A large body of epidemiologic evidence suggests associations between ambient air pollution and cardiovascular mortality and

morbidity (Peters and Pope 2002; Pope et al.

2004). All of these studies focus on events

occurring at a late stage of vascular disease

processes. The impact of air pollution on the

underlying preclinical conditions remains

poorly understood. We hypothesize that current levels of ambient particulate matter (PM)

up to 2.5 μm in aerodynamic diameter

(PM2.5) may contribute to atherosclerosis,

leading to subclinical anatomical changes that

play a major role in cardiovascular morbidity

and mortality later in life. Animal studies support our hypothesis by showing that inhalation

of ambient PM promotes oxidative lung damage, including alveolar and systemic inflammatory responses (Becker et al. 1996; Dye et al.

2001; Fujii et al. 2002; Goto et al. 2004; Suwa

et al. 2002; van Eeden et al. 2001).

We investigated the association between

residential ambient PM2.5 and carotid artery

intima-media thickness (CIMT) using prerandomization baseline data from two recent

clinical trials conducted in Los Angeles,

California (Hodis et al. 2002). CIMT is a

well-established quantitative measure of

generalized atherosclerosis that correlates well

with all of the major cardiovascular risk factors, with coronary artery atherosclerosis, and

with clinical cardiovascular events (Mack et al.

2000). It is an established tool for investigating

the contribution of long-term exposures such

as smoking or passive smoking to subclinical

stages of atherosclerosis at any given age (DiezRoux et al. 1995; Howard et al. 1994, 1998).

This is the first study to assess the association

of atherosclerosis with air pollution.

Materials and Methods

Population and health assessment. We used

baseline health data from two randomized,

double-blind, placebo-controlled clinical trials

conducted at the University of Southern

California Atherosclerosis Research Unit

(Hodis et al. 2002). The Vitamin E

Atherosclerosis Progression Study (VEAPS)

investigated the effects of vitamin E on the

progression of atherosclerosis measured by

CIMT. The B-Vitamin Atherosclerosis

Intervention Trial (BVAIT) focused on the

effect of vitamin B supplements on the progression of atherosclerosis (trial in progress).

Baseline assessment in both trials included

CIMT measured between 1998 and 2003

using the same standardized methods

(Hodis et al. 2002; Selzer et al. 1994, 2001).

Recruitment of volunteers occurred over the

entire Los Angeles Basin, covering a geographic area of approximately 64,000 km2.

Eligible subjects for the VEAPS trial

(n = 353) were men and women ≥ 40 years of

age with slightly increased LDL cholesterol

(≥ 3.37 mmol/L) but with no clinical signs or

symptoms of cardiovascular disease (CVD)

(Hodis et al. 2002). Subjects with diabetes,

diastolic blood pressure > 100 mm Hg, thyroid

disease, serum creatinine > 0.065 mmol/L, lifethreatening diseases, or high alcohol intake

were excluded.

BVAIT (n = 506) had a similar design to

that of VEAPS. Men and women > 40 years

of age were prescreened to meet study criteria

(fasting plasma homocysteine ≥ 8.5 μmol/L;

postmenopausal for women; no evidence of

diabetes, heart disease, stroke, or cancer).

Subjects were excluded on the basis of any

clinical signs or symptoms of CVD, diabetes

or fasting serum glucose ≥ 140 mg/dL, triglyceride levels ≥ 150 mg/dL, serum creatinine

> 1.6 mg/dL, high blood pressure, untreated

thyroid disease, life-threatening disease with

prognosis < 5 years, or high alcohol intake.

Thus, our study included “healthy” subjects

with biomarkers (elevated LDL cholesterol or

homocysteine) that suggested an increased risk

of future CVDs (n = 859). Fifty-eight subjects

were excluded in the exposure assignment

process because they lived outside the area

with PM2.5 data. Three subjects had missing

data in at least one of the covariates used in

the models. Our total sample consisted of 798

participants.

Health measures, including CIMT. Our

main outcome of interest is CIMT. In both

trials, high-resolution B-mode ultrasound

images of the right common carotid artery

were obtained before the intervention (baseline) with a 7.5-MHz linear array transducer

attached to an ATL Ultramark-4 Plus

Ultrasound System (Ultramark, Bothell, WA).

We used this baseline CIMT measurement as

the outcome. Details of this highly reproducible method are published (Hodis et al.

