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Keywords:

  • physical activity;
  • risk factor;
  • inflammation;
  • insulin sensitivity

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Objective: This study investigated the relationship between physical activity and the obesity-related inflammatory markers C-reactive protein, interleukin-6, and soluble tumor necrosis factor receptors (sTNF-Rs) 1 and 2. Furthermore, we examined the relationship between physical activity and insulin sensitivity (insulin, C-peptide, and hemoglobin A1c levels) and whether inflammatory markers mediate this association.

Research Methods and Procedures: Biomarkers were measured in 405 healthy men and 454 healthy women from two large ongoing prospective studies. Information about physical activity and other variables was assessed by questionnaires.

Results: After adjustment for other predictors of inflammation, physical activity was inversely associated with plasma levels of sTNF-R1, sTNF-R2, interleukin-6, and C-reactive protein (p = 0.07, p = 0.004, p = 0.04, and p = 0.009). After further adjustment for BMI and leptin, as a surrogate for fat mass, most of these associations were no longer significant. Physical activity was also inversely related to insulin and C-peptide levels (p = 0.008 and p < 0.001); however, in contrast to BMI and leptin, levels of inflammatory markers explained only very little of this inverse relationship.

Discussion: These results suggest that frequent physical activity is associated with lower systemic inflammation and improved insulin sensitivity. These associations can partially be explained by a lower degree of obesity in physically active subjects. Although inflammatory markers may mediate obesity-dependent effects of physical activity on inflammatory related diseases such as type 2 diabetes or coronary heart disease, our study suggests that they do not directly account for the beneficial effects of physical activity on insulin resistance.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Physical activity improves insulin sensitivity and reduces the risk of type 2 diabetes and coronary heart disease (CHD)1 (1, 2, 3, 4); however, the mechanisms for these benefits are not completely understood. Obesity-related inflammatory markers may be important mediators in the pathophysiology of these diseases. Thus, the expression of interleukin-6 (IL-6), tumor necrosis factor (TNF)-α, and soluble TNF receptors (sTNF-Rs) from adipose tissue is increased in obese subjects (5, 6), and plasma levels of these cytokines and C-reactive protein (CRP) are associated with BMI and insulin resistance (7, 8, 9). Furthermore, these biomarkers are important risk factors for CHD and type 2 diabetes (10, 11, 12, 13). Thus, these data suggest the hypothesis that physical activity, which leads to a decrease in obesity, may reduce adipose-derived inflammatory markers and lower the risk of chronic diseases.

Results from several cross-sectional studies suggest that higher levels of physical activity are associated with lower CRP levels (14, 15, 16, 17, 18); however, only limited data are available on the association between leisure-time physical activity and IL-6, TNF-α, or sTNF-R in humans. One smaller study (18) found a reduction in TNF-α production by blood mononuclear cells after a 6-month exercise program in 43 healthy volunteers.

The aim of the present study was to investigate the relationship between physical activity and the obesity-related inflammatory markers sTNF-R1, sTNF-R2, IL-6, and CRP. Furthermore, we examine the relationship between physical activity and measures of insulin sensitivity [insulin, C-peptide, and hemoglobin A1c (HbA1c) levels] and whether inflammatory markers mediate this association.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

The Health Professionals Follow-up Study (HPFS)

The HPFS is a prospective cohort investigation of 51, 529 U.S. male health professionals, 40 to 75 years old at baseline in 1986, designed primarily to evaluate associations between diet and chronic disease incidence (19). Information about health and disease are assessed biennially by a self-administered questionnaire, and diet is assessed every 4 years by a validated 131-item self-administered semiquantitative food frequency questionnaire (SFFQ) (20). Between 1993 and 1995, a blood sample was requested from all subjects and returned from 18, 225 participants. This cohort and the method for blood collection have been described previously in detail (21). The men who provided samples were somewhat younger but otherwise similar to those who did not provide samples. Among those who returned the blood sample, subjects were excluded who did not have any information on diet, cigarette smoking, alcohol consumption, or physical activity between 1986 and 1994. In addition, subjects with a history of myocardial infarction, angina pectoris, stroke, type 2 diabetes, intermittent claudication, gastric or duodenal ulcers, gallbladder disease, liver disease, or cancers (except nonmelanoma skin cancer) before 1994 were excluded. From the remaining men, 468 participants were randomly selected based on different patterns of self-reported alcohol consumption determined by frequency, amount, and time (e.g., with meals) of alcohol intake. The purpose of selecting this subset was to investigate the association between alcohol and nutrient intake and blood lipids and inflammatory markers. Except for the exclusion criteria, there is little difference in various characteristics between the 468 men used in this study and the remaining 17, 757 who gave blood. The association between physical activity and biomarkers of obesity in this population has been described previously (22).

