Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research.

BackgroundThe National Institutes of Health's Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemicals for biomonitoring that may present health risk concerns.ObjectivesOur aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nationwide and to identify gaps needing additional research.MethodsWe searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemicals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency exposure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.ResultsFor the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, developmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro assays. Positive results for endocrine, developmental, neurotoxicity, and obesity were observed for 32, 11, 35, and 60 chemicals, respectively. Predictive modeling results suggested 90% are toxicants. Biomarkers were reported for 76 chemicals. Thirty-six were recommended for biomonitoring, 108 deferred pending additional research, and 11 as low priority for biomonitoring.DiscussionThe 108 deferred chemicals included those lacking biomonitoring methods or toxicity data, representing an opportunity for future research. Our evaluation was, in general, limited by the large number of unmeasured or untested chemicals. https://doi.org/10.1289/EHP5133

Prenatal metal(loid) mixtures and birth weight for gestational age: A pooled analysis of three cohorts participating in the ECHO program

Background: A growing number of studies have identified both toxic and essential metals which influence fetal growth. However, most studies have conducted single-cohort analyses, which are often limited by narrow exposure ranges, and evaluated metals individually. The objective of the current study was to conduct an environmental mixture analysis of metal impacts on fetal growth, pooling data from three geographically and demographically diverse cohorts in the United States participating in the Environmental Influences on Child Health Outcomes program. Methods: The pooled sample (N = 1,002) included participants from the MADRES, NHBCS, and PROTECT cohorts. Associations between seven metals (antimony, cadmium, cobalt, mercury, molybdenum, nickel, tin) measured in maternal urine samples collected during pregnancy (median: 16.0 weeks gestation) and birth weight for gestational age z-scores (BW for GA) were investigated using Bayesian Kernel Machine Regression (BKMR). Models were also stratified by cohort and infant sex to investigate possible heterogeneity. Chromium and uranium concentrations fell below the limits of detection for most participants and were evaluated separately as binary variables using pooled linear regression models. Results: In the pooled BKMR analysis, antimony, mercury, and tin were inversely and linearly associated with BW for GA, while a positive linear association was identified for nickel. The inverse association between antimony and BW for GA was observed in both males and females and for all three cohorts but was strongest for MADRES, a predominantly low-income Hispanic cohort in Los Angeles. A reverse j-shaped association was identified between cobalt and BW for GA, which was driven by female infants. Pooled associations were null for cadmium, chromium, molybdenum, and uranium, and BKMR did not identify potential interactions between metal pairs. Conclusions: Findings suggest that antimony, an understudied metalloid, may adversely impact fetal growth. Cohort- and/or sex-dependent associations were identified for many of the metals, which merit additional investigation

The Associations of Maternal Health Characteristics, Newborn Metabolite Concentrations, and Child Body Mass Index among US Children in the ECHO Program

We aimed first to assess associations between maternal health characteristics and newborn metabolite concentrations and second to assess associations between metabolites associated with maternal health characteristics and child body mass index (BMI). This study included 3492 infants enrolled in three birth cohorts with linked newborn screening metabolic data. Maternal health characteristics were ascertained from questionnaires, birth certificates, and medical records. Child BMI was ascertained from medical records and study visits. We used multivariate analysis of variance, followed by multivariable linear/proportional odds regression, to determine maternal health characteristic-newborn metabolite associations. Significant associations were found in discovery and replication cohorts of higher pre-pregnancy BMI with increased C0 and higher maternal age at delivery with increased C2 (C0: discovery: aβ 0.05 [95% CI 0.03, 0.07]; replication: aβ 0.04 [95% CI 0.006, 0.06]; C2: discovery: aβ 0.04 [95% CI 0.003, 0.08]; replication: aβ 0.04 [95% CI 0.02, 0.07]). Social Vulnerability Index, insurance, and residence were also associated with metabolite concentrations in a discovery cohort. Associations between metabolites associated with maternal health characteristics and child BMI were modified from 1–3 years (interaction: p < 0.05). These findings may provide insights on potential biologic pathways through which maternal health characteristics may impact fetal metabolic programming and child growth patterns

Birth Outcomes in Relation to Prenatal Exposure to Per- and Polyfluoroalkyl Substances and Stress in the Environmental Influences on Child Health Outcomes (ECHO) Program