Environmental Health Perspectives • VOLUME 113 | NUMBER 2 | February 2005 201

Address correspondence to N. Künzli, Keck School

of Medicine University of Southern California,

Division of Environmental Health, 1540 Alcazar St.

CHP 236, Los Angeles, CA 90033-9013 USA.

Telephone: (323) 442-2870. Fax: (323) 442-3272.

E-mail: kuenzli@usc.edu

This work was supported in part by the National

Institute on Aging [grants R01AG-13860 (Vitamin E

Atherosclerosis Prevention Study) and R01AG-17160

(B-Vitamin Atherosclerosis Intervention Trial)], the

National Institute of Environmental Health Sciences

(grants P30 ES07048, 5P01ES11627), the Wright

Foundation, the Hastings Foundation, and the

Health Effects Institute. 

The authors declare they have no competing

financial interests.

Received 26 August 2004; accepted 22 November

2004.

Research | Article

Ambient Air Pollution and Atherosclerosis in Los Angeles

Nino Künzli, Michael Jerrett, Wendy J. Mack, Bernardo Beckerman, Laurie LaBree, Frank Gilliland, 

Duncan Thomas, John Peters, and Howard N. Hodis

Divisions of Environmental Health and Biostatistics, Department of Preventive Medicine, and Atherosclerosis Research Unit, Division of

Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA

Associations have been found between long-term exposure to ambient air pollution and cardiovascular

morbidity and mortality. The contribution of air pollution to atherosclerosis that underlies many

cardiovascular diseases has not been investigated. Animal data suggest that ambient particulate matter

(PM) may contribute to atherogenesis. We used data on 798 participants from two clinical trials to

investigate the association between atherosclerosis and long-term exposure to ambient PM up to 2.5

μm in aerodynamic diameter (PM2.5). Baseline data included assessment of the carotid intima-media

thickness (CIMT), a measure of subclinical atherosclerosis. We geocoded subjects’ residential areas to

assign annual mean concentrations of ambient PM2.5. Exposure values were assigned from a PM2.5

surface derived from a geostatistical model. Individually assigned annual mean PM2.5 concentrations

ranged from 5.2 to 26.9 μg/m3 (mean, 20.3). For a cross-sectional exposure contrast of 10 μg/m3

PM2.5, CIMT increased by 5.9% (95% confidence interval, 1–11%). Adjustment for age reduced the

coefficients, but further adjustment for covariates indicated robust estimates in the range of 3.9–4.3%

(p-values, 0.05–0.1). Among older subjects (≥ 60 years of age), women, never smokers, and those

reporting lipid-lowering treatment at baseline, the associations of PM2.5 and CIMT were larger with

the strongest associations in women ≥ 60 years of age (15.7%, 5.7–26.6%). These results represent

the first epidemiologic evidence of an association between atherosclerosis and ambient air pollution.

Given the leading role of cardiovascular disease as a cause of death and the large populations

exposed to ambient PM2.5, these findings may be important and need further confirmation.

Key words: air pollution, atherosclerosis, particulate matter. Environ Health Perspect 113:201–206

(2005). doi:10.1289/ehp.7523 available via http://dx.doi.org/ [Online 22 November 2004]
2002; Selzer et al. 1994, 2001). Blood pressure, height, and weight were measured with

standard procedures.

The baseline questionnaires included an

assessment of all major CVD risk factors and

covariates, including clinical events, diet, use

of prescription medications, physical activity,

current and past smoking and passive smoking, and vitamin supplements. Age, education, and other sociodemographic factors

were available for each subject. Fasting blood

samples were also drawn for lipid measurements. Data used in our analyses were collected with the same tools in both trials.

Exposure assignment. To assess exposure

we chose a novel approach derived from a

geographic information system (GIS) and

geostatistics. This method allows for assignment of long-term mean ambient concentrations of PM2.5 to the ZIP code area of each

subject’s residential address (Künzli and Tager

2000). The resulting surface of PM2.5 covered

the entire Los Angeles metropolitan area. The

surface is derived from a geostatistical model

and data from 23 state and local district monitoring stations (during 2000). These monitors are located across the Los Angeles region

to characterize urban levels of pollution. To

assign exposure, PM2.5 data were interpolated

using a combination of a universal kriging

model with a quadratic drift and a multiquadric radial basis function model (Bailey

and Gatrell 1995; Burrough and McDonnell

1998). We averaged the two surfaces based on

25-m grid cells. Examination of errors from

the universal model showed that > 50% of the

study area had assigned values within 15% of

monitored concentrations, whereas 67% were

within 20%. The larger errors were on the

periphery of our study area, where the density

of study participants was the lowest. We

linked the ZIP code centroids of each subject

with the exposure surface through a geocoding database [Environmental Systems

Research Institute (ESRI) 2004]. Figure 1

illustrates the PM2.5 surface with the geolocated ZIP codes. Individually assigned PM2.5

data had a range from 5.2 to 26.9 μg/m3

(mean, 20.3), thus exceeding the range

Article | Künzli et al.

