Environmental Health Perspectives • volume 117 | number 5 | May 2009 751

Research

Studies of human populations show that 

ambient particulate matter (PM) air pollution is associated with coronary heart disease (CHD) morbidity and mortality. The 

consistent findings suggest direct associations 

between both short- and long-term exposure 

to PM and CHD incident [Brook et al. 2004; 

Dockery et al. 1993; Miller et al. 2007; Pope 

et al. 2006; U.S. Environmental Protection 

Agency (EPA) 2006]. However, biological mechanisms underlying the association 

between PM and CHD remain incompletely 

characterized in human populations despite 

calls for epidemiologic research designed 

to help elucidate them (Brook et al. 2004; 

Health Effects Institute 2002; National 

Research Council 2004).

Resting, supine, standard 12-lead electrocardiography (ECG) has been recognized as a 

potentially valuable epidemiologic tool in this 

regard, yet to date much of its value remains 

unrealized. ECG findings characteristic of 

myocardial ischemia, for example, have not 

been adequately evaluated in this context. 

These measures include ST-segment depression and T-wave inversion on the ECG.

Because of their clinical value, these ECG 

findings have also attracted environmental, 

toxicologic, and epidemiologic interest. Indeed, 

they are thought to be relevant in studies focusing on arrhythmogenic effects of air pollution 

(Zareba et al. 2001) and risk factors for sudden 

death due to cardiac arrhythmia (Huikuri et al. 

2001). The literature in this area has nevertheless focused on experimentation in animal 

models of coronary vasoconstriction (Campen 

et al. 2005; Godleski et al. 2000; Nikolov et al. 

2007; Wellenius et al. 2003) or examination 

of typically small, single-city panels of CHDburdened, Holter-monitored male participants 

during exercise (Gold et al. 2005; Henneberger 

et al. 2005; Lanki et al. 2006; Mills et al. 2007; 

Pekkanen et al. 2002; Yue et al. 2007). For 

this reason, epidemiologic evidence linking PM 

exposure to minor but prognostically important 

abnormalities of the ST segment and T wave in 

human populations at rest is quite limited.

We therefore designed the present study 

to investigate the association between acute 

exposures to ambient PM < 2.5 μm in diameter (PM2.5) and ECG measures of myocardial 

ischemia in a relatively large, geographically 

diverse, healthy female population enrolled 

in the Women’s Health Initiative (WHI) 

clinical trials.

Methods

Study design. We examined the association 

between PM2.5 and ischemia in the Environmental Epidemiology of Arrhythmogenesis in 

WHI (EEAWHI) study, an ancillary study of 

proarrhythmic mechanisms linking air pollution and CHD among 68,133 WHI clinical 

trial participants examined at 49 sites. The 

WHI clinical trial is a multicenter study of 

risk factors for prevention of common causes 

of mortality, morbidity, and impaired quality of life in U.S. postmenopausal women. 

Details of the study design, including recruitment procedures and selection and exclusion 

criteria, have been published elsewhere (WHI 

Study Group 1998). The study was approved 

by each study site’s institutional review board. 

All participants provided written informed 

consent.

Study population. Of the 68,133 WHI 

clinical trial participants at baseline, we 

excluded 2,082 (3.1%) with foreign, U.S. military, U.S. protectorate, Hawaiian, Alaskan, or 

missing addresses; 417 (0.6%) with missing or 

poor-quality ECGs; 2,379 (3.5%) with ECG 

QRS duration ≥ 120 msec; and 7,726 (11%) 

Address correspondence to Z. Zhang, Epidemiologic 

Cardiology Research Center (EPICARE), Department 

of Epidemiology and Prevention, Division of Public 

Health Sciences, Wake Forest University School of 

Medicine, 2000 West First St., Suite 505, WinstonSalem, NC 27104 USA. Telephone: (336) 716-0835. 

Fax: (336) 716-0834. E-mail: zmzhang@wfubmc.edu

Supplemental Material is available online at http://

www.ehponline.org/members/2009/0800046/suppl.pdf

We acknowledge the contributions of WHI investigators listed in the Short List of WHI Investigators 

(http://www.whiscience.org/publications/WHI_

investigators_shortlist.pdf).

