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Original Research

Immunometabolic Responses to Concurrent Training

The Effects of Exercise Order in Recreational Weightlifters

Inoue, Daniela S.; Panissa, Valéria L.G.; Monteiro, Paula A.; Gerosa-Neto, José; Rossi, Fabrício E.; Antunes, Barbara M.M.; Franchini, Emerson; Cholewa, Jason M.; Gobbo, Luís A.; Lira, Fábio S.

Author Information
The Journal of Strength & Conditioning Research: July 2016 - Volume 30 - Issue 7 - p 1960–1967
doi: 10.1519/JSC.0000000000001281
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Abstract

Introduction

The importance of muscular strength and aerobic fitness to improve or maintain health and physical function is well established. Resistance and aerobic exercise may be performed in separate exercise sessions on different days or in separate sessions on the same day to optimize the available time for physical activity (23). The performance of resistance and aerobic exercise in the same session is defined as concurrent exercise and has been demonstrated to interfere with strength adaptations acutely via impaired neuronal activation and glycogen storage, and chronically via interfering with muscle fiber hypertrophy and fiber type intraconversion to more oxidative phenotypes (7,17,21,35).

Concurrent training decreases acute strength performance and may impair long-term strength development (7,10,23). The acute interference hypothesis suggests that there is a reduction in performance (i.e., the maximum number of repetitions [MNRs] or total volume) during the resistance exercise session when an aerobic activity is executed before resistance exercise (3,22,30,31,38). A reduction in volume and/or intensity would theoretically decrease the training stimulus and over subsequent training sessions lead to a smaller magnitude of adaptation. Thus the acute interference, caused by residual fatigue accumulation throughout each session exercise training, may contribute to the long-term impairment in strength gains observed after a period of concurrent training (10,28).

Residual fatigue due to insufficient recovery between aerobic and strength activity likely contributes to reduced strength performance during concurrent exercise (31); however, only a few studies have investigated the underlying mechanisms responsible for this phenomenon (3,22,29). Lactate accumulation and the resultant muscle acidosis were suggested by Leveritt and Abernethy (23) as a possible factor that contributes to reduced strength performance when high-intensity aerobic exercise is performed before resistance exercise (19). The association between blood lactate and acute fatigue has been suggested to be causal (8,34). Abernethy (1) demonstrated reductions in force output 4 hours after an intense aerobic bout of exercise. Muscle pH likely returned to baseline values 4 hours after exercise, suggesting that mechanisms other than muscle acid-base balance, such as central fatigue and/or the acute immunological response, also contribute to impaired acute strength performance with concurrent exercise. However, studies have reported that the reverse order does not cause the interference effect (15,33). Pugh et al. (33) demonstrated that high-intensity intermittent aerobic exercise performed after resistance exercise did not negatively affect mTOR phosphorylation compared with resistance training alone but did increase MuRF-1 2 hours after training; however, increased MuRF-1 returned to baseline within 6 hours. This suggests that although strength training followed by aerobic training may not suppress protein synthesis, it may stimulate a short transient rise in protein breakdown. PGC-1α mRNA expression was elevated for at least 6 hours after concurrent exercise suggesting that, at least on a molecular level, resistance exercise before aerobic exercise does not cause an interference effect.

The relationship and/or role of inflammatory cytokines in muscular fatigue are less understood (22,27). Minetto et al. (26) demonstrated a significant correlation between isometric strength and mechanical fatigue during exercise with peak lactate and serum interleukin-6 (IL-6). Circulating tumor necrosis factor alpha (TNF-α) is immediately elevated in response to muscle damage by heavy resistance exercise, likely indicating the acute inflammatory response (39). Moreover, Donges et al. (12) have shown that cytokine mRNA expression is upregulated at 1 hour after concurrent exercise in middle-aged men. In addition, IL-6 and TNF-α have been considered energetic sensors capable of signalling in a hormone-like manner to mobilize extracellular glucose and free fatty acids during exercise (14). Kim et al. (20) suggest that IL-6 produced in response to aerobic exercise induces a pronounced lipolysis, especially when the exercise is performed in the evening. Interleukin-10 (IL-10) levels increase in response to TNF-α, and its suggested function is to suppress inflammatory stimuli from high-intensity exercise (24), and IL-10/TNF-α ratio could be used as an anti- or pro-inflammatory marker (25). Currently, it is unclear whether the inflammatory cytokines released in response to fatiguing exercise mediate fatigue or play a role in attenuating fatigue via their role in maintaining energy homeostasis.

