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Basic Sciences: Original Investigations

Influence of mode and carbohydrate on the cytokine response to heavy exertion


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Medicine & Science in Sports & Exercise: May 1998 - Volume 30 - Issue 5 - p 671-678
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Cytokines are low molecular weight proteins and peptides that help control and mediate interactions among cells involved in immune responses(2). Strenuous physical exercise of limb muscles typically results in muscle soreness and injury, especially when the exercise is intense and prolonged such as in long distance running (20). An inflammatory response to the muscle injury is initiated, characterized by movement of fluid, plasma proteins, and leukocytes into the injured area and metabolically active tissues (2). Cytokines help regulate the inflammatory cascade, with tumor necrosis factors (TNFα), interleukin (IL)-1β, IL-6, and interferons (IFNγ) working synergistically (2). An exaggerated response is prevented via several pathways, including the production of anti-inflammatory cytokines(IL-1 receptor antagonist (ra), IL-4, and IL-10) and mediators such as prostaglandin E2. Proin-flammatory cytokines also activate the hypothalamic-pituitary-adrenal (HPA) axis and the sympathoadrenergic system, which exert strong anti-inflammatory actions(1-5). As reviewed by Pedersen et al.(24), there is a growing consensus that prolonged and intensive exercise is linked to a strong increase in both pro- and anti-inflammatory cytokines. Exercise bouts that induce muscle cell injury cause a sequential release of the proinflammatory cytokines TNF-α, IL-1β, and IL-6, followed very closely by anti-inflammatory cytokines such as IL-4, IL-10, and IL-1ra(2,5-8,12,13,24,28-30). TNF-α and IL-1β stimulate the production of IL-6, which induces the acute phase response and the production of IL-1ra (3). Recent work using muscle biopsies and urine samples has shown more clearly the intimate link between all of these cytokines (2,24). The inflammatory cytokines help regulate a rapid migration of neutrophils, and then later monocytes, into areas of injured muscle cells and other metabolically active tissues to initiate repair(2,4). Moderate endurance exercise (e.g., 1 h of cycling at 60% ˙VO2max(27) or exercise associated with little or no muscle soreness (e.g., 30 min of moderate cycling) (5) seems to have little effect on inflammatory cytokines. Bruunsgaard et al. (5), for example, showed a significant increase in both creatine kinase and IL-6 following 30 min of eccentric cycling, but no increase in IL-6 following 30 min of moderate concentric cycling. Recent research has helped establish that endotoxemia(i.e., the presence in the blood of endotoxins, often from Gram-negative rod-shaped bacteria) is not the major cause of the inflammatory cytokine response (7).

A few attempts have been made to reduce plasma inflammatory cytokine concentrations during intensive and prolonged endurance exercise by nutritional and chemical means in the assumption that overall physiological stress and immunosuppression could be attenuated(2,8,20). Castell et al.(8) were unable to alter the increase in IL-6 following a marathon race competition with glutamine supplements. In a randomized, double-blind experiment with 30 marathon runners, we showed that carbohydrate ingestion attenuated both the IL-6 and IL-1ra responses to 2.5 h of intensive running (20). Carbohydrate versus placebo beverage ingestion before, during, and after the running bout was also associated with higher plasma glucose levels, lower plasma cortisol, and a diminished perturbation in most blood leukocyte and lymphocyte subset counts(20,21). A reduction in blood glucose levels has been linked to hypothalamic-pituitary-adrenal (HPA) activation, an increased release of adrenocorticotrophic hormone and cortisol, increased plasma growth hormone, decreased insulin, and a variable effect on blood epinephrine levels(9,16,18,19,21,26). Proinflammatory cytokines also activate the HPA, providing a natural negative feedback system through the anti-inflammatory actions of cortisol, which inhibits the release of IL-1 and IL-6 from monocytes and macrophages(1,2,10,23).

