### 840 volume 118 | number 6 | June 2010 • Environmental Health Perspectives 

# Research

Evidence from epidemiologic studies shows a consistent association between increased ambient air pollution and increased daily hospital admission (Lee et al. 2007; Santos

et al. 2008; Schwartz 1999; Zanobetti and Schwartz 2006) and premature death (Pope

et al. 2004; Schwartz 1994). Studies have further shown that the pollution-mediatedimpacts were largest for cardiovascularrelated illness and deaths (von Klot et al. 2009; Zanobetti and Schwartz 2007). Several biological mechanisms by which air pollution can elicit cardiovascular morbidity and mor

tality have been identified, including oxida

tive stress (Gurgueira et al. 2002), autonomic dysfunction (Gold et al. 2000), and systemic inflammation (Peters et al. 2001 b ; Ruckerl

et al. 2006)
, leading to endothelial dysfunc

tion (O’Neill et al. 2005), atheromatous plaque (Sun et al. 2005; Suwa et al. 2002), and thrombosis (Baccarelli et al. 2008). However, the specific underlying biologi

cal pathways are not fully understood
, and the identification of these pathways warrants further study. Of particular interest are recent studies that suggest that myocardial infarctions risk (Peters

et al. 2001 a, 2004), inflammation (Ruckerl

et al. 2006), and cardiac repolarization changes (Henneberger et al. 2005; Yue et al. 2007) may be associated with exposures to air pollu

tion on time scales of less than a day. Because standards for particles and nitrogen dioxide ( NO 2 ) currently involve longer-term averages, these studies suggest that the relation of hourly peaks in air pollution with indicators of cardio

vascular health should be examined. Of the cardiovascular health indicators, irregularities in myocardial repolarization may be especially important because they can lead to the development of cardiac arrhythmias. Two panel studies of patients with cardiovascular dis

ease, which were conducted in East Germany, showed evidence of an immediate effect of air pollution on ventricular repolarization dura

tion, morphology
, and variability (Henneberger

et al. 2005; Yue et al. 2007). Ljungman et al. (2008) found that patients with implantable cardioverter defibrillators also showed evidence of rapid effects of air pollution on the risk of life-threatening ventricular arrhythmias. These findings suggest a possible biological path

way linking an acute effect of air pollution on increased risk of ventricular repolarization, cardiovascular arrhythmias
, and cardiac death. However, the effect of air pollution on repolari

zation parameters in other populations at risk should be addressed and confirmed. We hypothesized that short-term expo

sures to traffic-related air pollutants may be associated with increases in ventricular repo

larization, as measured by changes in the heart-rate

– corrected QT interval (QTc) on the electrocardiogram (ECG)
, and that par

ticipant characteristics and genes related to oxidative stress may modify this association. Thus, in this study, we investigated the asso

ciation between ambient air pollution [black carbon (BC), carbon monoxide (CO), NO 2 , ozone (O 3 ), PM ≤ 2.5 μm in aerodynamic diameter (PM 2.5 ), and sulfur dioxide (SO 2 )] and a measure of ventricular repolarization, the QTc, among men residing in commu

nities in the Boston, Massachusetts (MA)
, area to examine whether this association is modified by participant characteristics and by genetic susceptibility to oxidative stress. We evaluated these aims using data from a prospective longitudinal study of 580 elderly male participants of the Veterans Affairs (VA) Normative Aging Study ( NAS )

. 

### Materials and Methods Study population. The NAS is an ongoing longitudinal study of aging, which was estab

lished in 1963 by the Veterans Administration (Bell et al. 1972). Briefly, 2,280 communitydwelling, healthy men living in the Greater

Address correspondence to E.S. Baja, Exposure, Epidemiology, and Risk Program, Harvard School of Public Health, 401 Park Dr., Suite 422 Landmark Center West, Boston, MA 02115 USA. Telephone: (617) 384-8848. Fax: (617) 384-8745. E-mail: ebaja@hsph.harvard.edu

Supplemental Material is available online (doi:10.1289/ehp.0901396 via http://dx.doi.org/).

This work was supported by grants from the National Institute of Environmental Health Sciences (ES014663-01A2 and P01 ES09825) and from the U.S. Environmental Protection Agency (EPA R827353 and R83241601).

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

Received 28 August 2009; accepted 1 March 2010. 

## Traffic-Related Air Pollution and QT Interval: Modification by Diabetes, Obesity, and Oxidative Stress Gene Polymorphisms in the Normative Aging Study
Emmanuel S. Baja, 1 Joel D. Schwartz, 1 Gregory A. Wellenius, 2 Brent A. Coull, 3 Antonella Zanobetti, 1 Pantel S. Vokonas, 4,5 and Helen H. Suh 1

1 Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 2 Department of Community Health, Brown University, Providence, Rhode Island, USA; 3 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA; 4 Veterans Affairs Normative Aging Study, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, USA; 5 Department of Internal Medicine, Boston University, Boston, Massachusetts, USA

B ackground : Acute exposure to ambient air pollution has been associated with acute changes in cardiac outcomes, often within hours of exposure.
O b jectives : We examined the effects of air pollutants on heart-rate

– corrected QT interval (QTc), an electrocardiographic marker of ventricular repolarization, and whether these associations were modified by participant characteristics and genetic polymorphisms related to oxidative stress.
M ethods : We studied repeated measurements of QTc on 580 men from the Veterans Affairs Normative Aging Study ( NAS) using mixed-effects models with random intercepts. We fitted a qua

dratic constrained distributed lag model to estimate the cumulative effect on QTc of ambient air pol

lutants including fine particulate matter ≤ 2.5 μm in aerodynamic diameter ( PM 2.5 ) , ozone ( O 3 ) , black carbon ( BC ) , nitrogen dioxide ( NO 2 ) , carbon monoxide ( CO ) , and sulfur dioxide (SO 2 ) concentrations during the 10 hr before the visit. We genotyped polymorphisms related to oxidative stress and analyzed pollution

– susceptibility score interactions using the genetic susceptibility score (GSS) method.
R

esults : Ambient traffic pollutant concentrations were related to longer QTc. An interquartile range (IQR) change in BC cumulative during the 10 hr before the visit was associated with increased QTc [1.89 msec change ; 95% confidence interval ( CI ), –0.16 to 3.93]. We found a similar association with QTc for an IQR change in 1- hr BC that occurred 4 hr before the visit ( 2.54 msec change ; 95% CI , 0.28–4.80). We found increased QTc for IQR changes in NO 2 and CO , but the change was statistically insignificant . In contrast, we found no association between QTc and PM 2.5 , SO 2 , and O 3 . The association between QTc and BC was stronger among participants who were obese, who had diabetes, who were nonsmokers, or who had higher GSSs.
C onclusions : Traffic-related pollutants may increase QTc among persons with diabetes, persons who are obese, and non smoking elderly individuals; the number of genetic variants related to oxida

tive stress increases this effect.
K

ey words : air pollution, diabetes, distributed lags, genes, obesity, oxidative stress, QT interval, smoking, traffic. Environ Health Perspect 118:840–846 (2010). doi:10.1289/ehp.0901396 [Online 1 March 2010]Traffic-related air pollution and QT interval

