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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance 6 Conclusions and Recommendations Trends in the conduct of military operations will continue toward the use of smaller operational units deployed more frequently and equipped with advanced technology, weaponry, and communications. Monitoring metabolic status is a way of optimizing the functioning of the individual service member and minimizing risk of fatigue and illness through the observation, interpretation, and transmission of physiological data both to the individual and to the command unit. The use of an array of sensors to monitor both the biomarkers of the individual’s physiological and cognitive status and the ambient environment can allow metabolic irregularities to be anticipated in a manner that will permit timely feedback and initiation of corrective actions. These same systems can continue to monitor the individual for compliance with recommendations and provide information on the efficacy of the corrective action taken. It is important to look forward a decade or two to imagine what might be possible with the evolution of new technologies in regard to metabolic monitoring. It is quite certain that sensors will get smaller, faster, more mobile, more versatile, and perhaps more affordable. However, this forecasting is hampered when only the technological tools available today are considered. One needs to stretch the imagination to make predictions in two major areas: (1) What will be the new biomarkers that will enhance monitoring of individual health and performance? and (2) What will be the new sensor technologies that will facilitate rapid assessment of individual status? Clearly, improvement in monitoring metabolic status requires significant research investments in the identification and validation of new biomarkers, the field validation of existing sensor technologies, the development of new sensor devices, and the enhancement of bioinformatics to evaluate the data. For example, in the last two decades biotechnology has provided great benefits to medicine, agriculture, criminology, and environmental sciences and holds great promise for future advancements in these fields. Biotechnology can be defined as using living material or molecular components of living material, to interact with small amounts of environmental substances, resulting in a physi-
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance cal or chemical change that can be measured by a detection system. A recent report (NRC, 2001) extensively reviewed the state of the art of biochips that might use proteins, nucleic acids, deoxyribonucleic acid, or even living cells to detect a wide array of chemical toxins in the environment, biochemical substances excreted by the body in response to stress, or exposure to toxins or pathogenic organisms. Although this technology is in its infancy and now is only reliable in the laboratory, some embedded sensors, such as glucose monitors, are already available and the committee envisions that externally worn sensors can be developed in the near future. Finally, the integration of self- (or peer-) reported and objective measures through a single wrist-worn device should be a priority in the military. Software and hardware are available to create such a device that would integrate information from ratings of perceived exertion, muscle soreness, fatigue scores, weight changes, urine specific gravity, as well as physical activity data from an accelerometer or pedometer. The implementation of any new approaches, however, should first be evaluated for feasibility of using them in specific environments and for the cost of the technology versus the cost of the mishap that the technology would be expected to prevent. Also, the effectiveness of monitoring, including physiological and self- (and peer-) assessments, depends largely on the standardized use of methodology and on taking appropriate decisions; therefore, it is important that military personnel be educated in basic physiology and psychology and monitoring methods. This chapter provides suggestions on which research efforts for monitoring metabolic status should take priority, along with the committee’s answers to the five questions posed by the military. QUESTION 1 What are the most promising biomarkers for the prediction of: (a) excessive rates of bone loss and muscle turnover, (b) reduced glucose and energy metabolism (e.g., bioelectrical indicators of muscle and mental fatigue), (c) dehydration, and (d) decrements in cognitive function? Irrespective of the biological or cognitive markers selected, there is a need for baseline measurements of individual combat service members so that it can be determined, on an individual basis, if a marker is significantly altered under stress. The committee recommends that, initially, simple protocol data of normal/abnormal ranges may be used. However, these ranges may be sufficiently imprecise to make individual-based predictions dubious. By evaluating a marker’s deviation from the individual’s baseline, rather than by comparing it with a population-based norm, an individual’s condition can be much more accurately described.