Given the link between plasma glucose, HPA activation, cortisol, and inflammatory cytokines and the implication that exercise associated with more muscle eccentric activity (e.g., running versus cycling) causes a greater cytokine inflammatory response, we designed a randomized, double-blind, placebo-controlled study to investigate the influence of carbohydrate ingestion on the inflammatory cytokine response to 2.5 h of intensive cycling and running. IL-6 and IL-1ra were measured to explore the effects of carbohydrate ingestion on the pro- and anti-inflammatory phases, respectively. Our hypothesis was that running would be associated with a greater plasma cytokine response than cycling and that carbohydrate versus placebo supplementation would keep plasma glucose levels at a higher level, attenuating the rise in cortisol and both pro- and anti-inflammatory cytokines.


Subjects. Ten experienced triathletes (8 men and 2 women) were recruited who met the following subject selection criteria: 25-50 yr of age, marathon race time of less than 4 h within the previous year, average training distance of ≥30 km·wk-1 running and 125 km·wk-1 cycling during the previous year, had competed in at least 2 triathlon competitions, and capable of running and cycling 2.5 h within a laboratory setting. Informed consent was obtained from each subject, and the experimental procedures were in accordance with the policy statements of the American College of Sports Medicine and the institutional review board of Appalachian State University.

Experimental design. The triathletes reported to the Human Performance Laboratory for baseline measurements of ˙VO2max and body composition and to receive orientation regarding the study. Body composition was assessed from hydrostatic weighing, and ˙VO2max was determined twice using graded maximal treadmill and cycle ergometer protocols(21,25). Oxygen uptake and ventilation were measured using the MedGraphics CPX metabolic system (MedGraphics Corporation, St. Paul, MN) using medical grade calibration gases supplied by MedGraphics Corporation. Maximal heart rate was measured using the Quinton Q4000 stress test system (Quinton Instrument Co., Seattle, WA). Training history and demographic factors were assessed through a questionnaire.

During the next four sessions (spread throughout a 4-6-wk period), subjects ran on treadmills or cycled using their own bicycles on electromagnetically braked tripod trainers for 2.5 h at ≈75% ˙VO2max (verified by testing for oxygen consumption and heart rate every 20 min). Subjects exercised under carbohydrate (6% carbohydrate beverage, Gatorade, Quaker Oats Company, Barrington, IL) or placebo conditions (double-blinded). Sessions were assigned in a random, counterbalanced order.

Subjects recorded food intake for 3 days before each test session, choosing foods from a list to ensure a carbohydrate intake of ≈55% of total energy intake. Nutrient intake was assessed using the computerized dietary analysis system, Food Processor Plus, version 6.0 (ESHA Research, Salem, OR).

Test sessions. For the four test sessions, subjects reported to the Human Performance Laboratory in a 12-h fasted and rested condition at 0700 h. Subjects indicated on surveys that they had avoided intensive exercise the day before testing and all exercise for at least 12-15 h, had avoided mineral and vitamin supplements of greater than 100% the recommended dietary allowance for 3 days before testing, and were free of symptoms associated with respiratory infections. After resting for 10-15 min, a blood sample was taken from each subject. Next, the triathletes consumed 12 mL·kg-1 body mass of a 6% carbohydrate (Gatorade) or placebo beverage. The beverages were prepared by the Gatorade Sports Science Institute (Barrington, IL). Treatments were double-blinded, and carbohydrate and placebo beverages were identical in appearance and taste. Except for carbohydrate concentration, the two fluids were identical in sodium (≈19.0 mEq·L-1) and potassium (≈3.0 Eq·L-1) concentrations and pH (≈3.0). At 0730, the triathletes began exercising and ingested 4 mL·kg-1 carbohydrate or placebo every 15 min of the 2.5-h exercise bout. The triathletes ran or cycled from 0730 h to 1000 h at a pace adjusted to elicit a workload approximating 75% ˙VO2max. Immediately after exercise(1000 h), another blood sample was taken, followed by a 1.5-h recovery sample(1130 h), 3-h recovery sample (1300 h), and 6-h (1600 h) recovery sample (five total samples). Subjects drank 8 mL·kg-1·h-1 of carbohydrate or placebo during the first 1.5 h of recovery and then 4 mL·kg-1·h-1 during the last 4.5 h of recovery. After the 1130-h blood sample, subjects ate a meal ad libitum, choosing foods from the same food list they had adhered to during the 3 days before the study.

