Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration

Lead-associated mortality in the US 1999-2020: a time-stratified analysis of a national cohort

OBJECTIVES

We undertook time-stratified analyses of the National Health and Nutrition Examination Survey in the US to assess time trends (1999-2020) in the associations of blood lead (BL) with blood pressure, mortality, the BL-associated population attributable fraction (PAF).

METHODS

Vital status of participants, 20-79 years old at enrolment, was ascertained via the National Death Index. Regressions, mediation analyses and PAF were multivariable adjusted and standardized to 2020 US Census data.

RESULTS

In time-stratified analyses, BL decreased from 1.76 μg/dl in 1999-2004 to 0.93 μg/dl in 2017-2020, while the proportion of individuals with BL < 1 μg/dl increased from 19.2% to 63.0%. Total mortality was unrelated to BL (hazard ratio (HR) for a fourfold BL increment: 1.05 [95% confidence interval, CI: 0.93-1.17]). The HR for cardiovascular death was 1.44 (1.01-2.07) in the 1999-2000 cycle, but lost significance thereafter. BL was directly related to cardiovascular mortality, whereas the indirect BL pathway via BP was not significant. Low socioeconomic status (SES) was directly related to BL and cardiovascular mortality, but the indirect SES pathway via BL lost significance in 2007-2010. From 1999-2004 to 2017-2020, cardiovascular PAF decreased ( P  < 0.001) from 7.80% (0.17-14.4%) to 2.50% (0.05-4.68%) and number of lead-attributable cardiovascular deaths from 53 878 (1167-99 253) to 7539 (160-14 108).

CONCLUSION

Due to implementation of strict environmental policies, lead exposure is no longer associated with total mortality, and the mildly increased cardiovascular mortality is not associated with blood lead via blood pressure in the United States.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Association of Metal Exposure with Novel Immunoinflammatory Indicators

Objective: We aimed to investigate the relationship between metal exposure and novel immunoinflammatory indicators. Methods: Data on adults participating in the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2018 were analyzed. Various statistical models were employed to assess the association between metal exposure and novel immune-inflammation-related indicators. Additionally, the impact of metal exposure on inflammation in different gender populations was explored. Results: This study included 4482 participants, of whom 51.1% were male. Significant correlations were observed among various metals. Both elastic net (ENET) and linear regression models revealed robust associations between cadmium (Cd), cobalt (Co), arsenic (As), mercury (Hg), and immunoinflammatory indicators. Weighted quantile sum (WQS) and Quantile g-computation (Q-gcomp) models demonstrated strong associations between barium (Ba), Co, and Hg and immunoinflammatory indicators. Bayesian kernel machine regression (BKMR) analysis indicated an overall positive correlation between in vivo urinary metal levels and systemic inflammation response index (SIRI) and aggregate index of systemic inflammation (AISI). Furthermore, Co, As, and Hg emerged as key metals contributing to changes in novel immunoinflammatory indicators. Conclusions: Metals exhibit associations with emerging immunoinflammatory indicators, and concurrent exposure to mixed metals may exacerbate the inflammatory response. Furthermore, this relationship varies across gender populations.

Long-term exposure to PM2.5 species and all-cause mortality among Medicare patients using mixtures analyses

BACKGROUND

The relationship between long-term exposure to PM2.5 and mortality is well-established; however, the role of individual species is less understood.

OBJECTIVES

In this study, we assess the overall effect of long-term exposure to PM2.5 as a mixture of species and identify the most harmful of those species while controlling for the others.

METHODS

We looked at changes in mortality among Medicare participants 65 years of age or older from 2000 to 2018 in response to changes in annual levels of 15 PM2.5 components, namely: organic carbon, elemental carbon, nickel, lead, zinc, sulfate, potassium, vanadium, nitrate, silicon, copper, iron, ammonium, calcium, and bromine. Data on exposure were derived from high-resolution, spatio-temporal models which were then aggregated to ZIP code. We used the rate of deaths in each ZIP code per year as the outcome of interest. Covariates included demographic, temperature, socioeconomic, and access-to-care variables. We used a mixtures approach, a weighted quantile sum, to analyze the joint effects of PM2.5 species on mortality. We further looked at the effects of the components when PM2.5 mass levels were at concentrations below 8 μg/m3, and effect modification by sex, race, Medicaid status, and Census division.

RESULTS

We found that for each decile increase in the levels of the PM2.5 mixture, the rate of all-cause mortality increased by 1.4% (95% CI: 1.3%-1.4%), the rate of cardiovascular mortality increased by 2.1% (95% CI: 2.0%-2.2%), and the rate of respiratory mortality increased by 1.7% (95% CI: 1.5%-1.9%). These effects estimates remained significant and slightly higher when we restricted to lower concentrations. The highest weights for harmful effects were due to organic carbon, nickel, zinc, sulfate, and vanadium.

CONCLUSIONS

Long-term exposure to PM2.5 species, as a mixture, increased the risk of all-cause, cardiovascular, and respiratory mortality.

