2016 Progress 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: Kaufman, Joel D. , Larson, Timothy V. , Sampson, Paul , Sheppard, Lianne (Elizabeth) A. , Szpiro, Adam , Vedal, Sverre , Yost, Michael
Current 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)
Project Period Covered by this Report: August 1, 2015 through July 31,2016
RFA: Clean Air Research Centers (2009) RFA Text |  Recipients Lists
Research Category: Health Effects , Air

Objective:

Project 5 has three primary objectives:

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 multi-pollutant 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: i) the outdoor residential concentration estimates from the multi-pollutant model; ii) estimates of residential infiltration rates; iii) road class- and traffic condition-specific estimates of on-roadway concentrations; and iv) 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 CT, intima-medial thickness as measured by ultrasound, and DNA methylation.

Progress Summary:

Aim 1: Developing spatial exposure model. For Aim 1 of Project 5, we are working closely with Project 1 and Biostatistics Core personnel to develop approaches to their high-dimensional data which can be applied to epidemiological analyses.  Methods for this approach are described in the Biostats Core Section above. Results for the cluster analysis were provided in last year's report; here we present the cluster assignments for MESA Air participants in Baltimore.  Health analyses are currently underway.

Heating

Membership in clusters identified by k-means from the heating season data for monitoring locations and MESA Air participants are presented in Figure 1.

Text Box:  
Figure 1. Cluster membership for MESA Air participants 
in Baltimore during the heating season.

Non-Heating

Membership in clusters identified by k-means from the non-heating season data for monitoring locations and for MESA Air participants are presented in Figure 2.

Text Box:  
Figure 2. Cluster membership for MESA Air participants 
in Baltimore during the non-heating season.

Aim 2: Understanding in-vehicle contribution to individual level multi-pollutant exposures.  In prior reports, we discussed in detail the field work portion of this project, in which data was collected to address much of the second aim of this project.  Through a combination of personal, residential and in-vehicle sampling, paired with intensive location tracking, we are seeking to understand the influence of time spent in transit on personal exposure, which will improve our individual-level exposure estimates and contribute to our epidemiological analysis.

Four exposure campaigns were conducted in two seasons each in Winston-Salem, NC and Los Angeles, CA. Each campaign involved assessment of time-location patterns using multiple methods and individual-level air monitoring in several microenvironments: residential outdoors, residential indoors, in-vehicle, and personal monitoring. A novel in-vehicle passive monitoring device was built specifically for this study to capture exposures while driving. A summary of participant characteristics and measured air pollutant concentrations by sampling location were presented in the last report.  

After reviewing the results for all four of the monitoring campaigns, and subsequent to analysis presented previously, we became concerned that the in-vehicle concentrations (e.g. of oxides of nitrogen) calculated based on measurements from the field study were higher than anticipated.  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. Table 1 shows the additional time per opening for each analyte. On average, participants took 38 trips during the two-week sampling period and the average additional sampling time ranged from 6 hours to 25 hours depending upon the parameter. Table 2 shows the median vehicle concentrations before and after the addition of the extra participant-specific sampling time.

Table 1. Additional sampling time per trip and in total for in-vehicle samples.

Analyte

Sampling time per trip (min)

Average total additional sampling time (hrs)

NO2

40.2

25

NO

22.7

14

O3

16.1

10

Pentanes

9.9

6

Isoprene

8.7

6

Nonane

14.2

9

Decane

15.2

10

Undecane

15.5

10

Dodecane

16.3

10

Benzene

9.9

6

Toluene

11.1

7

m-Xylene

12.8

8

o-Xylene

12.8

8

Table 2. Revised median in-vehicle concentrations for each of the analytes.