Immune cell counts and cytokines. Five blood samples per subject were drawn from an antecubital vein with subjects in the seated position(after 10-15 min of rest except for the immediate postrun sample). Routine complete blood counts (CBC) with hemoglobin, hematocrit, and total leukocyte and subsets were performed by a clinical hematology laboratory (Lab Corp., Burlington, NC).

Five blood samples per subject were drawn into heparinized tubes and were immediately chilled and centrifuged, with plasma samples frozen at -80 °C until analysis (cytokine, hormone, and glucose levels). IL-6, IL-1ra, cortisol, and growth hormone were measured from all five blood samples. Several parameters (glucose, insulin, and catecholamines) that were anticipated to be near baseline levels by 1.5 h postexercise were not measured at the 3- and 6-h postexercise time points.

Total plasma IL-6 and IL-1ra measurements. IL-6 was measured with the MEDGENIX solid phase Enzyme Amplified Sensitivity Immunoassay (EASIA; INCSTAR Corporation, Stillwater, MN) ELISA kits. A standard curve was constructed for each run using commercial standards for both cytokines. Concentrations for both controls and serum samples were generated from standard curves using linear regression analysis. These assays are based on an oligoclonal system in which a blend of monoclonal antibodies directed against distinct epitopes of each interleukin molecule are used as capture antibodies. The minimum detectable concentration of plasma IL-6 is 2 pg·mL-1. Total plasma IL-1ra was measured with a quantitative sandwich ELISA technique, using monoclonal antibodies specific for IL-1ra as capture antibodies (R&D Systems, Inc., Minneapolis, MN). The IL-1ra commercial standard provided by the manufacturer had expected absorbances, which resulted in a high linear regression value, and was then compared with our own as a quality control measure (R2 > 0.98 for all three plates). The minimum detectable level of plasma IL-1ra is < 14 pg·mL-1.

Hormones, glucose, lactate, and plasma volume. Plasma cortisol was assayed using a competitive solid-phase 125I radioimmunoassay (RIA) technique (Diagnostic Products Corporation, Los Angeles CA). RIA kits were also used to determine plasma concentrations of insulin and growth hormone according to manufacturers' instructions (Diagnostic Products). For plasma epinephrine and norepinephrine, blood samples were drawn into chilled tubes containing EGTA and glutathione (Amersham, RPN532 Vacutainer tubes), centrifuged, and the plasma was stored at -80 °C until analysis. Plasma concentrations of epinephrine were determined by high pressure liquid chromatography (HPLC) with electrochemical detection(17,20,21). Plasma was analyzed spectrophotometrically for glucose (prerun and immediate and 1.5-h postrun samples) (14). Blood samples were analyzed for lactate according to manufacturer guidelines (Diagnostic Products). Plasma volume changes were estimated using the method of Dill and Costill(11).

Statistical analysis. Data are expressed as mean ± SE. Leukocyte subsets, hormone values, and all immune function measures were analyzed using 2 (running and cycling modes) × 2 (carbohydrate and placebo conditions) × 3 or 5 (times of measurement) repeated measures ANOVA. If the condition × time or mode × time interactionP value was ≤0.05, the change from baseline for the immediate postexercise, 1.5-, 3-, and 6-h recovery values was compared between conditions or modes using paired t-tests. For the four multiple comparisons within conditions or modes, a Bonferroni adjustment was made, with statistical significance set at P < 0.013 and values between this and 0.05 treated as trends.


