Regular physical activity has many benefits, as it lowers the risk of developing several chronic diseases, including cardiovascular disease (CVD) (37). Although evidence of the positive effects of increased physical activity on the prevention of CVD is widely acknowledged (32), the precise biological mechanisms underlying this relationship still remain unclear. However, it has recently been suggested that the inverse association between CVD and physical activity can be explained by the impact of physical activity on blood pressure (BP), lipids, body mass index (BMI), and, for the most part, on inflammatory/hemostatic biomarkers (21). During the last decade, inflammation has been established as an important independent risk factor for CVD. Inflammatory mechanisms are seen as key players in mediating atherosclerosis, the underlying pathology of cardiovascular events (30).
Several inflammatory markers such as acute phase reactants or cytokines have been considered to predict future cardiovascular risk (16). Among these biomarkers, C-reactive protein (CRP), an acute phase protein primarily released by the liver in response to interleukin-6 (IL-6), has proven to be most conducive for clinical use. Furthermore, CRP seems to be the strongest inflammatory predictor of cardiovascular events (4).
Investigating the inflammatory response to physical activity has opened a relatively new field of interest in public health research. A variety of studies have shown inverse relationships between physical activity and concentrations of acute phase reactants such as CRP, fibrinogen, or plasma viscosity (11,17,36) as well as cytokines such as IL-6 (31). Previous studies have mainly concentrated on the effects of a single domain of physical activity, predominantly on leisure time or recreational physical activity. However, it is essential to also include other settings in which people spend most of their time, for instance, occupational or transportation physical activity.
In the present study, we hypothesized that physical activity performed at work, during transportation, in the household, and during leisure time is associated with lower levels of CRP, fibrinogen, and IL-6 among middle-aged men and women.
The 1989/1990 MONItoring trends and determinants in CArdiovascular disease (MONICA) Augsburg Survey was the second examination (S2) to be conducted in the city of Augsburg (Germany) and two adjacent counties as part of the multinational World Health Organization (WHO) MONICA project. The cross-sectional survey was performed to estimate the prevalence and distribution of CVD risk factors in both men and women. All subjects are currently followed within the frame of the cooperative research platform in the region of Augsburg (KORA). Details concerning the objectives and design of the worldwide WHO project as well as the data collection and sampling frame for the three independent Augsburg Surveys 1984-1995 have been described elsewhere (20,39). In brief, for S2 (1989/1990), 6637 individuals were drawn from a target population of 349,050 residents aged 25-74 yr, using two-stage random sampling stratified by age and sex. A total of 4940 individuals participated in the study. The present study was restricted to subjects aged 35-74 yr (n = 3994) because inflammatory markers had only been measured in these age groups. Of these, 3613 subjects had available blood samples and represented the study base for the selection of a random sample, in which measurements of inflammatory markers were performed (n = 840). Individuals with incomplete data on inflammatory markers or any of the covariables used in this study were excluded from further analyses (n = 19, 2.3%). Because the lipid-lowering drugs seem to be associated with decreasing CRP levels (2), individuals taking statins (n = 4, 0.5%) were removed from the data set. In addition, persons taking either anticoagulants (n = 2, 0.2%) or antiplatelet drugs (n = 19, 2.3%) were left out. Thus, final analyses were performed with the data of 796 subjects (437 men and 359 women). The study was approved by local authorities, and all subjects provided a written informed consent.
The MONICA core questionnaire.
Applying standardized face-to-face interviews, baseline information on sociodemographic factors, medical history, self-estimated limited physical activity due to health problems, addictive behavior such as smoking and drinking habits, medication use and menopause were assessed.
Smoking levels were categorized into current smokers, ex-smokers, and never smokers. Alcohol consumption was classified into no alcohol consumption, moderate alcohol consumption (for men: >0 to <40 g·d−1, for women: >0 to <20 g·d−1), and high alcohol consumption (for men: ≥40 g·d−1, for women: ≥20 g·d−1). Education status was disposed into low (≤11 yr of school) and high educational levels (≥12 yr of school).
The MONICA Optional Study on Physical Activity.
