Environmental risk score as a new tool to examine multi-pollutants in epidemiologic research: an example from the NHANES study using serum lipid levels

Classification of Suicide Attempt Risk Using Environmental and Lifestyle Factors in 3 Large Youth Cohorts

Importance

Suicide is the third-leading cause of death among US adolescents. Environmental and lifestyle factors influence suicidal behavior and can inform risk classification, yet quantifying and incorporating them in risk assessment presents a significant challenge for reproducibility and clinical translation.

Objective

To quantify the aggregate contribution of environmental and lifestyle factors to youth suicide attempt risk classification.

Design, Setting, and Participants

This was a cohort study in 3 youth samples: 2 national longitudinal cohorts from the US and the UK and 1 clinical cohort from a tertiary pediatric US hospital. An exposome-wide association study (ExWAS) approach was used to identify risk and protective factors and compute aggregate exposomic scores. Logistic regression models were applied to test associations and model fit of exposomic scores with suicide attempts in independent data. Youth from the Adolescent Brain Cognitive Development (ABCD) study, the UK Millennium Cohort Study (MCS), and the Children's Hospital of Philadelphia emergency department (CHOP-ED) were included in the study.

Exposures

A single-weighted exposomic score that sums significant risk and protective environmental/lifestyle factors.

Main Outcome and Measure

Self-reported suicide attempt.

Results

A total of 40 364 youth were included in this analysis: 11 564 from the ABCD study (3 waves of assessment; mean [SD] age, 12.0 [0.7] years; 6034 male [52.2%]; 344 attempted suicide [3.0%]; 1154 environmental/lifestyle factors were included in the ABCD study), 9000 from the MCS cohort (mean [SD] age, 17.2 [0.3] years; 4593 female [51.0%]; 661 attempted suicide [7.3%]; 2864 environmental/lifestyle factors were included in the MCS cohort), and 19 800 from the CHOP-ED cohort (mean [SD] age, 15.3 [1.5] years; 12 937 female [65.3%]; 2051 attempted suicide [10.4%]; 36 environmental/lifestyle factors were included in the CHOP-ED cohort). In the ABCD discovery subsample, ExWAS identified 99 risk and protective exposures significantly associated with suicide attempt. A single weighted exposomic score that sums significant risk and protective exposures was associated with suicide attempt in an independent ABCD testing subsample (odds ratio [OR], 2.2; 95% CI, 2.0-2.6; P < .001) and explained 17.6% of the variance (based on regression pseudo-R2) in suicide attempt over and above that explained by age, sex, race, and ethnicity (2.8%) and by family history of suicide (6.3%). Findings were consistent in the MCS and CHOP-ED cohorts (explaining 22.6% and 19.3% of the variance in suicide attempt, respectively) despite clinical, demographic, and exposure differences. In all cohorts, compared with youth at the median quintile of the exposomic score, youth at the top fifth quintile were substantially more likely to have made a suicide attempt (OR, 4.3; 95% CI, 2.6-7.2 in the ABCD study; OR, 3.8; 95% CI, 2.7-5.3 in the MCS cohort; OR, 5.8; 95% CI, 4.7-7.1 in the CHOP-ED cohort).

Conclusions and Relevance

Results suggest that exposomic scores of suicide attempt provided a generalizable method for risk classification that can be applied in diverse samples from clinical or population settings.

Prenatal exposure to multiple environmental chemicals and birth size

BACKGROUND

Epidemiological studies addressing the combined effects of exposure to chemical mixtures at different stages of pregnancy on birth size are scarce.

OBJECTIVE

To evaluate the association between prenatal exposure to chemical mixtures and birth size.

METHODS

Our previous study repeatedly measured the urinary concentrations of 34 chemical substances among 743 pregnant women and identified three distinct clusters of exposed population and six dominant principal components of exposed chemicals in each trimester. In this study, we assessed the associations of these exposure profiles with birth weight, birth length, and ponderal index using multivariable linear regression.

RESULTS

We found that compared with women in cluster 1 (lower urinary chemical concentrations), women in cluster 2 (higher urinary concentrations of metals, benzothiazole, benzotriazole, and some phenols), and women in cluster 3 (higher urinary concentrations of phthalates) were more likely to give birth to children with higher birth length [0.23 cm (95% CI: -0.03, 0.49); 0.29 cm (95%CI: 0.03, 0.54), respectively]. This association was observed only in 1st trimester. In addition, prenatal exposure to PC3 (higher benzophenones loading) was associated with reduced birth length across pregnancy [-0.07 cm (95% CI: -0.18, 0.03) in 1st and 2nd trimester; -0.13 cm (95% CI: -0.24, -0.03) in 3rd trimester]. Exposure to PC6 (higher thallium and BPA loading in 2nd trimester) was associated with increased birth length [0.15 cm (95% CI: 0.05, 0.26)]. Compared with other outcomes, associations of both clusters and PCs with birth length were stronger, and these associations were more pronounced in boys.

IMPACT STATEMENT

Exposure to multiple chemicals simultaneously, the actual exposure situation of pregnant women, was associated with birth size, indicating that chemical mixtures should be taken more seriously when studying the health effects of pollutants.

Machine learning-based health environmental-clinical risk scores in European children

BACKGROUND

Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data.

METHODS

In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions.

RESULTS

ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health.

CONCLUSIONS

Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

Environmental risk score of multiple pollutants for kidney damage among residents in vulnerable areas by occupational chemical exposure in Korea

This study aimed to develop an environmental risk score (ERS) of multiple pollutants (MP) causing kidney damage (KD) in Korean residents near abandoned metal mines or smelters and evaluate the association between ERS and KD by a history of occupational chemical exposure (OCE). Exposure to MP, consisting of nine metals, four polycyclic aromatic hydrocarbons, and four volatile organic compounds, was measured as urinary metabolites. The study participants were recruited from the Forensic Research via Omics Markers (FROM) study (n = 256). Beta-2-microglobulin (β2-MG), N-acetyl-β-D-glucosaminidase (NAG), and estimated glomerular filtration rate (eGFR) were used as biomarkers of KD. Bayesian kernel machine regression (BKMR) was selected as the optimal ERS model with the best performance and stability of the predicted effect size among the elastic net, adaptive elastic net, weighted quantile sum regression, BKMR, Bayesian additive regression tree, and super learner model. Variable importance was estimated to evaluate the effects of metabolites on KD. When stratified with the history of OCE after adjusting for several confounding factors, the risks for KD were higher in the OCE group than those in the non-OCE group; the odds ratio (OR; 95% CI) for ERS in non-OCE and OCE groups were 2.97 (2.19, 4.02) and 6.43 (2.85, 14.5) for β2-MG, 1.37 (1.01, 1.86) and 4.16 (1.85, 9.39) for NAG, and 4.57 (3.37, 6.19) and 6.44 (2.85, 14.5) for eGFR, respectively. We found that the ERS stratified history of OCE was the most suitable for evaluating the association between MP and KD, and the risks were higher in the OCE group than those in the non-OCE group.

Statistical methods for chemical mixtures: a roadmap for practitioners

Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. With many existing statistical methods and emerging approaches, it is important for practitioners to understand when each method is best suited for their inferential goals. In this study, we conduct a review and comparison of 11 analytical methods available for use in mixtures research, through extensive simulation studies for continuous and binary outcomes. These methods fall in three different classes: identifying important components of a mixture, identifying interactions and creating a summary score for risk stratification and prediction. We carry out an illustrative data analysis in the PROTECT birth cohort from Puerto Rico. Most importantly we develop an integrated package "CompMix" that provides a platform for mixtures analysis where the practitioner can implement a pipeline for several types of mixtures analysis. Our simulation results suggest that the choice of methods depends on the goal of analysis and there is no clear winner across the board. For selection of important toxicants in the mixture and for identifying interactions, Elastic net by Zou et al. (Enet), Lasso for Hierarchical Interactions by Bien et al (HierNet), Selection of nonlinear interactions by a forward stepwise algorithm by Narisetty et al. (SNIF) have the most stable performance across simulation settings. Additionally, the predictive performance of the Super Learner ensembling method by Van de Laan et al. and HierNet are found to be superior to the rest of the methods. For overall summary or a cumulative measure, we find that using the Super Learner to combine multiple Environmental Risk Scores can lead to improved risk stratification properties. We have developed an R package "CompMix: A comprehensive toolkit for environmental mixtures analysis", allowing users to implement a variety of tasks under different settings and compare the findings. In summary, our study offers guidelines for selecting appropriate statistical methods for addressing specific scientific questions related to mixtures research. We identify critical gaps where new and better methods are needed.

Joint Exposure to Ambient Air Pollutants, Genetic Risk, and Ischemic Stroke: A Prospective Analysis in UK Biobank

BACKGROUND

Our primary objective was to assess the association between joint exposure to various air pollutants and the risk of ischemic stroke (IS) and the modification of the genetic susceptibility.

