Lagged WQS regression for mixtures with many components

The developmental timing of exposures to toxic chemicals or combinations of chemicals may be as important as the dosage itself. This concept is called "critical windows of exposure." The time boundaries of such windows can be detected if exposure data are collected repeatedly in short time intervals. The development of tooth-matrix biomarkers which provide prenatal and postnatal exposure measures in repeated intervals can provide such data. Using teeth, we use reverse distributed lagged models (DLMs) to incorporate weekly prenatal and postnatal measures of exposures to estimate time-varying associations with developmental effects. The analysis of such data using lagged weighted quantile sum (WQS) regression as an extension to reverse DLMs for complex mixtures was first proposed by Bello et al. This prior algorithm was not operationally generalizable to large numbers of components (say, more than five or six). We propose a revised algorithm that may be useful for larger mixtures by combining time-specific WQS(t) indices in a reverse DLM. We demonstrate the new algorithm using tooth data in association with a neurodevelopmental score and in simulated data from 3 cases wherein different components of a mixture have time varying associations and in the case where none have associations. The new algorithm correctly detects the simulated associations when the number of samples within the time-specific analyses is moderate to large.

Persistent organic pollutants, pre-pregnancy use of combined oral contraceptives, age, and time-to-pregnancy in the SELMA cohort

BACKGROUND

We are exposed to several chemicals such as persistent organic pollutants (POPs) in our everyday lives. Prior evidence has suggested that POPs may have adverse effects on reproductive function by disrupting hormone synthesis and metabolism. While there is age-related decline of fertility, the use of hormonal combined oral contraceptives (COCs) and its association to return of fertility remains controversial. The goal of this study is to investigate the association between exposure to POPs, both individually and as a mixture, and fecundability measured as time-to-pregnancy (TTP) according to pre-pregnancy use of COCs and age.

METHODS

Using the SELMA (Swedish Environmental Longitudinal Mother and Child, Allergy and Asthma) study, we have identified 818 pregnant women aged 18-43 years (mean 29 years) with data on how long they tried to get pregnant and what was their most recently used contraceptive method. These data were collected at enrollment to the study (median week 10 of pregnancy). Concentrations of 22 POPs and cotinine were analyzed in the blood samples collected at the same time as the questions on TTP and pre-pregnancy use of contraceptive. Analyses were done on the association between POPs exposure and TTP measured as continuous (months) and binary (infertile for those with TTP > 12 months). To study the chemicals individually, Cox regression and logistic regression were used to estimate fecundability ratios (FRs) and odds ratios (ORs), respectively. Weighted quantile sum (WQS) regression was used to investigate the chemicals as a mixture where chemicals of concern were identified above the 7.6% threshold of equal weights. To perform the subgroup analysis, we stratified the sample according to use of COCs as the most recent pre-pregnancy contraception method and age (< 29 years, and ≥ 29 years). The models were adjusted for parity, regularity of menses, maternal body mass index (BMI) and smoking status, and stratified as described above.

RESULTS

Prior to stratification, none of the POPs were associated with fecundability while increased exposure to HCB, PCB 74 and 118 had higher odds of infertility. Upon stratification, POP exposure was significantly associated with longer TTP in women aged ≥29 years who did not use COC. Specifically, PCBs 156, 180, 183, and 187 were associated with reduced fecundability while PCBs 99, 153, 156, 180, 183, and 187 had higher odds of infertility. As a mixture, we identified the chemicals of concern for a longer TTP include PCBs 118, 156, 183, and 187. Moreover, chemicals of concern identified with increased odds of infertility were PCB 74, 156, 183, 187, and transnonachlor.

CONCLUSION

Serum concentrations of selected POPs, both as individual chemicals and as a mixture, were significantly associated with lower fecundability and increased odds of infertility in women aged 29 years and above not using COC as their most recent pre-pregnancy contraceptive. Our findings suggest that pre-pregnancy use of oral contraceptive and age may modify the link between POPs and fecundability. The differences of specific chemicals in the individual analysis and as a mixture support the need to study combination effects of chemicals when evaluating reproductive outcomes.

Long term transcriptional and behavioral effects in mice developmentally exposed to a mixture of endocrine disruptors associated with delayed human neurodevelopment

Accumulating evidence suggests that gestational exposure to endocrine disrupting chemicals (EDCs) may interfere with normal brain development and predispose for later dysfunctions. The current study focuses on the exposure impact of mixtures of EDCs that better mimics the real-life situation. We herein describe a mixture of phthalates, pesticides and bisphenol A (mixture N1) detected in pregnant women of the SELMA cohort and associated with language delay in their children. To study the long-term impact of developmental exposure to N1 on brain physiology and behavior we administered this mixture to mice throughout gestation at doses 0×, 0.5×, 10×, 100× and 500× the geometric mean of SELMA mothers' concentrations, and examined their offspring in adulthood. Mixture N1 exposure increased active coping during swimming stress in both sexes, increased locomotion and reduced social interaction in male progeny. The expression of corticosterone receptors, their regulator Fkbp5, corticotropin releasing hormone and its receptor, oxytocin and its receptor, estrogen receptor beta, serotonin receptors (Htr1a, Htr2a) and glutamate receptor subunit Grin2b, were modified in the limbic system of adult animals, in a region-specific, sexually-dimorphic and experience-dependent manner. Principal component analysis revealed gene clusters associated with the observed behavioral responses, mostly related to the stress axis. This integration of epidemiology-based data with an experimental model increases the evidence that prenatal exposure to EDC mixtures impacts later life brain functions.