Physical activity was derived from the 1994 questionnaire, which included a series of questions on the specific type of activity and the average total time per week spent on the activity over the previous year. The questionnaire also included a question on the weekly number of hours watching television and videocassettes with 13 response categories, ranging from 0 to 40 hours/week. Total weekly energy expenditure from leisure-time physical activity was expressed as metabolic equivalent-hours (MET-hrs) (23). One MET-hr is equivalent to sitting quietly for 1 hour. Total MET-hrs for vigorous activities, defined as requiring MET-hr values ≥ 6, were calculated by using MET-hr from jogging, running, bicycling, swimming laps, playing tennis/squash/racquetball, and doing calisthenics/rowing. Nonvigorous activities (MET-hr < 6) were walking, heavy outdoor work, and weightlifting. The validity and reproducibility of the physical activity measurement have been reported in detail elsewhere (24). Briefly, correlations between 1-week diaries and the questionnaire for reported overall activity and vigorous activity were 0.41 and 0.58, respectively, and the correlation between vigorous activity and resting pulse was −0.45 and −0.41 for postexercise pulse.

Average nutrient intake was derived from the SFFQ in 1994. BMI was calculated as the ratio of body weight (reported in 1994) to body height (reported in 1986) squared, expressed as kilograms per meter squared. The validity of the SFFQ and of self-reported weight and alcohol intake has been reported in detail elsewhere (20, 25, 26). Participants were also asked if they regularly used aspirin or other anti-inflammatory drugs during the past year.

The Nurses’ Health Study II (NHS2)

The NHS2 is a prospective cohort of 116, 671 United States registered female nurses, 25 to 42 years old at baseline in 1989 (27). Health information and disease status are assessed biennially by a self-administered questionnaire, and diet is assessed every 4 years by a self-administered SFFQ. Blood samples were obtained between 1996 and 1998 from over 29, 000 participants. Among those who responded to the questionnaire in the blood collection kit, blood samples were selected from premenopausal women who collected their blood during the luteal phase of their menstrual cycle and who were not using any hormones. After exclusion of women with any of the previously described preexisting conditions, a subset of 473 women was randomly selected from the remaining participants based on different categories of self-reported alcohol consumption pattern, which were similar to those used for the HPFS. As with HPFS, women who gave blood were very similar to those in the overall cohort. The only difference was a greater percentage of women with a family history of breast cancer in the subgroup.

Because diet is assessed only every 4 years in the NHS2, average nutrient intake and alcohol consumption for this analysis were derived from the SFFQ in 1995, which was similar to those used in the HPFS. Height was derived from the baseline questionnaire in 1989 and weight from the 1997 questionnaire, and BMI was calculated as described above. Physical activity, smoking status, and regular use of nonsteroidal anti-inflammatory drugs (aspirin or others) during the past 2 years were derived from the 1997 questionnaire. The validity and reproducibility of the questionnaire data and of physical activity measurements have been reported in detail elsewhere (28, 29). Briefly, for physical activity, the 2-year test-retest correlation was 0.59, and the correlation between activity reported on the questionnaire and that reported on recalls and in diaries was 0.79 and 0.62, respectively.

