82.8-148.9%). Using two satellite-derived datasets in conjunction resulted in substantial improvements; specifically, combining CropScape or NLCD with Landsat imagery had the highest percent agreement with the groundtruth data (92.8-93.8% agreement).

SIGNIFICANCE

Residential proximity to agriculture is frequently used as a proxy for pesticide exposure in epidemiologic investigations, and remote sensing-derived datasets are often the only practical means of identifying cultivated land. We found that estimates of agricultural proximity obtained from commonly-used satellite-based datasets are likely to result in exposure misclassification. We propose a novel approach that capitalizes on the complementary strengths of different sources of satellite imagery, and suggest the combined use of one dataset with high temporal resolution (e.g., Landsat imagery) in conjunction with a second dataset that delineates agricultural land use (e.g., CropScape or NLCD).

Exposure to metal mixtures and adverse pregnancy and birth outcomes: A systematic review

BACKGROUND

Prenatal exposure to metal mixtures is associated with adverse pregnancy and birth outcomes like low birth weight, preterm birth, and small for gestational age. However, prior studies have used individual metal analysis, lacking real-life exposure scenarios.

OBJECTIVES

This systematic review aims to evaluate the strength and consistency of the association between metal mixtures and pregnancy and birth outcomes, identify research gaps, and inform future studies and policies in this area.

METHODS

The review adhered to the updated Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) checklist, along with the guidelines for conducting systematic reviews and meta-analyses of observational studies of etiology (COSMOS-E). Our data collection involved searching the PubMed, MEDLINE, and SCOPUS databases. We utilized inclusion criteria to identify relevant studies. These chosen studies underwent thorough screening and data extraction procedures. Methodological quality evaluations were conducted using the NOS framework for cohort and case-control studies, and the AXIS tool for cross-sectional studies.

RESULTS

The review included 34 epidemiological studies, half of which focused on birth weight, and the others investigated neonate size, preterm birth, small for gestational age, miscarriage, and placental characteristics. The findings revealed significant associations between metal mixtures (including mercury (Hg), nickel (Ni), arsenic (As), cadmium (Cd), manganese (Mn), cobalt (Co), lead (Pb), zinc (Zn), barium (Ba), cesium (Cs), copper (Cu), selenium (Se), and chromium (Cr)) and adverse pregnancy and birth outcomes, demonstrating diverse effects and potential interactions.

CONCLUSION

In conclusion, this review consistently establishes connections between metal exposure during pregnancy and adverse consequences for birth weight, gestational age, and other vital birth-related metrics. This review further demonstrates the need to apply mixture methods with caution but also shows that they can be superior to traditional approaches. Further research is warranted to deeper understand the underlying mechanisms and to develop effective strategies for mitigating the potential risks associated with metal mixture exposure during pregnancy.

Weekly prenatal PM2.5 and NO2 exposures in preterm, early term, and full term infants: Decrements in birth weight and critical windows of susceptibility

BACKGROUND

Previous studies have observed associations between birth weight and prenatal air pollution exposure, but there is not consensus on timing of critical windows of susceptibility.

OBJECTIVE

We estimated the difference in birth weight among preterm, early term and full term births associated with weekly exposure to PM2.5 and NO2 throughout gestation.

METHODS

We included all singleton live births in the Lower Peninsula of Michigan (United States) between 2007 and 2012 occurring at or after 32 weeks gestational age (n = 497,897). Weekly ambient PM2.5 and NO2 concentrations were estimated at maternal residences using 1-km gridded data from ensemble-based models. We utilized a distributed lag nonlinear model to estimate the difference in birth weight associated with weekly exposures from the last menstrual period (week 0) through 31 weeks gestation for preterm births; through 36 weeks gestation for early term births; and through 38 weeks gestation for full term births.

RESULTS

In single-pollutant models, a 5 μg/m3 increase in PM2.5 exposure was associated with a reduction in birth weight among preterm births (-37.1 g [95% confidence interval [CI]: 60.8 g, -13.5 g]); early term births (-13.5 g [95% CI: 26.2 g, -0.67 g]); and full term births (-8.23 g [95% CI: 15.8 g, -0.68 g])]. In single-pollutant models, a 10 ppb increase in NO2 exposure was associated with a -11.7 g (95% CI: 14.46 g, -8.92 g) decrement in birth weight among full term births only. In models co-adjusted for PM2.5 and NO2, PM2.5 exposure was associated with reduced birth weight among preterm births (-36.9 g [95% CI: 61.9 g, -11.8 g]) and NO2 exposure was associated with reduced birth weight among full term births (-11.8 g [95% CI: 14.7 g, -8.94 g]). The largest decrements in birth weight were associated with PM2.5 exposure between approximately 10 and 26 weeks of pregnancy; for NO2 exposure, the largest decrements in birth weight in full term births were associated with exposure between weeks 6-18.

CONCLUSION

We observed the largest and most persistent adverse associations between PM2.5 exposure and birth weight in preterm infants, and between NO2 exposure and birth weight in full term infants. Exposure during the first half of pregnancy had a greater impact on birthweight.

A State-of-the-Science Review on Metal Biomarkers

PURPOSE OF REVIEW

Biomarkers are commonly used in epidemiological studies to assess metals and metalloid exposure and estimate internal dose, as they integrate multiple sources and routes of exposure. Researchers are increasingly using multi-metal panels and innovative statistical methods to understand how exposure to real-world metal mixtures affects human health. Metals have both common and unique sources and routes of exposure, as well as biotransformation and elimination pathways. The development of multi-element analytical technology allows researchers to examine a broad spectrum of metals in their studies; however, their interpretation is complex as they can reflect different windows of exposure and several biomarkers have critical limitations. This review elaborates on more than 500 scientific publications to discuss major sources of exposure, biotransformation and elimination, and biomarkers of exposure and internal dose for 12 metals/metalloids, including 8 non-essential elements (arsenic, barium, cadmium, lead, mercury, nickel, tin, uranium) and 4 essential elements (manganese, molybdenum, selenium, and zinc) commonly used in multi-element analyses.

RECENT FINDINGS

We conclude that not all metal biomarkers are adequate measures of exposure and that understanding the metabolic biotransformation and elimination of metals is key to metal biomarker interpretation. For example, whole blood is a good biomarker of exposure to arsenic, cadmium, lead, mercury, and tin, but it is not a good indicator for barium, nickel, and uranium. For some essential metals, the interpretation of whole blood biomarkers is unclear. Urine is the most commonly used biomarker of exposure across metals but it should not be used to assess lead exposure. Essential metals such as zinc and manganese are tightly regulated by homeostatic processes; thus, elevated levels in urine may reflect body loss and metabolic processes rather than excess exposure. Total urinary arsenic may reflect exposure to both organic and inorganic arsenic, thus, arsenic speciation and adjustment for arsebonetaine are needed in populations with dietary seafood consumption. Hair and nails primarily reflect exposure to organic mercury, except in populations exposed to high levels of inorganic mercury such as in occupational and environmental settings. When selecting biomarkers, it is also critical to consider the exposure window of interest. Most populations are chronically exposed to metals in the low-to-moderate range, yet many biomarkers reflect recent exposures. Toenails are emerging biomarkers in this regard. They are reliable biomarkers of long-term exposure for arsenic, mercury, manganese, and selenium. However, more research is needed to understand the role of nails as a biomarker of exposure to other metals. Similarly, teeth are increasingly used to assess lifelong exposures to several essential and non-essential metals such as lead, including during the prenatal window. As metals epidemiology moves towards embracing a multi-metal/mixtures approach and expanding metal panels to include less commonly studied metals, it is important for researchers to have a strong knowledge base about the metal biomarkers included in their research. This review aims to aid metals researchers in their analysis planning, facilitate sound analytical decision-making, as well as appropriate understanding and interpretation of results.