Prenatal exposure to per- and polyfluoroalkyl substances and maternal and neonatal thyroid function in the Project Viva Cohort: A mixtures approach

BACKGROUND

Maternal and neonatal thyroid function is critical for growth and neurodevelopment. Exposure to individual per- and polyfluoroalkyl substances (PFAS) can alter circulating thyroid hormone levels, but few studies have investigated effects of combined exposure to multiple PFAS.

OBJECTIVES

Estimate associations of exposure to multiple PFAS during early pregnancy with maternal and neonatal thyroid function.

METHODS

The study population consisted of 726 mothers and 465 neonates from Project Viva, a Boston, Massachusetts area longitudinal pre-birth cohort. We measured six PFAS [perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoate (PFNA), perfluorohexane sulfonate (PFHxS), 2-(N-ethyl-perfluorooctane sulfonamido)acetate (EtFOSAA), and 2-(N-methyl-perfluorooctane sulfonamido)acetate (MeFOSAA)] and thyroxine (T₄), Free T₄ Index (FT₄I), and thyroid stimulating hormone (TSH) in maternal plasma samples collected during early pregnancy, and neonatal T₄ in postpartum heel sticks. We estimated individual and joint effects of PFAS exposure with thyroid hormone levels using weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR), and evaluated potential non-linearity and interactions among PFAS using BKMR.

RESULTS

Higher concentrations of the PFAS mixture were associated with significantly lower maternal FT₄I, with MeFOSAA, EtFOSAA, PFOA, and PFHxS contributing most to the overall mixture effect in BKMR and WQS regression. In infants, higher concentrations of the PFAS mixture were associated with lower T₄ levels, primarily in males, with PFHxS and MeFOSAA contributing most in WQS, and PFHxS contributing most in BKMR. The PFAS mixture was not associated with maternal T₄ or TSH levels. However, in maternal BKMR analyses, ln-PFOS was positively associated with T₄ levels (Δ25^th to 75th percentile: 0.21 µg/dL; 95% credible interval: -0.03, 0.47) and ln-PFHxS was associated with a non-linear effect on TSH levels.

CONCLUSIONS

These findings support the hypothesis that there may be combined effects of prenatal exposure to multiple PFAS on maternal and neonatal thyroid function, but the direction and magnitude of these effects may vary across individual PFAS.

The ENDpoiNTs Project: Novel Testing Strategies for Endocrine Disruptors Linked to Developmental Neurotoxicity

Ubiquitous exposure to endocrine-disrupting chemicals (EDCs) has caused serious concerns about the ability of these chemicals to affect neurodevelopment, among others. Since endocrine disruption (ED)-induced developmental neurotoxicity (DNT) is hardly covered by the chemical testing tools that are currently in regulatory use, the Horizon 2020 research and innovation action ENDpoiNTs has been launched to fill the scientific and methodological gaps related to the assessment of this type of chemical toxicity. The ENDpoiNTs project will generate new knowledge about ED-induced DNT and aims to develop and improve in vitro, in vivo, and in silico models pertaining to ED-linked DNT outcomes for chemical testing. This will be achieved by establishing correlative and causal links between known and novel neurodevelopmental endpoints and endocrine pathways through integration of molecular, cellular, and organismal data from in vitro and in vivo models. Based on this knowledge, the project aims to provide adverse outcome pathways (AOPs) for ED-induced DNT and to develop and integrate new testing tools with high relevance for human health into European and international regulatory frameworks.

Per- and poly-fluoroalkyl substances and bone mineral density: Results from the Bayesian weighted quantile sum regression

Per- and poly-fluoroalkyl substances (PFAS) are chemicals, detected in 95% of Americans, that induce osteotoxicity and modulate hormones, thereby influencing bone health. Previous studies found associations between individual PFAS and bone mineral density in adults but did not analyze their combined effects.

OBJECTIVE

To extend weighted quantile sum (WQS) regression to a Bayesian framework (Bayesian extension of the WQS regression [BWQS]) and determine the association between a mixture of serum PFAS and mineral density in lumbar spine, total, and neck femur in 499 adults from the 2013 to 2014 National Health and Nutrition Examination Survey (NHANES).

METHODS

We used BWQS to assess the combined association of eight PFAS, as a mixture, with bone mineral density in adults. As secondary analyses, we focused on vulnerable populations (men over 50 years and postmenopausal women). Analyses were adjusted for sociodemographic factors. Sensitivity analyses included bone mineral density associations with individual compounds and results from WQS regressions.

RESULTS

The mean age was 55 years old (SD = 1) with average spine, total, and neck femur mineral densities of 1.01 (SD = 0.01), 0.95 (SD = 0.01), and 0.78 (SD = 0.01) gm/cm2, respectively. PFAS mixture levels showed no evidence of association with mineral density (spine: β = -0.004; 95% credible interval [CrI] = -0.04, 0.04; total femur: β = 0.002; 95% CrI = -0.04, 0.05; femur neck: β = 0.005; 95%CrI = -0.03, 0.04) in the overall population. Results were also null in vulnerable populations. Findings were consistent across sensitivity analyses.

CONCLUSIONS

We introduced a Bayesian extension of WQS and found no evidence of the association between PFAS mixture and bone mineral density.

