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SPORTS AND ATHLETICS PREPARATION, PERFORMANCE, AND PSYCHOLOGY

ATHLETE PERFORMANCE AND INJURIES

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SPORTS AND ATHLETICS PREPARATION, PERFORMANCE, AND PSYCHOLOGY

ATHLETE PERFORMANCE AND INJURIES

JOÃO H. BASTOS AND

ANDREIA C. SILVA EDITORS

Nova Science Publishers, Inc. New York

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Copyright © 2012 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. Library of Congress Cataloging-in-Publication Data Athlete performance and injuries / editors, Joco H. Bastos and Andreia C. Silva. p. cm. Includes index.

ISBN:  (eBook)

1. Sports injuries. 2. Sports--Competitions. I. Bastos, Joco H. II. Silva, Andreia C. RD97.A83 2011 617.1'027--dc23 2011051070 Published by Nova Science Publishers, Inc. † New York

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CONTENTS Preface

vii

Chapter 1

Exercise, Injuries and Athlete Performance Ana Luisa Miranda-Vilela

Chapter 2

Training Over the Edge: Understanding the Overtraining Syndrome Fernando Rocha, Mário C. Marques and Aldo M. Costa

Chapter 3

Evaluating the Dynamic Model of Psychological Response to Sport Injury and Rehabilitation Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert, Elizabeth A. Arendt, Susan J. Schwenz, Shelly S. Myers and Diane M. Gardetto-Heller

Chapter 4

Chapter 5

Cardiometabolic Injury due to Recombinant Human Erythropoietin Doping for Improvement of Sports Performance: Chronic (Training) versus Acute (Extenuating) Aerobic Exercise Edite Teixeira-Lemos, Helena M. Teixeira, Nuno Piloto, Margarida Teixeira, Belmiro Parada, Paulo Rodrigues-Santos, Lina Carvalho, Rui Alves, Elísio Costa, Luís Belo, Alice Santos-Silva, Frederico Teixeira and Flávio Reis Athletic Heart: The Possible Role of Impaired Repolarization Reserve in Development of Sudden Cardiac Death István Baczkó, Andrea Orosz, Csaba Lengyel and András Varró

Chapter 6

Sports Injuries and Risk-Taking Behaviors in Amateur Athletes Vanessa Lentillon-Kaestner

Chapter 7

Oral Glycosaminoglycans for 8-Week Recovery of Functional Abilities in Professional Male Athletes after Knee Injury Sergej M. Ostojic, Senka Rendulic-Slivar, Marko Stojanovic, Igor Jukic, Kemal Idrizovic and Boris Vukomanovic

Chapter 8

Evolution of the Achilles Tendon in Bipedal Locomotion: Advantages and Flaws B. Tucker and W. S. Khan

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vi Chapter 9

Contents Patellofemoral Syndrome A. Yetkil, W. S. Khan and P. Pastides

Index

177 183

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PREFACE In this book, the authors present current research in the study of athletic performance and injuries. Topics discussed include the role of antioxidants in combating exercise-induced oxidative stress; over-training syndrome and recovery; psychological response to sport injuries and rehabilitation processes; the cardiometabolic effects of rhEPO treatment on chronic vs. acute extenuating exercise; impaired repolarization reserve in the development of sudden cardiac death in the young athlete; sports injuries and risk-taking behaviors in amateur athletes; injuries in young martial arts athletes; the achilles tendon in bipedal locomotion and patellofemoral syndrome. Chapter 1 - Today it is unanimously accepted that physical exercise, when practiced on a regular basis, is vital for a healthy lifestyle, acting as a therapeutic agent and/or preventing numerous illnesses. However, despite its potential beneficial effects, exercise, especially if unusual or exhausting, or training above habitual intensity, may exceed the endogenous antioxidant system’s capacity and often results in oxidative stress and injuries. This oxidative overload, although felt most intensely in skeletal muscles, has also been reported in many other organs and body systems responsible for regulating and maintaining homeostasis, including the heart, liver, kidneys, lungs, erythrocytes and immune and osteoarticular systems. Sports-related injuries are one of the main reasons why athletes prematurely abandon a sports career, spend long periods excluded from training and competitions, or experience a decline in sports performance, even causing functional limitations at more advanced ages. Thus, many athletes and even individuals participating in regular exercise programs consume antioxidant supplements to prevent exercise-induced oxidative stress and injuries. However, antioxidant supplementation can inhibit the beneficial adaptive responses associated with improved athletic performance, which in turn is a multifactorial phenotype, where genetic and environmental factors interact to produce the final phenotype. Research into genetic variants that, when inherited, can lead to improved athletic performance, has therefore increased greatly. This chapter presents a comprehensive summary of free radicals and the antioxidant defense system and examines the role of reactive oxygen species in exercise-induced oxidative stress and injuries to skeletal muscle, myocardium, liver, erythrocytes, immune system, plasma lipoproteins and DNA. It also discusses the role of antioxidants in combating exercise-induced oxidative stress and provides a subset of genetic variants potentially related to exercise-induced oxidative stress and injuries, as well as some others widely studied in the context of performance.

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Chapter 2 - The improvement of elite athletes performance depends on two fundamental training variables: volume and intensity. The physiological adaptations and supercompensation require training and sufficient recovery periods. In fact, there is a strict relationship between training volume and recovery time that can be considered critic for the athlete’s physiological status. On this, overtraining syndrome appears to be caused by too much high intensity training and/or too little recovery time often combined with other training and nontraining stressors factors. The imbalance between effort and adequate recovery can bring serious physiological consequences, like decrease of training tolerance and performance and even susceptibility to respiratory tract infections. At present there is no one single diagnostic test that can define overtraining. The recognition of overtraining requires the identification of stress indicators, which do not return to baseline following a period of regeneration. Possible indicators include an imbalance of the neuroendocrine system, suppression of the immune system, indicators of muscle damage, depressed muscle glycogen reserves, deteriorating aerobic, ventilatory and cardiac efficiency, a depressed psychological profile, and poor performance in sport specific tests, e.g. time trials. Therefore, screening for overtraining and performance improvements must occur at the culmination of regeneration periods. Chapter 3 - Authors of the integrated model of psychological response to the sport injury and rehabilitation process (Wiese-Bjornstal, Smith, Shaffer, & Morrey, 1998) conceptualized sport injury as influenced by preinjury psychosocial factors (Williams & Andersen, 1998), acting as a negative life event stressor, and comprising a dynamic process of ongoing cognitive appraisals influencing emotional and behavioral responses affecting recovery outcomes (Wiese-Bjornstal, 2009; 2010; Wiese-Bjornstal, Smith, & LaMott, 1995). The purpose of this project was to simultaneously examine these three primary model components and associated predictions while controlling for within team and school-related factors through repeated measures sampling of injured and noninjured teammates. Within a prospective mixed factorial study design, NCAA Division I male and female athletes (N = 74) from four sports (women’s softball, track and field, and tennis, and men’s baseball) completed multiple psychosocial measures at repeated time points from baseline to postseason. Results supported (a) the ability of psychosocial variables to predict sport injury, (b) conceptualizing sport injury as a stressor, and, (c) the role of affect as a precursor and response to sport injury. A unique aspect to this study was reflected in the matching of psychological data from injured and noninjured teammates during the specific weeks in which injuries occurred, thus controlling for non-injury related factors such as team and school related variables that may have influenced the mood state and life event stress of all athletes on the teams aside from injury. Furthermore, this study lends support to the idea that negative mood states are not only responses to but also risk factors for sport injury, and thus provides grounding for identifying psychological interventions to ameliorate negative moods. Chapter 4 - Athletes who abuse recombinant human erythropoietin (rhEPO) consider only the benefit to performance and usually ignore the potential short and long-term liabilities. Elevated haematocrit and dehydratation associated with intense exercise may reveal undetected cardiovascular risk, but the mechanisms underlying it remain to be fully explained. This chapter intended to compare the cardiometabolic effects of rhEPO treatment on rats under chronic vs acute extenuating exercise. The following male Wistar rat groups were assessed: control – sedentary (Sed); rhEPO – 50 IU/Kg/wk; Exercise (Ex) – swimming: 1 hr, 3 times/wk; Ex+rhEPO. For the chronic exercise a period of 10 wks was assessed, while for the acute exercise, a single bout of

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extenuating swimming was performed, with a rhEPO treatment for 3 wks prior to the acute section. Blood pressure and heart trophism were analysed. Blood and tissue samples were assessed for: biochemical data, haematology, catecholamine and serotoninergic measures, redox status and heart gene expression profile. The chronic Ex+rhEPO rats showed higher values of RBC, Htc and Hb vs chronic Ex, as well as vs acute Ex+rhEPO. Both chronic and acute swimming showed a remarkable sympathetic and serotonergic activation. rhEPO treatment in chronic training has promoted oxidative stress, in contrast with the antioxidant effect on the acute exercise. rhEPO in trained rats promoted erythrocyte count increase, hypertension, heart hypertrophy, sympathetic and serotonergic overactivation. One rat of the chronic Ex+rhEPO group suffered a sudden death episode during the week 8 and the tissues analyzed showed: brain with vascular congestion; left ventricular hypertrophy, together with a “cardiac liver”, suggesting the hypothesis of heart failure as cause of sudden death. In the chronically trained rats, rhEPO per se promoted apoptosis, proliferation and angiogenesis, which was partially or totally prevented in the Ex+rhEPO rats. In conclusion, the effects of rhEPO doping in rats under exercise is notoriously more deleterious in circumstances that mimic high-performance athletes (chronic training) than in occasional consumers (acute sessions), particular due to increased cardiovascular risk. Chronic rhEPO doping in rats under chronic exercise promotes not only the expected RBC count increment, suggesting hyperviscosity, but also other serious deleterious cardiovascular and thromboembolic modifications, including mortality risk, which might be known and assumed by all sports authorities, including athletes and their physicians. Chapter 5 - A number of sudden deaths involving young competitive athletes were reported in recent years. Sudden death among athletes is rare, but in a significant number of these cases the cause is not established and is mostly attributed to ventricular fibrillation. Physical conditioning in competitive athletes induces cardiovascular adaptation including lower resting heart rate (increased vagal tone) and increased cardiac mass (hypertrophy) and volume as a consequence of increased demand on the cardiovascular system, called "athlete’s heart”. Myocardial hypertrophy has been shown to cause electrophysiological remodeling where the expression of different ion channels is altered. Since the duration of repolarization depends on cycle length, the low heart rate in athletes also leads to prolonged repolarization. It is conceivable that prolonged repolarization and a possibly impaired repolarization reserve due to myocardial hypertrophy-induced downregulation of potassium currents might represent increased risk for the development of ventricular arrhythmias, including Torsades de Pointes ventricular tachycardia (TdP) that can degenerate into ventricular fibrillation and lead to sudden cardiac death in athletes. The reliable prediction of TdP remains unsatisfactory. Short-term variability (STV) of the QT interval is a novel parameter used in the assessment of arrhythmic risk. STV of repolarization can increase in case of decreased repolarization reserve even when there are no noticable changes in the duration of cardiac repolarization. STV may be significantly larger in competitive athletes and may be an early indicator of increased instability of cardiac repolarization and a higher arrhythmia propensity in this population. Chapter 6 - Research conducted outside of the sports context has shown higher risk for all injuries (e.g., intentional injuries, injured drivers, fatal and non-fatal injuries) among persons with risk-taking behaviors (e.g., cannabis use, alcohol consumption). The purpose of this study was to investigate whether: (1) risk-taking behaviors, such as alcohol, cannabis or

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tobacco consumption increased the risk and the severity of sports injuries; (2) whether differences emerged between male and female athletes; and (3) whether differences emerged between recreational and competitive athletes. The sample consisted of 1,810 amateur athletes (993 men, 817 women), aged 16 to 22 years old (M=18.72; SD=2.08). Respondents completed a questionnaire, which queried frequency of risk-taking behaviors and sports injuries recorded in their lifetime. Sixty-seven percent (67%) of amateur athletes indicated at least one sports-related injury in their lifetime. For sixty-two percent (62%) of athletes, the most frequent sports injuries required ten days to three months of sport interruption. Results also indicated that risk and severity of sports injuries increased with increased alcohol consumption. Cannabis use also increased the risk but not the severity of sports injuries, while smoking was not associated with the risk but with the severity of sports injuries. Some differences were observed between males and females as well as between recreational and competitive athletes in associations between risk-taking behaviors, risk and/or severity of sports injuries. Prevention measures for risk-taking behaviors in athletic pursuits should be increased and improved to reduce the number and severity of injuries in sports played on an amateur level. Chapter 7 - The use of different glycosaminoglycans (GAGs; e.g. glucosamine salts, chondroitin sulfates, hyaluronan) is a common practice among athletes at all ages and levels of participation, with GAGs promoted as chondroprotective and therapeutic agents for musculoskeletal healing. Yet, the effectiveness of different common GAGs intake after acute joint injury in high-performance athletes is yet to be determined. The main aim of the present chapter was to present the effects of eight-week of oral glucosamine chloride, chondroitin sulfate and hyaluronic acid administration on the functional ability and the degree of pain intensity in competitive male athletes after acute knee injury. This research was a randomized, double-blind parallel trial of glucosamine chloride (1500 mg per day), chondroitin sulfate (1500 mg per day), hyaluronic acid (90 mg per day) or a placebo administration for 2 months, utilising 218 patients with an acute knee injury. Pain at rest and while walking and functional ability (e.g. passive knee flexibility, degree of knee swelling) were evaluated at the beginning of the study and every second week thereafter for the study duration. No significant differences were found between the experimental protocols in mean pain intensity scores for resting and walking, and degree of knee swelling during the study (p > 0.05). There were no significant differences for passive knee flexibility between the groups at the 14-day and 28-day assessment (p > 0.05). After 6 weeks of treatment the patients supplemented with glucosamine chloride demonstrated significant improvement in both knee flexion and extension as compared to other experimental protocols (p < 0.05). The findings of the present study indicate that administration with GAGs does not significantly alter pain score or degree of swelling after acute sports injury of knee. Yet, glucosamine chloride supplementation appears to be suitable as a flexibility improvement strategy in athletes after 6 weeks of treatment. In prescribed doses GAGs do not induce any acute side-effects. Chapter 8 - The Achilles tendon is a key structure separating humans from other primates, allowing the upright bipedal stance. There are many advantages to being a biped from hunting ability to energy expenditure. The Achilles tendon itself has the benefit of greatly enhancing endurance running. However, there are disadvantages to having an Achilles tendon such as its vulnerability to injury. This article outlines the advantages and disadvantages of the tendon and highlights some theories as to why humans may have evolved to have it.

Preface

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Chapter 9 - Patellofemoral pain syndrome (PFPS), an injury frequently observed in runners and is a very common presentation to sports medicine clinics. Although the exact cause is still unclear, the development of PFPS is almost certainly multifactorial. Disruption to the physiological tracking of the patella due to overuse, muscular imbalance or injury may changes the biomechanics of the joint and result in the development of PFPS. The focus of this work will be to examine the suspected aetiology of PFPS and then suggest how the recent evidence based exercise therapies in particular can be included to alleviate the pain and allow return to a normal level of activities.

In: Athlete Performance and Injuries Editors: João H. Bastos and Andreia C. Silva

ISBN 978-1-61942-658-0 © 2012 Nova Science Publishers, Inc.

Chapter 1

EXERCISE, INJURIES AND ATHLETE PERFORMANCE Ana Luisa Miranda-Vilela Department of Genetics and Morphology, Laboratory of Genetics, Institute of Biological Sciences, University of Brasilia, Brasilia/DF, Brazil

ABSTRACT Today it is unanimously accepted that physical exercise, when practiced on a regular basis, is vital for a healthy lifestyle, acting as a therapeutic agent and/or preventing numerous illnesses. However, despite its potential beneficial effects, exercise, especially if unusual or exhausting, or training above habitual intensity, may exceed the endogenous antioxidant system’s capacity and often results in oxidative stress and injuries. This oxidative overload, although felt most intensely in skeletal muscles, has also been reported in many other organs and body systems responsible for regulating and maintaining homeostasis, including the heart, liver, kidneys, lungs, erythrocytes and immune and osteoarticular systems. Sports-related injuries are one of the main reasons why athletes prematurely abandon a sports career, spend long periods excluded from training and competitions, or experience a decline in sports performance, even causing functional limitations at more advanced ages. Thus, many athletes and even individuals participating in regular exercise programs consume antioxidant supplements to prevent exercise-induced oxidative stress and injuries. However, antioxidant supplementation can inhibit the beneficial adaptive responses associated with improved athletic performance, which in turn is a multifactorial phenotype, where genetic and environmental factors interact to produce the final phenotype. Research into genetic variants that, when inherited, can lead to improved athletic performance, has therefore increased greatly. This chapter presents a comprehensive summary of free radicals and the antioxidant defense system and examines the role of reactive oxygen species in exercise-induced oxidative stress and injuries to skeletal muscle, myocardium, liver, erythrocytes, immune system, plasma lipoproteins and DNA. It also discusses the role of antioxidants in combating exercise-induced oxidative stress and provides a subset of genetic variants potentially related to exercise-induced oxidative stress and injuries, as well as some others widely studied in the context of performance.

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1. FREE RADICALS IN PHYSIOLOGICAL FUNCTIONS AND OXIDATIVE STRESS A free radical (FR) is any species capable of independent existence, containing one or more unpaired electrons in its outer orbit. These configurations make them highly unstable, very chemically reactive and with a very short half-life. They provoke or result from redox (shorthand for reduction-oxidation) reactions and multiply quickly in cascade by stealing electrons from other molecules, which also become FRs (Ferreira and Matsubara, 1997; Hermes-Lima, 2004; Bahorun et al., 2006; Fisher-Wellman and Bloomer, 2009). They can also be non-radical species (not presenting unpaired electrons in their outer orbit), but be capable of generating highly reactive and harmful species (Ferreira and Matsubara, 1997; Bahorun et al., 2006; Barreiros et al., 2006). Radicals are generally less stable than nonradical species, although their reactivity varies (Bahorun et al., 2006). By the simple fact of consuming oxygen, the cellular metabolism, even in basal situations, promotes a continuous generation of reactive oxygen species (ROS), FRs that present the unpaired electron centered on the oxygen atom and that play an important role in a variety of physiological and pathological processes (Niess et al., 1999; Hermes-Lima, 2004; Schneider and Oliveira, 2004). Because of its electron configuration, oxygen has a marked tendency to receive an electron from each time; the univalent reduction of oxygen leads to formation of the superoxide anion (O2). With that as a starting point, the other ROS are chemically derived from O2, formed as by-products of enzymatic reactions (Dröge, 2002; Hermes-Lima, 2004; Schneider and Oliveira, 2004; Ferreira et al., 2007). Thus, ROS are naturally produced in our bodies as a result of the normal oxidative metabolism that occurs in mitochondria, endoplasmic reticulum, lysosomes, cell membranes, peroxisomes and cytosol (Ferreira et al., 2007). ROS are also produced by phagocytic cells (neutrophils, monocytes, macrophages, eosinophils) and help them to inactivate viruses and bacteria (Bahorun et al., 2006). Mitochondria use 85-90% of the oxygen we breathe; the remaining 10-15% are used by various oxidases and oxygenases enzymes and also by direct oxidation of chemical reactions that occur in the other cellular compartments mentioned above (Schneider and Oliveira, 2004, Ferreira et al., 2007). Hence, mitochondria are the major intracellular sites of O2 generation under physiological conditions, followed by the NADPH oxidase enzymatic system, which is found in neutrophils, monocytes and macrophages. O2 is also generated by a variety of cytosolic and membrane-bound enzymes, including molybdenum hydroxylase reactions (xanthine, sufite, and aldehyde oxidases), the cytochrome 450 complex, the monoamine oxidase enzymatic system and those of arachidonic acid metabolism (phospholipase A2, lipoxygenases and cyclooxygenases) (Comhair and Erzurum, 2002; Bahorun et al., 2006). In cells, one-electron abstraction of molecules can also yield sulfur-, carbon- and nitrogen-centered radicals (Hermes-Lima, 2004), but those derived from oxygen and nitrogen represent the most important class of FR generated in living systems (Fisher-Wellman and Bloomer, 2009). Many regulatory effects are mediated by hydrogen peroxide (H2O2) and other ROS chemically derived from O2 (Dröge, 2002), while nitric oxide (NO), a reactive nitrogen species (RNS), plays a important role in cellular signalling, vasodilation, relaxing smooth muscle tissue, inhibition of platelet adhesion and innate immune response (Dröge, 2002; Drew and Leeuwenburgh, 2002; Iovine et al., 2008). Thus, the most relevant radicals in

Exercise, Injuries and Athlete Performance

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biological regulation are O2 and NO, the latter being typically generated by tightly regulated enzymes such as constitutive or inducible NO synthases (NOS) isoforms (Comhair and Erzurum, 2002; Dröge, 2002; Hermes-Lima, 2004; Iovine et al., 2008). Both radicals, as well as the non-radical species created via interaction with FRs, are collectively referred to as ROS/RNS or RONS (Fisher-Wellman and Bloomer, 2009). Because FRs are continuously formed in small quantities by the normal processes of aerobic metabolism, all body cells have an antioxidant defense system to soften their aggressive effects. This system is divided into enzymatic and non-enzymatic antioxidants. The former includes the enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPX), while the non-enzymatic system includes compounds synthesized by the body such as haptoglobin, bilirubin, ceruloplasmin, estrogens, melatonin, coenzyme Q, uric acid, and others obtained through the diet such as ascorbic acid (vitamin C), D-tocopherol (vitamin E), carotenoids and phenolic compounds of plants (Sies, 1993; Schneider and Oliveira, 2004; Tseng et al., 2004). So, radical and reactive non-radical species derived from radicals exist in biological cells and tissues at low concentrations and their concentrations are determined by the balance between their production rates and their clearance rates by various antioxidant compounds and enzymes (Figure 1) (Dröge, 2002). Despite their deleterious potential for cells, FRs are critical for maintaining normal physiological functions. In the body, they are involved in energy production, phagocytosis, cell growth regulation, intercellular signaling and synthesis of biologically important substances (Harman, 1956; Saeed et al., 2005; Barreiros et al., 2006; Valko et al., 2007; Lecarpentier, 2007). At moderate concentrations, RONS play an important role as regulatory mediators in signaling processes, such as regulation of vascular tone, monitoring of oxygen

ETC, electron transport chain; SOD, superoxide dismutase; CAT, catalase; GPX, glutathione peroxidase; GR, glutathione reductase; GSH, glutathione in the reduced state (reduced glutathione); GSSG, glutathione in the oxidized state (oxidized glutathione) Figure 1. Integrated action of enzymatic and non-enzymatic antioxidant system in neutralizing ROS.

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tension in the control of ventilation and erythropoietin production, and signal transduction from membrane receptors in various physiological processes (Dröge, 2002). ROS are also necessary for normal contractile activity of skeletal muscles (Lecarpentier, 2007). Many of the ROS-mediated responses actually protect the cells against oxidative stress and reestablish redox homeostasis (Dröge, 2002) by modulating several major regulatory systems of skeletal muscle performance such as mitochondria, sarcoplasmic reticulum, glucose transport and numerous enzymatic systems involved in the cellular metabolism (Lecarpentier, 2007). They may also have considerable positive effects on the immune system (Cruzat et al., 2007; Lecarpentier, 2007). However, under certain conditions, an imbalance between RONS generation and antioxidant capacity leads to oxidative stress, a state in which an imbalance between the prooxidant and antioxidant system occurs in such way that the pro-oxidant system prevails. As a result, RONS cause extensive cell damages, with harmful effects, such as the oxidation of the cellular membrane lipidic layer (leading to cell lysis) and aggression to proteins, carbohydrates and DNA (causing single and double strand breaks and chromosomal aberrations) (Dizdaroglu, 1992; Imlay and Linn, 1988; Cooke et al., 2003; Schneider and Oliveira, 2004; Barreiros et al., 2006; Traber, 2006; Valko et al., 2007; Radak et al., 2008). Therefore, RONS can contribute to the pathogenesis of a number of human diseases, such as cancer, neurological disorders, chronic inflammatory diseases, cardiovascular diseases and even muscle fatigue during strenuous exercise, besides being implicated in the mechanism of senescence, where they can be the cause or general aggravating factor (Comhair and Erzurum, 2002; Dröge, 2002; Cooke et al., 2003, Hermes-Lima, 2004; Schneider and Oliveira, 2004; Barreiros et al., 2006; Ferreira et al., 2007). Since oxidative stress can occur by excessive FR production, by deficient antioxidant capacity, or by a combination of both (Collins, 2009), the balance between RONS and the body's natural antioxidant functions plays a crucial role in the prevention of oxidative stress and development of related diseases (Cooke et al., 2003).

2. PHYSICAL EXERCISE  CLASSIFICATION Physical exercise can be classified according to the effort intensity as mild, moderate and intense, based on the performance of some maximal effort tests for evaluation of the percentage of maximal oxygen uptake (VO2max), the percentage of oxygen uptake reserve (VO2R), the percentage of maximal heart rate (HRmax) and the percentage of heart rate reserve (HRR), where VO2R and HRR are calculated from the difference between resting and maximal VO2 and resting and maximal heart rate respectively (ACSM, 1998; Baldwin et al., 2000; Leandro et al., 2007). Some studies have demonstrated that physiological and metabolic responses to exercise at the same relative intensity are the same regardless of training status, while several other studies have observed that the metabolic, cardiovascular, and hormonal changes differ between endurance trained and untrained individuals during exercise at the same relative oxygen consumption (45–75% VO2 peak) (Baldwin et al., 2000). Despite this, the amount of improvement in VO2max increases with training frequency, but the magnitude of change is smaller and tends to plateau when frequency exceeds 3 days/week. The value of the added

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improvement in VO2max that occurs with training more than 5 days/week is minimal to none, and the incidence of injury increases disproportionately (ACSM, 1998). The lactate threshold (LT), which is based on blood lactate concentration behavior at different exercise intensities, is another important indicator of cardiorespiratory endurance and provides a rating of perceived exertion (RPE). The LT may be thought of as the highest VO2 that can be maintained without a sustained rise in blood lactate, since it has been defined as the exercise intensity above which blood lactate concentration increases gradually and ventilation also intensifies in a non-linear way to the consumed oxygen, although other previous definitions exist (ACSM, 1998; Baldwin et al., 2000; Barros et al., 2004). Although the optimal training frequency for improving LT and metabolic fitness is not known and may or may not be similar to that for improving VO2max, exercise below the LT may be considered light-to-moderate RPE, while exercise above the LT may be considered hard-to-very hard RPE, depending upon the degree to which the VO2 exceeds the LT (ACSM, 1998). In exercises of light or moderate intensity, the blood lactate is produced at lower rates and its concentration remains steady (varying between 2 and 4 mmol/L) (Leandro et al., 2007), while for exercise intensities well above the LT (≥ 85% VO2max), blood lactate concentration rises continuously and exercise tolerance is compromised (ACSM, 1998). During high-intensity exercise, the intramuscular accumulation of lactic acid has long been considered one of the most important factors in fatigue (Cairns, 2006). Even though relative exercise intensities vary with the exercise type (endurance or resistance-type exercise), frequency, duration of training and age of subject, intensity classification, based on physical activity lasting up to 60 min, can be given according to Table 1 (ACSM, 1998; Leandro, 2007). Response to exercise is divided into acute response and chronic adaptation. Acute response is understood as the temporary physiological changes caused by an exercise session, while chronic adaptations are changes caused by multiple sessions of exercise, featuring training (Santos et al., 2007). Isolated exercise sessions elicit acute, transient cardiovascular and metabolic responses. Frequent repetition of these isolated sessions produces more permanent adaptations, referred to as the exercise training response. Exercise training increases exercise capacity, which permits longer individual exercise sessions and a greater acute effect (Thompson et al., 2001). Table 1. Classification of relative exercise intensities, based on physical activity lasting up to 60 min Relative intensities Endurance-type exercise VO2max (%) and HRmax VO2R (%) and Intensity RPE (%) HRR (%) Light 20–49 20–39 10–11 Moderate 50–69 40–59 12–13 Hard 70–89 60–84 14–16 Very hard ≥ 90 ≥ 85 17–19 *Based on 8–12 repetitions for persons under age 50–60 years and 10–15 50–60 yr and older.

Resistance-type exercise* Maximal voluntary contraction (%) 30–49 50–69 70–84 ≥ 85 repetitions for persons aged

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3. EXERCISE ADAPTATION Today it is unanimously accepted that exercise, when practiced regularly, is crucial for a healthy lifestyle, acting as a therapeutic agent and/or preventing numerous illnesses (Ferreira et al., 2007). Physical activity involves the entire body in terms of energy metabolism, hormonal mobilization and action, signal transduction process, immunological responses and adaptations (Ji et al., 2008). Exercise is a stress and the corresponding adaptations to regular training include improved cardiovascular function and antioxidant capacity, changes in body composition and body weight (mass), improved mood, lower blood pressure, insulin sensitivity (glucose tolerance) and biochemical cell changes (Wannamethee and Shaper, 2001; Laufs et al., 2005; Vancini et al., 2005), and enhanced immune function producing an anti-inflammatory environment and reducing the risk of infection (Gleeson, 2000; Nieman, 2000). Endurance athletes also have serum high-density lipoprotein (HDL) cholesterol (HDLC) concentrations 40-50% higher than their sedentary counter-parts, triglyceride (TG) levels 20% lower and low-density lipoprotein (LDL) cholesterol (LDL-C) concentrations often approximately 5-10% lower (Thompson et al., 2001). Epidemiological evidence indicates that physical activity is associated with reduced risk of coronary heart disease and mortality due to cardiovascular problems in middle age (Wannamethee and Shaper, 2001; Thompson et al., 2007). Moderate physical activity improves endothelial function and collateralization of vascularization and prevents the progression of carotid atherosclerosis. Regular training also favors increased expression (upregulation) of antioxidant enzymes with a consequent reduction in oxidative stress (Laufs et al., 2005), besides reducing the capacity of leukocytes for oxidant release; it also leads to an adaptation of antioxidant mechanisms, which may contribute to a limitation of exerciseinduced oxidative stress (Niess et al., 1999). It has also been observed that the extent of damage to the DNA of trained individuals is small compared to that of untrained individuals, suggesting that adaptation to endurance training can reduce the effects of oxidative stress on DNA damage (Nies et al., 1996; Mastaloudis et al., 2004; Schneider and Oliveira, 2004), thus perhaps preventing related diseases such as cancer. In summary, humans involved in regular exercise have shown reduced oxidative damage to DNA during physical exertion (Mastaloudis et al., 2004; Ji et al., 2008). Chronic exercise of moderate intensity positively alters the redox status of cells and tissues by reducing the basal levels of oxidative damage and increases resistance to oxidative stress, being beneficial to health (Vancini et al., 2005). Changes in the oxidant-antioxidant balance act as a trigger for redox homeostasis (Lecarpentier, 2007) and regular moderate exercise results in adaptations in antioxidant capacity, which protects cells against the deleterious effects oxidative stress, preventing subsequent cell damage (Vancini et al., 2005). After a temporary increase in cellular RONS concentrations, the initial redox state can be reestablished by numerous compensatory mechanisms. Some antioxidant genes, for example, can be rapidly activated to cope with acute oxidative stress caused by hypoxia and ischemia, whereas other genes are upregulated more slowly in response to chronic oxidative stress provoked by energy demand during exercise training (Ji, 2007; Lecarpentier, 2007). Additionally, nitric oxide production induces a direct feedback inhibition of NO synthase by NO.