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Biomarkers for Bone and Muscle Metabolism Bone There are no groups of intermediate markers of bone health that can provide a one-time identification of risk of fractures, including stress fracture. Bone remodeling is a relatively slow biological process and thus not amenable to monitoring in field situations. Prediction of bone changes that increase fracture risk may be of greater importance in initial entry training, when individuals are transitioning to a greater state of fitness, than in combat. Markers should be used both pre- and post-training and should include bone density (as measured by dual-energy X-ray absorptiometry), sensation of bone pain, menstrual status, and mental state as related to cortisol responsiveness. Bone turnover can be assessed by increases in 24-hour urinary n-telopeptide excretion over baseline. The role of cortisol in bone health during military exercises, however, may be transient and may not have long-term effects on bone health. Bone mineral density (BMD) is the most predictive measure of risk of fractures; this measure should be used in determining medical suitability for training and combat-related activities. Strategies should be developed to determine the BMD levels that are required to meet medical standards, and approaches should be identified to prevent significant loss of bone mass. In this sense, adequate countermeasures for preventing bone loss and fractures should be implemented prior, during, and after intensive physical training. Muscle Muscle fatigue is related to decreases in oxygen availability to muscle cells. This decrease in oxygen availability may result from, for example, decreases in cardiovascular function, decreased hemoglobin concentrations, or inadequate intravascular volume due to excessive water loss (dehydration). There are a number of biomarkers that may be indicative of muscle fatigue or increased catabolism (see Appendix A). Some of these include protein turnover and 24-hour urinary 3-methylhistidine. Protein turnover measures use stable or radioisotopes when amino acids, such as N15 glycine or C13 leucine, are infused and appearance and disappearance rates are measured. Twenty-four-hour urinary 3-methylhistidine, which is not metabolized, indicates rates of muscle protein catabolism. This speaks to the importance of renal function (see discussion below). However, there is insufficient evidence that can specifically correlate these biomarkers with actual decrements in muscle performance during activities such as weight lifting, timed running trials, and endurance running. In general, markers of muscle catabolism will overemphasize negative changes in muscle and must be coupled with markers of muscle protein synthesis. However, even with an imbalance of protein breakdown to synthesis, a loss of as much as 15 percent of muscle mass may occur without significant effects on muscle performance.
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance There is substantial evidence in the sports medicine literature that self- (or peer-) reported measures, such as perceived exertion, muscle soreness, muscle pain, ratings of sleep quality, and mood states, possess efficacy in predicting both physical performance and deterioration in performance and, in fact, have often been found to be superior to any physiological measures. Validation of these measurements in the field is necessary. Renal Function Potential markers of renal function deserve attention due to the essential role of the kidneys in maintaining protein status, hydration status, and electrolyte balance. Also, the loss of large amounts of nitrogen end products through the kidneys would be indicative of a negative protein balance. There are a number of markers and technologies now available that could be adapted for self- (or peer) monitoring during training or field operations. In order to assess renal function, it is suggested that the field measures presented in Figure 4–4 be taken at mid-day and in the evening after the day’s exertion. The military should consider providing and training personnel in the use of simple urine dipstick-type test strips that would provide information on levels of urine protein (a marker for potential kidney damage), ketones and glucose (potential markers for energy metabolism), and leukocyte esterase and nitrates (indicators of urinary tract infections) as indicators of muscle damage and hydration status. Biomarkers for Reduced Glucose Metabolism The development of specific biomedical markers under other situations, such as chemical exposures or psychological threats, would require an understanding of the metabolic processes resulting from such circumstances. The potential biomarkers for anaerobic glucose metabolism are: Borg’s 6–20 scale of perceived exertion (local, central, and overall); muscle soreness; tissue levels of lactate measured by near-infrared spectroscopy (NIRS); muscle biopsy for glycogen, cytokines, and enzymes; actigraphy; electroencephalography (EEG); heart-rate variability; profile of mood state; and visual analog scale. The use of these biomarkers for this purpose needs to be validated in the field. Biomarkers of Dehydration Heightened physical activity under adverse environmental conditions causes dehydration, which impacts exercise performance and other physiological functions. Even 1 percent dehydration can cause obvious signs of heat exhaustion if strenuous exercise occurs in hot (41°C or 105°F) environments. Dehydration increases hemoconcentration, blood viscosity and osmolality, core body temperature, and heart rate, while causing a decrease in stroke volume. Dehydration