Early life metal dysregulation in amyotrophic lateral sclerosis

OBJECTIVE

Deficiencies and excess of essential elements and toxic metals are implicated in amyotrophic lateral sclerosis (ALS), but the age when metal dysregulation appears remains unknown. This study aims to determine whether metal uptake is dysregulated during childhood in individuals eventually diagnosed with ALS.

METHODS

Laser ablation-inductively coupled plasma-mass spectrometry was used to obtain time series data of metal uptake using biomarkers in teeth from autopsies or dental extractions of ALS (n = 36) and control (n = 31) participants. Covariate data included sex, smoking, occupational exposures, and ALS family history. Case-control differences were identified in temporal profiles of metal uptake for individual metals using distributed lag models. Weighted quantile sum (WQS) regression was used for metals mixture analyses. Similar analyses were performed on an ALS mouse model to further verify the relevance of dysregulation of metals in ALS.

RESULTS

Metal levels were higher in cases than in controls: 1.49 times for chromium (1.11-1.82; at 15 years), 1.82 times for manganese (1.34-2.46; at birth), 1.65 times for nickel (1.22-2.01; at 8 years), 2.46 times for tin (1.65-3.30; at 2 years), and 2.46 times for zinc (1.49-3.67; at 6 years). Co-exposure to 11 elements indicated that childhood metal dysregulation was associated with ALS. The mixture contribution of metals to disease outcome was likewise apparent in tooth biomarkers of an ALS mouse model, and differences in metal distribution were evident in ALS mouse brains compared to brains from littermate controls.

INTERPRETATION

Overall, our study reveals direct evidence that altered metal uptake during specific early life time windows is associated with adult-onset ALS.

Dysregulated biodynamics in metabolic attractor systems precede the emergence of amyotrophic lateral sclerosis

Evolutionarily conserved mechanisms maintain homeostasis of essential elements, and are believed to be highly time-variant. However, current approaches measure elemental biomarkers at a few discrete time-points, ignoring complex higher-order dynamical features. To study dynamical properties of elemental homeostasis, we apply laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) to tooth samples to generate 500 temporally sequential measurements of elemental concentrations from birth to 10 years. We applied dynamical system and Information Theory-based analyses to reveal the longest-known attractor system in mammalian biology underlying the metabolism of nutrient elements, and identify distinct and consistent transitions between stable and unstable states throughout development. Extending these dynamical features to disease prediction, we find that attractor topography of nutrient metabolism is altered in amyotrophic lateral sclerosis (ALS), as early as childhood, suggesting these pathways are involved in disease risk. Mechanistic analysis was undertaken in a transgenic mouse model of ALS, where we find similar marked disruptions in elemental attractor systems as in humans. Our results demonstrate the application of a phenomological analysis of dynamical systems underlying elemental metabolism, and emphasize the utility of these measures in characterizing risk of disease.

The metabolism of essential elements in early life is essential to healthy growth and development. Elemental homeostasis is typically studied by characterizing distributions of elemental concentrations at the level of the population. Here, we introduce a new method of characterizing elemental metabolism at the level of the individual. Using tooth-based biomarkers, we tracked the longitudinal trajectory of essential elements throughout childhood at weekly temporal resolution from birth through approximately 10 years of life. We analyzed these trajectories to identify the formation of stable dynamic states (attractors) and transitions between these states throughout development. We found that metabolic dynamics were specific to discrete elemental pathways; copper metabolism typically involved the formation of multiple discrete states throughout childhood, whereas other elements, such as zinc, tended to persist in a single stable dynamic throughout development. Next, we compared elemental biodynamics in neurologically healthy cases and subjects that were later diagnosed with amyotrophic lateral sclerosis (ALS). We found these patterns were dysregulated in ALS, and also found similar results in a mouse model of ALS. Overall, our results provide a novel approach to characterize elemental biodynamics throughout development, and emphasize that the dysregulation of these processes may be predictive of later onset of disease.

Multi-media biomarkers: Integrating information to improve lead exposure assessment

Exposure assessment traditionally relies on biomarkers that measure chemical concentrations in individual biological media (i.e., blood, urine, etc.). However, chemicals distribute unevenly among different biological media; thus, each medium provides incomplete information about body burden. We propose that machine learning and statistical approaches can create integrated exposure estimates from multiple biomarker matrices that better represent the overall body burden, which we term multi-media biomarkers (MMBs). We measured lead (Pb) in blood, urine, hair and nails from 251 Italian adolescents aged 11-14 years from the Public Health Impact of Metals Exposure (PHIME) cohort. We derived aggregated MMBs from the four biomarkers and then tested their association with Wechsler Intelligence Scale for Children (WISC) IQ scores. We used three approaches to derive the Pb MMB: one supervised learning technique, weighted quantile sum regression (WQS), and two unsupervised learning techniques, independent component analysis (ICA) and non-negative matrix factorization (NMF). Overall, the Pb MMB derived using WQS was most consistently associated with IQ scores and was the only method to be statistically significant for Verbal IQ, Performance IQ and Total IQ. A one standard deviation increase in the WQS MMB was associated with lower Verbal IQ (β [95% CI] = -2.2 points [-3.7, -0.6]), Performance IQ (-1.9 points [-3.5, -0.4]) and Total IQ (-2.1 points [-3.8, -0.5]). Blood Pb was negatively associated with only Verbal IQ, with a one standard deviation increase in blood Pb being associated with a -1.7 point (95% CI: [-3.3, -0.1]) decrease in Verbal IQ. Increases of one standard deviation in the ICA MMB were associated with lower Verbal IQ (-1.7 points [-3.3, -0.1]) and lower Total IQ (-1.7 points [-3.3, -0.1]). Similarly, an increase of one standard deviation in the NMF MMB was associated with lower Verbal IQ (-1.8 points [-3.4, -0.2]) and lower Total IQ (-1.8 points [-3.4, -0.2]). Weights highlighting the contributions of each medium to the MMB revealed that blood Pb was the largest contributor to most MMBs, although the weights varied from more than 80% for the ICA and NMF MMBs to between 30% and 54% for the WQS-derived MMBs. Our results suggest that MMBs better reflect the total body burden of a chemical that may be acting on target organs than individual biomarkers. Estimating MMBs improved our ability to estimate the full impact of Pb on IQ. Compared with individual Pb biomarkers, including blood, a Pb MMB derived using WQS was more strongly associated with IQ scores. MMBs may increase statistical power when the choice of exposure medium is unclear or when the sample size is small. Future work will need to validate these methods in other cohorts and for other chemicals.