Association between gestational levels of toxic metals and essential elements and cerebral palsy in children

Introduction

Cerebral palsy (CP) is the most common motor disability in childhood, but its causes are only partly known. Early-life exposure to toxic metals and inadequate or excess amounts of essential elements can adversely affect brain and nervous system development. However, little is still known about these as perinatal risk factors for CP. This study aims to investigate the associations between second trimester maternal blood levels of toxic metals, essential elements, and mixtures thereof, with CP diagnoses in children.

Methods

In a large, population-based prospective birth cohort (The Norwegian Mother, Father, and Child Cohort Study), children with CP diagnoses were identified through The Norwegian Patient Registry and Cerebral Palsy Registry of Norway. One hundred forty-four children with CP and 1,082 controls were included. The relationship between maternal blood concentrations of five toxic metals and six essential elements and CP diagnoses were investigated using mixture approaches: elastic net with stability selection to identify important metals/elements in the mixture in relation to CP; then logistic regressions of the selected metals/elements to estimate odds ratio (OR) of CP and two-way interactions among metals/elements and with child sex and maternal education. Finally, the joint effects of the mixtures on CP diagnoses were estimated using quantile-based g-computation analyses.

Results

The essential elements manganese and copper, as well as the toxic metal Hg, were the most important in relation to CP. Elevated maternal levels of copper (OR = 1.40) and manganese (OR = 1.20) were associated with increased risk of CP, while Hg levels were, counterintuitively, inversely related to CP. Metal/element interactions that were associated with CP were observed, and that sex and maternal education influenced the relationships between metals/elements and CP. In the joint mixture approach no significant association between the mixture of metals/elements and CP (OR = 1.00, 95% CI = [0.67, 1.50]) was identified.

Conclusion

Using mixture approaches, elevated levels of copper and manganese measured in maternal blood during the second trimester could be related to increased risk of CP in children. The inverse associations between maternal Hg and CP could reflect Hg as a marker of maternal fish intake and thus nutrients beneficial for foetal brain development.

Polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans, pesticides, and diabetes in the Anniston Community Health Survey follow-up (ACHS II): single exposure and mixture analysis approaches

Dioxins and dioxin-like compounds measurements were added to polychlorinated biphenyls (PCBs) and organochlorine pesticides to expand the exposure profile in a follow-up to the Anniston Community Health Survey (ACHS II, 2014) and to study diabetes associations. Participants of ACHS I (2005-2007) still living within the study area were eligible to participate in ACHS II. Diabetes status (type-2) was determined by a doctor's diagnosis, fasting glucose ≥125 mg/dL, or being on any glycemic control medication. Incident diabetes cases were identified in ACHS II among those who did not have diabetes in ACHS I, using the same criteria. Thirty-five ortho-substituted PCBs, 6 pesticides, 7 polychlorinated dibenzo-p-dioxins (PCDD), 10 furans (PCDF), and 3 non-ortho PCBs were measured in 338 ACHS II participants. Dioxin toxic equivalents (TEQs) were calculated for all dioxin-like compounds. Main analyses used logistic regression models to calculate odds ratios (OR) and 95 % confidence intervals (CI). In models adjusted for age, race, sex, BMI, total lipids, family history of diabetes, and taking lipid lowering medication, the highest ORs for diabetes were observed for PCDD TEQ: 3.61 (95 % CI: 1.04, 12.46), dichloro-diphenyl dichloroethylene (p,p'-DDE): 2.07 (95 % CI 1.08, 3.97), and trans-Nonachlor: 2.55 (95 % CI 0.93, 7.02). The OR for sum 35 PCBs was 1.22 (95 % CI: 0.58-2.57). To complement the main analyses, we used BKMR and g-computation models to evaluate 12 mixture components including 4 TEQs, 2 PCB subsets and 6 pesticides; suggestive positive associations for the joint effect of the mixture analyses resulted in ORs of 1.40 (95% CI: -1.13, 3.93) for BKMR and 1.32 (95% CI: -1.12, 3.76) for g-computation. The mixture analyses provide further support to previously observed associations of trans-Nonachlor, p,p'- DDE, PCDD TEQ and some PCB groups with diabetes.

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.

Bayesian multiple index models for environmental mixtures

An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response-surface methods and exposure-index methods. Response-surface methods estimate high-dimensional surfaces and are thus highly flexible but difficult to interpret. In contrast, exposure-index methods decompose coefficients from a linear model into an overall mixture effect and individual index weights; these models yield easily interpretable effect estimates and efficient inferences when model assumptions hold, but, like most parsimonious models, incur bias when these assumptions do not hold. In this paper, we propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non-linear and non-additive relationships between exposure indices and a health outcome, while reducing the dimensionality of the exposure vector and estimating index weights with variable selection. This framework contains response-surface and exposure-index models as special cases, thereby unifying the two analysis strategies. This unification increases the range of models possible for analysing environmental mixtures and health, allowing one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability. In an analysis of the association between telomere length and 18 organic pollutants in the National Health and Nutrition Examination Survey (NHANES), the proposed approach fits the data as well as more complex response-surface methods and yields more interpretable results.

Exploring Health Effects under Specific Causes of Mortality Based on 90 Definitions of PM2.5 and Cold Spell Combined Exposure in Shanghai, China

In this study, a total of 90 definitions were set up based on six air pollution definitions, five cold spell definitions, and three combined exposure scenarios. The relative risks (RRs) on all-cause, circulatory, and respiratory mortality were explored by a model combining a distributed linear lag model with quasi-Poisson regression. The definition in which daily PM_2.5 increases more than 75 μg/m³ for at least 2 days and the average temperature falls below the 10th percentile for at least 2 days produced the best model fit performance in all-cause mortality. The high peaks of the health effect were generally observed around the lag days 6-9. The cumulative relative risks (CRRs) were more significant in the simultaneous-exposure scenario and higher in respiratory mortality, where the highest CRR (12.15, 3.69-40.03) was observed in definition P1T5, in which daily PM_2.5 increases more than 75 μg/m³, and the average temperature falls below the 2.5th percentile for at least two days. For relative risk due to interaction (RERI), we found positive additive interactions (RERI > 0) between PM_2.5 pollution and cold spell, especially in respiratory mortality. Clarifying the definition of combined events can help policymakers to capture health risks and construct more effective risk warning systems.