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If frequency, intensity, and duration of training are similar (total kcal expenditure), the training adaptations appear to be independent of the mode of aerobic activity (ACSM, 1998). Thus, although single bouts of aerobic and anaerobic exercise can induce an acute state of oxidative stress, which is indicated by an increased presence of oxidized molecules in a variety of tissues (Fisher-Wellman and Bloomer, 2009), the effects of exercise express characteristics of the hormesis phenomenon, which is a dose-dependent relationship in which a low dose of a substance may increase the body’s tolerance for greater toxicity (Ji, 2007; Ji et al., 2008). As a consequence, in accordance with the principle of hormesis, this ROS increase appears necessary to allow for an upregulation in endogenous antioxidant defenses (FisherWellman and Bloomer, 2009). Redox homeostasis can be maintained in a near-equilibrium stationary thermodynamic state or quasi-stable state (Forsberg et al., 2001a; Lecarpentier, 2007), and in a physiological range, the antioxidant response to a moderate increase in ROS may be sufficient to reset the balance between ROS production and ROS-scavenging capacity. Thus, cross-training that emphasizes the use of a variety of large muscle groups (activities) may be beneficial to achieving a more well-rounded training effect (ACSM, 1998).

4. EXERCISE, OXIDATIVE STRESS AND INJURIES FR production during exercise depends on several factors, such as frequency, intensity, duration and the type of exercise performed (aerobic or anaerobic), as well as the subject population tested (Vancini et al., 2005; Fisher-Wellman and Bloomer, 2009). When the redox system is subjected to dramatic and/or long-lasting perturbations, it may behave in a manner that is far from equilibrium, where instability and thermodynamic bifurcations toward chronic pathological states may appear (Lecarpentier, 2007). Consequently, in opposition to its potential beneficial effects, exercise, especially under circumstances such as unaccustomed intensity or duration, increases the production of RONS and can lead to oxidative stress, even in trained individuals (Ji, 1995; Niess et al., 1999; Lamprecht et al., 2004; Sureda et al., 2005). In this way, acute exercise, mainly if exhausting, or training above habitual intensity induces oxidative stress, causing muscular injuries with consequent inflammatory process (Ji and Leichtweis 1997; Sureda et al. 2005; Cruzat et al. 2007; Ferreira et al. 2007). Other harmful organic changes may also take place, especially when the tissues, organs or systems are not sufficiently adapted to withstand, without major homeostatic changes, the different types of burden imposed on them (Ferreira et al., 2007). These types of exercise generally overload the endogenous antioxidant system’s capacity, leading to an increase in plasma lipid peroxidation (Ferreira et al. 2007) plus oxidative damage to muscles and other tissues (Ji and Leichtweis 1997; Sureda et al. 2005; Traber 2006; Ferreira et al. 2007). Strong hormonal changes, as well as temperature changes associated with intense and exhaustive exercise, will be felt more or less intensely in most body cells (Ferreira et al., 2007).

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Figure 2. Oxidative damage and injuries induced by strenuous exercise.

Oxidative overload, although felt more intensely in the skeletal muscles, has also been reported in many systems (Cruzat et al., 2007; Ferreira et al., 2007; Kim et al., 2007; Lecarpentier, 2007), including heart (Ferreira et al., 2007), liver (Nagel et al., 1990; De Paz et al., 1995; Ferreira et al., 2007; Kim et al. 2007), kidneys, lungs (Ferreira et al., 2007), erythrocytes (Schmidt et al. 1988; Sureda et al. 2005; Yusof et al., 2007) and immune (Ji, 1999; Mooren et al. , 2002; Sureda et al. 2005; Belviranli and Gokbel, 2006; Cruzat et al., 2007; Kim et al., 2007) and osteoarticular (Ferreira et al., 2007; Kim et al., 2007) systems. The heart is affected because of high oxidative metabolic demand and large numbers of mitochondria (Ferreira et al., 2007; Judge and Leeuwenburgh, 2007). The liver and other tissues and organs are mainly affected by ischemia and reperfusion phenomena (Cruzat et al., 2007). Since circulation is deviated to the active muscles during exercise, other tissues and organs can suffer temporary hypoxia. As a consequence, after exercise these tissues receive large amounts of oxygen, favoring ROS generation (Cruzat et al., 2007; Radak et al., 2007). Additionally, there is an increase in the mechanical stress imposed on required skeletal muscle fibers and cells of the osteoarticular and cardiovascular systems (Figure 2) (Ferreira et al., 2007; Kim et al., 2007). These damaging effects, with their consequent inflammatory processes, can jeopardize performance and may lead to overtraining syndrome, besides potentially contributing to an increased future risk of cardiovascular disease (CVD) (Urso and Clarkson 2003; Cruzat et al. 2007; Ferreira et al. 2007; Radak et al. 2007; Thompson et al. 2007).

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4.1. Skeletal Muscle In skeletal muscle the mitochondria constitute the main source and, simultaneously, the main target of ROS. However, it is the activities of the enzymes xanthine oxidase and phospholipase A2, the deamination of catecholamines and the infiltration of leukocytes after exercise that constitute additional sources of ROS and consequently contribute decisively to oxidative damage (Ferreira et al., 2007). Myoglobin may also be oxidized by autooxidation or by FRs during ischemia/reperfusion, with production of H2O2. Then myoglobin can interact with H2O2 and produce other radicals such as the peroxyl radical (Finaud et al., 2006). Periods of intense exercise can increase oxidative stress due to temporary hypoxia and reoxygenation occurring in the exercised muscle, according to the cyclical contractions and relaxations established. During contraction, vascular compression provides a framework of ischemia and, therefore, hypoxia; in relaxation reperfusion occurs and, consequently, reoxygenation (Schneider and Oliveira, 2004). Since during exercise blood flow is diverted to the exercising muscles, other muscles and tissues may also suffer temporary hypoxia (Cruzat et al., 2007). As a result, these deprived muscles and tissues receive a large amount of oxygen after exercise, favoring ROS generation (Schneider and Oliveira, 2004; Finaud et al., 2006; Cruzat et al., 2007). In the ischemia/reperfusion process, increased oxidative stress during and after exercise is favored by the catabolism of purines. It has been proposed that the conversion of xanthine dehydrogenase to its oxidized form (xanthine oxidase) by proteases activated by intracellular Ca2+ favors ROS increase. During hypoxia, ATP is degraded to hypoxanthine, which accumulates in the tissues. As a result, there is failure of cellular homeostasis, allowing a Ca2+ influx into the cells, which activates intracellular proteases to convert the xanthine dehydrogenase to xanthine oxidase. During reperfusion, xanthine oxidase uses O2 to promote the conversion of hypoxanthine to xanthine and then uric acid, causing a univalent reduction of molecular oxygen to O2, with consequent generation of H2O2 e HO (hydroxyl anion) (Ji, 1999; Campos and Yoshida, 2004; Finaud et al., 2006; Cruzat et al., 2007; Ferreira et al., 2007). Acute and chronic exercise is associated with ultrastructural muscle damage, mainly centered on the Z disk that anchors thin filaments and several intermediate filaments within the sarcomere. Disturbances of the mitochondria, sarcoplasmic reticulum, A band, and extracellular matrix have also been reported (Magaudda et al., 2004). Muscle damage may range from an ultrastructural lesion of muscle fibers to trauma involving complete muscle rupture, and it can be directly accessed by histological techniques or electron microscopy, or indirectly by determining the specific efflux of cytosolic enzymes into the bloodstream (Cruzat et al., 2007; Foschini et al., 2007). The morphological and ultrastructural injuries induced by exercise are well documented in animal and human models (Armstrong et al., 1983; Gibala et al., 1995; Morgan and Allen, 1999; Proske and Morgan, 2001; Stupka et al., 2001; Magaudda et al., 2004). Muscle damage induced by one session of eccentric exercise may result from disruption of connective tissue attached to adjacent myofibrils, the muscle cell itself, the basal lamina adjacent to the plasma membrane, plasma membrane of the muscle cell, the sarcomere, the reticulum sarcoplasmic, or a combination of these components (Cruzat et al., 2007). In humans, for ethical and logistic reasons, the evidence for exercise-induced damage has been essentially studied in the blood, in both plasma and blood cells (Ferreira et al., 2007). In

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this context, an increase in the concentration of cytosolic proteins in the circulation after exercise reflects muscular injuries. The proteins that are often evaluated are creatine kinase (CK), lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and myoglobin, which are usually unable to cross the plasma membrane. The presence of these proteins in the blood reflects significant changes in the structure and permeability of the myofibrillar membrane (Cruzat et al., 2007; Apple et al., 1988; Agarwal and Ankra-Badu, 1999; Barbosa et al., 2003; Lac and Maso, 2004; Gasper and Gilchrist 2005; Foschini et al., 2007). Among them, CK is often best described as an indirect marker of muscle damage, especially after strength training or other exercises that require predominantly eccentric actions (Barbosa et al., 2003, Foschini et al., 2007).

4.2. Myocardium As described for skeletal muscle, today it is now widely accepted that acute exercise, especially if extensive, also promotes increased oxidative stress in the heart, leading to an increase in markers of oxidative damage in this organ (Ferreira et al., 2007). The myocardium appears to be particularly susceptible to all situations that promote an increase in metabolism, which include acute physical exercise, since the oxidative metabolic capacity is very high due to the abundance of mitochondria (Ferreira et al., 2007; Judge and Leeuwenburgh, 2007). Increased oxidative stress in this organ during acute exercise is the result of dramatic increases in oxygen consumption (Judge and Leeuwenburgh, 2007). Under these conditions, the CK reaction is important for rapid resynthesis of ATP from creatine phosphate and ADP, since the heart increases its work (Putney et al., 1984; Nascimben et al., 1996; Foschini et al., 2007). Considering that CK is a cytosolic enzyme, increased serum levels of this enzyme after acute exercise can also be indicative of oxidative damage in the myocardium. Evidence suggests that oxidative stress plays an important role in the pathogenesis of cardiovascular disease (Wattanapitayakul and Bauer, 2001; Molavi and Mehta, 2004; Abramson et al., 2005; Belardinelli, 2007; Kasap et al., 2007). The presence of elevated plasma levels of some markers of the inflammatory state, such as C-reactive protein (CRP) is a predictive risk factor for acute coronary syndrome, myocardial infarction, peripheral arterial occlusive disease and sudden cardiac death, both in healthy subjects and in patients with established atherosclerotic disease (Mitka, 2004; Dummer et al., 2007). Increased serum levels of AST enzyme have been used in the diagnosis of acute myocardial infarction since 1954. This transaminase is found in the cytoplasm and mitochondria of many cells, especially liver, heart, skeletal muscle, kidney, pancreas and blood cells (Dewar et al., 1958). Although the mentioned markers are not specific to assess myocardial injuries, increases in plasma levels of CRP and AST after acute exercise can also be indicative of oxidative damage in the myocardium.

4.3. Liver Exercise also induces ROS generation and oxidative damage in the liver, such as lipid peroxidation (Witt et al., 1992; Nagel et al., 1990; De Paz et al., 1995; Ferreira et al., 2007; Kim et al., 2007). The amount of damage will depend on the intensity of exercise and training

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status (Witt et al., 1992). The liver has a high metabolic rate, which is naturally associated with a high flow of oxygen. However, this flow decreases significantly during exercise, appearing to be similar to the ischemia/reperfusion phenomenon (Radak et al., 2008). Unlike skeletal muscle, liver contains high levels of xanthine dehydrogenase which, during exercise, is converted to xanthine oxidase, which in turn contributes as an additional source of ROS and, consequently, oxidative damage (Ferreira et al., 2007; Radak et al., 2008). While a single bout of intense exercise stimulates adaptations in the skeletal muscle’s antioxidant system (Cruzat et al. 2007; Ferreira et al., 2007; Radak et al. 2007), the same is not the case with the liver, which is oxidatively stressed (Radak et al. 2007). In animals, however, it has been shown that exercise training promotes improvements in the antioxidant capacity of the liver, inducing antioxidant enzymes and reducing ROS production (Ferreira et al., 2007; Radak et al., 2008). Liver damage may be evidenced by the efflux of some cytosolic enzymes into the bloodstream, particularly alanine aminotransferase (ALT) and AST; the first being found in high concentrations only in the liver (Dewar et al., 1958; Miranda-Vilela et al., 2009b). Therefore, increased serum levels of these enzymes after acute exercise serve as an indicator of oxidative damage in this organ.

4.4. Blood 4.4.1. Erythrocytes It has been suggested that intense exercise of long duration and exhausting training may also compromise our ability to detoxify ROS within the blood cells, and red blood cells (RBCs) appear much more vulnerable to oxidative damage (Petibois and Déléris, 2005; Sureda et al., 2005). This reflects their very limited biosynthetic capacity (nucleus absence), a poor repair mechanism (Santos-Silva et al., 2001; Cazzola et al., 2003; Sureda et al., 2005), the presence of large amounts of thiols (RSH) and polyunsaturated fatty acids (PUFA) in their membrane and the high internal concentration of oxygen and hemoglobin, sources that potentially promote oxidative processes (Ferreira and Matsubara, 1997; Cazzola et al., 2003). Despite having an elaborate antioxidant defense system, which includes enzymes such as catalase, superoxide dismutase and glutathione peroxidase, the efficiency of this system is overcome by the magnitude of oxidative processes. Thus, oxidative stress occurs, leading to hemolysis (Ferreira and Matsubara, 1997; Cazzola et al., 2003). In this case, the extracellular hemoglobin becomes toxic due to the nature of oxidative iron ions contained in the heme group, which participate in the Fenton reaction to produce ROS that cause cellular injury (Akimoto et al., 2010). During oxidative stress, the most common changes are lipid and membrane protein oxidation, which may destabilize the cytoskeleton and impair cell survival (Petibois and Déléris, 2005; Sureda et al., 2005; Yusof et al., 2007). Additionally, RBCs are highly exposed to mechanical stress, as well as changes in cytosolic and extracellular pH (Petibois and Déléris, 2005). ROS can alter the chemical and physical properties of the membrane by modifying the composition, packaging and distribution of their lipids, which leads to a structural change to reduce membrane fluidity. This in turn can modify the activity of several membrane proteins and accelerate erythrocyte senescence or even cause their premature removal from circulation

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(Cazzola et al., 2003; Petibois and Déléris, 2005). The oxidizing agents can convert the thiol groups into disulfide components (RSSG), leading to membrane protein denaturation. In this process, intracellular damage may occur, with hemoglobin (Hb) being oxidized to methemoglobin (metHb), which precipitates, forming erythrocytic inclusion bodies called Heinz bodies (Ferreira and Matsubara, 1997). In the human body, 3% of total hemoglobin (about 750 g) is transformed by self-oxidation. This reaction, which produces metHb and O2, can increase with exercise (Belviranli and Gökbel, 2006; Finaud et al., 2006). The products of the peroxidation of lipid components of erythrocyte membrane may also induce intracellular oxidative stress. The association of the phenomena of SH group oxidation, Heinz body formation and lipid peroxidation can promote damage in the RBC membrane (Ferreira and Matsubara, 1997). During exhaustive exercise or training, destruction of RBC can occur not only for the reasons mentioned above, but also due to plasma volume reduction. The displacement of water out of the vascular space may act as chemical stress, since the loss of water through the blood leads to dehydration of the erythrocyte, usually as a result of potassium loss. Dehydration of RBCs is also influenced by the ischemia/reperfusion phenomenon. However, in response to chemical stress, the body releases renin, aldosterone and vasopressin (antidiuretic hormone or ADH) and the end result is an increase in plasma volume (Eichner, 1998; Petibois and Déléris, 2005). The immediate consequence, known as "sports anemia", is characterized by reduced values of RBCs, hemoglobin and hematocrit and a change in mean cell volume (MCV) (Carlson and Mawdsley, 1986; Eichner, 1998; Fallon et al., 1999). Therefore it can be detected by hemogram, by the reduction of RBCs and a decreased hemoglobin concentration and hematocrit in the blood (Schmidt et al., 1988). However, sports anemia is a pseudoanemia, since the reduction in the values of RBCs, hemoglobin and hematocrit occurs as a physiological adaptation to strenuous exercise and is due to hemodilution caused by an increase in plasma volume (Carlson and Mawdsley, 1986; Eichner, 1998). As this is a false anemia, the hemolysis that occurs with physical activity is unlikely to cause anemia, and inadequate iron intake is the main cause of true anemia in athletes, particularly in females (Vilardi et al., 2001; Eichner, 1998).

4.4.2. Immune System Besides increasing oxygen consumption and inducing oxidative stress as a result of increased production of FR (Sureda et al., 2005), strenuous exercise can cause disturbances in the immune system and immune depression, increasing the risk of opportunistic infections, particularly those affecting the upper respiratory tract (Gleeson and Bishop, 2000; Woods et al., 2000). Immunosuppression in athletes involved in heavy training is undoubtedly multifactorial in origin (Gleeson and Bishop, 2000; Gleeson, 2007). The circulating numbers and functional capacities of leukocytes may be decreased by repeated bouts of intense, prolonged exercise, probably due to the increased levels of stress hormones during exercise and entry into the circulation of less mature leukocytes from the bone marrow. Falls in the blood concentration of glutamine have also been suggested as a possible cause of the immunodepression associated with heavy training, although the evidence for this is less compelling. Also, there is increased production of ROS, and some immune cell functions can be impaired by an excess of FR. During exercise, exposure to airborne pathogens is increased due to the higher rate and depth of breathing, while an increase in gut permeability may also

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allow increased entry of gut bacterial endotoxins into the circulation, particularly during prolonged exercise in the heat. Hence a variety of stressors can suppress immune function, and these effects, together with increased exposure to pathogens, can make the athlete more susceptible to infection (Gleeson, 2007). During exercise, leukocytes are recruited to the blood, and if muscle damage occurs the cytokine level is enhanced (Pedersen and Bruunsgaard, 1995). Initially neutrophils and later monocytes and lymphocytes are recruited to the site of inflammation where they produce ROS and proteolytic enzymes (Cruzat et al., 2007). Neutrophil infiltration is stimulated by chemotactic factors, including prostaglandins, tumor necrosis factor (TNF)-α, and interleukins (IL)-1β and IL-6 (Cruzat et al., 2007), while increased levels of adrenalin, and to a lesser degree noradrenalin, are believed to be the main factors responsible for recruitment of all lymphocyte subsets (NK, T and B cells) (Bruunsgaard and Pedersen, 2000). IL-6 acts as a primary mediator of the acute phase reaction, stimulating the hepatic production of acute phase proteins such as C-reactive protein (CRP) and protease inhibitors. It also restricts the extent of the inflammatory response by activating synthesis of inflammatory cytokines and stimulates the pituitary gland to release adrenocorticotropic hormone (ACTH), which promotes increased release of the cortisol hormone by the adrenal cortex (Cruzat et al., 2007). The catecholamines adrenalin and noradrenalin, together with growth hormone, may mediate the acute effects on neutrophils, whereas cortisol exerts its effect within a time lag of at least 2 hours (Bruunsgaard and Pedersen, 2000). After prolonged, intense exercise, the hormonal changes and the generation of ROS can inhibit the proliferation of lymphocytes, while oxidative damage can activate apoptotic processes in these cells (Sureda et al., 2005). Thus, the number of lymphocytes in the blood is reduced and the function of natural killer cells is suppressed, impairing cellular-mediated immunity; furthermore, secretory immunity is also impaired. During this temporary time of immunodepression, often referred to as “the open window”, the host may be more susceptible to micro-organisms bypassing the first line of defense (Pedersen and Bruunsgaard, 1995). Thus, training and competitive surroundings may increase the athlete’s exposure to pathogens and provide optimal conditions for pathogen transmission (Gleeson and Bishop, 2000). The data on whether competitive athletes have an increased incidence of infections compared with the general community has been inconclusive. However, it is generally agreed that the period of vulnerability for elite athletes coincides with the intense training undertaken immediately prior to or during a competition and may not follow the normal seasonal patterns observed in the general community (Gleeson, 2000). The relationship between exercise and susceptibility to infection has been modeled in the form of a “J”-shaped curve, where moderate activity may enhance immune function above sedentary levels, while excessive amounts of prolonged and high-intensity exercise may impair immune function (Nieman, 1994; Gleeson, 2007).

4.4.3. Plasma The increased metabolism imposed by exercise promotes increased ROS production, which can also induce oxidative damage in plasma, together with RBCs the fraction most susceptible to lipid peroxidation (Sureda et al., 2005). In humans, most studies investigating lipid peroxidation have examined the presence of lipid peroxides or lipid peroxidation byproducts, such as conjugated diene, lipid hydrocarbons (aliphatic hydrocarbons and polycyclic aromatic hydrocarbons - PAH) and thiobarbituric acid

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reactive substances (TBARS), such as malonaldehyde (MDA). Changes in plasma concentrations of vitamins C and E and oxidized glutathione (GSSG) have also been used to indicate increased oxidative reactions. It is thought that these antioxidant vitamins may be mobilized from the tissues to combat oxidative stress in another part of the body. The GSSG efflux into the plasma is considered an indicator of oxidative stress, since the reduced glutathione (GSH) is oxidized to GSSG in cells in response to the increase in FR (Clarkson and Thompson, 2000). After exhaustive exercise, there are also changes in serum iron and ferritin, total binding capacity of iron and transferrin saturation. Although such changes are similar to those found in chronic anemia, they just reflect the acute phase response (Fallon et al., 1999). Moreover, during intense exercise, large amounts of lactic acid can be produced by skeletal muscle and released into the bloodstream for subsequent removal by peripheral tissues. Lactacidemia exercises, i.e., with exercise intensity above that at which lactate begins to accumulate in the bloodstream (anaerobic threshold - AT), also have a direct influence on the level of plasma lipid peroxidation by inhibiting the antioxidant defense system of erythrocytes (Ribeiro et al., 2004; Petibois and Déléris, 2005; Manchado et al., 2006). 4.4.3.1. Lipoproteins There is significant evidence of the involvement of ROS in the pathogenesis of atherosclerosis. The oxidative modification of low density lipoproteins (LDL) plays a key role in amplifying the inflammatory response, mediating a variety of pro-inflammatory immune processes that determine the progression of atherosclerosis (Magnusson et al., 1994; Steinberg, 1997; Santos-Silva et al., 2001; Liu et al., 2004; Laufs et al., 2005; Kiechl et al., 2007). ROS contribute to the onset and progression of atherosclerotic lesions, which favor the infiltration and accumulation of lipids in the subendothelial space. ROS produced by smooth muscle cells, endothelial cells or macrophages can modify LDL that, when oxidized, promotes the recruitment of circulating monocytes, the accumulation of resident macrophages, phagocytosis and finally the accelerated formation of "foam cells" (composed of lipid-rich macrophages) and fatty streaks, characteristic of early lesions (Steinberg, 1997; Santos-Silva et al., 2001). Additionally, epidemiological studies suggest that increased blood viscosity is also related to atherogenesis and cardiovascular risk (Santos-Silva et al., 2001). In the presented context, exhaustive and high intensity exercise may promote atherosclerosis and cardiovascular risk (Schneider and Oliveira, 2004), since this type of exercise has been associated with increased oxidative stress in the vascular endothelium (Laufs et al., 2005; Petibois and Déléris, 2005) and increase in blood viscosity (Santos-Silva et al., 2001). Although prolonged exercise is associated with a small plasma TG reduction, which may reflect lipoprotein (LDL rich in triglycerides) use as a fuel or be attributed to reduced hepatic secretion of these lipoproteins (Hardman, 1998), inhibition of the antioxidant defense system of erythrocytes by chemical stress induced by high-intensity exercise (lactacidemia) may favor LDL oxidation and hence atherosclerosis (Santos-Silva et al., 2001; Petibois and Déléris, 2005).

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4.5. DNA Normal cellular metabolism is well established as an indigenous source of ROS and because these species are related to basal levels of DNA damage detected in normal tissues (Cooke et al., 2003), exhaustive exercise can also lead to damaged bases in DNA (Moller et al., 2001; Mastaloudis et al., 2004a). So, there has been growing interest in exercise-induced DNA damage due to its potential involvement in various disease states, such as carcinogenesis, the ageing process, lifestyle-related diseases and age-related degenerative diseases (Osterod et al., 2001; Kasai and Kawai, 2006; Loft and Møller, 2006). Endurance exercise increases whole body oxygen consumption 10-20 fold, which at the level of the skeletal muscle increases 100-200 fold. This increase in oxygen utilization may result in the production of ROS at rates that exceed the body’s detoxification capacity (Mastaloudis et al., 2004a). Because the byproducts of oxidative phosphorylation reactions can diffuse from mitochondria to reach nuclear DNA and induce damage (Zhao et al., 2007), this type of exercise can result in DNA strand breaks and oxidatively damaged bases in DNA (Mastaloudis et al., 2004a). Indeed, it has been shown that exhaustive exercise induces DNA damage in circulating leukocytes (Nies et al., 1996; Mastaloudis et al., 2004a; Demirbag et al., 2006) and that it can induce apoptosis through different mechanisms such as reduced levels of intracellular glutathione, alteration of mitochondrial proteins or by directly damaging the DNA (Mooren et al., 2002). Past studies have primarily used the 8-hydroxydeoxyguanosine assay to assess DNA damage, but this method has been criticized for its susceptibility to artifact formation and the large amount of DNA required for analysis (Mastaloudis et al., 2004a). More recently, the comet assay (also known as the single-cell gel electrophoresis assay) has come into favor to assess oxidatively damaged DNA, thanks to its greater simplicity, sensitivity, stability and accuracy (Mastaloudis et al., 2004a; Collins, 2009). This technique detects single and doublestrand breaks, alkali labile sites, incomplete repair sites, cross-linking DNA-DNA and DNAprotein in individual cells (Mastaloudis et al., 2004a; Brendler-Schwaab et al., 2005).

5. ANTIOXIDANTS AGAINST SPORTS-RELATED INJURIES Sports-related injuries are one of the main reasons why athletes prematurely abandon a sports career, spend long periods excluded from training and competitions, or experience a decline in sports performance, even causing functional limitations at more advanced ages (Artioli et al., 2007). Thus, many athletes and even individuals participating in regular exercise programs consume antioxidant supplements to avoid exercise-induced oxidative stress and injuries (Sureda et al. 2005; Cruzat et al. 2007; Ferreira et al. 2007; Radak et al. 2007; Yfanti et al. 2010). However, considering that ROS can act as signals that regulate molecular events of cellular adaptation to exercise, the practical consequence is that antioxidant supplements can inhibit beneficial adaptive responses associated with improved athletic performance. Therefore, the prudent recommendation for physically active individuals is a diet rich in antioxidants from natural foods (Clarkson and Thompson, 2000), and the recommendation of the use of antioxidant supplements should be made only for those

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cases in which exhaustive exercise causes oxidative stress and cell damage (Ji and Leichtweis, 1997; Gomez-Cabrera et al., 2008). Biological antioxidants play a fundamental role in protecting against oxidative stress induced by exercise. Deficiency or depletion of these antioxidants has been associated with exacerbated tissue damage, while antioxidant supplementation has generated variable results (Ji, 1995). Many studies have investigated the impact of antioxidant status in the oxidative damage induced by exercise (Ji, 1995) and for this purpose, most interventions have focused on nutritional factors such as antioxidant vitamins or drugs that serve as mediators of oxidative stress (Alessio et al., 2002). In order to reduce the deleterious effects promoted by strenuous exercise, the most studied alternatives include nutritional supplementation with vitamin E, vitamin C, creatine and glutamine (Clarkson and Thompson, 2000; Cruzat et al., 2007). Studies that have examined the effects of antioxidant supplementation used athletic performance only or together with changes in oxidative stress as outcome measures (Urso and Clarkson, 2003) or else serum inflammation markers and/or cell damage and DNA damage markers (Mastaloudis et al., 2004a,b; Miranda-Vilela et al. 2009a,b; Miranda-Vilela et al., 2010a; Miranda-Vilela et al., 2011a). Different studies have shown that supplementation with vitamin E, creatine and glutamine can attenuate oxidative stress or reduce the amount of cell damage caused by exhaustive exercise (Cruzat et al., 2007). Vitamin E administered together with vitamin C has also been shown to prevent increases in lipid peroxidation but had no apparent effect on DNA and muscle damage (Traber, 2006); nor did this combination have any effect on inflammatory markers (Mastaloudis et al., 2004). Vitamin C alone or in a mixture with vitamin E and beta-carotene had little or no effect, although the reduction in vitamin C body stores may contribute to increased oxidative stress (Kanter et al., 1993; Cruzat et al., 2007). On the other hand, there are some aspects that need to be better investigated before antioxidant intervention, because its potential effects can depend on the nature of the antioxidant, its dose and the prevailing oxygen partial pressure (PO2) in the tissues. Carotenoids, for example, are an effective antioxidant under low PO2 conditions (Borek, 2004; Hermes-Lima, 2004; Ferreira and Matsubara, 1997); while under high PO2 they are less efficient and may even act as pro-oxidants due to auto-oxidation (Borek, 2004). Conversely, vitamin E (α-tocopherol) is an efficient antioxidant for cells submitted to high PO2, such as those of the lungs (Borek, 2004). Compounds with antioxidant properties may also have antioxidant or pro-oxidant effects, depending on dose. While in nutritional levels these compounds seem to have a protective effect, at high doses they can have deleterious effects (Panayiotidis and Collins, 1997; Hercberg et al., 1998; Antunes and Takahashi, 1999; Collins, 2001; Paolini et al., 2003; Hercberg et al., 2006). Considering the entire context presented above, the importance of research on natural antioxidants has increased greatly in recent years. Since the 1980s there has been a considerable broadening of the search for natural antioxidants that can be included in the diet as substitutes for synthetic antioxidants (Degáspari and Waszczynskyj, 2004). Following this line, our group has demonstrated that a carotenoid-rich oil extracted from pequi (Caryocar brasiliense), a typical fruit of the Brazilian Cerrado, has anti-inflammatory properties, besides reducing arterial pressure, exercise-induced DNA and cell damages, lipid peroxidation and anisocytosis in runners (Miranda-Vilela et al., 2009a; Miranda-Vilela et al., 2009b; MirandaVilela et al., 2010a; Miranda-Vilela et al., 2011a). However, pequi oil was particularly

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efficient in reducing DNA damage for the age group of 20-40 years and a distance of 8-10 Km, indicating that long-distance races can be harmful, mainly for older athletes, due to oxidative stress above organism adaptability (Miranda-Vilela et al., 2011b). Although the protective effects of pequi oil are unquestionable, some of these responses were influenced by genetic polymorphisms related to oxidative stress and inflammatory markers. Given this, knowledge on how individual genetic differences can affect response to antioxidant supplementation and how diet interacts with the human genome to influence performance, health and disease is of unquestionable importance to the athlete’s performance and health.

6. ATHLETE’S PERFORMANCE Although performance is a term difficult to define, athletic performance is usually seen as a set of characteristics such as agility, muscle power, speed, equilibrium and coordination, flexibility, force and muscular resistance, cardio-respiratory resistance and corporal composition, among others, which lead to better physical aptitude. Endurance sports performance, for example, is associated with the interplay of several physiological factors, especially those related to lung and heart capacities, measured through VO2max, VO2R, HRmax, HRR and LT, among others related to energy metabolism or cardiorespiratory fitness. Muscle efficiency has been less studied in the scientific literature than other endurance phenotype traits, although it may be a critical factor determining endurance performance (GómezGallego et al., 2009). The phenomenon of human physical performance has long been of interest to specialists in sports medicine and exercise physiologists (Dias et al., 2007). In this context, anthropometric assessment, cardiopulmonary parameters and analysis of hormonal, immunological and enzymatic processes have been carried out (Bouchard et al., 1995; Apple et al., 1988; Lac and Maso, 2004; Ghorayeb et al., 2005; Cruzat et al., 2007; Dias et al., 2007; Dourado, 2007; Foschini et al., 2007; Foschini et al., 2008). However, some of those characteristics such as height, body mass, fat, muscle strength, flexibility, speed, aerobic capacity, muscle fiber composition and ability to adapt to the training are, to some extent, genetically determined (Bouchard et al., 1995; Beunen and Thomis, 1999; Smith, 2003; Lippi et al., 2009). Thus, the status of human physical performance is a multifactorial phenotype, influenced by several factors, including physique and biomechanical, physiological, metabolic, behavioral, psychological, and social characteristics (Bouchard et al. 1997; Rankinen et al. 2000; Ostrander et al 2009). Consequently, it requires the integrated combination of environmental (such as specific training and nutritional counseling) and genetic (which are beyond the control of athletes and technicians) factors; genetic predisposition has major implications for the genetic characterization of the individual as an outstanding athlete (Skinner, 2002; Smith, 2003, Dias et al., 2007, Lippi et al., 2009).

