Proteins, metabolites, electrolytes, and other small molecules may serve as biomarkers for athletes and recreationally active individuals. Advances in big data approaches to assessing health and performance of athletes suggest that using the newest technology with intrinsic data such as biochemical, hematological data can be powerful in identifying the balance between training and recovery in each unique individual. Indeed, many commercially available services are offering biochemical and genetic testing for athletes, and professional athletes are reportedly exploiting technology and biomarker testing to track performance and recovery during training. Numerous biomarkers may assess different aspects of health, sport performance, and recovery, but when tracking athletes, even useful biomarkers have limitations. Biomarker testing/analysis poses many challenges: (a) single biomarkers are not definitive for diagnosing broad physiological function such as “recovery” in sport, (b) sensitivity of single biomarkers to detect overtraining or injury risk is limited, (c) reference ranges for athletes and specific subgroups of athletes are not well defined, (d) interindividual variance in absolute values and relative changes in biomarkers, and (e) highly contextualized nature of biomarker testing and analysis.
A single measurement of a biomarker does not allow for precise determination of an individual's health status. For example, although the immune signaling molecule interleukin-6 (IL-6) is a cytokine that can indicate inflammation alone, it provides little diagnostic information about chronic inflammation during overtraining in an athlete; it has both pro inflammatory and anti-inflammatory roles and responds to many stimuli acutely and chronically. There seems to be evidence for the usefulness of IL-6 as a potential biomarker of overtraining. However, researchers agree that multiple cytokines should be measured together when attempting to detect chronic inflammation in athletes, and that other variables related to physiological/physical function and upstream stimuli for chronic inflammation should be measured simultaneously (80,114). Data on multiple inflammatory cytokines, endocrine markers of long-term dysregulation and overtraining like testosterone and cortisol, and muscle damage markers like creatine kinase (CK) can be integrated to provide precise and accurate information about an athlete's health and overtraining status. Relying on a single marker to sensitively and precisely detect overtraining is overly simplistic given the pleiotropic nature of most biological markers. Figure 1 outlines markers of hydration state, nutrition/metabolic health, oxygen transport, muscle status, inflammation, injury risk, and food allergies that can be integrated to help athletes interpret their blood biomarker data to meaningful practical application.
Further complicating biomarker analysis is the fact that isolated or infrequent testing of biomarkers provides limited information. There are few athlete-specific reference ranges for most biomarkers, and this is in part because there is large interindividual variance in biomarker values, and that measurement of biomarkers can vary by context. Again considering the example of overtraining biomarkers, people generally exhibit high variability in serum/plasma cytokine levels and responsiveness (68,74,130), and athletes could exhibit greater rates of variability or different ranges of values (compared with average/sedentary individuals) entirely as they do for other markers such as muscle damage marker CK (86). Markers such as the many inflammatory cytokines are elevated after exercise in healthy individuals and return to baseline values within minutes to hours after exercise (126). Absolute values of cytokines in a one-time blood sample might be meaningful if values are elevated or decreased outside of the large range of interindividual variability observed, but perhaps more meaningful might be the responsiveness of circulating cytokine levels to a challenge such as an acute training bout or weeks of training. The absolute resting levels of biomarkers may not change while the response to stress could be abnormal. Thus, timing of the measurement and an individual's average resting levels over multiple days are relevant to interpretation and important to understanding the normal fluctuation in biomarkers for a given individual over a short period of time and in response to exercise and recovery over the course of hours, days, or weeks.
Time course for when to take measurements and how frequently are included in the discussion of specific biomarkers. Although we do not recommend a precise testing schedule that is suitable for all athletes under any training conditions, we recommend 4 main considerations for determining frequency/timing of biomarker testing (Figure 2). The first recommendation is to test at the beginning and end the key points of training transitions. For example, testing before and at the end of preseason training will provide important information about the athlete coming out of off-season or rest periods and how preseason training has prepared the athlete for the competitive season without ideally, inducing any overtraining or injury. Second, it is recommended that during the competitive season, which may have training subcycles, that biomarker testing be completed around a single bout of exercise. Testing can be administered before and after (a) a bout of exercise during a particularly challenging training week, (b) a performance test, or (c) a bout of exercise after recovery from an injury or after some shift in training. This type of testing will elucidate any deficiencies or defects in biomarker responses to an acute stress. This would be valuable when resting values of biomarkers might not reveal any concerns, but the response and recovery from a bout of exercise would more sensitively detect concerns. A third recommendation is to test before and multiple times after a major competition event or injury. In this case, there is a severe stress imposed by either the competitive event or an injury and biomarker testing multiple times after the event will allow an athlete to determine whether recovery has occurred on a biochemically measurable level. This case highlights the potential of biomarker testing to precisely detect potential health/recovery concerns when an athlete might feel ready, but may not actually be ready at the tissue/cellular level. Finally, a recommendation to establish standards for each individual and address the variability in most biomarkers, is that biomarker testing be done on multiple days during off-season when an athlete is fit, healthy, and rested to determine the athlete's average resting values for all biomarkers to be tested under training conditions. Flexibility should be built into biomarker testing schedules to account for testing that can be associated with an athlete's subjective feelings of fatigue, measurable decreases in performance, and injury incidents.
Accurately and precisely assessing health and performance of athletes requires a more comprehensive, integrative, and dynamic approach to biomarker analysis. A simplistic approach to using molecular biology/biochemistry in applied/practical sport science will not be appropriate in maximizing the benefit of biomarker testing to diagnose and make training decisions. The application of biomarker testing to traditional sport assessment/coaching requires thoughtful selection of multiple biomarkers and schedule of biomarker testing, and informed interpretation of both biochemical results and physiological/physical data about athletes. Through this review, we present an example holistic approach to tracking athletes using biomarkers that assess nutritional health, metabolic health, hydration status, muscle status, endurance performance, injury status and risk, and inflammation. Diet and training affect these key aspects of health and performance that can be assessed with biomarkers that have been relatively well studied; examples of evidence-based biomarkers for each specific aspect of health/performance are suggested based on our review of the literature (Figure 1). We suggest ideally, a comprehensive approach to biomarker analysis, but markers are presented based on their respective physiological relevance for individuals seeking a more focused approach to hematological assessment.
Biomarkers of Nutrition and Metabolic Health
Athletic performance and recovery from exercise are enhanced by optimal nutrition according to a joint position stand by the American Dietetic Association, Dietitians of Canada, and the American College of Sports Medicine. Functional performance is impaired when nutritional intake is inadequate and a high prevalence of disordered eating in athletes, especially female athletes contributes to concerns about general health. Specific nutritional deficiencies are common in athletes particularly for vitamin D and iron, for which studies have reported deficiency rates of 73% (27) and 22–31% (in female athletes) (37,104). Other, less common nutritional deficiencies in nutrients such as folate, vitamin B12, or magnesium may result in reduced endurance work performance and muscle function. Individual nutritional needs depend largely on sport- and training-specific bioenergetic demands as well as on an athlete's metabolic tolerance, needs, and preferences. Frequent monitoring of macronutrient and micronutrient intake may help identify individual deficiencies and track changes, especially as training volume and nutritional demands increase. Nutritional assessment by objective biomarker testing eliminates bias associated with more traditional and subjective nutritional assessments (e.g., recall, questionnaire).
Glucose functions as the primary energy source. Unlike fats and proteins (e.g., ketones), which the body uses as energy sources in some conditions, glucose is the only energy substrate in the body that functions solely for providing energy to cells. Circulating glucose levels during exercise depend on energy status, food intake, event intensity, and glycogen storage levels. Reduced glycogen availability is commonly associated with fatigue. With glucose-depleting events, carbohydrate consumption before or during prolonged exercise has been shown to replenish glycogen, maintain blood glucose levels, and enhance performance, especially for high-intensity activity (136). Tracking and monitoring fasting and longer-term blood glucose through biomarkers such as glucose may help individual athletes monitor the nutritional adequacy of their diet. Although fasting blood glucose is not often related directly to performance, athletes tend to have lower fasting blood glucose (76), where levels are associated with the intensity of the training regimen (76). Adequate nutrition for a given training volume can reduce the risk of exercise-induced hypoglycemia in athletes. In addition, exercise training may reduce vulnerability to hypoglycemia in athletes because of a shift in substrate metabolism. However, overtraining may reverse this adaptation, making athletes more vulnerable to hypoglycemia in the over-trained state.
