The MESA Air and NPACT air monitoring campaign employed cohort-oriented fixed site monitors in which 2-week measurements of total PM2.5 and PM2.5 components were obtained. Using our primary approach to estimating exposure (nearest monitor) and our primary health effects model, there were cross-sectional associations of CIMT with OC, EC, and sulfur, as well as with total PM2.5, but not with silicon. The strongest associations were for OC and sulfur. The associations were reasonably robust to exposure estimation method (nearest monitor, IDW and city-wide mean) and to adjustment for additional potential risk factors and other PM2.5 components, but not to control for metropolitan area. These associations with CIMT, especially that for OC, were therefore primarily related to exposure differences between metropolitan areas.
Neither presence of CAC nor extent of CAC was positively associated with PM2.5, OC, or any other PM2.5 components evaluated. EC and silicon in some models were associated with CAC, but in the negative direction; these associations were sensitive to control for metropolitan area. While CIMT and CAC are highly correlated, they independently predict future cardiovascular events , suggesting that each provides somewhat different information on atherosclerosis and cardiovascular disease risk. CAC is a measure of plaque in the coronary arterial bed while CIMT can be regarded more as a continuous measure of generalized atherosclerosis. Our findings of associations with CIMT but not with CAC are consistent with earlier findings on PM in the MESA cohort  and may indicate differential pollutant effects on different vascular beds.
CIMT has been associated cross-sectionally with ambient PM2.5 concentrations estimated using regulatory monitoring data and different approaches to estimating within-urban concentrations. Künzli et al. reported an association in Los Angeles between CIMT and PM2.5 estimated using kriging, with exposure assigned at the zip code level . Diez Roux et al. reported an association between 20-year average PM2.5 concentration and CIMT in this same MESA cohort, with PM2.5 estimated using a spatio-temporal model to predict PM2.5 concentrations at each participant’s residence . In this study, as in the current study, there was no association between either presence or extent of CAC and 20-year average PM2.5. Hoffmann et al., however, reported an association in the Heinz Nixdorf Recall Study cohort in Germany between PM2.5 estimated from a dispersion model and the amount of CAC in the subset of study subjects not working full-time . Unlike our study, the primary focus in these prior studies was on PM2.5 total mass rather than PM2.5 chemical composition, and regulatory monitoring data were employed in estimating PM2.5 concentrations. Analyses involving longitudinal measures of subclinical outcomes and PM2.5 and PM2.5 components will help to assess the validity of our and others’ cross-sectional findings.
The cardiovascular effects of long-term exposure to several PM2.5 components have been examined in only one other study. Using the California Teachers Study (CTS) cohort, a prospective cohort of active and former female public school professionals, Ostro et al., in corrected analyses, reported an association of PM2.5 total mass with cardiopulmonary and ischemic heart disease (IHD) mortality; associations of IHD mortality with several PM2.5 components, including OC, EC, sulfate and silicon, were observed . Here we evaluated the associations between long-term concentrations of both PM2.5 total mass and selected PM2.5 components (EC, OC, silicon, and sulfur) and measures of subclinical atherosclerosis (CIMT and CAC). Of the PM components, the strongest and most consistent associations with CIMT were observed for OC and sulfur; only the association for OC was robust to control for the other components.
The choice of the four PM components was influenced by a priori notions that these components reflect different important sources, and together make up the majority of the PM2.5 mass. The OC carbon fraction reflects direct emissions from fossil fuel and biomass combustion and biogenic sources, as well as contributions from secondary atmospheric reactions [28, 29]. In addition to the long-term exposure associations, short-term exposure effects of OC have also been reported in several time series studies. For example, short-term exposure effects on daily cardiovascular mortality were observed in six California counties and in Phoenix, Arizona [30, 31]. Metzger et al. reported an association between OC and emergency department visits for cardiovascular disease in Atlanta, Georgia . Modification of short-term PM2.5 cardiovascular effects by long-term concentrations of OC has also been reported , although this has not been a consistent observation [34, 35].
Sulfur, used as a marker of sulfate, was also associated with CIMT. Sulfate is a secondary aerosol that is formed through photochemical reactions involving sulfur-based compounds, notably sulfur dioxide. Associations of sulfate with cardiopulmonary disease mortality were observed in the ACS study, the Six-Cities study and in the CTS cohort . The sulfate component modified the short-term effect of PM2.5 in one mortality time series study . Also, short-term effects of sulfate have been reported in some studies . It is not clear whether the observed associations with sulfate indicate direct effects of sulfate, or whether these reflect effects of components in the secondary pollutant mix that includes sulfate.
The EC carbon fraction is typically used to reflect diesel emissions and other combustion processes such as wood burning [37, 38]. We found some associations of EC with CIMT, but these were sensitive to inclusion of any one of the other PM components in the health model (results not shown). EC was negatively associated with presence of CAC, but not after controlling for metropolitan area. Long-term EC was associated with ischemic heart disease mortality in the CTS  and was reported to modify short-term PM2.5 effects in one study , but not in others [33, 34]. Also, short-term EC exposure effects have been reported in time series studies .