The association of prenatal exposure to intensive traffic with early preterm infant neurobehavioral development as reflected by the NICU Network Neurobehavioral Scale (NNNS)

INTRODUCTION

Traffic-related air pollution has been shown to be neurotoxic to the developing fetus and in term-born infants during early childhood. It is unknown whether there is an increased risk of adverse neurobehavioral outcome in preterm infants exposed to higher levels of air pollution during the fetal period.

OBJECTIVE

To assess the association between prenatal exposure to traffic-related air pollution on early preterm infant neurobehavior.

METHODS

Air pollution exposure was estimated by two methods: density of major roads and density of vehicle-miles traveled (VMT), each at multiple buffering areas around residential addresses. We examined the association between prenatal exposure to traffic-related air pollution and performance on the Neonate Intensive Care Unit (NICU) Network Behavioral Scale (NNNS), a measure of neurobehavioral outcome in infancy for 240 preterm neonates enrolled in the NICU-Hospital Exposures and Long-Term Health cohort. Linear regression analysis was conducted for exposure and individual NNNS subscales. Latent profile analysis (LPA) was applied to classify infants into distinct NNNS phenotypes. Multinomial logistic regression analysis was conducted between exposure and LPA groups. Covariates included gestational age, birth weight z-score, post-menstrual age at NNNS assessment, socioeconomic status, race, delivery type, maternal smoking status, and medical morbidities during the NICU stay.

RESULTS

Among all 13 NNNS subscales, hypotonia was significantly associated with VMT (10⁴ vehicle-mile/km²) in 150 m (β = 0.01, P-value<0.001), 300 m (β = 0.01, P-value = 0.003), and 500 m (β = 0.01, P-value = 0.002) buffering areas, as well as with road density in a 500 m buffering area (β = 0.03, P-value = 0.03). We identified three NNNS phenotypes by LPA. Among them, high density of major roads within 150 m, 300 m, and 500 m buffers of the residential address was significantly associated with the same phenotype (P < 0.05).

CONCLUSION

Prenatal exposure to intensive air pollution emitted from major roads may impact early neurodevelopment of preterm infants. Motor development may be particularly sensitive to air pollution-related toxicity.

Dynamic growth metrics for examining prenatal exposure impacts on child growth trajectories: Application to perfluorooctanoic acid (PFOA) and postnatal weight gain

BACKGROUND

Epidemiologic studies investigating prenatal exposures in relation to growth typically rely on cumulative growth measures such as weight or BMI. However, less is known about how prenatal exposure may impact other aspects of growth dynamics, including timing and velocity.

OBJECTIVES

To describe and apply a nonlinear growth model previously used in other health science fields to characterize postnatal growth trajectories for use in environmental epidemiology studies.

METHODS

We used a double logistic function to model child weight trajectories from birth to 5.5 years using data from the Swedish Environmental Longitudinal Mother and Child, Asthma and Allergy (SELMA) study. From this, we approximated several infant growth metrics: 1) duration of time needed to complete 90% of the infant growth spurt (Δt₁), 2) the maximum growth rate in infancy or infant peak growth velocity (PGV), 3) the age at infant PGV (δ₁), a measure of growth tempo, and 4) the weight plateau at the end of the infant growth spurt (α₁). We assessed these metrics in relation to prenatal perfluorooctanoic acid (PFOA) exposure among 1334 mother-child pairs, and differences between boys and girls.

RESULTS

Average estimated infant PGV and its timing (δ₁) were 0.68 kg/month and 3.4 months, respectively. Mean infant growth spurt duration (Δt₁) was 13 months, ending at an average weight plateau (α₁) of 8.2 kg. Higher prenatal PFOA concentrations were related to a longer duration of infant growth (Δt₁: 0.06; 95% CI = 0.01, 0.11). PGV was not impacted, but higher prenatal PFOA concentrations were significantly related to delayed infant PGV (δ₁: 0.58; 95% CI = 0.17, 0.99) and a higher post-spurt weight plateau (α₁: 0.81; 95% CI = 0.21, 1.41). After adjusting for false discovery, results were only significant for δ₁ and α₁. We observed a significant interaction by sex for the association with δ₁, and stratified analyses revealed the association was only significant among girls.

CONCLUSION

Model-derived growth metrics were consistent with published growth standards. This novel application of nonlinear growth modeling enabled detection of altered infant growth dynamics in relation to prenatal PFOA exposure. Our results may help describe how PFOA yields lower birthweights, but higher weight later in childhood. Future applications may characterize adolescent growth or additional metrics of biological interest.

