evaluations were performed approximately every 3 years. The eligibility criteria for this study were never smoking and residing in Massachusetts within 40 km of a BC monitor. We focused on never-smokers because tobacco smoke is a particle-rich mixture that has many attributes and effects (e.g., inflammation) in common with ambient particles but involves a much greater dose. Because this dose is meas- ured imperfectly by questionnaires, we felt the measurement error could be large compared with the particle doses we were examining and could obscure any association with our expo- sure. We identified 179 participants who met the eligibility criteria, and we obtained at least one telomere length measure on 165 (92%). The median age of these men was 74 years, and the range was 56–94 years; 43 (26%) of these subjects had coronary heart disease. Spatiotemporal model for mobile source particles. Concentration of BC, a surro- gate for particles from gasoline- and espe- cially diesel-powered motor vehicles, outside each subject’s home during the year before telomere measures was estimated based on a previously validated spatiotemporal model (Gryparis et al. 2007). Briefly, Gryparis et al. 2007) modeled 24-hr measures of BC based on > 6,000 observations from > 2,000 unique exposure days during the years 1999–2004 at 82 locations. Predictions were based on meteorologic and other characteristics (e.g., weekday/weekend) of a particular day, as well as measures of land use (e.g., cumula- tive traffic density within 100 m and percent urbanization) at each monitor location. The model also allowed for space–time interac- tions by including interaction terms between the temporal meteorologic predictors and the source-based geographic variables. The most important interactions of this type were mixing height (the height above the ground below which the atmosphere is well mixed) with percent urbanization and daily average wind speed with cumulative traffic density. Gryparis et al. (2007) used thin-plate splines to model residual spatial variability. They fit separate models for the warm and cold sea- son. The prediction equation also included central monitor concentrations as a predictor to reflect average concentration for a given day. The highest predicted daily concentra- tion level was more than three times that of the lowest predicted level, and the adjusted R2 for this model was 0.83. Monthly monitoring campaigns were conducted subsequently at 37 locations and used as a validation sample. The out-of-sample predicted R2 based on this validation sample was 0.53 for 24-hr meas- urements. All addresses of participants in the NAS were geocoded, and annual average BC concentration corresponding to each telo- mere length measurement was defined as the average BC concentration over the 365 days before the visit at which a blood sample was collected to assess the outcome. Telomere length measurements. We col- lected 7 mL of whole blood by venous phle- botomy in EDTA tubes. DNA was extracted from stored frozen buffy coat using the QiAmp DNA blood kits (Qiagen, Germantown, MD, USA) and used for leukocyte telomere length (LTL) measurement by means of quan- titative real-time polymerase chain reaction (qRT-PCR) (Cawthon 2002). Relative LTL was measured by determining the ratio of the telomere (T) repeat copy number to the sin- gle-copy gene (S) copy number (T:S ratio) in a given sample and reported as relative units expressing the ratio between LTL in the test DNA and LTL in a DNA pool used to gen- erate a standard curve in each PCR run. We used pooled DNA from 475 participants ran- domly selected from the NAS (50 ng for each sample) to create for each run a fresh standard curve, which ranged from 20 to 0.25 ng/μL . An eight-point standard curve, derived from serially diluted DNA pool, ranging from 30 to 0.234 ng/μL was included in each PCR plate, so that relative quantities of T and S could be determined. For each study sample, we prepared a 25-Lμ mixture of DNA sample (2 ng/μL ) containing Escherichia coli DNA (15 ng/Lμ ) used as a DNA carrier to increase PCR reproducibility; we heated these mixtures to 96°C × 10 min and then cooled them to room temperature. PCR primer sets for T and human beta-globin, taken as the reference S, as well as the PCR mix composition were pre- viously described (Hou et al. 2009). Using a MICROLAB STARlet Robot (Hamilton Life Science Robotics, Bonaduz, Switzerland), we transferred 2 Lμ (2 ng/Lμ ) DNA in 5 Lμ reac- tion mix in 384-well plates. We performed the PCR runs on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). After PCR amplification, the specificity of the product was confirmed by dissociation curve analysis. We ran all samples in tripli- cates, and the average of the three T meas- urements was divided by the average of the three S measurements to calculate the average T:S ratio (Farzaneh-Far et al. 2008). To test the reproducibility of this method, we ampli- fied T and S in 15 samples replicated three times on each of 3 consecutive days. The coef- ficient of variation for the average T:S ratio of samples analyzed over 3 consecutive days was 8.7%, similar to the reproducibility originally reported for this method (Cawthon 2002). Statistical analysis. Natural log-trans- formed LTL was the dependent variable in linear mixed effects regression models run using Proc Mixed from SAS (version 9.1; SAS Institute Inc., Cary NC, USA). To estimate the effect of annual moving-average BC con- centration on LTL, we included random inter- cepts for each subject, thereby accounting for Environmental Health Perspectives • volume 118 | number 11 | November 2010 Traffic particles may shorten telomeres correlation among repeated measures of LTL, and used the empirical estimator of variance for confidence intervals (CIs) and p-values (Zeger and Liang 1986). In the baseline model, we included as potential confounders age at first telomere measure, the difference between this first age and the age at each measure, and year of telomere measurement. In the next model, to adjust for potential confounding, we included education level (< high school, high school graduate, < 4 years of college, 4 years of college, > 4 years of college) because we assumed it is likely to be a common cause of BC exposure and LTL. We also included body mass index (BMI), white blood cell count, percent neutrophils, percent lymphocytes, treatment with a statin medication (yes/no; then-current use based on self-report), diagno- sis of diabetes (yes/no), and fasting blood glu- cose because we assumed these variables may share an unmeasured common cause with BC exposure and are likely to be determinants of LTL. Next, we built a model in which we also adjusted for living in an urban area, census- tract median income, and percentage of popu- lation below the poverty line (U.S. Census Bureau poverty thresholds from year 1999) as area-level covariates (U.S. Census Bureau 1999). Residential areas were defined as urban (yes/no) according to the National Atlas of the United States (U.S. Geological Survey 2009). For each continuous covariate, we evaluated the potential to further reduce confounding by including quadratic terms. We multiplied the regression coefficient for BC by the pollut- ant’s interquartile range (IQR) and calculated the percent difference in LTL associated with an IQR increase in BC. The telomere measures constitute an imbalanced longitudinal data set, with subjects having varying numbers of repeated measures. Loss to follow-up may lead to selection bias if data are not missing at random, conditional on the terms included in our regression mod- els (Rubin 1976). We repeated the above anal- yses after weighting follow-up observations by the inverse probability of attaining a follow-up response (Robins et al. 1995). A subject with a repeated measurement that occurred < 5 years before the end of the study (31 December 2006) was defined as complete, and a sub- ject for whom ≥ 5 years had passed between their last observation and the end of the study was defined as lost to follow-up. In a logistic regression model, the log odds of the response indicator (response = 1, missing = 0) was pre- dicted by covariates from the previous meas- urement occasion, including telomere length, annual BC, age, BMI, statin use, and educa- tion level. We then weighted the observed follow-up measures by the inverse of the esti- mated probabilities of obtaining a follow-up response, which gives more weight to observa- tions that are more likely to be missing, and 1565