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Background: Organophosphate esters (OPEs) are synthetic chemicals used as flame retardants and plasticizers in a variety of goods. Despite ubiquitous human exposures and laboratory evidence that prenatal OPE exposures may disrupt offspring metabolism, perinatal studies of OPE health effects are limited. The objectives of this study were to: 1) Determine predictors and reproducibility of urinary OPE biomarker concentrations during pregnancy, and 2) Estimate the relation of prenatal OPE exposures with birth outcomes and cord blood adipokine and insulin concentrations., Methods: We analyzed five OPE metabolites in urine samples collected at up to three visits during pregnancy from 90 women enrolled in the ORigins of Child Health And Resilience in Development (ORCHARD) pregnancy cohort in Baltimore, MD from 2017 to 2019. To quantify the variability of metabolite concentrations during pregnancy, we calculated intraclass correlation coefficients (ICCs) for each metabolite using mixed effects regression models. Using self-reported questionnaire data collected during gestation, we assessed possible sociodemographic and environmental/behavioral predictors of each OPE metabolite using generalized estimating equations to account for repeated exposure measures. We ascertained birth outcomes of 76 offspring from medical records, including weight-for-gestational age, length, ponderal index, and gestational age. In a subset of 37 infants, we measured cord blood concentrations of leptin, adiponectin, and insulin. To account for repeated exposure measures, we used linear structural equation models to assess the relations of standard deviation (SD) increases in prenatal OPE metabolite factor scores with continuous birth outcomes and cord blood biomarker concentrations., Results: ICCs ranged from 0.09 for isopropylphenyl-phenyl phosphate (ip-PPP) to 0.59 for bis(1,3-dichloro-2-propyl) phosphate (BDCIPP). We observed little consistency in environmental or behavioral predictors of OPE exposures, although concentrations were generally lower for samples collected in the afternoon compared to morning and winter compared to other seasons. In adjusted analyses, a SD increase in BDCIPP concentration was associated with a 0.06 g/cm 3 (95% CI: 0.00, 0.12) greater ponderal index. A SD increase in BDCIPP was associated with a 0.37 (95% CI: - 0.62, - 0.13) SD lower insulin concentration and 0.24 (95% CI: - 0.39, - 0.08) SD lower leptin concentration. Other OPEs were not associated with infant outcomes., Conclusions: These findings suggest some OPEs may be metabolic disruptors warranting investigation in larger studies.

Purpose of Review: Urine- and serum-based biomarkers are useful for assessing individuals' exposure to environmental factors. However, variations in urinary creatinine (a measure of dilution) or serum lipid levels, if not adequately corrected for, can directly impact biomarker concentrations and bias exposure-disease association measures., Recent Findings: Recent methodological literature has considered the complex relationships between creatinine or serum lipid levels, exposure biomarkers, outcomes, and other potentially relevant factors using directed acyclic graphs and simulation studies. The optimal measures of urinary dilution and serum lipids have also been investigated. Existing evidence supports the use of covariate-adjusted standardization plus creatinine adjustment for urinary biomarkers and standardization plus serum lipid adjustment for lipophilic, serum-based biomarkers. It is unclear which urinary dilution measure is best, but all serum lipid measures performed similarly. Future research should assess methods for pooled biomarkers and for studying diseases and exposures that affect creatinine or serum lipids directly.