The MONICA Optional Study on Physical Activity (MOSPA) aimed to assess different domains of physical activity covering a 1-yr period. Hence, participants were asked to report the time usually spent on physical activity during work, transportation to work, household, and leisure time during a normal week. The subjects' indications allowed the calculation of METs, expressed in minutes per week, using a standardized program on the basis of the compendium of physical activities (1) provided by the Centers for Disease Control and Prevention (CDC). Each physical activity domain was assigned an intensity category(light, moderate, vigorous) on the basis of the recommendations by the American College of Sports Medicine and the CDC (26). Individuals were graded into the category corresponding to the highest intensity level of their activities. For example, if subjects were engaged in light and moderate physical activities, they were categorized as moderately active. In addition, we a priori determined a minimum time spent while executing the level of physical activity in the respective category.
- Light physical activity, defined as activities causing no or little raise in breathing or heart rate (HR) (<3.0 METs): subjects reported activity in this category only.
- Moderate physical activity, defined as activities causing little to moderate raise in breathing and HR (3.0-6.0 METs): subjects could have engaged in light activity as well. Participants below the cutoff point of 120 min·wk−1 spent on physical activity were reassigned to light physical activity.
- Vigorous physical activity, defined as activities causing moderate to large raise in breathing and HR (>6.0 METs): subjects could have also indicated engagement in moderate and/or light activity. Participants below the cutoff point of 90 min·wk−1 were reassigned to moderate physical activity.
Furthermore, a category "no physical activity" was set up to classify subjects who did not report any vigorous, moderate, or light activity in the respective domain of physical activity. The MET-minutes per week of the four domains of physical activity were later added up, thereby creating a fifth rubric (summary physical activity) representing the overall activity level in the study population. For work and household, only light and moderate activity was offered as an answer possibility in the MOSPA questionnaire. For transportation, subjects could report engagement in moderate and/or vigorous physical activity. Six persons who stated no physical activity at all in any of the four physical activity domains were allotted to "no physical activity" in each single domain but were assigned to "light physical activity" of the summary.
We also calculated quartiles within each physical activity domain using the total of MET-minutes per week in the respective domain to support our results on the association between physical activity and markers of inflammation. However, classification into light, moderate, and vigorous physical activity allowed a more accurate assessment of physical activity taking both time and intensity into account. The results for the quartiles are shown in the annex as supporting material.
BP, BMI (weight divided by height2 (kg·m−2)), and waist-to-hip ratio (WHR) were measured by trained medical staff (mainly nurses). BP was measured with a Hawksley Random Zero sphygmomanometer. Three BP recordings were taken from each individual after completion of the interview, i.e., after being at rest in a sitting position for an average of 30 min. The BP results provided are based on the mean of the second and third BP recordings. Further details on the measurement procedures are reported elsewhere (12). Actual hypertension was defined as BP values ≥140/90 mm Hg and/or the usage of antihypertensive medication given that the subjects were aware that they had hypertension (12).
Nonfasting blood samples were collected from all subjects under standardized conditions in 1989/1990 (15). Until analysis, plasma and serum samples were stored at −80°C. Fibrinogen was measured in plasma by an immunonephelometric assay on a BN II analyzer (Behringwerke, Marburg, Germany) (19). CRP concentrations were determined in serum using a high-sensitivity latex-enhanced nephelometric assay also on a BN II analyzer (Dade Behring, Marburg, Germany) (33). Serum levels of IL-6 were measured by enzyme-linked immunosorbent assay (23). Total cholesterol was measured by an enzymatic method (CHOD-PAP; Boehringer Mannheim, Mannheim, Germany), HDL cholesterol after precipitation with phosphotungstic acid/Mg2+ (Boehringer Mannheim), and LDL cholesterol after precipitation with dextran sulfate (Quantolip LDL, Immuno AG).