Thus, the aim of this study was to evaluate the influence of concurrent strength and high-intensity aerobic exercise orders on strength performance, metabolic, and the inflammatory response. Our hypothesis is that fatigue-related markers (e.g., lactate and IL-6) will present higher levels when high-intensity aerobic exercise is performed before strength exercise but not when strength exercise is performed before high-intensity aerobic exercise.

Methods

Experimental Approach to the Problem

This cross-sectional study used a randomized controlled design with repeated measures to analyze the impact of concurrent exercise order on immunometabolic and strength performance responses in young recreationally trained weightlifting men. After anthropometric and maximal aerobic and strength tests, subjects performed 2 experimental sessions composed of high-intensity intermittent aerobic exercise and resistance exercise with different exercise orders. In each experimental test, blood samples were collected at 3 distinct times (pre-exercise [pre], and immediately after the first [Post-1] and second exercise tests [Post-2]) to investigate the immunometabolic profile. Strength performance was analyzed by the MNRs and the total volume (repetitions × weightlifted). The relationship between immunometabolic variables and performance parameters was also examined.

Subjects

Eleven male recreational weightlifters (18–32 years) with strength training experience (>6 months) (Table 1) participated in this study. Subjects were free from the use of drugs such as anabolic-androgenic steroids, psychotropic, and nutrients supplementation as well as free from health problems and/or neuromuscular disorders that could affect their ability to complete the study protocol. Subjects voluntarily took part in the study after being informed of the procedures, risks, and benefits. The informed consent documents were signed for all subjects. This study was approved by the ethics committee of Estadual Paulista University, Presidente Prudente, Brazil.

Table 1
Table 1:
Subject characteristics (n = 11).*†

Procedures

Subjects completed 6 sessions separated by at least 72 hours. During the first day of tests, anthropometric and maximal endurance running test (Vmax) measurements on treadmill were performed. During the second and third days, the participants performed a familiarization of the one repetition maximum (1RM) test, and the 1RM test was performed during the fourth session. Two experimental sessions followed in a randomized order preceded by a warm-up at 50% Vmax for 5 minutes: (a) 1 session in which subjects performed 4 sets of half-squat strength exercise on a Smith machine until exhaustion (at 80% 1RM with 2-min rest between sets) followed by a high-intensity intermittent run (1-minute running at Vmax velocity with 1-minute passive rest) for 5-km run (Strength-Aerobic Exercise Order [SA]); (b) 1 session in which subjects performed a high-intensity intermittent run (1-minute running at Vmax velocity with 1-minute passive rest) for 5-km run followed by 4 sets of half-squat strength exercise on a Smith machine until exhaustion (at 80% 1RM with 2-min rest between sets) (aerobic-strength exercise order [AS]) [adapted from Souza et al. (38)]. The MNRs performed was recorded and the total volume was calculated (repetitions × weightlifted) (28). The aerobic and strength exercise bouts were separated by a 10-min recovery interval in accord with Souza et al. (38). All tests were performed at the same time of the day for each subject. The subjects were instructed to abstain themselves from any strenuous exercise at least 48 hours before each testing session, to consume a meal 3 hours before testing, and were encouraged to replicate their pretest meal from the first test to the second to maintain nutritional and hydration status.

Maximal Endurance Running Test

The subjects performed an incremental test to volitional exhaustion. The initial treadmill (modelo MASTER CI; Inbramed, Porto Alegre, Brazil) speed was set at 8.0 km·h−1 and increased by 1 km·h−1 per 2-min stage until the subject could no longer continue. The Vmax reached in the test was defined as the maximal intensity attained. When the subject was not able to finish the 1-minute stage, the speed was expressed according to the time of permanence in the last stage, determined as the following: Vmax = velocity of penultimate stage + ([time, in seconds, remained at the last stage multiplied by 1 km·h−1]/60 s) (23).

Maximum Dynamic Strength Test

The subject completed 2 sessions of familiarization (4 sets of 10–12 repetition repetitions in half-squat exercise) with equipment 1 week before to perform the maximum dynamic strength test. The 1RM was conducted to determine the half-squat maximum dynamic strength by using a Smith machine (Ipiranga, São Paulo, Brazil). Initially, a warm-up was performed at a treadmill for 5 minutes at 50% of Vmax. Then, the subjects had up to 5 attempts to achieve the 1RM load (i.e., maximum weight that could be lifted once using a proper technique), with 3–5 minutes interval between trials, according to standard procedures (4).

For better control of the 1RM test procedures, each subject had his body and feet position placed according to the half-squat exercise recorded and reproduced throughout the study. In addition, a height adjustable seat was placed behind the participant to keep the bar displacement and knee angle (∼90°) constant on each half-squat repetition.

