Monitoring Metabolic Status: Predicting Decrements in Physiological and Cognitive Performance (2004)
Chapter: Appendix A: Examples of Physiological and Cognitive Markers of Performance
TABLE A-1 Examples of Metabolic Markers
|
Performance, Outcome |
Measure |
|
Heat stress Hypo- and hyperthermia |
Core temperature |
|
Cognitive performance De- and overhydration Fatigue Heat exhaustion Heat tolerance Muscular endurance |
Body-weight change Eye pressure Plasma volume, osmolarity Saliva flow Skin turgor Urine color Urine specific gravity, osmolarity Urine volume |
|
Cognition Hypo- and hyperglycemia Heat cramps Heat exhaustion Sunstroke |
Body weight Calorimetry (direct and indirect) Lean body mass |
TABLE A-2 Examples of Brain Function Markers
|
Tissue, Organ, Function |
Intermediate Marker |
Source |
|
Cognitive |
Blood flow Electrocardiogram Functional magnetic resonance imaging Imaging Magneto-electroencephalography Metabolism Positron emission tomography Spectroscopy |
|
|
Mood |
Odor profiles |
|
|
Sleep |
Ambulatory sleep monitor Electrocardiogram pattern |
|
|
Stress response |
Autonomic nervous system Cortisol Dehydroepiandrosterone Growth hormones Heart rate variability Impedance Insulin-like growth factor-1 Neuropeptide Y Neurotransmitters Norepinephrine Other hormones Prolactin Stress hormones Testosterone |
Blood Salivaa Urineb |
|
NOTE: Metabolic monitoring biomarkers can be categorized according to outcome function or intermediate measure that can be quantified to reflect the outcome function. This table summarizes outcome functions of various organs/systems/ physiological/psychological states and some intermediate biomarkers that might be used to predict or quantify these outcome functions and optimal performance. In general, it was felt that no single intermediate biomarker accurately predicts outcome function. Accurate measures of outcome function are often invasive and not applicable to field situations. More emphasis should be placed on developing noninvasive measures that accurately predict peak performance or catastrophic failure of a given organ/system or physiological/psychological state. |
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TABLE A-3 Examples of Bone Markers
TABLE A-4 Examples of Cardiac, Muscle, and Pulmonary Markers
|
Tissue, Organ, Function |
Intermediate Marker |
Source |
|
Cardiac |
Heart rate Heart rate variability Impedance |
|
|
Muscle |
Amino acids (glutamine, histidine, 3-methyl-histidine) |
|
|
|
Enzymes/molecules Carbonic anhydrase Isoenzymes of creatine kinase Myoglobin Myosin heavy chains Phosphocreatine levels Ubiquitin |
Muscle |
|
|
Immune Circulatory polymorphonuclear leukocytes Insulin-like growth factor-1 Interleukin-1, -6 Tumor necrosis factor |
Blood Salivaa Urineb |
|
|
Metabolism/catabolism/anabolism Lactate Glycogen Blood ammonia |
Blood Urine |
|
|
Protein turnover |
Blood Urine |
|
|
Structure/metabolism 3-methyl-/histidine excretion Glycogen Trace metals |
Muscle |
|
Pulmonary |
Expired air |
Oxygen and carbon dioxide saturation |
|
NOTE: Metabolic monitoring biomarkers can be categorized according to outcome function or intermediate measure that can be quantified to reflect the outcome function. This table summarizes outcome functions of various organs/systems/ physiological/psychological states and some intermediate biomarkers that might be used to predict or quantify these outcome functions and optimal performance. In general, it was felt that no single intermediate biomarker accurately predicts outcome function. Accurate measures of outcome function are often invasive and not applicable to field situations. More emphasis should be placed on developing noninvasive developing noninvasive measures that accurately predict peak performance or catastrophic failure of a given organ/system or physiological/psychological state. |
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