Environmental Health Perspectives • volume 119 | number 4 | April 2011 

### 481 

# Research

There is strong epidemiologic al evidence that short-term air pollution exposure (i.e. , < 24 hr to 3 weeks) is related to mortality and other cardio vascular events (Brook et al. 2010). Much of this evidence involves exposure to ambient particulate matter (PM) with aero

dynamic diameter ≤ 2.5 μm (PM 2.5 ) , which comprises many components and varies regionally. Contributions of specific compo

nents and sources to these effects are not well understood but are critical for informing the develop ment of regulations. Short-term exposure studies often use sta

tionary monitors to estimate exposure in a nearby region. However, specific components of traffic-related air pollution (TRAP) vary substantially within cities, and traffic variables may contribute to this variation (Brauer et al. 2003; Clougherty et al. 2008; Kinney et al. 2000). This suggests that a geographically based approach could substantially improve assessment of black carbon ( BC ) exposure. Long-term exposure to TRAP (i.e. , aver ages ≥ 1 year) has also been associated with cardio vascular mortality, often based on studies using nitrogen dioxide as a surrogate for traffic pollutants and spatial modeling of exposures (Brunekreef et al. 2009; Gehring et al. 2006; Yorifuji et al. 2010 ). In a Boston, Massachusetts–area case-crossover analysis, we reported an association between mortality and BC exposure on the day before death, using spatio temporally modeled BC exposure estimates as a marker of TRAP (Maynard
et al. 2007). Intercellular adhesion molecule-1 (ICAM

- 1) and vascular cell adhesion molecule-1 (VCAM

- 1) are markers of inflammation and endothelial function that are expressed on cell surfaces and are also found in soluble form in the plasma (sICAM

- 1 and sVCAM

- 1). These markers are independently and jointly associ

ated with increased risk of cardio vascular dis

ease (Albert and Ridker 1999; Pradhan et al. 2002; Rana et al. 2011 ; Ridker et al. 2003). Recent studies have reported associations between inflammatory markers and short-term exposure to TRAP , but studies have varied in both PM exposure type and inflamma

tory marker s examined (Delfino et al. 2008; Madrigano et al. 2010 ; Zeka et al. 2006). Obesity and diabetes are becoming increas

ingly prevalent, and these conditions may increase susceptibility to the adverse health effects of air pollution (Forastiere et al. 2008; O’Neill et al. 2005; Zanobetti and Schwartz 2002). Prospective and randomized clini

cal trials, as well as laboratory studies, have demon strated that statins, a widely prescribed class of drugs with anti-inflammatory and anti

oxidant activity (Haendeler et al. 2004), can decrease C-reactive protein ( CRP ) , ICAM

- 1 , and VCAM

- 1 levels (Albert et al. 2001; Blanco-Colio et al. 2007; Liang et al. 2008; Montecucco et al. 2009). Thus, it is plausible that statin use could attenuate pro inflammatory effects of air pollution, and reduced effects of air pollution on inflammation and heart-rate variability among statin users compared with
non users have been reported (Schwartz et al. 2005; Zeka et al. 2006). The primary objective of the present study was to estimate the cumulative effects of exposure to TRAP over several weeks on inflammation and endothelial function. Previous findings based on BC measured at a central-site monitor suggested associations between inflammatory markers and air pollu

tion exposure averaged over the prior 4 weeks (Zeka et al. 2006). Because BC concentration varies spatially and averaging over a longer period of time decreases the temporal varia

tion in the data, we wanted to examine effects of 4

- to 12-week average exposures estimated using a land-use regression model. We chose sICAM

- 1 and sVCAM

- 1 as outcomes because they reflect changes in inflammation, are asso

ciated with cardio vascular disease risk, and are relatively stable within individuals over 4

- week periods (Eschen et al. 2008). We hypothesized that increases in medium-term BC concentrations estimated by land-use regressions would be associated with an increased inflammatory response in elderly
Address correspondence to S.E. Alexeeff, 401 Park Dr., Ste. 415 West, Landmark Center, Boston, MA 02215 USA. Telephone: (617) 384-8753. Fax: (617) 384-8745. E-mail: salexeeff@fas.harvard.edu

This work was supported by grants ES015172-01 and ES00002 from the National Institute of Environmental Health Sciences, and grant R832416 from the U.S. Environmental Protection Agency. The Normative Aging Study is supported by the Cooperative Studies Program/Epidemiology Research and Information Center of the U.S. Department of Veterans Affairs and is a component of the Massachusetts Veterans Epidemiology Research and Information Center (Boston, MA, USA).