Characteristics of the study population were depicted by means and SD for continuous variables. Discrete variables were expressed as absolute and relative frequencies. The normality of the distribution was analyzed applying the Kolmogorov-Smirnov test. Because of a skewed distribution, CRP and IL-6 were log-transformed and shown as geometric means. To test for differences in median values between MET-minutes per week in the different domains of physical activity and the classification into low, moderate, and vigorous physical activity, Kruskal-Wallis test was applied. To avoid multicollinearity, Pearson correlation was computed to determine the relationships between the variables of interest. Because waist circumference was highly correlated with both BMI and WHR (r = 0.76 and r = 0.81, respectively), waist circumference was not used for further analyses. ANCOVA was applied to examine the association between the predictor variable physical activity and the outcome variables fibrinogen, CRP, and IL-6. Means and geometric means of the inflammatory markers according to the level of each physical activity domain were calculated unadjusted, partially adjusted for age and sex (model 1), and fully adjusted for age, sex, BMI, WHR, smoking status, hypertension, diabetes, total-to-HDL cholesterol ratio, education, and self-reported limited physical activity due to health problems (model 2). Potential confounding factors were selected on the basis of their age- and sex-adjusted associations with physical activity measures and inflammatory markers in the present study and a review of the literature. All chosen confounders were simultaneously entered into the regression model. Multivariate linear regression was used to examine the relation between CRP, fibrinogen, and IL-6 as outcome variables and domains of physical activity as well as potential confounding factors as explanatory variables. In addition, possible interactions between physical activity and sex, BMI, WHR, and current smoking as well as ex-smoking regarding the inflammatory markers were tested. The association of the summary physical activity with CRP/fibrinogen/IL-6 was then analyzed stratified by smoking status because of the identified interactions. P values for trend were calculated from the multiple linear regression models treating exercise categories as ordinal covariables. All reported P values are based on a significance level of 5%. The statistical software package SAS 9.1.3 (SAS Institute, Inc., Cary, NC) was used for all statistical analyses.
Characteristics of the study population are presented in Table 1. Physical activity levels are shown for the summary rubric. Both men (61.3%) and women (59.6%) primarily engaged in moderate physical activity, whereas the category with the lowest activity was occupied by the fewest (men: 7.8%; women: 8.1%). MET-minutes per week were considerably higher in men (11,581) compared with women (7398). A more detailed description of the MET-minutes per week (median, range, and P value) is given in the Appendix (Supplementary Table 1, http://links.lww.com/mss/A6).
Table 2 illustrates the association between the inflammatory markers of interest and the different domains and levels of physical activity for the partially adjusted model 1 and the fully adjusted model 2. In the model adjusted for age and sex (model 1), significant results for the trend test in all physical activity domains, except for household (fibrinogen, CRP, IL-6) and work (IL-6), indicate a strong inverse relationship between the examined biomarkers of inflammation and physical activity. After further adjustments for BMI, WHR, smoking status, hypertension, diabetes, total-to-HDL cholesterol ratio, education, and self-reported limited physical activity due to health problems (model 2), the association examined above remained significant for fibrinogen in each physical activity domain (work: P trend = 0.038, transportation: P trend = 0.025, leisure time: P trend = 0.013, summary: P trend < 0.001). CRP stayed significant in the summary (P trend = 0.003). IL-6 revealed significant results for transportation (P trend = 0.031), leisure time (P trend = 0.016), and the summary (P trend < 0.001).
An outline of the relation between physical activity (summary) and other potential confounding factors as well as the three biomarkers as outcome variables is depicted in Table 3. Parameter estimates are listed in addition to the P value to underline the effect of physical activity and other cardiovascular risk factors on the inflammatory parameters. The multivariate model reveals that physical activity serves as a good predictor for the variability of the inflammatory markers. Both moderate (P = 0.002) and vigorous (P < 0.001) physical activity had an independent effect on fibrinogen when examining the summary physical activity. CRP revealed similar results for moderate and vigorous (both P < 0.001) physical activity. Also, parameter estimates of CRP were intensified for moderate (β = −0.462) and vigorous physical activity (β = −0.543) concerning the reference group. In contrast, IL-6 was associated with vigorous physical activity (P = 0.004) only. Furthermore, age, BMI, and current smoking status proved significant independent positive correlates across all inflammatory parameters.