We found little evidence of an association of silicon with CIMT. In contrast, silicon was, along with several PM2.5 components, associated with cardiopulmonary mortality in the CTS cohort . Silicon is a crustal element that is a large component of soil and resuspended road dust . It may therefore reflect constituents found in road dust, including combustion-based material, brake dust, tire debris, and semivolatile compounds. It may also serve as a general marker for proximity to traffic. Short-term exposure to silicon has been associated with cardiovascular effects in a few time-series studies in Arizona and California [2, 30, 31, 40, 41]. In contrast, in a study of six eastern and Midwestern cities in the U.S., no association between mortality and daily exposure to silicon was seen . Long-term concentrations of silicon did not modify short-term PM2.5 effects in one study . Silicon PM was associated with exacerbation of myocardial ischemia in a dog model of coronary artery disease . While relatively limited exposure contrasts for silicon may have hampered our ability to detect associations with silicon, previously reported findings on silicon effects on cardiovascular outcomes have not been consistent.
All of our measures of PM exposure were based only on the MESA Air fixed ambient air monitors (Figure 1), as we were not able to integrate CSN data into our exposure estimates due to the well-documented poor comparability of monitoring methods, notably for OC and EC, during the monitoring period . Three approaches were employed in assigning exposure to PM2.5 total mass and PM2.5 components. The nearest monitor and the IDW approaches assigned exposure based on the participant’s address, as has been done in a few other studies of long-term PM exposure effects on cardiovascular diseases [12, 14]. The nearest monitor is assumed to provide a more valid estimate of exposure than city-wide mean, although it is not clear whether it is superior to the IDW approach. Both nearest monitor and IDW estimates attempt to capture some within-city variability in exposure, as opposed to the city-wide mean. We chose, somewhat arbitrarily, the nearest monitor approach as our primary exposure estimation approach, using the IDW and city-wide mean approaches in sensitivity analyses. Our findings were not highly sensitive to the approach to estimating exposure. Future analyses of PM2.5 component effects in the MESA cohort will take advantage of more sophisticated spatio-temporal modeling of pollutants, which will allow for assessment of the impact on the health findings of using these more sophisticated exposure estimates.
A limitation of all of our approaches to estimating exposure is that we only estimate outdoor residential concentrations rather than concentrations to which people are actually exposed. While outdoor concentrations have been shown to be reasonable proxies for indoor concentrations and for personal exposure to particles of outdoor origin [44, 45], estimates of outdoor concentrations necessarily mismeasure exposures that are influenced by time-activity patterns that take people away from home, such as work and travel to work. Time-activity studies, however, show that people spend most of their time in or around home , justifying the common practice of basing exposure estimates on place of residence. In MESA Air, time-activity data from the entire MESA cohort confirmed that study participants spent most of their time in their homes; the elderly or Chinese participants spent relatively more time in their homes . Future analyses will assess whether our estimates of health effect are modified by incorporation of data collected on time-activity patterns and infiltration of particles indoors.
Adding to the complexity of PM exposure measurement error is the likely differential measurement error across the different PM2.5 components. It is expected that sulfate, with relatively homogeneous concentrations within a metropolitan area, would exhibit less measurement error than OC, for example, whose concentrations vary within an urban area . To the extent that increased exposure measurement error biases effect estimates toward the null , it is possible that the association with sulfate is underestimated to a lesser extent than that with OC. In spite of that, we observed that the effect per IQR was higher for OC than any of our PM2.5 components, suggesting that the true association for OC may have been even larger.
Exposure misclassfication could also result from estimating exposure for a time period that is not relevant to the exposure responsible for the observed effect. Exposure was assigned based on one year of monitoring from 2007 to 2008, whereas our endpoint measurements were obtained during the period 2000 to 2002. To address the issue of PM component concentration stability over time, we examined CSN PM2.5 component data monitoring sites in the six MESA areas for the years 2002 and 2007. There was generally good correlation over that 5-year span (Figure 3). It is therefore reasonable to assume that concentrations in 2000–2002, while likely higher than those in more recent years, were nevertheless highly correlated with them. In the MESA cohort, PM2.5 concentrations were highly correlated over a 20-year period , as they were in the American Cancer Society study .
Although we included a reasonably comprehensive list of potential individual-level confounder variables in our health effect analyses, it is possible that uncontrolled confounding from unmeasured confounders associated with metropolitan area is present. This motivated control for metropolitan area in Model 4. Our findings were variably sensitive to control for metropolitan area. For example, when study area was added to models using our primary estimation approach (nearest monitor), there was no longer an association of some of the PM2.5 components, especially OC, with CIMT. Because much of the variability in exposure was due to variability between areas, control for metropolitan area substantially reduced exposure variability, which limits our power to detect associations. While we put most interpretive weight on models that did not control for metropolitan area, it may have been preferable to place more weight on findings from models with control for metropolitan area if it had been possible to accomplish that without dramatically reducing variability in exposure.
Strengths of this study include the wealth of detailed information on cardiovascular risk factors, the standardized assessment of outcomes, the attempt to incorporate some features of within-city variability in our exposure estimates based on PM2.5 species air monitoring carried out specifically on the MESA cohort, and the assessment of sensitivity of findings to employing three commonly-used approaches for estimating exposure. Future work will employ more sophisticated methods for estimating individual-level exposure to PM components that incorporate land use regression modeling and geostatistical methods, as well as time-activity data. Effects on longitudinal change in CIMT and CAC, in addition to the cross-sectional effects described in this report, will also be assessed when those data are available.