Fats are used as a primary energy source in endurance events or when carbohydrate availability is low. In particular, medium-chain fatty acids are preferred for oxidation, as they enter circulation more rapidly and are primarily absorbed by the liver. Fat utilization during exercise impacts lipid profiles by reducing resting levels of total cholesterol and triglycerides (59), thereby improving cardiovascular health profiles. In addition to providing energy, some types of fats play important roles in recovery. Omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) reduce inflammation, muscle soreness, and the perception of pain from exercise (14,63,129). Moreover, omega-3 fatty acids may influence performance through their effects on neuromuscular function (123), nerve conduction velocity (123), and neuromuscular sensitivity of the acetylcholine receptor. In addition, omega-3 fatty acids may support increased training volume (63) and support adaptations to exercise training. Levels of omega fatty acids measured in the blood reflect their clinical role more so than dietary intake. Nevertheless, the recommended daily intake of omega-3 fatty acids (EPA + DHA) is ≤3 g·d−1 for average individuals or those moderately physically active, but recommendations may be as high as 6–8 g·d−1 (2:1 ratio of EPA:DHA) for elite athletes. Greater training demands may increase requirements for omega-3 fatty acid intake.
Proteins serve as the building blocks of hormones and enzymes used in all cells and tissues in the body, including muscle. Generally, protein intakes of 1.3–2.0 grams per kg body mass per day are recommended for athletes to support muscle protein synthesis, facilitate training adaptations, and prevent lean muscle mass loss. An imbalance between dietary protein intake and dietary protein needs may result in net protein loss in athletes. With protein deficiency, tissue protein breakdown becomes a source of essential amino acids needed to maintain critical body functions. While it is generally recognized that athletes require higher protein intake than the recommended daily allowance, defining individual needs is challenging. As outlined below, a combination of biomarkers including total protein, albumin, globulin (calculated), urea nitrogen (blood urea nitrogen [BUN] or urinary urea nitrogen), nitrogen balance (calculated), and amino acid analysis may help athletes to gauge their protein status and make dietary alterations to improve training outcomes.
Protein deficiency decreases blood proteins, especially of albumin, and low protein intake seems to decrease the rate of albumin synthesis (61). However, albumin may also serve as a marker of other aspects of athlete health and performance, and this measure requires contextualization when assessed in athletes. The need for contextualization is a common feature of many biomarkers in a comprehensive panel approach to tracking biomarkers and performance. Contextualizing albumin levels, traditionally defined for sedentary or nonathlete populations, for athletes training or competing is important for interpreting the implications of either decreased or elevated plasma albumin. In addition, it is important to understand that measures such as albumin may also be related to performance and recovery through nontraditional functions or signaling in athletes. For example, albumin has been associated with human growth hormone (GH) concentrations in the blood (101), and although the mechanism by which these 2 markers are related is unknown, this type of result suggests that albumin may require additional interpretation when tracking athletes.
Similarly, while urea nitrogen (blood or in urine) is a product of protein degradation and suggests protein breakdown, elevations can be due to a variety of factors such as protein intake, endogenous protein catabolism, fever, infection, glucocorticoids, state of hydration, hepatic urea synthesis, and renal urea excretion. Lower urea nitrogen may be due to low protein intake, malnutrition starvation, or impaired metabolic activity in the liver. Higher urea nitrogen may be due to exhaustive exercise training, catabolism (59), and high dietary protein intake (148). As maintaining a positive protein balance is essential to facilitating optimal recovery and training adaptations, protein status should be optimized to avoid nutritional insufficiencies and excessive protein catabolism. In the absence of disease, low blood protein, low albumin, and elevated urea nitrogen may be indicative of insufficient protein intake in athletes. In circumstances where protein intake seems to be sufficient for an athlete's estimated needs, albumin and urea nitrogen may indicate other relevant athlete health issues.
Many athletes follow nontraditional diets, such as low-carbohydrate or ketogenic diets. Athletes are able to sustain performance on diets comprising as little as 7% carbohydrates without effects of gluconeogenesis (146), but dramatic effects on fat oxidation to maintain similar muscle glycogen use and repletion to that of athletes on traditional high carbohydrate diets (139). As with all biomarkers, we recommend contextualizing nutritional biomarkers with each individual's habitual diet in a dynamic fashion. In other words, absolute values for certain biomarkers may not direct action for a given athlete, but changes with training that coincide with reduced capacity to recover and decreased performance should be monitored on an individual basis. This approach to biomarker monitoring will allow coaches and staff to better monitor groups of highly variable athletes who will inevitably have highly different diets and other behaviors that affect performance.
A variety of vitamins and minerals support physiological processes that underlie performance. For example, vitamin D, in addition to being involved in bone maintenance, has a role in muscle function and protein synthesis. Many athletes monitor vitamin D with a goal of achieving levels of greater than 50 ng·ml−1 because of the many potential ergogenic effects of vitamin D on sport performance rev. in Dahlquist et al (31). Although some studies have determined that specific vitamin D supplementation regimens do not affect power-specific performance variables, there is promising evidence that vitamin D supplementation enhances aerobic performance (62) and that vitamin D levels are correlated to aerobic performance (31). B complex vitamins (thiamin, riboflavin, niacin, pyridoxine, folate, biotin, pantothenic acid, and choline) also play an important role in performance by regulating energy metabolism by modulating the synthesis and degradation of carbohydrate, fat, protein, and bioactive compounds. Other vitamins play important supporting roles in recovery processes. For example, vitamin E functions as an antioxidant in cell membranes and subcellular structures (65). Deficiencies in vitamin E may relate to neurologic damage and erythrocyte hemolysis, as well as muscle degradation (79). Similarly, beta-carotene, a precursor of vitamin A, acts as antioxidants in reducing muscle damage and enhancing recovery after exercise (65). Low calcium, iron, B-vitamins, and vitamin D have been associated with increased injury risk, specifically lower extremity stress fractures (81).
Several essential minerals, such as magnesium and iron, affect physical performance (79). For example, magnesium is important for energy metabolism as well as nerve and muscle function (79). Deficiencies may lead to muscle weakness (79), muscle spasms (43), and altered CK and lactate response to exercise (49). In addition, specific nutrients including iron, folic acid, and vitamin B12 (cyanocobalamin) are essential to hemoglobin synthesis and subsequently oxygen transport (79). Deficiencies may lead to fatigue, anemia, cognitive impairment, and immune deficiencies (79,81). Iron deficiency is prevalent in athletes from a variety of sports (79,104), with prevalence as high as 31% in some sports (104). In addition to decreased iron concentrations, other biomarkers are useful in the assessment of iron deficiency including ferritin (concentration less than 12 μg·L−1) and transferrin (saturation less than 16%) (79). Moreover, red blood cell indices may provide early indications of nutritional deficiencies. For example, hematocrit, hemoglobin, and red blood cell indices may suggest iron, vitamin B12, or folate deficiency (79).
Other micronutrients including zinc and chromium also have important supporting roles in athletes. Zinc is required for a variety of functions including protein synthesis, cellular function, glucose use, hormone metabolism, immunity, and wound healing (135). Low zinc is prevalent (22–25%) in endurance athletes (35). Chromium is a provisionally essential mineral that functions broadly in the regulation of glucose, lipid, and protein metabolism by potentiating the action of insulin at the cellular level. Athletes excrete higher amounts of chromium (3), which may result in increased nutritional needs. Monitoring micronutrient levels may help athletes to identify deficiencies and increase nutritional needs early to reduce the potential performance-impairing impact of nutritional deficiencies.