7. GENETICS-BASED PERFORMANCE The variability of individual biological and mechanical responses, particularly in the elite athletes of each specific modality, allows the screening of candidate genes. A large number of

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genetic markers have been well documented, showing association with physical performance phenotypes. However, it is important to note that multiple biological and environmental factors are determinants of performance, and the analysis of a single gene alone does not determine the phenotype of an athlete, since the athletic characteristics responsible for contributing to a good sports performance are determined by a number of genes which are subjected to environmental action (Bouchard et al., 1997; Skinner, 2002; Dias et al., 2007). Thus, human genetic research is based on the application of biomarkers to assess the genetic characteristics and their relation to the environment, aiming to understand the desired phenotype that, in this case, would be sports performance (Bouchard et al., 1997; Dias et al., 2007). Currently, the genes themselves and their products have been used as biomarkers in human genetic research (Bouchard et al., 1997; Dias et al., 2007), and genetic mapping with newer approaches such as genome-wide association may yield novel insights into the physiological responses to exercise. Over the last two decades, a number of groups have begun to investigate the influence of candidate gene polymorphisms on endurance performance and, arising from their studies, a large number of genetic variants have been well documented, showing association with physical performance-related phenotypes (Ostrander et al. 2009; Schoenfelder, 2010). By 2005, 170 genetic variations (165 autosomal and 5 X-linked) had been identified to improve athletic performance when inherited (Dias et al., 2007; Bray et al., 2008; Ostrander et al. 2009); most of them involved some aspect of energy metabolism or cardiovascular function, and presented heritability estimates varying from 20% to 75% (MacArthur and North 2005; Schoenfelder 2010). Muscle efficiency has been less studied in the scientific literature than these traits (Gómez-Gallego et al. 2009), and the genetic contribution to variation in the relative proportions of skeletal muscle fiber types is estimated as lying between 40% and 50% (MacArthur and North 2005). The 2007 update included 239 genes and quantitative trait loci (QTL) related to human performance and health-related fitness genes, besides mitochondrial genes that that have been shown to be associated with exercise intolerance, fitness, or performance-related phenotypes (Bray et al., 2008). Moreover, superimposed on this genetic variability, epigenetic1 modifications thought to be modulated by environmental and lifestyle factors, such as nutrition and hormonal status, could amplify biological diversity (Schoenfelder, 2010). Given the wide range of genetic factors connected to performance, this chapter intends to approach a small number of potential genetic variants related to oxidative stress and injuries previously addressed, as well as some others widely studied in the context of performance and health-related fitness phenotypes.

7.1. Polymorphisms Related to Oxidative Stress Many potentially significant genetic variants related to oxidative stress have already been identified (Forsberg et al., 2001; Morgenstern, 2004). Among them, several single nucleotide polymorphisms (SNPs) in the antioxidant enzyme genes have been reported to produce 1

Epigenetics is any regulatory activity of genes that does not involve changes in DNA sequence (genetic code) and can persist for a generation or more. Epigenetic processes include covalent modifications of histones, methylation of cytosines in DNA, and gene regulation by noncoding RNA.

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altered levels or activities of those enzymes, leading to abnormal free radical defense mechanisms (Bastaki et al., 2006). In such circumstances, ROS may interact with cellular biomolecules, such as DNA, with potentially serious consequences for the cell (Cooke et al., 2003). Similarly, activation of the renin angiotensin system has been associated with increased vascular superoxide anion production (Münzel and Keaney, 2001), so the insertion/deletion polymorphism of the angiotensin I-converting enzyme (ACE) gene can influence vascular oxidative stress, besides being associated with performance in endurance sports and with an increased response to endurance training. Moreover, the ability of the serum glycoprotein haptoglobin (Hp) to block hemoglobin-induced oxidative stress and damage is reportedly phenotype-dependent (Carter and Worwood, 2007).

7.1.1. Polymorphisms of Antioxidant Enzymes’ Genes 7.1.1.1. Superoxide Dismutase (SOD) Superoxide dismutase (SOD) is one of the most important enzymes to act against superoxide anions in tissues, by catalyzing the dismutation reaction of O2• to molecular oxygen and H2O2 (Akyol et al., 2005; Nakamura et al., 2005). It is divided into three isoforms: copper zinc superoxide dismutase (Cu/ZnSOD or SOD1) in the intracellular cytoplasmic compartments, manganese superoxide dismutase (MnSOD or SOD 2) in the mitochondria, and extracellular SOD (ECSOD or SOD3) on the endothelial membrane surface (Nakamura et al., 2005). Cu/ZnSOD (EC 1.15.1.1) is coded by the SOD1 gene located at chromosome 21q22.1 and it is specifically expressed in cytosol and associated with organelles, including mitochondrial intermembrane space (Niwa et al., 2007; Wang et al., 2008; Magrané et al., 2009). More than 100 mutations in the SOD1 gene have been reported, with numerous variants causing familial amyotrophic lateral sclerosis through the gain of a toxic function (Niwa et al., 2007). There are several hypotheses for mutant SOD1 toxicity, and among them are the contribution to mitochondrial dysfunction (Magrané et al., 2009) and enhanced redox stress caused by dominant mutations (Harraz et al., 2008). Although none of these mutations have been studied in the context of physical fitness or performance-related phenotypes, both contributions could affect them, by increasing oxidative stress and decreasing the energy supply during exercise. Moreover, Cu/ZnSOD has been shown to inhibit the superoxidestimulated osteoclastic bone resorption in vitro (Wang et al., 2008), besides exerting an impact on femoral mechanical characteristics, impairing femoral bending strength and stiffness in growing knockout female mice. Also, an important role has been suggested for Cu/ZnSOD in inflammation, suggesting that alterations in the activity of Cu/ZnSOD may affect the immune response and pathologies in which inflammation is involved (Marikovsky et al., 2003). Mitochondria are the major source of reactive oxygen species in a resting cell. If antioxidant protection is inadequate this will result in oxidative stress and cause mitochondrial dysfunction. MnSOD (EC1.15.1.1) is the only known enzyme that scavenges the superoxide radical within mitochondria, providing the main defense against oxidative stress in this organelle (Elsakka et al., 2007). It is coded by a nuclear gene located on chromosome 6q25.3 and synthesized with a mitochondrial targeting sequence (MTS), which is cleaved in the mitochondrial matrix to produce the active enzyme (Bastaki et al., 2006; Akyol et al., 2005; Elsakka et al., 2007). The common polymorphism consisting of a single

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nucleotide change in the region of the DNA encodes the signal sequence, such that the presence of either an alanine or a valine has been suggested; these would induce a conformational change from an α-helix to a β-sheet. This change has been reported to impact mitochondrial processing efficiency, affect the transport of MnSOD to mitochondria, and decrease MnSOD efficiency against oxidative stress, being associated with diseases related to oxidative stress and abnormal free radical defense mechanisms (Rosenblum et al., 1996; Mitrunen et al., 2001; Kinnula et al., 2004; Olson et al., 2004; Akyol et al., 2005; Choi et al., 2008). Despite these associations, it has been suggested that an overexpression of MnSOD such as that in the wild type genotype could increase production of H2O2, which, if not subsequently neutralized and converted to H2O and O2, could contribute to further generations of ROS (Slanger et al., 2006). In this context, MnSOD heterozygosis has been suggested to favor defense against oxidative stress in runners, since the Val/Ala genotype presented the lowest damages to DNA and tissues, as well as the lowest lipid peroxidation levels, besides having a better response to the antioxidant supplementation with pequi oil against exercise-induced damages (MirandaVilela et al., 2009a). Moreover, MnSOD has been reported to significantly influence results of CK in runners, with a possible association between the variant genotype with lower muscle damage and higher DNA damage, suggesting that the mechanisms of muscle and DNA damages are independent and could operate in different ways (Akimoto et al., 2010). Because MnSOD expression is induced by oxidative stress, hypoxia, tumor necrosis factor  (TNF) and interleukin-1 (IL-1) (Elsakka et al., 2007), and all of these factors are involved in the response to endurance exercise, this polymorphism in the MTS of MnSOD deserves to be further investigated in the context of improving performance, mainly among elite athletes of different modalities. Extracellular SOD (EC 1.15.1.1) is encoded on 4p15.3-p15.1 (OMIM*185490) and is the major SOD isoenzyme in extracellular fluids such as plasma, lymph, and synovial fluid, although it also occurs in tissues. Ninety percent of ECSOD is located on the bond to the endothelial cell surfaces (Juul eta l., 2004; Nakamura et al., 2005) and is distinguished from the other SOD isoenzymes by its heparin-binding capacity: ECSOD binds to the surface of endothelial cells through the heparan sulfate proteoglycan in the glycocalyx of endothelial cell surfaces and in connective tissue matrix, especially in the arterial wall (Juul et al., 2004). In the vascular system, ECSOD reduces free radical toxicity by scavenging superoxide anions in and around the endothelial cells (Nakamura et al., 2005). It has also been reported that ECSOD effectively prevents LDL oxidation in vitro and plays an important role in attenuating free radical damage of LDL in the tissues (Nakamura et al., 2005). Although ECSOD provides important antioxidant defense against damage from free radicals, increased levels of this enzyme, such as those that occur in the polymorphism Arg213Gly, may in some cases lead to cell damage by an overproduction of H2O2, especially in individuals with a decreased capacity to remove this highly reactive compound by glutathione peroxidase or catalase. Indeed, the concentration of ECSOD in the plasma of individuals who are homozygous for this variant is increased 10- to 30-fold compared with non-carriers, and it has been suggested that this phenotype is caused by a reduction in heparin affinity (Juul et al., 2004; Petersen et al., 2005). ECSOD Arg213Gly cannot dismutate superoxide anions in and around the endothelial cells and may facilitate atherosclerosis because the mutated ECSOD has a lower affinity for endothelial cells via the heparin-binding domain than the native form (Nakamura et al., 2005; Petersen et al., 2005). Because

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inflammation is important in the start, progression, and clinical outcome of atherosclerosis (Brull et al., 2003), strenuous exercise, mainly if above habitual intensity of effort, or training with very elevated frequency, can exceed the benefits of physical activity, compromising performance and potentially contributing to an increased future risk of cardiovascular disease (CVD) in athletes (Urso and Clarkson 2003; Cruzat et al. 2007; Ferreira et al. 2007; Thompson et al. 2007). Sudden deaths in athletes are usually caused by previously unsuspected cardiovascular disease, with the vast majority of deaths in middle-aged athletes caused by atherosclerotic coronary artery disease (CAD) (Maron et al., 1996; Maron and Zipes, 2005; Thompson et al., 2007). Thus, the Arg213Gly polymorphism seems to be important in contexts, performance and CAD risk for middle-aged athletes, needing to be better investigated in these respects. 7.1.1.2. Catalase (CAT) CAT (EC 1.11.1.6) is a heme enzyme whose main role involves controlling H2O2 concentrations in human cells, converting H2O2 into H2O and O2. With SOD and glutathione peroxidase, CAT constitutes a primary defense against oxidative stress (Ambrosone et al. 2005). Although some polymorphisms in the CAT gene (locus 11p13) have been reported to cause acatalasemia (Wen et al., 1990) or to be associated with sporadic aniridia (congenital absence of the iris) (Boyd et al., 1986) or essential hypertension (Jiang et al., 2001), the CAT gene presents a benign polymorphism, CAT 21A/T (ref SNP ID: rs7943316), located inside the promoter region close to the start site (Ukkola et al. 2001; Góth et al. 2004). For this polymorphism, no effects on catalase expression, catalase activity or association with disease/pathological changes have been reported (Góth et al., 2004), besides the heterozygous AT being associated with higher values of mean corpuscular hemoglobin (MCH) in runners. Because MCH indicates the average amount of oxygen-carrying hemoglobin inside and in view of the fact that CAT’s high concentrations in erythrocytes provides defense against high concentrations of hydrogen peroxide, results suggest that the CAT 21A/T variant allele can influence endurance performance to improve the blood oxygen-carrying capacity (MirandaVilela et al., 2010a). Moreover, the variant genotype (TT) has been positively influenced by pequi-oil supplementation against DNA damage, which did not occur with the wild genotype (Miranda-Vilela et al., 2011b). 7.1.1.3. Glutathione Peroxidase (GPX) The glutathione peroxidase family is the largest of the selenoprotein gene families. Glutathione peroxidases (GPX, EC 1.11.1.9) are named for their ability to use glutathione as a reducing substrate. GPX1 and GPX2 appear to have similar substrate specificity, catalyzing the reduction of hydrogen peroxide to water, but differ in their tissue distribution, with GPX1 expression being particularly abundant in erythrocytes and GPX2 expression being restricted primarily to the gastrointestinal tract and the liver in humans (Chu et al., 1993; Foster et al., 2006). GPX3 (extracellular or plasma) is a circulating plasma selenoprotein and is able to utilize thioredoxin reductase, thioredoxin or glutaredoxin as reductants. GPX4 reduces phospholipid hydroperoxides, localizes to the mitochondria or to the nucleus and the cytosol, and appears to be essential for survival (Foster et al., 2006). In view of what has been said in this chapter and elsewhere about exercise-induced oxidative stress and injuries, the altered expression of GPX isoenzymes could compromise performance by promoting oxidative stress and injuries mediated by H2O2, mainly for those

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athletes overexpressing SOD enzymes, but deficient in neutralizing H2O2, while decreased GPX2 activity could also favor infections in athletes who exercise strenuously. From this point of view, polymorphisms in the GPX genes associated with decreased enzyme activities could be a target for further investigations in the context of performance. For example, it has been reported that CAT 21A/T and GPX1 Pro198Leu polymorphisms influenced results of mean cellular volume (MCV) and MCH of erythrocytes in runners, consequently influencing their capacity to carry oxygen (Miranda-Vilela et al., 2010a). Moreover, GPX1 knockout mice have a normal phenotype, but are highly sensitive to oxidative stressors, raising the issue of the role of GPX1, especially under conditions of oxidative stress (de Haan et al., 1998). Considering these findings, polymorphisms in the GPX1 gene which decrease the GPX1 enzyme activity could favor hemolytic events, mainly in those endurance athletes overexpressing SOD enzymes, although this deleterious effect could be attenuated by CAT activity. Also, plasma GPX3 deficiency related to genetic polymorphisms in the promoter region of the GPX3 gene has been reported to increase extracellular oxidant stress, decreases bioavailable nitric oxide, and promotes platelet activation (Voetsh et al., 2007). Such decreased GPX3 activity can compromise performance and potentially contribute to an increased future risk of cardiovascular disease (CVD) in athletes, mainly those who exercise extraneously and had overexpression of ECSOD due to the Arg213Gly polymorphism.

7.1.2. Haptoglobin (Hp) Haptoglobin (Hp) is an integral part of the immune acute phase response, which binds free hemoglobin (Hb), preventing oxidative damage and modulating immune function. The complex Hp-Hb also functions as a scavenger of nitric oxide (NO), a free radical vital in basal blood flow regulation and vascular homeostasis, regulating NO bioavailability and vascular homeostasis (Carter and Worwood, 2007). Several functional differences reported between Hp phenotypes could have important biological and clinical consequences. These differences are explained by a phenotype-dependent modulation of oxidative stress and prostaglandin synthesis (Carter and Worwood, 2007; Alayash, 2011). Hp polymorphism is associated with the prevalence and clinical evolution of many inflammatory diseases (Alayash, 2011) and recently, it has also been suggested as a possible determining factor in runners’ performance (Akimoto et al., 2010), influencing aerobic capacity (Miranda-Vilela et al., 2010a) and, mainly when in interaction with angiotensin-converting enzyme (ACE) polymorphism, also influencing lipid peroxidation and CK values (Akimoto et al., 2010). 7.2. I/D Polymorphism of the Angiotensin-Converting Enzyme (ACE) The angiotensin-converting enzyme I (ACE, EC 3.4.15.1) plays an important role in circulatory homeostasis and blood pressure control, by cleaving angiotensin I to produce the potent vasoconstrictor angiotensin II and by inactivating the vasodilator bradykinin (Can et al., 2005; Dias et al., 2007). Plasma levels of ACE in humans are related to I/D polymorphism in the ACE gene (locus 17q23). This polymorphism consists of the absence (deletion or "D" allele) or presence (insertion or "I" allele) of an Alu sequence of 287 base pairs (bp) in intron 16 of the ACE gene (Fonseca and Izar, 2004; Dias et al., 2007). The deletion is associated with higher levels of transcription of messenger RNA and, consequently, with higher expression of ACE. Thus, carriers of the DD genotype have higher ACE levels than those with ID or II genotypes (Fonseca and Izar, 2004).

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The ACE I/D polymorphism has attracted considerable attention regarding its association with human physical performance (Dias et al., 2007), since it has been associated with better performance in endurance sports and with an increased response to endurance training (Can et al., 2005). Studies have shown that the I allele is more frequent in endurance athletes, whereas the D allele occurs more in athletes of strength and muscular explosion (Dias et al., 2007). Additionally, activation of the renin angiotensin system has been associated with increased vascular superoxide anion production (Münzel and Keaney. 2001), so the ACE I/D polymorphism can influence vascular oxidative stress.

7.3. Polymorphisms Related to Aerobic Capacity 7.3.1. Erythropoietin (EPO) and Polymorphisms in EPO Gene and Its Receptor (EpoR) Erythropoietin (EPO) is the main endogenous hormone regulator of erythropoiesis. It is a glycoprotein and its expression is induced in the kidneys and liver by anemia or hypoxia (Semenza et al., 1991; Bento et al., 2003). Due to its inherent ability to stimulate production of red blood cells and consequently increase the oxygen supply to tissues, its use in sport has been banned by the International Olympic Committee (IOC) since 1987, and its use is considered as doping (Bento et al., 2003; Artioli et al., 2007). However, its recombinant (synthetic) forms have been used indiscriminately by athletes, mainly in endurance sports, to increase the concentration of red blood cells, generating higher oxygen supply to muscle tissue (Pardos et al., 1999; Bento et al., 2003; De Rose et al., 2004). Because changes in EPO production generate an imbalance in its plasma concentration and can cause several pathologies related to the hematopoietic system, its use in sport is certainly questionable (Bento et al., 2003). On the other hand, benign mutations in the gene of its receptor (EPOR, locus 19p13.3-p13.2) may favor aerobic physical performance (de la Chapelle et al., 1993a). In fact, so far only one allelic variant in the promoter region of the EPO gene (locus 7q21) has been described, being associated with complications in diabetes (Tong et al., 2008), while several polymorphisms have been described for EpoR (Prchal et al., 1985; Juvonen et al.,1991; de la Chapelle et al., 1993a; Sokol et al., 1995; Le Couedic et al., 1996; Arcasoy et al.; 1997; Kralovics et al., 1997; Kralovics et al., 1998; Watowich et al., 1999), one of them associated with favoring performance in skiing competitions (OMIM *133171). 7.3.2. Vascular Endothelial Growth Factor (VEGF) and Its Receptor (VEGFR) Many polypeptide mitogens, such as fibroblast growth factor (FGFB) and plateletderived growth factors, are active in a wide range of different cell types. In contrast, vascular endothelial growth factor (VEGF) is a mitogen primarily for vascular endothelial cells, being structurally related to platelet-derived growth factor (OMIM +192240). VEGF constitutes a family of hypoxia-inducible regulatory peptides capable of controlling blood vessel formation and permeability, being a potent stimulator of endothelial cell proliferation, besides having vasodilatory function, and neurotrophic and neuroprotective effects. It interacts with receptor tyrosine kinases on endothelial cells to promote angiogenesis (Jin et al., 2002; Medford and Millar, 2006). VEGF increases microvascular permeability 20,000 times more potently than histamine (Medford and Millar, 2006) and its expression is stimulated by a great number of proangiogenic factors, including the hypoxia-induced factor (HIF) and epidermal (EGF) and

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fibroblast (FGF) growth factors. Also, its level is influenced by the pH value of the blood and the partial pressure and concentration of oxygen in inhaled air (Ahmetov et al., 2008). VEGF expression significantly increases under aerobic physical exercise, and the improvement of the VO2max as a result of training is mainly determined by an increased maximal blood flow and higher density of muscle capillaries in active tissues (Ahmetov et al., 2008). Moreover, VEGF expression in response to exercise is stronger in deep regions of the muscle possessing a high proportion of oxidative fibers (Ahmetov et al., 2009). Because individual differences in the degree of adaptive changes, such as growth of blood vessels of skeletal muscles and the myocardium, are to a greater extent accounted by genetic factors that determine the genetic predisposition to performing physical exercises of different intensities and durations (Ahmetov et al., 2008), polymorphisms that increase VEGF expression could result in a hyper supply of oxygen and other nutrients to the tissues. With better vascularization in muscles, the heart and other parts of the body, exhaustion would be delayed. The VEGF gene is located in chromosome 6 (6p12) and alternate splicing of the the gene transcript leads to the generation of several splice variants (isoforms) of differing sizes (Medford and Millar, 2006; Ahmetov et al., 2008). Among the identified polymorphisms, of special interest are the variants located in the promoter (regulatory) region (Ahmetov et al., 2008). In this way, the substitution of cytosine for guanine at position –634 (the G-634C polymorphism; rs2010963) increases the gene activity and, accordingly, determines individual differences in the level of expression (Ahmetov et al., 2008), the VEGF C allele being associated with a greater increase in the VO2max level as a result of aerobic physical exercise (Prior et al., 2006). All VEGF isoforms bind to the tyrosine kinase receptors, VEGF receptor 1 (VEGF-R1) and VEGF receptor 2 (VEGF-R2), but VEGF-R2 is the main signaling receptor for VEGF bioactivity (angiogenesis, proliferation and permeability) and can cause proliferation in cells lacking VEGF-R1 (Medford and Millar, 2006). VEGFR2 is essential to induce the full spectrum of VEGF angiogenic responses to aerobic training and one polymorphism in its gene, His472Gln, has been pinpointed as important in the context of performance, where the allele VEGFR2 472Gln has been associated with elite athlete status, endurance performance and muscle fiber type composition in females (Ahmetov et al., 2009).

7.4. Polymorphisms Related yo Muscle Energy, Structure and Strength 7.4.1. Creatine Kinase (CK) and the Nco I and Taq I Polymorphisms in the 3' Untranslated Region of the CKM Gene Creatine kinase (CK) is an enzyme that catalyzes the rapid reaction of ATP resynthesis from phosphocreatine (CP) and ADP, playing an important role in energy metabolism of muscle cells and brain (Zhou et al., 2006; Foschini et al., 2007). In its active form it consists of two subunits, M and B, expressed by distinct genes. The gene for the subunit M (CKM; M= muscle), with 17.5 kilo base pairs (Kbp), eight exons and seven introns, is located on chromosome 19q13.2-q13.3, while the gene for subunit B (CKB; B = brain) is located on chromosome 14q32.3. By the combination of CKM and CKB subunits, three dimeric isoforms are formed, structuring in CK-MM, predominantly in skeletal muscle; CK-BB, predominantly in the brain; and CK-MB, predominantly in the myocardium. These three

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isoforms are found in the cytosol or associated with the myofibrillar structures (Dias et al., 2007; Foschini et al., 2007). Skeletal muscle contains almost entirely CK-MM, with small amounts of CK-MB. The major activity of this enzyme in heart muscle is also attributed to CK-MM, with approximately 20% of CK-MB (Foschini et al., 2007). The muscle-specific creatine kinase (CK-MM) is present both in type I fibers (slow twitch or slow oxidative fibers) and in type II fibers (white fibers, fast twitch or fast oxidative fibers) (Zhou et al., 2006). However, these fibers differ in their CK-MM activities, which are two times more active in fast-twitch fibers (Zhou et al., 2006; Dias et al., 2007). Additionally, type I muscle fibers, predominantly recruited in endurance sports and recognized by the predominance of aerobic oxidative metabolism, present an inverse relationship with CK-MM activity. Consequently, lower CKMM activity may be essential for endurance athletes (Zhou et al., 2006; Dias et al., 2007). Using the polymerase chain reaction technique (PCR) and subsequent DNA digestion with restriction enzymes NcoI and TaqI (PCR-based RFLP or restriction fragment length polymorphism), two polymorphisms were detected in the 3' untranslated region of the CKM gene (Rivera et al., 1997a; Dias et al, 2007). For the NcoI enzyme, the allele with the restriction site was first designated as 985 +185 bp and subsequently, after sequencing, as A allele; while the allele without the restriction site was originally designated as 1170 bp and later as G allele (Rivera et al., 1997a; Zhou et al., 2005; Zhou et al., 2006). For the TaqI enzyme, the allele with the restriction site was designated as 1020 bp + 150 bp and the allele without the restriction site as 1170 bp, because it is related to the size of the fragment amplified by PCR (Rivera et al.a, 1997a). These polymorphisms, analyzed together or separately, have been indicated as potential contributors to athletic performance in some studies (Zhou et al., 2006; Rivera et al. 1997b; Heled et al., 2007), while in others, no association was made (Rivera et al., 1997a). Because both polymorphisms are located in the 3' untranslated region of the gene, outside the coding region and regulatory region, it has been proposed that there is little likelihood that this mutation is the direct cause of any observed association, suggesting thus that this polymorphism could only serve as a marker of genetic difference (Dias et al., 2007). However, because the nature of athletic performance is multigenic and multifactorial, further investigation becomes necessary, mainly due to the fact that investigations have examined these polymorphisms together with VO2max assessments and/or running economy, without, however, assessing serum concentrations of total CK or CK-MM. Therefore, our group recently demonstrated that CK-MM TaqI polymorphism significantly influenced results of serum AST, total CK and high-sensitivity C-reactive protein (hs-CRP) (Miranda-Vilela et al., 2011c), indicating that the heterozygous to the CK TaqI polymorphism can favor minor exercise-induced damage and also the reduction of its subsequent inflammatory process as shown by the hs-CRP results, thus corroborating previous reports of its contribution to athletic performance.

7.4.2. R577X Polymorphism Alpha-Actinin 3 (ACTN3) Gene The alpha-actinin 3 (ACTN3) gene (locus 11q13-q14) encodes alpha-actinin 3, a structural protein of the sarcomeric Z line of type II muscle fibers, related to power and muscular strength, and with predominance of anaerobic energy metabolism type (Dias et al., 2007; Yang et al., 2007; Massidda et al., 2009).

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Genetic variation of ACTN3 has been associated with elite athletic status and resistance training (MacArthur and North, 2004). In a common polymorphism in the ACTN3 gene, the exchange of a cytosine (C) for a thymine (T) at position 1747 of exon 16 causes the amino acid arginine (R) to be replaced by a premature stop codon (X) at position 577 of the protein (arg577-to-ter) (North et al., 1999). When in homozygozis (577XX genotype), this polymorphism, named R577X, results in complete deficiency of the protein alpha-actinin 3 and is present in about 16% of humans globally (North et al., 1999; Dias et al., 2007; Massidda et al., 2009). In the presence of the R allele, the ACTN3 gene has a protective function in the sarcomere of skeletal muscle against destructive mechanisms caused during repetitive efforts, like short running distances (100-200 metros) (Moran et al., 2007). Interestingly, the genotype for deficiency of alpha-actinin 3 (577XX) does not result in a pathological phenotype such as muscular dystrophy or myopathy and has been found more often in endurance athletes than in the general population, suggesting that it contributes to a better performance in endurance tests (North et al., 1999; Dias et al., 2007; Yang et al., 2007; Eynon et al., 2009). On the other hand, the 577RR genotype has been associated with better performance in tests that require muscle strength and explosiveness (Druzhevskaya et al., 2008; Eynon et al., 2009; Massidda et al., 2009).

7.4.3. Myostatin (MSTN or GDF8) The transforming growth factor-beta (TGF-beta) superfamily encompasses a large number of growth and differentiation factors that play important roles in regulating embryonic development and in maintaining tissue homeostasis in adult animals (McPherron et al., 1997). Myostatin (MSTN) or growth/differentiation factor-8 (GDF8) is a member of this superfamily with a role in the control and maintenance of skeletal muscle mass (McPherron et al., 1997; Gonzalez-Cadavid et al., 1998). During early stages of embryogenesis, GDF8 expression is restricted to the myotome compartment of developing somites. At later stages and in adult animals, it is expressed in many different muscles throughout the body (McPherron et al., 1997; Gonzalez-Cadavid et al., 1998). Myostatin is a genetic determinant of skeletal muscle growth (Gonzalez-Cadavid et al., 1998), exerting negative regulation on muscle mass (inhibitor of muscle growth) (Schuelke et al., 2004; Huygens et al., 2005; Ye et al., 2007), by inhibiting the activation of satellite cells, which are stem cells resident in skeletal muscle (Schuelke et al., 2004). MSTN gene (locus 2q32.2) comprises three exons and two introns and is highly conserved in gene structure among vertebrate species (Thomis et al., 2004; Ye et al., 2007). Several polymorphisms and mutations have been identified in this gene, with diverse functional consequences (González-Freire et al., 2010; Santiago et al., 2011). It is transcribed as a 3.1-kb mRNA species that encodes a 335-amino acid precursor protein, being expressed uniquely in human skeletal muscle, in both type I and type II fibers, as a 26-kD mature glycoprotein and secreted into the plasma (Gonzalez-Cadavid et al., 1998). Mice with null mutations of the myostatin gene have increased muscle mass. Similarly, mutations of the myostatin gene inactivating the protein cause bovine muscular hypertrophy (GonzalezCadavid et al., 1998; Miranda et al., 2002; Ye et al., 2007). In humans, a loss-of-function mutation in the myostatin gene in a child increased muscle bulk and strength. This child was the son of a woman who was a former professional athlete, and several members of her family were reported to be unusually strong (Schuelke et al., 2004).

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Of the identified polymorphisms, the Lys(K)153Arg(R) variation located in exon 2 (rs1805086, 2379 A>G replacement) is a candidate to influence skeletal muscle phenotypes (González-Freire et al., 2010; Santiago et al., 2011), with the variant R allele appearing to contribute to a worse performance (Thomis et al., 2004; Santiago et al., 2011). However, reports on association studies of MSTN polymorphisms with baseline muscle strength or responses to strength training in humans are scarce (Thomis et al., 2004). Moreover, the frequency of the mutant R allele is low (about 3-4% among Caucasians), with the frequency of the homozygotes RR (below 1%) being even lower, which certainly limits the possibility of studying large groups of people carrying the R variant (Thomis et al., 2004; GonzálezFreire et al., 2010). Also other genes within the myostatin pathway as well as regulatory elements in myostatin expression should be studied as candidate genes. In fact, it has been reported that several genes involved in the myostatin pathway, but not the myostatin gene itself, are important quantitative trait loci (QTLs)2 for human muscle strength. An additional set of valuable candidate genes that were not part of the myostatin pathway was also found in the chromosome 12 and 13 genomic regions, including the insulin-like growth factor-1 (IGF1), among others (Huygens et al., 2005).

