Relationship between cytokines and running economy in marathon runners

Luiz Antonio Luna Junior; Juliana de Melo Batista dos Santo; André Luis Lacerda Bachi; Roberta Foster; Alexandre Slowetzky Amaro; Ana Paula Ligeiro de Oliveira; Ana Paula Rennó Sierra; Maria Augusta Peduti Dal Molin Kiss; Mauro Walter Vaisberg

doi:10.1515/biol-2016-0041

Article Open Access

Relationship between cytokines and running economy in marathon runners

Luiz Antonio Luna Junior , Juliana de Melo Batista dos Santo , André Luis Lacerda Bachi , Roberta Foster , Alexandre Slowetzky Amaro , Ana Paula Ligeiro de Oliveira , Ana Paula Rennó Sierra , Maria Augusta Peduti Dal Molin Kiss and Mauro Walter Vaisberg

Published/Copyright: November 11, 2016

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From the journal Open Life Sciences Volume 11 Issue 1

Abstract

Background

Running economy (RE), expresses the relationship between the energy cost of running (Cr) and the work performed by a runner and is an predictor of performance. Given the intense effort of marathon runners during training and competition and the dearth of studies that address performance and cytokines in this population, the objective of the current study was to investigate the relationship between RE and cytokines in marathon runners.

Methods

A total of 22 recreational marathon runners were examined. Using data obtained from VO_2max assessments and sub-maximal tests, the following formula was applied to determine RE: Cr (mLO₂·kg^-1·km^-1) = VO₂ (mL·kg^-1·h^-1) × 60 ÷ speed (km·h^-1).

Results

Cr values shows no correlation with levels of the serum IL-1β, IL-4, IL-8, IL-10 and TNF-a 24h before, immediately after or 72h after the completion of an official marathon. However, the IL-6 level shows a significant correlation with Cr.

Discussion and conclusion

The relationship between higher values of IL-6 and lower RE leads to the hypothesis of a physical under-recovery state by some athletes. Considering the stress caused by training, associated with the higher energetic cost in less economic athletes, it’s possible that the period of resting may not totally compensate for the inflammatory state.

Keywords: Running Economy; maximal oxygen; consumption; cytokine; inflammation and recreational athletes

1 Introduction

The number of amateur runners participating in marathons has increased in recent years [1,2]. Their performance in these tests is largely dependent on three factors: maximal aerobic capacity (VO_2max), running economy (RE) and anaerobic threshold (AT) [3,4].

RE represents the energy consumption necessary to run at a given speed³. RE can be measured by the energy cost of running (Cr), which can be calculated using di Prampero’s equation: Cr (mL O₂·kg^-1·km^-1) = VO₂ (mL·kg^-1·h^-1) × 60 ÷ speed (km·h^-1) [5]. The value of Cr represents the energy required to transport a runner’s body mass at a given running speed (in km·h^-1). Higher RE values are associated with lower Cr values and therefore with better prospects for sustaining higher rates of speed and greater percentages of VO_2max with improved performance and reduced fatigue [6].

Although sustaining speeds at high percentages of VO_2max can be a good race strategy, while training, marathon runners must be careful to remain below the AT (4 mmol·L^-1), which represents the respiratory compensation point (RCP), because beyond this threshold the individual becomes exhausted within a few minutes [7].

An important consideration in studies of performance is that physical exercise not only relates to metabolism but also triggers a vigorous inflammatory response that causes increased production of both pro- and anti-inflammatory cytokines [8,9]. These changes become more evident as exercise intensity increases [10,11].

A previous study by our group demonstrated that athletes that produced higher serum inflammatory cytokine in response to exercise had significantly lower VO_2max levels, compared to athletes that showed a more equilibrated production of pro- and anti-inflammatory cytokines [12].

The influence of the inflammatory response on RE remains unknown, therefore, the objective of this study was to evaluate if there is some relation between RE and cytokine production in amateur marathon runners.

2 Methods

A total of 22 male runners between 18 to 50 years of age volunteered for this study. These subjects agreed to participate as volunteers according to an informed consent form approved by the Ethics Committee of the Federal University of São Paulo [Universidade Federal de São Paulo] (UNIFESP) (no. 572/11). The exclusion criteria were: a history of cardiopulmonary disease; use of medications and the inability to complete the 2012 São Paulo International Marathon (the race from which biological materials were collected). All participants were advised to maintain their normal training routine.

Initial evaluation of subjects V˙O2max values was determined in a treadmill test, using a gas analyser (GERAR, Fitmate®). Cardiopulmonary testing was performed using a ramp protocol involving a fixed slope of 1% and a load increase (of 1 km·h^-1) each minute until the treadmill’s maximum speed was reached, whereupon the incline began to be increased by 2% each minute. These tests were maximal and interrupted only by intense physical fatigue.