BackgroundWidespread exposure to organophosphate ester (OPE) flame retardants with potential reproductive toxicity raises concern regarding the impacts of gestational exposure on birth outcomes. Previous studies of prenatal OPE exposure and birth outcomes had limited sample sizes, with inconclusive results.ObjectivesWe conducted a collaborative analysis of associations between gestational OPE exposures and adverse birth outcomes and tested whether associations were modified by sex.MethodsWe included 6,646 pregnant participants from 16 cohorts in the Environmental influences on Child Health Outcomes (ECHO) Program. Nine OPE biomarkers were quantified in maternal urine samples collected primarily during the second and third trimester and modeled as log2-transformed continuous, categorized (high/low/nondetect), or dichotomous (detect/nondetect) variables depending on detection frequency. We used covariate-adjusted linear, logistic, and multinomial regression with generalized estimating equations, accounting for cohort-level clustering, to estimate associations of OPE biomarkers with gestational length and birth weight outcomes. Secondarily, we assessed effect modification by sex.ResultsThree OPE biomarkers [diphenyl phosphate (DPHP), a composite of dibutyl phosphate and di-isobutyl phosphate (DBUP/DIBP), and bis(1,3-dichloro-2-propyl) phosphate] were detected in >85% of participants. In adjusted models, DBUP/DIBP [odds ratio (OR) per doubling=1.07; 95% confidence interval (CI): 1.02, 1.12] and bis(butoxyethyl) phosphate (OR for high vs. nondetect=1.25; 95% CI: 1.06, 1.46), but not other OPE biomarkers, were associated with higher odds of preterm birth. We observed effect modification by sex for associations of DPHP and high bis(2-chloroethyl) phosphate with completed gestational weeks and odds of preterm birth, with adverse associations among females. In addition, newborns of mothers with detectable bis(1-chloro-2-propyl) phosphate, bis(2-methylphenyl) phosphate, and dipropyl phosphate had higher birth weight-for-gestational-age z-scores (β for detect vs. nondetect=0.04-0.07); other chemicals showed null associations.DiscussionIn the largest study to date, we find gestational exposures to several OPEs are associated with earlier timing of birth, especially among female neonates, or with greater fetal growth. https://doi.org/10.1289/EHP13182.

BackgroundPhthalate exposures are ubiquitous during pregnancy and may contribute to racial and ethnic disparities in preterm birth.ObjectivesWe investigated race and ethnicity in the relationship between biomarkers of phthalate exposure and preterm birth by examining: a) how hypothetical reductions in racial and ethnic disparities in phthalate metabolites might reduce the probability of preterm birth; and b) exposure-response models stratified by race and ethnicity.MethodsWe pooled individual-level data on 6,045 pregnancies from 16 U.S. cohorts. We investigated covariate-adjusted differences in nine urinary phthalate metabolite concentrations by race and ethnicity [non-Hispanic White (White, 43%), non-Hispanic Black (Black, 13%), Hispanic/Latina (38%), and Asian/Pacific Islander (3%)]. Using g-computation, we estimated changes in the probability of preterm birth under hypothetical interventions to eliminate disparities in levels of urinary phthalate metabolites by proportionally lowering average concentrations in Black and Hispanic/Latina participants to be approximately equal to the averages in White participants. We also used race and ethnicity-stratified logistic regression to characterize associations between phthalate metabolites and preterm birth.ResultsIn comparison with concentrations among White participants, adjusted mean phthalate metabolite concentrations were consistently higher among Black and Hispanic/Latina participants by 23%-148% and 4%-94%, respectively. Asian/Pacific Islander participants had metabolite levels that were similar to those of White participants. Hypothetical interventions to reduce disparities in metabolite mixtures were associated with lower probabilities of preterm birth for Black [13% relative reduction; 95% confidence interval (CI): -34%, 8.6%] and Hispanic/Latina (9% relative reduction; 95% CI: -19%, 0.8%) participants. Odds ratios for preterm birth in association with phthalate metabolites demonstrated heterogeneity by race and ethnicity for two individual metabolites (mono-n-butyl and monoisobutyl phthalate), with positive associations that were larger in magnitude observed among Black or Hispanic/Latina participants.ConclusionsPhthalate metabolite concentrations differed substantially by race and ethnicity. Our results show hypothetical interventions to reduce population-level racial and ethnic disparities in biomarkers of phthalate exposure could potentially reduce the probability of preterm birth. https://doi.org/10.1289/EHP12831.