Building towards an adolescent neural urbanome: Expanding environmental measures using linked external data (LED) in the ABCD study

Many recent studies have demonstrated that environmental contexts, both social and physical, have an important impact on child and adolescent neural and behavioral development. The adoption of geospatial methods, such as in the Adolescent Brain Cognitive Development (ABCD) Study, has facilitated the exploration of many environmental contexts surrounding participants' residential locations without creating additional burdens for research participants (i.e., youth and families) in neuroscience studies. However, as the number of linked databases increases, developing a framework that considers the various domains related to child and adolescent environments external to their home becomes crucial. Such a framework needs to identify structural contextual factors that may yield inequalities in children's built and natural environments; these differences may, in turn, result in downstream negative effects on children from historically minoritized groups. In this paper, we develop such a framework - which we describe as the "adolescent neural urbanome" - and use it to categorize newly geocoded information incorporated into the ABCD Study by the Linked External Data (LED) Environment & Policy Working Group. We also highlight important relationships between the linked measures and describe possible applications of the Adolescent Neural Urbanome. Finally, we provide a number of recommendations and considerations regarding the responsible use and communication of these data, highlighting the potential harm to historically minoritized groups through their misuse.

Association of household solid fuel use and long-term exposure to ambient air pollution with estimated 10-year high cardiovascular disease risk among postmenopausal women

This study aimed to explore the separate and joint effects of long-term ambient air pollution and household air pollution exposure on 10-year high cardiovascular disease (CVD) risk among postmenopausal women. A total of 4679 postmenopausal women from the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. Information of fuel type was collected by standard questionnaires and use of solid fuel was considered as a proxy for household air pollution. Data of ambient air pollutants (PM1, PM2.5, PM10, SO2, NO2, CO, O3) were obtained from the ChinaHighAirPollutants (CHAP) datasets. Logistic regression models were performed to assess the separate and joint effects of long-term exposure to ambient air pollution and use of solid fuel on 10-year high CVD risk. We found use of solid fuel and its duration and ambient air pollutants (PM1, PM2.5, PM10, SO2, NO2) were all positively associated with 10-year high CVD risk among postmenopausal women (P < 0.05). Compared to those used clean fuel and exposed to low ambient air pollution levels, odds ratios (ORs) and 95% confidence intervals (CIs) for participants using solid fuels and exposed to high ambient air pollution levels (PM1, PM2.5, PM10, SO2, NO2, CO, O3) were 1.66 (1.35, 2.05), 1.66 (1.35, 2.04), 1.49 (1.22, 1.83), 1.28 (1.05, 1.57), 1.67 (1.34, 2.07), 1.28 (1.04, 1.57), 1.46 (1.18, 1.80), respectively. Moreover, significant additive interactions of solid fuel use with PM1 and PM2.5 on 10-year high CVD risk were observed, with approximately 18% and 23% of 10-year high risk of CVD attributable to the interaction. Overall, indoor and outdoor air pollution had separate and joint effects on 10-year high CVD risk among postmenopausal women. Therefore, simultaneously improving indoor and outdoor air quality are of great importance and could have a joint impact on prevention of CVD and improved health among postmenopausal women.

Long-term exposure to ambient fine particulate components and leukocyte epigenome-wide DNA Methylation in older men: the Normative Aging Study

BACKGROUND

Epigenome-wide association studies of ambient fine particulate matter (PM2.5) have been reported. However, few have examined PM2.5 components (PMCs) and sources or included repeated measures. The lack of high-resolution exposure measurements is the key limitation. We hypothesized that significant changes in DNA methylation might vary by PMCs and the sources.

METHODS

We predicted the annual average of 14 PMCs using novel high-resolution exposure models across the contiguous U.S., between 2000-2018. The resolution was 50 m × 50 m in the Greater Boston Area. We also identified PM2.5 sources using positive matrix factorization. We repeatedly collected blood samples and measured leukocyte DNAm with the Illumina HumanMethylation450K BeadChip in the Normative Aging Study. We then used median regression with subject-specific intercepts to estimate the associations between long-term (one-year) exposure to PMCs / PM2.5 sources and DNA methylation at individual cytosine-phosphate-guanine CpG sites. Significant probes were identified by the number of independent degrees of freedom approach, using the number of principal components explaining > 95% of the variation of the DNA methylation data. We also performed regional and pathway analyses to identify significant regions and pathways.

RESULTS

We included 669 men with 1,178 visits between 2000-2013. The subjects had a mean age of 75 years. The identified probes, regions, and pathways varied by PMCs and their sources. For example, iron was associated with 6 probes and 6 regions, whereas nitrate was associated with 15 probes and 3 regions. The identified pathways from biomass burning, coal burning, and heavy fuel oil combustion sources were associated with cancer, inflammation, and cardiovascular diseases, whereas there were no pathways associated with all traffic.

CONCLUSIONS

Our findings showed that the effects of PM2.5 on DNAm varied by its PMCs and sources.

Socioeconomic and racial disparities in source-apportioned PM2.5 levels across urban areas in the contiguous US, 2010

Fine particulate matter (PM2.5) air pollution exposure is associated with short and long-term health effects. Several studies found differences in PM2.5 exposure associated with neighborhood racial and socioeconomic composition. However, most focused on total PM2.5 mass rather than its chemical components and their sources. In this study, we describe the ZIP code characteristics that drive the disparities in exposure to PM2.5 chemical components attributed to source categories both nationally and regionally. We obtained annual mean predictions of PM2.5 and fourteen of its chemical components from spatiotemporal models and socioeconomic and racial predictor variables from the 2010 US Census, and the American Community Survey 5-year estimates. We used non-negative matrix factorization to attribute the chemical components to five source categories. We fit generalized nonlinear models to assess the associations between the neighborhood predictors and each PM2.5 source category in urban areas in the United States in 2010 (n=25,790 zip codes). We observed higher PM2.5 levels in ZIP codes with higher proportions of Black individuals and lower socioeconomic status. Racial exposure disparities were mainly attributed to Heavy Fuel, Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Economic disparities were mainly attributed to Soil and Crustal Dust, Heavy Fuel Oil and Industrial, Metal Processing Industry and Agricultural, and Motor Vehicle sources. Upon further analysis through stratifying by regions within the United States, we found that the associations between ZIP code characteristics and source-attributed PM2.5 levels were generally greater in Western states. In conclusion, racial, socioeconomic, and geographic inequalities in exposure to PM2.5 and its components are driven by systematic differences in component sources that can inform air quality improvement strategies.

Evaluating Chemical Mixtures in Epidemiological Studies to Inform Regulatory Decisions

BACKGROUND

Epidemiological studies are increasingly going beyond the evaluation of health effects of individual chemicals to consider chemical mixtures. To our knowledge, the advantages and disadvantages of addressing chemical mixtures for informing regulatory decisions-as opposed to obtaining a more comprehensive understanding of etiology-has not been carefully considered.

OBJECTIVES

We offer a framework for the study of chemical mixtures in epidemiological research intended to inform regulatory decisions. We identify a) the different ways mixtures originate (product source, pollution source, shared mode of action, or shared effect on health outcome), b) the use of indicator chemicals to address mixtures, and c) the requirements for epidemiological studies to be informative for regulatory purposes.

DISCUSSION

The principal advantage of considering mixtures is to obtain a more complete understanding of the role of the chemical environment as a determinant of health. Incorporating other exposures may improve the assessment of the net effect of the chemicals of interest. However, the increased complexity and potential loss of generalizability may limit the value of studies of mixtures, especially for mixtures based on mode of action or shared health outcomes. Our recommended strategy is to successively assess the marginal contribution of individual chemicals, joint effects with other specific chemicals, and hypothesis-driven evaluation of mixtures rather than applying hypothesis-free data exploration methods. Although more ambitious statistical approaches to mixtures may, in time, be helpful for guiding regulation, the authors believe conventional methods for assessing individual and combined effects of chemicals remain preferable. https://doi.org/10.1289/EHP11899.

Association between short- and long-term exposures to air pollutants and internalizing/externalizing behavior in children aged 4 to 7 years

The effects of air pollutants on psychological health have attracted increasing attention worldwide. However, there is limited evidence on the association between air pollution and children's psychological development. This study explores the association between short- and long-term exposures to air pollutants and children's internalizing and externalizing behaviors. A total of 2303 children of 4-7 years were included in this study. We assessed their behavior using the Child Behavior Checklist (4-16 years). The prevalence of internalizing and externalizing behavior was 4.77% and 4.43%, respectively. For short-term exposure, CO pollution was associated with children's internalizing behaviors, with each 1 mg/m³ increment leading to an odds ratio (OR) of 1.063 (95% CI 1.005, 1.124), 1.065 (95% CI 1.009, 1.124), 1.067 (95% CI 1.007, 1.131), and 1.122 (95% CI 1.018, 1.236) at lag04, lag05, lag06, and lag0120, respectively. O₃ (per 1 g[Formula: see text]/m³) was negatively associated with internalizing problems at lag2 [OR = 0.991 (95% CI 0.983, 0.999)]. NO₂ (per 1 g[Formula: see text]/m³) was significantly associated with externalizing behaviors, with the ORs of 1.067 (95% CI 1.024, 1.111) at lag060 and 1.060 (95% CI 1.010, 1.113) at lag0120. For long-term exposure, it indicated that 1-year exposure to CO (per 1 mg/m³) and PM_2.5 (per 1 g[Formula: see text]/m³) was positively associated with internalizing behavioral risk [OR = 1.724 (95% CI 1.187, 2.504); PM_2.5: OR = 1.236 (95% CI 1.114, 1.371)], whereas NO₂ (per 1 g[Formula: see text]/m³) exposure was associated with an increased risk of externalizing behavior [OR = 1.123 (95% CI 1.003, 1.256)]. In addition, the interaction analysis showed that boys were at a higher risk of abnormal behaviors associated with long-term exposure to CO, PM_2.5, and NO₂. Our findings reveal a potential link between air pollution exposure and abnormal behaviors in kindergarten children after short-/long-term exposure, which is an essential supplement to the studies on the association between air pollution and children's behavioral problems.