Environmental Health Perspectives • volume 119 | number 4 | April 2011 481

Research

There is strong epidemiological evidence that 

short-term air pollution exposure (i.e., < 24 hr 

to 3 weeks) is related to mortality and other 

cardiovascular events (Brook et al. 2010). 

Much of this evidence involves exposure to 

ambient particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5), which 

comprises many components and varies 

regionally. Contributions of specific components and sources to these effects are not well 

understood but are critical for informing the 

development of regulations.

Short-term exposure studies often use stationary 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., averages ≥ 1 year) has also been associated with 

cardiovascular 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 spatiotemporally 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 associated with increased risk of cardiovascular disease (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 inflammatory markers examined (Delfino et al. 2008; 

Madrigano et al. 2010; Zeka et al. 2006).

Obesity and diabetes are becoming increasingly 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 clinical trials, as well as laboratory studies, have 

demonstrated that statins, a widely prescribed 

class of drugs with anti-inflammatory and antioxidant 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 proinflammatory 

effects of air pollution, and reduced effects of 

air pollution on inflammation and heart-rate 

variability among statin users compared with 

nonusers 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 pollution 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 variation 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 associated with cardiovascular 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 Schwartz1

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

Massachusetts, USA

Background: Exposure to traffic-related air pollution (TRAP) contributes to increased cardiovascular risk. Land-use regression models can improve exposure assessment for TRAP.

Objectives: 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 inflammatory and endothelial response. 

Methods: 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 spatiotemporal 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.

Results: We found statistically significant positive associations between BC and sICAM-1 for averages of 4, 8, and 12 weeks. An interquartile-range increase in 8-week BC exposure (0.30 μg/m3) was 

associated with a 1.58% increase in sICAM-1 (95% confidence interval, 0.18–3.00%). Overall associations 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 interaction with statin use, which 

was statistically significant for sVCAM-1 and suggestive for sICAM-1. We found no evidence of an 

interaction with obesity.

Conclusion: Our results suggest that medium-term exposure to TRAP may induce an increased 

inflammatory/endothelial response, especially among diabetics and those not using statins.

Key words: adhesion molecules, air, cardiovascular, 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 examined 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 longitudinal 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 participating institutions. Participants visited the 

Boston VA Hospital study center every 3 years 

to undergo physical examinations. At each 

of these visits, blood samples and extensive 

physical examination, laboratory, anthropometric, and questionnaire data were collected. 

Information about cigarette smoking, medical 

history, and medication use were obtained 

by self-administered questionnaire. Each subject was interviewed to confirm the identity 

and purpose of medications used, and all new 

disease diagnoses were noted.

Diabetes was defined as a physician diagnosis of diabetes, and obesity was defined as 

a body mass index (BMI) of at least 30 kg/

m2. 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 measured in 

duplicate using the enzyme-linked immunosorbent 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 measured BC continuously using aethalometers, 

and other sites collected particles on a filter 

over 24 hr and measured elemental carbon 

(EC) using reflectance analysis. The monitoring data used to develop our model included 

6,031 observations from 2,079 unique 

exposure days.

Using a spatiotemporal 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 R2 = 0.83). A validation 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 estimates 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, population density, distance to nearest major roadway, and percent urbanization), geographic 

information system (GIS) location (latitude, 

longitude), daily meteorological factors (apparent temperature, wind speed, and height of the 

planetary boundary layer), and other characteristics (day of week, day of season). The 

Boston central-site monitor was also included as 

a predictor to reflect average pollutant concentrations over the entire region on each day.

Separate models were fit for warm and 

cold seasons. Interaction terms between the 

temporal meteorological predictors and sourcebased geographic variables allowed for space–

time interactions. Regression splines allowed 

main effect terms to nonlinearly predict exposure 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, unobservable traffic exposure variable; see Gryparis 

et al. (2007) for further details.