The Environmental Influences on Child Health Outcomes (ECHO) Program will evaluate environmental factors affecting children's health (perinatal, neurodevelopmental, obesity, respiratory, and positive health outcomes) by pooling cohorts composed of >50,000 children in the largest US study of its kind. Our objective was to identify opportunities for studying chemicals and child health using existing or future ECHO chemical exposure data. We described chemical-related information collected by ECHO cohorts and reviewed ECHO-relevant literature on exposure routes, sources, and environmental and human monitoring. Fifty-six ECHO cohorts have existing or planned chemical biomonitoring data for mothers or children. Environmental phenols/parabens, phthalates, metals/metalloids, and tobacco biomarkers are each being measured by ≥15 cohorts, predominantly during pregnancy and childhood, indicating ample opportunities to study child health outcomes. Cohorts are collecting questionnaire data on multiple exposure sources and conducting environmental monitoring including air, dust, and water sample collection that could be used for exposure assessment studies. To supplement existing chemical data, we recommend biomonitoring of emerging chemicals, nontargeted analysis to identify novel chemicals, and expanded measurement of chemicals in alternative biological matrices and dust samples. ECHO's rich data and samples represent an unprecedented opportunity to accelerate environmental chemical research to improve the health of US children.

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

BACKGROUND: Widespread exposure to organophosphate ester (OPE) flame retardants with potential reproductive toxicity raises concern regarding the impacts of gestational exposure on birth outcomes. Previous studies of prenatal OPE exposure and birth outcomes had limited sample sizes, with inconclusive results. OBJECTIVES: We conducted a collaborative analysis of associations between gestational OPE exposures and adverse birth outcomes and tested whether associations were modified by sex. METHODS: We included 6,646 pregnant participants from 16 cohorts in the Environmental influences on Child Health Outcomes (ECHO) Program. Nine OPE biomarkers were quantified in maternal urine samples collected primarily during the second and third trimester and modeled as log2-transformed continuous, categorized (high/low/nondetect), or dichotomous (detect/nondetect) variables depending on detection frequency. We used covariate-adjusted linear, logistic, and multinomial regression with generalized estimating equations, accounting for cohort-level clustering, to estimate associations of OPE biomarkers with gestational length and birth weight outcomes. Secondarily, we assessed effect modification by sex. RESULTS: Three OPE biomarkers [diphenyl phosphate (DPHP), a composite of dibutyl phosphate and di-isobutyl phosphate (DBUP/DIBP), and bis (1,3-dichloro-2-propyl) phosphate] were detected in >85% of participants. In adjusted models, DBUP/DIBP [odds ratio (OR) per doubling= 1.07; 95% confidence interval (CI): 1.02, 1.12] and bis(butoxyethyl) phosphate (OR for high vs. nondetect=1.25; 95% CI: 1.06, 1.46), but not other OPE biomarkers, were associated with higher odds of preterm birth. We observed effect modification by sex for associations of DPHP and high bis(2- chloroethyl) phosphate with completed gestational weeks and odds of preterm birth, with adverse associations among females. In addition, newborns of mothers with detectable bis(1-chloro-2-propyl) phosphate, bis(2-methylphenyl) phosphate, and dipropyl phosphate had higher birth weight-for-gestational- age z-scores (β for detect vs. nondetect= 0.04–0.07); other chemicals showed null associations. DISCUSSION: In the largest study to date, we find gestational exposures to several OPEs are associated with earlier timing of birth, especially among female neonates, or with greater fetal growth. [ABSTRACT FROM AUTHOR]

Epidemiologic studies often quantify exposure using biomarkers, which commonly have statistically skewed distributions. Although normality assumption is not required if the biomarker is used as an independent variable in linear regression, it has become common practice to log-transform the biomarker concentrations. This transformation can be motivated by concerns for nonlinear dose-response relationship or outliers; however, such transformation may not always reduce bias. In this study, we evaluated the validity of motivations underlying the decision to log-transform an independent variable using simulations, considering eight scenarios that can give rise to skewed X and normal Y. Our simulation study demonstrates that (1) if the skewness of exposure did not arise from a biasing factor (e.g., measurement error), the analytic approach with the best overall model fit best reflected the underlying outcome generating methods and was least biased, regardless of the skewness of X and (2) all estimates were biased if the skewness of exposure was a consequence of a biasing factor. We additionally illustrate a process to determine whether the transformation of an independent variable is needed using NHANES. Our study and suggestion to divorce the shape of the exposure distribution from the decision to log-transform it may aid researchers in planning for analysis using biomarkers or other skewed independent variables. [ABSTRACT FROM AUTHOR]