Measurement of Biochemical Variables

Blood samples were collected in three 10-mL liquid EDTA blood tubes for the men and sodium heparin blood tubes for the women, placed on ice packs, stored in Styrofoam containers, and returned to our laboratory using overnight courier, with over 95% arriving within 24 hours. After arrival, blood samples were centrifuged and aliquoted for storage in the vapor phase of liquid nitrogen freezers (−130 °C or colder). Fewer than 15% of the samples were slightly hemolyzed, and very few were moderately hemolyzed (<3%), lipemic (<1%), or not cooled on arrival (<0.5%). Plasma IL-6, sTNF-R, and leptin concentrations were measured by enzyme-linked immunosorbent assay (R&D Systems, Minneapolis, MN for IL-6 and sTNF-R1; Genzyme Diagnostics, Cambridge, MA for sTNF-R2 in HPFS; R&D Systems for sTNF-R2 in NHS2; and Diagnostic Systems, Webster TX for leptin). CRP was measured spectrophotometrically using a high-sensitivity assay on a Hitachi 911 analyzer (Roche Diagnostics, Indianapolis, IN) and reagents and calibrators from Denka Seiken (Niigata, Japan). Insulin and C-peptide were measured by radioimmunoassay (Linco Research, St. Charles, MO). HbA1c was measured with turbiometric immunoinhibition by using a Hitachi 911 analyzer (Roche Diagnostics). Day-to-day coefficients of variations of all assays were below 10%, and transport conditions did not substantially affect the inflammatory marker plasma levels (30). Furthermore, in a subsample of 82 men from the HPFS who provided blood samples 4 years apart, there were good to excellent intraclass correlations for the inflammatory markers (sTNF-R1, 0.81; sTNF-R2, 0.78; IL-6, 0.47; and CRP, 0.67). All participants gave written informed consent, and the Harvard School of Public Health Human Subjects Committee Review Board approved the study protocol.

Exclusions

For the present study, we excluded participants who did not provide information on BMI, physical activity, diet, or smoking status on the questionnaires returned in 1994 (men) and 1995 and 1997 (women), respectively. This resulted in a sample size of 859 (405 men, 454 women).

Statistical Analyses

Age-adjusted data of the study subjects are presented as means according to the quintile distribution of physical activity. To investigate the association between BMI and the biomarkers, we used Spearman's partial correlation coefficient, adjusting for age and gender. The association between physical activity and the biomarkers was investigated in a model adjusting for age, gender, smoking status, alcohol, intake of nonsteroidal anti-inflammatory drugs, saturated fat, polyunsaturated fat, eicosapentaenoic acid and docosahexaenoic acid, and hours watching television. We included television watching in our regression models because we found in a previous analysis in the HPFS (22) that physical activity and hours of watching television, as measured by our questionnaires, reflect two largely unrelated behaviors that may independently predict biomarker levels. BMI and leptin (the latter as a surrogate for fat mass) were included in secondary analyses to assess their importance as intermediate variables in the biological pathways between physical activity and the biomarkers. To assess the impact of the inflammatory markers on HbA1c, insulin, and c-peptide levels, models were run with and without inclusion of these markers. Relative changes in biomarker levels due to physical activity were calculated by dividing the effect estimates from the regression models by the mean biomarker levels of the total sample.

We used spline regression to test for nonlinearity between physical activity and the biomarkers (31). With the exception of leptin, the test for nonlinearity was not statistically significant for these markers. Therefore, and because leptin was not the main outcome of our study, we conducted the analysis with linear regression using the main exposure variable (i.e., physical activity) and the outcome (i.e., biomarkers) continuously. To adjust for covariates, we used indicator variables in our models, which take into account any potential nonlinear relationship between independent and dependent variables. We used the following categories: age (<35, 35 to 39, 40 to 44, 45 to 49, 50 to 54, 55 to 59, 60 to 64, 65 to 69, and ≥70 years), smoking status (never smoked, past smoker, nonsmoker with unknown past history, current smoker of 1 to 14 cigarettes/day, and current smoker ≥ 15 cigarettes/d), and BMI (<20, 20 to 24, 25 to 29, 30 to 34, and ≥35 kg/m2). All other continuous variables were categorized as quintiles. We also included a cross-product term (gender × independent variable) to evaluate the interaction between gender and the exposures.

