Abstract
Background and Aims
Commuting by public transportation (PT) entails more physical activity and energy expenditure than by cars, but its biologic consequences are unknown.
Methods
In 2009-2010, we randomly sampled New York adults, usually commuting either by car (n=79) or PT (n=101). Measures comprised diet and physical activity questionnaires, weight and height, white blood cell (WBC) count, C reactive protein, (CRP) gene-specific methylation (IL-6), and global genomic DNA methylation (LINE-1 methylation).
Results
Compared to the 101 PT commuters, the 79 car drivers were about 9 years older, 2 kg/m2 heavier, more often non-Hispanic whites, and ate more fruits and more meats. The 2005 guidelines for physical activity were met by more car drivers than PT users (78.5% vs. 65.0%). There were no differences in median levels of CRP (car vs. PT: 0.6 vs. 0.5 mg/dl), mean levels of WBC (car vs. PT: 6.7 vs. 6.5 cells/mm3), LINE-1 methylation (car vs. PT: 78.0% vs. 78.3%), and promoter methylation of IL-6 (car vs. PT: 56.1% vs. 58.0%).
Conclusions
PT users were younger and lighter than car drivers, but their commute mode did not translate into a lower inflammatory response or a higher DNA methylation, maybe because, overall, car drivers were more physically active.
Introduction
Work or school commute offers a logical option to integrate more physical activity in daily life as a means of counterbalancing the sedentary forces behind the on-going obesity epidemic. Even though biking and walking to work and school would be most effective, for most Americans the choice, if any, is between car and public transportation (PT). PT users walk and climb stairs more than car commuters do, as a result of moving to, from, and within stations. (Besser and Dannenberg 2005;Edwards 2008;Lachapelle and Frank 2009;Ogilvie et al., 2004) We have documented the higher physical energy expenditure of PT users during their work commute compared to car drivers.(Morabia et al., 2009;Morabia et al., 2010)
After the introduction of a new commuter light rail transit in North Carolina, MacDonald et al. (MacDonald et al., 2010) found that the rail commuters had an 18% reduction in body mass index compared to those who kept commuting by car, corresponding to the loss of 6.5 pounds for a person 5’5” (165 cm) tall over seven months. This was equivalent to an average excess energy expenditure of about 100 kcal/d, compatible with simulation studies suggesting that an average loss of 100 kcal/d can stabilize the progression of a population’s weight.(Hill et al., 2003;Morabia and Costanza 2004)
Increased energy expenditure and potentially associated loss of body weight can reduce inflammatory responses, as assessed by total white blood cell (WBC) count and C-Reactive Protein (CRP), (Ford 2002;Hammett et al., 2004;Kasapis and Thompson 2005) and epigenetic markers such as global genomic DNA methylation (Zhang et al., 2011a) and gene-specific methylation.(Coyle et al., 2007) Inflammatory processes are involved in atherogenesis (Mora et al., 2007) and carcinogenesis. (Coussens and Werb 2002;Rogers et al., 2008) There is, however, no research yet evaluating whether commute-specific physical activity is involved in chronic disease pathways.
This study was therefore designed and conducted to provide first evidence about the inflammatory markers and epigenetic characteristics of random samples of people commuting to a college campus in Queens, NY, either by car or by PT.
Methods
Participants were identified using a campus-wide survey about commuting habits which had been performed every winter since 2007.(Morabia and Zheng 2009) Over the years, 4,213 respondents agreed to be contacted for research projects related to transportation. They comprised 43% of car commuters and 51% of PT commuters; 6% only commuted by bike, motorcycle, or walked. We recruited and financially remunerated for time a sample of those who were nonsmokers, had no work-related exposure to air pollutants, were students or employees of Queens College, City University of New York, and commuted 5 days/week to and from the campus either by car or by PT. Subjects were not eligible if they had recently used anti-inflammatory drugs, such as aspirin, NSAID, or corticoid drugs.
The car and PT commuters were sent several recruitment emails and were entered into the study in the order in which they volunteered between September 2009 and December 2010. The initial objective was to recruit 100 car (“cases”) and 100 PT commuters (“controls”).
WBC, CRP, LINE-1 and IL-6 DNA methylation, diet (including alcohol intake), overall energy expenditure, and body weight were measured on all participants. Body weight and height were measured using a Detecto® medical scale and gauge. The protocol had been approved by the Institutional Review Board of Queens College.