One additional aspect of nutritional and metabolic health that may have value in tracking athletes is that of an athlete's unique responses to certain foods. Blood-based biomarkers are available for testing food allergen sensitization that may or may not be known to the athlete. Adverse reactions (food allergy/intolerance) to specific foods may result from immunoglobulin E (IgE)-mediated mechanisms, where IgE is produced against specific food components in food-allergic individuals (38). Immunoglobulin E triggers immune responses within minutes to hours after consuming the food by way of mast cell degranulation resulting in the release of vasoactive and proinflammatory mediators. In adults, allergies to peanuts, tree nuts (walnut, hazel, cashew, pistachio, Brazil nut, pine nut, almond), fish, shellfish (shrimp, crab, lobster, oyster, scallops), fruits, vegetables, seeds (cotton, sesame, psyllium, mustard), milk, egg and spices are most prevalent (38). Reactions vary from aggravation of the skin, nose, eyes, lungs, and gastrointestinal tract to severe cardiovascular effects. Symptoms may be noticed as swelling and itching of the lips, tongue, or palate, abdominal pain and cramping, nausea, vomiting, diarrhea, respiratory challenges, and asthma. While a variety of methods exist to assess food allergies, IgE-based testing is considered an acceptable approach to assess suspected food allergies. Immunoglobulin E–based blood tests measure IgE directed against specific antigens, where levels can predict reactions to certain foods with greater than 95% certainty (112). Specific IgE levels higher than 0.35 kU·L−1 suggest sensitization (112). The accurate identification of causative foods is important for creating effective treatment plans in athletes, especially when pharmaceutical interventions are subject to the World Anti-Doping Agency regulations. Food allergen testing may be performed under resting conditions as part of athletes' preseason physicals. Identification of potential food allergens is particularly important due to a condition known as food-dependent exercise-induced anaphylaxis (85). In this condition, exercise in combination with ingestion of the food agent triggers the allergic response (85), possibly because of altered absorption from the gastrointestinal tract (85), or altered IgE levels from exercise (2), or hyperosmolar conditions (93). Although the direct effects of IgE-mediated responses on exercise tolerance and performance are yet to be examined, symptoms like anaphylaxis, eosinophilic inflammation, bronchial hyperresponsiveness, urticaria-angioedema, dermatitis, rhinitis or asthma, and gastrointestinal disorders (oral allergy syndrome, colic, nausea, vomiting, diarrhea, abdominal pain) may present a barrier to exercise tolerance.
Biomarkers of Hydration Status
Water is the most essential nutrient of the body undergoing continuous recycling, functioning as a solvent, and regulating cell volume, while playing a critical role in thermoregulatory and overall function. Water balance is mainly regulated by thirst and antidiuretic hormone, known also as vasopressin, through its renal effect. Acute decrease in body weight has been used as the gold standard to evaluate the degree of dehydration because it reflects mainly a decrease in total body water and not energy substrates (e.g., fat, protein). In this case, we assume that both cutaneous (sweating) or renal (urination) water losses have a specific gravity of approximately 1.000, resulting in a 1-gram change in body weight for every milliliter of sweat and urine output. This technique is accurate, assuming there is no bowel movement or food consumption and of course body weight is taken before exercise in a euhydrated state. A hypohydrated state of greater than −2% body mass has been linked to decreases in exercise/sport performance, cognitive function, mood, and increases in risk of exertional heat illness or exertional heatstroke for individuals exercising in hot and humid environments (107).
During exercise, especially in the heat, most people tend to drink less than what they lose through sweating, resulting in a water deficit (involuntary dehydration). Because sweat is hypotonic, exercise-induced dehydration leads mainly to a decrement of the extracellular fluid volume. This state is described as hypertonic hypovolemia due to water loss from the plasma. Blood biomarkers of hemoconcentration have thus been widely used as an indexes of dehydration.
Both blood osmolality and sodium levels have been used for hydration assessment because both values increase in a linear fashion with the levels of dehydration. Blood osmolality is considered by many as the gold standard for the assessment of hydration state, especially for acute and dynamic changes of hydration state (6,25). Even a small degree of dehydration (e.g., −1% of body weight) can significantly increase plasma osmolality. Most studies suggest that the threshold of dehydration for blood osmolality is 295 mmol·kg−1 of plasma water (25,45,108). In addition, because dehydration has a negative impact on kidney function, the ratio of urea nitrogen to creatinine has been used as a strong indicator of hydration state with a suggested threshold of 20 for dehydration (45).
Timing of blood hydration biomarkers depends on the intent. Pre-exercise hydration state may be used to assess whether an athlete is hypohydrated before a training session or competition; in this case, the result will define fluid consumption recommendations to optimize training benefit or performance during an event. Postexercise hydration state will also define fluid consumption recommendations for an individual, but for the purposes of promoting optimal recovery. Tracking hydration state over a number of days can elucidate whether fluid and food intake is providing sufficient hydration to maintain a hydrated state during critical times in training and before and after competition.
The hemoconcentration-driven elevation in plasma osmolality and decrement in plasma volume stimulate arginine vasopressin (AVP) secretion by osmotic receptor stimulation and unloading of the baroreceptors. Even though AVP could be used as a marker of dehydration, its analytical process is laborious and expensive. Although this 8–amino acid molecule is very sensitive, it tends to degrade quickly, making its measurement challenging. Luckily, AVP has a strong effect on the renal system by increasing water reabsorption in the nephron tubules. As a result, urine volume is smaller and more concentrated. Therefore, urinary markers of concentration have also been widely used as an index of hydration. Urine-specific gravity (USG) and urine osmolality (UOsm) are sensitive to changes in hydration state. Both the American College of Sports Medicine and the National Athletic Training Association recommend cut-off points for dehydration of ≥1.020 for USG and ≥700 mmol·kg−1 for UOsm, respectively (22,108). Based on the link between dehydration and urine concentration, urine color has been shown to be a valid practical marker of hydration assessment both in adults and in children (5,66). Based on the 8-point urine color scale developed by Dr. Lawrence Armstrong, the threshold of dehydration is color 4, with 1 being the lightest and 8 the darkest color. A urine color of 5 or above is consistently and reliably associated with dehydration (83).
Thirst has also been suggested as a surrogate perceptual marker of dehydration. Thirst and AVP are similarly stimulated by decreases in plasma volume and increases in plasma osmolality (100). However, both are more sensitive to small osmotic stimulation than to baroreceptor activation. What is interesting is that the osmotic threshold for thirst activation is significantly greater than the one for AVP secretion (100). Mild dehydration-induced increase in plasma osmolality will rapidly increase AVP and elevate urinary markers of dehydration, even in the absence of thirst. Of course, because thirst is stimulated by significant dehydration, people may already be dehydrated by the time they notice thirst. This phenomenon could also explain why both recreational and professional athletes often start their training or competition in a suboptimal hydration state, as indicated by their elevated urine hydration markers. We therefore recommend that although thirst be a useful measure of hydration state particularly at rest, blood or urine biomarkers may be more precise and accurate.
Other Measures of Hydration
Researchers have studied novel biomarkers including saliva, sweat, and even tears as possible biological samples in which to measure hydration state. Saliva osmolality and saliva flow rate have shown promise as hydration markers (142); other studies raise doubts about the usefulness of salivary osmolarity as a biomarker except in highly controlled conditions, during physical activity (89) or in special clinical populations (45). Similarly, sweat osmolality, electrolytes, and other variables, as well as tear osmolality (44) show promise as potential biomarkers, but research is limited. Although these options currently cannot serve as valid biomarkers of hydration status for athletes, when considering biomarkers to select for a comprehensive panel, it is critical to consider newly studied markers as potential options. Table 1 provides options for well-validated hydration biomarkers.
