Effects of High-Carbohydrate and Low-Fat Versus High-Protein and Low-Carbohydrate

Diets on High-Intensity Aerobic Exercise

A dissertation presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Kumika Toma

August 2009

This dissertation titled

Effects of High-Carbohydrate and Low-Fat Versus High-Protein and Low-Carbohydrate

Diets on High-Intensity Aerobic Exercise

KUMIKA TOMA

has been approved for

the Department of Biological Sciences

and the College of Arts and Sciences by

Robert S. Hikida

Distinguish Professor of Biomedical Sciences

Benjamin M. Ogles

Dean, College of Arts and Sciences 3

ABSTRACT

TOMA, KUMIKA, Ph.D., August 2009, Biological Sciences

Effects of High-Carbohydrate and Low-Fat Versus High-Protein and Low-Carbohydrate

Diets on High-Intensity Aerobic Exercise (226 pp.)

Director of Dissertation: Robert S. Hikida

Adequate physical activity and a balanced diet (60 % carbohydrates, 30 % fats, and 10 % proteins of total caloric intake) are two important factors for a healthy life.

Individuals who engage in limited physical activities and who have poor eating habits are more likely to be overweight/obese and to have many diseases. Many people who want to maximize their weight loss attempt to combine exercise and diet regimens. Recently a low-carbohydrate high-protein diet has become popular as a weight reduction method.

However, the effectiveness and safety of this diet regimen are unclear. Moreover, the effectiveness and safety of this diet regimen when combined with exercise is unknown. In this study, seventeen healthy male subjects were assigned to either a traditional, high- carbohydrate low-fat diet (57.5 % carbohydrates, 25.4 % fats, and 14.2 % protein of total caloric intake) group or an experimental, low-carbohydrate high-protein diet (30.6 % carbohydrates, 38.5 % fats, and29.9 % protein of total caloric intake) group while participating in seven weeks of high-intensity rowing training. Subjects rowed twice a week during the first three weeks and three times a week during the last four weeks.

During the rowing training, post-rowing heart rates were recorded. The rowing intensity was gradually increased to maintain the same post-rowing heart rate. The duration of each rowing session was also gradually extended while the resting time was reduced. 4

After the seven-week rowing training and diet intervention, the traditional diet group showed improvements in cardiovascular function, lipoprofiles, rowing performance, and skeletal muscle size, while the experimental diet group showed improvements in cardiovascular function and lipoprofile but not skeletal muscle size. These results showed that regardless of what people eat, the high-intensity rowing exercise improves their physical fitness level and benefits their health status. However, a high-carbohydrate diet, rather than a high-protein diet, would increase the skeletal muscle fiber size when combined with high-intensity rowing exercise.

Approved: ______

Robert S. Hikida

Distinguish Professor of Biomedical Sciences

ACKNOWLEDGMENTS

This dissertation project has been supported by many people and organizations. I would like to thank to hard-working subjects and helpers, my advisor and committee members, courtesies from Dr. Loucks and Holzer Clinic, and Ohio University Student

Writing Center. 6

TABLE OF CONTENTS

Page

ABSTRACT ...... 3 ACKNOWLEDGMENTS ...... 5 LIST OF TABLES ...... 9 LIST OF FIGURES ...... 10 LIST OF ABBREVIATION ...... 11 CHAPTER 1: INTRODUCTION ...... 12 Energy Systems and Energy Sources ...... 13 Macronutrients and Exercise Performance ...... 17 Carbohydrates ...... 18 Fats ...... 20 Protein ...... 22 Skeletal Muscle ...... 26 Skeletal Muscle Development ...... 26 Satellite Cells ...... 27 Skeletal Muscle Fiber Types ...... 28 Skeletal Muscle and Physical Activity ...... 33 Testosterone Effect on Skeletal Muscle ...... 36 Physical Fitness ...... 39 Maximum Oxygen Consumption ...... 39 Overweightness/Obesity ...... 40 Obesity Related Health Risks and Exercise ...... 42 Carbohydrate-Restricted Diet ...... 44 Purpose of Study ...... 46 Chapter Summary ...... 47 CHAPTER 2: METHODS ...... 49 Experimental Design ...... 49 Subjects ...... 51 Familiarization ...... 52 Diet ...... 53 Training ...... 55 Measurements/Tests ...... 56 Anthropometry ...... 57 Blood Analyses ...... 60 Blood Sampling Procedure ...... 60 FastingBlood Glucose Concentration ...... 60 -hydroxybutyrate Concentration ...... 61 Total Cholesterol Concentration ...... 61 Total Testosterone Concentration ...... 62 Muscular and Cardiovascular Fitness Tests ...... 63 7

Isokinetic Maximal Strength and Endurance Tests ...... 63 Maximal Aerobic Capacity Test ...... 64 Skeletal Muscle Analyses ...... 65 Muscle Biopsy Procedure ...... 65 Skeletal Muscle Fiber Type Analysis ...... 68 Skeletal Muscle Fiber Cross-Sectional Size ...... 71 Skeletal Muscle Oxidative Activity ...... 71 Myonuclear Domain ...... 74 Satellite Cell Ratio ...... 77 Statistics ...... 77 Statistical Test Procedures ...... 79 Analyses of Compliance of Diet and Rowing Training ...... 80 Analyses of Diet-Rowing Training Effects ...... 81 A Priori Test ...... 82 Direction of Hypotheses ...... 84 Correlation ...... 86 A Priori Power Analyses ...... 87 Chapter Summary ...... 89 CHAPTER 3: RESULTS ...... 90 Characteristics and Compliance of Subjects ...... 90 Diet and Rowing Training ...... 91 Diet ...... 91 Rowing Training ...... 96 Anthropometry ...... 98 Blood Analyses ...... 101 Fasting Blood Glucose Concentration ...... 101 Fasting β-hydroxybutyrate Concentration ...... 102 Total Plasma Cholesterol Concentration ...... 104 Total Plasma Testosterone Concentration ...... 105 Isokinetic Muscular Strength and Endurance Tests ...... 107 Maximum Oxygen Consumption and Maximum Power Output ...... 108 Skeletal Muscle Characteristics ...... 111 Skeletal Muscle Fiber Type Analysis ...... 111 Skeletal Muscle Fiber Cross-Sectional Size ...... 113 Skeletal Muscle Oxidative Activity ...... 115 Relationships between Protein Intake and Power, Muscle Size, ...... 119 Chapter Summary ...... 120 CHAPTER 4: DISCUSSION ...... 122 Results of Diet and Training ...... 123 Diets ...... 123 Rowing Training ...... 126 Ketosis ...... 130 Results of Anthropometry ...... 131 Results of Blood Analyses ...... 132 8

Fasting Blood Glucose Concentration ...... 132 Fasting Plasma β-hydroxybutyrate Concentration ...... 134 Fasting Total Cholesterol Concentration ...... 135 Resting Total Testosterone Concentration ...... 137 Result of Muscular and Cardiovascular Fitness Tests ...... 138 Isokinetic Maximal Strength Test ...... 138 Maximal Aerobic Capacity Test ...... 139 Skeletal Muscle Characteristics ...... 141 Skeletal Muscle Fiber Type Analysis ...... 141 Skeletal Muscle Fiber Cross-Sectional Size ...... 141 Skeletal Muscle Oxidative Activity ...... 143 Cytoplasm-to-Nucleus Ratio (C/N) and Satellite Cell Ratio ...... 143 Chapter Summary ...... 145 CHAPTER 5: CONCLUSION ...... 146 Safety and Effectiveness of High-Intensity Aerobic Training with Different Diet Regimens ...... 147 The Benefits against Cardiovascular Risk Factors ...... 149 Skeletal Muscle Adaptation and Exercise Performance ...... 151 Limitations and Suggestions for Future Study ...... 153 Suggestions for Future Study ...... 155 REFERENCES ...... 157 APPENDIX A: INSTITUTENAL REVIEW BOARD APPROVAL ...... 196 APPENDIX B: FLYER ...... 200 APPENDIX C: CONCENT FORM ...... 201 APPENDIX D: QUESTIONNAIRE...... 207 APPENDIX E: FOOD LOG ...... 216 APPENDIX F: EXMAPLES OF FOOD PROPORTION ...... 218 APPENDIX G: TRAINING LOG SHEET ...... 220 APPENDIX H: MUSCLE BIOPSY HANDOUT...... 221 APPENDIX I: MYOFIBRILLAR ATPASE STAINING PROCEDURE ...... 223 APPENDIX J: NADH TETRAZOLIUM REDUCTASE PROCEDURE ...... 225 APPENDIX K: TISSUE FIXATION PROCEDURE FOR ELECTRON MICROSCOPY ...... 226

LIST OF TABLES

Page

Table 1 Three Linear Contrasts and Their Contrast Coefficients ...... 83 Table 2 Proportion of Daily Food Intake (Mean and SD) ...... 92 Table 3 Carbohydrate, Protein, and Caloric Intake (Mean and SD) ...... 94 Table 4 Total Work (Mean and SD) ...... 97 Table 5 Anthropometry (Mean and SD) ...... 99 Table 6 Fasting Blood Glucose Analysis (Mean and SD) ...... 101 Table 7 Beta-hydroxybutyrate Analysis (Mean and SD) ...... 103 Table 8 Total Cholesterol Analysis (Mean and SD) ...... 104 Table 9 Total Testosterone Analysis (Mean and SD) ...... 106 Table 10 Maximal Isokinetic Strength Tests (Mean and SD) ...... 107 Table 11 Maximal Oxygen Consumption Tests (Mean and SD) ...... 109 Table 12 Skeletal Muscle Fiber Type Analysis (Mean and SD) ...... 112 Table 13 Skeletal Muscle Fiber Cross-Sectional Area (Mean and SD) ...... 114 Table 14 Skeletal Muscle Oxidative Activity (Mean and SD) ...... 115 Table 15 Cytoplasm-to-nucleus Ratio and Satellite Cell Ratio(Mean and SD) ...... 117 Table 16 Previous Exercise-Diet Studies ...... 128

LIST OF FIGURES

Page

Figure 1. Time course of diet-training intervention and test measurements...... 50 Figure 2. A biopsy needle ...... 67 Figure 3. Illustration of different skeletal muscle fiber ...... 70 Figure 4. Skeletal muscle fibers stained for mATPase analysis ...... 70 Figure 5. Skeletal muscle fibers stained for NADH activity analysis ...... 73 Figure 6. Pictures of adjacent cross-sections of skeletal muscle ...... 75 Figure 7. An electron micrograph of skeletal muscle...... 76 Figure 8. Food proportions...... 93 Figure 9. Carbohydrate, protein, and caloric Intake analyses...... 95 Figure 10. Total work analysis...... 97 Figure 11. Anthropometry: body weight; fat mass; fat-free mass ...... 100 Figure 12. Fasting blood glucose analysis...... 102 Figure 13. Beta hydroxybutyrate analysis...... 103 Figure 14. Total cholesterol analysis...... 105 Figure 15. Total testosterone analysis...... 106 Figure 16. Maximal isokinetic strength...... 108 Figure 17. Maximal oxygen consumption tests ...... 110 Figure 18. Skeletal muscle fiber type analysis...... 112 Figure 19. Skeletal muscle fiber cross-sectional area...... 114 Figure 20. Skeletal muscle oxidative activity ...... 116 Figure 21. Cytoplasm-to-nucleus ratio and satellite cell ratio ...... 118 Figure 22. Relationship between daily protein intake and the post-training resting total testosterone concentration...... 120 Figure 23. Average post-exercise heart rate per minute ...... 129 Figure 24. Average daily work...... 129 Figure 25. Relationship between the pre-training total cholesterol concentration and its reduction after the training...... 137

LIST OF ABBREVIATION

ADP - Adenosine diphosphate ANCOVA - Analysis of covariance ANOVA - Analysis of variance ATP - Adenosine triphosphate BCAA – Branched-chain amino acid CLFS – Chronic low frequency stimulation FAD - Flavin adenine dinucleotide GDP - Guanosine diphosphate GLUT 4 - Glucose transporter 4 GnRH - Gonadotropin-releasing hormone GTP - Guanosine triphosphate HA - Alternative hypothesis HC – High-carbohydrate, low-fat diet HDL - High-density lipoprotein HO - Null hypothesis HP – High-protein, low-carbohydrate diet IMF - Intermyofibrillar kcal - Kilocalories kg - Kilograms LDL - Low-density lipoprotein MANOVA – Multivariate analysis of variance mATPase – Myofibrillar ATPase MS - Mean square NBT - Nitroblue tetrazolium SD – standard deviation SS - Subsarcolemmal VO2 max - Maximum oxygen consumption 12

CHAPTER 1: INTRODUCTION

Human systems are pliable and adaptable to stimuli. If stimuli are applied long enough, adaptation occurs. Selye (1950) proposes that fewer stimuli would lower biological function; adequate stimuli would enhance biological function maximally; and too much stimuli would terminate biological function. Adequate physical stimulus, such as exercise, is beneficial to the body’s healthy functioning. Aerobic exercise causes cardiovascular adaptation, e.g. increased blood circulation, heart volume, capillary density, ventilation, oxygen uptake, and substrate storage for fuel. Anaerobic exercise also causes neuromuscular adaptation, e.g. increased muscular strength and muscle size, and increased oxidative capacity. Because of these physiological effects, exercise has been used not only to improve sports performance but also to improve health. These adaptations benefit people regardless of their age or gender (Falk & Eliakim, 2003;

Hartman et al., 2007; Pogliaghi et al., 2006).

When the level of physical activity increases, an adequate amount of energy has to be taken into the body. Food intake provides energy sources and ingredients to build up tissues. For average adults, the U.S. Food and Drug Administration (FDA) proposes a balanced 2000-kilocalorie diet which is high in carbohydrates (60 %), low in fats (30 %), and contains moderate proteins (10 %) (FDA Backgrounder, 1999). However, several different individuals and institutions proposed different proportions of nutrients in diets

(Kennedy et al., 2001). An imbalance of energy, less energy expenditure and excess energy intake, results in increased fat storage in the body. This accumulation of subcutaneous and visceral fat as well as increased body weight is known to lead to many 13

disease conditions. Therefore, studying energy supply and physical activity gives us a

better understanding of a person’s overall health.

This chapter starts with a review of energy systems and energy sources followed

by a review of skeletal muscle properties. Then, the relationship between macronutrients

and exercise performance is discussed. Cardiovascular risk factors are also reviewed with

an emphasis on nutrients and physical activity. Lastly, the purpose of this study is

addressed.

Energy Systems and Energy Sources

It is known that energy demands increase during exercise, thereby facilitating the

enzyme activities to metabolize nutrients in order to generate energy. Throughout life, the

body continuously undergoes chemical processes called metabolism, which is composed

of catabolism and anabolism. Catabolism is the process of breaking down chemical

compounds into smaller molecules such as digested food to nutrients for energy; and

anabolism is the process of building up cells and tissues through molecules such as

building skeletal muscle with amino acids. Physiological adaptation occurs during

catabolic and anabolic processes.

Adenosine triphosphate (ATP) is the energy used by organisms. Limited amounts

of ATP are stored in muscle tissues. ATP is generated with (aerobically) or without

(anaerobically) oxygen by metabolizing nutrient molecules. Carbohydrates and fats are two of the three major nutrients that provide energy aerobically. Carbohydrates are 14

broken down to glucose and stored as glycogen in the tissues. Fat is broken down into a

small amount of glycerol and large amounts of free fatty acids.

When glucose is metabolized anaerobically, ATP is synthesized. However, this

glycolysis, occurring in the cytosol, only supports energy supply for less than 90 seconds.

Energy requirements beyond 90 seconds rely on the oxidation of substrates. Through

glycolysis one molecule of glucose is broken down to form two molecules of pyruvate.

When oxygen is available pyruvate is converted into two molecules of acetyl Co-A. Each

acetyl Co-A then enters the mitochondrion for oxidative phosphorylation. When oxygen

availability is low, pyruvate is converted into lactates, which enters blood circulation and

then are used as fuel in cardiac and skeletal muscle (Brooks, 2000; Gertz et al., 1988;

Gladden, 2000) and/or are converted into glucose in the liver (Cori, 1931).

Oxidative phosphorylation is composed of two parts: the Krebs cycle and the

electron transport chain. Going through the Krebs cycle, one guanosine triphosphate

(GTP) is resynthesized from a guanosine diphosphate (GDP); and hydrogen ions are

transferred to coenzymes, nicotinamide adenine dinucleotide (NAD+) and flavin adenine

dinucleotide (FAD), to form NADH and FADH2 by oxidative enzymes. Energy from

GTP is used to resynthesize adenosine diphosphate (ADP) to ATP. NADH and FADH2

are transported through the electron transport chain, where the hydrogen ions of NADH and FADH2 are pushed out from the mitochondrial matrix to the inner membrane space.

Chemiosmotic theory explains how this pH gradient created by hydrogen ions produces

free energy for ATP resynthesis in the electron transport chain. Triglycerides, a form of

lipid, are broken down to form fatty acids and glycerol. Glycerol can be converted to an 15

intermediate molecule of glycolysis and fatty acids can be further broken down to acetyl

Co-A, which can enter the Krebs cycle only when sufficient glucose is available to be

oxidized in the Krebs cycle.

The majority of ATP is synthesized in mitochondria. In skeletal muscle, there are

two subtypes of mitochondria: subsarcolemmal (SS) mitochondria located close to the

sarcolemma and intermyofibrillar (IMF) mitochondria located among the myofibrils

(Hoppeler, 1986). SS mitochondria are thought to produce ATP for membrane function,

while IMF mitochondria are for myofibril contraction (Hood, 2001). IMF mitochondria

reportedly respond more sensitively and quickly to muscle contraction as their contents

can increase two-fold within a two-week endurance training period (Connor et al., 2000;

Hood, 2001).

Fats have a higher energy potential than carbohydrates. One molecule of glucose

(6 carbons) yields 39 molecules of ATP, while one molecule of palmitic acid, one of the

common fatty acids (16 carbons) yields 129 molecules of ATP. One gram of glucose

produces 4.1 Kcal, while one gram of fat produces 9.4 Kcal. However, it is estimated that

carbohydrates yield 5.05 Kcal per liter of oxygen, while fats yield 4.70 Kcal. Presumably,

the rate of energy released from fat is too slow for most energy demands (Wilmore &

Costill, 2004). Triglycerides are found in food and also stored in the body: adipose tissues

and intramuscular fat droplets. Triglycerides are broken down to free fatty acids, which

will be catabolized to be acetyl CoA. Acetyl CoA can enter the Krebs cycle to generate

ATP. However, oxidative phosphorylation occurs only when a sufficient amount of glucose is available. When there is not a sufficient amount of glucose available for 16

oxidative phosphorylation, acetyl CoA is condensed to form β-hydroxybutyrate (Helge &

Kiens, 1997).

Beta-hydroxybutyrate is one of ketone bodies used as fuel for the cardiac and

skeletal muscles and the brain. Brains can utilize only glucose and ketone bodies as fuel.

Therefore, when glucose is not sufficient, for example during starvation, β- hydroxybutyrate becomes a major fuel for the brain. However, ketone bodies are very acidic. While normal arterial pH is about 7.4, acidosis is a condition when hydrogen ion concentration is higher than normal resulting in pH lower than 7.35. A high concentration of ketone bodies, such as in severe diabetes, can result in an acidotic coma. Therefore, β-

hydroxybutyrate is not a perfect substitute for glucose.

Protein is broken down to amino acids. Amino acids have three parts: amino

group, carboxylic acid group, and side chain. Based on the different structure of side

chains, amino acids are classified into 20 different forms. Eleven of the 20 amino acids

are called non-essential amino acids because they can be synthesized in the body, and the

other nine are essential amino acids because they are not synthesized in the body. While

all amino groups are basic and all carboxylic acid groups are acidic, some side chains are

neutral, basic, or acidic. Therefore, amino acid concentration affects a person’s acid-base

balance since some amino acids are hydrogen ion donors and some are acceptors.

Although protein turnover (the breaking down of old skeletal muscle and the

building up of new muscle) continuously occurs in the body, amino acids do not serve as

a fuel source under normal conditions. Digested amino acids enter the circulation and are

pooled to build skeletal muscle, connective tissues, and various enzymes. However, 17 skeletal muscle becomes a fuel source in extreme conditions such as starvation. The liver can oxidize most of the 20 amino acids that are present in proteins. Amino acids can be converted into intermediate substrates of oxidative phosphorilation process

(gluconeogenesis) or into Acetyl-Co A (ketogenesis). Proteins are broken down into amino acids, and then nitrogens are removed from amino acids or transferred to other chemical compounds. Remaining carbon skeletons become fuel sources by either entering various stages of glycolysis and the Krebs cycle or being converted to Acetyl-Co

A (Wagenmakers, 1998). However, their contribution to energy production in normal life is much less than that of carbohydrates and fats (Berg et al., 2002).

Proper nutrition provides fuel for biological work. It also provides chemicals for enzymes that extract and use potential energy for energy production, repairing existing cells, and synthesizing new tissues. Because nutrients provide energy and regulate the physiological processes associated with exercise, changes in diet can improve exercise performance.

Macronutrients and Exercise Performance

Increased duration and/or intensity of physical activity demands more energy production in the body. The abilities to provide adequate fuel and oxygen and to maintain the internal environment are important during exercise. Human systems involve many chemical reactions using enzymes, which are composed of amino acids. Enzymatic activity is sensitive to pH; and it can be altered by slightly lowered pH in the body.

During exercise, enzymatic activities, such as energy producing pathways, are enhanced. 18

A slower rate of energy release (Wilmore & Costill, 2004) and lower enzymatic activity

caused by acidity are limiting factors of energy production during exercise (Greenhaff et al., 1988a). Therefore, food intake is one of the key factors affecting exercise performance.

Carbohydrates

Carbohydrates are the main energy source at maximum aerobic exercise levels because the oxidation of glucose generates ATP more efficiently than free fatty acids

(Brooks & Mercier, 1994). The intensity of aerobic exercise determines which nutrients

(glucose or free fatty acids) are oxidized. At rest, fats are mainly used as fuel. As exercise intensity increases, the proportion of fats used decreases and that of carbohydrates

increases. Because both carbohydrates and fats have advantages and disadvantages in terms of their efficiency in energy production related to quantity and timing, researchers have been investigating the effects of carbohydrate and fat intake in exercise events.

Carbohydrates are stored in muscle in the form of glycogen. In glycogenolysis, glycogen is broken down into glucose. The amount of muscle glycogen stored depends on the amount of consumed carbohydrates. Because muscle glycogen is the primary energy source during exercise and muscle glycogen depletion is one of the factors causing fatigue and decreasing exercise performance, increasing and maintaining muscle glycogen is thought to be a key for endurance training and competitive events. The pre- exercise muscle glycogen concentration was shown to be positively related to time to exhaustion in endurance performance (Åstrand, 1967). Based on this finding, effects of 19

different amounts of carbohydrate ingestion have been investigated by changing the

proportions of carbohydrates and fats in food consumption. Costill and his colleagues

(1971) showed that a high-carbohydrate diet (70 % of total calories) allowed almost

complete muscle glycogen replenishment after two hours of high-intensity running, while

a low-carbohydrate diet (40% of total calories) only replenished half of muscle glycogen

supplied by of a high-carbohydrate diet. Additionally, they reported that the amount of

muscle glycogen declined if participants ate a low percentage of carbohydrates when they

continued daily training. A study comparing the effects of high (70 %) and moderate (42

%) dietary carbohydrates on rowing training results showed that although both groups

improved their VO2 max, the high-carbohydrate group showed significant increases in average power output and muscle glycogen concentration (Simonsen et al., 1991).