Blood Measurements
Blood was obtained by venipuncture at Queens College by a nurse into coded EDTA-tubes. WBC count (cells/mm3) and hs-CRP (mg/dl) were assayed by a commercial clinical laboratory (Quest). WBC counts were determined immediately after collection, while, for the other measures, a 7ml tube was taken in a refrigerated box to Columbia University, plasma and WBC isolated and stored at -80°C. Samples were analyzed in batches at the middle and end of the study. Each batch had a mix of PT and car commuter bloods.
DNA was extracted from the WBC using FlexiGene DNA Kits (Qiagen, Valencia, CA) at Columbia University. Bisulfite modification was conducted using an EZ DNA Methylation-Gold kit (Zymo Research, Irvine, CA) following the manufacturer’s recommendations. The biotinylated PCR products were purified and pyrosequencing was run on a PyroMark Q24 (Qiagen, Valencia, CA). We used non-CpG cytosine residues as internal controls to verify efficient sodium bisulfite DNA conversion, and universal unmethylated (whole genome amplified) and methylated DNA (CpGenome Universal Methylated DNA, Millipore, Billerica, MA) were run as controls. Methylation quantification was performed using the PyroMark Q24 1.010 software. The degree of methylation was expressed for each DNA locus as percentage methylated cytosine over the sum of methylated and unmethylated cytosine. For LINE-1, values across the 3 CpG sites were averaged while for IL-6, values for the 6 sites were averaged. For LINE-1, the PCR primers and sequencing probe used were previously described.(Bollati et al., 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 between 5.3 and 14.8%.
Dietary and Physical Activity Measurements
We administered the validated 108-item Block food frequency questionnaire (FFQ), (Block et al., 1990;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.
Statistical analyses
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 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 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 minutes 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 minutes on at least five days a week, or engaged in vigorous physical activity for 20 minutes on at least three days per week). We used logistic regression to compare differences in distributions across quartiles of durations of the various types of physical activity.
Then, we compared the mean, median and adjusted geometric mean levels of CRP, WBC, LINE-1 methylation and IL-6 promoter methylation between car drivers and PT users. Linear regression was used to estimate the difference and associated 95% confidence intervals. Because CRP levels did not follow a normal distribution, it was log-transformed in linear regression models.
Last, we created case-PT pairs of participants matched on age (± 5yrs) and gender and compared their differences in WBC, CRP, LINE-1 methylation and IL-6 methylation using paired-T tests. All statistical analysis was performed using SAS (version 9.1; SAS institute, Cary, NC).
Results
There were 79 car drivers and 101 PT users. Car drivers were older, had higher BMI, and included a greater proportion of males and non-hispanic whites than PT commuters.(Table 1)
Table 1.
Demographics of car drivers and public transportation (PT) users, New York, 2009-2010
| Car | PT | Difference (95%CI) | |
|---|---|---|---|
| N | 79 | 101 | |
| Age, y, mean (SD) | 34.5 (15.6) | 25.4 (8.9) | -9.2 (-12.8, -5.5) |
| Body Mass Index, kg/m2, mean (SD) | 26.0 (5.3) | 24.2 (3.7) | -1.8 (-3.1, -0.5) |
| Women, n (%) | 36 (45.6) | 58 (57.4) | 11.8 (-2.9, 26.6) |
| Race/ethnicity, n (%) | |||
| Non-Hispanic whites | 45 (59.2) | 30 (32.6) | -26.6 (-41.4, -11.8) |
| Non-Hispanic blacks | 9 (11.8) | 16 (17.4) | 5.5 (-5.4, 16.5) |
| Hispanics | 15 (19.7) | 29 (31.5) | 11.8 (1.6, 25.2) |
| Others | 7 (9.2) | 17 (18.5) | 9.3 (-1.4, 19.9) |
Car drivers ate more fruits and more meats than PT users (p=0.02 and 0.04 respectively, Table 2). We identified two dietary patterns in the study population: the prudent dietary pattern was characterized by high intakes of vegetables and fruits; and the western dietary pattern was characterized by high intakes of meats, grains and dairy products. Overall the two groups did not differ in the adherence to the two dietary patterns, either for the prudent or for the western diet. (Table 3)
Table 2.
