4
Physiological Biomarkers for Predicting Performance

This chapter provides scientific background on biomarkers that could be useful in monitoring metabolic status in the field. It includes a discussion of the most promising biomarkers for the prediction of: (a) excessive rates of bone and muscle turnover, (b) renal function and hydration, and (c) stress and immune function. Intermediate biomarkers that might be predictive of outcome function, performance, or injury of these systems are addressed, as are factors that influence the validity of each marker as a predictor of performance (e.g., individual variability, gender differences, and environmental exposures). The sensitivity of these biomarkers as surrogates for predicting performance outcomes under a variety of conditions is also explored. In addition, other potential markers of physiological status that have not yet been thoroughly researched are discussed. The measures presented in this chapter are meant as a comprehensive list from which selected measures can be chosen as appropriate, depending on circumstance and feasibility for measurement in the field. Therefore, appropriate groupings of biomarkers can be selected from this list, depending on specific conditions and goals.

BIOMARKERS OF BONE HEALTH

Healthy bone is essential to minimize fracture incidence, including stress fractures that decrease the availability of combat military personnel for training and combat action (Burr, 1997; IOM, 1998). The most accepted predictor of fractures is bone mineral density (BMD) (Black et al., 1992; Chailurkit et al., 2001; Cummings et al., 1993; Gluer et al., 1996; Kelly and Eisman, 1992; Kelsey et al., 1992; Marshall et al., 1996; Melton et al., 1993; Watts, 1999). BMD is measured by dual-energy X-ray absorptiometry, ultrasound, or quantitative computed tomography (Bass and Myburgh, 2000; Bennell et al., 1998; Ingle et al., 1999; IOM, 1998; Ravn et al., 1999).

Bone remodeling is a continuous process of breakdown (bone resorption by osteoclasts) and resynthesis (bone formation by osteoblasts) (Kleerekoper, 2003)



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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 4 Physiological Biomarkers for Predicting Performance This chapter provides scientific background on biomarkers that could be useful in monitoring metabolic status in the field. It includes a discussion of the most promising biomarkers for the prediction of: (a) excessive rates of bone and muscle turnover, (b) renal function and hydration, and (c) stress and immune function. Intermediate biomarkers that might be predictive of outcome function, performance, or injury of these systems are addressed, as are factors that influence the validity of each marker as a predictor of performance (e.g., individual variability, gender differences, and environmental exposures). The sensitivity of these biomarkers as surrogates for predicting performance outcomes under a variety of conditions is also explored. In addition, other potential markers of physiological status that have not yet been thoroughly researched are discussed. The measures presented in this chapter are meant as a comprehensive list from which selected measures can be chosen as appropriate, depending on circumstance and feasibility for measurement in the field. Therefore, appropriate groupings of biomarkers can be selected from this list, depending on specific conditions and goals. BIOMARKERS OF BONE HEALTH Healthy bone is essential to minimize fracture incidence, including stress fractures that decrease the availability of combat military personnel for training and combat action (Burr, 1997; IOM, 1998). The most accepted predictor of fractures is bone mineral density (BMD) (Black et al., 1992; Chailurkit et al., 2001; Cummings et al., 1993; Gluer et al., 1996; Kelly and Eisman, 1992; Kelsey et al., 1992; Marshall et al., 1996; Melton et al., 1993; Watts, 1999). BMD is measured by dual-energy X-ray absorptiometry, ultrasound, or quantitative computed tomography (Bass and Myburgh, 2000; Bennell et al., 1998; Ingle et al., 1999; IOM, 1998; Ravn et al., 1999). Bone remodeling is a continuous process of breakdown (bone resorption by osteoclasts) and resynthesis (bone formation by osteoblasts) (Kleerekoper, 2003)