METHODS

This observational cohort study included 307 304 British participants from the United Kingdom Biobank, who were stroke-free and possessed comprehensive baseline data on genetics, air pollutant exposure, alcohol consumption, and dietary habits. All participants were initially enrolled between 2006 and 2010 and were followed up until 2022. An air pollution score was calculated to assess joint exposure to 5 ambient air pollutants, namely particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, as well as nitrogen oxide and nitrogen dioxide. To evaluate individual genetic risk, a polygenic risk score for IS was calculated for each participant. We adjusted for demographic, social, economic, and health covariates. Cox regression models were utilized to estimate the associations between air pollution exposure, polygenic risk score, and the incidence of IS.

RESULTS

Over a median follow-up duration of 13.67 years, a total of 2476 initial IS events were detected. The hazard ratios (95% CI) of IS for per 10 µg/m3 increase in particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, nitrogen dioxide, and nitrogen oxide were 1.73 (1.33-2.14), 1.24 (0.88-1.70), 1.13 (0.89-1.33), 1.03 (0.98-1.08), and 1.04 (1.02-1.07), respectively. Furthermore, individuals in the highest quintile of the air pollution score exhibited a 29% to 66% higher risk of IS compared with those in the lowest quintile. Notably, participants with both high polygenic risk score and air pollution score had a 131% (95% CI, 85%-189%) greater risk of IS than participants with low polygenic risk score and air pollution score.

CONCLUSIONS

Our findings suggested that prolonged joint exposure to air pollutants may contribute to an increased risk of IS, particularly among individuals with elevated genetic susceptibility to IS.

Associations of multiple plasma metals with osteoporosis: findings from the Dongfeng-Tongji cohort

With the aging population, osteoporosis has become a more prevalent public health issue. Existing researches have indicated significant relations of single metal exposure with osteoporosis (e.g., lead, copper, and zinc), whereas the evidence regarding the joint association of metal mixtures with osteoporosis remain limited and inconclusive. A total of 4924 participants from the Dongfeng-Tongji cohort were included in the present study. Plasma levels of 23 metals were determined by inductively coupled plasma mass spectrometry, and the presence of osteoporosis was defined as a bone mineral density T-score ≤  - 2.5. We applied stepwise regression, plasma metal score, and quantile g-computation model to evaluate the association between plasma metal mixtures and osteoporosis risk. Of the 4924 participants, the prevalence of osteoporosis was 10.9% (N = 265) in males and 27.5% (N = 684) in females. In the multiple-metals model, arsenic was positively associated with osteoporosis in males, while zinc was positively associated with osteoporosis in females. Comparing extreme quartiles, the multivariate-adjusted ORs of osteoporosis were 2.20 (95% CI, 1.29, 3.79; P-trend = 0.006) for arsenic in males and 2.16 (95% CI, 1.44, 3.23; P-trend < 0.001) for zinc in females. The plasma metal score was significantly and positively associated with a higher risk of osteoporosis, with ORs (95% CI) comparing extreme quartiles were 5.00 (95% CI, 3.36, 7.65; P-trend < 0.001) in males and 1.76 (95% CI, 1.35, 2.29; P-trend < 0.001) in females. Furthermore, the results of quantile g-computation revealed a consistent positive trend of metal mixtures with risk of osteoporosis and suggested the dominant role of arsenic in males and zinc in females, respectively. Our findings highlighted the importance of controlling metal mixtures exposure for the prevention of osteoporosis in the middle-aged and elder population. Further prospective studies in larger populations are warranted to confirm our findings.

Associations of phthalates, phthalate replacements, and their mixtures with eicosanoid biomarkers during pregnancy

Humans are exposed to complex mixtures of phthalates. Gestational exposure to phthalates has been linked to preeclampsia and preterm birth through potential pathways such as endocrine disruption, oxidative stress, and inflammation. Eicosanoids are bioactive signaling lipids that are related to a variety of homeostatic and inflammatory processes. We investigated associations between urinary phthalates and their mixtures with plasma eicosanoid levels during pregnancy using the PROTECT cohort in Puerto Rico (N = 655). After adjusting for covariates, we estimated pair-wise associations between the geometric mean of individual phthalate metabolite concentrations across pregnancy and eicosanoid biomarkers using multivariable linear regression. We used bootstrapping of adaptive elastic net regression (adENET) to evaluate phthalate mixtures associated with eicosanoids and subsequently create environmental risk scores (ERS) to represent weighted sums of phthalate exposure for each individual. After adjusting for false-discovery, in single-pollutant analysis, 14 of 20 phthalate metabolites or parent compound indices showed significant and primarily negative associations with multiple eicosanoids. In our mixture analysis, associations with several metabolites of low molecular weight phthalates - DEP, DBP, and DIBP - became prominent. Additionally, MEHHTP and MECPTP, metabolites of a new phthalate replacement, DEHTP, were selected as important predictors for determining the concentrations of multiple eicosanoids from different pathway groups. A unit increase in phthalate ERS derived from bootstrapping of adENET was positively associated with several eicosanoids mainly from Cytochrome P450 pathway. For example, an increase in ERS was associated with 11(S)-HETE (β = 1.6, 95% CI: 0.020, 3.180), (±)11,12-DHET (β = 2.045, 95% CI: 0.250, 3.840), 20(S)-HETE (β = 0.813, 95% CI: 0.147, 1.479), and 9 s-HODE (β = 2.381, 95% CI: 0.657, 4.104). Gestational exposure to phthalates and phthalate mixtures were associated with eicosanoid levels during pregnancy. Results from the mixture analyses underscore the complexity of physiological impacts of phthalate exposure and call for further in-depth studies to examine these relationships.

The Association between Maternal Urinary Phthalate Concentrations and Blood Pressure in Pregnancy: A Systematic Review and Meta-Analysis

Phthalates are commonly found in a wide range of environments and have been linked to several negative health outcomes. While earlier research indicated a potential connection between phthalate exposure and blood pressure (BP) during pregnancy, the results of these studies remain inconclusive. The objective of this meta-analysis was to elucidate the relationship between phthalate exposure and BP in pregnancy. A comprehensive literature search was carried out with PubMed, EMBASE, and Web of Science, and pertinent studies published up until 5 March 2023 were reviewed. Random-effects models were utilized to consolidate the findings of continuous outcomes, such as diastolic and systolic BP, as well as the binary outcomes of hypertensive disorders of pregnancy (HDP). The present study included a total of 10 studies. First-trimester MBP exposure exhibited a positive association with mean systolic and diastolic BP during both the second and third trimesters (β = 1.05, 95% CI: 0.27, 1.83, I² = 93%; β = 0.40, 95% CI: 0.05, 0.74, I² = 71%, respectively). Second-trimester monobenzyl phthalate (MBzP) exposure was positively associated with systolic and diastolic BP in the third trimester (β = 0.57, 95% CI: 0.01, 1.13, I² = 0; β = 0.70, 95% CI: 0.27, 1.13, I² = 0, respectively). Conversely, first-trimester mono-2-ethylhexyl phthalate (MEHP) exposure demonstrated a negative association with mean systolic and diastolic BP during the second and third trimesters (β = -0.32, 95% CI: -0.60, -0.05, I² = 0; β = -0.32, 95% CI: -0.60, -0.05, I² = 0, respectively). Additionally, monoethyl phthalate (MEP) exposure was found to be associated with an increased risk of HDP (OR = 1.12, 95% CI: 1.02, 1.23, I² = 26%). Our study found that several phthalate metabolites were associated with increased systolic and diastolic BP, as well as the risk of HDP across pregnancies. Nevertheless, given the limited number of studies analyzed, additional research is essential to corroborate these findings and elucidate the molecular mechanisms linking phthalates to BP changes during pregnancy.

Long-term exposure to ambient air pollutants and their interaction with physical activity on insomnia: A prospective cohort study

Exposure to air pollution or lack of physical activity (PA) increases the risk of insomnia. However, evidence on joint exposure to air pollutants is limited, and the interaction of joint air pollutants and PA on insomnia is unknown. This prospective cohort study included 40,315 participants with related data from the UK Biobank, which recruited participants from 2006 to 2010. Insomnia was assessed by self-reported symptoms. The annual average air pollutant concentrations of particulate matter (PM_2.5, PM₁₀), nitrogen oxides (NO₂, NO_X), sulfur dioxide (SO₂) and carbon monoxide (CO) were calculated based on participants' addresses. We applied a weighted Cox regression model to evaluate the correlation between air pollutants and insomnia and newly proposed an air pollution score to assess joint air pollutants effect using a weighted concentration summation after obtaining the weights of each pollutant in the Weighted-quantile sum regression. With a median follow-up of 8.7 years, 8511 participants developed insomnia. For each 10 μg/m³ increase in NO₂, NO_X, PM₁₀, SO₂, the average hazard ratios (AHRs) and 95% confidence interval (CI) of insomnia were 1.10 (1.06, 1.14), 1.06 (1.04, 1.08), 1.35 (1.25, 1.45) and 2.58 (2.31, 2.89), respectively; For each 5 μg/m³ increase in PM_2.5 and each 1 mg/m³ increase in CO, the corresponding AHRs (95%CI) were 1.27 (1.21, 1.34) and 1.83 (1.10, 3.04), respectively. The AHR (95%CI) for insomnia associated with per interquartile range (IQR) increase in air pollution scores were 1.20 (1.15, 1.23). In addition, potential interactions were examined by setting cross-product terms of air pollution score with PA in the models. We observed an interaction between air pollution scores and PA (P = 0.032). The associations between joint air pollutants and insomnia were attenuated among participants with higher PA. Our study provides evidence on developing strategies for improving healthy sleep by promoting PA and reducing air pollution.