We found a strong interaction between physical activity and smoking as well as ex-smoking concerning the inflammation markers (smoking: fibrinogen, P = 0.003; ex-smoking: CRP, P < 0.001; IL-6, P = 0.049). Therefore, we stratified the results by smoking status (Fig. 1 and Supplementary Tables 2 and 3, http://links.lww.com/mss/A7, http://links.lww.com/mss/A8). The highest levels of fibrinogen, CRP, and IL-6 were found in participants with light physical activity in combination with current smoking or with ex-smoking (smoking: fibrinogen, 5.05 g·L−1; ex-smoking: CRP, 3.44 mg·L−1; IL-6, 3.85 pg·mL−1). In contrast, the lowest inflammatory levels were found in subjects with vigorous physical activity in combination with either never smoking or ex-smoking (never smoking: CRP, 1.19 mg·L−1; ex-smoking: fibrinogen, 3.86 g·L−1; IL-6, 1.6pg·mL−1). Among smokers, higher levels of physical activity were inversely related with lower levels of fibrinogen (P trend < 0.001) and IL-6 (P trend = 0.046). Regarding ex-smokers, a statistically significant inverse association with IL-6 was found (P trend = 0.021). Among never smokers, only CRP (P trend = 0.003) showed significant results. No significant interactions were found between summary physical activity and age (except for IL-6), sex, BMI, or WHR concerning any of the three biomarkers (Supplementary Table 4, http://links.lww.com/mss/A9).
These results confirm and extend previous findings indicating that, beyond leisure time physical activity, activities performed in daily routines, for instance, cycling/walking to work as means of transportation, may also have a protective effect on inflammatory markers such as fibrinogen, CRP, and IL-6 (5,9,11,25,36).
Only very few studies, mainly among the elderly, have assessed exercise beyond leisure time or recreational physical activity (7,28). For example, as part of the MacArthur Studies of Successful Aging, data on recreational, house/yard work, and work activity were collected. Beyond the frequently reported effect of recreational physical activity on inflammatory biomarkers, high levels of house/yard work activity also had a protective effect on CRP levels, whereas high levels of work activity failed to demonstrate an inverse association with CRP or IL-6 (28). To our knowledge, no published study has specifically examined fibrinogen in this context. However, Wannamethee et al. (36) showed that self-reported physical activity, which included travel to and from work, had a beneficial effect on fibrinogen in elderly men. About the results of the present study, it should be mentioned that the fact that household activities did not reach vigorous intensities and rarely reached moderate intensities could have contributed to the negative findings. The concept of lifestyle physical activity aims at actively opposing the increasing physically inactive lifestyle and at firmly embedding physical activity into every day life. Lifestyle physical activity has been defined as "the daily accumulation of at least 30 minutes of self-selected activities, which includes all leisure, occupational or household activities that are at least moderate to vigorous in their intensity and could be planned or unplanned activities that are part of everyday life" (8). Walking or biking to work instead of choosing the motorized alternative, for instance, has previously been shown to improve health-related fitness (24). Thus, incorporating lifestyle activity for health in daily routines seems to contribute to decreased inflammatory levels.
An interesting interaction between smoking status and physical activity in regard to inflammatory markers was found in the present study. It has previously been shown that in current smokers, blood levels of IL-6 (38), fibrinogen, and CRP (10,35) were elevated compared with never smokers. Although cessation from smoking lowers levels of inflammatory markers considerably, they still stay significantly elevated for one to two decades until reaching levels of never smokers (35). A very recent review on the association between physical activity and smoking drew the conclusion that, overall, physical activity and smoking seem to be inversely related, although the possibility of confounding factors such as physiological, psychological, or sociodemographic factors interfering with the association cannot fully be ruled out (14). Interestingly, our results suggest that increased engagement in physical activity has the greatest effect on smokers and ex-smokers in efficiently lowering levels of inflammatory markers. It is particularly notable that the effect of vigorous physical activity seems to be most beneficial on CRP among both smokers and ex-smokers; however, vigorous physical activity had a positive effect on fibrinogen mainly among smokers and on IL-6 mainly among ex-smokers. Further exploration of this finding in future studies, especially why increased physical activity does not seem to have a great impact among never smokers regarding lower levels of fibrinogen or IL-6, will yield important insights in this topic.
