Physical activity helps protect against numerous chronic diseases. 1 Twenty-five percent of adults in the United States are sedentary, and more than 60% are not regularly active. 1 During the past decade, little change has occurred in the proportion of adults in the United States who are meeting current physical activity recommendations. 2 One estimate of the direct health care delivery costs of physical inactivity is $24 billion. 3
The evidence showing the benefits of physical activity for cardiovascular disease is overwhelming. 4 There are several plausible biological mechanisms to explain this relation. Physical activity is inversely linked to high-density lipoprotein cholesterol concentration, blood pressure, body mass index, glucose intolerance, and fibrinolytic activity, all risk factors for cardiovascular disease. Furthermore, physical activity promotes the hemodynamic functioning of the heart. In addition, people who are physically active often have other behaviors associated with a decreased risk for cardiovascular disease, such as lower rates of smoking and higher rates of fruit and vegetable consumption.
Inflammation is of critical importance in the pathogenesis of cardiovascular disease. 5 Thus, it is of considerable interest to know whether physical activity can affect the inflammatory process. Several studies have shown inverse associations between physical activity levels and concentrations of acute phase reactants such as fibrinogen and C-reactive protein. 6–14
Because previous studies of physical activity and C-reactive protein concentrations have been limited to special populations, I explored this relation in a representative sample of the U.S. population. Furthermore, previous studies may not have adequately controlled for potential confounders. Although the focus of this study is on the association between leisure-time physical activity and C-reactive protein concentration, I also analyzed the associations between leisure-time physical activity and fibrinogen (a positive acute phase reactant), white blood cell count, and albumin (a negative acute phase reactant).
The Third National Health and Nutrition Examination Survey (NHANES III) was conducted between 1988 and 1994. A representative sample of the noninstitutionalized civilian U.S. population, selected by using a multistage, stratified sampling design, was interviewed and invited for a clinical examination. For most participants, blood was drawn at the examination clinic, but for some who were unable to attend the examination because of health reasons, a blood sample was obtained during the home interview. Persons age ≥60 years, African Americans, and Mexican Americans were oversampled. Details about the survey and its methods have been previously published. 15,16
Participants attended one of three examination sessions: morning, afternoon, or evening. Those attending the morning sessions were asked to fast for 10–16 hours before the session; those attending the afternoon and evening sessions were asked to fast for at least 6 hours. C-reactive protein was measured at the University of Washington Department of Laboratory Medicine by using latex-enhanced nephelometry. 16 The lower detection limit was 3.0 mg/L. An elevated C-reactive protein concentration was defined as one at or above the 85th percentile of the sex-specific distribution: ≥4.4 mg/dL for men and ≥7.0 mg/dL for women. 17 Albumin was measured on a Hitachi Model 737 multichannel analyzer after binding with bromcresol purple (Boehringer Mannheim Diagnostics, Indianapolis, IN). Fibrinogen concentration was determined by comparing the clotting time from a sample with that of a standardized fibrinogen preparation obtained on a Coagamate XC Plus automated coagulation analyzer (Organon Teknika, Durham, NC). White blood cell count was determined on a Coulter Counter Model S-PLUS JR (Coulter Electronics, Hialeah, FL). Details about these laboratory procedures and quality control have been published. 16
Respondents were asked whether they participated and, if so, their frequency of participation in the following activities during the previous month: walking, jogging or running, bicycling or bicycling on an exercise bicycle, swimming, aerobics or aerobic dancing, other dancing, calisthenics or exercises, gardening or yard work, and lifting weights. Participants could also report up to four additional physical activities. Four levels of physical activity were defined: vigorously active, moderately active, lightly active, and sedentary. “Vigorously active” was defined as participating three or more times per week in an activity with a metabolic equivalent (MET) level of ≥6 for participants who were 60 years or older and ≥7 for participants who were younger than 60 years. One MET is the energy expenditure of approximately 3.5 ml oxygen per kg body weight per minute or 1 kcal per kg body weight per hour. “Moderately active” was defined as participating five or more times per week in activities of which no more than two could be considered vigorous activities. “Lightly active” was defined as participation that was not vigorous or moderate. Sedentary was defined as engaging in no leisure-time physical activity. In addition, a summary measure of physical activity was created by summing the products of the frequency of participation by the MET levels for each reported activity.
