Identifying Subsets of Complex Mixtures Most Associated With Complex Diseases: Polychlorinated Biphenyls and Endometriosis as a Case Study

Prenatal ambient air pollutant mixture exposure and neurodevelopment in urban children in the Northeastern United States

BACKGROUND

Studies of prenatal air pollution (AP) exposure on child neurodevelopment have mostly focused on a single pollutant. We leveraged daily exposure data and implemented novel data-driven statistical approaches to assess effects of prenatal exposure to a mixture of seven air pollutants on cognitive functioning in school-age children from an urban pregnancy cohort.

METHODS

Analyses included 236 children born at ≥37 weeks gestation. Maternal prenatal daily exposure levels for nitrogen dioxide (NO2), ozone (O3), and constituents of fine particles [elemental carbon (EC), organic carbon (OC), nitrate (NO3-), sulfate (SO42-), ammonium (NH4+)] were estimated based on residential addresses using validated satellite-based hybrid models or global 3-D chemical-transport models. Children completed Wide Range Assessment of Memory and Learning (WRAML-2) and Conners' Continuous Performance Test (CPT-II) at 6.5 ± 0.9 years of age. Time-weighted levels for mixture pollutants were estimated using Bayesian Kernel Machine Regression Distributed Lag Models (BKMR-DLMs), with which we also explored the interactions in the exposure-response functions among pollutants. Resulting time-weighted exposure levels were used in Weighted Quantile Sum (WQS) regressions to examine AP mixture effects on outcomes, adjusted for maternal age, education, child sex, and prenatal temperature.

RESULTS

Mothers were primarily ethnic minorities (81% Hispanic and/or black) reporting ≤12 years of education (68%). Prenatal AP mixture (per unit increase in WQS estimated AP index) was associated with decreased WRAML-2 general memory (GM; β = -0.64, 95%CI = -1.40, 0.00) and memory-related attention/concentration (AC; β = -1.03, 95%CI = -1.78, -0.27) indices, indicating poorer memory functioning, as well as increased CPT-II omission errors (OE; β = 1.55, 95%CI = 0.34, 2.77), indicating increased attention problems. When stratified by sex, association with AC index was significant among girls, while association with OE was significant among boys. Traffic-related pollutants (NO2, OC, EC) and SO42- were major contributors to these associations. There was no significant evidence of interactions among mixture components.

CONCLUSIONS

Prenatal exposure to an AP mixture was associated with child neurocognitive outcomes in a sex- and domain-specific manner.

Endometriosis: Update of Pathophysiology, (Epi) Genetic and Environmental Involvement

Endometriosis is a chronic disease caused by ectopic endometrial tissue. Endometriotic implants induce inflammation, leading to chronic pain and impaired fertility. Characterized by their dependence on estradiol (via estrogen receptor β (ESRβ)) and their resistance to progesterone, endometriotic implants produce their own source of estradiol through active aromatase. Steroidogenic factor-1 (SF1) is a key transcription factor that promotes aromatase synthesis. The expression of SF1 and ESRβ is enhanced by the demethylation of their promoter in progenitor cells of the female reproductive system. High local concentrations of estrogen are involved in the chronic inflammatory environment favoring the implantation and development of endometriotic implants. Similar local conditions can promote, directly and indirectly, the appearance and development of genital cancer. Recently, certain components of the microbiota have been identified as potentially promoting a high level of estrogen in the blood. Many environmental factors are also suspected of increasing the estrogen concentration, especially prenatal exposure to estrogen-like endocrine disruptors such as DES and bisphenol A. Phthalates are also suspected of promoting endometriosis but throughmeans other than binding to estradiol receptors. The impact of dioxin or tobacco seems to be more controversial.

Independent and combined associations of urinary arsenic exposure and serum sex steroid hormones among 6-19-year old children and adolescents in NHANES 2013-2016

Arsenic exposure may disrupt sex steroid hormones, causing endocrine disruption. However, human evidence is limited and inconsistent, especially for children and adolescents. To evaluate the independent and combined associations between arsenic exposure and serum sex steroid hormones in children and adolescents, we conducted a cross-sectional analysis of data from 1063 participants aged 6 to 19 years from the 2013-2016 National Health and Nutrition Examination Survey (NHANES). Three urine arsenic metabolites were examined, as well as three serum sex steroid hormones, estradiol (E₂), total testosterone (TT), and sex hormone-binding globulin (SHBG). The ratio of TT to E₂ (TT/E₂) and the free androgen index (FAI) generated by TT/SHBG were also assessed. Linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were used to evaluate the associations of individual or arsenic metabolite combinations with sex steroid hormones by gender and age stratification. Positive associations were found between total arsenic and arsenic metabolites with TT, E₂, and FAI. In contrast, negative associations were found between arsenic metabolites and SHBG. Furthermore, there was an interaction after gender-age stratification between DMA and SHBG in female adolescents. Notably, based on the WQS and BKMR model results, the combined association of arsenic and its metabolites was positively associated with TT, E₂, and FAI and negatively associated with SHBG. Moreover, DMA and MMA dominated the highest weights among the arsenic metabolites. Overall, our results indicate that exposure to arsenic, either alone or in mixtures, may alter sex steroid hormone levels in children and adolescents.

Bayesian multiple index models for environmental mixtures

An important goal of environmental health research is to assess the risk posed by mixtures of environmental exposures. Two popular classes of models for mixtures analyses are response-surface methods and exposure-index methods. Response-surface methods estimate high-dimensional surfaces and are thus highly flexible but difficult to interpret. In contrast, exposure-index methods decompose coefficients from a linear model into an overall mixture effect and individual index weights; these models yield easily interpretable effect estimates and efficient inferences when model assumptions hold, but, like most parsimonious models, incur bias when these assumptions do not hold. In this paper, we propose a Bayesian multiple index model framework that combines the strengths of each, allowing for non-linear and non-additive relationships between exposure indices and a health outcome, while reducing the dimensionality of the exposure vector and estimating index weights with variable selection. This framework contains response-surface and exposure-index models as special cases, thereby unifying the two analysis strategies. This unification increases the range of models possible for analysing environmental mixtures and health, allowing one to select an appropriate analysis from a spectrum of models varying in flexibility and interpretability. In an analysis of the association between telomere length and 18 organic pollutants in the National Health and Nutrition Examination Survey (NHANES), the proposed approach fits the data as well as more complex response-surface methods and yields more interpretable results.