Although our results indicate that physical activity may mitigate inflammation, conflicting results have previously been reported in particular cases as well, in which no association was found between CRP and physical activity among women (3) or between CRP as well as fibrinogen and physical activity in both sexes (34). Other studies favored a low BMI as a predictor of decreased levels of inflammatory markers such as CRP or fibrinogen over physical activity (22,27).
Furthermore, the limitations of the study, which may have influenced our findings, need to be addressed: first, the study was cross-sectional in design. Causal inference cannot be made because only a snapshot of the population can be produced. Second, as well as in most other epidemiological studies, our data on physical activity were self-reported, and therefore, reporting bias cannot be fully avoided. Nevertheless, the MOSPA questionnaire we used for assessing physical activity levels has been validated in previous studies (13,29). The strict classification into low, moderate, and vigorous physical activity may seem somewhat arbitrary. However, this solution worked best to combine the multiple answer options (e.g., subjects engaged in moderate and vigorous physical activity were categorized as vigorously active) and our division most probably reflects the participants' activity level correctly because the results were comparable to the calculation of quartiles (Supplementary Tables 5 and 6, http://links.lww.com/mss/A10, http://links.lww.com/mss/A11). Third, only one inflammatory marker measurement was available, which can lead to misclassification. CRP levels, for instance, vary considerably within subjects depending on the health condition of the subjects. Normally, CRP circulates at low levels in the blood, but in the case of acute infection, CRP levels often temporarily exceed the 10-mg·L−1 limit. Hence, three serial measurements should be undertaken to determine CRP in an accurate manner (18). One strength of the study was the incorporation of different domains of physical activity performed in daily routines. The nonassessment of occupational physical activity, for instance, has been already described as limiting factor (25). In addition, cardiovascular risk factors were carefully evaluated in the MONICA Augsburg Survey. Therefore, potential confounding by these factors could be controlled for in advance. Further, as of 1989/1990, many drugs potentially affecting inflammation markers were not yet being used as standard common treatments for cardiovascular risk factors as they are today. Thus, by exclusion of a very small proportion of subjects, we could eliminate any potential confounding by these drugs.
The mechanism of how physical activity could influence the inflammatory response associated with CVD yet remains unclear. The modulation in the inflammatory process through increased physical activity may be ascribed to the following factors: enhanced endothelial function, increased insulin sensitivity, reduced arterial BP, improved lipid profile, and reduction in adipose tissue (6).
In conclusion, the salient findings of this cross-sectional study were as follows: 1) Transportation, leisure time, and summary physical activity are inversely associated with the inflammatory biomarkers fibrinogen and IL-6 in a representative sample of 796 middle-aged persons, even after adjustments for multiple confounding factors. In addition, lower levels of fibrinogen are significantly correlated with higher levels of work physical activity, whereas CRP was inversely associated with summary physical activity. 2) There is no statistically significant inverse relationship between household physical activity and any of the investigated markers of inflammation. 3) Observing the main results stratified by smoking status leads to the conclusion that smokers and ex-smokers benefit the most from moderate and vigorous activities by efficiently lowering their levels of inflammatory markers.
Further research, especially prospective studies or clinical trials, will be needed for confirmation of these results. In addition, if a positive effect of physical activity on the reduction of inflammatory markers can be proven, research has to aim at ascertaining the appropriate dose of physical activity (duration, frequency and intensity) that would best achieve lower levels of inflammation and, consequently, the reduction of various chronic diseases related to inflammation such as CVD, type 2 diabetes, and neurodegenerative diseases.
The MONICA/KORA study group consists of: KORA: H.-E. Wichmann (speaker), A. Peters, C. Meisinger, T. Illig, R. Holle, J. John, and their coworkers who are responsible for the design and conduct of the KORA studies; MONICA: U. Keil (PI), A. Döring, B. Filipiak, H.W. Hense, H. Löwel, J. Stieber, and their coworkers who were responsible for the design and conduct of the MONICA studies.