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance of bone that begins after puberty and continues throughout life. Homeostatic processes involve both the cortical and trabecular bone, with remodeling of mature bone occurring more rapidly in trabecular regions. Net bone growth is seen primarily at the growth plate during longitudinal growth. Once the growth plate is closed, bone remodeling occurs at both the trabecular and cortical sites, but it is much slower in cortical bone. Bone health is related to both adult peak bone mass and the rate of bone loss after peak bone mass (Recker et al., 1992). Peak bone mass occurs in individuals between 20 and 30 years of age (Bass and Myburgh, 2000). Since fracture risk is related to bone density, BMD is the primary predictor of risk regardless of age or health status of an individual (Black et al., 1992; Chailurkit et al., 2001; Cummings et al., 1993; Gluer et al., 1996; Kelly and Eisman, 1992; Kelsey et al., 1992; Marshall et al., 1996; Melton et al., 1993; Watts, 1999). As a sole measure, BMD provides a good indication of the state of bone health over the lifetime of the individual. Currently available instrumentation for measuring BMD has a level of precision from 1 to 3 percent of BMD, depending on the machine, the site of measurement, and the operator (Chailurkit et al., 2001; LeBlanc et al., 1986; Nguyen et al., 1997). This limits the use of BMD for determining short-term changes because it generally takes months to years for a significant change to be detected (Nguyen et al., 1997). Consequently, intermediate biochemical markers of bone resorption and formation may provide earlier indications of potential fractures. (See Appendix A for the available biochemical markers for bone health.) Biochemical Markers of Bone Resorption Intermediate markers of bone resorption are used as early indicators of changes in bone homeostasis. Historically, urinary hydroxyproline, a bone breakdown product, was the marker for resorption (Latner, 1975; Lueken et al., 1993). However, this marker was not specific for bone changes and is affected by diet. Currently the most commonly used markers of bone loss are the collagen breakdown products N-telepeptide, carboxy-terminal telopeptide, and the pyridinium cross-links pyridinoline and deoxypridinoline (Chailurkit et al., 2001; Fukuoka et al., 1994; Ladlow et al., 2002; LeBlanc et al., 2002; Lueken et al., 1993; Nishimura et al., 1994; Ravn et al., 1999; Smith et al., 1998). Calcium balance and increases in urinary (24-h) calcium excretion levels are also used to indicate changes in bone resorption (LeBlanc et al., 1995; Matkovic and Heaney, 1992; Weaver et al., 2000), as is tartrate-resistant acid phosphatase, a specific gene product of the osteoclast related to bone resorption (Ingle et al., 1999; Nishimura et al., 1994). These intermediate biochemical markers track well with the elevated bone resorption that is found in individuals during space flight, suggesting that they are good indicators. (These compounds increased immediately upon entrance into microgravity [Smith et al., 1998] and correlated with changes in BMD.)

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance There is great intraindividual variability in the urinary products of collagen breakdown, making these products difficult to use as a one-time measure to predict bone health (Ingle et al., 1999; Ladlow et al., 2002; Smith et al., 1998). These products, like other measures of bone metabolism, have circadian rhythms, with their highest excretion point at waking and their lowest point 12 hours later (Ladlow et al., 2002). Smith and colleagues (1998) reported on the variability of these markers: an average baseline level for eight individuals was determined over 5 or 10 weeks of 24-hour urinary collections. The longer period of collections reduced error from daily variation and circadian rhythms. The urinary excretion of N-telepeptide varied from 375±66 nmol/day to 1,065± 118 nmol/day. This threefold difference reflects the interindividual variability (i.e., the between-subject variation). When these same individuals participated in a bed-rest study for 1 week, their levels of N-telepeptide increased. The increases ranged from 63 percent for the individual with the lowest baseline level to 7 percent for the individual with the highest baseline level. In fact, with the exception of very elevated bone resorption due to space flight or diseases such as Paget’s disease, these urinary products of collagen breakdown are only helpful in indicating a change from an individual’s baseline (Kleerekoper, 2003; Ladlow et al., 2002). As a measure of change in bone resorption status, these early markers could be used in clinical assessment of decreases in bone resorption after therapy or after a return to health. Endocrine markers of bone resorption are important for understanding the balance between bone loss and bone formation. Hormonal measures of bone resorption include 1, 25-dihydroxyvitamin D, osteocalcin, and parathyroid hormone (PTH) (LeBlanc et al., 1995). These markers were determined in spaceflight studies and in bed-rest studies—periods known to increase bone resorption (LeBlanc et al., 1995; Lueken et al., 1993; Smith et al., 1999; Weaver et al., 2000). In spaceflight-induced bone resorption, PTH and osteocalcin increased, compared with 17 weeks of bed rest where PTH and osteocalcin were unchanged. In both the spaceflight and bed-rest studies, 1, 25-dihydroxyvitamin D decreased (LeBlanc et al., 1995; Smith et al., 1999). The loss of bone density was similar between these two studies, but the endocrine changes were somewhat different, making it difficult to draw conclusions about the best endocrine markers. Both chronic and acute exercise affects endocrine makers for bone metabolism. For instance, Chilibeck (2000) summarized several studies showing that with acute exercise, PTH increased bone resorption when continuously released, but increased bone formation when intermittently released. In contrast, extreme training decreased calcitonin (which decreased bone resorption) and increased vitamin D (which increased calcium absorption). Extreme training also impacts the reproductive hormones estrogen and testosterone, thyroid hormones, and growth hormone, all of which affect bone health (Chilibeck, 2000).