A systematic evidence map for the evaluation of noncancer health effects and exposures to polychlorinated biphenyl mixtures

Assessing health outcomes associated with exposure to polychlorinated biphenyls (PCBs) is important given their persistent and ubiquitous nature. PCBs are classified as a Group 1 carcinogen, but the full range of potential noncancer health effects from exposure to PCBs has not been systematically summarized and evaluated. We used systematic review methods to identify and screen the literature using combined manual review and machine learning approaches. A protocol was developed that describes the literature search strategy and Populations, Exposures, Comparators, and Outcomes (PECO) criteria used to facilitate subsequent screening and categorization of literature into a systematic evidence map of PCB exposure and noncancer health endpoints across 15 organs/systems. A comprehensive literature search yielded 62,599 records. After electronic prioritization steps, 17,037 studies were manually screened at the title and abstract level. An additional 900 studies identified by experts or supplemental searches were also included. After full-text screening of 3889 references, 1586 studies met the PECO criteria. Relevant study details such as the endpoints assessed, exposure duration, and species were extracted into literature summary tables. This review compiles and organizes the human and mammalian studies from these tables into an evidence map for noncancer health endpoints and PCB mixture exposure to identify areas of robust research as well as areas of uncertainty that would benefit from future investigation. Summary data are available online as interactive visuals with downloadable metadata. Sufficient research is available to inform PCB hazard assessments for most organs/systems, but the amount of data to inform associations with specific endpoints differs. Furthermore, despite many years of research, sparse data exist for inhalation and dermal exposures, which are highly relevant human exposure routes. This evidence map provides a foundation for future systematic reviews and noncancer hazard assessments of PCB mixtures and for strategic planning of research to inform areas of greater uncertainty.

Associations between prenatal multiple metal exposure and preterm birth: Comparison of four statistical models

BACKGROUND

Exposure to some heavy metals has been demonstrated to be related to the risk of preterm birth (PTB). However, the effects of multi-metal mixture are seldom assessed. Thus, we aimed to investigate the associations of maternal exposure to metal mixture with PTB, and to identify the main contributors to PTB from the mixture.

METHODS

The population in the nested case-control study was from a prospective cohort enrolled in Wuhan, China between 2012 and 2014. Eighteen metals were measured in maternal urine collected before delivery. Logistic regression, elastic net regularization (ENET), weighted quantile sum regression (WQSR), and Bayesian kernel machine regression (BKMR) were used to estimate the overall effect and identify important mixture components that drive the associations with PTB.

RESULTS

Logistic regression found naturally log-transformed concentrations of 13 metals were positively associated with PTB after adjusting for the covariates, and only V, Zn, and Cr remained the significantly positive associations when additionally adjusting for the 13 metals together. ENET identified 11 important metals for PTB, and V (β = 0.23) had the strongest association. WQSR determined the positive combined effect of metal mixture on PTB (OR: 1.44, 95%CI: 1.32, 1.57), and selected Cr and V (weighted 0.41 and 0.32, respectively) as the most weighted metals. BKMR analysis confirmed the overall mixture was positively associated with PTB, and the independent effect of V was the most significant. Besides, BKMR showed the non-linear relationships of V and Cu with PTB, and the potential interaction between Zn and Cu.

CONCLUSION

Applying different statistical models, the study found that exposure to the metal mixture was associated with a higher risk of PTB, and V was identified as the most important risk factor among co-exposed metals for PTB.

Repeated measurements of 21 urinary metabolites of volatile organic compounds and their associations with three selected oxidative stress biomarkers in 0-7-year-old healthy children from south and central China

Human beings are extensively and concurrently exposed to multiple volatile organic compounds (VOCs, including some Class I human carcinogens), which may induce oxidative stress in human body. Data on urinary metabolites of VOCs (mVOCs) among young children are limited. No studies have examined their inter-day variability of mVOCs and their associations with oxidative stress biomarkers (OSBs) using repeated urine samples from children. In this study, we measured twenty one mVOCs and three OSBs [8-hydroxy-2'-deoxyguanosine (8-OHdG; for DNA), 8-hydroxyguanosine (8-OHG; for RNA], and 4-hydroxy nonenal mercapturic acid (HNEMA; for lipid)] in 390 urine samples of 130 children (three samples on three consecutive days provided by each participant) aged 0-7 years from September 2018 to January 2019 in Shenzhen, south China, and Wuhan, central China. HPMMA (3-hydroxypropyl-1-methyl mercapturic acid/N-Acetyl-S-(3-hydroxypropyl-1-methyl)-l-cysteine), 3HPMA (3-hydroxypropyl mercapturic acid/N-Acetyl-S-(3-hydroxypropyl)-l-cysteine), and ATCA (2-aminothiazoline-4-carboxylic acid) had higher specific gravity-adjusted median concentrations (1 383, 286, and 273 μg/L, respectively) than the others. Intraclass correlation coefficients of mVOCs ranged from 0.29 to 0.71. After false-discovery rate (FDR, defined as FDR q-value < 0.05) adjustment, linear mixed-effects models revealed that 14 mVOCs were positively associated with 8-OHdG (β range: 0.09-0.37), 11 mVOCs were positively associated with 8-OHG (β range: 0.08-0.30), and 11 mVOCs were positively associated with HNEMA (β range: 0.21-0.70) in urine. Considering the weight of the mVOC index accounted for the associations, based on the weighted quantile sum regression model, parent compounds of DHBMA (3,4-dihydroxybutyl mercapturic acid/N-Acetyl-S-(3,4-dihydroxybutyl)-l-cysteine) and t,t-MA (trans,trans-muconic acid) should be listed as priority VOCs for management to mitigate health risks. For the first time, this study characterized the inter-day variability of urinary mVOCs and their associations with selected OSBs (8-OHdG, 8-OHG, and NHEMA) in young, healthy Chinese children.

Prenatal Exposure to Mixtures of Persistent Endocrine-disrupting Chemicals and Birth Size in a Population-based Cohort of British Girls

BACKGROUND

Previous studies of endocrine-disrupting chemicals have examined one of these chemicals at a time in association with an outcome; studying mixtures better approximates human experience. We investigated the association of prenatal exposure to mixtures of persistent endocrine disruptors (perfluoroalkyl and polyfluoroalkyl substances [PFAS], polychlorinated biphenyls [PCBs], and organochlorine pesticides) with birth size among female offspring in the Avon Longitudinal Study of Parents and Children (ALSPAC), based in the United Kingdom in 1991-1992.

METHODS

We quantified concentrations of 52 endocrine-disrupting chemicals in maternal serum collected during pregnancy at median 15-week gestation. Birth weight, crown-to-heel length, and head circumference were measured at birth; ponderal index and small for gestational age were calculated from these. We used repeated holdout Weighted Quantile Sum (WQS) regression and Bayesian kernel machine regression to examine mixtures in 313 mothers.

RESULTS

Using WQS regression, all mixtures (each chemical class separately and all three together) were inversely associated with birth weight. A one-unit increase in WQS index (a one-decile increase in chemical concentrations) for all three classes combined was associated with 55 g (β = -55 g, 95% confidence interval [CI] = -89, -22 g) lower birth weight. Associations were weaker but still inverse using Bayesian kernel machine regression. Under both methods, PFAS were the most important contributors to the association with birth weight. We also observed inverse associations for crown-to-heel length.

CONCLUSIONS

These results are consistent with the hypothesis that prenatal exposure to mixtures of persistent endocrine-disrupting chemicals affects birth size.