Association of single and joint metals with albuminuria and estimated glomerular filtration longitudinal change in middle-aged adults from Spain: The Aragon workers health study

The nephrotoxicity of low-chronic metal exposures is unclear, especially considering several metals simultaneously. We assessed the individual and joint association of metals with longitudinal change in renal endpoints in Aragon Workers Health Study participants with available measures of essential (cobalt [Co], copper [Cu], molybdenum [Mo] and zinc [Zn]) and non-essential (As, barium [Ba], Cd, chromium [Cr], antimony [Sb], titanium [Ti], uranium [U], vanadium [V] and tungsten [W]) urine metals and albumin-to-creatinine ratio (ACR) (N = 707) and estimated glomerular filtration rate (eGFR) (N = 1493) change. Median levels were 0.24, 7.0, 18.6, 295, 3.1, 1.9, 0.28, 1.16, 9.7, 0.66, 0.22 μg/g for Co, Cu, Mo, Zn, As, Ba, Cd, Cr, Sb, Ti, V and W, respectively, and 52.5 and 27.2 ng/g for Sb and U, respectively. In single metal analysis, higher As, Cr and W concentrations were associated with increasing ACR annual change. Higher Zn, As and Cr concentrations were associated with decreasing eGFR annual change. The shape of the longitudinal dose-responses, however, was compatible with a nephrotoxic role for all metals, both in ACR and eGFR models. In joint metal analysis, both higher mixtures of Cu-Zn-As-Ba-Ti-U-V-W and Co-Cd-Cr-Sb-V-W showed associations with increasing ACR and decreasing eGFR annual change. As and Cr were main drivers of the ACR change joint metal association. For the eGFR change joint metal association, while Zn and Cr were main drivers, other metals also contributed substantially. We identified potential interactions for As, Zn and W by other metals with ACR change, but not with eGFR change. Our findings support that Zn, As, Cr and W and suggestively other metals, are nephrotoxic at relatively low exposure levels. Metal exposure reduction and mitigation interventions may improve prevention and decrease the burden of renal disease in the population.

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

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

Explainable Gated Recurrent Unit to explore the effect of co-exposure to multiple air pollutants and meteorological conditions on mental health outcomes

Mental health conditions have the potential to be worsened by air pollution or other climate-sensitive factors. Few studies have empirically examined those associations when we faced to co-exposures, as well as interaction effects. There would be an urgent need to use deep learning to handle complex co-exposures that might interact in multiple ways, and the model performance reinforced by SHapely Additive exPlanations (SHAP) enabled our predictions interpretable and hence actionable. Here, to evaluate the mixed effect of short-term co-exposure, we conducted a time-series analysis using approximately 1.47 million hospital outpatient visits of mental disorders (i.e., depressive disorder-DD, Schizophrenia-SP, Anxiety Disorder-AD, Bipolar Disorder-BD, Attention Deficit and Hyperactivity Disorder-ADHD, Autism Spectrum Disorder-ASD), with matched meteorological observations from 2015 through 2019 in Nanjing, China. The global insights of gated recurrent unit model revealed that most of input features with similar effect size caused the illness risk of SP and ASD increase, and most markedly, 73% of relative humidity, 44.6 µg/m³ of NO₂, and 14.1 µg/m³ of SO₂ at 5-year average level associated with 2.27, 1.14, and 1.29 visits increase for DD, SP, and AD, respectively. Both synergic and antagonistic effect among informative paired-features were distinguished from local feature dependence. Interestingly, variation tendencies of excessive visits of bipolar disorder when atmospheric pressure, PM_2.5, and O₃ interacted with one another were inconsistent. Our results provided added qualitative and quantitative support for the conclusion that short-term co-exposure to ambient air pollutants and meteorological conditions posed threats to human mental health.

Perinatal Metal and Metalloid Exposures and Offspring Cardiovascular Health Risk

PURPOSE OF REVIEW

Toxic metal exposures have been associated with cardiovascular disease in adults and growing evidence suggests metal exposures also adversely affect cardiovascular phenotypes in childhood and adolescence. However, to our knowledge, the influence of perinatal metals exposure, particularly metal mixtures, in relation to cardiovascular-related outcomes have not been comprehensively reviewed.

RECENT FINDINGS

We summarized 17 contemporary studies (2017-2021) that investigated the impact of perinatal metal exposures on measures of cardiovascular health in children. Accumulating evidence supports a potential adverse impact of perinatal Pb exposure on BP in children. Fewer recent studies have focused on perinatal As, Hg, and Cd; thus, the cardiovascular impacts of these metals are less clear. Studies of metal mixtures demonstrate that interactions between metals may be complex and have identified numerous understudied elements and essential metals, including Mo, Co, Ni, Se, Zn, and Mn, which may influence cardiovascular risk. A key question that remains is whether perinatal metals exposure influences cardiovascular health into adulthood. Comparisons across studies remain challenging due to several factors, including differences in the timing of exposure/outcome assessments and exposure biomarkers, as well as variability in exposure levels and mixture compositions across populations. Future studies longitudinally investigating trajectories of cardiovascular outcomes could help determine the influence of perinatal metals exposure on long-term effects of clinical relevance in later life and whether interventions, which reduce metals exposures during this key developmental window, could alter disease development.

Joint exposure to air pollution, ambient temperature and residential greenness and their association with metabolic syndrome (MetS): A large population-based study among Chinese adults

Previous studies assessing adverse health have traditionally focused on a single environmental exposure, failing to reflect the reality of various exposures present simultaneously. Air pollution, ambient temperature and greenness have been proposed as critical environmental factors associated with metabolic syndrome (MetS). However, evidence exploring their joint relationships with MetS is needed for identifying interactive factors and developing more targeted public health interventions. The baseline data was obtained from China Multi-Ethnic Cohort (CMEC). Environmental data of air pollutants (PM_2.5, O₃) and NDVI for greenness was calculated from satellites data. Ambient temperature data were obtained from European Center for Medium-Range Weather Forecasts (ECMWF). MetS was classified based on National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) using anthropometric measures and biomarkers. Logistic regression models were utilized to examine the combined relationship of MetS with three-year exposure to air pollutants, temperature and NDVI. Relative excess risk due to interaction (RERI) was calculated to evaluate interaction on an additive scale. We found associations between prevalent MetS and interquartile range (IQR) increases in PM_2.5 (OR: 1.38; 95% confidence interval [95% CI]: 1.23, 1.55) and O₃ (OR: 1.15; 95% CI: 1.09, 1.22). Additive and multiplicative interactions were observed between air pollutants and temperature exposure. Compared to low-temperature level, the relationship between PM_2.5 and MetS attenuated (RERI: 0.22, 95% CI: 0.44, -0.04) at high-temperature level, while the relationship between O₃ and MetS enhanced (RERI: 0.05, 95% CI: 0.02, 0.11). At low NDVI 250 m, the association between PM_2.5 and MetS was stronger (RERI: 0.13, 95% CI: 0.05, 0.19) with high NDVI 250 m as the reference group. Our findings showed that ambient temperature and residential greenness could affect the relationship between air pollutants and MetS.

Principal Component Pursuit for Pattern Identification in Environmental Mixtures

BACKGROUND

Environmental health researchers often aim to identify sources or behaviors that give rise to potentially harmful environmental exposures.

OBJECTIVE

We adapted principal component pursuit (PCP)-a robust and well-established technique for dimensionality reduction in computer vision and signal processing-to identify patterns in environmental mixtures. PCP decomposes the exposure mixture into a low-rank matrix containing consistent patterns of exposure across pollutants and a sparse matrix isolating unique or extreme exposure events.