Food and nutrients differences by car versus public transportation (PT) commute modes. New York, 2009-2010
| Car (n=79) | PT (n=101) | |
|---|---|---|
| Median (IQR) | ||
| Food groups | ||
| Vegetables (cups/d) | 2.5 (1.3-4.8) | 2.0 (1.1-3.5) |
| Leafy green vegetables (cups/d) | 0.4 (0.2-0.7) | 0.2 (0.1-0.6) |
| Orange/yellow vegetables (cups/d) | 0.09 (0.05-0.2) | 0.07 (0.04-0.1) |
| Legumes (cups/d) | 0.1 (0.03-0.3) | 0.2 (0.05-0.3) |
| Other vegetables (cups/d) | 0.8 (0.5-1.3) | 0.7 (0.4-1.1) |
| Starchy vegetables (cups/d) | 0.2 (0.1-0.4) | 0.2 (0.1-0.3) |
| Fruits (cups/d) | 1.1 (0.8-0.4) | 0.9 (0.5-1.6) |
| Total grains (oz./d) | 5.0 (3.6-7.1) | 4.8 (3.5-6.4) |
| Whole grains (oz./d) | 1.1 (0.7-2.0) | 1.0 (0.5-1.6) |
| Meats (oz./d) | 2.9 (1.7-5.2) | 2.2 (1.1-4.1) |
| Dairy (cups/d) | 1.2 (0.7-1.6) | 1.0 (0.6-1.7) |
| Oils (tsp./d) | 2.6 (1.3-3.8) | 2.4 (1.4-3.2) |
| Nutrients | ||
| Total energy intake (kcal/d) | 1836 (1302-2415) | 1600 (1299-2135) |
| % calorie from fat | 35.0 (30.0-38.6) | 33.3 (30.4-37.6) |
| % calorie from saturated fat | 10.0 (8.3-12.0) | 9.7 (8.0-11.4) |
| % calorie from trans-fat | 0.9 (0.7-1.2) | 1.0 (0.7-1.2) |
| Cholesterol (mg/d) | 213.0 (131.7-307.8) | 171.3 (120.5-292.9) |
| % calorie from sweets | 10.2 (5.8-16.9) | 13.2 (7.0-20.3) |
| Glycemic index | 50.6 (47.6-52.5) | 51.0 (49.3-53.3) |
| Sodium (mg/d) | 2802 (2072-3889) | 2604 (1849-3533) |
| Fiber (g/1,000kcal) | 9.6 (7.2-13.1) | 9.2 (6.6-12.3) |
Table 3.
Differences in dietary patterns by car versus public transportation (PT) commute modes. New York, 2009-2010
| Car (n=79) | PT (n=101) | |||
|---|---|---|---|---|
| Dietary pattern factor scores (quartile range) | ||||
|
| ||||
| Prudent dietary pattern mean score (SD) | 0.2 (1.2) | -0.1 (0.8) | Difference (95% CI)1 -0.3 (-0.6, 0.05) | |
| N (%) | OR (95%CI)1 | |||
| Q1 (< -0.7) | 15 (19.0) | 29 (29.0) | Ref. | |
| Q2 (-0.7, -0.31) | 20 (25.3) | 25 (25.0) | 1.3 (0.5, 3.4) | |
| Q3 (-0.3, 0.49) | 19 (24.1) | 26 (26.0) | 1.3 (0.5, 3.3) | |
| Q4 (≥ 0.5) | 25 (31.7) | 20 (20.0) | 2.1 (0.8, 5.8) | |
| Western dietary pattern mean score (SD) | 0.03 (0.9) | -0.02 (0.9) | -0.03 (-0.3, 0.3) | |
|
| ||||
| N (%) | OR (95%CI)1 | |||
| Q1(< -0.6) | 18 (22.8) | 29 (26.0) | Ref. | |
| Q2 (-0.6, -0.21) | 18 (22.8) | 27 (27.0) | 0.8 (0.3, 2.1) | |
| Q3 (-0.2, 0.39) | 22 (27.9) | 23 (23.0) | 1.2 (0.5, 3.2) | |
| Q4 (≥ 0.4) | 21 (26.6) | 24 (24.0) | 1.3 (0.5, 3.4) | |
CI= Confidence Interval, IQR=Interquartile Range (Q1-Q3), OR=Odds Ratio, Q=Quartiles,
Linear regression models were adjusted for age, gender, race/ethnicity and BMI.
Car drivers reported a higher level of light job activities and a lower level of sedentary activities than PT commuters (p=0.007 and 0.004 respectively). Overall, car drivers had higher adherence to 2005 DGA for physical activity than PT commuters (78.5% vs. 65.0%). However, after adjusting for age, gender, race/ethnicity and BMI, the difference in adherence to the 2005 DGA for physical activity became statistically insignificant (difference: -14.2%, 95%CI: -29.0, 0.5).(Table 4)
Table 4.
