Household Air Pollution and Child Lung Function: The Ghana Randomized Air Pollution and Health Study.

Rationale: The impact of a household air pollution (HAP) stove intervention on child lung function has been poorly described. Objectives: To assess the effect of a HAP stove intervention for infants prenatally to age 1 on, and exposure-response associations with, lung function at child age 4. Methods: The Ghana Randomized Air Pollution and Health Study randomized pregnant women to liquefied petroleum gas (LPG), improved biomass, or open-fire (control) stove conditions through child age 1. We quantified HAP exposure by repeated maternal and child personal carbon monoxide (CO) exposure measurements. Children performed oscillometry, an effort-independent lung function measurement, at age 4. We examined associations between Ghana Randomized Air Pollution and Health Study stove assignment and prenatal and infant CO measurements and oscillometry using generalized linear regression models. We used reverse distributed lag models to examine time-varying associations between prenatal CO and oscillometry. Measurements and Main Results: The primary oscillometry measure was reactance at 5 Hz, X5, a measure of elastic and inertial lung properties. Secondary measures included total, large airway, and small airway resistance at 5 Hz, 20 Hz, and the difference in resistance at 5 Hz and 20 Hz (R5, R20, and R5-20, respectively); area of reactance (AX); and resonant frequency. Of the 683 children who attended the lung function visit, 567 (83%) performed acceptable oscillometry. A total of 221, 106, and 240 children were from the LPG, improved biomass, and control arms, respectively. Compared with control, the improved biomass stove condition was associated with lower reactance at 5 Hz (X5 z-score: ß = -0.25; 95% confidence interval [CI] = -0.39, -0.11), higher large airway resistance (R20 z-score: ß = 0.34; 95% CI = 0.23, 0.44), and higher AX (AX z-score: ß = 0.16; 95% CI = 0.06, 0.26), which is suggestive of overall worse lung function. The LPG stove condition was associated with higher X5 (X5 score: ß = 0.16; 95% CI = 0.01, 0.31) and lower small airway resistance (R5-20 z-score: ß = -0.15; 95% CI = -0.30, 0.0), which is suggestive of better small airway function. Higher average prenatal CO exposure was associated with higher R5 and R20, and distributed lag models identified sensitive windows of exposure between CO and X5, R5, R20, and R5-20. Conclusions: These data support the importance of prenatal HAP exposure on child lung function. Clinical trial registered with www.clinicaltrials.gov (NCT01335490).

EDC mixtures during pregnancy and body fat at 7 years of age in a Swedish cohort, the SELMA study.

BACKGROUND: Some endocrine disrupting chemicals (EDC), are "obesogens" and have been associated with overweight and obesity in children. Daily exposure to different classes of EDCs demands for research with mixtures approach. OBJECTIVES: This study evaluates the association, considering sex-specific effects, between prenatal exposure to EDC mixture and children's body fat at seven years of age. METHODS: A total of 26 EDCs were assessed in prenatal urine and serum samples from first trimester in pregnancy from 737 mother-child pairs participating in the Swedish Environmental Longitudinal, Mother and child, Asthma and allergy (SELMA) study. An indicator for children's "overall body fat" was calculated, using principal component analysis (PCA), based on BMI, percent body fat, waist, and skinfolds measured at seven years of age. Weighted quantile sum (WQS) regression was used to assess associations between EDC mixture and children's body fat. RESULTS: Principal component (PC1) represented 83.6 % of the variance, suitable as indicator for children's "overall body fat", with positive loadings of 0.40-0.42 for each body fat measure. A significant interaction term, WQS*sex, confirmed associations in the opposite direction for boys and girls. Higher prenatal exposure to EDC mixture was borderline significant with more "overall body fat" for boys (Mean ß = 0.20; 95 % CI: -0.13, 0.53) and less for girls (Mean ß = -0.23; 95 % CI: -0.58, 0.13). Also, higher prenatal exposure to EDC mixture was borderline significant with more percent body fat (standardized score) for boys (Mean ß = 0.09; 95 % CI: -0.04, 0.21) and less for girls (Mean ß = -0.10 (-0.26, 0.05). The chemicals of concern included bisphenols, phthalates, PFAS, PAH, and pesticides with different patterns for boys and girls. DISCUSSION: Borderline significant associations were found between prenatal exposure to a mixture of EDCs and children's body fat. The associations in opposite directions suggests that prenatal exposure to EDCs may present sex-specific effects on children's body fat.