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance also increases the onset of fatigue and makes any given exercise intensity appear harder than it would be if the individual was well hydrated. However, the most serious effect of progressive dehydration is that due to a lower cardiac output, the body decreases its ability to sweat via decreased blood flow to the skin. This in turn decreases the body’s ability to cool itself, which leads to an increased core body temperature and the risk of heat illness and collapse and, in rare situations, life-threatening heat stroke. In the military setting, changes in water and osmotic balance are usually synergistic with increases in water loss (dehydration reflected by reductions in body volume and increases in osmolality). One of the most sensitive indicators of hydration status is short-term changes in body weight since most day-to-day variation in body weight is due to hydration status. The assessment of weight loss or loss of body mass, plasma sodium or plasma osmolality, urinary specific gravity, fluid balance, and the recovery of weight 24 hours after dehydration can be used for the identification of extent and type of dehydration. In the military setting, where dehydration is the most common condition, weight changes over a short period of time reflect fluid changes and loss of body water coupled with measures of serum sodium or serum osmolality can define the degree of concomitant salt loss. Renal function is also a good indicator of hydration status. Biomarkers of Cognitive Function The most promising techniques for accomplishing continuous assessments of ground combat service member cognitive readiness in field settings are actigraphy, EEG, and heart-rate variability. Actigraphy is useful because it offers a field-practical way of monitoring the sleep of combat service members, and insufficient sleep is the primary cause of cognitive degradations in operational environments. EEG is useful because it offers a relatively noninvasive assessment of the brain activity that underlies all types of performance, including vigilance and judgment. Heart-rate variability is a peripheral nervous system measure that also reflects the brain activity that underlies performance attention and mood. In vehicle operators or in radar or other fixed-based system operators, eye-movement monitoring is also promising. Saccadic velocity and percentage-of-eye-closure measures have been shown to reflect the status of the central nervous system. In all probability, most of these measures will be useful only in laboratory environments or in fixed-based operational facilities (such as posts in which radar or sonar equipment is monitored or stations from which remote-controlled vehicles are piloted) where complex equipment can be housed, lengthy recording procedures can be conducted, and rigid controls can be maintained. Only a small subset of these methods will likely be suitable for operational settings. Besides these objective measures, subjective ratings of alertness and fatigue should be considered for use in the field since these have been shown to correlate with performance changes in some situations. However, it should be recog-
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance nized that self-report data can be influenced by peer pressure (or supervisor pressure); also, there is evidence that self-reports may lose a degree of sensitivity when the stress or fatigue becomes so chronic that the individual has difficulty referencing his or her present feelings to more normal past experiences. QUESTION 2 What monitoring technologies would be required (that may not currently exist) to predict these intermediate targets in critical metabolic pathways? New biomarkers are likely to be identified in the future; still, the greater need lies in: (1) the development of easier systems to measure and transmit data, and (2) the development of new mathematical models to provide enhanced data integration and analysis by using nonlinear discriminant algorithms. Future monitoring technologies should consist of an integrated system that incorporates noninvasive or minimally invasive sensor technology, communication interface and integration, data analysis tools, and local area networks. This infrastructure should be both redundant and noncentralized. A “black box” or “medical hub” is needed to gather data from multiple sensors or devices, standardize the outputs, and submit these data to a data reduction system or decision-making tool for the creation of both prioritized alarm signals and recommended interventions. In summary, the major obstacles likely to be encountered in the implementation