Length of gestation and birth weight are associated with indices of combined kidney biomarkers in early childhood

Infants born prematurely or with low birth weights are more susceptible to kidney dysfunction throughout their lives. Multiple proteins measured in urine are noninvasive biomarkers of subclinical kidney damage, but few studies have examined the joint effects of multiple biomarkers. We conducted an exploratory study of 103 children in the Programing Research in Obesity, Growth, Environment, and Social Stressors (PROGRESS) longitudinal birth cohort, and measured nine proteins selected a priori in banked spot urine samples collected at ages 4-6. The goal of our study was to explore the combined effects of kidney damage biomarkers previously associated with birth outcomes. To do this, we generated kidney biomarker indices using weighted quantile sum regression and assessed associations with length of gestation or birth weight. A decile increase in each kidney biomarker index was associated with 2-day shorter gestations (β = -2.0, 95% CI: -3.2, -0.9) and 59-gram lower birth weights (β = -58.5, 95% CI: -98.3, -18.7), respectively. Weights highlighting the contributions showed neutrophil gelatinase-associated lipocalin (NGAL) (60%) and osteopontin (19%) contributed most to the index derived for gestational age. NGAL (66%) and beta-2-microglobulin (10%) contributed most to the index derived for birth weight. Joint analyses of multiple kidney biomarkers can provide integrated measures of kidney dysfunction and improved statistical assessments compared to biomarkers assessed individually. Additionally, shorter gestations and lower birth weights may contribute to subclinical kidney damage measurable in childhood.

Quantitative methods for metabolomic analyses evaluated in the Children's Health Exposure Analysis Resource (CHEAR)

With advances in technologies that facilitate metabolome-wide analyses, the incorporation of metabolomics in the pursuit of biomarkers of exposure and effect is rapidly evolving in population health studies. However, many analytic approaches are limited in their capacity to address high-dimensional metabolomics data within an epidemiologic framework, including the highly collinear nature of the metabolites and consideration of confounding variables. In this Children's Health Exposure Analysis Resource (CHEAR) network study, we showcase various analytic approaches that are established as well as novel in the field of metabolomics, including univariate single metabolite models, least absolute shrinkage and selection operator (LASSO), random forest, weighted quantile sum (WQS_RS) regression, exploratory factor analysis (EFA), and latent class analysis (LCA). Here, in a Bangladeshi birth cohort (n = 199), we illustrate research questions that can be addressed by each analytic method in the assessment of associations between cord blood metabolites (¹H NMR measurements) and birth anthropometric measurements (birth weight and head circumference).

Early prenatal exposure to suspected endocrine disruptor mixtures is associated with lower IQ at age seven

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics with the ability to interfere with hormone action, even at low levels. Prior environmental epidemiology studies link numerous suspected EDCs, including phthalates and bisphenol A (BPA), to adverse neurodevelopmental outcomes. However, results for some chemicals were inconsistent and most assessed one chemical at a time.

OBJECTIVES

To evaluate the overall impact of prenatal exposure to an EDC mixture on neurodevelopment in school-aged children, and identify chemicals of concern while accounting for co-exposures.

METHODS

Among 718 mother-child pairs from the Swedish Environmental Longitudinal, Mother and child, Asthma and allergy study (SELMA) study, we used Weighted Quantile Sum (WQS) regression to assess the association between 26 EDCs measured in 1st trimester urine or blood, with Wechsler Intelligence Scale for Children (IV) Intelligence Quotient (IQ) scores at age 7 years. Models were adjusted for child sex, gestational age, mother's education, mother's IQ (RAVEN), weight, and smoking status. To evaluate generalizability, we conducted repeated holdout validation, a machine learning technique.

RESULTS

Using repeated holdout validation, IQ scores were 1.9-points (CI = -3.6, -0.2) lower among boys for an inter-quartile-range (IQR) change in the WQS index. BPF made the largest contribution to the index with a weight of 14%. Other chemicals of concern and their weights included PBA (9%), TCP (9%), MEP (6%), MBzP (4%), PFOA (6%), PFOS (5%), PFHxS (4%), Triclosan (5%), and BPA (4%). While we did observe an inverse association between EDCs and IQ among all children when training and testing the WQS index estimate on the full dataset, these results were not robust to repeated holdout validation.

CONCLUSION

Among boys, early prenatal exposure to EDCs was associated with lower intellectual functioning at age 7. We identified bisphenol F as the primary chemical of concern, suggesting that the BPA replacement compound may not be any safer for children. Future studies are needed to confirm the potential neurotoxicity of replacement analogues.

Sources of clinically significant neonatal intensive care unit phthalate exposure

In the United States each year, more than 300,000 infants are admitted to neonatal intensive care units (NICU) where they are exposed to a chemical-intensive hospital environment during a developmentally vulnerable period. Although multiple studies have demonstrated elevated phthalate biomarkers in NICU patients, specific sources of NICU-based phthalate exposure have not been identified.In this study, premature newborns with birth weight <1500 g were recruited to participate in a prospective environmental health cohort during the NICU hospitalization. Exposure to specific NICU equipment was recorded daily during the NICU hospitalization. One hundred forty-nine urine specimens from 71 infants were analyzed for phthalate metabolites using high-performance liquid chromatography/tandem mass spectrometry.In initial analyses, exposure to medical equipment was directly related to phthalate levels, with DEHP biomarkers 95-132% higher for infants exposed to specific medical equipment types compared to those without that equipment exposure (p < 0.001-0.023). This association was mirrored for clinically relevant phthalate mixtures whether composed of DEHP metabolites or not (p = 0.002-0.007). In models accounting for concurrent equipment use, exposure to respiratory support was associated with DEHP biomarkers 50-136% higher in exposed compared to unexposed infants (p = 0.007-0.036). Phthalate mixtures clinically relevant to neurobehavioral development were significantly associated with non-invasive respiratory support (p = 0.008-0.026). Feeding supplies and intravenous lines were not significantly associated with clinically important phthalate mixtures.Respiratory support equipment may be a significant and clinically relevant NICU source of phthalate exposure. Although manufacturers have altered feeding and intravenous supplies to reduce DEHP exposure, other sources of exposure to common and clinically impactful phthalates persist in the NICU.