Estimation of health effects. We log-transformed 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 season. We used mixed models to account for 

correlation among measurements on the same 

subject across different medical visits. Mixed 

models have the form

Yij = β0 + βlagei + β2BMIi

 + ... + βpBCij + ui + εij, [1]

where Yij is log(sICAM-1) or log(sVCAM-1) 

in subject i on day j, and ui represents a 

subject-specific intercept that reflects unexplained heterogeneity in the outcome. BC 

averages and model covariates are modeled as 

fixed linear effects, and ui

 is modeled as a random effect. We assume that the ui

 are generated from a normal distribution with common 

variance, yielding the simple compound symmetry variance structure. This model requires 

estimation of two variance components, which 

represent between- and within-subject variation. Models with unbalanced data (i.e., varying numbers of repeated measurements on 

each subject) typically yield accurate estimates 

of within-subject variation, provided a sufficient number of repeated measurements contribute to the estimate.

Models used to examine effect modification by obesity, diabetes, and statin use 

included interaction terms that allowed associations between BC and the outcomes to vary 

among subgroups. Diabetes, obesity, and statin use were treated as time-varying covariates, 

where the status was updated at each visit to 

reflect changes since the last visit. The percentages of subjects whose status changed for these 

factors over the study period were 3% for diabetes, 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/m3 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/m2;

range, 18–52 kg/m2). Average 4-week BC 

exposure estimates ranged from 0 to 3.26

μg/m3 (mean ± SD, 0.42 ± 0.30 μg/m3;

median, 0.37 μg/m3; IQR, 0.30 μg/m3). For 

both 8- and 12-week exposures, exposure 

estimates were 0.42 ± 0.29 μg/m3 (median,

0.38 μg/m3; IQR, 0.30 μg/m3). Figure 1 illustrates 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, statin use, or smoking status, and we found no 
Traffic particles and endothelial function

Environmental Health Perspectives • volume 119 | number 4 | April 2011 483

significant differences in the mean BC exposure assessed at baseline.

Regression analysis. We found significant positive associations between sICAM-1 

and BC averaged over the previous 4, 8, and 

12 weeks (Table 2). Associations between average BC for 4, 8, and 12 weeks and sVCAM-1 

were in the same direction, with slightly 

smaller effect size, but were not statistically significant. As a sensitivity analysis, we also estimated 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 statistically significant. A 0.30-μg/m3 increase in 

BC exposure during weeks 5–8 was associated 

with a 1.22% increase in sICAM-1 [95% confidence 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 statistically 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 individually 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 

nondiabetics showed no effect (Figure 2;

interaction 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 associations among statin users (Figure 3; interaction 

p-values < 0.05 for sVCAM-1 only). In contrast, we found no difference in the estimated 

effect of BC by obesity status on either outcome (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). These differences could 

reflect some overall difference in health 

between statin users and nonusers because 

statin use was not randomized.

Discussion

We found that address-specific BC exposures averaged over 4, 8, and 12 weeks were 

positively associated with markers of inflammation 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 nondiabetics, 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/m2

) 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-yearsa 30.4 (27.9)

sICAM-1 (ng/mL) 295.4 ± 80.6

sVCAM-1 (ng/mL) 1058.9 ± 351.8

aFor current and former smokers.

Table 2. Estimated percent increase in sICAM-1 and sVCAM-1 levels per 0.3-μg/m3 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/m3)

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

0.87–2.5
Alexeeff 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. Several 

studies have examined the effects of shortterm (i.e., < 24 hr to 3 weeks) TRAP and 

various markers of inflammation. For example, in a large epidemiological study, daily 

increases in ambient PM levels were positively associated with plasma fibrinogen levels, 

with the strongest association observed in participants with chronic obstructive lung disease (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 components were significantly associated with CRP 

and interleukin-6, and observed positive but 

nonsignificant associations with sICAM-1 

and sVCAM-1.

In other studies of NAS participants, traffic-related PM (BC and organic carbon) exposure has also been positively associated with 

plasma total homocysteine concentrations 

(Park et al. 2008), and this association is modified by polymorphisms in genes related to 

oxidative stress (Ren et al. 2010). PM2.5 and 

BC averages of 1–3 days measured at the central 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 positively 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 peripheral 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 compared with nondiabetics (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 brachial artery flow-mediated dilation (FMD) 

were stronger in diabetics than in nondiabetics (O’Neill et al. 2005). Associations 

between 24-hr exposure to PM2.5 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 sediment rate (Zeka et al. 2006), and sVCAM-1 

(Madrigano et al. 2010) than nonobese 

participants.