Difference in physical activity by car versus public transportation (PT) commute modes. New York, 2009-2010
| Car | PT | ||
|---|---|---|---|
| Physical activity levels (minutes/day) | Median (IQR) | ||
| Moderate leisure activities | 23.7 (5.4-50.7) | 20.0 (2.7-52.1) | |
| N (%) | OR (95%CI)2 | ||
| Q1 (<3.9) | 15 (19.0) | 28 (28.0) | Ref. |
| Q2 (3.9, 23.7) | 23 (29.1) | 23 (23.0) | 1.5 (0.5, 4.2) |
| Q3 (23.7, 51.6) | 22 (27.9) | 23 (23.0) | 2.4 (0.9, 6.7) |
| Q4 (≥ 51.6) | 19 (24.1) | 26 (26.0) | 2.1 (0.7, 6.2) |
| Moderate or vigorous leisure activities | 46.8 (16.1-82.4) | 33.7 (10.5-89.4) | |
| N (%) | OR (95%CI) | ||
| Q1 (<12.3) | 16 (20.3) | 28 (28.0) | Ref. |
| Q2 (12.3, 36.7) | 20 (25.3) | 25 (25.0) | 0.9 (0.3, 2.5) |
| Q3 (36.7, 85.5) | 25 (31.7) | 20 (20.0) | 1.6 (0.6, 4.3) |
| Q4 (≥85.5) | 18 (22.8) | 27 (27.0) | 1.3 (0.5, 3.5) |
| Moderate household activities | 22.4 (5.4-59.0) | 14.9 (5.4-29.2) | |
| N (%) | OR (95%CI) | ||
| Q1 (<5.4) | 19 (24.1) | 24 (24.0) | Ref. |
| Q2 (5.4, 20) | 14 (17.7) | 32 (32.0) | 0.5 (0.2, 1.2) |
| Q3 (20, 45) | 22 (27.9) | 22 (22.0) | 1.2 (0.5, 3.0) |
| Q4 (≥45) | 24 (30.4) | 22 (22.0) | 1.1 (0.4, 3.0) |
| Light job activities | 300.0 (171.4-342.9) | 205.7 (94.3-325.7) | |
| N (%) | OR (95%CI) | ||
| Q1 (<128.6) | 13 (16.5) | 29 (28.0) | Ref. |
| Q2 (128.6, 214.3) | 15 (19.0) | 28 (28.0) | 1.3 (0.5, 3.5) |
| Q3 (214.3, 342.9) | 23 (29.1) | 18 (18.0) | 2.6 (1.0, 7.1) |
| Q4 (≥342.9) | 28 (35.4) | 25 (25.0) | 1.5 (0.5, 3.9) |
| Sedentary activities1 | 241 (153-366) | 311 (320-458) | |
| N (%) | OR (95%CI) | ||
| Q1 (<225.9) | 27 (34.2) | 17 (17.0) | Ref. |
| Q2 (225.9, 325.8) | 20 (25.3) | 25 (25.0) | 1.5 (0.5, 4.2) |
| Q3 (325.8, 476.1) | 17 (21.5) | 28 (28.0) | 2.4 (0.9, 6.7) |
| Q4 (≥476.1) | 15 (19.0) | 30 (30.0) | 2.1 (0.7, 6.2) |
| Adherence to physical activity guidelines | n (%) | Differences2 (95% CI) | |
| Meet USDA 2005 DGA for physical activity | 62 (78.5) | 65 (65.0) | -14.2 (-29.0, 0.5) |
| Meet Healthy People 2010 Guidelines for physical activity | 47 (59.5) | 47 (47.0) | -11.2 (-27.8, 5.4) |
CI= Confidence Interval, DGA= Dietary Guidelines for Americans, IQR=Interquartile Range (Q1-Q3), OR=Odds Ratio, Q=Quartiles, USDA= US Department of Agriculture
Minutes/day of sedentary activities include times spent watching TV or movie, on the internet or computer work, and sitting.
Logistic regression and inear regression models were adjusted for age, gender, race/ethnicity and BMI.
In Table 5, there were no differences in median level of CRP (car vs. PT: 0.6 vs. 0.5 mg/dl, difference in log-CRP: 0.2, 95%CI: -0.2, 0.5) and mean level of WBC (car vs. PT: 6.7 vs. 6.5 cells/mm3, difference: -0.4, 95%CI: -0.9, 0.2). In Table 6, there were no differences in mean levels of LINE-1 methylation (car: 78.0%; PT: 78.3%, difference: 0.2, 95%CI: -0.5, 1.0), and IL-6 promoter methylation (car: 56.1%; PT: 58.0%, difference: 1.7, 95%CI: -2.4, 5.8). Missing values in Table 6 are due to low DNA yield following extraction from the buffy or low quality calls on pyrosequencing LINE-1 methylation or IL-6 promoter methylation.
Table 5.