Biomarkers of Muscle Status
Skeletal muscle tissue quality (size, structure, composition, metabolic capacity, and contractile indices) is an important aspect of athletic health and performance. Strength, power, fatigue, and endurance in athletes are directly affected by muscle status or the fatigue and recovery state of the muscle. Also, insufficient recovery from exercise-induced muscle damage caused by training impairs performance, likely because of increased sense of effort, reduced exercise tolerance, reduced strength, and reduced power. Monitoring indices of muscle status will help athletes to tailor their training/competition and recovery regimens to optimize performance. Blood-based biomarker muscle status assessment should focus on endocrine regulation of muscle repair/adaptations, metabolic homeostasis (anabolic-catabolic balance, protein/amino acid deficiencies, substrate availability), muscle damage, and muscle excitability. There are well-validated markers (Table 2) related to fatigue, recovery, protein synthesis, or fueling strategies, which are all major athlete concerns. Because hormone and amino acid concentrations in the blood are highly variable among individuals, these types of biomarkers are best assessed by analyzing progressive increases/decreases away from a baseline measure for each person (Table 2). This requires monitoring for these types of biomarkers at multiple time points throughout training, off-season, and competition cycles. To monitor chronic changes across a season, athletes may be tested every 4–6 weeks under similar conditions (i.e., fasted, in the morning, before training, the day after a rest day or similar training day).
Proper hormonal signaling is essential for the physiological adaptations to exercise training. Dependent on the magnitude of the training stimulus, often defined by acute program variables such as load, volume, duration, modality, and rest, hormones elicit specific training adaptations. Testosterone, cortisol, dehydroepiandrosterone (DHEA), GH, insulin-like growth factor 1 (IGF-1), sex-hormone binding globulin, and luteinizing hormone (LH) are among the key hormones demonstrated to be critical to athletes.
Testosterone is required for promoting protein synthesis, red blood cell production, and glycogen replenishment and for reducing protein breakdown. Decreased testosterone levels accompanied by decreased performance, energy, or strength observed during a training season may indicate that that training volume is too high. In this case, an athlete may benefit from temporarily reducing training volume. Cortisol works antagonistically to testosterone, inhibiting protein synthesis by interfering with testosterone's binding to its androgen receptor and by blocking anabolic signaling through testosterone-independent mechanisms. When chronically elevated, cortisol is catabolic and immunosuppressive leading to circumstances that make it more difficult for an athlete to build/maintain muscle mass and recover from training.
In addition to monitoring testosterone and cortisol separately, monitoring their relative levels (T:C ratio) during a training season may provide a relative indication of anabolic-catabolic balance, especially in male athletes (133). T:C ratio is considered more sensitive to training stresses than either measure alone. A prolonged decrease in T:C is associated with detriments to performance through increased proteolysis (muscle protein breakdown) and decreased protein synthesis. A 30% decrease in T:C has been suggested as an indicator of insufficient recovery (8,138), whereas a value of 0.35 × 10−3 has been considered to be the threshold of overtraining (138). Poor performance outcomes and suboptimal training adaptations have been reported in both soccer athletes (70) and tactical athletes (26) with a low T:C ratio.
As other hormones moderate physiological adaptations to training, especially in female athletes, monitoring other hormones, such as SHBG or DHEA-S in relation to cortisol may provide additional insights into the anabolic to catabolic balance in both male and female athletes. Dehydroepiandrosterone is a precursor hormone to both estrogen and testosterone. In addition to affecting body composition (56) in athletes, changes in DHEA in relation to cortisol have been reported to be a useful marker of susceptibility to overtraining in the female athlete (16,42). Similarly, SHBG is a useful indicator of training status and performance (strength and rate of force development) (40). SHBG transports hormones such as testosterone in the body and increases in response to exercise training in both male and female athletes. Increased SHBG is believed to protect sex hormones from being degraded by protecting the biologically active free sex hormones in circulation. Increased SHBG and decreased testosterone may indicate insufficient recovery (53). Low SHBG may merely represent an individual's chronic diet (1); diets high in fat and protein may be associated with low levels of SHBG and high levels of sex hormones (1) and may be considered a sign of suboptimal capacity to adapt to training (133).
Other key hormones inform us about training adaptations. These include GH, IGF-1, and LH. Growth hormone stimulates anabolism by promoting muscle protein synthesis and inhibiting protein breakdown. Growth hormone concentrations have been correlated to exercise volume and intensity. Growth hormone increases levels of circulating IGF-I, both of which hormones are involved in muscle mass regulation, making IGF-1 and GH together potentially useful biomarkers. Luteinizing hormone is associated with reproductive function in men and women. Luteinizing hormone may be another useful marker to detect overtraining or insufficient energy intake.
Athletes require greater daily intakes of protein (in the range of 1.3–1.8 g·kg−1·d−1) to maximize muscle protein synthesis as compared to the general population. As discussed, markers of nitrogen balance (e.g., urea nitrogen) are important for assessing the nutritional status of an athlete, but a number of specific amino acids can reveal information about protein synthesis, nutrition, and fatigue. For example, the branched-chain amino acids (BCAA), leucine, isoleucine, and valine, increase the rates of protein synthesis and degradation in resting human muscle (13). Branched-chain amino acids levels have been informative about whether BCAA supplementation is directly affecting skeletal muscle protein synthesis signaling (4). With some special considerations for measurement (131), BCAA can also indicate whether diet, stress, or disease states are affecting an athlete's skeletal muscle. There are a few other examples in which specific amino acids may indicate muscle status based on their unique roles in skeletal muscle. The amino acid taurine is not incorporated into protein, but is abundant in muscle tissue and is needed for the differentiation and growth of skeletal muscle. Taurine deficiency can impair muscle development, structure, and function (118). Researchers have interpreted elevated taurine levels, perhaps because of release from muscle fibers, as a marker of damage or impaired muscle function (29,144). Others have used urine excretion of taurine as a biomarker in athletes (28). Another amino acid, glycine, is involved in the biosynthesis of heme, creatine, nucleic acids, and uric acid (143), deficiencies in which may affect various aspects of the metabolic pathways. Other amino acid patterns (e.g., elevated tryptophan, decreased glutamine) have been associated with fatigue and suboptimal training capacity in athletes (23,67,115) and suggest specific amino acids that may serve as biomarkers of muscle quality/status. While some amino acids change in response to acute exercise (103), monitoring resting amino acids across a season as part of a comprehensive panel under similar conditions (i.e., fasted, in the morning, before training, the day after a rest day or similar training day) may provide insights into training (39) and fatigue (67).
Recovery (Urea Nitrogen and Creatine Kinase)
After muscle-damaging exercise, the enzyme CK leaks from the muscle into the circulation (69,86). It is typical for athletes to have elevated CK during training, with reference ranges of 82–1,083 U·L−1 in male and 47–513 U·L−1 in female athletes suggested as athletic norms (86). Monitoring CK levels during training in comparison with baseline levels may help athletes to monitor muscle status. Creatine kinase levels peak approximately 24 hours after damaging exercise such as heavy strength training, but may remain elevated up to 7 days after exercise. Chronically elevated CK may indicate insufficient recovery. Because other components of muscle such as myoglobin may leak into circulation during muscle damage (peak 1–3 hours after exercise), and urea nitrogen can indicate overall protein synthesis vs. breakdown (59), using all 3 markers to determine an athlete's muscle status during training and recovery will be useful to athletes, coaches, and clinicians.