Sex-specific associations between co-exposure to multiple metals and externalizing symptoms in adolescence and young adulthood.

Externalizing disorders, such as attention-deficit/hyperactivity disorder (ADHD), account for the majority of the child/adolescent referrals to mental health services and increase risk for later-life psychopathology. Although the expression of externalizing disorders is more common among males, few studies have addressed how sex modifies associations between metal exposure and adolescent externalizing symptoms. This study aimed to examine sex-specific associations between co-exposure to multiple metals and externalizing symptoms in adolescence and young adulthood. Among 150 adolescents and young adults (55% female, ages: 15-25 years) enrolled in the Public Health Impact of Metals Exposure (PHIME) study in Brescia, Italy, we measured five metals (manganese (Mn), lead (Pb), copper (Cu), chromium (Cr), nickel (Ni)) in four biological matrices (blood, urine, hair, and saliva). Externalizing symptoms were assessed using the Achenbach System of Empirically Based Assessment (ASEBA) Youth Self-Report (YSR) or Adult Self Report (ASR). Using generalized weighted quantile sum (WQS) regression, we investigated the moderating effect of sex (i.e., assigned at birth) on associations between the joint effect of exposure to the metal mixture and externalizing symptoms, adjusting for age and socioeconomic status. We observed that metal mixture exposure was differentially associated with aggressive behavior in males compared to females (ß = -0.058, 95% CI [-0.126, -0.009]). In males, exposure was significantly associated with more externalizing problems, and aggressive and intrusive behaviors, driven by Pb, Cu and Cr. In females, exposure was not significantly associated with any externalizing symptoms. These findings suggest that the effect of metal exposure on externalizing symptoms differs in magnitude between the sexes, with males being more vulnerable to increased externalizing symptoms following metal exposure. Furthermore, our findings support the hypothesis that sex-specific vulnerabilities to mixed metal exposure during adolescence/young adulthood may play a role in sex disparities observed in mental health disorders, particularly those characterized by externalizing symptoms.

Maternal nutrition during mid-pregnancy and children's body composition at 7 years of age in the SELMA study.

Optimal nutrition during pregnancy is vital for both maternal and child health. Our objective was to explore if prenatal diet is associated with children's height and body fat. Nutrient intake was assessed through a FFQ from 808 pregnant women and summarised to a nutrition index, 'My Nutrition Index' (MNI). The association with children's height and body fat (bioimpedance) was assessed with linear regression models. Secondary analysis was performed with BMI, trunk fat and skinfolds. Overall, higher MNI score was associated with greater height (ß = 0·47; (95 % CI 0·00, 0·94), among both sexes. Among boys, higher MNI was associated with 0·15 higher BMI z-scores, 0·12 body fat z-scores, 0·11 trunk fat z-scores, and larger triceps, and triceps + subscapular skinfolds (ß = 0·05 and ß = 0·06; on the log2 scale) (P-value < 0·05). Among girls, the opposite associations were found with 0·12 lower trunk fat z-scores, and smaller subscapular and suprailiac skinfolds (ß = -0·07 and ß = -0·10; on the log2 scale) (P-value < 0·05). For skinfold measures, this would represent a ± 1·0 millimetres difference. Unexpectedly, a prenatal diet in line with recommended nutrient intake was associated with higher measures of body fat for boys and opposite to girls at a pre-pubertal stage of development.

Machine Learning Assisted Discovery of Interactions between Pesticides, Phthalates, Phenols, and Trace Elements in Child Neurodevelopment.

A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, investigating the effect of interactions among these ECs can be challenging. We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression and a machine-learning method termed Signed iterative Random Forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multiordered synergistic interactions among 62 ECs measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs no). WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD.