While it is known that insulin signals glucose transporter 4 (GLUT 4) translocation to skeletal muscle cell membrane (sarcolemma), exercise is also known to induce GLUT 4 translocation and facilitate skeletal muscle glycogen storage. Consuming low-carbohydrate food after exercise decreased GLUT 4 (Koshinaka et al., 2004) and lowered GLUT 4 mRNA expression in skeletal muscle (MacLean et al., 2002).

Conversely, consuming high-carbohydrate food after exercise increased gene expression for glucose regulation (Arkinstall et al., 2003). These results indicate that muscle glycogen storage is affected by both exercise and glucose availability.

Carbohydrates affect not only muscle glycogen storage, but also skeletal muscle metabolism. Consuming carbohydrates combined with proteins after exercise is reported to minimize skeletal muscle catabolism by promoting insulin secretion (Rasmussen & 20

Phillips, 2003; Rennie, 2007) and enhancing skeletal muscle anabolism (Børsheim et al.,

2004; Rasmussen & Phillips, 2003). These reports indicate the importance of

carbohydrate intake for muscle glycogen storage and skeletal muscle protein synthesis.

Carbohydrates are also the fuel for the central nervous system. Therefore, there is

a competition for fuel between working skeletal muscle and the brain. As a result, the

availability of glucose for skeletal muscle is a limiting factor for performance in

endurance events and high-intensity exercise (Burke et al., 2004; Coyle, 1995).

Fats

Because fats are fuel sources and abundant in the body, studies have compared the

effects of fats to carbohydrates on exercise performance. As previously explained, fats

are broken down to acetyl CoA and metabolized in the Krebs cycle. When there are

excess acetyl CoA molecules or an insufficient amount of glucose available, acetyl CoA

condenses to form ketone bodies, which are used as fuel for cardiac and skeletal muscles

and the brain. A low-carbohydrate but eucaloric diet causes carbohydrate-starvation, which increases ketone body production (Brooks et al., 1996). However, ketone bodies are very acidic. Research has shown that a low carbohydrate and high fat diet results in a lowered plasma pH level, which reduces the total cycling time during maximum oxygen consumption (VO2 max) tests (Greenhaff et al., 1987).

In a previous study, low carbohydrate intake before endurance cycling exercise resulted in high serum concentrations of β-hydroxybutyrate and free fatty acids. This indicates that fats are used to compensate for insufficient glucose (Pitsiladis & Maughan, 21

1999). Another study reported that after seven weeks of moderate endurance exercise, the

time to exhaustion on a cycle ergometer was shorter among the subjects on a low-

carbohydrate high-fat diet, compared to those on a high-carbohydrate diet (Helge et al.,

1996). Another study reported that a high-fat diet combined with six weeks of varied

endurance training (walking, running, cycling, and cross-training) lowered VO2 max and maximum power in cycling tests (Fleming et al., 2003).

Researchers have also investigated the effects of a high-fat diet during high- intensity exercise. A Wingate cycle ergometer test is a test of maximum anaerobic power production in which subjects cycle against a load at their maximum effort for 30 seconds to measure their anaerobic power and capacity. A low carbohydrate intake resulted in a reduced mean power output, lower lactic acid concentration after exercise, and higher serum concentrations of β-hydroxybutyrate. This compensation using fat oxidation was not sufficient for high-intensity work (Langfort et al., 1997). High fat intake resulted in increased intracellular lipids and longer running times among long distance runners, but also caused decreased power output (Fleming et al., 2003; Hoppeler et al., 1999; Vogt et al., 2003). Therefore, consuming a large amount of fat (fat loading) is more beneficial than carbohydrate loading for athletes participating in events such as triathlons and ultra- marathons.

Protein

Protein is one of three nutrients besides carbohydrates and fats that are abundant in the body. As previously explained, carbohydrates and fats are the body’s main energy sources, while proteins are not used as fuel, except in extreme conditions such as starvation. During starvation, they become energy sources through liver gluconeogenesis.

Compared to the resting state, exercise demands a larger amount of energy and/or a longer duration of energy supply to maintain the body’s function. During four hours of low intensity cycle exercise, protein turnover and fatty acids turnover increased, which indicated these nutrients were used as fuel sources (Ahlborg et al., 1974). Reportedly, the rate of protein catabolism during exercise among male subjects increased when initial muscle glycogen concentration was low (Lemon & Mullin, 1980), which also indicates that proteins were catabolized as fuel sources.

Among the essential amino acids, leucine, isoleucine, and valine are called branched-chain amino acids (BCAA). Unlike other amino acids that are oxidized in the liver, these BCAA are oxidized in the skeletal muscle. Therefore, they are thought to be the third fuel for skeletal muscles during exercise (Wagenmakers & van Hall, 1994;

Wagenmakers, 1999; Wagenmakers, 1998).

Leucine oxidation for energy in trained rats was calculated to be 37 % of their total energy expenditure during exercise (Dohm et al., 1977). It has been estimated that amino acids provide 5-15 % of the total energy expenditure in humans during exercise

(Evans et al., 1983; Dohm, 1983; Rennie et al., 1981). This difference might be due to the fact that humans have different energy pathways than other animals (Langfort et al., 23

1996). In rats, glycogen concentration is seven times higher in their livers than in their skeletal muscles (Alonso et al., 1995). When glucose is not available, rats rely on glucose from glycogenolysis and gluconeogenesis in the liver, rather than fat oxidation (Torgan et al., 1990). During exercise, they also rely on glucose from glycogenolysis and gluconeogeneisis rather than from muscle glycogen (Coyle & Hodgkinson, 1999).

However, humans rely on muscle glycogen rather than gluconeogenesis in the liver.

Differences in the mechanisms of skeletal muscle protein turnover among vertebrates were also reported (Mittendorfer et al., 2005). Therefore, it is difficult to apply animal studies to human performance. The quantity of protein catabolism contributing to energy production seems to differ among species.

Because the quantification of protein metabolism is more complicated than the quantification of carbohydrates and fats, the proportion of protein catabolism contributing to energy production is difficult to analyze. Amino acids derive from external (from digesting food) and internal sources (from breaking down tissues). All amino acids enter the free amino acid pool and are used in the synthesis of the body tissues or used for energy production. Nitrogen balance analysis is a commonly used technique to assess protein metabolism. In order to analyze nitrogen balance, urine, feces, and sweat have to be analyzed for their nitrogen content. However, it is difficult to collect an exact amount of sweat during exercise. In previous studies, the amount of nitrogen in sweat was measured with the sweat collected by wiping a subject’s body surface with towels

(Friedman & Lemon, 1988), and by estimating the amount with calculations (Meredith et al., 1989). However, either method can result in inaccurate measurements. Isotopic tracer 24

analysis is a new technique to quantify protein metabolism. Isotopically labeled amino acid is analyzed after its infusion/ingestion. This method brings more accurate analysis

than the nitrogen balance method. However, using isotopic tracers for twenty different

amino acids is not practical. Commonly one or two amino acids are selected. Therefore,

the methodologies to examine protein metabolism have limitations.

After high-intensity exercise, muscle glycogen levels decrease; and skeletal muscle is damaged due to intense exercise and/or is degraded to be a fuel. As previously discussed, glucose and fats are the main fuel sources for oxidative energy production; and proteins are the elements of contractile proteins in skeletal muscle. Many studies compared the effects of two diets: high-carbohydrates and high-fat and/or high-protein.

One of the few studies conducted to see the effects of solely protein intake on exercise performance after consuming experimental diets for three days reported that both a high- fat high-protein diet and a low-fat high-protein diet equally resulted in a shorter time to exhaustion during the cycle ergometer test at an intensity of 100 % VO2 max compared

with consuming either a normal diet or a high-carbohydrate low-fat diet. It was also

reported that the plasma pH level during exercise was lower among subjects who

consumed a high-fat high-protein diet and a low-fat high-protein diet than those who

consumed a normal diet or a high-carbohydrate low-fat diet, which indicated that there

was diet induced metabolic acidosis (Greenhaff et al., 1988a; Greenhaff et al., 1988b;

Maughan et al., 1997). While amino acids from protein catabolism provide energy for maintaining exercise, amino acids can deteriorate exercise performance by lowering the plasma pH level. Some amino acids are acidic and lower plasma pH level (Rossing et al., 25

1986). Research has shown that lowered plasma pH level due to a large amount of dietary

protein intake reduced the total cycling time during VO2 max tests (Greenhaff et al.,

1987).

In order to maintain daily exercise levels, the quantity of muscle glycogen has to

be replenished and the quality and quantity of skeletal muscle has to be maintained. For

those reasons, studies were conducted to evaluate the effects of glucose and amino acids supplements on muscle glycogen load and protein turnover after exercise. As previously discussed, post-exercise glucose ingestion promotes muscle glycogen loading. Because increased muscle contraction facilitates protein catabolism (Rennie et al., 1981) causing negative nitrogen balance, protein has to be ingested to maintain nitrogen balance.

Proteins consumed after exercise increased skeletal muscle protein synthesis (Bolster et al., 2005; Gaine et al., 2007; Kimball & Jefferson, 2002; Tipton et al., 1999), which promotes maintaining and/or increasing skeletal muscle mass. While the American

Dietetic Association (ADA) recommends 0.8 g / kg of body weight of daily protein intake for average adults, many studies recommend a higher intake (1.1 g to 1.8 g / kg of body weight) for both resistance and endurance training athletes (Gaine et al., 2006;

Gaine et al., 2007; Friedman & Lemon, 1988; Meredith et al., 1989; Tarnopolsky et al.,

1998; Tarnopolsky, 2004).

Skeletal Muscle

Skeletal Muscle Development

As previously mentioned, skeletal muscle is composed of amino acids. It is a major structural component accounting for about 40 to 45% of body mass (Rasmussen &

Phillips, 2003). About half of proteins in the body are contractile proteins in the form of skeletal muscle (Wagenmakers, 1998). Skeletal muscle is made-up of a bundle of muscle cells called skeletal muscle fibers. A skeletal muscle fiber is a large cell with multiple nuclei compared to other types of cells. Skeletal muscle derives from the somites of the embryo. Myoblasts are the muscle forming cells from mesodermal cells. After proliferative mitoses, each cell undergoes a final division and then prepares for fusion into a myotube (McComas, 1996; Stockdale & Holtzer, 1961). During embryonic development, mononucleated myoblasts cluster and fuse to form multinucleated myotubes. Myoblasts at embryogenesis are called the primary myoblasts or embryonic myoblasts. These primary myoblasts form primary myotubes that function as a guiding form for the developing skeletal muscle fiber. The secondary myoblasts (fetal myoblasts) aggregate around the primary myotubes and form the secondary myotubes (Kelly &

Zacks, 1969). These secondary myotubes do not fuse with the primary myotubes; instead they become oriented parallel to the primary myotubes (Harris et al., 1989). The nuclei in the myotubes do not undergo any further mitosis (Bischoff & Holtzer, 1969; Moss &

Leblond, 1970). As the myotubes enlarge and differentiate, the primary and secondary myotubes are separated and transformed into mature skeletal muscle fibers. As a result, the skeletal muscle fiber is a syncytium, and is larger and longer than other cells, and it 27

has multiple myonuclei. Each myonucleus has a theoretical domain called the

myonuclear domain, which is the volume of cytoplasm controlled by each myonucleus.

Satellite Cells

During embryonic development, myoblasts undergo a cell division and fuse into myotubes, which will develop into skeletal muscle. However, not all myoblasts form myotubes. Some myoblasts remain as undifferentiated myogenic precursor cells and are called satellite cells. When the primary and secondary myotubes are separated to become a mature skeletal muscle fiber, satellite cells associate to each of them and locate themselves between the plasma membrane and basal lamina (Muir et al., 1965). Satellite cells are one of the adult stem cells. Satellite cells proliferate during early development, but withdraw from the cell cycle and become quiescent (MacConnachie et al., 1964).

While myonuclei do not undergo mitosis (Moss & Leblond, 1970), satellite cells are

activated during muscle injury, muscle growth, or denervation (Hawke & Garry, 2001;

McGeachie, 1989; Schultz et al., 1985), and undergo mitosis. Half of the daughters of satellite cells remain satellite cells (Moss & Leblond, 1971); and the other half proliferate, replace damaged myonuclei, and regenerate skeletal muscle (Bischoff, 1994;

Moss & Leblond, 1970; Shafiq et al., 1968).

Because radiation prevents nuclear division, radiated satellite cells do not undergo mitosis. Reportedly, irradiation of muscle prior to the muscle injury prevents satellite cells from repairing the damage (Gross & Morgan, 1999; Phelan & Gonyea, 1997;

Rosenblatt et al., 1994). Irradiation is also reported to prevent skeletal muscle from 28

hypertrophy (Mitchell & Pavlath, 2001; Phelan & Gonyea, 1997). These results indicate

that mitosis of satellite cells is necessary to add myonuclei for repair and hypertrophy.

Satellite cell response to injury is not limited to the injured muscle. It also occurs in

undamaged areas distant from the injury sites; and studies using radioisotopically labeled

satellite cells have shown that satellite cells migrate across the basal lamina and

participate in muscle repair (Hughes & Blau, 1990; Lipton & Schultz, 1979; Schultz et

al., 1985). The question has often risen if a satellite cell is programmed to be a specific

skeletal muscle fiber type or has ability to be any type of fiber. One study indicated that

satellite cells are capable of forming any type of skeletal muscle fiber (LaFramboise et

al., 2003). Others indicate that there are at least two different satellite cells, similar to

myoblasts, because satellite cells obtained from different fiber types form those fiber types in vitro (Barjot et al., 1995; Feldman & Stockdale, 1991). However, as previously explained, satellite cells migrate. Therefore, it is very difficult to identify the actual origin

of satellite cells.

Skeletal Muscle Fiber Types

Skeletal muscle fibers are classified into two groups: type I and type II fibers.

These fibers have subtypes. These classifications are based on contraction speed

(Wuerker et al., 1965), oxidative enzymatic capacity (Ogata & Mori, 1964), myofibrillar

ATPase (mATPase) activity (Henneman & Olsen, 1965), and specific MHC isoforms

(Pette & Staron, 1990; Pette at al., 1999; Staron & Pette, 1986). The mATPase-based fiber type classification is based on the pH sensitivity of mATPase. During histochemical 29 analysis, pre-incubation at different pH levels (pH 4.3, 4.6, and 10.4) results in different staining patterns among the different fiber types. Type I fiber is stable in the acid but labile in the alkaline pre-incubation; and type II fiber is stable in the alkaline but labile at the acidic pre-incubation. Fiber type determination by MHC isoforms has been proven to be the most useful method of classification (Pette et al., 1999). MHC isoforms are identified by electrophoresis and immunoblotting methods. These methods allow for fiber type determination in a single muscle fiber.

In mammals, three different subtypes of type II fiber have been identified: IIA,

IIX, and IIB (Schiaffino & Reggiani, 1994). Besides the four pure fibers (I, IIA, IIX, and

IIB), hybrid fibers that have both characteristics of nearest neighboring MHC isoforms have been found. Type C fiber has been identified as co-expressing MHC I and IIA in variable ratios (Staron & Hikida, 1992). Therefore, a fiber type continuum of I, IC, IIC,

IIAC, IIC, IIA, IIAX, IIXA, IIX, IIXB, IIBX, and IIB is possible. In adult human skeletal muscle, three MHC isoforms have been identified. Type I fiber has MHCI, type IIA fiber has MHCIIA, and type IIX fiber has MHCIIX. Human skeletal muscle type IIB fiber was found to express type IIX rather than type IIB MHC isoform and was classified as type

IIX fibers (Smerdu et al., 1994; Ennion et al., 1995). While a direct correlation between fiber types defined by mATPase and MHC isoform has been reported (Staron & Pette,

1986), large overlaps in oxidative enzymatic activity levels between type I and II fibers, defined by their MHC isoform profiles, have also been reported (Pette & Staron, 1990).

Although there are large inter-individual differences in the diameter of skeletal muscle fibers, generally type I is smaller in diameter than type II fiber (Prince et al., 30

1976). As the skeletal muscle diameter determines its strength, it is reasonable that type II fiber generates more force than type I (Leiber, 1992). As previously explained, various methods are used to determine fiber type; and the different classifications of fiber types

are closely correlated but not exactly the same. However, type I fiber is often called slow-

twitch-oxidative fiber; and type IIB fiber is often called fast-twitch-glycolytic fiber.

Characteristics of type IIA fiber are between that of type I and that of type IIB (Barnard

et al., 1971; Peter et al., 1972); and type IIA fiber is often called fast-twitch-oxidative-

glycolytic fiber. These characteristics correspond to the fiber type continuum previously

listed.

Skeletal muscle fiber type is also related to the motor unit. A motor unit is

composed of an alpha (α) motor neuron and the skeletal muscle fibers that a motor

neuron innervates. When the primary and secondary myotubes are separated and

transformed into mature skeletal muscle fibers, the motor neurons innervate the muscle

fibers and form neuromuscular junctions (Lieber, 1992). Motor neurons with smaller

axon diameters innervate slow-twitch fibers and have a lower threshold than those with

larger axon diameters innervating fast-twitch fibers to trigger an action potential. The

relationship between axon diameter of motor neurons and the activation order of skeletal

muscle fibers is called the size principle (Henneman et al., 1973). This motor unit

recruitment order has an additive effect. Therefore, type I units are always recruited first;

and type IIA and IIB units are recruited in addition to type I fibers as exercise intensity increases (Andersen & Sjogaard, 1975). Since motor neurons and fiber types have such a 31

distinctive relationship, the pattern of motor unit recruitment could be a factor in fiber

type properties.

Slow twitch fibers are activated by low frequency stimulation by their motor neurons, while fast twitch fibers are activated with high electrical frequency. Cross-re- innervation, or innervation is the alteration of nerves to different skeletal muscles. As previously mentioned, motor neurons innervating slow twitch fibers are easily activated because they have a low threshold. Therefore, they are recruited first and are continuously recruited while other motor unit types are also recruited. As a result, the electrical pattern of motor neurons innervating slow twitch fibers is defined as chronic low frequency. On the other hand, the electrical pattern of a motor neuron innervating fast units is defined as phasic high frequency (Lieber, 1992). Chronic low frequency stimulation (CLFS) is a commonly used electrical stimulation pattern to activate skeletal muscle by imitating a motor neuron pattern that innervates slow twitch fiber.

Electrical stimulation studies examine the effect of electrical patterns on denervated skeletal muscle fibers. A muscle fiber stimulated with a fast or slow pattern after denervation results in fast or slow properties respectively (Hämäläinen & Pette,

1996; Pette & Staron, 2000). Satellite cell activity seems to be involved in the fast-to- slow twitch shift by CLFS. As previously explained, irradiation prevents satellite cell proliferation. When an irradiated muscle fiber receives CLFS, it results in a lesser degree

of fast-to-slow twitch shift (Martins et al., 2006). These results indicate that the skeletal

muscle fiber type is strongly affected by extrinsic stimulation (Buller at al., 1960).

However, these studies also showed that fiber type conversion after cross-innervation was 32

not complete. Cell culture studies of the primary and secondary myoblasts showed that

there are different fiber type expressions between them, which indicates that skeletal

muscle fiber type is also determined by intrinsic factors. This may be the reason why

extrinsic stimulation results in incomplete fiber type conversion (Ausoni et al., 1990;

Fugl-Meyer et al., 1982; Miller & Stockdale, 1986; Pin & Merrifield, 1993; Pin et al.,

2002).

Fiber type composition is different in each muscle. For example, extensor

digitorum longus has mostly type II fibers, while soleus has mostly type I fibers. Some

muscles, such as vastus lateralis, have both type I and type II fibers. The proportion of fiber types can be changed after long-term stimulation or training. However, a genetic influence on the composition of mixed fiber type muscle has been reported which is thought to limit fiber type change (Fugl-Meyer et al., 1982; Komi et al., 1977; Simoneau

& Bouchard, 1995).

A skeletal muscle fiber has thousands of myonuclei. Each myonucleus maintains and controls a certain part of a fiber, which is called its myonuclear domain. Myonuclear domain sizes are different because fiber types have different functions. For example, oxidative fibers, such as type I and type IIA, have more enzymes involved in their energy

production; and they have a smaller domain than less oxidative fiber types (Allen et al.,

1996; Kelly, 1978; Tseng et al., 1994). Exercise training increases skeletal muscle’s

oxidative activity and muscle fiber size. When a muscle fiber type becomes more

oxidative and/or increases in diameter, quiescent satellite cells are activated, undergo

mitosis, and one of the daughter nuclei becomes incorporated into the fiber (Moss & 33

Leblond, 1970; Shafiq et al. 1968). As a result, the number of myonuclei increases to

maintain the suitable size of the myonuclear domain (Winchester & Gonyea, 1992). In

comparison, when skeletal muscle fiber becomes atrophied and smaller in diameter, the

number of myonuclei decreases (Hikida et al., 1997; Mitchell & Pavlath, 2001). It has been reported that atrophy precedes a reduction of the myonuclear number and skeletal muscle adaptation affects gene expression and the availability of myonuclei among existing myonuclei (Allen et al., 1996).

When exercise training causes fibers to be more oxidative, the number of satellite cells increases (Bischoff, 1994). Similar to the size of myonuclear domain, slow fibers have more satellite cells per given cytoplasmic volume than fast fibers (Kelly, 1978;

Matthew & Moore, 1987; Schmalbruch & Hellhammer, 1997). Chronic low-frequency stimulation (CLFS) induces a fast-to-slow fiber type shift (Hamalainen & Pette, 1996). It has been reported that this shift is associated with an increased number of satellite cells.

This increase allows the muscle fiber to maintain the appropriate size of the myonuclear domain and muscle function when its diameter is increased and/or its metabolism is shifted to a more oxidative state (Putman et al., 1998).

Skeletal Muscle and Physical Activity

Skeletal muscle adapts to a given stimulus. For this reason, habitual physical activity such as exercise training, causes several functional and morphological changes in

skeletal muscle. Although human skeletal muscle fiber type is not believed to shift from type I to type II, or type II to type I, fiber type shift among subtypes of type I and type II 34 has been reported. Any exercise training, including strength and sprint training, makes skeletal muscle more oxidative as the percentage of type IIX fiber decreases, while that of type IIA fiber increases (Fry et al., 2003; Staron at al., 1989). Because disuse of skeletal muscle mostly decreases the percentage of type I fiber, disuse due to, for instance, bed rest, spinal cord injury, limb suspension, and space flight, causes skeletal muscle to be less oxidative and causes a shift toward type IIX (Allen at al., 1996).

Although some slow fibers such as IC, IIC, and IIAC co-express the characteristics of fast fibers, it is not clear if a slow fiber becomes entirely fast, or if a fast fiber becomes entirely slow. It has been assumed that type I fiber would not shift to type IIA and type

IIA fiber would not shift to type I fiber (Allemeier et al., 1994; Andersen & Henriksson,

1977; Pette & Staron, 2000; Staron & Johnson, 1993).

As previously indicated, type I fibers are smaller in diameter than type II fibers; this makes it optimal to facilitate the transportation of oxygen and nutrients from capillaries because of the shorter distance between capillaries and all components of the muscle fiber. The type II fiber tends to be larger in diameter than type I. It contracts faster and can generate more power without oxygen and nutrient supply from capillaries.