Biomarkers of Cardiovascular Endurance Performance
Iron is an important mineral in oxygen transport and oxidative phosphorylation which are fundamental physiological processes required for aerobic metabolism and cardiovascular endurance performance (60). Endurance athletes, especially females (113), are particularly susceptible to iron deficiency because of one or a combination of the following factors: menstrual bleeding, poor dietary intake, exercise-related gastrointestinal tract bleeding, hematuria, sweating, poor intestinal iron absorption due to subclinical exercise-induced inflammation (97), and erythrocyte destruction through repeated foot striking (98), elevated intramuscular pressure in swimmers and cyclists (110), and increased mechanical loading and hepcidin release in response to subclinical exercise-related inflammation (97,105). Other factors affecting iron status biomarkers in athletes include regular nonsteroidal anti-inflammatory drug (NSAID) use, blood donation, and chronic alcohol consumption (12). Athletes with compromised iron status may experience decreases in performance because of the inability to optimally metabolize substrates into energy (51). Iron deficiencies also prevent adaptations to endurance and altitude training (11,60). Also, iron deficiency with anemia may have a role in the greater prevalence of upper respiratory tract infections in marathon runners (88). Given the physiological role of iron and its association with aerobic performance, health, and adaptation, athletes and coaches should consider tracking iron, iron binding capacity, transferrin saturation, and ferritin levels during training. Approaches to timing and frequency of iron status testing for individual athletes can be customized to address issues with when cardiovascular endurance performance may be affected by changes in training programs/cycles or general health (e.g., during infection or personal stress experienced during training). Iron status assessments acutely before competition will also be contextually useful. Practical considerations of cost of biomarker assessments may define frequency of testing.
The compliment of widely used biomarkers includes iron, total iron binding capacity (TIBC), transferrin saturation, and ferritin, with more recent biomarkers such as soluble transferrin receptor and hepcidin peptide assay possibly improving diagnosis. Iron status markers should be interpreted in the context of recent events (e.g., competition season, recent training intensity, frequency, and duration, inflammation state, and diet changes). Changes in iron status markers indicate a number of well-studied, potential effects on performance (Table 3).
Iron concentration reflects total iron content with a reference ranges within 50–175 μg·dl−1 (9). Between and within-day variation of iron concentration is high (10–26%) and as a consequence iron concentration must be interpreted cautiously and cannot be rendered a useful measure of iron status alone (15). Serum ferritin can be falsely elevated in an inflammatory state (e.g., postexercise, infection) but inflammatory markers such as C-reactive protein (CRP) or alpha-1-acid glycoprotein can aid in the interpretation of ferritin in the assessment of iron status (9). A more stable indicator of iron status is TIBC (reference range: 250–425 μg·dl−1), which reflects the total number of binding sites on the blood iron transporting peptide transferrin. Daily variation of TIBC is relatively low (8–12%) and does not change before iron stores are depleted (9), thus reducing the likelihood of falsely detecting iron depleted states. Total iron binding capacity would rise in iron deficiency as more free transferrin binding sites are available. In addition, transferrin is not an acute phase reactant or affected by other diseases and therefore is a valuable biomarker panel addition for determining iron status (152). Transferrin is an iron-carrying monomeric glycoprotein within blood that transports iron to tissues. Transferrin saturation (reference range: 15–50%) is the percentage of iron to TIBC, with values under 15% consistent with iron deficiency. Because TIBC is quite stable, alterations in iron concentration will also affect transferrin saturation (9). Soluble transferrin receptor reflects iron deficiency at the tissue level and is believed to be a more sensitive measure of functional iron deficiency assessed by ferritin (152). In 2 iron supplementation studies examining aerobic training adaptation in females, improvements were only noted when soluble transferrin receptor was elevated before training (>8 mg·L−1) compared with those with adequate iron status (<8 mg·L−1) (18,19). This biomarker seems not to be affected by inflammation and has low within-subject variability in athletes undergoing training. The combination of at least transferrin and transferrin saturation, TIBC, serum ferritin, and hemoglobin is required for accurate determination of the presence and severity of iron deficiency. Including additional clinical parameters such as soluble transferrin receptor, among others, may increase the confidence in iron status diagnosis.
Endurance performance suffers when iron levels are insufficient (serum ferritin <12 μg·L−1) for hemoglobin (Hb) to efficiently transport oxygen to exercising muscle tissue (Hb, females, <12 g·d−1; males, <13 g·dL−1). Yet, serum ferritin stores can be depleted before hemoglobin has declined to levels required for diagnosis of anemia (32). Functional iron deficiency has been defined as ferritin <35 μg·L−1, Hb <11.5 g·dl−1, and transferrin (iron transport molecule) saturation <16% (96); others have used more precise serum ferritin ranges of 12–20 μg·L−1. Iron deficiency without anemia is more common than iron deficiency with anemia in endurance athletes, but it is critical to consider multiple aspects of iron metabolism that may affect an athlete.
Supplementation with iron is known to correct low levels of ferritin, transferrin, and hemoglobin, but in some cases may not affect endurance performance (96). However, a vast amount of research supports that tracking these variables and introducing supplementation regimens is effective in improving endurance performance in athletes with low ferritin, both anemic and nonanemic (33,34,58,73,82,106,109,151). A recent review determined that in 73% of studies, implementing low-moderate doses of iron supplementation resulted in improvements in aerobic/endurance performance in female athletes (32).
Biomarkers of Injury Status and Risk
Although biomarkers have been studied in human performance, there has been limited use of biomarkers to determine injury states (both risk for injury, severity of injury, and recovery from injury). No previous work, to our knowledge, has examined the use of biomarkers for injury prevention or for recovery after injury. Concussions are a major concern in sports. One of the major concerns in concussion is understanding when injured athletes have recovered. Previous work has examined biomarkers of concussion recovery with the goal of detecting and monitoring changes in the central nervous system to provide objective measures of when athletes are ready to return to athletic pursuits safely (111). Previous work in this area has focused on the examination of biomarkers in the cerebrospinal fluid, with specific emphasis on markers of axonal damage (total tau, neurofilament light), which have been shown to be elevated in boxers after repeated punches to the head even without a knockout (92,149). However, because of the invasiveness, difficulty, and expense of completing a lumbar puncture, researchers began to explore the possibility of assessing blood-based biomarkers of brain injury. Two blood-based biomarkers of interest have been neuron-specific enolase and the glial cell biomarker S-100 calcium binding protein B (S-100B), with most studies focusing on changes in S-100B levels (36,90,95,119–122). Serum levels of both markers have been reported to be increased after boxing matches in which the athlete sustained direct or repetitive blows to the head (50,150). By contrast, when examining these same markers in concussed hockey players, only S-100B was found to be increased in the serum (111). Based on this work and the 2015 review article by Papa, it is clear that the study of biomarkers of concussion is beginning to identify potentially diagnostic as well as recovery markers (99). However, no biomarkers have yet been identified for clinical diagnosis or tracking of concussions in athletic populations. This remains an active area for examination.
Another area of musculoskeletal health that has received substantial attention is stress fractures, specifically female stress fractures. Women had a 10-fold higher risk of sustaining a stress fracture when compared with men in a study of military recruits, and the risk has been reported to be as much as 50% higher in female athletes (10,41). Stress fractures are known to result in significant medical costs, lost duty time in the military, and lost game time for athletes. The female athlete triad is a medical condition that affects physically active females and is characterized by 3 components: (a) low-energy availability with or without disordered eating, (b) amenorrhea or menstrual dysfunction, and (c) low bone mineral density (BMD) (91). This condition has been associated with osteoporosis and low BMD, which have been proposed as risk factors for stress fracture development (41,102). Although specific biomarkers have not been associated with the female athlete triad, some biomarkers of bone breakdown have been associated with poor bone quality or bone density. Insulin-like growth factor (IGF-I), one biomarker associated with bone quality, has been reported to be significantly lower in osteoporotic women with poor bone quality and to be positively associated with BMD (87,116). In addition, reduced concentrations of IGF-I have been associated with fracture risk in women (64,125). Only a few studies have examined the use of biomarkers to assess stress fracture risk, none of which have identified a single set of bone turnover biomarkers that could be used for stress fracture prediction (124,147). However, Strohbach et al. (124) did report that serum IGF-I was decreased in subjects who sustained a stress fracture when compared with their noninjured control subject. The results of these few studies as well as an improved understanding of the female athlete triad will allow for the continued exploration of biomarkers that could potential identify individuals at risk of stress fracture development.
