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

Association of combined lead, cadmium, and mercury with systemic inflammation

Background

Exposure to environmental metals has been increasingly associated with systemic inflammation, which is implicated in the pathogenesis of various chronic diseases, including those with neurodegenerative aspects. However, the complexity of exposure and response relationships, particularly for mixtures of metals, has not been fully elucidated.

Objective

This study aims to assess the individual and combined effects of lead, cadmium, and mercury exposure on systemic inflammation as measured by C-reactive protein (CRP) levels, using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018.

Methods

We employed Bayesian Kernel Machine Regression (BKMR) to analyze the NHANES 2017-2018 data, allowing for the evaluation of non-linear exposure-response functions and interactions between metals. Posterior Inclusion Probabilities (PIP) were calculated to determine the significance of each metal's contribution to CRP levels.

Results

The PIP results highlighted mercury's significant contribution to CRP levels (PIP = 1.000), followed by cadmium (PIP = 0.6456) and lead (PIP = 0.3528). Group PIP values confirmed the importance of considering the metals as a collective group in relation to CRP levels. Our BKMR analysis revealed non-linear relationships between metal exposures and CRP levels. Univariate analysis showed a flat relationship between lead and CRP, with cadmium having a positive relationship. Mercury exhibited a U-shaped association, indicating both low and high exposures as potential risk factors for increased inflammation. Bivariate analysis confirmed this relationship when contaminants were combined with lead and cadmium. Analysis of single-variable effects suggested that cadmium and lead are associated with higher values of the h function, a flexible function that takes multiple metals and combines them in a way that captures the complex and potentially nonlinear relationship between the metals and CRP. The overall exposure effect of all metals on CRP revealed that exposures below the 50th percentile exposure level are associated with an increase in CRP levels, while exposures above the 60th percentile are linked to a decrease in CRP levels.

Conclusions

Our findings suggest that exposure to environmental metals, particularly mercury, is associated with systemic inflammation. These results highlight the need for public health strategies that address the cumulative effects of metal exposure and reinforce the importance of using advanced statistical methods to understand the health impact of environmental contaminants. Future research should focus on the mechanistic pathways of metal-induced inflammation and longitudinal studies to ascertain the long-term effects of these exposures.

Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression

Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research.Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect.Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains.Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.

Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer

Arsenic is a bladder carcinogen though less is known regarding the specific temporal relationship between exposure and bladder cancer diagnosis. In this study, we modeled time-varying mixtures of arsenic exposures at many historic temporal windows to evaluate their association with bladder cancer risk in the New England Bladder Cancer Study. We used arsenic exposure estimates up to 60 years prior to study entry and compared the goodness of fit of models using these mixtures to those using summary measures of arsenic exposures. We used the Bayesian index low rank kriging multiple membership model (LRK-MMM) to estimate the associations of these mixtures with bladder cancer and estimate cumulative spatial risk for bladder cancer using participants' residential histories. We found consistent evidence that modeling arsenic exposures as a time-varying mixture provided better fit to the data than using a single arsenic exposure summary measure. We estimated several positive though not significant associations of the time-varying arsenic mixtures with bladder cancer having odds ratios (ORs) of 1.03-1.14 and identified many significant and positive associations for an interaction among those who consumed water from a private dug well (ORs 1.28-1.60). Arsenic exposures 40-50 years before study entry received elevated importance weights in these mixtures. Additionally, we found two small areas of elevated cumulative spatial risk for bladder cancer in southern New Hampshire and in south central Maine. These results emphasize the importance of considering time-varying mixtures of exposures for diseases with long latencies such as bladder cancer.

Investigating the Interplay of Toxic Metals and Essential Elements in Liver Disease

Liver diseases, including non-alcoholic fatty liver disease (NAFLD), are a growing global health issue. Environmental exposure to toxic metals can harm the liver, increasing the risk of NAFLD. Essential elements are vital for liver health, but imbalances or deficiencies can contribute to the development of NAFLD. Therefore, understanding the interplay between toxic metals and essential elements in liver disease is important. This study aims to assess the individual and combined effects of toxic metals (lead(Pb), cadmium (Cd), mercury (Hg)), and essential elements (manganese and selenium) on the risk of liver disease. Methods: We assessed the individual and combined effects of Pb, Cd, Hg, manganese (Mn), and selenium (Se) on liver disease risk using data from the National Health and Nutrition Examination Survey between 2017 and 2018. We performed descriptive statistics and linear regression analysis and then utilized Bayesian Kernel Machine Regression (BKMR) techniques such as univariate, bivariate, and overall effect analysis. BKMR enabled the assessment of non-linear exposure-response functions and interactions between metals and essential elements. Posterior Inclusion Probabilities (PIPs) were calculated to determine the importance of each metal and essential element in contributing to liver disease. Regarding our study results, the regression analysis of liver injury biomarkers ALT, AST, ALP, GGT, total bilirubin, and the FLI-an indicator of NAFLD-with toxic metals and essential elements, adjusting for covariates such as age, sex, BMI, alcohol consumption, ethnicity, income, and smoking status, demonstrated the differential effects of these contaminants on the markers of interest. Our BKMR analysis provided further insights. For instance, the PIP results underscored Pb's consistent importance in contributing to liver disease (PIP = 1.000), followed by Hg (PIP = 0.9512), Cd (PIP = 0.5796), Se (PIP = 0.5572), and Mn (PIP = 0.4248). Our univariate analysis showed a positive trend with Pb, while other exposures were relatively flat. Our analysis of the single-variable effects of toxic metals and essential elements on NAFLD also revealed that Pb significantly affected the risk of NAFLD. Our bivariate analysis found a positive (toxic) trend when Pb was combined with other metals and essential elements. For the overall exposure effect of exposure to all the contaminants together, the estimated risk of NAFLD showed a steady increase from the 60th to the 75th percentile. In conclusion, our study indicates that Pb exposure, when combined with other toxic metals and essential elements, plays a significant role in bringing about adverse liver disease outcomes.

