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

The correlation between single and mixed trace elements exposure in systemic lupus erythematosus: A case-control study

BACKGROUND

Recent studies have shown an association between trace elements and systemic lupus erythematosus (SLE), but the relationship between trace elements and SLE is still unclear. This study aims to determine the distribution of plasma trace elements in newly diagnosed SLE patients and the association between these essential and toxic element mixtures and SLE.

METHODS

In total, 110 SLE patients and 110 healthy controls were included. Blood samples were collected. 15 plasma trace elements were quantified using an inductively coupled plasma mass spectrometer (ICP-MS). Multivariate logistic regression, restricted cubic spline (RCS), weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) are used to analyze the association between single and mixed exposure of elements and SLE.

RESULTS

The logistic regression model shows that, plasma lithium (Li) [OR (95 % CI): 1.963 (1.49-2.586)], vanadium (V) [OR (95 % CI): 2.617(1.645-4.166)] and lead (Pb) [OR (95 % CI): 1.603(1.197-2.145)] were positively correlated with SLE, while selenium (Se) [OR (95 % CI): 0.055(0.019-0.157)] and barium (Ba) [OR (95 % CI): 0.792(0.656-0.957)] had been identified as protective factors for SLE. RCS results showed a non-linear correlation between the elements Li, V, Ni, copper, Se, rubidium and SLE. In addition, WQS regression, qgcomp, and BKMR models consistently revealed significant positive effects of plasma Li and Pb on SLE, as well as significant negative effects of plasma Se.

CONCLUSIONS

Exposure to heavy metals such as Li and Pb is significantly positively correlated with SLE, but Se may be protective factors for SLE. In addition, there is a nonlinear correlation between the elements Li and Se and SLE, and there are complex interactions between the elements. In the future, larger populations and prospective studies are needed to confirm these associations.

Bayesian Kernel Machine Regression for Social Epidemiologic Research

BACKGROUND

Little attention has been devoted to framing multiple continuous social variables as a "mixture" for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects.

METHODS

Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable.

RESULTS

We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% confidence interval [CI]: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31).

CONCLUSION

With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.

The causal effect of exposure to air pollution on risk of adverse pregnancy outcomes: A two-sample Mendelian randomisation study

BACKGROUND

Epidemiological studies have examined the relation between air pollution (NOx, NO2, PM2.5, PM2.5-10, and PM10) and adverse pregnancy outcomes (APOs). There's increasing evidence that air pollution increases the risk of APOs. However, the results of these studies are controversial, and the causal relation remains uncertain. We aimed to assess whether a genetic causal link exists between air pollution and APOs and the potential effects of this relation.

METHODS

A novel two-sample Mendelian randomisation (MR) study used pooled data from a large-scale complete genome correlation study. The primary analysis method was inverse variance weighting (IVW), which explored the expose-outcome relationship for assessing single nucleotide polymorphisms (SNPs) associated with air pollution. Further sensitivity analysis, including MR-PRESSO, MR-Egger regression, and leave-one analysis, was used to test the consistency of the results.

RESULTS

There was a significant correlation between air pollution-related SNPs and APOs. A robust causal link was found between genetic susceptibility to air pollution and APOs.

CONCLUSIONS

Our MR analysis reveals a genetic causal relation between air pollution and APOs, which may help provide new insights into further mechanisms and clinical studies in air pollution-mediated APOs.

Toxic metals and pediatric clinical immune dysfunction: A systematic review of the epidemiological evidence

BACKGROUND

Children are at high risk for exposure to toxic metals and are vulnerable to their effects. Significant research has been conducted evaluating the role of these metals on immune dysfunction, characterized by biologic and clinical outcomes. However, there are inconsistencies in these studies. The objective of the present review is to critically evaluate the existing literature on the association between toxic metals (lead, mercury, arsenic, and cadmium) and pediatric immune dysfunction.

METHODS

Seven databases (PubMed (NLM), Embase (Elsevier), CINAHL (Ebsco), Web of Science (Clarivate Analytics), ProQuest Public Health Database, and ProQuest Environmental Science Collection) were searched following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in February 2024. Rayaan software identified duplicates and screened by title and abstract in a blinded and independent review process. The remaining full texts were reviewed for content and summarized. Exclusions during the title, abstract, and full-text reviews included: 1) not original research, 2) not epidemiology, 3) did not include toxic metals, 4) did not examine an immune health outcome, or 5) not pediatric (>18 years). This systematic review protocol followed the PRISMA guidelines. Rayaan was used to screen records using title and abstract by two blinded and independent reviewers. This process was repeated for full-text article screening selection.

RESULTS

The search criteria produced 7906 search results; 2456 duplicate articles were removed across search engines. In the final review, 79 studies were included which evaluated the association between toxic metals and outcomes indicative of pediatric immune dysregulation.

CONCLUSIONS

The existing literature suggests an association between toxic metals and pediatric immune dysregulation. Given the imminent threat of infectious diseases demonstrated by the recent COVID-19 epidemic in addition to increases in allergic disease, understanding how ubiquitous exposure to these metals in early life can impact immune response, infection risk, and vaccine response is imperative.

Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches

BACKGROUND

Identifying the impact of environmental mixtures on human health is an important topic. However, such studies face challenges when exposure measurements lie below limit of detection (LOD). While various approaches for accommodating a single exposure subject to LOD have been used, their impact on mixture analysis has not been thoroughly investigated. Our study aims to understand the impact of five popular LOD accommodation approaches on mixture analysis results with multiple exposures subject to LOD, including omitting subjects with any exposures below LOD (complete case analysis); single imputations by LOD/ 2 , and by estimates from a censored accelerated failure time (AFT) model; and multiple imputation (MI) with or without truncation based on LOD.

METHODS

In extensive simulation studies with high-dimensional and highly correlated exposures and a continuous health outcome, we examined the performance of each LOD approach on three mixture analysis methods: elastic net regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR). We further analyzed data from the National Health and Nutrition Examination Survey (NHANES) on how persistent organic pollutants (POPs) influenced leukocyte telomere length (LTL).

RESULTS

Complete case analysis was inefficient and could result in severe bias for some mixture methods. Imputation by LOD/ 2 showed unstable performance across mixture methods. Conventional MI was associated with consistent mild biases, which can be reduced by using a truncated distribution for imputation. Estimating censored values by AFT models had a minimal impact on the results. In the NHANES analysis, imputation by LOD/ 2 , truncated MI and censored AFT models performed similarly, with a positive overall effect of POPs on LTL while PCB126, PCB169 and furan 2,3,4,7,8-pncdf being the most important exposures.

CONCLUSIONS

Our study favored using truncated MI and censored AFT models to accommodate values below LOD for the stability of downstream mixture analysis.

Prenatal exposure to air pollutant mixtures and birthweight in the upstate KIDS cohort

BACKGROUND

Single-pollutant models have linked prenatal PM2.5 exposure to lower birthweight. However, analyzing air pollutant mixtures better captures pollutant interactions and total effects. Unfortunately, strong correlations between pollutants restrict traditional methods.

OBJECTIVES

We explored the association between exposure to a mixture of air pollutants during different gestational age windows of pregnancy and birthweight.

METHODS

We included 4,635 mother-infant dyads from a New York State birth cohort born 2008-2010. Air pollution data were sourced from the EPA's Community Multiscale Air Quality model and matched to the census tract centroid of each maternal home address. Birthweight and gestational age were extracted from vital records. We applied linear regression to study the association between prenatal exposure to PM2.5, PM10, NOX, SO2, and CO and birthweight during six sensitive windows. We then utilized Bayesian kernel machine regression to examine the non-linear effects and interactions within this five-pollutant mixture. Final models adjusted for maternal socio-demographics, infant characteristics, and seasonality.

RESULTS

Single-pollutant linear regression models indicated that most pollutants were associated with a decrement in birthweight, specifically during the two-week window before birth. An interquartile range increase in PM2.5 exposure (IQR: 3.3 µg/m3) from the median during this window correlated with a 34 g decrement in birthweight (95 % CI: -54, -14), followed by SO2 (IQR: 2.0 ppb; β: -31), PM10 (IQR: 4.6 µg/m3; β: -29), CO (IQR: 60.8 ppb; β: -27), and NOX (IQR: 7.9 ppb; β: -26). Multi-pollutant BKMR models revealed that PM2.5, NOX, and CO exposure were negatively and non-linearly linked with birthweight. As the five-pollutant mixture increased, birthweight decreased until the median level of exposure.

DISCUSSION

Prenatal exposure to air pollutants, notably PM2.5, during the final two weeks of pregnancy may negatively impact birthweight. The non-linear relationships between air pollution and birthweight highlight the importance of studying pollutant mixtures and their interactions.

Prenatal exposure to a mixture of organophosphate flame retardants and infant neurodevelopment: A prospective cohort study in Shandong, China

BACKGROUND

Previous studies have suggested that prenatal exposure to organophosphate flame retardants (OPFRs) may have adverse effect on early neurodevelopment, but limited data are available in China, and the overall effects of OPFRs mixture are still unclear.

OBJECTIVE

This study aimed to investigate the association between prenatal exposure to OPFR metabolites mixture and the neurodevelopment of 1-year-old infants.

METHODS

A total of 270 mother-infant pairs were recruited from the Laizhou Wan (Bay) Birth Cohort in China. Ten OPFR metabolites were measured in maternal urine. Neurodevelopment of 1-year-old infants was assessed using the Gesell Developmental Schedules (GDS) and presented by the developmental quotient (DQ) score. Multivariate linear regression and weighted quantile sum (WQS) regression models were conducted to estimate the association of prenatal exposure to seven individual OPFR metabolites and their mixture with infant neurodevelopment.

RESULTS

The positive rates of seven OPFR metabolites in the urine of pregnant women were greater than 70% with the median concentration ranged within 0.13-3.53 μg/g creatinine. The multivariate linear regression model showed significant negative associations between bis (1-chloro-2-propyl) phosphate (BCIPP), din-butyl phosphate (DnBP), and total OPFR metabolites exposure and neurodevelopment in all infants. Results from the WQS model consistently revealed that the OPFR metabolites mixture was inversely associated with infant neurodevelopment. Each quartile increased in the seven OPFR metabolites mixture was associated with a 1.59 decrease (95% CI: 2.96, -0.21) in gross motor DQ scores, a 1.41 decrease (95% CI: 2.38, -0.43) in adaptive DQ scores, and a 1.08 decrease (95% CI: 2.15, -0.02) in social DQ scores, among which BCIPP, bis (1, 3-dichloro-2-propyl) phosphate (BDCIPP) and DnBP were the main contributors.

CONCLUSION

Prenatal exposure to a mixture of OPFRs was negatively associated with early infant neurodevelopment, particularly in gross motor, adaptive, and social domains.

Childhood exposure to outdoor air pollution in different microenvironments and cognitive and fine motor function in children from six European cohorts

BACKGROUND

Exposure to air pollution during childhood has been linked with adverse effects on cognitive development and motor function. However, limited research has been done on the associations of air pollution exposure in different microenvironments such as home, school, or while commuting with these outcomes.

OBJECTIVE

To analyze the association between childhood air pollution exposure in different microenvironments and cognitive and fine motor function from six European birth cohorts.

METHODS

We included 1301 children from six European birth cohorts aged 6-11 years from the HELIX project. Average outdoor air pollutants concentrations (NO2, PM2.5) were estimated using land use regression models for different microenvironments (home, school, and commute), for 1-year before the outcome assessment. Attentional function, cognitive flexibility, non-verbal intelligence, and fine motor function were assessed using the Attention Network Test, Trail Making Test A and B, Raven Colored Progressive Matrices test, and the Finger Tapping test, respectively. Adjusted linear regressions models were run to determine the association between each air pollutant from each microenvironment on each outcome.

RESULTS

In pooled analysis we observed high correlation (rs = 0.9) between air pollution exposures levels at home and school. However, the cohort-by-cohort analysis revealed correlations ranging from low to moderate. Air pollution exposure levels while commuting were higher than at home or school. Exposure to air pollution in the different microenvironments was not associated with working memory, attentional function, non-verbal intelligence, and fine motor function. Results remained consistently null in random-effects meta-analysis.

CONCLUSIONS

No association was observed between outdoor air pollution exposure in different microenvironments (home, school, commute) and cognitive and fine motor function in children from six European birth cohorts. Future research should include a more detailed exposure assessment, considering personal measurements and time spent in different microenvironments.

Integrating Data Across Multiple Sites in the Northeastern United States to Examine Associations Between a Prenatal Metal Mixture and Child Cognition

We applied a novel hierarchical Bayesian weighted quantile sum (HBWQS) regression to combine data across 3 study sites to examine associations between prenatal exposure to metals and cognitive functioning in childhood. Data from 326 mother-child dyads enrolled in an ongoing cohort study, the Programming of Intergenerational Stress Mechanisms (PRISM) Study, based in New York, New York (recruitment in 2013-2020) and Boston, Massachusetts (recruitment 2011-2013), and the First Thousand Days of Life (FTDL) cohort study (recruitment 2012-2019), based in northern Virginia, were used. Arsenic, cadmium, manganese, lead, and antimony were measured in urine collected during pregnancy. Cognitive functioning was assessed in children aged 3-11 years using the National Institutes of Health Toolbox Cognition Battery. The HBWQS regression showed a negative association between the urinary metal mixture and the Cognition Early Childhood Composite Score in the PRISM New York City (β = -3.67, 95% credible interval (CrI): -7.61, -0.01) and FTDL (β = -3.76, 95% CrI: -7.66, -0.24) samples, with a similar trend in the PRISM Boston sample (β = -3.24, 95% CrI: -6.77, 0.144). We did not detect these associations in traditionally pooled models. HBWQS regression allowed us to account for site heterogeneity and detect associations between prenatal metal-mixture exposure and cognitive outcomes in childhood. Given the ubiquity of metals exposure, interventions aimed at reducing prenatal exposure may improve cognitive outcomes in children. This article is part of a Special Collection on Environmental Epidemiology.

Combined exposure to multiple essential elements and cadmium at early pregnancy on gestational diabetes mellitus: a prospective cohort study

Background

Minerals and trace elements were involved in the pathogenesis and progression of diabetes. However, the association of mixed exposure to essential elements and toxic elements with gestational diabetes mellitus (GDM) is poorly understood.

Objective

This study aims to examine the associations between serum calcium (Ca), iron (Fe), zinc (Zn), copper (Cu), magnesium (Mg), and cadmium (Cd) concentrations in early pregnancy and GDM risk in Chinese pregnant women.

Method

A total of 1,168 pregnant women were included in this prospective cohort study. The concentrations of serum elements were measured using the polarography method before 14 gestational weeks and an oral glucose tolerance test was conducted at 24-28 gestational weeks to diagnose GDM. Binary logistic regression analysis and restricted cubic spline were applied to evaluate the association between serum individual element and GDM. Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression were used to assess the associations between mixed essential elements and Cd exposure and GDM risk.

Results

The mean concentrations of Zn (124.65 vs. 120.12 μmol/L), Fe (135.26 vs. 132.21 μmol/L) and Cu (23.33 vs. 23.03 μmol/L) in the GDM group were significantly higher than those in the control group. Single-element modeling results suggested that second and fourth-quartile maternal Zn and Fe concentration, third and fourth-quartile Cu concentration and fourth-quartile Ca concentration were associated with an increased risk of GDM compared to first-quartile values. Restricted cubic spline analysis showed U-shaped and non-linear relationships between Cd and GDM. According to the BKMR models and WQS analyses, a six-element mixture was significantly and positively associated with the risk of GDM. Additionally, Cd, Zn, and Cu contributed the most strongly to the association.

Conclusion

Serum Zn, Cu, Fe, and Ca exposure during early pregnancy showed a positive association with GDM in the individual evaluation. The multiple-evaluation showed that high levels of elements mixture, particularly Cd, Zn, and Cu, may promote the development of GDM.

A State-of-the-Science Review on Metal Biomarkers

PURPOSE OF REVIEW

Biomarkers are commonly used in epidemiological studies to assess metals and metalloid exposure and estimate internal dose, as they integrate multiple sources and routes of exposure. Researchers are increasingly using multi-metal panels and innovative statistical methods to understand how exposure to real-world metal mixtures affects human health. Metals have both common and unique sources and routes of exposure, as well as biotransformation and elimination pathways. The development of multi-element analytical technology allows researchers to examine a broad spectrum of metals in their studies; however, their interpretation is complex as they can reflect different windows of exposure and several biomarkers have critical limitations. This review elaborates on more than 500 scientific publications to discuss major sources of exposure, biotransformation and elimination, and biomarkers of exposure and internal dose for 12 metals/metalloids, including 8 non-essential elements (arsenic, barium, cadmium, lead, mercury, nickel, tin, uranium) and 4 essential elements (manganese, molybdenum, selenium, and zinc) commonly used in multi-element analyses.

RECENT FINDINGS

We conclude that not all metal biomarkers are adequate measures of exposure and that understanding the metabolic biotransformation and elimination of metals is key to metal biomarker interpretation. For example, whole blood is a good biomarker of exposure to arsenic, cadmium, lead, mercury, and tin, but it is not a good indicator for barium, nickel, and uranium. For some essential metals, the interpretation of whole blood biomarkers is unclear. Urine is the most commonly used biomarker of exposure across metals but it should not be used to assess lead exposure. Essential metals such as zinc and manganese are tightly regulated by homeostatic processes; thus, elevated levels in urine may reflect body loss and metabolic processes rather than excess exposure. Total urinary arsenic may reflect exposure to both organic and inorganic arsenic, thus, arsenic speciation and adjustment for arsebonetaine are needed in populations with dietary seafood consumption. Hair and nails primarily reflect exposure to organic mercury, except in populations exposed to high levels of inorganic mercury such as in occupational and environmental settings. When selecting biomarkers, it is also critical to consider the exposure window of interest. Most populations are chronically exposed to metals in the low-to-moderate range, yet many biomarkers reflect recent exposures. Toenails are emerging biomarkers in this regard. They are reliable biomarkers of long-term exposure for arsenic, mercury, manganese, and selenium. However, more research is needed to understand the role of nails as a biomarker of exposure to other metals. Similarly, teeth are increasingly used to assess lifelong exposures to several essential and non-essential metals such as lead, including during the prenatal window. As metals epidemiology moves towards embracing a multi-metal/mixtures approach and expanding metal panels to include less commonly studied metals, it is important for researchers to have a strong knowledge base about the metal biomarkers included in their research. This review aims to aid metals researchers in their analysis planning, facilitate sound analytical decision-making, as well as appropriate understanding and interpretation of results.

Cytokines as mediators of the associations of prenatal exposure to phenols, parabens, and phthalates with internalizing behaviours at age 3 in boys: A mixture exposure and mediation approach

Childhood internalizing disorders refer to inwardly focused negative behaviours such as anxiety, depression, and somatic complains. Interactions between psychosocial, genetic, and environmental risk factors adversely impact neurodevelopment and can contribute to internalizing disorders. While prenatal exposure to single endocrine disruptors (EDs) is associated with internalizing behaviours in infants, the associations with prenatal exposure to EDs in mixture remain poorly addressed. In addition, the biological mediators of EDs in mixture effects on internalizing behaviours remain unexplored. EDs do not only interfere with endocrine function, but also with immune function and inflammatory processes. Based on this body of evidence, we hypothetised that inflammation at birth is a plausible biological pathway through which prenatal exposure to EDs in mixture could operate to influence offspring internalizing behaviours. Based on the EDEN birth cohort, we investigated whether exposure to a mixture of EDs increased the odds of internalizing disorders in 459 boy infants at age 3, and whether the pro-inflammatory cytokines IL-1β, IL-6, and TNF-α measured at birth were mediators of this effect. To determine both the joint and individual associations of prenatal exposure to EDs with infant internalizing behaviours and the possible mediating role of cytokines, we used the counterfactual hierarchical Bayesian Kernel Machine Regression (BKMR) regression-causal mediation analysis. We show that prenatal exposure to a complex mixture of EDs has limited effects on internalizing behaviours in boys at age 3. We also show that IL-1β, IL-6, and TNF-α are unlikely mediators or suppressors of ED mixture effects on internalizing behaviours in boys at age 3. Further studies on larger cohorts are warranted to refine the deleterious effects of EDs in mixtures on internalizing behaviours and identify possible mediating pathways.

Mixed exposure to phthalates and organic UV filters affects Children's pubertal development in a gender-specific manner

BACKGROUND

Previous studies showed phthalates and UV filters are endocrine-disruptive and associated with puberty. However, few studies have examined effects of mixed exposure.

METHODS

Six phthalate metabolites and 12 organic UV filters were detected among 223 school-age children. Puberty development was evaluated at baseline and after 18 months of follow-up. Ordered logistic regression models, least absolute shrinkage and selection operator (LASSO) regression and quantile-based g-computation (qgcomp) were used to evaluate relationships between phthalate metabolites or UV filters exposure and pubertal development.

RESULTS

Six phthalate metabolites and 5 UV filters were detectable in urine samples. In boys, BP-3 and 4'-MAP were negatively associated with genital (ORBP-3 = 0.52, (0.27, 0.93), OR4'-MAP = 0.45, (0.25, 0.74)) and pubic hair development (ORBP-3:0.24, (0.05, 0.76), OR4'-MAP:0.24, (0.05, 0.77)). In girls, MEP levels were associated with advanced breast development (OR: 1.29, (1.04, 1.64)). LASSO regression identified BP-3, 4'-MAP, and OD-PABA for inverse associations with pubertal development in boys. MEP was related to an increase in girls' breast development (OR: 1.64, (1.08, 2.63)). Overall mixture was related to a 70% reduction in boys' genital development stage, with a larger effect size than a single chemical in qgcomp. Mixed exposure was associated with girls' earlier puberty onset (OR: 2.61, (1.06, 6.42)).

CONCLUSIONS

Our results suggested higher levels of phthalate metabolites and UV filters were associated with delayed pubertal development in boys but with earlier puberty in girls. Higher effect size of joint exposure than single chemicals suggested phthalates and UV filters might have synergistic effects on puberty and distort adolescent endocrine function together.

Bayesian multiple index models for environmental mixtures

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

A multipollutant low-grade exposure regulates the expression of miR-30b, Let-7a and miR-223 in maternal sera: Evidence from the NEHO cohort

There is growing evidence that environmental pollutants can induce epigenetic modifications altering the balance of miRNAs and inducing the onset of pathological conditions in animals. In this study, we measured the serum concentration of a suite of inorganic and organic pollutants (Cu, Zn, Se, Hg, HCB, p,p'-DDE, PCBs) and their association to serum miR-30b, miR-223 and Let-7a microRNA expression in 68 healthy pregnant women from the NEHO birth cohort sited in a highly industrialized area. The effects of the pollutants on the modulation of circulating miRNAs' expression were first investigated using linear continuous regression models with a single-compound approach showing that miR-223 expression was significantly associated with serum concentration of Se and Zn (pSe = 0.0336; pZn = 0.0225) and miR-30b was associated with Hg levels (pHg = 0.019). Furthermore, when contaminants were categorized into tertiles, miR-223 and miR-30b showed a positive association with higher tertiles of Zn, p,p'-DDE (pZn = 0.023; pDDE = 0.041) and Hg (pHg = 0.008), respectively. Moreover, Let-7a expression was exclusively influenced by medium tertiles levels of Se (low vs medium tertiles, p = 0.001). Simultaneous exposure to multi-pollutant mixture was approached by WQS regression model. Statistical analysis shows a driving effect of Zn, Se, Cu, Hg and HCB on significant increased expression of Let-7a (p = 0.045). Mercury and Se significantly amplified the expression for miR-30b (p = 0.038). Differently, the combined effect of p,p'-DDE, Zn and Se decreased miR-223 expression (p = 0.0001). The documented modified expression of circulating miRNAs in the serum of pregnant women, exposed to low-medium dose contaminants mixtures offers innovative early-warning approaches to human health risk assessment.

Prenatal exposure to ambient air pollution and adverse birth outcomes: An umbrella review of 36 systematic reviews and meta-analyses

Multiple systematic reviews and meta-analyses linked prenatal exposure to ambient air pollutants to adverse birth outcomes with mixed findings, including results indicating positive, negative, and null associations across the pregnancy periods. The objective of this study was to systematically summarise systematic reviews and meta-analyses on air pollutants and birth outcomes to assess the overall epidemiological evidence. Systematic reviews with/without meta-analyses on the association between air pollutants (NO2, CO, O3, SO2, PM2.5, and PM10) and birth outcomes (preterm birth; stillbirth; spontaneous abortion; birth weight; low birth weight, LBW; small-for-gestational-age) up to March 30, 2022 were included. We searched PubMed, CINAHL, Scopus, Medline, Embase, and the Web of Science Core Collection, systematic reviews repositories, grey literature databases, internet search engines, and references of included studies. The consistency in the directions of the effect estimates was classified as more consistent positive or negative, less consistent positive or negative, unclear, and consistently null. Next, the confidence in the direction was rated as either convincing, probable, limited-suggestive, or limited non-conclusive evidence. Final synthesis included 36 systematic reviews (21 with and 15 without meta-analyses) that contained 295 distinct primary studies. PM2.5 showed more consistent positive associations than other pollutants. The positive exposure-outcome associations based on the entire pregnancy period were more consistent than trimester-specific exposure averages. For whole pregnancy exposure, a more consistent positive association was found for PM2.5 and birth weight reductions, particulate matter and spontaneous abortion, and SO2 and LBW. Other exposure-outcome associations mostly showed less consistent positive associations and few unclear directions of associations. Almost all associations showed probable evidence. The available evidence indicates plausible causal effects of criteria air pollutants on birth outcomes. To strengthen the evidence, more high-quality studies are required, particularly from understudied settings, such as low-and-middle-income countries. However, the current evidence may warrant the adoption of the precautionary principle.

Prenatal exposure to multiple organochlorine compounds and childhood body mass index

Background

Prenatal exposure to organochlorine compounds (OCs) has been associated with increased childhood body mass index (BMI); however, only a few studies have focused on longitudinal BMI trajectories, and none of them used multiple exposure mixture approaches.

Aim

To determine the association between in-utero exposure to eight OCs and childhood BMI measures (BMI and BMI z-score) at 4 years and their yearly change across 4-12 years of age in 279 Rhea child-mother dyads.

Methods

We applied three approaches: (1) linear mixed-effect regressions (LMR) to associate individual compounds with BMI measures; (2) Bayesian weighted quantile sum regressions (BWQSR) to provide an overall OC mixture association with BMI measures; and (3)Bayesian varying coefficient kernel machine regressions (BVCKMR) to model nonlinear and nonadditive associations.

Results

In the LMR, yearly change of BMI measures was consistently associated with a quartile increase in hexachlorobenzene (HCB) (estimate [95% Confidence or Credible interval] BMI: 0.10 [0.06, 0.14]; BMI z-score: 0.02 [0.01, 0.04]). BWQSR results showed that a quartile increase in mixture concentrations was associated with yearly increase of BMI measures (BMI: 0.10 [0.01, 0.18]; BMI z-score: 0.03 [0.003, 0.06]). In the BVCKMR, a quartile increase in dichlorodiphenyldichloroethylene concentrations was associated with higher BMI measures at 4 years (BMI: 0.33 [0.24, 0.43]; BMI z-score: 0.19 [0.15, 0.24]); whereas a quartile increase in HCB and polychlorinated biphenyls (PCB)-118 levels was positively associated with BMI measures yearly change (BMI: HCB:0.10 [0.07, 0.13], PCB-118:0.08 [0.04, 012]; BMI z-score: HCB:0.03 [0.02, 0.05], PCB-118:0.02 [0.002,04]). BVCKMR suggested that PCBs had nonlinear relationships with BMI measures, and HCB interacted with other compounds.

Conclusions

All analyses consistently demonstrated detrimental associations between prenatal OC exposures and childhood BMI measures.

Exposure to multiple trace elements and miscarriage during early pregnancy: A mixtures approach

BACKGROUND

Exposure to some conventional trace elements has been found to be associated with miscarriage; however, evidence for combined exposure is inconclusive. Therefore, it is important to explore the joint associations between toxic and essential trace elements and miscarriage.

METHODS

This cross-sectional study measured a wide range of element levels in the whole blood of pregnant women by using inductively coupled plasma mass spectrometry. The associations between individual elements and miscarriage were appraised using logistic regression model. Multi-exposure models, including Bayesian kernel machine regression (BKMR) and weighted quantile sum regression (WQS), were used to explore the mixed exposure to elements. Furthermore, grouped weighted quantile sum (GWQS) considered multiple elements with different magnitudes and directions of associations.

RESULTS

In logistic regression, the odds ratios (ORs) with a 95% confidence interval (CI) in the highest quartiles were 5.45 (2.00, 15.91) for barium, 0.28 (0.09, 0.76) for copper, and 0.32 (0.12, 0.83) for rubidium. These exposure-outcome associations were confirmed and supplemented by BKMR, which indicated a positive association for barium and negative associations for copper and rubidium. In WQS, a positive association was found between mixed elements and miscarriage (OR: 1.71; 95% CI: 1.07, 2.78), in which barium (75.7%) was the highest weighted element. The results of GWQS showed that the toxic trace element group dominated by barium was significantly associated with increased ORs (OR: 2.71; 95% CI: 1.74, 4.38). Additionally, a negative association was observed between the essential trace element group and miscarriage (OR: 0.32; 95% CI: 0.18, 0.54), with rubidium contributing the most to the result.

CONCLUSIONS

As a toxic trace element, barium was positively associated with miscarriage both by individual and multiple evaluations, while essential trace elements, particularly rubidium and copper, exhibited negative associations. Our findings provide significant evidence for exploring the effects of trace elements on miscarriage.

How to investigate human health effects related to exposure to mixtures of per- and polyfluoroalkyl substances: A systematic review of statistical methods

BACKGROUND

Humans are exposed to several per- and polyfluoroalkyl substances (PFAS) daily; however, most previous studies have focused on individual PFAS. Although attention to effects of exposure to mixtures of PFAS has grown in recent years, there is no consensus on the appropriate statistical methods that can be used to assess their combined effect on human health.

OBJECTIVES

We aim to perform a comprehensive review of the statistical methods used in the existing studies which evaluate the association between exposure to mixtures of PFAS and any adverse human health effect.

METHODS

The online databases PubMed, Embase and Scopus were searched for eligible studies, published during the last ten years (last search performed on April 08, 2021). Covidence software was used by two different reviewers to perform a title/abstract screening, followed by a full text revision of the selected papers.

RESULTS

A total of 3640 papers were identified, and after the screening process, 53 papers were included in the current review. Most of the studies were published between 2019 and 2021 and were conducted mainly in North America and Europe; more than half of the studies (28 out of 53) were conducted on mother and child pairs. WQS (Weighted Quantile Sum) Regression and BKMR (Bayesian Kernel Machine Regression) were used in 36 out of 53 papers to model mixtures' effects. Health outcomes included in the studies are immunotoxicity (n = 8), fetal development (n = 7), neurodevelopment (n = 9), reproductive hormones (n = 6), thyroid hormones (n = 7), outcomes related to metabolic pathways (n = 16).

CONCLUSION

Studies on human exposure to PFAS as complex mixtures and health consequences have substantially increased in the last few years. Based on our findings, we propose that addressing risk from PFAS mixtures will likely require combinations of approaches and implementation of constantly evolving statistical methods. Specific guidelines and tools for quality assessment and publication of mixture observational studies are warranted.

Interactions of agricultural pesticide use near home during pregnancy and adverse childhood experiences on adolescent neurobehavioral development in the CHAMACOS study

BACKGROUND

Studies have documented independent adverse associations between prenatal and early-life exposure to environmental chemicals and social adversity with child neurodevelopment; however, few have considered these exposures jointly. The objective of this analysis is to examine whether associations of pesticide mixtures and adolescent neurobehavioral development are modified by early-life adversity in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) cohort.

METHODS

We used linear mixed effects Bayesian Hierarchical Models (BHM) to examine the joint effect of applications of 11 agricultural pesticides within 1 km of maternal homes during pregnancy and youth-reported Adverse Childhood Experiences (ACEs) with maternal and youth-reported internalizing behaviors, hyperactivity, and attention problems assessed via the Behavior Assessment for Children (BASC) (mean = 50, standard deviation = 10) at ages 16 and 18 years (n = 458).

RESULTS

The median (25th-75th percentiles) of ACEs was 1 (0-3); 72.3% of participants had low ACEs (0-2 events) and 27.7% had ACEs (3+ events). Overall, there was little evidence of modification of exposure-outcome associations by ACEs. A two-fold increase in malathion use was associated with increased internalizing behaviors among those with high ACEs from both maternal- (β = 1.9; 95% Credible Interval (CrI): 0.2, 3.7 for high ACEs vs. β = -0.1; 95% CrI: 1.2, 0.9 for low ACEs) and youth-report (β = 2.1; 95% CrI: 0.4, 3.8 for high ACEs vs. β = 0.2; 95% CrI: 0.8, 1.2 for low ACEs). Applications of malathion and dimethoate were also associated with higher youth-reported hyperactivity and/or inattention among those with high ACEs.

CONCLUSION

We observed little evidence of effect modification of agricultural pesticide use near the home during pregnancy and adolescent behavioral problems by child ACEs. Future studies should examine critical windows of susceptibility of exposure to chemical and non-chemical stressors and should consider biomarker-based exposure assessment methods.

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.

Prenatal and postnatal exposures to four metals mixture and IQ in 6-year-old children: A prospective cohort study in South Korea

BACKGROUND

Humans are exposed to a mixture of metals during their lifetime; however, evidence of neurotoxicity of such mixtures in critical time windows is still insufficient. We aimed to elucidate the associations of four metals mixture across multiple time points with children's intelligence quotient (IQ) in a prospective cohort study.

METHODS

Prenatal exposure and exposure at age 4 and 6 years to four types of blood metals, namely lead, mercury, cadmium, and manganese were quantified in 502 pregnant women and their children who participated in the Environment and Development Cohort study. Children' s IQ scores were assessed using the Wechsler Intelligence Scale at age 6. Bayesian kernel machine regression (BKMR), quantile g-computation models, and elastic net (ENET) models were used to assess the associations of their blood metals mixture with IQ scores.

RESULTS

Multivariate linear regression models indicated that postnatal blood manganese exposure at the age of 4 years was significantly negatively associated with children's IQ [β =  - 5.99, 95% confidence interval (CI): -11.37 to - 0.61]. In the multi-chemical BKMR and quantile g-computation model, statistically significant inverse associations were found between the mixture of prenatal and postnatal metals and children's IQ score (Difference in children' IQ per quartile increase: -2.83; 95% CI: -5.28, -0.38). Interestingly, we found that manganese levels at both age of 4 and 6 years were contributing factors to children's IQ in the mixture models, namely, BKMR, quantile g-computation, and ENET models.

CONCLUSIONS

Multi-pollutant mixtures of prenatal and postnatal exposures to four metals affected child IQ at 6 years of age. We found a relationship between manganese exposure at both age 4, and 6 years and children's IQ. Additional studies are warranted to confirm these associations and to control the exposure to different metals during pregnancy and preschool childhood.

Environmental exposure to perfluoroalkyl substances in early pregnancy, maternal glucose homeostasis and the risk of gestational diabetes: A prospective cohort study

BACKGROUND

Humans are widely exposed to environmental perfluoroalkyl substances (PFAS), which may affect glucose homeostasis. However, research linking PFAS exposure to glucose homeostasis during pregnancy is limited and the results were inconsistent. We aimed to investigate the association between PFAS exposure and glucose homeostasis in pregnancy in a large prospective cohort.

METHODS

A total of 2747 pregnant women who participated in the Shanghai Birth Cohort, had blood samples in early pregnancy and completed a 75 g oral glucose tolerance test (OGTT) at 24-28 gestational weeks were included. 10 PFAS were determined by high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS-MS) in the plasma samples in early pregnancy. Logistic regression was used to explore the associations between PFAS concentrations and gestational diabetes mellitus (GDM), while multiple linear regression was used to model the associations between PFAS and OGTT fasting, 1-h and 2-h glucose levels. Potential confounders were adjusted. Bayesian kernel machine regression (BKMR) and a quantile-based g-computation approach (qgcomp) were employed to explore the joint and independent effects of PFAS on glucose homeostasis.

RESULTS

The incidence of GDM was 11.8%. One log-unit increment in plasma concentrations in early pregnancy was associated with an increased risk of GDM for perfluorobutane sulfonate (PFBS) (adjusted odd ratio (aOR) = 1.23, 95% confidence interval (95% CI): 1.05, 1.44) and perfluoroheptanoic acid (PFHpA) (aOR = 1.25, 95% CI: 1.07, 1.46). Perfluorooctane sulfonic acid (PFOS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluorohexanesulfonate (PFHxS) and PFHpA were positively correlated with 1-h and 2-h glucose levels. Results of the mixed exposure model showed that the joint effects of PFAS were significantly associated with abnormal glucose homeostasis; In the BKMR model, PFAS mixture exposure was positively associated with the GDM incidence, 1-h and 2-h glucose levels and negatively correlated with FBG level. A similar trend could be observed in qgcomp and the positive correlation between PFAS and 2-h glucose level was significant (β = 0.12, 95% CI: 0.04, 0.20). PFOS, PFNA and PFHpA may be the main contributors after controlling for other PFAS congeners. PFOS was significantly correlated with GDM incidence and 2-h glucose level, and PFHpA was significantly associated with FBG and 2-h glucose levels. The above associations were more prominent among women with a normal prepregnant BMI.

CONCLUSIONS

Environmental exposure to PFAS may affect glucose homeostasis in pregnancy and increase the risk of GDM, especially in normal weight women.

