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Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches.

ABSTRACT: OBJECTIVES:We applied three statistical approaches for evaluating associations between prenatal urinary concentrations of a mixture of phthalate metabolites and birth weight. METHODS:We included 300 women who provided 732 urine samples during pregnancy and delivered a singleton infant. We measured urinary concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, di-n-butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear regressions; 2) classification methods [principal component analysis (PCA) and structural equation models (SEM)]; and 3) Bayesian kernel machine regression (BKMR), to evaluate associations between phthalate metabolite mixtures and birth weight adjusting for potential confounders. Data were presented as mean differences (95% CI) in birth weight (grams) as each phthalate increased from the 10th to the 90th percentile. RESULTS:When analyzing individual phthalate metabolites using linear regressions, each metabolite demonstrated a modest inverse association with birth weight [from -93 (-206, 21) to -49 (-164, 65)]. When simultaneously including all metabolites in a multivariable model, inflation of the estimates and standard errors were noted. PCA identified two principal components, both inversely associated with birth weight [-23 (-68, 22), -27 (-71, 17), respectively]. These inverse associations were confirmed when applying SEM. BKMR further identified that monoethyl and mono(2-ethylhexyl) phthalate and phthalate concentrations were linearly related to lower birth weight [-51(-164, 63) and -122 (-311, 67), respectively], and suggested no evidence of interaction between metabolites. CONCLUSIONS:While none of the methods produced significant results, we demonstrated the potential issues arising using linear regression models in the context of correlated exposures. Among the other selected approaches, classification techniques identified common sources of exposures with implications for interventions, while BKMR further identified specific contributions of individual metabolites.

SUBMITTER: Chiu YH

PROVIDER: S-EPMC5866233 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches.

Chiu Yu-Han YH Bellavia Andrea A James-Todd Tamarra T Correia Katharine F KF Valeri Linda L Messerlian Carmen C Ford Jennifer B JB Mínguez-Alarcón Lidia L Calafat Antonia M AM Hauser Russ R Williams Paige L PL

Environment international 20180220

<h4>Objectives</h4>We applied three statistical approaches for evaluating associations between prenatal urinary concentrations of a mixture of phthalate metabolites and birth weight.<h4>Methods</h4>We included 300 women who provided 732 urine samples during pregnancy and delivered a singleton infant. We measured urinary concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, di-n-butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear regressions; 2) classification ...[more]

PMID: 29453090

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Project description:BackgroundPrevious studies of prenatal phthalate exposure and childhood asthma are inconsistent. These studies typically model phthalates as individual, rather than co-occurring, exposures. We investigated whether prenatal phthalates are associated with childhood wheeze and asthma using a mixtures approach.MethodsWe studied dyads from two prenatal cohorts in the ECHO-PATHWAYS consortium: CANDLE, recruited 2006-2011 and TIDES, recruited 2011-2013. Parents reported child respiratory outcomes at age 4-6 years: ever asthma, current wheeze (symptoms in past 12 months) and current asthma (two affirmative responses from ever asthma, recent asthma-specific medication use, and/or current wheeze). We quantified 11 phthalate metabolites in third trimester urine and estimated associations with child respiratory outcomes using weighted quantile sum (WQS) logistic regression, using separate models to estimate protective and adverse associations, adjusting for covariates. We examined effect modification by child sex and maternal asthma.ResultsOf 1481 women, most identified as White (46.6%) or Black (44.6%); 17% reported an asthma history. Prevalence of ever asthma, current wheeze and current asthma in children was 12.3%, 15.8% and 12.3%, respectively. Overall, there was no adverse association with respiratory outcomes. In sex-stratified analyses, boys' phthalate index was adversely associated with all outcomes (e.g., boys' ever asthma: adjusted odds ratio per one quintile increase in WQS phthalate index (AOR): 1.42; 95% confidence interval (CI): 1.08, 1.85, with mono-ethyl phthalate (MEP) weighted highest). Adverse associations were also observed in dyads without maternal asthma history, driven by MEP and mono-butyl phthalate (MBP), but not in those with maternal asthma history. We observed protective associations between the phthalate index and respiratory outcomes in analysis of all participants (e.g., ever asthma: AOR; 95% CI: 0.81; 0.68, 0.96), with di(2-ethylhexyl)phthalate (DEHP) metabolites weighted highest.ConclusionsResults suggest effect modification by child sex and maternal asthma in associations between prenatal phthalate mixtures and child asthma and wheeze.