Environmental Health Perspectives • volume 118 | number 6 | June 2010 

### 841 Boston area were enrolled between 1963 and 1968. Every 3–5 years, participants visited the VA Medical Center NAS clinic after an over

night fast and abstention from smoking for an extensive physical examination, laboratory tests, blood collection, and a self-administered questionnaire on alcohol consumption, food intake, medical history, medication use , smok

ing history, and other factors that could affect health. From November 2000 to December 2008, ECG measurements were also obtained during each participant’s regularly scheduled visit. To date, ECG measurements have been collected for 712 participants. Of these, we excluded 132 participants because the record

ing time of the ECG measurements was

< 3.5 min, the T- wave amplitude was insuf

ficient, or the heart beats were non normal

or non supraventricular. The remaining 580 men had one ( n = 318), two ( n = 178), or three ( n = 84) ECG measurements, for a total of 926 valid readings. All participants gave written consent , and we received institutional review board approval for this study. ECG measurement and analysis. The ECG was recorded for 5–10 min between 0530 and 1400 hours with a two-channel (five lead) ECG monitor (Trillium 3000; Forest Medical, Inc., East Syracuse, NY) using a sampling rate of 256 Hz per chan

nel. A detailed description of the protocol is provided elsewhere (Park et al. 2005; Pope

et al. 2001). Briefly, beats were automatically labeled
, and the QT interval was measured from the QRS onset to the end of the T-wave only on normal or supraventricular beats. The QT interval was not calculated if the T- wave did not have sufficient amplitude. The QTc values were calculated using the Bazett’s for

mula (Bednar et al. 2001). We calculated the mean QTc for the length of the recording at the time of each participant’s visit. Air pollution and meteorology. Ambient BC and PM 2.5 concentrations were contin

uously measured at the stationary ambient monitoring site at the Harvard University Countway Library, which is located < 1 km from the clinical laboratory where subjects were examined. Hourly ambient CO, O 3 , NO 2 , and SO 2 concentrations were obtained from several local monitoring sites of the Massachusetts Department of Environmental Protection. We used the following locations to calculate the mean concentrations of the gases: Roxbury, Bremen, Kenmore Square, the North End of Boston
, Lynn
, and Waltham
, MA

. The distance of the sites from the clinical laboratory varied from < 2 km to < 20 km. Temperature data were obtained from the weather station at Boston Logan airport. Genotyping and genetic susceptibility

score. Participants were genotyped for two gene deletions [glutathione S -transferase mu-1 ( GSTM1 ) and glutathione S -transferase theta-1 ( GSTT1 )], eight single-nucleotide polymor

phism s (SNP s ) [ glutathione S -transferase pi-1 ( GSTP1 ) Ile105Val and Ala114Val, hemo

chromatosis ( HFE ) C282Y and H63D, NAD(P)H dehydrogenase, quinone-1 ( NQO1 ) C609T, and catalase ( CAT ) RS2300181, RS2284367
, and RS769217 ] , and a microsat

ellite GTn-repeat polymorphism [heme oxyge

nase (decycling)-1 ( HMOX1 ) ] . The dominant genetic model was used to classify the SNPs of the participants. The SNPs were catego

rized as either a wild type (WT) or any variant [ for a detailed description , see Supplemental Material (doi:10.1289/ehp.0901396 ) ]

. To reduce multiple comparisons by inves

tigations of multiple gene deletions and poly

morphisms, we created a variable for the genetic susceptibility score (GSS) that was related to the genetic susceptibility of a participant to oxi

dative stress. The GSS assumed that all the dele

tions and polymorphisms have an equal weight contribution to the genetic susceptibility of a participant to oxidative stress. A genotype was defined as unfavorable based on findings from the scientific literature of the polymorphisms or of gene

– air pollution interactions. Catalase SNPs were not included in the scoring method because of the lack of prior studies reporting their effects. The role of the proteins encoded by these genes in the oxidative stress pathway is summarized in Supplemental Material, Table 1 (doi:10.1289/ehp.0901396 ). For every gene null deletion, SNP
, or microsatellite GTnrepeat polymorphism, a score of 1 was given if the participant had the unfavorable geno

type and 0 if the participant did not have this genotype. The scores were added for every par

ticipant. Based on the observed distribution of the score, the GSS of every participant was categorized into low [GSS < 4 (< distributional median)] and high [GSS ≥ 4 (above median)] susceptibility to oxidative stress. Statistical analysis. We calcuated descrip

tive statistics for QTc, air pollutants, and covariates, as well as correlation coefficients to evaluate the relationship among QTc, covari

ates, and air pollutants. Associations between each air pollut

ant and the change in mean QTc were esti

mated using linear mixed-effects models with random subject-specific intercepts
, which is a standard approach for analyzing longitudinal data with repeated measures on the same sub

ject (Fitzmaurice et al. 2004). Mixed models with a random subject-specific intercept were fit to capture residual correlation among mea

surements within the same participant and to account for the heterogeneity in the subject’s overall QTc. In all models we controlled for age, body mass index (BMI), sitting mean arte

rial blood pressure (MAP), cholesterol level, alcohol intake, smoking status, diabetic status, and cardiac medication use ( alpha blockers, angiotensin-converting enzyme inhibitors,angiotensin receptor antagonist/blockers, betablockers, and calcium channel blockers). These covariates were chosen a priori as potentially important predictors of QTc. To account for seasonal variation and other long-term time trends in QTc, we used a natural spline of cal

endar date with 4 degrees of freedom per year. We also controlled 1 -hour mean temperatureusing a linear term and modeled day-of-theweek effects with indicator variables. Separate single- pollutant models were evaluated. To examine the appropriate expo

sure window for each pollutant and to mini

mize multiple comparisons
, our primary analysis fit a quadratic constrained distributed lag (QCDL) model to estimate the cumula

tive effect of each pollutant during a 10- hr time window before the visit (Schwartz 2000)
, which
, using the Akaike information crite

rion, gave the best model fit for the cumula

tive effect of the pollutant. We verified the results of the QCDL model by fitting separate hourly exposure lag models for each lag. The fixed covariates were included in all analyses. To evaluate obesity as an effect modi