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Biochemical Markers of Bone Formation Bone remodeling combines resorption with formation (Kohrt and Jankowski, 2003). When bone resorption is high, bone formation is often also high, and it is the balance of these two that produce healthy bones. Intermediate markers of bone formation are also important to ensure a correct clinical evaluation of the balance between resorption and formation before changes in BMD can be detected. Markers of bone formation have been difficult to elucidate (Ingle et al., 1999; Ladlow et al., 2002; LeBlanc et al., 2002; Lueken et al., 1993; Smith et al., 1999). In one study, total alkaline phosphatase and bone-specific alkaline phosphatase were measured to evaluate the efficacy of bisphosphonates to reduce bone resorption. These two enzymes decreased with reduction of resorption (LeBlanc et al., 2002). In another study of bone healing after fractures, osteocalcin, procollagen type I, N-terminal propeptide, and bone-specific alkaline phosphatase increased; however, the variability was two- to threefold (Ingle et al., 1999). It is unclear if these are good markers for a one-time determination of bone formation status. Biomarkers of Stress and Bone Metabolism Other indicators of changes in bone health are related to markers of stress. The cytokines interleukin (IL-1 and IL-6), tumor necrosis factor, transforming growth factor, and insulin-like growth factor-1 (IGF-1) have been studied (Conover, 1996; Margolis et al., 1996). Increases in stress indicators have been shown to correlate with increases in bone resorption. However, a clinical prediction of bone health using these stress markers cannot yet be made because often stress indicators appear over a limited time and may not result in significant bone loss and an increase in fracture risk. Since there is still a need for intermediate markers of bone metabolism, research is on-going with other markers, such as specific IGF-1 markers. Recent work (Rosen et al., 2003; Zhang et al., 2002; Zhao et al., 2000) suggests that the determination of IGF-1 may relate directly to signaling in the bone matrix. Other studies of specific genes may lead to a better marker for bone health (Simon et al., 2002). Cortisol as a Biomarker of Bone Health Glucocorticoid excess directly affects bone resorption and formation (Ziegler and Kasperk, 1998) (Figure 4–1). Chronic elevated corticoid levels stimulate the loss of BMD through decreased formation and increased resorption. The chronic nature of corticoid-induced bone loss is of particular concern as this may cause the fracture rate to increase, causing reduced mobility and general health. Possible glucocorticoid-induced osteoporosis is documented in patients with endocrine-related diseases, such as Cushing’s syndrome (Ziegler

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance FIGURE 4–1 Mechanisms of bone loss due to glucocorticoid excess. SOURCE: Reprinted from Ziegler and Kasperk (1998), with permission from Elsevier. and Kasperk, 1998), patients with depression (Cizza et al, 2001; Robbins et al., 2001; Wong et al., 2000), and transplant patients. Cortisol, measured in U.S. Army Rangers during 8 weeks of training (Friedl et al., 2000), did not significantly increase until the Rangers’ fourth week of training and remained elevated, albeit within normally accepted levels (Figure 4–2). During training, the Rangers experienced sleep deprivation, heavy exercise, and inadequate food intake. Yet these service members’ cortisol levels did not change until their body fat was significantly reduced; their muscle and hepatic glycogen were probably also depleted. The authors concluded that the Rangers’ cortisol response was related to their increased need to catabolize alternate body-energy sources, similar to that found in research with starvation. In contrast to the cortisol response, the Rangers’ IGF-1 levels decreased by week 2 (Figure 4–3), showing earlier adjustments to the physical activity of training. In comparison, astronauts (Smith et al., 1997) had increased plasma cortisol levels immediately upon entry into space. These levels remained above baseline during space flight, but all levels were within their normal ranges with considerable variation between crew members.

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance FIGURE 4–2 Serum cortisol for group 1 (solid lines) and group 2 (dashed lines) over an 8-week Ranger training course. Values are means±standard deviation. Letters indicate means that are not significantly different (Scheffé’s test); shaded regions represent areas outside of normal range for morning serum concentrations in normal young men. There were differences between group means at all of the common measurement points for cortisol. SOURCE: Reprinted, with permission from JAP 88:1820 by Friedl et al. (2000). FIGURE 4–3 Serum insulin-like growth factor-1 (IGF-1) for group 1 (solid lines) and group 2 (dashed lines) over an 8-week Ranger training course. Values are means±standard deviation. Letters indicate means that are not significantly different (Scheffé’s test); shaded regions represent areas outside of normal range for morning serum concentrations in normal young men. There were differences between group means at all of the common measurement points for IGF-1. SOURCE: Reprinted, with permission from JAP 88:1820 by Friedl et al. (2000).