METHODS

We adapted PCP to accommodate nonnegative data, missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions < LOD and three noise structures. We applied PCP-LOD to evaluate its performance in comparison with principal component analysis (PCA). We next applied principal component pursuit with limit of detection (PCP-LOD) to an exposure mixture of 21 persistent organic pollutants (POPs) measured in 1,000 U.S. adults from the 2001-2002 National Health and Nutrition Examination Survey (NHANES). We applied singular value decomposition to the estimated low-rank matrix to characterize the patterns.

RESULTS

PCP-LOD recovered the true number of patterns through cross-validation for all simulations; based on an a priori specified criterion, PCA recovered the true number of patterns in 32% of simulations. PCP-LOD achieved lower relative predictive error than PCA for all simulated data sets with up to 50% of the data < LOD . When 75% of values were < LOD , PCP-LOD outperformed PCA only when noise was low. In the POP mixture, PCP-LOD identified a rank-three underlying structure and separated 6% of values as extreme events. One pattern represented comprehensive exposure to all POPs. The other patterns grouped chemicals based on known structure and toxicity.

DISCUSSION

PCP-LOD serves as a useful tool to express multidimensional exposures as consistent patterns that, if found to be related to adverse health, are amenable to targeted public health messaging. https://doi.org/10.1289/EHP10479.

Leveraging Multiple Data Streams for Prioritization of Mixtures for Hazard Characterization

There is a growing need to establish alternative approaches for mixture safety assessment of polycyclic aromatic hydrocarbons (PAHs). Due to limitations with current component-based approaches, and the lack of established methods for using whole mixtures, a promising alternative is to use sufficiently similar mixtures; although, an established framework is lacking. In this study, several approaches are explored to form sufficiently similar mixtures. Multiple data streams including environmental concentrations and empirically and predicted toxicity data for cancer and non-cancer endpoints were used to prioritize chemical components for mixture formations. Air samplers were analyzed for unsubstituted and alkylated PAHs. A synthetic mixture of identified PAHs was created (Creosote-Fire Mix). Existing toxicity values and chemical concentrations were incorporated to identify hazardous components in the Creosote-Fire Mix. Sufficiently similar mixtures of the Creosote-Fire Mix were formed based on (1) relative abundance; (2) toxicity values; and (3) a combination approach incorporating toxicity and abundance. Hazard characterization of these mixtures was performed using high-throughput screening in primary normal human bronchial epithelium (NHBE) and zebrafish. Differences in chemical composition and potency were observed between mixture formation approaches. The toxicity-based approach (Tox Mix) was the most potent mixture in both models. The combination approach (Weighted-Tox Mix) was determined to be the ideal approach due its ability to prioritize chemicals with high exposure and hazard potential.

Extracellular microRNA and cognitive function in a prospective cohort of older men: The Veterans Affairs Normative Aging Study

BACKGROUND

Aging-related cognitive decline is an early symptom of Alzheimer's disease and other dementias, and on its own can have substantial consequences on an individual's ability to perform important everyday functions. Despite increasing interest in the potential roles of extracellular microRNAs (miRNAs) in central nervous system (CNS) pathologies, there has been little research on extracellular miRNAs in early stages of cognitive decline. We leverage the longitudinal Normative Aging Study (NAS) cohort to investigate associations between plasma miRNAs and cognitive function among cognitively normal men.

METHODS

This study includes data from up to 530 NAS participants (median age: 71.0 years) collected from 1996 to 2013, with a total of 1,331 person-visits (equal to 2,471 years of follow up). Global cognitive function was assessed using the Mini-Mental State Examination (MMSE). Plasma miRNAs were profiled using small RNA sequencing. Associations of expression of 381 miRNAs with current cognitive function and rate of change in cognitive function were assessed using linear regression (N = 457) and linear mixed models (N = 530), respectively.

RESULTS

In adjusted models, levels of 2 plasma miRNAs were associated with higher MMSE scores (p < 0.05). Expression of 33 plasma miRNAs was associated with rate of change in MMSE scores over time (p < 0.05). Enriched KEGG pathways for miRNAs associated with concurrent MMSE and MMSE trajectory included Hippo signaling and extracellular matrix-receptor interactions. Gene targets of miRNAs associated with MMSE trajectory were additionally associated with prion diseases and fatty acid biosynthesis.

CONCLUSIONS

Circulating miRNAs were associated with both cross-sectional cognitive function and rate of change in cognitive function among cognitively normal men. Further research is needed to elucidate the potential functions of these miRNAs in the CNS and investigate relationships with other neurological outcomes.

Joint association between ambient air pollutant mixture and pediatric asthma exacerbations

Exposure to air pollutants is known to exacerbate asthma, with prior studies focused on associations between single pollutant exposure and asthma exacerbations. As air pollutants often exist as a complex mixture, there is a gap in understanding the association between complex air pollutant mixtures and asthma exacerbations. We evaluated the association between the air pollutant mixture (52 pollutants) and pediatric asthma exacerbations.

Method

This study focused on children (age ≤ 19 years) who lived in Douglas County, Nebraska, during 2016-2019. A seasonal-scale joint association between the outdoor air pollutant mixture adjusting for potential confounders (temperature, precipitation, wind speed, and wind direction) in relation to pediatric asthma exacerbation-related emergency department (ED) visits was evaluated using the generalized weighted quantile sum (qWQS) regression with repeated holdout validation.

Results

We observed associations between air pollutant mixture and pediatric asthma exacerbations during spring (lagged by 5 days), summer (lag 0-5 days), and fall (lag 1-3 days) seasons. The estimate of the joint outdoor air pollutant mixture effect was higher during the summer season (adjusted-β_WQS = 1.11, 95% confidence interval [CI]: 0.66, 1.55), followed by spring (adjusted-β_WQS = 0.40, 95% CI: 0.16, 0.62) and fall (adjusted-β_WQS = 0.20, 95% CI: 0.06, 0.33) seasons. Among the air pollutants, PM_2.5, pollen, and mold contributed higher weight to the air pollutant mixture.

Conclusion

There were associations between outdoor air pollutant mixture and pediatric asthma exacerbations during the spring, summer, and fall seasons. Among the 52 outdoor air pollutant metrics investigated, PM_2.5, pollen (sycamore, grass, cedar), and mold (Helminthosporium, Peronospora, and Erysiphe) contributed the highest weight to the air pollutant mixture.

The associations of prenatal exposure to dioxins and polychlorinated biphenyls with neurodevelopment at 6 Months of age: Multi-pollutant approaches

BACKGROUND

Prenatal exposure to persistent organic pollutants, including polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), dioxin-like polychlorinated biphenyls (DL-PCBs), and nondioxin-like PCBs (NDL-PCBs), has been hypothesized to have a detrimental impact on neurodevelopment. However, the association of prenatal exposure to a dioxin and PCB mixture with neurodevelopment remains largely inconclusive partly because these chemical levels are correlated.

OBJECTIVES

We aimed to elucidate the association of in utero exposure to a mixture of dioxins and PCBs with neurodevelopment measured at 6 months of age by applying multipollutant methods.

METHODS

A total of 514 pregnant women were recruited between July 2002 and October 2005 in the Sapporo cohort, Hokkaido Study on Environment and Children's Health. The concentrations of individual dioxin and PCB isomers were assessed in maternal peripheral blood during pregnancy. The mental and psychomotor development of the study participants' infants was evaluated using the Bayley Scales of Infant Development-2nd Edition (n = 259). To determine both the joint and individual associations of prenatal exposure to a dioxin and PCB mixture with infant neurodevelopment, Bayesian kernel machine regression (BKMR) and quantile-based g-computation were employed.