2.1 RE testing

At least 48 h after the initial test, the participating athletes underwent a submaximal cardiopulmonary exercise test to determine their RE. The treadmill test for RE was performed using a 1% slope. This test involved 5 min of warm-up at a speed corresponding to ventilatory threshold (VT₁) exhibited at VO_2max, followed by 8 min of running at a speed equal to 90% of the speed at the RCP. The VO₂ during the sixth minute was correlated with the calculated speed. RE was determined using the following equation [5]: Cr (mL O₂·kg^-1·km^-1) = VO₂ (mL·kg^-1·h^-1) × 60 ÷ speed (km·h^-1).

2.2 Analysis of blood samples

Each sample of peripheral blood collected was coagulated in the collection tube, and centrifugation of the supernatant was performed at 2500 rpm for 10 min to obtain serum (500 μL), which was frozen at -80°C for the subsequent determination of circulating cytokine concentrations.

2.3 Cytokines

The cytokines analyzed were IL-1β, IL-4, IL-6, IL-8, IL-10 and TNF-a. All samples were analyzed using Multiplex MILLIPLEX kits (Merck Millipore, USA) in accordance with the manufacturer’s instructions.

2.4 Statistical analysis

Anthropometric, systemic and performance data are summarized through mean and standard deviation 24 hours before (baseline), immediately and 72 hours after marathon race. Differences between levels of cytokines before, immediately and 72 hours after were investigated through T-test for paired observations. Spearman’s correlation analysis was used to investigate how RE (as measured by Cr) related to cytokine concentrations obtained 24 h before, immediately and 72 h after the marathon. A significance threshold of p < 0.05 was established.

3 Results

The studied group had the following characteristics (each expressed as the mean ± standard deviation): ages of 34.50 ± 6.70 y, weights of 75.90 ± 11.77 kg; heights of 176.81 ± 6.45 cm; BMI values of 24.17 ± 2.59; and body fat percentages of 18.66 ± 4.09%. The results of the cardiopulmonary and RE tests, as determined using RCP, the formula for Cr and runners’ speeds obtained during the sixth minute of the cardiopulmonary test, are presented in Table 1.

Table 1

Results of cardiopulmonary exercise testing and Running Economy assessment in marathon runners (n = 22).

Parameters	Mean ± SD	maximum	minimum
VO_2max (mL·kg^-1·min^-1)	58.6 ± 7.20	67.7	41.9
Speed at VO_2max (km·h^-1)	18.90 ± 1.54	22	16
VO₂ at VT₁ (mL·kg^-1·min^-1)	36.43 ± 4.93	45.4	26
VT speed1 (km·h^-1)	13 ± 1.19	15	10
VO₂ at the RCP (mL·kg^-1·min^-1)	49.3 ± 7.5	60.4	36.6
RCP speed (km·h^-1)	17.18 ± 1.29	20	15
90% of the RCP speed (km·h^-1)	15.46 ± 1.16	18	13.5
Cr at 6 min (mLO₂·kg^-1·km^-1)	9899.8 ± 1317.3	12011.11	7076.667

3.1 Cytokines

Cytokine levels show no significant differences relative to the baseline immediately after and 72 hours after marathon race, as showed in Table 2.

Table 2

Cytokines levels analysis in marathon runners (n = 22) immediately after and 72 h after the run.