of future monitoring technologies will be the selection of variables and the building of models that truly predict health performance status. QUESTION 3 What tools currently exist for monitoring metabolic status that could be useful in the field? Metabolic status can be defined in part by energy metabolism, intermediary fuels (glucose, fatty acids, and amino acids), acid/base and hydration status, and psychophysiological data. One methodology that can examine many different biomarkers of metabolism and shows great promised in the field is MRS. MRS can concurrently monitor muscle oxygenation and deoxygenation, intramuscular pH, lactate, and skin hydration status. With NIRS, muscle function and hydration status can be measured under field conditions with telemetry units. Muscle Fatigue Individuals experience fatigue when muscle energy sources are inadequate or when faced with inadequate energy substrates when oxygen delivery falls below the point for lactate accumulation. One measure that could be useful in the field, after validation in military settings, is self-perception, as described in Chapter 3. Predictors of fatigue at an earlier state have also been proposed. The
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance challenge remains to differentiate diagnosis between acute damage from muscle injury, fatigue due to overuse or overconditioning, exercise until exhaustion, hydration, and nutritional status given the interactions of these factors in the subjective feeling of fatigue. Renal Function and Hydration Simple methods that measure renal function and hydration already exist. As mentioned previously, the military should train personnel in the use of simple urine dipstick-type test strips that would provide information on levels of urine protein, ketones and glucose, and leukocyte esterase and nitrates as indicators of muscle damage and hydration status. Also, a practical method of monitoring weight changes in the field would be of value for monitoring hydration. Energy Expenditure An individual is said to be in energy balance if energy input (calories consumed) matches energy expenditure and weight is maintained. Research has focused on measuring the energy expenditure side of the energy balance equation as a measurement of total energy needs since these methods may not rely on individual data recording. If energy balance is not maintained, weight is lost and available energy is decreased. This situation can dramatically impair physical performance and cognitive ability in high-stress situations. Several field methods have been tested for predicting total daily energy expenditure, including heart-rate monitors, pedometers, and accelerometers. Accelerometer- and pedometer-based monitors provide valid indicators of overall physical activity, but they are less accurate at predicting energy expenditure. In addition, single-axis accelerometers or pedometers and most multidimensional accelerometers are not useful in detecting the increased energy costs of high-intensity exercise, upper-body exercise, carrying a load, or changes in surface or terrain. The combination of doubly labeled water, as a measure of total energy expenditure, and hand-held indirect calorimetry to assess resting energy expenditure could be used to monitor metabolic status and assess energy metabolism over periods of up to 2 weeks. Self-selected pace, foot-strike devices, and activity monitors that integrate pulse, temperature, and movement can estimate activity and total energy expenditure and may be useful in the field. If predicting total energy expenditure is the goal of monitoring the activity of the combat service member, then more sophisticated devices must be developed (multidimensional devices that include multiple types of metabolic measurements). Accurate measurement of total daily energy expenditure in military personnel will require the development of motion sensors that are inexpensive, but more convenient and reliable than current pedometers or accelerometers.
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Stress and Immune Function The precise combination of measures chosen to monitor stress and immune function depends on the flexibility of the collection of the measures in the field setting. There is a substantial body of research that conclusively links both physical and psychological stress to derangements in immune function. In both military and civilian populations, immunosuppression has been reported after exhaustive aerobic exercise. It is hypothesized that trauma in muscle and connective tissue stimulates the production of cytokines that suppress cellular immune responses consistent with reported higher levels of stress hormones. Higher levels of stress hormones also occur in response to psychological stress. A full evaluation of the effects of activation of stress response systems on immune function requires measures of multiple functional and molecular biomarkers at multiple time points prior to, during, and after the stress exposure. Monitoring biomarkers of the stress response should include molecular and functional measures of the hypothalamic-pituitary-adrenal (HPA) axis, the adrenergic