Our finding that statin users are less susceptible than those not taking statins is generally consistent with other studies. In diabetics, 

stronger associations of PM2.5 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 mechanism 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 unresolved. Differences in the associations 

reported in epidemiological 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 interactions were the same, even though we found 

differences in statistical significance. Thus, our 

study does not support the idea of a different underlying mechanism for the effects of 

TRAP on these two CAMs.

The expression of both ICAM-1 and 

VCAM-1 on the surface of vascular endothelial cells is associated with the formation of 

early atherosclerotic lesions. Although the 

relationship between the degree of cellular 

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 cardiovascular 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 intrasubject 

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 characterize the individual-level differences in exposures instead of classifying exposure based on 

measurements at the nearest monitor, landuse regression models without BC measurements, 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 measurements 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/m3

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

–4.0

PercentPercent

4-week 8-week 

sICAM-1 sVCAM-1

12-week 4-week 8-week 12-week 

Not diabetic

Diabetic

Figure 3. Estimated percent increase in sICAM-1 (A) and sVCAM-1 (B) levels by statin use, per 0.3-μg/m3

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

–4.0

PercentPercent

4-week 8-week 12-week 4-week 8-week 12-week 

No statin

Statin

sICAM-1 sVCAM-1
Traffic 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 measurement 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 considerable 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 shows 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. The 

4-week block lags do not isolate the effects of 

those time windows because each week is correlated in both space and time; thus, future 

studies to model the specific contributions 

of different lag weeks would be beneficial in 

understanding these effects.

Sources and components. Many studies 

have examined PM at different diameters, but 

fewer studies have looked at which components of PM are associated with adverse health 

effects. Our study links a health biomarker 

with BC, a specific component of PM2.5. 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 PM2.5 or PM2.5 components other 

than BC in this study. We think it is unlikely 

that total PM2.5 or any other copollutant is 

driving the effect we observed for BC because 

the BC model is based largely on the daily 

spatial variation exhibited by BC. Other components of PM2.5, such as sulfates and organic 

PM, are more homogeneous over the study 

region. Although we cannot completely rule 

out confounding by copollutants or other 

factors, the correlation between the modelpredicted 4-week BC and the corresponding 4-week averages of PM2.5 measured at 

the central site was low (0.108), which supports our belief that the effects observed are 

not driven by any temporal correlation with 

PM2.5 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 population 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 possibly 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, particularly among diabetics, and that statin use 

may be protective against this effect.

References

Albert MA, Danielson E, Rifai N, Ridker PM. 2001. Effect of 

statin therapy on C-reactive protein levels: the pravastatin 

inflammation/CRP evaluation (PRINCE): a randomized trial 

and cohort study. JAMA 286(1):64–70.

Albert MA, Ridker PM. 1999. The role of C-reactive protein in 

cardiovascular disease risk. Curr Cardiol Rep 1(2):99–104.

Bell B, Rose C, Damon A. 1972. The Normative Aging Study: 

an interdisciplinary and longitudinal study of health and 

aging. Aging Hum Dev 3:4–17.

Blanco-Colio LM, Martin-Ventura JL, de Teresa E, Farsang C, 

Gaw A, Gensini G, et al. 2007. Elevated ICAM-1 and MCP-1 

plasma levels in subjects at high cardiovascular risk 

are diminished by atorvastatin treatment. Atorvastatin 

on Inflammatory Markers study: a substudy of Achieve 

Cholesterol Targets Fast with Atorvastatin Stratified 

Titration. Am Heart J 153(5):881–888.

Brauer M, Hoek G, van Vliet P, Meliefste K, Fischer P, Gehring U, 

et al. 2003. Estimating long-term average particulate air 

pollution concentrations: application of traffic indicators and geographic information systems. Epidemiology 

14(2):228–239.

Brook RD, Rajagopalan S, Pope CA III, Brook JR, Bhatnagar A, 

Diez-Roux AV, et al. 2010. Particulate matter air pollution 

and cardiovascular disease: an update to the scientific 

statement from the American Heart Association. Circulation 

121(21):2331–2378.