fier, subjects were classified into two groups according to BMI ( obese, ≥ 30 kg/m 2 ; non
obese, < 30 kg/m 2 ). We also assessed effect modification by a history of diabetes [ diabetic vs. nondiabetic : doctor’s diagnosis of disease or fasting blood glucose (FBG) > 126 mg/dL vs. no disease diagnosis or FBG ≤ 126 mg/ dL)], smoking ( ever smoker vs. never smoker ), and genetic susceptibility to oxidative stress ( high vs. low GSS, ≥ 4 vs. < 4). In addition, we assessed effect modification by each gene deletion and polymorphism to check if these deletions and polymorphisms individually modify the associations of air pollutants with QTc. We included in the models interaction terms between the dichotomized modifier variable and each air pollutant. The likelihood ratio test (LRT) was used to test the signifi

cance of the interaction terms. Effect size estimates were reported as change in mean QTc per interquartile range ( IQR ) change of a pollutant. In addition, effect estimates were also scaled to percent age of a standard deviation (SD) change in QTc per IQR change of a pollutant and are detailed in the Supplemental Material (doi:10.1289/ ehp.0901396) . We considered p -value s of

≤ 0.05 to be statistically significant. R (version 2.8.1; R Foundation for Statistical Computing 2008), SAS (version 9.1) and and JMP (ver

sion 8; SAS Institute Inc., Cary, NC ) were used in the analysis [ see Supplemental Material ( doi:10.1289/ehp.0901396)]

. 

### Results Eligible study participants included 580 NAS participants, who had a total of 926 valid ECG recordings available for analysis. TheBaja et al.



### 842 volume 118 | number 6 | June 2010 • Environmental Health Perspectives participants were older men with a mean age (± SD) of 74.8 years ± 6.8 years who were generally overweight with a mean BMI (± SD) of 27.9 kg/m 2 ± 4.1 kg/m 2

. Table 1 shows other participant characteristics. Table 2 shows the descriptive statistics of pollutant concentrations and temperature. All pollutant concentrations during the actual hour of ECG monitoring ( 0- hr lag) were significantly correlated with each other, with BC and PM 2.5 having the highest correlation (Spearman corre

lation coefficient, ρ = 0.69). Among the gaseous pollutants, NO 2 and CO were most strongly correlated ( ρ = 0.64), whereas the correlation between particulate and gaseous pollutants was highest for BC and NO 2 ( ρ = 0.60). In con

trast, O 3 was negatively correlated with every other pollutant ( ρ < –0.12). Figure 1 shows the estimated cumulative effect of each pollutant for exposures for the 10 hr before the QTc measurement using the QCDL model. BC, CO, and NO 2 were positively associated with QTc, but the results were not statistically significant [BC: 1.89 msec change in mean QTc ; 95% confidence interval ( CI ), –0.16 to 3.93 ; p -value = 0.07 ; CO: 3.83 msec change in mean QTc ; 95% CI
, –0.17 to 7.82 ; p -value = 0.06 ; NO 2 : 2.56 msec change in mean QTc ; 95% CI
, –0.88

to 6.00 ; p -value = 0.14]. In contrast, PM 2.5 , O 3 , and SO 2 were not clearly associated with QTc [s ee Supplemental Material, Table 2 (doi:10.1289/ehp.0901396 ) ]

. In Figure 2, we compare the results for cumulative exposure for a 10- hr time win

dow (based on QCDL models) and hourly exposure lag models. The two approaches showed an increase in QTc during a simi

lar time course for BC, NO 2 , and CO. For BC, the strongest associations were observed with pollutant measures 4 hr before the ECG measurement. IQR changes in BC, NO 2 , and CO in the 4 th hr were associated with a 2.54 msec (95% CI
, 0.28–4.80), 2.72 msec (95% CI
, –0.01 to 5.44), and 2.36 msec (95% CI
, –0.07 to 4.80) change in mean QTc, respectively. Not all of the measures were statistically significant, but IQR changes in BC, NO 2 , and CO at other hourly lags were also related to longer QTc. This finding shows a consistent trend across hourly lags. In contrast, we found no significant associa

tion between QTc and ambient PM 2.5 , SO 2 , and O 3 [s ee Supplemental Material, Table 2 (doi:10.1289/ehp.0901396 ) ]

. Effect modification by diabetes, obesity and smoking. We assessed whether being diabetic, obese, or a smoker modified the effect of BC, NO 2 , and CO on QTc. 6% of the study population were obese, diabetic, and non smoker, 8% were obese and diabetic, 6% were non smokers and obese, and 5% were

non smokers and diabetic. Figure 3 shows the results of effect modi

fication by diabetes, obesity
, and smoking for both the QCDL model (for cumulative exposure during the 10 hr before QTc meas

urement) and the hourly exposure model (with a 4- hr lag) for BC, NO 2 , and CO. The estimated effect of BC on QTc is consistent for both models. Cumulative BC exposure was associated with a 5.28 msec (95% CI
, 0.67–9.90) change in mean QTc among par

ticipants with diabetes, but we estimated a smaller association for non diabetic partici

pants ( 1.43 msec change in mean QTc ; 95% CI
, –0.79 to 3.65 ; p -value for interaction = 0.26 using the LRT). Additionally, the asso

ciation with cumulative BC was also stronger for obese versus non obese participants and never-smokers versus ever-smokers, although differences between estimates were not statis

tically significant (LRT
, p -values for interac

tion > 0.53; for details, see Table 3). Effect modification by GSS

. We also assessed whether having a high GSS (unfa

vorable genotype) modified associations of traffic-related pollutants with QTc ; 94%, 90% and 85% of the participants in the **Table 1.** Characteristics of the study population (NAS participants, n = 580). Characteristic Value Age [years (mean ± SD)] 74.8 ± 6.8 BMI [kg/m 2 (mean ± SD)] 27.9 ± 4.1

≥ 30 kg/m 2 (%) 27.6 MAP [mmHg (mean ± SD)] 90.0 ± 11.1 Cholesterol [mg/dL (mean ± SD)] 246.2 ± 131.2 Alcohol intake ≥ 2 drinks/day (%) 19.5 Ever diabetic (%) a 22.9 Cigarette smoking (%) b

Former 69.0 Never 28.4 Current 5.0 Medication use (%) Beta blockers 42.9 Angiotensin-converting enzyme

inhibitors 30.9

Alpha blockers 17.6 Calcium blockers 17.6 Angiotensin receptor

antagonist/blockers 9.3
Genotypes (%)

HFE C282Y WT c 82.6 GSTP1 Ala114Val WT c 80.0 HFE H63D WT c 72.8 CAT rs769217 WT c 51.0 CAT rs2284367 WT c 50.7 GSTM1 deletion d 49.0 HMOX1 long/long alleles (≥ 25 GT repeats) 46.2

GSTP1 Ile105Val any variant e 45.7 CAT rs2300181 any variant e 41.6 NQO1 C609T any variant e 29.5 GSTT1 deletion d 16.7 QTc interval [msec (mean ± SD)] f 393.3 ± 29.7

a Report of doctor’s diagnosis of disease or FBG > 126 mg/dL. b Total does not equal 100% because of changes in smoking habit

. c Homozygous WT. d Homozygous null

. e Heterozygous + homozygous variant

. f Bazett corrected, mean QTc interval

.

