Exposure-response associations between chronic exposure to fine particulate matter and risks of hospital admission for major cardiovascular diseases: population based cohort study.

OBJECTIVE: To estimate exposure-response associations between chronic exposure to fine particulate matter (PM2.5) and risks of the first hospital admission for major cardiovascular disease (CVD) subtypes. DESIGN: Population based cohort study. SETTING: Contiguous US. PARTICIPANTS: 59 761 494 Medicare fee-for-service beneficiaries aged ≥65 years during 2000-16. Calibrated PM2.5 predictions were linked to each participant's residential zip code as proxy exposure measurements. MAIN OUTCOME MEASURES: Risk of the first hospital admission during follow-up for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, valvular heart disease, thoracic and abdominal aortic aneurysms, or a composite of these CVD subtypes. A causal framework robust against confounding bias and bias arising from errors in exposure measurements was developed for exposure-response estimations. RESULTS: Three year average PM2.5 exposure was associated with increased relative risks of first hospital admissions for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, and thoracic and abdominal aortic aneurysms. For composite CVD, the exposure-response curve showed monotonically increased risk associated with PM2.5: compared with exposures ≤5 µg/m3 (the World Health Organization air quality guideline), the relative risk at exposures between 9 and 10 µg/m3, which encompassed the US national average of 9.7 µg/m3 during the study period, was 1.29 (95% confidence interval 1.28 to 1.30). On an absolute scale, the risk of hospital admission for composite CVD increased from 2.59% with exposures ≤5 µg/m3 to 3.35% at exposures between 9 and 10 µg/m3. The effects persisted for at least three years after exposure to PM2.5. Age, education, accessibility to healthcare, and neighborhood deprivation level appeared to modify susceptibility to PM2.5. CONCLUSIONS: The findings of this study suggest that no safe threshold exists for the chronic effect of PM2.5 on overall cardiovascular health. Substantial benefits could be attained through adherence to the WHO air quality guideline.

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.

Epigenome-wide DNA methylation in leukocytes and toenail metals: The normative aging study.

BACKGROUND: Environmental metal exposures have been associated with multiple deleterious health endpoints. DNA methylation (DNAm) may provide insight into the mechanisms underlying these relationships. Toenail metals are non-invasive biomarkers, reflecting a medium-term time exposure window. OBJECTIVES: This study examined variation in leukocyte DNAm and toenail arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg) among elderly men in the Normative Aging Study, a longitudinal cohort. METHODS: We repeatedly collected samples of blood and toenail clippings. We measured DNAm in leukocytes with the Illumina HumanMethylation450 K BeadChip. We first performed median regression to evaluate the effects of each individual toenail metal on DNAm at three levels: individual cytosine-phosphate-guanine (CpG) sites, regions, and pathways. Then, we applied a Bayesian kernel machine regression (BKMR) to assess the joint and individual effects of metal mixtures on DNAm. Significant CpGs were identified using a multiple testing correction based on the independent degrees of freedom approach for correlated outcomes. The approach considers the effective degrees of freedom in the DNAm data using the principal components that explain >95% variation of the data. RESULTS: We included 564 subjects (754 visits) between 1999 and 2013. The numbers of significantly differentially methylated CpG sites, regions, and pathways varied by metals. For example, we found six significant pathways for As, three for Cd, and one for Mn. The As-associated pathways were associated with cancer (e.g., skin cancer) and cardiovascular disease, whereas the Cd-associated pathways were related to lung cancer. Metal mixtures were also associated with 47 significant CpG sites, as well as pathways, mainly related to cancer and cardiovascular disease. CONCLUSIONS: This study provides an approach to understanding the potential epigenetic mechanisms underlying observed relations between toenail metals and adverse health endpoints.

The role of short-term air pollution and temperature on arterial stiffness in a longitudinal closed cohort of elderly individuals.

