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

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.

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.

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.

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.

Relationship of long-term air pollution exposure with asthma and rhinitis in Italy: an innovative multipollutant approach

BACKGROUND

air pollution is a complex mixture; novel multipollutant approaches could help understanding the health effects of multiple concomitant exposures to air pollutants.

AIM

to assess the relationship of long-term air pollution exposure with the prevalence of respiratory/allergic symptoms and diseases in an Italian multicenter study using single and multipollutant approaches.

METHODS

14420 adults living in 6 Italian cities (Ancona, Pavia, Pisa, Sassari, Turin, Verona) were investigated in 2005-2011 within 11 different study cohorts. Questionnaire information about risk factors and health outcomes was collected. Machine learning derived mean annual concentrations of PM₁₀, PM_2.5, NO₂ and mean summer concentrations of O₃ (μg/m³) at residential level (1-km resolution) were used for the period 2013-2015. The associations between the four pollutants and respiratory/allergic symptoms/diseases were assessed using two approaches: a) logistic regression models (single-pollutant models), b) principal component logistic regression models (multipollutant models). All the models were adjusted for age, sex, education level, smoking habits, season of interview, climatic index and included a random intercept for cohorts.

RESULTS

the three-year average (± standard deviation) pollutants concentrations at residential level were: 20.3 ± 6.8 μg/m³ for PM_2.5, 29.2 ± 7.0 μg/m³ for PM₁₀, 28.0 ± 11.2 μg/m³ for NO₂, and 70.9 ± 4.3 μg/m³ for summer O_3. Through the multipollutant models the following associations emerged: PM₁₀ and PM_2.5 were related to 14-25% increased odds of rhinitis, 23-34% of asthma and 30-33% of night awakening; NO₂ was related to 6-9% increased odds of rhinitis, 7-8% of asthma and 12% of night awakening; O₃ was associated with 37% increased odds of asthma attacks. Overall, the Odds Ratios estimated through the multipollutant models were attenuated when compared to those of the single-pollutant models.

CONCLUSIONS

this study enabled to obtain new information about the health effects of air pollution on respiratory/allergic outcomes in adults, applying innovative methods for exposure assessment and multipollutant analyses.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The Association of Combined Per- and Polyfluoroalkyl Substances and Metals with Allostatic Load Using Bayesian Kernel Machine Regression

Background/Objective: This study aimed to investigate the effect of exposure to per- and polyfluoroalkyl substances (PFAS), a class of organic compounds utilized in commercial and industrial applications, on allostatic load (AL), a measure of chronic stress. PFAS, such as perfluorodecanoic acid (PFDE), perfluorononanoic acid (PFNA), perfluorooctane sulfonic acid (PFOS), perfluoroundecanoic acid (PFUA), perfluorooctanoic acid (PFOA), and perfluorohexane sulfonic acid (PFHS), and metals, such as mercury (Hg), barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (TI), tungsten (W), and uranium (U) were investigated. This research was performed to explore the effects of combined exposure to PFAS and metals on AL, which may be a disease mediator. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014 were used to conduct this study on persons aged 20 years and older. A cumulative index of 10 biomarkers from the cardiovascular, inflammatory, and metabolic systems was used to calculate AL out of 10. If the overall index was ≥ 3, an individual was considered to be chronically stressed (in a state of AL). In order to assess the dose-response connections between mixtures and outcomes and to limit the effects of multicollinearity and other potential interaction effects between exposures, Bayesian kernel machine regression (BKMR) was used. Results: The most significant positive trend between mixed PFAS and metal exposure and AL was revealed by combined exposure to cesium, molybdenum, PFHS, PFNA, and mercury (posterior inclusion probabilities, PIP = 1, 1, 0.854, 0.824, and 0.807, respectively). Conclusions: Combined exposure to metals and PFAS increases the likelihood of being in a state of AL.

Modelling socioeconomic position as a driver of the exposome in the first 18 months of life of the NINFEA birth cohort children

BACKGROUND

The exposome drivers are less studied than its consequences but may be crucial in identifying population subgroups with unfavourable exposures.

OBJECTIVES

We used three approaches to study the socioeconomic position (SEP) as a driver of the early-life exposome in Turin children of the NINFEA cohort (Italy).

