Medicine & Science in Sports & Exercise:
Basal, Circadian, and Acute Inflammation in Normal versus Overweight Men
MILES, MARY P.; KELLER, JAN M.; KORDICK, LINDSAY K.; KIDD, JESSY R.
Department of Health and Human Development, Montana State University, Bozeman, MT
Address for correspondence: Mary P. Miles, Ph.D., Department of Health and Human Development, Montana State University, Box 173540, Bozeman, MT 59717; E-mail: email@example.com.
Submitted for publication February 2012.
Accepted for publication June 2012.
Increased inflammation is present in obese compared with normal weight individuals, but inflammation characteristics of nonobese, overweight individuals are less clear.
Purpose: The objective of this study was to determine whether basal, circadian, and posteccentric exercise inflammation levels differ between normal and overweight men.
Methods: Men (18–35 yr old) classified as normal weight (body mass index ≤25 kg·m−2, n = 20) and overweight (body mass index = 25–30 kg·m−2, n = 10) completed exercise (EX) and control (CON) conditions in random order. Maximal voluntary effort and eccentric actions (3 × 15) using the elbow flexor muscles of one arm were performed, and blood was collected preexercise and 4, 8, 12, and 24 h postexercise at 7:00 a.m., 12:00 p.m., 4:00 p.m., 8:00 p.m., and 7:00 a.m. Blood was collected on a time-matched schedule without exercise for CON. Soluble tumor necrosis factor receptor-1, interleukin-6, C-reactive protein (CRP), and cortisol responses (EX value − time-matched CON value) were measured.
Results: Basal CRP was higher in the overweight compared with normal weight group (mean ± SD, 0.542 ± 0.578 vs 1.395 ± 1.041 mg·L−1). Soluble tumor necrosis factor receptor-1 increased (P < 0.05) 8 h postexercise in both groups, and the response was greater 12 and 24 h postexercise in the overweight compared with normal weight groups. Interleukin-6 increased (P < 0.05) 8 h postexercise, with a trend (P = 0.09) to be greater in the overweight group. CRP and cortisol responses were not detected.
Conclusions: The low-grade inflammation state in overweight compared with normal weight men includes both higher basal CRP concentrations and enhanced acute inflammation, but not in changes to the circadian patterns of cortisol and inflammation variables.
There is a shift toward a chronic, low-level inflammatory state of the body that occurs as body mass index (BMI) and adiposity increase (33,34,37). As a result, obesity is considered a pro-inflammatory state, and individuals who are obese often have chronic low-grade inflammation, sometimes called microinflammation or metainflammation. Inflammatory processes contribute to the development of insulin resistance, CAD, neurodegenerative diseases, and cancer (30); thus, it is important to understand when and how the pro-inflammatory state develops. C-reactive protein (CRP) is a biomarker indicative of the acute phase response portion of inflammation, and basal levels of this biomarker have been stratified to identify risk for inflammation-based chronic diseases, particularly cardiovascular disease (29). Development of a pro-inflammatory state and higher disease risk may occur long before an obese BMI is reached, and evaluation of biomarkers other than CRP may be important to understanding the progression of inflammation as weight increases. The development of effective inflammation and disease risk reduction strategies will be more effective if it is determined whether low-grade inflammation occurs as simply an increase in selected biomarkers or as a fundamental change in inflammation regulation and responses.
In addition to CRP, several mediators of inflammation with a range of functions may have particular relevance to inflammation in younger populations. In addition to CRP, soluble tumor necrosis factor receptor-1 (sTNFR1) and interleukin (IL)-6 are among the inflammation markers predictive of metabolic syndrome extent in young adults (15). sTNFR1 modulates tumor necrosis factor (TNF)-α bioavailability and may promote increases in adiposity by decreasing thermogenesis (35). This may be an example of a mediator of inflammation actually participating in the mechanism underlying increased adiposity. IL-6 has a wide range of effects on inflammation and metabolism. Elevations in resting IL-6 occur with increasing adiposity and physical inactivity, and in this context, IL-6 plays a role in the pathology of cardiovascular disease development (13,30). Glycoprotein 130 (gp130) is an IL-6 receptor, and the soluble form of this receptor (sgp130) can block biological activity of IL-6. sgp130 also associates with development of metabolic syndrome (41). IL-10 is an anti-inflammatory cytokine, and it has been proposed that the increased expression of IL-10 in visceral adipose tissue of children may be a response to dampen inflammation in the early stages of development of the inflammation state (39). Soluble intercellular adhesion molecule-1 (sICAM-1) is a marker of cell adhesion and migration considered to be an early indicator of atherosclerotic processes within arterial walls in young adults (9). Although there is evidence supporting a progression of inflammation characteristics over time and across lean, overweight, and obese groups, the details of this progression have not been adequately identified. These details may be useful in advancing the use of inflammation measurements to assess disease risk, the disease process, and the efficacy of interventions to reduce inflammation.