Therefore, changes in physical activity cause not only a shift within fiber subtypes, but also a change in the size of skeletal muscle fibers. It has been reported that highly trained weightlifters’ skeletal muscle fiber diameters are larger than highly trained marathon runners’ fiber diameters (Fry et al., 2003; Prince et al., 1976). However, any exercise training seems to increase skeletal muscle strength by increasing fiber diameters; this is why highly trained endurance runners’ fiber diameters are larger than sedentary subjects’ 35 fiber diameters (Hermansen & Wachtlova, 1971). Disuse of skeletal muscles causes the fiber to become smaller and weaker (Allen et al., 1996; Clark et al., 2006; Hikida et al.,

1997; Itai et al., 2004; Widrick et al., 2002).

The change in physical activity changes the demand on skeletal muscle.

Increasing the duration of exercise demands ATP production and subsequently increases oxygen supply to skeletal muscle. Therefore, endurance exercises increase enzymes for substrate oxidation and capillarization of skeletal muscle (Spina et al., 1996). During endurance muscle activity, skeletal muscle relies on its energy supply from oxidation.

Endurance training makes skeletal muscle fiber diameters larger and also capillary density higher so that the oxygen supply can be maintained for muscle hypertrophy

(Andersen & Henriksson, 1977; Hermansen & Wachtlova, 1971).

The results of muscular adaptation depend on the type, duration, and intensity of exercise training. Campos et al. (2002) conducted a resistance training study, varying the loads and numbers of repetitions. The findings showed that heavy-loaded, low-repetition resistance training resulted in more hypertrophy than the light-loaded, high-repetition resistance training; and that there was a positive relationship between the degree of hypertrophy and the amount of the load during training (Campos et al, 2002). Endurance training has been thought to result in a higher oxidative capacity and a smaller diameter in muscle fibers. However, it has also been reported that high-intensity bicycle endurance training caused hypertrophy (Andersen & Henriksson, 1977); and that endurance athletes had larger muscle fiber diameters than control subjects (Hermansen & Wachtlova, 1971), 36

which indicates that the skeletal muscle fiber size of endurance athletes depends on the

type, duration, and intensity of training (Zumstein et al., 1983).

Testosterone Effect on Skeletal Muscle

It is known that not only exercise, but also hormones affect the size and function

of skeletal muscle (McComas, 1996). Hormones are chemical messengers released from

glands into the bloodstream, and they circulate though the whole body. Some hormone

secretion is controlled by physiological conditions. Secreted hormones have physiological effects on target tissues. The seminiferous tubules and skeletal muscles are

target tissues for the anabolic effect of exogenous and endogenous testosterone (Evans,

2004). Testosterone is a steroid hormone released from the Leydig cells of the testes.

Testosterone enhances male reproductive function and skeletal muscle anabolism and

decreases body fat (Herbst & Bhasin, 2004; Rogozkin, 1979).

To observe the effects of testosterone, studies often suppress male endogenous

testosterone secretion then inject exogenous testosterone on male subjects. Studies using

this method showed skeletal muscle fiber hypertrophy and an increased number of

myonuclei and satellite cells in a dose-dependent manner (Sinha-Hikim et al., 2003;

Sinha-Hikim et al., 2002). In another study in which male subjects were given exogenous

testosterone injections without suppressing endogenous testosterone, it was concluded

that catabolized proteins were directly used for protein synthesis rather than transported

into the blood stream because fractional protein synthesis increased but there was no

change in protein breakdown and amino acid transportation (Ferrando et al., 1998). These 37

studies indicate that skeletal muscle hypertrophy is related to testosterone. One study using the mesenchymal pluripotent cells of mice showed that testosterone promoted the myogenic lineage while inhibiting the adipogenic lineage (Singh et al., 2003), thus promoting fat-free mass. These results support the effects of testosterone on promoting skeletal muscle hypertrophy and reducing fat mass.

When resistance training and exogenous testosterone injections were combined, skeletal muscle hypertrophy among the subjects was enhanced compared to those who did not undergo resistance training (Bhasin et al., 1996). This study indicates that the testosterone effect on skeletal muscle hypertrophy would be enhanced when combined with exercise. Both resistance exercise and endurance exercise cause an elevation of testosterone concentration during and after exercise (Tremblay et al., 2004). Reportedly, this elevation depends on intensity, rather than duration or volume (intensity x duration)

(Ahtiainen et al., 2003; Raastad et al., 2000). The testosterone elevation timeline is similar to the elevation of protein degradation and protein synthesis during and after

exercise. As previously explained, protein synthesis in skeletal muscle fibers increases

during and after exercise. Because protein synthesis requires nutrient input, the effects of

nutrients on skeletal muscle protein synthesis and testosterone concentration have been

studied.

The training effect on the resting total testosterone concentration has been

investigated. While resistance training is believed to increase skeletal muscle fiber

hypertrophy and force production (Faulkner & White, 1990: McComas, 1996; Sinha-

Hikim et al., 2003; Sinha-Hikim et al., 2002), the effects of training on resting total 38

testosterone concentration are inconclusive. Six-months of military training increased the

resting total testosterone concentration of men, especially those who improved their

cardiovascular fitness (Remes et al., 1979). Some studies have shown that two years of resistance training caused an increase in resting testosterone concentration (Ahtiainen et

al., 2003; Häkkinen et al., 1988); and other studies showed no effect on resting

testosterone concentration (Kraemer & Ratamess, 2005). These findings indicate that

plasma testosterone concentration is influenced by other factors. Analytical methods are

one of the factors influencing testosterone measurement (Diver, 2009). Testosterone

release from Leydig cells has a diurnal circadian rhythm: high in the morning and low in

the afternoon. (Diver et al., 2009; Diver, 2003; Nieschlag, 1975). This circadian rhythm

becomes weak and total testosterone concentration decreases among older men (Diver,

2009). Plasma testosterone concentration is also affected by physical activity and sexual activity. It was reported that plasma testosterone concentration was elevated even 48 hours after the last resistance training (Ahtiainen et al., 2003), and a sharp testosterone increase was seen during and immediately after coitus (Fox et al., 1972). Therefore, unless the sampling condition and subjects are controlled in the same manner, it is difficult to interpret basal plasma testosterone concentration from different reports.

Because testosterone facilitates protein synthesis in skeletal muscle, its relationship with dietary supplements has been studied. Nutrient ingestion during and after exercise affects skeletal muscle protein synthesis responses and elevates testosterone concentration. However, it is also reported that different types of nutrients result in 39

different responses in serum testosterone concentration (Chandler et al., 1945; Hulmi et

al., 2008; Kraemer et al., 1998; Sallinen et al., 2004).

The above findings show that the skeletal muscle fiber is adaptable to changes in physical activity and internal environments in the aspects of function and morphology.

When the number of muscle contractions and/or the load against contraction is increased, energy production needs to match energy expenditure. As previously explained, oxidative phosphorylation relies on the amounts oxygen and nutrients derived from the blood supply. Therefore, the functions of both skeletal muscle and the cardiovascular system will increase when physical fitness levels increase.

Physical Fitness

Maximum Oxygen Consumption

VO2 max is the maximal capacity for oxygen consumption by the body during

maximal exertion (Wilmore & Costill, 2004); and VO2 is increased by increasing both cardiac output and cells’ ability to extract oxygen from blood. VO2 max has been used as

an indicator of cardiovascular fitness (Foss & Keteyian, 1998; Wilmore & Costill, 2004).

It was reported that there is a negative correlation between the level of VO2 max and mortality; and that improved cardiovascular fitness benefits a person’s health status

(Farrell et al., 1998; Laukkanen et al., 2001). 40

Overweightness/Obesity

Increasing energy demands increases energy expenditure because ATP is

produced using substrates that come from food. Therefore, adequate food intake is

required to maintain body function. Modern trend of a combination of more food intake

and less physical activity has been reported. Digested carbohydrates and fat that are not

used for energy production are stored in the body and are converted to subcutaneous fats,

leading to increased body weight. Previously the ADA recommended the food proportion as 45-65 % carbohydrates, 20-25 % fat, and 10-35 % protein of total caloric intake. Since

fat has a high energy potential, the ADA focused on reducing fat intake. This

recommendation was widely accepted as Recommended Dietary Allowances (RDAs).

However, the problem of becoming overweight is not only the proportion but also the

amount of food that is eaten. As an article of The New York Times reported, the average

American ate 2.6 lbs of food daily in 2006 compared to 2.3 lbs in 1970 (Marsh, 2008).

Considering this fact, a new standard, Daily Reference Values (DRVs), recommends a

diet of 60 % carbohydrates, 30 % fats, and 10 % proteins of 2000 kilocalories (Kcal)

daily food intake. The proportion of nutrients and the proportion of daily reference are

shown on food labels as Daily Values (DVs).

Physical inactivity is highly related to obesity and overweightness (American

Heart Association; Centers for Disease Control and Prevention, 2007). Regardless of

what type of food is eaten, excess caloric intake combined with less physical activity

promotes accumulation of increased body fat and body weight. As previously explained,

fats are high-energy compounds; therefore, a small amount of fats provides large amounts 41 of energy for longer physical activity. As a result, once they are deposited in the body as subcutaneous fat, it is difficult to eliminate these fats. In order to maintain an adequate body weight, it is highly recommended that food intake and energy expenditure be balanced (Lichtenstein et al., 2006).

Overweightness and obesity are defined using body mass index (BMI) that is calculated as weight in kilograms (kg) divided by the square of height in meters. Centers for Disease Control and Prevention (CDC) list that BMI between 25.0 and 29.9 is overweight and BMI 30 and higher is obese (Centers for Disease Control and Prevention,

2008). The survey shows that between 2003-2004 in the United States, approximately 66

% of adults over 20 years of age were either overweight or obese (Ogden et al., 2006).

Overweight and obesity trends did statistically increase during 2005-2006 (Ogden et al.,

2007). These trends have been seen among children and adolescents (Ogden et al., 2006;

Ogden et al., 2008), indicating earlier onset of obesity and related diseases.

Overweightness and obesity lead to many diseases and health risks including hypertension (high blood pressure), osteoarthritis, hyperlipidemia (high fat concentration in plasma), hypercholesterolemia (high cholesterol concentration in plasma), type 2 diabetes, coronary heart disease, stroke, gallbladder disease, sleep apnea, respiratory disease, and some cancers (Centers for Disease Control and Prevention, 2008; National

Heart Lung and Blood Institute, 2008).

Obesity Related Health Risks and Exercise

Heart disease, such as coronary heart disease and heart attacks, is the leading

cause of death. About 700,000 people die of heart disease in the United States each year,

with about 71 % of all heart disease deaths due to a coronary heart disease called

atherosclerosis (American Heart Association; Centers for Disease Control and

Prevention, 2007). The possibility of developing heart disease increases with high blood

cholesterol levels, high blood pressure, diabetes mellitus, tobacco use, a high

salt/saturated fats/cholesterol diet, physical inactivity, obesity, and alcohol use. It is also

affected by aging, gender (higher in males), and heredity (including race) (Centers for

Disease Control and Prevention, 2007). Among the above factors which affect

cardiovascular disease, physical inactivity is related to high blood cholesterol, diabetes

mellitus, and high blood pressure. Exercise can help to manage blood lipids including

cholesterol, reduce body weight by increasing energy expenditure, stimulate glucose

transport without using insulin, and lower blood pressure among patients with some types

of hypertension (Fletcher et al., 1996).

Cholesterol, synthesized from fat in the liver and also consumed as food, is

important as a precursor of bile salt and steroid hormones. Cholesterol is not a fuel source for energy production. Therefore, excess cholesterol tends to accumulate in tissues and causes pathological conditions such as atherosclerosis. Self-reported physical activity and

fitness levels determined by VO2 max have been negatively correlated with total plasma

cholesterol concentration. A physically active person is more likely to have low total 43

cholesterol (Lehtonen & Viikari, 1978) and is less likely to develop cardiovascular

disease (Haskell, 1984; Orakzai et al., 2006) than an inactive person.

Many controlled studies have shown that exercise alters plasma concentration

and/or the types of lipoproteins that carry cholesterol in blood. High-density lipoprotein

(HDL) carries cholesterol to the liver and is thought to be involved in cholesterol

catabolism, while low-density lipoprotein (LDL) carries it to the tissues. Studies have

shown that exercise increases the concentration of HDL and/or the ratio of HDL to LDL

(Leon & Sanchez, 2001; Peltonen et al., 1981). However, a meta-analysis study shows that only 25% of controlled exercise studies reported a decreased total cholesterol

concentration (Durstine et al., 2001). It has been reported that the cholesterol lowering

effect is associated with high energy expenditure, reduced body weight, and/or reduced

fat mass (Alterkruse & Wilmore, 1973; Arciero et al., 2006; Durstine et al., 2001;

Durstine et al., 2002; Goldberg et al., 1984; Peltonen et al., 1981; Toriola 1984). It was

also reported that the decrease in total cholesterol was associated with the increased

proportion of HDL to LDL, which might be a reason for decreased total plasma

cholesterol concentration since cholesterol elimination can only occur via the liver

(Brooks et al., 1996).

Diabetes, which is another cardiovascular disease risk factor, is a pathological

condition in which blood glucose is not transported to tissues. Insulin is a hormone that

stimulates tissues to take up glucose. Type I diabetes is caused by the lack of insulin

secretion from the β cells of the pancreas and requires dietary manipulation and insulin

injection. Type II diabetes is due to the failure of insulin’s signal transduction, despite 44

normal or even higher insulin secretion. Although the precise cause of Type II diabetes is

still under investigation, a person who is overweight/obese and inactive is more likely to develop this disease. Common treatments for Type II diabetes are oral drug administration, diet modification, and increasing physical activity. Increasing physical activity would not only reduce body weight and fat mass but also stimulates translocation of glucose transporters to the surface of the skeletal muscle and facilitates glucose uptake without insulin signaling (MacLean et al., 2002). Therefore, exercise is beneficial for blood glucose management among Type II diabetic patients.

Increasing physical activity, such as exercise training, demands an increase in energy production by oxidizing substrates, such as glucose and fatty acids. Therefore, increasing physical activity leads to reduced body weight. It also increases the body’s efficiency when delivering oxygen throughout the cardiovascular system by decreasing the heart rate due to increased stroke volume. Consequently, exercise training and/or

increasing physical activity can decrease heart disease risk factors and reduce cardiovascular disease mortality (Farrell et al., 1998; Fletcher et al., 1992; Laukkanen et

al., 2001; Vainionpaa et al., 2007).

Carbohydrate-Restricted Diet

While the importance of carbohydrate intake has been emphasized in exercise

studies, a high-protein and low-carbohydrate diet has recently resurfaced and gained

popularity, especially as a method for weight reduction. One of the rationales for

supporting this diet is that an excess intake of carbohydrates stimulates insulin secretion, 45

promoting glucose storage as body fat (Nestel & Barter, 1973), which leads to obesity/overweightness, another cardiovascular risk factor (American Heart Association;

Centers for Disease Control and Prevention, 2007). Many professional health

organizations have warned against the potential risks of a high-protein and low-

carbohydrate diet because fats and cholesterol tend to accompany dietary proteins, and

weight reduction would be caused by ketosis (Fleming, 2000; Tapper-Gardzina et al.,

2002; White, 1973).

Despite these possible adverse outcomes, studies of the effects of a low-

carbohydrate high-protein diet on weight loss among overweight/obese people indicate

that this diet not only decreases body weight (Brehm et al., 2003; Larosa et al., 1980;

Yancy et al., 2004; Wood et al., 2006), but also maintains weight loss (Brehm et al.,

2003; Larosa et al., 1980; Lejeune et al., 2005; Shai et al., 2008; Westerterp-Plantega et

al., 2004). Advocates for a low-carbohydrate diet claim that a low-carbohydrate high-

protein diet prevents insulin release by lowering blood glucose, while a high-

carbohydrate diet elevates blood glucose concentration and possibly causes type 2

diabetes (Atkins, 2002).

Because the amount of food containing large portions of cholesterol is not limited

in a high-protein low-carbohydrate diet, professional health organizations are concerned

that this type of diet results in hyper-lipidemia, hyper-cholesterolemia, ketosis, and

kidney and liver disorders (American Heart Association; American Heart Association

Media Advisory, 2002; JAMA Council on Foods and Nutrition, 1973; St. Joer et al.,

2001; White, 1973). 46

Studies of the effects of a high-protein low-carbohydrate diet also report that it

decreases LDL (Johnston et al., 2004; Laroasa et al., 1980; Sargrad et al., 2005; Wood et

al, 2006), decreases triglycerides (Foster et al., 2003: Samaha et al., 2003; Stern et al.,

2004; Wood et al., 2006; Volek et al., 2004: Yancy et al., 2004), and decreases blood

pressure (Sargrad et al., 2005). However, these studies also report that there are negative effects, such as an increase in total cholesterol (Larosa et al., 1980) and LDL (Larosa et al., 1980; Volek et al., 2004). These studies show that a high-protein low-carbohydrate diet results in positive and negative effects in reducing cardiovascular risk factors.

Therefore, the overall benefits from this diet are still inconclusive.

Purpose of Study

Because a high-protein low-carbohydrate diet is so popular and because the

majority of the U.S. population is at risk for being overweight many people who are not

severely obese, but who want to reduce their body weight eat high-protein low-

carbohydrate foods while increasing physical activity. This combination aims for weight

reduction as the result of multiple effects. However, there are very few studies done with

human subjects to investigate the effects of a high-protein low-carbohydrate diet during

high metabolic activity such as exercise training (Kennedy et al., 2001). Therefore, the purpose of this study was to use an a priori comparison to compare the effects of a traditional high-carbohydrate low-fat (HC) diet to the effects of a high-protein low-

carbohydrate (HP) diet when each diet is combined with high-intensity aerobic training.

The study compared the effects of: (1) the safety and effectiveness of high-intensity 47 aerobic training, (2) the benefits against cardiovascular risk factors, and (3) the ability of skeletal muscle to adapt, focusing on muscular hypertrophy.

Chapter Summary

In this Chapter 1, characteristics of the energy systems and their relationships to nutrients and exercise were introduced. The literature related to the effects of different nutrients on exercise performance was reviewed. Characteristics of skeletal muscle and its adaptability to physical activity were also reviewed. The importance of physical activity and proper diet for a healthy life were discussed, and the current trends of physical activity and popular diets were examined. The purposes of this study were also addressed.

In the following Chapter 2, the design and methods employed in this study are explained. The design of study and schedule are listed. Subject criteria, diet composition, and training schedule are explained. The training apparatus and monitoring methods of training and diet are discussed. Pre-and post-intervention measurements and their analytical methods, and statistics are also discussed.

In Chapter 3, the results of this study are shown. The compliance of subjects to diet-training intervention is reported. The group differences in post-training heart rate and the amount of total work are also reported. The statistical group differences of pre- and post-intervention measurements are reported and their means and standard deviations of groups are shown in tables. The correlations of test measurements are also reported. 48

Addition to the statistical analyses introduced in the chapter 3, the physiological importance of results are explained and are also compared with the results of previous studies in Chapter 4. The conclusions to the three study questions addressed in Chapter 1 are followed in Chapter 5. The limitations of this study and the application of the results obtained from this study are also addressed in the final section of this chapter.

CHAPTER 2: METHODS

In the previous chapter, some aspects of energy systems, energy sources, and

skeletal muscle were reviewed. Cardiovascular risk factors to physical activity and diet

were also explained. In this chapter, the methods used in this study to examine the effects

of diets and a high-intensity aerobic exercise intervention are discussed. Additionally, this chapter includes a discussion of the selection of test measurements. This chapter begins with the experimental design, including diet and training protocols, followed by the selection criteria for participants, analytical methods used to examine blood and skeletal muscle samples, and the statistical tests used to analyze the findings. The different statistical methods that were selected for specific measurements are also discussed.

Experimental Design

The experimental procedure was approved by the Ohio University Institutional

Review Board in August, 2004 (Appendix A); and subject recruitment started in

September, 2004. Subjects were recruited via flyers (Appendix B) distributed in Ohio

University’s lecture, dining, and dormitory halls and through verbal announcements at introductory biology, human anatomy, and physiology classes. The introductory meeting with potential subjects was held in November, 2004. After the written consents were obtained, the experimental procedure started in January and ended in March 2005.

The subjects were introduced to the indoor rowing machine during a familiarization period, and underwent the pre-intervention tests listed later in this chapter. 50

Based on these test results, subjects were paired, and then, randomly assigned to one of two diet groups, a traditional high-carbohydrate low fat diet (HC) group or an experimental low-carbohydrate high-protein diet (HP) group, and then rowing training started. At the completion of a seven-week diet and training intervention, the subjects underwent the post-intervention tests, and then were allowed to resume their own diets.

The time course of pre- and post-intervention tests and diet-training period is illustrated in Figure 1.

Figure 1. Time course of diet-training intervention and test measurements. 51

Subjects

Subjects who participated in this study satisfied the following criteria:

1. Male

2. College-age (18-30 years old)

3. Non-smoker

4. Non-diabetic

5. No neuromuscular complications or diseases for three months prior to the beginning of the study

6. Active, but not participating in any training programs for six months prior to the beginning of the study

7. Not engaging in any special diet/beverage plans for three months prior to the beginning of the study

8. Not on any medication which would affect neuromuscular and metabolic responses

After the written and verbal informed consents (Appendix C) were obtained, the potential subjects filled out the health and family medical history questionnaire

(Appendix D). This questionnaire was used to confirm if the potential subjects met the criteria listed above. All protocols including the recruitment of potential subjects began after the researcher obtained approval from the Ohio University Institutional Review

Board.

A total of twenty subjects volunteered to participate in this study. After screening, familiarization, and pre-training tests, ten subjects were assigned to the traditional high- 52 carbohydrate low-fat diet (HC) group; and the other ten subjects were assigned to the experimental high-protein low-carbohydrate diet (HP) group. One subject of the HP group failed to report to the scheduled rowing training sessions twice in the second week and was eliminated from participating in this study. Only the data of the subjects who attended at least fourteen of the total seventeen training sessions (82 %) were used for analysis. Therefore, there were nine subjects in the HC group; and eight subjects in the

HP group.

Familiarization

Subjects had two rowing practice sessions before the pre-intervention maximal aerobic capacity test (VO2 max test) on an indoor rowing machine. It has been reported that the greatest learning effect occurs at the second trial (Schenck & Forward, 1965).

The rowing technique for an indoor rowing ergometer was introduced via a commercial videotape before participating in rowing practice sessions. The subjects learned not only how to row but also how to control the intensity of the exercise. During this familiarization period, recording food consumption was introduced to the subjects. The subjects learned how to distinguish food groups, how to measure food portions, and how to record them in a food log (Appendix E). 53

Diet

After the pre-intervention VO2 max measurements, subjects with similar VO2 max were paired and then assigned to either a traditional high-carbohydrate low-fat diet group or to an experimental high-protein low-carbohydrate diet group (Volek et al.,

2004).

A traditional high-carbohydrate low-fat diet (HC) consists of 55 % carbohydrate,

30 % fat, and 15 % protein, based on their total caloric intake (Butki et al., 2003). In this research, it was determined that an experimental high-protein low-carbohydrate diet (HP) consisted of 50 % protein, 25 % fat, and 25 % carbohydrate. Subjects were allowed to consume any amount of food they wanted as long as they maintained the assigned nutrient proportions. After pre-intervention testing, the subjects were given the list of restricted and recommended foods based on the dietary group to which they were assigned (Appendix F); and they were encouraged to ask any questions regarding food choices during the seven weeks of training and diet intervention. All subjects started consuming their assigned diets on a Monday, the first week of the rowing training; and they continued them until the post-training muscle biopsy was taken.