The authors declare they have no actual or potential competing financial interests.

Received 15 June 2010; accepted 24 February 2011. 

## Medium-Term Exposure to Traffic-Related Air Pollution and Markers of Inflammation and Endothelial Function Stacey E. Alexeeff, 1 Brent A. Coull, 2 Alexandros Gryparis, 2 Helen Suh, 1 David Sparrow, 3 Pantel S. Vokonas, 3 and Joel Schwartz 1

1 Department of Environmental Health, and 2 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA; 3 VA Normative Aging Study, VA Boston Healthcare System, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA

B ackground : Exposure to traffic-related air pollution (TRAP) contributes to increased cardio

vascular risk. Land-use regression models can improve exposure assessment for TRAP. O b jectives : We examined the association between medium-term concentrations of black carbon (BC) estimated by land-use regression and levels of soluble intercellular adhesion molecule-1 ( sICAM-1 ) and soluble vascular cell adhesion molecule-1 ( sVCAM-1 ) , both markers of inflamma

tory and endothelial response. M ethods : We studied 642 elderly men participating in the Veterans Administration (VA) Normative Aging Study with repeated measurements of sICAM

- 1 and sVCAM

- 1 during 1999–2008. Daily estimates of BC exposure at each geocoded participant address were derived using a validated spatio

temporal model and averaged to form 4

- , 8

- , and 12-week exposures. We used linear mixed models to estimate associations, controlling for confounders. We examined effect modification by statin use, obesity, and diabetes. R

esults : We found statistically significant positive associations between BC and sICAM

- 1 for aver

ages of 4, 8, and 12 weeks. An interquartile-range increase in 8-week BC exposure (0.30 μg/m 3 ) was associated with a 1.58% increase in sICAM

- 1 (95% confidence interval, 0.18

– 3.00%). Overall asso

ciations between sVCAM

- 1 and BC exposures were suggestive but not statistically significant. We found a significant interaction with diabetes—where diabetics were more susceptible to the effect of BC—for both sICAM

- 1 and sVCAM

- 1. We also observed an inter action with statin use, which was statistically significant for sVCAM

- 1 and suggestive for sICAM

- 1. We found no evidence of an interaction with obesity. C onclusion : Our results suggest that medium-term exposure to TRAP may induce an increased inflammatory/endothelial response, especially among diabetics and those not using statins. K

ey words : adhesion molecules, air, cardio vascular, environmental, outdoor air, roadway proximity. Environ Health Perspect 119:481–486 (2011). doi:10.1289/ehp.1002560 [Online 24 February 2011]Alexeeff et al.



### 482 volume 119 | number 4 | April 2011 • Environmental Health Perspectives men in the Greater Boston area. We also exam

ined whether that effect was modified by statin use, obesity, and diabetes. 

### Materials and Methods Study population. We studied participants in the Normative Aging Study (NAS), a longi

tudinal study established by the VA in 1963 (Bell et al. 1972). In brief, the NAS enrolled 2,280 men from the Greater Boston area who were initially free of known chronic medical conditions. All participants provided written informed consent, and the study was approved by the institutional review boards of all par

ticipating institutions. Participants visited the Boston VA Hospital study center every 3 years to under go physical examinations. At each of these visits, blood samples and extensive physical examination, laboratory, anthropo

metric, and questionnaire data were collected. Information about cigarette smoking, medical history, and medication use were obtained by self-administered questionnaire. Each sub

ject was interviewed to confirm the identity and purpose of medications used, and all new disease diagnoses were noted. Diabetes was defined as a physician diag

nosis of diabetes, and obesity was defined as
a body mass index ( BMI ) of at least 30 kg/
m 2 . Self-reported data on diabetes status and statin use were updated at each study visit. In addition, BMI and obesity were updated based on height and weight measurements at each visit. Thus, NAS data reflect changes in disease status and medication use over time. Measurements of sICAM