Association of Combined Exposure to Ambient Air Pollutants, Genetic Risk, and Incident Rheumatoid Arthritis: A Prospective Cohort Study in the UK Biobank

BACKGROUND

Evidence for a potential link between air pollution and rheumatoid arthritis (RA) is inconsistent, and the modified effect of genetic susceptibility on the relationship between air pollution and RA has not been well studied.

OBJECTIVE

Using a general population cohort from the UK Biobank, this study aimed to investigate the associations between various air pollutants and the risk of incident RA and to further estimate the impact of combined exposure to ambient air pollutants on the risk of developing RA under the modification effect of genetic predisposition.

METHODS

A total of 342,973 participants with completed genotyping data and who were free of RA at baseline were included in the study. An air pollution score was constructed by summing the concentrations of each pollutant weighted by the regression coefficients with RA from single-pollutant models to assess the combined effect of air pollutants, including particulate matter (PM) with diameters ≤ 2.5 μ m (PM 2.5 ), between 2.5 and 10 μ m (PM 2.5 - 10 ), and ≤ 10 μ m (PM 10 ), as well as nitrogen dioxide (NO 2 ) and nitrogen oxides (NO x ). In addition, the polygenic risk score (PRS) of RA was calculated to characterize individual genetic risk. The Cox proportional hazard model was used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) of associations of single air pollutant, air pollution score, or PRS with incident RA.

RESULTS

During a median follow-up time of 8.1 y, 2,034 incident events of RA were recorded. The HRs (95% CIs) of incident RA per interquartile range increment in PM 2.5 , PM 2.5 - 10 , PM 10 , NO 2 , and NO x were 1.07 (1.01, 1.13), 1.00 (0.96, 1.04), 1.01 (0.96, 1.07), 1.03 (0.98, 1.09), and 1.07 (1.02, 1.12), respectively. We also found a positive exposure-response relationship between air pollution score and RA risk (p Trend = 0.000053 ). The HR (95% CI) of incident RA was 1.14 (1.00, 1.29) in the highest quartile group compared with the lowest quartile group of the air pollution score. Furthermore, the results of the combined effect of air pollution score and PRS on the RA risk showed that the risk of RA incidence in the highest genetic risk and air pollution score group was almost twice that of the lowest genetic risk and air pollution score group [incidence rate (IR) per 100,000 person-years: 98.46 vs. 51.19, and HR = 1.73 (95% CI: 1.39, 2.17) vs. 1 (reference)], although no statistically significant interaction between the air pollution and genetic risk for incident RA was found (p Interaction > 0.05 ).

DISCUSSION

The results revealed that long-term combined exposure to ambient air pollutants might increase the risk of RA, particularly in those with high genetic risk. https://doi.org/10.1289/EHP10710.

Joint Exposure to Ambient Air Pollutants Might Elevate the Risk of Small for Gestational Age (SGA) Infants in Wuhan: Evidence From a Cross-Sectional Study

Objective: To investigate the effect of exposure to multiple ambient air pollutants during pregnancy on the risk of children being born small for gestational age (SGA). Methods: An Air Pollution Score (APS) was constructed to assess the effects of being exposed to six air pollutants simultaneously, PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ (referred to as joint exposure). A logistic regression model was applied to estimate the associations of APS and SGA. Results: The adjusted odds ratios (ORs) of SGA per 10 ug/m³ increased in APS during the first and second trimesters and the entire pregnancy were 1.003 [95% confidence intervals (CIs): 1.000, 1.007], 1.018 (1.012, 1.025), and 1.020 (1.009, 1.031), respectively. The ORs of SGA for each 10 μg/m³ elevated in APS during the whole pregnancy were 1.025 (1.005, 1.046) for mothers aged over 35 years old vs. 1.018 (1.005, 1.031) for mothers aged under 35 years old. Women who were pregnant for the first time were more vulnerable to joint ambient air pollution. Conclusion: In summary, the results of the present study suggested that joint exposure to ambient air pollutants was associated with the increment in the risks of SGA.

Long-Term Air Pollution, Genetic Susceptibility, and the Risk of Depression and Anxiety: A Prospective Study in the UK Biobank Cohort

BACKGROUND

Depression and anxiety are two mental disorders that are often comorbid. However, the associations of long-term air pollution exposure with depression and anxiety remain inconclusive.

OBJECTIVE

We conducted a cross-sectional and prospective study to examine the associations of ambient exposure to particulate matter (PM) with a diameter of ≤2.5μm (PM2.5), ≤10μm (PM10), and 2.5-10μm (PMcoarse), nitrogen oxides (NOx), and nitrogen dioxide (NO2) with the risk of depression and anxiety in the UK Biobank.

METHODS

This study included 398,241 participants from the UK Biobank, 128,456 of whom participated the 7-y online mental health survey. A total of 345,876 individuals were free of depression and anxiety at baseline; of those, 16,185 developed incident mental disorders during a median of 8.7 y of follow-up. Depression and anxiety were assessed using hospital admission records and mental health questionnaires. Associations of air pollution with prevalent and incident mental disorders were examined using logistic regression and Cox regression models, respectively.

RESULTS

Elevated levels of the five air pollutants were associated with higher odds of mental disorders at baseline. Levels of four pollutants but not PMcoarse were also associated with higher odds and risks of mental disorders during follow-up; specifically, hazard ratios [HR, 95% confidence interval (CI)] of an interquartile range increase in PM2.5, PM10, NOx, and NO2 for incident mental disorders were 1.03 (95% CI: 1.01, 1.05), 1.06 (95% CI: 1.04, 1.08), 1.03 (95% CI: 1.01, 1.05), and 1.06 (95% CI: 1.04, 1.09), respectively. An air pollution index reflecting combined effects of pollutants also demonstrated a positive association with the risk of mental disorders. HR (95% CI) of incident mental disorders were 1.11 (95% CI: 1.05, 1.18) in the highest quintile group in comparison with the lowest quintile of the air pollution index. We further observed that the associations between air pollution and mental disorders differed by a genetic risk score based on single nucleotide polymorphisms previously associated with genetic susceptibility to mental disorders in the UK Biobank cohort.

DISCUSSION

To our knowledge, this research is one of the largest cohort studies that demonstrates an association between mental health disorders and exposure to long-term air pollution, which could be further enhanced by genetic predisposition. https://doi.org/10.1289/EHP10391.

Using Latent Profile Analysis to Identify Associations Between Gestational Chemical Mixtures and Child Neurodevelopment

BACKGROUND

Unsupervised machine learning techniques have become increasingly popular for studying associations between gestational exposure mixtures and human health. Latent profile analysis is one method that has not been fully explored.

METHODS

We estimated associations between gestational chemical mixtures and child neurodevelopment using latent profile analysis. Using data from the Maternal-Infant Research on Environmental Chemicals (MIREC) research platform, a longitudinal cohort of pregnant Canadian women and their children, we generated latent profiles from 27 gestational exposure biomarkers. We then examined the associations between these profiles and child Verbal IQ, Performance IQ, and Full-Scale IQ, measured with the Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI-III). We validated our findings using k-means clustering.

RESULTS

Latent profile analysis detected five latent profiles of exposure: a reference profile containing 61% of the study participants, a high monoethyl phthalate (MEP) profile with moderately low persistent organic pollutants (POPs) containing 26%, a high POP profile containing 6%, a low POP profile containing 4%, and a smoking chemicals profile containing 3%. We observed negative associations between both the smoking chemicals and high MEP profiles and all IQ scores and between the high POP profile and Full-Scale and Verbal IQ scores. We also found a positive association between the low POP profile and Full-Scale and Performance IQ scores. All associations had wide 95% confidence intervals.

CONCLUSIONS

Latent profile analysis is a promising technique for identifying patterns of chemical exposure and is worthy of further study for its use in examining complicated exposure mixtures.

Association of urinary or blood heavy metals and mortality from all causes, cardiovascular disease, and cancer in the general population: a systematic review and meta-analysis of cohort studies

Amounting epidemiological evidence has shown detrimental effects of heavy metals on a wide range of diseases. However, the effect of heavy metal exposure on mortality in the general population remains unclear. The primary objective of this study was to clarify the associations between heavy metals and mortality from all causes, cardiovascular disease (CVD), and cancer based on prospective studies. We comprehensively searched Pubmed, Embase, and Web of Science electronic databases to identify studies published from their inception until 1 March 2022. Investigators identified inclusion criteria, extracted study characteristics, and assessed the methodological quality of included studies according to standardized guidelines. Meta-analysis was conducted if the effect estimates of the same outcome were reported in at least three studies. Finally, 42 original studies were identified. The results of meta-analysis showed that cadmium and lead exposure was significantly associated with mortality from all causes, CVD, and cancer in the general population. Moderate evidence suggested there was a link between arsenic exposure and mortality. The adverse effects of mercury and other heavy metals on mortality were inconclusive. Epidemiological evidence for the joint effect of heavy metal exposure on mortality was still indeterminate. In summary, our study provided compelling evidence that exposure to cadmium, lead, and arsenic were associated with mortality from all causes, CVD, and cancer, while the evidence on other heavy metals, for example mercury, was insignificant or indeterminate. Nevertheless, further prospective studies are warranted to explore the joint effects of multiple metal exposure on mortality.

Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

Metals and risk of incident metabolic syndrome in a prospective cohort of midlife women in the United States

Exposure to metals may contribute to the development of metabolic syndrome (MetS); however, evidence from midlife women who are at greater risk of cardiometabolic disease is limited. We assessed the associations of 15 urinary metal concentrations with incident MetS in a prospective cohort of midlife women in the United States. The study population included 947 White, Black, Chinese and Japanese women, aged 45-56 years, free of MetS at baseline (1999-2000), who participated in the Study of Women's Health Across the Nation Multi-Pollutant Study. Fifteen metals were detected in almost all participants urine samples using inductively coupled plasma mass spectrometry at the baseline. Incident MetS was identified annually through 2017 as having at least three of the following five components: high blood pressure, impaired fasting glucose, abdominal obesity, high triglycerides, and poor high-density lipoprotein cholesterol. We used the Cox proportional hazards models to investigate the associations between individual metals and MetS incidence. The adjusted hazard ratios (HR) (95% CI) for MetS in associations with each doubling of urinary metal concentration were 1.14 (1.08, 1.23) for arsenic, 1.14 (1.01, 1.29) for cobalt, and 1.20 (1.06, 1.37) for zinc. We further evaluated the associations between metal mixtures and MetS using the elastic net penalized Cox model and summarized the results into the environmental risk score (ERS). Arsenic, barium, cobalt, copper, nickel, antimony, thallium, and zinc had positive weights, and cadmium, cesium, mercury, molybdenum, lead, and tin had negative weights in the construction of the ERS. The adjusted HR of MetS comparing 75th vs. 25th percentiles of the ERS was 1.45 (1.13, 1.87). These findings support the view that arsenic, cobalt, zinc, as well as metal mixtures, might influence the risks of incident MetS in midlife women.

KERNEL MACHINE AND DISTRIBUTED LAG MODELS FOR ASSESSING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL MIXTURES IN CHILDREN'S HEALTH STUDIES

Exposures to environmental chemicals during gestation can alter health status later in life. Most studies of maternal exposure to chemicals during pregnancy have focused on a single chemical exposure observed at high temporal resolution. Recent research has turned to focus on exposure to mixtures of multiple chemicals, generally observed at a single time point. We consider statistical methods for analyzing data on chemical mixtures that are observed at a high temporal resolution. As motivation, we analyze the association between exposure to four ambient air pollutants observed weekly throughout gestation and birth weight in a Boston-area prospective birth cohort. To explore patterns in the data, we first apply methods for analyzing data on (1) a single chemical observed at high temporal resolution, and (2) a mixture measured at a single point in time. We highlight the shortcomings of these approaches for temporally-resolved data on exposure to chemical mixtures. Second, we propose a novel method, a Bayesian kernel machine regression distributed lag model (BKMR-DLM), that simultaneously accounts for nonlinear associations and interactions among time-varying measures of exposure to mixtures. BKMR-DLM uses a functional weight for each exposure that parameterizes the window of susceptibility corresponding to that exposure within a kernel machine framework that captures non-linear and interaction effects of the multivariate exposure on the outcome. In a simulation study, we show that the proposed method can better estimate the exposure-response function and, in high signal settings, can identify critical windows in time during which exposure has an increased association with the outcome. Applying the proposed method to the Boston birth cohort data, we find evidence of a negative association between organic carbon and birth weight and that nitrate modifies the organic carbon, elemental carbon, and sulfate exposure-response functions.

Exposure to Metal Mixtures in Association with Cardiovascular Risk Factors and Outcomes: A Scoping Review

Since the National Institute of Environmental Health Sciences (NIEHS) declared conducting combined exposure research as a priority area, literature on chemical mixtures has grown dramatically. However, a systematic evaluation of the current literature investigating the impacts of metal mixtures on cardiovascular disease (CVD) risk factors and outcomes has thus far not been performed. This scoping review aims to summarize published epidemiology literature on the cardiotoxicity of exposure to multiple metals. We performed systematic searches of MEDLINE (PubMed), Scopus, and Web of Science to identify peer-reviewed studies employing statistical mixture analysis methods to evaluate the impact of metal mixtures on CVD risk factors and outcomes among nonoccupationally exposed populations. The search was limited to papers published on or after 1998, when the first dedicated funding for mixtures research was granted by NIEHS, through 1 October 2021. Twenty-nine original research studies were identified for review. A notable increase in relevant mixtures publications was observed starting in 2019. The majority of eligible studies were conducted in the United States (n = 10) and China (n = 9). Sample sizes ranged from 127 to 10,818. Many of the included studies were cross-sectional in design. Four primary focus areas included: (i) blood pressure and/or diagnosis of hypertension (n = 15), (ii) risk of preeclampsia (n = 3), (iii) dyslipidemia and/or serum lipid markers (n = 5), and (iv) CVD outcomes, including stroke incidence or coronary heart disease (n = 8). The most frequently investigated metals included cadmium, lead, arsenic, and cobalt, which were typically measured in blood (n = 15). The most commonly utilized multipollutant analysis approaches were Bayesian kernel machine regression (BKMR), weighted quantile sum regression (WQSR), and principal component analysis (PCA). To our knowledge, this is the first scoping review to assess exposure to metal mixtures in relation to CVD risk factors and outcomes. Recommendations for future studies evaluating the associations of exposure to metal mixtures with risk of CVDs and related risk factors include extending environmental mixtures epidemiologic studies to populations with wider metals exposure ranges, including other CVD risk factors or outcomes outside hypertension or dyslipidemia, using repeated measurement of metals to detect windows of susceptibility, and further examining the impacts of potential effect modifiers and confounding factors, such as fish and seafood intake.

Biomarkers of Exposure to Phthalate Mixtures and Adverse Birth Outcomes in a Puerto Rico Birth Cohort

BACKGROUND

Humans are exposed to complex mixtures of phthalate chemicals from a range of consumer products. Previous studies have reported significant associations between individual phthalate metabolites and pregnancy outcomes, but mixtures research is limited.

OBJECTIVES

We used the Puerto Rico Testsite for Exploring Contamination Threats longitudinal pregnancy cohort to investigate associations between phthalate metabolite mixtures and pregnancy outcomes.

METHODS

Women (n=462 carrying females, n=540 carrying males) provided up to three urine samples throughout gestation (median 18, 22, and 26 wk), which were analyzed for 13 phthalate metabolites. Pregnancy outcomes including preterm birth (PTB), spontaneous PTB, small and large for gestational age (SGA, LGA), birth weight z-score, and gestational age at delivery were abstracted from medical records. Environmental risk scores (ERS) were calculated as a weighted linear combination of the phthalates from ridge regression and adaptive elastic net, which are variable selection methods to handle correlated predictors. Birth outcomes were regressed on continuous ERS. We assessed gestational average and visit-specific ERS and stratified all analyses by fetal sex. Finally, we used Bayesian kernel machine regression (BKMR) to explore nonlinear associations and interactions between metabolites.

RESULTS

Differences in metabolite weights from ridge and elastic net were apparent between birth outcomes and between fetal sexes. An interquartile range increase in gestational average phthalate ERS was associated with increased odds of PTB [male odds ratio (OR)=1.56; 95% confidence interval (CI): 1.08, 2.27; female OR=1.91; 95% CI: 1.23, 2.98], spontaneous PTB (male OR=2.32; 95% CI: 1.46, 3.68; female OR=2.00; 95% CI: 1.04, 3.82), and reduced gestational age at birth (male β=-0.39 wk, 95% CI: -0.62, -0.15; female β=-0.29 wk, 95% CI: -0.52, -0.05). Analyses by study visit suggested that exposure at ∼22 wk (range 20-24 wk) was driving those associations. Bivariate plots from BKMR analysis revealed some nonlinear associations and metabolite interactions that were different between fetal sexes.

DISCUSSION

These results suggest that exposure to phthalate mixtures was associated with increased risk of early delivery and highlight the need to study mixtures by fetal sex. We also identified various metabolites displaying nonlinear relationships with measures of birth weight. https://doi.org/10.1289/EHP8990.

Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression

There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.

Associations of plasma metal concentrations with the risks of all-cause and cardiovascular disease mortality in Chinese adults

BACKGROUND

Exposure to metals/metalloids from both the natural environment and anthropogenic sources have a complex influence on human health. However, relatively few studies have explored the relations of exposure to multiple metals/metalloids with mortality. Therefore, this prospective study aims to examine the relations of multiple metal/metalloids exposures with all-cause and cardiovascular disease (CVD) mortality.

METHODS

A total of 6155 participants within the Dongfeng-Tongji (DF-TJ) cohort were involved in this analysis, which were followed for mortality until December 31, 2018. We applied inductively coupled plasma mass spectrometry (ICP-MS) to measure baseline plasma concentrations of 23 metals. We utilized Cox regression models to calculate the hazard ratios (HRs) for all-cause and CVD mortality associated with metal concentrations. We proposed plasma metal score to assess the simultaneous exposure to multiple metals through summing each metal concentration weighted by the regression coefficients with all-cause mortality.