Table 1 summarizes subject characteristics for the 10 triathletes. Subject characteristics and test results of the two female subjects were within the range of the eight male subjects, and they fully complied with all aspects of the study design; thus, results for all 10 subjects are presented without reference to gender. (The body fat percent of the two female triathletes was 13.1 and 8.0%, and their running˙VO2max was 51.0 and 55.3 mL·kg-1·min-1). Although not an elite group, the data portray the athletes as highly experienced and committed to triathlon training and competition. The maximal performance data indicate a 5% difference between running and cycling for ˙VO2max, a slightly lower maximal heart rate for cycling, and similar maximal ventilation, respiratory exchange ratio (RER), and respiratory rate values for the two modes.

Subject characteristics (N = 10) (mean ± SE).

Nutrient analysis of the 3-d food records before each of the four test sessions revealed a consistent dietary intake: mean energy intake for the 10 triathletes ranged from 9,920 to 11,302 kJ·d-1, with the proportion of energy as carbohydrate ranging from 54.6 to 57.5%, fat 23.1 to 25.3%, and protein 16.7 to 17.4%.

Percent ˙VO2max, percent maximal heart rate, respiratory rate, and ending lactate values did not differ significantly between any of the four test sessions indicating similar relative workloads (Table 2). Ending RER values were significantly lower in the placebo versus carbohydrate conditions for both test modes. Ending rating of perceived exertion (RPE) values were lowest for the cycling-carbohydrate test session but were similar for the other sessions (6-20 scale). The laboratory temperature ranged from 24 to 30 °C, with a relative humidity of 20-40% for all four test sessions. All triathletes consumed fluids according to the research design, including 2.5 L during the 2.5-h run. Body mass remained stable pre- to postexercise (within ± 0.6 kg of preexercise body mass for all test sessions). Plasma volume changes were minimal and did not differ significantly between the four test sessions (Table 2).

Treadmill running and cycling performance under carbohydrate and placebo conditions (average of seven measurements taken over 2.5 h of exercise except for RER, RPE, lactate, and change in body mass) (mean ± SE).

The blood leukocyte subset data are summarized in Table 3. The pattern of change over time between test modes did not differ significantly except for slight differences in neutrophils and monocytes. For both modes, the placebo versus carbohydrate condition, however, resulted in significantly higher blood concentrations of neutrophils throughout recovery. Immediately postexercise, monocytes and lymphocytes were higher in the placebo conditions, with lymphocytes falling markedly lower from 1.5 to 3 h postexercise. As a result, the neutrophil/lymphocyte ratio was elevated in the placebo conditions for both modes throughout recovery. Blood eosinophils tended to be lower during recovery for the placebo conditions.

Response of blood leukocyte subsets following 2.5-h treadmill running and cycling under carbohydrate or placebo conditions.

Plasma glucose concentrations were lower in the placebo versus carbohydrate conditions immediately postexercise (F (2,18) = 11.29, P< 0.001) (Fig. 1). The lowest plasma glucose was associated with the placebo-cycling test session (change from pre- to postexercise compared with the placebo-running test session, P = 0.006). The pattern of change in insulin did not vary according to test mode but was significantly lower in the placebo conditions (F (2,18) = 4.32, P = 0.029) (Fig. 2). The pattern of change in plasma catecholamines did not differ between placebo and carbohydrate conditions, although for norepinephrine, levels tended to be lower in the cycling test sessions (Table 4). The pattern of change in plasma cortisol was significantly different between placebo and carbohydrate conditions (F (4,36) = 4.74, P = 0.004) (but not modes), with values higher in the placebo conditions during most of recovery(Fig. 3). The pattern of change in plasma growth hormone was significantly different between placebo and carbohydrate conditions(F (4,36) = 6.61, P < 0.001) (but not modes), highlighted by significantly higher values in the placebo conditions immediately postexercise (Fig. 4).