BACKGROUND: There are few existing methods to quantify total exposure burden to chemical mixtures, independent of a health outcome. A summary metric could be advantageous for use in biomonitoring, risk assessment, health risk calculators, and mediation models. OBJECTIVE: We developed a novel exposure burden score method for chemical mixtures, applied it to estimate exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures, and estimated associations of PFAS burden scores with cardio-metabolic outcomes in the general U.S. population. METHODS: We applied item response theory (IRT) to biomonitoring data from 1,915 children and adults 12–80 years of age in the 2017–2018 National Health and Examination Survey to quantify a latent PFAS burden score, using serum concentrations of eight measured PFAS biomarkers, each considered an “item.” The premise of IRT is that through using both information about a participant’s concentration of an individual PFAS bio-marker, as well as their exposure patterns for the PFAS mixture, we can estimate the participant’s latent PFAS exposure burden, independent of a health outcome. We used linear regression to estimate associations of the PFAS burden score with cardio-metabolic outcomes and compared our findings to results using summed PFAS concentrations as the exposure metric. RESULTS: PFAS burden scores and summed PFAS concentrations had moderate-high correlation (ρ=0.75). Isomers of PFOS [푛-perfluorooctane sulfonic acid (푛-PFOS) and perfluoromethylheptane sulfonic acid isomers (Sm-PFOS)] were the most informative to the PFAS burden scores. PFAS burden scores and summed PFAS concentrations were both significantly associated with cardio-metabolic outcomes, but associations were generally closer to the null for summed PFAS concentrations vs. the PFAS burden score. Adjusted associations (95% CIs) with total cholesterol (in milligrams per deciliter) were 8.6 (95% CI: 5.2, 11.9) and 2.4 (95% CI: 0.5, 4.2) per interquartile range increase in the PFAS burden score and summed concentrations, respectively. Sensitivity analyses showed similar associations with cardio-metabolic outcomes when only a subset of PFAS biomarkers was used to estimate PFAS burden. In a validation study, associations between PFAS burden scores and cholesterol were consistent with primary analyses but null when using summed PFAS concentrations. DISCUSSION: IRT offers a straightforward way to include exposure biomarkers with low detection frequencies and can reduce exposure measurement error. Further, IRT enables comparisons of exposure burden to chemical mixtures across studies even if they did not measure the exact same set of chemicals, which supports harmonization across studies and consortia. We provide an accompanying PFAS burden calculator (https://pfasburden. shinyapps.io/app%5Fpfas%5Fburden/), enabling researchers to calculate PFAS burden scores based on U.S. population exposure reference ranges. [ABSTRACT FROM AUTHOR]

Background: Guidance is lacking for how to combine urinary biomarker data across studies that use different measures of urinary dilution, that is, creatinine or specific gravity.Methods: Among 741 pregnant participants from four sites of The Infant Development and Environment Study (TIDES) cohort, we assessed the relation of maternal urinary di-2-ethylhexyl phthalate (DEHP) concentrations with preterm birth. We compared scenarios in which all sites measured either urinary creatinine or specific gravity, or where measure of dilution differed by site. In addition to a scenario with no dilution adjustment, we applied and compared three dilution-adjustment approaches: a standard regression-based approach for creatinine, a standard approach for specific gravity (Boeniger method), and a more recently developed approach that has been applied to both (covariate-adjusted standardization method). For each scenario and dilution-adjustment method, we estimated the association between a doubling in the molar sum of DEHP (∑DEHP) and odds of preterm birth using logistic regression.Results: All dilution-adjustment approaches yielded comparable associations (odds ratio [OR]) that were larger in magnitude than when we did not perform dilution adjustment. A doubling of ∑DEHP was associated with 9% greater odds of preterm birth (OR = 1.09, 95% confidence interval [CI] = 0.91, 1.30) when applying no dilution-adjustment method, whereas dilution-adjusted point estimates were higher, and similar across all scenarios and methods: 1.13-1.20 (regression-based), 1.15-1.18 (Boeniger), and 1.14-1.21 (covariate-adjusted standardization).Conclusions: In our applied example, we demonstrate that it is possible and straightforward to combine urinary biomarker data across studies when measures of dilution differ. [ABSTRACT FROM AUTHOR]