Multivariate linear regression with robust variance (using PROC MIXED in SAS; SAS Institute, Cary, NC) was used to insure validity without the need of normal distribution assumptions (32). All p values presented are two-tailed, and p values below 0.05 were considered statistically significant.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Table 1 shows the biomarker levels that we measured for the present study in men, in women, and in the combined cohort. Plasma levels of sTNF-R1, sTNF-R2, and leptin were generally higher in women, whereas IL-6 and CRP concentrations were comparable between the two groups. HbA1c, insulin, and c-peptide levels were slightly higher in men.

Table 1.  Biomarker levels in men, women, and in the combined cohort (mean ± SD)
BiomarkerMen (n = 405)Women (n = 454)Total (n = 859)
  • *

    Includes fasting subjects only (n = 241) for men; in women, all subjects were fasting.

sTNF-R1 (pg/mL)932 ± 2351004 ± 217970 ± 229
sTNF-R2 (pg/mL)1414 ± 3922117 ± 4301785 ± 541
IL-6 (pg/mL)1.55 ± 2.171.53 ± 2.271.54 ± 2.23
CRP (mg/L)1.88 ± 3.031.74 ± 4.291.81 ± 3.75
Leptin (ng/mL)6.58 ± 4.4815.58 ± 11.4411.31 ± 9.92
HbA1c (%)5.73 ± 0.685.24 ± 0.195.47 ± 0.55
Insulin (μU/mL)*12.66 ± 8.9311.36 ± 6.7111.81 ± 7.58
C-peptide (ng/mL)*2.05 ± 1.141.92 ± 0.911.96 ± 0.96

The age-standardized characteristics and biomarker levels of the study subjects according to quintiles of physical activity are presented in Table 2. Men were more active, older, had higher body mass indices, consumed more alcohol, and had a higher energy intake than women. In both men and women, participants with higher physical activity had lower body mass indices and higher alcohol and energy but lower fat intake compared to those with lower physical activity.

Table 2.  Characteristics of the study population by quintile of physical activity (N = 859)*
  • *

    Values are means (except physical activity). All values (except physical activity, age, and number of subjects) are age-standardized.

  • EPA + DHA, eicosapentaenoic and docosahexaenoic fatty acid; NSAID, non-steroidal anti-inflammatory drugs.

  • Includes fasting subjects only (n = 241) in men; in women, all subjects were fasting.

 Men Quintile
Characteristics12345
Median physical activity (MET-hrs/week)4.916.029.149.586.0
N8181808281
BMI (kg/m2)26.325.424.924.625.1
Age (years)60.158.861.160.359.5
Current smoker (%)9.44.83.70.86.2
Alcohol intake (g/d)23.422.520.823.023.8
Total calories (kcal/d)20082020220021832226
Total fat (% energy)30.229.128.830.028.8
EPA + DHA (% energy)0.110.110.150.150.15
Intake of NSAID (%)39.043.549.447.349.2
sTNF-R1 (pg/mL)955944905939907
sTNF-R2 (pg/mL)14571494135014381330
IL-6 (pg/mL)1.441.781.861.421.26
CRP (mg/L)2.042.231.871.641.45
Leptin (ng/mL)8.376.676.645.545.25
HbA1c (%)5.755.675.705.785.77
Insulin (μU/mL)13.314.312.410.510.9
C-peptide (ng/mL)2.162.182.211.831.72
 Women Quintile
Characteristics12345
Median physical activity (MET-hrs/week)2.48.015.124.751.9
N8992919191
BMI (kg/m2)25.824.623.624.022.7
Age (years)42.441.842.442.942.3
Current smoker (%)8.06.710.27.07.1
Alcohol intake (g/d)10.39.712.412.212.3
Total calories (kcal/d)18101907181719311850
Total fat (% energy)30.928.928.327.526.7
EPA + DHA (% energy)0.080.060.070.040.10
Intake of NSAID (%)42.550.149.755.950.4
sTNF-R1 (pg/mL)103510361006966986
sTNF-R2 (pg/mL)21452187211720752064
IL-6 (pg/mL)1.641.661.571.321.52
CRP (mg/L)2.771.681.941.650.88
Leptin (ng/mL)20.8118.5013.6614.3310.47
HbA1c (%)5.265.265.225.245.20
Insulin (μU/mL)11.8011.0612.4410.7610.43
C-peptide (ng/mL)2.171.981.881.771.73