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are persistent and ubiquitous chemicals associated with risk of adverse birth outcomes. Results of previous studies have been inconsistent. Associations between PFAS and birth outcomes may be affected by psychosocial stress. OBJECTIVES: We estimated risk of adverse birth outcomes in relation to prenatal PFAS concentrations and evaluate whether maternal stress modifies those relationships. METHODS: We included 3,339 participants from 11 prospective prenatal cohorts in the Environmental influences on the Child Health Outcomes (ECHO) program to estimate the associations of five PFAS and birth outcomes. We stratified by perceived stress scale scores to examine effect modification and used Bayesian Weighted Sums to estimate mixtures of PFAS. RESULTS: We observed reduced birth size with increased concentrations of all PFAS. For a 1-unit higher log-normalized exposure to perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorononanoic acid (PFNA), and perfluorohexane sulfonic acid (PFHxS), we observed lower birthweight-for-gestational-age z-scores of formula presented [95% confidence interval (CI): formula presented ], formula presented (95% CI: formula presented ), formula presented (95% CI: formula presented ), formula presented (95% CI: formula presented , 0.06), and formula presented (95% CI: formula presented ), respectively. We observed a lower odds ratio (OR) for large-for-gestational-age: formula presented (95% CI: 0.38, 0.83), formula presented (95% CI: 0.35, 0.77). For a 1-unit increase in log-normalized concentration of summed PFAS, we observed a lower birthweight-for-gestational-age z-score [formula presented ; 95% highest posterior density (HPD): formula presented ] and decreased odds of large-for-gestational-age (formula presented ; 95% HPD: 0.29, 0.82). Perfluorodecanoic acid (PFDA) explained the highest percentage (40%) of the summed effect in both models. Associations were not modified by maternal perceived stress. DISCUSSION: Our large, multi-cohort study of PFAS and adverse birth outcomes found a negative association between prenatal PFAS and birthweight-for-gestational-age, and the associations were not different in groups with high vs. low perceived stress. This study can help inform policy to reduce exposures in the environment and humans. https://doi.org/10.1289/EHP10723

Maternal tobacco smoking and offspring autism spectrum disorder or traits in ECHO cohorts

Given inconsistent evidence on preconception or prenatal tobacco use and offspring autism spectrum disorder (ASD), this study assessed associations of maternal smoking with ASD and ASD-related traits. Among 72 cohorts in the Environmental Influences on Child Health Outcomes consortium, 11 had ASD diagnosis and prenatal tobaccosmoking (n = 8648). and 7 had Social Responsiveness Scale (SRS) scores of ASD traits (n = 2399). Cohorts had diagnoses alone (6), traits alone (2), or both (5). Diagnoses drew from parent/caregiver report, review of records, or standardized instruments. Regression models estimated smoking-related odds ratios (ORs) for diagnoses and standardized mean differences for SRS scores. Cohort-specific ORs were meta-analyzed. Overall, maternal smoking was unassociated with child ASD (adjusted OR, 1.08; 95% confidence interval [CI], 0.72–1.61). However, heterogeneity across studies was strong: preterm cohorts showed reduced ASD risk for exposed children. After excluding preterm cohorts (biased by restrictions on causal intermediate and exposure opportunity) and small cohorts (very few ASD cases in either smoking category), the adjusted OR for ASD from maternal smoking was 1.44 (95% CI, 1.02–2.03). Children of smoking (versus non-smoking) mothers had more ASD traits (SRS T-score + 2.37 points, 95% CI, 0.73–4.01 points), with results homogeneous across cohorts. Maternal preconception/prenatal smoking was consistently associated with quantitative ASD traits and modestly associated with ASD diagnosis among sufficiently powered United States cohorts of non-preterm children. Limitations resulting from self-reported smoking and unmeasured confounders preclude definitive conclusions. Nevertheless, counseling on potential and known risks to the child from maternal smoking is warranted for pregnant women and pregnancy planners. Lay Summary: Evidence on the association between maternal prenatal smoking and the child's risk for autism spectrum disorder has been conflicting, with some studies reporting harmful effects, and others finding reduced risks. Our analysis of children in the ECHO consortium found that maternal prenatal tobacco smoking is consistently associated with an increase in autism-related symptoms in the general population and modestly associated with elevated risk for a diagnosis of autism spectrum disorder when looking at a combined analysis from multiple studies that each included both pre- and full-term births. However, this study is not proof of a causal connection. Future studies to clarify the role of smoking in autism-like behaviors or autism diagnoses should collect more reliable data on smoking and measure other exposures or lifestyle factors that might have confounded our results

Cardiometabolic Pregnancy Complications in Association With Autism-Related Traits as Measured by the Social Responsiveness Scale in ECHO

Prior work has examined associations between cardiometabolic pregnancy complications and autism spectrum disorder (ASD) but not how these complications may relate to social communication traits more broadly. We addressed this question within the Environmental Inf luences on Child Health Outcomes program, with 6,778 participants from 40 cohorts conducted from 1998–2021 with information on ASD-related traits via the Social Responsiveness Scale. Four metabolic pregnancy complications were examined individually, and combined, in association with Social Responsiveness Scale scores, using crude and adjusted linear regression as well as quantile regression analyses. We also examined associations stratified by ASD diagnosis, and potential mediation by preterm birth and low birth weight, and modification by child sex and enriched risk of ASD. Increases in ASD-related traits were associated with obesity (β = 4.64, 95% confidence interval: 3.27, 6.01) and gestational diabetes (β = 5.21, 95% confidence interval: 2.41, 8.02), specifically, but not with hypertension or preeclampsia. Results among children without ASD were similar to main analyses, but weaker among ASD cases. There was not strong evidence for mediation or modification. Results suggest that common cardiometabolic pregnancy complications may inf luence child ASD-related traits, not only above a diagnostic threshold relevant to ASD but also across the population