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Although cortisol is a marker of physical and emotional stress (Hackney and Viru, 1999; Obminski et al., 1997), its circadian rhythms make it difficult to obtain reliable measures during field operations. Circadian rhythms are also disrupted during operations (especially with sleep deprivation), further exacerbating reliable measures of change. When the cortisol elevations are not chronic, there may be no long-lasting effect on bone health. Finally, the levels of cortisol found in healthy individuals performing Ranger-like activities and in astronauts were within normal ranges. Although the relationship of cortisol and bone turnover is well known, this relationship has not been verified under actual operational experiences, such as during Ranger training. Astronauts did have elevated urinary excretion of collagen cross-links within the first week of space flight (Smith et al., 1998), but the relationship between the urinary excretion of collagen cross-links and cortisol levels has not been studied. However, there is evidence that cortisol decreases BMD in the healthy population when cortisol levels are chronically elevated. Because increased cortisol levels did not occur in Rangers until their fourth week of training, it may be expected that after training, cortisol levels would return to pretraining levels and, similar to astronauts, their bone formation would increase. BIOMARKERS OF MUSCLE METABOLISM AND FATIGUE Skeletal muscle structure is highly adaptable in that the individual muscle cells (myocytes) comprising the complex muscle system have the ability to change their mass, metabolic capacity, and contractile properties in accordance with the level of demand placed on them (Baldwin et al., 2003). In the context of this report, skeletal muscle function is defined as the composite of muscle activities needed for strength, endurance, and rapid-burst, quick movements like short sprints. Each component is essential for military training and combat activities; however, endurance is probably most critical (IOM, 1998). Reduction in any of these muscle capabilities may lead to decrements in overall performance (Behm et al., 2001; Budgett, 1998; Clarkson et al., 1992; Davis, 1995). Chronic muscle fatigue is a generalized problem caused by inadequate recovery from multiple acute bouts of muscle activity. Lack of adequate rest, hydration, and nutritional support (Ardawi et al., 1989; Barac-Nieto et al., 1980; Fitts, 1994) increase the time needed for recovery from muscle fatigue. Skeletal muscle activity, which can involve either single muscles or muscle groups (for review, see Wilmore and Costill, 1994), includes fine intricate movements, heavy lifting, long-duration traveling, or very fast (rapid-burst) movements. Muscle contraction is initiated through the nervous system by a combination of biochemical and electrochemical reactions that cause shortening of the fibers (Fitts, 1994; Saltin and Gollnick, 1983). Muscle capabilities can be

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance improved or lost through training, overuse that produces fatigue, and disuse that produces atrophy (ACSM, 2002; Ferrando et al., 1996; LeBlanc et al., 1992). The muscle contractible unit (sarcomere) contains myofibrils, composed of actin and myosin filaments (Fitts, 1994). Through neurological activation, the membrane potential of the muscle cell changes, which in turn causes the filaments to slide together (interdigitate), producing a contraction. All the muscle fibers innervated by a single motor neuron (anterior horn cell of the spinal cord) by contact with its axon are termed a motor unit (Fitts, 1994). Muscle Fatigue A common definition of muscle fatigue is “failure to maintain the required or expected force” (Edwards, 1981; Fitts, 1994). Muscle fatigue limits physical activity. The etiology can be of either local or central origin. Local fatigue originates within the muscle, whereas central fatigue is secondary to alterations within the brain. Several neurological and biochemical changes may cause local muscle fatigue, including the following (Fitts, 1994): inability of the sodium-potassium pump to maintain the membrane excitability necessary for contraction, inability of the muscle fiber to maintain normal contractility because calcium ions are not efficiently removed from the intermyofibrillar space into the sarcoplasmic reticulum, inability to provide oxygen to the muscle cell for energy metabolism (oxidative phosphorylation), lack of available energy substrates, such as glucose and phosphocreatine, to provide sufficient adenosine triphosphate (ATP), and increased lactic acid that reduces intracellular muscle fiber pH, thereby inhibiting further contractions. It is postulated that in central mechanisms, exercising muscle releases factors that act systemically and impact the central nervous system. In the context of military performance, systemic effects are likely to be of greater significance because of their potential to impact both physical and mental performance. Muscle fatigue is not the same as muscle soreness. Muscle soreness is the pain that occurs about a day after exercise and peaks 2 to 3 days postexercise (Clarkson et al., 1992). The underlying mechanisms for delayed-onset muscle fatigue and soreness are different. Soreness is believed to be due to a localized inflammatory response (Smith, 1991), and so the appropriate markers are markers for an inflammatory response. The onset of pain is also not considered to be a marker for muscle fatigue. Pain by itself is performance limiting and therefore is not a “predictor.” The majority of studies of muscle fatigue have assumed that the fatigue is the result of events localized within skeletal muscle (Davies and White, 1981;