Project description:BackgroundHumans are exposed to complex mixtures of phthalate chemicals from a range of consumer products. Previous studies have reported significant associations between individual phthalate metabolites and pregnancy outcomes, but mixtures research is limited.ObjectivesWe used the Puerto Rico Testsite for Exploring Contamination Threats longitudinal pregnancy cohort to investigate associations between phthalate metabolite mixtures and pregnancy outcomes.MethodsWomen (n=462 carrying females, n=540 carrying males) provided up to three urine samples throughout gestation (median 18, 22, and 26 wk), which were analyzed for 13 phthalate metabolites. Pregnancy outcomes including preterm birth (PTB), spontaneous PTB, small and large for gestational age (SGA, LGA), birth weight z-score, and gestational age at delivery were abstracted from medical records. Environmental risk scores (ERS) were calculated as a weighted linear combination of the phthalates from ridge regression and adaptive elastic net, which are variable selection methods to handle correlated predictors. Birth outcomes were regressed on continuous ERS. We assessed gestational average and visit-specific ERS and stratified all analyses by fetal sex. Finally, we used Bayesian kernel machine regression (BKMR) to explore nonlinear associations and interactions between metabolites.ResultsDifferences in metabolite weights from ridge and elastic net were apparent between birth outcomes and between fetal sexes. An interquartile range increase in gestational average phthalate ERS was associated with increased odds of PTB [male odds ratio (OR)=1.56; 95% confidence interval (CI): 1.08, 2.27; female OR=1.91; 95% CI: 1.23, 2.98], spontaneous PTB (male OR=2.32; 95% CI: 1.46, 3.68; female OR=2.00; 95% CI: 1.04, 3.82), and reduced gestational age at birth (male β=-0.39 wk, 95% CI: -0.62, -0.15; female β=-0.29 wk, 95% CI: -0.52, -0.05). Analyses by study visit suggested that exposure at ∼22 wk (range 20-24 wk) was driving those associations. Bivariate plots from BKMR analysis revealed some nonlinear associations and metabolite interactions that were different between fetal sexes.DiscussionThese results suggest that exposure to phthalate mixtures was associated with increased risk of early delivery and highlight the need to study mixtures by fetal sex. We also identified various metabolites displaying nonlinear relationships with measures of birth weight. https://doi.org/10.1289/EHP8990.

Project description:BackgroundParental preconception exposure to select phenols and phthalates was previously associated with increased risk of preterm birth in single chemical analyses. However, the joint effect of phenol and phthalate mixtures on preterm birth is unknown.MethodsWe included 384 female and 211 male (203 couples) participants seeking infertility treatment in the Environment and Reproductive Health (EARTH) Study who gave birth to 384 singleton infants between 2005 and 2018. Mean preconception urinary concentrations of bisphenol A (BPA), parabens, and eleven phthalate biomarkers, including di(2-ethylhexyl) phthalate (DEHP) metabolites, were examined. We used principal component analysis (PCA) with log-Poisson regression and Probit Bayesian Kernel Machine Regression (BKMR) with hierarchical variable selection to examine maternal and paternal phenol and phthalate mixtures in relation to preterm birth. Couple-based BKMR model was fit to assess couples' joint mixtures in relation to preterm birth.ResultsPCA identified the same four factors for maternal and paternal preconception mixtures. Each unit increase in PCA scores of maternal (adjusted Risk Ratio (aRR): 1.36, 95%CI: 1.00, 1.84) and paternal (aRR: 1.47, 95%CI: 0.90, 2.42) preconception DEHP-BPA factor was positively associated with preterm birth. Maternal and paternal BKMR models consistently presented the DEHP-BPA factor with the highest group Posterior Inclusion Probability (PIP). BKMR models further showed that maternal preconception BPA and mono(2-ethyl-5-hydroxyhexyl) phthalate, and paternal preconception mono(2-ethylhexyl) phthalate were positively associated with preterm birth when the remaining mixture components were held at their median concentrations. Couple-based BKMR models showed a similar relative contribution of paternal (PIP: 61%) and maternal (PIP: 77%) preconception mixtures on preterm birth. We found a positive joint effect on preterm birth across increasing quantiles of couples' total mixture concentrations.ConclusionIn this prospective cohort of subfertile couples, maternal BPA and DEHP, and paternal DEHP exposure before conception were positively associated with preterm birth. Both parental windows jointly contributed to the outcome. These results suggest that preterm birth may be a couple-based pregnancy outcome.

