Final Report: Effects of Long-Term Exposure to Traffic-Derived Particles and Gases on Subclinical Measures of Cardiovascular Disease in a Multi-Ethnic Cohort

EPA Grant Number: R834796C005
Subproject: this is subproject number 005 , established and managed by the Center Director under grant R834796
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: University of Washington Center for Clean Air Research
Center Director: Vedal, Sverre
Title: Effects of Long-Term Exposure to Traffic-Derived Particles and Gases on Subclinical Measures of Cardiovascular Disease in a Multi-Ethnic Cohort
Investigators: Vedal, Sverre , Kaufman, Joel D. , Larson, Timothy V. , Sampson, Paul , Sheppard, Lianne (Elizabeth) A. , Szpiro, Adam , Yost, Michael
Institution: University of Washington
EPA Project Officer: Callan, Richard
Project Period: December 1, 2010 through November 30, 2015 (Extended to November 30, 2017)
RFA: Clean Air Research Centers (2009) RFA Text |  Recipients Lists
Research Category: Health Effects , Air

Objective:

Project 5 has three primary objectives, which are unchanged from those described previously:

1. Employ the small-scale gradient data acquired as part of the mobile monitoring campaign in Project 1, in conjunction with central fixed-site data, regulatory monitoring data and geographic covariates, to build a multipollutant exposure model for traffic-derived air pollutants. This model will incorporate complex spatial information on primary and secondary traffic-derived particles and gases.

2. Develop and validate individual-level exposure estimates for traffic-derived air pollutants, integrating (1) the outdoor residential concentration estimates from the multipollutant model, (2) estimates of residential infiltration rates, (3) road class- and traffic condition-specific estimates of on-roadway concentrations and (4) individual-level questionnaire-derived time-location information. These individual-level exposure estimates will also utilize personal monitoring data designed to clarify the in-transit component of total exposure.

3. Estimate the effect of individual-level exposure to traffic-derived air pollution on subclinical cardiovascular disease using these exposure models. Health outcomes will include left ventricular myocardial mass as ascertained by MRI, arteriolar diameters as measured by retinal photography, coronary artery calcium as ascertained by computed tomography (CT), intima-medial thickness as measured by ultrasound, and DNA methylation.

Summary/Accomplishments (Outputs/Outcomes):

Aim 1: Developing spatial exposure model. For Aim 1 of Project 5, we worked closely with Project 1 and Biostatistics Core personnel to develop approaches to their high-dimensional data, which can be applied to epidemiological analyses.

As described in previous annual reports, we applied traditional k-means cluster analysis methods to the Center for Clean Air Research (CCAR) monitoring data with the goal of identifying spatial differences in the multipollutant sources. We performed the analysis separately within each sampling season for each city, since we have ample reason to believe that source profiles change by season and by city. The four cities studied were Baltimore, Maryland; Los Angeles,, California; St. Paul, Minnesota; and Winston-Salem, North Carolina. All pollutant data in this analysis were normalized by the concentration of NO­x measured at each location. Although we have completed k-mean cluster analyses in all four cities, our health analyses have focused thus far on Baltimore.

The data sources and clustering method are described in more detail in the Year 5 Annual Report. Briefly, the data sources included passive badges (volatile organic compounds [VOCs], NOx, NO2, ozone) and real-time monitoring data for carbon monoxide, black carbon and size-fractioned PM count (25 nm–400nm, <1 µm, 1–2.5 µm). We performed k-means clustering, with Eulerian distance measures, separately by city and season over the 43 different monitoring locations. We selected the number of clusters using three criteria: (1) minimizing the number of clusters with fewer than five members, (2) maximizing the separation between the first two principal components describing each cluster and (3) maximizing the agreement between different validated indices for selecting k.

Aim 2: Understanding in-vehicle contribution to individual level multi-pollutant exposures. A major component of Project 5 involved field monitoring using a combination of personal, residential and novel in-vehicle sampling, paired with intensive location tracking. The field work component of this project occurred twice in two seasons each in Winston-Salem and Los Angeles and involved individual-level air monitoring in multiple microenvironments, GPS tracking over a relatively long duration and proximity monitoring, each of which required unique methods for novel equipment development. Specifically, we designed and built in-vehicle passive monitoring devices that capture exposures during driving. We also designed and built proximity monitors, which record time spent in specific microenvironments (inside the residence and inside the vehicle), and we customized off-the-shelf GPS units to allow continuous location tracking for periods up to and exceeding two weeks.