7.4.4. Insulin-Like Growth Factor-1 (IGF-1) The somatomedins or insulin-like growth factors (IGFs) comprise a family of peptide hormones structurally related to insulin that play important roles in mammalian growth and development, having a pleiotropic effect on cell growth and metabolism. Insulin-like growth factor-1 (IGF-1 or somatomedin C) mediates many of the growth-promoting effects of growth hormone (OMIM*147440; Lisa et al., 2011). The primary source of circulating IGF-I is the liver, although the skeleton also contributes to total serum levels and has anabolic effects, its concentration being related to that of growth hormone (GH) (Rosen et al., 1998; Haisma and Hon, 2006). These biological actions include the ability to release cytokines, promotion of angiogenesis and stimulation of extracellular matrix production (Lisa et al., 2011), besides exerting mitogenic, myogenic and anabolic tissue actions (Nindl et al., 2002), stimulating skeletal muscle hypertrophy by increasing protein synthesis, inhibiting proteolysis and increasing the uptake of glucose and amino acids (Cordeiro et al., 2005). IGF-1 is also important for bone cell proliferation, differentiation, and collagen synthesis (Rosen et al., 1998). Accordingly, it has been proposed that a combination of resistance training and overexpression of IGF-1 could be an effective measure for attenuating the loss of traininginduced adaptations (Lee et al., 2004). Because quantitative genetic analyses in humans, mice, pigs and cattle have shown that the levels of circulating IGF-I are under genetic control, with heritability estimates around 30% (Estany et al., 2007), polymorphisms in the IGF-1 gene which increase the hormone’s expression could favor performance of resistance training athletes. The IGF-1 gene (locus 12q23.2) contains 6 exons, 4 of which are alternatively spliced depending on tissue type and hormonal environment (Smith et al., 2002). Sequencing of the rat and human IGF-1 genes revealed the presence of a cytosine-adenosine short tandem repeat

2

Quantitative trait loci (QTLs) are stretches of DNA containing or linked to the genes that underlie a quantitative trait, which in turn are phenotypes (characteristics) that vary in degree and can be attributed to polygenic effects, i.e., product of two or more genes, and their environment.

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(STRs)3 in the promoter region (Estany et al., 2007). In humans evidence is accumulating that the length of these highly polymorphic STR sequences may be associated with circulating IGF-1 concentration (Rosen et al., 1998). There are also lines of evidence indicating that this polymorphism is associated with body weight and height characteristics, although results were not always coincident and their molecular basis remains to be elucidated (Estany et al., 2007). Since it has been demonstrated that the injection of a recombinant adeno-associated virus directing overexpression of IGF-1 in differentiated muscle fibers gave rise to an increase in muscle bulk in young adult mice in the absence of any special exercise program (Barton-Davis et al., 1998) and the combination of resistance training and overexpression of IGF-1 induced greater hypertrophy than either treatment alone (Lee et al., 2004), this polymorphism could favor performance by contributing, for example, to the strengthening a tennis player’s shoulder muscles, a sprinter’s calves or a boxer’s biceps.

7.5. Polymorphisms in the Cytokine Genes and Inflammation Intense physical activities, such as resistance exercise, with a strong eccentric component, cause micro-injury to skeletal muscle, and inflammation appears to play an important role in the repair and regeneration of skeletal muscle after damage (Dennis et al., 2004; Mann et al., 2011). There is evidence that susceptibility to inflammation is influenced by genetic variation in cytokine genes, and accumulated data have indicated that both pro- and anti-inflammatory responses can play a role in athletic performance. They may influence muscle repair, hypertrophy and strength (Cauci et al., 2010), particularly those SNPs located in the promoter regions of the cytokine genes that may alter their expression. SNPs in the cytokine genes and alterations in associated gene expression may also influence risk for upper respiratory symptoms (URS) in some athletes (Cox et al., 2010).

7.5.1. Tumor Necrosis Factor Alpha (TNF-) With micro-injury to the muscle, the first genes activated by the quiescent resident macrophages are the pro-inflammatory cytokines TNF-α and IL-1β (Dennis et al., 2004). It is of note that accumulation and activation of muscle resident macrophages is a rich source of growth factors postulated to stimulate myogenesis. Thus, inflammation may serve as a mechanism promoting hypertrophy. However, pre- and post-exercise levels of inflammatory factors display considerable variation among people, and this is likely influenced, at least partially, by genetic variation (Cauci et al., 2010). TNF- is a multifunctional proinflammatory cytokine that has effects on lipid metabolism, coagulation, insulin resistance and endothelial function. It is secreted predominantly by monocytes/macrophages, although significant amounts are also secreted by several other cell types (Skoog et al., 1999). TNF-α is also a potent catabolic factor to skeletal muscle (Liu et al., 2008), besides stimulating the production of interleukin (IL-6) and thereby inducing the hepatic production of C-reactive protein (CRP), a sensitive biomarker of the 3

STRs or microsatellites are hypervariable short sequences of DNA, normally of length 2-5 base pairs, that are interspersed throughout the human genome and repeated numerous times in tandem (in a head-to-tail fashion at a specific chromosomal locus). Ex.: the 16 bp sequence of "gatagatagatagata" would represent 4 head-tail copies of the tetramer "gata". The polymorphisms in STRs are due to the dfferent number of copies of the repeat element that can occur in a population of individuals.

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inflammatory status of the individual and exercise-induced oxidative stress (Djarova et al., 2011). TNF- is synthesized as a 26-kD membrane-bound protein (pro-TNF) that is cleaved by TNF-processing enzymes to release a soluble 17-kD TNF-α molecule. The mature TNF-α protein can then bind to its main receptors TNFR1 and TNFR2, which are expressed in most nucleated cells. After interacting with its receptors, a variety of responses are elicited which affect the regulation of a large number of genes (Skoog et al., 1999). TNF-α gene is located on the short arm of chromosome 6 (6p21.3), which is within the highly polygenic and polymorphic major histocompatibility complex (MHC) region of the human genome (Liu et al., 2008). TNF- expression is indicated to be partly genetically determined, and polymorphic sites closely linked to the TNF-α locus (inside the MHC region) are associated with differences in cellular TNF-α secretion. While there is evidence for transcriptional regulation of TNF-α gene expression, polymorphisms in the promoter region of the TNF-α gene may be important for TNF-α gene expression and protein production (Skoog et al., 1999). Many SNPs and microsatellites have been identified in the TNF locus, and the ones in the promoter region are thought to influence TNF transcription rate and to affect the circulating CRP levels (Lakka et al., 2006; Liu et al., 2008). It is also believed that the interaction between nuclear proteins and these TNF SNPs is an important pathway for the allele-specific modulation of TNF expression (Liu et al., 2008). Supporting this hypothesis, five SNPs in the promoter region have been shown to influence gene expression (Liu et al., 2008), being linked to various infectious and autoimmune diseases, obesity and obesity-associated insulin resistance, age-related diseases, including sarcopenia (age-related loss of muscle mass and strength), as well as longevity (Lakka et al., 2006; Liu et al., 2008). Among them, a guanine (G) to adenine (A) substitution located at position −308 of the transcription start site in the promoter region (308G/A; rs1800629) has been reported to affect the transcription rate of the TNF-α gene, with association between the AA genotype with higher plasma CRP levels and less favorable CRP response to regular exercise (Lakka et al., 2006). Because CRP can amplify the proinflammatory response through complement activation, tissue damage, and activation of endothelial cells (Libby et al. 2002), this polymorphism may contribute to a worse performance and also, over time, to loss of muscle mass and strength and CVD risk in athletes, particularly middle-aged athletes who exercise extensively.

7.5.2. Interleukins (IL) The interleukin-1 (IL-1) family of cytokines and IL-6 are other cytokines involved in the inflammatory and repair reactions of skeletal muscle during and after exercise (Cauci et al., 2010; Eynon et al., 2011). IL-6 also plays a role in the regulation of metabolism during physical exercise, improving skeletal muscle energy supply and assisting in the maintenance of stable blood glucose levels by stimulating lipolysis in the adipose tissue and augmenting glycogenolysis in the liver (Pedersen et al., 2004; Huuskonen et al., 2009). In general, IL-1 acts synergistically with TNF-, activating proinflammatory responses in a wide range of cells and promoting the acute phase response. IL-1 is able to induce the secretion of several inflammatory factors, including IL-6 and TNF- (Cauci et al., 2010). IL6 plays an important role in the homeostasis of the neuroendocrine and immune systems, in the balance of pro- and anti-inflammatory pathways and in response to oxidative stress,

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besides regulating hematopoiesis and bone resorption (Chung et al., 2003; Pereira et al., 2011). Additionally, it may modify the regulation of energy balance, by actings as an energy sensor, being dependent on the glycogen content in the muscle. IL-6 is released from contracting muscles in high amounts and exerts its effect on adipose tissue, inducing lipolysis and gene transcription in abdominal subcutaneous fat (Pedersen et al., 2004). During exercise, IL-6 is produced by muscle fibers in contraction, even without any muscle damage, via a TNF-independent pathway increasing plasma IL-6 levels dramatically (100-fold) (Pedersen et al., 2004). IL-6 stimulates the appearance in the circulation of the anti-inflammatory cytokine IL-10 and the cytokine inhibitors such as IL-1 receptor antagonist (IL-1ra or IL-1RN) and TNF- receptor, inhibiting the production of the proinflammatory cytokine TNF- (Pedersen et al., 2004; Petersen and Pedersen, 2005). Consequently, IL-6 induces an anti-inflammatory environment, may inhibit TNF--induced insulin resistance and have an important role in mediating the beneficial health effects of exercise in inactivity and obesity-related disorders such as diabetes and CVD (Pedersen et al., 2004; Petersen and Pedersen, 2005; Petersen and Pedersen, 2006). Polymorphisms in the promoter region of the IL-6 gene (locus 7p21) that affect the IL-6 expression level may consequently influence performance, immunodepression and risk of upper respiratory symptoms (URS), besides contributing to insulin resistance and CVD risk. The IL-1 and IL-1 genes are located on the long arm of chromosome 2 (2q14) and are tightly linked (D'Eustachio et al., 1987; Nicklin et al., 1994). They are synthesized as a large precursor of 30.6 and 30.7 kD, respectively, which is processed to a smaller form (March et al., 1985). Another gene map close to IL-1  and  genes is the IL-1ra gene (locus 2q14.2) (Nicklin et al., 1994). IL-1ra is a protein that binds to IL-1 receptors (IL-1RI) and inhibits the binding of IL-1 and IL-1, neutralizing the biologic activity of these 2 cytokines in physiologic and pathophysiologic immune and inflammatory responses (Arend, 1991). Because IL-1ra acts as an antagonist of IL-1RI and prevents IL-1-dependent signaling, deficiency of IL-1ra in humans, which may be caused by certain polymorphisms, can lead to IL-1-mediated systemic and local inflammation (Cauci et al., 2010). Several studies showed that polymorphisms in the IL-1 and IL-1ra genes correlate with altered protein expression (Cauci et al., 2010). Two SNPs in IL-1 representing C-to-T base transitions have been studied for disease predisposition, one at position 511 in the promoter region and another at position +3954 in exon 5 (TaqI restriction site polymorphism). In addition, a polymorphism in the intron 2 region of the IL-1ra gene consisting of a variable number of tandem repeats (VNTR)4 of 86 base pairs (bp) has been extensively investigated in relation to a variety of pathological conditions, including inflammatory myopathies (Cauci et al., 2010). Whether polymorphisms in the interleukin genes can affect the severity of the inflammatory response or the athletic status has been also investigated. A study performed on sedentary subjects selected on the basis of their haplotype pattern of specific combinations of five SNPs in the IL-1 gene cluster showed that the wild type genotype for IL-1 +3954 or the variant genotype for IL-1 −3737 in combination with the variant allele at IL-1ra +2018 were associated with inflammation of skeletal muscle, following acute resistance exercise. This 4

VNTRs or minisatellites are hypervariable regions of human DNA of length 10-100 base pairs that are repeated numerous times in tandem (in a head-tail manner, like STRs). VNTRs are similar to STRs, the difference being that in a VNTR, the repeated sequence is longer.

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study indicated that IL-1 haplotype can influence the inflammatory response of skeletal muscle after exercise and that it is necessary to test whether the specific IL-1 haplotype is beneficial or detrimental for muscle repair and the adaptability to resistance training (Dennis et al., 2004). Another study carried out with professional and non-professional Italian athletes and non-athlete controls assessing the IL-1 511 and +3954 polymorphisms and the VNTR IL-1ra polymorphism showed that variants in the IL-1ra gene was associated with athletic status. The authors suggested that as VNTR IL-1ra polymorphism is implicated in several disease conditions, athlete status may constitute a confounding variable that will need to be accounted for when examining associations of this polymorphism with disease risk (Cauci et al., 2010). For IL-6 gene, of the three main polymorphisms reported so far in the promoter region of the IL-6 gene, such as 174G/C, 572G/C and 597G/A (Wang et al., 2011), the −174G/C polymorphism has been reported as a candidate to explain individual variations in health and exercise related phenotypes, with GG genotype favoring sprint/power sports performance in European (Spanish) Caucasian males (Ruiz et al., 2010) but not in Israeli Caucasians (Eynon et al., 2011), and CG genotype favoring increased plasma IL-6 levels, greatest gains in VO2max and decreased BMI (Huuskonen et al., 2009). These apparently contradictory findings support the need to replicate association results between genetic polymorphisms and athletic status in populations of different ethnic backgrounds with the largest possible population, since they vary among different ethnicities. The change of guanine bases to cytosine (G → C) at position 174 bp from the transcriptional start site seems to affect the transcription of the IL-6 gene and therefore the plasma levels of this cytokine in young, elderly and centenarian individuals (Pereira et al., 2011), with an increased inflammatory response associated with the G allele (Bennermo et al., 2004). It has been shown that, in German Caucasian surgical patients, the −174GG was not associated with the incidence of sepsis, although it increased their survival in sepsis (Schlüter et al., 2002), while in highly-trained athletes, this genotype has been associated with an increased likelihood of ≥ 3 URS episodes in a 12 month period (Cox et al., 2010). These contradictory results indicate the need to study cytokine haplotypes in association with studies involving elite and trained athletes, at least those involved in the systemic IL-6 response, such as IL-10, IL-1ra and TNF- receptor.

7.5.3. Methylenetetrahydrofolate Reductase (MTHFR) Gene Polymorphisms in the MTHFR gene have been reported in association with an altered plasma homocysteine (Hcy) level (Morita et al. 1997; van Bockxmeer et al. 1997; Fujimura et al. 2000; Miller et al. 2005), which is in turn an independent factor risk for CVD (Graham et al. 1997; Morita et al. 1997; Eikelboom et al. 1999; Anderson et al. 2000). Because elevated Hcy levels are associated with coronary and peripheral vascular obstructive events (Misawa et al., 2008), the variant alleles of the MTHFR gene could contribute, in the course of time, to a lower oxygen supply to the heart in those endurance athletes who exercise strenuously, reducing cardiovascular fitness and thus performance, since they have been associated with increased Hcy. In fact, MTHFR C677T polymorphism has been associated with a lower hemoglobin level in a healthy exercise-trained population (Fortunato et al. 2007). Moreover, hemolysis can occur as a result of mechanical trauma in the capillaries of runners’ feet

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(Carlson and Mawdsley, 1986), which would compromise performance still more in carriers of these variants. Previous reports from our research group have demonstrated an association between both MTHFR polymorphisms (C677T and A1298C) with plasma CRP levels, and thus with a slightly higher inflammatory process (Miranda-Vilela et al., 2011c). Hence, these results indicate that MTHFR polymorphisms should be better investigated in the context of performance and future CVD risk in athletes, particularly middle-aged athletes who exercise extensively, because besides the facts discussed above, CRP is present in atherosclerotic plaques, where it might exert several potential proinflammatory and atherogenic actions that include the binding of oxidized LDL cholesterol, induction of adhesion molecule expression, activation of complement, and stimulation of tissue factor production by monocytes (Brull et al. 2003). Furthermore, the existence of a sport-related hyperhomocysteinemia has been reported, independent of the variables found in the general population such as decreased folate or vitamin B12 (Borrione et al. 2008), particularly if a race took place close to the anaerobic threshold speed (Benedini et al. 2010). This suggests that it would represent an adaptation to training but the possibility of secondary vascular damage cannot be excluded (Borrione et al. 2008).

7.6. Some Considerations Although more than 200 performance enhancing polymorphisms have been reported until now, several of them vary among ethnic groups. This possibly explains, at least in part, the fact that athletes from a specific ethnic origin seem to have an advantage over others in certain Olympic sports and affirms the need to replicate association results between genetic polymorphisms and athletic status in populations of different ethnic backgrounds with the largest possible population. Moreover, in contrast to monogenic inheritance, in which mutations of a single gene result in a specific phenotype, sports performance is multigenic and multifactorial. Thus, several genes are involved and, although genetic predisposition has a strong influence on the characterization of the individual as an outstanding athlete, it alone does not produce an elite athlete. Other aspects, such as psychological and environmental factors, will ultimately determine whether these individuals will be top athletes.

CONCLUSION Exercise has a classic Janus effect, which means that the difference between "medicine" and "poison" is in the dose. When practiced moderately and regularly, it is crucial for a healthy lifestyle, acting as a therapeutic agent and/or preventing numerous illnesses. When exhaustive, exercise can leads to oxidative stress and cell damage even in trained individuals. Thus, the prudent recommendation to maintain a healthy lifestyle is the regular practice of moderate exercise and a diet rich in antioxidants from natural foods, whatever the genetic inheritance. For those people who undergo strenuous exercise or training that exceeds the antioxidant defenses, the use of antioxidant supplements may be of use, providing that they are closely monitored by sports nutritionists. For these athletes, genetic screening may also be

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used to select specific training methods to enhance or improve their genetic predisposition. It is worth remembering, however, that sudden deaths in athletes are usually caused by previously unsuspected cardiovascular disease, and the vast majority of deaths in middle-aged athletes are caused by atherosclerotic cardiovascular disease. Moreover, even children of Olympic athletes, who certainly have a genetic advantage over many other babies born, are not guaranteed the same performance as their parents, because other aspects such as psychological and environmental factors will ultimately determine whether these individuals will be top athletes.

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ingestion of antioxidant supplementation based on pequi oil (Caryocar brasiliense Camb.): a Before-After study. Genes Nutr 2011a. doi: 10.1007/s12263-011-0217-y. Miranda-Vilela, AL, Pereira LCS, Gonçalves CA, Grisolia CK. Pequi fruit (Caryocar brasiliense Camb.) pulp oil reduces exercise-induced inflammatory markers and blood pressure of male and female runners. Nutr Res 2009a;29:850-858. Misawa AK, Suzuki H, Maia Júnior OO, Bonanomi MTBC, Melo CSN. Obstrução arterial retiniana periférica associada com hiperhomocisteinemia: relato de caso. Arq Bras Oftalmol 2008;71(5):729-33. Mitka M. Biomarkers for Coronary Heart Disease: Predictive Value or Background Noise? J Am Med Assoc 2004;292(23):2824-2825. Mitrunen K, Sillanpaa P, Kataja V, Eskelinen M, Kosma VM, Benhamou S, Uusitupa M, Hirvonen A. Association between manganese superoxide dismutase (MnSOD) gene polymorphism and breast cancer risk. Carcinogenesis 2001;22:827-829. Molavi B, Mehta JL. Oxidative stress in cardiovascular disease: molecular basis of its deleterious effects its detection and therapeutic considerations. Curr Opin Cardiol 2004;19:488-493. Møller P; Loft S; Lundby C; Olsen NV. Acute hypoxia and hypoxic exercise induce DNA strand breaks and oxidative DNA damage in humans. FASEB J 2001;15:1181-1186. Mooren FC, Blöming D, Lechtermann A, Lerch MM, Völker K . Lymphocyte apoptosis after exhaustive and moderate exercise. J Appl Physiol 2002;93:147-153. Moran CN, Yang N, Bailey MES, Tsiokanos A, Jamurtas A, MacArthur D, North KN, Pitsiladis YP, Wilson RH. Association analysis of the ACTN3 R577X polymorphism and complex quantitative body composition and performance phenotypes in adolescent Greeks. Eur J Human Genet 2007;15:88-93. Morgan DL, Allen DG. Early events in stretch-induced muscle damage. J. Appl. Physiol. 1999;87:2007-2015. Morgenstern R. Oxidative Stress and Human Genetic Variation. J Nutr 2004;134:3173S3174S. Morita H, Taguchi J, Kurihara H, Kitaoka M, Kaneda H, Kurihara Y, Maemura K, Sindo T, Minamino T, Ohno M, Yamaoki K, Ogasawara K, Aizawa T, Suzuki S, Yazaki Y. Genetic polymorphism of 5,10-methylentetrahydrofolate reductase (MTHFR) as a risk factor for coronary artery disease. Circulation 1997;95:2032-2036. Münzel T, Keaney JF. Are ACE Inhibitors a "Magic Bullet" Against Oxidative Stress? Circulation. 2001;104:1571-1574. Nagel D, Seiler D, Franz H, Jung K. Ultra-long-distance running and the liver. Int J Sports Med 1990;11(6):441-445. Nakamura S, Ando Y, Sasada K, Haraoka K, Ueda M, Okabe H, Motomiya Y. Role of Extracellular Superoxide Dismutase in Patients under Maintenance Hemodialysis. Nephron Clin Pract 2005;101:c109-c115. Nascimben L, Ingwall JS, Pauletto P, Friedrich J, Gwathmey JK, Saks V, Pessina AC Allen PD. Creatine Kinase System in Failing and Nonfailing Human Myocardium. Circulation 1996;94:1894-1901. Nicklin MJH, Weith A, Duff,GW. A physical map of the region encompassing the human interleukin-1-alpha, interleukin-1-beta, and interleukin-1 receptor antagonist genes. Genomics 1994;19: 382-384. Nieman DC. Exercise, infection, and immunity. Int J Sports Med 1994;15(Suppl 3):S131-41.

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Zhou DQ, Hu Y, Liu G, Wu J, Gong L. An A/G polymorphism in muscle-specific creatine kinase gene in Han population in northern China. Yi Chuan 2005;27:535-538. Zhou, DQ, Hu Y, Liu G, Gong L, Xi Y, Wen L. Muscle-specific creatine kinase gene polymorphism and running economy responses to an 18-week 5000-m training programme. Br J Sports Med 2006;40:988-991.

In: Athlete Performance and Injuries Editors: João H. Bastos and Andreia C. Silva

ISBN 978-1-61942-658-0 © 2012 Nova Science Publishers, Inc.

Chapter 2

TRAINING OVER THE EDGE: UNDERSTANDING THE OVERTRAINING SYNDROME Fernando Rocha1,2, Mário C. Marques1,2 and Aldo M. Costa1,2 1

University of Trás-os-Montes and Alto Douro, Department of Sport Sciences, Vila Real, Portugal 2 Research Centre in Sports, Health and Human Development, Vila Real, Portugal

ABSTRACT The improvement of elite athletes performance depends on two fundamental training variables: volume and intensity. The physiological adaptations and supercompensation require training and sufficient recovery periods. In fact, there is a strict relationship between training volume and recovery time that can be considered critic for the athlete’s physiological status. On this, overtraining syndrome appears to be caused by too much high intensity training and/or too little recovery time often combined with other training and nontraining stressors factors. The imbalance between effort and adequate recovery can bring serious physiological consequences, like decrease of training tolerance and performance and even susceptibility to respiratory tract infections. At present there is no one single diagnostic test that can define overtraining. The recognition of overtraining requires the identification of stress indicators, which do not return to baseline following a period of regeneration. Possible indicators include an imbalance of the neuroendocrine system, suppression of the immune system, indicators of muscle damage, depressed muscle glycogen reserves, deteriorating aerobic, ventilatory and cardiac efficiency, a depressed psychological profile, and poor performance in sport specific tests, e.g. time trials. Therefore, screening for overtraining and performance improvements must occur at the culmination of regeneration periods.

Keywords: Overtraining, overreaching, recovery, training load, sports performance

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INTRODUCTION Top-level sport inevitably requires a rigorous training and control regimen and therefore lies with the coach the decision of selecting the best training load. However, the complexity of this task results from the dynamic nature of the athlete’s individual trainability. Thus, the capacity to adapt to successive training loads during a defined period of time - translated into improved performance – depends on a number of endogenous (age, gender, morphology, training experience, etc.) and exogenous factors (nutrition, social support, etc.). As such, the adaptation capacity varies over time, inclusively facing the same applied load. Providing training loads that are effective in improving performance is not new for sports coaches. Unfortunately, the common acceptance of the classical theory of training, do not converge to the requirements of modern high-level sport. Here, the amount of volume and training intensity imposed on athletes today has grown to the point of no return. Moreover, this wild scheme of training can became dubious in its true effectiveness, beyond to the possible deleterious effects on the athlete’s physical and mental integrity. So, nowadays, consider a proper recovery for the current demands of training load and competition, became a primary concern. Successful training must involve overload; however the combination of excessive overload plus inadequate recovery must be avoided. As consequence of a disrupted balance between training stress and recovery, the athletes may experience acute feelings of fatigue, changes in mood state, staleness and even decreases in overall performance. If no other explanation for this observed changes can be found, the state of overtraining may be diagnose - overtraining syndrome. While many athletes and coaches unaware this phenomenon, its high prevalence in toplevel sport has gradually been highlighted. Therefore, the purpose of this chapter is to deepen the knowledge about overtraining, bringing actual scientific data to help coaches and athletes to recognize but particularly to avoid and overcame the overtraining syndrome. The following issues will be discussed: (i) misconception of overtraining terminology (overtraining, overtraining syndrome, overreaching); (ii) understanding the multifactorial etiology;; (iii) the assessment of overtraining (monitoring performance, immunological, hematological, hormonal and psychological parameters); (iv) prevention and treatment of the overtraining syndrome.

MISCONCEPTION OF OVERTRAINING TERMINOLOGY The definition of the term overtraining has considerably conflicting viewpoints. Researchers have used too many terms in different ways to describe both processes and outcomes associated with overtraining. Indeed, there has been confusion “about whether overtraining may have positive or negative aftereffects; about whether it should be considered a process, an outcome, or both; about whether various aspects of overtraining are causes or consequences; and about the varied usage of terms in the fields associated with overtraining” (Richardson, Anderson & Morris, 2008, p. 6). The difficulty of having a standardized diagnosis helps this misconception, demonstrating that this issue needs further investigation.

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The competitive sports requires an athlete, beyond their natural talent, have their physical and mental capacity in an optimal level and this can only happen through the training process. Through training , coaches and athletes must adapt to and cope with all demands, in manner to avoid exhaustion (Grantham, 2006). With this concern, which must be based not only in training loads, but also in recovery process, it may be possible to reach the limits of human performance. Sometimes (more often that we would like), by carelessness or lack of knowledge, that ceiling are exceeded resulting in a state of chronic fatigue and in a decrease of physical performance (Gleeson, 2002). The same author identifies this state as overtraining, adding that “it is also a situation defined by excessive training, characterized by long-lasting fatigue and worsening of competitive performance with further attempts to improve physical condition” (p. 32). Indeed, overtraining is an imbalance between training/competition and recovery with atypical cellular adaptations and responses (Steinacker & Lehmann, 2002). Therefore, the state of overtraining is characterized by the inability to recover properly after successive training sessions (Kuipers, 1998). That’s why the feeling of fatigue persists even after a regular rest period and leads to an emotional, physical and behavioral changes. This accumulation of training and/or non-training stress results in long-term decrement in performance capacity (Kreider et al., 1998). However, besides performance incompetence, many other clinical problems may arise as a result of overtraining; including sports injuries, infections or mood disturbances (Steinacker & Lehmann, 2002.) Moreover, stress factors not caused by training such as monotony, intra and interpersonal conflicts, can exacerbate the risk of resulting in overtraining (Lehmann et al., 1997). That’s why the term overtraining seems insufficient to describe what was going on with athletes in their everyday battles to balance stressors with recoveries (Richardson, Anderson & Morris, 2008). With effect, quite a few authors (Hooper & Mackinnon, 1995; O’Toole, 1998, Steinacker and Lehmann, 2002) have provided a definition that describes overtraining as a process and also an outcome (i.e., overtraining syndrome). The term overtraining seems appropriate to label the process, whereas overtraining syndrome is an outcome, representing the end state of nonadaptation that results from overtraining (Hooper & Mackinnon, 1995). By using the expression ’syndrome’, the emphasis is placed on a multifactorial etiology, recognizing that exercise (training) is not necessarily the only cause of this phenomenon (Meeusen, Duclos, Gleeson & Rietjens, 2005). Israel (1976), says that the overtraining can be classified in two categories: the parasympathetic and sympathetic. The sympathetic form, or the classic overtraining is characterized by increase sympathetic nervous system activity at rest. The sympathetic nervous system causes changes of the basic functions of the body, making easily the motor response to acute stress or physical activity. It occurs more frequently in athletes that rely primarily on anaerobic metabolism (lactic and alactic) to supply their muscle energetic demands,. The parasympathetic overtraining form is characterized by the predominance of parasympathetic tone at rest and during exercise, and is observed with greater frequency in endurance athletes. Lehmann, Foster, Gastmann, Keizer & Steinacker (1999) distinguished overtraining by time frame (i.e., short- or long-term overtraining). The short-term overtraining is presented as a common part of athletic training, which leads to a so-called state of overreaching. This positive state “is characterized by transient underperformance, which is reversible within short-term recovery period” (p. 2). Therefore, in search of peak performance, the state of

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overreaching seems to be a regular part of athletic training in which restoration of performance capacity usually take one or two weeks and can be rewarded by an increase in performance ability. On the other hand, when overreaching is too profound or is extended for too long (i.e., long-term overtraining) the athlete runs the risk of a resulting overtraining syndrome. Nederhof, Lemmink, Visscher, Meeusen & Mulder (2006) described the overtraining process occurring in three progressive stages: (i) Functional overreaching; (ii) Non-functional overreaching and; (iii) Overtraining syndrome. According to this author, functional overreaching occurs as a result of heavy training process, where there is a momentary decrease in performance, however, this reduction is reversible in a short time if we consider an appropriate recovery plan. The functional overreaching occurs after several days of intense training and is associated with muscle fatigue or peripheral and, according to Lehman, Foster & Keul (1993), can be defined as pre-overtraining. Many coaches use training camps to increase the training load (intensity and volume) so that athletes are subjected to a stimulus that creates the functional overreaching. Promoting the so-called super -compensation period, usually enable the athlete to reach higher performance levels. , Non functional overreaching or extreme overreaching, can occurs if the athlete neglecting the balance between training and recovery, typically, situations where the training load is markedly heavy during recovery periods; when the athlete drops down to a low level of performance and energy are not restored after a planned short-term recovery period; and when the impact of the non training stressors in life are underestimate (Saunders, 2009; Meeusen et al., 2005). Non functional overreaching is, therefore a quite severe level of fatigue where athletes can experience the first signs and symptoms of prolonged training distress such as performance decrements, psychological disturbance (decreased vigor, increased fatigue) and hormonal disorder. Recovery happens if athletes refrain from training for a few weeks (or even mouths). At this stage, the action of the coach is very important because realizing that the athlete is in a non-functional overreaching state, may delay the next training session.. Facing such performance decrease, an anxious coach may even increase training Table 1. Overload training progression Process Outcome

Training (overload) Acute fatigue

Intensified Training Functional overreaching (short-term overreaching) Moderate

Non-functional overreaching (extreme overreaching) Fatigue level Ordinary Moderatesevere Recovery time Day(s) Days to Weeks Weeks to Months Performance Increase Temporary Stagnation performance Decrease decrement (e.g training camp) Based on Saunders (2009) and Meeusen et al. (2005).