Identifying critical windows of prenatal phenol, paraben, and pesticide exposure and child neurodevelopment: Findings from a prospective cohort study

BACKGROUND

This study aimed to investigate how exposure to a mixture of endocrine disrupting chemicals (EDCs) during two points in pregnancy affects early childhood neurodevelopment.

METHODS

We analyzed publicly-available data from a high-risk cohort of mothers and their children (2007-2014) that measured six EDCs including methyl-, ethyl- and propyl parabens (MEPB, ETPB, PRPB), Bisphenol-A (BPA), 3,5,6-trichloro-2-pyridinol (TCPy), 3-phenoxybenzoic acid (3-PBA) in prenatal urine samples during the second and third trimesters. Neurodevelopmental scores were assessed using Mullen Scales of Early Learning (MSEL) at age 3. We used mean field variational Bayes for lagged kernel machine regression (MFVB-LKMR) to investigate the association between trimester-specific co-exposure to the six EDCs and MSEL scores at age 3, stratified by sex.

RESULTS

The analysis included 130 children. For females, the relationship between BPA and 3PBA with MSEL score varied between the two trimesters. In the second trimester, effect estimates for BPA were null but inversely correlated with MSEL score in the third trimester. 3PBA had a negative relationship with MSEL in the second trimester and positive correlation in the third trimester. For males, effect estimates for all EDCs were in opposing directions across trimesters. MFVB-LKMR analysis identified significant two-way interaction between EDCs for MSEL scores in both trimesters. For example, in females, the MSEL scores associated with increased exposure to TCPy were 1.75 units (95%credible interval -0.04, -3.47) lower in the 2nd trimester and 4.61 (95%CI -3.39, -5.84) lower in the third trimester when PRPB was fixed at the 75th percentile compared to when PRPB was fixed at the 25th percentile.

CONCLUSION

Our study provides evidence that timing of EDC exposure within the prenatal period may impact neurodevelopmental outcomes in children. More of these varying effects were identified among females. Future research is needed to explore EDC mixtures and the timing of exposure during pregnancy to enhance our understanding of how these chemicals impact child health.

Prenatal Metal Exposures and Child Social Responsiveness Scale Scores in 2 Prospective Studies

Background

Prenatal exposure to metals is hypothesized to be associated with child autism. We aim to investigate the joint and individual effects of prenatal exposure to urine metals including lead (Pb), mercury (Hg), manganese (Mn), and selenium (Se) on child Social Responsiveness Scale (SRS) scores.

Methods

We used data from 2 cohorts enriched for likelihood of autism spectrum disorder (ASD): Early Autism Risk Longitudinal Investigation (EARLI) and the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) studies. Metal concentrations were measured in urine collected during pregnancy. We used Bayesian Kernel Machine Regression and linear regression models to investigate both joint and independent associations of metals with SRS Z-scores in each cohort. We adjusted for maternal age at delivery, interpregnancy interval, maternal education, child race/ethnicity, child sex, and/or study site.

Results

The final analytic sample consisted of 251 mother-child pairs. When Pb, Hg, Se, and Mn were at their 75th percentiles, there was a 0.03 increase (95% credible interval [CI]: -0.11, 0.17) in EARLI and 0.07 decrease (95% CI: -0.29, 0.15) in MARBLES in childhood SRS Z-scores, compared to when all 4 metals were at their 50th percentiles. In both cohorts, increasing concentrations of Pb were associated with increasing values of SRS Z-scores, fixing the other metals to their 50th percentiles. However, all the 95% credible intervals contained the null.

Conclusions

There were no clear monotonic associations between the overall prenatal metal mixture in pregnancy and childhood SRS Z-scores at 36 months. There were also no clear associations between individual metals within this mixture and childhood SRS Z-scores at 36 months. The overall effects of the metal mixture and the individual effects of each metal within this mixture on offspring SRS Z-scores might be heterogeneous across child sex and cohort. Further studies with larger sample sizes are warranted.

Serum HDL partially mediates the association between exposure to volatile organic compounds and kidney stones: A nationally representative cross-sectional study from NHANES

Environmental exposure to volatile organic compounds (VOCs) is ubiquitous, and this study explored whether exposure to VOCs is associated with the risk of kidney stones. We performed a nationally representative US cross-sectional study using data from five survey cycles (2011-2020) of the National Health and Nutrition Examination Survey (NHANES) program. Exposure to VOCs was determined by urine creatinine-corrected metabolites of VOCs (mVOCs). In total 5505 participants and 15 urine mVOCs were included for analysis, and the prevalence of kidney stones was 9.57 % (527/5505). Multivariable logistic regression showed that urine AMCC (parent VOCs (pVOCs): N, N-Dimethylformamide), 3,4-MHA (pVOCs: xylene), MA (pVOCs: ethylbenzene; styrene), DHBMA (pVOCs: 1,3-butadiene), HMPMA (pVOCs: crotonaldehyde) and 2HPMA (pVOCs: propylene oxide) were significantly associated with an increased risk of kidney stones in US general population. Sub-analysis revealed that there was a more pronounced association in women and the overweight/obesity group (body mass index ≥ 25). Moreover, the weighted quantile sum (WQS) regression model and the Bayesian kernel machine regression (BKMR) model consistently identified a positive association between co-exposure to VOCs and the risk of kidney stones, in which AMCC played the most important role among the 15 mVOCs. Mediation analysis further identified serum high-density lipoprotein cholesterol (HDL) as a mediator of the association between VOC co-exposure and kidney stones. Our study draws attention to the previously unknown positive associations between non-occupational VOC exposure and the risk of kidney stones in the general population. However, further studies are required to clarify the existence of such causation.