T h e E n v i r o n m e n t a l E p i d e m i o l o g y o f 

Arrhythmogenesis in the Women’s Health Initiative 

(WHI) study is supported by the National Institute 

of Environmental Health Sciences (5-R01-

ES012238); an ancillary study of the WHI program is funded by the National Heart, Lung, and 

Blood Institute, Department of Health and Human 

Services. 

The authors declare they have no competing 

financial interests.

Received 25 July 2008; accepted 23 January 2009.

Ambient Fine Particulate Matter Exposure and Myocardial Ischemia in the 

Environmental Epidemiology of Arrhythmogenesis in the Women’s Health 

Initiative (EEAWHI) Study

Zhu-ming Zhang,1 Eric A. Whitsel,2,3 P. Miguel Quibrera,2 Richard L. Smith,4 Duanping Liao,5 Garnet L. Anderson,6

and Ronald J. Prineas1

1Epidemiologic Cardiology Research Center, Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake 

Forest University School of Medicine, Winston-Salem, North Carolina, USA; 2Departments of Epidemiology, 3Medicine, and 4Statistics 

and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 5Department of Health Evaluation 

Sciences, Pennsylvania State University, Hershey, Pennsylvania, USA; 6Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

Background: Ambient particulate matter (PM) air pollution is associated with coronary heart 

disease, but the pathways underlying the association remain to be elucidated.

Methods: We studied the association between PM and ischemia among 57,908 Women’s Health 

Initiative clinical trial participants from 1999–2003. We used the Minnesota Code criteria to 

identify ST-segment and T-wave abnormalities, and estimated T amplitude (microvolt) from 

resting, standard 12-lead electrocardiogram (ECG). We used U.S. Environmental Protection 

Agency’s monitor data to estimate concentrations of PM < 2.5 μm (PM2.5) at geocoded participant 

addresses over 6 days before the ECGs (lag0 through lag5). We excluded 2,379 women with ECG 

QRS duration ≥ 120 msec.

Results: Overall, 6% of the remaining 55,529 women (52–90 years of age; 83% non-Hispanic 

white) had ST abnormalities and 16% had T abnormalities. Lead-specific T amplitude was normally distributed (range of means from –14 to 349 μV). PM2.5 (mean ± SD) averaged over lag0–2

was 14 ± 7 μg/m3. In logistic and linear regression models adjusted for demographic, clinical, temporal, and climatic factors, a 10-μg/m3 increase in lag0–2 PM2.5 was associated with a 4% [95% confidence interval (CI), –3%, to 10%] increase in the odds of ST abnormality and a 5% (95% CI, 0% 

to 9%) increase in the odds of T abnormality. We observed corresponding decreases in T amplitude 

in all exam sites and leads except lead V1, reaching a minimum of –2 μV (95% CI, –5 to 0 μV) in 

lead V3.

Conclusions: Short-term PM2.5 exposure is associated with ECG evidence of myocardial ischemia 

among postmenopausal women. The principal manifestations include subclinical but potentially 

arrhythmogenic ST–T abnormalities and decreases in T amplitude.

Key words: air pollution, cardiovascular disease, electrocardiography, myocardial ischemia, particulate matter. Environ Health Perspect 117:751–756 (2009). doi:10.1289/ehp.0800046 available via 

http://dx.doi.org/ [Online 23 January 2009]
Zhang et al.

752 volume 117 | number 5 | May 2009 • Environmental Health Perspectives

without an ECG recording between 1999 and 

2003, the initial 5-year period in which PM2.5

data were routinely collected by the U.S. EPA 

Air Quality System (AQS). The present study 

focuses on the initial high-quality, resting, standard 12-lead ECGs recorded during this period 

among the remaining 55,529 participants, 

all of whom had addresses in the contiguous 

United States.

ECG methodology. Identical ECGs (MAC 

PC, Marquette Electronics, Inc., Milwaukee, 

WI) were used in all clinic sites, and resting, 

standard 12-lead ECGs were recorded for all 

participants using strictly standardized procedures. The chest electrodes were located in 

precise positions. All ECGs were processed in 

a central ECG laboratory (Epidemiological 

Cardiology Research Center, Wake Forest 

University, Winston-Salem, NC), where they 

were visually inspected for technical errors and 

inadequate quality. The ECGs were initially 

processed with the Dalhousie ECG program,

and for the present study we reprocessed them 

with the 2001 version of the GE Marquette 

12-SL program (GE Healthcare 2008; Prineas 

et al. 1982; Rautaharju et al. 1998). The 

Marquette measurement matrix contained several ECG measures of myocardial ischemia that 

we used as outcome variables in this study.