Prenatal Lead Exposure Is Associated with Reduced Abundance of Beneficial Gut Microbial Cliques in Late Childhood: An Investigation Using Microbial Co-Occurrence Analysis (MiCA).

Many analytical methods used in gut microbiome research focus on either single bacterial taxa or the whole microbiome, ignoring multibacteria relationships (microbial cliques). We present a novel analytical approach to identify microbial cliques within the gut microbiome of children at 9-11 years associated with prenatal lead (Pb) exposure. Data came from a subset of participants (n = 123) in the Programming Research in Obesity, Growth, Environment and Social Stressors cohort. Pb concentrations were measured in maternal whole blood from the second and third trimesters of pregnancy. Stool samples collected at 9-11 years old underwent metagenomic sequencing to assess the gut microbiome. Using a novel analytical approach, Microbial Co-occurrence Analysis (MiCA), we paired a machine learning algorithm with randomization-based inference to first identify microbial cliques that were predictive of prenatal Pb exposure and then estimate the association between prenatal Pb exposure and microbial clique abundance. With second-trimester Pb exposure, we identified a two-taxa microbial clique that included Bifidobacterium adolescentis and Ruminococcus callidus and a three-taxa clique that also included Prevotella clara. Increasing second-trimester Pb exposure was associated with significantly increased odds of having the two-taxa microbial clique below the median relative abundance (odds ratio (OR) = 1.03, 95% confidence interval (CI) [1.01-1.05]). Using a novel combination of machine learning and causal inference, MiCA identified a significant association between second-trimester Pb exposure and the reduced abundance of a probiotic microbial clique within the gut microbiome in late childhood.

A cross-validation based approach for estimating specific gravity in elementary-school aged children using a nonlinear model.

Environmental research often relies on urinary biomarkers which require dilution correction to accurately measure exposures. Specific gravity (SG) and creatinine (UCr) are commonly measured urinary dilution factors. Epidemiologic studies may assess only one of these measures, making it difficult to pool studies that may otherwise be able to be combined. Participants from the National Health and Nutrition Examination Survey 2007-2008 cycle were used to perform k-fold validation of a nonlinear model estimating SG from UCr. The final estimated model was applied to participants from the School Inner-City Asthma Intervention Study, who submitted urinary samples to the Children's Health Exposure Analysis Resource. Model performance was evaluated using calibration metrics to determine how closely the average estimated SG was to the measured SG. Additional models, with interaction terms for age, sex, body mass index, race/ethnicity, relative time of day when sample was collected, log transformed 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), and asthma status were estimated and assessed for improvement. The association between monobenzyl phthalate (MBZP) and asthma symptom days, controlling for measured UCr, measured SG, and each estimated SG were compared to assess validity of the estimated SG. The model estimating SG from UCr alone, resulted in a beta estimate of 1.10 (95% CI: 1.01, 1.19), indicating agreement between model-predicted SG and measured SG. Inclusion of age and sex in the model improved estimation (ß = 1.06, 95% CI: 0.98, 1.15). The full model accounting for all interaction terms with UCr resulted in the best agreement (ß = 1.01, 95% CI: 0.93,1.09). Associations between MBZP and asthma symptoms days, controlling for each estimated SG, were within the range of effect estimates when controlling for measured SG and measured UCr (Rate ratios = 1.28-1.34). Our nonlinear modeling provides opportunities to estimate SG in studies that measure UCr or vice versa, enabling data pooling despite differences in urine dilution factors.

NICU-based stress response and preterm infant neurobehavior: exploring the critical windows for exposure.