The analyses for this paper included the following variables: age, sex, ethnicity, education, working status, smoking status, serum cotinine concentration, hypertension, body mass index, waist-to-hip ratio, total cholesterol concentration, high-density lipoprotein cholesterol concen-tration, low-density lipoprotein cholesterol, triglyceride concentration, apolipoprotein A1, apolipoprotein B, lipoprotein(a), uric acid, aspirin use, alcohol consumption, and fruit and vegetable intake. Most of these variables were selected based on published findings suggesting relations with C-reactive protein concentrations. 10,17–22
Participants who answered the question “During the past 2 weeks, did you work at any time at a job or business, not counting work around the house?” in the affirmative were determined to have worked during that time period. Serum cotinine concentration was determined by using high-performance liquid chromatography atmospheric-pressure chemical ionization tandem mass spectrometry. Three levels of smoking status were created: participants who currently smoked (had smoked 100 cigarettes and were currently smoking), those who had quit smoking (had smoked 100 cigarettes and were not currently smoking), and those who had never smoked (had never smoked 100 cigarettes). Three blood pressure readings were obtained in the mobile examination center. The average of the second and third blood pressure readings (both systolic and diastolic) was used in the analyses. Hypertension was defined as a systolic blood pressure ≥140 mmHg, a diastolic blood pressure ≥90 mmHg, or the current use of antihypertensive medication.
Concentrations of total cholesterol, high-density lipoprotein cholesterol, and triglycerides (after hydrolyzation to glycerol) were measured enzymatically on a Hitachi 704 Analyzer. High-density lipoprotein cholesterol was measured after the precipitation of other lipoproteins with a heparin-manganese chloride mixture. Low-density lipoprotein cholesterol was calculated using the Friedewald equation for participants who reported fasting at least 9 hours, 23 were examined in the morning, and were randomly assigned to the morning fasting sample. Apolipoproteins A1 and B were measured only for participants from Phase 1 (1988–1991) of NHANES III. These apolipoproteins were measured by using nephelometry on a Beckman Nephelometer Automated Array (Beckman Instrument, Inc, Brea, CA). Lipoprotein(a) concentration was measured only for participants from Phase 2 (1991–1994) of NHANES III and was measured by using an enzyme-linked immunosorbent assay (Strategic Diagnostics, Newark, DE). Uric acid was measured on a Hitachi Model 737 multichannel analyzer after oxidation by uricase to form allantoin and H2O2 (Boeh-ringer Mannheim Diagnostics, Indianapolis, IN). Details about the laboratory procedures of all these tests are found elsewhere. 16
Body mass index was calculated from measured weight and height (weight in kilograms divided by height in meters squared). The circumferences of the waist (at the level of the right iliac crest) and hips (at the maximum extension of the buttocks) were measured to the nearest 0.1 cm. Aspirin use was estimated from the following question: “In the past month, have you taken any aspirin, Anacin, Bufferin, Ecotrin, Ascriptin, or Midol?” Participants were then asked how often they used these products in the previous month. Consumption of beer, wine, or hard liquor during the past month, determined from a food-frequency questionnaire, was used to estimate alcohol consumption. Fruit and vegetable intake was determined by summing the responses to 21 items on a food-frequency questionnaire.
Because C-reactive protein concentrations are elevated in numerous diseases, I examined whether coronary heart disease (self-reported myocardial infarction, or probable or possible myocardial infarction diagnosed by electrocardiogram), cancer other than skin cancer (self-reported), diabetes (self-reported or based on fasting blood glucose concentration), arthritis (self-reported), or asthma (self-reported) possibly confounded the association between leisure-time physical activity and C-reactive protein concentration. Electrocardiograms were only offered to participants ≥40 years of age.