Prenatal exposure to mixtures of persistent endocrine disrupting chemicals and early menarche in a population-based cohort of British girls

Exposure to endocrine disrupting chemicals (EDCs) is ubiquitous. EDC exposure, especially during critical periods of development like the prenatal window, may interfere with the body's endocrine system, which can affect growth and developmental outcomes such as puberty. Most studies have examined one EDC at a time in relation to disease; however, humans are exposed to many EDCs. By studying mixtures, the human experience can be more closely replicated. We investigated the association of prenatal exposure to persistent EDCs (poly- and perfluoroalkyl substances (PFAS), polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCPs)) as mixtures with early menarche among female offspring in a nested case-control study within the Avon Longitudinal Study of Parents and Children (ALSPAC) recruited in the United Kingdom in 1991-1992. Concentrations of 52 EDCs were quantified in maternal serum samples collected during pregnancy. Daughter's age at menarche was ascertained through mailed questionnaires sent annually. We used repeated holdout weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) to examine the association between prenatal exposure to multiple EDCs and early menarche (<11.5 (n = 218) vs. ≥11.5 years (n = 230)) for each chemical class separately (PFAS, PCBs, and OCPs) and for all three classes combined. Models adjusted for maternal age at menarche, maternal education, parity, pre-pregnancy body mass index, maternal age, prenatal smoking, and gestational week at sample collection. Mixture models showed null associations between prenatal exposure to EDC mixtures and early menarche. Using WQS regression, the odds ratio for early menarche for a one-decile increase in chemical concentrations for all three classes combined was 0.89 (95% CI: 0.76, 1.05); using BKMR, the odds ratio when all exposures were at the 60th percentile compared to the median was 0.98 (95% CI: 0.91, 1.05). Results suggest the overall effect of prenatal exposure to persistent EDC mixtures is not associated with early menarche.

Prenatal metal mixtures and sex-specific infant negative affectivity

Prenatal exposure to metals has been associated with a range of adverse neurocognitive outcomes; however, associations with early behavioral development are less well understood. We examined joint exposure to multiple co-occurring metals in relation to infant negative affect, a stable temperamental trait linked to psychopathology among children and adults.

METHODS

Analyses included 308 mother-infant pairs enrolled in the PRISM pregnancy cohort. We measured As, Ba, Cd, Cs, Cr, Pb, and Sb in urine, collected on average during late pregnancy, by ICP-MS. At age 6 months, we assessed negative affect using the Infant Behavior Questionnaire-Revised. We used Weighted Quantile Sum (WQS) regression with repeated holdout validation to estimate the joint association between the metals and global negative affectivity, as well as four subdomains (Fear, Sadness, Distress to Limitations, and Falling Reactivity). We also tested for a sex interaction with estimated stratified weights.

RESULTS

In adjusted models, urinary metals were associated with higher scores on the Fear scale (βWQS = 0.20, 95% confidence interval [CI]: 0.09, 0.30), which captures behavioral inhibition, characterized by startle or distress to sudden changes in the environment and inhibited approach to novelty. We observed a significant sex interaction (95% CI for the cross-product term: -0.19, -0.01), and stratified weights showed girls (61.6%) contributed substantially more to the mixture effect compared with boys (38.4%). Overall, Ba contributed the greatest mixture weight (22.5%), followed by Cs (14.9%) and As (14.6%).

CONCLUSIONS

Prenatal exposure to metals was associated with increased infant scores on the temperamental domain of fear, with girls showing particular sensitivity.Key words: Prenatal; Metals; Mixtures; Temperament; Infancy; Negative affect.

Association of multiple metals with lipid markers against different exposure profiles: A population-based cross-sectional study in China

We sought to evaluate whether essential and toxic metals are cross-sectionally related to blood lipid levels using data among adults from Shimen (n = 564) and Huayuan (n = 637), two counties with different exposure profiles in Hunan province of China. Traditional and grouped weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were performed to assess association between exposure to a mixture of 22 metals measured in urine or plasma, and lipid markers. Most of the exposure levels of metals were significantly higher in Shimen area than those in Huayuan area (all P-values < 0.001). Traditional WQS regression analyses revealed that the WQS index were both significantly associated with lipid markers in two areas, except for the HDL-C. Grouped WQS revealed that essential metals group showed significantly positive associations with lipid markers except for HDL-C in Huayuan area, while toxic metals group showed significantly negative associations except for HDL-C and LDL-C in Huayuan area. There were no significant joint effects, but potential non-linear relationships between metals mixture and TC or LDL-C levels were observed in BKMR analyses. Although consistent significantly associations of zinc and titanium with TG levels were found in both areas, the metals closely related to other lipid markers were varied by sites. Additionally, the BKMR analyses revealed an inverse U shaped association of iron with LDL-C levels and interaction effects of zinc and cadmium on LDL-C in Huayuan area. The relationship between metal exposure and blood lipid were not identical against different exposure profiles.

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 (βWQS = -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: βWQS = -0.92, 95% CI: -1.53, -0.30; high: βWQS = -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.

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.

Combined exposure to phthalate esters and phosphate flame retardants and plasticizers and their associations with wheeze and allergy symptoms among school children

BACKGROUND

Phthalate esters and phosphate flame retardants and plasticizers (PFRs) are both used as plasticizers and are commonly detected in indoor environments. Although both phthalates and PFRs are known to be associated with children's wheeze and allergic symptoms, there have been no previous studies examining the effects of mixtures of these exposures.

OBJECTIVES

To investigate the association between exposure to mixtures of phthalate esters and PFRs, and wheeze and allergic symptoms among school-aged children.

METHODS

A total of 128 elementary school-aged children were enrolled. Metabolites of 3 phthalate esters and 7 PFRs were measured in urine samples. Parent-reported symptoms of wheeze, rhinoconjunctivitis, and eczema were evaluated using the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. In the primary model, we created a phthalate ester and PFR mixture exposure index, and estimated odds ratios (ORs) using weighted quantile sum (WQS) regression and quantile g (qg)-computation. The two highest chemicals according to qg-computation weight %s were combined to create a combination high × high exposure estimate, with ORs calculated using the "low × low" exposure group as the reference category. Concentrations of each metabolite were corrected by multiplying this value by the sex- and body size-Standardised creatinine concentration and dividing by the observed creatinine value. All models were adjusted for sex, grade, dampness index and annual house income.

RESULTS

The odds ratio of rhinoconjunctivitis for the association between exposure to chemical mixtures according to the WQS index positive models was; OR = 2.60 (95% confidence interval [CI]: 1.38-5.14). However, wheeze and eczema of the WQS index positive model, none of the WQS index negative models or qg-computation result yielded statistically significant results. Combined exposure to the two highest WQS weight %s of "high-high" ΣTCIPP and ΣTPHP was associated with an increased prevalence of rhino-conjunctivitis, OR = 5.78 (1.81-18.43) to the "low × low" group.