The shift toward a pro-inflammatory state measured in basal or resting inflammation markers may include or be influenced by shifts in the circadian profile of cytokines and cortisol. A variety of research findings support the involvement of circadian rhythms in the development of metabolic syndrome including the demonstration of circadian rhythmicity in gene expression of adipocytes, glucose control, insulin action, inflammation, and disruption of feedback loops between inflammation and stress hormones (noradrenaline and cortisol) (7,17,20,21,32). There is also a substantial body of evidence linking obesity-associated chronodisruption (disturbance of circadian rhythms, such as change in rhythm amplitude), increased incidence of metabolic syndrome, type 2 diabetes mellitus, and cardiovascular diseases (7,21). Cortisol is the main neuroendocrine modulator of circadian variation in immune function (31). We previously reported that plasma concentrations of both sTNFR1 and IL-6 parallel the circadian rhythm of cortisol with levels that decrease from morning highs to evening lows (23). The rate of decrease in cortisol is a variable of growing interest because a flattened cortisol profile is influenced by BMI and may associate with health status and outcomes (16). Thus, the circadian patterns of both cortisol and inflammatory mediators are important elements to be examined to characterize the progression of inflammation with increases in body mass.
Differences in acute inflammation responses related to BMI may occur within and contribute to the chronic, low-level inflammatory state. We and others have found that acute inflammation is enhanced in overweight or obese individuals under either high carbohydrate intake or hyperglycemic conditions (4,14,25). For example, TNF-α is a potent pro-inflammatory cytokine that interferes with insulin signaling, and its production is suppressed under hyperglycemic conditions. Kirwan et al. (14) found that the suppression of TNF-α was lost as BMI and waist circumference increased. In addition, we previously reported a positive association of BMI and waist-to-hip ratio with the magnitude of the increase in another pro-inflammatory cytokine, IL-1β, following eccentric exercise with high carbohydrate intake (25). Thus, there is evidence to suggest that inhibition of inflammation is diminished and acute inflammation may be enhanced in overweight individuals; however, research comparing acute inflammation responses across weight groups is limited.
High-force eccentric exercise elicits inflammation and is a useful model to study acute inflammatory responses. The sequence of inflammation events and regulation is for the eccentric exercise to elicit focal disruptions and tissue damage in muscle, followed by initiation of inflammation including local production of TNF-α and IL-1β, a robust increase in plasma IL-6 approximately 8 h postexercise that associate with perceived severity of delayed onset muscle soreness, and finishing with an increase in CRP if the systemic response is of sufficient magnitude to elicit an acute phase response (23). The damage to muscle tissue can be verified via measurement of creatine kinase (CK) activity in serum (24). Using this model of exercise-induced muscle damage, investigation of acute inflammation responses between normal and overweight individuals may help to determine whether the magnitude of or regulation of inflammation is altered or impaired as individuals become overweight.
The purpose of this study was to determine whether basal, circadian, or posteccentric exercise inflammation levels differ between normal and overweight men. Basal samples were fasting blood samples collected first thing in the morning, whereas circadian samples included basal and additional samples collected throughout the course of a nonexercise day. To achieve this purpose, we measured a mediator of the early activation of the inflammatory response (sTNFR1), a mediator in the middle of the inflammatory cascade with both feedback and feedforward roles (IL-6), an acute phase protein to identify inflammation at the systemic level (CRP), and an immunoregulatory hormone that influences whole body metabolism and circadian rhythms (cortisol). While TNF-α is a cytokine of interest, it is difficult to measure changes in this cytokine in the plasma after eccentric exercise involving a small muscle mass. This difficulty may be related to the short, approximately 6-min half-life of TNF-α in the circulation (2). However, an increase TNF-α in the circulation elicits shedding of TNF receptors that are more stable in the circulation (1). Given the potential roles of TNFR1 in promoting obesity and as a marker of changes in the less stable TNF-α, we measured sTNFR1 rather than TNF-α. High-force eccentric exercise is being used as a tool to induce inflammation so that the characteristics of the response may be compared between normal weight and overweight individuals. We hypothesized that differences in inflammation markers and cortisol would be consistent with a shift toward enhanced inflammation in the overweight individuals.