METHODS

Forty-two environmental exposures, collected at 18 months of age (N = 1989), were classified in 5 groups (lifestyle, diet, meteoclimatic, traffic-related, built environment). We performed cluster analysis to identify subjects sharing similar exposures, and intra-exposome-group Principal Component Analysis (PCA) to reduce the dimensionality. SEP at childbirth was measured through the Equivalised Household Income Indicator. SEP-exposome association was evaluated using: 1) an Exposome Wide Association Study (ExWAS), a one-exposure (SEP) one-outcome (exposome) approach; 2) multinomial regression of cluster membership on SEP; 3) regressions of each intra-exposome-group PC on SEP.

RESULTS

In the ExWAS, medium/low SEP children were more exposed to greenness, pet ownership, passive smoking, TV screen and sugar; less exposed to NO₂, NO_X, PM_25abs, humidity, built environment, traffic load, unhealthy food facilities, fruit, vegetables, eggs, grain products, and childcare than high SEP children. Medium/low SEP children were more likely to belong to a cluster with poor diet, less air pollution, and to live in the suburbs than high SEP children. Medium/low SEP children were more exposed to lifestyle PC1 (unhealthy lifestyle) and diet PC2 (unhealthy diet), and less exposed to PC1s of the built environment (urbanization factors), diet (mixed diet), and traffic (air pollution) than high SEP children.

CONCLUSIONS

The three approaches provided consistent and complementary results, suggesting that children with lower SEP are less exposed to urbanization factors and more exposed to unhealthy lifestyles and diet. The simplest method, the ExWAS, conveys most of the information and is more replicable in other populations. Clustering and PCA may facilitate results interpretation and communication.

Relationship of multiple metals mixture and osteoporosis in older Chinese women: An aging and longevity study

Osteoporosis has become a major health problem in older women. Previous studies have linked individual metals exposure with osteoporosis, but combined effects remain inconclusive. We aimed to explore the individual and combined association between multiple metals mixture and osteoporosis risk in older Chinese women. A total of 2297 older women (aged ≥60) from the Hongshuihe region of Guangxi, southern China included. We measured 22 blood metal levels through inductively coupled plasma mass spectrometry. And osteoporosis was defined as a T score ≤ -2.5. The least absolute shrinkage and selection operator (LASSO) penalized regression, and Bayesian kernel machine regression (BKMR) models were performed to explore the association between blood metals and osteoporosis risk. Of 2297 older women, there were 829 osteoporosis and 1468 non-osteoporosis participants. The median age was 71 and 68 years old in the osteoporosis and the non-osteoporosis group, respectively. In the single-metal model, rubidium and vanadium were negatively associated with osteoporosis (P for trend = 0.02 and 0.002, respectively), and lead presented the reverse trend (P for trend = 0.01). The LASSO penalized regression model selected nine metals (calcium, cadmium, cobalt, lead, magnesium, rubidium, strontium, vanadium and zinc), which were included in the subsequent analysis. And the multiple-metal model presented a consistent trend with the single-metal model using the selected metals. Furthermore, we performed BKMR to explore the combined effect, and found an overall negative effect between metals mixture and osteoporosis risk when all the metals were fixed at 50th, and rubidium and vanadium were the main contributors. In addition, blood Rb and V were significantly negatively related to OP risk with other metals at different levels (25th, 50th and 75th percentiles). The study suggests metal mixture exposure and osteoporosis risk in older Chinese women, and further studies need to be conducted.

Association between chemical mixtures and female fertility in women undergoing assisted reproduction in Sweden and Estonia

OBJECTIVE

Women of reproductive age are exposed to ubiquitous chemicals such as phthalates, parabens, and per- and polyfluoroalkyl substances (PFAS), which have potential endocrine disrupting properties and might affect fertility. Our objective was to investigate associations between potential endocrine-disrupting chemicals (EDCs) and female fertility in two cohorts of women attending fertility clinics.

METHODS

In a total population of 333 women in Sweden and Estonia, we studied the associations between chemicals and female fertility, evaluating ovarian sensitivity index (OSI) as an indicator of ovarian response, as well as clinical pregnancy and live birth from fresh and frozen embryo transfers. We measured 59 chemicals in follicular fluid samples and detected 3 phthalate metabolites, di-2-ethylhexyl phthalate (DEHP) metabolites, 1 paraben, and 6 PFAS in >90% of the women. Associations were evaluated using multivariable-adjusted linear or logistic regression, categorizing EDCs into quartiles of their distributions, as well as with Bayesian Kernel Machine Regression.

RESULTS

We observed statistically significant lower OSI at higher concentrations of the sum of DEHP metabolites in the Swedish cohort (Q4 vs Q1, β = -0.21, 95% CI: -0.38, -0.05) and methylparaben in the Estonian cohort (Q3 vs Q1, β = -0.22, 95% CI: -0.44, -0.01). Signals of potential associations were also observed at higher concentrations of PFUnDA in both the combined population (Q2 vs. Q1, β = -0.16, 95% CI -0.31, -0.02) and the Estonian population (Q2 vs. Q1, β = -0.27, 95% CI -0.45, -0.08), and for PFOA in the Estonian population (Q4 vs. Q1, β = -0.31, 95% CI -0.61, -0.01). Associations of chemicals with clinical pregnancy and live birth presented wide confidence intervals.

CONCLUSIONS

Within a large chemical mixture, we observed significant inverse associations levels of DEHP metabolites and methylparaben, and possibly PFUnDA and PFOA, with OSI, suggesting that these chemicals may contribute to altered ovarian function and infertility in women.

Human health risk assessment for contaminated sites: A retrospective review

Soil contamination is a serious global hazard as contaminants can migrate to the human body through the soil, water, air, and food, threatening human health. Human Health Risk Assessment (HHRA) is a commonly used method for estimating the magnitude and probability of adverse health effects in humans that may be exposed to contaminants in contaminated environmental media in the present or future. Such estimations have improved for decades with various risk assessment frameworks and well-established models. However, the existing literature does not provide a comprehensive overview of the methods and models of HHRA that are needed to grasp the current status of HHRA and future research directions. Thus, this paper aims to systematically review the HHRA approaches and models, particularly those related to contaminated sites from peer-reviewed literature and guidelines. The approaches and models focus on methods used in hazard identification, toxicity databases in dose-response assessment, approaches and fate and transport models in exposure assessment, risk characterization, and uncertainty characterization. The features and applicability of the most commonly used HHRA tools are also described. The future research trend for HHRA for contaminated sites is also forecasted. The transition from animal experiments to new methods in risk identification, the integration and update and sharing of existing toxicity databases, the integration of human biomonitoring into the risk assessment process, and the integration of migration and transformation models and risk assessment are the way forward for risk assessment in the future. This review provides readers with an overall understanding of HHRA and a grasp of its developmental direction.

Chemical mixture exposure patterns and obesity among U.S. adults in NHANES 2005-2012

BACKGROUND

The effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated.

OBJECTIVES

To discover obesity-related mixed chemical exposure patterns in the general U.S.

METHODS

Sparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR).

RESULTS

Eight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity.

CONCLUSION

Our study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.

Associations between prenatal multiple plasma metal exposure and newborn telomere length: Effect modification by maternal age and infant sex

Exposure to metals during pregnancy may affect maternal and infant health. However, studies on the combined effects of metals on the telomere length (TL) of newborns are limited. A prospective cohort study was conducted among 1313 mother-newborn pairs in the Guangxi Zhuang Birth Cohort. The concentrations of metals in maternal plasma during the first trimester were measured using inductively coupled plasma-mass spectrometry. We explored the associations between nine plasma metals and newborn TL using generalized linear models (GLMs), principal component analysis (PCA), quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). The GLMs revealed the inverse association between plasma arsenic (percent change, -5.56%; 95% CI: -7.69%, -3.38%) and barium concentrations (-9.84%; 95% CI: -13.81%, -5.68%) and newborn TL. Lead levels were related to significant decreases in newborn TL only in females. The PCA revealed a negative association between the PC3 and newborn TL (-4.52%; 95% CI: -6.34%, -2.68%). In the BKMR, the joint effect of metals was negatively associated with newborn TL. Qgcomp indicated that each one-tertile increase in metal mixture levels was associated with shorter newborn TL (-9.39%; 95% CI: -14.32%, -4.18%). The single and joint effects of multiple metals were more pronounced among pregnant women carrying female fetuses and among pregnant women <28 years of age. The finding suggests that prenatal exposure to arsenic, barium, antimony, and lead and mixed metals may shorten newborn TLs. The relationship between metal exposures and newborn TL may exhibit heterogeneities according to infant sex and maternal age.

Associations between the urban exposome and type 2 diabetes: Results from penalised regression by least absolute shrinkage and selection operator and random forest models

BACKGROUND

Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety of exposures in their association with prevalent T2D by applying penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches. We contrasted the findings with single-exposure models including consistently associated risk factors reported by previous studies.

METHODS

Baseline data (n = 14,829) of the Occupational and Environmental Health Cohort study (AMIGO) were enriched with 85 exposome factors (air pollution, noise, built environment, neighbourhood socio-economic factors etc.) using the home addresses of participants. Questionnaires were used to identify participants with T2D (n = 676(4.6 %)). Models in all applied statistical approaches were adjusted for individual-level socio-demographic variables.

RESULTS

Lower average home values, higher share of non-Western immigrants and higher surface temperatures were related to higher risk of T2D in the multivariable models (LASSO, RF). Selected variables differed between the two multi-variable approaches, especially for weaker predictors. Some established risk factors (air pollutants) appeared in univariate analysis but were not among the most important factors in multivariable analysis. Other established factors (green space) did not appear in univariate, but appeared in multivariable analysis (RF). Average estimates of the prediction error (logLoss) from nested cross-validation showed that the LASSO outperformed both RF and ANN approaches.

CONCLUSIONS

Neighbourhood socio-economic and socio-demographic characteristics and surface temperature were consistently associated with the risk of T2D. For other physical-chemical factors associations differed per analytical approach.

Air pollution, road traffic noise and lack of greenness and risk of type 2 diabetes: A multi-exposure prospective study covering Denmark

OBJECTIVE

Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective.

METHODS

We estimated 5-year residential mean exposure to fine particles (PM_2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO₂) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates.

RESULTS

In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO₂ remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO₂, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149).

CONCLUSIONS

Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.

Air Pollution: Possible Interaction between the Immune and Nervous System?

Exposure to environmental pollutants is a serious and common public health concern associated with growing morbidity and mortality worldwide, as well as economic burden. In recent years, the toxic effects associated with air pollution have been intensively studied, with a particular focus on the lung and cardiovascular system, mainly associated with particulate matter exposure. However, epidemiological and mechanistic studies suggest that air pollution can also influence skin integrity and may have a significant adverse impact on the immune and nervous system. Air pollution exposure already starts in utero before birth, potentially causing delayed chronic diseases arising later in life. There are, indeed, time windows during the life of individuals who are more susceptible to air pollution exposure, which may result in more severe outcomes. In this review paper, we provide an overview of findings that have established the effects of air pollutants on the immune and nervous system, and speculate on the possible interaction between them, based on mechanistic data.

Identification of Health Effects of Complex Air Pollution in China

After the Chinese government introduced a series of policies to strengthen the control of air pollution, the concentration of particulate matter has decreased, but the concentration of ozone has increased, and the problem of complex air pollution still exists, posing a serious threat to public health. Therefore, disentangling the health effect of multi-pollutants has been a long-discussed challenge in China. To evaluate the adverse effects of complex air pollution, a generalized additive model was used to assess the health risks of different pollution types in eight metropolises in different climates in China from 2013 to 2016. Instead of directly introducing multiple pollutant concentrations, we integrated the concentration levels of PM_2.5, NO₂, and O₃ into a set of predictors by grouping methods and divided air pollution into three high single-pollutant types and four high multi-pollutant types to calculate mortality risk in different types. The comprehensive results showed that the impact of high multi-pollutant types on mortality risk was greater than that of high single-pollutant types. Throughout the study period, the high multi-pollutant type with high PM_2.5, NO₂, and O₃ and the high multi-pollutant type with high PM_2.5 and NO₂ were more associated with death, and the highest RRs were 1.129 (1.080, 1.181) and 1.089 (1.066, 1.113), respectively. In addition, the pollution types that most threaten people are different in different cities. These differences may be related to different pollution conditions, pollutant composition, and indoor-outdoor activity patterns in different cities. Seasonally, the risk of complex air pollution is greater in most cities in the warm season than in the cold season. This may be caused by the modifying effects of high temperature on pollutants in addition to different indoor-outdoor activity patterns in different seasons. The results also show that calculating the effect of individual air pollutants separately and adding them together may lead to an overestimation of the combined effect. It further highlights the urgency and need for air pollution health research to move towards a multi-pollutant approach that considers air pollution as a whole in the context of atmospheric abatement and global warming.

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.

Associations of multiple plasma metals with chronic kidney disease in patients with diabetes

As common contaminants, metals are non-negligible risk factors for diabetes and chronic kidney disease. However, whether there is an association between multiple metals exposure and incident chronic kidney disease (CKD) risk in patients with diabetes is unclear. We conducted a prospective study to evaluate these associations. In total, 3071 diabetics with baseline estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m² from the Dongfeng-Tongji cohort were included. We measured baseline plasma concentrations of 23 metals and investigated the associations between plasma metal concentrations and CKD in diabetics using logistic regression, the least absolute shrinkage and selection operator (LASSO), and the Bayesian Kernel Machine Regression (BKMR) models. During average 4.6 years of follow-up, 457 diabetics developed CKD (14.9 %). The three models consistently found plasma levels of zinc, arsenic, and rubidium had a positive association with incident CKD risk in patients with diabetes, while titanium, cadmium, and lead had an inverse correlation. The results of BKMR showed a significant and positive overall effect of 23 metals on the risk of CKD, when all of the metals were above the 50th percentile as compared to the median value. In addition, potential interactions of zinc and arsenic, zinc and cadmium, zinc and lead, titanium and arsenic, and cadmium and lead on CKD risk were observed. In summary, we found significant associations of plasma titanium, zinc, arsenic, rubidium, cadmium, and lead with CKD in diabetes and interactions between these metals except for rubidium. Co-exposure to multiple metals was associated with increased CKD risk in diabetics.

Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort

Despite continuous growth in dietary pattern research, the relative importance of each dietary component in the overall pattern and their joint effects on mortality risk have not been examined adequately. We explored the individual and joint associations of multiple food groups with all-cause and cause-specific mortality (cardiovascular disease (CVD) or cancer), by analyzing data from a cohort of 3995 Hong Kong Chinese older adults in the Mr. Osteoporosis (OS) and Ms. OS Study. Cox proportional hazards models were used to examine the associations of food groups with mortality risk. The individual and joint contribution of food groups to mortality risk has been quantified by a machine learning approach, i.e., the Quantile G-Computation. When comparing the highest with the lowest quartile of intake, dark green and leafy vegetables (hazard ratio (HR) = 0.82, 95% confidence interval (CI) = 0.70 to 0.96, Ptrend = 0.049), fruit (HR = 0.79, 95% CI = 0.68 to 0.93, Ptrend = 0.006), legumes (HR = 0.75, 95% CI = 0.63 to 0.87, Ptrend = 0.052), mushroom and fungi (HR = 0.76, 95% CI = 0.65 to 0.88, Ptrend = 0.023), soy and soy products (HR = 0.77, 95% CI = 0.66 to 0.90, Ptrend = 0.143), and whole grains (HR = 0.76, 95% CI = 0.65 to 0.89, Ptrend = 0.008) were inversely associated with all-cause mortality. Legume intake was associated with a lower risk of CVD mortality, while fruit, nuts, soy and soy products were associated with a lower risk of cancer mortality. From the Quantile G-Computation, whole grains, legumes, fruits, mushroom and fungi, soy and soy products had a higher relative weighting on mortality risk, and the joint effect of food groups was inversely associated with the mortality risk due to all-causes (HR = 0.39, 95% CI = 0.27 to 0.55), CVD (HR = 0.78, 95% CI = 0.67 to 0.91), and cancer (HR = 0.31, 95% CI = 0.15 to 0.65). From a sex-stratified analysis, most associations between food groups (whole grains, legumes, fruits, mushroom and fungi, soy and soy products) and mortality risk remained significant among men. In conclusion, whole grains, legumes, fruits, mushroom and fungi, soy and soy products were the main contributors to a reduction in mortality risk, and their joint effects were stronger than individual food groups. Moreover, the sex-specific association of sweets and desserts with cancer mortality may be worth further investigation.

Patterns of Perceived Indoor Environment in Danish Homes

The indoor environment is composed of several exposures existing simultaneously. Therefore, it might be useful to combine exposures into common combined measures when used to assess the association with health. The aim of our study was to identify patterns of the perceived indoor environment. Data from the Danish Health and Morbidity Survey in the year 2000 were used. The perceived indoor environment was assessed using a questionnaire (e.g., annoyances from noise, draught, and stuffy air; 13 items in total). Factor analysis was used to explore the structure of relationships between these 13 items. Furthermore, groups of individuals with similar perceived indoor environment were identified using latent class analysis. A total of 16,688 individuals ≥16 years participated. Their median age was 46 years. Four factors were extracted from the factor analysis. The factors were characterized by: (1) a mixture of items, (2) temperature, (3) traffic, and (4) neighbor noise. Moreover, three groups of individuals sharing the same perception of their indoor environment were identified. They were characterized by: a low (n = 14,829), moderate (n = 980), and large number of annoyances (n = 879). Observational studies need to take this correlation and clustering of perceived annoyances into account when studying associations between the indoor environment and health.

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

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