CONCLUSIONS

Significant associations of mixed exposures to phthalates and PFRs and increased prevalence of rhinoconjunctivitis was found among elementary school-aged children in the WQS positive model. Mixed exposures were not associated with any of allergic symptoms in the WQS negative model or qg-computation approach. However, the combined effects of exposure to two PFRs suggested an additive and/or multiplicative interaction, potentially increasing the prevalence of rhinoconjunctivitis. A further study with a larger sample size is needed to confirm these results.

Associations between organophosphate esters and sex hormones among 6-19-year old children and adolescents in NHANES 2013-2014

BACKGROUND

Organophosphate esters (OPEs) are a class of alternative replacements for polybrominated diphenyl ethers. In vitro and in vivo studies suggested that OPEs may disrupt the homeostasis of sex steroid hormones. However, human evidence in children and adolescents is limited.

OBJECTIVES

We conducted a cross-sectional analysis of the associations between OPE biomarkers and sex steroid hormones among children (6-11 years) and adolescents (12-19 years) in the U.S. National Health and Nutrition Examination Survey, 2013-2014.

METHODS

Participants aged 6-19 years who had available data on urinary OPE metabolites, serum sex hormones [total testosterone (TT), estradiol (E₂)] and sex hormone binding globulin (SHBG) were included (n = 544). Free androgen index (FAI) calculated as TT divided by SHBG and a ratio of TT to E₂ (TT/E₂) were generated. Five urinary OPE metabolites were examined. A constructed puberty status was defined as either high steroid hormone levels (TT ≥ 50 ng/dL in males and E₂ ≥ 20 pg/ml in females) or onset of menarche. Multiple linear regression and weighted quantile sum (WQS) regression analyses stratified by sex-age and sex-puberty-status groups were conducted to examine the associations of OPE metabolites and its mixture with sex hormone levels.

RESULTS

After adjusting for covariates, dibutyl phosphate (DBUP) and dibutyl phosphate (DPHP) were significantly inversely associated with TT (or FAI) and E₂; DBUP was negatively associated with SHBG; and DPHP was positively associated with SHBG and TT/E₂ in female adolescents. In male adolescents, we observed monotonic negative associations of bis(1,3-dichloro-2-propyl) phosphate (BDCPP), DBUP or DPHP with TT (or FAI) and E₂, and positive associations of BDCPP and DPHP with SHBG. Among adolescents, the OPEs index was negatively associated with TT [WQS beta = -0.29 (95% confidence interval: -0.51, -0.07) in males and -0.15 (-0.28, -0.01) in females ], FAI [-0.46 (-0.71, -0.2) in males and -0.23 (-0.41, -0.05) in females] and E₂ [-0.25 (-0.41, -0.1) in males and -0.33 (-0.59, -0.08) in females], with stronger associations with TT and FAI in males and a slightly stronger association with E₂ in females. In addition, the OPEs index presented a comparable positive association with SHBG in both sexes of adolescents. In contrast, significant associations of individual OPE metabolites or OPEs index with sex hormones were sparse in children. Results by sex-puberty status in single pollutant and WQS regression analyses presented a similar pattern, where most of the significant associations were limited to the pubertal individuals. Of note, stronger inverse associations of the OPEs index with TT and FAI remained in pubertal boys. But the association between the OPEs index and E₂ was non-significant in pubertal girls, and only in pubertal boys did the OPEs index show a significant and stronger inverse association with E₂.

CONCLUSIONS

Exposure to OPEs, either individually or as a mixture, was associated with decreased levels of certain sex steroid hormones (TT, FAI, and E₂) and increased levels of SHBG in adolescents or pubertal individuals, with the associations presenting somewhat sex-dependent pattern. However, there is little evidence of the significant associations in children or prepubescent ones. Given the cross-sectional nature of the analysis, our findings need further confirmation.

Association between mixture of persistent organic pollutants and thyroid pathologies in a Belgian population

Previous years, the incidence of autoimmune thyroid diseases has increased worldwide. The presence of many pollutants in the environment suspected to be thyroid disruptors may have contributed to the observed increase. Unfortunately, the results from epidemiological studies assessing the association between pollution and thyroid disorders remain inconsistent, maybe due to a nearly complete neglect of the mixture effect. The blood levels of 12 brominated flame retardants, 3 polychlorinated biphenyls, 16 organochlorine pesticides, 7 perfluoroalkyl substances and 16 phenolic organohalogens were measured in 35 hypothyroid and 44 hyperthyroid volunteers and in 160 individuals from the general population designed as controls. Weighted quantile sum (WQS) regressions were performed to compute indexes representing the mixture of POPs, and we assessed the relations with thyroid disorders. Nineteen pollutants were detected in more than 40% of the individuals and were thus included in the WQS indexes. The WQS index was statistically significantly associated with an increased odds of hypothyroidism (odds ratio (OR) = 98.1; 95% CI: 5.51-1747) with the highest weights attributed to PCB 138 (w = 0.210), 3-OH-CB 180 (w = 0.197), 4-OH-CB 146 (w = 0.188), 4',4-DDE (w = 0.156) while there were no evidence of a relation with increased odds of hyperthyroidism. Given the relative low number of individuals included in the present investigation, standard WQS methodology could not be used, this study should thus be considered as a preliminary, hypothesis-generating study. Nevertheless, these results highlighted the importance of considering the potential effect of chemical mixture when studying endocrine disruptors.

Assessment of polychlorinated biphenyls (PCBs) in the Himalayan Riverine Network of Azad Jammu and Kashmir

The emission of polychlorinated biphenyls (PCBs) in South Asian countries is one of the great environmental concerns and has resulted in the contamination of surrounding high altitude regions such as Azad Jammu and Kashmir (AJK), Pakistan. This first investigation of Polychlorinated Biphenyl (PCBs) concentrations in the ambient air, water and surface soil was conducted along the extensive stream network in the AJK valley of the Himalayan Region. In 2014, surface soil samples were taken and passive air and water samplers were deployed along the four main rivers, namely Jhelum, Neelum, Poonch and Kunhar, and analysed for PCBs (33 congeners) using GC-MS/MS. The ∑₃₃PCBs concentrations ranged from 31.17 to 175.2 (mean ± SD: 81 ± 46.4 pg/L), ND to 1908 (1054 ± 588.5 pg/g), and 29.8 to 94.4 (52.9 ± 22.7 pg/m³) in surface water, soil and air matrices, respectively. The levels of dioxin-like PCBs (∑₈DL-PCBs) contributed considerably towards the total PCBs concentrations: 60.63% (water), 43.87% (air) and 13.76% (soil). The log transformed air-water fugacity (log fa/fw) ratios ranged from -9.37 to 2.58; with 86.3% of the sampling sites showing net volatilization of selected PCB congeners. Similarly, the fugacity fractions for air-soil exchange exhibited narrow variation (0.8 to < 1) indicating net volatilization of PCBs. The ecological risk assessment showed low potential ecological risks (E_rⁱ  = 1.58-7.63) associated with PCB contamination. The present findings provide baseline data that suggest cold trapping of POPs in the remote mountainous areas of Pakistan and can support environmental management of POPs at the regional level. This pioneer investigation campaign to assess the PCBs concentrations in Himalayan Riverine Network of Azad Jammu and Kashmir, Pakistan helps to develop baseline data of PCBs from the strategically important riverine environment that would help in future regional as well as global ecological studies. However, the effects of temperature variations on the sampling rates of chemicals across a wide spectrum of volatility along the elevation gradient were not taken under consideration for PCBs atmospheric concentrations.