Fluoride exposure and sleep patterns among older adolescents in the United States: a cross-sectional study of NHANES 2015-2016

BACKGROUND

Fluoride from environmental sources accumulates preferentially in the pineal gland which produces melatonin, the hormone that regulates the sleep-wake cycle. However, the effects of fluoride on sleep regulation remain unknown. This population-based study examined whether chronic low-level fluoride exposure is associated with sleep patterns and daytime sleepiness among older adolescents in the United States (US).

METHOD

This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (2015-2016). We analyzed data from adolescents who had plasma fluoride (n = 473) and water fluoride (n = 419) measures and were not prescribed medication for sleep disorders. Relationships between fluoride exposure and self-reported sleep patterns or daytime sleepiness were examined using survey-weighted linear, binomial logistic or multinomial logistic regression after covariate adjustment. A Holm-Bonferroni correction accounted for multiple comparisons.

RESULTS

The average age of adolescents was 17 years (range = 16-19). Median (IQR) water and plasma fluoride concentrations were 0.27 (0.52) mg/L and 0.29 (0.19) μmol/L respectively. An IQR increase in water fluoride was associated with 1.97 times higher odds of reporting symptoms suggestive of sleep apnea (95% CI: 1.27, 3.05; p = 0.02), a 24 min later bedtime (B = 0.40, 95% CI: 0.10, 0.70; p = 0.05), a 26 min later morning wake time (B = 0.43, 95% CI: 0.13, 0.73; p = 0.04), and among males, a 38% reduction in the odds of reporting snoring (95% CI: 0.45, 0.87, p = 0.03).

CONCLUSIONS

Fluoride exposure may contribute to changes in sleep cycle regulation and sleep behaviors among older adolescents in the US. Additional prospective studies are warranted to examine the effects of fluoride on sleep patterns and determine critical windows of vulnerability for potential effects.

Prenatal phenol and paraben exposures in relation to child neurodevelopment including autism spectrum disorders in the MARBLES study

BACKGROUND

Environmental phenols and parabens are endocrine disrupting chemicals (EDCs) with the potential to affect child neurodevelopment including autism spectrum disorders (ASD). Our aim was to assess whether exposure to environmental phenols and parabens during pregnancy was associated with an increased risk of clinical ASD or other nontypical development (non-TD).

METHODS

This study included mother-child pairs (N = 207) from the Markers of Autism Risks in Babies - Learning Early Signs (MARBLES) Cohort Study with urinary phenol and paraben metabolites analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) from repeated pregnancy urine samples. Because family recurrence risks in siblings are about 20%, MARBLES enrolls pregnant women who already had a child with ASD. Children were clinically assessed at 3 years of age and classified into 3 outcome categories: ASD, non-TD, or typically developing (TD). Single analyte analyses were conducted with trinomial logistic regression and weighted quantile sum (WQS) regression was used to test for mixture effects.

RESULTS

Regression models were adjusted for pre-pregnancy body mass index, prenatal vitamin use (yes/no), homeowner status (yes/no), birth year, and child's sex. In single chemical analyses phenol exposures were not significantly associated with child's diagnosis. Mixture analyses using trinomial WQS regression showed a significantly increased risk of non-TD compared to TD (OR = 1.58, 95% CI: 1.04, 2.04) with overall greater prenatal phenol and paraben metabolites mixture. Results for ASD also showed an increased risk, but it was not significant.

DISCUSSION

This is the first study to provide evidence that pregnancy environmental phenol exposures may increase the risk for non-TD in a high-risk population.

Cohort profile: the Neonatal Intensive Care Unit Hospital Exposures and Long-Term Health (NICU-HEALTH) cohort, a prospective preterm birth cohort in New York City

PURPOSE

The Neonatal Intensive Care Unit Hospital Exposures and Long-Term Health (NICU-HEALTH) longitudinal preterm birth cohort studies the impact of the NICU exposome on early-life development. NICU-HEALTH collects multiple biospecimens, complex observational and survey data and comprehensive multisystem outcome assessments to allow measurement of the impact of modifiable environmental exposures during the preterm period on neurodevelopmental, pulmonary and growth outcomes.

PARTICIPANTS

Moderately preterm infants without genetic or congenital anomalies and their mothers are recruited from an urban academic medical centre level IV NICU in New York City, New York, USA. Recruitment began in 2011 and continues through multiple enrolment phases to the present with goal enrolment of 400 infants. Follow-up includes daily data collection throughout the NICU stay and six follow-up visits in the first 2 years. Study retention is 77% to date, with the oldest patients turning age 8 in 2019.