- 1 and sVCAM

- 1 began in 1999. For the present study, we included the 642 NAS participants with at least one measurement of sICAM

- 1 and sVCAM

- 1 and whose home address was in the Greater Boston area (1,423 total person-visits). Subjects who moved out of the area were excluded. Measurements of sICAM‐1 and sVCAM‐1. Blood samples routinely collected during medical exam visits from 1999 through 2008 were analyzed for sICAM

- 1 and sVCAM

- 1 in N. Rafai’s laboratory at Children’s Hospital Boston (Boston, MA ) . Plasma sICAM

- 1 and sVCAM

- 1 concentrations were meas ured in duplicate using the enzyme-linked immuno

sorbent assay (ELISA) method (R&D Systems, Minneapolis, MN), with a sensitivity of 0.35 ng/mL for sICAM

- 1 and 2.0 ng/mL for sVCAM

- 1 (Lim et al. 1999). BC exposure prediction. The BC exposure model and the stationary air monitors used to develop the model have been described in detail previously (Gryparis et al. 2007). Briefly, 82 sites were used; most sites meas

ured BC continuously using aethalometers, and other sites collected particles on a filter over 24 hr and meas ured elemental carbon (EC) using reflectance analysis. The monitor

ing data used to develop our model included 6,031 observations from 2,079 unique exposure days. Using a spatio temporal model that we developed and validated previously (Gryparis
et al. 2007), we estimated the 24

- hr average BC concentration at each geocoded participant address. Predicted daily concentrations showed
a > 3

- fold range of variation in exposure across measurement sites (adjusted R 2 = 0.83). A vali

dation sample at 30 additional monitoring sites showed an average correlation of 0.59 between predicted and observed daily BC levels. We averaged the 24

- hr predictions to form esti

mates for the 4, 8, and 12 weeks before each participant visit. We also averaged the 24

- hr predictions to form estimates for the 4

- week average during the 5–8 weeks before the study visit and the 4

- week average during the 9–12 weeks before the study visit, which are components of the 8

- and 12

- week averages, to use as a sensitivity analysis. Covariates in the BC prediction model included measures of land use for each address (cumulative traffic density within 100 m, popu lation density, distance to nearest major road

way, and percent urbanization), geographic information system (GIS) location (latitude, longitude), daily meteorological factors (appar

ent temperature, wind speed, and height of the planetary boundary layer) , and other charac

teris tics (day of week, day of season). The Boston central-site monitor was also included as a predictor to reflect average pollutant concen

trations over the entire region on each day. Separate models were fit for warm and cold seasons. Interaction terms between thetemporal meteorological predictors and sourcebased geographic variables allowed for space

– time inter actions. Regression splines allowed main effect terms to non linearly predict expo

sure levels, and thin-plate splines modeled the residual spatial variability not explained by the spatial predictors. A latent variable framework was used to integrate BC and EC exposure data, where BC and EC measurements were treated as surrogates of some true, unobserv

able traffic exposure variable; see Gryparis
et al. (2007) for further details. Estimation of health effects. We log-trans

formed sICAM

- 1 and sVCAM

- 1 levels to increase normality and stabilize variance in the residuals. Model covariates were selected
a priori , and all models included age, BMI, diabetes, smoking status, pack

- years , and sea

son. We used mixed models to account for correlation among measurements on the same subject across different medical visits. Mixed models have the form
Y

ij = β 0 + β l age i + β 2 BMI i + ... + β p BC ij + u i + ε ij , [1]
where Y ij is log(sICAM