Multiple exposure distributed lag models with variable selection

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time periods during which exposure to a pollutant adversely affects health outcomes. Recent studies have focused on estimating the health effects of a large number of environmental exposures, or an environmental mixture, on health outcomes. In such settings, it is important to understand which environmental exposures affect a particular outcome, while acknowledging the possibility that different exposures have different critical windows. Further, in studies of environmental mixtures, it is important to identify interactions among exposures and to account for the fact that this interaction may occur between two exposures having different critical windows. Exposure to one exposure early in time could cause an individual to be more or less susceptible to another exposure later in time. We propose a Bayesian model to estimate the temporal effects of a large number of exposures on an outcome. We use spike-and-slab priors and semiparametric distributed lag curves to identify important exposures and exposure interactions and discuss extensions with improved power to detect harmful exposures. We then apply these methods to estimate the effects of exposure to multiple air pollutants during pregnancy on birthweight from vital records in Colorado.

Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures

Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).

Association of heavy metal complex exposure and neurobehavioral function of children

Background

Exposure to heavy metals is a public health concern worldwide. Previous studies on the association between heavy metal exposure and neurobehavioral functions in children have focused on single exposures and clinical manifestations. However, the present study evaluated the effects of heavy metal complex exposure on subclinical neurobehavioral function using a Korean Computerized Neurobehavior Test (KCNT).

Methods

Urinary mercury, lead, cadmium analyses as well as symbol digit substitution (SDS) and choice reaction time (CRT) tests of the KCNT were conducted in children aged between 10 and 12 years. Reaction time and urinary heavy metal levels were analyzed using partial correlation, linear regression, Bayesian kernel machine regression (BKMR), the weighted quantile sum (WQS) regression and quantile G-computation analysis.

Results

Participants of 203 SDS tests and 198 CRT tests were analyzed, excluding poor cooperation and inappropriate urine sample. Partial correlation analysis revealed no association between neurobehavioral function and exposure to individual heavy metals. The result of multiple linear regression shows significant positive association between urinary lead, mercury, and CRT. BMKR, WQS regression and quantile G-computation analysis showed a statistically significant positive association between complex urinary heavy metal concentrations, especially lead and mercury, and reaction time.

Conclusions

Assuming complex exposures, urinary heavy metal concentrations showed a statistically significant positive association with CRT. These results suggest that heavy metal complex exposure during childhood should be evaluated and managed strictly.

The associations between prenatal phthalate exposure and childhood glycolipid metabolism and blood pressure: An updated systematic review and a pilot meta-analysis of prospective cohort studies

This is the first pilot meta-analysis on the association of prenatal phthalate exposure with childhood cardiometabolic risks. A systematic literature search was performed in MEDLINE, Web of Science and CNKI (Chinese National Knowledge Infrastructure) until June 5, 2023. A total of seven studies with 5746 children (2646 girls and 3100 boys) were finally included. Four, three and two studies investigated the effects of maternal phthalate exposure on childhood blood pressure (BP), blood lipids and blood glucose profiles, respectively. The pilot meta-analysis suggested that di-2-ethylhexyl phthalate (DEHP) metabolite exposure was associated with a decrease in childhood z-systolic BP (SBP, β = -0.169, 95% CI = -0.338-0.001). Furthermore, the pooled results showed negative relationships of prenatal ∑DEHP exposure with z-SBP (β = -0.109, 95% CI = -0.163 to -0.055) and z-diastolic BP (DBP, β = -0.126, 95% CI = -0.182 to -0.069) in girls. In addition, MEP exposure was associated with z-SBP in girls (β = -0.227, 95% CI = -0.387 to -0.066). The pooled result showed a positive relationship between prenatal ∑DEHP exposure and triglycerides (β = 0.103, 95% CI = 0.028-0.178). The overall results revealed that exposure to ∑DEHP throughout gestation was associated with a decrease in insulin (β = -0.074, 95% CI = -0.144 to -0.004) and glucose (β = -0.129, 95% CI = -0.199 to -0.058) in boys. Interestingly, there was an inverse relationship of prenatal mono- 3 -carboxypropyl phthalate (MCPP) exposure with glucose in pubertal boys (β = -3.749, 95% CIs = -6.758 to -0.741) but not found in postpubertal children. In conclusion, prenatal phthalate exposure interfered with cardiovascular risk in children with gender-specific differences and was influenced by puberty. Overall, prenatal ∑DEHP was negatively associated with systolic blood pressure in girls and with insulin and glucose in boys but increased the level of triglycerides.

Gestational exposure to organophosphate esters and infant anthropometric measures in the first 4 weeks after birth

BACKGROUND

Few studies have examined whether gestational exposure to organophosphate esters (OPEs), widely used chemicals with potential endocrine-disrupting potency and developmental toxicity, is associated with impaired infant growth.

METHODS

We analyzed data from 329 mother-infant pairs in the Health Outcomes and Measures of the Environment (HOME) Study (2003-2006, Cincinnati, Ohio, USA). We quantified concentrations of four OPE metabolites in maternal urine collected at 16 and 26 weeks of gestation, and at delivery. We calculated z-scores using 2006 World Health Organization (WHO) child growth standards for the 4-week anthropometric measures (weight, length, and head circumference), the ponderal index, and weekly growth rates. We used multiple informant models to examine window-specific associations between individual OPE metabolites and anthropometric outcomes. We further modeled OPEs as a mixture for window-specific associations with 4-week anthropometric outcomes using mean field variational Bayesian inference procedure for lagged kernel machine regression (MFVB-LKMR). We stratified the models by infant sex.