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Edwards, 1981). Prior studies of muscle fatigue have focused on the relationship of a putative marker to the underlying biochemical or histological changes (Banister et al., 1985). For a marker to be of practical use, certain conditions must be met. The marker must apply to all subjects; a statistical relationship is inadequate when applied to the individual (Barron et al., 1985). In addition, the measurement must be technically feasible on a large number of subjects. These criteria currently limit assays to “spot” blood and urine measurements. In-line sensors in a selected muscle are not likely to be of much use because an isolated muscle may not reflect the whole musculoskeletal system, and the muscle selected may not be one of the muscles that is becoming fatigued. Fatigue may occur with the inability of the sodium-potassium pump to maintain the muscle membrane excitability necessary for contraction (Evans and Cannon, 1991; Fitts, 1994). Determined by electrophysiological measurement, this fatigue is transient and probably not related to the phenomenon of chronic muscle fatigue (Fitts, 1994). The muscle cell membrane (sarcolemma) is electrically excitable due to selective permeability to potassium and sodium (Fitts, 1994). The electrical potential across the resting muscle cell membrane is due to the ion concentration gradients maintained by the ATPase-dependent sodium-potassium pump that transports sodium ions out of the cell in exchange for potassium ions back into the cell. The neurological excitation is through the release of acetylcholine at the neural membrane; this neurotransmitter causes conformational changes that open channels for the movement of calcium, sodium, and potassium ions. Initially, more sodium ions flow through these channels, resulting in a negative potential on the muscle membrane, which produces a contraction. With the metabolism of acetylcholine, the potassium ions move across the cell membrane to reduce the negative potential. Generally, this fast reaction is not primarily related to fatigue, but research with artificial electrical stimulation demonstrates that there is a point where the rate of destruction of acetylcholine is limited. At that point, the cell membrane cannot recover the resting potential for a subsequent contraction. Muscle contraction requires movement of calcium ions into the intermyofibrillar fluid (intermyofibrillar space) (Fitts, 1994; Westerblad et al., 1991). Muscle fatigue may relate to the process by which free calcium ions are removed from the intermyofibrillar space back into the sarcoplasmic reticulum (Westerblad et al., 1991). Essentially, if calcium ions are not efficiently removed (increased sarcoplasmic free calcium), then the muscle fiber will not be able to maintain normal contractility. Also, chronic increases of sarcoplasmic calcium ions are associated with the activation of calcium-dependent proteases, which in turn can lead to destruction of the contractile proteins and muscle atrophy. Myofibrils are surrounded by the transverse tubule-sarcoplasmic reticulum system. These tubules transverse the entire cell and, by branching, form planes of T tubules that interlace the myofibrils (Fitts, 1994). In the resting state, the troponin-tropomyosin complex blocks the action-filaments binding sites, which