- 1) or log(sVCAM

- 1) in subject i on day j , and u i represents a subject-specific intercept that reflects unex

plained hetero geneity in the outcome. BC averages and model covariates are modeled as fixed linear effects, and u i is modeled as a ran

dom effect. We assume that the u i are gener ated from a normal distribution with common variance, yielding the simple compound sym

metry variance structure. This model requires estimation of two variance components, which represent between- and within-subject varia

tion. Models with unbalanced data (i.e. , varying numbers of repeated measurements on each subject) typically yield accurate estimates of within-subject variation, provided a suf

ficient number of repeated measurements con

tribute to the estimate. Models used to examine effect modi

fication by obesity, diabetes, and statin use included inter action terms that allowed asso

ciations between BC and the outcomes to vary among subgroups. Diabetes, obesity, and sta

tin use were treated as time-varying covariates, where the status was updated at each visit to reflect changes since the last visit. The percent

ages of subjects whose status changed for these factors over the study period were 3% for dia

betes , 9% for obesity , and 21% for statin use

. We also performed a sensitivity analysis to investigate whether the interaction with statin use was heavily influenced by the people who began taking statins after the study period began. Specifically, we limited the interaction analysis to the participants who never used statins during the study period ( n = 284) and those who used statins throughout the entire study period ( n = 223). Because the outcomes were log-transformed, effect estimates are reported as percent changes in sICAM

- 1 and sVCAM

- 1 concentrations associated with a 0.30

- μg/m 3 increase in BC, which corresponds to the interquartile range (IQR) for average BC exposures over all three time intervals (4, 8 , and 12 weeks). A level of α = 0.05 was used to determine statistical significance. 

### Results Descriptive data. Subjects were elderly, with a mean age of 72 years (range , 56–100 years ) at the first study visit (Table 1). Most subjects were overweight (median BMI , 27.5 kg/m 2 ; range , 18–52 kg/m 2 ). Average 4

- week BC exposure estimates ranged from 0 to 3.26 μg/m 3 (mean ± SD, 0.42 ± 0.30 μg/m 3 ; median , 0.37 μg/m 3 ; IQR , 0.30 μg/m 3 ). For both 8

- and 12-week exposures, exposure estimates were 0.42 ± 0.29 μg/m 3 (median , 0.38 μ g/m 3 ; IQR , 0.30 μg/m 3 ) . Figure 1 illus

trates the spatial distribution of predicted BC in the study area during an arbitrary 4-week period (February 2001). The distributions of BC exposures looked similar among subjects classified according to obesity, diabetes, sta

tin use, or smoking status, and we found noTraffic particles and endothelial function

Environmental Health Perspectives • volume 119 | number 4 | April 2011 

### 483 significant differences in the mean BC expo

sure assessed at baseline. Regression analysis. We found signifi

cant positive associations between sICAM

- 1 and BC averaged over the previous 4, 8 , and 12 weeks (Table 2). Associations between aver

age BC for 4, 8, and 12 weeks and sVCAM

- 1 were in the same direction , with slightly smaller effect size, but were not statistically sig

nificant. As a sensitivity analysis, we also esti

mated the effects of BC exposure in weeks 5–8 and weeks 9–12 separately. For sICAM

- 1, the association with the longer BC lags were positive with effect size slightly smaller than the effect size for weeks 1–4 and not quite sta

tistically significant. A 0.30

- μg/m 3 increase in BC exposure during weeks 5–8 was associated with a 1.22% increase in sICAM

- 1 [ 95% con fidence interval ( CI ),

– 0.10% to 3.00% ] , and during weeks 9–12 , with a 1.09% increase in sICAM

- 1 (95% CI , – 0.18% to 2.38%). For sVCAM

- 1, the estimated effects for the longer lags had the same effect size and direction as the effects for weeks 1–4 but were not statisti

cally significant (data not shown). Overall, we
saw the same pattern of effects in the lagged 4-week intervals, with only slight weakening in effect when the lags were considered indi

vidually rather than cumulatively. Regression analysis and interactions. When we examined the effect of BC on sICAM

- 1 and sVCAM

- 1 by diabetes status, we saw effects only among diabetics, whereas
non diabetics showed no effect (Figure 2 ; inter action p -values < 0.05 for all models except 12-week BC and sVCAM