MATERIALS AND METHODS
Individuals age 18–35 yr were recruited to participate in this investigation. The men included in the analysis for the present study were extracted from a larger pool of both men (n = 30) and women (n = 21) for which a summary of findings has been published previously (23). Individuals who reported performing activities in which lifting and lowering of heavy objects were performed or who recalled experiencing muscle soreness in the arm muscles at any time in the 6 months preceding the investigation were excluded from participation. The aim of these criteria was to eliminate confounding influences of the repeated bout effect, in which muscle that has been exposed to high-force eccentric exercise will have a blunted muscle damage response to subsequent eccentric exercise bouts that occur within the next several months. Additional exclusion criteria included known anemia, musculoskeletal limitations, known inflammatory conditions, diabetes, heart disease, known kidney problems (excluding kidney stones), smoking, chronic use of anti-inflammatory medications (including over-the-counter nonsteroidal anti-inflammatory drugs), lipid-lowering medications, and regular performance of physical activity in which muscle soreness or bruising occurs. The research protocol and informed consent document for this investigation were approved by the Human Subjects Committee at Montana State University. Participants were informed of the procedures and potential risks associated with the study and gave written informed consent before participation in this investigation. Data for the men were then grouped as normal weight (BMI = 18–24.9 kg·m−2, n = 20) and overweight (BMI = 25–30 kg·m−2, n = 10) for the analysis in the present study. CRP levels above 10 mg·L−1 are considered to reflect the presence of acute inflammation (29), and data from one man were excluded because of high initial CRP concentrations throughout both conditions (>10 mg·L−1).
All participants performed both an exercise (EX) and a control (CON) protocol in randomized order with equal numbers beginning in each of the two conditions to avoid a confounding effect of order (Table 1). Several restrictions were placed on participants to minimize variability in physiological status. Standardized conditions for blood collections in the morning included an overnight fast and minimal physical activity before reporting to the laboratory at 7:00 a.m. for blood collection and assessments. Strenuous physical exercise that was judged to be near maximal in intensity or longer than 60 min in duration was not allowed while participants were active in the EX and CON protocols, i.e., not on the day of or the day before research activities took place. To avoid the influence of illness on inflammatory parameters, participants were only tested if they were free of known infection for at least 1 wk. The EX protocol consisted of baseline assessments at 7:00 a.m., followed by a bout of high-force eccentric resistance exercise using the elbow flexor muscles of the nonpreferred limb (according to self-reported handedness), and follow-up assessments 4, 8, 12, 24, 48, 96, and 120 h postexercise. Assessments included muscle soreness, blood collection for blood-borne variables, and maximal force production. During the EX condition, the nonexercised arm was measured as a CON for maximal force production. A time-matched CON condition identical with the experimental condition but without the high-force eccentric exercise was performed for blood-borne variables. The experimental and control protocols were separated by at least 3 and no more than 6 wk.
High-force eccentric exercise
The protocol for inducing muscle damage in the flexor muscles (primarily m. biceps brachii and m. brachialis) of the nonpreferred arm was performed using a computer-controlled, isokinetic dynamometer (Kin Com125 E+; Chattecx Corporation, Chattanooga, TN). The dynamometer was adjusted to the body height and limb length of the individual. The arm was supported by a padded bench at approximately 0.79 rad of shoulder abduction, the axis of rotation of the dynamometer was aligned with the axis of rotation of the elbow, and the forearm was secured to the lever arm of the dynamometer with padded support just proximal to the wrist joint. Three sets of 15 repetitions of eccentric elbow flexion were performed with maximal effort at a rate of one repetition per 15 s and 5 min of rest between sets. Repetitions began with the elbow fully flexed and ended with the elbow fully extended. Using maximal effort, participants attempted to keep the elbow in the fully flexed position as the dynamometer pulled the arm to a fully extended position at an angular velocity of 0.79 rad·s−1. The dynamometer returned the arm to the fully flexed position and paused for 10 s before beginning the next repetition. Participants were verbally encouraged to give a maximal effort with each repetition.
Maximal force production
Maximal isometric force production for elbow flexion at an enclosed elbow angle of 1.57 rad was measured before exercise, immediately after exercise (to identify the amount of fatigue induced by the exercise), and 24, 48, 96, and 120 h postexercise (to identify prolonged strength loss, which is an indicator of muscle damage ). To perform the isometric strength measurement, the dynamometer and subject position were adjusted as for the high-force eccentric exercise, and the lever arm was fixed such that the elbow was positioned with a 1.57 rad angle. Participants were instructed to pull (flex) for 3 s using maximal effort. Three maximal efforts were performed with 30-s rest between repetitions. To ensure uniformity of measurements from day to day, all dynamometer position settings for each subject were recorded and reproduced at each testing session. To eliminate variation due to initial strength levels, data were converted to percentages of the initial strength measurement before analysis.
A subjective assessment of muscle soreness was made by participants using a 100-mm visual analog scale anchored at one end with “no soreness” and at the other end with “very, very sore.” Participants were instructed to fully flex and extend the elbow while holding a 1-kg weight and gently squeezing the elbow flexor muscles and then to place a tick mark on the analog scale that represented the degree of soreness. Participants also were instructed to think of their ratings in terms of muscle soreness, not of fatigue or relative to other types of pain, e.g., a broken bone.