response systems, and the immune system at multiple levels. The HPA axis can be monitored by measuring levels of the corticotropin-releasing hormone adrenocorticotropin and cortisol in plasma, cerebrospinal fluid, urine, saliva, and sweat. Measuring heart-rate variability should be considered as an accurate, sensitive, and noninvasive way to measure the relative activity of the sympathetic and parasympathetic nervous systems. Immunological evaluation could include measuring the numbers, maturity, activation, and function of immune cells, including such measures as macrophage phagocytosis, lymphocyte proliferation in stimulation test, natural killer-cell activity, cytokine production patterns, expression of genes and receptors, antibody production, skin delayed-type hypersensitivity, antibody response to vaccine, wound healing, and infection rate. Indicators of stress and immune responses that are currently in use and in development include cortisol levels (measured from saliva, sweat, or urine), and heart-rate variability as measured with high-impedance electrocardiogram (ECG) electrodes that are currently available and are being further developed. Sleep Several companies currently offer wrist-worn actigraphs that are capable of estimating the quantity and quality of sleep in a variety of environments. These devices can collect data for periods ranging from a few hours to several weeks. Associated software can present sleep/wake histories in a number of user-friendly formats. Electrophysiological measures (EEG and ECG) are more difficult to collect in field settings because of the requirement to attach sensors to the body and to maintain low sensor impedance. However, significant progress has already been made in developing and validating high-impedance sensors that could soon be
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance mounted in helmets or clothing. The technology for field-portable, individual-worn systems for amplifying, recording, and to some extent analyzing these data already exist. Assessment of eye movements and eye closures will only be possible in limited situations in which monitoring equipment can be mounted and aimed at the combat service member. A substantial amount of literature has already shown that a subset of oculomotor measures is sensitive to cognitive fatigue, but further work is necessary to validate the utility of these measures for predicting performance. Self-assessments, on the other hand, are quite easy to collect. Questionnaires can be administered via paper and pencil or hand-held computer. As noted above, there are a variety of self-assessments available, and many have been shown to be sensitive to operational stressors, such as mental and physical fatigue. However, readers are cautioned that self-assessments can be significantly confounded by motivational factors or peer pressure or in chronic-demand conditions (e.g., people who are very tired for several days at a time may lose their subjective ability to determine how tired they actually are). As with other measurements, when deciding where new objective, physiologically based cognitive monitoring approaches (e.g., EEG-based or eye-movement based strategies) will be implemented, an assessment of the feasibility of using these approaches in specific environments and an analysis of the cost of the technology versus the cost of the prevented mishap should be performed. QUESTION 4 What algorithms are available that might provide useful predictions from combined sensor signals? What additional measurements would improve specificity of the predictions? Simple algorithms that are already in use include wet bulb globe temperature and cold strain-wind chill index. Other models, such as the Acute Physiologic and Chronic Health Evaluation Scores and the simpler Simplified Applied Physiological Score, also use physiological variables to predict health outcomes. Although these tools have worked quite well in the intensive care unit setting where pathological changes in physiological parameters are the rule, there is little compelling evidence that similar algorithms would be equally effective in the military setting where such parameters vary over a narrower range. NASA (National Aeronautics and Space Administration) also has undertaken a major research effort in this area (see Appendix B), the design of which may be quite compatible with the military environment. Although it would be reasonable to explore whether new variables made possible by new field technologies, such as serum osmolality, sodium concentration, or tissue pH, would be predictive using