Figure 1-The pattern of change in plasma glucose over time was influenced by carbohydrate versus placebo ingestion (
Figure 1-The pattern of change in plasma glucose over time was influenced by carbohydrate versus placebo ingestion (:
F(2,18) = 11.29,P < 0.001) but not exercise mode(F (2,18) = 1.85,P = 0.186).*,P < 0.05; **,P < 0.013; change from preexercise, carbohydrate versus placebo, same mode.††,P < 0.013; change from preexercise, running versus cycling, placebo condition.
Figure 2-The pattern of change in plasma insulin over time was influenced by carbohydrate versus placebo ingestion (
Figure 2-The pattern of change in plasma insulin over time was influenced by carbohydrate versus placebo ingestion (:
F(2,18) = 4.32,P = 0.029) but not exercise mode(F (2,18) = 0.32,P = 0.730).*,P < 0.05; change from preexercise, carbohydrate versus placebo, running or cycling modes.
Response of catecholamines following 2.5-h treadmill running and cycling under carbohydrate or placebo conditions.
Figure 3-The pattern of change in plasma cortisol over time was influenced by carbohydrate versus placebo ingestion (
Figure 3-The pattern of change in plasma cortisol over time was influenced by carbohydrate versus placebo ingestion (:
F(4,36) = 4.74,P = 0.004) but not exercise mode(F (4,36) = 1.46,P = 0.236).*,P < 0.05; change from preexercise, carbohydrate versus placebo, cycling mode.
Figure 4-The pattern of change in plasma growth hormone over time was influenced by carbohydrate versus placebo ingestion(
Figure 4-The pattern of change in plasma growth hormone over time was influenced by carbohydrate versus placebo ingestion(:
F (4,36) = 6.61,P < 0.001) but not exercise mode (F (4,36) = 0.36,P = 0.838). *,P < 0.05; change from preexercise, carbohydrate versus placebo, running or cycling modes.

The pattern of change over time for IL-6 was significantly different between placebo and carbohydrate conditions (F (4,36)=3.32,P = 0.021) and between running and cycling modes (F (4,36)= 9.84, P < 0.001), with the lowest postexercise values seen in the carbohydrate-cycling sessions (10.7 ± 1.8 pg·mL-1) and the highest in the placebo-running sessions (51.6 ± 14.2 pg·mL-1) (Fig. 5). The pattern of change over time between the carbohydrate and placebo conditions (but not modes) was significantly different for IL-1ra (F (4,36) = 4.80, P = 0.003), with values once again lowest for the carbohydrate-cycling sessions(1.5 h postexercise, 301 ± 114 pg·mL-1) and highest for the placebo-running sessions (1171 ± 439 pg·mL-1)(Fig. 6).

Figure 5-The pattern of change in plasma IL-6 over time was influenced by carbohydrate versus placebo ingestion (
Figure 5-The pattern of change in plasma IL-6 over time was influenced by carbohydrate versus placebo ingestion (:
F(4,36) = 3.32,P = 0.021) and exercise mode(F (4,36) = 9.84,P < 0.001).**,P < 0.013; change from preexercise, carbohydrate versus placebo, cycling mode. ††,P < 0.013; change from preexercise, running versus cycling, carbohydrate condition.
Figure 6-The pattern of change in plasma IL-1ra over time was influenced by carbohydrate versus placebo ingestion (
Figure 6-The pattern of change in plasma IL-1ra over time was influenced by carbohydrate versus placebo ingestion (:
F(4,36) = 4.80,P = 0.003) but not exercise mode(F (4,36) = 1.52,P = 0.216).*,P < 0.05; change from preexercise, carbohydrate versus placebo, same mode.