Blood collection and analysis
Participants sat for 10–15 min before blood was collected from an antecubital vein into evacuated tubes using a standard venipuncture technique. Blood was collected in a vacuum tube without additive for analysis of CRP, cortisol, and CK, and containing ethylenediaminetetraacetic acid for IL-6 and sTNFR1. After clotting, serum was separated from cells using a refrigerated 21000R Marathon centrifuge (Fisher Scientific, Pittsburgh, PA). All samples were stored at −80°C until analysis. Fasting, basal, serum cholesterol, and triglyceride (TG) concentrations were measured in duplicate by standard laboratory techniques using an VitrosDT60 Ektachem analyzer (Eastman Kodak Co., Rochester, NY) and the procedures described by Lie et al. (19). Serum CRP (high sensitivity assay; MP Biomedicals, Irvine, CA), plasma IL-6 (high sensitivity assay; R&D Systems, Minneapolis, MN), plasma sTNFR1 (R&D Systems), serum sICAM (R&D Systems), serum IL-10 (R&D Systems), and serum cortisol (Diagnostic Systems Laboratories, Inc., Webster, TX) concentrations were measured using commercially available enzyme-linked immunosorbent assay kits according to the instructions of the manufacturers. Absorbance of 96-well assay plates was read using a μQuant Universal microplate spectrophotometer (Bio-Tek Instruments, Winooski, VT). All samples were run in duplicate. Average intraassay coefficients of variation for CRP, IL-6, sTNFR1, and cortisol were 11.0%, 7.6%, 3.8%, and 3.4%, respectively. On the basis of the anticipated time course for changes in these variables and the need to identify potential circadian variations throughout the day, CON samples were measured for the first 24 h postexercise only for these variables.
As an indirect marker of muscle damage to determine whether muscle damage responses were comparable between normal and overweight groups, serum CK activity was measured using an ultraviolet, kinetic assay at 37°C (Thermo Scientific, Waltham, MA). The assay was modified for microplate analysis and read using a μQuant Universal microplate spectrophotometer (Bio-Tek Instruments). Samples were run in duplicate, and all samples for a given participant were run in the same assay. The intraassay coefficient of variation was 4.5%.
Data were analyzed using the Statistical Package for Social Sciences for Windows (version 20.0; IBM Corporation, Somers, NY). Variables were tested for normal distribution using the Kolmogorov–Smirnov test. Nonnormally distributed variables CRP and IL-6 were log transformed before statistical analyses. Baseline measures were compared between BMI groups using an independent t-test for continuous variables and chi-square analysis for categorical variables (condition order). Baselines for inflammatory variables were the mean of the initial values for the EX and CON conditions. Delta scores for inflammation variables of sTNFR1, IL-6, CRP, and cortisol were calculated by subtracting each value from the CON condition from the time-matched value from the EX condition. A general linear model two-way repeated-measures ANOVA was used to compare CON condition values (circadian variation) and delta scores (acute inflammation responses) over time and between groups. Post hoc analysis to determine the location of differences when significant main effects or interactions were detected was performed using paired t-tests to detect time differences and independent samples t-tests to detect differences between groups. The Bonferroni correction to alpha was used for multiple comparisons. Statistical significance was set at the alpha = 0.05 level.
Basal CRP was the only variable that was higher in the overweight compared with normal weight group (Table 1). TGs, cholesterol, sTNFR1, IL-6, IL-10, sICAM-1, cortisol, and serum CK activity were similar between groups.
Circadian variation was similar between groups for cortisol, sTNFR1, and IL-6 (Table 2). Cortisol was highest at 7:00 a.m. and decreased (P < 0.05) at 12:00, 4:00, and 8:00 p.m., reaching a nadir at 8:00 p.m. in both groups. sTNFR1 followed a circadian pattern similar to that of cortisol with the highest value measured in the early morning and lower values throughout the rest of the day. IL-6 also was highest early in the morning; however, lower concentrations were measured only at 12:00 p.m. compared with 7:00 a.m. Observed β values were calculated when trends were detected to determine the statistical power for detecting differences of the magnitude that occurred within the current data set. The observed β value for the group by time interaction trend (P = 0.073) for sTNFR1 was less than 0.80. Thus, the potential for an interaction exists, but differences over time between groups were not sufficiently robust to achieve statistical significance.