BACKGROUND/AIMS: Our study adds to the sparse literature that examines whether arterial stiffness, related to cardiovascular risk, increases with exposure to air pollution. We assessed the associations between spatiotemporally resolved air pollutants and vascular and hemodynamic parameters in an elderly population-based in Eastern Massachusetts. METHODS: Among 397 men living in Eastern Massachusetts between 2007 and 2013, we utilized time-varying linear mixed-effects regressions to examine associations between central augmentation index (%) and central pulse pressure (mmHg) and short-term (0-7 days) exposure to air pollution concentrations (fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3)), and temperature adjusted for known cardiovascular risk factors. Central augmentation index (AIx) and pulse pressure (AP) were measured at each visit using radial artery applanation tonometry for pulse wave analysis. Each air pollutant and temperature were geocoded to the participant's residential address using validated ensemble and hybrid exposure models and gridMET predictions. RESULTS: We found consistent results that higher short-term PM2.5 concentrations (0-7 day moving averages) were associated with significantly higher measures of arterial stiffness. Each 4.52 µg/m3 interquartile range (IQR) increase in daily PM2.5 for a 3-day moving average was associated with a 0.63% (95% confidence interval (CI): 0.11, 1.15) increase in AIx and a 1.65 mmHg (95% CI: 0.42, 2.88) increase in pulse pressure. Furthermore, each 3.83 µg/m3 IQR increase in daily PM2.5 for a 7-day moving average was associated with a 0.57% (95% CI: -0.01, 1.14) increase in AIx and a 1.91 mmHg (95% CI: 0.54, 3.28) increase in pulse pressure. Smaller increases in AIx and AP were observed for the other short-term moving averages of PM2.5 exposure apart from days zero and five for AIx. We found no clear association between O3, NO2, temperature, and the outcomes. CONCLUSIONS: Short-term PM2.5 exposure was associated with markers of arterial stiffness and central hemodynamics.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to PM2.5 and Mortality.

BACKGROUND: Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed. OBJECTIVES: We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)] and all-cause mortality. METHODS: In this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders. RESULTS: We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels. DISCUSSION: Our findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.

Quantile regression to examine the association of air pollution with subclinical atherosclerosis in an adolescent population.

BACKGROUND: Air pollution has been associated with carotid intima-media thickness test (CIMT), a marker of subclinical atherosclerosis. To our knowledge, this is the first study to report an association between ambient air pollution and CIMT in a younger adolescent population. OBJECTIVE: To investigate the associations beyond standard mean regression by using quantile regression to explore if associations occur at different percentiles of the CIMT distribution. METHODS: We measured CIMT cross-sectionally at the age of 16 years in 363 adolescents participating in the Dutch PIAMA birth cohort. We fit separate quantile regressions to examine whether the associations of annual averages of nitrogen dioxide (NO2), fine particulate matter (PM2.5), PM2.5 absorbance (a marker for black carbon), PMcoarse and ultrafine particles up to age 14 assigned at residential addresses with CIMT varied across deciles of CIMT. False discovery rate corrections (FDR, p < 0.05 for statistical significance) were applied for multiple comparisons. We report quantile regression coefficients that correspond to an average change in CIMT (µm) associated with an interquartile range increase in the exposure. RESULTS: PM2.5 absorbance exposure at birth was statistically significantly (FDR < 0.05) associated with a 6.23 µm (95% CI: 0.15, 12.3) higher CIMT per IQR increment in PM2.5 absorbance in the 10th quantile of CIMT but was not significantly related to other deciles within the CIMT distribution. For NO2 exposure we found similar effect sizes to PM2.5 absorbance, but with much wider confidence intervals. PM2.5 exposure was weakly positively associated with CIMT while PMcoarse and ultrafine did not display any consistent patterns. CONCLUSIONS: Early childhood exposure to ambient air pollution was suggestively associated with the CIMT distribution during adolescence. Since CIMT increases with age, mitigation strategies to reduce traffic-related air pollution early in life could possibly delay atherosclerosis and subsequently CVD development later in life.

Air Pollutants and Asthma Hospitalization in the Medicaid Population.

Rationale: Risk of asthma hospitalization and its disparities associated with air pollutant exposures are less clear within socioeconomically disadvantaged populations, particularly at low degrees of exposure. Objectives: To assess effects of short-term exposures to fine particulate matter (particulate matter with an aerodynamic diameter of ⩽2.5 µm [PM2.5]), warm-season ozone (O3), and nitrogen dioxide (NO2) on risk of asthma hospitalization among national Medicaid beneficiaries, the most disadvantaged population in the United States, and to test whether any subpopulations were at higher risk. Methods: We constructed a time-stratified case-crossover dataset among 1,627,002 hospitalizations during 2000-2012 and estimated risk of asthma hospitalization associated with short-term PM2.5, O3, and NO2 exposures. We then restricted the analysis to hospitalizations with degrees of exposure below increasingly stringent thresholds. Furthermore, we tested effect modifications by individual- and community-level characteristics. Measurements and Main Results: Each 1-µg/m3 increase in PM2.5, 1-ppb increase in O3, and 1-ppb increase in NO2 was associated with 0.31% (95% confidence interval [CI], 0.24-0.37%), 0.10% (95% CI, 0.05 - 0.15%), and 0.28% (95% CI, 0.24 - 0.32%) increase in risk of asthma hospitalization, respectively. Low-level PM2.5 and NO2 exposures were associated with higher risk. Furthermore, beneficiaries with only one asthma hospitalization during the study period or in communities with lower population density, higher average body mass index, longer distance to the nearest hospital, or greater neighborhood deprivation experienced higher risk. Conclusions: Short-term air pollutant exposures increased risk of asthma hospitalization among Medicaid beneficiaries, even at concentrations well below national standards. The subgroup differences suggested individual and contextual factors contributed to asthma disparities under effects of air pollutant exposures.

Solar and geomagnetic activity enhance the effects of air pollutants on atrial fibrillation.

AIMS: Cardiac arrhythmias have been associated with intense solar and geomagnetic activity (SGA) and exposures to air pollution. METHODS AND RESULTS: We examined whether oscillations of SGA can modify the effect of hourly exposures to air pollutants on atrial fibrillation ≥30 s (AF) risk in patients with dual-chamber implantable cardioverter-defibrillators. The effects of SGA on ambient particulate matter <2.5 µm (PM2.5), black carbon (BC), ultrafine particles (PN), and associations with AF were assessed. Measures of SGA included solar wind proton density (SW), total interplanetary magnetic field strength (IMF), and Kp index, a measure of global geomagnetic activity. Overall time lags between 0 and 24 h, periods of increased SGA (>50th percentile in IMF, SW, and Kp index) enhanced the effects of all three air pollutants on AF, while during periods of reduced SGA the associations were considerably weaker or absent. During periods of intense SW 6 h prior to an AF event, the odds ratio (OR) for PM2.5 exposure per interquartile range (IQR) of 5.6 µg/m3 was 1.7 [95% confident interval (CI) 1.3-2.3, P = 0.0001]. For periods of reduced SW, the OR for PM2.5 exposure per IQR was 1.2 (95% CI 0.9-1.5; P = 0.27). There were similar effects for PN and BC exposures. In patients with multiple AF events per hour, the associations with air pollutants during intense SGA were even greater. CONCLUSION: The effects of air pollutants up to 24 h before AF events were enhanced during periods of increased SGA. Our results suggest that these effects may account for variation in AF risk.

A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States.

Particulate radioactivity, a characteristic of particulate matter, is primarily determined by the abundance of radionuclides that are bound to airborne particulates. Exposure to high levels of particulate radioactivity has been associated with negative health outcomes. However, there are currently no spatially and temporally resolved particulate radioactivity data for exposure assessment purposes. We estimated the monthly distributions of gross beta particulate radioactivity across the contiguous United States from 2001 to 2017 with a spatial resolution of 32 km, via a multi-stage ensemble-based model. Particulate radioactivity was measured at 129 RadNet monitors across the contiguous U.S. In stage one, we built 264 base learning models using six methods, then selected nine base models that provide different predictions. In stage two, we used a non-negative geographically and temporally weighted regression method to aggregate the selected base learner predictions based on their local performance. The results of block cross-validation analysis suggested that the non-negative geographically and temporally weighted regression ensemble learning model outperformed all base learning model with the smallest rooted mean square error (0.094 mBq/m3). Our model provided an accurate estimation of particulate radioactivity, thus can be used in future health studies.

Predicting Monthly Community-Level Domestic Radon Concentrations in the Greater Boston Area with an Ensemble Learning Model.

Inhaling radon and its progeny is associated with adverse health outcomes. However, previous studies of the health effects of residential exposure to radon in the United States were commonly based on a county-level temporally invariant radon model that was developed using measurements collected in the mid- to late 1980s. We developed a machine learning model to predict monthly radon concentrations for each ZIP Code Tabulation Area (ZCTA) in the Greater Boston area based on 363,783 short-term measurements by Spruce Environmental Technologies, Inc., during the period 2005-2018. A two-stage ensemble-based model was developed to predict radon concentrations for all ZCTAs and months. Stage one included 12 base statistical models that independently predicted ZCTA-level radon concentrations based on geological, architectural, socioeconomic, and meteorological factors for each ZCTA. Stage two aggregated the predictions of these 12 base models using an ensemble learning method. The results of a 10-fold cross-validation showed that the stage-two model has a good prediction accuracy with a weighted R2 of 0.63 and root mean square error of 22.6 Bq/m3. The community-level time-varying predictions from our model have good predictive precision and accuracy and can be used in future prospective epidemiological studies in the Greater Boston area.

Associations between acute and long-term exposure to PM2.5 components and temperature with QT interval length in the VA Normative Aging Study.

AIMS: Our study adds to the sparse literature on the effect of multiple fine particulate matter (PM2.5) components on QT interval length, an outcome with high clinical relevance in vulnerable populations. To our knowledge, this is the first study to examine the association between spatiotemporally resolved exposures to PM2.5 components and QT interval length. METHODS AND RESULTS: Among 578 men living in Eastern Massachusetts between 2000 and 2011, we utilized time-varying linear mixed-effects regressions with a random intercept to examine associations between acute (0-3 days), intermediate (4-28 days), and long-term (1 year) exposure to PM2.5 components, temperature, and heart-rate corrected QT interval (QTc). Each of the PM2.5 components and temperature was geocoded to the participant's residential address using validated ensemble and hybrid exposure models and gridMET predictions. We also evaluated whether diabetic status modified the association between PM2.5 components and QTc interval. We found consistent results that higher sulfate levels and colder temperatures were associated with significant longer QTc across all moving averages except the day of exposure. The greatest effect of sulfate and temperature was detected for the 28-day moving average. In the multi-pollutant model, each 1.5 µg/m3 IQR increase in daily sulfate was associated with a 15.1 ms [95% confidence interval (CI): 10.2-20.0] increase in QTc interval and in the single-pollutant models a 15.3 ms (95% CI: 11.6-19.1) increase in QTc interval. Other secondary particles, such as nitrate and organic carbon, also prolonged QT interval, while elemental carbon decreased QT interval. We found that diabetic status did not modify the association between PM2.5 components and QTc interval. CONCLUSION: Acute and long-term exposure to PM2.5 components and temperature are associated with changes in ventricular repolarization as measured by QT interval.

Associations between PM2.5 metal components and QT interval length in the Normative Aging Study.

BACKGROUND: Several studies have found associations between increases in QT interval length, a marker of cardiac electrical instability, and short-term fine particulate matter (PM2.5) exposures. To our knowledge, this is the first study to examine the association between specific PM2.5 metal components and QT interval length. METHODS: We measured heart-rate corrected QT interval (QTc) duration among 630 participants in the Normative Aging Study (NAS) based in Eastern Massachusetts between 2000 and 2011. We utilized time-varying linear mixed-effects regressions with a random intercept for each participant to analyze associations between QTc interval and moving averages (0-7 day moving averages) of 24-h mean concentrations of PM2.5 metal components (vanadium, nickel, copper, zinc and lead) measured at the Harvard Supersite monitoring station. Models were adjusted for daily PM2.5 mass estimated at a 1 km × 1 km grid cell from a previously validated prediction model and other covariates. Bayesian kernel machine regression (BKMR) was utilized to assess the overall joint effect of the PM2.5 metal components. RESULTS: We found consistent results with higher lead (Pb) associated with significant higher QTc intervals for both the multi-pollutant and the two pollutant (PM2.5 mass and a PM2.5 component) models across the moving averages. The greatest effect of lead on QTc interval was detected for the 4-day moving average lead exposure. In the multi-pollutant model, each 2.72 ng/m3 increase in daily lead levels for a 4-day moving average was associated with a 7.91 ms (95% CI: 3.63, 12.18) increase in QTc interval. In the two-pollutant models with PM2.5 mass and lead, each 2.72 ng/m3 increase in daily lead levels for a 4-day moving average was associated with an 8.50 ms (95% CI: 4.59, 12.41) increase in QTc interval. We found that 4-day moving average of copper has a negative association with QTc interval when compared to the other PM2.5 metal components. In the multi-pollutant model, each 1.81 ng/m3 increase in daily copper levels for a 4-day moving average was associated with an -3.89 ms (95% CI: -6.98, -0.79) increase in QTc interval. Copper's essential function inside the human body could mediate its cardiotoxicity on cardiac conductivity and explain why we found that copper in comparison to the other metals was less harmful for QTc interval. CONCLUSIONS: Exposure to metals contained in PM2.5 are associated with acute changes in ventricular repolarization as indicated by QT interval characteristics.

Exposure to Air Pollution and Particle Radioactivity With the Risk of Ventricular Arrhythmias.

BACKGROUND: Individuals are exposed to air pollution and ionizing radiation from natural sources through inhalation of particles. This study investigates the association between cardiac arrhythmias and short-term exposures to fine particulate matter (particulate matter ≤2.5 µm aerodynamic diameter; PM2.5) and particle radioactivity. METHODS: Ventricular arrhythmic events were identified among 176 patients with dual-chamber implanted cardioverter-defibrillators in Boston, Massachusetts between September 2006 and June 2010. Patients were assigned exposures based on residential addresses. Daily PM2.5 levels were estimated at 1-km×1-km grid cells from a previously validated prediction model. Particle gross ß activity was used as a surrogate for particle radioactivity and was measured from several monitoring sites by the US Environmental Protection Agency's monitoring network. The association of the onset of ventricular arrhythmias (VA) with 0- to 21-day moving averages of PM2.5 and particle radioactivity (2 single-pollutant models and a 2-pollutant model) before the event was examined using time-stratified case-crossover analyses, adjusted for dew point and air temperatures. RESULTS: A total of 1,050 VA were recorded among 91 patients, including 123 sustained VA among 25 of these patients. In the single-pollutant model of PM2.5, each interquartile range increase in daily PM2.5 levels for a 21-day moving average was associated with 39% higher odds of a VA event (95% CI, 12%-72%). In the single-pollutant model of particle radioactivity, each interquartile range increase in particle radioactivity for a 2-day moving average was associated with 13% higher odds of a VA event (95% CI, 1%-26%). In the 2-pollutant model, for the same averaging window of 21 days, each interquartile range increase in daily PM2.5 was associated with an 48% higher odds of a VA event (95% CI, 15%-90%), and each interquartile range increase of particle radioactivity with a 10% lower odds of a VA event (95% CI, -29% to 14%). We found that with higher levels of particle radioactivity, the effect of PM2.5 on VAs is reduced. CONCLUSIONS: In this high-risk population, intermediate (21-day) PM2.5 exposure was associated with higher odds of a VA event onset among patients with known cardiac disease and indication for implanted cardioverter-defibrillator implantation independently of particle radioactivity.