FINDINGS TO DATE

NICU-HEALTH has already contributed significantly to our understanding of phthalate exposure in the NICU. Phase I produced the first evidence of the clinical impact of phthalate exposure in the NICU population. Further study identified specific sources of exposure to clinically relevant phthalate mixtures in the NICU.

FUTURE PLANS

Follow-up from age 3 to 12 is co-ordinated through integration with the Environmental Influences on Child Health Outcomes (ECHO) programme. The NICU-HEALTH cohort will generate a wealth of biomarker, clinical and outcome data from which future studies of the impact of early-life chemical and non-chemical environmental exposures can benefit. Findings from study of this cohort and other collaborating environmental health cohorts will likely translate into improvements in the hospital environment for infant development.

TRIAL REGISTRATION NUMBERS

This observational cohort is registered with ClinicalTrials.gov (NCT01420029 and NCT01963065).

Repeated holdout validation for weighted quantile sum regression

Weighted Quantile Sum (WQS) regression is a method commonly used in environmental epidemiology to assess the impact of chemical mixtures in relation to a health outcome of interest. Data are partitioned into a single training and test set to reduce sample-specific chemical weights. However, in typical epidemiology sample sizes, this may produce unstable chemical weights and WQS index estimates, and investigators may resort to training and testing on the same data. To solve this problem, we propose repeated holdout validation whereby data are randomly partitioned 100 times, producing a distribution of validated results. Taking the mean as the final estimate, confidence estimates may also be calculated for inference. Further, this method helps characterize the variability in chemical weights, aiding in the identification of chemicals of concern. This is important since it may direct future research into specific chemicals.

Using data from 718 mother-child pairs in the Swedish Environmental Longitudinal, Mother and Child, Asthma and Allergy (SELMA) study, we assessed the association between prenatal exposure to 26 endocrine disrupting chemicals and child Intelligence Quotient (IQ). Results using a single partition were unstable, varying by random seed. The WQS index estimate was significant when all data was used (e.g. no partition) (β = −2.2 CI = −3.43, −0.98), but attenuated and nonsignificant using repeated holdout validation (β = −0.82 CI = −2.11, 0.45). When implementing WQS in epidemiologic studies with limited sample sizes, repeated holdout validation is a viable alternative to using a single, or no partitioning. Repeated holdout can both stabilize results and help characterize the uncertainty in identifying chemicals of concern, while maintaining some of the the rigor of holdout validation.

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Repeated holdout validation improves the stability of WQS estimates in finite study samples

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Uncertainty in identifying toxic chemicals of concern is acknowledged and characterized

Time-varying associations between prenatal metal mixtures and rapid visual processing in children

BACKGROUND

Humans are exposed to mixtures of chemicals across their lifetimes, a concept sometimes called the "exposome." Mixtures likely have temporal "critical windows" of susceptibility like single agents and measuring them repeatedly might help to define such windows. Common approaches to evaluate the effects of chemical mixtures have focused on their effects at a single time point. Our goal is to expand upon these previous techniques and examine the time-varying critical windows for metal mixtures on subsequent neurobehavior in children.

METHODS

We propose two methods, joint weighted quantile sum regression (JWQS) and meta-weighted quantile sum regression (MWQS), to estimate the effects of chemical mixtures measured across multiple time points, while providing data on their critical windows of exposure. We compare the performance of both methods using simulations. We also applied both techniques to assess second and third trimester metal mixture effects in predicting performance in the Rapid Visual Processing (RVP) task from the Cambridge Neuropsychological Test Automated Battery (CANTAB) assessed at 6-9 years in children who are part of the PROGRESS (Programming Research in Obesity, GRowth, Environment and Social Stressors) longitudinal cohort study. The metals, arsenic, cadmium (Cd), cesium, chromium, lead (Pb) and antimony (Sb) were selected based on their toxicological profile.

RESULTS

In simulations, JWQS and MWQS had over 80% accuracy in classifying exposures as either strongly or weakly contributing to an association. In real data, both JWQS and MWQS consistently found that Pb and Cd exposure jointly predicted longer latency in the RVP and that second trimester exposure better predicted the results than the third trimester. Additionally, both JWQS and MWQS highlighted the strong association Cd and Sb had with lower accuracy in the RVP and that third trimester exposure was a better predictor than second trimester exposure.

CONCLUSIONS

Our results indicate that metal mixtures effects vary across time, have distinct critical windows and that both JWQS and MWQS can determine longitudinal mixture effects including the cumulative contribution of each exposure and critical windows of effect.

Prenatal Exposure to PM2.5 and Cardiac Vagal Tone during Infancy: Findings from a Multiethnic Birth Cohort

BACKGROUND

The autonomic nervous system plays a key role in maintaining homeostasis and responding to external stimuli. In adults, exposure to fine particulate matter (PM2.5) has been associated with reduced heart rate variability (HRV), an indicator of cardiac autonomic control.

OBJECTIVES

Our goal was to investigate the associations of exposure to fine particulate matter (PM2.5) with HRV as an indicator of cardiac autonomic control during early development.

METHODS

We studied 237 maternal-infant pairs in a Boston-based birth cohort. We estimated daily residential PM2.5 using satellite data in combination with land-use regression predictors. In infants at 6 months of age, we measured parasympathetic nervous system (PNS) activity using continuous electrocardiogram monitoring during the Repeated Still-Face Paradigm, an experimental protocol designed to elicit autonomic reactivity in response to maternal interaction and disengagement. We used multivariable linear regression to examine average PM2.5 exposure across pregnancy in relation to PNS withdrawal and activation, indexed by changes in respiration-corrected respiratory sinus arrhythmia (RSAc)-an established metric of HRV that reflects cardiac vagal tone. We examined interactions with infant sex using cross-product terms.