Analyses were limited to participants ≥20 years of age. Age-adjustment was based the 1980 U.S. population ≥20 years of age using the direct method. Tests for linear trend were carried out for proportions and means across levels of physical activity. In addition, I examined the associations between the dichotomized C-reactive protein concentrations and the independent variables by using logistic regression analysis. P-values for linear trend were calculated by using the medians of the frequency-MET variable for each level of the four-level leisure-time physical activity variable. Age, sex, race or ethnicity, and education were included in all multiple-adjusted logistic regression models. In addition, variables that were substantially related to both leisure-time physical activity and C-reactive protein concentration (Tables 1 and 2) were initially included in the regression models. Subsequently, variables that were not substantially associated with leisure-time physical activity were removed from the models, as long as their removal did not seriously change the odds ratios (ORs). I explored the associations between leisure-time physical activity and albumin concentration, fibrinogen concentration, and white blood cell count using both linear regression and logistic regression analyses. The same set of covariates identified for the logistic regression model for leisure-time physical activity and C-reactive protein concentration was used in these models. After inspecting the distributions of these variables, I performed additional linear regression models with log-transformations of fibrinogen and white blood cell count to improve their distributional properties. In addition, the associations between leisure-time physical activity and these variables were examined by using logistic regression analysis. To be consistent with the logistic regression model for C-reactive protein concentration, I dichotomized albumin concentration, fibrinogen concentration, and white blood cell count using the 85th percentile of their weighted distributions as the cutpoint. Because the 85th percentiles of these variables were very similar for men and women—unlike those for C-reactive protein concentration—I did not use sex-specific cutpoints. To account for the complex survey design, analyses were conducted with SUDAAN. 24 I used the medical examination clinic sampling weights to produce weighted estimates.
Of the 16,573 participants age ≥20 years who attended the medical examination, C-reactive protein was determined for 15,604. After participants with missing data for the study variables were excluded, this analysis was based on 13,748 participants age ≥20 years. C- reactive protein concentrations ranged from <3.0 mg/L to 252.0 mg/L (<3.0 mg/L to 198.0 mg/L for men and <3.0 mg/L to 252.0 mg/L for women). About three-quarters of the men (78%) and two-thirds of the women (66%) had a value of <3.0 mg/L.
Physical activity levels were inversely related to age, the proportion of participants who had worked during the past 2 weeks, the percentage of current smokers, serum cotinine concentration, the percentage of participants with hypertension, body mass index and waist- to-hip ratio, total cholesterol concentration, non– high-density lipoprotein cholesterol concentration, triglyceride concentration, and apolipoprotein B concentration (Table 1). Furthermore, leisure-time physical activity was directly related to the proportion of men, the proportion of white participants, years of education, high-density lipoprotein cholesterol concentration, alcohol consumption, and fruit and vegetable intake. Sedentary participants had the lowest prevalence of aspirin use during the previous month.
After age adjustment, 8% of vigorously active participants, 13% of moderately active participants, 17% of lightly active participants, and 21% of sedentary participants had an elevated C-reactive protein concentration (P for linear trend <0.001) (Table 1). In addition, leisure-time physical activity was positively associated with serum albumin concentration (P for linear trend <0.001), and inversely associated with white blood cell count (P for linear trend <0.001) and plasma fibrinogen concentration (P for linear trend <0.001).
Participants with an elevated C-reactive protein concentration differed from participants with a low C-reactive protein concentration for all variables except total cholesterol and LDL-cholesterol concentrations (Table 2).
After adjusting for age, the ORs for having an elevated C-reactive protein concentration were 0.78 (95% confidence interval [CI] = 0.64–0.96), 0.59 (0.49–0.70), and 0.30 (0.22–0.41) for lightly active participants, moderately active participants, and vigorously active participants, respectively, compared with sedentary participants. To examine whether this association was independent of covariates other than age, I used logistic regression analysis to adjust for potential confounders. Non–high-density lipoprotein cholesterol concentration, alcohol consumption, and fruit and vegetable consumption were removed one at a time from the logistic model, and odds ratios remained virtually unchanged. With sedentary participants serving as the reference group, odds ratios were 0.98 (CI = 0.78–1.23) for lightly active participants, 0.85 (0.70–1.02) for moderately active participants, and 0.53 (0.40–0.71) for vigorously active participants (Table 3). Results for the full logistic regression model for C-reactive protein are presented in Table 4. In a separate analysis of participants who reported fasting ≥8 hours, triglyceride concentration was only modestly associated with leisure-time physical activity (per mmol/Lβ = 0.05092, SE = 0.04827).
To explore possible confounding by chronic disease, I reran a logistic regression model after adding the following self-reported conditions: heart attack, cancer other than skin cancer, diabetes, asthma, and arthritis. The odds ratios changed little: 0.98 (CI = 0.79–1.23), 0.86 (0.71–1.04), and 0.53 (0.40–0.71) for light, moderate, and vigorous leisure-time physical activity, respectively.
In another analysis limited to 8435 persons who reported fasting at least 8 hours, C-reactive protein concentration remained inversely associated with physical activity after additional adjustment for serum insulin concentration. Compared with sedentary participants, odds ratios were 0.92 for participants who were lightly active, 0.85 for participants who were moderately active, and 0.54 for participants who were vigorously active. Fasting insulin was positively associated with C-reactive protein status (per pmol/Lβ = 0.00231, SE = 0.00076).
Leisure-time physical activity was positively associated with albumin concentration and inversely associated with log-transformed fibrinogen concentration and log-transformed white blood cell counts in linear regression models. In these logistic regression models, in which the inflammatory markers were dichotomized using the 85th percentile of the distribution for men and women combined, participants who were engaged in vigorous leisure-time physical activity were more likely to have had an elevated albumin concentration (OR = 1.54, CI = 1.10–2.16) and less likely to have had an elevated fibrinogen concentration (OR = 0.43, CI = 0.26–0.70) and an elevated white blood cell count (OR = 0.64, CI = 0.41–0.99) compared with participants who did not engage in leisure-time physical activity (Table 3). Linear trends were observed for the associations of leisure-time physical activity with albumin and fibrinogen concentrations but not with white blood cell count.
In a representative sample of adults in the United States, leisure-time physical activity was inversely associated with C-reactive protein concentration in a dose-response manner. Leisure-time physical activity was also directly associated with serum albumin concentration and inversely with fibrinogen concentration and white blood cell count. Thus, the results from this study support findings from other studies that physical activity favorably affects concentrations of acute phase reactants. 6–14 Because this was a cross-sectional study, cause and effect cannot be directly determined.
Previous cross-sectional studies with large sample sizes have generally found an inverse association between physical activity and C-reactive protein concentration. Among 936 men 45–64 years of age who were part of the Monitoring Trends and Determinants in Cardiovascular Disease Augsburg Cohort Study, C-reactive protein concentration was inversely associated with leisure-time physical activity during winter and summer but was directly associated with work activity. 9 Among 880 men and women age 70–79 years, moderate and strenuous physical activity were inversely associated with C-reactive protein concentrations. 13 Similar findings emerged from a cross-sectional analysis of 5888 participants, age ≥65 years, in the Cardiovascular Health Study. 14 In a study of 1172 male physicians age 40–84 years, exercise frequency was found to be inversely associated with C-reactive protein concentration by univariate but not multivariate analysis. 10
Interestingly, heavy bouts of exercise result in short-lived inflammation. 25–31 The etiology of this inflammation differs from inflammation that accompanies atherosclerosis or other chronic conditions. The effect of single bouts of exercise may have mixed effects on acute phase reactants, however. For example, subjects who took a 30-minute treadmill test had increased concentrations of alpha1-antitrypsin and haptoglobin but not C-reactive protein or alpha1-acid glycoprotein. 32
It is not clear how physical activity could influence the specific inflammatory activity associated with cardiovascular disease or other diseases. C-reactive protein is produced by hepatocytes. The major stimulants of production are interleukin-6 (IL-6) and, to a lesser degree, IL-1. Many stimuli can result in C-reactive protein production and elevations in its blood concentrations. One such stimulus is the increased IL-6 production by adipocytes as body mass index increases. By reducing adipose mass, physical activity could decrease IL-6 production and hence, C-reactive protein production. However, after adjusting for body mass index and waist-to-hip ratio, C-reactive protein concentration was still strongly related to level of physical activity in the present study, suggesting that physical activity influences the inflammatory process through other mechanisms. Geffken and colleagues 14 suggest that physical activity can reduce inflammation by improving insulin resistance because concentrations of several inflammatory markers were raised in insulin-resistant subjects. However, after adjustment of NHANES III participants’ fasting insulin concentration, which is often used as a measure of insulin resistance, the odds ratios changed little, suggesting that physical activity influences the inflammatory process through other mechanisms. Physical activity has been shown to improve endothelial function. 33 Endothelial cells are known to secrete IL-1 and IL-6, 34,35 inducers of an acute phase response. Activated endothelial cells can increase production of interleukins. 36 The possibility remains that the adjustments for excess weight were incomplete and that residual confounding could still be present.
In conclusion, the results of this study showed that physical activity is inversely associated with C-reactive protein concentrations, suggesting that physical activity may mitigate inflammation. Research to delineate the exact mechanisms through which physical activity influences the inflammatory process will help improve our understanding of some of the benefits of physical activity. Furthermore, additional research concerning the relation of the intensity, duration, and type of physical activity with inflammation could yield additional insights into how physical activity might influence inflammation.
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