Anterior cruciate ligament (ACL) injuries have been reported to result in the development of osteoarthritis (OA) in up to 50% of patients (77). The development of OA in ACL patients has been reported to occur within 10–15 years of the primary injury (77,78,141). While the examination and exploration of both inflammatory biomarkers as well as markers of cartilage breakdown have been extensive in the study of OA, very few studies have explored these markers in ACL patients after injury and surgery; no studies, to our knowledge, have determined biomarkers that can be used to predict ACL injuries. The biomarkers of greatest interest in the early postoperative recovery period after ACL reconstruction have been serum concentrations of collagen type I and type II cleavage products as well as inflammatory responses in both human and animal models (55,127,132). Immediately after ACL injury, the serum concentration of these biomarkers indicates an imbalance between cartilage breakdown and synthesis that could be indicative of posttraumatic changes in cartilage metabolism and signal the onset of posttraumatic OA (127,132). Haslauer et al. examined changes in the IL-6, IL-8, markers of tissue damage (CRP), as well as vascular endothelial growth factor (VEGF), and transforming growth factor β (TGFβ) in Yucatan minipigs to examine the immediate response after ACL transection (55). The results of this study indicate that in the early postinjury period, there is an increase in IL-6 and IL-8 in the synovium as well as an increase in CRP in the ligament, whereas there was no change in TGFβ or VEGF. Similar to human studies, the CRP returned to normal levels by 15 days after injury or after surgery, wheras IL-6 and IL-8 returned to normal levels by approximately 5 days after injury or after surgery (21,55). In a study of ACL reconstruction patients, similar results were found regarding CRP, but this study reported an increase in TGFβ and myostatin in the early postoperative period and then returned to normal by approximately 12 weeks after surgery (84). Although these studies have identified biomarkers that change with injury and after surgical intervention, no studies to date have examined the potential for using biomarkers to identify individuals at increased risk. Thus, further research is required before these biomarkers should be assayed as a standard, clinical approach for injury assessment; it is important to note, that contextualized with results from other biomarkers, of muscle status for example, potential markers of injury like certain cytokines or CRP may be indicative of simply, exercise-induced muscle damage, or more seriously, overtraining. The overlap of biomarkers in many areas of diagnosis is one of the reasons that we suggest panels that will help define the true reason for changes in intersecting biomarkers.
Biomarkers of Inflammation
Muscle damage is an expected part of exercise training, as are the physiological and immune responses that occur during and after muscle tissue damage. Athletes monitoring their performance during training may track inflammation indirectly through key components of the inflammation process that can enter systemic blood circulation. Chronic inflammation that persists after damage results from positive feedback of multiple signals indicating injury or stress from overtraining, or results from infection/illness can also be tracked in specimens by assessing proteins and other molecules that control inflammation (Figure 3). Chronic inflammation can also result from infection, autoimmune disease, cardiovascular disease, or other major health concerns. In both instances, chronic inflammation is a positive feedback phenomenon that can impact health and performance of an individual. Creatine kinase, for example, is released in response to skeletal muscle damage or cardiac muscle damage during myocardial infarction. Creatine kinase levels have remained a valuable biomarker for muscle damage despite several limitations, including individual variability in CK response to damaging exercise (140), the need for information on CK isoforms to determine whether elevated CK is due to cardiac or skeletal muscle damage (30), and other complicating factors. In addition to CK, myoglobin released is a more of a short-term marker of damage measurable in blood (117). More specific markers including skeletal muscle troponin I, skeletal muscle specific enzymes, and markers indicating an oxidative stress-antioxidant response during muscle damage have also been extensively reviewed and used to track muscle damage during exercise (17). Concurrently measuring muscle damage markers when assessing biomarkers of inflammation in an athlete is critical to contextualizing the potential source of inflammation and define the subsequent action required for athlete health and optimal performance.
As markers of muscle damage are released into circulation, at the tissue level resident or locally surveying naive immune cells migrate to the site of tissue injury and differentiate into mature proinflammatory macrophages that function to phagocytose and clear debris and degenerate damaged tissue. Mature activated macrophages also release a number of growth factors, cytokines, and other signaling molecules to promote the inflammatory process by recruiting other cells required for skeletal muscle regeneration to differentiate and function in repair. As inflammation progresses, macrophages convert to anti-inflammatory profiles and release different growth factors, cytokines, and have distinct effects to encourage the progression of repair stages. Shifts in circulating immune cells as cell populations move in and out of tissue vs. systemic circulation can be measured through a complete blood count with differential (CBC/diff). Although the CBC/diff assay cannot be used alone to assess an athlete's level of inflammation, it is another assay that provides valuable information about shifts in immune cell populations that may occur during muscle damage-induced inflammation. Another benefit of assessing CBC/diff profiles in athletes is that CBC/diff can be used to diagnose potential infection or disease that might also cause inflammation and increases in biomarkers that are common with muscle damage-induced inflammatory biomarkers. Additional recruitment of monocytes or other immune cells during inflammation can be tracked using blood measures of chemicals that attract immune cells to an area (e.g., monocyte chemoattractant protein-1 or soluble intracellular adhesion molecule-1) while activation of other immune cell types can be measured through cellular components like CD40 ligand (CD40L, CD154) that are only expressed or released as soluble factors by activated immune cells.
The blood biomarkers that indicate proinflammatory macrophages activity include growth factors and cytokines released by macrophages. The most standard of these are generalized signaling molecules termed “cytokines.” Cytokines are numerous and diverse in function, making it difficult to use the presence of these alone as a direct measure of inflammation in an athlete. However, we can assess inflammation through increases from an individual's normal baselines in cytokines classically considered proinflammatory such as IL-1β, TNF-α, IL-6, IL-10, IL-8, and IL-12p40. There is no recommendation for a threshold above which increases in inflammatory markers are universally interpretable as “elevated.” The recommendation is to use repeat testing at rested, healthy baseline states to establish individual reference ranges for normal values, and also test at key changing points in training, health status, performance status during competition, and heavy training periods to determine what are normal fluctuations in inflammatory markers, and which are fluctuations and values associated with physical concerns. This dynamic approach to biomarker testing begs coaches and athletes to use biomarker testing to observe normal changes in biomarkers during healthy states and document dramatic changes in biomarkers that are associated with performance effects. This may require a period of adjustment during which biomarker testing is essentially calibrated to each individual, but this approach will provide the greatest accuracy and precision independent of the biological diversity that we know occurs among all individuals.
Hallmarks of prolonged, severe inflammations include markers of tissue damage associated with chronic inflammation. One aspect of prolonged or severe inflammation involves hepatic signaling by circulating cytokines. During inflammation, liver tissue may be stimulated to produce an acute phase response. The acute phase response and acute phase reactant proteins produced by the liver trigger a systemic inflammation response that recruits vascular tissue activation, systemic immune response, endocrine function, and other multiorgan involvement in positive feedback of inflammation. Classic acute phase reactant proteins that are measured include CRP, serum amyloid A, E-selectin, von Willebrand factor (endothelial dysfunction), plasminogen activator inhibitor-1, fibrinogen, P-selectin, and inflammatory cytokines.
Because muscle damage, inflammation, and acute phase response may normally occur during exercise training designed to optimize performance, it is critical to contextualize assessment of inflammation biomarkers with other assays concurrently. For example, the assessment of CBC/diff could indicate the presence of an infection that is temporary and requires no long-term change in an exercise training program. Chronic or prolonged inflammation should be evaluated with such markers that might indicate chronic disease states that will direct long-term and dramatic changes in training. Additional markers that overlap with other aspects of an athlete's health will also provide valuable information about action from insight. An athlete that consistently and chronically exhibits high levels of inflammatory markers should also, for example, be evaluated for chronic stress, which can be tested for by physical assessment of fatigue or performance decrements, subjective perceptual scales, or assays measuring levels of stress hormones such as cortisol (48,72,134). We reiterate the recommendation that repeat testing during rested, healthy states will provide average values for each individual, as markers of inflammation may be highly varied person to person, and establishing per-individual reference ranges will be most practical and useful.
