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-mediated 

impacts 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 mortality have been identified, including oxidative stress (Gurgueira et al. 2002), autonomic 

dysfunction (Gold et al. 2000), and systemic 

inflammation (Peters et al. 2001b; Ruckerl 

et al. 2006), leading to endothelial dysfunction (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 biological 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. 2001a, 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 pollution on time scales of less than a day. Because

standards for particles and nitrogen dioxide 

(NO2) currently involve longer-term averages, 

these studies suggest that the relation of hourly 

peaks in air pollution with indicators of cardiovascular 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 disease, which were conducted in East Germany, 

showed evidence of an immediate effect of air 

pollution on ventricular repolarization duration, 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 pathway 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 repolarization parameters in other populations at risk 

should be addressed and confirmed.

We hypothesized that short-term exposures to traffic-related air pollutants may be 

associated with increases in ventricular repolarization, as measured by changes in the 

heart-rate–corrected QT interval (QTc) on 

the electrocardiogram (ECG), and that participant characteristics and genes related to 

oxidative stress may modify this association. 

Thus, in this study, we investigated the association between ambient air pollution [black 

carbon (BC), carbon monoxide (CO), NO2, 

ozone (O3), PM ≤ 2.5 μm in aerodynamic 

diameter (PM2.5), and sulfur dioxide (SO2)] 

and a measure of ventricular repolarization, 

the QTc, among men residing in communities 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 established 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. Suh1

1Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 2Department of Community 

Health, Brown University, Providence, Rhode Island, USA; 3Department of Biostatistics, Harvard School of Public Health, Boston, 

Massachusetts, USA; 4Veterans Affairs Normative Aging Study, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, 

USA; 5Department of Internal Medicine, Boston University, Boston, Massachusetts, USA

Background: Acute exposure to ambient air pollution has been associated with acute changes in 

cardiac outcomes, often within hours of exposure.

Objectives: 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.

Methods: 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 quadratic constrained distributed lag model to estimate the cumulative effect on QTc of ambient air pollutants including fine particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5), ozone (O3), black 

carbon (BC), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2) 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.

Results: 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 NO2 and CO, but 

the change was statistically insignificant. In contrast, we found no association between QTc and 

PM2.5, SO2, and O3. The association between QTc and BC was stronger among participants who 

were obese, who had diabetes, who were nonsmokers, or who had higher GSSs.

Conclusions: Traffic-related pollutants may increase QTc among persons with diabetes, persons 

who are obese, and nonsmoking elderly individuals; the number of genetic variants related to oxidative stress increases this effect.

Key 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 overnight 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, smoking 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 recording time of the ECG measurements was 

< 3.5 min, the T-wave amplitude was insufficient, or the heart beats were nonnormal 

or nonsupraventricular. 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 channel. 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 formula (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 PM2.5 concentrations were continuously 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, O3, 

NO2, and SO2 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 polymorphisms (SNPs) [glutathione S-transferase pi-1 

(GSTP1) Ile105Val and Ala114Val, hemochromatosis (HFE) C282Y and H63D, 

NAD(P)H dehydrogenase, quinone-1 (NQO1)

C609T, and catalase (CAT) RS2300181, 

RS2284367, and RS769217], and a microsatellite GTn-repeat polymorphism [heme oxygenase (decycling)-1 (HMOX1)]. The dominant 

genetic model was used to classify the SNPs 

of the participants. The SNPs were categorized 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 investigations of multiple gene deletions and polymorphisms, we created a variable for the genetic 

susceptibility score (GSS) that was related to 

the genetic susceptibility of a participant to oxidative stress. The GSS assumed that all the deletions 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 genotype and 0 if the participant did not have this 

genotype. The scores were added for every participant. 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 descriptive statistics for QTc, air pollutants, and 

covariates, as well as correlation coefficients to 

evaluate the relationship among QTc, covariates, and air pollutants.

Associations between each air pollutant and the change in mean QTc were estimated 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 subject (Fitzmaurice et al. 2004). Mixed models 

with a random subject-specific intercept were 

fit to capture residual correlation among measurements 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 arterial 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 calendar date with 4 degrees of freedom per year. 

We also controlled 1-hour mean temperature 

using a linear term and modeled day-of-theweek effects with indicator variables.

