, 2007). For IL-6, the PCR primers and sequencing probe were designed

to target sites within a CpG island located in the promoter region of the gene using the Pyromark Assay Design Software Version 2.0 (Qiagen). The sequences were as follows: TTTTGAGAAAGGAGGTGGGTAG (Forward PCR primer), ACCCCCTTAACCTCAAATCTACAATACTCT (5′ biotinylated Reverse PCR primer), and AAGGAGGTGGGTAGG (Sequencing primer). The coefficients of variation (CV) for the LINE-1 methylation assay range from 0.5 to 2.6% and the CVs for IL-6 promoter methylation assay range DZNeP cell line between 5.3 and 14.8%. We administered the validated 108-item Block food frequency questionnaire (FFQ), (Block et al., 1990 and Subar et al., 2001) and the Block Adult Energy Expenditure Survey (Block et al., 2009). The nutrient and energy expenditure computations of the de-identified questionnaires

were performed by NutritionQuest, the distributor of the two questionnaires. We first compared the demographics between car drivers and PT users. Linear regression was used to estimate the difference and associated 95% confidence intervals (95%CI). We then compared the median and interquartile range (IQR) of daily intakes of foods and nutrients between the two groups. To construct dietary patterns, we performed factor analysis of 13 food groups using the principal factor method followed by an learn more orthogonal rotation. Based on the scree test results, the proportion of variance accounted and the interpretability criteria, we identified two factors, i.e. two dietary patterns. For each subject, we estimated factor scores for the two dietary patterns by summing the frequency consumption of very each food group weighted by their scoring coefficients. Subjects were then categorized into quartiles of factor scores for two dietary patterns, with high scores corresponding to a better adherence to a particular dietary pattern. We also estimated the car-vs-PT mean differences in factor scores for each of the two dietary patterns and associated 95%CIs using the beta coefficients of linear regression models and their standard

errors. Next, we compared the median levels of reported daily physical activities between car drivers and PT users. Using linear regression, we also evaluated whether two groups differed in their adherence to physical activity guidelines by assessing the proportion of subjects meeting the U.S. Department of Agriculture 2005 Dietary Guidelines for Americans (DGA) for physical activity (i.e., engaged in approximately 60 min of moderate- to vigorous-intensity activity on most days of the week), or meeting the Healthy People 2010 Guidelines for physical activity (i.e., engaged in moderate physical activity for at least 30 min on at least 5 days a week, or engaged in vigorous physical activity for 20 min on at least 3 days/week). We used logistic regression to compare differences in distributions across quartiles of durations of the various types of physical activity.