Overtraining syndrome

Severe Months … Decrease

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load, contributing to the deterioration of the non functional overreaching state, that is, a deeper level of fatigue, impairing the capacity for regeneration and recovery of the body. If this tune persists, may lead to overtraining syndrome. Despite the importance of correct terminology, many coaches and athletes unaware this phenomenon whereas their main object of interest is sport performance. Thus, sport scientist should focus on distinguish and monitor positive from negative training adaptation in order to get always positive results and avoid damaging the athlete health (Richardson et al., 2008). In order to summarize and demonstrate how thin is the line between overtraining and overreaching, we present the table 1, which represents the overload training progression, referring to the fatigue level, recovery time and level of performance. By this time, we are able to say that overtraining syndrome often can be accompanied by several biochemical, physiological, psychological and hormonal changes, and some common manifestation are chronic muscle pain, joint pain, mood and personality changes, elevated resting heart rate, and of course, decreased performance (Gleeson, 2002; Brenner, 2011). The difficulty of knowing whether an a athlete is in a state of peak fitness or if he is at the beginning of a decline in performance due to overtraining is very complex, especially regard to the physiological and biochemical factors (Meeusen et al., 2005, p.5). Moreover, overtraining signs and symptoms vary from individual to individual, are non-specific, anecdotal and numerous. These symptoms can also be confused with other clinical disturbances, and many times, the chronic fatigue syndrome and clinical depression are the most confoundable factors.

UNDERSTANDING THE MULTIFACTORIAL ETIOLOGY The progress of knowledge in this area has been delayed because there are few scientific prospective researches and lack of well-controlled studies about individual responses to overload training (Halson &Jeukendrup, 2004). This lack of studies happens because is not ethical to “overtrain” an athlete. Thus, identifying possible events that trigger or initiate overtraining (imbalance between load and recovery, training monotony, exaggerated number of competitions, glycogen deficiency, infections, emotional demands – affective and professional) is, perhaps, a rational study design, although cannot fully explain the entire mechanism of overload training. Since the phenomena involved in overtraining and recovery are clearly multifactorial, qualitative descriptive case studies can also assist in understanding the complex relationships involved (Botterill & Wilson, 2002). It could be useful to conduct research looking into many variables as possible; nevertheless it is not an easy task in understanding problems in a holistic way. The physiopathology of overtraining syndrome, ranges from muscle soreness and weakness, cytokine actions, moods swings, hormonal and hematological changes, psychological depression and nutritional problems, but the number of symptoms reported by overtrained athletes is very large, more than 200 (Fry et al., 1991). Table 2 shows, the physiological and psychological symptoms that are most commonly associated with a clinical diagnosis of overtraining (base on Gleeson, 2002).

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Fernando Rocha, Mário C. Marques and Aldo M. Costa Table 2. Common reported physiological and psychological changes associated with overtraining

Symptoms Underperformance Muscle weakness Chronic fatigue Sore muscles Increased perceived exertion during exercise Reduced motivation Sleep disturbance Increased early morning or sleeping heart rate Altered mood states (e.g. low scores for vigor; increase scores for fatigue and depression) Loss of appetite Gastrointestinal disturbance Recurrent infections Reference: based on Gleeson (2002)

To understand the etiology of overtraining syndrome seek first to exclude some organic diseases or infections and other nutritional factors (negative energy balance, insufficient carbohydrates and proteins intake, iron and magnesium deficiency). Despite the existence of several hypotheses about the causes of overtraining syndrome, there seem to be also some consensus. Situations that can trigger overtraining syndrome are the imbalance between training / load and recovery, excess competition, the monotony of training, emotional issues. Other less mentioned causes, relies on exercise heat stress and training at altitude (Meeusen, 2005), but the scientific evidence to support or refute these hypotheses are scarce, and the diagnosis is reached when you cannot identify and justify the cause of such symptoms. In the following subsections we point some main reasons that seem to trigger overtraining. What we need to retain, is that the etiology of overtraining syndrome varies from individual to individual, depending a lot on your state (physical and psychological) and stressors factors that are put upon it. Nevertheless, high intensity training and/or too little regeneration (recovery) is always the starting point.

Variations of the Hypothalamic-Pituitary-Adrenal Axis Lehmann et al., (1993) introduced the concept that hypothalamic function reflects the state of overreaching or overtraining syndrome because the hypothalamus integrates many of the stressors. The same author in 1998 suggested that a regulation disorder at the hypothalamus-pituitary might be the central disorder in overtraining syndrome. Increased training loads as well as other stresses can influence the neuroendocrine system in a chronic way. The endocrine system acts to promote the adaptation to the stimulus (load or other life stressors) through the activation of the autonomic nervous system. These actions result in changes in blood catecholamine, glucocorticoid, testosterone levels (Cunha et al., 2006), adrenocorticotrophin (ACTH), cortisol and prolactina (Gleeson, 2002).

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In response to stress, greater quantities of hormones are released by changing the sensitivity of specific receptors for these hormones, and tissues became less responsive to its action. Some authors (Fry et al., 1991; Lehmann et al., 1998) refers that the negative feedbacks responses reduce sympathetic drive and down –regulation of anterior pituitary gland receptors for hypothalamic releasing factors (corticotrophin) and/or inhibition of pituitary hormone pulse generators could result in a decreased pituitary hormone - ACTH; growth hormone; follicle stimulating hormone, (FSH); luteinising hormone, (LH)- response to stress. This and/or a down regulation of receptors for ACTH on the cells of the adrenal cortex could result in a decreased release of cortisol in response to stress. In a normal training state, with high loads and other life stressors, there is a decrease in the adrenal responsiveness; this decrease is compensated by an increase in the pituitary release of ACTH. In an early stage of overtraining, we still record a decrease in the adrenal responsiveness to ACTH, but at this time it is not compensated, and a decrease in cortisol response will be verified. A more advanced state of overtraining continues to show a reduction of the ACTH release by the pituitary, a decrease in sympathetic activity and a decrease sensitivity to catecholamine’s (adrenaline and noradrenaline). Those catecholamine´s and cortisol, are responsible to redistribute metabolic fuels, maintain blood glucose and enhance responsiveness of the cardiovascular system. A repeated exposure to stress can change the responsiveness, through alterations in neurotransmitter and receptors functions, impairing the behavioral adaptations.

Imbalance of Circulating Amino Acids During exercise there may be a decrease in circulating amino acids (including the branched chain - BCCA´s, isoleucine, leucine and valine) due to oxidation in skeletal muscle to ATP production, while there is the formation of an aromatic amino acid, tryptophan, it binds to albumin in the blood. Free fatty acids may also be oxidized to form ATP (when the muscle and liver glycogen is depleted) and because they are not soluble, they also circulate in the blood bound to albumin. Consequently, there will be a competition for that link - albumin-tripotophan and albumin- free fatty acids (Petibois, Cazola, Poortmans & Deleris, 2002). Tryptophan is the serotonin precursor. As 90% of tryptophan is bound to albumin, and the 10% remaining is free in blood. The more free fatty acids bound to albumin, greater amount of free tryptophan will exist. Tryptophan also competes with BCAA's to pass the blood brain barrier. During physical activity there is a decrease in BCCA's circulating and a greater concentration of tryptophan than BCCA's will take place, thus, tryptophan will have the preference to pass to the brain, and that can result in fatigue of cerebral origin (Budgett, 1998). In the brain, tryptophan acts as a neurotransmitter (5HT), and level changes in that neurotransmitter can provoke overtraining symptoms (see figure 1), causing central fatigue, loss of appetite, affecting sleeping, and even inhibiting the release of factors from the hypothalamus that control pituitary hormones (Blomstrand, 1989; Rang 1987 cited by Budgett, 1998).

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Fernando cited by Budgett, 1998). Rocha, Mário

C. Marques and Aldo M. Costa Exercise

Free fatty acids

Oxidation

Formation

(bind to albumin)

Triptophan (bind to albumin)

Oxidation

BCAA´S

Competition for the link with ALBUMIN

Brain Barrier

Triptophan

Competitive barrier to cross

Triptophan

Triptophan

Brain Barrier

BCCA´S

BCCA´S Triptophan

Acts like a neurotransmissor OVERTRAINING SYMPTONS

that leads to central fatigue. Figure 1. Process Figure 1. Process that leads to central fatigue.

The glutamine, a nonessential amino acid synthesized by isoleucine and valine, very abundant in skeletal muscle, also seems to play a role in the overtraining syndrome. Glutamine can be used for hepatic gluconeogenesis and its main target is the kidneys, where it is used in maintaining the pH balance (Rowbottom, Goodman & Morton, 1995). A negative arterio venous difference in plasma glutamine concentration occurs during prolonged exercise (Graham, 1995) and some evidences shows that, this concentration of amino acid is higher in slow-twitch fibers compared with fast-twitch fibers. Long-duration exercise, with aerobic characteristics and in periods of intense training, the concentration of glutamine rises, decreasing during the recovery period. Since the white blood cells (lymphocytes in particular) cannot synthesize glutamine for energy, being dependent on syntheses and release by skeletal muscles the decrease in glutamine cause a muscle acidosis (cannot do the buffering of hydrogen ions) and provoke a decline in the immune response, especially in overtraining (Gleeson, 2008). Indeed, plasma glutamine has been suggested to be a potential cause of the exercise-induced immune impairment and increased susceptibility to infection in athletes and therefore, as a possible indicator of excessive training stress. However, not all studies have found a fall during periods of increased training and overtraining (Walsh, Blannin, Robson & Gleeson, 1998).

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Cytokine and Inflammation Cytoquines have also been linked to overtraining as these appear to be mediators of this syndrome, a situation justified by the activation of monocytes to produce and release inflammatory cytokines such as IL-1b, IL-6 and TNF-α. Repetitive exercise, high volume and inadequate rest generate a high inflammatory response, which can cause micro-trauma in joints, muscles and connective tissue (Mackinnon, 2000). These cytokines would then initiate a ‘whole-body’ response, involving chronic systemic inflammation, ‘sickness behavior’, suppressed immune function and mood state changes. It has also been suggested that cytokines may activate the hypothalamic-pituitary adrenal axis, and therefore, may underlie the neuroendocrine changes observed in overtrained athletes. With overtraining there are also increases in the plasma concentrations of others substances that are known to influence leukocyte functions (besides the ones that already were pronounced) like the inflammatory cytokines (Mackinnon, 1998b quoted in Gleesson, 2002). It appears that the high release of pro-inflammatory cytokines (interleukins 1, 2 and 6, interferon α, tumor necrosis alfa and protein c-reactive) triggered by the systemic inflammation process – due to excessive training – acts on the central nervous system, changing the hormonal balance. Cytokines also activate the sympathetic nervous system, while suppressing the activity of hypothalamic-pituitary-gonad, and thus responsible for the observed changes in blood concentrations of gonadal hormones and catecholamines, which are present in a state of overtraining athletes (Rogero, Mendes & Tirapegui, 2005).

THE ASSESSMENT OF OVERTRAINING At present, it still is a very hard task to differentiate acute fatigue and decreased performance resulting from isolated training sessions from any overtraining progression states (Halson & Jeukendrup 2004). Additionally, it is also complicated to identify a specific marker that can register difference between the states of overtraining and overreaching. acoording to Meeusen, Nederhof, Buyse, Roelands, Shutter & Piacentini (2010), a keyword in the detection and recognition of the overtraining syndrome may be the prolonged inability to adapt, not only to the level of aspects of athletic performance, but also in relation to other regulatory mechanisms, such as biological mechanisms, hormonal and neurochemicals. The marker of choice for detecting overtraining syndrome should address the following two criteria: (i) the marker should be sensitive to training load and, preferably, should not be affected by other factors such as diet; (ii) changes in the marker value should occur before reaching the state of overtraining syndrome, and responses due to the acute exercise should be possible to distinguish in relation to chronic responses. As this marker would be extremely useful for coaches and athletes, a criterion of easy applicability and low cost also is a point to be fulfilled (Meuseen, 2005), however, so far, the literature does not identify any marker that has all these requirements. The mechanisms that are consistently documented to occur with overtraining and together may provide a significant support to expose the overtraining syndrome, include the list below. (Mackinnon, 2000, p. 503):

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Fernando Rocha, Mário C. Marques and Aldo M. Costa

      

Performance decrements; Reduce ability to performance high intensity exercise; Persistent high fatigue ratings; Decreased maximal heart rate; Changes in blood lactate variables, such as the blood lactate threshold or blood lactate concentration at maximal exercise; Neuroendocrine changes, such as reduced nocturnal excretion of noreepinephrine (Nep); Changes in athletes self-reported indicators of “wellbeing” such as fatigue and quality of sleep

Prevention is a important point in this thematic, therefore, a very well structured planning train is necessary, where coaches and athletes can register and track all adaptations to short and long term training.

Monitoring Performance Identify the prevalence of overtraining is difficult because it requires a long-term monitoring of several athletes from different sports, and on the other hand, the coaches have a great reluctance to identify athletes who are overtrained, but some studies indicate that about 7 % to 20% of athletes in specific individuals active phase of his sports life may have symptoms of overtraining (Hooper, 1993;, 1987; Raglin, 1994). The type of sport most likely to cause overtraining appears to be the endurance modes, where the very intense training volume is more present than those where the strength is the predominant capacity. But in sports like judo and weight-lifting can also occur overtraining symptoms (Callister, Fleck & Dudley, 1990). Meeusen (2005, citing Budgett, 2000; Lehmann, 1999 and Urhausen, 1995) refer that athletes suffering from overtraining syndrome, normally are able to start a regular training sequence at their usual capacities, but they are not capable to complete the training load, so, as mentioned before, one very good indicator is the unexplainable decrease in performance. Of course it is clear that the type of tests should be sport-specific. How to apply it is still involved in academic discussions: maximal or incremental test? Halson (2004) refers that in general, time to fatigue test are more likely to show greater changes in exercise capacity as a result of overreaching and overtraining syndrome than incremental exercise tests, beyond that, allows the evaluation of substrate kinetics, hormonal response and the possibility of setting specific intensities and durations for the collection of sub-maximal results. Meeusen et al., (2010), used a two-bout maximal exercise protocol to objectively and immediately make a distinction between non-functional overreaching and overtraining syndrome in underperforming athletes who were diagnosed with suspicion of non-functional overreaching or overtraining syndrome. With this protocol, they measured physical performance and stress induced hormonal reactions. The protocol was applied with 4 hours of interval, obtaining the following main results: the maximal blood lactate was lower in overtraining syndrome subjects, compared with the non-functional overreaching subjects

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while resting concentrations of cortisol, adrenocorticotrophic hormone (ACTH) and prolactin (PRL) concentrations were higher. However, sensitivity of these measures was low. Both, ACTH and PRL had a higher reaction in the second bout in non-functional overreaching athletes compared with overtraining syndrome and showed the highest sensitivity for making that distinction. This study suggests that using a two-bout maximal exercise protocol can be useful to early detect non-functional overreaching and overtraining syndrome. The authors used the cycle ergometer and the treadmill, obviously, depending on the specific type of athlete/sport to be tested, proving that the specificity of the test may be sensitive not only to variation in performance but also in the variation of other parameters that may be associated with overtraining syndrome. Monitoring sports performance involves having a set of standardized and validated instruments, for the changes over time can be explained. However there are no ways to measure the individual capacity of response or the athelet’s adaptation to exercise/training. For such task we can always use questionnaires, diaries, monitoring physiological parameters or even use direct observation (Borresen & Lambert2009) to track the physiological adaptations of training. The baseline individual data and the need of high standardized conditions are the most frequent problems and represent a limitation for the use of performing test as a detector of overtraining syndrome.

Monitoring Heart Rate Heart rate (HR) appears as one the most preferable indicator for the evaluation of training load response and physical fitness. In addition to HR responses to exercise, research has recently focused on heart rate variability (HRV). HRV is an index of interbeat intervals; the higher the HRV, the higher the cardiovascular autonomic responsiveness (Bosquet et al., 2008), which also means a increase in vagal (parasympathetic) tone relative to sympathetic activity (Uusitalo et al., 2000). It seems that trained individual have higher HRV than untrained individuals. As enunciated in the following texts, both HR and HRV, could potentially play a role in the prevention and detection of overtraining (Achten & Jeukendrup, 2003). Training stress interferes with the autonomic nervous system and therefore with HR. According to Fry et al., (1991), this influence may be one of the reasons why HR is considered an indicator for overreaching and overtraining syndrome. However, the effects of overreaching on submaximal HR are controversial, with some studies showing decreased rates and others no difference. Maximal HR appears to be decreased in almost all 'overreaching' studies, but concerning the HRV, it appears that in overreaching or overtraining there is no differences (Achten and Jeukendrup, 2003) or the one´s are very inconsistent (Uusitalo et al., 2000). Meeusen et al. (2005) underlined the study of Halson et al., 2005, in which they sought to understand the influence of increased training intensity for 7 days (overreaching) on HRV. The results showed a significant effect on HRV values when the intensity of training was intensified. This suggests an increase in the relative contribution of parasympathetic to sympathetic nervous system activity. In a meta-analysis developed by Bosquet et al. (2008), overeaching resulted in a small decrease in the HR measured during submaximal and maximal exercise, together with a small

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increase in the cardiovascular autonomic balance at rest. The decrease in HR in a submaximal effort was more evident during long term increase in training load, suggesting that this marker cannot be used as valid short-term fatigue indicator; it probably suits better for long-term fatigue. The results also show that maximum HR, in addition to identifying the training intensity, can also be a possible indicator of overtraining syndrome, functional overreaching and non-functional overreaching, because it was the only variable that changes with increased training load during short and long term periods. The overall effect size showed only a small increase in resting HR, suggesting that it cannot be a valid indicator for overtraining syndrome or for both states of overreaching. Although, the results also show a moderate increase in resting HR after short term interventions (2 weeks) of increasing training load, but no alterations when the intervention was longer than 2 weeks. According to the authors the increasing in resting HR suggests that this indicator can be used as a valid marker of shortterm fatigue, probably for functional overreaching, but not for a long-term intervention of increasing load (possibly non functional overreaching or overtraining syndrome). Another variable widely used in training is the heart-rate recovery. Bosquet et al. (2008) found no data/studies supported by experimental data that would enable them to make considerations about this parameter, noting that any conclusion about the validity of post-exercise HR recovery as a marker of functional overreaching, non-functional overreaching and overtraining syndrome will be hazardous. In a meta-analysis is important to note that the results are primarily statistical, and in this context Bosquet et al reported that the moderate amplitude of the alterations founded in their research limits the clinical usefulness, as this difference may be justified with the day-to-day variability. Consequently, the correct interpretation of HR or HRV fluctuations during the training process requires the comparison of these markers with other objective signs and symptoms of functional overreaching, non-functional overreaching and overtraining syndrome. Indeed, HR or HRV alone does not provide consistent results due to the difficulty of standardized procedures. Moreover, it seems that it is also difficult to to distinguish between changes in physiological measures resulting from functional overreaching, nonfunctional overreaching and overtraining syndrome (Meeusen et al., 2005).

Immunological Parameters The immune system composed by several white cells seems to be affected with exercise. For instance, leukocytes, whose change in number and its functions are closely correlated with being active. According to Mackinnen (1998b, quoted by Gleeson, 2002) in intense and repeated exercises bouts there is a decreased in the leukocytes ability, suspecting that the change in plasma concentration of hormones such as adrenaline, cortisol, growth hormone and b-endorphin is considered the neuroendocrine cause that leads to immunosuppression induced by exercise (Niemann, 1997).The falls in blood concentration of glutamine as seen before, also seems to be implicated in causing immunossupression associated with heavy training. One situations that is often reported with increased training intensity and the overreaching and overtraining syndrome is the risk of upper respiratory tract infections (URTI) (Niemann, 1997; Mackinnon, 2000; Meeusen et al., 2005), but it has been suggested that the predisposition increase of high performance athletes to URTI is not necessary

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accompanied by a state of overreaching or overtraining syndrome; URTI can be the consequence of an intense workout. In a study using swimmers submitted to training intensified training for 4 weeks, it was found that the high rate of URTI among athletes who have not reached a state of overreaching was a protective factor since, in a way, forced them to reduce the training loads for some time, allowed sufficient rest to prevent overreaching (Jonsdottir et al. in IV International Symposium Exercise and Immunology, 1999 quoted by Mackinnen, 2000). When trying to support the theory that overtraining alone is associated with increased risk of URTI, the studies are inconclusive, but there are good evidence that when the training is truly intense (beyond the limits of an individual athlete capacities and before the presence of some monotony in training), the risk of URTI is increased, suspecting that the overtraining syndrome and URTI may result from a common denominator - excessive training load and insufficient recovery time. How intense training causes an immunosuppression has several explanations. The question of increasing the duration and degree of “open window” is a trigger to induce overtraining syndrome and to infections. On the other hand, when a athlete is subjected to prolonged exercise, there is an increase in neutrophils (bone marrow) and if that training continues to occur over extended time for weeks and/or months, the bone marrow can deplete the ability to neutrophils release, particularly those who have reached a mature state. This decreased number of neutrophils in overtrained athletes may also predispose athletes to infections. During the recovery period after a workout, the number of neutrophils increases, however, the number of lymphocytes decreases and the ratio neutrophil/lymphocyte seems to be a good indicator of stress induced by exercise, and also for the recovery capacity (Nieman, 1998). The normal value of this ratio usually takes 6-9 hours after exercise to be replaced, but if it is a prolonged and intense training, the same ratio can be elevated for 24 hours after exercise (Gleeson, 2002). The same author also claims that using the indicator given by expression of CD45RO+ over the CD4+ cells can indentify overtrained athletes with high sensitivity and specificity. CD45RO+ and CD4+ cells are subsets of T lymphocytes, changing both with exercise or training. CD45RO cells are markers of T-memory cells and T-cells activators; therefore, an expression of CD45RO on T-cells may be only an indicator of the presence of an acute infection, which may be a possible cause of underperformance (Meeusen et al., 2005). Another changing in the leukocytes function with intense training is the ratio CD4+/CD8+ (helper/suppressor). This ratio under conditions of intense training falls, but it seems not be different enough in overtrained athletes when compared with healthy or well trained athletes (Gleeson et al, 2005). Regarding the number and functions of natural killer cells (NK, CD16*/CD56*) the percentage and number are normal in athletes, although their cytotoxic activity at rest may be higher in athletes than in non-athletes (Nieman, 1995, cited by Mackinnon, 2000). Nieman and coworkers in Suzui et al., (2004) reported that NK cell cytolytic activity was greater in marathon runners, rowers, and active elderly than in untrained individuals, although there were no intergroup differences in NK cell count. Such results suggest that chronic exercise increases cytotoxicity per NK cell. On the other hand, many studies have failed to establish positive relationships between NK cell cytolytic activity and chronic exercise (Boas et al, 1996; Shepard, 1997; Shepard and Shek, 1999, cited by

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Suzui et al., 2004). Acute exercise temporarily increases NK citolytic activity (Gleeson, 2002) but decreases after exercise, usually for no more than a few hours (Suzui et al., 2004). Natural killer cells citotoxic activity have been shown to sensitive increases in the training load in already well trained athletes, however, it is not possible to identify differences between overtraining and healthy athletes (Verde, Thomas & Shepard,., 1992). Mackinnon says that in terms of functionality and number of NK is not possible, yet, to distinguish a state of overtraining and overreaching, but during periods of intense training the number of NK cells may decrease: a military study found that for 10 days of intense workout (running) the number of CD56* cells decreased more than 40%, and these values remained low for 5 days of light training for recovery (Fry et al., 1992).In another study whose reference is direct to the number of NK and citotoxic activity, it is reported that both conditions (number and activity) after an intense workout plan for 4 weeks in swimmers, are lower. A special feature of this intervention is that athletes who reported these results are not achieved during the four weeks a state of overreaching (Gedge, Mackinnon & Hooper, 1997). For competitive athletes who often train twice a day is possible that the number of NK cells and their function need more time to recover, since it is reported that intense and prolonged training sessions has a transient response of suppression of citotoxic activity of NK cells, which may take at least 6 hours, and perhaps can reach 12 hours to normal activity (Nieman et al., 1995, Mackinnon et al., 1997 as cited in Mackinnon, 2000). That downregulates of Nk function, seem to support the “open window” theory, whereby some athletes become susceptible to upper respiratory infections (URTI) for a brief period after heavy exercise (Pedersen and Ullum, 1994, as cited in Suzui et al., 2004). Another important parameter in relation to the immune system is the concentration of immunoglobulin A (IgA), which also constitutes a barrier to infectious agents in the body, particularly against pathogens that cause URTI. IgA is found in external secretions (eg. mucous, saliva, tears), and with intensified training, it seems that the concentration of IgA falls, continuing low after several hours in the recovery period (Nieman, 1997). Some studies documented a negative relationship between salivary IgA and the concentration and occurrence of URTI: for example, lower IgA levels early in the training season have been correlated with the number of URTI episodes throughout the season (Mackinnon, 2000). Low levels of IgA have been reported in overtrained athletes (Mackinnon, 1996, as cited in Gleeson, 2002) demonstrating that monitoring salivary IgA may be useful in indicating overtraining, although the inter individual variation in salivary IgA is quite large. It seems that the immunity system is fairly sensitive to intense training, and although it is not possible to distinguish those alterations (mainly in functions and not in numbers) from the well trained stage (overreaching) and a maladaptation state (overtraining). Like for all stress parameters that we have referred before is important to establish a reference/normal value for the each individual athlete. Given the biological variability, comparing values between individuals seem not a sufficiently reliable method. Moreover, testing immunological markers for overtraining, despite being wrapped up in lengthy and costly process, the data presented in the scientific literature are inconsistent, which leads us to use and analyze these markers with relative care.

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Hematological Parameters The values of hematological parameters are affected by a number of factors even in apparently healthy populations. These factors include age, sex, ethnic background, social, nutritional and environmental factors, and it has been shown in several studies that some of the hematological parameters exhibit considerable variations at different periods of life (ElHazmi & Warsy, 2001). For the overtraining syndrome and overreaching a variety of responses to the increased training load have been studied in an attempt to achieve a reliable indicator for those two states. Mackinnon et al. (1997, quoted by Silva, 2006) refers that the hematocrit tends to decrease with overtraining in swimmers after two and four weeks of gradually increasing the training volume. In the same study, the amount of red blood cells also decreased with overtraining after four weeks of increased training volume. The reduction was about 8-12%, very similar to the decline in magnitude to the concentration of hemoglobin in amounts of 59%. Likewise, according to Silva, (2006) others authors (Newhouse and Clement, 1988; Smith, 1995) also reported a reduction concentration of red blood cells and hemoglobin after high intensity training. The concentration of plasma glutamine has been suggested as a possible indicator of excessive training stress (Rowbottom et al., 1995, as cited is Meeusen et al, 2005; Rowbottom et al, 1996, as cited in Gleeson, 2002). During periods of high demand of immune system a increased production of glutamine is observed, but during prolonged period of training, glutamine levels fall - the same don´t happen in short and intense training bouts – the same decrease of glutamine can also been seen during the existence of physical trauma, burns, inflammations and infections (Walsh et a.l, 1998;Gleeson, 2002). Because of these changes in glutamine levels and also because of its relationship with the immune system, it has been associated this amino acid with the overtraining. Halson, (2004) refer Parry-Byllings et al. (1992) where they reported a lower plasma glutamine concentrations in 40 athletes diagnosed as overtrained when compared with controls; the same study reported lower increased glutamate levels in overtrained athletes. We refer the glutamate because the mechanisms behind the performance decrements associated with overtraining are unclear, and a combination of a number of markers is needed for early diagnosis. Smith and Norris (2000), in an attempt to track the training tolerance through the glutamine and glutamate concentrations founded changes in the plasma glutamine/glutamate ratio (Gln/Glu) suggesting this ratio as a predictor of overreaching or overtraining in athletes. They also observed a elevated plasma glutamate and hence a reduced Gln/Glu ratio in athletes who were classified as overtrained. However, no studies have investigated changes in glutamine, glutamate, and reported concurrent performance measures during a period of intensified training that has resulted in overreaching (Halson et al., 2003) Although not all studies have found a fall during periods of increased training and overtraining, plasma glutamine may provide a useful biochemical marker of overtraining, but since plasma glutamine level is influenced by short term exercise, nutritional status, diet, infection and physical trauma, it is important that standardized evaluations of these parameters are taken into account (Walsh et al., 1998; Gleeson, 2002). Other marker that seems to be also a help to detect overtraining is urea. Urea is an end product of the degradation of nitrogenous or protein materials. Measurements recorded in the field as a component of the training program represents the concentration of serum urea (can

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be regarded as equal to plasma), i.e., balance of urea synthesized in the liver and urea excreted renally (Hartmann & Mester, 2000), briefly, the extent of protein degradation can be very helpful to the coach. An increase in exercise can promote an increase in serum urea value, but the conclusion of catabolic/metabolic activity does not automatically result from such elevated values. If those increased values are associated with a reduced exercise tolerance after a long phase of intensive physical effort, the possibility of a catabolic/metabolic activity or insufficient exercise tolerance becomes much more likely (Hartmann & Mester, 2000). According to the same investigators, it is only appropriate to speak of a catabolic/metabolic activity only when serum urea levels are elevated for 2-3 days. If the levels remain high for more than 3 to 5 days, it may be suspect a massive loss of protein. In Halson et al., (2003) study, plasma urea concentration tended to be slightly elevated during a intense training period, and also declined to pre-intensive training levels after recovery, which means that elevation is temporary, and the concentration of urea is markedly influenced by the recent diary protein intake, so serum urea hardly fulfill a reliable indicator for the onset of overtraining (Gleeson, 2002). Other parameter very associated with muscle function is the creatine kinase, an enzyme present in the blood when the muscle cell membranes are damaged as a result of an intense muscle contraction, and that is often used as an indicator of muscle damage (Diaz, Ruiz, Hoyos, Zubero, Gravina, Gil & Irazusta 2010). One consequence of the high level of creatine kinase in plasma is the temporary decline in athletic performance, probably caused by muscle aches, muscle stiffness, decreased range of motion, changes in lactate concentrations, loss of strength and decrease in the maximum dynamic power (Jones, Newham, Round & Tolfree, 1986). Although Gleeson (2002) noted that in highly trained athletes the eccentric work does not cause large increases in creatine kinase activity, yet, athletes experience muscle aches. The same author quoting O´Reilly et al. (1997), also notes that once installed elevated creatine kinase levels in the body impairs the re-synthesis of muscle glycogen, resulting in decrease performance. Halson et al (2003), in the same study that evaluated the serum urea, found that plasma creatine kinase activity was significantly elevated during the intensive training period, and also returned to baseline levels during recovery. Regarding the activity of creatina kinase, it seems that the characteristics of the effort, intensity and volume, are both important, since they have an influence on the reduction of high-energy phosphate in muscle cell. Creatine kinase seems to have the highest concentrations in men than in women, probably due to the influence of sex hormones, the greatest resistance to muscle cell damage, or simple due to a smaller amount of this enzyme in women (Hartmann & Mester, 2000). There is another peculiarity of creatine kinase, which is the inter and intra-individual variability. As mentioned by Hartman and Mester, there are athletes who have low levels of creatine kinase at rest, others a mean value and there are still those who have high values when compared with normal values. In this study, athletes who had chronic low values of creatine kinase have demonstrated a lower variability, whereas those with chronically higher values exhibited considerable variability of this parameter. This information becomes important for the coach, since he can better tailor training schemes to a more individualized intervention. As noted for serum urea, increases in creatine kinase concentrations at rest when measured on standardized conditions, can provide a set of information concerning an elevated

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muscle and /or metabolic strain, but they are not suitable to indicate an overreaching or overtraining state (Urhausen et al., 1998a, as cited in Meeusen et al., 2005). Another proposed marker of overtraining is a paradoxical decrease in plasma lactate levels in submaximal and maximal exercise. While lower lactate levels during submaximal exercise generally indicate improved endurance capacity (Foster et al., 1988; Jacobs, 1986 as cited in Jeukendrup and Hessellink, 1994), in overtraining, paradoxically lower maximal and submaximal lactate values have been reported (Jeukendrup et al., 1992; Lehmann, 1988 as cited in Jeukendrup and Hesselink, 1994). This has been explained on the basis of low muscle glycogen levels, a decreased catecholamine response to exercise or decreased muscle tissue responsiveness to the effects of catecholamines (Jeukendrup et al., 1992, as cited in Gleeson, 2002). Associate ratings of perceived exertion with lactate values, seems to be a possible way to distinguish the state of training with overtraining. This is supported by the explanation that for a given exercise intensity, a decrease in blood lactate concentration is accompanied by a increase of rating of perceived exertion during overtraining, while ratings of perceived exertion remains unchanged or decreases when the athlete is tested during intensive training (Snyder et al., 1993, as cited in Bosquet et al., 2000). So, the blood lactate /rating of perceived exertion quotient would be expected to decrease with overtraining, but stay relatively the same with intensive training. This theory seems to be true in overreaching athletes, but never has been tested with overtraining athletes. Bosquet et al., (2000) in a study with the objective of determine if it is possible to disassociate the changes in the lactate curve brought about by training and overtraining, with the hypotheses that overtraining would result in a decrease in the blood lactate /ratings of perceived exertion quotient after an overtraining period and a 2 weeks recovery period. The study showed that rating of perceived exertion does not provide useful information for detecting overtraining during an incremental test. Therefore, the proposed ratio is not a better marker for overtraining than blood lactate alone. Another interesting fact was that the authors noted that the right shift of the curve of lactate was accompanied by a decrease in the peak blood lactate when there was a decrease in performance capacity, which remained after the 2 recovery weeks when athletes were in overtraining, but not when they were in overreaching. Following this observation, Bosquet et al. propose to retain a decrease in the peak blood lactate as a marker of overtraining in events of long duration, and repeating its measurement after a sufficient period of rest to make the distinction with overreaching. Beyond what already mentioned, the literature presents some limitations as to lactate be a marker for overtraining - lactate differences are sometimes subtle (lying within the measuring error of the apparatus) and depend on the modus of the exercise test used; and no lactate changes reported in strength athletes (Meeusen et al., 2005).