RESULTS

In adjusted models we found that a 1-unit increase in PM2.5 (in micrograms per cubic meter) was associated with a 3.53% decrease in baseline RSAc (95% CI: -6.96, 0.02). In models examining RSAc change between episodes, higher PM2.5 was generally associated with reduced PNS withdrawal during stress and reduced PNS activation during recovery; however, these associations were not statistically significant. We did not observe a significant interaction between PM2.5 and sex.

DISCUSSION

Prenatal exposure to PM2.5 may disrupt cardiac vagal tone during infancy. Future research is needed to replicate these preliminary findings. https://doi.org/10.1289/EHP4434.

A Novel Approach to Chemical Mixture Risk Assessment-Linking Data from Population-Based Epidemiology and Experimental Animal Tests

Humans are continuously exposed to chemicals with suspected or proven endocrine disrupting chemicals (EDCs). Risk management of EDCs presents a major unmet challenge because the available data for adverse health effects are generated by examining one compound at a time, whereas real-life exposures are to mixtures of chemicals. In this work, we integrate epidemiological and experimental evidence toward a whole mixture strategy for risk assessment. To illustrate, we conduct the following four steps in a case study: (1) identification of single EDCs ("bad actors")-measured in prenatal blood/urine in the SELMA study-that are associated with a shorter anogenital distance (AGD) in baby boys; (2) definition and construction of a "typical" mixture consisting of the "bad actors" identified in Step 1; (3) experimentally testing this mixture in an in vivo animal model to estimate a dose-response relationship and determine a point of departure (i.e., reference dose [RfD]) associated with an adverse health outcome; and (4) use a statistical measure of "sufficient similarity" to compare the experimental RfD (from Step 3) to the exposure measured in the human population and generate a "similar mixture risk indicator" (SMRI). The objective of this exercise is to generate a proof of concept for the systematic integration of epidemiological and experimental evidence with mixture risk assessment strategies. Using a whole mixture approach, we could find a higher rate of pregnant women under risk (13%) when comparing with the data from more traditional models of additivity (3%), or a compound-by-compound strategy (1.6%).

Assessing the contributions of metals in environmental media to exposure biomarkers in a region of ferroalloy industry

Residential proximity to ferroalloy production has been associated with increased manganese exposure, which can adversely affect health, particularly among children. Little is known, however, about which environmental samples contribute most to internal levels of manganese and other ferroalloy metals. We aimed to characterize sources of exposure to metals and evaluate the ability of internal biomarkers to reflect exposures from environmental media. In 717 Italian adolescents residing near ferromanganese industry, we examined associations between manganese, lead, chromium, and copper in environmental samples (airborne particles, surface soil, indoor/outdoor house dust) and biological samples (blood, hair, nails, saliva, urine). In multivariable regression analyses adjusted for child age and sex, a 10% increase in soil Mn was associated with increases of 3.0% (95% CI: 1.1%, 4.9%) in nail Mn and 1.6% (95% CI: -0.2%, 3.4%) in saliva Mn. Weighted-quantile-sum (WQS) regression estimated that higher soil and outdoor dust Mn accounted for most of the effect on nail Mn (WQS weights: 0.61 and 0.22, respectively, out of a total of 1.0). Higher air and soil Mn accounted for most of the effect on saliva Mn (WQS weights: 0.65 and 0.29, respectively). These findings can help inform biomarker selection in future epidemiologic studies and guide intervention strategies in exposed populations.

An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length

BACKGROUND

Numerous methods exist to analyze complex environmental mixtures in health studies. As an illustration of the different uses of mixture methods, we employed methods geared toward distinct research questions concerning persistent organic chemicals (POPs) as a mixture and leukocyte telomere length (LTL) as an outcome.

METHODS

With information on 18 POPs and LTL among 1,003 U.S. adults (NHANES, 2001-2002), we used unsupervised methods including clustering to identify profiles of similarly exposed participants, and Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) to identify common exposure patterns. We also employed supervised learning techniques, including penalized, weighted quantile sum (WQS), and Bayesian kernel machine (BKMR) regressions, to identify potentially toxic agents, and characterize nonlinear associations, interactions, and the overall mixture effect.

RESULTS

Clustering separated participants into high, medium, and low POP exposure groups; longer log-LTL was found among those with high exposure. The first PCA component represented overall POP exposure and was positively associated with log-LTL. Two EFA factors, one representing furans and the other PCBs 126 and 118, were positively associated with log-LTL. Penalized regression methods selected three congeners in common (PCB 126, PCB 118, and furan 2,3,4,7,8-pncdf) as potentially toxic agents. WQS found a positive overall effect of the POP mixture and identified six POPs as potentially toxic agents (furans 1,2,3,4,6,7,8-hxcdf, 2,3,4,7,8-pncdf, and 1,2,3,6,7,8-hxcdf, and PCBs 99, 126, 169). BKMR found a positive linear association with furan 2,3,4,7,8-pncdf, suggestive evidence of linear associations with PCBs 126 and 169, and a positive overall effect of the mixture, but no interactions among congeners.

CONCLUSIONS

Using different methods, we identified patterns of POP exposure, potentially toxic agents, the absence of interaction, and estimated the overall mixture effect. These applications and results may serve as a guide for mixture method selection based on specific research questions.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.