Separate single-pollutant models were 

evaluated. To examine the appropriate exposure window for each pollutant and to minimize multiple comparisons, our primary 

analysis fit a quadratic constrained distributed 

lag (QCDL) model to estimate the cumulative effect of each pollutant during a 10-hr

time window before the visit (Schwartz 2000),

which, using the Akaike information criterion, gave the best model fit for the cumulative 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 modifier, subjects were classified into two groups 

according to BMI (obese, ≥ 30 kg/m2; nonobese, < 30 kg/m2). 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 significance 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 percentage

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-values 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 (version 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. The 
Baja 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/m2 ± 4.1 kg/m2. 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 PM2.5

having the highest correlation (Spearman correlation coefficient, ρ = 0.69). Among the gaseous 

pollutants, NO2 and CO were most strongly 

correlated (ρ = 0.64), whereas the correlation 

between particulate and gaseous pollutants was 

highest for BC and NO2 (ρ = 0.60). In contrast, O3 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 NO2 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; NO2: 2.56 

msec change in mean QTc; 95% CI, –0.88

to 6.00; p-value = 0.14]. In contrast, PM2.5, 

O3, and SO2 were not clearly associated with 

QTc [see Supplemental Material, Table 2

(doi:10.1289/ehp.0901396)].

In Figure 2, we compare the results for 

cumulative exposure for a 10-hr time window (based on QCDL models) and hourly 

exposure lag models. The two approaches 

showed an increase in QTc during a similar time course for BC, NO2, and CO. For 

BC, the strongest associations were observed 

with pollutant measures 4 hr before the ECG 

measurement. IQR changes in BC, NO2, 

and CO in the 4th 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, NO2, 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 association between QTc and ambient PM2.5, SO2, 

and O3 [see 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, NO2, and CO on QTc. 6% of 

the study population were obese, diabetic, 

and nonsmoker, 8% were obese and diabetic, 

6% were nonsmokers and obese, and 5% were 

nonsmokers and diabetic.

Figure 3 shows the results of effect modification by diabetes, obesity, and smoking 

for both the QCDL model (for cumulative 

exposure during the 10 hr before QTc measurement) and the hourly exposure model 

(with a 4-hr lag) for BC, NO2, 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 participants with diabetes, but we estimated a 

smaller association for nondiabetic participants (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 association with cumulative BC was also stronger 

for obese versus nonobese participants and 

never-smokers versus ever-smokers, although 

differences between estimates were not statistically significant (LRT, p-values for interaction > 0.53; for details, see Table 3).

Effect modification by GSS. We also 

assessed whether having a high GSS (unfavorable 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/m2 (mean ± SD)] 27.9 ± 4.1

≥ 30 kg/m2 (%) 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 WTc 82.6

GSTP1 Ala114Val WTc 80.0

HFE H63D WTc 72.8

CAT rs769217 WTc 51.0

CAT rs2284367 WTc 50.7

GSTM1 deletiond 49.0

HMOX1 long/long alleles 

(≥ 25 GT repeats)

46.2

GSTP1 Ile105Val any variante 45.7

CAT rs2300181 any variante 41.6

NQO1 C609T any variante 29.5

GSTT1 deletiond 16.7

QTc interval [msec (mean ± SD)]f 393.3 ± 29.7

aReport of doctor’s diagnosis of disease or FBG > 126 

mg/dL. bTotal does not equal 100% because of changes 

in smoking habit. cHomozygous WT. dHomozygous null. eHeterozygous + 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/m3) 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

NO2 (ppm) 0 0.021 ± 0.008 0.020 0.010

10 0.019 ± 0.009 0.018 0.013

PM2.5 (μg/m3) 0 11.42 ± 8.55 9.03 8.56

10 10.72 ± 7.88 8.75 7.92

O3 (ppm) 0 0.023 ± 0.016 0.021 0.018

10 0.021 ± 0.015 0.020 0.020

SO2 (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

aCurrent 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 measurement: 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/blockers, beta blockers, or calcium channel blockers;

day of the week (Monday through Sunday); temperature; 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 NO2 PM2.5

Pollutant

Change in mean QTc (msec)

SO2 O3

*

*
Traffic-related air pollution and QT interval

Environmental Health Perspectives • volume 118 | number 6 | June 2010 843

“high polymorphism” group had the unfavorable 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, NO2, 

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 NO2

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 contrast, we found no significant associations with 

PM2.5, O3, or SO2, generally indicators of nontraffic pollution. Our results further suggest 

that diabetic, nonsmoker, and obese participants 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), NO2 (B), and CO (C). (D–F) Singlepollutant model of hourly exposure with random intercept using hourly lags: BC (D), NO2 (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 10 0 2 4 6 8 10

024 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10

Lag (hr)

Change in mean QTc (msec)Change in mean QTc (msec)Change in mean QTc (msec)

Change in mean QTc (msec)Change in mean QTc (msec)Change in mean QTc (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

Obese

Nondiabetic

Nonobese

Never smoker

Ever smoker

Diabetic

Obese

Nondiabetic

Nonobese

Never smoker

Ever smoker

Diabetic

Obese

Nondiabetic

Nonobese

Never smoker

Ever smoker

Participant characteristic

Diabetic

Obese

Nondiabetic

Nonobese

Never smoker

Ever smoker

Diabetic

Obese

Nondiabetic

Nonobese

Never smoker

Ever smoker

Diabetic

Obese

Nondiabetic

Nonobese

Never smoker

Ever smoker

Participant characteristic

Change in mean QTc (msec)Change in mean QTc (msec)

* **

** *

*

*

*

*

* *

4-hr-lag model QCDL model BC

NO2

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 studies 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 depression (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 disease. They found no significant association 

at alpha = 0.05 between QTc and elemental 

carbon, but found significant associations with 

NO2 and CO during 6–11 hr and with SO2

during the whole 0–23 hr before ECG recording. Insignificant findings for EC in the East 

Germany study may be due to its smaller sample size or its younger study population compared 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 signaling 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 particles may also cross the pulmonary epithelium 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 generated proinflammatory cytokines and ROS 

may subsequently affect a variety of health 

measures, including autonomic cardiac control 

(Simkhovich et al. 2008). Changes in autonomic control may in turn cause a prolongation 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 characteristics. Our observation of effect modification by diabetic status in the associations 

between the cumulative effect and 4 hr before

ECG measurement lag effect of BC and NO2

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 diabetes. In addition, the greater baseline oxidative 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 consistent pattern in the acute estimated effects 

of BC and NO2 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 modification by obesity on associations between 

cumulative (for the 10 hr before ECG measurement) and hourly (lagged 4 hr before) 

BC and NO2 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 exposures (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

NO2 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

NO2 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

NO2 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 QTc (msec)Change in mean QTc (msec)

High Low High Low High Low High

4-hr-lag model QCDL model

Genetic susceptibility score Genetic susceptibility score

Low High Low High Low

*

*

**

**

BC

NO2

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 

nonobese individuals and a positive association exists between BMI and QTc. This may 

explain why we found a stronger association 

between BC and NO2, and QTc among obese 

participants compared with nonobese participants. 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 association between QTc and BC and NO2 exposures (both as the cumulative and 4-hr lag 

effect), with stronger associations estimated 

for nonsmokers. We hypothesize that nonsmokers are more susceptible to effects of 

traffic-related pollution, because particles and 

gases may penetrate the clear lungs of nonsmokers more easily than the lungs of smokers 

who have extensive lung damage. However, 

results from previous studies provide conflicting 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 

nonsmokers. 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 NO2, compared with those 

who are not diabetic, obese, and smokers.

Effect modification by genetic susceptibility to oxidative stress. Our QCDL model 

estimates showed significant QTc prolongation 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 leading to an intracellular excess and accumulation 

of positively charged ions inside the cardiac 

myocytes, which extends ventricular repolarization and results in QT interval prolongation (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 variants related to oxidative stress modified the 

associations between traffic-related pollutants and QTc. This approach is a statistically 

efficient method to evaluate gene–environment interactions especially in inferring biological pathways that would link exposure 

to outcome of interest. The use of the score 

method approach, together with the distributed 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 limitation of our study is the use of a single ambient monitoring site as a surrogate for recent 

ambient pollution exposure, which will not 

capture spatial variation in air pollutant concentrations. This spatial variation may result 

in exposure misclassification because participants lived 22 km on a median straight line 

distance from our ambient monitoring site. 

This exposure misclassification would probably be nondifferential 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 automatically detected beat labels and QTc intervals to analyze ECG recordings. This made 

the outcome assessment of QTc less susceptible 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 factor 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 polymorphisms 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 oxidative 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 association between elevated short-term exposure to 

traffic-related pollution and prolonged QTc, 

a marker of ventricular arrhythmias commonly associated with heart attack, among 

older men. The study also showed that traffic-related pollutants is related to prolonged 

QTc, given the generally significant association findings for BC, NO2, 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 protected from QTc prolongation with traffic 

exposure than older men with high genetic 

variants related to oxidative stress. Moreover, 

the study provides further evidence that traffic 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 pollutant 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

NO2 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

References

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 suppl 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 mechanisms 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 intervals. 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 bronchial 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 concentration 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 pollution and hospital admissions for congestive heart failure 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 particles 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 ultrafine 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 carbon 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 vulnerability to particulate air pollution-associated impairment 

in vascular reactivity and endothelial function. Circulation 

111(22):2913–2920.

Park SK, O’Neill MS, Vokonas PS, Sparrow D, Schwartz J. 2005. 

Effects of air pollution on heart rate variability: the VA normative 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 associated 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. 2001b. 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 disease. 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 inflammation 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 visits 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 matter 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 variability 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, obesity, 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 myocardial 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 variability 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 pollution: 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 relationship 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, progression, 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(suppl 4):533–538.