All subjects kept a dietary record that was recorded in a food log. The subjects were taught to record food intake in the food log during the introductory period. The subjects reported their diet three days per week, including one weekend day during the weeks 1, 3, 4, and 6 (Nathan et al., 1996; Rebro et al., 1998). The previous studies reported that three days of food reporting analysis resulted in better compliance among the subjects than four days or more a week (Nathan et al., 1996). The food log consisted 54

of six sections: meal/snack time, food consumed, meal location, meal/snack companion, feelings during the meal, and a hunger rating. A computer software program, Nutritionist

Pro Nutrition Analysis (ESHA Research, Inc., Salem, OR), was used to analyze and interpret the data. After data processing was complete, Client Diet Record Report and

Client Diet Record Nutrition Summary were generated. The Client Diet Record Report listed all the foods and drinks consumed in a specified time and calculated the amounts of: sodium, calories, protein, carbohydrate, fat, cholesterol, saturated fat,

monounsaturated fat, polyunsaturated fat, and dietary fiber in each food or drink. The

Client Diet Record Nutrition Summary averaged the total amount of most macronutrients, vitamins, and minerals over a specified time. The analysis of the food log was done by

Tim Werner (2006) as his master’s thesis project.

Because low-carbohydrate diets could cause low blood glucose concentration, there was a concern that ketone bodies might be generated. A large amount of ketone bodies could lead to metabolic acidosis. Ketosis is a condition where the concentration of ketone bodies, such as ß-hydroxybutyrate, is abnormally high in the body, which can result in an acidosis coma. In order to ensure the well-being of the subjects during the seven-week diet and training intervenion, urinary ketone analysis using dipsticks,

Kitostix (Bayer Corp., Elkhart, IN) was done weekly for all subjects before the training session started. If a high concentration of urinary ketone bodies was observed, the training session was suspended and another test was done the following day. If the second ketone body test result was within the normal range, the subject was permitted to 55

resume training. However, if high ketone body concentration was observed two days in

row, increasing the carbohydrate consumption would be recommended for that subject.

Training

Following the introduction and pre-intervention measurements, the subjects began to train for seven weeks on rowing ergometers. Rowing is one of the few non-weight

bearing endurance sports that require the effort of the whole body. Because the energy used to support such a high-intensity muscular effort relies on an aerobic energy system, rowing is a strength-endurance sport; and its performance depends on aerobic and anaerobic power (Mäestu et al., 2005). As a result, rowing training improves the

cardiovascular system’s capacity to deliver oxygen and nutrients to working muscles.

Because rowing motion requires the repetitive high tension of skeletal muscle contraction

at a relatively slow speed of movement, power production is the product of large size

oxidative fibers, such as type I and type IIA, rather than type IIB fibers (Larsson &

Forsberg, 1980). Studies of rowing-trained athletes show that rowers have high VO2 max

and a large diameter of skeletal muscle fibers (Hagerman, 2000; Hagerman, 1998).

Because rowing is an aerobic exercise and the cardiovascular system adapts to the

training, subjects’ heart rates were monitored during a rowing training period to adjust

their training intensity. The initial training intensity was set at 70 % of the maximal

power output obtained during the maximal oxygen consumption test. Rowing time,

number of sets, and resting times were adjusted as follows. The intensity was increased at intervals of 10 w, so that the subjects would maintain the same heart rate obtained on the

initial training day throughout the seven-week training. 56

Week 1: 2 days/week 5-minute rowing, 3 sets with 4-minute rest between sets

Week 2: 2 days/week 5-minute rowing, 4 sets with 4-minute rest between sets

Week 3: 2 days/week 10-minute rowing, 2 sets with 5-minute rest between sets

Week 4: 3 days/week 10-minute rowing, 2 sets with 5-minute rest between sets

Week 5: 3 days/week 10-minute rowing, 3 sets with 5-minute rest between sets

Week 6: 3 days/week 10-minute rowing, 3 sets with 4-minute rest between sets

Week 7: 3 days/week 10-minute rowing, 4 sets with 4-minute rest between sets

Immediately after each set, subjects recorded their post-exercise heart rate, actual average power, and rate of perceived exhaustion, which were averaged as weekly post exercise heart rate, actual average power, and rate of perceived exertion.

Measurements/Tests

Before the rowing training and diet intervention began, all subjects underwent the following procedures:

1. Anthropometry: height, weight, and body fat percentage using underwater weighing

2. Fasting blood sampling

3. Maximal muscular strength and endurance tests on an isokinetic dynamometer

4. Maximal oxygen consumption test on an indoor rowing machine

5. Muscle biopsy

The tests were carried out in the following order: (1) fasting blood sampling, (2) isokinetic muscular strength and endurance tests, (3) maximal oxygen consumption test, 57

(4) anthropometric measurements, and (5) muscle biopsy. Rowing familiarization sessions were done a day before the maximal oxygen consumption test. In order to minimize the effects of fatigue factors, the isokinetic and maximal oxygen consumption measurements were conducted after at least 24-hour of rest. Although rowing causes minimal damage to skeletal muscles, muscle biopsies were taken at least 48 hours after the maximal oxygen consumption test. All measurements and tests were repeated in the same order at the end of the seven-week diet and rowing training period.

Anthropometry

Increased physical activity results in increased fat free mass and decreased fat mass. Supporters of a high-protein low-carbohydrate diet claim that this diet is more effective in reducing fat mass and retaining fat free mass weight than traditional diets

(Jean et al., 2001; Larosa et al., 1980; Brehm et al., 2003; Yancy et al., 2004; Wood et al.,

2006). Therefore, anthropometric data were recorded before and after the diet-rowing intervention to determine whether there was a dietary effect on body weight and/or fat mass. All anthropometric data were recorded in metric units. Height and weight were measured during the underwater weighing session. Height was measured using a wall- mounted scale; and weight was measured using an electrical scale.

Prior to underwater weighing, the subjects performed a forced vital capacity test to estimate residual volume in their lungs. Since the percent body fat is calculated by body weight in the air and body weight in the water, estimating the amount of air

(residual volume) in the lungs is important to this calculation. Each subject put on a nose 58 clip and a mouthpiece, and breathed normally for five seconds. Then the subject was instructed to inhale and exhale once through the mouthpiece as hard and fast as possible.

The mouthpiece was connected to a spirometer, Servey Plus III (Warren E. Collins, Inc.,

Braintree, MA). The spirometer was connected to a computer, and the computer software

(Collins Plus SQL System; Warren E. Collins, Inc., Braintree, MA) calculated forced vital capacity. Each subject performed three forced vital capacity tests; and the highest expired volume was recorded as the subject’s maximum forced vital capacity. Residual volume was calculated using the following formula:

Estimated residual volume = maximum forced vital capacity x 0.24 (Wilmore, 1969).

Since exercise training has little influence on residual volume (Enright et al., 2006), the estimated residual volume calculated from the pre-training forced vital capacity was used at the post-training underwater weighing.

Each subject reported to the lab without food or drink at least one hour prior to underwater weighing. The subject urinated and defecated, if possible, before beginning the underwater weighing. The subject was weighed in the air by an electrical scale and in the water by underwater weighing procedures. During the underwater weighing tests, the subject sat in a chair located in a water tank. The chair was connected to a force transducer which sent an analog signal to the computer. The subject immersed himself in the water while exhaling as much air as possible from his lungs. He was asked to remain submerged until a stable waveform was obtained. Immediately after collecting the data, the most stable waveform segment was selected and the mean value of this stable waveform segment was recorded as the underwater weight for the trial. This procedure 59 was repeated until the values of at least two trials were within a 3% difference, or until ten trials had been completed. The mean value was then calculated for the final underwater weight. Body density was calculated using the following formula:

where Wa is body weight in the air in kg; Ww is body weight in the water in kg; Dw is the density of the water; RV is the estimated residual volume in liters; and 0.1 L (100 ml) is estimated air volume in the gastrointestinal tract. The percent body fat was calculated by the Siri formula (Siri, 1961):

Percent body fat = (4.95/Db – 4.5) x 100 where Db is density of body.

Calculated the percent body fat was used to calculate fat mass (FM) and fat-free mass (FFM) using following formulae:

FM = body weight x fraction of the percent body fat

FFM = body weight - FM 60

Blood Analyses

Blood Sampling Procedure

Subjects reported to the laboratory early in the morning of the blood sampling day. They were asked to refrain from engaging in vigorous exercise 24 hours prior to blood sampling and from having any food or beverage except water after 8:00 pm the night before the blood sampling. About 10 cc of venous blood was taken from the median cubital vein. The blood was centrifuged and plasma was collected and stored in a –80 ° C freezer for later analyses of fasting blood glucose concentration, β-hydroxybutyrate concentration, total cholesterol concentration, and total testosterone concentration.

FastingBlood Glucose Concentration

As previously discussed in chapter 1, glucose is the main fuel source for the body.

Blood glucose concentration is maintained by hormones but is also affected by exercise.

Although glucose is the main fuel during exercise and exercise decreases fasting blood glucose concentration, advocates of a low-carbohydrate diet warn that a high- carbohydrate diet elevates blood glucose concentration and possibly causes type 2 diabetes (Atkins, 2002). Therefore, fasting blood glucose concentration was analyzed to determine if a high-intensity exercise decreases fasting blood glucose concentration regardless of different diet regimens. Blood glucose concentration was analyzed by an enzyme analyzer, YSI 2300 (YSI, Inc., Yellow Springs, OH).

β-hydroxybutyrate Concentration

As previously discussed, when fewer carbohydrates are available in a high-protein

low-carbohydrate diet, more fats are metabolized to acetyl CoA, which is broken down to

acetone and β- hydroxybutyrate. Elevated serum acetone and β-hydroxybutyrate

concentrations indicate an increase in fat metabolism. Because acetone is not stable and

tends to evaporate orally, β-hydroxybutyrate concentration was analyzed to determine if a

HP diet combined with high-intensity exercise elevated the state of fat metabolism. β-

hydroxybutyrate concentration was analyzed with an assay kit (Sigma Chemical Co., St.

Louis, MO) by a spectrophotometer (courtesy of Dr. Anne B. Loucks).

Total Cholesterol Concentration

Cholesterol is not a fuel source, but is an important compound as a part of membranes and a precursor of steroid hormones. However, as previously explained, excess cholesterol has been a cause of many disease conditions. Exercise tends to decrease the concentration of serum cholesterol (Lehtonen & Viikari, 1978). However, health professionals warn that a high-protein low-carbohydrate diet will elevate serum cholesterol and lipoproteins (White, 1973). Therefore, analyses of concentrations of total cholesterol, HDL-C, LDL-C, and triglycerides were planned to determine if a HP diet with high-intensity exercise elevated lipoproteins. However, because our analytical equipment was broken, only total cholesterol concentrations were analyzed by Mr. Cliff

Haak and Ms. Dana Becker through the courtesy of the Holzer Medical Clinic 62

(Gallipolis, OH) using an automated analyzer, Dimension RxL (Dade Behring, Newark,

DE).

Total Testosterone Concentration

Testosterone has an anabolic effect on skeletal muscle (Sinha-Hikim et al., 2002;

Sinha-Hikim et al., 2003). Although rowing training is an endurance sport, it also

requires multiple high-intensity muscular efforts. Previous studies indicated that intensive

exercise might elevate resting total testosterone concentration (Häkkinen at al., 1988;

Kraemer et al., 1999; Ahtiainen et al., 2003). It was also reported that testosterone concentration is affected by diet composition (Chandler et al., 1945; Hulmi et al., 2008;

Kraemer et al., 1998; Sallinen et al., 2004). Therefore, resting total testosterone concentration was analyzed to determine if high-intensity exercise increased the endogeneous anabolic steroid hormone and if the response differed between the two diets. Testosterone concentration was analyzed with a radioactive kit, Coat-A-Count total testosterone (DPC, Los Angeles, CA) by an automated gamma counter, Wizard 1470

(Perkin Elmer, Waltham, MA).

Although intensive training increases resting total testosterone concentration, negative correlations were reported between subject’s resting total testosterone concentration and protein intake per body weight (Sallinen et al., 2004; Volek et al.,

1997), and between subject’s total testosterone concentration and dietary protein/carbohydrate intake ratio (Anderson et al., 1987). Therefore, the relationships between post-training testosterone concentration and the average protein intake per body 63

weight, and those between post-intervention testosterone concentration and

protein/carbohydrate ratio were also analyzed.

Muscular and Cardiovascular Fitness Tests

Isokinetic Maximal Strength and Endurance Tests

As previously discussed, rowing training increases cardiovascular function but

also increases skeletal muscle strength; and rowing studies show that the quadriceps group of muscles provides the majority of power (Clarkson et al., 1984; Mäestu et al.,

2005; So et al., 2007; Tachibana et al., 2007). Therefore, isokinetic maximal strength and

endurance on leg extensor muscles were tested to determine if the diets combined with

high-intensity exercise increased muscular strength and endurance. Maximal dynamic

strength and local muscular endurance were measured on an isokinetic dynamometer,

BIODEX (BIODEX Medical Systems, Inc., Shirley, NY), through the knee extension

actions of each subject’s dominant leg. The maximal dynamic strength was tested using

three continuous knee extensions and flexions at the speed of 30° per second.

Local muscular endurance was measured, followed by the maximal dynamic

strength test. The subject was given at least five minutes between maximal strength and local endurance tests. The subject performed 30 continuous repetitions of knee extension

and flexion exercises at the speed of 180° per second. The fatigue index was then

calculated as the total work during the last ten repetitions, divided by the total work

during the first ten repetitions. Because rowing primarily trained the knee extensor 64

muscles, vastus lateralis, only the results of the knee extension were used (Pincivero &

Grandaio, 2003).

Maximal Aerobic Capacity Test

Rowing is an endurance exercise and improves aerobic capacity; therefore, VO2 max was tested to determine if the diets combined with high-intensity exercise increased this capacity. VO2 max was determined by a semi-computerized turbine system while

each subject rowed an indoor rowing ergometer, Concept 2 model B (Concept 2, Inc.,

Morrisville, VT). The subject was fitted with a nose clip and headgear comprised of a

mouthpiece connected to a tube, which collected expired air. While the subject produced

a target power by controlling the speed and/or the intensity of the rowing stroke, expired

air traveled through this tube to a mixing chamber. From the mixing chamber, expired air went to O2 and CO2 analyzers. Expired air volume was measured by a turbine system,

which was located between the tube and the mixing chamber. The gas concentration and

air volume were integrated by an interface, Vista with TurboFit (VacuMed, Ventura, CA)

and oxygen consumption was simultaneously analyzed by the computer software,

TurboFit 5.04a (VacuMed, Ventura, CA).

The target power of the maximal oxygen consumption test began at 75 W and was

increased by 25 W every minute during the exercise until one or more of the following

criteria were observed: 65

(1) a failure to maintain required power, (2) attaining age-predicted maximal heart rate,

(3) plateau or decline in VO2 in later power stages, (4) respiratory exchange ratio (RER)

of greater than 1.1, or (5) volitional cessation of exercise.

Rowing requires high-intensity muscular effort and increases skeletal muscle

strength. Therefore, maximum power during a VO2 max test was analyzed to determine if

either diet, combined with rowing training, increased post-intervention maximum power.

High-intensity endurance training involves not only the aerobic but also the

anaerobic energy system, which increases lactic acid after the maximum exercise.

Therefore, post-exercise lactic acid concentration was analyzed to determine if the

exercise subjects increased post-exercise lactic acid concentration. During a five-minute

recovery period, the subject remained seated without any cooling-down movements.

After a recovery period, 25 μl of blood was taken by a finger prick. Blood was lysed in a

buffer solution with Triton X-100 and sodium fluoride, kept at 4˚C until the end of each

testing day, and analyzed for lactic acid concentration by the enzymatic analyzer, YSI

2300.

Skeletal Muscle Analyses

Muscle Biopsy Procedure

As previously discussed, studies show that the quadriceps group of muscles provide the majority of power that the whole body can produce during rowing (Clarkson et al., 1984; Mäestu et al., 2005; So et al., 2007; Tachibana et al., 2007). Therefore, participants’ vastus lateralis muscle was examined. A small piece of muscle (60-100 mg) 66 was taken from the vastus lateralis muscle using a needle muscle biopsy procedure

(Hikida et al., 1998) to determine if the seven-week rowing and diet intervention caused any changes in the subject’s skeletal muscle.

Before starting the muscle biopsy procedure, subjects were given a hand-out with an explanation of the muscle biopsy, care needed after biopsy, and the possible risks and discomfort (Appendix H). For the procedure, a subject lay in a relaxed supine position.

The thigh was shaved and cleaned with an antiseptic povidone-iodine cleaning solution.

After an injection site was cleaned with an alcohol swab, two cc of 2 % lidocaine HCl and epinephrine solution (2 % Xylocaine®) was injected subcutaneously. When the subject reported no pain sensation on the injection site, the subject’s skin was cleaned with a povidone-iodine prep swab in a circular motion outward, then a sterile surgical scalpel blade was inserted about 2.5 cm inch deep over the vastus lateralis muscle so that an incision penetrated the fascia. A biopsy needle was used to extract a small piece of muscle. A biopsy needle is composed of two parts: an outer probe and an inner probe.

The outer probe is hollow with a closed conical tip. About 1 cm from the tip, there is a 5 mm wide 1 cm long window into which a small piece of skeletal muscle bulges. The inner probe is also hollow and has a sharp opening tip that chops off the skeletal muscle that bulges into the opening window of the outer probe. The inner probe was connected to a 50 ml syringe. A suction was applied through this syringe to maximize the amount of skeletal muscle that fits into the window. The biopsy needle is shown in Figure 2.

Figure 2. A biopsy needle: an inner cutting needle fits within an outer probe (left); an outer probe (right top) and an inner probe (right bottom) are seen separately in the figure on the right.

Once a muscle sample was obtained, pressure was manually applied on the

incision through a sterile gauze pad. When bleeding was stopped, sterile reinforced skin

closures were applied over the incision, and then covered with a small bandage (2 x 8 cm) and a large bandage (5 x 13 cm). The subject was asked to avoid vigorous physical activity and bathing for a day and to return to the laboratory within 24 hours to check the healing progress of the incision.

The sampled muscle was immediately divided into two pieces. One was quickly frozen in methyl butane cooled with liquid nitrogen (- 159 °C) for histochemical analysis; and another part was processed for electron and light microscopic analyses (Staron &

Hikida, 1992). Frozen muscles were stored at - 80˚C and the pre- and post-training muscles were analyzed together. 68

Skeletal Muscle Fiber Type Analysis

Since endurance training causes a skeletal muscle fiber type shift toward a more

oxidative type, muscle fiber type analysis using the mATPase method was performed to

determine if the training protocol shifted the muscle fiber type toward a more oxidative

fiber type. The enzyme, mATPase, catalyzes ATP into ADP and a free phosphate. This

dephosphorylation reaction releases energy to bind a myosin head to an actin filament, leading to skeletal muscle contraction. Each fiber type has a different mATPase isoform which is differentially inhibited with different pre-incubation pH levels. For example, type I fiber has high activity after acidic pre-incubation but low activity after an alkaline pre-incubation, while type IIA fiber has high activity after alkaline pre-incubation but low activity after acidic pre-incubation. Using three different pre-incubation pH levels, it is possible to identify seven different skeletal muscle fiber types (Staron & Hikida, 1992).

Pre- and post-intervention frozen muscles from the same subject were sliced in cross sections by a cryostat at 10 μm thickness and mounted on microscope cover slips that were pretreated with poly-L-lysine. This preparation was done by students of the

OUCOM Summer Scholar program and Summer Undergraduate Research Fellowship programs. Due to the small muscle mass for frozen sections, only histochemical analysis for fiber size and muscle fiber type was performed using mATPase analysis. Sections from all subjects were processed together. They were pre- incubated at pH 4.1, 4.6, or

10.4. Two sets of muscle samples were placed in acid preincubation solutions (at pH 4.1 or 4.6) for 7 minutes then rinsed by a solution of calcium chloride (CaCl2) and Tris. One

set of muscle samples was placed in an alkaline preincubation solution (at pH 10.4) for 69

20 minutes. After pre-incubation, the muscle samples were incubated in a solution of

ATP and CaCl2 for 45 minutes. They were then rinsed with a CaCl2 solution three times and once with distilled water. After the samples were incubated in 2 % ammonium sulfide solution for 45 seconds, they were dehydrated with ethanol solutions progressively (70 %, 95 %, 100 %) followed by Citru-solv solution, and then mounted on the microscope slides. The mATPase histochemical staining protocol is shown in

Appendix I.

The type I fiber, whose mATPase is stable in acid, was stained dark after pre- incubation at pH 4.1 while type II fiber, whose mATPase is stable in alkaline, was stained dark after pre-incubation at pH 10.4. The serial muscle sections at different pH pre- incubation levels allowed for identification of the skeletal muscle fiber type based on their mATPase activity. This pre-incubation and staining intensity pattern is shown in

Figure 3 and an example of staining is shown in Figure 4.

Figure 3. Illustration of different skeletal muscle fiber types that can be identified using mATPase histochemical analysis after pre-incubation at various pH levels. From “Histochemical, Biochemical, and Ultrastructural Analyses of Single Human Muscle Fibers, with Special Reference to the C-fiber Population,” by R. S. Staron and R. S. Hikida, 1992, Journal of Histochemistry and Cytochemistry, 40, p. 564. Reprinted with verbal permission from the first author.

Figure 4. Skeletal muscle fibers stained for mATPase analysis pre-incubation at pH 4.6.

Skeletal Muscle Fiber Cross-Sectional Size

Since rowing training increases muscular strength, it was expected to cause

skeletal muscle hypertrophy. Therefore, muscle fiber cross-sectional areas were analyzed

to determine if cross-sectional fiber size increased after the seven-week diet and rowing

intervention. The size of the cross-sectional area was measured by using the skeletal

muscle samples used in fiber type analysis. Stained muscle fibers were photographed

with a digital camera, a Nikon DMX 1200 (Nikon Corp., Tokyo, Japan), and computer

software, ACT-1 (Nikon Corp., Tokyo, Japan). The cross-sectional fiber size was analyzed using a public domain NIH Image software program, NIH Image 1.63. Muscle

sections on the microscope slides were photographed using a NIH Image 1.63.

Calibration for length was done prior to the analysis using a slide with a micrometer scale. Using a mouse, the perimeter of each skeletal muscle fiber was traced and the area was calculated. The cross-sectional fiber size was analyzed for type I fibers, type II fibers

and both fibers combined.

Skeletal Muscle Oxidative Activity

Endurance training increases VO2 max, as well as oxidative enzymatic activity in

the skeletal muscle fibers. NAD+ is a coenzyme involved in transporting electrons in the

oxidative process. During oxidation of substrates, hydrogen ions are transferred from the

substrate to a hydrogen acceptor, NAD+. NAD+ becomes NADH; and then, the reducing

potential stored in NADH is converted to ATP through the final aerobic energy system,

the electron transport chain. The NADH-tetrazolium reductase method allows for 72

analysis of relative oxidative enzymatic activities, using the intensity of color in skeletal

muscle.