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).

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.

Pesticide interactions and risks of sperm chromosomal abnormalities

Disentangling the separate and synergistic effects of chemicals poses methodological challenges for accurate exposure assessment and for investigating epidemiologically how chemicals affect reproduction. We investigated combined exposures to ubiquitous contemporary use pesticides, specifically organophosphates (OP) and pyrethroids (PYR), and their association with germ cell abnormalities among adult men. Fluorescence in situ hybridization was used to determine disomy in sperm nuclei and urine was analyzed for concentrations of PYR metabolites (3-phenoxybenzoic acid; 3PBA) and OP dialkyl phosphate (DAP) metabolites. Incidence rate ratios using Poisson models were estimated for each disomy type by exposure quartile of DAP metabolites and 3PBA, controlling for confounders. The shape of the associations between PYRs, OPs and disomy were frequently nonmonotonic. There were consistent interactions between OP and PYR metabolite concentrations and the risk for sperm abnormalities. Taking both chemicals into account simultaneously resulted in quantitatively different associations than what was reported previously for OPs and PYRs separately, demonstrating the importance of modeling multiple concentrations simultaneously. Methods investigating interactions using Poisson models are needed to better quantify chemical interactions and their effects on count-based health outcomes, the importance of which was shown here for germ cell abnormalities.

A generalized weighted quantile sum approach for analyzing correlated data in the presence of interactions

A weighted quantile sum (WQS) regression has been used to assess the associations between environmental exposures and health outcomes. However, the currently available WQS approach, which is based on additive effects, does not allow exploring for potential interactions of exposures with other covariates in relation to a health outcome. In addition, the current WQS cannot account for clustering, thus it may not be valid for analysis of clustered data. We propose a generalized WQS approach that can assess interactions by estimating stratum-specific weights of exposures in a mixture, while accounting for potential clustering effect of matched pairs of cases and controls as well as censored exposure data due to being below the limits of detection. The performance of the proposed method in identifying interactions is evaluated through simulations based on various scenarios of correlation structures among the exposures and with an outcome. We also assess how well the proposed method performs in the presence of the varying levels of censoring in exposures. Our findings from the simulation study show that the proposed method outperforms the traditional WQS, as indicated by higher power of detecting interactions. We also find no strong evidence that the proposed method falsely identifies interactions when there are no true interactive effects. We demonstrate application of the proposed method to real data from the Epidemiological Research on Autism Spectrum Disorder (ASD) in Jamaica (ERAJ) by examining interactions between exposure to manganese and glutathione S-transferase family gene, GSTP1 in relation to ASD.

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors

PURPOSE OF REVIEW

The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health?

RECENT FINDINGS

There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Statistical Methodology in Studies of Prenatal Exposure to Mixtures of Endocrine-Disrupting Chemicals: A Review of Existing Approaches and New Alternatives

BACKGROUND

Prenatal exposures to endocrine-disrupting chemicals (EDCs) during critical developmental windows have been implicated in the etiologies of a wide array of adverse perinatal and pediatric outcomes. Epidemiological studies have concentrated on the health effects of individual chemicals, despite the understanding that EDCs act together via common mechanisms, that pregnant women are exposed to multiple EDCs simultaneously, and that substantial toxicological evidence of adverse developmental effects has been documented. There is a move toward multipollutant models in environmental epidemiology; however, there is no current consensus on appropriate statistical methods.

OBJECTIVES

We aimed to review the statistical methods used in these studies, to identify additional applicable methods, and to determine the strengths and weaknesses of each method for addressing the salient statistical and epidemiological challenges.

METHODS

We searched Embase, MEDLINE, and Web of Science for epidemiological studies of endocrine-sensitive outcomes in the children of mothers exposed to EDC mixtures during pregnancy and identified alternative statistical methods from the wider literature.

DISCUSSION

We identified 74 studies and analyzed the methods used to estimate mixture health effects, identify important mixture components, account for nonmonotonicity in exposure–response relationships, assess interactions, and identify windows of exposure susceptibility. We identified both frequentist and Bayesian methods that are robust to multicollinearity, performing shrinkage, variable selection, dimension reduction, statistical learning, or smoothing, including methods that were not used by the studies included in our review.

CONCLUSIONS

Compelling motivation exists for analyzing EDCs as mixtures, yet many studies make simplifying assumptions about EDC additivity, relative potency, and linearity, or overlook the potential for bias due to asymmetries in chemical persistence. We discuss the potential impacts of these choices and suggest alternative methods to improve analyses of prenatal exposure to EDC mixtures. https://doi.org/10.1289/EHP2207.

Association between urinary concentration of phthalate metabolites and impaired renal function in Shanghai adults

BACKGROUND

Exposure to phthalates is reported to be associated with increased incidence of microalbuminuria and low-grade albuminuria in children and adolescents. However, this phenomenon of phthalate-related nephrotoxicity is unknown in adults.

METHODS

Urine samples of 1663 adults from the 2012 Shanghai Food Consumption Survey (SHFCS) were measured for 10 metabolites of 6 phthalates and for renal function parameters. Their associations were explored by linear and logistic regression models.

RESULTS

Multivariate linear regression analysis showed that all three renal function parameters (albumin-to-creatinine ratio (ACR), β2-microglobulin (B2M), and N-acetyl-β-d-glucosaminidase (NAG)) are positively associated with six metabolites, including mono-benzylphthalate (MBzP), mono-2-ethylhexylphthalate (MEHP), mono-2-ethyl-5-oxohexyphthalate (MEOHP), mono-2-ethyl-5-hydroxyhexylphthalate (MEHHP), mono-2-ethyl-5-carboxypentylphthalate (MECPP), and mono-2-carboxymethyl-hexyl phthalate (MCMHP) (P < 0.05). Logistic analysis showed that the prevalence of hyperALBuria, hyperB2Muria, hyperNAGuria, or potentially impaired renal function (PIRF) were positively associated with urinary levels of MBzP, MEOHP, and MECPP, respectively (P < 0.05). Co-exposure to identified risk metabolites monoethylphthalate (MEP), MBzP, MEHP, MEOHP, MECPP, MEHHP, and MCMHP increased the risk of having impaired renal function.

CONCLUSION

Certain metabolites of phthalates, including bis (2-ethylhexyl) phthalate (DEHP) and benzyle butyl phthalate (BBzP), were associated with impaired renal function in Shanghai adults.