Hormonal Parameters Halson et al, (2000, cited by Urhausen et al., 1998) reported that in overtraining endurance athletes there was no significant changes in cortisol, normal vs overtrained athletes, when subjects were examined prior to and during a state of short-term overtraining, however, maximal cortisol response appear to be reduced during overtraining. Compared to

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testosterone, some studies are contradictory, Urhausen referring to Flynn et al., (1994), indicates that after intensive training, a decrease in testosterone levels may be found, which can also coincide with a decrease in performance capacity. Vervoorn et al., (1991, as cited in Halson and Jeukendrup, 2004) obtained the same decrease in testosterone concentrations, however, no significant loss of performance was obtained. The ratio testosterone/cortisol is referred in some studies as a marker of the anbaboliccatabolic balance, which can be a tool for the diagnosis of overtraining syndrome (Adlercreutz et al., 1986, as cited in Urhausen and Kindermann, 2002). Cortisol and testosterone are both released in response to high intensity (>60% maximal oxygen uptake, VO2max) aerobic and anaerobic exercise and is believed that this ratio decreases in relation to the intensity and duration of training , and it is the indicator of the positive and negative effects of training due to the opposing effects that hormones have on growth, protein synthesis and muscle metabolism (Kreider et al., 1998 as cited in Halson and Jeukendrup, 2004). This ratio only indicates the actual physiological strain of training and cannot be used for diagnosis of overreaching or overtraining syndrome (Lehmann et al., 1995; Meeusen, 2005 quote Urhausen et al., 1995; Meeusen et al., 2004) because the ratio has been shown to remain unchanged in overreached athletes, although, a decrease ratio has been reported in athletes who show no performance decrements after intensive training (Vervoorn et al., 1991, as cited in Halson and Jeukendrup, 2004). The decrease in nocturnal urinary excretion of catecholamines has been suggested as sign of an advance state of overtraining syndrome, in overtraining athletes, and has also been interpreted as low intrinsic sympathetic activity (Lehmann et al., 1992, Mackinnon et al., 1997, as cited in Urhausen and Kindermann, 2002). This excretion appears to be lower than normal in overtrained athletes (Foster and Lehmann, 1999, as cited in Gleeson, 2002) indicating a negative correlation with fatigue ratings. Catecholamine levels in urine and plasma can reflect the activity of the sympathetic nervous system and can, therefore, examine the possibility of parasympathetic-sympathetic imbalance or autonomic imbalance (Halson et al., 2003). Lehmann et al., (1997) reported that athletes after been submit a intensive ergometer training, revealed 60% higher pituitary adrenocorticotropic hormone (ACTH) release to corticotrophin releasing hormone, which was also followed by a decrease of about 25% of adrenal cortisol release. Barron et al, (1985, as cited in Gleeson, 2002) have also presented evidence of an adrenocortical deficiency in athletes suffering from overtraining syndrome. They found that growth hormone, prolactin and ACTH responses to insulin-induced hypoglycaemia (a potent stimulus to sympathetic nervous activity) were lower in a small group of overtrained athletes compared with healthy well-trained controls. Urhausen et al, (1998, as cited in Halson and Jeukendrup, 2004), reported lower resting ACTH levels and lower exercise-induced ACTH release in overreached athletes. A reduced maximal plasma growth hormone (GH) concentration was also reported. In order to protect the target organs of inadequate or pathological loads during the process of overtraining, the body has several adaptations, and one that appears to prevent a catabolic state is a slight increase in pituitary sensitivity to GHRH (increased release of GH), which is more anabolic (Lehmann et al., 1993). This also happened in response to the unchanged testosterone/cortisol ratio.

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About the limitations for the use of hormonal markers to identify overtraining, the baseline data also appears a problem to avoid; it is needed to have a baseline measure to allow the comparisons among different stages/periods for the same athlete. Moreover, nutrients through the food intake can change the concentration of some hormones at rest and/or in response to exercise (Meeusen et al., 2005). The same author also highlighted that the concentrations of blood hormone are linked with the sample conditions, i.e., conservation and the assay variability.

Psychological Parameters Is clear the multifactorial etiology of the overtraining process, and several methods using physiological markers, hematological or immunological parameters have been used, however, Shepard &Shek (1994) show that the psychological evaluation is an easier and less expensive to detect the overtraining syndrome. In fact, there is a general agreement that the overtraining syndrome is characterized by psychological disturbances and negative affective states (Hooper et al., 1997, as cited in Halson and Jeukendrup, 2004). O´Connor (1998, as cited is Kentta and Hassmén, 1998) identify four advantages for using psychological markers: 1) Psychological changes are more reliable, i.e. mood shifts coincide with increases and decreases in training and are also highly replicable; 2) Some mood states are highly sensitive to increases in the training load (changes in these states occur early on and have large effects) while others are more sensitive to the staleness (overtraining) syndrome; 3) Variations in measures of mood often correlate with those of physiological markers; and 4) The titration of training loads based on mood responses to overtraining appears to have good potential for preventing overtraining. On the other end, psychological testing may reveal early-warning signs more readily than the various physiological or immunological markers (Kenta & Hassmén, 1998). The great advantage of psychometric instruments is the quick availability of information (Kellmann, 2002, as cited in Meeusen et al., 2005), therefore, some questionnaires such has the “Profile of Mood State” (POMS) (Morgan et al., 1988; Raglin et al., 1994; O´Connor, 1997; O´Connor et al., 1989; Rietjens et al., 2005, as cited in Meeusen et al., 2005) the “Recovery – Stress Questionnaire” (RestQ-Sport) (Kellmann, 2002, as cited in Meeusen et al., 2005), the “Daily Analysis of Life Demands of Athletes” (DALDA) (Halson et al., 2002, as cited in Meeusen et al., 2005), and the “Self-condition Scale” (Urhausen et al., 1998b, as cited in Meeusen et al., 2005) have been used to monitor psychological parameters in athletes. Although these questionnaires give a set of information that can predict a state of overreaching or overtraining syndrome, the results should not be interpreted without an association of decreased performance measurements. It has been said that sometimes there is confusion between burnout and overtraining, where the first is a sequel to the second. In psychological terms, it is necessary to treat the two situations differently, once an athlete burnout, have their motivation levels far below, and this is a central issue of burnout. Overtrained athletes may be highly motivated at the point of increasing their levels of training/load in order to try to reverse the decline in performance. Once again we present the limitations of use of questionnaires to assess psychological parameters/mood changes described in the Overtraining Position Statement, Task Force (Meesen et al., 2005): the application of the questionnaires must be in a well standardized

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conditions to avoid different in mood; the timing is also important for example, normally during the day the mood in the morning, afternoon and evening changes. Also in this parameter the measures of an individual must be compared with baseline data and the questionnaire as an instrument must detect the influenced in the results of other psychological parameters independently of moods state

PREVENTION AND TREATMENT OF OVERTRAINING SYNDROME Scientific research in the field of sports science has been keen on getting a set of effective strategies that promote physical recovery, indicating that talent is not enough to ensure success in sports. The concept that body needs a recovery time to adapt to the load of training and/or competition is not new, however, athletes and coaches often rely on the empiricism to act at this stage of training. Before one can develop a recovery strategy, it must be define what kind of fatigue we want to reverse. Because fatigue is a multifactorial condition, the type of stimulus induced will define the forms of fatigue that may manifest itself (Gleeson, 2002). The issue focuses on the occasion (the shortest amount of time) that a new load of training or competition can be applied, causing the athlete benefits from previous load. This requires that the same athlete is subjected to a recovering method at the point of fatigue that allows having a recovery time of the stimulus that was submitted, avoiding overtraining and all its negative repercussions on athletic and sports performance. Avoiding / preventing is the primary measure in the fight against the overtraining syndrome. Uusitalo (2001) refers that because there is still a huge difficulty in diagnosing the overtraining syndrome and distinguish it from a state of overreaching, it is best to prevent that overtraining happen, and the first step is to understand the fundamental principles of progressive load before of understanding the significance of recovery (Grantham, 2006, p. 12):      

Training is designed progressively to overload body systems and stores; If the training stress is insufficient to overload the body´s capabilities, no adaptations will occur; If the workload is to great (progressed too quickly, performed too often without adequate rest), then fatigue follows and subsequent performance will be reduced; Work alone is not enough to produce the best results; it takes time to adapt to training stress; To encourage adaptation to training, it is important to plan recovery activities that reduce residual fatigue; The sooner the recovery from fatigue, and fresher when undertake a training session, the better the chance of improving.

Figure 2 (based on Grantham, 2006) shows the principle of progressive overload, where it enters a recovery strategy in the fatigue point where it will decrease the length of time it will take to recover from training.

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Figure 2. Progressive overload.

As in Grantham manuscript (2006), the broken line represents the gain in the recovery time, and the light gray shaded area, the opportunity window to applied another training/competitive load, which will be sooner than if it hadn’t been a training unit for recovery. To achieve the situation described above is necessary to have a training plan, based on periodicity. Periodicity means that loads are given in an appropriate stimulus, followed by periods of recovery. This will also decrease the monotony of training (Uusitalo, 2001) and allow taper. Taper is a type of reduced training, which is implemented to maintain fitness and skill levels. Taper is NOT de-training and is needed to reduce the residual effects of fatigue resulting from training and peak performance will occur at a point where fitness and fatigue differences are maximized (Banister & Calvert, 1980). Training and recovery must be in balance to prevent non-functional overreaching and overtraining syndrome, and for that, it is very important that coaches and athletes make the registration of the load applied in practice/training session and total week training using a training log. The four methods, most frequently used to monitor training and prevent overtraining are: retrospective questionnaires, training diaries, physiological screening and the direct observational method (Hopkins, 1991, as cited in Meeusen et al., 2005). Also the psychological screening of athletes (Berglund & Safstrom, 1994, as cited in Meeusen et al., 2005; Hooper et al., 1995, as cited in Meeusen et al., 2005; Hooper & McKinnon, 1995, as cited in Meeusen et a.l, 2005; McKenzie 1999, as cited in Meeusen et al., 2005; Raglin et al., 1991, as cited in Meeusen et al., 2005; Urhausen et al., 1998b, as cited in Meeusen et al., 2005; Morgan et al., 1988, as cited in Meeusen et al., 2005; Kellmann, 2002, as cited in Meeusen et al., 2005; Steinacker et al., 2002, as cited in Meeusen et al., 2005 ) and the Ratings of Perceived Exertion (RPE) (Acevedo et al., 1994, as cited in Meeusen et al., 2005; Callister et al., 1990; Foster et al., 1996, as cited in Meeusen et al., 2005; Foster, 1998, as cited in Meeusen et al., 2005; Hooper et al., 1995; Hooper & McKinnon 1995, as cited in Meeusen et al., 2005; Kentta & Hassmen 1998; Snyder et al., 1993, as cited in Meeusen et al., 2005) have received more and more attention nowadays. So that the records of training load were objectives, Foster et al, (1996, 1998, as cited in Meeusen, 2005), have determined training load as the product of the subjective intensity of a

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training session using ‘session RPE’ and the total duration of the training session expressed in minutes. If these parameters are summated on a weekly base it is called the total training load of an individual. The ‘session RPE’ has been shown to be related to the average percent heart rate reserve during an exercise session and to the percentage of a training session during which the heart rate is in blood lactate derived heart rate training zones. With this method of monitoring training they have demonstrated the utility of evaluating experimental alterations in training and have successfully related training load to its performance. But, as training load are clearly not the only parameters that influences overtraining syndrome, the same investigators additionally to the weekly training load, daily mean training load as well as the standard deviation of training load were calculated during each week. The daily mean divided by the standard deviation was defined as the monotony. The product of the weekly training load and monotony was calculated as strain. The incidence of simple illness and injury was noted and plotted together with the indices of training load, monotony and strain. They noted the correspondence between spikes in the indices of training and subsequent illness or injury and thresholds that allowed for optimal explanation of illnesses were computed Kentta and Hassmé (1998), attest that there are many methods used to measure the training process but few with which to match the recovery process against it. One such framework for this is referred to as the total quality recovery (TQR) process. By using a TQR scale, structured around the scale developed for ratings of perceived exertion (RPE), the recovery process can be monitored and matched against the breakdown (training) process (TQR versus RPE). The TQR scale emphasizes both the athlete’s perception of recovery and the importance of active measures to improve the recovery process. Furthermore, directing attention to psychophysiological cues serves the same purpose as in RPE, i.e. increasing selfawareness, as opposed to relying on physiological cues alone. The TQR has a correspondence with the RPE, and is divided into two subscales, one more subjective (perception) and other more objectives (action). The idea is to integrate quantitative and qualitative aspects of overtraining syndrome, in order to speed up the recovery process with interventions and strategies that are optimized for one particular stimulus. In order to best overcome and prevent the overtraining is necessary to take into consideration that the more intense the training, the greater the breakdown. High intensity training therefore demands higher quality recovery than low intensity training. Consequently, high intensity training also demands a longer recovery period than low intensity training. The athlete undertaking high intensity training would therefore benefit from high quality recovery more than an athlete undertaking low intensity training (Kentta and Hassmén, 1998) and the whole process of recovery depends, among other factors (gender, age, level of experience, weather…) of the energy system used. Bompa and Cornacchia (1998), recommend the following recovery time (see table 3). According to Terjing and Hood (1988, cited by Bompa and Cornacchia, 1998), reported that to overcome the effects of short-term overtraining, the sessions should be discontinued for 3-5 days. After this rest period the training should be lowered, alternating one day of training and one day of rest. If overtraining is more severe and the initial rest period is longer, then for each week of rest will require two weeks training for the athlete reach his prior fitness state.

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Table 3. Suggested recovery time after intense training Recovery Process ATP/CP restoration Restoration of muscle glycogen After prolonged exercise After intermittent exercise (weight training) Renewal of blood and muscle lactic acid Restoration of enzymes and vitamins Recovery of strength training of high intensity (metabolic supercompensation and CNS) Payment of alactic O2 debt Payment of the lactic debt.

Recovery Time 3-5 minutes 10-48 hours 24 hours 1-2 hours 24 hours 2-3 days 5 minutes 30-60 minutes

Table 4. Type of fatigue and how they occur Occurs as a result of …  High volume training  Repeated workloads  Aerobic/anaerobic conditions  Mmultiple training sessions throughout day Tissue damage  Plyometrics  Eccentric loading  Contact sports Neurological  High intensity work (peripheral nervous system)  Resistance training (strength and power development)  Speed work  Skill sessions and introduction of new training techniques Psychological  Training monotony (central nervous system and  Lifestyle issues emotional fatigue)  Heavy game/competition/training periods  Pressure plays (training simulating match conditions  New training techniques Environmental  Hot and cold environmental  Travel (local, national, international)  Time differences  Competitions Reference: based on Grantham, 2006. Type of Fatigue Metabolic (energy stores)

It is important to take notice about the type of training effort, because this is what will determine which forms of fatigue an athlete will experience (Calder, 1996, as cited in Grantham, 2006, p.2). Table 4 illustrates the various types of fatigue (according to Grantham, 2006). The author, define an order in which recovery strategies should be applied. He called it “the recovery pyramid”. The pyramid is composed of four levels, namely:

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Fernando Rocha, Mário C. Marques and Aldo M. Costa    

Level 1 (base) – covers the rest (passive and active), sleep and nutrition (refueling and rehydratation); Level 2 – covers periodisation (training changes), reactive programming, cooldown, stretching); Level 3 – encompasses recovery pool work, compression skins, ice baths, massage, contrast bathing; Level 4 – is responsible for strategies that involve psychological/environmental (flotation tanks, etc), omega wave, integrated approach with individual focus.

The list is not exhaustive, and strategies at levels 3 and 4 should not form part of the equation until and unless the basic (levels 1 and 2) has already an established regime; first we should look for the simple intervention, sleep, nutrition and training. The repair of damage muscle tissue is in the category of short-term overtraining, requiring 5-7days to complete the process, while the total regeneration of muscle tissue takes approximately 20 days (Ebbing and Clarkson, 1989, as cited in Bompa and Cornacchia, 1998). The recovery of muscle damage in the acute phase is best treated with ice, elevation, compression and passive and active rest, depending on the degree of injury. After three days you can begin to introduce other methods of therapy such as massage. Alternate hot and cold can also be an effective way to decrease the stiffness associated with muscle damage caused by exercise (Cornheim, 1988, as cited is Bompa and Cornacchia, 1998; Prentice, 1990, Bompa and Cornacchia, 1998). The diet has a direct connection with the overtraining because it can be an important factor in the recovery of muscle tissue. Carbohydrates are essential to maintain muscle glycogen levels during intense training and become crucial in intense high-volume workouts, because the glycogen is the primary source of stored energy in the muscles being used. After exercise, in the first 30 min there is a window of opportunity to replenish muscle glycogen, this windows is caused by insulin-like effect of exercise which lasts for some time after exercise. If this time is spent with the consumption of carbohydrates, the replacement will happen much faster than if the intake of carbohydrates happen later. This action is sufficient to prevent the non-functional overreaching and give to athlete the opportunity to get the most out of the training, as showing in the figure 3 (based on Saunders, 2009). The effects of protein intake seems to add some benefits in the recovery process, especially when it is mixed with carbohydrates, however, there is no consensus regarding the role that protein play in the overtraining. Some studies have shown faster rates of muscle glycogen replenishment when carbohydrate-protein is consumed immediately following endurance exercise, compared to carbohydrate alone; a better protein balance, increasing protein synthesis and decreased protein breakdown; improvements in some muscle damage markers, resulting in lower blood creatina kinase, less muscle soreness and improve muscle function. But not all studies have shown significant improvements in subsequent performance following carbohydrate-protein intake. However, the positive effects of protein seem to appear more regularly in the studies that provide the more demanding training/recovery periods. So, it also seem that the longer and harder is the training, the more important the details of the recovery nutrition, including the inclusion of protein, become.

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Figure 3. Effects of carbohydrate intake during intensified training.

CONCLUSION The overtraining is a challenge for coaches, athletes and researchers, since there isn’t, yet, a valid and reliable instrument for diagnosing it. This syndrome is wrapped in a set of situations that can lead to a difficult, time consuming and tricky diagnosis. So far the best possible diagnosis is by exclusion of diseases that can mask the overtraining. The targets for diagnostic markers are lacking, although some such as heart rate variability, the perception of mood changes and feelings/self awareness of the athletes is a promising diagnostic tools. Until further studies reveal specific indicators, confirming the effectiveness of physiotherapists and psychotherapists interventions as a treatment, prevention is still the best cure.

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Richardson, S. O., Andersen, M. B., & Morris, T. (2008). Overtraining athletes: personal journeys in sport. Champaign, IL: Human Kinetics. Rogero, M. M., Mendes, R.R., & Tirapegui, J. (2005). Aspectos Neuroendocrinos e Nutricionais em atletas com overtraining.Arquivo Brasileiro de Endocrinologia e Metabolismo, 49(3), 359-368. Rowbottom, D., Goodman C., & Morton, A. (1995). The haematological, biochemical and immunological profile of athletes suffering from the overtraining syndrome. European Journal of Applied Physiology, 70, 502-509. Saunders, M. (2009). Recovery nutrition: why it´s a potent weapon against overtraining. Peak Performance, 276, 1-4. Shepard, R., J., & Shek, P. N. (1994). Potential impact of physical activity and sport on the immune system: a brief review. Brish Journal of Sports Mdicine, 28, 347-355. Silva, A., Santiago, V., & Gobatto, C. (2006). Understanding overtraining in sports: from definition to treatment. Revista Portuguesa de Ciências do Desporto, 6(2), 229-238. Small, E. (2002). Chronic musculoskeletal pain in young athletes. Pediatric Clinical North American, 49, 655-662. Steinaker, J. M., & Lehmann, M. (2002). Clinical findings and mechanisms of stress and recovery: preventing underperformance in athletes (pp. 103-118). Champaign, IL: Human Kinetics. Suzui, M., Takeshi, K., Kimura, H., Takeda, K., Yagita, H., Okumura, K., Shek, P. N., & Shepard, R. J. (2004). Natural killer cell lytic activity and CD56dim and CD56bright cell distributions during and after intensive training. Journal of Applied Physiology, 96, 21672173. Urhausen, A., & Kindermann, W. (2002). Diagnosis of Overtraining.What Tools Do We Have? Sports Medicine, 32(2), 95-102. Uusitalo, A. (2001). Overtraining. Making a difficult diagnosis and implementing target treatment. The Physician and Sportsmedicine, 29, 35-50. Uusitalo, A.L., Uusitalo, A. J., & Rusko, H. (2000). Heart rate and blood pressure variability during heavy training and overtraining in the female athlete. International Journal of Sports Medicine, 21, 45-53. Verde, T., Thomas, S., & Shephard, R.J. (1992). Potencial markers of heavy training in highly trained endurance runners. British Journal of Sports Medicine, 26, 167-175. Walsh N., Blannin A., Robson P., & Gleeson M. (1998). Glutamine, exercise and immune function: links and possible mechanisms. Sports Medicine, 26, 177-191.

In: Athlete Performance and Injuries Editors: João H. Bastos and Andreia C. Silva

ISBN 978-1-61942-658-0 © 2012 Nova Science Publishers, Inc.

Chapter 3

EVALUATING THE DYNAMIC MODEL OF PSYCHOLOGICAL RESPONSE TO SPORT INJURY AND REHABILITATION Diane M. Wiese-Bjornstal*, Courtney B. Albinson, Shaine E. Henert, Elizabeth A. Arendt, Susan J. Schwenz, Shelly S. Myers and Diane M. Gardetto-Heller University of Minnesota, Twin Cities, Minneapolis, MN, US

ABSTRACT Authors of the integrated model of psychological response to the sport injury and rehabilitation process (Wiese-Bjornstal, Smith, Shaffer, & Morrey, 1998) conceptualized sport injury as influenced by preinjury psychosocial factors (Williams & Andersen, 1998), acting as a negative life event stressor, and comprising a dynamic process of ongoing cognitive appraisals influencing emotional and behavioral responses affecting recovery outcomes (Wiese-Bjornstal, 2009; 2010; Wiese-Bjornstal, Smith, & LaMott, 1995). The purpose of this project was to simultaneously examine these three primary model components and associated predictions while controlling for within team and school-related factors through repeated measures sampling of injured and noninjured teammates. Within a prospective mixed factorial study design, NCAA Division I male and female athletes (N = 74) from four sports (women’s softball, track and field, and tennis, and men’s baseball) completed multiple psychosocial measures at repeated time points from baseline to postseason. Results supported (a) the ability of psychosocial variables to predict sport injury, (b) conceptualizing sport injury as a stressor, and, (c) the role of affect as a precursor and response to sport injury. A unique aspect to this study was reflected in the matching of psychological data from injured and noninjured teammates during the specific weeks in which injuries occurred, thus controlling for noninjury related factors such as team and school related variables that may have influenced the mood state and life event stress of all athletes on the teams aside from injury. * Correspondence concerning this article should be addressed to Diane M. Wiese-Bjornstal, School of Kinesiology, Cooke Hall, University of Minnesota, 1900 University Ave. SE, Minneapolis, MN 55455. E-mail: [email protected].

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Diane M. Wiese-Bjornstal, Courtney B. Albinson, Shaine E. Henert et al. Furthermore, this study lends support to the idea that negative mood states are not only responses to but also risk factors for sport injury, and thus provides grounding for identifying psychological interventions to ameliorate negative moods.

Keywords: intercollegiate athletes, sports medicine, sport psychology, mood state, life event stress

INTRODUCTION Once thought of in purely physical terms, interest in sport injury has long since moved into the psychosocial domain. Conceptual models of sport injury have focused on preinjury and postinjury dimensions. From a preinjury standpoint, the conceptual model of stress and athletic injury developed by Andersen and Williams (1988) has generated a significant amount of interest in psychosocial factors influencing injury vulnerability. This model provides a framework for the prediction and prevention of sport injuries and includes personality, stress history, and coping resources variables that may influence the occurrence of injury through the mechanism of the stress response. Andersen and Williams proposed that an athlete who has a lot of stress in his or her life, who possesses personality characteristics that tend to exacerbate the stress response, and who has few coping resources will, when placed in a potentially stressful situation, be more likely to appraise the situation as stressful and exhibit greater muscle tension and attentional disruptions. Considerable research support has been obtained for this preinjury stress model, particularly with respect to the history of stressors and coping resources portions (see Williams & Andersen, 1998 for a review). Other researchers have looked into to the postinjury psychosocial processes occurring among athletes after they have sustained sport injuries. A stress process based model of response to sport injury was first developed by Wiese and Weiss in 1987, who conceptualized sport injury as a stressor and the recovery from sport injury as a dynamic process of cognitive appraisals and emotional and behavioral responses influencing recovery outcomes. WieseBjornstal and Smith (1993) and Wiese-Bjornstal et al. (1995) expanded the development of this conceptual model of response to injury based on an inductive approach, analyzing the specific research findings of existing empirical research pieces and ordering them into the broader generalizations and patterns of a predictive model. Wiese-Bjornstal et al. posited that the stress-based precursors to injury described by Andersen and Williams continue to affect athletes’ postinjury responses by filtering through other moderating and mediating factors (classified into personal and social categories; see Wiese-Bjornstal et al. for a list of hypothesized moderators and mediators) to influence postinjury psychological responses. Athletes’ responses to injuries are considered cyclic longitudinal dynamic processes, in which athletes’ cognitions (defined as interpretations, beliefs and appraisals) influence their emotions (defined as affects, feelings, and moods) and behaviors (defined as efforts, actions, and activities). These psychological response cycles affect athlete recovery outcomes (defined as results, effects, and consequences) such as health status, recovery progress, or return-toplay (see Wiese-Bjornstal, 2009, 2010 for further specific examples of cognitions, emotions, behaviors, and outcomes). Since the initial development of this model several investigations have provided support for various postinjury components, particularly with respect to cognitive and emotional

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responses (e.g., Albinson & Petrie, 2003; LaMott, 1994; Morrey, Stuart, Smith, & WieseBjornstal, 1999; Shaffer, 1991; Smith, Stuart, Wiese-Bjornstal, Milliner, O’Fallon, & Crowson, 1993). The prospective repeated measures designs employed by several of the studies showed the greatest strengths for documenting that postinjury changes are a consequence of the injury and not of other stressors in the sport or school environment. Research has also supported the idea that injury is a source of stress for athletes (Gould, Udry, Bridges, & Beck, 1997; Selby, Weinstein, & Bird, 1990), although not conducted in direct tests of the Wiese-Bjornstal et al. (1998) model predictions. No one study has examined the model as a whole, yet testing the central predictions of the model simultaneously within the same sample would have great theoretical and practical value. The information could confirm or disconfirm stress-based models of response to injury and would allow sport psychologists and coaches to better assist injured athletes in coping with their injuries by understanding their influences and consequences. Therefore, the purpose of this project was to simultaneously examine the primary model components and associated predictions while controlling for within team- and school-related factors through prospective repeated measures sampling of injured and noninjured teammates. The following three research questions were examined. First, do the preinjury factors specified by the Williams and Andersen (1998) model, and incorporated into the Wiese-Bjornstal et al. (1998) model, predict future injury status among initially noninjured male and female intercollegiate athletes? Second, is sport injury a stressor, as predicted by the Wiese-Bjornstal et al. (1998) model? Third, do cognitive appraisal and emotional response factors differ between injured and noninjured teammates in the specific times surrounding injury, as predicted by the WieseBjornstal et al. (1998) model?

METHOD Participants Intercollegiate athletes were chosen for model evaluation because all athletes shared similar school and sport-related influences and thus it would be possible to control for more competing explanations if differences were found between injured and noninjured athletes. Therefore, the overall sample of athletes used in this investigation consisted of 74 National Collegiate Athletic Association (NCAA) Division I level university athletes competing on the following spring intercollegiate sport teams during the 1996 – 1997 season: women’s tennis, women’s softball, women’s track and field, and men’s baseball. The athletes ages ranged between 18 and 23 years (M = 20.32 years, SD = 1.40) and they self-reported having participated in their respective sports an average of 9.43 years (SD = 4.57). Participants were included in this study if they were a member of the university women’s varsity track and field (n = 32), softball (n = 20) or tennis (n = 7) team or men’s baseball team (n = 15), if they signed an informed consent form, and if they were not injured at the outset of the study. The NCAA definition of sport injury from the Injury Surveillance System (ISS; National Collegiate Athletic Association, 1995 - 96) was used in this study for establishing non-injury status at baseline and for reporting injury occurrence during the course of the study. Specifically, a sport injury was defined as: (a) having occurred as a result of participation in

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an organized intercollegiate practice or game, (b) as requiring medical attention by a team athletic trainer or physician, and (c) as having resulted in a restriction of the athlete’s sport participation or performance for one or more days beyond the day of injury.

PREINJURY

STRESS

FACTORS

 AIMS  ISP (base)

 ALES (base)  WES (base)  Scholarship

Personality

History of

SPORT

Coping

Stressors

INJURY

Resources

RESPONSE

 PSOM (base)  ACSI

Interventions

RESPONSE TO SPORT INJURY AND REHABILITATION PROCESS SITUATIONAL MODERATORS

PERSONAL MODERATORS  Gender  Race/ethnicity  Time played this sport

COGNITIVE APPRAISAL       

BEHAVIORAL RESPONSE  SIRAS (weekly for injured)

WES hassles (post) ALES negative (post) WES hassles (weekly) WES uplifts (weekly) PSOM (weekly) WT (weekly) HUS for validity

 Sport type  University competitive seasons

EMOTIONAL RESPONSE  

ISP (weekly) POMS-SF for validity

Figure 1. Measures included in the present study to evaluate multiple components of the integrated model of psychological response to the sport injury and rehabilitation process (Wiese-Bjornstal et al., 1998).