RESULTS

During the follow-up (mean duration, 9.8 years), we ascertained 876 deaths, including 416 deaths of CVD (157 deaths of coronary heart disease and 259 deaths of stroke). In the multiple-metals model, after adjusting for potential confounders, plasma copper, molybdenum, and vanadium were positively associated with all-cause mortality, whereas manganese, selenium, and thallium were negatively associated with the risk of all-cause mortality, with adjusted HRs (95% Confidence Interval, CI) of the fourth quartiles were 1.73 (1.42-2.11, P-trend < 0.001) for copper, 1.33 (1.09-1.63, P-trend = 0.005) for molybdenum, 1.43 (1.16-1.77, P-trend < 0.001) for vanadium, 0.74 (0.58-0.94, P-trend = 0.005) for manganese, 0.68 (0.56-0.83, P-trend < 0.001) for selenium, and 0.74 (0.59-0.92, P-trend = 0.002) for thallium, respectively. Positive associations were observed between plasma copper, molybdenum, vanadium concentrations and CVD mortality, whereas negative associations were found for plasma selenium and thallium concentrations with CVD mortality in the multiple-metals model. Compared with the first quartiles, the HRs of fourth quartiles were 1.94 (1.45-2.58, P-trend < 0.001) for copper, 1.72 (1.26-2.35, P-trend < 0.001) for molybdenum, 1.81 (1.32-2.47, P-trend < 0.001) for vanadium, 0.67 (0.50-0.89, P-trend = 0.003) for selenium, and 0.58 (0.41-0.81, P-trend < 0.001) for thallium, respectively. The plasma metal score was significantly associated with higher risks of all-cause and CVD death in dose-response fashions. When compared with the first quartiles of plasma metal score, the HRs of fourth quartiles were 2.16 (1.76-2.64; P-trend < 0.001) for all-cause mortality and 3.00 (2.24-4.02; P-trend < 0.001) for CVD mortality.

CONCLUSIONS

The study indicated that several plasma metals/metalloids were key determinants and predictors of all-cause and CVD death in the Chinese population. Our findings highlighted the importance to comprehensively assess and monitor multiple metals/metalloids exposures.

Urinary metals and metal mixtures and timing of natural menopause in midlife women: The Study of Women's Health Across the Nation

BACKGROUND

Exposure to metals and metal mixtures may influence ovarian aging. However, epidemiologic evidence of their potential impact is lacking.

OBJECTIVE

We prospectively examined the associations of 15 urinary metal concentrations and their mixtures with natural menopause in the Study of Women's Health Across the Nation Multi-Pollutant Study.

METHODS

The study population consisted of 1082 premenopausal women from multiple racial/ethnic groups, aged 45-56 years at baseline (1999-2000), with the median follow-up of 4.1 years. Urinary concentrations of 15 metals, including arsenic, barium, cadmium, cobalt, cesium, copper, mercury, manganese, molybdenum, nickel, lead, antimony, tin, thallium, and zinc, were measured at baseline. Natural menopause was defined as the final bleeding episode prior to at least 12 months of amenorrhea, not due to surgery or hormone therapy. Cox proportional hazards models were used to examine associations between individual metal concentrations and timing of natural menopause. The associations between metal mixtures and natural menopause were evaluated using elastic net penalized Cox regression, and an environmental risk score (ERS) was computed to represent individual risks of natural menopause related to metal mixtures.

RESULTS

The median age at natural menopause was 53.2 years. Using the Cox proportional hazards models, the adjusted hazard ratio (HR) (and its 95% confidence interval (CI)) for natural menopause was 1.32 (1.03, 1.67) for arsenic and 1.36 (1.05, 1.76) for lead, comparing the highest with the lowest quartiles of metal concentrations. The predicted ages at natural menopause in the highest and lowest quartiles were 52.7 and 53.5 years for arsenic; and 52.9 and 53.8 years for lead. A significant association between ERS and menopause was also observed. Women in the highest vs. the lowest quartiles of ERS had an HR of 1.71 (1.36, 2.15), equivalent to a 1.6 year earlier median time to natural menopause.

CONCLUSION

This study suggests that arsenic, lead, and metal mixtures are associated with earlier natural menopause, a risk factor for adverse health outcomes in later life.

Urinary concentrations of phenols and parabens and incident diabetes in midlife women: The Study of Women's Health Across the Nation

BACKGROUND

Environmental phenols have been suggested as diabetogens but evidence from prospective cohort studies is limited. We examined associations between urinary concentrations of phenols and parabens, assessed at two time-points, and incident diabetes in the Study of Women's Health Across the Nation (SWAN).

METHODS

We examined 1,299 women, aged 45-56 years, who were diabetes-free at baseline of the SWAN Multi-Pollutant Study (MPS) (1999-2000) and were followed through January 2017. Urinary concentrations of bisphenol-A, bisphenol-F, triclosan, 2,4-dichlorophenol, 2,5-dichlorophenol, benzophenone-3, methyl-paraben, ethyl-paraben, propyl-paraben, and butyl-paraben were measured twice at MPS baseline and 3 years later (2002-2003), and the two average concentrations were used as exposure variables. Associations of incident diabetes with individual phenols and parabens were examined using Cox regression. We evaluated the overall joint effects using quantile-based g-computation.

RESULTS

Adjusted hazard ratios (HRs) for incident diabetes of the third tertile compared with the first tertile of urinary concentrations were 0.40 (95% confidence interval [CI] = 0.29, 0.56) for methyl-paraben; 0.42 (0.30, 0.58) for propyl-paraben; 0.53 (0.38, 0.75) for 2,5-diclrorophenol; and 0.55 (0.39, 0.80) for benzophenone-3. Nonlinear associations were found for bisphenol-A and 2,4-dichlorophenol (significant positive associations in the second tertile but no associations in the third tertile compared with the first tertile). No significant associations were observed for the other individual chemicals or the joint effect of mixtures.

CONCLUSIONS

Our findings do not support diabetogenic effects of urinary parabens which were inversely associated with incident diabetes among mid-life women. Epidemiologic findings for biomarkers with short half-lives and high within-person variability need to be interpreted with caution.

Performance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting

BACKGROUND

Studies on the health effects of metal mixtures typically utilize biomarkers measured in a single biological medium, such as blood or urine. However, the ability to evaluate mixture effects are limited by the uncertainty whether a unified medium can fully capture exposure for each metal. Therefore, it is important to compare and assess metal mixtures measured in different media in epidemiology studies.

OBJECTIVES

The aim of this study was to examine the mixture predictive performance of urine and blood metal biomarkers and integrated multi-media biomarkers in association with birth outcomes.

METHODS

In our analysis of 847 women from the Puerto Rico PROTECT Cohort, we measured 10 essential and non-essential metals in repeated and paired samples of urine and blood during pregnancy. For each metal, we integrated exposure estimates from paired urine and blood biomarkers into multi-media biomarkers (MMBs), using intraclass-correlation coefficient (ICC) and weighted quantile sum (WQS) approaches. Using Ridge regressions, four separate Environmental risk scores (ERSs) for metals in urine, blood, MMBICC, and MMBWQS were computed as a weighted sum of the 10 metal concentrations. We then examined associations between urine, blood, and multi-media biomarker ERSs and birth outcomes using linear and logistic regressions, adjusting for maternal age, maternal education, pre-pregnancy body mass index (BMI), and second-hand smoke exposure. The performance of each ERS was evaluated with continuous and tertile estimates and 95% confidence intervals of the odds ratio of preterm birth using area under the curve (AUC).

RESULTS

Pb was the most important contributor of blood ERS as well as the two integrated multi-media biomarker ERSs. Individuals with high ERS (3rd tertile) showed increased odds of preterm birth compared to individuals with low ERS (1st tertile), with 2.8-fold (95% CI, 1.49 to 5.40) for urine (specific gravity corrected); 3.2- fold (95% CI, 1.68 to 6.25) for blood; 3.9-fold (95% CI, 1.72 to 8.66) for multi-media biomarkers composed using ICC; and 5.2-fold (95% CI, 2.34 to 11.42) for multi-media biomarkers composed using WQS. The four ERSs had comparable predictive performances (AUC ranging from 0.64 to 0.68) when urine is examined with specific gravity corrected concentrations.

CONCLUSIONS

Within a practical metal panel, measuring metals in either urine or blood may be an equally good approach to evaluate the metals as a mixture. Applications in practical study design require validation of these methods with other cohorts, larger panels of metals and within the context of other adverse health effects of interest.