202 VOLUME 113 | NUMBER 2 | February 2005 • Environmental Health Perspectives

5 7 9 11 14 16 18 21 23 25 27

330

264

198

132

66

0

PM2.5 exposure (μg/m3)

No. of subjects

0 5 10 20 30 40 Miles Kilometers0 5 10 20 30 40

5.0–7.8

7.9–10.5

10.6–13.3

13.4–16.1

16.2–18.8

18.9–21.6

21.7–24.4

24.5–27.1

Freeway

Subjects

PM2.5 μg/m3

PM2.5 assigned exposure distribution among subjects

Figure 1. ZIP code locations of the study population geocoded on the PM2.5 surface, modeled with 2000

PM2.5 data, and distribution of individually assigned concentrations.

Table 2. Percent change (and 95% CI) in CIMT (μm) associated with a 10 μg/m3 change in ambient outdoor PM2.5 concentration at the residential ZIP code in the

total population (n = 798).a

Modela Total sample (798) Females ≥ 60 years (186) Lipid-lowering therapy (109)

(with adjustment factors in the model) Percent change p-Value Percent change p-Value Percent change p-Value

None (unadjusted estimate) 5.9 (1.0–10.9) 0.018 19.2 (8.8–30.5) 0.001 15.8 (2.1–31.2) 0.024

Age, sex, education, incomeb 4.4 (0.0–9.0) 0.056 15.7 (5.7–26.6) 0.002 13.3 (0–28.5) 0.051

All above plus active and passive 4.2 (–0.2–8.9) 0.064 13.8 (4.0–24.5) 0.002 13.3 (–0.3–28.8) 0.060

smoking, multivitamins, alcohol

aUnadjusted association (crude model) and estimates from two multivariate models; 95% CIs of the estimates are shown in parentheses. The relative effects are based on a linear model

with log intima-media thickness as dependent variable. bFactors with univariate associations with both, CIMT and PM2.5.

Table 1. Description of assigned exposure (outdoor concentration in 2000) and CIMT, and main characteristics of the study population at the time of baseline

measurements in the total sample, men, women, women ≥ 60 years of age, and subjects under lipid-lowering therapy.

Females Lipid-lowering

Characteristic Total sample (n = 798) Males (n = 443) Females (n = 355) ≥ 60 years (n = 186) therapy (n = 109)

PM2.5 (μg/m3) 20.3 ± 2.6 20.1 ± 2.7 20.5 ± 2.4 20.7 ± 2.3 20.0 ± 2.5

Ozone (ppb, annual mean of daily maximum) 89.2 ± 17.9 89.6 ± 18.5 88.8 ± 17.3 87.1 ± 17.2 88.5 ± 18.6

CIMT (μm) 755 ± 148 767 ± 166 740 ± 118 775 ± 120 788 ± 140

Age (years) 59.2 ± 9.8 58.3 ± 10.3 60.4 ± 8.9 67.3 ± 5.3 63.3 ± 10.0

Diastolic blood pressure (mm Hg) 77.8 ± 9.2 79.2 ± 8.8 75.9 ± 9.3 74.8 ± 9.5 78.1 ± 8.9

Systolic blood pressure (mm Hg) 127.2 ± 16.3 126.7 ± 16.0 127.8 ± 16.6 130.5 ± 16.7 130.9 ± 16.2

LDL cholesterol (mg/dL) 137.9 ± 29.5 137.0 ± 30.9 139.0 ± 27.6 136.4 ± 26.9 125.7 ± 33.7

White (%) 67.3 67.7 66.8 65.0 69.7

Smoking status (%)

Never smokers 62.9 62.8 63.1 62.9 53.2

Former smokers 33.2 33.4 33.0 33.3 44.0

Current smokers 3.8 3.6 3.9 3.8 2.8

ETS at home (%) 33.5 21.9 47.9 55.4 37.5

Lipid-lowering therapy (%) 13.7 15.3 11.5 15.1 100

Antihypertensive prescriptions (%) 26.2 26.6 25.6 33.3 42.2

ETS, environmental tobacco smoke. Data are mean ± SD except where indicated.
observed across 156 metropolitan areas used

in the largest cohort study of air pollution and

mortality (Pope et al. 2002). All models were

implemented with ArcScript from ESRI

(Redlands, CA).