All of this equipment was tested during a series of pilot studies. In the first pilot study, we evaluated the ability of an external battery, connected to GPS units through a customized circuit-board, to provide sufficient data acquisition, data quality and sampling duration. This configuration was successful, and we determined that we could track participant locations continuously for up to a month. During a second pilot study, we evaluated the custom-built in-vehicle samplers, which consist of a stainless steel container fitted with a Teflon core and Ogawa and 3M VOC passive sampling badges. Our aims for this pilot study were to determine how much driving time was required to meet sample detection limits, evaluate sample reproducibility and ensure that the equipment did not leak (i.e., that blank samples were, in fact, blank). This pilot study occurred in December 2012 and included 20 samples (10 sets of duplicates). We observed generally high reproducibility among duplicates, low concentrations in blanks and determined that detection limits were reliably exceeded in samples of participants with driving times of 30 minutes/day or greater over a 2-week period. We also observed that our measured concentrations were consistent with those observed in previous studies. A second pilot study was conducted in March of 2013 to ensure that the Teflon cores we were using were not acting as “sinks” for the pollutants, and these results taken together provided confidence in our equipment.

After confirming the suitability of our sampling equipment and the reliability of our methods, we conducted the first field campaign in Winston-Salem from January 27–February 21, 2013. This campaign included 46 participants (96% of goal). We deployed 184 Ogawa and 184 3M samplers (46 each of personal, indoor residential, outdoor residential and in-vehicle), and we measured the following pollutants: oxides of nitrogen (NOX), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), pentanes, isoprene, n-nonane, n-decane, n-undecane, n-dodecane, benzene, toluene, m-xylene and o-xylene. We also deployed 17 blank samples (9%) and 13 duplicate samples (9% of possible maximum, as no personal duplicates were intended to be deployed, to reduce participant burden).

The second field campaign occurred in Winston-Salem from July 29–August 24, 2013. This campaign included 47 participants (98% of goal). Of the 47 participants, 29 returned from the February field campaign and 18 were new participants. We deployed 188 Ogawa and 188 3M samplers (47 each of personal, indoor residential, outdoor residential, and in-vehicle), and we measured the same pollutants as during the heating season campaign. We also deployed 19 blank samples (10%) and 15 duplicate samples (11% of possible maximum).

The third field campaign occurred in Los Angeles from January 27–February 20, 2014. This campaign included 47 participants (98% of goal). We deployed 188 Ogawa and 188 3M samplers (47 each of personal, indoor residential, outdoor residential, and in-vehicle), as well as 20 blank samples (11%) and 14 duplicate samples (10% of possible maximum).

The fourth and final field campaign occurred in Los Angeles from July 6–July 31, 2014. This campaign included 46 participants (96% of goal). Of the 46 participants, 28 returned from the February field campaign and 18 were new participants. We deployed 184 Ogawa and 184 3M samplers (46 each of personal, indoor residential, outdoor residential, and in-vehicle), and we measured the same pollutants as during previous campaigns. We also deployed 20 blank samples (11%) and 15 duplicate samples (11% of possible maximum).

In addition to the air monitoring described above, each of these field campaigns also included an intensive location-tracking component focused on time spent in five different microenvironments: at home indoors, at home outdoors, away from home indoors, away from home outdoors and in a motor vehicle. Time-location data were collected using three different methods simultaneously: time-location diaries, GPS tracking and proximity monitors. In order to analyze the GPS tracking data, a rule-based model using speed and location information is being developed to categorize each GPS point recorded into the microenvironments of interest.

Overall, the participants in this study matched the MESA Air cohort as a whole well with a couple of exceptions. Sampled participants tended to be a little younger and were more likely to be employed. These differences can be attributed to our requirement that sampled participants drive at least 30 minutes/day on average. We also ended up with fewer Chinese participants, partly because we restricted participation to English speakers and partly because there were no Chinese participants in Winston-Salem by design in the parent MESA study.

For all analytes except for ozone, the highest concentrations were found in the vehicle samples. For ozone, the highest concentrations were found in the outdoor samples. After reviewing the results for all four campaigns, the results for the in-vehicle samples seem higher than expected. We are conducting experiments to ensure that we are capturing the correct sampling time for the in-vehicle samples.