Considering environmental exposures to per- and polyfluoroalkyl substances (PFAS) as risk factors for hypertensive disorders of pregnancy

Hypertensive disorders of pregnancy (HDP), including preeclampsia and gestational hypertension, lead to significant maternal morbidity and in some cases, maternal mortality. Environmental toxicants, especially those that disrupt normal placental and endothelial function, are emerging as potential risk factors for HDP. Per- and polyfluoroalkyl substances (PFAS) are a large group of ubiquitous chemicals found in consumer products, the environment, and increasingly in drinking water. PFAS have been associated with a multitude of adverse health effects, including dyslipidemia, hypertension, and more recently, HDP. In this review, we present epidemiological and mechanistic evidence for the link between PFAS and HDP and recommend next steps for research and prevention efforts. To date, epidemiological studies have assessed associations between only ten of the thousands of PFAS and HDP. Positive associations between six PFAS (PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonic acid; PFHxS, perfluorohexane sulfonic acid; PFHpA, perfluoroheptanoic acid; PFBS, perfluorobutanesulfonic acid; and PFNA, perfluoronanoic acid) and risk for HDP have been reported in some, but not all, studies. PFAS disrupt placental and immune function, cause oxidative stress, and disrupt lipid metabolism. These physiological disruptions may be mechanisms through which PFAS can lead to HDP. Overall, limited epidemiological evidence and plausible mechanisms support PFAS as risk factors for HDP. More research is needed in diverse, well-powered cohorts that assess exposures to as many PFAS as possible. Such research should consider not only individual PFAS but also the totality of exposures to PFAS and other environmental chemicals. Pregnant women may be a group that is vulnerable to PFAS exposure, and as such HDP risk should be considered by policymakers setting PFAS exposure limits. In the interim, medical and public health professionals in regions with PFAS contamination could provide short-term solutions in the form of patient-level prevention, increased monitoring, and early intervention for HDP.

Joint exposure to various ambient air pollutants and incident heart failure: a prospective analysis in UK Biobank

AIMS

Little is known about the relation between the long-term joint exposure to various ambient air pollutants and the incidence of heart failure (HF). We aimed to assess the joint association of various air pollutants with HF risk and examine the modification effect of the genetic susceptibility.

METHODS AND RESULTS

This study included 432 530 participants free of HF, atrial fibrillation, or coronary heart disease in the UK Biobank study. All participants were enrolled from 2006 to 2010 and followed up to 2018. The information on particulate matter (PM) with diameters ≤2.5 µm (PM2.5), ≤10 µm (PM10), and between 2.5 and 10 µm (PM2.5-10) as well as nitrogen oxides (NO2 and NOx) was collected. We newly proposed an air pollution score to assess the joint exposure to the five air pollutants through summing each pollutant concentration weighted by the regression coefficients with HF from single-pollutant models. We also calculated the weighted genetic risk score of HF. During a median of 10.1 years (4 346 642 person-years) of follow-up, we documented 4201 incident HF. The hazard ratios (HRs) [95% confidence interval (CI)] of HF for a 10 µg/m3 increase in PM2.5, PM10, PM2.5-10, NO2, and NOx were 1.85 (1.34-2.55), 1.61 (1.30-2.00), 1.13 (0.80-1.59), 1.10 (1.04-1.15), and 1.04 (1.02-1.06), respectively. We found that the air pollution score was associated with an increased risk of incident HF in a dose-response fashion. The HRs (95% CI) of HF were 1.16 (1.05-1.28), 1.19 (1.08-1.32), 1.21 (1.09-1.35), and 1.31 (1.17-1.48) in higher quintile groups compared with the lowest quintile of the air pollution score (P trend <0.001). In addition, we observed that the elevated risk of HF associated with a higher air pollution score was strengthened by the genetic susceptibility to HF.

CONCLUSION

Our results indicate that the long-term joint exposure to various air pollutants including PM2.5, PM10, PM2.5-10, NO2, and NOx is associated with an elevated risk of incident HF in an additive manner. Our findings highlight the importance to comprehensively assess various air pollutants in relation to the HF risk.

Bayesian Group Index Regression for Modeling Chemical Mixtures and Cancer Risk

There has been a growing interest in the literature on multiple environmental risk factors for diseases and an increasing emphasis on assessing multiple environmental exposures simultaneously in epidemiologic studies of cancer. One method used to analyze exposure to multiple chemical exposures is weighted quantile sum (WQS) regression. While WQS regression has been demonstrated to have good sensitivity and specificity when identifying important exposures, it has limitations including a two-step model fitting process that decreases power and model stability and a requirement that all exposures in the weighted index have associations in the same direction with the outcome, which is not realistic when chemicals in different classes have different directions and magnitude of association with a health outcome. Grouped WQS (GWQS) was proposed to allow for multiple groups of chemicals in the model where different magnitude and direction of associations are possible for each group. However, GWQS shares the limitation of WQS of a two-step estimation process and splitting of data into training and validation sets. In this paper, we propose a Bayesian group index model to avoid the estimation limitation of GWQS while having multiple exposure indices in the model. To evaluate the performance of the Bayesian group index model, we conducted a simulation study with several different exposure scenarios. We also applied the Bayesian group index method to analyze childhood leukemia risk in the California Childhood Leukemia Study (CCLS). The results showed that the Bayesian group index model had slightly better power for exposure effects and specificity and sensitivity in identifying important chemical exposure components compared with the existing frequentist method, particularly for small sample sizes. In the application to the CCLS, we found a significant negative association for insecticides, with the most important chemical being carbaryl. In addition, for children who were born and raised in the home where dust samples were taken, there was a significant positive association for herbicides with dacthal being the most important exposure. In conclusion, our approach of the Bayesian group index model appears able to make a substantial contribution to the field of environmental epidemiology.

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction?

Background

To evaluate whether blood markers of lead, cadmium, and mercury can improve prediction for cardiovascular disease (CVD) mortality when added individually, jointly, or as an integrative index/Environmental Risk Score (ERS), in a model with established risk factors.

Methods and Results

Our study sample comprised 16 028 adults aged ≥40 years who were enrolled in the National Health and Nutrition Examination Survey 1999–2012 and followed up through December 31, 2015. The study sample was randomly split into training for the ERS construction (n=8043) and testing for the evaluation of prediction performance (n=7985). ERS was computed using elastic‐net penalized Cox's model based on the selected metal predictors predicting CVD mortality. During median follow‐up of 7.2 years, 517 died from CVD. In the training set, linear terms of cadmium and mercury, squared terms of lead and mercury, and all 3 pairwise interactions were selected by elastic‐net for ERS construction. In the testing set, the C‐statistic increased from 0.845 when only established CVD risk factors were in the model to 0.854 when the ERS was additionally added to the model. Addition of all linear, squared, and pairwise interaction terms of blood metals to the Cox's models improved C‐statistic from 0.845 to 0.857. The improvement remained significant when it was assessed by net reclassification improvement and integrated discrimination improvement.

Conclusions

Our findings suggest that blood markers of toxic metals can improve CVD risk prediction over the established risk factors and highlight their potential utility for CVD risk assessment, prevention, and precision health.

Joint and independent neurotoxic effects of early life exposures to a chemical mixture: A multi-pollutant approach combining ensemble learning and g-computation

BACKGROUND

Exposure to mercury (Hg) is associated with adverse developmental effects. However, Hg occurs with a multitude of chemicals. We assessed the associations of developmental exposure to multiple pollutants with children's neurodevelopment using a novel approach.

METHODS

Hg, polychlorinated biphenyls (PCBs), and perfluoroalkyl substances were measured in maternal and children's blood at 5-years (n=449 and 419). At 7-years, children were administered Boston Naming Test (BNT) and the Strengths and Difficulties Questionnaire (SDQ). We used the G-formula combined with SuperLearner to estimate independent and joint effects of chemicals at both ages. We constructed flexible exposure-response relationships and assessed interactions.

RESULTS

Most chemicals showed negative relationships with BNT scores. An inter-quartile range (IQR) increase in maternal Hg and perfluorooctanoic acid (PFOA) was associated with 0.15 standard deviation [SD] (95% Confidence Interval [CI]: -0.29,-0.03) and 0.14 SD (95%CI: -0.26,-0.05) lower scores in BNT, whereas a joint IQR increase in the mixture of chemicals was associated with 0.48 SD (95%CI: -0.69,-0.25) lower scores in BNT. An IQR increase in PFOA was associated with 0.11 SD (95%CI: 0.02,0.26) higher total SDQ difficulties scores. Maternal ∑PCBs concentrations were associated with lower SDQ scores (β=-0.09 SD; 95%CI: -0.19,0), whereas 5-years ∑PCBs showed a negative association (β=-0.09 SD; 95%CI: -0.21,0). Finally, a joint IQR increase in the mixture was associated with 0.22 SD (95%CI: 0.04,0.4) higher SDQ scores.

CONCLUSIONS

Using a novel statistical approach, we confirmed associations between prenatal mercury exposure and lower cognitive function. The potential developmental effects of PFASs need additional attention.

High plasma concentrations of organic pollutants negatively impact survival in amyotrophic lateral sclerosis

OBJECTIVE

To determine whether persistent organic pollutants (POP) affect amyotrophic lateral sclerosis (ALS) survival.

METHODS

ALS participants seen at the University of Michigan (Ann Arbor, MI, USA) provided plasma samples for measurement of POPs. ALS disease and clinical features were collected prospectively from the medical records. Survival models used a composite summary measure of exposure due to multiple POPs (environmental risk score or ERS).