RESULTS

Diphenyl phosphate (DPHP) in mothers at 16 weeks, and bis(2-chloroethyl) phosphate (BCEP) and bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) at delivery were positively associated with z-scores of weight, length, and head circumference in all infants at 4 weeks of age. After stratifying by infant sex, positive associations were only observed in males for DPHP at 16 weeks and BCEP at delivery and in females for BDCIPP at delivery. Negative associations not present in all infants were observed in males for di-n-butyl phosphate (DNBP) at 26 weeks of gestation with weight z-score and DPHP at delivery with head circumference z-score. Results were generally similar using MFVB-LKMR models with more conservative 95 % credible intervals. We did not identify consistent associations of gestational OPE metabolite concentrations with the ponderal index and weekly growth rates.

CONCLUSION

In this cohort, exposure to OPEs during gestation was associated with altered infant anthropometry at 4 weeks after birth.

Developing an Exposure Burden Score for Chemical Mixtures Using Item Response Theory, with Applications to PFAS Mixtures

BACKGROUND

There are few existing methods to quantify total exposure burden to chemical mixtures, independent of a health outcome. A summary metric could be advantageous for use in biomonitoring, risk assessment, health risk calculators, and mediation models.

OBJECTIVE

We developed a novel exposure burden score method for chemical mixtures, applied it to estimate exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures, and estimated associations of PFAS burden scores with cardio-metabolic outcomes in the general U.S.

POPULATION

METHODS

We applied item response theory (IRT) to biomonitoring data from 1,915 children and adults 12-80 years of age in the 2017-2018 National Health and Examination Survey to quantify a latent PFAS burden score, using serum concentrations of eight measured PFAS biomarkers, each considered an "item." The premise of IRT is that through using both information about a participant's concentration of an individual PFAS biomarker, as well as their exposure patterns for the PFAS mixture, we can estimate the participant's latent PFAS exposure burden, independent of a health outcome. We used linear regression to estimate associations of the PFAS burden score with cardio-metabolic outcomes and compared our findings to results using summed PFAS concentrations as the exposure metric.

RESULTS

PFAS burden scores and summed PFAS concentrations had moderate-high correlation (ρ=0.75). Isomers of PFOS [n-perfluorooctane sulfonic acid (n-PFOS) and perfluoromethylheptane sulfonic acid isomers (Sm-PFOS)] were the most informative to the PFAS burden scores. PFAS burden scores and summed PFAS concentrations were both significantly associated with cardio-metabolic outcomes, but associations were generally closer to the null for summed PFAS concentrations vs. the PFAS burden score. Adjusted associations (95% CIs) with total cholesterol (in milligrams per deciliter) were 8.6 (95% CI: 5.2, 11.9) and 2.4 (95% CI: 0.5, 4.2) per interquartile range increase in the PFAS burden score and summed concentrations, respectively. Sensitivity analyses showed similar associations with cardio-metabolic outcomes when only a subset of PFAS biomarkers was used to estimate PFAS burden. In a validation study, associations between PFAS burden scores and cholesterol were consistent with primary analyses but null when using summed PFAS concentrations.

DISCUSSION

IRT offers a straightforward way to include exposure biomarkers with low detection frequencies and can reduce exposure measurement error. Further, IRT enables comparisons of exposure burden to chemical mixtures across studies even if they did not measure the exact same set of chemicals, which supports harmonization across studies and consortia. We provide an accompanying PFAS burden calculator (https://pfasburden.shinyapps.io/app_pfas_burden/), enabling researchers to calculate PFAS burden scores based on U.S. population exposure reference ranges. https://doi.org/10.1289/EHP10125.

Prenatal metal mixture exposure and birth weight: A two-stage analysis in two prospective cohort studies

The understanding of the impact of prenatal exposure to metal mixtures on birth weight is limited. We aimed to identify metal mixture components associated with birth weight and to determine additional pairwise interactions between metals showing such associations. Concentrations of 18 metals were measured using inductively coupled plasma mass spectrometry in urine samples collected in the 3rd trimester from a prenatal cohort (discovery; n = 1849) and the Healthy Baby Cohort (replication; n = 7255) in Wuhan, China. In the discovery set, we used two penalized regression models, i.e., elastic net regression for main effects and a lasso for hierarchical interactions, to identify important mixture components associated with birth weight, which were then replicated. We observed that 8 of the 18 measured metals were retained by elastic net regression, with five metals (vanadium, manganese, iron, cesium, and barium) showing negative associations with Z-scores for birth weight and three metals (cobalt, zinc, and strontium) showing positive associations. In replication set, associations remained significant for vanadium (β = -0.035; 95% confidence interval [CI], -0.059 to -0.010), cobalt (β = 0.073; 95% CI, 0.049 to 0.097), and zinc (β = 0.040; 95% CI, 0.016 to 0.065) after Bonferroni correction. We additionally identified and replicated a single pairwise interaction between iron and copper exposure on birth weight (P < 0.001). Using a two-stage analysis, we identified and replicated individual metals and additional pairwise interactions-associated birth weight. The approach could be used in other studies estimating the effect of complex mixtures on human health.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

Associating multiple mycotoxin exposure and health outcomes: current statistical approaches and challenges

Mycotoxin contamination is a global challenge to food safety and population health. A diversity of adverse effects in human health such as organ damage, immunity disorders and carcinogenesis are attributed to acute and chronic exposure to mycotoxins. While there is a high likelihood of mycotoxin co-occurrence in the daily diet, multiple mycotoxin exposures represent a considerable challenge in understanding the accumulative effects of groups of exposures on health outcomes. Nevertheless, previous studies on mycotoxin exposure-health outcome associations have focused on a single or a limited number of exposures. To guide multi-exposure assessment, careful considerations of statistical approaches available are required. In addition, the issue of multicollinearity in high-dimensional settings of multiple exposure analysis underlies the controversy surrounding the reliability and consistency of statistical conclusions about the exposure-health outcome associations. Conventional approaches such as generalised linear regressions (GLR) in conjunction with regularisation methods, including ridge regression, lasso and elastic net, offer some clear advantages in terms of results’ interpretation and model selection. However, when highly-correlated variables are observed, these methods have shown a low specificity in variable selection. Principal component analysis (PCA) that has been widely used as a dimensionality reduction technique also has the limitation to identify important predictor variables as this approach may overlook the associations between certain components and health outcomes. Recently, some alternative approaches have been introduced to address the issues of high dimensionality and highly-correlated data in the context of epidemiological and environmental research. Two of the noticeable approaches are weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR). Combining different methods of inference allows us to interpret the role of certain exposures, their interactions and the combined effects on human health under diverse statistical perspectives, which ultimately facilitate the construction of the toxicological profile of multiple mycotoxins’ exposure.