Blood Samples and Analyses

The blood samples were collected at rest and immediately after each exercise condition as illustrated in Figure 1.

Figure 1
Figure 1:
Experimental designer AS and SA condition.

Blood samples (20 ml) were immediately allocated into two 5 ml vacutainer tubes (Becton Dickinson, Juiz de Fora, Brazil) containing EDTA for plasma separation and into two 5 ml dry vacutainer tubes for serum separation. The tubes were centrifuged at 3.000 rpm for 15 minutes at 4° C, and plasma and serum samples were stored at −20° C until analysis. Glucose and lactate were assessed by using commercial kits (Labtest, São Paulo, Brazil). Nonesterified fatty acid (NEFA) was assessed by a colorimetric method using a commercial kit (Wako Diagnostics, Richmond, VA, USA). Interleukins (IL-6, IL-10) and TNF-α cytokines were assessed by using ELISA commercial kits (affimetrix/eBioscience, Ambriex S/A, São Paulo, Brazil). The cytokines (IL-6, IL-10, and TNF-α), lactate, and glucose levels were assessed by using serum, and NEFA levels were assessed using plasma. To eliminate interassay variance, all samples were analyzed in identical runs resulting in an intra-assay variance of <7%. The standard curve range for TNF-α (7.81–500 pg·ml−1), IL-6 (3.12–200 pg·ml−1), and IL-10 (4.68–300 pg·ml−1), and NEFA (0.01–4.00 mEq·L−1) was mentioned, and the reference standard for glucose was 100 mg·dl−1.

Statistical Analyses

The data were analyzed using a statistical analysis system (Version 9.2; SAS Institute, Cary, NC, USA) and presented as mean and SD values. Linear mixed models were used to compare the MNRs in each set across the different conditions (condition × set), as well as blood variables in different conditions across time (condition × time). This type of analysis was chosen because it allows for missing data and can model covariate structures for repeated data (40). The effect size was calculated through Cohen's d. The Tukey's post hoc test was conducted if a significant interaction was found. Statistical significance was set at p ≤ 0.05.

In the SA condition, after strength exercise, 5 subjects finished the high-intensity intermittent endurance exercise before completion of 5 km, with 16.0 ± 8.1 minutes to exhaustion. The other 6 subjects completed all tests with a final time to complete 5 km of 41.5 ± 3.6 minutes. Thus, 6 subjects were analyzed for blood samples; however, data of all participants were used when performance data were analyzed.

Results

Table 2 presents MNRs and total volume performed in the strength exercise for the 2 different experimental conditions. For total repetitions, there was a main effect of condition (F1,30 = 10.28; p = 0.002), with a higher number of repetitions performed in SA compared with that of AS condition (p = 0.002; d = 0.85). There was also a main effect of the sets (F3,30 = 12.53; p < 0.001), with higher total repetitions performed in set 1 than during sets 2 (p < 0.001; d = 0.47), 3 (p < 0.001; d = 0.63), and 4 (p < 0.001, d = 0.79). There was no interaction between condition and sets.

Table 2
Table 2:
Comparison of the half-squat performance in SA and AS (n = 11).*†

For volume, there was a main effect of condition (F1,30 = 9.41; p = 0.003), with higher volume performed in SA condition compared with that in AS condition (p = 0.003; d = 0.70). There was also a main effect of the sets (F3,30 = 11.59; p < 0.001), with total volume performed in set 1 than during sets 2 (p = 0.002; d = 0.45), 3 (p < 0.001; d = 0.64), and 4 (p < 0.001; d = 0.79). There was no interaction between condition and sets.

Table 3 presents the metabolic variables for the 2 experimental conditions. For glucose, there was a main effect of time (F2,15 = 10.42; p = 0.001), with lower values in Pre compared with post-1 (p = 0.001). There was no effect of condition and interaction. For lactate, there was a main effect of condition (F1,43 = 47.4; p = 0.001), with lower values in AS compared with those in SA condition (p = 0.001; d = 0.72). There was a main effect of time (F2,44 = 78.1; p < 0.001) with pre values lower than post-1 (p < 0.001; d = 2.51) and post-2 (p < 0.001; d = 1.68) values, and post-1 lower than post-2 (p < 0.001; d = 0.67) values. There was also an interaction effect (F2,18 = 23.8; p = 0.044), with values in AS condition lower than those in SA condition at post-2 (p < 0.001; d = 1.99).