Project description:BackgroundPrenatal exposures to endocrine-disrupting chemicals (EDCs) during critical developmental windows have been implicated in the etiologies of a wide array of adverse perinatal and pediatric outcomes. Epidemiological studies have concentrated on the health effects of individual chemicals, despite the understanding that EDCs act together via common mechanisms, that pregnant women are exposed to multiple EDCs simultaneously, and that substantial toxicological evidence of adverse developmental effects has been documented. There is a move toward multipollutant models in environmental epidemiology; however, there is no current consensus on appropriate statistical methods.ObjectivesWe aimed to review the statistical methods used in these studies, to identify additional applicable methods, and to determine the strengths and weaknesses of each method for addressing the salient statistical and epidemiological challenges.MethodsWe searched Embase, MEDLINE, and Web of Science for epidemiological studies of endocrine-sensitive outcomes in the children of mothers exposed to EDC mixtures during pregnancy and identified alternative statistical methods from the wider literature.DiscussionWe identified 74 studies and analyzed the methods used to estimate mixture health effects, identify important mixture components, account for nonmonotonicity in exposure–response relationships, assess interactions, and identify windows of exposure susceptibility. We identified both frequentist and Bayesian methods that are robust to multicollinearity, performing shrinkage, variable selection, dimension reduction, statistical learning, or smoothing, including methods that were not used by the studies included in our review.ConclusionsCompelling motivation exists for analyzing EDCs as mixtures, yet many studies make simplifying assumptions about EDC additivity, relative potency, and linearity, or overlook the potential for bias due to asymmetries in chemical persistence. We discuss the potential impacts of these choices and suggest alternative methods to improve analyses of prenatal exposure to EDC mixtures. https://doi.org/10.1289/EHP2207.

Project description:A common practice when analyzing multi-site epidemiological data is to include a term for 'site' to account for unmeasured effects at each location. This practice should be carefully considered when site can have complex relationships with important demographic and exposure variables. We leverage data from three longitudinal North American pregnancy cohorts to demonstrate a novel method to assess study heterogeneity and potential combinability of studies for pooled analyses in order to better understand how to consider site in analyses. Results from linear regression and fixed effects meta-regression models run both prior to and following the proposed combinability analyses were compared. In order to exemplify this approach, we examined associations between prenatal exposure to particulate matter and birth weight. Analyses included mother-child dyads (N=1966) from the Asthma Coalition on Community Environment and Social Stress (ACCESS) Project and the PRogramming of Intergenerational Stress Mechanisms (PRISM) study in the northeastern United States, and the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study in Mexico City. Mothers' daily third trimester exposure to particulate matter?2.5?m in diameter (PM2.5) was estimated using a validated satellite-based spatio-temporally resolved model in all studies. Fenton birth weight for gestational age z-scores were calculated. Linear regression analyses within each cohort separately did not find significant associations between PM2.5 averaged over the third trimester and Fenton z-scores. The initial meta-regression model also did not find significant associations between prenatal PM2.5 and birthweight. Next, propensity scores and log linear models were used to assess higher order interactions and determine if sites were comparable with regard to sociodemographics and other covariates; these analyses demonstrated that PROGRESS and ACCESS were combinable. Adjusted linear regression models including a 2-level site variable according to the pooling indicated by the log linear models (ACCESS and PROGRESS as one level and PRISM as another) revealed that a 5?g/m3 increase in PM2.5 was associated with a 0.075 decrease in Fenton z-score (p<0.0001); linear models including a 3-level site variable did not reveal significant associations. By assessing the combinability of heterogeneous populations prior to combining data using a method that more optimally accounts for underlying cohort differences, we were able to identify significant associations between prenatal PM2.5 exposure and birthweight that were not detected using standard methods.

Project description:BackgroundMolar sums are often used to quantify total phthalate exposure, but they do not capture patterns of exposure to multiple phthalates.ObjectiveTo introduce an exposure burden score method for quantifying exposure to phthalate metabolites and examine the association between phthalate burden scores and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR).MethodsWe applied item response theory (IRT) to data from 3474 adults aged 20-60 years in the 2013-2018 National Health and Examination Survey (NHANES) to quantify latent phthalate exposure burden from 12 phthalate metabolites. We compared model fits of three IRT models that used different a priori groupings (general phthalate burden; low molecular weight (LMW) and high molecular weight (HMW) burdens; and LMW, HMW and DEHP burden), and used the best fitting model to estimate phthalate exposure burden scores. Regression models assessed the covariate-adjusted association between phthalate burden scores and HOMA-IR. We compared findings to those using molar sums. In secondary analyses, we examined how the IRT model could be used for data harmonization when a subset of participants are missing some phthalate metabolites, and accounted for measurement error of the phthalate burden scores in estimating associations with HOMA-IR through a resampling approach using plausible value imputation.ResultsA three correlated factors model (LMW, HMW and DEHP burdens) provided the best fit. One interquartile range (IQR) increase in DEHP burden score was associated with 0.094 (95% CI: 0.022, 0.164, p = 0.010) increase in log HOMA-IR, co-adjusted for LMW and HMW burden scores. Findings were consistent when using log molar sums. Associations of phthalate burden and insulin resistance were also consistent when participants were simulated to be missing some phthalate metabolites, and when we accounted for measurement error in estimating burden scores.ConclusionBoth phthalate molar sums and burden scores are sensitive to associations with insulin resistance. Phthalate burden scores may be useful for data harmonization.

Dataset Information

Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches.

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Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches.

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