Brunekreef B, Beelen R, Hoek G, Schouten L, BauschGoldbohm S, Fischer P, et al. 2009. Effects of long-term 

exposure to traffic-related air pollution on respiratory and 

cardiovascular mortality in the Netherlands: the NLCS-AIR 

study. Res Rep Health Eff Inst (139):5–89. 

Calles-Escandon J, Cipolla M. 2001. Diabetes and endothelial 

dysfunction: a clinical perspective. Endocr Rev 22(1):36–52.

Clougherty JE, Wright RJ, Baxter LK, Levy JI. 2008. Land use 

regression modeling of intra-urban residential variability in 

multiple traffic-related air pollutants. Environ Health 7:17; 

doi:10.1186/1476-069X-7-17 [Online 16 May 2008].

Delfino RJ, Staimer N, Tjoa T, Polidori A, Arhami M, Gillen DL, 

et al. 2008. Circulating biomarkers of inflammation, antioxidant activity, and platelet activation are associated 

with primary combustion aerosols in subjects with coronary artery disease. Environ Health Perspect 116:898–906.

Eschen O, Christensen JH, Dethlefsen C, Schmidt EB. 2008. 

Cellular adhesion molecules in healthy subjects: short 

term variations and relations to flow mediated dilation. 

Biomark Insights 3:57–62.

Forastiere F, Stafoggia M, Berti G, Bisanti L, Cernigliaro A, 

Chiusolo M, et al. 2008. Particulate matter and daily mortality: a case-crossover analysis of individual effect modifiers. Epidemiology 19(4):571–580.

Gehring U, Heinrich J, Kramer U, Grote V, Hochadel M, Sugiri D, 

et al. 2006. Long-term exposure to ambient air pollution 

and cardiopulmonary mortality in women. Epidemiology 

17(5):545–551.

Ghio AJ, Kim C, Devlin RB. 2000. Concentrated ambient air 

particles induce mild pulmonary inflammation in healthy 

human volunteers. Am J Resp Crit Care Med 162(3 pt 

1):981–988.

Gryparis A, Coull BA, Schwartz J, Suh HH. 2007. Semiparametric 

latent variable regression models for spatiotemporal modeling of mobile source particles in the Greater Boston 

area. Appl Stat 56(2):183–209.

Gryparis A, Paciorek CJ, Zeka A, Schwartz J, Coull BA. 2009. 

Measurement error caused by spatial misalignment in 

environmental epidemiology. Biostatistics 10(2):258–274.

Haendeler J, Hoffmann J, Zeiher AM, Dimmeler S. 2004. 

Antioxidant effects of statins via S-nitrosylation and activation of thioredoxin in endothelial cells: a novel vasculoprotective function of statins. Circulation 110(7):856–861.

Kinney PL, Aggarwal M, Northridge ME, Janssen NA, Shepard P. 

2000. Airborne concentrations of PM2.5 and diesel exhaust 

particles on Harlem sidewalks: a community-based pilot 

study. Environ Health Perspect 108:213–218.

Liang YJ, Shyu KG, Wang BW, Lai LP. 2008. Simvastatin inhibits 

C-reactive protein-induced pro-inflammatory changes 

in endothelial cells by decreasing mevalonate pathway 

products. Cardiology 110(3):182–190.

Lim SC, Caballero AE, Smakowski P, LoGerfo FW, Horton ES, 

Veves A. 1999. Soluble intercellular adhesion molecule, vascular cell adhesion molecule, and impaired microvascular 

reactivity are early markers of vasculopathy in type 2 diabetic individuals without microalbuminuria. Diabetes Care 

22(11):1865–1870.

Madrigano J, Baccarelli A, Wright R, Suh H, Sparrow D, 

Vokonas P, et al. 2010. Air pollution, obesity, genes, 

and cellular adhesion molecules. Occup Environ Med 

67(5):312–317. 

Maynard D, Coull BA, Gryparis A, Schwartz J. 2007. Mortality 

risk associated with short-term exposure to traffic particles 

and sulfates. Environ Health Perspect 115:751–755.

Montecucco F, Burger F, Pelli G, Poku NK, Berlier C, Steffens S, 

et al. 2009. Statins inhibit C-reactive protein-induced 

chemokine secretion, ICAM-1 upregulation and chemotaxis 

in adherent human monocytes. Rheumatology (Oxford) 

48(3):233–242.

O’Neill MS, Veves A, Sarnat JA, Zanobetti A, Gold DR, 

Economides PA, et al. 2007. Air pollution and inflammation 

in type 2 diabetes: a mechanism for susceptibility. Occup 

Environ Med 64(6):373–379.

O’Neill MS, Veves A, Zanobetti A, Sarnat JA, Gold DR, 

Economides PA, et al. 2005. Diabetes enhances vulnerability to particulate air pollution-associated impairment 

in vascular reactivity and endothelial function. Circulation 

111(22):2913–2920.

Park SK, O’Neill MS, Vokonas PS, Sparrow D, Spiro A III, 

Tucker KL, et al. 2008. Traffic-related particles are associated with elevated homocysteine: the VA Normative Aging 

Study. Am J Resp Crit Care Med 178(3):283–289.

Pradhan AD, Rifai N, Ridker PM. 2002. Soluble intercellular adhesion molecule-1, soluble vascular adhesion molecule-1, and 

the development of symptomatic peripheral arterial disease 

in men. Circulation 106(7):820–825.

Rana JS, Arsenault BJ, Després JP, Côté M, Talmud PJ, Ninio E, 

et al. 2011. Inflammatory biomarkers, physical activity, 

waist circumference, and risk of future coronary heart disease in healthy men and women. Eur Heart J 32(3):336–344.

Ren C, Park SK, Vokonas PS, Sparrow D, Wilker E, Baccarelli A, 

et al. 2010. Air pollution and homocysteine: more evidence 

that oxidative stress-related genes modify effects of particulate air pollution. Epidemiology 21(2):198–206.

Ridker PM, Buring JE, Cook NR, Rifai N. 2003. C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14719 initially 

healthy American women. Circulation 107(3):391–397.

Salvi S, Blomberg A, Rudell B, Kelly F, Sandstrom T, Holgate ST, 

et al. 1999. Acute inflammatory responses in the airways 

and peripheral blood after short-term exposure to diesel 

exhaust in healthy human volunteers. Am J Resp Crit Care 

Med 159(3):702–709.

Schneider A, Neas L, Herbst MC, Case M, Williams RW, 

Cascio W, et al. 2008. Endothelial dysfunction: associations with exposure to ambient fine particles in diabetic 

individuals. Environ Health Perspect 116:1666–1674.

Schwartz J. 2001. Air pollution and blood markers of cardiovascular risk. Environ Health Perspect 109(suppl 3):405–409.
Alexeeff et al.

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

Schwartz J, Park SK, O’Neill MS, Vokonas PS, Sparrow D, 

Weiss S, et al. 2005. Glutathione-S-transferase M1, obesity, statins, and autonomic effects of particles: gene-bydrug-by-environment interaction. Am J Respir Crit Care 

Med 172(12):1529–1533.

U.S. Census Bureau, Population Division. 2005. Interim Projections 

of the Population by Selected Age Groups for the United 

States and States: April 1, 2000 to July 1, 2030. Available: 

http://www.census.gov/population/www/projections/

files/SummaryTabB1.pdf [accessed 14 February 2011].

Yorifuji T, Kashima S, Tsuda T, Takao S, Suzuki E, Doi H, et al. 

2010. Long-term exposure to traffic-related air pollution 

and mortality in Shizuoka, Japan. Occup Environ Med

67:111–117. 

Zanobetti A, Schwartz J. 2002. Cardiovascular damage by 

airborne particles: are diabetics more susceptible? 

Epidemiology 13(5):588–592.

Zeka A, Sullivan JR, Vokonas PS, Sparrow D, Schwartz J. 

2006. Inflammatory markers and particulate air pollution: 

characterizing the pathway to disease. Int J Epidemiol 

35(5):1347–1354.