Baja et al. 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 Table 1. Characteristics of the study population (NAS participants, n = 580). Value 74.8 ± 6.8 27.9 ± 4.1 27.6 90.0 ± 11.1 246.2 ± 131.2 19.5 22.9 69.0 28.4 5.0 42.9 30.9 17.6 17.6 9.3 Characteristic Age [years (mean ± SD)] BMI [kg/m2 (mean ± SD)] ≥ 30 kg/m2 (%) MAP [mmHg (mean ± SD)] Cholesterol [mg/dL (mean ± SD)] Alcohol intake ≥ 2 drinks/day (%) Ever diabetic (%)a Cigarette smoking (%)b Former Never Current Medication use (%) Beta blockers Angiotensin-converting enzyme inhibitors Alpha blockers Calcium blockers Angiotensin receptor antagonist/blockers 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 46.2 (≥ 25 GT repeats) 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. fBazett corrected, mean QTc interval. Variable BC (µg/m3) CO (ppm) NO2 (ppm) PM2.5 (µg/m3) O3 (ppm) SO2 (ppm) Temperature (°C)a aCurrent 1-hr mean temperature 842 HR Lag 10 10 10 10 0 0 0 0 0 0 10 10 Table 2. Concentration of ambient air pollutants and temperature during or 10 hr before ECG monitoring (November 2000–December 2008). having the highest correlation (Spearman corre- lation 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 con- trast, 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 win- dow (based on QCDL models) and hourly exposure lag models. The two approaches showed an increase in QTc during a simi- lar time course for BC, 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 associa- tion 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 modi- fication by diabetes, obesity, and smoking for both the QCDL model (for cumulative exposure during the 10 hr before QTc meas- urement) and the hourly exposure model (with a 4-hr lag) for BC, 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 par- ticipants with diabetes, but we estimated a smaller association for nondiabetic partici- pants (1.43 msec change in mean QTc; 95% CI, –0.79 to 3.65; p-value for interaction = 0.26 using the LRT). Additionally, the asso- ciation with cumulative BC was also stronger for obese versus nonobese participants and never-smokers versus ever-smokers, although differences between estimates were not statis- tically significant (LRT, p-values for interac- tion > 0.53; for details, see Table 3). Effect modification by GSS. We also assessed whether having a high GSS (unfa- vorable genotype) modified associations of traffic-related pollutants with QTc; 94%, 90% and 85% of the participants in the 10 8 ) c se 6 m ( c Q T 4 n a e m 2 n i e g an 0 h C –2 –4 * * Mean ± SD 1.08 ± 0.84 0.64 ± 0.51 0.436 ± 0.299 0.332 ± 0.280 0.021 ± 0.008 0.019 ± 0.009 11.42 ± 8.55 10.72 ± 7.88 0.023 ± 0.016 0.021 ± 0.015 0.0052 ± 0.0045 0.0042 ± 0.0037 13.03 ± 9.65 Median 0.85 0.50 0.381 0.266 0.020 0.018 9.03 8.75 0.021 0.020 0.0038 0.0030 13.33 IQR 0.85 0.55 0.302 0.271 0.010 0.013 8.56 7.92 0.018 0.020 0.0039 0.0030 14.96 BC CO NO2 O 3 PM2.5 SO2 Pollutant Figure 1. Effect estimates of change in mean QTc per IQR change in cumulative exposure to pollutant during the 10 hr before ECG measurement: single- pollutant model with random intercept using QCDL. All models were adjusted for age; BMI; MAP; cholesterol; diabetic status (doctor’s diagnosis of disease or FBG > 126 mg/dL vs. no diagnosis or FBG ≤ 126 mg/dL); alcohol intake (≥ 2 drinks/day, < 2 drinks/day); cigarette smoker (never, ever); use of alpha blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor antagonist/block- ers, beta blockers, or calcium channel blockers; day of the week (Monday through Sunday); tem- perature; and a natural spline for long-term time trend (date). Error bars indicate 95% CI. *p < 0.10. ovlume 118 | number 6 | June 2010 • Environmental Health Perspectives