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.

Mixture analysis on associations between semen quality and sperm DNA integrity and occupational exposure to polycyclic aromatic hydrocarbons

The objective of this study was to assess relationships between exposure to PAHs at occupational levels and outcomes of human semen quality and sperm DNA integrity. Personal breathing zone air samples were collected to quantify exposure of 16 targeted PAHs to coke-oven workers at a steel company in southern Taiwan. Semen quality, including concentration, motility, morphology, and viability, were assessed. Sperm DNA fragmentation, 8-oxodGuo, bulky PAH adducts, and benzo[a]pyrene diol epoxide-DNA adducts served as biomarkers for assessment of sperm DNA integrity. The Bayesian Kernel Machine Regression modeling was employed to estimate mixture effects of the PAH mixture on the outcomes of semen quality and sperm DNA integrity and to identify individual compounds of PAH mixtures associated with the mixture effects. Exposure to the PAH mixture was inversely associated with sperm viability, while benzo(b)fluoranthene (B[b]F) was identified as the main predictor for sperm viability. Exposure to the PAH mixture also exhibited a positive trend with sperm DNA fragmentation. B[b]F and benzo(a)anthracene (B[a]A) were identified as individual PAH compounds associated with increased sperm DNA fragmentation.

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Human Health Exposure Analysis Resource (HHEAR): A model for incorporating the exposome into health studies

BACKGROUND

Characterizing the complexity of environmental exposures in relation to human health is critical to advancing our understanding of health and disease throughout the life span. Extant cohort studies open the door for such investigations more rapidly and inexpensively than launching new cohort studies and the Human Health Exposure Analysis Resource (HHEAR) provides a resource for implementing life-stage exposure studies within existing study populations. Primary challenges to incorporation of environmental exposure assessment in health studies include: (1) lack of widespread knowledge of biospecimen and environmental sampling and storage requirements for environmental exposure assessment among investigators; (2) lack of availability of and access to laboratories capable of analyzing multiple environmental exposures throughout the life-course; and (3) studies lacking sufficient power to assess associations across life-stages. HHEAR includes a consortium of researchers with expertise in laboratory analyses, statistics and logistics to overcome these limitations and enable inclusion of exposomics in human health studies.

OBJECTIVE

This manuscript describes the structure and strengths of implementing the harmonized HHEAR resource model, and our approaches to addressing challenges. We describe how HHEAR incorporates analyses of biospecimens and environmental samples and human health studies across the life span - serving as a model for incorporating environmental exposures into national and international research. We also present program successes to date.

DISCUSSION

HHEAR provides a full-service laboratory and data analysis exposure assessment resource, linking scientific, life span, and toxicological consultation with both laboratory and data analysis expertise. HHEAR services are provided without cost but require NIH, NCI, NHLBI, or ECHO funding of the original cohort; internal HHEAR scientific review and approval of a brief application; and adherence to data sharing and publication policies. We describe the benefits of HHEAR's structure, collaborative framework and coordination across project investigators, analytical laboratories, biostatisticians and bioinformatics specialists; quality assurance/quality control (QA/QC) including integrated sample management; and tools that have been developed to support the research (exposure information pages, ontology, new analytical methods, common QA/QC approach across laboratories, etc.). This foundation supports HHEAR's inclusion of new laboratory and statistical analysis methods and studies that are enhanced by including targeted analysis of specific exposures and untargeted analysis of chemicals associated with phenotypic endpoints in biological and environmental samples.

CONCLUSION

HHEAR is an interdisciplinary team of toxicologists, epidemiologists, laboratory scientists, and data scientists across multiple institutions to address broad and complex questions that benefit from integrated laboratory and data analyses. HHEAR's processes, features, and tools include all life stages and analysis of biospecimens and environmental samples. They are available to the wider scientific community to augment studies by adding state of the art environmental analyses to be linked to human health outcomes.

Metal mixtures and neurodevelopment: recent findings and emerging principles

Children are constantly exposed to a wide range of environmental factors including essential and non-essential metals. In recent years, the mixtures paradigm has emerged to foster the examination of combined effects that emerge from exposures to multiple elements. In this review, we summarized recent literature studying the relationship between prenatal and childhood metal mixtures with neurodevelopmental outcomes. Our review highlights two basic principles to emerge from this approach. First, recent findings emphasize that the effect of a given exposure is contextual and may be dependent on past or concurrent metal exposures. Second, the timing of exposures is equally critical to the mixture composition in determining neurodevelopmental effects. Our discussion emphasizes how these principles may apply to future exposure-related neurodevelopmental studies.

Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

PURPOSE OF REVIEW

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

RECENT FINDINGS

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.

IDENTIFYING MAIN EFFECTS AND INTERACTIONS AMONG EXPOSURES USING GAUSSIAN PROCESSES

This article is motivated by the problem of studying the joint effect of different chemical exposures on human health outcomes. This is essentially a nonparametric regression problem, with interest being focused not on a black box for prediction but instead on selection of main effects and interactions. For interpretability we decompose the expected health outcome into a linear main effect, pairwise interactions and a nonlinear deviation. Our interest is in model selection for these different components, accounting for uncertainty and addressing nonidentifiability between the linear and nonparametric components of the semiparametric model. We propose a Bayesian approach to inference, placing variable selection priors on the different components, and developing a Markov chain Monte Carlo (MCMC) algorithm. A key component of our approach is the incorporation of a heredity constraint to only include interactions in the presence of main effects, effectively reducing dimensionality of the model search. We adapt a projection approach developed in the spatial statistics literature to enforce identifiability in modeling the nonparametric component using a Gaussian process. We also employ a dimension reduction strategy to sample the nonlinear random effects that aids the mixing of the MCMC algorithm. The proposed MixSelect framework is evaluated using a simulation study, and is illustrated using data from the National Health and Nutrition Examination Survey (NHANES). Code is available on GitHub.