Cytokines		24 h before (baseline)	Differences
Cytokines		24 h before (baseline)	Immediately after D₁	t-test for D₁ = 0	72 h after D₂	t-test for D₂ = 0
IL-1b	Min-Max	290.91, 1046. 92	298.69, 1678. 38	^tdf = ²¹	245.34, 825.9	^tdf = ²¹
	Mean	494.93	550.69	p = 0.687	501.84	p = 0.948
	SD	175.81	303.28		159.83
	CI95%		-1.68, 4.12		-1.93, 2.53
IL-4	Min-Max	12.27, 426.16	23.29, 737.44	^tdf = ²¹	18.1, 1852, 44	tdf = 21
	Mean	155.57	163.77	p = 0.435	205.12	p = 0.492
	SD	117.33	168.01		422.01
	CI95%		-107.55, 113.25		-5.39, 0.80
IL-6	Min-Max	45.6, 632.51	27.84, 1644.4	^tdf = ²¹	52.12, 718.07	tdf = 21
	Mean	229.75	287.73	p = 0.744	197.47	p = 0.523
	SD	165.24	358.13		172.05
	CI95%		-43.23, -20.76		-4.17, 0.20
IL-8	Min-Max	16.91, 17.65	27.14, 144.95	tdf = 21	18.6, 131.31	tdf = 21
	Mean	68.3	52.15	p = 0.779	46.82	p = 0.678
	SD	62.43	62.44		34.72
	CI95%		-22.35, -10.30		-4.93, 1.64
IL-10	Min-Max	0, 518.7	0, 879.11	tdf = 21	0, 23.33	tdf = 21
	Mean	35.06	60.96	p = 0.064	3.80	p = 0.433
	SD	113.92	190.15		6.86
	CI95%		-235.95, -95.52		-5.39, 0.80
HS-PCR	Min-Max	0.01, 0.7	0.01, 0.88		0.15, 2.16	tdf = 21
	Mean	0.11	0.14	tdf = 21	1.05	p = 0
	SD	0.14	0.19	p = 0.166	0.77
	CI95%		0.01, 0.70		0.15, 2.60
TNF	Min-Max	2.23, 611.3	2.27, 186.91	tdf = 21	2.16, 132.99	tdf = 21
	Mean	93.10	56.68	p = 0.398	34.26	p = 0.177
	SD	138.06	53.33		40.25
	CI95%		-6.88, -2.38		-6.02, 0.44

There were no significant correlations between Cr and levels of the cytokines IL-1β, IL-4, IL-8, IL-10 and TNF-a before, immediately after and 72 h after the marathon (Table 3). However, a significant positive correlation (r = 0.568, p = 0.009) between Cr and serum IL-6 concentration at baseline was observed (Figure 1).

Figure 1

Correlation between serum IL-6 basal level and Cr (p = 0.009; correlation = 0.568) in marathon runners after the run.

Table 3

Spearman correlation between Cr 6 minutes and levels of plasma cytokines in marathon runners (n = 22) immediately after and 72 h after the run.

Cytokine	24 h before	Immediately after	72 h after
TNF-α	-0.03	-0.342	-0.146
IL-1β	-0.108	-0.122	0.123
IL-6	0.568^[*]	-0.214	-0.226
IL-8	0.032	0.1	-0.437
IL-10	0.326	-0.016	-0.119

4 Discussion

It has been long demonstrated that exercise training induces an alteration in cytokine profile [13-15]. However, no evidence could be found in the literature of any study correlating RE to IL-6, a modulatory cytokine, in marathon runners. In the present study, it was demonstrated that athletes with higher Cr have highest baseline levels of serum IL-6. These data mean that the most economic athlete presents lowest levels of serum IL-6 concentration at rest, noting that the relation between values of Cr and RE are inversely proportional.

IL-6 demonstrates both pro- and anti-inflammatory actions. Usually this cytokine has an anti-inflammatory profile when produced by muscles during exercise and a pro-inflammatory pattern when produced by other tissues, especially adipose. As a result, it is understood that IL-6 acts as a pro-inflammatory cytokine at baseline in this group.

The inflammatory process could be triggered by physical exercise and is extremely important considering the role of cytokines in tissue damage repair. This function occurs in an environment of balanced cytokine production [16]. Acutely strenuous exercise leads to a proinflammatory state, while regular and systemic physical training induces an anti-inflammatory state [17].

Study of ultra-marathon runners showed that proinflammatory cytokines such as IL-6 increased in response to exercise and remain elevated at rest throughout competition. The increase occurred despite overnight recovery between competition days [18].

Considering the finding of elevated IL-6 values and the role of this cytokine at rest, the relationship between higher values of IL-6 and lower running economy leads to the hypothesis of a physical under-recovery state by some athletes. Taking into account the stress caused by training, associated with the higher energetic cost in less economic athletes, it’s possible that the period of resting may not totally compensate for the inflammatory state.

These findings demonstrate the importance of assessing athletes’ inflammatory condition, due to the potential effect of these states on their performance. Sport physicians and exercise physiologists should be alerted to this factor and take account of it during their evaluations.

Future studies should be done with the objective of elucidating how RE may be affected by other factors.

Acknowledgements

CAPES - Coordinator of Improvement of Higher Level Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior).

Fundação de Amparo à Pesquisa de São Paulo (FAPESP), São Paulo, Brazil (12/51698-5).

Funding source

Fundação de Amparo à Pesquisa de São Paulo (FAPESP), São Paulo, Brazil (12/51698-5).

Conflict of Interests

Dr. Luna Junior reports grants from Fundação de Amparo à Pesquisa de São Paulo (FAPESP), São Paulo, Brazil (12/51698-5) during the conduct of the study.

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Received: 2016-3-6

Accepted: 2016-8-29

Published Online: 2016-11-11

Published in Print: 2016-1-1

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.