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Measures Decisions were made about which measures to use in this study based on evaluating key components and predictions of the Wiese-Bjornstal et al. (1998) model. In line with the model, multiple measures were selected to assess constructs related to personality, history of stressors, coping resources, cognitions, affects, and behaviors (see Figure 1). Measures were identified based on previous use in publications related to the psychosocial aspects of sport injury prediction and response. Because of the large number of measures employed, efforts were made to use the most brief but reliable forms of assessment for these constructs. All measures were completed in a paper-and-pencil format. Athletic identity. The Athletic Identity Measurement Scale (AIMS) measures athletic identity, defined as the extent to which one identifies with the athlete role (Brewer, Van Raalte, & Linder, 1993). It is considered to be a part of the broader construct of self-concept. The scale contains 10 items each rated on a 1 to 7 continuum (1 = “strongly agree” to 7 = “strongly disagree”). Example items include, “I consider myself an athlete”, “Sport is the most important thing in my life”, and “I would be very depressed if I were injured and could not compete in sport”. A single total score is calculated that can range from 10 to 70, with higher scores indicating a greater identification with the athlete role. Developers of the scale report data supporting it as a reliable and valid measure of athletic identity (Brewer et al.). This measure was completed one time as part of the baseline packet, and scores were used as a personality measure in the preinjury analysis. Coping resources. The Athletic Coping Skills Inventory (ACSI) was used as a measure of coping skills and resources. The ACSI was developed by Smith, Smoll, Schutz, and Ptacek (1995) and consists of 28 items loading on seven sport-specific coping subscales (coping with adversity, peaking under pressure, goal setting/mental preparation, concentration, freedom from worry, confidence and achievement motivation, coachability) that are summed to create a personal coping resources score. Example items include “When I fail to reach my goals it makes me try even harder”, “I maintain emotional control no matter how bad things are going for me”, and “I handle unexpected situations in my sport very well”. Each of the 28 items is rated on a 4-point Likert scale (0 = “almost never” to 3 = “almost always”). Each subscale contains four items with scores with scores ranging from 0 to 12; the composite personal coping resources score ranges from 0 to 84. Ratings of internal consistency have been reported to range from .56 to .86 for individual subscales and from .85 to .88 for the scale as a whole (Smith et al.). Demographics. The demographic data sheet consisted of questions concerning the athlete’s age, gender, and athletic scholarship status (i.e., full, partial, none). In addition, participants were asked to indicate on which sport team they participated, the number of competitive seasons they completed at their university, and the total number of years they had participated in their respective sports. Athletic scholarship status was used as one of the life event stressors. Injury. The injury data sheet consisted of questions regarding the athlete’s name, the original injury diagnosis, the dates of the original and/or recurrent injuries, where the injury occurred (e.g., practice, competition, other), illnesses or any other complications the athlete was having, and additional comments. This form was to be completed by university certified athletic trainers (ATCs) each time an athlete became injured and sought medical attention. The only injury data that were ultimately allowed to use was whether or not the athlete was

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injured during the course of the season. Thus athletes were categorized as to “yes” or “no” based on whether or not they sustained an injury that met the ISS definition. Major life event stress. Major life event stress was measured with a modified version of the Athletic Life Experiences Survey (ALES; Passer & Seese, 1983). Major life events are those significant events and changes that can result in feelings of stress. The scale used in this study consisted of the 19 sport-related items from the ALES, and the Life Experiences Survey (LES; Sarason, Johnson, & Siegel, 1978) in its entirety, including the 10 academic-related items. The modified scale consisted of 75 items examining positive and negative life events among athletes. In addition, 5 blank items were included to allow the athletes to fill in and rate other major life events that had impacted their lives, but were not listed on the scale. Individuals were asked to report each major life event they experienced over the past twelve months (baseline) or during the course of the sport season (posttest), whether the event was positive or negative, and the degree of impact or strain (i.e., 0 = no, 1 = some, 3 = moderate, or 4 = extreme) the event had on their lives at the time of its occurrence. Example items include: “conflict with head coach,” “failing an important exam,” and “death of a family member.” Three scores were calculated from the life event stress measure: total life event stress, the sum of the absolute value of positive and negative impact scores; negative life event stress, the sum of the absolute value of the negative impact scores; and positive life event stress, the sum of the positive impact scores. Passer and Seese (1983) did not report reliability and validity information for the ALES; Sarason et al. (1978) reported test-retest reliability coefficients between .56 and .88 for the LES negative life event stress score, between .19 and .53 for the LES positive life event stress score, and between .63 and .64 for the LES total life event stress score. Minor life event stress. The Weekly Events Scale (WES), a 16-item hassles and uplifts survey, was created for the purposes of this study to measure minor life event stress (hassles) and uplifts. Minor life event stress is “the stress from many minor daily problems, irritations, or changes” (Williams & Andersen, 1998, p. 10). Sixteen major themes of the Hassles and Uplifts Scale (HUS) (DeLongis, Folkman & Lazarus, 1988; see also DeLongis, 1985 for reliability and validity descriptions) relevant to university students were extracted to create a shorter version (WES) of the scale that could be used with a college athlete population on a weekly basis. Example themes included: “family,” “finances,” “school”, and “sport.” At baseline, posttest, and each week during the season participants provided WES responses to the following questions for each item: “How much of a hassle was this item for you this past week?” and “How much of an uplift was this item for you this past week?” using two 4-point Likert scales (0 = “none/not applicable” to 3 = “a great deal”). Thus, two scores were calculated each week: a) a WES-hassles score was calculated by summing the values of the responses to the hassles question (scores range 0 to 48) and b) a WES-uplifts score was calculated by summing the values of the responses to the uplifts question (scores range 0 to 48). Comparisons were made between the HUS and WES at one week within this investigation to determine the acceptability of using the shorter WES on a weekly basis in lieu of the longer but established HUS. A total of 52 athletes completed both the WES and the HUS. With this sample, adequate concurrent validity was found for both the WES hassles subscale (r = .44, p < .00) and the WES uplifts subscale (r = .79, p < .00) with the corresponding subscale on the HUS. Both the WES hassles subscale and the WES uplifts

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subscale showed reasonably high internal consistency with Cronbach’s alpha equaling .69 and .77 respectively. Mood state. The Incredibly Short Profile of Mood States (ISP; Dean, Whelan, & Meyers, 1990) is a 6-item state mood assessment using one item to assess each mood dimension. This instrument was created as a very brief alternative to the original validated 65-item Profile of Mood States (POMS; McNair, Lorr, & Droppleman, 1971) and the 30-item POMS-Short Form (POMS-SF) that could be administered in less than one minute (Dean et al.). Each week participants rated their current mood on the same 5-point Likert Scale as the POMS-SF (0 = “not at all” to 4 = “extremely”) expressing to what degree each adjective reflected how they had been feeling the past week. In line with the procedure established for the POMS, total mood disturbance scores were calculated by adding scores for the five negative mood states (anxious, sad/depressed, confused, angry, fatigued) and subtracting the score for the positive mood state (energetic). Thus ISP total mood disturbance scores could range between –4 to 20. As opposed to one-word adjectives, items on the ISP are phrased as questions and ask participants how they are feeling right now (e.g., “How anxious do you feel right now?”). For the purposes of this study, however, the questions were reworded to assess how participants were feeling during the past week (e.g., “How anxious have you felt this past week?”). The ISP has been shown previously to have good reliability with correlations between .91 to .97 for the individual subscales and .97 for total mood disturbance when correlated with the original 65-item POMS with a sport population (Fleming, Bourgeois, LeUnes, & Meyers, 1992). During one week of the present study, both the ISP and the POMS-SF were administered in order to verify the acceptability of using the ISP as a measure of mood in this sample. The POMS-SF is an established 30-item inventory consisting of adjectives corresponding to six subscales (i.e., depression, tension, fatigue, anger, confusion, and vigor). Concurrent validity was established for the ISP, in that each item on the ISP significantly correlated with the corresponding subscale on the POMS-SF (r = .32 to .66), and the total mood disturbance score on the ISP significantly correlated with the total mood disturbance score on the POMS-SF (r = .74, p < .001). The ISP also showed reasonably high internal consistency within this study; Cronbach’s alpha was .78. It was used as a predictor of injury and as a measure of emotional response to injury. Positive states of mind. The Positive States of Mind (PSOM) measure is an indicator of satisfying states of mind (Adler, Horowitz, Garcia, & Moyer, 1998). Scale developers suggest individuals’ capacities to enter positive states of mind help them to better tolerate negative stressful circumstances. The questionnaire asks about the respondent’s kinds of satisfying states of mind experienced in the last week. The scale consists of six items (focused attention, productivity, responsible caretaking, able to relax, enjoyment, sharing) each ranked in a four choice response format (“unable to”, “trouble in”, “limited in”, “able to”). Example items include, “Feeling able to attend and focus to whatever you want or need to do, without many distractions” and “Feeling that you are doing what you should do to take care of yourself or someone else”. Each item on the scale is scored separately for six subscale scores ranging from 0 (“unable to”) to 3 (“able to”), and a total positive indicator is based on a summation, thus having a range of 0 to 18. Adler et al. report data supporting satisfactory reliability and validity for the scale. This was used as a measure of coping resources. Rehabilitation behavior. The Sport Injury Rehabilitation Adherence Scale (SIRAS; Brewer , Van Raalte, Petitpas, Sklar, Pohlman, Krushell et al., 2000) typically completed by ATCs or other clinicians as a measure of the athlete’s quality of adherence to rehabilitation

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sessions. It contains three items (athlete intensity of effort, adherence to ATC advice, and receptivity to changes in rehabilitation protocol) each rated in this study by the ATC on 1 to 5 Likert Scales, with higher scores being indicative of better quality of adherence. Athletes who do not attend a rehabilitation session receive scores of zero for each item. A total score is calculated by summing the three ATC rankings for the rehabilitation session; thus the scores can range from 0 to 15 for a specific rehabilitation session. Scale developers suggest that the SIRAS shows good construct validity, test-retest reliability, internal consistency, and unidimensionality (Brewer et al.). This measure was used as an indicator of postinjury behavioral response in the present investigation. Self-efficacy and coping. The Weekly Thoughts (WT) inventory was developed for use in this investigation as a brief weekly measure of the cognitive appraisals of athletes regarding their confidence in physical performance and success in coping with stressful demands. Essential the inventory assessed their self-efficacy regarding these specific aspects, and as such, this scale was designed using based on Bandura’s (1997) recommendations for self-efficacy measures. This measure consisted of seven total items. The first three items were responded to with 7-point Likert scale ratings of confidence general physical abilities, performing the physical skills specific to the sport, and in completing physical conditioning and/or rehabilitative activities. The next two questions asked for two-part responses, first to fill-in-the-blank descriptive questions (major demands of sport participation, major sport and/or rehabilitation setbacks during the past week), and then for each a 7-point Likert scale rating of how well those demands and setbacks were handled or coped with during the past week.

Procedure Once Institutional Review Board approval was received the study was launched. At the beginning of the sport season and prior to the start of the competitive season, the head coach of each team was contacted to request permission to use practice and/or team meeting time to collect data from his or her athletes. After the coaches’ permission was obtained, the investigators met separately with each team to take the baseline assessments from consenting participants. The baseline packet consisted of the informed consent form, demographics, and the AIMS, ACSI, ALES, ISP, PSOM, WES, and WT. Athletes choosing not to participate in the study were instructed not to sign the consent form and to return the packet to the investigators. All athletes present at these initial meetings agreed to participate in the study. This first meeting lasted approximately 20 minutes. Following the baseline assessment period, meeting times were set each week after a practice or team meeting to take the weekly assessments. Weekly packets were given only to those athletes who had completed the first baseline assessment. If participating athletes were not present at a weekly meeting, psychological data were not collected from them that week. The weekly packets consisted of the ISP (6 items), PSOM (6 items), WES (16 items) and WT (7 items). Each meeting lasted approximately 5 minutes. The weekly assessments covered an interim period of 11 weeks; however, data was not able to be collected from all teams each week due to scheduling conflicts. Teams averaged 7 data collection weeks each. During the week following the week 11 assessment, the athletes were given the posttest packets. The posttest packets consisted of the ALES, HUS, ISP, POMS-SF, PSOM, WES and

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WT. The life event stress measure was reworded to assess to occurrence and impact of life events occurring since the study began (i.e., over the course of the sport season). The POMSSF and the HUS were included this week with the ISP and the WES to establish concurrent validity for the newly established scales. This final meeting lasted approximately 25 minutes. Throughout the course of the study, university ATCs were expected to recorded injury data on the injury data sheet. Each time an athlete sought medical attention for a sportsrelated injury, the ATCs recorded the type of injury, the date the injury originally occurred, and the date of the recurrence of the injury (for recurrent injuries). The ATCs also recorded where the injury was sustained (e.g., practice or competition) and whether the athlete was experiencing any other illness or complications. Each week, the investigators collected the previous weeks’ injury data from the ATCs and matched the injury dates with the corresponding weekly psychological data. For purposes of statistical analyses, a participant was included in the injured group if they sustained an injury at any point during the study. At the end of these data collection efforts, it was found that data from two of the psychosocial measures was inconsistent and therefore unusable. First, while the PSOM data for baseline and posttest was sufficient, there was not sufficient weekly data to allow for further analysis. Second, despite researcher efforts, the SIRAS was not completed by the ATCs on a consistent enough basis to be usable. Further, in terms of injury reports, the only injury data that investigators were ultimately allowed to include in the published report is the injury status of each athlete, i.e., whether they were injured or not during the course of the season based on the NCAA definition of injury. Thus no descriptive information about the nature of the specific injuries sustained is included in this report. Table 1. Demographic Information for All Participant Groups

Variable Age (M) yrs Time Played this Sport (M) yrs University Competitive Seasons (M) yrs Gender (%) Female Male Ethnicity (%) European American African American Asian American Other Ethnicities Sport Team (%) Track & Field (Women) Softball (Women) Tennis (Women) Baseball (Men) Scholarship Status (%) Full Partial None

Overall GroupInjured (n = 23) 20.6 10.9

Overall GroupNoninjured (n = 51) 20.2 8.8

Matched SubsetInjured (n = 6) 21.0 11.3

Matched SubsetNoninjured (n = 6) 20.0 7.5

2.1

1.8

87.0 13.0

76.5 23.5

100.0

100.0

73.9 8.7

66.7 33.3

83.3 16.7

17.4

84.4 3.9 3.9 7.8

43.5 39.1 4.3 13.1

43.1 21.6 11.8 23.5

66.7 33.3

66.7 33.3

47.8 39.1 13.1

19.6 35.3 45.1

83.3

33.3 50.0 16.7

16.7

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RESULTS Participant Characteristics Table 1 displays participant characteristics for all of the groupings used in the analyses of results. The Overall Groups (Injured and Noninjured) represent all of the athletes in the investigation from whom data was sufficient. The Matched Subsets (Injured and Noninjured) represent the matched samples. Out of the Overall Group-Injured sample, six injured female athletes had complete injury data and complete psychological data corresponding to 1 to 2 weeks prior to the occurrence of their injuries, the actual week of their injury, 1 to 2 weeks postinjury, and the posttest. These six athletes were closely matched by sport, scholarship status, race/ethnicity and age with six noninjured teammates who also had complete data for these corresponding weeks, becoming the Matched Subsets.

Research Question Findings Psychosocial factors as sport injury predictors. The first research question was: Do the preinjury factors specified by the Williams and Andersen (1998) model, and incorporated into the Wiese-Bjornstal et al. (1998) model, predict future injury status among initially noninjured male and female intercollegiate athletes? The Overall Group members with complete data on involved measures (n=70) were used to answer this research question. Baseline variables were selected for inclusion in the study and this analysis based on the theoretical predictors identified by the Williams and Andersen model of stress and athletic injury and incorporated into the dynamic model of psychological response to sport injury and rehabilitation of Wiese-Bjornstal et al. (1998): personality (athletic identity [AIMS], mood state [ISP total mood disturbance]), history of stressors (major life event stress [ALES total], minor life event stress [WES hassles], athletic scholarship status), and coping resources (uplifts [WES uplifts], positive states of mind [PSOM total]). These variables were entered into a logistic regression to determine the impact of psychosocial predictors on subsequent categorical injury status (injured [n = 21] vs. noninjured [n = 49]) during the course of the season). A test of the full model against a constant only model was statistically significant (chi square = 17.20, p = .02, with df = 7), indicating that the psychosocial predictors as a set reliably distinguished between athletes who were injured versus noninjured. Nagelkerke’s R2 of .31 indicated a modest relationship between prediction and grouping. Prediction accuracy overall was 71.4% (38.1% for injured and 85.7% for noninjured). The Wald criterion showed that athletic scholarship status (p = .00), baseline total mood disturbance (p = .05), and baseline minor life event stress (p = .05) made significant contributions to prediction. Athletes with a higher level of athletic scholarship (injured M = 1.65, SD = .71 versus noninjured M = 2.25, SD = .77), greater total mood disturbance (injured M = 6.33, SD = 4.36 versus noninjured M = 5.43, SD = 4.34), and less minor life event stress (injured M = 7.00, SD = 3.69 versus noninjured M = 8.54, SD = 5.32) at baseline were more likely to be subsequently injured. Athletic identity, major life event stress, uplifts, and positive states of mind at baseline did not contribute significantly to prediction of injury status during the season.

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Sport injury as a stressor. The second research question was: Is sport injury a stressor, as predicted by the Wiese-Bjornstal et al. (1998) model? In the Wiese-Bjornstal et al. (1998) model, sport injury is conceptualized as a stressor that would add perceived negative stress to an athlete’s life above and beyond that attributable to those stressors already inherent in the university, sport, and personal structures. Therefore it was hypothesized that injured athletes would report greater changes in negative stress variables from baseline to posttest than would noninjured teammates; baseline scores serve as controls for the initial stress variable status of athletes. Data from those in the Overall Group with complete baseline and posttest psychosocial stress-related data (n = 73) were used to answer this research question. Two measures of stress were included to evaluate individual score changes from baseline to posttest contrasting injured (n = 23) and noninjured (n = 50) athletes. Change scores were created by subtracting individual posttest scores from baseline scores for each individual for the following two stress assessment variables: major negative life event stress (ALES negative change), minor negative life event stress (WES hassles change). Higher scores on either of these measures indicate greater perceived negative stress, and therefore negative change scores would indicate more deterioration in negative stress perceptions from baseline to posttest. The two change scores for major and minor negative life event stress were entered as dependent variables into a one-way multivariate analysis of variance (MANOVA) comparing injured and noninjured groups to ascertain if injury occurrence affected negative stress levels. Results indicated a significant difference between injured and noninjured athletes, Wilks’ Lambda = .92, F (2, 70 df) = 3.17, p = .05; partial eta squared = .08. Follow-up univariate analyses of variance (ANOVAs) showed that both major (F = 4.20, p = .04, partial eta squared = .06) and minor (F = 4.34, p = .04, partial eta squared = .06) negative life event stress change scores distinguished between injury status groups. Injured athletes reported minimal change in their minor negative life event stress (hassles) at posttest compared to their own baselines (M change score = -.09, SD = 4.72) whereas noninjured athletes reported substantially less negative life event stress (hassles) at posttest compared to baseline (M change score = 2.62, SD = 5.34). Injured athletes reported far more major negative life event stress at posttest compared to their own baselines (M change score = -5.28, SD = 7.26) whereas noninjured athletes reported only a slight change from baseline to posttest (M change score = -.75, SD = 9.37). These findings should be interpreted with some caution because significant variability in major and minor life event stress change scores was noted in the data. In theory, being all on the same teams at the same university provides some control for other stressful life event factors related to sport or university, and comparing athletes changes from their own baseline scores provide stronger evidence that the changes in life event stress are related to injury rather than other personal or situational factors. Using data from the Matched Subsets (n = 12) allowed us to further examine the answer to this question in a tightly controlled way. A 2 x 2 (participant group by time) ANOVA with repeated measures on the last factor was conducted for total life event stress (ALES total) and revealed a significant main effect for time (F (1, 10 df) = 5.46, p = .04), with both the matched injured and the matched noninjured athletes scoring higher at baseline than at posttest. This was not entirely surprising since the athletes were responding to different time period at baseline (12 months) than at posttest (3 months). The more important finding is that the main effect for participant group was also significant for total life event stress (F (1, 10 df)

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45 40 35

ALES total

30 25

Matched Injured

20

Matched Noninjured

15 10

5 0 Baseline

Posttest

Figure 2. Mean baseline and posttest total life event stress (ALES total) scores for the Matched SubsetInjured (n = 6) and the Matched Subset-Noninjured (n = 6). Higher scores indicate greater total major life event stress. The ANOVA main effects for time and participant group were statistically significant.

= 13.16, p = .00), with the matched injured athletes scoring higher than the matched noninjured athletes at both baseline and posttest. Figure 2 displays these results, which also support the model contention that sport injury was a stressful experience. Differences in cognitive and emotional factors over time. The third research question was: Do cognitive appraisal and emotional response factors differ between injured and noninjured teammates in the specific times surrounding injury, as predicted by the WieseBjornstal et al. (1998) model? One of the powerful and unique aspects of this study was its collection of weekly data throughout the course of a season for both injured and noninjured teammates. This simultaneous data collection allowed for some measure of control of noninjury related factors that likely affect cognitions and emotions for all athletes, such as time in the academic year or sport season success. In this way the influence that injury has on cognitions and emotions could be better isolated. Using the Matched Subsets for these analyses allowed direct comparison of injured athletes with their noninjured team counterparts at the exact time points surrounding the injury occurrence. Thus, in order to evaluate changes in two cognitive appraisal elements at key time points surrounding injury, minor negative life event stress (hassles) and uplifts over these weeks were examined for the Matched Subsets. A separate 2 x 4 (injury status by time) ANOVA with repeated measures on the last factor was conducted each for minor negative life event stress (WES hassles) and WES uplifts. No results were statistically significant for either dependent variable. A visual inspection of the data graph for WES hassles (see Figure 3) proves interesting for future consideration, however. Although not found to be statistically significant perhaps due to large variability within the data, the finding illustrates the possibility that injuries themselves are a hassle.

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12 10

WES hassles

8 Matched Injured

6

Matched Noninjured 4 2

0 Preinjury

Injury

Postinjury Posttest

Figure 3. Mean minor negative life event (WES-hassles) scores during the preinjury, injury, postinjury, and posttest weeks for the Matched Subset-Injured (n = 6) and the Matched Subset-Noninjured (n = 6). Higher scores indicate greater minor life event stress. The ANOVA results were not significant.

10

ISP total mood disturbance

9

8 7 6 5

Matched Injured

4

Matched Noninjured

3 2

1 0 Preinjury

Injury

Postinjury

Posttest

Figure 4. Mean weekly mood disturbance (ISP-total mood disturbance) scores during the preinjury, injury, postinjury, and posttest weeks for the Matched Subset-Injured (n = 6) and the Matched SubsetNoninjured (n = 6). Higher scores indicate more negative mood state.The ANOVA main effect for participant group was statistically significant.

To evaluate changes in emotional response at key time points surrounding injury, a 2 x 4 (injury status by time) ANOVA with repeated measures on the last factor was conducted with mood disturbance (ISP total mood disturbance) data for the Matched Subsets. The only significant finding was a main effect for injury status, F (1, 10 df) = 6.95, p = .03. Post-hoc comparisons illustrated that matched injured athletes showed significantly (p < .05) higher

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total mood disturbance scores than the matched noninjured athletes at all four time points surrounding the specific injury weeks for each injured athlete (see Figure 4). It was interesting to note that in addition to the significant magnitude differences at each of the four time points, in a qualitative sense the patterning of the mood state data was quite similar for injured and noninjured athletes, likely illustrating the role of school and sport factors affecting all athletes.

DISCUSSION Evaluating various aspects of the Wiese-Bjornstal et al. (1998) integrated model of psychological response to the sport injury and rehabilitation process led generally to support for the model, with some exceptions. Findings from this study regarding the first research question examining preinjury and baseline predictors derived from the model of stress and athletic injury (Andersen & Williams, 1998) showed higher athletic scholarship status to be the most powerful predictor of sport injury. This finding demands further attention in future investigation in order to examine the stressors and actions that appear to differentially affect scholarship athletes. More negative mood state at preseason was also a significant predictor of injury, which is in line with other investigations. Individual mood states such as fatigue have been shown to predict sport injury (e.g., Smith, Stuart, Wiese-Bjornstal, & Gunnon, 1997), and higher levels of tension can interfere with normal skill execution and quality sleep in ways that could increase vulnerability. Minor life event stress in the form of weekly hassles proved predictive of injury, but in an inverse way contrary to what was expected. Unlike previous research findings other constructs such as major life event stress and athletic identity did not contribute to the prediction of injury status in the present investigation. Psychological variables had a stronger ability to predict status among noninjured athletes than they did among injured athletes; perhaps noninjured athletes are more stable or similar in their psychological profiles in a way that works to a protective advantage. With respect to the second research question, the results of this study support WieseBjornstal et al.’s (1995) model conceptualization of sport injury as a stressor. The injured athletes in this study reported a similar amount of major negative life event stress during the 13 week sport season as they did during the entire year preceding the sport season, while the noninjured athletes reported significantly less total major life event stress during the sport season than they did for the year prior to it. The results from minor negative life event stress (hassles) were less clear; although the hassles of noninjured athletes dropped significantly from pre- to posttest, those for the injured athletes did not. When a matched subset of injured athletes was compared with a subset of noninjured athletes from the same team, support for the conceptualization of sport injury as a stressor was again evident. This subset of injured athletes reported greater amounts of total major life event stress than the subset of noninjured athletes during both the year preceding the sport season and during the course of the sport season. Thus, it appears that injured athletes experience a significant amount of major life event stress as a result of their injuries, particularly in terms of total life event stress. As for the third research question, the findings surrounding the model predictions about cognitive and emotional responses of athletes to injury were somewhat mixed. Statistical results did not support the model hypothesis that injured athletes would experience a greater

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intensity of weekly hassles than their noninjured teammates, although a visual inspection of the mean scores showed the matched injured athletes to experience an increase in weekly hassles during the injury and postinjury weeks. The matched noninjured athletes, on the other hand, showed a steady decrease in weekly hassles over the same time periods. Thus, injuries may be a temporary hassle that athletes soon adjust to, or, since we did not have the actual injury data, it could be that injured athletes were fully recovered and returned to play within a couple of weeks following injury and therefore the hassle (injury) was removed. Based on the model it would also be expected that the injured athletes would experience significantly less uplifts than their noninjured teammates. But no significant differences were found between the matched injured and matched noninjured athletes in terms of weekly uplifts. Thus, it does not appear that the experience of uplifts is affected by the occurrence of sport injury. A larger sample size and a more sport injury-sensitive weekly hassles and uplifts measure could possibly have increased the significance of the results. Support was found for the model prediction that the injured athletes would experience greater mood disturbance than their noninjured teammates. The matched injured athletes reported greater weekly mood disturbance than the matched noninjured athletes during the preinjury, injury, postinjury, and posttest weeks. Interestingly, an examination of both the matched injured and the matched noninjured athletes’ weekly mood disturbance scores over all four time points revealed that the temporal pattern of the matched noninjured athletes’ weekly mood disturbance scores approximated the pattern found with the matched injured athletes, but to a lesser degree. It was suggested that these patterns may reflect to a certain extent, team and academic-related factors that could influence both the injured and noninjured athletes. But the degree or magnitude aspect of the mood disturbance scores at baseline and prior to injury seems to speak to the observation that negative mood state is a risk factor for sport injury as much as a response. This was supported by the results for research question one, in which total mood disturbance at baseline was in fact found to be one of the significant predictors of subsequent injury. As with all forms of scientific inquiry, this investigation had a number of strengths and limitations. The main strength of this study was its prospective, repeated measures design. The prospective research design permitted both pre- and postinjury assessments of the psychological variables of interest to be made. Such a design is beneficial because it allows researchers to have a better understanding of cause and effect relationships. Since stress and mood disturbance were assessed both prior to, and following the athletes’ injuries, greater confidence was obtained that the postinjury changes in these variables were actually due to the injuries. Without the use of a prospective research design, less certainty could be obtained as to whether the differences between the injured and noninjured athletes were due to sport injury or simply pre-existing differences. Thus, this approach allowed for greater confidence in the results. Another major strength of this investigation was the use of repeated measurements. Repeated measurements are important for a couple of reasons. First, the levels of many psychological variables are not static, but rather change over time. Therefore, measurements taken at the beginning of the sport season may not yield the same results as measurements taken during the middle of the season or at the end of the season. The use of only one or two assessment periods (e.g., baseline and/or posttest) would likely not provide an accurate indication of the psychological variable levels around the time of injury. The state-like variables assessed in this study (e.g., hassles, uplifts, mood state, self-efficacy and coping,

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positive states of mind) are by nature, in constant flux, and thus require multiple assessments. Infrequent assessments of such variables have plagued previous sport injury research; therefore, this investigation represents a significant improvement over past research. Second, with repeated measurements, each athlete serves as his or her own control. In other words, repeated measurements allow for intraindividual comparisons to be made—an individual athlete’s earlier scores can be compared to his or her later scores. Since the same participant’s scores are compared over time, greater confidence can be obtained that the resulting effects are due to a certain treatment or event (e.g., sport injury), as opposed to between-subjects variability. The short recall period used to assess life event stress, hassles, uplifts, and mood disturbance was another advantage to this study. For the posttest life event stress measurement, the athletes were asked to recall life events occurring during the sport season (i.e., over a period of 3 months). Such a short recall period is beneficial because it maximizes the accuracy of the athletes’ reports (Petrie & Falkstein, 1998). For the weekly measures (i.e., hassles, uplifts, mood states, positive states of mind, self-efficacy and coping) athletes reflected upon the past week and provided the appropriate responses. The weekly measurements essentially allowed for the assessment of the psychological variables as they were occurring. Therefore, the athletes were able to provide more immediate and accurate reflections of their stress levels and mood states, and thus, more immediate reactions to their injuries. This constitutes a significant improvement over past investigations which usually assess the psychological variables of interest only at baseline and/or posttest. If a longer recall period were used, as often done with previous research, considerable time lapse could have resulted between the time sport injury occurred and the time the individual responded to it, introducing the possibility of memory loss and contamination from the occurrence of subsequent events and outcomes. Finally, the selection of noninjured athletes to match the injured athletes primarily in terms of team membership, and secondarily, in terms of scholarship status, race, and age, was another benefit to this study. The use of a subset of matched noninjured athletes enabled the effects of team and academic-related factors on stress, uplifts, and mood disturbance to be controlled. Past sport injury research has not considered the importance of controlling for such factors. The benefit of this approach was particularly evident with the analyses of the matched subsets’ weekly mood disturbance scores. It was found that the pattern of the matched noninjured athletes’ weekly mood disturbance scores approximated that of the matched injured athletes’ scores. The difference between the two subsets was that the matched injured athletes’ scores were significantly higher than the matched noninjured athletes’ scores at each time point. The similarity in weekly mood disturbance patterns was likely due to team-related and academic-related occurrences that affected all of the athletes from that particular team. For instance, a team loss in a big competition or intense travel demands during a certain week would have affected the entire team, not just the injured or the noninjured athletes. Furthermore, academic-related occurrences such as midterms or finals week would have affected both the matched injured and the matched noninjured athletes, since psychological data were examined from the same weeks for both groups. If a subset of matched noninjured athletes was not used, such team and academic-related effects on mood disturbance could have been mistakenly attributed to injuries, resulting in inaccurate results and conclusions. Future injury-related research should incorporate groups of noninjured athletes that match injured athletes on various factors such as team membership, scholarship

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status, gender, race, and age, and should collect data from both groups during the same points in time whenever possible to minimize such inaccuracies. While there were several advantages to this investigation, certain weaknesses and limitations were also present. First, a major disadvantage of this study was its small sample size. It is likely that several of the analyses could have reached statistical significance had a larger sample size resulted from the initial pool of athletes. A larger sample size would have been particularly beneficial for the matched subset analyses, especially with the weekly hassles and weekly uplifts measures. While there are more advantages to using prospective, repeated measures designs with matched noninjured groups than there are disadvantages, significant disadvantages of such research designs are that they require considerably large sample sizes and data collection is quite labor intensive. These research designs are often affected by participant attrition, incomplete data sets, and a limited sample of injured athletes, therefore, the beginning sample size needs to be quite large to counteract these effects. The present study was affected by all of these factors. But with larger samples more elegant and parsimonious statistical analyses such as structural equation modeling would be possible for simultaneously testing model predictions and paths of influence. Second, this investigation was unable to assess injury severity, time loss from sport, or the athletes’ perceived rates of recovery. Consequently, the sample of injured athletes in this study was comprised of athletes with varying levels of injury severity and varying lengths of rehabilitation periods. The investigators originally intended to collect information regarding injury severity and time loss from sport; however, despite initial approval from institutional review boards, athletes and ATCs, they were ultimately denied access to the athletes’ personal medical records kept by the university ATCs. Therefore, these variables could not be incorporated into this investigation. Furthermore, due to the extensive number of inventories already employed in this investigation, an inventory asking the injured athletes to rate their perceived recovery statuses was not included. Nonetheless, researchers should continue to make attempts to include all of these factors in their investigations, if at all possible, as they have been related to subsequent mood disturbance in several studies (e.g., Smith et al., 1993). Finally, certain limitations were present due to the weekly hassles and uplifts inventory employed in this investigation. The Weekly Events Scale (WES), which was developed for the purposes of this study, may not be the most appropriate scale for assessing the hassles and uplifts specific to sport and sport injury. While this scale was found to be reliable, valid, and useful for repeated measures investigations, it is not particularly sport- or injury-sensitive. It is possible that the analyses of weekly hassles and weekly uplifts in this study did not reach statistical significance because the WES was not sensitive to hassles and uplifts that would be most affected by sport injury. For instance, items on the WES such as “sexual/intimate relationships,” “legal matters,” and “world issues,” would most likely not be affected by sport injury. Therefore, future research investigating pre- and postinjury hassles and uplifts would benefit from the employment of a more age and sport-specific weekly measure.