**Table 2.** Concentration of ambient air pollutants and temperature during or 10 hr before ECG monitoring (November 2000–December 2008). Variable HR Lag Mean ± SD Median IQR BC (μg/m 3 ) 0 1.08 ± 0.84 0.85 0.85 10 0.64 ± 0.51 0.50 0.55 CO (ppm) 0 0.436 ± 0.299 0.381 0.302 10 0.332 ± 0.280 0.266 0.271 NO 2 (ppm) 0 0.021 ± 0.008 0.020 0.010 10 0.019 ± 0.009 0.018 0.013 PM 2.5 (μg/m 3 ) 0 11.42 ± 8.55 9.03 8.56 10 10.72 ± 7.88 8.75 7.92 O 3 (ppm) 0 0.023 ± 0.016 0.021 0.018 10 0.021 ± 0.015 0.020 0.020 SO 2 (ppm) 0 0.0052 ± 0.0045 0.0038 0.0039 10 0.0042 ± 0.0037 0.0030 0.0030 Temperature (°C) a 13.03 ± 9.65 13.33 14.96

a Current 1-hr mean temperature **Figure 1**

**.** Effect estimates of change in mean QTc per IQR change in cumulative exposure to pollutant during the 10 hr before ECG measur e ment : singlepollutant model with random intercept using QCDL. All models were adjusted for age; BMI ; MAP ; cholesterol ; diabetic status (doctor’s diagnosis of disease or FBG > 126 mg/dL vs. no diagnosis or FBG ≤ 126 mg/dL) ; alcohol intake (≥ 2 drinks/day,

< 2 drinks/day) ; cigarette smoker (never, ever) ; use of alpha blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor antagonist/block

ers, beta blockers, or calcium channel blockers ; day of the week (Monday through Sunday) ; tem

perature ; and a natural spline for long-term time trend (date). Error bars indicate 95% CI.

* p

< 0.10

.

10

8

6

4

2

0

–2

–4
BC CO NO 2 PM 2.5 **Pollutant Change in mean QT** **c (msec)**

SO 2 O 3 * *Traffic-related air pollution and QT interval

Environmental Health Perspectives • volume 118 | number 6 | June 2010 

### 843 “high polymorphism” group had the unfavor

able polymorphism of HFE C282Y, GSTP1 A114V, and HFE H63D, respectively. Figure 4 shows the results of the analysis of effect modification by GSS for BC, NO 2 , and CO. Cumulative BC exposure (over the previous 10 hr ) was associated with a 3.85 msec (95% CI
, 0.78–6.93) increase in mean QTc in participants with a high GSS, but no association was evident among participants with a low GSS (– 0.56 msec ; 95% CI
, –3.90

to 2.78 ; LRT
, p -value for interaction = 0.57; for details, see Table 4). 

### Discussion This study provides evidence that the trafficrelated pollution markers BC, CO, and NO 2 are associated with prolongation of QTc, a marker of ventricular repolarization and a risk factor for ventricular arrhythmias (e.g
., torsades de pointes) and sudden cardiac death. In con

trast, we found no significant associations with PM 2.5 , O 3 , or SO 2 , generally indicators of non
traffic pollution. Our results further suggest that diabetic, non smoker, and obese partici

pants and individuals with a high number of unfavorable genotypes related to oxidative stress

**Figure 2**

**.** Effect estimates of change in mean QTc per IQR change of traffic-related pollutant. Error bars and hatch-marked regions indicate 95% CI. ( A

– C ) Singlepollutant model of cumulative exposure for a 10- hr time window with random intercept using QCDL with hourly lags : BC ( A ), NO 2 ( B ), and CO ( C )

. ( D

– F

) Singlepollutant model of hourly exposure with random intercept using hourly lags : BC ( D ), NO 2 ( E ), and CO ( F )

. 6

4

2

0

–2 6

4

2

0

–2 6

4

2

0

–2

6

4

2

0

–2 6

4

2

0

–2 6

4

2

0

–2 024 6 8 10 0 2 4 6 8 1 0 0 2 4 6 8 10

024 6 8 10 0 2 4 6 8 1 0 0 2 4 6 8 10 **Lag (hr) Change in mean QT** **c (msec) Change in mean QT** **c (msec) Change in mean QT** **c (msec) Change in mean QT** **c (msec) Change in mean QT** **c (msec) Change in mean QT** **c (msec) Lag (hr)**  **Lag (hr)**

**Lag (hr) Lag (hr)**  **Lag (hr)** Black carbon Nitrogen dioxide Carbon monoxide

Black carbon Nitrogen dioxide Carbon monoxide **A B**  **C**

**D**

**Figure 3**

**.** Adjusted change in mean QTc per IQR change of traffic-related pollutant : hourly exposure with a 4 -hr lag ( A ) or cumulative exposure during 10 hr before ECG measurement ( B )

, by participant characteristics [diabetic status (doctor’s diagnosis of diabetes or FBG > 126 mg/dL, vs. no diagnosis or FBG ≤ 126 mg/dL), smoking (never, ever), and obesity (BMI ≥ 30, < 30)]. Error bars indicate 95% CI.

* p -Value interaction < 0.01; ** p -Value interaction < 0.05. 15

10

5

0

–5 15

10

5

0

–5 Diabetic Obes e Nondiabeti c Nonobese Never smoker Ever smoker Diabetic Obes e Nondiabeti c Nonobese Never smoker Ever smoker Diabetic Obes e Nondiabeti c Nonobese Never smoker Ever smoker

**Participant characteristic** Diabetic Obes e Nondiabeti c Nonobese Never smoker Ever smoker Diabetic Obes e Nondiabeti c Nonobese Never smoker Ever smoker Diabetic Obes e Nondiabeti c Nonobese Never smoker Ever smoker

**Participant characteristic Change in mean QT** **c (msec) Change in mean QT** **c (msec)** *
**

**

* *

*

* *

*

* QCDL model 4-hr -lag model BC NO 2 CO **A B** Baja et al.