We conducted a series of additional assessments to better understand this issue and to evaluate some hypotheses we developed about sources of potential errors from equipment issues, such as biases from actual device sampling times or canister leakage. Tests were conducted in the laboratory with known levels of nitrogen dioxide (NO2) in order to measure the level of NO2 inside of the sealed in-vehicle monitor at varying time points after it the lid was closed. A decrease in NO2 levels within the canister over time suggested that the Ogawa badges continued to absorb NO2 from the air trapped inside of the canister once it was closed. Though these novel samplers were designed to minimize empty space, approximately 350 mL of air was trapped inside the canister upon closure. Failing to account for this led to a bias (correctable) in actual sampling time used in the concentration calculations.

Using the sampling rate or molecular weight and molar volume of air at standard temperature and pressure for each pollutant and assuming a relative humidity of 43%, sampling of the 350mL volume of air would take 10–40 minutes depending on the analyte. Each time a participant opened and closed the canister, this additional time to sample the air inside the canister after it was closed was added to the aggregate sampling time for the Ogawa or 3M badge. On average, participants took 38 trips during the 2-week sampling period, and the average additional sampling time ranged from 6 hours to 25 hours depending upon the parameter.

In addition to the air monitoring described above, each of the field campaigns also included intensive methods for time-location measurement. Time-location data during these two-week periods was collected using GPS units and Time-Location Diaries (TLDs) simultaneously. GPS units were customized to allow continuous location tracking for periods up to and exceeding two weeks. In order to analyze the GPS tracking data, an automated rule-based method was developed to process the large quantity of GPS data collected. To produce the single best estimate of time-location patterns during the monitoring periods, the GPS and TLD measurements were integrated in order to capitalize on the strengths of each tool. The GPS measurements of time at home and in other locations was divided into indoors and outdoors based on proportions indoors and outdoors reported in the TLD. On average during these 2-week monitoring periods, participants spent 4–5 percent of their time in vehicles, 2–6 percent of their time outdoors and the remainder (89–94 percent) indoors.

The percent of time spent indoors, outdoors and in-vehicle based on this integration of intensive 2-week measurement methods was compared to questionnaire data previously collected as part of the MESA Air study. The magnitude of the average amount of time spent in each microenvironment, particularly time spent in-vehicle, is similar across measurement methods at the cohort level but time-location patterns were less well correlated at the individual level. A manuscript describing this work, entitled “Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution,” was published in the Journal of Exposure Science and Environmental Epidemiology (Hazlehurst et al 2017).

As published in Hazlehurst et al. (2018), we conducted a detailed analysis of the impact of the in-vehicle environment on NO2 exposures. On average, indoor exposure contributed 69 percent and in-vehicle exposure contributed 24 percent of participants’ ambient-source NO2 exposure. For participants in the highest quartile of time in-vehicle (≥1.3 hours/day), indoor and in-vehicle contributions were 60 and 31 percent, respectively. Incorporating infiltrated indoor and measured in-vehicle NO2 produced exposure estimates 5.6 ppb lower, on average, than using only outdoor concentrations. The indoor microenvironment accounted for the largest proportion of ambient-source exposure in this older population, despite higher concentrations of NO2 outdoors and in vehicles than indoors. In-vehicle exposure was more influential among participants who drove the most and for participants residing in areas with lower outdoor air pollution. Failure to characterize exposures in these microenvironments may contribute to exposure misclassification in epidemiologic studies.

Aim 3: Epidemiological Analyses. As described above, we created representative traffic-related air pollution (TRAP) profiles for heating and non-heating seasons by applying the predictive k-means clustering method to measurements made during two 2-week periods at fixed-site and on-road locations. The heating season data yielded two clusters, notable for higher ratios of gases and ultrafine particles, respectively. We predicted cluster membership for 1,005 participants in the MESA Air study with follow-up between 2000 and 2012. This provides a partitioning of the cohort into people predicted to be exposed to different types of TRAP. We estimated cluster-specific relationships between coronary artery calcification (CAC) progression and long-term exposure to fine particulate matter (PM2.5) and oxides of nitrogen (NOx). The association between long-term PM2.5 exposure and CAC progression in the heating season varied by cluster. We found evidence of greater CAC progression rates per unit PM2.5 exposures among people living in areas characterized by high ratios of ultrafine particle counts relative to NOx concentrations. Similar trends occurred using clusters identified from non-heating season measurements. These results are presented in Keller et al., 2018.

