McCracken et al. by an analysis of the Third National Health and Nutrition Examination Survey by Chen and Schwartz (2008) showing that exposure to annual average PM10 (PM with aerody- namic diameter ≤ 10 mμ) was associated with higher white blood cell count. Toxicologic experiments demonstrate that combustion particles promote atherosclerosis (Mills et al. 2007), widely recognized as an inflammatory disease (Ross 1999). Moreover, Künzli et al. (2005) found that a 10-μg/m 3 increase in annual near-residence PM2.5 (PM with aero- dynamic diameter ≤ 2.5 mμ) in Los Angeles, California, was associated with approximately a 4% increase in carotid intima-media thick- ness, a measure of subclinical atherosclerosis. There is also clear evidence that diesel exhaust exposure causes oxidative DNA damage, and human panel studies have shown that guanine oxidation, in particular, is associated with par- ticle exposure (Gonzalez-Flecha 2004; Risom et al. 2005). Oxidative stress mediated by PM may arise from direct contact with reactive oxygen species on the surface of particles that deposit in the lungs, soluble compounds such as transition metals or organic compounds that enter the bloodstream, or activation of inflammatory cells capable of generating reactive oxygen and nitrogen species (Risom et al. 2005). Traffic-particle exposure, therefore, may increase both the rate of replication of hematopoietic stem cells, due to inflamma- tion, as well as the extent of telomere loss per replication, due to oxidative stress. In our study, approximately an 8% shorter LTL was associated with an IQR increase in annual BC. Telomere shortening, which leads to endothelial dysfunction, one of the earliest features of atherosclerosis, may be an under- lying cause of atherosclerosis (van der Harst et al. 2007). An important research ques- tion, therefore, becomes whether telomere shortening mediates the effects of particle- induced chronic, low-grade inflammation, and oxidative stress on the development and progression of atherosclerotic coronary artery disease. The mechanisms described above, however, fail to explain the wide temporal variability in telomere length that led to increases in LTL among some subjects rather than a monotonic decrease in length with aging, and the causes and health implica- tions of this aspect of telomere dynamics, also observed in previous studies (Aviv et al. 2009; Ehrlenbach et al. 2009; Martin-Ruiz et al. 2005), are unclear. The small coefficient of variation (8.7%) we observed among the triplicate measures of the T:S ratios taken over 3 consecutive days suggests that these increases in LTL with age are not attributable to measurement error alone. A key concept that has come to the fore in air pollution epidemiology is interindividual 1568 variability in susceptibility. Demographic and socioeconomic characteristics, medical history, and genetic variants have been identified as predictors of susceptibility (Baccarelli et al. 2008a; Chahine et al. 2007; O’Neill et al. 2007). In our study of annual average BC and LTL levels, we found statistically significant interactions with both age and statin treat- ment, with larger effect estimates among sub- jects ≥ 75 years of age compared with younger participants, and among those not taking statins compared with statin users. Similarly, previous studies have reported stronger asso- ciations between daily particle exposure and both inflammatory markers and heart rate variability among subjects not taking statins (O’Neill et al. 2007; Schwartz et al. 2005), as well as between annual PM2.5 and carotid intima-media thickness among older subjects (Künzli et al. 2005). The spatiotemporal statistical model we used to estimate near-residence con- centrations of BC was built upon measure- ments at 82 locations in the greater Boston, Massachusetts, area. We took the pollutant measurements that informed the model dur- ing varying time intervals from January 1999 through September 2004. The model elimi- nates a substantial amount of measurement error that would have been incurred if esti- mates had been derived based on exposure levels collected at a central site monitor, due to the large amount of spatial heterogeneity in BC levels. This is evident when one com- pares the increase in out-of-sample R2 for the model-based estimates (0.53) with that from a model based on central site measurements alone (0.10). However, because we have used relatively simple structures to approximate complex spatiotemporal interactions, there is still some error associated with these estimates, which will likely attenuate our estimated health effects toward the (Gryparis et al. 2009). However, the fact that we are aggregat- ing predicted daily BC levels over yearly peri- ods to estimate chronic effects serves to lessen the effect of this form of model misspecifica- tion, because much of the measurement error is due to random fluctuations that average out over longer periods. For instance, Gryparis et al. (2009) considered the impact of using 9-month integrated predictions from this same model to assess the association between birth weight and BC exposure during gesta- tion. Using newly developed measurement error correction techniques, these authors esti- mated that the attenuation resulting from using the predicted 9-month values is on the order of 20%. Indeed, when we restricted our study population to those living within 10 km of a BC monitor, our effect estimates were slightly larger, which may be attributable to a reduction in exposure measurement error due to spatial extrapolation. BC is used in our study as a surrogate for traffic pollution and may not itself be causally related to LTL, so the estimated effect of traf- fic pollution may be biased if BC is not a reli- able surrogate for the overall toxicity of this pollutant mixture. Although the use of BC as a surrogate for traffic-related particles is sup- ported by the predictive value of cumulative traffic density in the spatiotemporal model we used for near-residence ambient BC con- centrations (Gryparis et al. 2007), as well as other studies that have shown ambient BC to be associated with proximity to automobile traffic (Kim et al. 2004; Patel et al. 2009), there are other sources of ambient BC, such as wood burning (LaRosa et al. 2002), so BC is an imperfect surrogate for particles from automobiles. The direction of the bias intro- duced by this type of measurement error is unknown because the relative toxicity of the other sources is unknown, but we assume that the bias would be small because automobile traffic is the major source of ambient BC in the study area (Gryparis et al. 2007). Another limitation of our study is that we used relative telomere length as a surrogate for absolute telomere length. Although these two measures have been shown to be moder- ately correlated, prediction of absolute lengths from the relative measures involves a nonzero intercept term (Ehrlenbach et al. 2009), so the percent changes we report are specific to relative LTL measures and are not associated with equivalent proportional changes in abso- lute LTL. This nonzero intercept in the rela- tionship between these two types of measures, rather than differences in measurement error between the PCR and Southern blot meth- ods, also explains the much wider coefficient of variation of relative LTL compared with that of absolute LTL. As in most longitudinal studies, there was imbalance in the number of repeated mea- sures per subject, here largely due to loss to follow-up. Although the similar results after using inverse probability of response weight- ing suggest that a spurious association due to loss to follow-up was unlikely, this approach relies on correctly specifying a model for the dropout process, so it does not rule out potential selection bias due to dependent censoring. It is also possible that we did not include or correctly adjust for confounders of the relationship between BC and LTL. A par- ticularly important concern when evaluating effects of air pollution across a metropolitan area is that higher levels of air pollution are associated with lower socioeconomic status (O’Neill et al. 2007), a determinant of many factors likely associated with shorter telom- eres. Although we adjusted for education level, census-tract median income, and per- centage of population below the poverty line, residual and unmeasured confounding by ovlume 118 | number 11 | November 2010 • Environmental Health Perspectives