Some elements of the acute inflammation response were greater in the overweight compared with normal weight men. The response of each variable was calculated as the value from the EX condition minus the value from the CON condition. Significant (P < 0.05) time, group, and time-by-group interactions were measured for sTNFR1. Compared with preexercise, there was an increased sTNFR1 response (P < 0.05, time main effect) at 8 h postexercise. Post hoc analysis identified that the sTNFR1 response was greater (P < 0.0125) at 8 h postexercise in the overweight group, and the sTNFR1 response was greater in the overweight compared with the normal weight group 12 and 24 h postexercise (Fig. 1). A significant time effect (P < 0.05) and trends for group (P = 0.09) and the time-by-group interaction (P = 0.10) were measured for IL-6. The IL-6 response increased (P < 0.05) 8 h postexercise and tended (P = 0.09) to be higher in the overweight group (Fig. 2). An effect for time (P < 0.05) was detected for the CRP (Fig. 3) and cortisol (data not shown) responses, but post hoc comparisons were not significant. Observed β for the group main effects that were trends for IL-6 and CRP were less than 0.80. Thus, the potential for group differences exists, but the differences between groups were not sufficiently robust to achieve statistical significance.
The increase in markers of muscle damage was similar between the normal and overweight groups (Table 3). Decreased (P < 0.05) strength was measured immediately through 120 h postexercise in both groups. Similarly, muscle soreness and serum CK activity were increased at all postexercise time points but did not differ between groups.
Whether low-grade inflammation occurs as a change in selected biomarkers reflecting aspects of inflammation, as a change in the underlying neuroendocrine regulation (e.g., circadian variation), as a change in responses to acute stimuli (e.g., muscle damage), or as a combination of these is important to determine. This information may provide insights to understand disease risk and to develop inflammation reduction interventions. The purpose of this study was to determine whether basal, circadian, or posteccentric exercise inflammation levels differ between normal and overweight men. The primary findings of this investigation were that there is evidence of basal, low-grade inflammation and enhanced acute inflammation responses in nonobese, overweight, young men when compared with normal weight men. This is consistent with our hypothesis and provides evidence that acute inflammation differences may play a role in the shift toward the pro-inflammatory state that coincided with increased CRP. Circadian variations in mediators of inflammation and cortisol were similar between groups.
Low-grade, basal inflammation is predictive of cardiovascular disease risk connected to metabolic syndrome and considered to be part of the mechanism for the development of a variety of disease conditions including neurodegenerative diseases, cancer, type 2 diabetes mellitus, and cardiovascular diseases (10,29,30). The positive association between basal inflammation, particularly CRP, and proxies for adiposity such as BMI, waist circumference, or waist to hip ratio is well established (33,34,37). Our finding that overweight men with an average BMI of about 27 kg·m−2 had higher CRP concentrations compared with normal weight men with a BMI of about 22 kg·m−2 is not surprising, except that this is an early indication of increased risk for cardiovascular disease events (CRP >1.0 mg·L−1) in younger men (29). However, we did not detect basal differences between normal and overweight young men for sTNFR1, IL-6, sgp130, IL-10, or sICAM-1. Thus, although CRP was the only marker of inflammation that was higher in overweight compared with normal weight men in the basal state, we infer from this finding that the pro-inflammatory state associated with obesity is present in young, overweight, otherwise low disease risk men. This finding is consistent with previous research with elevations in inflammation in young, overweight youths 10–14 yr old (36).
Eccentric exercise involves forcing muscles to lengthen against resistance and has been used as a research model to induce inflammation (12,23), and this exercise was used as a model to study inflammation differences between normal weight and overweight men. Inflammation is the general response of the body to tissue injury, with the overall goal being healing. The inflammatory response is a sequential process involving cytokine and chemokine signals, leukocytes, oxidative stress, and acute phase proteins (38). Strength loss, serum CK activity, and muscle soreness are indicators of muscle damage (5). The changes that we measured in this study indicate that muscle damage was induced by the eccentric exercise, but there were no differences between groups. As a result, we infer that differences between groups in the level of inflammation induced by the eccentric exercise resulted from differences related to BMI, the grouping variable, and not from differences in the degree of muscle damage. Previous research comparing responses of high-force eccentric exercise of the knee extensor muscles in women measured greater changes in indices of muscle damage, peak torque, soreness, and serum CK activity, in overweight compared with normal weight women (28). Our findings may differ because of difference in sex of the participants or because of our use of the arm versus the previous researchers’ use of the weight-bearing leg muscles.
Higher levels of sTNFR1 and IL-6 in the eccentric exercise compared with control condition indicate that early (sTNFR1) and middle (IL-6) phases of inflammation occurred eight or more hours after the exercise bout. Although there was a trend (P = 0.09) for the IL-6 response to be greater in the overweight group of men, the sTNFR1 response was significantly greater in the overweight group. Thus, we infer from this finding that physiological mechanisms related to higher BMI are linked to the enhancement of the acute inflammation response. McMurray et al. (22) found that cytokine responses (TNF-α, IL-6, and IL-1 receptor antagonist) to high-intensity, intermittent exercise in adolescent boys and girls did not differ between normal and overweight groups, but this exercise may not have produced muscle damage and measurements were only made 0 and 2 h postexercise. The peak of IL-6 occurred 8 h postexercise in our study. Our findings concur with those of Gonzalez et al. (8) who found that inflammation responses were greater in obese compared with normal weight women in response to hyperglycemia. Our findings are evidence that exercise-induced muscle damage is a stimulus to which overweight individuals have a greater inflammation response and that the influence of increased body mass begins before obese body mass levels are reached.