BACKGROUND: Exposure to maternal stress in utero negatively impacts cognitive and behavioral outcomes of children born at term. The neonatal intensive care unit (NICU) can be stressful for preterm infants during a developmental period corresponding to the third trimester of gestation. It is unknown whether stress in the NICU contributes to adverse neurodevelopment among NICU graduates. The aim was to examine the association between salivary cortisol and early neurodevelopment in preterm infants. METHODS: We examined the association between cortisol levels during the NICU hospitalization and subsequent performance on the NICU Network Neurobehavioral Scales (NNNS), estimating time-specific associations and considering sex differences. RESULTS: Eight hundred and forty salivary cortisol levels were measured from 139 infants. Average cortisol levels were inversely associated with NNNS Regulation scores for both male and female infants (ß = -0.19; 95% CI: -0.44, -0.02). Critical developmental windows based on postmenstrual age were identified, with cortisol measured <30 weeks PMA positively associated with Habituation and Lethargy scores (ß = 0.63-1.04). Critical developmental windows based on chronological age were identified, with cortisol measured in the first week of life inversely associated with Attention score (ß = -1.01 for females; -0.93 for males). CONCLUSIONS: Stress in the NICU at specific developmental time points may impact early preterm infant neurodevelopment. IMPACT: Stress in the neonatal intensive care unit can impact the neurodevelopmental trajectory of premature infants. The impact of stress is different at different points in development. The impact of stress is sexually dimorphic.

Association between prenatal metal exposure and adverse respiratory symptoms in childhood.

INTRODUCTION: Manganese and lead have been cross-sectionally associated with adverse respiratory outcomes in childhood but there is limited data on their combined effects starting in utero. We examined associations between in utero exposure to metals and childhood respiratory symptoms. METHODS: We assessed 633 mother-child dyads enrolled in the Programming Research in Obesity, Growth, Environment, and Social Stressors (PROGRESS) birth cohort in Mexico City. Blood manganese (BMn) and lead (BPb) were measured in mothers at 2nd and 3rd trimester. Ever wheeze, current wheeze and asthma diagnosis were ascertained at 4-5 and 6-7 year visits through the International Study of Asthma and Allergies in Childhood survey. Logistic mixed model regression was used to assess the association between prenatal metals and respiratory outcomes in children across the 4-5 and 6-7 year visits. Covariates included mother's age, education and asthma, environmental tobacco smoke, child's sex and assessment time. RESULTS: In adjusted models, higher 2nd trimester BPb had a significant association with elevated odds of ever wheeze (Odds Ratio (OR): 1.97, 95% CI: 1.05, 3.67). BMn at 2nd trimester was associated with decreased (OR: 0.06, 95% CI: 0.01, 0.35) odds of current wheeze. We did not find any statistically significant associations with 3rd trimester blood metals. CONCLUSION: Prenatal exposure to Pb was associated with higher odds of ever wheeze while Mn was negatively associated with odds of current wheeze. These findings underscore the need to consider prenatal metal exposure, including low exposure levels, in the study of adverse respiratory outcomes.

A Mixture of Endocrine Disrupting Chemicals Associated with Lower Birth Weight in Children Induces Adipogenesis and DNA Methylation Changes in Human Mesenchymal Stem Cells.

Endocrine Disrupting Chemicals (EDCs) are man-made compounds that alter functions of the endocrine system. Environmental mixtures of EDCs might have adverse effects on human health, even though their individual concentrations are below regulatory levels of concerns. However, studies identifying and experimentally testing adverse effects of real-life mixtures are scarce. In this study, we aimed at evaluating an epidemiologically identified EDC mixture in an experimental setting to delineate its cellular and epigenetic effects. The mixture was established using data from the Swedish Environmental Longitudinal Mother and child Asthma and allergy (SELMA) study where it was associated with lower birth weight, an early marker for prenatal metabolic programming. This mixture was then tested for its ability to change metabolic programming of human mesenchymal stem cells. In these cells, we assessed if the mixture induced adipogenesis and genome-wide DNA methylation changes. The mixture increased lipid droplet accumulation already at concentrations corresponding to levels measured in the pregnant women of the SELMA study. Furthermore, we identified differentially methylated regions in genes important for adipogenesis and thermogenesis. This study shows that a mixture reflecting human real-life exposure can induce molecular and cellular changes during development that could underlie adverse outcomes.

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.

Exposure to environmental chemical mixtures is associated with nasal colonization by Staphylococcus aureus: NHANES 2001-2004.

