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 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 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 PM2.5 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, 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 polymor- phisms (SNPs) [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 deletion, SNP, or microsatellite GTn- repeat 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, Environmental Health Perspectives • volume 118 | number 6 | June 2010 Traffic-related air pollution and QT interval angiotensin-converting enzyme inhibitors, angiotensin receptor antagonist/blockers, beta- blockers, 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 temperature using a linear term and modeled day-of-the- week 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/m2; non- obese, < 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 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 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 (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. The 841