Statistical analyses. We tested the univariate and multivariate associations between

CIMT and ambient PM2.5 using linear regression analyses. Extensive residual diagnostics

indicated some heteroskedasticity, which was

rectified with the natural log-transformed

CIMT. We adjusted for factors that were statistically associated with both CIMT and

ambient PM2.5 (age, male sex, low education,

and low income). Next, we expanded the

models using covariates that were associated

with either PM2.5 or CIMT, including indicator variables for current second-hand smoke

exposure and current and former personal

smoking. We then added covariates that play a

role in atherosclerosis such as blood pressure,

LDL cholesterol, or proxy measures such as

reporting treatment with antihypertensives or

lipid-lowering medications at study entry.

These factors may affect the pathophysiologic

pathways linking air pollution exposure and

atherosclerosis (Ross 1999); thus, such models

may overadjust the coefficients. We chose this

conservative approach to test the sensitivity of

the effect estimates under a broad range of

model assumptions.

There is increasing evidence that host

factors such as age, sex, or underlying disease

and risk profiles may modify the effects of

air pollution (Pope et al. 2002; Zanobetti

and Schwartz 2002). Furthermore, the finding of atherosclerosis in PM-exposed rabbits

was based on a hyperlipidemic trait (Suwa

et al. 2002). Therefore, we also stratified by

sex, age (< 60 years, ≥ 60 years), smoking

status, and lipid-lowering drug therapy.

Results

Table 1 summarizes the main characteristics of

the study population and among main subgroups. Table 2 presents the percent change in

CIMT in association with a 10 μg/m3 contrast

in ambient PM2.5 concentrations for three

cross-sectional regression models. The unadjusted model indicates a 5.9% [95% confidence interval (CI), 1–11%] increase in CIMT

per 10 μg/m3 PM2.5. For the observed contrast

between lowest and highest exposure

(20 μg/m3 PM2.5), this corresponds to a 12.1%

(2.0–23.1%) increase in CIMT. The only

covariate with a substantial effect on the point

estimate was age, which reduced the effect

from 5.9 to 4.3% (0.4–9%) per 10 μg/m3

PM2.5. This change agrees with the age-related

effect modification. Otherwise, effect estimates

across the models remained robust, in the

range of 3.9–4.3% with p-values from 0.05 to

0.1. To corroborate the exposure–response

relationship, we also categorized PM2.5 levels

into quartiles. Figure 2 shows the adjusted

mean CIMT across these four groups of equal

sample size at the mean levels of the covariates

(age, sex, education, and income). The trend

across the exposure groups was statistically significant (p = 0.041). The unadjusted means of

CIMT among these quartiles of exposure were

734, 753, 758, and 774 μm, respectively.

The associations between CIMT and

PM2.5 were substantially stronger among

109 subjects reporting lipid-lowering medication at study entry, both in men and in women

(Table 2, Figure 3). The crude effect reached

15.8% (2–31%) per 10 μg/m3 PM2.5, with

adjusted values ranging between 12 and 16%.

Despite the small sample size, p-values of all

models were mostly < 0.1 and often < 0.05.

Results also suggest significant age and sex

interactions, with much larger effects in

women and in the older age group (Figure 3).

Effect estimates in women were statistically

significant and typically in the range of 6–9%

per 10 μg/m3 PM2.5. Associations were

strongest among women ≥ 60 years of age

(n = 186), leading to crude estimates of

19.2% (9–31%). Adjusted coefficients ranged

from 14 to 19%, being statistically significant

in all models and sensitivity analyses.

Among never smokers (n = 502), the effect

estimate reached 6.6% (1.0–12.3%). The estimate was small and not significant in current

(n = 30) and former smokers (n = 265).

Discussion

Our study presents the first evidence for an

association between CIMT and long-term

exposure to ambient air pollution. As recently

reviewed in a statement of the American Heart

Association (Brook et al. 2004) substantial epidemiologic and experimental evidence suggests

a contribution of ambient air pollutants on

cardiovascular mortality and morbidity.