We also have focused on DNA methylation. To gain biologic insight into genes where both DNA methylation and gene expression are associated with air pollution, we conducted pathway analyses using two publicly available databases: the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We conducted overrepresentation analyses comparing the set of genes where both DNA methylation and gene expression were associated with air pollution with the total 2,713 genes in the study. The analyses identified several overrepresented pathways from the KEGG pathway, including leukocyte transendothelial migration and B cell receptor signaling pathway for NOx and Notch signaling for PM2.5. These results provide evidence that air pollution-associated atherosclerosis may be mediated in part by aberrant gene expression driven by alterations in DNA methylation. No significantly overrepresented GO pathways were identified.

We investigated candidate methylation sites that were previously associated with expression of nearby genes in the same sample of participants that we used in our study and found five methylation sites significantly associated with PM2.5 exposure. Three of the CpG sites not only had DNA methylation associated with PM2.5 which was statistically significant after correction for multiple comparisons, but the cis-gene transcript also had mRNA expression associated with PM2.5 as well. These may be more plausibly involved in the pathogenesis of air pollution-related disease. The following genes were associated in this manner with PM2.5.

  • ANKHD1—Ankyrin repeat and KH domain containing 1 and may support proliferation and cell cycle progression of cancer cells.
  • LGALS2—Galectin 2 polymorphisms linked to MI and coronary artery disease and may modulate both pro- and anti-inflammatory molecules.
  • ANKRD11—Regulates chromatin modification and was linked to autism. Inflammation is suggested potential common mechanism between air pollution-related CVD and autism.
  • BAZ2B—Bromodomain containing chromatin remodeling protein that epigenetically regulates transcription and polymorphism associated with sudden cardiac death.
  • PPIE—Stimulates folding and conformational changes in proteins and may be linked to leukemia, colorectal cancer and body mass index.

We did not find any CpG sites significantly associated with NOx.

Conclusions:

For Aim 1, we developed a method for identifying spatial differences in multipollutant clusters and then utilized that approach to understand how these differences were related to CAC progression (Aim 3). We found evidence of greater CAC progression per unit PM2.5 exposures is areas with high ratios of ultrafine particle counts relative to NOx concentrations. Another important aspect of Project 5 focused on understanding the contribution of in-vehicle exposure to overall exposure (Aim 2). Through concurrent monitoring at home indoors, at home outdoors, in vehicles, and on personal samplers, we were able to characterize the contribution of in-vehicle exposures and demonstrate that it is a key source of exposure to traffic-related air pollutants, especially for individuals who spend a lot of time inside vehicles on the road.

References:

  1. Hazlehurst MF, Spalt EW, Curl CL, Davey ME, Vedal S, Burke GL, Kaufman JD. Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Journal of Exposure Science and Environmental Epidemiology 2017;27(6):569.
  2. Keller JP, Larson TV, Austin E, Barr RG, Sheppard L, Vedal S, Kaufman JD, Szpiro AA. Pollutant composition modification of the effect of air pollution on progression of coronary artery calcium: The Multi-Ethnic Study of Atherosclerosis. Environmental Epidemiology 2018; July 9, doi: 10.1097/EE9.0000000000000024 .   