Dietary zinc, calcium and nickel are associated with lower childhood blood lead levels

The potential mitigation of elevated blood lead (PbB) levels with nutrient intake remains debatable. A comprehensive review by Kordas (2017) concluded that careful examination of the links between nutrition (nutritional status, nutrients, diet) and lead (Pb) exposure revealed limited and tenuous evidence. We have measured 20 elements including calcium (Ca), chromium (Cr), copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), nickel (Ni), zinc (Zn), and Pb from 6-day duplicate diets of 108 young children over a 5-year period and expressed these as intakes per body weight. Bivariate analyses showed a weak positive association between the Pb content in the diets of the participants and the level of Pb in their blood, as might be expected. Weak, but negative, associations occurred between the other elements in the diet and PbB. The associations for Ca, Mg, Ni and Zn were statistically significant for both subject-based (between subjects) and within-subject effects: that is, as the levels of elements in diet increased, the PbB level decreased. The largest percentage of variance of PbB in the context of the bivariate model accounted for was 4.23% for Zn, followed by Ca (3.91%) and Fe (2.20%). Supplementary analyses indicated that the between- and within-subject effects did not vary with the age at which participants entered the study, or with the levels of elements at their first measurement. A multivariable analysis using Weighted Quantile Sum Regressions showed that a weighted composite comprised of all the dietary elements had a significant association with PbB when adjusted for Pb in the diet and other covariates and also when adjusted for Pb in house dust; the latter was found to have the strongest association with PbB in earlier analyses. The highest weights were for Ca (0.29), Ni (0.27) and Zn (0.22); these results are generally consistent with those from the mixed model analyses.

Description of exposure profiles for seven environmental chemicals in a US population using recursive partition mixture modeling (RPMM)

Biomonitoring studies have shown that humans are exposed to numerous environmental chemicals. Previous work provides limited insights into the dynamic relationship between different chemicals within a population. The objective of this study is to develop an analytical method identifying exposure profiles of seven common environmental chemicals and determine how exposure profiles differ by sociodemographic groups and National Health and Nutrition Examination Survey (NHANES) 2003-2012 cycle year. We used recursive partition mixture modeling (RPMM) to define classes of the population with similar exposure profiles of lead, cadmium, 2,4-dichlorophenol, 2,5-dichlorophenol, bisphenol A (BPA), triclosan, and benzophenone-3 in individuals aged ≥6 years. Additionally, quasibinomial logistic regression was used to examine the association between each class and selected demographic characteristics. Eight exposure profiles were identified. Individuals who clustered together and had the highest chemical exposures were more likely to be older, to be Non-Hispanic Black (NHB) or Other Hispanic (OH), more likely to live below the poverty line, more likely to be male, and more likely to have participated in the earlier NHANES cycle (2003-2004). The developed method described the dynamic relationship between chemicals and shows that this relationship is different for subpopulations based on their sociodemographic characteristics.

Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression

BACKGROUND

Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables.

METHODS

This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code.

RESULTS

Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability.

CONCLUSIONS

This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.

The child health exposure analysis resource as a vehicle to measure environment in the environmental influences on child health outcomes program

PURPOSE OF REVIEW

Demonstrate the role of environment as a predictor of child health.

RECENT FINDINGS

The children's health exposure analysis resource (CHEAR) assists the Environmental influences on child health outcomes (ECHO) program in understanding the time sensitive and dynamic nature of perinatal and childhood environment on developmental trajectories by providing a central infrastructure for the analysis of biological samples from the ECHO cohort awards. CHEAR will assist ECHO cohorts in defining the critical or sensitive period for effects associated with environmental exposures. Effective incorporation of these principles into multiple existing cohorts requires extensive multidisciplinary expertise, creativity, and flexibility. The pursuit of life course - informed research within the CHEAR/ECHO structure represents a shift in focus from single exposure inquiries to one that addresses multiple environmental risk factors linked through shared vulnerabilities. CHEAR provides ECHO both targeted analyses of inorganic and organic toxicants, nutrients, and social-stress markers and untargeted analyses to assess the exposome and discovery of exposure-outcome relationships.

SUMMARY

Utilization of CHEAR as a single site for characterization of environmental exposures within the ECHO cohorts will not only support the investigation of the influence of environment on children's health but also support the harmonization of data across the disparate cohorts that comprise ECHO.

Latent class analysis to model multiple chemical exposures among children

BACKGROUND

Children are exposed to multiple potentially harmful chemicals simultaneously. Efforts to understand the patterns and consequences of these exposures have been hampered by statistical limitations in estimations of higher order interactions.

OBJECTIVES

The current study uses latent class analysis, a form of person-centered modeling to identify unobservable subgroups within populations and examine relationships between latent classes and measures of immune function.

METHODS

Data from the National Health and Nutrition Examination Survey 2011-2012 were analyzed. A sample of 721 children aged 6-19 years were included who provided data on 47 chemicals of interest representing six chemical classes. Groups were identified using latent class analysis controlling for race/ethnicity, age, sex and poverty status.

RESULTS

Two alternative approaches to identifying latent classes each resulted in similar three class solutions, including one group of children characterized by low co-exposures across chemicals, a group with moderate co-exposure levels, and a group characterized by high co-occurring levels of polycyclic aromatic hydrocarbons, volatile organic compounds, phenols and phthalates. Under one of the approaches, latent classes were significantly associated with immune function as measured by lymphocyte and neutrophil counts.

CONCLUSIONS

Latent class analysis offers a potential approach to measuring and understanding interactions among multiple co-occurring chemical stressors. However, additional work is needed to test the ability of latent classes to predict health variables.

Occurrence, ecological risk assessment, and spatio-temporal variation of polychlorinated biphenyls (PCBs) in water and sediments along River Ravi and its northern tributaries, Pakistan

Ecological risk assessment, spatio-temporal variation, and source apportionment of polychlorinated biphenyls (PCBs) were studied in surface sediments and water from River Ravi and its three northern tributaries (Nullah Deg, Nullah Basantar, and Nullah Bein) in Pakistan. In total, 35 PCB congeners were analyzed along 27 sampling stations in pre-monsoon and post-monsoon seasons. The ∑₃₅PCB concentration ranged from 1.06 to 95.76 ng/g (dw) in sediments and 1.94 to 11.66 ng/L in water samples, with hexa-CBs and tetra-CBs as most dominant homologs in sediments and water matrixes, respectively. The ∑₈DL-PCB levels were 0.33-22.13 ng/g (dw) and 0.16-1.95 ng/L in sediments and water samples, respectively. The WHO-toxic equivalent values were ranged from 1.18 × 10^-6 to 0.012 ng/L and 1.8 × 10^-6 to 0.031 ng/g in water and sediments matrixes, respectively. The ecological risk assessment indicates considerable potential ecological risk during pre-monsoon season ([Formula: see text] = 95.17) and moderate potential ecological risk during post-monsoon season ([Formula: see text] = 49.11). The industrial and urban releases were recognized as key ongoing sources for high PCB levels in environment. Therefore, we recommend more freshwater ecological studies to be conducted in the study area and firm regulatory initiatives are required to be taken in debt to the Stockholm Convention, 2001 to cop up with PCB contamination on emergency basis.