- 1). When we examined the effects of BC by statin use, interaction models suggested that effects were present only in participants who did not use statins, with little evidence of associa

tions among statin users (Figure 3 ; inter action p -values < 0.05 for sVCAM

- 1 only). In con

trast, we found no difference in the estimated effect of BC by obesity status on either out

come (data not shown). We restricted our sensitivity analysis of statin use interaction to participants who never used statins or always used statins during the study period. The results were comparable, with the effect sizes estimated to be slightly larger in all categories in the restricted model, and statistically significant (data not shown). T hese differences could reflect some overall difference in health between statin users and non users because statin use was not randomized

. 

### Discussion We found that address-specific BC expo

sures averaged over 4, 8 , and 12 weeks were positively associated with markers of inflam

mation and endothelial dysfunction in this elderly cohort. Exposure estimates based on our validated land-use regression model are more accurate than estimates based on ambient monitoring, which are often used for cohorts of this size. Our analyses suggest that diabetics are more susceptible to adverse effects of TRAP than are non diabetics, but we found no evidence of effect modification by obesity. In addition, we observed a null effect among participants who used statins

**Table 1.** Characteristics of the NAS population at first visit. Covariate Mean ± SD or n (%) Age (years) 72.4 ± 7.0 BMI (kg/m 2 ) 28.3 ± 4.2 Diabetes 126 (19.6) Obesity 180 (28.0) Statin use 242 (37.7) Smoking status

Former smoker 657 (69.7) Current smoker 28 (2.9) Never smoker 258 (27.4) Pack-years a 30.4 (27.9) sICAM-1 (ng/mL) 295.4 ± 80.6 sVCAM-1 (ng/mL) 1058.9 ± 351.8

a F or current and former smokers

. **Table 2.** Estimated percent increase in sICAM-1 and sVCAM-1 levels per 0.3- μg/m 3 increase in BC , for 4-, 8-, and 12-week averages. sICAM-1 sVCAM-1 Time period Percent increase 95% CI Percent increase 95% CI 4 weeks 1.50 0.22 to 2.80* 1.00 –0.65 to 2.67 8 weeks 1.58 0.18 to 3.00* 1.20 –0.58 to 3.02 12 weeks 1.49 0.04 to 2.95* 1.26 –0.58 to 3.14

* p

< 0.05. **Figure 1.** Predicted average BC concentration in the Greater Boston area over a 4-week period. Predicted BC (μg/m 3 ) 0.03–0.18 0.18–0.24 0.24–0.29 0.29–0.35 0.35–0.42 0.42–0.48 0.48–0.56 0.56–0.64 0.64–0.87 0.87–2.5Alexeeff et al.



### 484 volume 119 | number 4 | April 2011 • Environmental Health Perspectives and a positive association for those not taking statins, but further investigation is needed to clarify this potential effect. TRAP and inflammatory response. Severalstudies have examined the effects of shortterm (i.e. , < 24 hr to 3 weeks) TRAP and various markers of inflammation. For exam

ple, in a large epidemiological study, daily increases in ambient PM levels were posi

tively associated with plasma fibrinogen levels, with the strongest association observed in par

ticipants with chronic obstructive lung dis

ease (Schwartz 2001). In a study of a small ( n = 29) elderly population, Delfino et al. (2008) examined inflammatory markers and PM components measured at each subject’s home for 1–9 days on average. The authors reported that several traffic-related compo

nents were significantly associated with CRP and interleukin -6, and observed positive but
non significant associations with sICAM