BACKGROUND: Understanding the health effects of exposure to chemical mixtures is critically important given the broad range of concurrent exposures throughout the life-course. While investigations of environmental chemicals and components of the human microbiome are becoming more common, few have examined associations with chemical mixtures. This study assesses the association between exposure to mixtures of 66 different environmental chemicals and nasal colonization of Staphylococcus aureus (SA) and methicillin resistant SA (MRSA). METHODS: Data came from the National Health and Nutrition Examination Survey (NHANES) 2001-2004. The analytical sample consists of 10,312 participants, age 6 years and older, subdivided into 8 groups with different chemical exposure mixtures. Within each of 6 chemical classes (metals, phthalates, polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs), polyfluorochemicals (PFCs), and phenols), weighted quantile sum (WQS) regression was used to analyze the joint association of the component compounds and nasal SA colonization. WQS was also used to assess the joint association of 3 chemical mixtures (metals, metal and PAHs, and metal and triclosan) and nasal MRSA colonization. All regression models were adjusted for confounders. RESULTS: The analytical sample was between ages 6-85, slightly more female, and predominantly non-smokers. Prevalence of SA carriage was 29.2%, and MRSA colonization prevalence was 1.2%. Within each chemical class, odds of SA colonization increased statistically significantly with exposure to mixtures of metals (OR = 1.11, 95% CI = 1.02-1.20), phthalates (OR = 1.09, 95% CI = 1.04-1.14), and phenols (OR = 1.08, 95% CI = 1.01-1.15). Exposure to a mixture of metals combined with PAHs was also associated with increased odds of MRSA carriage (OR = 1.38, 95% CI = 1.02-1.86). CONCLUSION: Results indicate an association between multiple environmental chemical mixtures and SA colonization, including MRSA. These findings support the need for further analysis of associations between chemical mixtures and SA colonization, as well as other components of the human microbiome.

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.

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.

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 (ß = 0.01, P-value<0.001), 300 m (ß = 0.01, P-value = 0.003), and 500 m (ß = 0.01, P-value = 0.002) buffering areas, as well as with road density in a 500 m buffering area (ß = 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.

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 (ß [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.

Prenatal toxic metal mixture exposure and newborn telomere length: Modification by maternal antioxidant intake.

BACKGROUND: Telomere length (TL) predicts the onset of cellular senescence and correlates with longevity and age-related disease risk. While telomeres erode throughout life, adults display fixed ranking and tracking of TL, supporting the importance of the early environment in determining inter-individual variability across the life course. Given their guanine-rich structure, telomeres are highly susceptible to oxidative stress (OS). We examined maternal metal exposure, which can induce OS, in relation to newborn TL. We also considered the modifying role of maternal antioxidant intake. METHODS: Analyses included 100 mother-newborn pairs enrolled in the Boston and New York City-based PRogramming of Intergenerational Stress Mechanisms (PRISM) pregnancy cohort. We measured As, Ba, Cd, Ni, and Pb in maternal late-pregnancy urine by ICP-MS and quantified relative leukocyte TL (rLTL) in cord blood using qPCR. We used Weighted Quantile Sum (WQS) regression to estimate the metal mixture - rLTL association and conducted repeated holdout validation to improve the stability of estimates across data partitions. We examined models stratified by high (>median) versus low (≤median) maternal antioxidant intake, estimated from Block98 Food Frequency Questionnaires. We considered urinary creatinine, week of urine collection, maternal age, and race/ethnicity as covariates. RESULTS: In adjusted models, urinary metals were inversely associated with newborn rLTL (ßWQS = -0.50, 95% CI: -0.78, -0.21). The top metals contributing to the negative association included Ba (weight: 35.4%), Cd (24.5%) and Pb (26.9%). In models stratified by antioxidant intake, the significant inverse association between metals and rLTL remained only among mothers with low antioxidant intake (low: ßWQS = -0.92, 95% CI: -1.53, -0.30; high: ßWQS = -0.03, 95% CI: -0.58, 0.52). Results were similar in unadjusted models. CONCLUSIONS: Relative LTL was shorter among newborns of mothers with higher exposure to metals during pregnancy. Higher maternal antioxidant intake may mitigate the negative influence of metals on newborn rLTL.

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.

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.

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.