RESULTS

167 participants (40.7% female, n=68) with ALS were recruited, of which 119 died during the study period. Median diagnostic age was 60.9 years (IQR 52.7-68.2), median time from symptom onset to diagnosis was 1.01 years (IQR 0.67-1.67), bulbar onset 28.7%, cervical onset 33.5% and lumbar onset 37.7%. Participants in the highest quartile of ERS (representing highest composite exposure), adjusting for age at diagnosis, sex and other covariates had a 2.07 times greater hazards rate of mortality (p=0.018, 95% CI 1.13 to 3.80) compared with those in the lowest quartile. Pollutants with the largest contribution to the ERS were polybrominated diphenyl ethers 154 (HR 1.53, 95% CI 0.90 to 2.61), polychlorinated biphenyls (PCB) 118 (HR 1.50, 95% CI 0.95 to 2.39), PCB 138 (HR 1.69, 95% CI 0.99 to 2.90), PCB 151 (HR 1.46, 95% CI 1.01 to 2.10), PCB 175 (HR 1.53, 95% CI 0.98 to 2.40) and p,p'-DDE (HR 1.39, 95% CI 1.07 to 1.81).

CONCLUSIONS

Higher concentrations of POPs in plasma are associated with reduced ALS survival, independent of age, gender, segment of onset and other covariates. This study helps characterise and quantify the combined effects of POPs on ALS and supports the concept that environmental exposures play a role in disease pathogenesis.

Urinary metals and metal mixtures in midlife women: The Study of Women's Health Across the Nation (SWAN)

BACKGROUND

Little is known about the extent of exposure to metals and metal mixtures among midlife women.

OBJECTIVES

We assessed exposure to multiple metals in the Study of Women's Health Across the Nation (SWAN), a multi-site, multi-racial/ethnic cohort of women at midlife.

METHODS

We measured urinary concentrations of 21 metals (arsenic, barium, beryllium, cadmium, cobalt, chromium, cesium, copper, mercury, manganese, molybdenum, nickel, lead, platinum, antimony, tin, thallium, uranium, vanadium, tungsten and zinc) using high-resolution inductively coupled plasma-mass spectrometry among 1335 white, black, Chinese and Japanese women aged 45-56 years at the third SWAN annual visit (1999-2000). Least squared geometric mean concentrations were compared across race/ethnicity, education, financial hardship, smoking, secondhand smoking, seafood intake and rice intake groups. Overall exposure patterns of multiple metals were derived using k-means clustering method.

RESULTS

The percentage of women with detectable concentrations of metals ranged from 100% for arsenic, cesium, molybdenum and zinc, to less than 5% for platinum; 15 metals had detection rates of 70% or more. Asian women, both Chinese and Japanese, had higher urinary concentrations of arsenic, cadmium, copper, mercury, molybdenum, lead and thallium, compared with other race/ethnic groups, independent of sociodemographic, lifestyle, dietary, and geographic characteristics. Seafood and rice intake were important determinants of urinary arsenic, cesium, mercury, molybdenum and lead levels. Two distinct overall exposure patterns- "high" vs. "low" -- were identified. Women in the "high" overall exposure pattern were more likely to be Asians, current smokers, and to report high consumption of seafood and rice. Black women were less likely to have the high exposure pattern.

CONCLUSIONS

Metal exposure of midlife women differs by racial/ethnic, sociodemographic, lifestyle, dietary, and geographic characteristics. Asian women may be experiencing the highest exposures to multiple metals compared with other racial/ethnic groups in the United States.

Complex Mixtures, Complex Analyses: an Emphasis on Interpretable Results

PURPOSE OF REVIEW

The purpose of this review is to outline the main questions in environmental mixtures research and provide a non-technical explanation of novel or advanced methods to answer these questions.

RECENT FINDINGS

Machine learning techniques are now being incorporated into environmental mixture research to overcome issues with traditional methods. Though some methods perform well on specific tasks, no method consistently outperforms all others in complex mixture analyses, largely because different methods were developed to answer different research questions. We discuss four main questions in environmental mixtures research: (1) Are there specific exposure patterns in the study population? (2) Which are the toxic agents in the mixture? (3) Are mixture members acting synergistically? And, (4) what is the overall effect of the mixture? We emphasize the importance of robust methods and interpretable results over predictive accuracy. We encourage collaboration with computer scientists, data scientists, and biostatisticians in future mixture method development.

Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes

In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. For understanding the health effects of multiple environmental exposures, it is often important to identify and estimate complex interactions among exposures. However, this issue becomes analytically challenging in the presence of potential nonlinearity in the outcome-exposure response surface and a set of correlated exposures. Through simulation studies and analyses of test datasets that were simulated as a part of a data challenge in multipollutant modeling organized by the National Institute of Environmental Health Sciences (http://www.niehs.nih.gov/about/events/pastmtg/2015/statistical/), we illustrate the advantages of our proposed method in comparison with existing alternative approaches. A particular strength of our method is that it demonstrates very low false positives across empirical studies. Our method is also used to analyze a dataset that was released from the Health Outcomes and Measurement of the Environment Study as a benchmark beta-tester dataset as a part of the same workshop.

Emerging concepts and challenges in implementing the exposome paradigm in allergic diseases and asthma: a Practall document

Exposome research can improve the understanding of the mechanistic connections between exposures and health to help mitigate adverse health outcomes across the life span. The exposomic approach provides a risk profile instead of single predictors and thus is particularly applicable to allergic diseases and asthma. Under the PRACTALL collaboration between the European Academy of Allergy and Clinical Immunology (EAACI) and the American Academy of Allergy, Asthma, and Immunology (AAAAI), we evaluated the current concepts and the unmet needs on the role of the exposome in allergic diseases and asthma.

Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma

Background: Non-hodgkin lymphoma (NHL) is one of the most common and deadly cancers. There is limited analysis of gene-environment interactions for the risk of NHL. This study intends to explore the interactions between genetic variants and environmental factors, and how they contribute to NHL risk.

Methods: A case-control study was performed in Shanghai, China. The cases were diagnosed between 2003 and 2008 with patients aged 18 years or older. Samples and SNPs which did not satisfy quality control were excluded from the analysis. Weighted and unweighted genetic risk scores (GRS) and environmental risk scores were generated using clustering analysis algorithm. Univariate and multivariable logistic regression analyses were conducted. Moreover, genetics and environment interactions (G × E) were tested on the NHL cases and controls.

Results: After quality control, there are 22 SNPs, 11 environmental variables and 5 demographical variables to be explored. For logistic regression analyses, 5 SNPs (rs1800893, rs4251961, rs1800630, rs13306698, rs1799931) and environmental tobacco smoking showed statistically significant associations with the risk of NHL. Odds ratio (OR) and 95% confidence interval (CI) was 10.82 (4.34–28.88) for rs13306698, 2.84 (1.66–4.95) for rs1800893, and 2.54 (1.43–4.58) for rs4251961. For G × E analysis, the interaction between smoking and dichotomized weighted GRS showed statistically significant association with NHL (OR = 0.23, 95% CI = [0.09, 0.61]).

Conclusions: Several genetic and environmental risk factors and their interactions associated with the risk of NHL have been identified. Replication in other cohorts is needed to validate the results.

Assessing the cumulative health effect following short term exposure to multiple pollutants: An evaluation of methodological approaches using simulations and real data

BACKGROUND

Assessment of the cumulative effect of correlated exposures is an open methodological issue in environmental epidemiology. Most previous studies have applied regression models with interaction terms or dimension reduction methods. The combined effect of pollutants has been also evaluated through the use of exposure scores that incorporate weights based on the strength of the component-specific associations with health outcomes.

METHODS

We compared three approaches addressing multi-pollutant exposures in epidemiological models: main effects models, the adaptive least absolute shrinkage and selection operator (LASSO) and a weighted exposure score. We assessed the performance of the methods by simulations under various scenarios for the pollutants' correlations. We further applied these methods to time series data from Athens, Greece in 2007-12 to investigate the combined effect of short-term exposure to six regulated pollutants on all-cause and respiratory mortality.

RESULTS

The exposure score provided the least biased estimate under all correlation scenarios for both mortality outcomes. The adaptive LASSO performed well in the case of low and medium correlation between exposures while the main effect model resulted in severe bias. In the real data application, the cumulative effect estimate was similar between approaches for all-cause mortality ranging from 0.7% increase per interquartile range (IQR) (score) to 1.1% (main effects), while for respiratory mortality conclusions were contradictive and ranged from - 0.6% (adaptive LASSO) to 2.8% (score).

CONCLUSIONS

Τhe use of a weighted exposure score to address cumulative effects of correlated metrics may perform well under different exposure correlation and variability in the health outcomes.

Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression

There is substantial interest in assessing how exposure to environmental mixtures, such as chemical mixtures, affect child health. Researchers are also interested in identifying critical time windows of susceptibility to these complex mixtures. A recently developed method, called lagged kernel machine regression (LKMR), simultaneously accounts for these research questions by estimating effects of time-varying mixture exposures, and identifying their critical exposure windows. However, LKMR inference using Markov chain Monte Carlo methods (MCMC-LKMR) is computationally burdensome and time intensive for large datasets, limiting its applicability. Therefore, we develop a mean field variational Bayesian inference procedure for lagged kernel machine regression (MFVB-LKMR). The procedure achieves computational efficiency and reasonable accuracy as compared with the corresponding MCMC estimation method. Updating parameters using MFVB may only take minutes, while the equivalent MCMC method may take many hours or several days. We apply MFVB-LKMR to PROGRESS, a prospective cohort study in Mexico. Results from a subset of PROGRESS using MFVB-LKMR provide evidence of significant positive association between second trimester cobalt levels and z-scored birthweight. This positive association is heightened by cesium exposure. MFVB-LKMR is a promising approach for computationally efficient analysis of environmental health datasets, to identify critical windows of exposure to complex mixtures.