RESULTS

Suggestive inverse associations were observed between in utero exposure to a dioxin and PCB mixture and infant psychomotor development in both the BKMR and quantile g-computation models. In contrast, we found no association of a dioxin and PCB mixture with mental development. When group-specific posterior inclusion probabilities were estimated, BKMR suggested prenatal exposure to mono-ortho PCBs as the more important contributing factors to early psychomotor development compared with the other dioxin or PCB groups. No evidence of nonlinear exposure-outcome relationships or interactions among the chemical mixtures was detected.

CONCLUSIONS

Applying the two complementary statistical methods for chemical mixture analysis, we demonstrated limited evidence of inverse associations of prenatal exposure to dioxins and PCBs with infant psychomotor development.

Identifying cleaning products associated with short-term work-related respiratory symptoms: A workforce-based study in domestic cleaners

Domestic cleaners have an increased risk of asthma-like and other respiratory symptoms and conditions. Uncertainty exists about which products are most hazardous. We aimed to investigate, among professional domestic cleaners, the associations of ocular/respiratory outcomes with using specific types of products at work and with the ability to choose their own products. Among domestic cleaners employed by "service vouchers" companies in Belgium, we administered an online questionnaire on ocular/respiratory symptoms (frequency and time relation to workdays), frequency of use of 40 types of products, and ability to choose one's own products. Work-relatedness was defined as symptoms improving/disappearing on days off-work. We studied associations between frequency of product-use with work-related outcomes (eye irritation, rhinitis symptoms, sore throat, laryngeal symptoms, asthma symptoms, cough) and with chronic bronchitis, using multivariable logistic and elastic net regression. Adjusted odds ratios (OR) with 95%-confidence intervals were obtained per time a product was used per week. Among 1,586 domestic cleaners (99% women), the number of times sprays were used (median 13/week) was significantly associated with all outcomes (ORs between 1.012 and 1.024 per time sprays were used per week). Bleach/disinfectant-containing liquid products were associated with all outcomes, except for laryngeal symptoms (ORs 1.086 to 1.150); ammonia with work-related upper airway symptoms and chronic bronchitis. Cleaners able to choose their own products had fewer work-related eye symptoms (OR 0.728;0.556-0.954), rhinitis (OR 0.735;0.571-0.946) and cough (OR 0.671;0.520-0.865). Using elastic net regression, work-related rhinitis was most strongly associated with mould removal spray (OR 1.108;1.006-1.248), carpet/seat/curtain spray (OR 1.099;1.001-1.304) and ammonia (OR 1.081;1.002-1.372); work-related asthma with carpet/seat/curtain spray (OR 1.103;1.017-1.322), mould removal spray (OR 1.029;0.995-1.199) and drain cleaner (OR 1.023;0.979-1.302). In a large group of domestic cleaners, we documented that cleaning products have a range of adverse respiratory effects. Empowering cleaners to choose their products may reduce the burden of symptoms.

How to investigate human health effects related to exposure to mixtures of per- and polyfluoroalkyl substances: A systematic review of statistical methods

BACKGROUND

Humans are exposed to several per- and polyfluoroalkyl substances (PFAS) daily; however, most previous studies have focused on individual PFAS. Although attention to effects of exposure to mixtures of PFAS has grown in recent years, there is no consensus on the appropriate statistical methods that can be used to assess their combined effect on human health.

OBJECTIVES

We aim to perform a comprehensive review of the statistical methods used in the existing studies which evaluate the association between exposure to mixtures of PFAS and any adverse human health effect.

METHODS

The online databases PubMed, Embase and Scopus were searched for eligible studies, published during the last ten years (last search performed on April 08, 2021). Covidence software was used by two different reviewers to perform a title/abstract screening, followed by a full text revision of the selected papers.

RESULTS

A total of 3640 papers were identified, and after the screening process, 53 papers were included in the current review. Most of the studies were published between 2019 and 2021 and were conducted mainly in North America and Europe; more than half of the studies (28 out of 53) were conducted on mother and child pairs. WQS (Weighted Quantile Sum) Regression and BKMR (Bayesian Kernel Machine Regression) were used in 36 out of 53 papers to model mixtures' effects. Health outcomes included in the studies are immunotoxicity (n = 8), fetal development (n = 7), neurodevelopment (n = 9), reproductive hormones (n = 6), thyroid hormones (n = 7), outcomes related to metabolic pathways (n = 16).

CONCLUSION

Studies on human exposure to PFAS as complex mixtures and health consequences have substantially increased in the last few years. Based on our findings, we propose that addressing risk from PFAS mixtures will likely require combinations of approaches and implementation of constantly evolving statistical methods. Specific guidelines and tools for quality assessment and publication of mixture observational studies are warranted.

Satellite data and machine learning reveal a significant correlation between NO2 and COVID-19 mortality

The Coronavirus disease 2019 (COVID-19) pandemic has officially spread all over the world since the beginning of 2020. Although huge efforts are addressed by scientists to shed light over the several questions raised by the novel SARS-CoV-2 virus, many aspects need to be clarified, yet. In particular, several studies have pointed out significant variations between countries in per-capita mortality. In this work, we investigated the association between COVID-19 mortality with climate variables and air pollution throughout European countries using the satellite remote sensing images provided by the Sentinel-5p mission. We analyzed data collected for two years of observations and extracted the concentrations of several pollutants; we used these measurements to feed a Random Forest regression. We performed a cross-validation analysis to assess the robustness of the model and compared several regression strategies. Our findings reveal a significant statistical association between air pollution (NO₂) and COVID-19 mortality and a significant role played by the socio-demographic features, like the number of nurses or the hospital beds and the gross domestic product per capita.

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.

Exposure Assessment for Tropical Cyclone Epidemiology

PURPOSE OF REVIEW

Tropical cyclones impact human health, sometimes catastrophically. Epidemiological research characterizes these health impacts and uncovers pathways between storm hazards and health, helping to mitigate the health impacts of future storms. These studies, however, require researchers to identify people and areas exposed to tropical cyclones, which is often challenging. Here we review approaches, tools, and data products that can be useful in this exposure assessment.

RECENT FINDINGS

Epidemiological studies have used various operational measures to characterize exposure to tropical cyclones, including measures of physical hazards (e.g., wind, rain, flooding), measures related to human impacts (e.g., damage, stressors from the storm), and proxy measures of distance from the storm's central track. The choice of metric depends on the research question asked by the study, but there are numerous resources available that can help in capturing any of these metrics of exposure. Each has strengths and weaknesses that may influence their utility for a specific study. Here we have highlighted key tools and data products that can be useful for exposure assessment for tropical cyclone epidemiology. These results can guide epidemiologists as they design studies to explore how tropical cyclones influence human health.