In both groups, individuals with higher physical activity had lower plasma levels of sTNF-R1, sTNF-R2, IL-6, CRP, and leptin. Thus, levels of inflammatory markers in men in the highest quintile of physical activity compared with men in the lowest quintile of physical activity were 5%, 9%, 13%, and 25% lower for sTNF-R1, sTNF-R2, IL-6, and CRP, respectively; the comparison values among women in the two quintile categories were 5%, 4%, 7%, and 68% lower, respectively. Insulin and c-peptide levels were also lower in subjects with higher physical activity.

Because the association between physical activity and the biomarkers may be, in part, mediated by body weight, we next analyzed the association between BMI and inflammatory markers and leptin. As expected, leptin was correlated with BMI (Spearman age-adjusted partial correlation coefficient: men, r = 0.62, p < 0.001; women, r = 0.73, p < 0.001; combined age- and sex-adjusted, r = 0.65, p < 0.001). IL-6 and CRP were moderately correlated with BMI (men, r = 0.24, p < 0.001 and 0.35, p < 0.001, respectively; women, r = 0.27, p < 0.001, and r = 0.48, p < 0.001, respectively; combined, r = 0.26, p < 0.001, and r = 0.45, p < 0.001, respectively), whereas the relationship was somewhat lower for sTNF-R1 (men, r = 0.10, p = 0.04; women, r = 0.15, p = 0.001; combined, r = 0.14, p < 0.001) and sTNF-R2 (men, r = −0.01, p = 0.88; women, r = 0.13, p = 0.005; combined, r = 0.09, p = 0.01).

Table 3 shows the association between physical activity and inflammatory markers and leptin levels, adjusted for other potential predictors of inflammation as absolute and relative changes in biomarker levels associated with an increase in physical activity of 20 MET-hrs/week. These associations were generally comparable between men and women (data not shown). Without adjustment for BMI, there were significant inverse relationships between physical activity and plasma levels of sTNF-R2 (p = 0.004), IL-6 (p = 0.04), and CRP (p = 0.009), but less so for sTNF-R1 (p = 0.07). Thus, subjects running 4 or more hours per week had ∼4% lower sTNF-R1 and sTNF-R2 levels, 6% lower IL-6 levels, and 49% lower CRP levels than those running less than 0.5 hours per week. Adjustment for BMI reduced the association of physical activity on these inflammatory markers (Table 3). After further adjustment for leptin, as a surrogate for fat mass, only the association between sTNF-R2 and physical activity remained significant. When we restricted the analyses to vigorous physical activity, the effect estimates became somewhat stronger in the base model without BMI adjustment (data not shown). However, adjustment for BMI, and especially leptin, again substantially reduced the effect estimates. When both vigorous and nonvigorous physical activity were included in one model to control for each variable, the effect estimates for both variables were not substantially different (data not shown).

Table 3.  Association between physical activity and obesity-related inflammatory markers and leptin levels in men and women (N = 859), with and without adjustment for BMI and leptin
  Model
BiomarkerChange*Base modelBase model + BMIBase model + BMI + leptin
  • Estimated absolute and relative changes associated with an increase in physical activity of 20 MET-hrs per week.

  • *

    Absolute change in biomarker levels, percentage change relative to mean biomarker levels in the cohort, and corresponding p value.

  • Base model is adjusted for gender, age, smoking status, alcohol intake, intake of non-steroidal anti-inflammatory drugs, saturated fat, polyunsaturated fat, eicosapentaenoic and docosahexaenoic fatty acid, and television watching.