The concentration of plasma IL-6 has been shown by several researchers to rise strongly following prolonged and intensive aerobic exercise, returning to close to preexercise levels within 6 h depending on the severity of the workload(7,8,12,13,20,22,24,28-30). Although relatively unstudied, IL-1ra also rises strongly following heavy exertion, showing a more delayed and prolonged elevation than IL-6(12,20,24). In agreement with our prior research involving marathon runners (20,21), carbohydrate relative to placebo ingestion before, during, and after 2.5 h of intensive exercise was associated with higher plasma levels of glucose and insulin, lower plasma cortisol and growth hormone, diminished perturbation in blood immune cell counts, and decreased proinflammatory (IL-6) and anti-inflammatory (IL-1ra) cytokine levels. In the present study, these findings have been extended, with results showing that this response pattern is exhibited during both running and cycling.

Mode of exercise had the strongest effect on IL-6, with levels higher following running relative to cycling. Bruunsgaard et al.(5) have shown that exercise involving eccentric muscle activity (e.g., braked cycling during reversed revolutions) is associated with a much higher IL-6 response than concentric exercise (normal bicycle exercise). In another study in which muscle biopsies and blood samples were collected before and after a marathon race (24), a comparative polymerase chain reaction technique detected mRNA for IL-6 in muscle following the race but not in blood where mRNA for IL-1ra was found. These findings indicate that exercise-induced injury of muscle fibers in the skeletal muscles triggers local production of IL-6 (probably stimulated by TNF-α and IL-1β) that stimulates the production of IL-1ra from blood mononuclear cells (24).

Our data are in part consistent with these findings. Running, which involves more eccentric activity than cycling, was associated with higher postexercise plasma concentrations of IL-6. IL-1ra, however, was unaffected by mode of exercise but was strongly diminished in the carbohydrate versus placebo test sessions. In the present study, IL-1β was not measured because it is difficult to detect this cytokine in plasma samples, although its presence in muscle biopsies and urine samples suggests that it is produced early following intensive exercise and then rapidly cleared from circulation(2,8,12,20,22,28). Our data suggest that production of IL-1ra (and by association, IL-1β) after intensive exercise is affected by carbohydrate ingestion independent of exercise mode, probably through linkage to plasma cortisol levels. Cortisol has been shown to inhibit release of IL-1 from monocytes(1). In agreement with other studies, our data have shown that athletes ingesting carbohydrate have higher plasma glucose and lower cortisol levels (9,19-21). As a result, IL-1β production is probably diminished, with a resulting lower production of IL-1ra. Cortisol can also inhibit IL-6 production, but studies suggest that following exercise, IL-6 production is more strongly linked to other factors (10,23).

Positive correlations between plasma IL-6 and both epinephrine and norepinephrine have been reported following high intensity running(23). Our data suggest that if catecholamines have a role in stimulating IL-6 production, their relative contribution compared with other factors such as muscle cell injury and cortisol is probably small. Growth hormone, which was significantly higher in the placebo versus carbohydrate trials with our triathletes, has been reported to promote neutrophilia but not have a significant effect on cytokine production(15).

In summary, carbohydrate versus placebo ingestion was found to have a significant influence on the hormone and cytokine response to 2.5 h of intensive running and cycling (≈75% ˙VO2max) by 10 triathletes who acted as their own controls. Other than IL-6, mode of exercise (running and cycling) had little effect on the plasma glucose, hormone, and immune response. Plasma IL-6 levels following exercise were affected by both carbohydrate ingestion and exercise mode, with levels after the carbohydrate cycling trial about one-fifth those measured after the placebo running trial. Unlike IL-6, IL-1ra was not affected by exercise mode but was decreased during several hours of recovery by about 60% after carbohydrate ingestion relative to placebo. Together, the data support that carbohydrate ingestion is associated with higher plasma glucose levels, an attenuated cortisol response, fewer perturbations in blood immune cell counts, and a diminished pro- and anti-inflammatory cytokine response. The clinical significance of these carbohydrate-induced effects on the endocrine and immune systems awaits further research, but the findings to date suggest a reduction in overall physiological stress.


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