The study was supported by research grants from the German Research Foundation (TH-784/2-1 and TH-784/2-2), by the European Foundation for the Study of Diabetes, and by additional funds provided by the University of Ulm, Germany, and the German Diabetes Center. The KORA research platform (KORA, Cooperative Research in the Region of Augsburg) and the MONICA Augsburg studies were initiated and financed by the Helmholtz Zentrum München, German Research Center for Environmental Health (formerly GSF, National Research Center for Environment and Health), which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. Fibrinogen measurements were financed by an unrestricted educational grant from Behring Marburg (PI Prof. Hans-Werner Hense). We thank all members of the Institute of Epidemiology of the Helmholtz Zentrum München and the field staff in Augsburg who were involved in the planning and conduct of the MONICA/KORA Augsburg. Furthermore, we thank Gerlinde Trischler (University of Ulm), Ulrike Poschen, and Karin Röhrig (both from the German Diabetes Center) for their excellent technical assistance.
The results of the present study do not constitute endorsement by ACSM.
1. Ainsworth BE, Haskell WL, Leon AS, et al. Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc
2. Albert MA, Danielson E, Rifai N, Ridker PM. Effect of statin therapy on C-reactive protein
levels: the pravastatin inflammation/CRP evaluation (PRINCE): a randomized trial and cohort study. JAMA
3. Albert MA, Glynn RJ, Ridker PM. Effect of physical activity
on serum C-reactive protein
. Am J Cardiol
4. Blake GJ, Ridker PM. Inflammatory bio-markers and cardiovascular risk prediction. J Intern Med
5. Borodulin K, Laatikainen T, Salomaa V, Jousilahti P. Associations of leisure time physical activity
, self-rated physical fitness, and estimated aerobic fitness with serum C-reactive protein
among 3,803 adults. Atherosclerosis
6. Bruunsgaard H. Physical activity
and modulation of systemic low-level inflammation. J Leukoc Biol
7. 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
8. Dunn AL, Andersen RE, Jakicic JM. Lifestyle physical activity
interventions. History, short- and long-term effects, and recommendations. Am J Prev Med
9. Ford ES. Does exercise reduce inflammation? Physical activity
and C-reactive protein
among U.S. adults. Epidemiology
10. Frohlich M, Sund M, Lowel H, Imhof A, Hoffmeister A, Koenig W. Independent association of various smoking characteristics with markers of systemic inflammation in men. Results from a representative sample of the general population (MONICA Augsburg Survey 1994/95). Eur Heart J
11. Geffken DF, Cushman M, Burke GL, Polak JF, Sakkinen PA, Tracy RP. Association between physical activity
and markers of inflammation in a healthy elderly population. Am J Epidemiol
12. Hense HW, Filipiak B, Döring A, Stieber J, Liese A, Keil U. Ten year trends of cardiovascular risk factors in the MONICA Augsburg region in southern Germany: results from the 1984/1985, 1989/1990, and 1994/1995 surveys. CVD Prev
13. Iqbal R, Rafique G, Badruddin S, Qureshi R, Gray-Donald K. Validating MOSPA questionnaire
for measuring physical activity
in Pakistani women. Nutr J
14. Kaczynski AT, Manske SR, Mannell RC, Grewal K. Smoking and physical activity
: a systematic review. Am J Health Behav
15. Keil U, Stieber J, Doring A, et al. The cardiovascular risk factor profile in the study area Augsburg. Results from the first MONICA survey 1984/85. Acta Med Scand Suppl
16. Koenig W, Khuseyinova N, Baumert J, et al. Increased concentrations of C-reactive protein
and IL-6 but not IL-18 are independently associated with incident coronary events in middle-aged men and women: results from the MONICA/KORA Augsburg case-cohort study, 1984-2002. Arterioscler Thromb Vasc Biol
17. Koenig W, Sund M, Doring A, Ernst E. Leisure-time physical activity
but not work-related physical activity
is associated with decreased plasma viscosity. Results from a large population sample. Circulation
18. Koenig W, Sund M, Frohlich M, Lowel H, Hutchinson WL, Pepys MB. Refinement of the association of serum C-reactive protein
concentration and coronary heart disease risk by correction for within-subject variation over time: the MONICA Augsburg studies, 1984 and 1987. Am J Epidemiol
19. Krobot K, Hense HW, Cremer P, Eberle E, Keil U. Determinants of plasma fibrinogen
: relation to body weight, waist-to-hip ratio, smoking, alcohol, age, and sex. Results from the second MONICA Augsburg survey 1989-1990. Arterioscler Thromb
20. Lowel H, Doring A, Schneider A, Heier M, Thorand B, Meisinger C. The MONICA Augsburg surveys-basis for prospective cohort studies. Gesundheitswesen
. 2005;67(Suppl 1):S13-8.
