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 associa- tion 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 par- ticipants. Because only 8% of the participants were both obese and diabetic, it is unlikely in this case that obesity is acting as a surrogate for diabetes. In addition, we observed evidence of effect modification by smoking status on the asso- ciation between QTc and BC and NO2 expo- sures (both as the cumulative and 4-hr lag effect), with stronger associations estimated for nonsmokers. We hypothesize that non- smokers are more susceptible to effects of traffic-related pollution, because particles and gases may penetrate the clear lungs of non- smokers more easily than the lungs of smokers who have extensive lung damage. However, results from previous studies provide conflict- ing information on the effect of smoking on prolonged QT interval (Andrassy et al. 2003), because Ileri et al. (2001) reported that QTc was significantly prolonged in smokers than in 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 suscepti- bility to oxidative stress. Our QCDL model estimates showed significant QTc prolonga- tion in association with increasing BC and CO in 50% of the study population with higher genetic susceptibility to oxidative stress (as measured by GSS). In contrast, no associations with traffic pollutants were seen in half of the study population with lower GSS. Our findings are supported by several gene–environment interaction studies of our group that showed effect modification by genes related to oxidative stress (Chahine et al. 2007; Park et al. 2006; Schwartz et al. 2005b). The observed interactions between traffic pollutants and genetic susceptibility to oxidative stress score imply that components of traffic pollutants may play a significant role in influencing oxidative stress via changes in autonomic signaling, and changes in the levels of oxidants and antioxidants (Rhoden et al. 2005; Simkhovich et al 2008). We hypothesize that participants with high polymorphisms related to oxidative stress are more susceptible to an increase in oxidant generation and/or a decrease in antioxidant protection and this susceptibility could lead to QTc prolongation. Oxidative stress could increase ROS in the endothelial cells and/or heart muscles that could induce endothelial dysfunction and myocardial inflammation. Endothelial dysfunction and inflammation could alter the function of ion channels lead- ing to an intracellular excess and accumulation of positively charged ions inside the cardiac myocytes, which extends ventricular repolari- zation and results in QT interval prolonga- tion (Al-Khatib et al. 2003; Viskin 1999). Oxidative stress also inhibits nitric oxide production (Bai et al. 2001). Nitric oxide inactivation is associated with a reduction in parasympathetic tone and with an elevation in sympathetic tone (Chowdhary et al. 2002), which could trigger QTc prolongation and lead to arrhythmia, and sudden cardiac death (Tapanainen et al. 2002; Tsuji et al. 1996). Our study used the GSS method to assess if having a higher number of genetic vari- ants related to oxidative stress modified the associations between traffic-related pollut- ants and QTc. This approach is a statistically efficient method to evaluate gene–environ- ment interactions especially in inferring bio- logical pathways that would link exposure to outcome of interest. The use of the score method approach, together with the dis- tributed lag models, reduces the problem of multiple comparisons. Results from the GSS method were consistent with those from the analysis of individual genetic polymorphisms [see Supplemental Material (doi:10.1289/ ehp.0901396)]. Nevertheless, more studies are needed to verify this approach. Table 4. Adjusted effect estimates for change in mean QTc with an IQR change in cumulative traffic pol- lutant exposure (during the 10 hr before ECG measurement) by genetic susceptibility to oxidative stress (high vs. low GSS). Pollutant BC NO2 CO Modifier High GSS Low GSS High GSS Low GSS High GSS Low GSS Change in Mean QTc [msec (95% CI)] 3.85 (0.78 to 6.93) –0.56 (–3.90 to 2.78) 3.50 (–1.86 to 8.87) 0.29 (–4.56 to 5.14) 6.52 (0.49 to 12.55) 3.94 (–1.28 to 9.16) QCDL model p-Value 0.01 0.74 0.20 0.90 0.03 0.14 p-Value interaction 0.57 < 0.01 0.08 GSS measures the genetic susceptibility of a participant to oxidative stress by adding all the unfavorable genotypes of the participant. Environmental Health Perspectives • volume 118 | number 6 | June 2010 Traffic-related air pollution and QT interval Strengths and limitations. One limita- tion of our study is the use of a single ambi- ent monitoring site as a surrogate for recent ambient pollution exposure, which will not capture spatial variation in air pollutant con- centrations. This spatial variation may result in exposure misclassification because partici- pants lived 22 km on a median straight line distance from our ambient monitoring site. This exposure misclassification would prob- ably be nondifferential and bias the results toward the (Sarnat et al. 2005). Unlike previous studies that read the ECG recordings manually, our study used a predefined computer algorithm that auto- matically detected beat labels and QTc inter- vals to analyze ECG recordings. This made the outcome assessment of QTc less suscep- tible to differential measurement errors and intertechnician variability. In addition, the use of a random subject intercept means that contrasts were predominantly within and not between subjects. Hence, confounding fac- tor bias should be less than that incurred in a purely cross-sectional design, although bias due to residual or unmeasured confounding cannot be ruled out. Another limitation of the study is the assumption that the deletions and poly- morphisms have equal “weight” contribution to the genetic susceptibility of a participant to oxidative stress. This assumption does not take into account the possible interaction between genes that may or may not affect the genetic susceptibility of a participant to oxida- tive stress. Because the study population consists of older men who are predominantly white, the results may not be generalizable to women, younger individuals, or to other racial and ethnic groups. The effect of traffic pollutants on QTc on these other populations should be addressed in future studies. Conclusions In summary, this study documents the associ- ation between elevated short-term exposure to traffic-related pollution and prolonged QTc, a marker of ventricular arrhythmias com- monly associated with heart attack, among older men. The study also showed that traf- fic-related pollutants is related to prolonged QTc, given the generally significant associa- tion findings for BC, 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 pro- tected from QTc prolongation with traffic exposure than older men with high genetic variants related to oxidative stress. Moreover, the study provides further evidence that traf- fic pollutants via the oxidative stress pathway may play a crucial role in cardiopulmonary toxicity. 845