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance maintains the muscle in a relaxed state. An action potential at the muscle fiber membrane spreads along these T tubules to the interior of the myofibril, and this causes the increase in calcium ions in the intermyofibrillar fluid. Calcium ions diffuse through the intermyofibrillar space and cause conformational changes in the fiber proteins (troponin-tropomyosin complex), which results in the availability of actin binding sites for the globular heads of the myosin filaments. Muscle contraction will continue as long as the calcium concentration is high in the sarcoplasmic tubules. Calcium-ATPase pumps the calcium into the sarcoplasmic reticulum, leaving few free calcium ions, so muscle relaxation occurs. This reaction time is extremely fast, 1/20 of a second, and continued repetitive contractions may prevent the reestablishment of calcium equilibrium prior to the next stimulus. Muscle fatigue is also related to decreases in the availability of oxygen to the muscle cell (Barac-Nieto et al., 1980; Fitts, 1994; Gollnick et al., 1972). This may be due to decreases in cardiovascular function, hemoglobin concentrations, and respiratory exchange rates. Dehydration, or decreased plasma volume (Nose et al., 1983), can also reduce the availability of oxygen to the cell. Even with very highly trained athletes, there are limits in the ability to perfuse the muscle cell with oxygen, and fatigue occurs. Obviously combat service members must maintain hydration, high levels of cardiovascular/pulmonary fitness, and good hemoglobin status (no iron or other nutrient deficiency). ATP provides energy for muscle contraction (Meyer and Foley, 1996). ATP is generated through several different pathways, including glycolysis and oxidative phosphorylation. The instantaneous source of energy, phosphocreatine, produces ATP immediately. When this ATP is used, the adenosine diphosphate is regenerated through the phosphocreatine shuttle to resynthesize ATP. With high resistance and/or fast repetitions of contractions, the source of phosphorus for ATP synthesis (phosphocreatine) is depleted and fatigue occurs. Replenishment of the phosphocreatine from glucose may take minutes before contractions can continue. Muscle fibers store energy as glycogen. It is estimated that 2,000 muscle fiber contractions may be required to deplete muscle glycogen stores, suggesting that this is a good source of energy for muscle contractions, particularly during quick-burst activities. Muscle glycogen depletion may occur with endurance types of muscle activity, such as marathon running, when no dietary glucose is available. Since muscle glycogen metabolism does not require oxygen (anaerobic metabolism), it is not dependent upon an immediate blood supply of oxygen. Under anaerobic conditions, for each glucose-6-phosphate molecule released from glycogen stores, three ATP molecules are generated with two molecules of lactic acid. Thus glycogen metabolism in the absence of oxygen leads to a buildup of lactic acid, which can be measured in the blood. Lactic acid levels become a problem when the intracellular muscle fiber pH changes, (an increase in H+ ions) producing muscle fatigue (Fitts, 1994; Westerblad et al., 1991).

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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Accumulating lactic acid reduces intracellular muscle fiber pH, which inhibits further contraction and results in fatigue. Nuclear magnetic resonance spectroscopy has shown that prolonged strong muscle contraction can reduce the intracellular pH from normal resting values near 7.02 to as low as 6.34 (de Kerviler et al., 1991). Adenosine monophosphate (AMP) accumulation also activates the enzyme myoadenylate deaminase (localized particularly in type II fibers), which hydrolyzes AMP to inosine monophosphate with the release of ammonia. This ammonia partially neutralizes lactic acid, modulating the drop in intracellular pH. Ultimately, muscle fatigue is accompanied by cessation of contraction, which restores capillary blood flow with the consequent removal of lactic acid and the recovery of normal intracellular pH. Regeneration of AMP is then possible from inosine monophosphate through a guanosine-triphosphate-mediated reaction (Meyer and Foley, 1996). Fiber Types and Muscle Performance The ability of the muscle tissue to perform burst versus endurance activities is due to the percentage of the two fiber types found in the muscles: types I and II, and two subtypes: types IIA and IIB; these are defined by their morphological, physiological, and biochemical characteristics (Pette and Staron, 1990; Saltin and Gollnick, 1983). Genetically predetermined, human muscles are a mosaic of these various fiber types. An individual with more type I (slow twitch) muscle cells excels at endurance activities, while an individual with more type II (fast twitch) excels at quickness and strength activities. Different muscles have different proportions of the fiber types. The leg muscles provide an example. The gastrocnemius has more than 50 percent fast-fiber types, while the soleus muscle may have less than 40 percent fast types. There is a great deal of interindividual variation in the percentage of fiber types found in muscles. Type 1 fiber types have low glycogen content, high resting levels of phosphocreatine (with ample mitochondria), a rich capillary network, and high blood flow—all indicative of reliance on aerobic metabolism. In general, these fiber types are considered to be resistant to fatigue due to their slower contraction speed and their postural and prolonged sustained contracile functions. Type 1 fibers rely on a continuous delivery of glucose and oxygen for energy production. As long as oxygen and glucose are available, these muscles will continue to function. With reduced oxygen availability due to altered cardiovascular-pulmonary function, dehydration (reduction of plasma volume), or low hemoglobin levels, glucose metabolism is limited to nonmitochondrial metabolism, resulting in increased lactic acid concentrations. Maintaining blood glucose through feeding during endurance exercise delays the onset of this fatigue by providing necessary energy. During long-duration exercise, both intracellular and plasma fatty acids can be used for energy metabolism by type I muscle fibers. A noteworthy sex difference is that women have higher muscle lipid levels,

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