Frozen muscle sections were incubated in a Tris solution with nitroblue

tetrazolium (NBT), cobalt chloride (CoCl2), and NADH at 37 °C for 30 minutes. After

incubation, muscle samples were rinsed with distilled water, then fixed with a formalin

solution at room temperature for 10 minutes. Fixed samples were rinsed with distilled

water again and mounted in a water-soluble mounting medium (Aqua Mount). This

NADH-Tetrazolium reductase procedure is shown in Appendix J.

Enzymatic activity releases hydrogen ions from the substrates and released hydrogen is transferred to the NBT. With hydrogen ions, NBT becomes purple-blue, which shows the site of enzyme activity. Therefore, the greater intensity staining represents more oxidative enzymatic activity. The intensity was determined by analyzing the density of muscle fiber with NIH Image 1.63 computer software. Stained muscle sections were mounted on the microscope slides and photographs were taken using NIH

Image 1.63. Each pixel of a digital picture has a gray scale with a numerical level between 0 and 255. Using a mouse, an area within each fiber was traced and selected, then the mean density was calculated by the NIH software function. The mean density was calculated as the sum of the gray scale of all the pixels, divided by the total number of pixels. The higher numerical scale represents a higher density, which indicates that muscle fiber has higher oxidative enzymatic activity. The examples of NADH analyses are shown in Figure 5.

Figure 5. Skeletal muscle fibers stained for NADH activity analysis. The left picture shows high density (the average density is 90) and the right shows low density (the average density is18).

It was reported that tetrazolium binds the sarcoplasmic reticulum and transverse tubular systems (Brooke & Engel, 1966; Brooke & Kaiser, 1972). This binding can result in artificial dark staining and could be wrongly concluded increased oxidative capacity.

To avoid such artificial staining, only the inner side of the muscle fiber was traced; and the mean density of the selected area was calculated. Although this method allowed for the elimination of artificial staining around membranes, it also eliminated a subpopulation of mitochondria that is abundant under the sarcolemma. Previous studies showed that endurance-resistance combined training increased oxidative enzyme activity in both subsarcolemmal (SS) and intermyofibrillar (IMF) mitochondria (Bizeau et al.,

1998; Chilibeck et al., 2002); these studies also reported that SS and IMF mitochondria may have a different time course for adaptation to training and fat metabolism (Koves et al., 2005; Bizeau et al., 1998). However, overall oxidative enzyme activity of mitochondria was this study’s interest; and it was concluded that avoiding possible artificial staining was more important than evaluating subpopulations’ mitochondrial oxidative activity. 74

Myonuclear Domain

Endurance muscular exercise training increases muscle fibers’ oxidative capacity.

Given that oxidative muscle fibers have a smaller myonuclear domain than less oxidative

fibers, post-intervention muscle fibers were predicted to have a smaller myonuclear

domain than pre-intervention fibers.

Immediately after the muscle biopsy, a portion of the sampled muscle was placed

into a fixative solution for 30 minutes and diced to about 1 x 1 x 5 mm with a razor blade,

then placed in a fixative solution in a 5 °C refrigerator overnight. The tissues were placed

in a buffered sucrose solution overnight, then placed in a 1 % buffered osmium solution

in a 0-4 °C refrigerator for one hour. The tissues were rinsed in buffered sucrose solution

twice and placed in 1 % fresh, filtered uranyl acetate in a 5 °C refrigerator overnight.

Afterwards, the tissue was dehydrated with ethanol solutions progressively (70 %, 95 %,

100 %), and placed in a propylene oxide solution for 20 minutes. Lastly, the tissues were

embedded in resin in the oven at 60 °C for 48 hours. The procedure for the preparation of

tissues for electron microscopic analysis is shown in Appendix K.

The samples were cut in cross sections of two range of thicknesses by a

microtome, Ultracut E (Reichert-Jung, Germany). Thick sections (0.5 to 0.7 μm) were

sliced from the sample for light microscopy; and thin sections (60 to 90 nm) were sliced

to observe adjacent cross-sectional areas with an electron microscope, EM109 (Carl

Zeiss, Germany). The thick section was mounted on microscope slides, stained with

toluidine blue, and photographed. Using a pre-calibrated NIH Image 1.63 program, photographed cross-sectional muscle fibers were traced at the sarcolemma; and the size 75

of cross-sectional areas of the fibers was determined as previously described. The

adjacent thin section was mounted on a copper grid and stained with uranyl acetate and

lead citrate, and examined under an electron microscope. Using the photographed thick

section, the number of myonuclei and satellite cells per muscle fiber were counted. An

example of two adjunct sections is shown in Figure 6. The example of myonucleus and

satellite cell seen in electron microscope is also shown in Figure 7.

Figure 6. Pictures of adjacent cross-sections of skeletal muscle: a thick section photographed by a light microscope (left) and a thin section photographed by an electron microscope (right).

Figure 7. An electron micrograph of skeletal muscle with a myonucleus (MN) and a satellite cell (SC).

These numbers of myonuclei and satellite cells were used to calculate the cytoplasm-to-nucleus ratio (C/N).

Satellite Cell Ratio

Exercise training activates quiescent satellite cells to undergo mitosis and become incorporated as myonuclei. Increase in the ratio of satellite cells to myonuclei was reported (Hikida et al., 1998; Roth et al., 2001). The numbers of myonuclei and satellite cells were also used to calculate the satellite cell ratio. Satellite cell ratio is calculated as follows:

Statistics

Statistics provides a mathematical interpretation and evaluation to help objectively understand data. In experiments, it is important to determine objectively if the outcome is a result of specific conditions or by chance. Inferential statistics are techniques to make general statements about the population. The goal of hypothesis testing is to make general statements about the population based on sample analysis.

Before a significant test is performed, a critical value (α) and two hypotheses are made.

A null hypothesis (HO) states that the means are the same and an alternative hypothesis

(HA) states that the means are not the same.

A critical value (α), also called the level of significance, defines the point of the distribution from which it is highly unlikely to obtain sample data if the HO is true.

Obtaining sample data from this part of the distribution, the critical region, would lead to 78

the rejection of the HO since sample data that fall in this critical region most likely did not occur by chance, but instead because of a treatment effect.

Based on the result of the test statistics, which measures comparability between the observed data and the HO, the decision is made. There are four possible decisions: (1)

rejecting the HO (accepting the HA) when the HO is correct, (2) rejecting the HO

(accepting the HA) when the HO is not correct, (3) failing to reject the HO (retaining the

HO) when the HA is correct and (4) failing to reject the HO (retaining the HO) when the HA is not correct. Two of them, rejecting the HO when the HO is not correct and failing to

reject the HO when the HO is correct, are right decisions; and others are called errors.

Rejecting the HO when the HO is correct is called a type I error and failing to reject the

HO when the HA is correct is called a type II error. A type I error is also called α and a

type II error is also called β. Correctly rejecting the HO is called power, which is

expressed as 1 - β.

All pre- and post-intervention results for each group are presented as a mean and

SD. The binary data, such as the ratio of the number of muscle fibers identified as type I

fiber over the total number of fibers analyzed, are proportions. The variance of the

sampling distribution of proportion is greater when the proportion value is near 0 or 1,

rather than when it is near 0.5. In order to remove this problem, the results in proportion

are presented as group means and SD after a transformation: Y’ = 2 x arcsine x (square root of Y) (Keppel & Wickens, 2004).

Statistical Test Procedures

Means and standard deviations (SD) represent the characteristics of different

sample groups. Often means allow us to compare groups. Several statistical test

procedures allow us to determine if differences between the means exist. One-sample t-

test shows whether the sample comes from a population when the mean is known but SD

is not. For example, when the researchers wanted to find out if their research subjects actually ate 30 % of total caloric intake in the form of carbohydrate as instructed, they can compare the mean of the subjects’ proportion of caloric intake of carbohydrates to 30

Analysis of variance (ANOVA) is a procedure of hypothesis tests comparing the means of a single variable with two or more groups. For example, if the researchers wanted to compare the residents’ BMI in different cities, they can compare the means of

BMI using ANOVA. Multivariate analysis of variance (MANOVA) allows them to compare more than two variables simultaneously. If multiple variables are compared separately, there is a higher possibility of type I errors, which will lead to a conclusion.

MANOVA is beneficial to minimize this possibility of errors.

When the means of different sample groups are compared, it is desirable that all groups have no commonality. However, in real life, it is an unlikely situation. Some physiological characteristics have a strong genetic influence; and this can be a bias for the treatment effect in research. For example, skeletal muscle fiber type is largely influenced by genetic factors. As previously explained, chronic low frequency stimulation (CLFS) can alter skeletal muscle fiber type. However, fiber type cannot be completely altered due 80

to intrinsic factors. Using the pre-treatment data as covariate, analysis of covariance

(ANCOVA) eliminates such a bias and compares unique differences.

Statistical test procedures can also determine if there is a relationship between quantitative variables. In addition to differences among group means, correlation tests also measure the direction and strength of the relationship. For example, one may wonder if there is a relationship between how much food is eaten and how much weight is gained. Then, correlation analysis determines if there is the relationship between caloric intake and weight gain. If there is a positive correlation, it would be concluded that one who eats more would gain more body weight. Correlation analysis can also identify the

strength of a relationship. In the previous example, it is possible to see how much caloric

intake contributes to weight gain.

Analyses of Compliance of Diet and Rowing Training

The assigned food proportion and the actual food proportion were compared using multiple simple t-tests to see if the subjects maintained their assigned food proportions.

Holm’s adjustment was performed to control type I error for the results of multiple t- tests. The actual food proportions of the HC and HP diets were compared with

MANOVA. If this omnibus test result was significant, multiple independent t-tests were performed for each nutrient. The actual average daily caloric intake and expected daily caloric intake of both groups were compared with a 2-way repeated omnibus ANOVA.

Changes in post-exercise heart rates and workloads were analyzed by repeated measure 81

MANOVA. Both groups’ total workloads were compared by independent t-tests. Critical value was set at 0.05 with 2-tails.

Analyses of Diet-Rowing Training Effects

Previous research has agreed that the effects of endurance training are increasing cardiovascular fitness and skeletal muscle diameter, shifting skeletal fiber type to more oxidative types, maintaining normal fasting blood glucose, and decreasing body weight, the percentage of body fat, and total cholesterol. Low carbohydrate intake is thought to cause carbohydrate starvation, which induces increasing plasma β-hydroxybutyrate

(Brooks et al., 1996).

Among the test measurements, it is known that skeletal muscle fiber type distribution and cross sectional area have wide normal ranges; and that they are genetically influenced, although exercise alters them (Bouchard et al., 1986; Hamel et al.,

1986; Komi et al., 1976; Simoneau et al., 1986; Willan et al., 2002). Therefore, pre- intervention values were used as covariance and the effects of training-diet intervention of skeletal muscle fiber size and fiber type distribution were examined using ANCOVA.

Critical value was set at 0.05 with 2-tails.

A Priori Test

An omnibus statistic test is an overall test to determine simultaneously whether a

variance in a set of data is significantly different from other sets of data. Only when the omnibus test shows significant mean differences, do researchers perform a posteriori

tests to determine which mean(s) is/are significantly different when comparing each pair of means. Many studies that do not have significant omnibus test results carry out a posteriori tests to compare the selected means of interest to the researchers. This practice indicates that the researchers have specific hypotheses among the analyses and/or that they are interested only in specific comparisons. In many experimental studies, researchers have a specific interest or hypothesis. Since an a priori test compares specific means, it would be a suitable analysis for such cases.

An HA can be directional when one of the means is expected to be greater than another. The directional alternative hypothesis can be proposed in only a priori test design. The non-directional alternative hypothesis is 2-tail, which means that the critical value, α, is set at both ends of the distribution curve. The directional alternative hypothesis is 1-tail, which means that the critical value, α, is set at one end of the distribution curve. A statistical test measures comparability between the HO and the data.

A priori comparison is performed for specific comparisons of group means before

data collection. These mean differences are represented as a linear contrast. In a linear

contrast, coefficient contrasts are used to assess the significance of this specific

relationship among the means. When comparing two means, the null hypothesis states

that there is no mean difference between two populations, which also means that a 83

combination of population means is 0; the mean of group 1 – the mean of group 2 = 0. In

this case, 1 is the contrast coefficient for the mean of group 1 and -1 is the contrast

coefficient for the mean of group 2.

The number of linear contrasts that can be made is equal to the number of means -

1 (the degree of freedom). In this study, since both the HC and HP groups were tested

pre- and post-intervention, there were four group means and three comparisons were

planned. Three linear contrasts were made to determine if post-training results were

higher/lower than pre-training results in both the HC and HP groups respectively; and if

the difference between pre- and post-training results was different between the HC and

HP groups (Table 1). Prior to performing a priori tests, two-way repeated measure

ANOVA was performed to obtain the mean square error (MSE) within subjects.

Table 1

Three Linear Contrasts and Their Contrast Coefficients

pre- HC post- HC pre- HP post- HP Sum

Contrast 1 (ψ1) -1 1 0 0 0

Contrast 2 (ψ2) 0 0 -1 1 0

Contrast 3 (ψ3) -1 1 -1 1 0

Direction of Hypotheses

Several hypotheses for the planned comparisons of the measurements were

expected to be higher in the post-intervention results; specifically, HO for the first

contrast (ψ1) was that the mean of the post-intervention value of the HC group was the same as that of the pre-intervention value. HA was that the mean of the post-intervention

value of the HC group was greater than that of the pre-intervention value. HO of the

second contrast (ψ2) was that the mean of the post-intervention value of the HP group is

the same as that of the pre-intervention value. HA was that the mean of the post-training

value of the HP group was greater than that of the pre-training value. HO of the third

contrast (ψ3) was that the mean difference between the post- and pre-training values of

the HC group was the same as that of the HP group; and HA was that the mean difference

between the post- and pre-training values of the HC group was different from that of the

HP group.

As previously described, investigators concluded that a high-carbohydrate diet

would be more beneficial than a low-carbohydrate diet to improve endurance

performance. In this study, an experimental high-protein, low-carbohydrate (HP) diet

combined with high-intensity endurance training was compared to a traditional high-

carbohydrate (HC) diet. The training effects of high-intensity aerobic exercise with a traditional high-carbohydrate diet have been established. Therefore, directional (1-tailed) tests were chosen to compare pre- and post-training results; and non-directional (2-tailed)

tests were chosen to compare the effects of training between the groups (Carlin & Doyle,

2001; Gravetter & Wallnau, 1988; Howell, 2004; Keppel & Wickens, 2004; Motulsky, 85

1995). The test measurements that were expected to be higher in the post-training results

were: (1) maximum oxygen consumption, (2) maximum power output, (3) maximum

isokinetic force output, (4) muscular endurance test on the isokinetic dynamometer, (5) skeletal muscle fiber size, (6) the percent of skeletal muscle type I fiber, (7) skeletal muscle fiber oxidative capacity, (8) satellite cell ratio to myonuclei and satellite cells, and

(9) testosterone concentration. The test measurements that were expected to be lower in the post-training results were: body weight, the percentage of body fat, total cholesterol concentration, plasma glucose concentration, β- hydroxybutyrate, and C/N ratio.

The remaining hypotheses for the planned comparisons of the measurements were expected to be lower in the post-training results. Specifically, HO of the first contrast (ψ1)

was that the mean of the post-training value of the HC group was the same as that of the

pre-training value. HA was that the mean of the post-training value of the HC group was

expected to be lower than that of the pre-training values. HO of the second contrast (ψ2)

was that the mean of the post-training value of the HP group was the same as that of the

pre-training value; and HA was that the mean of the post-training value of the HP group

was lesser than that of the pre-training value. HO of the third contrast (ψ3) was that the

mean difference between the post- and pre-training values of the HC group was the same

as that of the HP group. HA was that the mean difference between the post- and pre-

intervention values of the HC group was different from that of the HP group. The formula

for the F ratio was: 86

The formulae for the F ratio used for these analyses were:

The critical value was set at 0.05, which was adjusted to Dunn’s test for planned

comparisons as Fcritical (1, 15) = 5.4756 for 1-tail (Fψ1 and Fψ2) and Fcritical (1, 30) =

7.2361 for 2-tail (Fψ3).

Correlation

As previously discussed, testosterone concentration is believed to be a factor

causing skeletal muscle fiber hypertrophy, but it is also affected by different types of nutrients (Chandler et al., 19945; Hulmi et al., 2008; Kraemer et al., 1998; Sallinen et al.,

2004; Sinha-Hikim et al., 2002; Sinha-Hikim et al., 2003). Therefore, Pearson’s 87

correlation was employed to analyze the relationships between post-training testosterone

concentration and the average protein intake per body weight, and between post-training

testosterone concentration and the average carbohydrate intake per body weight.

Statistical software, SPSS 13.0.0 (SPSS, Inc., Chicago, IL) was used for the above

analyses.

A Priori Power Analyses

Because the goal of hypothesis testing is to make a conclusion for a wider

population based on a sample of the population, correctly rejecting the HO is desirable.

However, 80 % power is becoming the standard since many U.S. government agencies

provide their funds to research studies if their sample size is sufficient to detect results

with 80 % power at 5 % critical value (Moore & McCabe, 2003).

Power analysis is affected by four elements: (1) critical value (α), (2) effect size

(larger mean difference), (3) standard deviation (SD), and (4) sample size. Power can be

increased by increasing effect size, critical value, and sample size and by decreasing SD.

Power analysis can be performed before and after data collection. While an a posteriori

power analysis provides a probability that the HO is correctly rejected, an a priori power

analysis is conducted to determine a minimum sample size to correctly reject the HO.

Effect size, critical value, SD, and observed power can be obtained from previous studies.

If previous studies do not provide the above information, statistical software allows for the calculation of the minimum sample size needed for desired power using Cohen’s 88 effect size (Cohen, 1969) and critical value. Cohen’s effect size is most commonly used to interpret effect size.

Increasing critical value increases power; however, it also increases the possibility of a type I error. Therefore, it is not a desirable method to increase power. Increasing sample size is difficult since the total number of potential subjects is not large when training studies are combined with a muscle biopsy. This is a crucial problem if the test measurement has a large potential range since variance increases and is not homogeneous in such a test measurement. A small number of subjects become problematic when there are more than two groups to be compared. If there are more groups to be compared, more subjects are needed. However, when the numbers of potential subjects are limited, each group has a very small number of subjects, which causes very low power. Increasing effect size is also difficult when the length of treatment (training length) is not long enough to create large post-training test results compared to pre-training ones. A shorter length of treatment can also create less homogeneous results, which leads to larger variance. In general, it is difficult for a human subject experimental study to have high statistical power.

A minimum number of subjects was determined by using a priori power analyses with a free computer software, G*Power 3.0.4. (Faul, Erdfelder, Lang, & Buchner,

Germany). The minimum number of subjects needed for diet analysis using MANOVA was determined with the following conditions: MANOVA, expected effect size as medium (0.25), critical value as 0.05 at 2-tail, expected power as 0.8, group number as two, and the number of variables as four. The minimum number of subjects needed for 89

other analyses using a priori comparison was determined with the following conditions:

independent t-test, expected effect size as medium (0.5), critical value as 0.0177 (0.05 /

3) at 1-tail, and expected power as 0.8.

Chapter Summary

In this chapter, the methods and selection of test measurements used to examine the effects of diet and rowing training were discussed. These were experimental design, diet and training, subjects’ criteria, analytical methods of blood and skeletal muscle, and the statistical tests used to analyze findings. In Chapter 3, the characteristics of subjects and the statistical results of the diet-training intervention and pre-and post-intervention measurements are explained.

CHAPTER 3: RESULTS

In this chapter, several measurements conducted before and after rowing training

are statistically analyzed. This chapter begins with the subjects’ compliance to the diet-

training with analyses of food intake and rowing training progress followed by several analyses of the pre- and post-training measurements between the two groups.

Anthropometric measurements, blood analyses, muscular strength and endurance, maximum oxygen consumption (VO2) and maximum power, and skeletal muscle

properties are analyzed. The relationships among diet, maximal power, cross-sectional

skeletal muscle size, and testosterone concentration are also analyzed.

Characteristics and Compliance of Subjects

Ten volunteer subjects were assigned to the traditional high-carbohydrate low-fat

diet (HC) group; and the other ten volunteer subjects were assigned to the experimental

high-protein low-carbohydrate diet (HP) group. One subject in the HP group withdrew

from the study in the second week. Nineteen subjects finished the seven-weeks of diet

and high-intensity rowing training. However, because the data of the subjects who

attended at least fourteen of the total seventeen training sessions (82 %) were used for

data analysis, nine subjects in the HC group (22.8 ± 3.0 years old, 179.2 ± 6.4 cm in

height) were used and eight subjects in the HP group (23.8 ± 3.7 years old, 179.8 ± 7.7

cm in height).

Diet and Rowing Training

Diet

Subjects were allowed to consume any amount of food they wanted as long as

they maintained the assigned nutrient proportions. All subjects kept a dietary record that

was recorded in a food log for dietary analysis. For this study, a traditional diet consists

of 55 % carbohydrate, 30 % fat, and 15 % protein, based on their total caloric intake. An

experimental high-protein low-carbohydrate diet consists of 50 % protein, 25 % fat, and

25 % carbohydrate.

Multivariate analysis of variance (MANOVA) was used for diet proportion

analysis. The actual proportions of the two diets were significantly different, F(4,12) =

25.071. The post hoc tests showed that all the proportions except alcohol consumption were significantly different between the groups. One-sample t-tests were performed to

compare actual and target proportions of food. The actual proportion of fat in the HC

group was significantly lower than the target proportion. In comparison, the actual

proportion of fat in the HP group was significantly higher than its target proportion, while

that of protein was significantly lower. The actual proportion of alcohol consumption in

the HC group was significantly higher than its target proportion of alcohol consumption.

The target proportion of daily food intake and the average of the actual proportion are

listed in Table 2 and illustrated in Figure 8.

Table 2

Proportion of Daily Food Intake (Mean and SD)

HC (n=9) HP (n=8)

Target Actual Target Actual

Carbohydrate (%)* 55 57.5 ± 4.5 25 30.6 ± 7.3

Protein (%)* 15 14.2 ± 1.9 50 29.9 ± 7.3***

Fat (%)* 30 25.4 ± 4.3*** 25 38.5 ± 9.1***

Alcohol (%) 0 2.9 ± 3.4*** 0 1.0 ± 0.2 *Significant difference between actual HC and HP proportions, p< 0.05, 2-tailed. ***Significant difference between actual and target proportions, p< 0.05, 2-tailed.

Figure 8. Food proportions. *Significant difference between actual HC and HP proportions, p< 0.05, 2-tailed.

The average daily carbohydrate intake of the HC group subjects was significantly

higher than that of the HP group, t(1,15) =4.607. Conversely, the average protein intake

per body weight of the HP group was significantly higher than that of the HC group, t(15) 94

= 4.156. Repeated measure analysis of variance (repeated ANOVA) showed that actual

average daily caloric intake was significantly lower than the expected daily caloric intake, F(1, 15) = 78.397, but there was no significant difference between the groups,

F(1, 15) = 0.11. The average daily protein intake per body weight and the average of the

actual and expected daily caloric intakes are listed in Table 3 and illustrated in Figures 9.

Table 3

Carbohydrate, Protein, and Caloric Intake (Mean and SD)

HC (n=9) HP (n=8)

Carbohydrates (g)* 367.8 ± 78.9 196.7 ± 73.52

Protein/body weight (g / kg)* 1.1135 ± 0.206 2.5975 ± 0.976

Caloric intake (Kcal)*** Actual 2540.5 ± 396.1 2605.5 ± 820.2

Expected 4216.7 ± 423.5 4025.0 ± 397.3 *Significant difference between groups, p< 0.05, 2-tailed. ***Significant difference between actual and expected caloric intake, p< 0.05, 2-tailed.