However, these studies focus on acute and subacute effects on cardiac autonomic function,

inflammatory or thrombogenic markers,

arrhythmia, myocardial infarction, cardiovascular hospital admission, and death. The

only outcome considered in long-term air pollution studies has been mortality. The relative

risks for acute effects on mortality have been

substantially smaller than those observed for

long-term associations (Pope et al. 2002;

Samet et al. 2000). As shown previously,

cohort studies are capable of capturing acute

and chronic effects of air pollution on the

course of diseases that ultimately lead to premature death (Künzli et al. 2001). In contrast,

time-series and panel studies investigate only

the associations of event occurrence with the

most recent exposure (Künzli et al. 2001).

Article | Air pollution and atherosclerosis

Environmental Health Perspectives • VOLUME 113 | NUMBER 2 | February 2005 203

750

740

730

720

710

700

690

(5.2–19.1) (19.11–20.73) (20.74–22.0) (22.1–26.9)

CIMT (μm)

Quartiles of PM2.5 (range, μg/m3)

40

35

30

25

20

15

10

5

0

–5

–10

–15

–20

Change in CIMT (%)

Women, age ≥ 60 years (186)

Lipid-LT (109)

Women, with lipid-LT (41)

Men, with lipid-LT (68)

Age ≥ 60 years (348)

Women (355)

Women, no lipid-LT (314)

All (798)

Men, age ≥ 60 years (198)

Women, age ≥ 60 years (169)

No lipid-LT (689)

Men (443)

Age < 60 years (414)

Men, no lipid-LT (375)

Men, age < 60 years (245)

Figure 2. Mean CIMT ± 1 SE among quartiles of the

PM2.5 distribution. The y-axis shows mean CIMT levels at the population average of the adjustment

covariates (age, sex, education, and income). The

first quartile is the reference group.

Figure 3. Percent difference and 95% CI in CIMT associated with a 10 μg/m3 contrast in ambient PM2.5 in all

subjects and in subgroups. Lipid-LT, lipid-lowering therapy. All estimates are based on the cross-sectional

linear model with log intima-media thickness as the dependent variable and home outdoor PM2.5 as the

independent variable, adjusted for sex, age, education, and income. Numbers in parentheses are numbers

of subjects per group. Data are ordered by size of point estimate; the null effect line is indicated by a dash.
Thus, if air pollution has both acute and

cumulative long-term effects, one expects

larger mortality coefficients in cohort studies.

CIMT reflects long-term past exposure; thus,

we provide the first evidence for chronic effects

of air pollution on atherogenesis that may in

part explain the above mentioned discrepancy

between acute and long-term risk estimates

(Pope et al. 2002; Samet et al. 2000).

There are several major aspects to be considered in the interpretation of this new finding, mainly the strength in the exposure

assignment, the limited evidence for bias, the

differences in effects within subgroups, and

plausibility.

Exposure assignment. The individual residence-based assignment of exposure represents

a substantial improvement over most studies

that have relied on central monitors or on

binary road buffers combined with basic interpolation (Hoek et al. 2002; Pope et al. 2004).

As a sensitivity analysis, we used weighted

least-squares models with the weights specified

as the inverse of the standard errors from the

universal kriging model to down-weight estimates with larger error. In addition, we implemented models based solely on the universal

kriging estimate. In both instances results were

robust and similar to what we found with our

main model.

Time–activity studies show that people

spend most of their time in or around home,

and our restriction of exposure assessment on

residential address captures the most relevant

part of exposure (Leech et al. 2002). PM2.5

generally displays spatially homogeneous distributions across small areas such as neighborhoods and blocks, and as a result, the ambient

conditions at the ZIP code centroid likely

reflect the levels expected at home outdoors

(Roosli et al. 2000). PM2.5 of outdoor origin

will also penetrate indoors, and correlations

between long-term outdoor PM concentrations and indoor levels of PM from outdoor

origin is high (Sarnat et al. 2000). Exposure

to ambient air pollution while working and

during commute are not included in our

exposure term but are considered to be a relevant source of exposure (Riediker et al. 2003).

Although most likely a random misclassification with biases toward the null, the errors

may affect subgroups differently, thus

explaining part of the observed interactions.

In Los Angeles, no clear trends have been

observed in PM2.5 concentrations over the past

5–10 years. The year 2000 surface characterizes

the prevailing mean PM2.5 concentrations

across several years and can be considered a

measure of long-term past exposure. This year

also sits in the middle of the baseline recruitment period. Overall, the various limitations

in our exposure assignment may add some

random error, biasing results toward weaker

associations (Thomas et al. 1993).