### 844 volume 118 | number 6 | June 2010 • Environmental Health Perspectives may be particularly at risk from traffic-related exposures. Our findings are supported by those from previous studies that also show evidence of greater cardiovascular effects for traffic-related pollutants than for regional nontraffic-related pollutants (Laden et al. 2000
, 2006; Schwartz

et al. 2005a). In the Six City Study, for example, the estimated effect of particles from mobile sources on mortality was greater than that from coal power plants (Laden

et al. 2000, 2006). Consistent with these findings, ambient BC concentrations have been associated in several Boston-area stud

ies with mortality (Maynard et al. 2007) as well as a variety of intermediate cardiovascular indicators, including heart rate variability (Schwartz et al. 2005a), ST-segment depres

sion (Chuang et al. 2008; Gold et al. 2005)
, and homocysteine (Park et al. 2008). Few studies have been conducted that have examined pollution-mediated effects on QTc. In a prospective panel study in East Germany, Henneberger et al. (2005) examined pollutioninduced repolarization changes in 56 males (mean age, 66 years) with ischemic heart dis

ease. They found no significant association at alpha = 0.05 between QTc and elemental carbon, but found significant associations with NO 2 and CO during 6–11 hr and with SO 2 during the whole 0–23 hr before ECG record

ing. Insignificant findings for EC in the East Germany study may be due to its smaller sam

ple size or its younger study population com

pared with our study, in which we estimated significant main effects of BC 4 hr before ECG recording (Figure 2) in our population of 580 men with a mean age of 74.8 years. Plausible pathophysiologic mechanisms. Our observed associations can be explained by plausible pathophysiological mechanisms. Toxicity of traffic particles may have a direct effect on the blood, cardiovascular system, and lung receptors (Brook et al. 2004; Simkhovich

et al. 2008). Deposition in the airways and lung alveoli may trigger proinflammatory sig

naling via a reactive oxygen species (ROS)- dependent mechanism (Donaldson and Stone 2003 ; Ghio and Devlin 2001 ; Ghio et al. 2000; Li et al. 1999). Moreover, traffic par

ticles may also cross the pulmonary epithe

lium and may be able to reach the heart via the vasculature (Nemmar et al. 2001
, 2002; Oberdörster et al. 2002), where they may induce oxidative stress and proinflammatory changes in the vasculature and myocardial substrate (Simkhovich et al. 2008). The gen

erated proinflammatory cytokines and ROS may subsequently affect a variety of health measures, including autonomic cardiac control (Simkhovich et al. 2008). Changes in auto

nomic control may in turn cause a prolonga

tion in ventricular repolarization and QTc interval through the altering of the function of the sodium and calcium channels (Goldenberg

et al. 2008; Moss and Kass 2005; Utell et al. 2002; Zareba et al. 2001). Effect modification by participant charac

teristics. Our observation of effect modifi

cation by diabetic status in the associations between the cumulative effect and 4 hr before ECG measurement lag effect of BC and NO 2 exposure and prolonged QTc for the QCDL and hourly lag models may be due to a higher likelihood of persons with diabetes to have prolonged QTc than those without diabe

tes. In addition, the greater baseline oxida

tive stress among persons with diabetes has been shown to mediate the autonomic effects of particles (Chahine et al. 2007; Schwartz

et al. 2005b; Rhoden et al. 2005). Both the QCDL and hourly lag models showed a con

sistent pattern in the acute estimated effects of BC and NO 2 on QTc. These results are supported by those from a population based cohort study that showed a high prevalence of prolonged QTc among participants with type 2 diabetes (Veglio et al. 2002). We also observed evidence of effect modi

fication by obesity on associations between cumulative (for the 10 hr before ECG meas

urement) and hourly (lagged 4 hr before ) BC and NO 2 exposures and prolonged QTc. Obesity and the cardiac autonomic nervous system are related, an increase in body weight is associated with a decline in parasympathetic **Table 3.** Adjusted effect estimates for change in mean QTc with an IQR increase in traffic pollutant expo

sures (cumulative during the 10 hr before ECG measurement) by patient characteristics (diabetic, obesity, and smoking status).
QCDL model

Status/pollutant Modifier Change in mean QTc [msec (95% CI)] p -Value p -Value interaction Diabetic status BC DM 5.28 (0.67 to 9.90) 0.03 0.26 Non-DM 1.43 (–0.79 to 3.65) 0.21 NO 2 DM 7.10 (–0.12 to 14.32) 0.054 < 0.01 Non-DM 1.22 (–2.67 to 5.12) 0.54 CO DM 3.78 (–3.59 to 11.14) 0.31 0.15 Non-DM 3.91 (–0.62 to 8.44) 0.09 Obesity status

BC Obese 3.97 (–0.07 to 8.01) 0.054 0.54 Nonobese 1.23 (–1.14 to 3.60) 0.31 NO 2 Obese 4.02 (–3.01 to 11.04) 0.26 < 0.01 Nonobese 2.28 (–1.65 to 6.21) 0.25 CO Obese 3.38 (–4.27 to 11.02) 0.38 0.29 Nonobese 3.90 (–0.57 to 8.37) 0.09 Smoking Status

BC Never 4.32 (0.54 to 8.09) 0.03 0.74 Ever 0.57 (–1.83 to 2.98) 0.64 NO 2 Never 4.18 (–2.41 to 10.77) 0.21 < 0.01 Ever 1.97 (–2.02 to 5.95) 0.33 CO Never 4.67 (–2.33 to 11.68) 0.19 0.14 Ever 3.27 (–1.41 to 7.96) 0.17 DM, diabetic (diagnosis of diabetes mellitus).

**Figure 4**

**.** Adjusted change in mean QTc per IQR change of traffic-related pollutant: hourly exposure with a 4 -hr lag ( A ) or cumulative exposure during 10 hr before ECG measurement ( B ), by GSS ( low GSS, high GSS). Error bars indicate 95% CI.