Post-GWAS in Psychiatric Genetics: A Developmental Perspective on the "Other" Next Steps

As psychiatric genetics enters an era where gene identification is finally yielding robust, replicable genetic associations and polygenic risk scores, it is important to consider next steps and delineate how that knowledge will be applied to ultimately ameliorate suffering associated with substance use and psychiatric disorders. Much of the post-genome-wide association study discussion has focused on the potential of genetic information to elucidate the underlying biology and use this information for the development of more effective pharmaceutical treatments. In this review we focus on additional areas of research that should follow gene identification. By taking genetic findings into longitudinal, developmental studies, we can map the pathways by which genetic risk manifests across development, elucidating the early behavioral manifestations of risk, and studying how various environments and interventions moderate that risk across developmental stages. The delineation of risk across development will advance our understanding of mechanism, sex differences and risk and resilience processes in different racial/ethnic groups. Here, we review how the extant twin study literature can be used to guide these efforts. Together, these new lines of research will enable us to develop more informed, tailored prevention and intervention efforts.

Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES

BACKGROUND

There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutant-pollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress (gamma-glutamyl transferase (GGT)), a common disease pathway linking environmental exposure and numerous health endpoints.

METHODS

We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003-2004 to 2013-2014, n = 9664). We randomly split the data evenly into training and testing sets and constructed ERS's of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints.

RESULTS

ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS's showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS's showed non-significant positive associations with mortality outcomes.

CONCLUSIONS

ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints.

Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies

A growing number and increasing diversity of factors are available for epidemiological studies. These measures provide new avenues for discovery and prevention, yet they also raise many challenges for adoption in epidemiological investigations. Here, we evaluate 1) designs to investigate diseases that consider heterogeneous and multidimensional indicators of exposure and behavior, 2) the implementation of numerous methods to capture indicators of exposure, and 3) the analytical methods required for discovery and validation. We find that case-control studies have provided insights into genetic susceptibility but are insufficient for characterizing complex effects of environmental factors on disease development. Prospective and two-phase designs are required but must balance extended data collection with follow-up of study participants. We discuss innovations in assessments including the microbiome; mass spectrometry and metabolomics; behavioral assessment; dietary, physical activity, and occupational exposure assessment; air pollution monitoring; and global positioning and individual sensors. We claim the the availability of extensive correlated data raises new challenges in disentangling specific exposures that influence cancer risk from among extensive and often correlated exposures. In conclusion, new high-dimensional exposure assessments offer many new opportunities for environmental assessment in cancer development. Cancer Epidemiol Biomarkers Prev; 26(9); 1370-80. ©2017 AACR.

A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey

The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.

Number of Persistent Organic Pollutants Detected at High Concentrations in Blood Samples of the United States Population

Human exposure to environmental chemicals as persistent organic pollutants (POPs) is usually assessed considering each pollutant individually, with little attention to concentrations of mixtures in individuals or social groups. Yet, it may be relatively common for humans to have low and high concentrations of numerous POPs. The study objectives were to analyze the number of POPs detected per person at high concentrations in the U.S. population, and the associations between such type of indicators and socioeconomic factors as gender, race / ethnicity, education, and poverty level. From 91 POPs analyzed in serum samples of 4,739 individuals in three subsamples of the National Health and Nutrition Examination Survey (NHANES) 2003-2004 (the last period with valid updated individual data for the compounds considered in the present study), we computed the number of POPs whose serum concentrations were above selected cutoff points. POPs included were 13 organochlorine compounds (OCs), 10 polybrominated diphenyl ethers (PBDEs), the polybrominated biphenyl (PBB) 153, 38 polychlorinated biphenyls (PCBs), 17 polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDDs/Fs), and 12 perfluorinated compounds (PFCs). Over 13% of participants had ≥10 of the 37 most detected POPs each at a concentration in the top decile (P90). Over 30% of subjects with total toxic equivalency (TEQ) ≥P75, had ≥10 of 24 POPs not included in TEQ calculations at concentrations ≥P90. Compared to non-Hispanic whites, the adjusted odds ratio of having ≥10 of the 37 POPs at P90 was 9.2 for non-Hispanic blacks and 0.18 for Mexican Americans. Poverty, body mass index, age, and gender were also independently associated with having ≥10 POPs in the top decile. More than one tenth of the US population may have ≥10 POPs each at concentrations in the top decile. Such pattern is nine times more frequent in Non-Hispanic blacks and four times less frequent in Mexican Americans than in non-Hispanic whites.

Adolescent epigenetic profiles and environmental exposures from early life through peri-adolescence

Epigenetic perturbations induced by environmental exposures at susceptible lifestages contribute to disease development. Even so, the influence of early life and ongoing exposures on the adolescent epigenome is rarely examined. We examined the association of exposure biomarkers for lead (Pb), bisphenol A (BPA), and nine phthalates metabolites with blood leukocyte DNA methylation at LINE-1 repetitive elements and environmentally responsive genes ( IGF2 , H19 , and HSD11B2 ) in peri-adolescents. Participants ( n  = 247) from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) birth cohorts were followed-up once between the ages of 8 and 14 years, and concurrent exposures were measured in biospecimen collected at that time (blood Pb, urinary BPA, and phthalate metabolites). Prenatal and childhood exposures to Pb were previously approximated using maternal and child samples. BPA and phthalate metabolites were measured in third trimester maternal urine samples. Significant associations ( P  < 0.05) were observed between DNA methylation and exposure biomarkers that were gene and biomarker specific. For example, Pb was only associated with LINE-1 hypomethylation during pregnancy ( P  = 0.04), while early childhood Pb was instead associated with H19 hypermethylation ( P  = 0.04). Concurrent urinary mono (2-ethylhexyl) phthalate (MEHP) was associated with HSD11B2 hypermethylation ( P  = 0.005). Sex-specific associations, particularly among males, were also observed. In addition to single exposure models, principal component analysis was employed to examine exposure mixtures. This method largely corroborated the findings of the single exposure models. This study along with others in the field suggests that environment-epigenetic relationships vary by chemical, exposure timing, and sex.

Combined Effects of Prenatal Exposures to Environmental Chemicals on Birth Weight

Prenatal chemical exposure has been frequently associated with reduced fetal growth by single pollutant regression models although inconsistent results have been obtained. Our study estimated the effects of exposure to single pollutants and mixtures on birth weight in 248 mother-child pairs. Arsenic, copper, lead, manganese and thallium were measured in cord blood, cadmium in maternal blood, methylmercury in maternal hair, and five organochlorines, two perfluorinated compounds and diethylhexyl phthalate metabolites in cord plasma. Daily exposure to particulate matter was modeled and averaged over the duration of gestation. In single pollutant models, arsenic was significantly associated with reduced birth weight. The effect estimate increased when including cadmium, and mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) co-exposure. Combining exposures by principal component analysis generated an exposure factor loaded by cadmium and arsenic that was associated with reduced birth weight. MECPP induced gender specific effects. In girls, the effect estimate was doubled with co-exposure of thallium, PFOS, lead, cadmium, manganese, and mercury, while in boys, the mixture of MECPP with cadmium showed the strongest association with birth weight. In conclusion, birth weight was consistently inversely associated with exposure to pollutant mixtures. Chemicals not showing significant associations at single pollutant level contributed to stronger effects when analyzed as mixtures.

Development of exposome correlation globes to map out environment-wide associations

The environment plays a major role in influencing diseases and health. The phenomenon of environmental exposure is complex and humans are not exposed to one or a handful factors but potentially hundreds factors throughout their lives. The exposome, the totality of exposures encountered from birth, is hypothesized to consist of multiple inter-dependencies, or correlations, between individual exposures. These correlations may reflect how individuals are exposed. Currently, we lack methods to comprehensively identify robust and replicated correlations between environmental exposures of the exposome. Further, we have not mapped how exposures associated with disease identified by environment-wide association studies (EWAS) are correlated with other exposures. To this end, we implement methods to describe a first "exposome globe", a comprehensive display of replicated correlations between individual exposures of the exposome. First, we describe overall characteristics of the dense correlations between exposures, showing that we are able to replicate 2,656 correlations between individual exposures of 81,937 total considered (3%). We document the correlation within and between broad a priori defined categories of exposures (e.g., pollutants and nutrient exposures). We also demonstrate utility of the exposome globe to contextualize exposures found through two EWASs in type 2 diabetes and all-cause mortality, such as exposure clusters putatively related to smoking behaviors and persistent pollutant exposure. The exposome globe construct is a useful tool for the display and communication of the complex relationships between exposure factors and between exposure factors related to disease status.