To better understand the dynamic and integrative aspects of how diet, hydration, training, and competition affect athletes, assessment of biomarkers should include select, diverse, and well-validated markers of performance (muscle status and oxygen transport), health (nutritional and hydration status, allergies), and recovery (inflammation, injury risk, muscle damage) (Figure 1). Because many validated biomarker reference ranges are appropriate for generalized populations rather than for athletes, repeat measurements will allow each clinician/coach to establish personalized reference ranges; from these individualized “normal” values that may fluctuate day-to-day or week-to-week, an athlete or sports professional can track chronic changes in directions that are associated with risk of injury, overtraining, or decreased performance. We have provided examples of useful biomarkers. It is important that the coach and athlete determine priorities for tracking training and competition and adapt their biomarker panels accordingly. As new biomarkers are being tested and validated, researchers will identify more universal, consistent biomarkers of multiple aspects of athlete health and performance.
E. C. Lee, S. A. Kavouras, R. M. Queen, J. L. Pryor, and D. J. Casa are members of the Quest Diagnostics' Sports Science and Human Performance Medical and Scientific Advisory Board. M. S. Fragala is an employee of Quest Diagnostics. No grant support contributed to the development of the manuscript. The content of the manuscript does not constitute endorsement of a product by the authors or the NSCA.
1. Adlercreutz H. Western diet and Western diseases: Some hormonal and biochemical mechanisms and associations. Scand J Clin Lab Invest Suppl 201: 3–23, 1990.
2. Aldred S, Love JA, Tonks LA, Stephens E, Jones DS, Blannin AK. The effect of steady state exercise on circulating human IgE and IgG in young healthy volunteers with known allergy. J Sci Med Sport 13: 16–19, 2010.
3. Anderson RA, Bryden NA, Polansky MM, Deuster PA. Exercise effects on chromium excretion of trained and untrained men consuming a constant diet. J Appl Physiol (1985) 64: 249–252, 1988.
4. Apro W, Blomstrand E. Influence of supplementation with branched-chain amino acids in combination with resistance exercise on p70S6 kinase phosphorylation in resting and exercising human skeletal muscle. Acta Physiol (Oxf) 200: 237–248, 2010.
5. Armstrong LE, Maresh CM, Castellani JW, Bergeron MF, Kenefick RW, LaGasse KE, Riebe D. Urinary indices of hydration status. Int J Sport Nutr 4: 265–279, 1994.
6. Armstrong LE, Maughan RJ, Senay LC, Shirreffs SM. Limitations to the use of plasma osmolality as a hydration biomarker. Am J Clin Nutr 98: 503–504, 2013.
7. Bacharach DW, Petit M, Rundell KW. Relationship of blood urea nitrogen to training intensity of elite female biathlon skiers. J Strength Cond Res 10: 105–108, 1996.
8. Banfi G, Marinelli M, Roi GS, Agape V. Usefulness of free testosterone/cortisol ratio during a season of elite speed skating athletes. Int J Sports Med 14: 373–379, 1993.
9. Beard JL, Murray-Kolb LE, Rosales FJ, Solomons NW, Angelilli ML. Interpretation of serum ferritin concentrations as indicators of total-body iron stores in survey populations: The role of biomarkers for the acute phase response. Am J Clin Nutr 84: 1498–1505, 2006.
10. Beck TJ, Ruff CB, Shaffer RA, Betsinger K, Trone DW, Brodine SK. Stress fracture in military recruits: Gender differences in muscle and bone susceptibility factors. Bone 27: 437–444, 2000.
11. Berglund B. High-altitude training. Aspects of haematological adaptation. Sports Med 14: 289–303, 1992.
12. Bermejo F, Garcia-Lopez S. A guide to diagnosis of iron deficiency and iron deficiency anemia in digestive diseases. World J Gastroenterol 15: 4638–4643, 2009.
13. Blomstrand E, Eliasson J, Karlsson HK, Köhnke R. Branched-chain amino acids activate key enzymes in protein synthesis after physical exercise. J Nutr 136(1 Suppl): 269S–73S, 2006.
14. Bloomer RJ, Larson DE, Fisher-Wellman KH, Galpin AJ, Schilling BK. Effect of eicosapentaenoic and docosahexaenoic acid on resting and exercise-induced inflammatory and oxidative stress biomarkers: A randomized, placebo controlled, cross-over study. Lipids Health Dis 8: 36, 2009.
15. Borel MJ, Smith SM, Derr J, Beard JL. Day-to-day variation in iron-status indices in healthy men and women. Am J Clin Nutr 54: 729–735, 1991.
16. Bouget M, Rouveix M, Michaux O, Pequignot JM, Filaire E. Relationships among training stress, mood and dehydroepiandrosterone sulphate/cortisol ratio in female cyclists. J Sports Sci 24: 1297–1302, 2006.
17. Brancaccio P, Lippi G, Maffulli N. Biochemical markers of muscular damage. Clin Chem Lab Med 48: 757–767, 2010.
18. Brownlie T IV, Utermohlen V, Hinton PS, Haas JD. Tissue iron deficiency without anemia impairs adaptation in endurance capacity after aerobic training in previously untrained women. Am J Clin Nutr 79: 437–443, 2004.
19. Brownlie T IV, Utermohlen V, Hinton PS, Giordano C, Haas JD. Marginal iron deficiency without anemia impairs aerobic adaptation among previously untrained women. Am J Clin Nutr 75: 734–742, 2002.
20. Buresh R, Berg K, French J. The effect of resistive exercise rest interval on hormonal response, strength, and hypertrophy with training. J Strength Cond Res 23: 62–71, 2009.
21. Calvisi V, Lupparelli S. C-reactive protein changes in the uncomplicated course of arthroscopic anterior cruciate ligament reconstruction. Int J Immunopathol Pharmacol 21: 603–607, 2008.
22. Casa DJ, Armstrong LE, Hillman SK, Montain SJ, Reiff RV, Rich BS, Roberts WO, Stone JA. National athletic trainers' association position statement: Fluid replacement for athletes. J Athl Train 35: 212–224, 2000.
23. Castell LM, Yamamoto T, Phoenix J, Newsholme EA. The role of tryptophan in fatigue in different conditions of stress. Adv Exp Med Biol 467: 697–704, 1999.
24. Cevada T, Vasques PE, Moraes H, Deslandes A. Salivary cortisol levels in athletes and nonathletes: A systematic review. Horm Metab Res 46: 905–910, 2014.
25. Cheuvront SN, Kenefick RW, Charkoudian N, Sawka MN. Physiologic basis for understanding quantitative dehydration assessment. Am J Clin Nutr 97: 455–462, 2013.
26. Chicharro JL, López-Mojares LM, Lucía A, Pérez M, Alvarez J, Labanda P, Calvo F, Vaquero AF. Overtraining parameters in special military units. Aviat Space Environ Med 69: 562–568, 1998.
27. Constantini NW, Arieli R, Chodick G, Dubnov-Raz G. High prevalence of vitamin D insufficiency in athletes and dancers. Clin J Sport Med 20: 368–371, 2010.
28. Corsetti R, Barassi A, Perego S, Sansoni V, Rossi A, Damele CA, Melzi D'Eril G, Banfi G, Lombardi G. Changes in urinary amino acids excretion in relationship with muscle activity markers over a professional cycling stage race: In search of fatigue markers. Amino Acids 48: 183–192, 2016.
29. Cuisinier C, Ward RJ, Francaux M, Sturbois X, de Witte P. Changes in plasma and urinary taurine and amino acids in runners immediately and 24h after a marathon. Amino Acids 20: 13–23, 2001.
30. Cummins P, Young A, Auckland ML, Michie CA, Stone PC, Shepstone BJ. Comparison of serum cardiac specific troponin-I with creatine kinase, creatine kinase-MB isoenzyme, tropomyosin, myoglobin and C-reactive protein release in marathon runners: Cardiac or skeletal muscle trauma? Eur J Clin Invest 17: 317–324, 1987.
31. Dahlquist DT, Dieter BP, Koehle MS. Plausible ergogenic effects of vitamin D on athletic performance and recovery. J Int Soc Sports Nutr 12: 33, 2015.