Our interest in the difference in acute inflammation between normal and overweight men stems not only from the interaction between adipose tissue and inflammation mediators but also from recent evidence that inflammation mediators influence thermogenesis and the accumulation of fat mass (26). In particular, sTNFR1, as demonstrated using knock-out mice lacking this receptor, has a protective role against diet-induced obesity and insulin resistance (27,35,40). As a crucial element in the initiation and amplification of the inflammation response, TNF-α induces cellular effects primarily through signal transduction involving either TNFR1 or TNFR2 (11). Only TNFR1 results in activation of nuclear-factor-κB to induce production of pro-inflammatory pathways, and only TNFR1 has been implicated in the interference of insulin signaling and insulin resistance (11,40). Cell-associated TNFRs are shed when TNF-α increases, resulting in an increase in soluble receptors in the circulation, sTNFR1 and sTNFR2 (1). One rationale for the shedding of the soluble receptors is that they can sequester the TNF-α to increase its half-life in the circulation from 6 min to greater than 2.5 h (2). However, independent roles of sTNFRs with respect to influencing obesity-associated inflammation, thermogenesis, and fat mass need to be evaluated. BMI is a rough proxy for adiposity used in this investigation to differentiate normal and overweight without a specific measure of fat mass. Our finding that sTNFR1 levels were higher during exercise-induced inflammation in overweight but not normal weight men is a unique finding that should be further investigated so that it may be determined whether this difference is linked specifically to body fat (not measured in the present investigation). This may be an indication that overweight men had greater increases in TNF-α to induce shedding of TNFR1. Alternatively, there may be an up-regulation of TNFR1 that leads to greater increases in overweight compared with normal weight men.
The interrelationships of the neuroendocrine and immune systems are complex; thus, the linkage between circadian patterns in endocrine and immune parameters and the development of obesity and related consequences also is complex. This issue is important because some studies have measured associations between short sleep duration (a disruptor of circadian rhythms) and obesity, diabetes mellitus, and hypertension incidence (6). It also has been demonstrated in animal models that inducing obesity with a high-fat diet disrupts circadian patterns for insulin resistance and mediators of inflammation including TNF-α and IL-6 (3). Thus, there is evidence not only that disruption of circadian patterns, for example, by altering sleep patterns, increases the likelihood of becoming obese or acquiring some diseases, but also that becoming obese disrupts circadian variation and influences development of some diseases. In humans, a flattening of the circadian variation in cortisol occurred in the highest (>31 kg·m−2) and lowest (<21 kg·m−2) BMI groups, a U-shaped association, and for men (n = 2915) and women (n = 1041) in the Whitehall II study (16). Other researchers found flattening of cortisol variation with increasing abdominal obesity in women but not in men (18). Our finding that circadian patterns in cortisol, sTNFR1, and IL-6 were similar for normal and overweight men may be an indication that men are not sensitive enough to body mass–related flattening of circadian patterns for this to be measured in nonobese, overweight men. Alternatively, it may be that overweight men fall within the same region of the U-shaped association as normal weight men and do not have circadian disruptions. Regardless, we found no evidence that normal and overweight men differed in circadian patterns for the endocrine and immune variables measured.
We conclude that the elements of the pro-inflammatory state characteristic of obesity that can be detected in overweight men are an increase basal concentrations of CRP and a modest enhancement of the acute inflammation response. This enhancement was most evident for sTNFR1 than other mediators of inflammation. Whether the enhanced response is a result of a greater TNF-α response or the result of the sTNFR1-specific role in obesity and insulin resistance requires further investigation. Circadian patterns of cortisol and mediators of inflammation are unchanged in young, healthy, overweight men. This finding adds to the body of evidence suggesting that being overweight, not just obese, influences the risk of diseases associated with inflammation and can do so at a relatively early age. Furthermore, our findings suggest that body mass–associated increases in inflammation include both basal levels of mediators of inflammation and the magnitude of the acute inflammation response. It is possible that additional differences between groups are present but too small to detect with our experimental methods and samples size; thus, it may be most reasonable to conclude that the differences we measured are the most pronounced and additional differences were not of sufficient magnitude for reliable detection. Accordingly, the trends for circadian differences in sTNFR1 and greater IL-6 and CRP during acute inflammation may be emerging differences that will become more pronounced as BMI increases further. The primary limitations of this study are that cardiovascular fitness, chronic physical activity levels, and body composition were not measured. These variables influence inflammation, and adjustment for their influence would allow for greater discrimination between BMI groups. Future research that includes these measurements to aid in interpretation of inflammation differences is recommended.