Chemical mixtures and neurobehavior: a review of epidemiologic findings and future directions

Background Epidemiological studies have historically focused on single toxicants, or toxic chemicals, and neurodevelopment, even though the interactions of chemicals and nutrients may result in additive, synergistic, antagonistic, or potentiating effects on neurological endpoints. Investigating the impact of environmentally-relevant chemical mixtures, including heavy metals and endocrine disrupting chemicals (EDCs), is more reflective of human exposures and may result in more refined environmental policies to protect the public. Objective In this review, we provide a summary of epidemiological studies that have analyzed chemical mixtures of heavy metals and EDCs and neurobehavior utilizing multi-chemical models, including frequentist and Bayesian methods. Content Studies investigating chemicals and neurobehavior have the opportunity to not only examine the impact of chemical mixtures, but they can also identify chemicals from a mixture that may play a key role in neurotoxicity, investigate interactive effects, estimate non-linear dose response, and identify potential windows of susceptibility. The examination of neurobehavioral domains is particularly challenging given that traits emerge and change over time and subclinical nuances of neurobehavior are often unrecognized. To date, only a handful of epidemiological studies examining neurodevelopment have utilized multi-pollutant models in the investigation of heavy metals and EDCs. However, these studies were successful in identifying contaminants of importance from the exposure mixtures. Summary and Outlook Investigators are encouraged to broaden their focus to include more environmentally relevant mixtures of chemicals using advanced statistical approaches, particularly to aid in identifying potential mechanisms underlying associations.

Performance of variable and function selection methods for estimating the nonlinear health effects of correlated chemical mixtures: A simulation study

Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure-response relationships. Nonmonotonic relationships are increasingly recognized (eg, for endocrine-disrupting chemicals); however, the impact of nonmonotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression (BKMR), Bayesian additive regression trees (BART), Bayesian structured additive regression with spike-slab priors (BSTARSS), generalized additive models with double penalty (GAMDP) and thin plate shrinkage smoothers (GAMTS), multivariate adaptive regression splines (MARS), and lasso penalized regression. We simulated realistic exposure data based on pregnancy exposure to 17 phthalates and phenols in the US National Health and Nutrition Examination Survey using a multivariate copula. We simulated data sets of size N = 250 and compared methods across 32 scenarios, varying by model size and sparsity, signal-to-noise ratio, correlation structure, and exposure-response relationship shapes. We compared methods in terms of their sensitivity, specificity, and estimation accuracy. In most scenarios, BKMR, BSTARSS, GAMDP, and GAMTS achieved moderate to high sensitivity (0.52-0.98) and specificity (0.21-0.99). BART and MARS achieved high specificity (≥0.90), but low sensitivity in low signal-to-noise ratio scenarios (0.20-0.51). Lasso was highly sensitive (0.71-0.99), except for quadratic relationships (≤0.27). Penalized regression methods that assume linearity, such as lasso, may not be suitable for studies of environmental chemicals hypothesized to have nonmonotonic relationships with outcomes. Instead, BKMR, BSTARSS, GAMDP, and GAMTS are attractive methods for flexibly estimating the shapes of exposure-response relationships and selecting among correlated exposures.

Periconceptional and prenatal exposure to metal mixtures in relation to behavioral development at 3 years of age

Supplemental Digital Content is available in the text.

Behavioral effects of prenatal exposure to mixtures of essential and toxic metals are incompletely understood.

Prenatal exposure to a mixture of persistent organic pollutants (POPs) and child reading skills at school age

BACKGROUND

Prenatal exposure to persistent organic pollutants (POPs) may affect child neurobehavior; however, exposures to mixtures of POPs have rarely been examined.

METHODS

We estimated associations of prenatal serum concentrations of 17 POPs, namely 5 polybrominated diphenyl ethers (PBDEs), 6 polychlorinated biphenyls (PCBs), dichlorodiphenyldichloroethylene (DDE), dichlorodiphenyltrichloroethane (DDT), and 4 per- and polyfluoroalkyl substances (PFAS), with Wide Range Achievement Test-4 reading composite scores at age 8 years in 161 children from a pregnancy and birth cohort (Health Outcomes and Measures of the Environment [HOME] Study, 2003-present) in Cincinnati, OH. We applied 6 statistical methods: least absolute shrinkage and selection operator (LASSO), elastic net (ENET), Sparse Principal Component Analysis (SPCA), Weighted Quantile Sum (WQS) regression, Bayesian Kernel Machine Regression (BKMR), and Bayesian Additive Regression Trees (BART), to estimate covariate-adjusted associations with individual and their mixtures in multi-pollutant models.

RESULTS

Both LASSO and ENET models indicated inverse associations with reading scores for BDE-153 and BDE-28, and positive associations for CB-118, CB-180, perfluoroctanoate (PFOA), and perfluorononanoate (PFNA). The SPCA identified inverse associations for BDE-153 and BDE-100 and positive associations for perfluorooctane sulfonate (PFOS), PFOA, and PFNA, as parts of different principal component scores. The WQS regression showed the highest weights for BDE-100 (0.35) and BDE-28 (0.16) in the inverse association model and for PFNA (0.29) and CB-180 (0.21) in the positive association model. The BKMR model identified BDE-100 and BDE-153 for inverse associations and CB-118, CB-153, CB-180, PFOA, and PFNA for positive associations. The BART method found dose-response functions similar to the BKMR model. No interactions between POPs were identified.