We also assigned ambient ozone to ZIP

code centroids. Inclusion of ozone in the

models had no impact on the PM2.5 coefficients or the SEs. Ozone and PM2.5 were not

correlated (r = –0.17), and the PM2.5 estimates were not substantially different in lowand high-ozone regions. The estimates of

association for ozone were positive but not

statistically significant and much smaller than

for PM2.5. This finding must be put in context of the specific challenges in determining

long-term exposure to ozone, which are substantially different than in the case of PM

exposure. In contrast to PM2.5 from outdoor

origin, ambient ozone levels have lower correlations with personal exposure (Avol et al.

1998; Sarnat et al. 2000, 2002); therefore, the

ability to detect effects of ozone will likely be

reduced due to greater misclassification.

Biases. Our subjects were a nonrandom

sample of “healthy” volunteers with aboveaverage education, meeting strict inclusion

criteria for the two clinical trials. Although we

cannot exclude some systematic selection

biases affecting the cross-sectional data, it is

unlikely that subjects with preclinical signs of

atherosclerosis would have been more likely to

volunteer if they lived in more polluted areas.

Although the selection of subjects limits the

generalization to other populations, we do not

expect this to lead to over- or underestimating

the cross-sectional associations. The two trials

recruited subjects independently; thus, the

effects may be compared across trials to evaluate the potential influence of selecting volunteers. The populations differed with regard to

age, smoking habits, baseline LDL and treatment, blood pressure, active and passive smoking, and other relevant factors; thus, the PM2.5

coefficients were smaller and were not statistically significant in the VEAPS trial with its

younger population. However, after taking

these factors into account, the associations

with ambient PM2.5 were similar. For example, among elderly women of VEAPS (n = 70)

and BVAIT (n = 116), the effect estimate

was 18.1% (–0.1 to 36.3.%) and 13.6%

(2.8–24.4.%), respectively. There is some evidence for larger effects in subjects with cardiovascular risk factors, indicated by prescriptions

of lipid-lowering treatment. Our trials

excluded subjects with clinically manifest

CVDs. Moreover, if air pollution amplifies systemic inflammation among those prone to

atherosclerosis, exclusion of subjects with high

LDL may be a source of bias. One may expect

effect estimates in a less selected, less healthy

population to be larger than those reported.

The wealth of baseline data from these

clinical trials offered the opportunity to control for a broad array of covariates. Apart

from the effect of age adjustment, estimates

were robust to numerous combinations of

covariates, including income, education,

active and passive tobacco smoke, cardiovascular prescriptions, vitamin intake, and

physical activity. Uncontrolled or residual

confounding appears to be an unlikely explanation for these results. Among women,

adjustment for hormone replacement therapies did not affect the PM2.5 estimates.

In previous studies, we found that spatial

autocorrelation in the residuals could affect

the size and significance of pollution coefficients (Jerrett et al. 2003a). We investigated

spatial autocorrelation of the unstandardized

residuals. We assessed autocorrelation with

first-order, adjusted first-order, and secondorder spatial weight matrices based on nearest

neighbor contiguity, but we found no evidence of spatial autocorrelation. This supports

the conclusion that the models supply efficient

unbiased estimates (Jerrett et al. 2003b). As

part of our sensitivity analyses, we also derived

PM2.5 surfaces using different interpolations

and weighted least squares with weights equal

to the inverse of the standard error of the

exposure estimate. All approaches produced

very similar results.

Evidence for effect modification. The data

suggest substantial interactions with age, sex,

smoking, and underlying cardiovascular risk

factors. Given the reduced sample size among

subgroups, the recruitment of volunteers, and

the cross-sectional nature of the data, it is difficult to fully explore the causes of the observed

modifications of associations and to establish

susceptibility profiles. If the exposure misclassifications differed across subgroups, part of the

interactions may be explained by differential

exposure error. The sex and age difference

could also be an artifact due to measurement

error in the assigned exposure because time

spent in commuting and location of work

places may be different in men and women

and in the young and elderly. Empirical studies

on mobility suggest women have smaller activity spaces than men and younger groups,

meaning they tend to spend more time in and

around the home (Kwan and Lee 2004), and

the same is probably true of the elderly compared with younger groups. Exposure measurement error may be reduced in those spending

more time at home, leading to stronger effects

(Thomas et al. 1993). Moreover, differences in

statistical power may play a role as well; as

shown at least for the 25–40-year age range,

power to detect effects on CIMT is larger in

women than in men (Stein et al. 2004).