- 1 and sVCAM

- 1. In other studies of NAS participants, traf

fic-related PM (BC and organic carbon) expo

sure has also been positively associated with plasma total homo cysteine concentrations (Park et al. 2008), and this association is mod

ified by poly morphisms in genes related to oxidative stress (Ren et al. 2010). PM 2.5 and BC averages of 1–3 days measured at the cen

tral site have been associated with increased vascular cell adhesion in NAS participants (Madrigano et al. 2010 ). Traffic-related PM (particle number and BC) has also been posi

tively associated with inflammatory markers (CRP, white blood cell count, sediment rate, and fibrinogen) in NAS participants, with stronger associations with particle numbers than with BC and stronger associations with BC averaged over 4 weeks than averaged over 48 hr or 1 week (Zeka et al. 2006). Few controlled studies of PM on human inflammatory response have been conducted, but one reported that plasma fibrinogen increased after exposure to urban PM (Ghio
et al. 2000), and another reported that periph

eral neutrophils, sVCAM

- 1, and sICAM

- 1 increased after a 1-hr exposure to diesel PM (Salvi et al. 1999). Mechanisms and interactions. Diabetics have impaired endothelial function com

pared with non diabetics (Calles-Escandon and Cipolla 2001), and there is increasing evidence that diabetics are more susceptible to the effects of air pollution. Inverse associations between 60-day average BC exposure and bra

chial artery flow-mediated dilation (FMD) were stronger in diabetics than in non diabetics (O’Neill et al. 2005). Associations between 24

- hr exposure to PM 2.5 and FMD were stronger among diabetics with markers of severe insulin resistance compared with other diabetics (Schneider et al. 2008). In contrast with our findings, obese NAS participants have been reported to have stronger associations between short-term BC exposure and plasma CRP, erythrocyte sedi

ment rate (Zeka et al. 2006), and sVCAM

- 1 (Madrigano et al. 2010 ) than non obese participants. Our finding that statin users are less sus

ceptible than those not taking statins is gener

ally consistent with other studies. In diabetics, stronger associations of PM 2.5 and BC with sVCAM

- 1 were reported among those not using statins than in statin users (O’Neill et al. 2007). Associations between CRP and traffic PM exposures of 5 and 9 days were reported to be stronger among those using statins than those not using statins (Delfino et al. 2008). Statins promote endothelial nitric oxide release, which reduces cell adhesion, thus suggesting a mecha nism for this association. However, we cannot rule out the possibility that statin use is an indicator of health status or some other unmeasured factor that may explain why BC did not appear to influence sICAM

- 1 and sVCAM

- 1 in statin users. The question of which CAM is most closely associated with PM remains unre

solved. Differences in the associations reported in epidemiologic al studies of the two CAMs could reflect differences in cell types that express the molecules or differences in the process of cleavage and shedding from endothelial cells. In the present study, the effects on both sICAM

- 1 and sVCAM

- 1 were consistent: The effect sizes were similar and the direction of the effects and the inter

actions were the same, even though we found differences in statistical significance. Thus , our study does not support the idea of a differ

ent under lying mechanism for the effects of TRAP on these two CAMs. The expression of both ICAM

- 1 and VCAM

- 1 on the surface of vascular endothe

lial cells is associated with the formation of early athero sclerotic lesions. Although the relationship between the degree of cellu lar ICAM

- 1 and VCAM

- 1 expression and plasma concentrations of soluble forms is not entirely clear, multiple studies have shown that sICAM

- 1 and sVCAM

- 1 predict risk of car

diovascular disease (Albert and Ridker 1999; Pradhan et al. 2002; Rana et al. 2011 ; Ridker
et al. 2003). In a recent study investigating the variability of sICAM

- 1 and sVCAM

- 1
measures over a 4

- week period, Eschen et al. (2008) reported an estimated intra subject variability of 7.6% for sICAM

- 1 and 9.5% for sVCAM

- 1, which suggests that these markers are relatively stable over a 4

- week period. Exposure estimation. A major advantage of the present study is the use of a validated land-use regression model to character

ize the individual-level differences in expo

sures instead of classifying exposure based on
measure ments at the nearest monitor, landuse regression models without BC measure

ments, or a weighted form of distance to roadway. Although the study is still limited by the lack of individual-level monitoring data at the home, our validation study suggests that our estimates are highly correlated with actual exposure measure ments at locations in Boston other than those used to fit the model and
are much more closely correlated with these measurements than are exposure estimates **Figure 2.** Estimated percent increase in sICAM-1 ( A ) and sVCAM-1 ( B ) levels by diabetic status, per 0.3

- μg/m 3 increase in BC , for 4-, 8- , and 12- week averages.