Differences in inflammatory response by car versus public transportation (PT) commute modes. New York, 2009-2010
| Car | PT | Differences1 (95% CI) | |
|---|---|---|---|
| n | 78 | 101 | |
| CRP, mg/dl, mean (SD) | 1.3 (1.9) | 1.2 (1.7) | 0.2 (-0.2, 0.5) |
| Median (IQR)2 | 0.6 (0.2-1.7) | 0.5 (0.2-1.5) | |
| Adjusted geometric means 3 | 0.7 | 0.7 | |
| WBC, /mm3, mean (SD) | 6.7 (1.6) | 6.5 (1.8) | -0.4 (-0.9, 0.2) |
| Median (IQR) | 6.7 (5.6-7.4) | 6.4 (5.4-7.4) | |
| Adjusted mean | 6.7 | 6.5 |
CI=Confidence Interval, IQR=Interquartite range
Linear regression adjusted for age, gender, race/ethnicity and BMI. Log transformation was performed for CRP in linear regression models.
Table 6.
Differences in DNA methylation by car versus public transportation (PT) commute modes. New York, 2009-2010
| Car | PT | ||
|---|---|---|---|
| LINE-1 methylation, n | 72 | 93 | |
|
| |||
| Difference1 (95% CI) | |||
| %, Mean (SD) | 78.0 (2.1) | 78.3 (2.0) | 0.2 (-0.5, 1.0) |
| Median (IQR) | 78.4 (77.2 - 79.6) | 78.3 (77.3 – 79.3) | |
| Adjusted means | 78.1 | 78.2 | |
| Quartiles (%) | n (%) | n (%) | OR (95%CI) |
| Q1 (<77.2) | 18 (25.0) | 21 (22.6) | Ref. |
| Q2 (77.2, 78.2) | 17 (23.6) | 25 (26.9) | 0.6 (0.2, 1.7) |
| Q3 (78.3, 79.4) | 15 (20.8) | 25 (26.9) | 0.6 (0.2, 1.6) |
| Q4 (≥79.5) | 22 (30.6) | 22 (23.7) | 1.1 (0.4, 2.8) |
| IL-6 promoter methylation, N | 72 | 85 | Difference1 (95% CI) |
| %, Mean (SD) | 56.1 (11.8) | 58.0 (12.1) | 1.7 (-2.4, 5.8) |
| Median (IQR) | 56.0 (47.6-63.5) | 55.6 (50.9-66.1) | |
| Adjusted means | 55.6 | 58.4 | |
| Quartiles (%) | n (%) | n(%) | OR (95%CI) |
| Q1 (<49.8) | 21 (29.2) | 18 (21.2) | Ref. |
| Q2 (49.8, 55.8) | 14 (19.4) | 25 (29.4) | 0.4 (0.1, 1.0) |
| Q3 (55.9, 64.5) | 20 (27.8) | 19 (22.4) | 0.7 (0.2, 1.8) |
| Q4 (≥64.6) | 17 (23.6) | 23 (27.1) | 0.5 (0.2, 1.3) |
CI=Confidence Interval, IQR=Interquartile Range, OR= Odds Ratio
Linear regression and logistic regression models were adjusted for age, gender, race/ethnicity and BMI.
A total of 58 1-to-1, age-gender matched pairs comprising one PT commuter and one car commuter were formed. No statistically significant differences were found for WBC (difference= 0.07 cells/mm3, 95%CI: -0.64, 0.77), CRP (difference = 0.03 mg/dl, 95%CI:-0.67, 0.74), LINE-1 methylation (difference=-0.07%, 95% CI:-0.91, 0.77) and IL-6 methylation (difference=-3.81%, 95%CI:-10.15, 2.52) between pairs. There remained, however, an age difference of about 1 year (difference=0.98yr, 95% CI: 0.58,1.39) between pairs.
Discussion
In this first assessment of the biological impact of commute modes, we found that PT commuters to a New York college campus had a similar inflammatory and epigenetic status compared to car drivers, even though they were about 10 year younger and had 2kg/m2 lower BMI than the car commuters. However, compared to PT commuters, car drivers ate more fruits and were overall more physically active. These results are compatible with the American Time Use Survey (ATUS) which shows that daily commute tends to squeeze the time dedicated to other essential activities such as exercise, food preparation, and sleeping. (Basner et al., 2007;Christian 2012) A transportation survey conducted every year since 2007 in the study target population at Queens College has consistently shown that the median commute time of car drivers is 60 minutes, per day, versus one hundred and twenty minutes for PT users.(Morabia & Zheng 2009) In a scenario in which car drivers commute in one hour, and PT users in two hours, ATUS predicts that the PT commuters will lack 2.2 minutes of exercise, 1.4 minutes of food preparation, and 15.6 minutes of sleeping per day.(Christian 2012) The reduction of exercise time seems too modest to explain the present study results, but a compounded loss of 16.4 minutes per day in health-related activities (-5.2% for a two-hour commuter compared to a one-hour commuter) may make a difference. Thus, the time saved by car drivers in their commute can be allocated to health-related activities and may explain a higher adherence to physical activity guideline in car drivers than in PT commuters.