Polychlorinated biphenyls and its potential role in endometriosis

With the progress of global industrialization and environmental deterioration, the relationship between human health and the living environment has become an increasing focus of attention. Polychlorinated biphenyls (PCBs, including dioxin-like polychlorinated biphenyls and non-dioxin-like polychlorinated biphenyls), as part of the organic chlorine contaminants, have been suspected as playing a role in the etiopathogenesis of endometriosis. Several population-based studies have proposed that exposure to PCBs may increase the risk of developing endometriosis, while some epidemiological studies have failed to find any association between PCBs and endometriosis. The purpose of this review is to discuss the potential pathophysiological relationship between endometriosis and PCBs with a focus on both dioxin-like polychlorinated biphenyls and non-dioxin-like polychlorinated biphenyls.

Personal Care Product Use in Men and Urinary Concentrations of Select Phthalate Metabolites and Parabens: Results from the Environment And Reproductive Health (EARTH) Study

BACKGROUND

Personal care products (PCPs) are exposure sources to phthalates and parabens; however, their contribution to men's exposure is understudied.

OBJECTIVES

We examined the association between PCP use and urinary concentrations of phthalate metabolites and parabens in men.

METHODS

In a prospective cohort, at multiple study visits, men self-reported their use of 14 PCPs and provided a urine sample (2004-2015, Boston, MA). We measured urinary concentrations of 9 phthalate metabolites and methylparaben, propylparaben, and butylparaben. We estimated the covariate-adjusted percent change in urinary concentrations associated with PCP use using linear mixed and Tobit mixed regressions. We also estimated weights for each PCP in a weighted binary score regression and modeled the resulting composite weighted PCP use.

RESULTS

Four hundred men contributed 1,037 urine samples (mean of 3/man). The largest percent increase in monoethyl phthalate (MEP) was associated with use of cologne/perfume (83%, p-value<0.01) and deodorant (74%, p-value<0.01). In contrast, the largest percent increase for parabens was associated with the use of suntan/sunblock lotion (66-156%) and hand/body lotion (79-147%). Increases in MEP and parabens were generally greater with PCP use within 6 h of urine collection. A subset of 10 PCPs that were used within 6 h of urine collection contributed to at least 70% of the weighted score and predicted a 254-1,333% increase in MEP and parabens concentrations. Associations between PCP use and concentrations of the other phthalate metabolites were not statistically significant.

CONCLUSIONS

We identified 10 PCPs of relevance and demonstrated that their use within 6 h of urine collection strongly predicted MEP and paraben urinary concentrations. https://doi.org/10.1289/EHP1374.

Extending the Distributed Lag Model framework to handle chemical mixtures

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures.

Relative effect potency estimates of dioxin-like activity for dioxins, furans, and dioxin-like PCBs in adults based on cytochrome P450 1A1 and 1B1 gene expression in blood

BACKGROUND

In the risk assessment of PCDDs, PCDFs, and dioxin-like (DL) PCBs, regulatory authorities support the use of the toxic equivalency factor (TEF)-scheme derived from a heterogeneous data set of the relative effect potency (REPs) estimates.

OBJECTIVES

We sought to determine REPs for dioxin-like compounds (DLCs) using expression of cytochrome P450 (CYP) 1A1 and 1B1 mRNA in human peripheral blood mononuclear cells representing two different pathways.

METHODS

We used a sex and age adjusted regression-based approach comparing the strength of association between each DLC and the cytochrome P450 (CYP) 1A1 and 1B1 mRNA expression in 320 adults residing in an organochlorine-polluted area of eastern Slovakia.

RESULTS

We calculated REPs based on CYP1A1 expression for 4 PCDDs, 8 PCDFs, and 1 PCB congener, and based on CYP1B1 expression for 5 PCDFs and 11 PCB congeners. REPs from CYP1A1 correlated with REPs previously derived from thyroid volume (ρ=0.85; p<0.001) and serum FT4 (ρ=0.77; p=0.009). The 13 log REPs from CYP1A1 correlated with log WHO-TEFs (r=0.63; p=0.015) and 11 log PCB REPs with PCB consensus toxicity factors (CTFs) for compounds with WHO-TEFs (r=0.80; p=0.003). The complete set of derived 56 log REPs correlated with the log CTFs (r=0.77; p=0.001) and log WHO-TEFs (r=0.81; p<0.001).

CONCLUSIONS

REPs calculated from thyroid and cytochrome P450 endpoints realistically reflect human exposure scenarios because they are based on human chronic and low-dose exposures. While the CYP 1A1 seems more suitable for toxicity evaluation of PCDD/Fs, the CYP 1B1 is more apt for PCDFs and PCBs and reflects different pathways.

Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting

In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies "bad actors" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set.

Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures

Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications.

Multiple classes of environmental chemicals are associated with liver disease: NHANES 2003-2004

Biomonitoring studies show that humans carry a body burden of multiple classes of contaminants which are not often studied together. Many of these chemicals may be hepatotoxic. We used the 2003-2004 National Health and Nutrition Examination Survey to evaluate the relationship between alanine aminotransferase (ALT) and 37 environmental contaminants, comprising heavy metals, non-dioxin-like polychlorinated biphenyls (PCBs), and dioxin-like compounds, using a novel method. Linear regression models were constructed for each chemical separately, then as a class, using quartiles to represent exposure and adjusting for age, sex, race, income, and BMI. We then used an optimization approach to compile a weighted sum of the quartile scores, both within and across chemical classes. Using the optimization approach to construct weighted quartile scores, the dioxin like PCB, the non-dioxin like PCB and metal class-level scores were significantly associated with elevated ALT. A significant interaction was detected between the class-level score for metals, and the score for non-dioxin-like PCBs. When including all chemicals in one model, 3 chemicals accounted for 78% of the weight (mercury, PCB 180, 3,3',4,4',5-PNCB) with the remaining 22% associated with 4 chemicals (a dioxin and 3 PCBs). Validation with a holdout dataset indicated that the weighted quartile sum estimator efficiently identifies reproducible significant associations.

A cohort study evaluation of maternal PCB exposure related to time to pregnancy in daughters

BACKGROUND

Polychlorinated biphenyls (PCBs) remain ubiquitous environmental contaminants. Developmental exposures are suspected to impact reproduction. Analysis of mixtures of PCBs may be problematic as components have a complex correlation structure, and along with limited sample sizes, standard regression strategies are problematic. We compared the results of a novel, empirical method to those based on categorization of PCB compounds by (1) hypothesized biological activity previously proposed and widely applied, and (2) degree of ortho- substitution (mono, di, tri), in a study of the relation of maternal serum PCBs and daughter's time to pregnancy.