Table 3
Table 3:
Metabolic parameters.*†

Table 4 presents the inflammatory responses for the 2 experimental conditions. For TNF-α, there was an interaction between condition and time (F2,61 = 6.4; p = 0.008) with values in AS post-1 higher than those in AS pre. Values in AS pre were lower than those in SA pre (p = 0.018; d = 0.10). There was no main effect of condition or time. For IL-6, there was a main effect of time (F2,44 = 4.3; p = 0.001) with values pre lower than values post-1 (p = 0.016; d = 0.62). There was no main effect to condition or interaction. For NEFA, IL-10, and IL-10/TNF ratio, there were no main effects of condition or time, nor an interaction.

Table 4
Table 4:
Inflammatory parameters.*†

Discussion

In this study, we aimed to evaluate the influence of concurrent strength and high-intensity aerobic exercise order on strength performance, metabolic, and inflammatory responses. The main finding of this study was that in the AS condition the strength performance was decreased as well as small yet statistically significant changes in glucose and lactate between conditions at the conclusion of both exercise tests. In addition, we observed that IL-6 concentration was increased after both exercise protocols; however, TNF-α increased only in AS condition.

Reductions in the MNRs and total volume of approximately 25% were in agreement with previous studies (11,24,26,29), which have reported comparable reductions (∼27%) in similar protocols (high-intensity aerobic exercise: 5-km run on a treadmill intermittently [1 minute at the Vmax separated by 1-minute passive recovery]), followed by 4 sets at 80% 1RM in leg press. Our results reinforce that the negative effects of concurrent exercise (high-intensity aerobic and endurance before strength) may in part be caused by metabolic mechanisms because glucose was decreased in AS condition. Bentley et al. (3) concluded that maximal voluntary contraction force and electrical stimulated force of the quadriceps were reduced at least 6 hours after exhausting cycling, suggesting that nonmetabolic mechanisms, such as altered nerve conduction, synaptic transmission, or excitation-contraction disruptions, may also be responsible for reductions in strength performance after high-intensity endurance exercise. In contrast, Panissa et al. (29) used the same Souza et al. (37) protocol and reported an interference effect without alterations in electromyography signal (root mean square of the vastus lateralis). Both studies support the hypothesis that the acute interference effect with concurrent exercise on strength performance may be due to peripheral fatigue; however, complex biochemical mechanisms may be involved beyond those already identified.

The main objective of this study was not to analyze performance but to investigate the immunometabolic mechanisms underlying the interference effects of 2 different concurrent training schematics. Our study differs from the aforementioned (2,13,37) in that they focused on cardiorespiratory and not immunometabolic responses. It was surprising and unexpected that 5 subjects did not complete the endurance training protocol because in a previous study (38) all subjects were able to complete the exercise protocol.

Souza et al. (37) used a high-intensity intermittent aerobic exercise protocol (1:1 at Vmax) and 2 strength training protocols (5 × 5RM and 2 × 15RM in leg press) with a total volume similar to this study without any reported dropout. Conceição et al. (9) reported that the time of exhaustion in a cycle ergometer (load correspondent to second ventilatory threshold) was lower when preceded by a strength session (6 × 8RM at 75% of 1RM half-squat). Thus, it is important to consider the interference effect that resistance training has on aerobic exercise sessions (2,9,13,37).

Research has investigated the effect of concurrent training on performance (7,11,17,23,31); however, few studies investigated the metabolic responses of the concurrent exercise. A better understanding of energy metabolism in concurrent exercise and of the humoral molecules that affect substrate availability and energy production will allow future researchers to better investigate the physiological mechanisms underlying reduced strength performance due to peripheral fatigue. In this context, cytokines with endocrine actions may play a role in fatigue because of their effects on substrate availability and energy homeostasis.

Our hypothesis was that, first, molecules related to fatigue such as lactate (metabolic marker) and IL-6 (inflammatory marker) could exhibit high concentrations in AS when compared with those in SA condition; however, our data did not support this hypothesis. With respect to lactate response, we can summarize in 2 points: (a) the exercise done second in the order began with lactate levels already elevated above those measured at rest; (b) aerobic exercise performed after strength leads to increased lactate concentrations that do not occur when the order is done inversely.

With respect to the first point, lactate levels have been a metabolite widely studied as a limiting factor of performance, because it was believed that its accumulation could release H+ ions increasing the intramuscular acidosis and inhibiting enzymes responsible for glycolytic flux and excitation-contraction coupling (16). However, it is also believed that the association between blood lactate and acute fatigue processes can only be correlative, because the published data reveal that the muscle buffer capacity (structural and metabolic) is almost double the lactate production. Moreover, there is no direct evidence that lactate production directly releases protons and causes lactic acidosis (8,34). For example, although Leveritt and Abernethy (23) observed a decrease of performance in strength exercise when performed after intermittent aerobic exercise accompanied by an increase of lactate concentration, Ruas et al. (36) showed an increase in lactate levels without an interference effect.