Figure 9. Carbohydrate (top), protein (middle), and caloric Intake (bottom) analyses. *Significant difference between groups, p< 0.05, 2-tailed. ***Significant difference between actual and expected caloric intake, p< 0.05, 2-tailed. 96

There was a concern that ketone bodies might be generated due to low carbohydrate intake during the high intensity aerobic training, and this could cause acidosis. In order to ensure the well-being of the subjects during the training, urinary ketone body concentration was measured before the subjects started rowing on each training day. One subject of the HP group showed a trace of ketone bodies (5 mg/ml) twice but additional urinary tests on the following days showed negative results both times. Another subject of the HP group showed a moderate amount of ketone bodies (30 mg/ml) once. An additional urinary test on the following day also showed a negative result. Therefore, the proportion of carbohydrate intake was not adjusted for these subjects.

Rowing Training

Subjects’ heart rates were monitored during a rowing training period to adjust their training intensity. The initial training intensity was set at 70 % of the maximal power output obtained during the maximal oxygen consumption test. Rowing time, number of sets, and resting times were adjusted as indicated in Chapter 2. The intensity was increased at intervals of 10 w so that the subjects would maintain the same heart rate

obtained on the initial training day throughout the seven-week training. The post exercise

heart rate fluctuated through the seven week training, F(6, 10) = 6.169; but there was no significant difference during the training or between the groups, F(6, 10) = 0.917.

Workload significantly increased during the training, F(6, 10) = 283.916, but not between the groups, F(6, 10) = 2.021. The post-exercise heart rate changes during training and 97 dairy work changes during training sessions are illustrated in Figure 10. The total workload of the HC group was significantly higher than that of the HP group, t(15) =

2.224. The means and SD of total workload are shown in Table 4 and illustrated in Figure

10.

Table 4

Total Work (Mean and SD)

HC (n=9) HP (n=8)

Total work (kjoules)* 4996.1 ± 626.8 4369.5 ± 520.8 *Significant difference between groups, p< 0.05, 2-tailed.

Figure 10. Total work analysis. *Significant difference between groups, p< 0.05, 2-tailed. 98

Anthropometry

It was expected that increased physical activity increased fat-free mass (FFM) and

decreased fat mass (FM). Because supporters of a high-protein low-carbohydrate diet

claim that this diet is more effective in reducing FM and retaining FFM weight than a

traditional diet, anthropometric data was recorded before and after the rowing training to

determine whether there was a dietary effect on body weight and/or fat mass. Because the

rowing training was only seven weeks and subjects were in the post-growth age, their age

and height were measured to explain subject characteristics.

The average body weight of the HC group did not significantly decrease, Fψ1(1,

15) = 0.0358, but the average body weight of the HP group significantly decreased,

Fψ2(1, 15) = 6.7915. However, there was no significant difference between the groups,

Fψ3(1, 30) = 3.1026. For both groups, the participants’ FM significantly decreased, Fψ1

(1, 15) = 18.4224 and Fψ2(1, 15) = 27.072. However, there was no significant difference

between the groups, Fψ3(1, 30) = 0.847. Neither group had a significant increase in FFM,

Fψ1(1, 15) = 5.409 and Fψ2(1, 15) = 0.484. There was no significant difference between the groups, Fψ3(1, 30) = 1.477. The means and SD of anthropometry are shown in Table

5 and illustrated in Figures 11.

Table 5

Anthropometry (Mean and SD)

HC (n=9) HP (n=8)

Body weight (kg) Pre 80.22 ± 11.57 75.33 ± 11.44

Post 80.11 ± 11.29 73.76 ± 10.67**

Fat mass (kg) Pre 12.5 ± 6.70 12.28 ± 4.82

Post 11.1 ± 6.6** 10.34 ± 4.75**

Fat-free mass (kg) Pre 67.70 ± 9.01 63.04 ± 8.89

Post 69.04 ± 8.38 63.39 ± 8.62 **Significantly decreased from pre-training, p< 0.05, 1-tailed.

100

Figure 11. Anthropometry: body weight (top);fat mass (middle); fat-free mass (bottom). **Significantly decreased from pre-training, p< 0.05, 1-tailed. 101

Blood Analyses

Fasting Blood Glucose Concentration

Blood glucose concentration is maintained by hormones; but it is also affected by exercise. Advocates of a low-carbohydrate diet warn that a high-carbohydrate diet elevates blood glucose concentration and possibly causes type 2 diabetes. Therefore, fasting blood glucose concentration was analyzed to determine if a high-intensity exercise decreases fasting blood glucose concentration regardless of different diet regimens.

The concentration of fasting serum glucose did not significantly decrease for either group, Fψ1(1, 15) = 0.9317 and Fψ2(1, 15) = 0.1769. There was no significant difference between the groups, Fψ3(1, 30) = 0.0152. The means and SD of blood glucose analysis are shown in Table 6 and illustrated in Figure 12.

Table 6

Fasting Blood Glucose Analysis (Mean and SD)

HC (n=9) HP (n=8)

Blood glucose (mg / dl) Pre 78.61 ± 2.88 78.35 ± 7.16

Post 80.11 ± 5.36 79.41 ± 2.66

102

Figure 12. Fasting blood glucose analysis.

Fasting β-hydroxybutyrate Concentration

When fewer carbohydrates are available in a high-protein low-carbohydrate diet, more fats are metabolized to acetyl CoA, which is broken down to acetone and β- hydroxybutyrate. Elevated serum acetone and β-hydroxybutyrate concentrations indicate an increase in fat metabolism. Because acetone is not stable and tends to evaporate orally,

β-hydroxybutyrate concentration was analyzed to determine if a HP diet combined with high-intensity exercise elevated the state of fat metabolism.

The concentration of β- hydroxybutyrate did not significantly decrease for either group, Fψ1 (1, 15) = 0.2288 and Fψ2(1, 15) = 0.8792. There was no significant difference between the groups, Fψ3(1, 30) = 1.0209. The means and SD of β-hydroxybutyrate

Concentration are shown in Table 7 and illustrated in Figure 13. 103

Table 7

Beta-hydroxybutyrate Analysis (Mean and SD)

HC (n=9) HP (n=8)

β-hydroxybutyrate (µmol / L) Pre 24.69 ± 25.49 25.13 ± 21.43

Post 19.28 ± 13.38 36.38 ± 44.22

Figure 13. Beta hydroxybutyrate analysis.

104

Total Plasma Cholesterol Concentration

Although cholesterol is an important compound because it is a part of cell membranes and is a precursor of steroid hormones, excess cholesterol has been a cause of many disease conditions. Exercise tends to decrease the concentration of serum cholesterol. However, health professionals warn that a high-protein low-carbohydrate diet will elevate serum cholesterol and lipoproteins. Therefore, the concentration of total cholesterol was analyzed to determine if a HP diet with high-intensity exercise elevated serum cholesterol.

The concentration of total cholesterol significantly decreased for the HC group,

Fψ1(1, 15) = 6.9393; but it did not significantly decrease for the HP group, Fψ2(1, 15) =

0.5364. However, there was no significant difference between the groups, Fψ3(1, 30) =

0.0152. The means and SD of total plasma concentration are shown in Table 8 and illustrated in Figure 14.

Table 8

Total Cholesterol Analysis (Mean and SD)

HC (n=9) HP (n=8)

Total cholesterol (mg / dl) Pre 181.89 ± 29.84 165.13 ± 40.83

Post 158.56 ± 10.38** 1858.25 ± 25.10 **Significantly decreased from pre-training, p< 0.05, 1-tailed.

105

Figure 14. Total cholesterol analysis. **Significantly decreased from pre-training, p< 0.05, 1-tailed.

Total Plasma Testosterone Concentration

Testosterone has an anabolic effect on skeletal muscle hypertrophy. Previous studies indicated that intensive exercise might elevate resting total testosterone concentration; and that testosterone concentration is affected by diet composition.

Therefore, resting free testosterone concentration was analyzed to determine if high- intensity exercise increased the endogeneous anabolic steroid hormone and if the response differed between the two diets.

The concentration of free testosterone did not significantly increase for either group, Fψ1(1, 15) = 0.0859 and Fψ2(1, 15) = 4.0383. After the seven-week rowing training, the concentration of free testosterone was expected to increase for both groups.

Conversely, it decreased for the HP group. However, there was no significant difference 106 between the groups, Fψ3(1, 30) = 2.7664. The means and SD of total testosterone concentration are shown in Table 9 and illustrated in Figure 16.

Table 9

Total Testosterone Analysis (Mean and SD)

HC (n=9) HP (n=8)

Total testosterone (nmol / L) Pre 23.09 ± 3.60 21.75 ± 4.82

Post 23.60 ± 3.31 18.03 ± 3.52

Figure 15. Total testosterone analysis. 107

Isokinetic Muscular Strength and Endurance Tests

Rowing training increases cardiovascular function but also increases skeletal

muscle strength; and rowing studies show that the thigh muscles provide the majority of

power. Therefore, isokinetic maximal strength and endurance for leg extensor muscles were tested to determine if the diets combined with high-intensity exercise increased muscular strength and endurance.

The maximum torque during isokinetic testing did not significantly increase for either group, Fψ1(1, 15) = 1.8394 and Fψ2(1, 15) = 0.5624. There was no significant

difference between the groups, Fψ3(1, 30) = 0.1480. The means and SD of the maximum

torque during isokinetic testing are shown in Table 10 and illustrated in Figure 16.

Despite verbal encouragement and practice during familiarization, some subjects did not maintain the maximum effort during the isokinetic endurance test, which caused a negative fatigue index. A negative fatigue index indicates an increased power output at the end of an endurance test compared to the beginning. Therefore, the results of the endurance tests on the isokinetic dynamometer were eliminated from the analysis.

Table 10

Maximal Isokinetic Strength Tests (Mean and SD)

HC (n=9) HP (n=8)

Maximal isokinetic torque (ft-lb) Pre 222.0 ± 29.5 199.1 ± 54.3

Post 231.1 ± 31.1 204.5 ± 42.8

108

Figure 16. Maximal isokinetic strength.

Maximum Oxygen Consumption and Maximum Power Output

Rowing is an endurance exercise and improves aerobic capacity; therefore, VO2 max was tested to determine if the diets combined with high-intensity exercise increased this capacity. Rowing also requires high-intensity muscular effort and increases skeletal muscle strength. Therefore, maximum power during a VO2 max test was analyzed to

determine if either diet, combined with rowing training, increased post-training maximum

power.

The relative VO2 max significantly increased in both groups, Fψ1(1, 15) = 6.0844

and Fψ2(1, 15) = 9.0658. However, there was no significant difference between the

groups, Fψ3(1, 30) = 0.2487. The whole blood lactate concentration sampled five minutes

after the maximum rowing test significantly increased in both groups, Fψ1(1, 15) = 5.83

and Fψ2(1, 15) = 13.63. However, there was no significant difference between the groups, 109

Fψ3(1, 30) = 1.06. The maximum power output during the progressive indoor rowing test

significantly increased for both groups, Fψ1(1, 15) = 32.2918 and Fψ2(1, 15) = 24.2350.

However, there was no significant difference between the groups, Fψ3(1, 30) = 0.1000.

The means and SD of maximum oxygen consumption tests, post-exercise blood lactate concentration, and maximum power output during rowing test are shown in Table 11 and illustrated in Figure 17.

Table 11

Maximal Oxygen Consumption Tests (Mean and SD)

HC (n=9) HP (n=8)

Relative VO2 max (ml /min / kg) Pre 56.46 ± 9.25 55.61 ± 7.43

Post 61.33 ± 8.43** 61.91 ± 8.08**

Post-exercise lactate (mmol / L) Pre 12.05 ± 2.86 12.06 ± 2.04

Post 13.72 ± 3.39** 14.77 ± 2.84**

Maximum power output (w) Pre 232.5 ± 40.2 206.5 ± 17.4

Post 281.8 ± 33.0** 251.8 ± 28.1** **Significantly increased from pre-training, p< 0.05, 1-tailed.

110

Figure 17. Maximal oxygen consumption tests: relative maximal oxygen consumption (top); post-exercise blood lactate concentration (middle); maximal power output (bottom). **Significantly increased from pre-training, p< 0.05, 1-tailed. 111

Skeletal Muscle Characteristics

Since endurance training causes a skeletal muscle fiber type shift toward a more

oxidative type, muscle fiber type analysis using the mATPase method was performed to

determine if the training protocol shifted the muscle fiber type toward a more oxidative

fiber type; and the NADH-tetrazolium reductase method was performed to analyze

relative oxidative enzymatic activities. Rowing training increases muscular strength

because of skeletal muscle hypertrophy. Therefore, muscle fiber cross-sectional areas

were analyzed to determine if cross-sectional fiber size increased after the seven-week

diet and rowing training.

Skeletal Muscle Fiber Type Analysis

The investigator in this research planned to analyze skeletal muscle fiber type

composition using three different staining intensities based on three different pre-

incubation pH levels. However, due to problems with the pH electrode, several samples were wasted. As a result, these three sections were not precise serial sections. As a result, it was difficult to identify the same muscle fiber. Pre-incubation at pH 4.6 permitted identification of type I and type II fibers. Therefore, samples pre-incubated at pH 4.6 were used for fiber typing analysis. Although it is believed that shifts of human skeletal muscle fiber type occur only within the subtypes but not between type I and II (Staron &

Hikida, 1992), the proportion of type I fiber was analyzed.

The average numbers of skeletal muscle fibers analyzed for skeletal muscle fiber diameter and the proportion of fiber types were 626 ± 331 for the pre-training and 565 ± 112

194 for the post-training for the HC group; and those were 495 ± 180 for the pre-training and 642 ± 454 for the post-training for the HP group. The proportion of skeletal muscle fiber types did not change after the training, F(1, 15) = 0.815. There was no difference between the groups, F(1, 15) = 0.815. The means and SD of skeletal muscle fiber type proportion are shown in Table 12 and illustrated in Figure 18.

Table 12

Skeletal Muscle Fiber Type Analysis (Mean and SD)

HC (n=9) HP (n=8)

Fiber type (%) Type I Pre 48.5 ± 14.3 44.1 ± 11.9

Post 48.9 ± 13.1 42.4 ± 12.3

Type II Pre 51.5 ± 14.3 55.9 ± 11.9

Post 51.1 ± 13.1 57.6 ± 12.3

Figure 18. Skeletal muscle fiber type analysis. 113

Skeletal Muscle Fiber Cross-Sectional Size

After the rowing training, the average skeletal muscle fiber cross-sectional size did not significantly increase for either group, Fψ1(1, 15) = 0.9002 and Fψ2(1, 15) =

0.1592. There was no significant difference between the groups, Fψ3(1, 30) = 0.13.

Similarly, the type I skeletal muscle fiber size did not significantly increase for either group, Fψ1(1, 15) = 0.1914 and Fψ2(1, 15) = 0.0005. There was no significant difference between the groups, Fψ3(1, 30) = 0.0987. The type II skeletal muscle fiber size did not significantly increase for either group, Fψ1(1, 15) = 1.3391 and Fψ2(1, 15) = 0.1267.

There was no significant difference between the groups, Fψ3(1, 30) = 0.2861. The means and SD of skeletal muscle cross-sectional area are shown in Table 13 and illustrated in

Figure 20. 114

Table 13

Skeletal Muscle Fiber Cross-Sectional Area (Mean and SD)

HC (n=9) HP (n=8)

Fiber cross sectional area (μm2)

Type I Pre 4574.5 ± 1004.3 4215.2 ± 1441.3

Post 4723.9 ± 1018.0 4207.4 ± 1106.9

Type II Pre 5144.5 ± 957.8 4584.4 ± 831.7

Post 6220.4 ± 2061.4 4717.0 ± 1241.4

Total Pre 5144.5 ± 957.8 4382.4 ± 954.8

Post 5435.7 ± 1369.9 4512.3 ± 1099.5

Figure 19. Skeletal muscle fiber cross-sectional area. 115

Skeletal Muscle Oxidative Activity

Due to the lack of volume of frozen samples described previously, only seven HC

subjects’ muscles and five HP subjects’ muscles were analyzed for oxidative enzymatic

activity. The average numbers of skeletal muscle fibers analyzed for this analysis were

855 ± 423 for the pre-training and 819 ± 238 for the post-training for the HC group; and

those were 717 ± 334 for the pre-training and 849 ± 670 for the post-training for the HP

group.The oxidative capacity was analyzed according to the staining intensity of the

NADH reaction. The NADH activity based on staining intensity did not significantly

increase for either group, Fψ1(1, 15) = 1.187 and Fψ2(1, 15) = 0.13. There was no

significant difference between the groups, Fψ3(1, 30) = 0.265. The means and SD of skeletal muscle oxidative activity analysis are shown in Table 14 and illustrated in Figure

20.

Table 14

Skeletal Muscle Oxidative Activity (Mean and SD)

HC (n=9) HP (n=8)

Color intensity of NADH activity analysis

Pre 42.5 ± 22.7 52.9 ± 29.7

Post 52.4 ± 18.3 56.0 ± 26.9

116

Figure 20. Skeletal muscle oxidative activity

Cytoplasm-to-Nucleus Ratio (C/N) and Satellite Cell Ratio

The average numbers of skeletal muscle fibers analyzed for cytoplasm-to-nucleus

ratio (C/N) and satellite cell ratio were 273 ± 51 for the pre-training and 273 ± 34 for the

post-training for the HC group; and those were 240 ± 65 for the pre-training and 293 ± 42

for the post-training for the HP group. The size per myonucleus (C/N) significantly

decreased for the HC group, Fψ1(1, 15) = 7.6916; but it did not decrease for the HP

group, Fψ2(1, 15) = 0.21583. However, there was no significant difference between the groups, Fψ3(1, 30) = 0.6949. The ratio of the satellite cell number to the total number of

the myonuclei and satellite cells was not significantly different for either group, Fψ1(1,

15) = 0.019 and Fψ2(1, 15) = 0.017. There was no significant difference between the

groups, Fψ3(1, 30) = 0.003. The means and SD of cytoplasm-to-nucleus ratio are shown

in Table 15 and illustrated in Figure 21. 117

Table 15

Cytoplasm-to-nucleus Ratio and Satellite Cell Ratio(Mean and SD)

HC (n=9) HP (n=8)

Cytoplasm-to-nucleus ratio (# / μm2)

Pre 2722.9 ± 594.9 2398.9 ± 727.1

Post 2067.7 ± 292.5** 2027.8 ± 384.7

Satellite cell ratio Pre 0.0198 ± 0.0085 0.0177 ± 0.0112

Post 0.0211 ± 0.0122 0.0195 ± 0.0101 **Significantly decreased from pre-training, p< 0.05, 1-tailed.

118

Figure 21. Cytoplasm-to-nucleus ratio (top) and satellite cell ratio (bottom). **Significantly decreased from pre-training, p< 0.05, 1-tailed.

119

Relationships between Protein Intake and Power, Muscle Size,

and Free Testosterone Concentration

Although intensive training increases resting total testosterone concentration, negative correlations were reported between resting total testosterone concentration and protein intake per body weight. Therefore, the relationships between post-training testosterone concentration and the average protein intake per body weight, and those between post-training testosterone concentration were also analyzed.

The relationship between the post-training testosterone concentration and the maximum isokinetic strength was not significant, r = 0.056, p = 0.832. The relationship between the average daily protein intake per body weight and the post-training maximum isokinetic strength showed a negative trend, r = - 0.472, p = 0.056. The relationships between protein intake and the post-training skeletal muscle size were not significant, r =

-0.433, p = 0.83, r = - 0.418, p = 0.95 for type I fibers, r = - 0.422, p = 0.091 for type II fibers. The relationship between daily protein intake per body weight in kg and post- training resting total testosterone concentration was significant, r = - 0.60, p = 0.011

(Figure 22).

120

Figure 22. Relationship between daily protein intake per body weight in kg and the post- training resting total testosterone concentration.

Chapter Summary

In this chapter, several measurements conducted before and after rowing training were statistically analyzed. Compared with the target proportion of both diet groups, the

HC group consumed more carbohydrates and less fats and the HP group consumed more fats. Statistically food proportions of these two diet groups were different. Both groups consumed significantly less amounts of food than estimated by their physical activity.

Total workload accomplished during the seven-week training was significantly higher among the HC subjects than the HP subjects.

Compared to the pre-training measurements, the HP group lost body weight after training while the HC group did not. However, both groups lost significant amounts of fat mass (FM) after the training. The HC subjects decreased total plasma cholesterol 121

significantly. Both groups significantly increased relative maximal oxygen consumption

(VO2 max), post-exercise lactate concentration, and maximal power output generated during VO2 max test. The cytoplasm-to-nucleus ratio among the HC subjects

significantly decreased but that of the HP group did not. Although some pre- and post-

measurement comparisons (ψ1 and ψ2) significantly increased or decreased, there was no statistical group difference (ψ3) observed. In the next chapter, the physiological importance of these results is addressed.

122

CHAPTER 4: DISCUSSION

Many people have become more interested in promoting their health status in the

U.S. Overeating and a sedentary life style leads to overweightness/obesity, which is a risk factor for many diseases, such as heart disease, diabetes, and cancer. Balancing energy intake and expenditure (e.g. amount of food eaten and physical activity using energy) is a key to controlling body weight and body fat. While the amount of food intake can be measured by calories, the types of nutrients people should consume is widely debated.

Recently, a high-protein low-carbohydrate diet resurfaced as a diet regimen to decrease body weight. In most prior studies, it has been recommended that the amount of carbohydrates is the determining factor for duration of exercise and power output during a high-intensity aerobic exercise. However, the effects of a high-protein low- carbohydrate diet on high-intensity aerobic training are unknown. Thus, this study aimed to see if different diet regimens resulted in different results when combined with high- intensity aerobic training.

Twenty college-aged male volunteer subjects participated in seven weeks of progressive high-intensity rowing training while consuming either a traditional high- carbohydrate low-fat diet (HC) or an experimental high-protein low-carbohydrate diet

(HP). Chapter 1 reviewed the nutrient effects on exercise performance, skeletal muscle characteristics and muscle property changes after exercise, current health concerns related to overweightness/obesity, and the purpose of this study. Chapter 2 described the diet composition, training schedule, pre- and post-training test measurements, and 123

statistics. The results and statistical analyses of diet, training, and pre- and post-training

test measurements were presented in Chapter 3.

In this chapter, physiological significance of the results, followed by the study findings to three research aims stated in Chapter 1 are discussed. These aims were to

compare the effects of different diet regimens when combined with high-intensity aerobic

training on: (1) safety and effectiveness of training, (2) the benefits of reducing

cardiovascular risk factors, and (3) the ability of skeletal muscle to adapt, focusing on

muscular hypertrophy. At the end of this chapter, limitations of this study and

suggestions for further research are addressed.

Results of Diet and Training

Diets

The target proportion of daily food intake of the HC was 55 % carbohydrates, 15

% protein, and 30 % fats and that of the HP was 25 % carbohydrates, 50 % protein, and

25 % fats. Within a week after subjects submitted their 3-day per week food logs, the

subjects received the analyses of their food consumption. If these analyses showed that their food proportion was not in the range of their assigned diets, the subjects were instructed to choose proper foods and/or cooking methods and were encouraged to maintain their target proportion of these foods.