ST-segment and T-wave amplitudes 

(microvolt). We defined lead-specific depth 

(or height) of the ST segment (ST) 60 msec

after the J point and T-wave amplitude (T) 

relative to the isoelectric line.

ST, T, and ST–T abnormalities. We 

applied Minnesota Code (MC) criteria to the 

former interval-scale measures to identify ST 

abnormality (MC 4.1–4.4), T abnormality 

(MC 5.1–5.4), or any ST–T abnormality (MC 

4.1–4.4 or MC 5.1–5.4) (Prineas et al. 1982).

QRS/T angles (degrees). The frontal 

plane QRS/T angle was defined as the absolute value of the difference between the frontal 

plane QRS and T axes (range, –89° to 270°). 

When > 180°, it was adjusted to the minimal 

angle as follows: 360° – frontal plane QRS/T 

angle (Zhang et al. 2007). We defined the 

spatial QRS/T angle as the angle between the 

mean QRS and T vectors, which we calculated from quasi-orthogonal X, Y, and Z leads 

reconstructed from the standard ECG leads 

via matrix transformation (Rautaharju et al. 

2006b).

Air pollutant concentrations, weather variables, and other participant characteristics.

We obtained all ambient PM2.5 concentration data recorded at monitors operating in 

the contiguous United States during the study 

from the U.S. EPA AQS. The data included 

the longitude and latitude of each monitor. 

We used a semiautomated program built on 

ArcView GIS (version 8.3) software and its 

Geostatistical Analyst extension (ESRI, Inc. 

Redlands, CA) to produce kriging-estimated 

daily mean concentrations (and their SEs) 

at each geocoded participant and exam site 

address from the baseline examination through 

2004 (Liao et al. 2006, 2007; Whitsel et al. 

2006). The program relied on a spherical 

model to perform national-scale, lognormal 

ordinary kriging and the weighted least-squares 

method to estimate semivariograms, and ran 

and cross-validated each daily semivariogram. 

From the kriging-estimated, daily mean, location-specific PM2.5 concentrations, we then 

computed a matrix of 15 short-term pollutant exposures by averaging within overlapping 1-, 2-, and 3-day lag combinations 

inside a 6-day exposure window beginning 

5 days before and ending on the examination 

date, that is, days 0–5. Associations between 

PM and ischemia were strongest on days 0–2 

(lag0–2), the emphasis of the present report.

We obtained all meteorologic data 

recorded at relevant stations and times from 

the National Climatic Data Center (U.S. 

National Climatic Data Center 2007): ambient temperature (degrees centigrade), dew 

point (degrees centigrade), and barometric 

pressures (kilo pascal), as well as station longitudes, latitudes, and altitudes. Daily mean 

temperature, dew point, and pressure at each 

geocoded participant and exam site address 

from baseline to closeout were estimated by 

averaging daily means across all stations within 

50 km, a distance over which their stationto-station correlations exceed 0.90 (Ito et al. 

2001). Thereafter, we computed weather 

variables at lag0–2/lag3–5 as described above 

for PM2.5.

Self-reported education, medication use, 

health history, and a variety of other attributes 

were determined at each visit by standardized 

participant interview and examination by the 

WHI staff. Interim health events were identified via standardized medical record review 

and physician adjudication. Specifically, diabetes was defined by antidiabetic medication use 

or history; hypertension by antihypertensive 

medication use, systolic blood pressure (SBP) 

≥ 140 mmHg, diastolic blood pressure (DBP) 

≥ 90 mmHg, or history; body mass index 

(BMI) as the ratio of weight (kilograms) to 

height (square meters); hypercholesterolemia by 

antihyperlipidemic medication use or history; 

smoking as current, former, or never; chronic 

lung disease by history of asthma, emphysema,

or lung cancer; CHD by antianginal medication use, history of angina, or myocardial infarction and medical record review/adjudication; 

revascularization by history of coronary artery 

angioplasty, stent, or bypass and medical record 

review/adjudication; and congestive heart failure by cardiac glycoside/diuretic use, history,

and medical record review/adjudication.

Statistical analysis methods. We inspected 

frequency distributions of all variables for outliers but did not identify influential values. 

Table 1. The demographic and clinical characteristics of the study population on the date of ECG 

examination (n = 55,529).

Characteristic Mean ± SD or percent

Age in years on ECG exam date 67 ± 7

Race

White 83

Black 10

Other 7

Education < college 23

BMI (kg/m2 ) 29 ± 6

SBP (mmHg) 126 ± 17

DBP (mmHg) 73 ± 9

Hypertension 52

Hypercholesterolemia 20

Diabetes 8

Current smoking 6

CHDa 11

Chronic lung disease 10

Beta-blocker use 13

Exam season

Spring 27

Summer 26

Fall 25

Winter 22

Exam day

Monday–Thursday 86

Friday 12

Saturday–Sunday 2

Exam time

Morning 64

Afternoon 36

aCHD includes congestive heart failure, coronary revascularization, or myocardial infarction by Minnesota Code 

or Novacode.

Table 2. The ECG parameters of the study 

population on the date of ECG examination 

(n = 55,529). 

ECG parameter Mean ± SD or percent

Minnesota codes

MC 4a 6

MC 5b 16

MC 4 or MC 5c 18

T-wave amplitude (μV)

Lead I 202 ± 105

Lead II 241 ± 105

Lead aVL 86 ± 93

Lead V1 –14 ± 116

Lead V2 313 ± 197

Lead V3 349 ± 203

Lead V4 324 ± 193

Lead V5 284 ± 167

Lead V6 221 ± 126

ST-segment amplitude (μV)

Lead I 13 ± 21

Lead II 19 ± 25

Lead aVL 4 ± 17

Lead V1 27 ± 27

Lead V2 64 ± 46

Lead V3 47 ± 41

Lead V4 29 ± 35

Lead V5 18 ± 29

Lead V6 11 ± 23

QRS/T angles and heart rate

Heart rate (beats/min) 66 ± 10

QRS/T angle–spatial (°) 60 ± 28

QRS/T angle–frontal plane (°) 29 ± 27

aAny ST abnormality (MC 4.1–4.4). bAny T abnormal- ny T abnormality (MC 5.1–5.4).cAny ST–T abnormality (MC 4.1–4.4 or 

MC 5.1–5.4).
PM2.5 and myocardial ischemia

Environmental Health Perspectives • volume 117 | number 5 | May 2009 753

Initially ignoring the influences of exam site, 

we explored the associations between ischemia 

and PM2.5 in unadjusted linear and logistic 

regression models. To control for residual confounding by exam season, day of week, time 

of day, health, and weather, we added temporal, demographic, clinical, and meteorologic 

characteristics to the models. We then conducted hierarchical analyses in two stages. In 

the first stage we used site-specific, adjusted 

regression models to estimate strength of the 

ischemia–PM2.5 associations. The models 

allowed these estimates to vary among sites. 

In the second stage we used random effects 

meta-regression of the site-specific, linear 

regression coefficients from the first stage to 

estimate overall strength of the ischemia–PM 

associations across sites. These weighted, iterative regression models allowed for an additive, 

between-study component of residual variance 

estimated via restricted maximum likelihood 

(Berkey et al. 1995). We repeated the secondstage analyses by placing uninformative priors 

on the second-stage parameters and Monte 

Carlo sampling from the conditional distribution of each unknown using standard Bayesian 

hierarchical regression algorithms (Robert 

and Casella 2004). We evaluated effect measure modification by including cross-product 

terms for the interaction between PM2.5 and 

CHD and its major risk factors at the first 

stage. Analyses based on logistic and linear 

regression models adjusting for exam site, and 

both the random-effects meta-analyses and 

Bayesian hierarchical regression analyses of the 

adjusted, site-specific regression coefficients 

produced similar results. We conducted all 

analyses using SAS version 9.1 (SAS Institute, 

Inc., Cary, NC) and MATLAB, version 7.0, 

software (MathWorks, Inc., Natick, MA). By 

convention, we report resulting estimates as 

odds ratios for ST, T, and ST–T abnormalities, or microvolt (μV) changes in ST-segment 

and T-wave amplitudes associated with 

10 μg/m3 increases in PM2.5 concentrations.

Results

Of the 55,529 female participants (age range,

52–90 years), 83% were non-Hispanic 

white, 52% hypertensive, and 20% hypercholesterolemia (Table 1). Somewhat smaller 

percentages were current smokers (6%) or

had diabetes (8%), CHD (11%), or chronic 

lung disease (10%). Six percent of the study 

population had ST abnormalities, and 16% 

had T abnormalities. Lead-specific ST and 

T amplitudes were normally distributed 

(range of means = 4, 64 μV and –14, 349 μV, 

respectively). Mean ± SD spatial and frontal plane QRS/T angles were 60° ± 28° and 

29° ± 27°, with heart rate 66 ± 10 beats/min

(Table 2). Mean PM2.5 averaged over the first 

3 days and second 3 days preceding the ECG 

(lag0–2 and lag3–5) were equivalent, as were 

mean temperature, dew point, and barometric 

pressure (Table 3).

In logistic and linear regression models 

adjusted for demographic, clinical, temporal, and climatic factors, 10 μg/m3 increases 

in lag0–2 PM2.5 were associated with 4–5% 

increases in odds ratios (ORs) for ST, T,

and ST–T abnormalities, small decreases in 

lead-specific ST and T amplitudes, and slight 

increases in the QRS/T angles and heart 

rate (Table 4). We observed decreases in T 

amplitude in all leads except V1, reaching a 

minimum of –2.3 μV [95% confidence interval (CI), –5.2 to 0.5 μV] in lead V3 even 

after controlling for its reported dependence 

on heart rate (Couderc et al. 2007). These 

observations were consistent across exam sites 

(Figure 1). The observed magnitude and direction of effects associated with lag3–5 PM2.5

were often weaker and less consistent [Table 4 

and Supplemental Material, Tables 1 and 2 

(available online at http://www.ehponline.

org/members/2009/0800046/suppl.pdf)]. 

Findings based on unadjusted models and 

those controlling for randomization status as 

well as additional participant- and contextuallevel measures of education, occupation, 

income, and housing were comparable.

Effects of lag0–2 PM2.5 were slightly 

stronger among users versus nonusers of 

beta-blockers: 10% (1%, 20%) versus 3% 

(–1%, 8%) increases in the odds of T abnormality (p = 0.098) and –8 (–15, –1) versus

–2 (–4, 1) μV decreases in T amplitude in lead 

V3 (p = 0.076), but in general, there was little 

suggestion of effect measure modification by 

CHD or its major risk factors.

Discussion

Judging from the U.S. EPA’s Provisional 

Assessment of Recent Studies on Health Effects 

of Particulate Matter Exposure (National 

Center for Environmental Assessment 

2006), interest in subclinical, atherosclerotic 

manifestations of long-term ambient PM2.5

exposure has dramatically increased. Indeed, 

Table 3. PM concentrations and weather variables for the study population on the date of ECG 

examination.

Characteristic Mean ± SD

PM2.5a

Lag0 14.1 ± 8

Lag1 13.8 ± 8

Lag2 13.8 ± 8

Lag3 13.8 ± 8

Lag4 13.9 ± 8

Lag5 14.1 ± 8

Lag0–2b 13.9 ± 7

Weather

Temperature lag0–2b 13.9 ± 9

Dew point lag0–2b 7.6 ± 9

Barometric pressure lag0–2b 102 ± 1

aConcentrations at geocoded participant addresses on 

day of exam (lag0) and preceding 5 days (lag1–5). bThe 

mean ± SD for PM2.5 and weather variables were the same 

at lag3–5.

Table 4. Multivariable-adjusteda ORs and changes (95% CI) per 10-μg/m3 increase in PM2.5.

ECG parameter Lag0–2 (95% CI) Lag3–5 (95% CI)

Minnesota Codes (OR)

MC 4b 1.04 (0.97 to 1.10) 1.04 (0.98 to 1.11)

MC 5c 1.05 (1.00 to 1.09) 1.04 (1.00 to 1.08)

MC 4 or MC 5d 1.04 (1.00 to 1.09) 1.03 (0.99 to 1.07)

ST-segment amplitude (μV, change)

Lead I –0.07 (–0.36 to 0.21) 0.18 (–0.10 to 0.46)

Lead II –0.12 (–0.47 to 0.23) 0.16 (–0.18 to 0.50)

Lead aVL –0.01 (–0.25 to 0.23) 0.11 (–0.12 to 0.34)

Lead V1 –0.02 (–0.39 to 0.35) –0.22 (–0.58 to 0.14)

Lead V2 0.07 (–0.57 to 0.70) –0.01 (–0.61 to 0.62)

Lead V3 –0.11 (–0.68 to 0.47) –0.02 (–0.58 to 0.54)

Lead V4 –0.03 (–0.51 to 0.45) 0.24 (–0.23 to 0.71)

Lead V5 –0.01 (–0.41 to 0.39) 0.35 (–0.04 to 0.74)

Lead V6 0.02 (–0.30 to 0.33) 0.35 ( 0.04 to 0.65)

T-wave amplitude (μV, change)

Lead I –1.60 (–3.07 to –0.13) –0.31 (–1.73 to 1.11)

Lead II –0.54 (–1.99 to 0.92) 0.71 (–0.70 to 2.13)

Lead aVL –1.21 (–2.50 to 0.10) –0.55 (–1.81 to 0.71)

Lead V1 1.45 (–0.16 to 3.06) 0.03 (–1.53 to 1.59)

Lead V2 –0.18 (–2.96 to 2.60) 0.57 (–2.12 to 3.27)

Lead V3 –2.33 (–5.15 to 0.49) –0.13 (–2.87 to 2.60)

Lead V4 –2.03 (–4.69 to 0.63) 0.64 (–1.94 to 3.22)

Lead V5 –1.92 (–4.22 to 0.38) 0.55 (–1.69 to 2.78)

Lead V6 –0.63 (–2.36 to 1.10) 0.82 (–0.86 to 2.49)

QRS/T angles and heart rate (change)

QRS/T angle–spatial (°) 0.19 (–0.21 to 0.59) –0.20 (–0.59 to 0.19)

QRS/T angle–frontal plane (°) 0.13 (–0.24 to 0.50) 0.35 (–0.01 to 0.71)

Heart rate (beats/min) 0.16 ( 0.02 to 0.30) 0.04 (–0.10 to 0.18)

aEstimates based on logistic and linear regression models adjusted for demographic, clinical, and weather variables: age;

race/ethnicity; education; exam site; BMI; current smoking status; history of CHD, diabetes, hypertension, SBP, chronic 

lung disease, or hypercholesterolemia; day of week; time of day; temperature; dew point; pressure and season; bAny ST 

abnormality (MC 4.1–4.4). cAny T abnormality (MC 5.1–5.4). dAny ST–T abnormality (MC 4.1–4.4 or MC 5.1–5.4).
Zhang et al.

754 volume 117 | number 5 | May 2009 • Environmental Health Perspectives

the July 2006 assessment included only one 

such study (of coronary artery calcium), yet 

in the interim, several others have also examined associations between PM2.5 exposures 

sustained over months to years and noninvasive measures of atherosclerosis, including 

carotid intimal-medial thickness, abdominal 

aortic calcification, and ankle-brachial index 

(Allen et al. 2009; Diez Roux et al. 2008; 

Hoffmann et al. 2007; Kunzli et al. 2005). 

The epidemiologic associations observed in 

these studies are not entirely consistent with 

one another or collectively as strong as those 

that may have been anticipated on the basis 

of recently reported associations between 

chronic PM exposure and cardiovascular disease (e.g., Miller et al. 2007), and the role of 

atherosclerotic mechanisms in the association remains uncertain.

We examined the role of acute, ischemic 

mechanisms in the association between PM2.5

and CHD among WHI clinical trial participants with this uncertainty in mind, using 

ECG—a simple, noninvasive, and underused 

research tool. In this epidemiologic context, 

however, we did not limit the examination to 

characteristics of clinically manifest ischemia 

(e.g., ≥ 0.1 mV of downsloping ST segment 

depression and/or deep, symmetrical, precordial T-wave inversion). We opted instead 

to include minor abnormalities of ventricular repolarization (ST–T abnormalities, ST 

and T amplitudes) and depolarization (QRS/T 

angles) to increase sensitivity of the ECG for 

subclinical forms of myocardial ischemia. 

Using this tool, we found that a 10 μg/m3

increment in lag0–2 PM2.5 is associated with 

4–5% increased odds of minor ST, T, and 

ST–T abnormalities, small decreases in ST and 

T amplitudes across most leads and exam sites,

and corresponding increases in QRS/T angles.

Previous findings from the WHI clinical 

trials cohort include significant prospective 

relationships between isolated ST–T abnormalities, lead-specific ST and T amplitudes, 

and wide QRS/T angle at baseline, and CHD 

mortality, incident CHD, and congestive heart 

failure (Rautaharju et al. 2006b). Similar findings also have been described in populationbased settings, such as the Atherosclerosis Risk 

in Communities study (Zhang et al. 2007), 

Cardiovascular Health Study (Rautaharju et al. 

2006a), and Chicago Western Electric Study 

(Daviglus et al. 1999), among others (Engel 

et al. 2004; Larsen et al. 2002; Okin et al. 

2005, 2007; Schouten et al. 1992). However, 

the clinical utility of these ECG measures 

remains limited.

Considering the consistent associations 

between PM2.5 and the ECG measures of 

myocardial ischemia observed in this context 

and the established, prospective association of 

these measures with CHD, the findings from 

the present study provide additional support 

for the hypothesis that subclinical myocardial 

ischemia has a pathophysiologic role in the 

adverse effects of acute exposure to PM2.5 on 

the heart, including predisposition to ventricular arrhythmogenesis and sudden cardiac 

death (Rubart and Zipes 2005). This is the first 

study to examine such an association in a large 

population of postmenopausal women. Indeed, 

prior studies have focused on either animal 

models of coronary vasoconstriction (Campen 

et al. 2005; Godleski et al. 2000; Nikolov et al. 

2007; Wellenius et al. 2003) or typically small, 

single-city panels of CHD-burdened, Holtermonitored male participants during exercise 

(Gold et al. 2005; Henneberger et al. 2005; 

Lanki et al. 2006; Mills et al. 2007; Pekkanen 

et al. 2002; Yue et al. 2007) (Table 5). The 

findings described herein are nonetheless consistent with and thereby extend the findings 

previously described in these populations. They 

do not imply, however, that ischemia is the 

sole process by which PM2.5 exerts its adverse 

effects on the heart. Autonomic, thrombotic, 

endothelial, inflammatory, oxidative, and other 

mechanisms of disease have all been implicated 

in cardiovascular effects of PM (Chuang et al. 

2007; Mills et al. 2007).

This study has several limitations that may 

affect interpretation of its findings. First, it is 

an ancillary study of participants in the WHI 

clinical trials. Inference is therefore limited to 

postmenopausal women 52–90 years of age living in the contiguous 48 United States between 

1999 and 2003, the initial 5-year period during which PM2.5 data were routinely collected 

by the U.S. EPA AQS. Because women in 

the WHI clinical trials were randomized to 

estrogen with or without progestin treatment, 

Table 5. Human studies of association of PM and ischemia.

 Mean age Detection

Reference No. Location (years) Female (%) Outcomes method

Pekkanen et al. 2002 45 Helsinki, Finland 68 47 STD E Holter

Henneberger et al. 2005 56 Efurt, Germany 66 0 TWA R Holter

Gold et al. 2005 24 Boston, MA, USA 73 75 STD E Holter

Lanki et al. 2006 45 Helsinki, Finland 68 47 STD E Holter

Yue et al. 2007 56 Efurt, Germany 66 0 TWA R Holter

Mills et al. 2007 20 Southampton, UK 60 0 STD E Holter

Zhang et al. 2008 55,529 49 exam sitesa 67 100 STT, STA, TWA Resting ECG

Abbreviations: E, exercise; R, rest; STA, ST-segment amplitude; STD, ST-segment depression; STT, ST-segment and 

T-wave abnormalities; TWA, T-wave amplitude. ECG involved resting, supine, standard 12-lead ECG. aIn the contiguous United States. 

Figure 1. Multivariable-adjusted ORs for ST–T abnormality and changes in T amplitude (μV) in lead 

V3 (95% CI) per 10-μg/m3 increase in lag0–2 PM2.5, by exam site. We based estimates on Bayesian 

hierarchical regression models adjusted for demographic, clinical, and weather variables: age;

race/ethnicity; education; BMI; current smoking status; history of CHD, diabetes, hypertension, SBP, 

chronic lung disease, or hypercholesterolemia; season; day of week; time of day; temperature; dew point;

and pressure.

Nevada

Portland

Torrance Cincinnati Chicago Rush GWU Iowa City

Tucson Worcester

Chicago Jacksonville

Milwaukee

Birmingham Madison Memphis Stanford Pawtucket Memphis satellite

Davenport La Jolla

Seattle Minneapolis Houston Buffalo

Newark New York Pittsburgh

Phoenix Winston-Salem Chapel Hill

Los Angeles

San Antonio Winston-Salem Satellite Fall River Miami Oakland Chula Vista Evanston Stony Brook

Des Moines

Gainsville

Columbus Irvine New Brunswick Boston

Medstar Detroit

Davis Atlanta

Davis Fall River GWU Birmingham Nevada San Antonio New Brunswick Milwaukee

Memphis Iowa City

Madison

Stanford Memphis Satellite Houston Irvine Evanston

Boston Pittsburgh

Chula Vista Pawtucket

Jacksonville

Chicago Rush Phoenix Cincinnati Des Moines Buffalo Winston-Salem satellite Miami Tucson

Chicago

New York Winston-Salem Detroit Stony Brook Torrance Davenport Chapel Hill

Gainsville Minneapolis Newark La Jolla

Medstar

Oakland Los Angeles Atlanta Worcester Seattle Columbus

Portland

0.95 1.0 1.05 1.10 1.15 –10 –5 0 5

OR μV

1.04 (1.00, 1.09) –2.20 (–5.38,1.06) All sites All sites
PM2.5 and myocardial ischemia

Environmental Health Perspectives • volume 117 | number 5 | May 2009 755

calcium/vitamin D supplementation, and/or 

dietary modification, these exposures could 

have affected the ECG measures examined 

in this setting. Because ambient PM2.5 concentrations also decreased 11% in the United 

States between 2000 and 2007 (U.S. EPA 

2008a), observed associations between PM and 

ischemia could be weaker if this study were 

repeated today. Findings were nevertheless 

robust to adjustment for randomization status. 

Moreover, seasonally weighted annual average 

PM2.5 concentrations in the United States still 

exceed the 15 μg/m3 national ambient air quality standard at approximately 10% of trend 

sites (U.S. EPA 2008b).

Second, we did not measure PM2.5 in 

the personal breathing space of participants. 

Instead, it was spatially interpolated at each 

participant’s geocoded address, raising questions about its validity (Szpiro et al. 2007). 

Third, the associations described herein also 

may be subject to residual socioeconomic 

confounding because we defined diabetes and 

hypercholesterolemia by self-reported history 

of the disease and/or use of antihyperlipidemic 

or antidiabetic medications rather than fasting 

glucose or lipid concentrations, which were 

only available for 6% site- and race-stratified 

minority participant oversample.

In the face of these additional limitations, 

in this study we took several precautions. We 

carefully established the validity of the geocodes relative to criterion standards (Whitsel 

et al. 2006) and the spatial interpolations 

using standard cross-validation statistics: the 

prediction error (PE = predicted minus measured pollutant concentration at each monitor site), standardized prediction error (SPE 

= PE divided by its estimated standard error), 

root mean square standardized error (RMSS 

= standard deviation of SPE across sites), root 

mean square prediction error (RMS = empirical standard error based on the mean square of 

the predictions), and mathematically calculated 

standard error (SE). Observed values of PE and 

SPE near 0 and RMSS near 1, and similarity of 

RMS and SE provide evidence of model validity (Liao et al. 2006, 2007). In addition, the 

study documented modest effects of geocoding 

and kriging error on PM–CHD associations 

(Crooks et al. 2008; Whitsel et al. 2006). 

Finally, the study demonstrated high reliability 

of treated diabetes and hypercholesterolemia 

(Langer et al. 2003), and we adjusted effect 

sizes for additional participant- and contextuallevel measures of socioeconomic status, noting 

only small changes as a result of adjustment.

Conclusion

We conclude that short-term exposure to 

PM2.5 is associated with ECG evidence of 

myocardial ischemia among postmenopausal 

women. The principal manifestations include 

ST-segment and T-wave abnormalities, 

decreases in ST segment and T amplitude, and 

increases in QRS/T angles. These findings suggest that the adverse effects of PM2.5 on CHD 

risk among postmenopausal women may be 

related to its subclinical, ischemic effects on 

the myocardium.

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