Regarding the second point, the results showed that the strength exercise exacerbated the lactate levels in aerobic exercise, but the aerobic exercise did not cause this response in strength exercise (order AS). We can conclude that the higher levels of lactate measured in aerobic exercise can indicate a greater contribution of the glycolytic system (5). It is possible that, because the aerobic exercise began with elevated lactate values, the short rest intervals during the 40 minutes of intermittent aerobic work were insufficient for adequate lactate metabolism. Alternatively, when the intramuscular pressure exceeds hydrostatic pressure, lactate efflux out of the muscle cell is interrupted. It is therefore possible that during the aerobic exercise a high volume of lactate efflux from the strength training occurred resulting in the elevated lactate levels observed in the post-2 measurement in SA (41).

In response to an altered intramuscular milieu (impaired calcium homeostasis and glucose availability, and increased reactive oxygen species level) by exercise, the IL-6 concentration during exercise can show increases of up to 100-fold, and these increases depend on the exercise type (endurance/strength) and duration (32). Recently, Bustamante et al. (6) have reported that several intracellular signalling pathways are needed for sustained skeletal-muscle work efficiency during exercise and suggested that the production of IL-6 by skeletal muscle is mediated by extracellular ATP and nucleotide receptors involving IP3-dependent calcium signals as an early step that triggers a positive IL-6 autocrine loop. In this study, we observed that, after AS condition, lactate levels were unaffected but there was reduced strength performance. We suggest that the concurrent training, in which the interference phenomenon is observed, can lead to a disturbance in the intracellular signalling pathways that regulate contractility of skeletal muscle during exercise, affected all apparatus related to muscle work during exercise, and impair glucose uptake and/or oxidation and IL-6 production. However, more studies are needed to substantiate this hypothesis.

In response to the energy demand imposed by high-intensity aerobic exercise and to the possibility of inefficiency of the glycolytic pathway (observed in lactate stabilization), we suggest that the increasing of TNF-α (post-1 AS condition) was triggered to restore the energy demand by hepatic glycogenolysis and stimulating lipolysis to increase substrate availability, despite that NEFA levels did not increase (18). However, the slight increase of glucose concentrations compared with pre values was not associated with the increase of lactate levels (post-2 AS condition), indicating that there may have been a reduction in glucose uptake and/or ATP production via the glycolytic pathway. This in turn may have compromised the contractile activity of the muscle and resulted in the reduced performance. We did not find changes in IL-10. This is possibly because the peak in IL-10 occurs after TNF-α elevation around 30 minutes after exercise (24). Interleukin-10 functions to suppress inflammation and re-establish the homeostasis; however, more studies are needed to better understand the response to and mechanisms of IL-10 in concurrent training.

Despite the importance of the results found here, some limitations should be mentioned. First, although representative, our sample is small. Second, diet was not standardized; however, participants were required to eat and drink the same foods 3 hours before all testing sessions; thus, we believe that dietary intakes within subjects did not affect the results.

Taken together, our data demonstrate that the acute decline in strength performance induced by previous aerobic exercise in a concurrent exercise session was associated with alterations in the metabolic and inflammatory response. In this context, the inflammatory response seems to act on the substrate availability to modulate energy production. Thus, more invasive molecular research is needed to investigate the relationship between peripheral fatigue, cytokine signalling pathway, and direct measure of glycolysis.

Practical Applications

This study demonstrates to coaches and trainers who use strength and high-intensity aerobic exercise in the same session of training that the acute interference induced by residual fatigue accumulation (especially by H+ ion accumulation) affects performance in the second exercise condition regardless of the order. Although repetitions decreased across sets in both conditions, the repetition range completed was typical of most training programs. However, some caution must be taken when applying the effects of the interference on performance, because many professionally supervised strength training exercises do not commonly incorporate multiple sets of squats to volitional fatigue. Therefore, these results may be more practically applied to recreational trainers engaged in “extreme conditioning programs” (i.e., Crossfit) where concurrent training often involves multiple sets of multijoint movements performed to fatigue. When aerobic or glycolytic conditioning is the objective, strength exercise should be performed second in order, and, when strength is the primary objective, it should be performed first. Second, a longer recovery interval and different periods of day for each stimulus (i.e., morning and evening sessions) may be required for optimal improvements in performance.

Acknowledgments

Fabio Santos Lira thanks FAPESP for their support (2013/25310-2). The authors declare that they have no conflict of interest.

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Keywords:

high-intensity aerobic exercise; metabolism; inflammation; performance; acute fatigue

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