The finding that those reporting prescriptions of lipid-lowering medications at baseline

showed stronger associations of CIMT with

PM2.5 merits further investigation. This result

agrees with the observed effects of PM on atherosclerosis in experiments conducted in

hyperlipidemic rabbits (Goto et al. 2004; Suwa

et al. 2002). The systemic inflammatory and

atherogenic reaction in these rabbits was

Article | Künzli et al.

204 VOLUME 113 | NUMBER 2 | February 2005 • Environmental Health Perspectives
related to the amount of PM contained in the

alveolar macrophages. In our study, being

under lipid-lowering therapy is an indicator for

risk profiles prone to atherogenesis. Those subjects were mostly men (64%) and, on average,

older, more often active or passive smokers,

and almost twice as likely to report antihypertensive treatment. The systemic response

to ambient PM may amplify and expand the

oxidation of LDL cholesterol among these susceptible subjects, consequently contributing to

injury in the artery wall (Goto et al. 2004; Ross

1999). Investigations of short-term effects of

ambient air pollution on mortality also suggest

that underlying risk profiles such as diabetes

may amplify susceptibility to ambient PM

(Zanobetti and Schwartz 2002), and similar

findings have been shown with smoking and

diabetes mellitus in association with CIMT

(Karim et al. 2005). To clarify the relevance of

lipid status, it would be interesting to investigate our hypothesis among cohorts with

familial hypercholesteremia (Wiegman et al.

2004; Wittekoek et al. 1999).

As shown in Figure 3, the size of the point

estimate was larger among the older subjects.

Future research needs to clarify whether air

pollution contributes to atherosclerosis only

after a certain age or early on. Effects of air

pollution on lung development have been

observed during adolescence and may be a

result of both pulmonary and chronic systemic inflammatory effects (Gauderman et al.

2002); thus, it is conceivable that atherogenic

responses may occur early in life. The age

dependence of the effects may also be codetermined by genetic factors (Humphries and

Morgan 2004; Ross 1999).

We also observed larger effects in women.

If other cardiovascular risk factors such as

occupational exposures dominate atherosclerosis in men, we would expect a smaller

effect signal and less precision in the estimates

among men. We also hypothesize that interactions may reflect biologic causes. If premenopausal women are protected against

atherosclerosis by endogenous hormones, loss

of hormonal protection would lead to

increased vulnerability after menopause

(Kannel et al. 1976). This could explain part

of the interaction by both age and sex.

Active and passive smoking did not confound results in either the total sample or

among subgroups. Adjustment for active

tobacco smoke led to a slight increase in the

effect estimate; thus, residual confounding is

unlikely to overestimate the effects. However,

PM2.5 associations were clearly stronger in

never smokers compared with smokers (data

not shown). This gradient was also observed

in all subgroups with significant PM2.5 associations (Figure 3). Oxidative and inflammatory effects of smoking may dominate to such

an extent that the additional exposure to

ambient air pollutants may not further

enhance effects along the same pathways. The

difference in the effects of PM2.5 in smokers

and nonsmokers needs further investigation.

The American Cancer Society cohort study

does not reveal a clear pattern of a smoking

interaction for the association of ambient air

pollution and cardiovascular death (Krewski

et al. 2004; Pope et al. 2004). In the Study on

Air Pollution and Lung Diseases in Adults

(SAPALDIA), associations between air pollution and level of pulmonary function did not

differ by smoking status (Ackermann-Liebrich

et al. 1997).

Some U.S. studies indicate effect modification of air pollution by socioeconomic status

(SES) with much stronger effects among the

less educated (Pope et al. 2002). The cause of

this interaction pattern is not well understood.

SES status was rather homogeneous in these

mostly well-educated volunteers, providing little power to investigate interactions of pollution with SES. If lower SES also positively

modifies effects of air pollution on atherosclerosis, our population would provide an

underestimate of the health effects in the general population (O’Neill et al. 2003). Further

research on samples representative of the population will be needed to assess whether the high

SES in the clinical trials biases the effects

toward the null.

Future research should focus on identifying factors that determine susceptibility to

PM2.5. We are initiating studies on subjects

with inflammatory metabolic syndromes

prone to accelerated atherosclerosis such as

postmenopausal women, diabetics, or obese

or physically inactive people. To corroborate

the cross-sectional findings, follow-up studies

are ultimately needed to investigate the association of concurrent levels of air pollution

exposure with the progression of CIMT.

Plausibility. From a biologic perspective,

our results support the hypothesis that longterm exposure to ambient PM contributes to

systemic inflammatory pathways, which are a

relevant aspect of atherogenesis (Ross 1999).

The findings indicate a biologically plausible

link between the observed acute effects of

ambient air pollution on systemic inflammation (Glantz 2002) and the long-term consequences of sustained vascular inflammation

leading to increased atherosclerosis and, ultimately, cardiovascular death (Hoek et al.

2002; Pope et al. 2004). Among susceptible

people, this may lead to artery wall lesions

similar to those observed in the rabbit model

(Fujii et al. 2002; Suwa et al. 2002). In these

hyperlipidemic rabbits, 4-week PM exposure

was associated with the progression of atherosclerotic lesions, coupled with an enhanced

release of bone marrow monocytes. These precursors of macrophages play an important role

in the atherogenic inflammatory responses

(Goto et al. 2004; Ross 1999; Suwa et al.

2002). Given the central role of oxidized LDL

in the initiation and progression of atherogenesis, suggestions that the plasma of automotive workers with high exposure to traffic

exhaust is more susceptible to oxidation is also

of interest (Sharman et al. 2002).

As a quantitative plausibility check, we

compared the size of the PM2.5 effects with

effects of other risk factors on CIMT. Using

smoking and environmental tobacco smoke

(ETS) as a model for air pollution exposure,

the size of our estimates appear plausible

(Diez-Roux et al. 1995; Howard et al. 1994).

Associations of ETS and current levels of air

pollution with various respiratory outcomes

are similar and support the notion of common underlying pathways (Künzli 2002).

Smoking and ETS associate with stiffer and

thicker artery walls, reflecting the systemic

effect of these exposures (Howard et al. 1994;

Mack et al. 2003). Exposure to ETS was associated with 2–3% thicker intima-media,

which approximate the effects observed for a

10 μg/m3 change in PM2.5 (Diez-Roux et al.

1995; Howard et al. 1994). Using never

smokers without ETS exposure as the referent

group in our data, never smokers with ETS at

home had 0.9% (–2.7 to 4.5%) thicker artery

walls; former smokers’ CIMT was increased

on average by 3.4% (0.7–6.3%), and the

30 current smokers had 5% (–1.5 to 11.6%)

thicker CIMT. The trend across these four

categories of tobacco exposure was statistically

significant. As shown in Table 1, smokers

were underrepresented in these volunteers of

well-educated participants.

The observed percent change in CIMT

corresponds to an increase in the thickness of

approximately 20–40 μm per 10 μg/m3 contrast in PM2.5. This difference in CIMT translates into some 3–6% increase in the long-term

risk for myocardial infarction (O’Leary et al.

1999). Pope et al. (2004) reported that longterm exposure to PM2.5 was associated with an

18% (14–23%) increase in ischemic heart disease. Effect sizes reported here concur with

these findings, indicating that a fraction of the

total effect of ambient PM on cardiovascular

mortality may be mediated through sustained

long-term effects of air pollution on atherosclerosis (Künzli et al. 2001). This is in line

with the proposed model (Künzli et al. 2001)

in which some of the effects observed in cohort

studies must reflect long-term contributions of

air pollution to the underlying disease progression, whereas in other cases, air pollution contributes only to triggering of cardiovascular

events or death (Bell et al. 2004; Künzli et al.

2001; Peters and Pope 2002).

From a biologic and policy perspective, we

emphasize that PM2.5 probably serves as a surrogate for the mixture of urban air pollution

and constituents of PM. It is premature to

Article | Air pollution and atherosclerosis

Environmental Health Perspectives • VOLUME 113 | NUMBER 2 | February 2005 205
conclude that PM2.5 and its constituents are

the atherogenic culprit per se. Atherosclerosis

results from complex processes that may

include a combination of various urban pollutants, host factors, and pathways that ultimately lead to the findings of a CIMT–PM2.5

association.

In conclusion, we have presented the first

epidemiologic evidence supporting the idea of

a chronic vascular response to respiratory and

systemic effects of PM exposure. Given the

leading role of heart disease as a cause of death

in most westernized countries and the growing

contribution in developing countries, these

findings may be of high public health relevance. Further investigations need to focus on

susceptible groups and follow-up of cohorts to

investigate the effect of air pollution on the

progression of CIMT.

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Article | Künzli et al.

206 VOLUME 113 | NUMBER 2 | February 2005 • Environmental Health Perspectives