CONCLUSIONS

Despite some inconsistency among biomarkers, these analyses revealed inverse associations between prenatal PBDE concentrations and children's reading scores. Positive associations of PCB congeners and PFAS with reading skills were also found.

Environmental mixtures and children's health: identifying appropriate statistical approaches

PURPOSE OF REVIEW

Biomonitoring studies have shown that children are constantly exposed to complex patterns of chemical and nonchemical exposures. Here, we briefly summarize the rationale for studying multiple exposures, also called mixture, in relation to child health and key statistical approaches that can be used. We discuss advantages over traditional methods, limitations and appropriateness of the context.

RECENT FINDINGS

New approaches allow pediatric researchers to answer increasingly complex questions related to environmental mixtures. We present methods to identify the most relevant exposures among a high-multitude of variables, via shrinkage and variable selection techniques, and identify the overall mixture effect, via Weighted Quantile Sum and Bayesian Kernel Machine regressions. We then describe novel extensions that handle high-dimensional exposure data and allow identification of critical exposure windows.

SUMMARY

Recent advances in statistics and machine learning enable researchers to identify important mixture components, estimate joint mixture effects and pinpoint critical windows of exposure. Despite many advantages over single chemical approaches, measurement error and biases may be amplified in mixtures research, requiring careful study planning and design. Future research requires increased collaboration between epidemiologists, statisticians and data scientists, and further integration with causal inference methods.

A spatially varying distributed lag model with application to an air pollution and term low birth weight study

Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called 'SpGPCW' and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM_2:5 during pregnancy by using birth records from North Carolina. SpGPCW is designed to accommodate areal level spatial correlation between lagged health effect parameters and temporal smoothness in risk estimation across pregnancy. Through simulation and a real data application, we show that the consequences of ignoring spatial variability in the lagged health effect parameters include less reliable inference for the parameters and diminished ability to identify true critical window sets, and we investigate the use of existing Bayesian model comparison techniques as tools for determining the presence of spatial variability. We find that exposure to PM_2:5 is associated with elevated term low birth weight risk in selected weeks and counties and that ignoring spatial variability results in null associations during these periods. An R package (SpGPCW) has been developed to implement the new method.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Environmental contaminants and child's growth

Experimental data have suggested that some contaminants in the environment may increase the risk of obesity. Infants can be exposed to chemicals either prenatally, by trans-placental passage of chemicals, or postnatally by their own diet and by other external pathways (air inhalation, dust, hand-to-mouth exposure) after birth. To provide a review of epidemiological evidence on the association between prenatal exposure to chemicals and prenatal and postnatal growth, we present the literature from systematic review articles and international meta-analyses, when available, or recent research articles when summarizing articles were not available. The most studied contaminants in this field were persistent organic pollutants (e.g. organochlorinated pesticides, polychlorinated biphenyls), non-persistent pollutants (e.g. phthalates, bisphenol A), toxic heavy metals (i.e. cadmium, lead and mercury), arsenic, mycotoxins and acrylamide. Mounting evidence suggests that child's growth may be associated with prenatal or postnatal exposures to environmental contaminants. Improving exposure assessment and studying the contaminants as mixtures should allow to gain knowledge about the environmental determinants of growth and obesity.

Connecting environmental exposure and neurodegeneration using cheminformatics and high resolution mass spectrometry: potential and challenges

Connecting chemical exposures over a lifetime to complex chronic diseases with multifactorial causes such as neurodegenerative diseases is an immense challenge requiring a long-term, interdisciplinary approach. Rapid developments in analytical and data technologies, such as non-target high resolution mass spectrometry (NT-HR-MS), have opened up new possibilities to accomplish this, inconceivable 20 years ago. While NT-HR-MS is being applied to increasingly complex research questions, there are still many unidentified chemicals and uncertainties in linking exposures to human health outcomes and environmental impacts. In this perspective, we explore the possibilities and challenges involved in using cheminformatics and NT-HR-MS to answer complex questions that cross many scientific disciplines, taking the identification of potential (small molecule) neurotoxicants in environmental or biological matrices as a case study. We explore capturing literature knowledge and patient exposure information in a form amenable to high-throughput data mining, and the related cheminformatic challenges. We then briefly cover which sample matrices are available, which method(s) could potentially be used to detect these chemicals in various matrices and what remains beyond the reach of NT-HR-MS. We touch on the potential for biological validation systems to contribute to mechanistic understanding of observations and explore which sampling and data archiving strategies may be required to form an accurate, sustained picture of small molecule signatures on extensive cohorts of patients with chronic neurodegenerative disorders. Finally, we reflect on how NT-HR-MS can support unravelling the contribution of the environment to complex diseases.

Statistical Approaches for Investigating Periods of Susceptibility in Children's Environmental Health Research

PURPOSE OF REVIEW

Children's environmental health researchers are increasingly interested in identifying time intervals during which individuals are most susceptible to adverse impacts of environmental exposures. We review recent advances in methods for assessing susceptible periods.

RECENT FINDINGS

We identified three general classes of modeling approaches aimed at identifying susceptible periods in children's environmental health research: multiple informant models, distributed lag models, and Bayesian approaches. Benefits over traditional regression modeling include the ability to formally test period effect differences, to incorporate highly time-resolved exposure data, or to address correlation among exposure periods or exposure mixtures. Several statistical approaches exist for investigating periods of susceptibility. Assessment of susceptible periods would be advanced by additional basic biological research, further development of statistical methods to assess susceptibility to complex exposure mixtures, validation studies evaluating model assumptions, replication studies in different populations, and consideration of susceptible periods from before conception to disease onset.

