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 remainspoorly understood. We hypothesize that current levels of ambient particulate matter (PM) up to 2.5 μm in aerodynamic diameter (PM 2.5 ) may contribute to atherosclerosis, leading to subclinical anatomical changes that play a major role in cardiovascular morbidityand mortality later in life. Animal studies support our hypothesis by showing that inhalationof 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 PM 2.5 and carotid arteryintima-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 wellwith 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 subclinicalstages 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 theeffect 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 theentire Los Angeles Basin, covering a geographic area of approximately 64,000 km 2

. 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, thyroiddisease, 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 PM 2.5 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 arterywere 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 asthe 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, Divi sion 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 (PM 2.5 ). 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 PM 2.5 . Exposure values were assigned from a PM 2.5 surface derived from a geostatistical model. Individually assigned annual mean PM 2.5 concentrations ranged from 5.2 to 26.9 μg/m 3 (mean, 20.3). For a cross-sectional exposure contrast of 10 μg/m 3

PM 2.5 , 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 PM 2.5 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 PM 2.5 , 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 bloodsamples 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) andgeostatistics. This method allows for assignment of long-term mean ambient concentrations of PM 2.5 to the ZIP code area of each subject’s residential address (Künzli and Tager 2000). The resulting surface of PM 2.5 covered the entire Los Angeles metropolitan area. The surface is derived from a geostatistical modeland 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, PM 2.5 data were interpolated using a combination of a universal krigingmodel 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 subjectwith the exposure surface through a geocoding database [Environmental Systems Research Institute (ESRI) 2004]. Figure 1 illustrates the PM 2.5 surface with the geolocated ZIP codes. Individually assigned PM 2.5 data had a range from 5.2 to 26.9 μg/m 3 (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

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PM 2.5 exposure ( μ g/m 3 )No. of subjects

0 5 10 20 30 40 Miles Kilometers 0 5 10 20 30 40 5.0–7.8

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18.9–21.6

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24.5–27.1 Freeway Subjects

PM 2.5 μ g/m 3

PM 2.5 assigned exposure distribution among subjects

Figure 1. ZIP code locations of the study population geocoded on the PM 2.5 surface, modeled with 2000
PM 2.5 data, and distribution of individually assigned concentrations.

Table 2. Percent change (and 95% CI) in CIMT (μm) associated with a 10 μg/m 3 change in ambient outdoor PM 2.5 concentration at the residential ZIP code in the total population ( n = 798). a

Model a 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, income b 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

a Unadjusted association (crude model) and estimates from two multivariate models; 95% CIs of the estimates are shown in parenthe ses. The relative effects are based on a linear model with log intima-media thickness as dependent variable. b Factors with univariate associations with both, CIMT and PM 2.5

. 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)

PM 2.5 (μg/m 3 ) 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 PM 2.5 using linear regression analyses. Extensive residual diagnostics indicated some heteroskedasticity, which was rectified with the natural log-transformedCIMT. We adjusted for factors that were statistically associated with both CIMT and ambient PM 2.5 (age, male sex, low education, and low income). Next, we expanded the models using covariates that were associated with either PM 2.5 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; Zanobettiand 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 ofthe study population and among main subgroups. Table 2 presents the percent change in CIMT in association with a 10 μg/m 3 contrast in ambient PM 2.5 concentrations for threecross-sectional regression models. The unadjusted model indicates a 5.9% [95% confidence interval (CI), 1–11%] increase in CIMT per 10 μg/m 3 PM 2.5 . For the observed contrast between lowest and highest exposure (20 μg/m 3 PM 2.5 ), 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/m 3

PM 2.5 . 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 PM 2.5 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 trendacross 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

PM 2.5 were substantially stronger among109 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/m 3 PM 2.5 , 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/m 3 PM 2.5 . 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 effectestimate 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 HeartAssociation (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. Theonly 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 thecourse 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

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690
(5.2–19.1) (19.11–20.73) (20.74–22.0) (22.1–26.9)CIMT (μm)

Quartiles of PM 2.5 (range, μ g/m 3 ) 40

35

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–5

–10

–15

–20Change 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
PM 2.5 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/m 3 contrast in ambient PM 2.5 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 PM 2.5 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 onbinary 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 theuniversal 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). PM 2.5generally 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). PM 2.5 of outdoor origin will also penetrate indoors, and correlationsbetween 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 ourexposure 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 PM 2.5 concentrations over the past 5–10 years. The year 2000 surface characterizes the prevailing mean PM 2.5 concentrations across several years and can be considered a measure of long-term past exposure. This yearalso 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 PM 2.5 coefficients or the SEs. Ozone and PM 2.5 were not correlated ( r = –0.17), and the PM 2.5 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 PM 2.5 . This finding must be put in context of the specific challenges in determininglong-term exposure to ozone, which are substantially different than in the case of PM exposure. In contrast to PM 2.5 from outdoororigin, 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 nonrandomsample 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, theeffects may be compared across trials to evaluate the potential influence of selecting volunteers. The populations differed with regard toage, smoking habits, baseline LDL and treatment, blood pressure, active and passive smoking, and other relevant factors; thus, the PM 2.5coefficients 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 PM 2.5 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 manifestCVDs. 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 theseclinical 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 residualconfounding appears to be an unlikely explanation for these results. Among women,adjustment for hormone replacement therapies did not affect the PM 2.5 estimates. In previous studies, we found that spatial autocorrelation in the residuals could affectthe size and significance of pollution coefficients (Jerrett et al. 2003a). We investigated spatial autocorrelation of the unstandardized residuals. We assessed autocorrelation withfirst-order, adjusted first-order, and secondorder spatial weight matrices based on nearestneighbor 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

PM 2.5 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, andthe cross-sectional nature of the data, it is difficult to fully explore the causes of the observed modifications of associations and to establishsusceptibility 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 studieson 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), andthe 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

PM 2.5 merits further investigation. This resultagrees 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 Perspectivesrelated to the amount of PM contained in the alveolar macrophages. In our study, being under lipid-lowering therapy is an indicator forrisk 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 theoxidation 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 oflipid 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 aresult 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 agedependence 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 asoccupational exposures dominate atherosclerosis in men, we would expect a smaller effect signal and less precision in the estimatesamong 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,

PM 2.5 associations were clearly stronger in never smokers compared with smokers (data not shown). This gradient was also observed in all subgroups with significant PM 2.5 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 PM 2.5 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 thesemostly well-educated volunteers, providing little power to investigate interactions of pollution with SES. If lower SES also positivelymodifies effects of air pollution on atherosclerosis, our population would provide anunderestimate of the health effects in the general population (O’Neill et al. 2003). Furtherresearch 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

PM 2.5 . 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 studiesare 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 ofambient air pollution on systemic inflammation (Glantz 2002) and the long-term consequences of sustained vascular inflammationleading 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 exposurewas associated with the progression of atherosclerotic lesions, coupled with an enhancedrelease 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 LDLin 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 PM 2.5 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 outcomesare 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/m 3 change in PM 2.5 (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/m 3 contrast in PM 2.5 . 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 PM 2.5 was associated with an18% (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 sustainedlong-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 ofair 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 PM 2.5 probably serves as a surrogate for the mixture of urban air pollution and constituents of PM. It is premature to Article 

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### 205conclude that PM 2.5 and its constituents are the atherogenic culprit per se. Atherosclerosis results from complex processes that mayinclude a combination of various urban pollutants, host factors, and pathways that ultimately lead to the findings of a CIMT–PM 2.5 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, thesefindings 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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