Despite this feedback, the HP subjects ate less protein and more carbohydrates

and fats (30.6 % carbohydrates, 29.9 % protein, and 38.5 % fats), while the HC subjects maintained their assigned proportion (57.5 % carbohydrates, 14.2 % protein, and 25.4 % 124

fats). It was reported that even highly motivated and educated subjects who closely

monitored their food intake experienced difficulty trying to achieve assigned diet proportions (Katan, 2009). Also as previously explained, food that is high in protein

contents tends to be high in fats (Tapper-Gardzina et al., 2002). Most food preparation

methods use fats and/oil. This might have been the reason that the HP subjects consumed

a smaller proportion of protein and a larger proportion of fats than the target proportions.

This increased fat intake among the HP subject adheres with health professionals’

warnings that fats associate with protein in a high protein diet (Fleming, 2000; Tapper-

Gardzina et al., 2002; White, 1973). Although the HP subjects ate more fats than the

assigned proportion, their overall diet was not a high-fat diet if it is compared to previous

high-fat diet studies; and the actual proportions of nutrients were different between the

HC and HP groups.

In some diet studies in which a small number of subjects participated for a short

period, food was prepared by researchers and provided to subjects (Horswill et al., 1990;

Johnston et al., 2004; Westerterp-Plantenga et al., 1999), which results in better diet

control than self-report diet studies. In this study, subjects were taught how to select and

prepare their assigned food and record their proportion size. A high-protein low-

carbohydrate diet is reported to have high satiety and result in low food intake (Lejeune

et al., 2005; Paddon-Jones et al., 2008; Westman et al., 2007). However, in this study,

both groups recorded a significantly less than expected amount of food intake in their

food logs; and they did not lose much body weight after the seven-week high-intensity

training. This result indicates that they were careful to identify the food group but 125 underestimated the amount of food consumed. It was reported that estimating portion sizes and food intake was empirical and a longer introductory period would be required to standardize participants’ estimations (Martin et al., 2007). It was also reported that while all subjects reported the correct proportions of food, the training group subjects tended to underreport their food intake during the exercise program; however, the control group subjects did not (Ambler et al., 1998). Although this weakness of the self-reporting food log was observed, it would be safe to say that the purpose of this study, maintaining the proportion of macronutrients, was not affected.

The acceptable macronutrient distribution ranges for adults has a wide range for each nutrient: 45 to 65 % carbohydrates, 20 to 35 % fats, and 10 to 35 % proteins of the percent of energy intake (Institute of Medicine, 2002). Researchers’ definition of a high- protein low-carbohydrate diet also varies. Therefore, there is no definitive macronutrient proportion describing a specific diet. One of the definitions for a low-carbohydrate diet describes it as a daily carbohydrate intake of less than 200 g (Westman et al., 2007).

Another definition for a low-carbohydrate diet describes it as a diet with less than 35 % of daily caloric intake of carbohydrates (Krieger et al., 2006). In the current study, the

HC group consumed 57.5 % of total caloric intake (367.8 g ± 78.9 g) of carbohydrates daily, while the HP group consumed 30.6 % of total caloric intake (196.7 g ± 73.5 g) of carbohydrates daily. Therefore, the HC group subjects consumed a high-carbohydrate diet and the HP subjects consumed a low-carbohydrate diet.

Interestingly, although consuming a large amount of carbohydrate was emphasized to the HC group, the HC group subjects ate an average 1.1 g / kg protein 126 daily, which is a sufficient amount of protein intake when compared to the recommended value for endurance training (1.1 g / kg) (Friedman & Lemon, 1989; Gaine et al., 2007;

Gaine et al., 2006; Meredith et al., 1989; Tarnopolsky, 2004; Tarnopolsky et al., 1998).

Often, a characteristic of specific diets is expressed by the proportion of nutrients or by the amount of nutrients. Such a characteristic is based on the total daily caloric intake of

2000 to 2500 kcal, which is recommended for the average, non-athletic adult. The use of proportions is not recommended to explain diet types when a large amount of food is consumed or a small amount of food is consumed. This tendency was seen in this study.

The HC subjects consumed 1.1 g / kg body weight of protein daily, which is classified as a high-protein diet (Krieger et al., 2006). Previous studies suggested that when the total caloric requirement is either higher or lower than the caloric requirement for average adults, the actual weights of necessary nutrients are more important than the proportion of nutrients, such as the minimum daily protein requirement for adult is 0.7 g / kg body weight (ADA Reports, 2000; Paddon-Jones et al., 2008; Rozenek et al., 2002).

Rowing Training

As previously explained, rowing is a strength-endurance sport because the repetitive high tension of skeletal muscle contraction increases skeletal muscle diameter and the energy used to support such a high-intensity muscular effort relies on aerobic and anaerobic power to improve the cardiovascular system. Most training studies last 8 to 20 weeks. During this training period, muscular strength increases in a linear manner (Sale,

1988). Because the participants were Ohio University students and Ohio University 127 employs a quarter system, there was a maximum of 11 weeks to introduce and conduct the study, including subjects’ screening, familiarization, pre-training measurements, rowing training, and post-training measurements. As a result, the training period was 7 weeks. Seven weeks of training is a short research duration. However, when training is combined with dietary experiments, seven-weeks of training is a reasonable amount of time. A comparison of the design of this study to similar diet-training studies, the characteristics of subjects, experimental durations, and the macronutrient proportions of previous studies are summarized in Table 16. 128

Table 16

Previous Exercise-Diet Studies

Author (Year) Subjects Duration Type of Experimental diet of study Exercise (% of C : F : P)

Current study 17 active males 7 wks Rowing 31 : 39 : 30 @ 70% VO2 max

Horswill et al. 12 male wrestlers 4 days Wrestling 42 : 46: 12 (1990)

Hoppeler et al. 7 male runners 4 wks Running 44 : 41 : 15 (1999)

Helge 41 males 7 wks 60 min 21 : 62 : 17 (2002) @ 60% VO2 max Various endurance training

Fleming et al. 20 males 6 wks Various 8 : 61 : 8 (2003) endurance exercise

Vogt et al. 11 male athletes 5 wks Various 30 : 55 : 15 (2003) endurance training

The training intensity was maintained by monitoring the post-rowing exercise

heart rate. Immediately after each rowing bout, subjects recorded their heart rate. The

target power output was increased when the post-rowing heart rate was lower than 70 % of maximum heart rate obtained prior to training. The post-rowing heart rates of both groups’ subjects fluctuated but remained close to the target heart rate while the training intensity was increased throughout the seven-week training period (Figures 23 & 24).

129

Figure 23. Average post-exercise heart rate per minute (bpm).

Figure 24. Average daily work (kjoules). 130

Comparing the average work of the first week to the 7th week, subjects in both groups increased their work. Although statistically there was no significant group difference in the changes of the weekly training intensity, the difference between the weekly work of the HC and the HP groups increased as the weeks went by. As a result, the total work during the seven weeks of the HC group was significantly higher than that of the HP group.

There was no change in target power, duration, sets, or resting time between the

6th and 7th weeks. During these last two weeks, the HP group’s average heart rate

decreased indicating improved cardiovascular function, but the HP group slightly decreased their work, indicating failure to maintain their work (Figure 23 & 24). This result agrees with the previous studies that a high-fat diet group improves exercise

duration, but not the intensity of the exercise; and this would be a limiting factor if the

competitive endurance event lasts a relatively short period, such as less than 30 minutes,

and if improving power is the issue during an endurance event rather than the duration

(Fleming et al., 2003; Hoppeler et al., 1999; Vogt et al., 2003).

Ketosis

As previously explained, when there are excess acetyl CoA molecules or an

insufficient amount of glucose available, acetyl CoA condenses to form ketone bodies as

fuel. However, ketone bodies are also acidic. A high amount of ketone bodies is harmful

and may possibly induce a coma. It is reported that when a daily carbohydrate intake is

50 to 150 g, ketosis is observed (Westman et al., 2007). In this study, the HC group 131

consumed an average 367.8 g of carbohydrates daily, while the HP group consumed an

average 196.7 g of carbohydrates daily. Consequently, neither diet group was in danger

of ketosis. However, increasing exercise intensity shifts the fuel source from fats to

carbohydrates; as a result, more carbohydrates are required to perform high-intensity

aerobic exercise. Therefore, metabolically ketosis may be induced.

Previous studies have shown that ketosis occurs during high-intensity aerobic

training (Brooks et al., 1996; Greenhaff et al., 1988a; Greenhaff et al., 1987). Therefore,

ketosis due to the combination of low carbohydrate intake and high-intensity aerobic exercise in the HP group was one of the health concerns in this study. In this study, one

HP subject showed a slight amount of urinary ketosis twice on separate occasions and another HP subject showed a moderate amount of urinary ketosis once during the 17 training sessions. This may be because the HP subjects did not adhere to the target diet proportion and consumed a higher amount of carbohydrate.

Results of Anthropometry

While after the growth period, the height of men is not influenced by the amount and types of food that are consumed, their body weight and body composition are largely influenced by physical activity and/or the food that is consumed. Some studies showed that a high-protein low-carbohydrate diet decreases body weight more than a high- carbohydrate low-calorie diet (Larosa et al., 1980; Brehm et al., 2003; Yancy et al., 2004;

Wood et al, 2006). To evaluate the diet effects on the body weight and body composition, the body weight and the percent of body fat were measured before and after the training. 132

The body weight of the HC group did not change while that of the HP group

decreased 1.5 kg. Although these differences were not statistically significant, it may be

an indication that a low-carbohydrate high-protein diet helps to reduce body weight if it is

consumed over a longer period with an exercise regimen.

Similarly, both groups decreased their percent of body fat after the training

without demonstrating a group difference. However, when the actual fat free mass (FFM)

was calculated using the percent of body fat and the body weight, the HC group gained

1.4 kg (from 67.9 kg to 69.3 kg) of FFM but the HP group gained only 0.4 kg (from 63.2

kg to 63.6 kg) of FFM. Some studies reported that a high-protein diet reduces body

weight and retains FFM better than a low-calorie diet (Lejeune et al., 2005; Volek et al.,

2002). However, in this study, FFM was retained in the HP group but did not increase. In

the current study, subjects were normal weight and physically active compared to most

dietary studies having overweight/obese subjects who are less active. In this study, the

combination of diet and high-intensity exercise increased energy deficits more than diet-

only or diet-moderate-intensity exercise experiments. That this energy deficit may affect

skeletal muscle metabolism is discussed later in this chapter.

Results of Blood Analyses

Fasting Blood Glucose Concentration

High-protein low-carbohydrate diet advocates claim that a high-carbohydrate diet would cause high fasting blood glucose, promoting type 2 diabetes and obesity.

Carbohydrate digestion elevates blood glucose concentration, which causes insulin 133 release to induce tissues to take up glucose. This study showed that after a high-intensity aerobic training, neither HC nor HP group had elevated resting blood glucose levels. As previously explained, besides insulin, physical activity also induces GLUT 4 translocation, and therefore, induces glucose uptake by tissues. The subjects of the HC group consumed 87 % more carbohydrates daily (680 g) than those of the HP group.

Nevertheless, their post-training fasting blood glucose concentration remained the same as pre-training concentration and both were within the normal range. This well-controlled blood glucose concentration among the HC group subjects may be the result of insulin and/or exercise-induced GLUT 4 translocation; and indicates that a high-carbohydrate diet combined with high-intensity aerobic exercise may not affect normal blood glucose regulation.

A previous study showed that a seven-week high-carbohydrate diet with moderate intensity exercise increased insulin sensitivity, while a low-carbohydrate, high-fat diet did not (Helge, 2002). Hypoenergetic high-carbohydrate diets without exercise among overweight female subjects has been shown to increase insulin sensitivity (Vogt et al.,

2003). High-carbohydrate diets without exercise have also caused increased insulin sensitivity among normal weight male subjects (Koutsari & Sidossis, 2003). Therefore, the high-carbohydrate diet would not increase fasting blood glucose nor decrease insulin sensitivity among healthy subjects or overweight/obese people if the total caloric intake does not exceed caloric requirements. 134

Fasting Plasma β-hydroxybutyrate Concentration

Glucose is the main fuel for all organs. When there are excess acetyl CoA molecules or an insufficient amount of glucose available, acetyl CoA condenses to form ketone bodies to become fuel. Therefore, the plasma concentration of β-hydroxybutyrate,

a form of circulating ketone body, is an indicator of the availability of glucose.

As expected, fasting plasma β-hydroxybutyrate concentration decreased among

the HC subjects and increased among the HP subjects. This result suggests that there was

enough glucose to be used as fuel among the HC subjects; and that there was an

accumulation of acetyl CoA and/or a lack of glucose due to a low-carbohydrate diet

among the HP subjects. This ketone body formation was also observed in urinary ketone

analysis. As previously explained, urinary ketone analysis was performed just before the

first rowing bout to monitor each subject’s health condition. Although on a few

occasions, a moderate amount of urinary ketone bodies was observed among the HP

subjects, they were not found among the HC subjects. It was reported that less than 20

μmol / L of plasma β-hydroxybutyrate concentration indicates compliance with a low-

carbohydrate diet (Volek et al., 2002). Therefore, although the HP subjects failed to

achieve the assigned food proportion, their food consumption was enough to be classified

as consuming a low-carbohydrate diet.

135

Fasting Total Cholesterol Concentration

Both HP and HC groups decreased their fasting total cholesterol after the seven- week rowing training. Although statistically there was little group difference, their fasting

total cholesterol concentration decreased 13 % among the HC subjects, but only 4 % among the HP subjects. Exercise has been shown to decrease resting total cholesterol

concentration, which is positively related to the intensity of exercise (Durstine et al.,

2002; Durstine et al., 2001; Lehtonen & Viikari, 1978; Toriola 1984). However, the

effects of a high-protein and/or low-carbohydrate diet on the subclasses of cholesterol are

inconclusive. Previously a study showed that a high-protein low-fat diet decreased total

cholesterol, triglycerides, and HDL (Johnston et al., 2004). However, other studies

indicated that a high-protein low-carbohydrate diet decreased triglycerides but increased

total cholesterol, HDL, and LDL (Sharman et al., 2002; Volek et al., 2003), and

decreased total cholesterol and LDL but also decreased HDL (Vidon et al, 2001). In the

current study, the analyses of the subclasses of cholesterol were not performed.

Therefore, the effects of the diets on the subclass of cholesterol were not investigated.

Although only the HC group showed a significant amount of total cholesterol

reduction, overall Pearson’s correlation analysis showed that the higher pre-training

concentration resulted in more reduction (r = - 0.86, p< 0.0001). While a high total

cholesterol concentration is known to have a strong correlation to mortality due to

cardiovascular diseases (Fletcher et al., 1996), a low total cholesterol concentration

(lower than 140 mg / dl) in males is reported to have a strong correlation to mortality due

to non-cardiovascular diseases, such ascerebral hemorrhage (Epstein, 1994; Jacobs et al., 136

1992; Law et al., 1994; NIH guide, 1994). Although the mechanism by which low total

cholesterol concentration causes cerebral hemorrhage is not known, it was reported that cellular dysfunction, such as disrupting cell membranes, would likely occur when total

cholesterol concentration is lower than 160 mg / dl (Jacobs et al., 1992).

In this study, a high-carbohydrate intake combined with high-intensity aerobic

rowing training resulted in a significant reduction of total cholesterol concentration. It

was also observed that both those who had high fasting total cholesterol concentration

before the training and that those who had a very low total cholesterol concentration

before participating in the study had a total cholesterol concentration that fell in the

normal range after the training (Figure 25). The relationship may be due to the subjects

paying more attention to what they ate and engaging in regular exercise. However, the actual reasons for this increased total cholesterol concentration are beyond the scope of this study’s analyses.

137

Figure 25. Relationship between the pre-training total cholesterol concentration and its reduction after the training.

Resting Total Testosterone Concentration

Testosterone is known to induce skeletal muscle hypertrophy. Endogenous testosterone concentration is related to the intensity of exercise. Some studies have shown that training increased the resting total testosterone concentration of men (Ahtiainen et al., 2003; Häkkinen et al., 1988; Remes et al., 1979). Others have shown that training had no effect on resting testosterone concentration (Kraemer & Ratamess, 2005). 138

The results of this study showed that statistically there was no significant

difference between the groups or within the group over time. However, the post-training

testosterone concentration decreased 17 % from the pre-training concentration in the HP

group. The precursor of testosterone is cholesterol, which is abundant in a high-protein

high-fat diet. As previously reported, the HP subjects decreased body weight and the

percent of body fat after training. The HP group did not increase FFM as much as the HC

group after the training. Skeletal muscle mass makes up the majority of FFM. This failure

to increase testosterone concentration after the training may be the reason why FFM among the HP subjects did not increase. Although it was reported that nutrient ingestion affects skeletal muscle protein synthesis and testosterone concentration, these responses

vary with different types of nutrients (Chandler et al., 1994; Hulmi et al., 2008; Kraemer

et al., 1998; Sallinen et al., 2004). It was also observed that the total work did not

increase in the HP group as much as the HC group. Because endogenous testosterone secretion is positively related to the intensity of exercise, this lower total work may be

related to the lower total resting testosterone concentration. These group differences in

FFM and testosterone concentration are reviewed in the section of skeletal muscle response to the training later in this chapter.

Result of Muscular and Cardiovascular Fitness Tests

Isokinetic Maximal Strength Test

During rowing training, the subjects perform repeated arm-pulls and leg-presses at very high-intensity; because of this, skeletal muscle strength gain was expected. 139

Because previous studies show that the thigh muscles provide the majority of power

during rowing motion (Clarkson et al., 1984; Mäestu et al., 2005; So et al., 2007;

Tachibana et al., 2007), the strength of leg extensor muscles was assessed to determine

the effect of diet and high-intensity exercise.

The results of this study showed there was little isokinetic strength increase in leg

extensor muscle after the rowing training; and there was no diet group difference. These

measurements were made using the isokinetic dynamometer. As it was explained in

Chapter 3, the maximal power output measured during maximal aerobic capacity test on

the rowing ergometer significantly increased after training for both groups. This

difference in the two tests is due to training specificity. Many studies have shown that physiological adaptation differs if the stimulation is different (Campos et al., 2002; Dinn

& Behm, 2007; Strømme et al., 1977; Wilson & Murphy, 1995). The training in this

study was conducted using a rowing ergometer and the maximal strength test was

conducted using the isokinetic dynamometer. Therefore, it is not unusual that the

maximum isokinetic strength did not increase after high-intensity training.

Maximal Aerobic Capacity Test

Subjects in this study underwent the seven weeks of high-intensity progressive

aerobic training. As seen in the early part of this chapter, subjects maintained their target

heart rate while intensity and duration of each rowing session increased with shorter

resting time between rowing sessions. The rowing training significantly increased maximal oxygen consumption (VO2 max) to supply energy aerobically; the HC groups 140

increased VO2 max an average of 7 % while the HP group increased an average of 11 %

compared with the pre-training values. As explained in Chapter 1, less oxygen is required

by glucose than fats during oxidation. Therefore, at the same intensity, glucose uses less

oxygen than fats to achieve the task. The HP group consumed less glucose than the HC

group, resulting in a higher fasting plasma β-hydroxybutyrate concentration than the HC group. This higher fasting plasma β-hydroxybutyrate concentration may have increased

the average relative VO2 max among the HP group more than the HC group.

When energy demand exceeds energy production aerobically, pyruvate cannot be

converted to acetyl CoA but is converted to lactate. Hydrogen ions are released from

lactates, creating acidic condition in the body; this is one of the limiting factors for

continuing high-intensity exercise (Greenhaff et al., 1987). At the later stages of the

progressive-intensity exercise test, blood lactate concentration increased due to

insufficient oxygen supply. High-intensity aerobic training is known to delay the onset of

this energy supply shift from aerobic to the anaerobic system, and to improve lactate

clearance (MacRae et al., 1992; Phillips et al., 1995). In this study, the post-exercise

blood lactate concentration increased 14 % among the HC subjects and 22 % among the

HP subjects after the training. It is difficult to distinguish if the elevated post-exercise

blood lactate concentration is due to anaerobic energy supply or due to poor lactate

clearance. However, considering the higher fasting plasma β-hydroxybutyrate

concentration, this larger lactate concentration among the HP subjects may be the result

of early onset of anaerobic energy supply. 141

As previously seen, the rowing training resulted in little isokinetic strength increase in leg extensor muscle after the rowing training. However, the maximum power

output generated during the progressive-intensity exercise test increased equally in both

groups (21 % among the HC subjects and 22 % among the HP subjects). This provides

further evidence that skeletal muscle adaptation and improvement is specific to types of

training, as also previously discussed.

Skeletal Muscle Characteristics

Skeletal Muscle Fiber Type Analysis

Although human skeletal muscle fiber type shift among subtypes of type I and

type II has been reported, fiber type is not believed to shift from type I to type II, or type

II to type I. As expected, in this study, neither group showed any change in the ratio of

type I to type II fiber after the training.

Skeletal Muscle Fiber Cross-Sectional Size

After the seven-week rowing training, the cross-sectional area of the muscle

fibera increased as expected: 6 % among the HC subjects and 3 % among the HP

subjects. Weight reduction studies show that a low-carbohydrate diet reduces more body

weight and retains more fat free mass (FFM) than a low-fat diet (Brehm et al., 2003;

Larosa et al., 1980; Shai et al., 2008; Yancy et al., 2004; Westerterp-Plantega et al., 2004;

Wood et al, 2006). When skeletal muscle fiber size increases, FFM increases. However,

in this study, when a high-protein low-carbohydrate diet was combined with high- 142

intensity aerobic training, it did not cause skeletal muscle hypertrophy as a high- carbohydrate diet did.

When the cross-sectional size is considered based on the fiber type, type II fiber size increased 21 % in the HC subjects, but only 3 % in the HP subjects. This result agrees with the reports from the previous studies. A rowing motion incorporates maximal leg press efforts (Mäestu et al., 2005); and this repetitive high-tension movement results in power production from large size oxidative fibers (Hagerman, 2000; Hagerman, 1998;

Larsson & Forsberg, 1980). High-intensity aerobic exercise increases oxidative capacity and cross-sectional size of type IIA fibers and causes fiber type shift from IIX to IIAX.

Although subtypes of type II fiber were not identified in this study, this increased type II fiber size may be due to increased cross-sectional size of type IIA and IIAX fibers.

Increased muscle contraction facilitates protein catabolism (Rennie et al., 1981) and proteins consumed after exercise increase skeletal muscle anabolism (Bolster et al.,

2005; Gaine et al., 2007; Kimball & Jefferson, 2002; Tipton et al., 1999). Because of this,

many studies recommend higher intake of protein (1.1 g to 1.8 g / kg of body weight)

than ADA recommends (0.8 g / kg of body weight) for both resistance and endurance

training athletes, to facilitate skeletal muscle protein synthesis (Gaine et al., 2006; Gaine

et al., 2007; Friedman & Lemon, 1989; Meredith et al., 1989; Tarnopolsky et al., 1998;

Tarnopolsky, 2004). However, the average cross-sectional size among the HP group was

not larger than that of the HC group.

As previously reported in this chapter, the total work of the HP group during the

rowing training was lower than that of the HC group; fat free mass (FFM) increased less 143 among the HP subjects than the HC subjects; and the resting total testosterone concentration of the HP group was lower than that of the HC group. This study showed a negative relationship between the daily protein intake and the post-training resting testosterone concentration. All these results indicate that higher protein intake combined with the rowing training did not increase skeletal muscle size as much as the higher carbohydrate intake did.