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance simpler algorithms, a parallel initiative to explore presently available physiological measurements with more complex models seems appropriate. The future development of algorithms must include the development of nonlinear models that allow discrimination of more complex decision surfaces (e.g., a graphical representation of a problem space). Given the enormous number of variables present, nonlinear models may permit improved optimization of the solution. Generally, univariate analysis is overly simplistic and thus impractical for such situations because it fails to capture significant interactions among the numerous variables. For example, more complex models involving artificial neural networks are needed. Although it is often thought that additional measures enhance the validity to discriminate between metabolic status, depending on the desired purpose of the algorithms, different or additional measurements may not be needed. However, additional work is clearly required to create models that comprise variables in nonlinear ways, utilizing modeling such as neural networks. For example, more complex algorithms can be developed that result in more accurate predictions to prescribe actions (e.g., rest, hydration, or active cooling) and prevent the unwelcome result. In this case, additional research will be needed to better understand the nature and mechanism of the outcome so that interventions can be targeted. In addition, as described in the responses to questions 2 and 5, the technology must evolve to permit the integration of data in multiple forms from different devices. Like in all analysis programs, the value of complex, multivariate, nonlinear analysis relies upon the data provided. Until all available information from multiple sensors can be utilized by the algorithms, the system will remain constrained. Last, it is crucial to develop baseline data for each individual (combat service member) in order to implement effective field strategies for monitoring metabolic status. Repeated measures of individuals to determine and validate individual normal response patterns are essential. QUESTION 5 What is the committee’s “blue sky” forecast for useful metabolic monitoring approaches (i.e., 10- to 20-year projection)? What are the current research investments that may lead to revolutionary advances? Evolution of New Cognitive Measurement Approaches Stress and fatigue can be induced by high physical and cognitive workloads, such as exercise, extreme environmental temperature, dehydration, heat exhaustion, constant battlefield threats to personal safety, sleep deprivation, circadian-rhythm disruptions, and other common operational demands. The prediction of cognitive responses to stress and fatigue needs to be improved. In addition to performing more research on the utility of traditional approaches that use self-reported data, a significant focus should be placed on further developing and
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance implementing new psychophysiological methods for monitoring brain activity, heart-rate variability, eye movements, and metabolites and validating these techniques as predictors of cognitive responses to stress and fatigue. New performance-assessment methodologies may soon be available for computerized tasks in which cognitive probes can be unobtrusively introduced during the completion of primary operational demands. In addition, the use of hand-held computers to record ecological momentary assessments of cognitive function should be further developed. In addition to developing new psychophysiological methods, more work needs to be undertaken on the mathematical integration of these data and the computer models that will synthesize numerous inputs into a field-useable status assessment. Optimization of Markers to Monitor Stress and Immune Function A limited battery of selected stress-response and immune markers should be validated to monitor physiological adaptations to changes in the environment and to evaluate the readiness of individuals for impending deployment. Odors as Biomarkers Since odors evolved to communicate distinct information about individuals, it would seem to be an ideal system for monitoring organic states of individual combat service members in the field. Further studies on the role of human odors as a future source of biomarkers should be performed. These studies should assess the role of the major histocompatability complex and other gene expressions on odor profiles. Studies should also identify the specific information that human odor profiles convey, and should determine their predictive value in assessing individual identity, stress, cognitive performance, and health status. Further development of sensor technologies, such as the e-nose or other methodologies, for monitoring in the field should also be pursued. Studies linking human perception of odors with emotion and cognitive states are currently in their infancy and need to be encouraged in order to ascertain the full range of information that human odors might convey. The military should promote innovative research in chemical signaling that will accelerate these advances. Also, research in the development of sensor technology is likely to yield smaller, more automated devices that reduce analysis time and increase reliability—two factors that are critical for field applications. These advances will go hand-in-hand with the development of sweat patches that can be uniquely designed to capture the substances of interest. It seems highly plausible that new insights from these diverse areas will converge in 5 to 10 years, making odor biomarkers a viable technology for military field applications.