Journal Articles on this Report : 13 Displayed | Download in RIS Format

Other subproject views: All 29 publications 15 publications in selected types All 15 journal articles
Other center views: All 187 publications 87 publications in selected types All 86 journal articles
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Journal Article Chan SH, Van Hee VC, Bergen S, Szpiro AA, DeRoo LA, London SJ, Marshall JD, Kaufman JD, Sandler DP. Long-term air pollution exposure and blood pressure in the Sister Study. Environmental Health Perspectives 2015;123(10):951-958. R834796 (2015)
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  • Journal Article Chi GC, Liu Y, MacDonald JW, Barr RG, Donohue KM, Hensley MD, Hou L, McCall CE, Reynolds LM, Siscovick DS, Kaufman JD. Long-term outdoor air pollution and DNA methylation in circulating monocytes: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Environmental Health 2016;15(1):119 (12 pp.). R834796 (2016)
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  • Journal Article Chi GC, Hajat A, Bird CE, Cullen MR, Griffin BA, Miller KA, Shih RA, Stefanick ML, Vedal S, Whitsel EA, Kaufman JD. Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular disease. Environmental Health Perspectives 2016;124(12):1840-1847. R834796 (Final)
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  • Journal Article Hazlehurst MF, Spalt EW, Curl CL, Davey ME, Vedal S, Burke GL, Kaufman JD. Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Journal of Exposure Science and Environmental Epidemiology 2017;27(6):569-574. R834796 (2016)
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  • Journal Article Hazlehurst MF, Spalt EW, Nicholas TP, Curl CL, Davey ME, Burke GL, Watson KE, Vedal S, Kaufman JD. Contribution of the in-vehicle microenvironment to individual ambient-source nitrogen dioxide exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Journal of Exposure Science & Environmental Epidemiology 2018;28(4):371-380. R834796 (Final)
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  • Journal Article Keller JP, Larson TV, Austin E, Barr RG, Sheppard L, Vedal S, Kaufman JD, Szpiro AA. Pollutant composition modification of the effect of air pollution on progression of coronary artery calcium:the Multi-Ethnic Study of Atherosclerosis. Environmental Epidemiology 2018;2:e024. R834796 (Final)
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  • Journal Article Spalt EW, Curl CL, Allen RW, Cohen M, Williams K, Hirsh JA, Adar SD, Kaufman JD. Factors influencing time-location patterns and their impact on estimates of exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Journal of Exposure Science & Environmental Epidemiology 2016;26(4):341-348. R834796 (2014)
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  • Journal Article Spalt EW, Curl CL, Allen RW, Cohen M, Adar SD, Stukovsky KH, Avol E, Castro-Diehl C, Nunn C, Mancera-Cuevas K, Kaufman JD. Time-location patterns of a diverse population of older adults:the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Journal of Exposure Science & Environmental Epidemiology 2016;26(4):349-355. R834796 (2014)
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  • Journal Article Sun M, Kaufman JD, Kim S-Y, Larson TV, Gould TR, Polak JF, Budoff MJ, Diez Roux AV, Vedal S. Particulate matter components and subclinical atherosclerosis:common approaches to estimating exposure in a Multi-Ethnic Study of Atherosclerosis cross-sectional study. Environmental Health 2013;12:39. R834796 (2013)
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  • Journal Article Szpiro AA, Sheppard L, Adar SD, Kaufman JD. Estimating acute air pollution health effects from cohort study data. Biometrics 2014;70(1):164-174. R834796 (2013)
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  • Journal Article Vedal S, Kaufman JD. What does multi-pollutant air pollution research mean? American Journal of Respiratory and Critical Care Medicine 2011;183(1):4-6. R834796 (2012)
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  • Journal Article Weuve J, Kaufman JD, Szpiro AA, Curl C, Puett RC, Beck T, Evans DA, Mendes de Leon CF. Exposure to traffic-related air pollution in relation to progression in physical disability among older adults. Environmental Health Perspectives 2016;124(7):1000-1008. R834796 (Final)
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  • Journal Article Young MT, Sandler DP, DeRoo LA, Vedal S, Kaufman JD, London SJ. Ambient air pollution exposure and incident adult asthma in a nationwide cohort of U.S. women. American Journal of Respiratory and Critical Care Medicine 2014;190(8):914-921. R834796 (2015)
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  • Supplemental Keywords:

    Cardiovascular disease, subclinical, Health, Scientific Discipline, Air, ENVIRONMENTAL MANAGEMENT, Air Quality, air toxics, Health Risk Assessment, Risk Assessments, mobile sources, Risk Assessment, ambient air quality, atmospheric particulate matter, particulate matter, aerosol particles, air pollutants, motor vehicle emissions, vehicle emissions, air quality models, motor vehicle exhaust, airway disease, bioavailability, air pollution, particle exposure, atmospheric aerosols, ambient particle health effects, vascular dysfunction, cardiotoxicity, atmospheric chemistry, exposure assessment

    Relevant Websites:

    University of Washington Center for Clear Air Research (UW CCAR) Exit

     

    Progress and Final Reports:

    Original Abstract
  • 2011 Progress Report
  • 2012 Progress Report
  • 2013 Progress Report
  • 2014
  • 2015 Progress Report
  • 2016 Progress Report

  • Main Center Abstract and Reports:

    R834796    University of Washington Center for Clean Air Research

    Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
    R834796C001 Exposure Mapping – Characterization of Gases and Particles for ExposureAssessment in Health Effects and Laboratory Studies
    R834796C002 Simulated Roadway Exposure Atmospheres for Laboratory Animal and Human Studies
    R834796C003 Cardiovascular Consequences of Immune Modification by Traffic-Related Emissions
    R834796C004 Vascular Response to Traffic-Derived Inhalation in Humans
    R834796C005 Effects of Long-Term Exposure to Traffic-Derived Particles and Gases on Subclinical Measures of Cardiovascular Disease in a Multi-Ethnic Cohort