METHODS

We measured PCBs in maternal serum samples collected in the early postpartum in 289 daughters in the Child Health and Development Studies birth cohort. We queried time to pregnancy in these daughters 28-31 years later. We applied a novel weighted quantile sum approach to find the bad-actor compounds in the PCB mixture found in maternal serum. The approach includes empirical estimation of the weights through a bootstrap step which accounts for the variation in the estimated weights.

RESULTS

Bootstrap analyses indicated the dominant functionality groups associated with longer TTP were the dioxin-like, anti-estrogenic group (average weight, 22%) and PCBs not previously classified by biological activity (54%). In contrast, the unclassified PCBs were not important in the association with shorter TTP, where the anti-estrogenic groups and the PB-inducers group played a more important role (60% and 23%, respectively). The highly chlorinated PCBs (average weight, 89%) were mostly associated with longer TTP; in contrast, the degree of chlorination was less discriminating for shorter TTP. Finally, PCB 56 was associated with the strongest relationship with TTP with a weight of 47%.

CONCLUSIONS

Our empirical approach found some associations previously identified by two classification schemes, but also identified other bad actors. This empirical method can generate hypotheses about mixture effects and mechanisms and overcomes some of the limitations of standard regression techniques.

Environmental Influences on the Development of Endometriosis

Purpose

Several environmental toxicants (ETs), including dioxins and dioxin-like compounds (DLCs), perfluorochemicals, organochlorine pesticides, phthalates, and heavy metals (especially cadmium with its estrogen-like properties in animal models) have been investigated as possibly being related to endometriosis.

Methods

Systematic review of pertinent literature.

Results

DLCs have been the most investigated ETs. DLCs are persistent organic pollutants with highly toxic potential and include three types of compounds: polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) or furans, and polychlorinated biphenyls (PCBs). The most toxic is 2, 3, 7, 8- tetrachlorodibenzo-p-dioxin (TCDD). The connection mechanism between dioxins and endometriosis is still unclear. However, dioxins and DLCs are endocrine-disrupting compounds that can affect the pathobiology of endometriosis at multiple levels. Part of the dioxin and DLCs toxic effects can be accounted for by their interaction with the aryl hydrocarbon receptor (AhR). It has been proposed that dioxin can initiate or promote endometriosis by means of interaction with estrogen receptors or by suppressing the expression of progesterone receptors. Furthermore, TCDD alters the expression of cytokines and growth factors, remodeling enzymes and cytochrome P450 expression and activity.

Conclusions

Studies in rhesus models have revealed a correlation between dioxin exposure and endometriosis. However, evidence from epidemiologic studies is inconclusive.

Assessing Chemical Mixtures and Human Health: Use of Bayesian Belief Net Analysis

BACKGROUND: Despite humans being exposed to complex chemical mixtures, much of the available research continues to focus on a single compound or metabolite or a select subgroup of compounds inconsistent with the nature of human exposure. Uncertainty regarding how best to model chemical mixtures coupled with few analytic approaches remains a formidable challenge and served as the impetus for study. OBJECTIVES: To identify the polychlorinated biphenyl (PCB) congener(s) within a chemical mixture that was most associated with an endometriosis diagnosis using novel graphical modeling techniques. METHODS: Bayesian Belief Network (BBN) models were developed and empirically assessed in a cohort comprising 84 women aged 18-40 years who underwent a laparoscopy or laparotomy between 1999 and 2000; 79 (94%) women had serum concentrations for 68 PCB congeners quantified. Adjusted odds ratios (AOR) for endometriosis were estimated for individual PCB congeners using BBN models. RESULTS: PCB congeners #114 (AOR = 3.01; 95% CI = 2.25, 3.77) and #136 (AOR = 1.79; 95% CI = 1.03, 2.55) were associated with an endometriosis diagnosis. Combinations of mixtures inclusive of PCB #114 were all associated with higher odds of endometriosis, underscoring its potential relation with endometriosis. CONCLUSIONS: BBN models identified PCB congener 114 as the most influential congener for the odds of an endometriosis diagnosis in the context of a 68 congener chemical mixture. BBN models offer investigators the opportunity to assess which compounds within a mixture may drive a human health effect.

Linking empirical estimates of body burden of environmental chemicals and wellness using NHANES data

Biomonitoring of industrial chemicals in human tissues and fluids has shown that all people carry a "body burden" of synthetic chemicals. Although measurement of an environmental chemical in a person's tissues/fluids is an indication of exposure, it does not necessarily mean the exposure concentration is sufficient to cause an adverse effect. Since humans are exposed to multiple chemicals, there may be a combination effect (e.g., additive, synergistic) associated with low-level exposures to multiple classes of contaminants, which may impact a variety of organ systems. The objective of this research is to link measures of body burden of environmental chemicals and a "holistic" measure of wellness. The approach is demonstrated using biomonitoring data from the National Health and Nutrition Examination Surveys (NHANES). Forty-two chemicals were selected for analysis based on their detection levels. Six biological pathway-specific indices were evaluated using groups of chemicals associated with each pathway. Five of the six pathways were negatively associated with wellness. Three non-zero interaction terms were detected which may provide empirical evidence of crosstalk across pathways. The approach identified five of the 42 chemicals from a variety of classes (metals, pesticides, furans, polycyclic aromatic hydrocarbons) as accounting for 71% of the weight linking body burden to wellness. Significant interactions were detected indicating the effect of smoking is exacerbated by body burden of environmental chemicals. Use of a holistic index on both sides of the exposure-health equation is a novel and promising empirical "systems biology" approach to risk evaluation of complex environmental exposures.

A methodological approach to assessing the health impact of environmental chemical mixtures: PCBs and hypertension in the National Health and Nutrition Examination Survey

We describe an approach to examine the association between exposure to chemical mixtures and a health outcome, using as our case study polychlorinated biphenyls (PCBs) and hypertension. The association between serum PCB and hypertension among participants in the 1999–2004 National Health and Nutrition Examination Survey was examined. First, unconditional multivariate logistic regression was used to estimate odds ratios and associated 95% confidence intervals. Next, correlation and multicollinearity among PCB congeners was evaluated, and clustering analyses performed to determine groups of related congeners. Finally, a weighted sum was constructed to represent the relative importance of each congener in relation to hypertension risk. PCB serum concentrations varied by demographic characteristics, and were on average higher among those with hypertension. Logistic regression results showed mixed findings by congener and class. Further analyses identified groupings of correlated PCBs. Using a weighted sum approach to equalize different ranges and potencies, PCBs 66, 101, 118, 128 and 187 were significantly associated with increased risk of hypertension. Epidemiologic data were used to demonstrate an approach to evaluating the association between a complex environmental exposure and health outcome. The complexity of analyzing a large number of related exposures, where each may have different potency and range, are addressed in the context of the association between hypertension risk and exposure to PCBs.

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.