Skeletal Muscle Oxidative Activity

Exercise training reportedly makes skeletal muscle fiber more oxidative (Fry et al., 2003; Staron at al., 1989). In this study, skeletal muscle oxidative activity expressed in the color intensity was 23 % increased in the HC group and 6 % in the HP group. This result agrees with the previous report that rowing motion requires large oxidative muscle fibers to perform high-tension muscle contractions repetitively (Larsson & Forsberg,

1980). The group difference between the HC and HP groups must be due to the HC subjects training at higher intensity than the HP subjects, as was reported in a previous section.

Cytoplasm-to-Nucleus Ratio (C/N) and Satellite Cell Ratio

Exercise training increases skeletal muscle’s oxidative activity and muscle fiber size. Each myonucleus of skeletal muscle fiber maintains and controls a certain part of a fiber which is called its myonuclear domain. Oxidative fibers having more enzymes involved in their function have a smaller domain than less oxidative fiber types (Allen et 144 al., 1996; Kelly, 1978; Tseng et al., 1994). In this study, the cytoplasm-to-nucleus ratio was used to examine the relationship between each myonucleus to fiber size. After the training, the cytoplasm-to-nucleus ratio decreased 24 % in the HC group and 15 % in the

HP group. Although it was not statistically significant, the previous section reported that skeletal muscle fibers of the HC group became more oxidative than that of the HP group.

This difference in the reduction of the cytoplasm-to-nucleus ratio between the groups also indicates that skeletal muscle of the HC group became more oxidative than that of the HP group.

When exercise training causes fibers to be more oxidative, the number of satellite cells increases (Moss & Leblond, 1970; Shafiq et al. 1968; Winchester & Gonyea, 1992).

This allows the muscle fiber to maintain the appropriate size of the myonuclear domain and muscle function when its diameter is increased and/or its metabolism is shifted to a more oxidative state (Putman et al., 1998). In this study, the results of the rowing training showed slight tendencies to increases in muscle fiber diameter and skeletal muscle oxidative activity. These increases require more myonuclei per skeletal muscle fiber to maintain proper function. As previously explained, exercise training activates quiescent satellite cells to undergo mitosis and merge into muscle fibers. Therefore, the number of myonuclei increases (Hikida et al., 1998; Roth et al., 2001). Although in this study, it was not a statistically significant increase, increases in fiber size and oxidative capacity were associated with an increased number of satellite cells, especially among the HC subjects.

145

Chapter Summary

In this chapter, results of diet regimens, rowing training, several pre- and post- training measurements were discussed. Diet group differences observed in these findings were also examined. During the rowing training, post-exercise heart rate adapted to progressively increased training intensity among all subjects but the HP subjects seemed to reach plateau earlier than the HC subjects, which resulted in less total work of the HP than that of the HC. After the training, the body weight among the HP subjects decreased as well as their percent of body fat. However, they also lost fat free mass (FFM) while the

HC subjects gained it. Fasting total cholesterol concentration decreased after the training regardless of diet regimens. This decrease had a negative relationship to the pre-training

concentration. Tendency of skeletal muscle hypertrophy and increased oxidative capacity were observed in skeletal muscle fibers in the HC groups but not in those of the HP group. These results were associated with decreased cytoplasm-to-nucleus ratio of the HC group. In the next chapter, the conclusions of three research aims proposed in Chapter 1 are addressed.

146

CHAPTER 5: CONCLUSION

The studies show that over 60 % of adults in the U. S. are either overweight or

obese, and that this trend is increasing (Ogden et al., 2007; Ogden et al., 2006). Limited

physical activities and poor eating habits are thought to be main reasons for this

increasing overweightness/obesity. To prevent weight gain and reduce body weight,

increasing energy expenditure by increasing physical activity and decreasing energy

intake by decreasing food intake are needed. Thus, exercise and/or diet modification is

recommended (Lichtenstein et al., 2006). Because lowering food intake leads to

decreasing energy intake, and, because fats have high energy potential, reducing total

caloric intake and reducing fat intake have been a traditional diet regimen for weight

control. However, changing food proportion has recently been suggested as a weight

reduction strategy (Kennedy et al., 2001).

The American Dietetic Association recommended a food proportion of 60 %

carbohydrates, 30 % fats, and 10 % proteins of a total 2000 kilocalories (kcal) intake for

average adults. Because carbohydrates are the main fuel source for all organs, a high-

carbohydrate diet has been recommended to those who engage in high-energy physical

activities (Arkinstall et al., 2003; Børsheim et al., 2004; Burke et al., 2004; Costill et al.,

1971; Coyle 1995; Rasmussen & Phillips, 2003; Simonsen et al., 1991). Because participating in exercise has become popular and no longer only for the athletic population, diet regimens among people who exercise are diverse. Some people who consume high-protein and/or low-carbohydrate diets also participate in endurance exercise to maximize weight reduction/maintenance effect. Therefore, the purpose of this 147

research was to identify if this experimental diet combined with high-intensity aerobic

training would result in similar results as a traditionally recommended high-carbohydrate

diet. In this study, rowing was selected as an endurance training tool because it is an

endurance sport that requires high-intensity muscular effort and it relies on aerobic and

anaerobic power (Mäestu et al., 2005). Rowing training improves the cardiovascular

system and causes skeletal muscle fiber adaptation, such as increased fiber size and

oxidative capacity (Hagerman, 2000; Hagerman, 1998; Larsson & Forsberg, 1980).

In the previous chapter, the physiological importance of the results of this study

were discussed. In this chapter, training results associated with an experimental high-

protein low-carbohydrate diet (HP) are compared with those with a traditional high-

carbohydrate diet (HC), and practical implications are also discussed. Finally, the

limitations of this study and suggestions for future studies are presented.

Safety and Effectiveness of High-Intensity Aerobic Training with Different Diet

Regimens

A low-carbohydrate diet is known to increase fat oxidation as compensation for

low amount of glucose (Pitsiladis & Maughan, 1999). Although fat intake increases

exercise time in endurance sports (Fleming et al., 2003; Hoppeler et al., 1999; Vogt et al.,

2003), it decreases power output in short-duration high-intensity exercise events

(Langfort et al., 1997). Maximal oxygen consumption (VO2 max) indicates a person’s health status and improving VO2 max benefits cardiovascular fitness (Farrell et al., 1998;

Laukkanen et al., 2001). 148

When carbohydrate availability becomes extremely low, fats are converted into

ketone bodies (Brooks et al., 1996). Not only low carbohydrate intake, but also exercise

and low insulin levels can induce metabolic ketosis. High ketone body concentration is

called metabolic acidosis, which lowers exercise performance (Greenhaff et al., 1987).

Therefore, the first question was whether consuming a high-protein, low-carbohydrate

diet during a high-intensity aerobic training would safely produce results similar to that

of consuming a traditionally recommended high-carbohydrate diet.

Contrary to the traditionally recommended high-carbohydrate food consumption, low-carbohydrate food consumption prior to high-intensity exercise causes early glucose depletion (Costill et al., 1971) and induces fat oxidation (Brooks et al., 1996), which

aggravates exercise-induced acidosis. Acidosis leads to lower power output, maximal

oxygen consumption (VO2), and shorter exercise time due to a slower rate of energy

release (Åstrand, 1967; Burke et al., 2004; Coyle 1995; Greenhaff et al., 1988b; Wilmore

& Costill, 2004).

In this study, increased fat utilization was observed among the subjects who

consumed an experimental high-protein low-carbohydrate diet as it was observed that

their resting β-hydroxybutyrate concentration increased. Urinary ketone bodies were also

observed in some of the HP subjects during the training, but the amounts of ketone bodies

were moderate and there were few occurrences of their presence. Therefore, no dietary

modification was applied to these HP subjects. It was concluded that the diet proportion

chosen for this study was safe for people who engage in high-intensity aerobic training. 149

During the seven-week rowing training, the adaptation of post-exercise heart rates

to increased training intensity was observed in both diet groups. This cardiovascular

adaptation was associated with the improvement of VO2 max at the post-training

measurement. However, at the end of the seven-week rowing training, daily work

training of the HP group tapered off in comparison to that of the HC group. Total work

over the seven-week training of the HP group was significantly lower than that of the HC

group. These results indicate that people can participate in high-intensity aerobic training

safely while consuming a high-protein low-carbohydrate diet, and can improve

cardiovascular fitness.

The Benefits against Cardiovascular Risk Factors

About 700,000 people die of heart disease, such as coronary heart disease and

heart attacks, in the United States each year. Seventy one percent of all heart disease

deaths is due to atherosclerosis (American Heart Association; Centers for Disease

Control and Prevention, 2007). Aging, gender (higher in males), and heredity (including

race) contribute to an individual developing heart disease (Centers for Disease Control and Prevention, 2007). Heart disease is also related to high blood cholesterol levels, high blood pressure, diabetes mellitus, tobacco use, a high salt/saturated fats/cholesterol diet, physical inactivity, obesity, and alcohol use.

Most of these factors are inter-correlated and also related to lifestyle. Increasing

physical activity can help to reduce cholesterol, reduce body weight, and lower blood

pressure (Altekruse & Wilmore, 1973; Fletcher et al., 1996; Arciero et al., 2006; Durstine 150

et al., 2002; Durstine et al., 2001; Goldberg et al., 1984; Haskell, 1984; Lehtonen &

Viikari, 1978; Orakzai et al., 2006; Peltonen et al., 1981; Toriola 1984). Therefore, the

second question was whether consuming a high-protein, low-carbohydrate diet during a

high-intensity aerobic training would decrease some of the cardiovascular risk factors just

as a traditionally recommended high-carbohydrate diet does.

Advocates for a high-protein low-carbohydrate diet claim that this diet reduces

more body weight than a high-carbohydrate diet (Brehm et al., 2003; Larosa et al., 1980;

Yancy et al., 2004; Wood et al., 2006). After training, the body weights of those who

consumed a high-protein low-carbohydrate diet decreased more than those who

consumed a high-carbohydrate diet.

In this study, the HP group showed a trend of decreasing fasting total cholesterol

concentration after the training. On the other hand, the HC group significantly decreased

the post-training total cholesterol concentration. These results reflect warnings announced

by professional health organizations that a high-protein and low-carbohydrate diet would

result in hyper-lipidemia and hyper-cholesterolemia (American Heart Association;

American Heart Association Media Advisory, 2002; JAMA Council on Foods and

Nutrition, 1973; Fleming, 2002; St. Joer et al., 2001; Tapper-Gardzina et al., 2002;

White, 1973).

The subjects of both groups increased cardiovascular fitness, represented as VO2 max, after the seven-week training. Decreased body weight, tendency of total cholesterol reduction, and increased VO2 max indicate that regardless of the types of food that are

consumed, high-intensity aerobic training reduces some cardiovascular risk factors. 151

Skeletal Muscle Adaptation and Exercise Performance

Human body adapts in response to stimuli. Exercise can cause several physiological and morphological changes. Rowing is an endurance sport that requires high-intensity muscular efforts and a cardiovascular system that supports them (Mäestu et al., 2005). As a result, cardiovascular system adaptation, as well as several functional and morphological adaptations in skeletal muscle occur. Therefore, the third question was whether consuming a high-protein, low-carbohydrate diet during a high-intensity aerobic training would increase oxidative capacity and/or skeletal muscle fiber hypertrophy as well as produce benefits in exercise performance as a traditionally recommended high- carbohydrate diet does.

Previous studies showed that rowing training causes skeletal muscle fibers to increase in size and oxidative capacity (Hagerman et al., 2006; Hagerman, 2000;

Hagerman, 1998; Larsson & Forsberg, 1980). This study showed that regardless of what proportions of macronutrients were consumed, skeletal muscle fibers increased their oxidative capacity after training. However, it was more prominent among the skeletal muscle fibers of the HC subjects than those of the HP subjects. This difference was also observed in cytoplasm-to-nucleus ratio. When skeletal muscle fiber increases its oxidative capacity, the nuclear domain of each myonucleus decreases (Putman et al.,

1998; Winchester & Gonyea, 1992). Cytoplasm-to-nucleus ratio of the HC group decreased more than the HP group’s ratio, which also indicates that skeletal muscle response to high-intensity aerobic training was better among the HC subjects than the HP subjects. 152

The cross-sectional area of skeletal muscle fibers of HC subjects increased more

than that of the HP subjects. This difference agrees with another finding that fat free mass

(FFM), such as the weight of skeletal muscle, increased among the HC subjects but not

among the HP subjects. The post-training resting testosterone concentration increased

among the HC subjects but decreased among the HP subjects. There was a strong

negative correlation between the protein intake and testosterone concentration: the HC

subjects consumed an average 1.1 g / kg protein daily and the HP subjects consumed 2.6

g / kg and there was a negative correlation between testosterone concentration and

protein intake, r = -0.68, p< 0.01 (Figure 22). A low testosterone concentration in high

protein intake was also reported in studies looking at the relationship between diet and

serum anabolic hormone responses (Sallinen et al., 2004; Volek et al., 1997). Overload

during the training is one of the factors that facilitate muscle hypertrophy. In this study, the total workload of the seven-week training period of the HC group was significantly higher than that of the HP group. Because endogenous testosterone release is proportional to the intensity of training (Ahtiainen et al., 2003; Raastad et al., 2000), the HC subjects

had higher total resting testosterone, which may cause more skeletal muscle fiber

hypertrophy than in he HP subjects. Although correlation does not explain causation,

factors among protein intake, training workload, and testosterone concentration appeared to affect skeletal muscle hypertrophy. Previous studies found that strength-trained athletes consumed more daily proteins than sedentary people (Friedman & Lemon, 1989;

Tarnopolsky et al., 1988). However, the results of this study and other studies indicate 153

that high protein intake may attenuate skeletal muscle hypertrophy by reducing resting

testosterone concentration.

In this study, rowing training increased VO2 max among all the subjects of both diet groups. This indicates that exercise improved cardiovascular fitness. Fat oxidation yields less energy (4.7 kcal per one liter of oxygen) than glucose oxidation (5.05 kcal per one liter of oxygen). Thus, more oxygen is needed to accomplish the same amount of work. Post-training fasting plasma β- hydroxybutyrate concentration indicates that the

HP subjects had less blood glucose than the HC subjects. Therefore, this increased VO2 max among the HP subjects may not be due to a true cardiovascular fitness improvement, but it may due to increased oxidative capacity in skeletal muscle fibers.

In this study, food choice restrictions were not strict. However, group differences due to moderate dietary manipulation were observed in skeletal muscle fiber diameter and exercise performance. This suggests that careful food selection would improve athletic performance, especially for those who participate in sport events that have weight restrictions.

Limitations and Suggestions for Future Study

Limitations of This Study

As an experiment involving exercise training and diet modification, this study had a relatively long training period and had moderate sample size compared to other studies shown in Table 10. Because self-recorded food logs are commonly used in diet studies and are cost effective, subjects selected the food choices and recorded them in a food log. 154

However, the accuracy of estimated food portion/size is typically low (Martin et al.,

2007). It was also noticed that subjects in this study underestimated the size of the food portions they consumed. As a result, caloric intake and expenditure analysis could not be performed to analyze if reduced body weight among the HP subjects was due to low total caloric intake or food proportion. Thus, improvement in methods to record food consumption is needed for future studies. A longer familiarization period for completing the food log is also needed. It is necessary to closely monitor food recording. For example, when subjects eat in dining halls, researchers may need to help them estimate accurate portion sizes. If possible, using a scale helps subjects to record the amount of food.

Although a priori design was used in this study, statistical power was still low.

This may be due to small sample size and large standard deviation. This problem has been observed frequently in studies employing human subjects and using muscle biopsies, which tend to have fewer participants. Although fiber type analysis cannot be performed without muscle biopsies, skeletal muscle fiber diameter can be measured by an ultrasound apparatus. This non-invasive technique may encourage more volunteer subjects to participate in this type of study. Some changes in test measurements were observed after the training, but those were not statistically significant. Some test measurements, such as the mean size of a muscle fiber cross-sectional area, have large inter-individual differences and lead to large standard deviations. Because statistical design could change the outcome, careful selection of statistical methods is needed in data analysis. 155

Suggestions for Future Study

This study used a high-intensity aerobic training as a stimulus and recruited healthy young males as subjects. Due to limited analytical instrumentation,

subclassification of cholesterol was not performed. As previously explained, it was reported that HDL and LDL concentrations were altered by exercise and that different diet regimens resulted in different lipoprofile changes. Therefore, the lipoprofile changes caused by training among the different diet regimens need to be investigated. Similarly, due to the small size of the skeletal muscle biopsy, a subclassification of skeletal muscle fiber type analysis was not performed. Because the post-training skeletal muscle fibers showed more oxidative capacity than the pre-training, it would be necessary to identify fiber type shift among type II fibers.

Different types of training and different groups of subjects may bring different results or may confirm the findings of this study. A resistance training causes skeletal muscle fiber hypertrophy and increases skeletal muscle strength (Campos et al., 2002).

Thus, using resistance exercise as a stimulus would show distinct effects on skeletal

muscle hypertrophy and skeletal muscle strength. Such results will be useful to those who participate in weight lifting and bodybuilding.

Because testosterone is abundant among young males, female subjects and older male subjects may have different muscle adaptation. Previous resistance training study

with older male subjects showed that older males increased both muscular strength and

diameter of fibers without increasing the number of myonuclei (Hikida et al., 2000;

Hikida et al., 1998). Another study showed that female subjects increased muscular strength after resistance training without significant hypertrophy (Kraemer et al., 1991; 156

Staron et al., 1994). Therefore, if resting plasma testosterone concentration plays a major

role in muscle fiber hypertrophy, there would be different diet effects on exercise

training. Estrogen is a female sex hormone; and one of its effects is that it lowers plasma

cholesterol (Mosca et al., 2001; Rybaczyk et al., 2005; World Heart Federation). Because

estrogen concentration becomes low after menopause, an exercise effect on reducing total

cholesterol may be different for pre- and post-menopausal female subjects.

A low-carbohydrate diet is recommended to diabetic patients to control their

blood glucose concentration. Exercise is also recommended for them because exercise

promotes GLUT 4 translocation without insulin and facilitate glucose uptake. It is known that not only insulin but also other hormonal functions among overweight/obese people are different from non-obese people. Thus, investigating interactions between diet and exercise among diabetic patients may bring different diet effects on exercise training. A study suggested a relationship between obesity and the proportion of type IIX fibers

(Tanner et al., 2002). A recent study using obese mice reported that body weight reduction and type IIB fiber hypertrophy were positively related, possibly due to Akt1 signaling pathways (Izumiya et al., 2008). Therefore, future studies are recommended to investigate if similar results are observed among different types of subjects and depend on different types of training.

This study aimed to investigate if different diet regimens affect high-intensity aerobic exercise training for young men. The positive results regarding health promotion may encourage more people to regularly exercise. At the same time, some results may also suggest the importance of food choices for athletes.

157

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APPENDIX A: INSTITUTENAL REVIEW BOARD APPROVAL

197

198

199

200

APPENDIX B: FLYER

201

APPENDIX C: CONCENT FORM

202

203

204

205

206

207

APPENDIX D: QUESTIONNAIRE

208

209

210

211

212

213

214

215

216

APPENDIX E: FOOD LOG

Instruction 217

Food Log Sheet 218

APPENDIX F: EXMAPLES OF FOOD PROPORTION

High-Carbohydrate Diet 219

Low-Carbohydrate, High-Protein Diet 220

APPENDIX G: TRAINING LOG SHEET 221

APPENDIX H: MUSCLE BIOPSY HANDOUT

Procedure 222

Care after Biopsy 223

APPENDIX I: MYOFIBRILLAR ATPASE STAINING PROCEDURE

1. Solutions: a. Acid pre-incubation solution Solution A (Stock): Sodium acetate 1.94 g Sodium barbiturate 2.94 g dH2O 100 ml

Solution B (Stock) HCl 0.075 M dH2O 500 ml

Mix 5 ml of Solution A, 10 ml of Solution B, and 8 ml of dH2O. Bring pH to 4.6 with 0.1 M HCL, then remove half of it. For the rest of the solution, bring pH to 4.2 – 4.35.

b. Alkaline pre-incubation solution Sigma 221 buffer 1.434 ml 0.18 M CaCl2 solution 2 ml dH2O 15 ml Adjust pH to 10.4 with 0.1 M NaOH and/or 0.1 M HCl

c. Incubation solution Sigma 221 buffer 1.434 ml 0.18 M CaCl2 solution 2 ml dH2O 15 ml ATP 0.05 g Bring pH to 9.4 with HCl

d. 2% CoCl2 (0.6 g CoCl2 in 30 ml dH2O). e. 1% (NH4)2S (6 drops ammonium sulfide in 30 ml dH2O).

2. Procedure a. Pre-incubate in aid pre-incubation for 5 min. b. Rinse for 1 min in 18 mM Cacl2 in 100 mM Tris, pH 7.8. c. Incubate for 45 min in Incubation solution. d. Rince in 3 changes of 1% CaCl23 min each. e. 4 min 2% cobalt chloride twice 2 min each. f. Blot, then 4 changes in 0.01 M sodium barbital, 30 sec each. g. 30 sec, dH2O h. 30 sec, 1% (NH4)2S i. Running tap water, 5 min. j. Dehydrate in 70, 95, 100, and 100% alcohols, 2 min each. 224

k. Clear in 2 changes of CitriSolv, 2 min each. i. Mount.

For alkaline pre-incubation: a. Alkaline pre-incubation solution for 15 min. b. Incubate in ATP solution for 10 min. c. Follow steps d through i above. 225

APPENDIX J: NADH TETRAZOLIUM REDUCTASE PROCEDURE

Incubation medium: 2.5 ml Tris 6.5 ml dH2O

Add 9 mg NBT 0.149 g CoCl2 9 mg NADH

Procedure: a. Incubate for 30 min at 37 °C b. Rinse with dH2O c. Formalin fix for 10 min d. Rinse with dH2O twice 10 min each e. Mount in AquaMount f. Day or two later, seal edge with nail polish g. Take pictures as soon as possible. 226

APPENDIX K: TISSUE FIXATION PROCEDURE FOR ELECTRON MICROSCOPY

1. Remove tissues and place into fixative (either in refrigerator or leave at room temperature).

2. After about 30 min, dice tissues with razor blades, being sure to keep the tissues from drying out.

3. Fix for about 1.5 hours longer or overnight in the refrigerator.

4. Remove fixative and place in buffered sucrose for 15 min to overnight.

5. Post-fix tissues in 1% buffered osmium at 0 – 4 °C

6. Rinse in buffered sucrose two times (15 min each), if fixed in a non-phosphate buffered fixative. If fixed in phosphate buffered solution, rinse about four times (15 min each).

7. Place in 1% fresh, filtered uranyl actate in the refrigerator overnight.

8. Dehydrate tissues for 5 min each in the following ethanol solutions: 70% 95% 100% (10 min) 100% (10 min)

Note: if tissues are very delicate or easily damaged, you may want to have a more gradual series of ethanols and thus add 30%, 50%, and 80% to the series.

9. Put in limonene for 20 min.

10. Remove limonene, then add 1 ml of fresh limonene and 1 ml of freshly prepared resin (without DMP-30). Put on the rotator for one hour.

11. Add another ml of freshly prepared resin (without DMP-30). Keep on the rotator for 3 – 24 hours

12. Embed in freshly prepared resin in the oven at 60 °C for 48 hours.

13. Clean vials as indicated on separate sheets.