Sociodemographic Differences in COVID-19 Pandemic Experiences Among Families in the United States

Few population-based studies in the US collected individual-level data from families during the COVID-19 pandemic.To examine differences in COVID-19 pandemic–related experiences in a large sociodemographically diverse sample of children and caregivers.The Environmental influences on Child Health Outcomes (ECHO) multi-cohort consortium is an ongoing study that brings together 64 individual cohorts with participants (24 757 children and 31 700 caregivers in this study) in all 50 US states and Puerto Rico. Participants who completed the ECHO COVID-19 survey between April 2020 and March 2022 were included in this cross-sectional analysis. Data were analyzed from July 2021 to September 2022.Exposures of interest were caregiver education level, child life stage (infant, preschool, middle childhood, and adolescent), and urban or rural (population &lt;50 000) residence. Dependent variables included COVID-19 infection status and testing; disruptions to school, child care, and health care; financial hardships; and remote work. Outcomes were examined separately in logistic regression models mutually adjusted for exposures of interest and race, ethnicity, US Census division, sex, and survey administration date.Analyses included 14 646 children (mean [SD] age, 7.1 [4.4] years; 7120 [49%] female) and 13 644 caregivers (mean [SD] age, 37.6 [7.2] years; 13 381 [98%] female). Caregivers were racially (3% Asian; 16% Black; 12% multiple race; 63% White) and ethnically (19% Hispanic) diverse and comparable with the US population. Less than high school education (vs master’s degree or more) was associated with more challenges accessing COVID-19 tests (adjusted odds ratio [aOR], 1.88; 95% CI, 1.06-1.58), lower odds of working remotely (aOR, 0.04; 95% CI, 0.03-0.07), and more food access concerns (aOR, 4.14; 95% CI, 3.20-5.36). Compared with other age groups, young children (age 1 to 5 years) were least likely to receive support from schools during school closures, and their caregivers were most likely to have challenges arranging childcare and concerns about work impacts. Rural caregivers were less likely to rank health concerns (aOR, 0.77; 95% CI, 0.69-0.86) and social distancing (aOR, 0.82; 95% CI, 0.73-0.91) as top stressors compared with urban caregivers.Findings in this cohort study of US families highlighted pandemic-related burdens faced by families with lower socioeconomic status and young children. Populations more vulnerable to public health crises should be prioritized in recovery efforts and future planning

Combining Effect Estimates Across Cohorts and Sufficient Adjustment Sets for Collaborative Research: A Simulation Study.

BackgroundCollaborative research often combines findings across multiple, independent studies via meta-analysis. Ideally, all study estimates that contribute to the meta-analysis will be equally unbiased. Many meta-analyses require all studies to measure the same covariates. We explored whether differing minimally sufficient sets of confounders identified by a directed acyclic graph (DAG) ensures comparability of individual study estimates. Our analysis applied four statistical estimators to multiple minimally sufficient adjustment sets identified in a single DAG.MethodsWe compared estimates obtained via linear, log-binomial, and logistic regression and inverse probability weighting, and data were simulated based on a previously published DAG.ResultsOur results show that linear, log-binomial, and inverse probability weighting estimators generally provide the same estimate of effect for different estimands that are equally sufficient to adjust confounding bias, with modest differences in random error. In contrast, logistic regression often performed poorly, with notable differences in effect estimates obtained from unique minimally sufficient adjustment sets, and larger standard errors than other estimators.ConclusionsOur findings do not support the reliance of collaborative research on logistic regression results for meta-analyses. Use of DAGs to identify potentially differing minimally sufficient adjustment sets can allow meta-analyses without requiring the exact same covariates

Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research.

BackgroundThe National Institutes of Health's Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemicals for biomonitoring that may present health risk concerns.ObjectivesOur aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nationwide and to identify gaps needing additional research.MethodsWe searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemicals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency exposure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.ResultsFor the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in&nbsp;vivo endocrine, developmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro assays. Positive results for endocrine, developmental, neurotoxicity, and obesity were observed for 32, 11, 35, and 60 chemicals, respectively. Predictive modeling results suggested 90% are toxicants. Biomarkers were reported for 76 chemicals. Thirty-six were recommended for biomonitoring, 108 deferred pending additional research, and 11 as low priority for biomonitoring.DiscussionThe 108 deferred chemicals included those lacking biomonitoring methods or toxicity data, representing an opportunity for future research. Our evaluation was, in general, limited by the large number of unmeasured or untested chemicals. https://doi.org/10.1289/EHP5133

Erratum: "Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research".

In the published article, incorrect versions of the supplemental materials, both the Word document and the Excel file, were included. The correct versions can be found in this erratum and are now associated with the published article. EHP regrets the error