  • Indicates a significant interaction between gender and physical activity on absolute change in leptin levels. An increase in physical activity of 20 MET-hrs/week (adjusted for the variables in the base model, except gender) is associated with an absolute change in leptin levels of −0.548 ng/mL in men and of −2.048 ng/mL in women (p < 0.001 for each). After additional adjustment for BMI, changes are −0.421 ng/mL in men and −1.244 ng/mL in women (p < 0.001 for each). The corresponding changes relative to mean leptin levels in men and women are −8.32% (men) and −13.15% (women), respectively, before, and −6.40% (men) and −7.98% (women), respectively, after adjustment for BMI.

sTNF-R1pg/mL−8.71−6.27−2.14
 %−0.90−0.65−0.22
 p0.070.190.66
sTNF-R2pg/mL−23.16−19.71−17.44
 %−1.30−1.10−0.98
 p0.0040.010.03
IL-6pg/mL−0.069−0.064−0.059
 %−4.48−4.16−3.83
 p0.040.060.10
CRPmg/L−0.167−0.120−0.064
 %−9.23−6.63−3.54
 p0.0090.060.26
Leptinng/mL−1.136−0.715-
 %−10.04−6.32-
 p<0.001<0.001-

We also assessed the association between hours of television watching and these biomarkers to determine whether measures of a sedentary lifestyle were important predictors of inflammation (data not shown). In the BMI-unadjusted model, hours of television watching were significantly associated with sTNF-R1 and leptin; however, after adjustment for BMI, none of these associations was statistically significant. Results were similar in men and women (data not shown).

Physical activity was inversely associated with plasma insulin (p = 0.008) and c-peptide levels (p < 0.001, Table 4). These associations were not significantly different between men and women (data not shown). Adjustment for the soluble TNF receptors, IL-6, or CRP slightly weakened these relationships. In contrast, adjustment for BMI and leptin markedly reduced these effect estimates.

Table 4.  Association between physical activity and HbA1c, insulin, and c-peptide levels with and without further adjustment for different inflammatory markers (IM) (including sTNF-R1, sTNF-R2, IL-6, and CRP), BMI, and leptin, in men and women (N = 859)
  Model
BiomarkerChange*Base modelBase model + sTNF-R1 + sTNF-R2Base model + IL-6Base model + CRPBase model + BMIBase model + BMI + leptinBase model + BMI + leptin + IM
  • Estimated absolute and relative changes associated with an increase in physical activity of 20 MET-hrs/week.

  • IM, inflammatory marker(s).

  • *

    Absolute change in biomarker levels, percentage change relative to mean biomarker levels in the cohort, and corresponding p value.

  • Adjusted for gender, age, smoking status, alcohol intake, intake of non-steroidal anti-inflammatory drugs, saturated fat, polyunsaturated fat, eicosapentaenoic and docosahexaenoic fatty acid, and television watching.

  • Models for insulin and c-peptide include fasting subjects only (n = 695).

HbA1c% (absolute)0.0070.0080.0070.0100.0140.0250.025
 % (relative)0.130.150.130.180.260.460.46
 p0.620.560.610.450.310.060.07
InsulinμU/mL−0.459−0.408−0.429−0.395−0.317−0.234−0.195
 %−3.89−3.45−3.63−3.34−2.68−1.98−1.65
 p0.0080.020.010.020.040.140.23
C-peptideng/mL−0.097−0.091−0.092−0.079−0.060−0.039−0.035
 %−4.95−4.64−4.69−4.03−3.06−1.99−1.79
 p<0.001<0.001<0.0010.0020.0070.090.13

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

In this cross-sectional study, we observed statistically significant inverse associations between physical activity and plasma levels of obesity-related inflammatory markers. Further adjustment for BMI and leptin, as a surrogate for fat mass, weakened these associations, suggesting that the beneficial association between physical activity and inflammation is partially due to less body fat in subjects with higher levels of physical activity. These results extend further the findings that frequent physical activity is associated with lower systemic inflammation and higher insulin sensitivity. Furthermore, we found that in contrast to fat mass, inflammatory markers explained only very little of the inverse association between physical activity and insulin markers.