10.0 8.0 6.0 4.0 2.0 0 –2.0 –4.0 10.0 8.0 6.0 4.0 2.0 0 –2.0 –4.0PercentPercent

4-week 8-week sICAM-1 sVCAM-1

12-week 4-week 8-week 12-week Not diabeti c Diabeti c

**Figure 3.** Estimated percent increase in sICAM-1 ( A ) and sVCAM-1 ( B ) levels by statin use, per 0.3

- μg/m 3 increase in BC , for 4-, 8- , and 12-week averages.

10.0 8.0 6.0 4.0 2.0 0 –2.0 –4.0 10.0 8.0 6.0 4.0 2.0 0 –2.0 –4.0PercentPercent

4-week 8-week 12-week 4-week 8-week 12-week No statin Statin sICAM-1 sVCAM-1Traffic particles and endothelial function

Environmental Health Perspectives • volume 119 | number 4 | April 2011 

### 485 based on a central- site monitor. Although some exposure misclassification will still occur based on our model estimates, we expect that most of the residual error is Berkson type , based on a previous validation study for this model analyzing measure ment error (Gryparis et al. 2009). Thus, we expect that the exposure misclassification will not bias effect estimates. The lack of individual activity data also presents a limitation, but the NAS population consists of elderly men who spend a consider

able amount of time at or near their home address compared with other population groups. Our sensitivity analysis of the block lags of weeks 5–8 and weeks 9–12 show s slight attenuation compared with the cumulative lags, suggesting that the cumulative effect may be dominated by the more recent weeks. The effect sizes for cumulative exposures do not attenuate when averaged up to 12 weeks. Th e 4-week block lags do not isolate the effects of those time windows because each week is cor

related in both space and time ; thus , future studies to model the specific contributions of different lag weeks would be bene ficial in understanding these effects. Sources and components. Many studies have examined PM at different diameters, but fewer studies have looked at which compo

nents of PM are associated with adverse health effects. Our study links a health biomarker with BC, a specific component of PM 2.5 . In addition, we consider BC to be a surrogate for primary traffic PM (a specific source of air pollution), where the spatial variability of BC on a given day reflects the variability of traffic in the region, which is the basis of the BC model we developed and used for this study. We did not examine other pollutants such as total PM 2.5 or PM 2.5 components other than BC in this study. We think it is unlikely that total PM 2.5 or any other co pollutant is driving the effect we observe dfor BC because the BC model is based largely on the daily spatial variation exhibited by BC. Other com

ponents of PM 2.5 , such as sulfates and organic PM, are more homo geneous over the study region. Although we cannot completely rule out confounding by co pollu tants or other factors, the correlation between the modelpredicted 4-week BC and the correspond

ing 4-week averages of PM 2.5 measured at the central site was low (0.108), which sup

ports our belief that the effects observed are not driven by any temporal correlation with PM 2.5 or one of its components. Generalizability. A limitation of this study is the restricted demographics of the study population. Study subjects were all elderly men, most of them white. Thus, we cannot generalize our results to other populations. However, the elderly represent a particularly susceptible population, and the growth in the number and proportion of older adults in the United States is unprecedented: by 2030, the proportion of the U.S. population ≥ 65 years of age will double to about 71 million older adults, or one in every five Americans (U

. S

. Census 2005). 

### Conclusions We observed positive associations between BC exposures and blood levels of sICAM

- 1 and sVCAM

- 1 , with statistically significant effect estimates for sICAM

- 1 in the popula

tion as a whole and for sVCAM

- 1 among diabetics and participants who were not using statins. Effects of BC on both markers appeared to be limited to diabetics and pos

sibly those not using statins. Overall, our results suggest that exposure to traffic PM over 4–12 weeks may induce an increased inflammatory and endothelial response, par

ticularly among diabetics, and that statin use may be protective against this effect.
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