32. DellaValle DM. Iron supplementation for female athletes: Effects on iron status and performance outcomes. Curr Sports Med Rep 12: 234–239, 2013.
33. DellaValle DM, Haas JD. Impact of iron depletion without anemia on performance in trained endurance athletes at the beginning of a training season: A study of female collegiate rowers. Int J Sport Nutr Exerc Metab 21: 501–506, 2011.
34. DellaValle DM, Haas JD. Iron supplementation improves energetic efficiency in iron-depleted female rowers. Med Sci Sports Exerc 46: 1204–1215, 2014.
35. Deuster PA, Day BA, Singh A, Douglass L, Moser-Veillon PB. Zinc status of highly trained women runners and untrained women. Am J Clin Nutr 49: 1295–1301, 1989.
36. Dietrich MO, Tort AB, Schaf DV, Farina M, Gonçalves CA, Souza DO, Portela LV. Increase in serum S100B protein level after a swimming race. Can J Appl Physiol 28: 710–716, 2003.
37. Dubnov G, Constantini NW. Prevalence of iron depletion and anemia in top-level basketball players. Int J Sport Nutr Exerc Metab 14: 30–37, 2004.
38. Ebo DG, Stevens WJ. IgE-mediated food allergy–extensive review of the literature. Acta Clin Belg 56: 234–247, 2001.
39. Einspahr KJ, Tharp G. Influence of endurance training on plasma amino acid concentrations in humans at rest and after intense exercise. Int J Sports Med 10: 233–236, 1989.
40. Fahrner CL, Hackney AC. Effects of endurance exercise on free testosterone concentration and the binding affinity of sex hormone binding globulin (SHBG). Int J Sports Med 19: 12–15, 1998.
41. Feingold D, Hame SL. Female athlete triad and stress fractures. Orthop Clin North Am 37: 575–583, 2006.
42. Filaire E, Duche P, Lac G. Effects of amount of training on the saliva concentrations of cortisol, dehydroepiandrosterone and on the dehydroepiandrosterone: Cortisol concentration ratio in women over 16 weeks of training. Eur J Appl Physiol Occup Physiol 78: 466–471, 1998.
43. Flink EB. Magnesium deficiency. Etiology and clinical spectrum. Acta Med Scand Suppl 647: 125–137, 1981.
44. Fortes MB, Diment BC, Di Felice U, Gunn AE, Kendall JL, Esmaeelpour M, Walsh NP. Tear fluid osmolarity as a potential marker of hydration status. Med Sci Sports Exerc 43: 1590–1597, 2011.
45. Fortes MB, Owen JA, Raymond-Barker P, Bishop C, Elghenzai S, Oliver SJ, Walsh NP. Is this elderly patient dehydrated? Diagnostic accuracy of hydration assessment using physical signs, urine, and saliva markers. J Am Med Dir Assoc 16: 221–228, 2015.
46. Friedmann B, Weller E, Mairbaurl H, Bärtsch P. Effects of iron repletion on blood volume and performance capacity in young athletes. Med Sci Sports Exerc 33: 741–746, 2001.
47. Gillespie CA, Edgerton VR. The role of testosterone in exercise-induced glycogen supercompensation. Horm Metab Res 2: 364–366, 1970.
48. Gleeson M. Biochemical and immunological markers of over-training. J Sports Sci Med 1: 31–41, 2002.
49. Golf SW, Bender S, Gruttner J. On the significance of magnesium in extreme physical stress. Cardiovasc Drugs Ther 12(Suppl 2): 197–202, 1998.
50. Graham MR, et al. Direct hits to the head during amateur boxing is associated with a rise in serum biomarkers for brain injury. Int J Immunopathol Pharmacol 24: 119–125, 2011.
51. Haas JD, Brownlie T. Iron deficiency and reduced work capacity: A critical review of the research to determine a causal relationship. J Nutr 131: 676S–688S, 2001; discussion 688S–690S.
52. Häkkinen K, Pakarinen A. Acute hormonal responses to two different fatiguing heavy-resistance protocols in male athletes. J Appl Physiol (1985) 74: 882–887, 1993.
53. Häkkinen K, Pakarinen A, Alén M, Kauhanen H, Komi PV. Neuromuscular and hormonal adaptations in athletes to strength training in two years. J Appl Physiol (1985) 65: 2406–2412, 1988.
54. Häkkinen K, Pakarinen A, Alén M, Kauhanen H, Komi PV. Relationships between training volume, physical performance capacity, and serum hormone concentrations during prolonged training in elite weight lifters. Int J Sports Med 8(Suppl 1): 61–65, 1987.
55. Haslauer CM, Proffen BL, Johnson VM, Hill A, Murray MM. Gene expression of catabolic inflammatory cytokines peak before anabolic inflammatory cytokines after ACL injury in a preclinical model. J Inflamm (Lond) 11: 34, 2014.
56. Hernandez-Morante JJ, Pérez-de-Heredia F, Luján JA, Zamora S, Garaulet M. Role of DHEA-S on body fat distribution: Gender- and depot-specific stimulation of adipose tissue lipolysis. Steroids 73: 209–215, 2008.
57. Hinton PS, Giordano C, Brownlie T, Haas JD. Iron supplementation improves endurance after training in iron-depleted, nonanemic women. J Appl Physiol (1985) 88: 1103–1111, 2000.
58. Hinton PS, Sinclair LM. Iron supplementation maintains ventilatory threshold and improves energetic efficiency in iron-deficient nonanemic athletes. Eur J Clin Nutr 61: 30–39, 2007.
59. Hong CZ, Lien IN. Metabolic effects of exhaustive training of athletes. Arch Phys Med Rehabil 65: 362–365, 1984.
60. Hood DA, Kelton R, Nishio ML. Mitochondrial adaptations to chronic muscle use: Effect of iron deficiency. Comp Biochem Physiol Comp Physiol 101: 597–605, 1992.
61. James WP, Hay AM. Albumin metabolism: Effect of the nutritional state and the dietary protein intake. J Clin Invest 47: 1958–1972, 1968.
62. Jastrezebski Z. Effect of vitamin D supplementation on the level of physical fitness and blood parameters of rowers during the 8-week high intensity training. Facicula Educ Fiz Si Sport 2: 57–67, 2014.
63. Jouris KB, McDaniel JL, Weiss EP. The effect of Omega-3 fatty acid supplementation on the inflammatory response to eccentric strength exercise. J Sports Sci Med 10: 432–438, 2011.
64. Kanazawa I, Yamaguchi T, Sugimoto T. Serum insulin-like growth factor-I is a marker for assessing the severity of vertebral fractures in postmenopausal women with type 2 diabetes mellitus. Osteoporos Int 22: 1191–1198, 2011.
65. Kanter MM. Free radicals, exercise, and antioxidant supplementation. Int J Sport Nutr 4: 205–220, 1994.
66. Kavouras SA, Johnson EC, Bougatsas D, Arnaoutis G, Panagiotakos DB, Perrier E, Klein A. Validation of a urine color scale for assessment of urine osmolality in healthy children. Eur J Nutr 55: 907–915, 2016.
67. Kingsbury KJ, Kay L, Hjelm M. Contrasting plasma free amino acid patterns in elite athletes: Association with fatigue and infection. Br J Sports Med 32: 25–32, 1998; discussion 32–3.
68. Kleiner G, Marcuzzi A, Zanin V, Monasta L, Zauli G. Cytokine levels in the serum of healthy subjects. Mediators Inflamm 2013: 434010, 2013.
69. Koch AJ, Pereira R, Machado M. The creatine kinase response to resistance exercise. J Musculoskelet Neuronal Interact 14: 68–77, 2014.
70. Kraemer WJ, French DN, Paxton NJ, Häkkinen K, Volek JS, Sebastianelli WJ, Putukian M, Newton RU, Rubin MR, Gómez AL, Vescovi JD, Ratamess NA, Fleck SJ, Lynch JM, