Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science

Associations of non-essential metal mixture with biological aging and the mediating role of inflammation in Chinese older adults

BACKGROUND

Individual non-essential metals (NMs) have been linked with biological aging. However, the effects of NM mixture and their mechanisms remain unclear.

OBJECTIVE

To characterize the relationships of individual NMs and their mixture to biological aging, and to explore the mediating roles of inflammatory factors.

METHODS

This cross-sectional study recruited 3251 individuals aged 60 years or above in China. Urine gallium, arsenic, cadmium, cesium, thallium, and barium were tested using ICP-MS. The Klemera-Doubal method was used to construct the KDMAge, reflecting the estimation of biological age, and ΔKDMAge, defined as the difference between KDMAge and chronological age, reflecting the deviation in aging rate. Four blood cell counts, including neutrophil, lymphocyte, platelet, and monocyte, were used to calculate inflammatory indices: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and systemic immune-inflammation index. Linear regression, generalized additive model (GAM), weighted quantile sum (WQS), quantile-based computation (QGC), and Bayesian kernel machine regression (BKMR) were employed to assess the associations between the NMs and ΔKDMAge. Mediation analysis was further performed to examine the roles of inflammatory factors.

RESULTS

KDMAge strongly correlated with chronological age (r = 0.863). Linear regression showed significant positive associations of Gallium (β = 0.88, 95 % CI = 0.30, 1.46), arsenic (β = 1.11, 95 % CI = 0.54, 1.69), and cesium (β = 0.75, 95 % CI = 0.19, 1.30) with ΔKDMAge. GAMs further exhibited a "J-shaped" relationship for gallium, arsenic with ΔKDMAge, a linear trend for cesium, and a "U-shaped" relationship for barium. The mixture models demonstrated a positive association between the NM mixture and ΔKDMAge, with gallium, arsenic, and cesium identified as the primary contributors. Mediation analyses further suggested that neutrophil-to-lymphocyte ratio and systemic immune-inflammation index partially mediated this association.

CONCLUSIONS

The NM mixture accelerates biological aging, mainly driven by gallium, arsenic, and cesium, with partial mediation by inflammation. Future longitudinal studies are necessary to verify these findings.

Machine learning-based analysis on factors influencing blood heavy metal concentrations in the Korean CHildren's ENvironmental health Study (Ko-CHENS)

Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing research focusing on pregnant women's heavy metal concentration has considered individual environmental factor. In this study, we aim to comprehensively consider lifestyle, food, and environmental factors to determine the most influential factor affecting heavy metal concentration in pregnant women. The Ko-CHENS (Korean CHildren health and ENvironmental Study) is a nationwide prospective birth cohort study in South Korea enrolling pregnant women from 2015 to 2020. A total of 5458 eligible pregnant women were included in this study, and 897 variables were included in questionnaire comprising: maternal general information, indoor and living environment, dietary habits, health behavior, exposure to chemicals. Lead, cadmium and mercury concentration on blood were measured in early, late pregnancy and in cord blood at birth. Variables that might be related to heavy metal concentrations were included in machine learning models. Random forest and XGBoost machine learning models were conducted for predictions. Both models had similar but better performance than multiple linear regression. Kimchi (β = 1.55), seaweed (β = 0.40), fatty fish (β = 1.55), intakes respectively affected lead, cadmium, and mercury exposure through early, late pregnancy and cord blood.

Short-term exposure to ambient nitrogen dioxide and fine particulate matter and cause-specific mortality: A causal modeling approach in four regions

Ambient air pollution still represents a major health burden. While the link between short-term air pollution exposures and mortality has been well-documented globally, few studies have applied causal modeling approaches. Therefore, we aimed to quantify the relationship between day-to-day changes in ambient particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) and nitrogen dioxide (NO2) levels and changes in daily natural, cardiovascular (including all-cardiovascular, cardiac, and stroke), as well as respiratory mortality rates using a causal modeling framework. Daily air pollution data and cause-specific death counts at the county, district, or municipality level from California (US), Jiangsu (China), Germany, and Lazio (Italy) were obtained for the years 2015-2019, including urban and rural populations. We used interactive fixed effects models to analyze the effects of air pollutants across different lag periods (0-2, 3-7, and 0-7 days after exposure) while accounting for both measured and unmeasured time-varying spatial unit-specific confounding factors. We observed increases in daily cardiovascular deaths (per 1 million people) per a 10 μg/m3 increase in daily NO2 at lag 0-7: 0.18 (95 % confidence interval: 0.02, 0.38) in California, 0.23 (0.14, 0.32) in Jiangsu, 0.48 (0.27, 0.70) in Germany, and -0.35 (-2.63, 1.92) in Lazio. For PM2.5, the related increases in cardiovascular mortality rates were 0.00 (-0.18, 0.18) in California, 0.04 (0.00, 0.09) in Jiangsu, 0.22 (0.06, 0.37) in Germany, and 1.96 (0.76, 3.16) in Lazio. Additionally, associations were seen for natural, cardiac, stroke, and respiratory mortality, particularly pronounced among individuals aged 75 and older. These associations were strongest with prolonged exposures and remained consistent even in two-pollutant models. This study, using a causal modeling approach and including urban and rural populations, contributes to the growing body of evidence linking increases in short-term exposure to NO2 and PM2.5 with increased cause-specific mortality rates.

Relationship of long-term air pollution exposure with chronic obstructive pulmonary disease: an Italian multicentre observational study

BACKGROUND

Recent evidence showed that 50% of chronic obstructive pulmonary disease (COPD) may be attributable to air pollution. We aimed to investigate the association between long-term air pollution exposure and COPD symptoms/diagnosis in an Italian epidemiological study.

METHODS

A total of 14 420 adults living in Ancona, Pavia, Pisa, Sassari, Turin and Verona were investigated in 2005-2011. Data on risk factors and health outcomes were collected by questionnaires; mean annual concentrations of particulate matters (PM) like PM10 and PM2.5 as well as NO2 and mean summer concentrations of O3 (µg/m3) at residential level with a 1 km resolution (period 2013-2015) were obtained by machine learning techniques. The relationship of pollutant exposure and COPD prevalence was assessed by logistic regression models (single pollutant) and principal component logistic regression models (multipollutant) adjusting for sex, age, education level, smoking habits, season of interview, and city-specific climatic index and including a random intercept for cohorts.

RESULTS

A 10 µg/m3 increase of PM10, PM2.5 and NO2 exposure was related to COPD diagnosis and symptoms (OR 1.31, 95% CI 1.03 to 1.65 for PM2.5; OR 1.26, 95% CI 1.03 to 1.54 for PM10 and OR 1.07, 95% CI 1.00 to 1.15 for NO2) using a multipollutant approach. Similar results emerged for dyspnoea (OR 1.24, 95% CI 1.05 to 1.47 for PM2.5; OR 1.21, 95% CI 1.05 to 1.39 for PM10 and OR 1.06, 95% CI 1.01 to 1.11 for NO2). Associations between COPD symptoms and summer O3 were less clear. By multipollutant models, OR estimates were lower than those by single pollutant models.

CONCLUSIONS

Further evidence about the relationship between air pollution and respiratory effects in Italian adults was provided indicating PM as the main driver.

LOW-RANK LONGITUDINAL FACTOR REGRESSION WITH APPLICATION TO CHEMICAL MIXTURES

Developmental epidemiology commonly focuses on assessing the association between multiple early life exposures and childhood health. Statistical analyses of data from such studies focus on inferring the contributions of individual exposures, while also characterizing time-varying and interacting effects. Such inferences are made more challenging by correlations among exposures, nonlinearity, and the curse of dimensionality. Motivated by studying the effects of prenatal bisphenol A (BPA) and phthalate exposures on glucose metabolism in adolescence using data from the ELEMENT study, we propose a low-rank longitudinal factor regression (LowFR) model for tractable inference on flexible longitudinal exposure effects. LowFR handles highly-correlated exposures using a Bayesian dynamic factor model, which is fit jointly with a health outcome via a novel factor regression approach. The model collapses on simpler and intuitive submodels when appropriate, while expanding to allow considerable flexibility in time-varying and interaction effects when supported by the data. After demonstrating LowFR's effectiveness in simulations, we use it to analyze the ELEMENT data and find that diethyl and dibutyl phthalate metabolite levels in trimesters 1 and 2 are associated with altered glucose metabolism in adolescence.

Identification of exposome clusters based on societal, social, built and natural environment - results of the ABCD cohort study

Exposome research has seen a recent increase. The conceptual framework of the Social Exposome extends initial concepts by considering the entirety of societal, social, built and natural environmental exposures which are assumed to holistically impact development and health across the lifecourse. The aim of this study is the identification and characterisation of exposome clusters. Additionally, their relevance for mental health is investigated. To this end 2,850 participants aged 11-12 of the Amsterdam Born Children and their Development (ABCD) Cohort Study were analysed. The exposome was characterized by 60 variables representing the societal, social, built and natural environment. Uniform manifold approximation and projection (UMAP) was applied for dimensionality reduction, and subsequently clustering was performed on the retrieved low-dimensional embedding. Mental health symptoms and behaviour related outcomes were assessed by the Strength and Difficulties Questionnaire (SDQ) as well as the Substance Use Risk Profile Scale (SURPS). The results suggest that exposome clusters are mainly driven by contextual socioeconomic and physical characteristics such as neighborhood income and deprivation rather than social characteristics at the individual level. Moreover, prevalence of children's mental health problems was more prominent within exposome clusters characterized at the contextual level by more deprived neighborhoods and at the individual level by higher prevalence of maternal mental health problems. This exploratory exposome cluster identification emphasized the relevance of socioeconomic neighborhood characteristics, thus structural inequalities.

Profiles of the maternal occupational exposome during pregnancy and associations with intrauterine growth: Analysis of the French Longitudinal Study of Children - ELFE study

BACKGROUND

Numerous agents in the workplace are suspected of impairing fetal growth. To date, no epidemiological studies have specifically described the occupational exposome during pregnancy.

OBJECTIVE

The objectives were to determine maternal occupational exposome profiles and study their associations with intrauterine growth characteristics measured by small for gestational age (SGA), birthweight (BW), and head circumference (HC).

METHODS

We used data from the French national ELFE cohort. Occupational exposures to 47 agents (chemical, physical, biological, biomechanical, organizational and psychosocial), were identified using job exposure matrices. Mothers were classified as occupationally not exposed, uncertainly exposed, or exposed depending on their probability of exposure. Outcomes of interest were BW, SGA and HC. Maternal profiles of the occupational exposome were determined using hierarchical clustering of principal components. Associations between profiles and intrauterine growth outcomes were studied using linear or logistic regression models adjusted for potential confounders. Analyses were carried out depending on whether mothers stopped working during pregnancy.

RESULTS

The 12,851 included women were exposed to a median of 6 factors. Four occupational exposome profiles were identified, characterized by "low exposure, stress at work"; "strenuous, high organization, low decision"; "postural constraints, psychosocial factors", "postural and strength constraints, chemical and biological factors". In multivariate analyses, and among women who stopped working during the third trimester of pregnancy, analyses found associations between the profile "postural constraints, psychosocial factor" and SGA, and HC. None of the other exposure profiles were statistically significantly associated with foetal growth outcomes.

CONCLUSION

The results show that the specific profile "postural constraints, psychosocial factors" may increase the risk of foetal growth retardation. Although these results need to be replicated, this study provides a first better understanding of the exposome of pregnant women at the workplace which may help to better adapt prevention strategies.

Pregnant women's lifestyles and exposure to endocrine-disrupting chemicals: A machine learning approach

Women have ubiquitous exposure to various endocrine disrupting chemicals (EDCs) present in personal care products, food packaging, and processing. Pregnancy is a phase of increased vulnerability to environmental stressors. Therefore, we aimed to identify questionnaire based variables of pregnant women's lifestyle factors affecting the prenatal concentrations of EDCs: bis-phenol A (BPA), triclosan (TCS), parabens, and phthalates. We also aimed to explore the association between these lifestyle factors and EDC exposure in pregnant women in South Korea. This study is a part of Korean CHildren's ENvironmental health Study (Ko-CHENS). The following lifestyle factors: usage of personal care products, eating habits, cooking practices, food storage practices, and chemical exposure were evaluated through questionnaire. We examined prenatal EDCs: phenols (BPA), TCS, parabens (MEP, ETP, and PRP), and phthalates (MEHHP, MEOHP, MECPP, MBZP, MCOP, MCPP, MCNP, and MNBP). The random forest and least absolute shrinkage and selection operator regression machine learning models were used to predict the important lifestyle factors affecting the prenatal EDC concentrations in pregnant women. Next, we calculated the lifestyle score and evaluated its association with prenatal EDCs, respectively. Our results show that pregnant women who used makeup [β: 1.01, 95% C.I.: 0.01,2.00] >6 times/week had a significant increase in early-pregnancy (EP) ΣParaben exposure. Using perfume up to 3 times/month was significantly associated with EP TCS exposure (β: 0.05, 95% C.I.: 0.01,0.23). While, using perfume >6 times/week was significantly associated to late-pregnancy (LP) ΣParaben exposure, and consuming cup noodles significantly increased LP ΣDEHP exposure. Linear model analysis showed that the lifestyle score significantly increased the EP (β: 0.24, 95% C.I.: 0.07,0.40) and LP (β:0.10, 95% C.I.: 0.01,0.20) ΣParaben exposure. Therefore, pregnant women's lifestyle factors, such as using makeup and perfume and eating habits (e.g., cup noodle consumption), were associated with prenatal EDC exposure.

Are metals in exhaled breath condensate and urine associated with oxidative/nitrosative stress and metabolism-related biomarkers? Results from 303 randomly selected Parisian subway workers

BACKGROUND

Subway particles can cause oxidative stress, with metals being a key factor. Only few epidemiological studies have examined the role of metal mixtures in this effect for subway workers.

OBJECTIVES

This cross-sectional study examined the relationship between metal concentrations in exhaled breath condensate (EBC) and urine, and biomarkers of oxidative/nitrosative stress and metabolism in subway workers.

METHODS

The study involved 303 randomly selected Parisian metro workers exposed to various levels of subway particles. Metals in EBC and urine were measured using ICP-MS, and biomarkers were analyzed through liquid chromatography-mass spectrometry. Factor analysis as dimension reduction strategy and cluster analysis to account for metal mixtures and multiple multi-media effect biomarkers was used along with multivariable linear regression analysis on factor variables adjusted for potential confounders.

RESULTS

Significant positive associations were observed between urinary metals and oxidative stress biomarkers, despite similar metal levels in workers and the general population. Metals in EBC were linked to nitrosative stress and other metabolites in EBC. Worker occupation correlated with small chain fatty acids in EBC and urinary levels of barium and titanium. Smoking was associated with effect biomarkers but not with exposure biomarkers.

CONCLUSIONS

Elevated metal levels in EBC and urine are associated with altered bronchopulmonary metabolites and increased systemic oxidative stress. While Ba and Ti may originate from brake wear, other metals identified in EBC and urine are not clearly related with subway particles and may be from non-occupational sources. Smoking showed a stronger relationship with the workers' oxidative stress status than occupation.

Association between fat-soluble vitamin co-exposure patterns and blood pressure in people with hypertension: a cross-sectional study

Background

Existing epidemiological studies investigated the association between a single vitamin and hypertension. However, the potential relationship between the level of circulating multivitamins and blood pressure has not been explored. We aimed to investigate the association between multiple fat-soluble vitamin levels and blood pressure.

Methods

A total of 2052 participants with essential hypertension were sampled nationwide. The plasma concentrations of fat-soluble vitamins (A, E, D, and K) were assessed using liquid chromatography coupled with the mass spectrometry method. Participants were categorized into different co-exposure patterns using the unsupervised K-means clustering method. The multiple linear regression model was used for subsequent analyses.

Results

Participants were classified into two co-exposure patterns of fat-soluble vitamins. The levels of vitamins were relatively low in pattern 1, compared to pattern 2. Participants in pattern 2 had no significantly different blood pressure levels compared to pattern 1. However, the plasma 25-hydroxyvitamin D3 (VD3) levels were negatively associated with SBP (logarithmic 10 transformed) (β = -0.002, 95% CI: -0.004, 0); participants in the fourth α-tocopherol quartile had mean SBP levels that were 1.02% (95% CI: 0.43, 1.61%) greater than those in the lowest quartile (p for trend <0.01). In addition, no significant relationships were found between plasma VA/VK concentrations and blood pressure.

Discussion

Although no significant association between fat-soluble vitamin co-exposure patterns and blood pressure was found, further analyses could imply that plasma α-tocopherol levels may offset the potential protective effect of plasma VD3 on blood pressure among hypertensive adults. This provided a novel perspective for exploring the joint effects of fat-soluble vitamins on blood pressure. Further studies are warranted to better understand the implications.

The relationships of metals exposure and disturbance of the vaginal microbiota with the risk of PROM: Results from a birth cohort study

The vaginal microbiota is proposed to be associated with reproductive health. Exposure to metals during pregnancy is a risk factor for premature rupture of membranes (PROM). PROM can lead to serious maternal complications, thus, identifying the cause and therapeutic targets for it is crucial. However, the role of vaginal microbiota in the association between metals exposure and the risk of PROM are not clear. Based on a prospective birth cohort study including 668 pregnant women, maternal blood levels of 15 metals in the first trimester (n=668) and microbiota of vaginal secretions in the third trimester (n=244) were assessed. The metals that significantly associated with the risk of PROM were screened out via four statistical models, the top three were barium (Ba), chromium (Cr) and thallium (Tl) according to their weight indices. The results from the BKMR model showed a positive association of the mixture (Ba, Cr and Tl) with the risk of PROM. PROM and non-PROM were characterised by different beta diversities, moreover, the relative abundances of Bifidobacterium, Corynebacterium and Collinsella were statistically and negatively related to the risk of PROM [the adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) were 0.06 (0.00, 0.82), 0.32 (0.14, 0.74) and 0.50 (0.30, 0.84), respectively]. On the other hand, women with different levels of Ba exposure were also characterised by different beta diversities (p value = 0.047); and blood Ba levels were also negatively associated with the relative abundances of Collinsella; additionally, Cr levels were positively associated with alpha diversity indices [Shannon index: β (95 % CI) = 0.25 (0.01, 0.50); Simpson index: β (95 % CI) = 0.08 (0.00, 0.17), respectively]. The results from mediation analysis showed the proportion of the relationship between Ba exposure and PROM risk mediated by the relative abundance of Collinsella was 26.4 %. Further verification analysis exploring the potential cause of the above phenomenon indicated that the neutrophil count, one of blood inflammation indicators for PROM, was higher in women with the absence of Collinsella (p value = 0.039), moreover, the cumulative hazard of PROM for women with the presence of Collinsella was also significantly lower than that of those without Collinsella (p value = 0.007). Collectively, the changes in the diversity and composition of the bacterial community, especially the reduction in Collinsella abundance caused by metal exposure, may be related to the occurrence of PROM, which provides a new microbiota-based perspective for intervention in metal exposure-related PROM. Confirming these relationships and determining the possible processes at play will require more investigation.

Sensitivity Analysis for Effects of Multiple Exposures in the Presence of Unmeasured Confounding: Non-Gaussian and Time-to-Event Outcomes

In epidemiological studies, evaluating the health impacts stemming from multiple exposures is one of the important goals. To analyze the effects of multiple exposures on discrete or time-to-event health outcomes, researchers often employ generalized linear models, Cox proportional hazards models, and machine learning methods. However, observational studies are prone to unmeasured confounding factors, which can introduce the potential for substantial bias in the multiple exposure effects. To address this issue, we propose a novel outcome model-based sensitivity analysis method for non-Gaussian and time-to-event outcomes with multiple exposures. All the proposed sensitivity analysis problems are formulated as linear programming problems with quadratic and linear constraints, which can be solved efficiently. Analytic solutions are provided for some optimization problems, and a numerical study is performed to examine how the proposed sensitivity analysis behaves in finite samples. We illustrate the proposed method using two real data examples.

Single- and two-pollutant concentration-response functions for PM2.5 and NO2 for quantifying mortality burden in health impact assessments

OBJECTIVE

Health Impact Assessments (HIAs) for air pollutant mixtures are challenging because risk estimates are primarily derived from single-pollutant models. Combining risk estimates from multiple pollutants requires new approaches, as a simple addition of single pollutant risk estimates from correlated air pollutants may result in double counting. We investigated approaches applying concentration-response functions (CRFs) from single- and two-pollutant models in HIAs, focusing on long-term exposure to particulate matter with a diameter less than 2.5 μm (PM2.5) and nitrogen dioxide (NO2) and their associations with all-cause mortality.

METHODS

A systematic literature search of MEDLINE and EMBASE identified cohort studies employing single- and two-pollutant models of long-term exposure to PM2.5 and NO2 with all-cause mortality. Pooled CRFs were calculated through random-effects meta-analyses of risk estimates from single- and two-pollutant models. Coefficient differences were calculated by comparing single- and two-pollutant model estimates. Four approaches to estimating population-attributable fractions (PAFs) were compared: PM2.5 or NO2 single-pollutant models to represent the mixture, the sum of single-pollutant models, the sum of two-pollutant models and the sum of single-pollutant models from a larger body of evidence adjusted by coefficient difference.

RESULTS

Seventeen papers reported both single and two-pollutant estimates. Pooled hazard ratios (HRs) for mortality from single- and two-pollutant models were 1.053 (95% confidence interval: 1.034-1.071) and 1.035 (1.014-1.057), respectively, for a 5 μg/m3 increase in PM2.5. HRs for a 10 μg/m3 increase in NO2 were 1.032 (1.014-1.049) and 1.024 (1.000-1.049) for single- and two-pollutant models, respectively. The average coefficient difference between single- and two-pollutant models was 0.017 for PM2.5 and 0.007 for NO2. Combined PAFs for the PM2.5-NO2 mixture using joint HRs from single- and two-pollutant model CRFs were 0.09 and 0.06, respectively.

CONCLUSION

Utilizing CRFs from two-pollutant models or applying the coefficient difference to a more extensive evidence base seems to mitigate the potential overestimation of mixture health impacts from adding single-pollutant CRFs.

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.

Exposure to ambient polycyclic aromatic hydrocarbons and early-onset female breast cancer in a case-control study in Ontario, Canada

Background

Ambient polycyclic aromatic hydrocarbons (PAHs) are a class of toxicologically important and understudied air pollutants. Epidemiologic evidence suggests that chronic exposure to PAHs increases breast cancer risk; however, there are few studies in nonoccupational settings that focus on early-onset diagnoses.

Methods

The relationship between residentially-based ambient PAH concentrations and female breast cancer, among those 18-45 years of age, was characterized in the Ontario Environment and Health Study (OEHS). The OEHS was a population-based case-control study undertaken in Ontario, Canada between 2013 and 2015. Primary incident breast cancers were identified within 3 months of diagnosis, and a population-based series of controls were recruited. Concentrations of ambient PAHs, using fluoranthene as a surrogate, were derived using a chemical transport model at a 2.5 km spatial resolution. These estimates were assigned to participants' residences at the time of the interview and 5 years prior. Logistic regression was used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) based on a quartile categorization of fluoranthene exposure while adjusting for a series of individual- and area-level risk factors. The shape of the exposure-response trend was evaluated using cubic splines.

Results

Median fluoranthene exposure for cases and controls was 0.0017 µg/m3 and 0.0014 µg/m3, respectively. In models adjusted for a parsimonious set of risk factors, the highest quartile of exposure was associated with an increased risk of breast cancer (OR = 2.16; 95% CI = 1.22, 3.84). Restricted spline analyses revealed nonlinear dose-response patterns.

Conclusions

These findings support the hypothesis that ambient PAH exposures increases the risk of early-onset breast cancer.

Residential exposure to ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) and incident breast cancer among young women in Ontario, Canada

BACKGROUND

Air pollution has been classified as a human carcinogen based largely on findings for respiratory cancers. Emerging, but limited, evidence suggests that it increases the risk of breast cancer, particularly among younger women. We characterized associations between residential exposure to ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) and breast cancer. Analyses were performed using data collected in the Ontario Environmental Health Study (OEHS).

METHODS

The OEHS, a population-based case-control study, identified incident cases of breast cancer in Ontario, Canada among women aged 18-45 between 2013 and 2015. A total of 465 pathologically confirmed primary breast cancer cases were identified from the Ontario Cancer Registry, while 242 population-based controls were recruited using random-digit dialing. Self-reported questionnaires were used to collect risk factor data and residential histories. Land-use regression and remote-sensing estimates of NO2 and PM2.5, respectively, were assigned to the residential addresses at interview, five years earlier, and at menarche. Logistic regression was used to estimate odds ratios (OR) and their 95 % confidence intervals (CI) in relation to an interquartile range (IQR) increase in air pollution, adjusting for possible confounders.

RESULTS

PM2.5 and NO2 were positively correlated with each other (r = 0.57). An IQR increase of PM2.5 (1.9 µg/m3) and NO2 (6.6 ppb) at interview residence were associated with higher odds of breast cancer and the adjusted ORs and 95 % CIs were 1.37 (95 % CI = 0.98-1.91) and 2.33 (95 % CI = 1.53-3.53), respectively. An increased odds of breast cancer was observed with an IQR increase in NO2 at residence five years earlier (OR = 2.16, 95 % CI: 1.41-3.31), while no association was observed with PM2.5 (OR = 0.96, 95 % CI 0.64-1.42).

CONCLUSIONS

Our findings support the hypothesis that exposure to ambient air pollution, especially those from traffic sources (i.e., NO2), increases the risk of breast cancer in young women.

Air quality in a small city: criteria pollutants, volatile organic compounds, metals, and microbes

This work presents a year-long integral study of air quality parameters in Ciudad Real, a small city in the center of Spain, and its influence on the nearby national park, Las Tablas de Daimiel. The study covers meteorological parameters and criteria pollutants such as O3, NO, NO2, SO2, and PM10. Additionally, for each month, a 1-week campaign was performed sampling air in sorbent tubes with 8-h time resolution to analyze anthropogenic volatile organic compounds and the effects of seasons, daytime, and working-weekend days. During these campaigns, 24-h PM2.5 samples were also collected to measure the load of bacteria and fungi, as well as the trace concentrations of elements.The city and the national park NOx profiles showed that emissions from the town had a non-perceivable effect on the protected area. PM10 levels in Ciudad Real were influenced by Saharan intrusions, as was the national park; however, Ciudad Real had a higher contribution from anthropogenic sources. Ozone levels were lower in the city during the cold season due to the higher concentration of NOx and have not changed significantly in the last decade.The VOCs with higher average concentrations were toluene, m,p-xylene, benzene, methylene chloride, and o-xylene, with traffic being the main source of these pollutants in the city. For benzene and carbon tetrachloride levels, weak carcinogenic risks were estimated. In PM2.5, the most abundant metals were Na, Zn, Mg, Ca, Al, Fe, and K. The carcinogenic and non-carcinogenic risks estimated from the levels of the studied metals were negligible. Bacterial and fungal counts positively correlated with the concentration of PM2.5. Microbial community composition showed seasonal variability, with the dominance of human pathogenic bacteria which correlated with certain pollutants such as SO2. Bacillus and Cutibacterium were the most abundant genera.

The Association Between the Non-essential Metal Mixture and Handgrip Strength in Chinese Community-Dwelling Older Adults

There is limited research on the effects of non-essential metal (NEM) mixture on handgrip strength in the elderly. This study aimed to assess the associations of single NEMs and their mixture with handgrip strength in Chinese community-dwelling older adults. A total of 3807 elderly people aged 60 years or above were included in this study. Measurement of urinary aluminum (Al), arsenic (As), barium (Ba), cadmium (Cd), and gallium (Ga) concentrations was conducted by inductively coupled plasma mass spectrometry (ICP-MS). Handgrip strength was measured using a hand dynamometer. Four statistical models, including general linear regression and generalized additive models (GAMs), as well as Bayesian kernel machine regression (BKMR) and quantile-based computation regression (QGC) models, were used to assess the individual and joint effects of urine NEMs with handgrip strength, respectively. After adjusting for covariates, Ga (ß = - 0.27; 95% CI, - 0.54 ~  - 0.01) and As ( β  = - 0.34; 95% CI, - 0.61 ~  - 0.07) were negatively associated with handgrip strength. The GAMs and BKMR further suggested that the negative associations of Ga and As with handgrip strength were linear and inverted U-shaped, respectively. The BKMR and QGC models showed that the NEM mixture was negatively related to handgrip strength, with Ga and As contributing the most within the mixture. Moreover, we also observed an interaction between As and Ga on handgrip strength. Longitudinal studies are needed to verify these findings.

Trajectories of long-term exposure to PCB153 and Benzo[a]pyrene (BaP) air pollution and risk of breast cancer

BACKGROUND

While genetic, hormonal, and lifestyle factors partially elucidate the incidence of breast cancer, emerging research has underscored the potential contribution of air pollution. Polychlorinated biphenyls (PCBs) and benzo[a]pyrene (BaP) are of particular concern due to endocrine-disrupting properties and their carcinogenetic effect.

OBJECTIVE

To identify distinct long term trajectories of exposure to PCB153 and BaP, and estimate their associations with breast cancer risk.

METHODS

We used data from the XENAIR case-control study, nested within the ongoing prospective French E3N cohort which enrolled 98,995 women aged 40-65 years in 1990-1991. Cases were incident cases of primary invasive breast cancer diagnosed from cohort entry to 2011. Controls were randomly selected by incidence density sampling, and individually matched to cases on delay since cohort entry, and date, age, department of residence, and menopausal status at cohort entry. Annual mean outdoor PCB153 and BaP concentrations at residential addresses from 1990 to 2011 were estimated using the CHIMERE chemistry-transport model. Latent class mixed models were used to identify profiles of exposure trajectories from cohort entry to the index date, and conditional logistic regression to estimate their association with the odds of breast cancer.

RESULTS

5058 cases and 5059 controls contributed to the analysis. Five profiles of trajectories of PCB153 exposure were identified. The class with the highest PCB153 concentrations had a 69% increased odds of breast cancer compared to the class with the lowest concentrations (95% CI 1.08, 2.64), after adjustment for education and matching factors. The association between identified BaP trajectories and breast cancer was weaker and suffered from large CI.

CONCLUSIONS

Our results support an association between long term exposure to PCB153 and the risk of breast cancer, and encourage further studies to account for lifetime exposure to persistent organic pollutants.

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.

Inflammatory biomarkers mediate the association between polycyclic aromatic hydrocarbon exposure and dyslipidemia: A national population-based study

Exploring the association between exposure to polycyclic aromatic hydrocarbons (PAHs) and the risk of dyslipidemia and possible mediating effects is essential for conducting epidemiological health studies on related lipid disorders. Therefore, our study aimed to elucidate the potential association between PAH exposure and dyslipidemia risk and further identify the mediating effects based on blood cell-based inflammatory biomarkers. This cross-sectional study was conducted on 8380 individuals with complete survey data from the National Health and Nutrition Examination Survey (2001-2016). Multiple models (generalized linear regression model, restricted cubic spline model, Bayesian kernel machine regression, weighted quantiles sum regression) were used to assess the relationship between PAH co-exposure and the dyslipidemia risk and further identify potential mediating effects. Among the 8380 subjects, 2886 (34.44 %) had dyslipidemia. After adjusting for the confounding factors, the adjusted OR and 95% CI for dyslipidemia in the highest quartile of subjects were 1.30 (1.11, 1.51), 1. 22 (1.04, 1.43), 1.21 (1.03, 1.42), 1.29 (1.10, 1.52), 1.18 (1.01, 1.37), and 1.04 (0.89, 1.23) for 1-hydroxynaphthalene, 2-hydroxynaphthalene, 3-hydroxyfluorene, 2-hydroxyfluorene (2-FLU), 1-hydroxyphenanthrene, and 1-hydroxypyrene. The Bayesian kernel machine regression model also showed a positive correlation between PAH mixtures and dyslipidemia, and 2-FLU has the highest contribution. Mediation effect analyses showed that white blood cells and neutrophils were statistically significant in the association between PAHs and dyslipidemia. The present study suggests that individual and mixed PAH exposures may increase the risk of dyslipidemia in adults. Inflammatory biomarkers significantly mediated the relationship between PAH exposure and dyslipidemia. Environmental pollutants and their mechanisms should be more intensively monitored and studied.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next?

Although concentrations of ambient air pollution continue to decline in high-income regions, epidemiological studies document adverse health effects at levels below current standards in many countries. The Health Effects Institute (HEI) recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in the United States (U.S.), Canada, and Europe. We provide an overview and synthesis of the results of this initiative along with other key research, the strengths and limitations of the research, and remaining research needs. The three studies funded through the HEI initiative estimated the effects of long-term ambient exposure to fine particulate matter (PM2.5), nitrogen dioxide, ozone, and other pollutants on a broad range of health outcomes, including cause-specific mortality and cardiovascular and respiratory morbidity. To ensure high quality research and comparability across studies, HEI worked actively with the study teams and engaged independent expert panels for project oversight and review. All three studies documented positive associations between mortality and exposure to PM2.5 below the U.S. National Ambient Air Quality Standards and current and proposed European Union limit values. Furthermore, the studies observed nonthreshold linear (U.S.), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality. Heterogeneity was found in both the magnitude and shape of this association within and across studies. Strengths of the studies included the large populations (7-69 million), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. Future work is needed to better understand potential sources of heterogeneity in the findings across studies and regions. Other areas of future work include the changing and evolving nature of PM components and sources, including wildfires, and the role of indoor environments. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions could yield larger benefits than previously anticipated.

Housing conditions and risk of incident COPD: a Danish cohort study, 2000-2018

BACKGROUND

More knowledge is needed on the risk of developing chronic obstructive pulmonary disease (COPD) associated with housing conditions and indoor environment based on cohort studies with a long follow-up time.

OBJECTIVE

To examine the association between housing conditions and indoor environment and the risk of developing COPD.

METHODS

In this cohort study, we followed 11,590 individuals aged ≥ 30 years free of COPD at baseline. Information on incident COPD and housing conditions and indoor environment was obtained from the Danish national registers and the Danish Health and Morbidity Survey year 2000. Poisson regression of incidence rates (IRs) were used to estimate incidence rate ratios (IRRs) of COPD.

RESULTS

The overall IR of COPD was 8.6 per 1,000 person-years. Individuals living outside the biggest cities vs. living in the biggest cities (≥ 50,000) had a lower risk of COPD (200-4,999; IRR 0.77 (95% CI 0.65-0.90). Individuals living in semi-detached houses had a higher risk compared to individuals living in detached houses (IRR 1.29 (95% CI 1.07-1.55)). Likewise, individuals living in rented homes had a higher risk (IRR 1.47 (95% CI 1.27-1.70)) compared to individuals living in owned homes. The IR of COPD was 17% higher among individuals living in dwellings build > 1982 compared with individuals living in older dwellings (< 1962), not statistically significant though (IRR 0.83 (95% CI 0.68-1.03)). Likewise, the IR of COPD was 15% higher among individuals living in the densest households compared with individuals living in the least dense households, not statistically significant though (IRR 1.15 (95% CI 0.92-1.45)). This was primary seen among smokers. There was no difference in risk among individuals with different perceived indoor environments. Overall, similar patterns were seen when stratified by smoking status with exception of perceived indoor environment, where opposite patterns were seen for smokers and never smokers.

CONCLUSION

Individuals living in semi-detached houses or rented homes had a higher risk of developing COPD compared to individuals living in detached or owned homes. Individuals living in cities with < 50.000 residents had a lower risk of COPD compared to individuals living in cities with ≥ 50.000 residents.

Multiple xenoestrogen air pollutants and breast cancer risk: Statistical approaches to investigate combined exposures effect

Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5222 cases and 5222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants' residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03-1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03-1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution.

Beyond lung cancer: air pollution and bladder, breast and prostate cancer incidence

BACKGROUND

The carcinogenicity of air pollution and its impact on the risk of lung cancer is well known; however, there are still knowledge gaps and mixed results for other sites of cancer.

METHODS

The current study aimed to evaluate the associations between ambient air pollution [fine particulate matter (PM2.5) and nitrogen oxides (NOx)] and cancer incidence. Exposure assessment was based on historical addresses of >900 000 participants. Cancer incidence included primary cancer cases diagnosed from 2007 to 2015 (n = 30 979). Cox regression was used to evaluate the associations between ambient air pollution and cancer incidence [hazard ratio (HR), 95% CI].

RESULTS

In the single-pollutant models, an increase of one interquartile range (IQR) (2.11 µg/m3) of PM2.5 was associated with an increased risk of all cancer sites (HR = 1.51, 95% CI: 1.47-1.54), lung cancer (HR = 1.73, 95% CI: 1.60-1.87), bladder cancer (HR = 1.50, 95% CI: 1.37-1.65), breast cancer (HR = 1.50, 95% CI: 1.42-1.58) and prostate cancer (HR = 1.41, 95% CI: 1.31-1.52). In the single-pollutant and the co-pollutant models, the estimates for PM2.5 were stronger compared with NOx for all the investigated cancer sites.

CONCLUSIONS

Our findings confirm the carcinogenicity of ambient air pollution on lung cancer and provide additional evidence for bladder, breast and prostate cancers. Further studies are needed to confirm our observation regarding prostate cancer. However, the need for more research should not be a barrier to implementing policies to limit the population's exposure to air pollution.

Exposome-Wide Association Study of Body Mass Index Using a Novel Meta-Analytical Approach for Random Forest Models

BACKGROUND

Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors.

OBJECTIVES

Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies.

METHODS

Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies.

RESULTS

Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5 -km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to € 300,000 . The directions of associations were less consistent for walkability and share of single residents.

DISCUSSION

Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393.

Association between Combined Metals and PFAS Exposure with Dietary Patterns: A Preliminary Study

Background

The global burden of chronic diseases has been increasing, with evidence suggesting that diet and exposure to environmental pollutants, such as per- and polyfluoroalkyl substances (PFAS) and heavy metals, may contribute to their development. The Dietary Inflammatory Index (DII) assesses the inflammatory potential of an individual's diet. However, the complex interplay between PFAS, heavy metals, and DII remains largely unexplored.

Objective

The goal of this cross-sectional study was to investigate the associations between diet operationalized as the DII with individual and combined lead, cadmium, mercury, perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS) exposures using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018.

Methods

Descriptive statistics, a correlational analysis, and linear regression were initially used to assess the relationship between the variables of interest. We subsequently employed Bayesian kernel Machine regression (BKMR) to analyze the data to assess the non-linear, non-additive, exposure-response relationships and interactions between PFAS and metals with the DII.

Results

The multi-variable linear regression revealed significant associations between the DII and cadmium and mercury. Our BKMR analysis revealed a complex relationship between PFAS, metal exposures, and the DII. In our univariate exposure-response function plot, cadmium and mercury exhibited a positive and negative linear relationship, respectively, which indicated a positive and negative relationship across the spectrum of exposures with the DII. In addition, the bivariate exposure-response function between two exposures in a mixture revealed that cadmium had a robust positive relationship with the DII for different quantiles of lead, mercury, PFOA, and PFOS, indicating that increasing levels of cadmium are associated with the DII. Mercury's bivariate plot demonstrated a negative relationship across all quantiles for all pollutants. Furthermore, the posterior inclusion probability (PIP) results highlighted the consistent importance of cadmium and mercury with the inflammatory potential of an individual's diet, operationalized as the DII in our study, with both showing a PIP of 1.000. This was followed by PFOS with a PIP of 0.8524, PFOA at 0.5924, and lead, which had the lowest impact among the five environmental pollutants, with a PIP of 0.5596.

Conclusion

Our study suggests that exposures to environmental metals and PFAS, particularly mercury and cadmium, are associated with DII. These findings also provide evidence of the intricate relationships between PFAS, heavy metals, and the DII. The findings underscore the importance of considering the cumulative effects of multi-pollutant exposures. Future research should focus on elucidating the mechanistic pathways and dose-response relationships underlying these associations in a study that examines causality, which will enable a deeper understanding of the dietary risks associated with environmental pollutants.

A cohort study of the multipollutant effects of PM2.5, NO2, and O3 on C-reactive protein levels during pregnancy

Background

PM2.5, NO2, and O3 contribute to the development of adverse pregnancy complications. While studies have investigated the independent effects of these exposures, literature on their combined effects is limited. Our objective was to study the multipollutant effects of PM2.5, NO2, and O3 on maternal systemic C-reactive protein (CRP) levels.

Methods

We used data from 1170 pregnant women enrolled in the Maternal-Infant Research on Environmental Chemicals Study (MIREC) study in Canada. Air pollution exposures were assigned to each participant based on residential location. CRP was measured in third-trimester blood samples. We fit multipollutant linear regression models and evaluated the effects of air pollutant mixtures (14-day averages) using repeated-holdout Weighted Quantile Sum (WQS) regression and by calculating the Air Quality Health Index (AQHI).

Results

In multipollutant models adjusting for NO2, O3, and green space, each interquartile range (IQR) increase in 14-day average PM2.5 (IQR: 6.9 µg/m3) was associated with 27.1% (95% confidence interval [CI] = 6.2, 50.7) higher CRP. In air pollution mixture models adjusting for green space, each IQR increase in AQHI was associated with 37.7% (95% CI = 13.9, 66.5) higher CRP; and an IQR increase in the WQS index was associated with 78.6% (95% CI = 29.7, 146.0) higher CRP.

Conclusion

PM2.5 has the strongest relationship of the individual pollutants examined with maternal blood CRP concentrations. Mixtures incorporating all three pollutants, assessed using the AQHI and WQS index, showed stronger relationships with CRP compared with individual pollutants and illustrate the importance of conducting multipollutant analyses.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Grouped mixtures of air pollutants and seasonal temperature anomalies and cardiovascular hospitalizations among U.S. Residents

BACKGROUND

Air pollution is a recognized risk factor for cardiovascular disease (CVD). Temperature is also linked to CVD, with a primary focus on acute effects. Despite the close relationship between air pollution and temperature, their health effects are often examined separately, potentially overlooking their synergistic effects. Moreover, fewer studies have performed mixture analysis for multiple co-exposures, essential for adjusting confounding effects among them and assessing both cumulative and individual effects.

METHODS

We obtained hospitalization records for residents of 14 U.S. states, spanning 2000-2016, from the Health Cost and Utilization Project State Inpatient Databases. We used a grouped weighted quantile sum regression, a novel approach for mixture analysis, to simultaneously evaluate cumulative and individual associations of annual exposures to four grouped mixtures: air pollutants (elemental carbon, ammonium, nitrate, organic carbon, sulfate, nitrogen dioxide, ozone), differences between summer and winter temperature means and their long-term averages during the entire study period (i.e., summer and winter temperature mean anomalies), differences between summer and winter temperature standard deviations (SD) and their long-term averages during the entire study period (i.e., summer and winter temperature SD anomalies), and interaction terms between air pollutants and summer and winter temperature mean anomalies. The outcomes are hospitalization rates for four prevalent CVD subtypes: ischemic heart disease, cerebrovascular disease, heart failure, and arrhythmia.

RESULTS

Chronic exposure to air pollutant mixtures was associated with increased hospitalization rates for all CVD subtypes, with heart failure being the most susceptible subtype. Sulfate, nitrate, nitrogen dioxide, and organic carbon posed the highest risks. Mixtures of the interaction terms between air pollutants and temperature mean anomalies were associated with increased hospitalization rates for all CVD subtypes.

CONCLUSIONS

Our findings identified critical pollutants for targeted emission controls and suggested that abnormal temperature changes chronically affected cardiovascular health by interacting with air pollution, not directly.

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.

Association and mediation analyses among multiple metal exposure, mineralocorticoid levels, and serum ion balance in residents of northwest China

Toxic metals are vital risk factors affecting serum ion balance; however, the effect of their co-exposure on serum ions and the underlying mechanism remain unclear. We assessed the correlations of single metal and mixed metals with serum ion levels, and the mediating effects of mineralocorticoids by investigating toxic metal concentrations in the blood, as well as the levels of representative mineralocorticoids, such as deoxycorticosterone (DOC), and serum ions in 471 participants from the Dongdagou-Xinglong cohort. In the single-exposure model, sodium and chloride levels were positively correlated with arsenic, selenium, cadmium, and lead levels and negatively correlated with zinc levels, whereas potassium and iron levels and the anion gap were positively correlated with zinc levels and negatively correlated with selenium, cadmium and lead levels (all P < 0.05). Similar results were obtained in the mixed exposure models considering all metals, and the major contributions of cadmium, lead, arsenic, and selenium were highlighted. Significant dose-response relationships were detected between levels of serum DOC and toxic metals and serum ions. Mediation analysis showed that serum DOC partially mediated the relationship of metals (especially mixed metals) with serum iron and anion gap by 8.3% and 8.6%, respectively. These findings suggest that single and mixed metal exposure interferes with the homeostasis of serum mineralocorticoids, which is also related to altered serum ion levels. Furthermore, serum DOC may remarkably affect toxic metal-related serum ion disturbances, providing clues for further study of health risks associated with these toxic metals.

Association between Multiple Trace Elements, Executive Function, and Cognitive Impairment with No Dementia in Older Adults

Many studies suggest a significant association between individual essential trace elements (ETEs) and cognitive impairment in older adults, but evidence of the synchronized effect of multiple ETEs on cognitive function is lacking. We investigated the association between multiple ETEs, cognitive impairment with no dementia (CIND), and executive function in older Korean adults, using the Bayesian kernel machine regression (BKMR) model. Three hundred and thirty-six older adults were included as the study population and classified as the CIND and control groups. Blood manganese (Mn), copper (Cu), zinc (Zn), selenium (Se), and molybdenum (Mo) were measured as relevant ETEs. The frontal/executive tests included digit symbol coding (DSC), the Korean color word Stroop test (K-CWST), a controlled oral word association test (COWAT), and a trial-making test (TMT). Overall, the BKMR showed a negative association between multiple ETEs and the odds of CIND. Mn was designated as the most dominant element associated with the CIND (PIP = 0.6184), with a U-shaped relationship. Cu and Se levels were positively associated with the K-CWST percentiles (β = 31.78; 95% CI: 13.51, 50.06) and DSC percentiles (β = 25.10; 95% CI: 7.66, 42.53), respectively. Our results suggest that exposure to multiple ETEs may be linked to a protective mechanism against cognitive impairment in older adults.

Investigating the spatiotemporal associations between meteorological conditions and air pollution in the federal state Baden-Württemberg (Germany)

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone ( O 3 ), nitrogen dioxide ( NO 2 ), particulate matter ( P M 2.5 , P M 10 ) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (-0.84), April (-0.47), and October (-0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O 3 and NO 2 . Spatially, NO 2 , P M 2.5 , and P M 10 concentrations were significantly higher in urban than rural regions. For O 3 , this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies.

Joint associations among non-essential heavy metal mixtures and nutritional factors on glucose metabolism indexes in US adults: evidence from the NHANES 2011-2016

Dietary intake can modify the impact of metals on human health, and is also closely related to glucose metabolism in human bodies. However, research on their interaction is limited. We used data based on 1738 adults aged ≥20 years from the National Health and Nutrition Examination Survey 2011-2016. We combined linear regression and restricted cubic splines with Bayesian kernel machine regression (BKMR) to identify metals associated with each glucose metabolism index (P < 0.05 and the posterior inclusion probabilities of BKMR >0.5) in eight non-essential heavy metals (barium, cadmium, antimony, tungsten, uranium, arsenic, lead, and thallium) and glucose metabolism indexes [fasting plasma glucose (FPG), blood hemoglobin A1c (HbA1c) and homeostatic model assessment of insulin resistance (HOMA-IR)]. We identified two pairs of metals associated with glucose metabolism indexes: cadmium and tungsten to HbA1c and barium and thallium to HOMA-IR. Then, the cross-validated kernel ensemble (CVEK) approach was applied to identify the specific nutrient group (nutrients) that interacted with the association. By using the CVEK model, we identified significant interactions between the energy-adjusted diet inflammatory index (E-DII) and cadmium, tungsten and barium (all P < 0.05); macro-nutrients and cadmium, tungsten and barium (all P < 0.05); minerals and cadmium, tungsten, barium and thallium (all P < 0.05); and A vitamins and thallium (P = 0.043). Furthermore, a lower E-DII, a lower intake of carbohydrates and phosphorus, and a higher consumption of magnesium seem to attenuate the positive association between metals and glucose metabolism indexes. Our finding identifying the nutrients that interact with non-essential heavy metals could provide a feasible nutritional guideline for the general population to protect against the adverse effects of non-essential heavy metals on glucose metabolism.

Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia

BACKGROUND

Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts.

OBJECTIVES

Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach.

METHODS

BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO 2 ), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups.

RESULTS

Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO 2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO 2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants].

DISCUSSION

Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

The Associations between Exposure to Multiple Heavy Metals and Total Immunoglobulin E in U.S. Adults

Immunoglobulin E (IgE) is a type of immunoglobulin, and elevated serum total IgE is often present in allergic diseases. Exposure to environmental heavy metals has been markedly linked to allergic diseases, leading to elevated total IgE levels. However, studies concerning the effects of multiple metal exposures on total IgE levels are limited. Therefore, the current study seeks to explore the correlation between heavy-metal co-exposure and total IgE levels based on the National Health and Nutrition Examination Survey (NHANES, 2005-2006). Participants possessed complete data on total IgE levels, 11 urinary metal concentrations and other covariates. The correlations between 11 metals and total IgE levels were analyzed using multiple linear regression, and total IgE levels were a continuous variable. Total IgE levels exceeding 150 kU/L were considered sensitized. Binary logistic regression analyses were employed to assess the correlation between metal exposure and the occurrence of an allergic state. Then, the association between co-exposure to the 11 metals and total IgE levels or the occurrence of sensitization status was further analyzed by Bayesian kernel machine regression (BKMR), a multi-contaminant model. There were 1429 adults with complete data included. Based on the median concentration, molybdenum (Mo) had the highest concentration (46.60 μg/L), followed by cesium (Cs), barium (Ba), lead (Pb), and mercury (Hg). And the median (interquartile range) for total IgE levels was 43.7 (17.3, 126.0) kU/L. Multiple linear regression results showed that Pb was significantly and positively associated with total IgE levels (β = 0.165; 95% CI: 0.046, 0.284). Binary logistic regression showed a significant positive correlation between urinary Pb (OR: 1.258; 95% CI: 1.052, 1.510) and tungsten (W) (OR: 1.251; 95% CI: 1.082, 1.447). Importantly, the BKMR model found a positive correlation between combined-metal exposure and total IgE levels and the occurrence of sensitization status. The mixed heavy-metal exposure was associated with increased total IgE levels, and this association may be driven primarily by the exposure of Pb and W. This study provides new insights into the relationship between heavy-metal exposure and allergic diseases. More research is needed to confirm these findings.

Exposure to metal mixtures and adverse pregnancy and birth outcomes: A systematic review

BACKGROUND

Prenatal exposure to metal mixtures is associated with adverse pregnancy and birth outcomes like low birth weight, preterm birth, and small for gestational age. However, prior studies have used individual metal analysis, lacking real-life exposure scenarios.

OBJECTIVES

This systematic review aims to evaluate the strength and consistency of the association between metal mixtures and pregnancy and birth outcomes, identify research gaps, and inform future studies and policies in this area.

METHODS

The review adhered to the updated Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) checklist, along with the guidelines for conducting systematic reviews and meta-analyses of observational studies of etiology (COSMOS-E). Our data collection involved searching the PubMed, MEDLINE, and SCOPUS databases. We utilized inclusion criteria to identify relevant studies. These chosen studies underwent thorough screening and data extraction procedures. Methodological quality evaluations were conducted using the NOS framework for cohort and case-control studies, and the AXIS tool for cross-sectional studies.

RESULTS

The review included 34 epidemiological studies, half of which focused on birth weight, and the others investigated neonate size, preterm birth, small for gestational age, miscarriage, and placental characteristics. The findings revealed significant associations between metal mixtures (including mercury (Hg), nickel (Ni), arsenic (As), cadmium (Cd), manganese (Mn), cobalt (Co), lead (Pb), zinc (Zn), barium (Ba), cesium (Cs), copper (Cu), selenium (Se), and chromium (Cr)) and adverse pregnancy and birth outcomes, demonstrating diverse effects and potential interactions.

CONCLUSION

In conclusion, this review consistently establishes connections between metal exposure during pregnancy and adverse consequences for birth weight, gestational age, and other vital birth-related metrics. This review further demonstrates the need to apply mixture methods with caution but also shows that they can be superior to traditional approaches. Further research is warranted to deeper understand the underlying mechanisms and to develop effective strategies for mitigating the potential risks associated with metal mixture exposure during pregnancy.

Combined health effects of PM2.5 components on respiratory mortality in short-term exposure using BKMR: A case study in Sichuan, China

One of the major causes of global mortality is respiratory diseases. Fine particulate matter (PM2.5) increased the risk of respiratory death in short-term exposure. PM2.5 is the chemical mixture of components with different health effects. The combined health effects of PM2.5 are determined by the role of each component and the potential interaction between components, but they have not been studied in short-term exposure. Sichuan Province (SC), with high respiratory mortality and heavy PM2.5 pollution, had distinctive regional differences in four regions in sources and proportions of PM2.5, so it was divided into four regions to explore the combined health effects of PM2.5 components on respiratory mortality in short-term exposure and to identify the main hazardous components. Due to the multicollinear, interactive, and nonlinear characteristics of the associations between PM2.5 components and respiratory mortality, Bayesian kernel machine regression (BKMR) was used to characterize the combined health effects, along with quantile-based g-computation (QGC) as a reference. Positive combined effects of PM2.5 were found in all four regions of Sichuan using BKMR with excess risks (ER) of 0.0101-0.0132 (95 % CI: 0.0093-0.0158) and in the central basin and northwest basin using QGC with relative risks (RR) of 1.0064 (95 % CI: 1.0039, 1.0089) and 1.0044 (95 % CI: 1.0022, 1.0066), respectively. In addition, the adverse health effect was larger in cold seasons than that in warm seasons, so vulnerable people should reduce outdoor activities in heavily polluted days, especially in the cold season. For the components of PM2.5, the BC and OM mainly from traffic, dominated the adverse health effects on respiratory mortality. Furthermore, NO3- might aggravate the adverse health effects of BC/OM. Therefore, BC/OM and NO3- should be focused together in air pollution control.

Short-term air pollution exposure and exacerbation events in mild to moderate COPD: a case-crossover study within the CanCOLD cohort

BACKGROUND

Infections are considered as leading causes of acute exacerbations of chronic obstructive pulmonary disease (COPD). Non-infectious risk factors such as short-term air pollution exposure may play a clinically important role. We sought to estimate the relationship between short-term air pollutant exposure and exacerbations in Canadian adults living with mild to moderate COPD.

METHODS

In this case-crossover study, exacerbations ('symptom based': ≥48 hours of dyspnoea/sputum volume/purulence; 'event based': 'symptom based' plus requiring antibiotics/corticosteroids or healthcare use) were collected prospectively from 449 participants with spirometry-confirmed COPD within the Canadian Cohort Obstructive Lung Disease. Daily nitrogen dioxide (NO2), fine particulate matter (PM2.5), ground-level ozone (O3), composite of NO2 and O3 (Ox), mean temperature and relative humidity estimates were obtained from national databases. Time-stratified sampling of hazard and control periods on day '0' (day-of-event) and Lags ('-1' to '-6') were compared by fitting generalised estimating equation models. All data were dichotomised into 'warm' (May-October) and 'cool' (November-April) seasons. ORs and 95% CIs were estimated per IQR increase in pollutant concentrations.

RESULTS

Increased warm season ambient concentration of NO2 was associated with symptom-based exacerbations on Lag-3 (1.14 (1.01 to 1.29), per IQR), and increased cool season ambient PM2.5 was associated with symptom-based exacerbations on Lag-1 (1.11 (1.03 to 1.20), per IQR). There was a negative association between warm season ambient O3 and symptom-based events on Lag-3 (0.73 (0.52 to 1.00), per IQR).

CONCLUSIONS

Short-term ambient NO2 and PM2.5 exposure were associated with increased odds of exacerbations in Canadians with mild to moderate COPD, further heightening the awareness of non-infectious triggers of COPD exacerbations.

Micronuclei, Pesticides, and Element Mixtures in Mining Contexts: The Hormetic Effect of Selenium

The contexts where there are mining and agriculture activities are potential sources of risk to human health due to contamination by chemical mixtures. These contexts are frequent in several Colombian regions. This study explored the potential association between the frequency of micronuclei and pesticides and elements in regions with ferronickel (Montelibano, Córdoba) and gold (Nechí, Antioquia) mining, and a closed native mercury mine (Aranzazu, Caldas), with an emphasis in the potential effect of selenium as a potential chelator. A cross-sectional study was carried out with 247 individuals. Sociodemographic, occupational, and toxicological variables were ascertained. Blood and urine samples were taken for pesticide analysis (5 organophosphates, 4 organochlorines, and 3 carbamates), 68 elements were quantified in hair, and micronuclei were quantified in lymphocytes. The mixtures of elements were grouped through principal component analysis. Prevalence ratios were estimated with robust variance Poisson regressions to explore associations. Interactions of selenium with toxic elements were explored. The highest concentrations of elements were in the active mines. The potentially most toxic chemical mixture was observed in the ferronickel mine. Pesticides were detected in a low proportion of participants (<2.5%), except paraoxon-methyl in blood (27.55%) in Montelibano and paraoxon-ethyl in blood (18.81%) in Aranzazu. The frequency of micronuclei was similar in the three mining contexts, with means between 4 to 7 (p = 0.1298). There was great heterogeneity in the exposure to pesticides and elements. The "hormetic effect" of selenium was described, in which, at low doses, it acts as a chelator in Montelibano and Aranzazu, and at high doses, it can enhance the toxic effects of other elements, maybe as in Nechí. Selenium can serve as a protective agent, but it requires adaptation to the available concentrations in each region to avoid its toxic effects.

Exposure of Akwesasne Mohawk women to polychlorinated biphenyls and hexachlorobenzene is associated with increased serum levels of thyroid peroxidase autoantibodies

Persistent organic pollutants (POPs) including polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB), and dichlorodiphenyltrichloroethane (p,p'-DDT) were reported to influence immunological activity. As endocrine-disrupting chemicals (EDC), these pollutants may disrupt normal thyroid function and act as catalysts for development of autoimmune thyroid disease by directly and indirectly affecting levels of thyroid peroxidase antibodies (TPOAbs). Native American communities are disproportionately exposed to harmful toxicants and are at an increased risk of developing an autoimmune disease. The aim of this study was to determine the association between POPs and TPOAbs in serum obtained from Native American women. This assessment was used to measure whether increased risk of autoimmune thyroid disease occurred as a result of exposure to POPs. Data were collected from 183 Akwesasne Mohawk women, 21-38 years of age, between 2009 and 2013. Multivariate analyses were conducted to determine the association between toxicant exposure and levels of TPOAbs. In multiple logistic regression analyses, exposure to PCB congener 33 was related to elevated risk of individuals possessing above normal levels of TPOAbs. Further, HCB was associated with more than 2-fold higher risk of possessing above normal levels of TPOAbs compared to women with normal levels of TPOAbs. p,p'-DDE was not associated with TPOAb levels within this study. Exposure to PCB congener 33 and HCB was correlated with above normal levels of TPOAbs, a marker of autoimmune thyroid disease. Additional investigations are needed to establish the causes and factors surrounding autoimmune thyroid disease which are multiple and complex.

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).

Exposure to multiple air pollutant mixtures and the subtypes of hypertensive disorders in pregnancy: A multicenter study

BACKGROUND

Hypertensive disorders in pregnancy (HDP) have heterogeneous etiologies. Previous studies have linked individual air pollutants to overall HDP with inconsistent results. Moreover, it has not been explored how exposure to a mixture of multiple air pollutants may affect the risks of the subtypes of the disorders.

OBJECTIVES

To investigate the associations of exposure to air pollutant mixture in the 1st and 2nd trimesters of pregnancy with the risks of HDP and its subtypes.

METHODS

Pregnancy data were obtained from the China Labor and Delivery Survey, a nationwide cross-sectional survey in 2015 and 2016. Levels of air pollutants [including fine particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2)] in the 1st and 2nd trimesters were estimated based on the model developed by the Institution of Atmospheric Physics, Chinese Academy of Science. Generalized linear mixed models were built to assess the single-exposure effects of air pollutants in early gestation on HDP. The restricted cubic spline function was further applied to assess the potential non-linearity. The weighted quantile sum (WQS) regression was used to investigate the effects of co-exposure to multiple air pollutants.

RESULTS

A total of 67,512 pregnancies were included, and 2,834 were HDP cases. The single-effect analysis showed that CO, PM2.5, and SO2 exposure in the 2nd trimester was positively associated with the risks of gestational hypertension (GH), with adjusted odds ratios (aORs) and 95% confidence intervals (CI) of 1.16 (1.04, 1.28), 1.19 (1.04, 1.37), and 1.13 (1.04, 1.22), respectively. The first-trimester O3 exposure was also associated with an increased preeclampsia/eclampsia (PE) risk (aOR = 1.17; 95%CI: 1.02, 1.33). WQS regression confirmed positive associations of air pollutant mixture with HDP subtypes, with PM2.5 as the main contributing pollutant to GH, and CO and O3 as the main pollutants to PE.

CONCLUSIONS

Exposure to multiple air pollutant mixtures in early pregnancy was associated with increased risks of hypertensive disorders in pregnancy.

Concomitant exposure to air pollution, green space, and noise and risk of stroke: a cohort study from Denmark

Background

Air pollution, road traffic noise, and green space are correlated factors, associated with risk of stroke. We investigated their independent relationship with stroke in multi-exposure analyses and estimated their cumulative stroke burden.

Methods

For all persons, ≥50 years of age and living in Denmark from 2005 to 2017, we established complete address histories and estimated running 5-year mean exposure to fine particles (PM_2.5), ultrafine particles, elemental carbon, nitrogen dioxide (NO₂), and road traffic noise at the most, and least exposed façade. For air pollutants, we estimated total, and non-traffic contributions. Green space around the residence was estimated from land use maps. Hazard ratios (HR) and 95% confidence limits (CL) were estimated with Cox proportional hazards models and used to calculate cumulative risk indices (CRI). We adjusted for the individual and sociodemographic covariates available in our dataset (which did not include information about individual life styles and medical conditions).

Findings

The cohort accumulated 18,344,976 years of follow-up and 94,256 cases of stroke. All exposures were associated with risk of stroke in single pollutant models. In multi-pollutant analyses, only PM_2.5 (HR: 1.058, 95% CI: 1.040-1.075) and noise at most exposed façade (HR: 1.033, 95% CI: 1.024-1.042) were independently associated with a higher risk of stroke. Both noise and air pollution contributed substantially to the CRI (1.103, 95% CI: 1.092-1.114) in the model with noise, green space, and total PM_2.5 concentrations.

Interpretation

Environmental exposure to air pollution and noise were both independently associated with risk of stroke.

Funding

Health Effects Institute (HEI) (Assistance Award No. R-82811201).

A weighted quantile sum regression with penalized weights and two indices

Background

New statistical methodologies were developed in the last decade to face the challenges of estimating the effects of exposure to multiple chemicals. Weighted Quantile Sum (WQS) regression is a recent statistical method that allows estimating a mixture effect associated with a specific health effect and identifying the components that characterize the mixture effect.

Objectives

In this study, we propose an extension of WQS regression that estimates two mixture effects of chemicals on a health outcome in the same model through the inclusion of two indices, one in the positive direction and one in the negative direction, with the introduction of a penalization term.

Methods

To evaluate the performance of this new model we performed both a simulation study and a real case study where we assessed the effects of nutrients on obesity among adults using the National Health and Nutrition Examination Survey (NHANES) data.

Results

The method showed good performance in estimating both the regression parameter and the weights associated with the single elements when the penalized term was set equal to the magnitude of the Akaike information criterion of the unpenalized WQS regression. The two indices further helped to give a better estimate of the parameters [Positive direction Median Error (PME): 0.022; Negative direction Median Error (NME): -0.044] compared to the standard WQS without the penalization term (PME: -0.227; NME: 0.215). In the case study, WQS with two indices was able to find a significant effect of nutrients on obesity in both directions identifying sodium and magnesium as the main actors in the positive and negative association, respectively.

Discussion

Through this work, we introduced an extension of WQS regression that improved the accuracy of the parameter estimates when considering a mixture of elements that can have both a protective and a harmful effect on the outcome; and the advantage of adding a penalization term when estimating the weights.

The influence of the co-exposure to polycyclic aromatic hydrocarbons and toxic metals on DNA damage in brazilian lactating women and their infants: A cross-sectional study using machine learning approaches

Polycyclic aromatic hydrocarbons (PAHs) and toxic metals are widely spread pollutants of public health concern. The co-contamination of these chemicals in the environment is frequent, but relatively little is known about their combined toxicities. In this context, this study aimed to evaluate the influence of the co-exposure to PAHs and toxic metals on DNA damage in Brazilian lactating women and their infants using machine learning approaches. Data were collected from an observational, cross-sectional study with 96 lactating women and 96 infants living in two cities. The exposure to these pollutants was estimated by determining urinary levels of seven mono-hydroxylated PAH metabolites and the free form of three toxic metals. 8-Hydroxydeoxyguanosine (8-OHdG) levels in the urine were used as the oxidative stress biomarker and set as the outcome. Individual sociodemographic factors were also collected using questionnaires. Sixteen machine learning algorithms were trained using 10-fold cross-validation to investigate the associations of urinary OH-PAHs and metals with 8-OHdG levels. This approach was also compared with models attained by multiple linear regression. The results showed that the urinary concentration of OH-PAHs was highly correlated between the mothers and their infants. Multiple linear regression did not show a statistically significant association between the contaminants and urinary 8OHdG levels. Machine learning models indicated that all investigated variables did not present predictive performance on 8-OHdG concentrations. In conclusion, PAHs and toxic metals were not associated with 8-OHdG levels in Brazilian lactating women and their infants. These novelty and originality results were achieved even after applying sophisticated statistical models to capture non-linear relationships. However, these findings should be interpreted cautiously because the exposure to the studied contaminants was considerably low, which may not reflect other populations at risk.

Combined Effects of Multiple Per- and Polyfluoroalkyl Substances Exposure on Allostatic Load Using Bayesian Kernel Machine Regression

This study aims to investigate the combined effects of per- and polyfluoroalkyl substances (PFAS) on allostatic load, an index of chronic stress that is linked to several chronic diseases, including cardiovascular disease and cancer. Using data from the National Health and Nutrition Examination Survey (NHANES) 2007-2014, this study examines the relationship between six PFAS variables (PFDE, PFNA, PFOS, PFUA, PFOA, and PFHS) and allostatic load using Bayesian Kernel Machine Regression (BKMR) analysis. The study also investigates the impact of individual and combined PFAS exposure on allostatic load using various exposure-response relationships, such as univariate, bivariate, or multivariate models. The analysis reveals that the combined exposure to PFDE, PFNA, and PFUA had the most significant positive trend with allostatic load when it was modeled as a binary variable, while PFDE, PFOS, and PFNA had the most significant positive trend with allostatic load when modeled as a continuous variable. These findings provide valuable insight into the consequences of cumulative exposure to multiple PFAS on allostatic load, which can help public health practitioners identify the dangers associated with potential combined exposure to select PFAS of interest. In summary, this study highlights the critical role of PFAS exposure in chronic stress-related diseases and emphasizes the need for effective strategies to minimize exposure to these chemicals to reduce the risk of chronic diseases. It underscores the importance of considering the combined effects of PFAS when assessing their impact on human health and offers valuable information for policymakers and regulators to develop strategies to protect public health.

The association between multi-heavy metals exposure and lung function in a typical rural population of Northwest China

BACKGROUND

Heavy metal exposure is acknowledged to be associated with decrease of lung function, but the relationship between metals co-exposure and lung function in rural areas of Northwest China remains unclear, particularly in an area famous for heavy metal pollution and solid fuel use. Therefore, the purpose of this study is to explore the effects of heavy metal exposure on lung function and the potential impacts of living habit in a rural cohort of Northwest China.

METHODS

The study area included five villages of two regions in Northwestern China-Gansu province. All participants were recruited from the Dongdagou-Xinglong (DDG-XL) rural cohort in the study area. Urine levels of 10 common and representative heavy metals were detected by ICP-MS, including Cobalt (Co), Nickel (Ni), Molybdenum (Mo), Cadmium (Cd), Stibium (Sb), Copper (Cu), Zinc (Zn), Mercury (Hg), Lead (Pb), and Manganese (Mn). The lung function was detected by measuring percentages of predicted forced vital capacity (FVC%) and predicted forced expiratory volume in one second (FEV1%) as well as the ratio of FEV1/FVC. We also analyzed the association between heavy metals and pulmonary ventilation dysfunction (PVD). Restricted cubic spline, logistic regression, linear regression, and bayesian kernel machine regression (BKMR) model were used to analyze the relationship between heavy metal exposure and lung function.

RESULTS

Finally, a total of 382 participants were included in this study with an average age of 56.69 ± 7.32 years, and 82.46% of them used solid fuels for heating and cooking. Single metal exposure analysis showed that the higher concentration of Hg, Mn, Sb, and lower Mo may be risk factors for PVD. We also found that FEV1% and FVC% were negatively correlated with Sb, Hg, and Mn, but positively correlated with Mo. The effect of mixed heavy metals exposure could be observed through BKMR model, through which we found the lung function decreased with the increase of heavy metal concentration. Furthermore, the males, BMI ≥ 24 kg/m2 and who used solid fuels showed a higher risk of PVD when exposed to Co, Zn, and Hg.

CONCLUSIONS

Our results suggested that heavy metal exposure was associated with decrease of lung function regardless of single exposure or mixed exposure, particularly for Sb, Hg, Mn and those who use solid fuels.