This study was funded by a grant from the American Heart Association.
The authors have no conflicts to disclose.
Publication of the results presented in this article does not constitute endorsement by the American College of Sports Medicine.
1. Aderka D, Sorkine P, Abu-Abid S, et al.. Shedding kinetics of soluble tumor necrosis factor (TNF) receptors after systemic TNF leaking during isolated limb perfusion. Relevance to the pathophysiology of septic shock. J Clin Invest
. 1998; 101 (3): 650–9.
2. Beutler BA, Milsark IW, Cerami A. Cachectin/tumor necrosis factor: production, distribution, and metabolic fate in vivo. J Immunol
. 1985; 135 (6): 3972–7.
3. Cano P, Cardinali DP, Rios-Lugo MJ, Fernandez-Mateos MP, Reyes Toso CF, Esquifino AI. Effect of a high-fat diet on 24-hour pattern of circulating adipocytokines in rats. Obesity (Silver Spring)
. 2009; 17 (10): 1866–71.
4. Depner CM, Kirwan RD, Frederickson SJ, Miles MP. Enhanced inflammation with high carbohydrate intake during recovery from eccentric exercise. Eur J Appl Physiol
. 2010; 109 (6): 1067–76.
5. Faulkner JA, Brooks SV, Opiteck JA. Injury to skeletal muscle fibers during contractions: conditions of occurrence and prevention. Phys Ther
. 1993; 73 (12): 911–21.
6. Gangwisch JE. Epidemiological evidence for the links between sleep, circadian rhythms and metabolism. Obes Rev
. 2009; 10 (Suppl): S237–45.
7. Garaulet M, Madrid JA. Chronobiological aspects of nutrition, metabolic syndrome and obesity. Adv Drug Deliv Rev
. 62 (9–10): 967–78.
8. Gonzalez F, Rote NS, Minium J, O’Leary VB, Kirwan JP. Obese reproductive-age women exhibit a proatherogenic inflammatory response during hyperglycemia. Obesity (Silver Spring)
. 2007; 15 (10): 2436–44.
9. Gross MD, Bielinski SJ, Suarez-Lopez JR, et al.. Circulating soluble intercellular adhesion molecule 1 and subclinical atherosclerosis: the Coronary Artery Risk Development in Young Adults Study. Clin Chem
. 2012; 58 (2): 411–20.
10. Grundy SM, Cleeman JI, Daniels SR, et al.. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation
. 2005; 112 (17): 2735–52.
11. Hehlgans T, Pfeffer K. The intriguing biology of the tumour necrosis factor/tumour necrosis factor receptor superfamily: players, rules and the games. Immunology
. 2005; 115 (1): 1–20.
12. Hirose L, Nosaka K, Newton M, et al.. Changes in inflammatory mediators following eccentric exercise of the elbow flexors. Exerc Immunol Rev
. 2004; 1075–90.
13. Hou T, Tieu BC, Ray S, et al.. Roles of IL-6–gp130 signaling in vascular inflammation. Curr Cardiol Rev
. 2008; 4 (3): 179–92.
14. Kirwan JP, Krishnan RK, Weaver JA, Del Aguila LF, Evans WJ. Human aging is associated with altered TNF-alpha production during hyperglycemia and hyperinsulinemia. Am J Physiol Endocrinol Metab
. 2001; 281 (6): E1137–43.
15. Kowalska I, Straczkowski M, Nikolajuk A, et al.. Insulin resistance, serum adiponectin, and proinflammatory markers in young subjects with the metabolic syndrome. Metabolism
. 2008; 57 (11): 1539–44.
16. Kumari M, Chandola T, Brunner E, Kivimaki M. A nonlinear relationship of generalized and central obesity with diurnal cortisol secretion in the Whitehall II study. J Clin Endocrinol Metab
. 2010; 95 (9): 4415–23.
17. la Fleur SE, Kalsbeek A, Wortel J, Fekkes ML, Buijs RM. A daily rhythm in glucose tolerance: a role for the suprachiasmatic nucleus. Diabetes
. 2001; 50 (6): 1237–43.
18. Larsson CA, Gullberg B, Rastam L, Lindblad U. Salivary cortisol differs with age and sex and shows inverse associations with WHR in Swedish women: a cross-sectional study. BMC Endocr Disord
. 2009; 9: 16.
19. Lie RF, Schmitz JM, Pierre KJ, Gochman N. Cholesterol oxidase–based determination, by continuous-flow analysis, of total and free cholesterol in serum. Clin Chem
. 1976; 22 (10): 1627–30.
20. Martin-Cordero L, Garcia JJ, Hinchado MD, Ortega E. The interleukin-6 and noradrenaline mediated inflammation-stress feedback mechanism is dysregulated in metabolic syndrome: effect of exercise. Cardiovasc Diabetol
. 2011; 10: 42.