Revealing the racial and spatial disparity in pediatric asthma: A Kansas City case study

Black and other socially disadvantaged children are disproportionately burdened by high rates of pediatric asthma. Intraurban variation in environmental risk factors and limited access to high-resolution health data make it difficult to identify vulnerable patients, communities, or the immediate exposures that may contribute to pediatric asthma exacerbation. This article presents a novel, interdisciplinary health disparities research and intervention strategy applied to the problem of pediatric asthma in Kansas City. First, address-level electronic health records from a major children's hospital in the Kansas City region are used to map the distribution of asthma encounters in 2012 at a high spatial resolution. Census tract Environmental Justice Screening Method (EJSM) indicators are then developed to scan for patterns in both the population health risks and vulnerabilities that may contribute to the burden of asthma in different communities. A Bayesian Profile Regression cluster analysis is used to systematically explore the complex relationships between census tract EJSM indicators and pediatric asthma incidence rates, helping to identify population characteristics and risk factors associated with both high and low rates of pediatric asthma throughout the region. The EJSM scanning exercise and BPR analysis demonstrate that each community faces a distinct set of risks and vulnerabilities that can contribute to the rate of acute pediatric asthma acute care encounters, providing targets for research and intervention. It is clear, however, that different forms of social disadvantage are driving high rates of pediatric asthma, which is closely tied to uneven development patterns and racial residential segregation. The results provide a starting point for designing place-based health disparities research and intervention strategies catered to the unique needs of vulnerable patients and communities; disparities-focused research and intervention strategies that leverage local knowledge and resources through community-based practices.

Prenatal exposure to organophosphate and pyrethroid insecticides and the herbicide 2,4-dichlorophenoxyacetic acid and size at birth in urban pregnant women

BACKGROUND

Organophosphate insecticides and the herbicide, 2,4-dichlorophenoxyacetic acid (2,4-D) are used to protect crops or control weeds. Pyrethroids are used to manage pests both in agriculture and in residences, and to reduce the transmission of insect-borne diseases. Several studies have reported inverse associations between exposure to organophosphates (as a larger class) and birth outcomes but these associations have not been conclusive for pyrethroids or 2,4-D, specifically. We aimed to investigate the association between birth outcomes and urinary biomarkers of pyrethroids, organophosphates and 2,4-D among healthy pregnant women living in New York City.

METHODS

We quantified urinary biomarkers of 2,4-D and of organophosphate and pyrethroid insecticides from 269 women from two cohorts: a) Thyroid Disruption And Infant Development (TDID) and b) Sibling/Hermanos cohort (S/H). We used weighted quantile sum regression and multivariable linear regression models to evaluate the associations between a mixture of urinary creatinine-adjusted biomarker concentrations and birth outcomes of length, birthweight and head circumference, controlling for covariates. We also used linear regression models and further classified biomarkers concentrations into three categories (i: non-detectable; ii: between the limit of detection and median; and iii: above the median) to investigate single pesticides' association with these birth outcomes. Covariates considered were delivery mode, ethnicity, marital status, education, income, employment status, gestational age, maternal age and pre-pregnancy BMI. Analyses were conducted separately for each cohort and stratified by child sex within each cohort.

RESULTS

In TDID cohort, we found a significant inverse association between weighted quantile sum of mixture of pesticides and head circumference among boys. We found that the urinary biomarkers of organophosphate chlorpyrifos, TCPy, and 2,4-D had the largest contribution to the overall mixture effect in the TDID cohort among boys (b = -0.57, 95%CI: -0.92, -0.22) (weights = 0.81 and 0.16 respectively) but not among girls. In the multivariable linear regression models, we found that among boys, for each log unit increase in 3,5,6-trichloro-2-pyridinol (TCPy, metabolite of organophosphate chlorpyrifos) in maternal urine, there was a -0.56 cm decrease in head circumference (95%CI: -0.92, -0.19). Among boys in the TDID cohort, 2,4-D was associated with smaller head circumference in the second (b = -1.57; 95%CI: -2.74, -0.39) and third (b = -1.74, 95%CI: -2.98, -0.49) concentration categories compared to the first. No associations between pyrethroid and organophosphate biomarkers and birth outcomes were observed in girls analyzed in WQS regression or individually in linear regression models in TDID cohort. In the S/H cohort, head circumference increased with higher concentrations of 3-phenoxybenzoic acid (3-PBA, a biomarker of several pyrethroids) (b = 0.53, 95%CI: 0.03, 1.04) among boys and head circumference was lower among girls in the high compared to low category of 2,4-D (b = -2.27, 95%CI: - 3.98, -0.56). Birth length was also positively associated with the highest concentration of 2,4-D compared to the lowest among boys (b = 4.01, 95%CI: 0.02,8.00).

CONCLUSIONS

Weighted quantile sum of pesticides was negatively associated with head circumference among boys in one cohort. Nonetheless, due to directional homogeneity assumption in WQS no positive associations were detected. In linear regression models with individual pesticides, concentrations of TCPy were inversely associated with head circumference in boys and higher concentrations of 2,4-D was inversely associated with head circumference among girls; 2,4-D concentrations were also associated with higher birth length among boys. Concentrations of 3-PBA was positively associated with head circumference among boys.

Gestational blood levels of toxic metal and essential element mixtures and associations with global DNA methylation in pregnant women and their infants

BACKGROUND

Pregnant women and their fetuses are exposed to multiple toxic metals that together with variations in essential element levels may alter epigenetic regulation, such as DNA methylation.

OBJECTIVES

The aim of the study was to investigate the associations between gestational levels of toxic metals and essential elements and mixtures thereof, with global DNA methylation levels in pregnant women and their newborn children.

METHODS

Using 631 mother-child pairs from a prospective birth cohort (The Norwegian Mother, Father and Child Cohort Study), we measured maternal blood concentration (gestation week ~18) of five toxic metals and seven essential elements. We investigated associations as individual exposures and two-way interactions, using elastic net regression, and total mixture, using quantile g-computation, with blood levels of 5-methylcytocine (5mC) and 5-hydroxymethylcytosine (5hmC) in mothers during pregnancy and their newborn children (cord blood). Multiple testing was adjusted for using the Benjamini and Hochberg false discovery rate (FDR) approach.

RESULTS

The most sensitive marker of DNA methylation appeared to be 5mC levels. In pregnant mothers, elastic net regression indicated associations between 5mC and selenium and lead (non-linear), while in newborns results indicated relationships between maternal selenium, cobalt (non-linear) and mercury and 5mC, as well as copper (non-linear) and 5hmC levels. Several possible two-way interactions were identified (e.g. arsenic and mercury, and selenium and maternal smoking in newborns). None of these findings met the FDR threshold for multiple testing. No net effect was observed in the joint (mixture) exposure-approach using quantile g-computation.

CONCLUSION

We identified few associations between gestational levels of several toxic metals and essential elements and global DNA methylation in pregnant mothers and their newborn children. As DNA methylation dysregulation might be a key mechanism in disease development and thus of high importance for public health, our results should be considered as important candidates to investigate in future studies.

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.

Pregnancy exposure to organophosphate esters and the risk of attention-deficit hyperactivity disorder in the Norwegian mother, father and child cohort study

BACKGROUND

Organophosphate esters (OPEs) are a class of flame retardants in common use. OPEs can easily leach from materials, resulting in human exposure. Increasing concentrations have been reported in human populations over the past decade. Recent studies have linked prenatal OPE exposure to hyperactivity and attention problems in children. Such behaviors are often found among children with attention-deficit hyperactivity disorder (ADHD), however, no study has investigated OPEs in relation to clinically assessed ADHD.