Pollutant

Winston-Salem, NC

Los Angeles, CA

Winter

Summer

Winter

Summer

NO2

27

18

33

39

NOx

51

27

104

24

O3

18

10

9

18

Pentanes

214

491

204

150

Isoprene

0.02

1.3

1.2

1.1

Nonane

0.6

0.6

0.4

0.3

Decane

1.4

2.4

2.7

2.2

Undecane

2.1

5.6

8.1

6.4

Dodecane

2.5

6.6

6.9

9.5

Benzene

1.1

1.7

1.4

1.8

Toluene

6.1

12.9

5.0

5.4

m-Xylene

3.3

4.1

2.1

2.2

o-Xylene

1.6

21

1.1

1.2

Using these concentrations, we have assessed the relative importance of the in-vehicle microenvironment for individual exposure to NO2. This work is currently being written up in a manuscript titled, "Contribution of the in-vehicle microenvironment to individual ambient source nitrogen dioxide exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution" that has been drafted and circulated to co-authors. A third paper is also in progress, and will examine the measured concentrations and comparisons of those concentrations between microenvironments for the entire suite of pollutants measured in this study. 

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 Global Positioning System (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 two-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 (Figure 3).

Figure 3. Percent of time spent in vehicle, outdoors, and in-vehicle by sampling campaign.

The percent of time spent indoors, outdoors, and in-vehicle based on this integration of intensive two-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," has been submitted to the Journal of Exposure Science and Environmental Epidemiology.

Aim 3: Epidemiological Analyses.  Several analyses relating to Aim 3 of Project 5 are in progress. These include analyses using the following outcomes: arteriolar diameters as measured by retinal photography, coronary artery calcium (CAC) as ascertained by CT, intima-medial thickness as measured by ultrasound, and DNA methylation.  As described in the Biostatistics section above, cluster membership will be used as an effect modifier of the association between NOx exposure and measurements of coronary artery calcium (CAC) to determine whether or not the association varies by multi-pollutant profile (as identified by cluster).

Much of the work for Aim 3 thus far has focused on DNA methylation, some of which was presented in last year's annual report. We did not find evidence that long-term ambient PM2.5 and NOX exposure were associated with global DNA hypo-methylation in monocytes at ALU or LINE-1 loci.

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 (Table 3). 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.

Table 3. Association Between PM2.5 (per 2.5 µg/m3) and eMS and Gene Expression

 

 

PM2.5 and DNA Methylation

PM2.5 and Gene Expression

Genea

Chr

CpG

β (95% CI)b

P-value

β (95% CI)b

P-value

ANKHD1

5

cg20455854

0.139 (0.074, 0.203)

2.77E-05

-0.048 (-0.074, -0.022)

3.71E-04

LGALS2

22

cg07855639

0.081 (0.043, 0.120)

3.28E-05

-0.147 (-0.240, -0.053)

0.002

ANKRD11

16

cg07598385

0.108 (0.056, 0.160)

4.97E-05

-0.075 (-0.142, -0.008)

0.028

BAZ2B

2

cg17360854

0.081 (0.042, 0.120)

5.08E-05

-0.016 (-0.060, 0.027)

0.463

PPIE

1

cg23599683

-0.057 (-0.085, -0.029)

7.17E-05

0.004 (-0.020, 0.028)

0.728

Note: Models adjusted for age, sex, race/ethnicity, site, smoking, socioeconomic status, body mass index, recent infection, residual cell contamination, methylation chip and position

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

Future Activities:

We are in the process of preparing two additional manuscripts related to Aim 2:
1) an analysis of the relative contributions of each microenvironment to overall exposure to ambient-source nitrogen dioxide; and
2) a manuscript discussing the results of all of traffic-related air pollutant sampling. 

The multipollutant work described in this summary and in the Biostats Core is in process and will continue with plans for manuscript(s). We will also continue our methylation analyses incorporating additional approaches including more conventional (epigenome-wide) and more innovative methods of interrogating high-dimensional methylation data (e.g. "bumphunting") and approaches that incorporate multi-pollutant framework.  We anticipate three manuscripts to be submitted related to the DNA methylation work in the upcoming year.


Journal Articles on this Report : 11 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 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 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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  • 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; doi:10.1289/EHP199 (Epub ahead of print]. R834796C005 (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 Exit   Exit

    Progress and Final Reports:

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