Advancing tools for human early lifecourse exposome research and translation (ATHLETE): Project overview

Early life stages are vulnerable to environmental hazards and present important windows of opportunity for lifelong disease prevention. This makes early life a relevant starting point for exposome studies. The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project aims to develop a toolbox of exposome tools and a Europe-wide exposome cohort that will be used to systematically quantify the effects of a wide range of community- and individual-level environmental risk factors on mental, cardiometabolic, and respiratory health outcomes and associated biological pathways, longitudinally from early pregnancy through to adolescence. Exposome tool and data development include as follows: (1) a findable, accessible, interoperable, reusable (FAIR) data infrastructure for early life exposome cohort data, including 16 prospective birth cohorts in 11 European countries; (2) targeted and nontargeted approaches to measure a wide range of environmental exposures (urban, chemical, physical, behavioral, social); (3) advanced statistical and toxicological strategies to analyze complex multidimensional exposome data; (4) estimation of associations between the exposome and early organ development, health trajectories, and biological (metagenomic, metabolomic, epigenetic, aging, and stress) pathways; (5) intervention strategies to improve early life urban and chemical exposomes, co-produced with local communities; and (6) child health impacts and associated costs related to the exposome. Data, tools, and results will be assembled in an openly accessible toolbox, which will provide great opportunities for researchers, policymakers, and other stakeholders, beyond the duration of the project. ATHLETE's results will help to better understand and prevent health damage from environmental exposures and their mixtures from the earliest parts of the life course onward.

Prenatal urinary concentrations of phthalate metabolites and behavioral problems in Mexican children: The Programming Research in Obesity, Growth Environment and Social Stress (PROGRESS) study

BACKGROUND

Phthalate exposure has been associated with increased childhood behavioral problems. Existing studies failed to include phthalate replacements and did not account for high correlations among phthalates. Phthalates' exposure is higher in Mexico than in U.S. locations, making it an ideal target population for this study.

AIM

To examine associations between 15 maternal prenatal phthalate metabolite concentrations and children's behavioral problems.

METHODS

We quantified phthalate metabolites in maternal urine samples from maternal-child dyads (n = 514) enrolled in the Programming Research in Obesity, Growth Environment and Social Stress (PROGRESS) birth cohort in Mexico City. We performed least absolute shrinkage and selection operator (LASSO) regressions to identify associations between specific-gravity adjusted log₂-transformed phthalate metabolites and parent-reported 4-6 year old behavior on the Behavior Assessment System for Children (BASC-2), accounting for metabolite correlations. We adjusted for socio-demographic and birth-related factors, and examined associations stratified by sex.

RESULTS

Higher prenatal mono-2-ethyl-5-carboxypentyl terephthalate (MECPTP) urinary concentrations were associated with increased hyperactivity scores in the overall sample (β = 0.57, 95% CI = 0.17, 1.13) and in girls (β = 0.54, 95% CI = 0.16, 1.08), overall behavioral problems in boys (β = 0.58, 95% CI = 0.20, 1.15), and depression scores in boys (β = 0.44, 95% CI = 0.06, 0.88). Higher prenatal monobenzyl phthalate (MBzP) concentrations were associated with reduced hyperactivity scores in girls (ß = -0.54, 95% CI = -1.08, -0.21).

DISCUSSION

Our findings suggested that prenatal concentrations of phthalates and their replacements altered child neurodevelopment and those associations may be influenced sex.

Early life exposure to air pollution, green spaces and built environment, and body mass index growth trajectories during the first 5 years of life: A large longitudinal study

Urban environments are characterized by multiple exposures that may influence body mass index (BMI) growth in early life. Previous studies are few, with inconsistent results and no evaluation of simultaneous exposures. Thus, this study aimed to assess the associations between exposure to air pollution, green spaces and built environment characteristics, and BMI growth trajectories from 0 to 5 years. This longitudinal study used data from an electronic primary care health record database in Catalonia (Spain), including 79,992 children born between January 01, 2011 and December 31, 2012 in urban areas and followed until 5 years of age. Height and weight were measured frequently during childhood and BMI (kg/m²) was calculated. Urban exposures were estimated at census tract level and included: air pollution (nitrogen dioxide (NO₂), particulate matter <10 μm (PM₁₀) and <2.5 μm (PM_2.5)), green spaces (Normalized Difference Vegetation Index (NDVI) and % green space) and built environment (population density, street connectivity, land use mix, walkability index). Individual BMI trajectories were estimated using linear spline multilevel models with several knot points. In single exposure models, NO_2, PM_10, PM_2.5, and population density were associated with small increases in BMI growth (e.g. β per IQR PM₁₀ increase = 0.023 kg/m², 95%CI: 0.013, 0.033), and NDVI, % of green spaces and land use mix with small reductions in BMI growth (e.g. β per IQR % green spaces increase = -0.015 kg/m^2, 95%CI: -0.026, -0.005). These associations were strongest during the first two months of life. In multiple exposure models, most associations were attenuated, with only those for PM₁₀ and land use mix remaining statistically significant. This large longitudinal study suggests that early life exposure to air pollution, green space and built environment characteristics may be associated with small changes in BMI growth trajectories during the first years of life, and that it is important to account for multiple exposures in urban settings.

New directions in understanding the role of environmental contaminants in child development: Four themes

Environmental contaminants, which include several heavy metals, persistent organic pollutants, and other harmful chemicals, impair several domains of child development. This article describes four themes from recent research on the impact of environmental contaminants on child development. The first theme, disparities in exposure, focuses on how marginalized communities are disproportionately exposed to harmful environmental contaminants. The second theme, complexity of exposures, encapsulates recent emphases on timing of exposures and mixtures of multiple exposures. The third theme, mechanisms that link exposures to outcomes, focuses on processes that elucidate how contaminants impact outcomes. The fourth theme, mitigating risks associated with exposures, sheds light on potential protective factors that could ameliorate many of the harmful effects of contaminant exposures. Developmental scientists are well positioned to contribute to interdisciplinary research that addresses these themes, which could foster additional conceptual and empirical innovations and inform policies and practices to mitigate risks and improve children's well-being.