* p -Value interaction < 0.01; ** p -value interaction < 0.10

. 15

12

9

6

3

0

–3 15

12

9

6

3

0

–3**Change in mean QT c (msec)** **Change in mean QT c (msec)** 

High Low Low Low High High High 4-hr -lag model QCDL model

**Genetic susceptibility score Genetic susceptibility score**  Low Low Low High High *

* **

** BC NO 2 CO **A B** Traffic-related air pollution and QT interval

Environmental Health Perspectives • volume 118 | number 6 | June 2010 

### 845 tone and accompanied by a rise in mean heart rate (Poirier et al. 2006). El-Gamal et al. (1995) have found that obese individuals are more likely to have prolonged QTc than

non obese individuals and a positive associa

tion exists between BMI and QTc. This may explain why we found a stronger association between BC and NO 2 , and QTc among obese participants compared with non obese par

ticipants. Because only 8% of the participants were both obese and diabetic, it is unlikely in this case that obesity is acting as a surrogate for diabetes. In addition, we observed evidence of effect modification by smoking status on the asso

ciation between QTc and BC and NO 2 expo
sures (both as the cumulative and 4- hr lag effect), with stronger associations estimated for non smokers. We hypothesize that non
smokers are more susceptible to effects of traffic-related pollution, because particles and gases may penetrate the clear lungs of non
smokers more easily than the lungs of smokers who have extensive lung damage. However, results from previous studies provide conflict

ing information on the effect of smoking on prolonged QT interval (Andrassy et al. 2003)
, because Ileri et al. (2001) reported that QTc was significantly prolonged in smokers than in

non smokers. Clearly, more studies are needed to verify our observed effect modification by smoking. Nevertheless, our results indicate that persons with diabetes, individuals who are obese, and persons who do not smoke may be more responsive to traffic-related pollutants, such as BC and NO 2 , compared with those who are not diabetic, obese, and smokers. Effect modification by genetic suscepti

bility to oxidative stress. Our QCDL model estimates showed significant QTc prolonga

tion in association with increasing BC and CO in 50% of the study population with higher genetic susceptibility to oxidative stress (as measured by GSS). In contrast, no associations with traffic pollutants were seen in half of the study population with lower GSS. Our findings are supported by several gene–environment interaction studies of our group that showed effect modification by genes related to oxidative stress (Chahine et al. 2007 ; Park et al. 2006 ; Schwartz et al. 2005b). The observed interactions between traffic pollutants and genetic susceptibility to oxidative stress score imply that components of traffic pollutants may play a significant role in influencing oxidative stress via changes in autonomic signaling, and changes in the levels of oxidants and antioxidants (Rhoden et al. 2005; Simkhovich et al 2008). We hypothesize that participants with high polymorphisms related to oxidative stress are more susceptible to an increase in oxidant generation and/or a decrease in antioxidant protection and this susceptibility could lead to QTc prolongation. Oxidative stress could increase ROS in the endothelial cells and/or heart muscles that could induce endothelial dysfunction and myocardial inflammation. Endothelial dysfunction and inflammation could alter the function of ion channels lead

ing to an intracellular excess and accumulation of positively charged ions inside the cardiac myocytes, which extends ventricular repolari

zation and results in QT interval prolonga

tion (Al-Khatib et al. 2003; Viskin 1999). Oxidative stress also inhibits nitric oxide production (Bai et al. 2001). Nitric oxide inactivation is associated with a reduction in parasympathetic tone and with an elevation in sympathetic tone (Chowdhary et al. 2002), which could trigger QTc prolongation and lead to arrhythmia, and sudden cardiac death (Tapanainen et al. 2002; Tsuji et al. 1996). Our study used the GSS method to assess if having a higher number of genetic vari

ants related to oxidative stress modified the associations between traffic- related pollut

ants and QTc. This approach is a statistically efficient method to evaluate gene

– environ

ment interactions especially in inferring bio

logical pathways that would link exposure to outcome of interest. The use of the score method approach, together with the dis

tributed lag models, reduces the problem of multiple comparisons. Results from the GSS method were consistent with those from the analysis of individual genetic polymorphisms [ see Supplemental Material (doi:10.1289/ ehp.0901396 ) ] . Nevertheless, more studies are needed to verify this approach. Strengths and limitations. One limita

tion of our study is the use of a single ambi

ent monitoring site as a surrogate for recent ambient pollution exposure, which will not capture spatial variation in air pollutant con

centrations. This spatial variation may result in exposure misclassification because partici

pants lived 22 km on a median straight line distance from our ambient monitoring site. This exposure misclassification would prob

ably be non differential and bias the results toward the null (Sarnat et al. 2005). Unlike previous studies that read the ECG recordings manually, our study used a predefined computer algorithm that auto

matically detected beat labels and QTc inter

vals to analyze ECG recordings. This made the outcome assessment of QTc less suscep

tible to differential measurement errors and intertechnician variability. In addition, the use of a random subject intercept means that contrasts were predominantly within and not between subjects. Hence, confounding fac

tor bias should be less than that incurred in a purely cross-sectional design, although bias due to residual or unmeasured confounding cannot be ruled out. Another limitation of the study is the assumption that the null deletions and poly

morphisms have equal “weight” contribution to the genetic susceptibility of a participant to oxidative stress. This assumption does not take into account the possible interaction between genes that may or may not affect the genetic susceptibility of a participant to oxida

tive stress. Because the study population consists of older men who are predominantly white, the results may not be generalizable to women, younger individuals
, or to other racial and ethnic groups. The effect of traffic pollutants on QTc on these other populations should be addressed in future studies. 

### Conclusions In summary, this study documents the associ

ation between elevated short-term exposure to traffic-related pollution and prolonged QTc, a marker of ventricular arrhythmias com

monly associated with heart attack, among older men. The study also showed that traf

fic-related pollutants is related to prolonged QTc, given the generally significant associa

tion findings for BC, NO 2 , and CO (Alm

et al. 1999; Chan et al. 1991). Furthermore, older men with few genetic variants related to oxidative stress appear to be better pro

tected from QTc prolongation with traffic exposure than older men with high genetic variants related to oxidative stress. Moreover, the study provides further evidence that traf

fic pollutants via the oxidative stress pathway may play a crucial role in cardiopulmonary toxicity. **Table 4.** Adjusted effect estimates for change in mean QTc with an IQR change in cumulative traffic pol

lutant exposure (during the 10 hr before ECG measurement) by genetic susceptibility to oxidative stress ( high vs. low GSS)

. QCDL model

Pollutant Modifier Change in Mean QTc [msec (95% CI)] p -Value p -Value interaction BC High GSS 3.85 (0.78 to 6.93) 0.01 0.57 Low GSS –0.56 (–3.90 to 2.78) 0.74 NO 2 High GSS 3.50 (–1.86 to 8.87) 0.20 < 0.01 Low GSS 0.29 (–4.56 to 5.14) 0.90 CO High GSS 6.52 (0.49 to 12.55) 0.03 0.08 Low GSS 3.94 (–1.28 to 9.16) 0.14 GSS measures the genetic susceptibility of a participant to oxidative stress by adding all the unfavorable genotypes of the participant

.Baja et al.