Bayesian varying coefficient kernel machine regression to assess neurodevelopmental trajectories associated with exposure to complex mixtures

Exposure to environmental mixtures can exert wide-ranging effects on child neurodevelopment. However, there is a lack of statistical methods that can accommodate the complex exposure-response relationship between mixtures and neurodevelopment while simultaneously estimating neurodevelopmental trajectories. We introduce Bayesian varying coefficient kernel machine regression (BVCKMR), a hierarchical model that estimates how mixture exposures at a given time point are associated with health outcome trajectories. The BVCKMR flexibly captures the exposure-response relationship, incorporates prior knowledge, and accounts for potentially nonlinear and nonadditive effects of individual exposures. This model assesses the directionality and relative importance of a mixture component on health outcome trajectories and predicts health effects for unobserved exposure profiles. Using contour plots and cross-sectional plots, BVCKMR also provides information about interactions between complex mixture components. The BVCKMR is applied to a subset of data from PROGRESS, a prospective birth cohort study in Mexico city on exposure to metal mixtures and temporal changes in neurodevelopment. The mixture include metals such as manganese, arsenic, cobalt, chromium, cesium, copper, lead, cadmium, and antimony. Results from a subset of Programming Research in Obesity, Growth, Environment and Social Stressors data provide evidence of significant positive associations between second trimester exposure to copper and Bayley Scales of Infant and Toddler Development cognition score at 24 months, and cognitive trajectories across 6-24 months. We also detect an interaction effect between second trimester copper and lead exposures for cognition at 24 months. In summary, BVCKMR provides a framework for estimating neurodevelopmental trajectories associated with exposure to complex mixtures.

Intrauterine multi-metal exposure is associated with reduced fetal growth through modulation of the placental gene network

BACKGROUND

Intrauterine metal exposures and aberrations in placental processes are known contributors to being born small for gestational age (SGA). However, studies to date have largely focused on independent effects, failing to account for potential interdependence among these markers.

OBJECTIVES

We evaluated the inter-relationship between multi-metal indices and placental gene network modules related to SGA status to highlight potential molecular pathways through which in utero multi-metal exposure impacts fetal growth.

METHODS

Weighted quantile sum (WQS) regression was performed using a panel of 16 trace metals measured in post-partum maternal toe nails collected from the Rhode Island Child Health Study (RICHS, n = 195), and confirmation of the derived SGA-related multi-metal index was conducted using Bayesian kernel machine regression (BKMR). We leveraged existing placental weighted gene coexpression network data to examine associations between the SGA multi-metal index and placental gene expression. Expression of select genes were assessed using RT-PCR in an independent birth cohort, the New Hampshire Birth Cohort Study (NHBCS, n = 237).

RESULTS

We identified a multi-metal index, predominated by arsenic (As) and cadmium (Cd), that was positively associated with SGA status (Odds ratio = 2.73 [1.04, 7.18]). This index was also associated with the expression of placental gene modules involved in "gene expression" (β = -0.02 [-0.04, -0.01]) and "metabolic hormone secretion" (β = 0.02 [0.00, 0.05]). We validated the association between cadmium exposure and the expression of GRHL1 and INHBA, genes in the "metabolic hormone secretion" module, in NHBCS.

CONCLUSION

We present a novel approach that integrates the application of advanced bioinformatics and biostatistics methods to delineate potential placental pathways through which trace metal exposures impact fetal growth.

Environmental exposure mixtures: questions and methods to address them

PURPOSE OF THIS REVIEW

This review provides a summary of statistical approaches that researchers can use to study environmental exposure mixtures. Two primary considerations are the form of the research question and the statistical tools best suited to address that question. Because the choice of statistical tools is not rigid, we make recommendations about when each tool may be most useful.

RECENT FINDINGS

When dimensionality is relatively low, some statistical tools yield easily interpretable estimates of effect (e.g., risk ratio, odds ratio) or intervention impacts. When dimensionality increases, it is often necessary to compromise this interpretablity in favor of identifying interesting statistical signals from noise; this requires applying statistical tools that are oriented more heavily towards dimension reduction via shrinkage and/or variable selection.

SUMMARY

The study of complex exposure mixtures has prompted development of novel statistical methods. We suggest that further validation work would aid practicing researchers in choosing among existing and emerging statistical tools for studying exposure mixtures.

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.

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

OBJECTIVES

We applied three statistical approaches for evaluating associations between prenatal urinary concentrations of a mixture of phthalate metabolites and birth weight.

METHODS

We included 300 women who provided 732 urine samples during pregnancy and delivered a singleton infant. We measured urinary concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, di-n-butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear regressions; 2) classification methods [principal component analysis (PCA) and structural equation models (SEM)]; and 3) Bayesian kernel machine regression (BKMR), to evaluate associations between phthalate metabolite mixtures and birth weight adjusting for potential confounders. Data were presented as mean differences (95% CI) in birth weight (grams) as each phthalate increased from the 10th to the 90th percentile.

RESULTS

When analyzing individual phthalate metabolites using linear regressions, each metabolite demonstrated a modest inverse association with birth weight [from -93 (-206, 21) to -49 (-164, 65)]. When simultaneously including all metabolites in a multivariable model, inflation of the estimates and standard errors were noted. PCA identified two principal components, both inversely associated with birth weight [-23 (-68, 22), -27 (-71, 17), respectively]. These inverse associations were confirmed when applying SEM. BKMR further identified that monoethyl and mono(2-ethylhexyl) phthalate and phthalate concentrations were linearly related to lower birth weight [-51(-164, 63) and -122 (-311, 67), respectively], and suggested no evidence of interaction between metabolites.

CONCLUSIONS

While none of the methods produced significant results, we demonstrated the potential issues arising using linear regression models in the context of correlated exposures. Among the other selected approaches, classification techniques identified common sources of exposures with implications for interventions, while BKMR further identified specific contributions of individual metabolites.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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