BACKGROUND: The 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. OBJECTIVES: Our 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. METHODS: We 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. RESULTS: For 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. DISCUSSION: The 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. [ABSTRACT FROM AUTHOR]

BACKGROUND: When a biologic mechanism of interest is anticipated to operate differentially according to sex, as is often the case in endocrine disruptors research, investigators routinely estimate sex-specific associations. Less attention has been given to potential sexual heterogeneity of confounder associations with outcomes. When relationships of covariates with outcomes differ according to sex, commonly applied statistical approaches for estimating sex-specific endocrine disruptor effects may produce divergent estimates. OBJECTIVES: We discuss underlying assumptions and evaluate the performance of two traditional approaches for estimating sex-specific effects, stratification and product terms, and introduce a simple modeling alternative: an augmented product term approach. METHODS: We describe the impact of assumptions regarding sexual heterogeneity of confounder relationships on estimates of sex-specific effects of the exposure of interest for three approaches: stratification, traditional product terms, and augmented product terms. Using simulated and applied examples, we demonstrate properties of each approach under a range of scenarios. RESULTS: In simulations, sex-specific exposure effects estimated using the traditional product term approach were biased when confounders had sexdependent associations with the outcome. Sex-specific estimates from stratification and the augmented product term approach were unbiased but less precise. In the applied example, the three approaches yielded similar estimates, but resulted in some meaningful differences in conclusions based on statistical significance. CONCLUSIONS: Investigators should consider sexual heterogeneity of confounder associations when choosing an analytic approach to estimate sexspecific effects of endocrine disruptors on health. In the presence of sex-dependent confounding, our augmented product term approach may be advantageous over stratification when there is prior knowledge available to fit reduced models or when investigators seek an automated test for effect measure modification. [ABSTRACT FROM AUTHOR]

• Urinary biomarkers are subject to measurement error due to urine dilution. • Urine creatinine or specific gravity are often used to correct for dilution. • Urine creatinine and specific gravity vary systematically by several factors. • Methods to correct for dilution should account for these factors to minimize bias. Urinary biomarkers are often corrected for sample dilution using creatinine, which is influenced by sociodemographic factors and certain health conditions. It is unknown whether these factors similarly influence specific gravity. To identify predictors of specific gravity and creatinine and compare methods for correcting estimated chemical concentrations for sample dilution using these measures. We assessed predictors of urinary specific gravity and creatinine among NHANES 2007–2008 participants (n = 7257). We corrected concentrations of mono-n-butyl phthalate (MnBP) for dilution using two methods, each applied to both specific gravity and creatinine: correction using a sample mean of the dilution indicator (i.e., specific gravity or creatinine) and covariate-adjusted standardization. We compared distributions and assessed the agreement of uncorrected or corrected concentrations visually using Bland-Altman plots and statistically by Kendall's τ a. We stratified all analyses by age category (i.e., 6–19 or 20+ years of age). Gender, race/ethnicity, body mass index, and height were associated with urinary specific gravity and creatinine. Distributions of corrected MnBP concentrations were comparable for both methods and dilution indicators, but agreement between methods was greater for specific gravity. Additionally, specific gravity- and creatinine-corrected MnBP concentrations had slightly greater agreement with each other when corrected using a covariate-adjusted standardization method. Specific gravity, like creatinine, is associated with sociodemographic and body composition variables. Accounting for these factors as part of the dilution correction method may be important to minimize bias. [ABSTRACT FROM AUTHOR]