Our results of an inverse association between physical activity and insulin status are in line with intervention and population studies reporting beneficial effects of physical activity on insulin sensitivity and on the prevention of type 2 diabetes (1, 2, 3). Similarly, several studies have shown that physical activity is associated with a lower risk of CHD (for review, see Ref. (4)). However, the mechanisms responsible for these effects are only poorly understood. A few cross-sectional studies have reported that physical activity is associated with lower levels of inflammatory markers such as fibrinogen (14, 15, 17, 22, 33, 34, 35, 36, 37, 38), blood leukocytes (14, 15, 17), and CRP (14, 15, 17). However, not all studies have controlled adequately for potential confounders, and some included persons with known existing chronic disease, which may preclude patients from being active. Several of these studies (14, 15, 17) found physical activity inversely associated with inflammatory markers even after adjustment for BMI. However, although BMI is widely used to measure adiposity, it may not perfectly reflect body fat mass (39). Thus, it was recently shown that leptin is a better surrogate for body composition (40). In line with this observation, we found that adjustment for leptin levels had a larger impact on the association between physical activity and biomarkers than controlling for BMI alone (Tables 3and 4). Thus, adjustment for BMI and leptin reduced the association of physical activity with inflammatory markers by 75% for sTNF-R1, by 62% for CRP, by 25% for sTNF-R2, and by 14% for IL-6, and for insulin and c-peptide by 49% and 60%, respectively, suggesting that body fat may partially mediate some of these associations. Of course, it should be noted that these cross-sectional observations cannot prove causation.

CRP is the principal downstream mediator of the acute phase response and is primarily secreted by the liver in response to TNF-α or IL-6 stimuli (41). Although CRP has been widely used clinically to measure inflammation, its biological significance is less clear. CRP can bind to damaged tissue, to nuclear antigens, and to certain pathogens. It activates complement, binds to Fc receptors, and acts as an opsonin for pathogens (41). Plasma levels of CRP have been shown to be associated with adiposity and insulin resistance (8) and are reduced after weight loss (42). However, whether elevated CRP levels are an epiphenomenon of insulin resistance or play a causative role remains speculative. Nevertheless, elevated plasma CRP levels are an important risk factor for type 2 diabetes (13) and cardiovascular disease (10) and, therefore, highlight the importance of examining preventive measures which decrease CRP levels.

TNF-α, its soluble receptors, and IL-6 are considered to play an important role in the pathophysiology of insulin resistance, type 2 diabetes, and CHD. TNF-α can mediate insulin resistance through indirect and direct effects, including increased free fatty acid oxidation, inhibition of glucose transporter protein GLUT4, reduced autophosphorylation of the insulin receptor, modifications of insulin receptor substrates, or reduced glucose-stimulated insulin release by pancreatic β-cells (5, 43). The soluble TNF receptors are derived by proteolytic cleavage from the TNF cell surface receptors after induction by TNF or other cytokines such as IL-6, IL-1β, or IL-2, have a longer half-life, and are detected with a higher sensitivity than TNF (44, 45). The biological function of these soluble receptors is not entirely clear, and it was suggested that by binding to TNF they might attenuate its bioactivity (44). However, other studies have shown that the soluble receptors promote formation of complexes, which preserve the active trimeric form of TNF and, thus, prevent the decay of TNF into inactive monomeric forms (46, 47). Therefore, the receptors may serve as binding proteins and/or as a slow release reservoir for TNF, thereby prolonging its half-life (48). Clinically, the soluble TNF receptors are excellent indicators of inflammatory processes (for review, see Refs. (44, 45)) and are associated with obesity, insulin resistance, CHD, and angina severity (12, 49). Plasma TNF levels also have been shown to reflect disease intensity in chronic heart failure (50), and, interestingly, recent studies confirmed that the soluble TNF receptors were more predictive of the disease status in heart failure than TNF itself (51, 52). The possible causative role of IL-6 in the pathophysiology of insulin resistance comes from several lines of evidence. As noted above, IL-6 concentrations have been found to be correlated with adiposity and insulin resistance in previous studies (6, 9), and weight loss—accompanied by improvement of insulin sensitivity—is associated with a reduction of IL-6 levels (53). IL-6 also has been shown to increase basal intracellular calcium, which negatively modulates insulin-mediated stimulation of GLUT4-dependent glucose transport (54), and infusion of recombinant IL-6 has been shown to increase plasma glucose levels in animals and humans (55, 56). Furthermore, plasma IL-6 levels predict subsequent development of type 2 diabetes independent of body weight (13), and it has also been shown that IL-6 is an important predictor of CHD (10, 11). Our study shows an inverse association between physical activity and the soluble TNF receptors, IL-6 and CRP levels, suggesting that regular physical activity reduces these obesity-related inflammatory markers, partially through the effects of physical activity on body weight. However, although inflammatory markers may mediate the obesity-dependent effects of physical activity on type 2 diabetes and CHD, our study suggests that they do not directly account for the beneficial effects of physical activity on insulin resistance.