21. Mora S, Cook N, Buring JE, Ridker PM, Lee IM. Physical activity
and reduced risk of cardiovascular events: potential mediating mechanisms. Circulation
22. Mora S, Lee IM, Buring JE, Ridker PM. Association of physical activity
and body mass index with novel and traditional cardiovascular biomarkers in women. JAMA
23. Muller S, Martin S, Koenig W, et al. Impaired glucose tolerance is associated with increased serum concentrations of interleukin 6 and co-regulated acute-phase proteins but not TNF-alpha or its receptors. Diabetologia
24. Oja P, Vuori I, Paronen O. Daily walking and cycling to work: their utility as health-enhancing physical activity
. Patient Educ Couns
. 1998;33(Suppl 1):S87-94.
25. Panagiotakos DB, Pitsavos C, Chrysohoou C, Kavouras S, Stefanadis C. The associations between leisure-time physical activity
and inflammatory and coagulation markers related to cardiovascular disease: the ATTICA Study. Prev Med
26. Pate RR, Pratt M, Blair SN, et al. Physical activity
and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA
27. Rawson ES, Freedson PS, Osganian SK, Matthews CE, Reed G, Ockene IS. Body mass index, but not physical activity
, is associated with C-reactive protein
. Med Sci Sports Exerc
28. Reuben DB, Judd-Hamilton L, Harris TB, Seeman TE. The associations between physical activity
and inflammatory markers in high-functioning older persons: MacArthur Studies of Successful Aging. J Am Geriatr Soc
29. Roeykens J, Rogers R, Meeusen R, Magnus L, Borms J, de Meirleir K. Validity and reliability in a Flemish population of the WHO-MONICA Optional Study of Physical Activity
Questionnaire. Med Sci Sports Exerc
30. Ross R. Atherosclerosis-an inflammatory disease. N Engl J Med
31. Rothenbacher D, Hoffmeister A, Brenner H, Koenig W. Physical activity
, coronary heart disease, and inflammatory response. Arch Intern Med
32. Thompson PD, Buchner D, Pina IL, et al. Exercise and physical activity
in the prevention and treatment of atherosclerotic cardiovascular disease: a statement from the Council on Clinical Cardiology (Subcommittee on Exercise, Rehabilitation, and Prevention) and the Council on Nutrition, Physical Activity
, and Metabolism (Subcommittee on Physical Activity
33. Thorand B, Kolb H, Baumert J, et al. Elevated levels of interleukin-18 predict the development of type 2 diabetes: results from the MONICA/KORA Augsburg Study, 1984-2002. Diabetes
34. Verdaet D, Dendale P, De Bacquer D, Delanghe J, Block P, De Backer G. Association between leisure time physical activity
and markers of chronic inflammation related to coronary heart disease. Atherosclerosis
35. Wannamethee SG, Lowe GD, Shaper AG, Rumley A, Lennon L, Whincup PH. Associations between cigarette smoking, pipe/cigar smoking, and smoking cessation, and haemostatic and inflammatory markers for cardiovascular disease. Eur Heart J
36. Wannamethee SG, Lowe GD, Whincup PH, Rumley A, Walker M, Lennon L. Physical activity
and hemostatic and inflammatory variables in elderly men. Circulation
37. Wannamethee SG, Shaper AG. Physical activity
in the prevention of cardiovascular disease: an epidemiological perspective. Sports Med
38. Woodward M, Rumley A, Tunstall-Pedoe H, Lowe GD. Associations of blood rheology and interleukin-6
with cardiovascular risk factors and prevalent cardiovascular disease. Br J Haematol
39. World Health Organization. The World Health Organization MONICA Project (monitoring trends and determinants in cardiovascular disease): a major international collaboration. WHO MONICA Project Principal Investigators. J Clin Epidemiol