CONCLUSION The evaluation of the integrated model of psychological response to the sport injury and rehabilitation process (Wiese-Bjornstal et al., 1998) with respect to conceptualizing sport

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injury as influenced by preinjury psychosocial factors (Williams & Andersen, 1998), acting as a negative life event stressor, and comprising a dynamic process of emotional responses (Wiese-Bjornstal, 2009; 2010; Wiese-Bjornstal et al., 1995) leads to the conclusion that the model is valid in these regards. Psychosocial factors did predict sport injury, sport injury was a stressful experience for injured athletes above and beyond the shared stressors of school and sport, and emotional responses did differ between injured and uninjured athletes. The repeated measures research approach used in this investigation was effective in gathering data over time for matched injured athletes with noninjured teammate controls when examining psychosocial changes linked to injury. Evaluating the core model predictions demonstrates the significant ways in which stress and mood disturbance affect injury vulnerability, and the impact sport injury can have on athletes in terms of stress and emotional disturbance. Researchers must continue to explore the effects injuries have on an athlete’s well-being, so that the appropriate interventions can be employed. Such information would greatly aid sport psychologists, coaches, and other members of the sports medicine team in facilitating the athlete’s return to physical and psychological health.

ABOUT THE AUTHORS Diane M. Wiese-Bjornstal, Courtney B. (Heniff) Albinson, Shaine E. Henert, Susan J. Schwenz, Shelly M. (Shaffer) Myers, and Diane M. (Gardetto)-Heller, School of Kinesiology, University of Minnesota, Twin Cities; Elizabeth A. Arendt, Department of Orthopaedic Surgery, University of Minnesota, Twin Cities. Courtney B. Albinson is now at Counseling and Psychological Services, Northwestern University, Evanston, IL; Shaine E. Henert is now at Division of Fitness, Wellness, and Sport, Rock Valley College, Rockford, IL; Susan J. Schwenz is now at School of Physical Therapy, Regis University, Denver, CO; Shelly S. Myers is now at Westlake Academy Foundation, Westlake, TX; and Diane M. Gardetto-Heller is now in Friday Harbor, WA. This research was supported in part by a Grant-in-Aid from the Graduate School at the University of Minnesota. Portions of this paper are derived from the analyses of project data presented in the University of Minnesota M.A. in Kinesiology thesis of Courtney B. Heniff (Albinson), A Comparison of Life Event Stress, Weekly Hassles, and Mood Disturbance between Injured and Noninjured Female University Athletes.

REFERENCES Adler, N. E., Horowitz, M., Garcia, A., & Moyer, A. (1998). Additional validation of a scale to assess positive states of mind. Psychosomatic Medicine, 60, 26-32. Albinson, C. B., & Petrie, T. A. (2003). Cognitive appraisals, stress, and coping: Preinjury and postinjury factors influencing psychological adjustment to sport injury. Journal of Sport Rehabilitation, 12(4), 306-322. Andersen, M. B., & Williams, J. M. (1988). A model of stress and athletic injury: Prediction and prevention. Journal of Sport & Exercise Psychology, 10, 294-306. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

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Brewer, B. W., Van Raalte, J. L., Petitpas, A. J., Sklar, J. H., Pohlman, M. H., Krushell, R. J., Ditmar, T. D., Daly, J. M., & Weinstock, J. (2000). Preliminary psychometric evaluation of a measure of adherence to clinic-based sport injury rehabilitation. Physical Therapy in Sport, 1, 68–74. Brewer, B. W., Van Raalte, J. L., & Linder, D. E (1993). Athletic identity: Hercules' muscles or Achilles' heel? International Journal of Sport Psychology, 24, 237-254. Dean, J. E., Whelan, J. P., & Meyers, A. W. (1990, September). An incredibly quick way to assess mood states: The Incredibly Short POMS. Paper presented at the annual meeting of the Association for the Advancement of Applied Sport Psychology, San Antonio, TX. DeLongis, A. (1985). The relationship of everyday stress to health and well-being: Inter- and intraindividual approaches. Unpublished doctoral dissertation, University of California, Berkeley. DeLongis, A., Folkman, S., & Lazarus, R. S. (1988). The impact of daily stress on health and mood: Psychological and social resources as mediators. Journal of Personality and Social Psychology, 54, 486-495. Fleming, S. L., Bourgeois, A. E., LeUnes, A. D., & Meyers, M. C. (1992). A psychometric comparison of the full-scale profile of mood states with other abbreviated POMS scales in selected athletic populations. Proceedings of the Association for the Advancement of Applied Sport Psychology, USA, 28. Gould, D., Udry, E., Bridges, D., & Beck, L. (1997). Stress sources encountered when rehabilitating from season-ending ski injuries. The Sport Psychologist, 11, 361-378. LaMott, E. E. (1994). The anterior cruciate ligament injured athlete: The psychological process. Unpublished doctoral dissertation, University of Minnesota, Minneapolis. McNair, D. M., Lorr, M., & Droppleman, L. F. (1971). Manual for Profile of Mood States. San Diego: Educational and Industrial Testing Service. Morrey, M. A., Stuart, M. J., Smith, A. M., & Wiese-Bjornstal, D. M. (1999). A longitudinal examination of athletes’ emotional and cognitive response to anterior cruciate ligament injury. Clinical Journal of Sports Medicine, 9(2), 63-69. National Collegiate Athletic Association. (1996). Injury Surveillance System (ISS) guidelines. Kansas City, MO: Author. Passer, M. W., & Seese, M. D. (1983). Life stress and athletic injury: Examination of positive versus negative events and three moderator variables. Journal of Human Stress, 9, 11-16. Petrie, T. A., & Falkstein, D. L. (1998). Methodological, measurement, and statistical issues in research on sport injury prediction. Journal of Applied Sport Psychology, 10, 26-45. Sarason, I. G., Johnson, J. H., & Siegel, J. M. (1978). Assessing the impact of life changes: Development of the Life Experiences Survey. Journal of Consulting and Clinical Psychology, 46, 932-946. Selby, R., Weinstein, H. M., & Bird, T. S. (1990). The health of university athletes: Attitudes, behaviors, and stressors. Journal of American College Health, 39, 11-18. Shaffer, S. M. (1991). Attributions and self-efficacy as predictors of rehabilitative success. Unpublished master’s thesis, University of Illinois, Urbana-Champaign. Smith, A. M., Stuart, M. J., Wiese-Bjornstal, D. M., & Gunnon, C. (1997). Predictors of injury in ice hockey players: A multivariate, multidisciplinary approach. American Journal of Sportsmedicine, 25(4), 500-507.

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Smith, A. M., Stuart, M. J., Wiese-Bjornstal, D. M., Milliner, E. K., O’Fallon, W. M., & Crowson, C. S. (1993). Competitive athletes: Preinjury and postinjury mood state and self-esteem. Mayo Clinic Proceedings, 68, 939-947. Smith, R. E., Smoll, F. L., Schutz, R. W., & Ptacek, J. T. (1995). Development and validation of a multidimensional measure of sport-specific psychological skills: The Athletic Coping Skills Inventory-28. Journal of Sport & Exercise Psychology, 17, 379-398. Wiese, D. M., & Weiss, M. R. (1987). Psychological rehabilitation and physical injury: Implications for the sportsmedicine team. Sport Psychologist, 1, 318-330. Wiese-Bjornstal, D. M. (2009). Sport injury and college athlete health across the lifespan. Journal of Intercollegiate Sport, 2, 64-80. Wiese-Bjornstal, D. M. (2010). Psychology and socioculture affect injury risk, response, and recovery in high intensity athletes: A consensus statement. Scandinavian Journal of Medicine and Science in Sports, 20 (Suppl. 2), 103-111. Wiese-Bjornstal, D. M., & Smith, A. M. (1993). Counseling strategies for enhanced recovery of injured athletes within a team approach. In D. Pargman (Ed.), Psychological bases of sport injuries (pp. 149-182). Morgantown, WV: Fitness Information Technology. Wiese-Bjornstal, D. M., Smith, A. M., & LaMott, E. E. (1995). A model of psychologic response to athletic injury and rehabilitation. Athletic Training: Sports Health Care Perspectives, 1, 17-30. Wiese-Bjornstal, D. M., Smith, A. M., Shaffer, S. M., & Morrey, M. A. (1998). An integrated model of response to sport injury: Psychological and sociological dynamics. Journal of Applied Sport Psychology, 10, 46-69. Williams, J. M., & Andersen, M. B. (1998). Psychosocial antecedents of sport injury: Review and critique of the stress and injury model. Journal of Applied Sport Psychology, 10, 525.

In: Athlete Performance and Injuries Editors: João H. Bastos and Andreia C. Silva

ISBN 978-1-61942-658-0 © 2012 Nova Science Publishers, Inc.

Chapter 4

CARDIOMETABOLIC INJURY DUE TO RECOMBINANT HUMAN ERYTHROPOIETIN DOPING FOR IMPROVEMENT OF SPORTS PERFORMANCE: CHRONIC (TRAINING) VERSUS ACUTE (EXTENUATING) AEROBIC EXERCISE Edite Teixeira-Lemos1,2, Helena M. Teixeira3,4,5, Nuno Piloto1, Margarida Teixeira1,6, Belmiro Parada1, Paulo Rodrigues-Santos6, Lina Carvalho7, Rui Alves8, Elísio Costa9,10, Luís Belo10,11, Alice Santos-Silva10,11, Frederico Teixeira1 and Flávio Reis*1 1

Laboratory of Pharmacology & Experimental Therapeutics, IBILI, Medicine Faculty, Coimbra University; 2 ESAV, Polytechnic Institute of Viseu; 3 National Institute of Legal Medicine – North Branch and CENCIFOR – Forensic Sciences Centre, Portugal; 4 Medicine Faculty, Porto University; 5 Medicine Faculty, Coimbra University; 6 Laboratory of Immunology and Oncology, Center for Neuroscience and Cell Biology, Coimbra; 7 Institute of Anatomic Patology and 8 Department of Nephrology,Medicine Faculty, Coimbra University; 9 Institute of Health Sciences, Catholic University, Porto; 10 Institute for Molecular and Cellular Biology, Porto University; 11 Biochemistry Department, Pharmacy Faculty, Porto University. Portugal

* Corresponding author: Flávio Reis, PhD, Laboratory of Pharmacology and Experimental Therapeutics, IBILI, Medicine Faculty, Sub-Unit 1 (Polo 3), Coimbra University, 3000-548 Coimbra, Portugal, Tel: +351 239 480 053; Fax: +351 239 480 065, E-mail: [email protected].

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ABSTRACT Athletes who abuse recombinant human erythropoietin (rhEPO) consider only the benefit to performance and usually ignore the potential short and long-term liabilities. Elevated haematocrit and dehydratation associated with intense exercise may reveal undetected cardiovascular risk, but the mechanisms underlying it remain to be fully explained. This chapter intended to compare the cardiometabolic effects of rhEPO treatment on rats under chronic vs acute extenuating exercise. The following male Wistar rat groups were assessed: control – sedentary (Sed); rhEPO – 50 IU/Kg/wk; Exercise (Ex) – swimming: 1 hr, 3 times/wk; Ex+rhEPO. For the chronic exercise a period of 10 wks was assessed, while for the acute exercise, a single bout of extenuating swimming was performed, with a rhEPO treatment for 3 wks prior to the acute section. Blood pressure and heart trophism were analysed. Blood and tissue samples were assessed for: biochemical data, haematology, catecholamine and serotoninergic measures, redox status and heart gene expression profile. The chronic Ex+rhEPO rats showed higher values of RBC, Htc and Hb vs chronic Ex, as well as vs acute Ex+rhEPO. Both chronic and acute swimming showed a remarkable sympathetic and serotonergic activation. rhEPO treatment in chronic training has promoted oxidative stress, in contrast with the antioxidant effect on the acute exercise. rhEPO in trained rats promoted erythrocyte count increase, hypertension, heart hypertrophy, sympathetic and serotonergic overactivation. One rat of the chronic Ex+rhEPO group suffered a sudden death episode during the week 8 and the tissues analyzed showed: brain with vascular congestion; left ventricular hypertrophy, together with a “cardiac liver”, suggesting the hypothesis of heart failure as cause of sudden death. In the chronically trained rats, rhEPO per se promoted apoptosis, proliferation and angiogenesis, which was partially or totally prevented in the Ex+rhEPO rats. In conclusion, the effects of rhEPO doping in rats under exercise is notoriously more deleterious in circumstances that mimic high-performance athletes (chronic training) than in occasional consumers (acute sessions), particular due to increased cardiovascular risk. Chronic rhEPO doping in rats under chronic exercise promotes not only the expected RBC count increment, suggesting hyperviscosity, but also other serious deleterious cardiovascular and thromboembolic modifications, including mortality risk, which might be known and assumed by all sports authorities, including athletes and their physicians.

Keywords: rhEPO doping – cardiometabolic injury – chronic vs acute aerobic exercise – hypertension – hyperviscosity – sympathetic and serotoninergic overactivation – oxidative stress – apoptosis, proliferation and angiogenesis profile

INTRODUCTION Erythropoietin (EPO) is a circulating glycosylated protein hormone, synthesized mainly in the kidneys, that is the primary regulator of RBC formation (Lacombe and Mayeux, 2006). The production of recombinant human erythropoietin (rhEPO), which has been widely used for correction of anaemia, allowed many patients to resume their normal daily activities due to increased energy (Fliser et al., 2006). The rationale for the use of rhEPO in sport, as doping, is based on the increased O2 capacity it provides, due to augmented erythropoietic stimulation (Adamson and Vapnek, 1991; Elliott et al., 2008). The availability of rhEPO allowed its use in doping (Gareau et al., 1996; Scott and Phillips, 2005). As soon as the anti-

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doping authorities were able to distinguish between the endogenous and the rhEPO (Lasne and de Ceaurriz, 2000), the scandal of its use in sport was revealed, with particular emphasis to cycling and cross-country skiing, compared to other sports modalities (Robinson et al., 2003; Bento et al., 2003). Sports authorities prohibited the use of rhEPO in 1988. The idea was, first, to limit the degree of health risk and, second, the degree of performance enhancement. Athletes who abuse rhEPO consider only the benefit to performance and usually ignore the potential short and long-term liabilities (Gauthier, 2001; Lipsic et al., 2006; Mastromarino et al., 2011). Illegal and abusive utilization of this hormone has been found in both endurance and short-duration sports, which require distinct energetic sources, but the potential deleterious effects and mechanism underlying, remain to be fully elucidated. In the early 1990s, there was a considerable speculation about the involvement of rhEPO doping in the death of professional cyclists (Gareau et al., 1996; Thein et al., 1995; Scheen, 1998). The artificial increase in RBC count and haematocrit, further enhanced by dehydratation during prolonged exercise, predisposes to thromboembolic complications, which might be connected to sudden death in sport practice (Thein et al., 1995). However, the cellular/molecular mechanisms underlying those sudden death episodes are poorly clarified, as well as whether rhEPO use was linked to this outrageous phenomenon. EPO has been recognized as a key player in a broad variety of processes in cardiovascular pathophysiology, including apoptosis, cell proliferation, ischaemia and the nitric oxide (NO) pathway, which reinforces their putative use in non-haematological conditions, as a pleiotropic protective factor (Ghezzi and Brines, 2004; Manolis et al., 2005), namely due to its cardio and neuroprotective actions (Parsa et al., 2003; Bogoyevitch, 2004; Riksen et al., 2008; Latini et al., 2008). Nevertheless, for a significant percentage of patients, rhEPO treatment losses efficacy, becoming resistant, recommending dose increment with further deterioration of heart function, most probably due to the expected hyperviscosity and thromboembolisms (van der Putten et al., 2008; Lipsic et al., 2006; Mastromarino et al., 2011). Therefore, although enhanced EPO synthesis is viewed as an appropriate compensatory mechanism in the cardio-renal syndrome, excessive EPO synthesis in the advanced stages of both the chronic renal failure and congestive heart failure appears to be predictive of higher mortality (Mastromarino et al., 2011). For rhEPO abuse as sports doping the same issues should be hypothesizes, but remains to be fully elucidated. Therefore, whether the above mentioned putative protective actions are present and could protect athletes from rhEPO use risks or prolonged and intense rhEPO abuse promotes the known deleterious cardiac and thromboembolic effects, remains to be fully characterized. This chapter intended to compare the cardiometabolic effects of rhEPO treatment on rats under chronic vs extenuating acute exercise, focusing on haemogram, lipid profile, blood pressure, circulating and tissue catecholamines and serotonin contents, redox status and heart gene expression profile.

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MATERIAL AND METHODS Animals and Experimental Protocol Male Wistar rats (Charles River Laboratories Inc., Barcelona, Spain), weighting 220250g, were maintained in an air conditioned room (22-24ºC) with humidity of 60%, subjected to 12-h dark-light cycles and given standard rat chow (AO4, Panlab, Letica, Barcelona, Spain) and water ad libitum. All experiments with animals were performed in accordance with the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes (Council of Europe no123, Strasbourg, 1985), as well as with ethical laws of the National Institutions for Science and Technology. For the chronic exercise (swimming), after a period of adaptation of 2 week, 4 groups (n=7) were evaluated for 10 wks-treatment: control – sedentary (Sed); rhEPO – 50 IU/Kg/wk beta-EPO Recormon®, Roche Pharm. (EPO); Exercised (Ex) – swimming (1 hr, 3 times/wk); Ex+rhEPO. The swimming rats were submitted to a 1 wk period of adaptation for minimizing the water stress (bath set at 351ºC). Sessions started with 15 min, increased 5 min each day until a 60 min continuous period was achieved. Excepting one animal of the EX+rhEPO group, which suffered a sudden death episode during an exercise session (week 8), all the animals have completed the 10-week protocol. For the acute exercise, the same 4 groups were tested and those treated with rhEPO received 50 IU/Kg/wk for 3 wks prior to the extenuating swimming session. Exercise was made without previous adaptation and until extenuation and duration was monitored. Body weight (BW) was monitored during the study, and blood pressure (BP) and heart rate (HR) measured using a tail-cuff sphygmomanometer LE 5001 (Letica, Barcelona, Spain).

Sample Collection and Preparation Blood: At the end of treatments the rats were subjected to intraperitoneal anesthesia with a 2 mg/kg BW of a 2:1 (v:v) 50 mg/mL ketamine (Ketalar®, Parke-Davis, Lab. Pfeizer Lda, Seixal, Portugal) solution in 2.5% chlorpromazine (Largactil®, Rhône-Poulenc Rorer, Lab. Vitória, Amadora, Portugal) and blood samples were immediately collected by venipuncture from the jugular vein into syringes without anticoagulant (for serum samples collection) or with the appropriate anticoagulant: EDTA, heparin or a solution of ACD (acid citratedextrose). Blood was centrifuged (160 g for 10 min. at 20ºC) to obtain platelet rich plasma (PRP), which was then centrifuged (730 g for 10 min. at 20ºC) to obtain the platelet pellet and poor platelet plasma (PPP). Tissues: The rats were sacrificed by cervical dislocation and the heart, the adrenals, the kidneys, the liver and the gastrocnenius muscle were immediately removed, placed in ice-cold Krebs’ buffer and carefully cleaned of adherent fat and connective tissue. The BW and the weights of heart (HW) and left ventricle (LVW) were measured in all the rats under study in order to be used as trophy indexes. The following tissues were removed from the rat that suffered a sudden death episode during an exercise session after 8 weeks of treatment: lungs, kidneys, brain, heart/left ventricle and liver. Tissues were analyzed for histomorphology with haematoxilin-eosin (H&E) staining.

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An aliquot of the following tissues collected were stored for further analysis: adrenals, left ventricle (LV) and brain in HClO4 and gastrocnenius muscle in liquid nitrogen.

Haematological Data Red blood cell (RBC) count, haematocrit (Hct), haemoglobin (Hb), platelets count, mean platelet volume (MPV), platelet distribution width (PDW) and plaquetocrit (PCT) were assessed by using an automatic Coulter Counter® (Beckman Coulter Inc., USA).

Biochemical Data Serum creatinine, ureia and uric acid concentrations were used as renal function indexes, aspartate (AST) and alanine aminotransferase (ALT) levels were assessed for liver evaluation and creatine cinase (CK) activity was assesses as a measure of muscle lesion, through automatic validated methods and equipments (Hitachi 717 analyser). Plasma glucose levels were measured using a Glucose oxidase commercial kit (Sigma, St. Louis, Mo, USA). Serum total cholesterol (Total-c) and triglycerides (TGs) were analysed on a Hitachi 717 analyser (Roche Diagnostics Inc., MA, USA) using standard methods.

Catecholamine and Serotonin Assay Noradrenaline (NA) and adrenaline (A) concentrations in plasma, platelet, adrenals, left ventricle and brain, as well as plasma, platelet and brain 5-hydroxy-tryptamine (5-HT) and 5hydroxyindoleacetic acid (5-HIAA) contents, were evaluated by high performance liquid chromatography with electrochemical detection (HPLC-ECD), according to previously described (Reis et al., 2005). Catecholamine measurement: The platelet pellet and the plasma samples from all the rat groups were prepared as previously described. In brief, to 2 ml of these fraction, in reduced glutathione (0.250 M) to prevent amine degradation, 100 ng/ml of DHBA, 50 mg of alumina and 1 ml of Tris-HCl buffer (1.5 M, pH 8.6) containing 0.1 M Na2-EDTA were added. The mixture was then shaken for 10 min, allowed to stand for a few min to sediment alumina, and the supernatant was aspirated. The alumina was then washed three times with ultra-pure water and transferred to an appropriate microfilter system, where adsorbed CAs were finally obtained by centrifugation (1,000 g, 1 min) after having added HClO4 (0.1 M). Concerning adrenals, ventricles and brain preparation, at the end of treatments, the rats were sacrificed and the adrenals, the heart and the brain were immediately removed, placed in ice-cold Krebs’ buffer and carefully cleaned of adherent fat and connective tissue. The two adrenals and ventricles and the brain were then homogenized in HClO4 (0.1 M, Sigma) at 4 °C and then centrifuged at 2,500 g for 15 min at 4 °C. The supernatant was filtered by microcentrifuge filter (Spin-X HPLC, Costar®, Corning Inc. NY, USA) and the filtrate was used for the CA assay. Serotonergic measures: The platelet pellet, obtained as above described, was suspended in 1 ml of a buffer solution (pH 7.4) containing (in mM): NaCl (145), KCl (5), MgSO4 (1),

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CaCl2 (1), Dglucose (10) and ascorbic acid (20 μM), in order to prevent metabolic degradation. After protein disruption, by adding perchloric acid followed by 15 min at ice temperature, the suspension was centrifuged (730 g for 10 min at 20 °C) and the supernatant containing the released serotonin (5-HT) and 5-hydroxyindol-acetic-acid (5-HIAA) was collected. One ml of plasma was prepared by adding 40 μM of ascorbic acid and 1 ml of perchloric acid (3 M) with EDTA (1%). After vigorous vortex, the samples were maintained all the night at 4 °C. The suspension was finally centrifuged at 1,200 g for 15 min at 4 °C and the supernatant collected. Platelet, plasma and brain (collected and prepared as above mentioned) 5-HT and 5-HIAA contents were determined by HPLC-ECD, according to the following chromatographic conditions. Chromatographic conditions: A Gilson Applied Chromatographic System with a 305 model pump and a 231 injection valve model, with a 50 μl loop, was used. A Biophase ODS RP18 analytical column (250×4.6, ∅=5 μ; Bioanalytical Systems Inc., U.S.A.) was used and separation was made possible by using an isocratic solvent system consisting of an acetatecitrate buffer (sodium acetate 0.1 M, citric acid 0.1 M), containing sodium 1-octanesulfonate (0.5 mM), EDTA (0.15 mM), dibutylamine (1 mM) and 10 % methanol. A flow rate of 1 ml/m was maintained and detection achieved by using a 141 Gilson electrochemical detector model (650 mV). Catecholamine and serotonin levels quantification: NA, AD, 5-HT and 5-HIAA contents were measured by using appropriate standards (Sigma Chemical Co., St. Louis, MO, U.S.A.) and software (Gilson 710). NA and AD concentrations were expressed in ng/ml for plasma and platelets and in g/g for adrenals, left ventricle and brain. 5-HT and 5-HIAA values were expressed in ng/ml for platelet and plasma levels and in ng/g for the brain contents.

Redox Status in Serum and Muscle The thiobarbituric acid reactive-species (TBARs) assay was used to assess serum and muscle products of lipid peroxidation (via malondialdehyde: MDA), according to previously described (Estepa et al., 2001; Baptista et al., 2008). In brief, a portion of the gastrocnemius muscle was homogeneized with a Tissumizer (Tekmar Industries, CI, USA) in 9 vol. of cold PBS, then quickly sonicated and thereafter centrifuged at 1,200 g, 10 min at 4 °C. The supernatant was recovered for determination of tissue MDA according to the same method. In brief, in a propylene tube 100 μl of serum or a sample of tissue extraction supernatant was mixed with 100 μl of FeCl3 .6H2O (2.7 g/l), 100 μl of butylated hydroxytoluene (BHT) dissolved in absolute ethanol (2.2 g/l), 1.5 ml HCl-glycin buffer solution (pH 3.5) and 1.5 ml of thiobarbituric acid (TBA) in sodium dodecyl sulphate (SDS) 0.3% (v/v). The tubes were kept in the dark at 5 °C for 1 h and then heated at 95 °C for further 1 h. Samples were thereafter cooled and extracted with a mixture nbuthanol-piridine-H2O (15:1:0.5 v/v). They were then submitted to a centrifugation at 1,200 g for 10 min. The organic phase was analysed spectrophotometrically at 532 nm using 1,1,3,3-tetramethoxypropane as external standard. The concentration of lipid peroxides (in MDA) was expressed as μmol/l in the serum and as μmol/g tissue in the skeletal muscle. Ferric reducing antioxidant potential (FRAP) assay was used to estimate serum and muscle total antioxidant status (TAS) (Benzie and Strain, 1996).

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Heart gene Expression Analysis by RT-qPCR Total RNA isolation: Hearts were isolated in autopsy and stored in RNA laterTM solution (Ambion, Austin, USA). Samples were removed from preservation solution and 1200 μl of RLT Lysis Buffer were added to proceed with disruption and homogenization for 2 minutes at 30Hz using TissueLyser (Qiagen, Hilden, Germany). Tissue lysate were processed according to the protocol from RNeasy® Mini Kit (Qiagen, Hilden, Germany). Total RNA was eluted in 50 µl of RNase-free water (without optional treatment with DNAse). In order to quantify the amount of total RNA extracted and verify RNA integrity (RIN, RNA Integrity Number), samples were analyzed using 6000 Nano Chip® kit, in Agilent 2100 bioanalyzer (Agilent Technologies, Walbronn, Germany) and 2100 expert software, following manufacturer instructions. The yield from isolation was from 0.5 to 3 µg; RIN values were 6.0-9.0 and purity (A260/A280) was 1.8-2.0. Reverse Transcription: RNA was reverse transcribed with SuperScriptTM III First-Strand Synthesis System for RT-PCR (Invitrogen Corp., Carlsbad, CA, USA). One microgram of total RNA was mixed with a 2X First-Strand Reaction Mix and a SuperScript™ III Enzyme Mix (Oligo(dT) plus Random hexamers). Reactions were carried out in a thermocycler Gene Amp PCR System 9600 (Perkin Elmer, Norwalk, USA), 10 min at 25 ºC, 50 min at 50 ºC and 5 min at 85 ºC . Reaction products were then digested with 1 µl RNase H for 20 min at 37 ºC and, finally, cDNA eluted to a final volume of 100 μl and stored at -20ºC. Relative quantification of gene expression: Performed using 7900 HT Sequence Detection System (Applied Biosystems, Foster City, USA). A normalization step preceded the gene expression quantification, using geNorm Housekeeping Gene Selection kit for Rattus norvegicus (Primer Design, Southampton, UK) and geNorm software (Ghent University Hospital, Center for Medical Genetics, Ghent, Belgium) to select optimal housekeeping genes to this study (Vandesompele et al., 2002). RT-PCR reactions used optimized specific primers (Proligo, Boulder, USA) for genes of interest, Bax, Bcl2, Fas, Faslg, Caspases 3 and 9, interleukin-2 (IL-2), tumour necrosis factor α (TNF-α), nitric oxide synthase 2 and 3 (NOS2 and NOS3), transforming growth factor1 (TGF-1), vascular endothelial growth factor (VEGF) and proliferating cell nuclear antigen (PCNA) and endogenous controls Actb, Gapdh and Top1 together with QuantiTect SYBR Green PCR Kit Gene expression. RT-PCR reactions were carried out with: 100ng cDNA sample, primers (50-200 nM) and 1X QuantiTect SYBR Green PCR Master Mix. Non template control reactions were performed for each gene, in order to assure no unspecific amplification. Reactions were performed with the following thermal profile: 10 min. at 95ºC plus 40 cycles of 15 seconds at 95ºC and 1 min. at 60ºC. Real-time PCR results were analyzed with SDS 2.1 software (Applied Biosystems, Foster City, USA) and quantification used the 2-ΔΔCt method (Livak and Schmittgen, 2001).

Data Analysis For statistical analysis, we used the GraphPad Prism version 5.00 software from GraphPad Software (San Diego, California, USA). Results are expressed as mean values ± standard errors of the mean (SEM). Differences between groups were tested by performing

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Factorial (two-way) analysis of variance (ANOVA), followed by the Bonferroni test Post’hoc test. Differences were considered to be significant at P