We explored differences in inflammatory response across commute modes because it is a plausible short-term effect of the type of moderate physical activity involved when commuting using PT. Physical activity can stimulate anti-inflammatory cytokine production, such as IL-1ra, IL-4 and IL-10, while sedentary behaviors can generate an excess of pro-inflammatory cytokines, such as IL-1, TNF and chemokines.(Colbert et al.,2004) However, we did not find differences in CRP and WBC between two commute modes. Cytokine balance may be under epigenetic regulation (Backdahl et al., 2009). DNA methylation is an epigenetic event that may contribute to cancer and other human disease occurrence by altering gene expression. Global hypomethylation, as indicated by low levels of LINE-1 methylation, has been associated with genome instability and elevated cancer risk, whereas methylation in the promoter region of specific genes is associated with gene silencing. Methylation patterns can be influenced by environmental factors such as diet, (Zhang et al., 2011b) physical activity, (Bjornsson et al., 2008;Coyle et al., 2007;Zhang et al., 2011a) and air pollution.(Miller and Ho 2008). In this study, we did not find that commuting modes affected the methylation levels of LINE-1 or IL-6 promoter. The lack of difference in global CRP, WBC, and methylation levels between car drivers and PT users may be due to other characteristics associated with their commute or their way of life. For example, our previous work indicates a slight increase in exposure to PM2.5 for a seven hour trip by PT (mostly subway) vs. by car, (Morabia et al., 2009) and air pollution increases inflammatory response. (Pope, III et al., 2004) Short-term (Liao et al., 2005;Schwartz 2001) and long-term (Chen and Schwartz 2008) elevation of ambient PM10 is associated with increased levels of inflammatory markers (Peters et al., 2001;Pope, III et al., 2004)
Limitations
As our previous research has already shown that PT commuters to Queens College expend more energy than car commuters, the physical activity questionnaire for the current study was mainly designed to assess the physical activity of the participants beyond their commute. We therefore did not have the possibility to factor in the specific extra energy spent during the commute in these analyses. Our results, however, indicate that future studies should use a more detailed measure of physical activity, such as diaries, in order to decompose it into commute, leisure, home, and work.
Limitations in the methodologies used to determine biomarker levels may have also hampered our ability to identify an association with commute mode. For the assessment of IL6 gene promoter methylation, the variability across the sites targeted within the IL6 promoter, as indicated by the coefficient of variation, may have reduced the robustness of the designed assay to capture the acute differences to be expected within this setting. Similarly, assay-based issues may have impacted the assessment of global methylation. LINE-1 is a retrotransposon distributed throughout the genome. As a repetitive element, it can be easily assessed using a PCR-based method, making it amenable for population-based studies. However, though commonly used, it has not been established how adequately this surrogate marker reflects true genome-wide methylation levels.
A strength of this study was its sampling method since participants were randomly selected, according to their commute type and duration, from a roster of about 4,000 persons who previously provided a detailed description of their commute mode in repeated college-wide surveys. Its design, analogous to a case-control study in which car drivers are the “cases” and PT commuters the “controls,” provides insight into potential differential selection processes. In particular, PT commuters responded better than car drivers to each of the multiple emails sent to all the eligible subjects. Our objective of 100 PT users was easily met, but we were not able to recruit during the same period more than 79 car drivers. We cannot therefore rule out that car drivers were selected among a more physically active and health conscious subset of the target population, therefore attenuating the observed differences.
These results need to be considered in a context of growing interest in public transportation as a means of reducing fossil-fuel consumption and global warming.(Zheng 2008) Americans took, in 2007, 10.3 billion PT trips, representing a 32% increase compared to 1995. Between January and September 2008, PT usership increased, for example, by 3.8% in New York, 8.1% in Atlanta, and 32.7% in Charlotte, NC.(APTA 2008) Plans of developing a rapid rail network across the US are under discussion.
The similar inflammatory and epigenetic traits observed in this study in car and PT commuters convey an important and apparently neglected prevention message that, if not integrated into a more general strategy to achieve overall dietary and physical activity objectives, society may miss the health benefit to be harvested if commute modes increasingly are switched from car to PT.
Acknowledgments
This COMIR (Commuting Mode and Inflammatory Response) project received financial support from the CUNY Collaborative Incentive Research Grant (CIRG) program, round 16, number 1606, from the NIEHS Center ES009089 at Columbia University, and from University of North Texas Health Science Center School of Public Health Seed Program. The authors thank Tashia Amstislavski and Steves Vanderpool for their help in the recruitment and data collection
Footnotes
Results has been presented orally at the Meeting of the International Society for Environmental Epidemiology (ISEE, Barcelona, September 14, 2011.)
Conflict of interest statement
None of the authors have conflict of interests with the content of the paper.
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References
- APTA. Public Transportation Fact Book. American Public Transportation Association; 2008. http://www.apta.com/research/stats/factbook/documents08/2008_fact_book_final_part_1.pdf. [Google Scholar]
- Backdahl L, Bushell A, Beck S. Inflammatory signalling as mediator of epigenetic modulation in tissue-specific chronic inflammation. Int J Biochem Cell Biol. 2009;41:176–84. doi: 10.1016/j.biocel.2008.08.023. [DOI] [PubMed] [Google Scholar]
- Basner M, Fomberstein KM, Razavi FM, et al. American time use survey: sleep time and its relationship to waking activities. Sleep. 2007;30:1085–95. doi: 10.1093/sleep/30.9.1085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Besser LM, Dannenberg AL. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med. 2005;29:273–80. doi: 10.1016/j.amepre.2005.06.010. [DOI] [PubMed] [Google Scholar]
- Bjornsson HT, Sigurdsson MI, Fallin MD, et al. Intra-individual change over time in DNA methylation with familial clustering. JAMA. 2008;299:2877–83. doi: 10.1001/jama.299.24.2877. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Block G, Jensen CD, Block TJ, et al. The work and home activities questionnaire: energy expenditure estimates and association with percent body fat. J Phys Act Health. 2009;6:S61–S69. doi: 10.1123/jpah.6.s1.s61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Block G, Woods M, Potosky A, Clifford C. Validation of a self-administered diet history questionnaire using multiple diet records. J Clin Epidemiol. 1990;43:1327–35. doi: 10.1016/0895-4356(90)90099-b. [DOI] [PubMed] [Google Scholar]
- Bollati V, Baccarelli A, Hou L, et al. Changes in DNA methylation patterns in subjects exposed to low-dose benzene. Cancer Res. 2007;67:876–80. doi: 10.1158/0008-5472.CAN-06-2995. [DOI] [PubMed] [Google Scholar]
- Chen JC, Schwartz J. Metabolic syndrome and inflammatory responses to long-term particulate air pollutants, Environ. Health Perspect. 2008;116:612–7. doi: 10.1289/ehp.10565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Christian T. Trade-Offs Between Commuting Time and Health-Related Activities. J Urban Health. 2012 doi: 10.1007/s11524-012-9678-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Colbert LH, Visser M, Simonsick EM, et al. Physical activity, exercise, and inflammatory markers in older adults: findings from the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2004;52:1098–104. doi: 10.1111/j.1532-5415.2004.52307.x. [DOI] [PubMed] [Google Scholar]
- Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420:860–7. doi: 10.1038/nature01322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Coyle YM, Xie XJ, Lewis CM, et al. Role of physical activity in modulating breast cancer risk as defined by APC and RASSF1A promoter hypermethylation in nonmalignant breast tissue. Cancer Epidemiol Biomarkers Prev. 2007;16:192–6. doi: 10.1158/1055-9965.EPI-06-0700. [DOI] [PubMed] [Google Scholar]
- Edwards RD. Public transit, obesity, and medical costs: assessing the magnitudes. Prev Med. 2008;46:14–21. doi: 10.1016/j.ypmed.2007.10.004. [DOI] [PubMed] [Google Scholar]
- Ford ES. Does exercise reduce inflammation? Physical activity and C-reactive protein among U.S. adults. Epidemiology. 2002;13:561–8. doi: 10.1097/00001648-200209000-00012. [DOI] [PubMed] [Google Scholar]
- Hammett CJ, Oxenham HC, Baldi JC, et al. Effect of six months’ exercise training on C-reactive protein levels in healthy elderly subjects. J Am Coll Cardiol. 2004;44:2411–3. doi: 10.1016/j.jacc.2004.09.030. [DOI] [PubMed] [Google Scholar]
- Hill JO, Wyatt HR, Reed GW, Peters JC. Obesity and the environment: where do we go from here? Science. 2003;299:853–5. doi: 10.1126/science.1079857. [DOI] [PubMed] [Google Scholar]
- Kasapis C, Thompson PD. The effects of physical activity on serum C-reactive protein and inflammatory markers: a systematic review. J Am Coll Cardiol. 2005;45:1563–9. doi: 10.1016/j.jacc.2004.12.077. [DOI] [PubMed] [Google Scholar]
- Lachapelle u, Frank LD. Transit and health: Mode of transport, employer-sponsored public transit pass programs, and physical activity. J Public Health Policy. 2009;30:S73–S94. doi: 10.1057/jphp.2008.52. [DOI] [PubMed] [Google Scholar]
- Liao D, Heiss G, Chinchilli VM, et al. Association of criteria pollutants with plasma hemostatic/inflammatory markers: a population-based study. J Expo Anal Environ Epidemiol. 2005;15:319–28. doi: 10.1038/sj.jea.7500408. [DOI] [PubMed] [Google Scholar]
- MacDonald JM, Stokes RJ, Cohen DA, Kofner A, Ridgeway GK. The effect of light rail transit on body mass index and physical activity. Am J Prev Med. 2010;39:105–12. doi: 10.1016/j.amepre.2010.03.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Miller RL, Ho SM. Environmental epigenetics and asthma: current concepts and call for studies. Am J Respir Crit Care Med. 2008;177:567–73. doi: 10.1164/rccm.200710-1511PP. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mora S, Cook N, Buring JE, Ridker PM, Lee IM. Physical activity and reduced risk of cardiovascular events: potential mediating mechanisms. Circulation. 2007;116:2110–8. doi: 10.1161/CIRCULATIONAHA.107.729939. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Morabia A, Amstislavski PN, Mirer FE, et al. Air pollution and activity during transportation by car, subway, and walking. Am J Prev Med. 2009;37:72–7. doi: 10.1016/j.amepre.2009.03.014. [DOI] [PubMed] [Google Scholar]
- Morabia A, Costanza MC. Does walking 15 minutes per day keep the obesity epidemic away? Simulation of the efficacy of a populationwide campaign. Am J Public Health. 2004;94:437–40. doi: 10.2105/ajph.94.3.437. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Morabia A, Mirer FE, Amstislavski TM, et al. Potential health impact of switching from car to public transportation when commuting to work. Am J Public Health. 2010;100:2388–91. doi: 10.2105/AJPH.2009.190132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Morabia A, Zheng Y. On the influence of a raffle upon responses to an urban transportation survey in New York City. Int J Public Health. 2009;54:31–4. doi: 10.1007/s00038-008-8118-6. [DOI] [PubMed] [Google Scholar]
- Ogilvie D, Egan M, Hamilton V, Petticrew M. Promoting walking and cycling as an alternative to using cars: systematic review. BMJ. 2004;329:763. doi: 10.1136/bmj.38216.714560.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Peters A, Frohlich M, Doring A, et al. Particulate air pollution is associated with an acute phase response in men; results from the MONICA-Augsburg Study. Eur Heart J. 2001;22:1198–204. doi: 10.1053/euhj.2000.2483. [DOI] [PubMed] [Google Scholar]
- Pope CA, III, Hansen ML, Long RW, et al. Ambient particulate air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly subjects. Environ Health Perspect. 2004;112:339–45. doi: 10.1289/ehp.6588. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rogers CJ, Colbert LH, Greiner JW, Perkins SN, Hursting SD. Physical activity and cancer prevention : pathways and targets for intervention. Sports Med. 2008;38:271–96. doi: 10.2165/00007256-200838040-00002. [DOI] [PubMed] [Google Scholar]
- Schwartz J. Air pollution and blood markers of cardiovascular risk, Environ. Health Perspect. 2001;109:405–9. doi: 10.1289/ehp.01109s3405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Subar AF, Thompson FE, Kipnis V, et al. Comparative validation of the Block, Willett, and National Cancer Institute food frequency questionnaires : the Eating at America’s Table Study. Am J Epidemiol. 2001;154:1089–99. doi: 10.1093/aje/154.12.1089. [DOI] [PubMed] [Google Scholar]
- Zhang FF, Cardarelli R, Carroll J, et al. Physical activity and global genomic DNA methylation in a cancer-free population. Epigenetics. 2011a;6:293–9. doi: 10.4161/epi.6.3.14378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zhang FF, Morabia A, Carroll J, et al. Dietary patterns are associated with levels of global genomic DNA methylation in a cancer-free population. J Nutr. 2011b;141:1165–71. doi: 10.3945/jn.110.134536. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zheng Y. The benefit of public transportation: physical activity to reduce obesity and ecological footprint. Prev Med. 2008;46:4–5. doi: 10.1016/j.ypmed.2007.11.019. [DOI] [PubMed] [Google Scholar]