OBJECTIVE

To evaluate prenatal exposure to OPEs as risk factors for clinically assessed ADHD using a case-cohort study nested within the Norwegian Mother, Father, and Child Cohort Study (MoBa).

METHODS

We included in the case group 295 ADHD cases obtained via linkage with the Norwegian Patient Registry, and the sub-cohort group 555 children sampled at baseline, irrespective of their ADHD case status. Prenatal concentrations of OPE metabolites were measured in maternal urine collected at 17 weeks of gestation, and included diphenyl phosphate (DPHP), di-n-butyl phosphate (DNBP), bis(2-butoxyethyl) hydrogen phosphate (BBOEP), and bis(1,3-dichloro-2-propyl) phosphate (BDCIPP). We estimated risk ratios and the corresponding 95% confidence intervals [95% CI] using logistic regression, adjusting for season of urine collection, child sex, birth year, and maternal depression, education, and sum of urinary di(2-ethylhexyl) phthalate metabolites (∑DEHP) concentration during pregnancy. To assess the overall impact of simultaneously decreasing exposure to all chemical constituents of an OPE-phthalate mixture, quantile based g-computation was implemented. The mixture constituents included OPE and phthalate metabolites commonly detected in our study. In all models, we considered effect measure modification by child sex and polymorphisms in genes encoding paraoxonase 1 (PON1) and cytochrome P450 (P450) enzymes. Mediation analysis was conducted using thyroid function biomarkers estimated from maternal blood collected at 17 weeks of gestation.

RESULTS

DPHP was detected in nearly all samples (97.2%), with a higher geometric mean among the case group (0.70 µg/L) as compared to the sub-cohort (0.52 µg/L). DNBP was commonly detected as well (93.8%), while BBOEP (52.9%) and BDCIPP (22.9%) were detected less frequently. A higher risk of ADHD was observed in children with greater than median exposure to DPHP during pregnancy (risk ratio: 1.38 [95% CI: 0.96, 1.99]), which was slightly higher among girls (2.04 [1.03, 4.02]) and children of mothers with PON1 Q192R genotype QR (1.69 [0.89, 3.19]) or PON1 Q192R genotype RR (4.59 [1.38, 15.29]). The relationship between DPHP and ADHD (total risk ratio: 1.34 [0.90, 2.02]) was partially mediated through total triiodothyronine to total thyroxine ratio (natural direct effect: 1.29 [0.87, 1.94]; natural indirect effect: 1.04 [1.00, 1.10]; 12.48% mediated). We also observed an elevated risk of ADHD in relation to BDCIPP detection during pregnancy (1.50 [0.98, 2.28]). We did not observe notable differences in ADHD by DNBP (0.88 [0.62, 1.26]) or BBOEP (1.03 [0.73, 1.46]) during pregnancy. Simultaneously decreasing all constituents of common-detect OPE-phthalate mixture, specifically DPHP, DNBP, and 6 phthalate metabolites, by a quartile resulted in an ADHD risk ratio of 0.68 [0.64, 0.72].

CONCLUSION

Prenatal exposure to DPHP and BDCIPP may increase the risk of ADHD. For DPHP, we observed potential modification by child sex and maternal PON1 Q192R genotype and partial mediation through maternal thyroid hormone imbalance at 17 weeks gestation.

Air pollution and lung function in children

In this narrative review, we summarize the literature and provide updates on recent studies of air pollution exposures and child lung function and lung function growth. We include exposures to outdoor air pollutants that are monitored and regulated through air quality standards, and air pollutants that are not routinely monitored or directly regulated, including wildfires, indoor biomass and coal burning, gas and wood stove use, and volatile organic compounds. Included is a more systematic review of the recent literature on long-term air pollution and child lung function because this is an indicator of future adult respiratory health and exposure assessment tools have improved dramatically in recent years. We present "summary observations" and "knowledge gaps." We end by discussing what is known about what can be done at the individual/household, local/regional, and national levels to overcome structural impediments, reduce air pollution exposures, and improve child lung function. We found a large literature on adverse air pollution effects on children's lung function level and growth; however, many questions remain. Important areas needing further research include whether early-life effects are fixed or reversible; and what are windows of increased susceptibility, long-term effects of repeated wildfire events, and effects of air quality interventions.

Interdisciplinary data science to advance environmental health research and improve birth outcomes

Rates of preterm birth and low birthweight continue to rise in the United States and pose a significant public health problem. Although a variety of environmental exposures are known to contribute to these and other adverse birth outcomes, there has been a limited success in developing policies to prevent these outcomes. A better characterization of the complexities between multiple exposures and their biological responses can provide the evidence needed to inform public health policy and strengthen preventative population-level interventions. In order to achieve this, we encourage the establishment of an interdisciplinary data science framework that integrates epidemiology, toxicology and bioinformatics with biomarker-based research to better define how population-level exposures contribute to these adverse birth outcomes. The proposed interdisciplinary research framework would 1) facilitate data-driven analyses using existing data from health registries and environmental monitoring programs; 2) develop novel algorithms with the ability to predict which exposures are driving, in this case, adverse birth outcomes in the context of simultaneous exposures; and 3) refine biomarker-based research, ultimately leading to new policies and interventions to reduce the incidence of adverse birth outcomes.

Metal-mixtures in toenails of children living near an active industrial facility in Los Angeles County, California

BACKGROUND

Children residing in communities near metalworking industries are vulnerable to multiple toxic metal exposures. Understanding biomarkers of exposure to multiple toxic metals is important to characterize cumulative burden and to distinguish potential exposure sources in such environmental justice neighborhoods impacted by industrial operations. Exposure to metal mixtures has not been well-characterized among children residing in the United States, and is understudied in communities of color.

METHODS

In this study we used toenail clippings, a noninvasive biomarker, to assess exposure to arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn), lead (Pb), antimony (Sb), selenium (Se), and vanadium (V). We used nonnegative matrix factorization (NMF) to identify "source" signatures and patterns of exposure among predominantly working class Latinx children residing near an industrial corridor in Southeast Los Angeles County. Additionally, we investigated the association between participant demographic, spatial, and dietary characteristics with identified metal signatures.

RESULTS

Through NMF, we identified three groupings (source factors) for the metal concentrations in children's toenails. A grouping composed of Sb, Pb, As, and Cd, was identified as a potential industrial source factor, reflective of known airborne elemental emissions in the industrial corridor. We further identified a manganese source factor primarily composed of Mn, and a potential dietary source factor driven by Se and Hg. We observed differences in the industrial source factor by age of participants, while the dietary source factor varied by neighborhood.

CONCLUSION

Utilizing an unsupervised dimension reduction technique (NMF), we identified a "source signature" of contamination in toenail samples from children living near metalworking industry. Investigating patterns and sources of exposures in cumulatively burdened communities is necessary to identify appropriate public health interventions.

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.

Bayesian Weighted Sums: A Flexible Approach to Estimate Summed Mixture Effects

OBJECTIVES

Methods exist to study exposure mixtures, but each is distinct in the research question it aims to address. We propose a new approach focused on estimating both the summed effect and individual weights of one or multiple exposure mixtures: Bayesian Weighted Sums (BWS).