21. Maury E, Ramsey KM, Bass J. Circadian rhythms and metabolic syndrome: from experimental genetics to human disease. Circ Res
. 2010; 106 (3): 447–62.
22. McMurray RG, Zaldivar F, Galassetti P, et al.. Cellular immunity and inflammatory mediator responses to intense exercise in overweight children and adolescents. J Investig Med
. 2007; 55 (3): 120–9.
23. Miles MP, Andring JM, Pearson SD, et al.. Diurnal variation, response to eccentric exercise, and association of inflammatory mediators with muscle damage variables. J Appl Physiol
. 2008; 104 (2): 451–8.
24. Miles MP, Clarkson PM. Exercise-induced muscle pain, soreness, and cramps. J Sports Med Phys Fitness
. 1994; 34 (3): 203–16.
25. Miles MP, Depner CM, Kirwan RD, Frederickson SJ. Influence of macronutrient intake and anthropometric characteristics on plasma insulin after eccentric exercise. Metabolism
. 2010; 59 (10): 1456–64.
26. Olszanecka-Glinianowicz M, Chudek J, Kocelak P, Szromek A, Zahorska-Markiewicz B. Body fat changes and activity of tumor necrosis factor alpha system—a 5-year follow-up study. Metabolism
. 2011; 60 (4): 531–6.
27. Pamir N, McMillen TS, Kaiyala KJ, Schwartz MW, LeBoeuf RC. Receptors for tumor necrosis factor-alpha play a protective role against obesity and alter adipose tissue macrophage status. Endocrinology
. 2009; 150 (9): 4124–34.
28. Paschalis V, Nikolaidis MG, Giakas G, et al.. Beneficial changes in energy expenditure and lipid profile after eccentric exercise in overweight and lean women. Scand J Med Sci Sports
. 2010; 20 (1): e103–11.
29. Pearson TA, Mensah GA, Alexander RW, et al.. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation
. 2003; 107 (3): 499–511.
30. Pedersen BK. The diseasome of physical inactivity—and the role of myokines in muscle–fat cross talk. J Physiol
. 2009; 587 (Pt 23): 5559–68.
31. Petrovsky N, McNair P, Harrison LC. Diurnal rhythms of pro-inflammatory cytokines: regulation by plasma cortisol and therapeutic implications. Cytokine
. 1998; 10 (4): 307–12.
32. Ptitsyn AA, Zvonic S, Conrad SA, Scott LK, Mynatt RL, Gimble JM. Circadian clocks are resounding in peripheral tissues. PLoS Comput Biol
. 2006; 2 (3): e16.
33. Raitakari M, Mansikkaniemi K, Marniemi J, Viikari JS, Raitakari OT. Distribution and determinants of serum high-sensitive C-reactive protein in a population of young adults: the Cardiovascular Risk in Young Finns Study. J Intern Med
. 2005; 258 (5): 428–34.
34. 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
. 2003; 35 (7): 1160–6.
35. Romanatto T, Roman EA, Arruda AP, et al.. Deletion of tumor necrosis factor-alpha receptor 1 (TNFR1) protects against diet-induced obesity by means of increased thermogenesis. J Biol Chem
. 2009; 284 (52): 36213–22.
36. Rubin DA, McMurray RG, Harrell JS, Hackney AC, Thorpe DE, Haqq AM. The association between insulin resistance and cytokines in adolescents: the role of weight status and exercise. Metabolism
. 2008; 57 (5): 683–90.
37. Santos AC, Lopes C, Guimaraes JT, Barros H. Central obesity as a major determinant of increased high-sensitivity C-reactive protein in metabolic syndrome. Int J Obes (Lond)
. 2005; 29 (12): 1452–6.
38. Smith LL, Miles MP. Exercise-induced muscle injury and inflammation. In: Garrett WE, Kirkendall DT, editors. Exercise and Sport Science
. Philadelphia: Lippincott Williams & Wilkins; 2000. pp. 401–11.
39. Tam CS, Heilbronn LK, Henegar C, et al.. An early inflammatory gene profile in visceral adipose tissue in children. Int J Pediatr Obes
. 2011; 6 (2-2): e360–3.
40. Uysal KT, Wiesbrock SM, Hotamisligil GS. Functional analysis of tumor necrosis factor (TNF) receptors in TNF-alpha–mediated insulin resistance in genetic obesity. Endocrinology
. 1998; 139 (12): 4832–8.
41. Zuliani G, Galvani M, Maggio M, et al.. Plasma soluble gp130 levels are increased in older subjects with metabolic syndrome. The role of insulin resistance. Atherosclerosis
. 2010; 213 (1): 319–24.
ACUTE INFLAMMATION; CORTISOL; INTERLEUKIN-6; SOLUBLE TNF RECEPTOR-1; C-REACTIVE PROTEIN; BODY MASS INDEX
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