In our age-standardized analysis, we observed lower sTNF receptors levels in men compared with women. We cannot exclude the possibility that these differences are partially due to gender differences in kidney function because sTNF receptor levels may depend on renal function and tend to increase with renal impairment (44, 45); however, kidney function is not likely associated with physical activity. Furthermore, we cannot rule out the possibility that slight differences in our blood collection procedures (different anticoagulants) or the use of different kits to determine the soluble TNF receptor levels are responsible for the differences in the absolute levels of sTNF-R between men and women. However, any potential bias between men and women should not affect our main analysis because we adjusted for sex, and the main results were similar in the male and female strata.

Our study has some limitations. The cross-sectional design complicates the drawing of causal inferences, and a single assessment of a biochemical indicator, especially acute-phase inflammatory markers, may be susceptible to short-term variation, which would bias the results toward the null. However, the biomarkers of inflammation we measured are reasonable stable over time (see above). Physical activity measured in this study represents the average exposure over the year previous to the blood drawing (24, 29). Measurement error from using self-reported lifestyle variables and dietary intake is relatively small (20, 24) and likely does not bias our results because reporting error is not likely associated with the biological measurements. The HPFS and the NHS2 do not represent random samples of the U.S. population; therefore, lifestyle and dietary patterns may not reflect those of the general population. However, the biological relationship between lifestyle and dietary factors and the biomarkers found in this study should be similar to men and women in general. Because the ranges of anthropometric parameters and the biological measures are quite broad and comparable with those of the general population, the associations found in this study most likely are generalizable to men and women of this age range.

In summary, we found physical activity inversely associated with obesity-related inflammatory markers. These results extend further the findings that frequent physical activity is associated with lower risk of systemic inflammation. Our study proposes one possible mechanism for the beneficial effects of physical activity on type 2 diabetes and CHD described in the literature and suggests that these effects are partially mediated by a reduction of fat mass. Furthermore, although inflammatory markers may mediate the obesity-dependent effects of physical activity on type 2 diabetes and CHD, our study suggests that they do not directly account for the beneficial effects of physical activity on insulin resistance.

Acknowledgment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

We would like to thank Dr. Walter Willett for his expert advice provided on this project and Lydia Liu for her helpful statistical assistance and programming review. This study was supported by NIH Grants AA11181, HL35464, CA55075, and CA67262 and Boston Obesity Nutrition Research Center (BONRC) Grant P30 DK46200. T. P. was supported by a grant from the German Academic Exchange Service (DAAD).

Footnotes
  • 1

    Nonstandard abbreviations: CHD, coronary heart disease; IL-6, interleukin-6; TNF, tumor necrosis factor; sTNF-R, soluble tumor necrosis factor receptor; CRP, C-reactive protein; HbA1c, hemoglobin A1c; HPFS, Health Professionals Follow-up Study; SFFQ, semiquantitative food frequency questionnaire; MET-hr, metabolic equivalent-hour; NHS2, Nurses’ Health Study II.

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  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References
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