METHODS

We applied BWS to simulated and real datasets with correlated exposures. The analytic context in our real-world example is an estimation of the association between polybrominated diphenyl ether (PBDE) congeners (28, 47, 99, 100, and 153) and Autism Spectrum Disorder (ASD) diagnosis and Social Responsiveness Scores (SRS).

RESULTS

Simulations demonstrate that BWS performs reliably. In adjusted models using Early Autism Risk Longitudinal Investigation (EARLI) data, the odds of ASD for a 1-unit increase in the weighted sum of PBDEs were 1.41 (95% highest posterior density 0.82, 2.50) times the odds of ASD for the unexposed and the change in z-score standardized SRS per 1 unit increase in the weighted sum of PBDEs is 0.15 (95% highest posterior density -0.08, 0.38).

CONCLUSIONS

BWS provides a means of estimating the summed effect and weights for individual components of a mixture. This approach is distinct from other exposure mixture tools. BWS may be more flexible than existing approaches and can be specified to allow multiple exposure groups based on a priori knowledge from epidemiology or toxicology.

Particulate matter pollution and risk of outpatient visits for psychological diseases in Nanjing, China

Exposure to air pollution has been associated with increased risk for a range of adverse mental health conditions. Less is known about whether air pollution is also associated with increases in the utilization of mental health services, especially outpatient mental health service utilization. This study aimed to examine the association between the number of daily outpatient visits at the psychological disease departments of two major hospitals (PSYC) and daily average concentrations of PM_2.5 and PM₁₀ in a heavily polluted city in China, Nanjing, from 2013/7/1 to 2019/2/28, using generalized additive models with a quasi-Poisson regression. Results showed that each 10 μg/m³ increase in PM_2.5 concentration on lag0 day was associated with a 0.40% increase (95% CI: 0.07-0.72) in PSYC visits, and each 10 μg/m³ increase in PM₁₀ concentration on the same day a 0.31% increase (95% CI: 0.09-0.54) in PSYC visits. Exposure-response curves suggested linear relationships between PM concentration and daily PSYC outpatient visits, without evidence of a threshold. Associations remained positive, but were non-significant, with adjustment for co-pollutants, SO₂, NO₂ and CO. Significantly larger effects were found for older and male participants, vs. their counterparts. These findings add to the growing literature linking air pollution to mental health service utilization, demonstrating the critical need for both air pollution mitigation measures and increased capacity of the mental health system in China.

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.

Prenatal maternal phthalate exposures and child lipid and adipokine levels at age six: A study from the PROGRESS cohort of Mexico City

BACKGROUND

Prenatal phthalate exposures may affect processes that underlie offspring cardiometabolic health, but findings from studies examining these associations are conflicting. We examined associations between biomarkers of phthalate exposures during pregnancy with child lipid and adipokine levels.

METHODS

Data were from 463 mother-child pairs in the PROGRESS cohort of Mexico City. We quantified 15 phthalate metabolites in 2nd and 3rd trimester maternal urine samples and created an average pregnancy measure using the geometric mean. We evaluated the 15 metabolites as nine biomarkers, including four metabolite molar sums. We measured fasting serum triglycerides, non-HDL cholesterol, leptin, and adiponectin in children at the six-year follow-up visit (mean = 6.8 years). We estimated associations using linear regression, Bayesian kernel machine regression (BKMR), and weighted quantile sum (WQS) and assessed effect modification by sex.

RESULTS

In BKMR and WQS models, higher concentrations of the total mixture of phthalate biomarkers were associated with lower triglycerides (β = -3.7% [-6.5, -0.78] per 1 unit increase in WQS biomarker index) and non-HDL cholesterol (β = -2.0 [-3.7, -0.25] ng/ml per increase in WQS biomarker index). Associations between individual biomarkers and child outcomes were largely null. We observed some evidence of effect modification by child sex for mono-3-carboxypropyl phthalate (β = 19.4% [1.26, 40.7] per doubling of phthalate) and monobenzyl phthalate (β = -7.6% [-14.4, -0.23]) in girls for adiponectin.

CONCLUSIONS

Individual prenatal phthalate biomarkers were not associated with child lipid or adipokine levels. Contrary to our hypothesis, the total phthalate mixture was associated with lower child triglycerides and non-HDL cholesterol.

Depression and Anxiety Associated with Exposure to Fine Particulate Matter Constituents: A Cross-Sectional Study in North China

The association between fine particulate matter (PM_2.5) exposure and mental disorders is attracting increasing attention, but the roles of specific PM_2.5 chemical constituents have yet to be explored. We conducted a multicenter cross-sectional study in nine cities located in the Beijing-Tianjin-Hebei region in China to assess the effects of PM_2.5 and chemical constituents on depression and anxiety. The Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scale were used to quantify the depression and anxiety status, atmospheric monitoring data from fixed stations was used to calculate exposure concentrations. We performed multiple logistic regression models to assess the associations of PM_2.5 chemical constituents exposure over the preceding 2 weeks with depression and anxiety. Overall, anxiety and depression were significantly associated with organic carbon (OC), elemental carbon (EC), copper (Cu), cadmium (Cd), nickel (Ni), and zinc (Zn). Subgroup analysis showed a stronger effect of PM_2.5 constituents on depression during the heating period. This study provide evidence for the possible link between PM_2.5 constituents and mental disorders among middle-aged and elderly Chinese adults, which requires further validation of the causal correlation. Our findings support the need for a stricter regulation on emissions of certain specific constituents, in addition to targeting control of total PM_2.5 emission concentration.

Environmental risk factors for endometriosis: A critical evaluation of studies and recommendations from the epidemiologic perspective

PURPOSE OF REVIEW

Recent studies of environmental chemicals and endometriosis were critically evaluated from the epidemiologic perspective to identify aspects of study design and analyses that may contribute to discrepant results across studies.

RECENT FINDINGS

Of the 29 studies reviewed, 12 studies used new approaches to population-based sampling. The remaining studies were conducted primarily among patients undergoing pelvic surgery; controls may not represent the exposure experience of the underlying study base, resulting in biased estimates of associations. Most studies used biologic specimens collected near diagnosis and varied in analytic approaches to minimize bias. Few studies investigated ovarian, deep-infiltrating, and peritoneal endometriosis presentations separately.

SUMMARY

Recommendations to move the field forward include: (1) control selection from a defined study base, (2) exposure characterization during the etiologically-relevant window, (3) employment of best practices to minimize bias in analyses, and (4) separate consideration of endometriosis presentations that may be etiologically-distinct entities.

Accelerating the Search for Interventions Aimed at Expanding the Health Span in Humans: The Role of Epidemiology

BACKGROUND

Extensive work in basic and clinical science suggests that biological mechanisms of aging are causally related to the development of disease and disability in late life. Modulation of the biological mechanisms of aging can extend both life span and health span in animal models, but translation to humans has been slow.

METHODS

Summary of workshop proceedings from the 2018-2019 Epidemiology of Aging Workshop hosted by the Intramural Research Program at the National Institute on Aging.

RESULTS

Epidemiologic studies play a vital role to progress in this field, particularly in evaluating new risk factors and measures of biologic aging that may influence health span, as well as developing relevant outcome measures that are robust and relevant for older individuals.

CONCLUSIONS

Appropriately designed epidemiological studies are needed to identify targets for intervention and to inform study design and sample size estimates for future clinical trials designed to promote health span.