### 846 volume 118 | number 6 | June 2010 • Environmental Health Perspectives **R efe**  **r ences** 

Al-Khatib SM, LaPointe NM, Kramer JM, Califf RM. 2003. What clinicians should know about the QT interval. JAMA 289(16):2120–2127. Alm S, Jantunen MJ, Vartiainen M. 1999. Urban commuter exposure to particle matter and carbon monoxide inside an automobile. J Expo Anal Environ Epidemiol 9(3):237–244. Andrassy G, Szabo A, Dunai A, Simon E, Nagy T, Trummer Z, et al. 2003. Acute effects of cigarette smoking on the QT interval in healthy smokers. Am J Cardiol 92(4):489–492. Baccarelli A, Martinelli I, Zanobetti A, Grillo P, Hou LF, Bertazzi PA, et al. 2008. Exposure to particulate air pollution and risk of deep vein thrombosis. Arch Intern Med 168(9):920–927. Bai Y, Suzuki AK, Sagai M. 2001. The cytotoxic effects of diesel exhaust particles on human pulmonary artery endothelial cells in vitro : role of active oxygen species. Free Radic Biol Med 30(5):555–562. Bednar MM, Harrigan EP, Anziano RJ, Camm AJ, Ruskin JN. 2001. The QT interval. Prog Cardiovasc Dis 43(5 s uppl 1):1–45. Bell B, Rose C, Damon A. 1972. The Normative Aging Study: an interdisciplinary and longitudinal study of health and aging. Aging Hum Dev 3(1):5–17. Brook RD, Franklin B, Cascio W, Hong Y, Howard G, Lipsett M, et al. 2004. Air pollution and cardiovascular disease: a statement for healthcare professionals from the Expert Panel on Population and Prevention Science of the American Heart Association. Circulation 109(21):2655–2671. Chahine T, Baccarelli A, Litonjua A, Wright RO, Suh H, Gold DR, et al. 2007. Particulate air pollution, oxidative stress genes, and heart rate variability in an elderly cohort. Environ Health Perspect 115:1617–1622. Chan CC, Spengler JD, Ozkaynak H, Lefkopoulou M. 1991. Commuter exposures to VOCs in Boston, Massachusetts. J Air Waste Manag Assoc 41(12):1594–1600. Chowdhary S, Ng GA, Nuttall SL, Coote JH, Ross HF, Townend JN. 2002. Nitric oxide and cardiac parasympathetic control in human heart failure. Clin Sci (Lond) 102(4):397–402. Chuang KJ, Coull BA, Zanobetti A, Suh H, Schwartz J, Stone PH, et al. 2008. Particulate air pollution as a risk factor for ST-segment depression in patients with coronary artery disease. Circulation 118(13):1314–1320. Donaldson K, Stone V. 2003. Current hypotheses on the mecha

nisms of toxicity of ultrafine particles. Ann Ist Super Sanita 39(3):405–410. el-Gamal A, Gallagher D, Nawras A, Gandhi P, Gomez J, Allison DB, et al. 1995. Effects of obesity on QT, RR, and QTc inter

vals. Am J Cardiol 75(14):956–959. Fitzmaurice GM, Laird NM, Ware JH. 2004. Applied Longitudinal Analysis. Hoboken, NJ:Wiley-Interscience. Ghio AJ, Devlin RB. 2001. Inflammatory lung injury after bron

chial instillation of air pollution particles. Am J Respir Crit Care Med 164(4):704–708. Ghio AJ, Kim C, Devlin RB. 2000. Concentrated ambient air particles induce mild pulmonary inflammation in healthy human volunteers. Am J Respir Crit Care Med 162(3 pt 1):981–988. Gold DR, Litonjua A, Schwartz J, Lovett E, Larson A, Nearing B, et al. 2000. Ambient pollution and heart rate variability. Circulation 101(11):1267–1273. Gold DR, Litonjua AA, Zanobetti A, Coull BA, Schwartz J, MacCallum G, et al. 2005. Air pollution and ST-segment depression in elderly subjects. Environ Health Perspect 113:883–887. Goldenberg I, Zareba W, Moss AJ. 2008. Long QT syndrome

. Curr Probl Cardiol 33(11):629–694.Gurgueira SA, Lawrence J, Coull B, Murthy GG, GonzalezFlecha B. 2002. Rapid increases in the steady-state con

centration of reactive oxygen species in the lungs and heart after particulate air pollution inhalation. Environ Health Perspect 110:749–755. Henneberger A, Zareba W, Ibald-Mulli A, Ruckerl R, Cyrys J, Couderc JP, et al. 2005. Repolarization changes induced by air pollution in ischemic heart disease patients. Environ Health Perspect 113:440–446. Ileri M, Yetkin E, Tandogan I, Hisar I, Atak R, Senen K, et al. 2001. Effect of habitual smoking on QT interval duration and dispersion. Am J Cardiol 88(3):322–325. Laden F, Neas LM, Dockery DW, Schwartz J. 2000. Association of fine particulate matter from different sources with daily mortality in six U.S. cities. Environ Health Perspect 108:941–947. Laden F, Schwartz J, Speizer FE, Dockery DW. 2006. Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities study. Am J Respir Crit Care Med 173(6):667–672. Lee IM, Tsai SS, Ho CK, Chiu HF, Yang CY. 2007. Air pollu

tion and hospital admissions for congestive heart fail

ure in a tropical city: Kaohsiung, Taiwan. Inhal Toxicol 19(10):899–904.Li XY, Brown D, Smith S, MacNee W, Donaldson K. 1999. Shortterm inflammatory responses following intratracheal instillation of fine and ultrafine carbon black in rats. Inhal Toxicol 11(8):709–731. Ljungman PL, Berglind N, Holmgren C, Gadler F, Edvardsson N, Pershagen G, et al. 2008. Rapid effects of air pollution on ventricular arrhythmias. Eur Heart J 29(23):2894–2901. Maynard D, Coull BA, Gryparis A, Schwartz J. 2007. Mortality risk associated with short-term exposure to traffic par

ticles and sulfates. Environ Health Perspect 115:751–755. Moss AJ, Kass RS. 2005. Long QT syndrome: from channels to cardiac arrhythmias. J Clin Invest 115(8):2018–2024. Nemmar A, Hoet PH, Vanquickenborne B, Dinsdale D, Thomeer M, Hoylaerts MF, et al. 2002. Passage of inhaled particles into the blood circulation in humans. Circulation 105(4):411–414. Nemmar A, Vanbilloen H, Hoylaerts MF, Hoet PH, Verbruggen A, Nemery B. 2001. Passage of intratracheally instilled ultra

fine particles from the lung into the systemic circulation in hamster. Am J Respir Crit Care Med 164(9):1665–1668. Oberdörster G, Sharp Z, Atudorei V, Elder A, Gelein R, Lunts A, et al. 2002. Extrapulmonary translocation of ultrafine car

bon particles following whole-body inhalation exposure of rats. J Toxicol Environ Health A 65(20):1531–1543. O’Neill MS, Veves A, Zanobetti A, Sarnat JA, Gold DR, Economides PA, et al. 2005. Diabetes enhances vulner

ability 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, Schwartz J. 2005. Effects of air pollution on heart rate variability: the VA nor

mative aging study. Environ Health Perspect 113:304–309. Park SK, O’Neill MS, Vokonas PS, Sparrow D, Spiro A III, Tucker KL, et al. 2008. Traffic-related particles are associ

ated with elevated homocysteine: the VA normative aging study. Am J Respir Crit Care Med 178(3):283–289. Park SK, O’Neill MS, Wright RO, Hu H, Vokonas PS, Sparrow D, et al. 2006. HFE genotype, particulate air pollution, and heart rate variability: a gene-environment interaction. Circulation 114(25):2798-2805. Peters A, Dockery DW, Muller JE, Mittleman MA. 2001a. Increased particulate air pollution and the triggering of myocardial infarction. Circulation 103(23):2810–2815. Peters A, Frohlich M, Doring A, Immervoll T, Wichmann HE, Hutchinson WL, et al. 2001 b . Particulate air pollution is associated with an acute phase response in men: results from the MONICA-Augsburg Study. Eur Heart J 22(14):1198–1204. Peters A, von Klot S, Heier M, Trentinaglia I, Hormann A, Wichmann HE, et al. 2004. Exposure to traffic and the onset of myocardial infarction. N Engl J Med 351(17):1721–1730. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX, et al. 2006. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 American Heart Association Scientific Statement on Obesity and Heart Disease from the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation 113(6):898–918. Pope CA III, Burnett RT, Thurston GD, Thun MJ, Calle EE, Krewski D, et al. 2004. Cardiovascular mortality and longterm exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of dis

ease. Circulation 109(1):71–77. Pope CA III, Eatough DJ, Gold DR, Pang Y, Nielsen KR, Nath P, et al. 2001. Acute exposure to environmental tobacco smoke and heart rate variability. Environ Health Perspect 109:711–716. R Foundation for Statistical Computing 2008. R Project for Statistical Computing. Available: http://www.r-project.org/ [accessed 5 May 2010].Rhoden CR, Wellenius GA, Ghelfi E, Lawrence J, GonzalezFlecha B. 2005. PM-induced cardiac oxidative stress and dysfunction are mediated by autonomic stimulation. Biochim Biophys Acta 1725(3):305–313. Ruckerl R, Ibald-Mulli A, Koenig W, Schneider A, Woelke G, Cyrys J, et al. 2006. Air pollution and markers of inflam

mation and coagulation in patients with coronary heart disease. Am J Respir Crit Care Med 173(4):432–441. Santos UP, Terra-Filho M, Lin CA, Pereira LA, Vieira TC, Saldiva PH, et al. 2008. Cardiac arrhythmia emergency room vis

its and environmental air pollution in São Paulo, Brazil. J Epidemiol Community Health 62(3):267–272. Sarnat JA, Brown KW, Schwartz J, Coull BA, Koutrakis P. 2005. Ambient gas concentrations and personal particulate mat

ter exposures: implications for studying the health effects of particles. Epidemiology 16(3):385–395. Schwartz J. 1994. Air pollution and daily mortality: a review and meta analysis. Environ Res 64(1):36–52. Schwartz J. 1999. Air pollution and hospital admissions for heart disease in eight U.S. counties. Epidemiology 10(1):17–22. Schwartz J. 2000. The distributed lag between air pollution and daily deaths. Epidemiology 11(3):320–326. Schwartz J, Litonjua A, Suh H, Verrier M, Zanobetti A, Syring M, et al. 2005a. Traffic related pollution and heart rate vari

ability in a panel of elderly subjects. Thorax 60(6):455–461. Schwartz J, Park SK, O’Neill MS, Vokonas PS, Sparrow D, Weiss S, et al. 2005b. Glutathione- S -transferase M1, obe

sity, statins, and autonomic effects of particles: gene-bydrug-by-environment interaction. Am J Respir Crit Care Med 172(12):1529–1533. Simkhovich BZ, Kleinman MT, Kloner RA. 2008. Air pollution and cardiovascular injury epidemiology, toxicology, and mechanisms. J Am Coll Cardiol 52(9):719–726. Sun Q, Wang A, Jin X, Natanzon A, Duquaine D, Brook RD, et al. 2005. Long-term air pollution exposure and acceleration of atherosclerosis and vascular inflammation in an animal model. JAMA 294(23):3003–3010. Suwa T, Hogg JC, Quinlan KB, Ohgami A, Vincent R, van Eeden SF. 2002. Particulate air pollution induces progression of atherosclerosis. J Am Coll Cardiol 39(6):935–942. Tapanainen JM, Thomsen PE, Kober L, Torp-Pedersen C, Makikallio TH, Still AM, et al. 2002. Fractal analysis of heart rate variability and mortality after an acute myocar

dial infarction. Am J Cardiol 90(4):347–352. Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, et al. 1996. Impact of reduced heart rate vari

ability on risk for cardiac events. The Framingham Heart Study. Circulation 94(11):2850–2855. Utell MJ, Frampton MW, Zareba W, Devlin RB, Cascio WE. 2002. Cardiovascular effects associated with air pollu

tion: potential mechanisms and methods of testing. Inhal Toxicol 14(12):1231–1247. Veglio M, Bruno G, Borra M, Macchia G, Bargero G, D’Errico N, et al. 2002. Prevalence of increased QT interval duration and dispersion in type 2 diabetic patients and its relation

ship with coronary heart disease: a population-based cohort. J Intern Med 251(4):317–324. Viskin S. 1999. Long QT syndromes and torsade de pointes. Lancet 354(9190):1625–1633. von Klot S, Gryparis A, Tonne C, Yanosky J, Coull BA, Goldberg RJ, et al. 2009. Elemental carbon exposure at residence and survival after acute myocardial infarction. Epidemiology 20(4):547–554. Yue W, Schneider A, Stolzel M, Ruckerl R, Cyrys J, Pan X, et al. 2007. Ambient source-specific particles are associated with prolonged repolarization and increased levels of inflammation in male coronary artery disease patients. Mutat Res 621(1–2):50–60. Zanobetti A, Schwartz J. 2006. Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health 60(10):890–895. Zanobetti A, Schwartz J. 2007. Particulate air pollution, pro

gression, and survival after myocardial infarction. Environ Health Perspect 115:769–775. Zareba W, Nomura A, Couderc JP. 2001. Cardiovascular effects of air pollution: what to measure in ECG? Environ Health Perspect 109 (s uppl 4 ):533–538.