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance Human Tears as Sources of Biomarkers Bodily excretions and secretions that are noninvasively accessible and that reflect actual internal concentrations of substances within physiologically relevant systems represent possible targets of metabolic monitoring technology. An often overlooked external secretion is lachrymal fluid, or tears. Although there appears to be little currently accepted clinical analytic use of tears as indicators of nonophthalmic internal status, a number of disparate studies suggest that there may be merit in examining tears as a possible medium for monitoring relevant aspects of metabolic status. For example, it has been reported that tear glucose concentrations are related to blood glucose levels. This is an area where little research has been done, but one that may have significant potential as a noninvasive monitoring technology for a variety of physiological biomarkers. New Algorithms to Integrate Complex Biological Information The use of technology and “smart systems” are required to bridge the cognitive gap created by the lack of skilled clinicians in the field to provide individualized recommendations to support end users. Predictive medical algorithms can be utilized to generate specific recommendations and interventions from complex biological information gathered by metabolic monitoring systems. Further research is needed to develop and validate these models, with a particular emphasis on identifying prognostic factors in asymptomatic subjects. The Impact of Biological and Chemical Hazards on Traditional Biomarkers of Health It is largely unknown how hazards and toxins encountered during deployment will affect the biomarkers used by the military for monitoring. For example, low chronic exposure to a bacterial toxin or a heavy metal may alter serum electrolytes, glucose, or enzymes and confound usual interpretation of these values. In contrast, other biomarkers might serve as critical indicators for biological or chemical toxin exposure; for example, pulse rate alterations may be used as an indication of (sublethal) nerve toxin exposure. Metabolomics/Nutrigenomics The human genome has essentially been sequenced and is estimated to contain about 35,000 genes. It is the differential expression of genes that creates individual differences or phenotypes. Gene arrays show differences in the expression of genes under various conditions. The long-term goal is to understand how the expression of groups of genes and the production of proteins affect performance. It is known that the single nucleotide polymorphisms can affect the way individuals respond to drugs, can affect individuals’ vulnerability to micro-
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance biological infections, and can have the potential to cause long-term degenerative diseases in individuals. Such knowledge is envisioned to enhance a combat service member’s performance and lower the risk of life-threatening injury. Further, it is possible that such determinations would allow for prophylactic vaccinations, prescription of preventative pharmaceuticals, and the possible use of special monitoring sensors. Although it may be a number of years before it becomes possible, it would be ideal to be able to predict how a single combat service member will perform under a variety of different dietary and other environmental conditions based upon his or her phenotype. In this manner, the identification of differences among individuals by the use of genomic and metabolomic information collected on each combat service member is the ultimate “blue sky.” RESEARCH RECOMMENDATIONS To develop new algorithms that employ currently measurable biomarkers and nonlinear modeling techniques. In circumstances where average group data may not appropriately correlate with the performance of an individual, prediction models will need to be based on data from repeated measures from individuals. To develop patterns of rates of changes and resiliency. For example, research is needed to elucidate individual patterns of rates of change of stress hormones and to determine the resiliency of these stress responses in returning to baseline after the stressors have been removed. To conduct research to evaluate and validate available technology in the field. For example, technology related to self-assessment of perceived exertion, preferred exertion, and mood states that have been tested extensively in sports settings but needs to be evaluated and validated in military settings. Optimal combinations for use with physiological markers need to be determined. To further perform research activities in areas with the greatest long-range benefits, such as genomics/metabolomics, odors as biomarkers, tears as a new media for potential biomarkers, new cognitive measurements approaches, optimization of monitoring stress and immune function markers, the development of new algorithms to integrate complex biological information, and the impact of biological and chemical hazards on traditional biomarkers of health. To continue military activities in bone research. These should include studies of markers of bone loss, especially related to fracture risk and the prevention of lost duty time during initial entry training, advanced training, and combat operations. To continue to study cortisol levels during training and operations to ensure that its elevation is not a contributor to bone loss.
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Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance To develop non- and minimally invasive technologies, particularly for the determination of muscle metabolism, hydration status, and cognitive function. To develop motion sensors that are inexpensive but more convenient and reliable than current pedometers and accelerometers. To conduct research to validate the use of self- (and peer-) assessment tools (e.g., the Borg 6–20 rating scale of perceived exertion) in the field as indicators of fatigue and cognitive ability. To continue research on the use of NIRS to monitor muscle oxygenation and deoxygenation, intramuscular pH, and skin hydration status concurrently. This particular technology also has the potential for detecting the occurrence of inflammation. To develop simple field-friendly tests for urine specific gravity as an indicator of hydration status. To develop a practical method of monitoring body-weight change in the field. To conduct research to be able to mount or integrate high impedance EEG and ECG electrodes in helmets or into combat clothing. Although this technology will soon make it possible to continuously record brain activity, heart-rate data, and other electrophysiological parameters, some remaining challenges limit its use in the field. REFERENCE NRC (National Research Council). 2001. Opportunities in Biotechnology for Future Army Applications. Washington, DC: National Academy Press.
Representative terms from entire chapter: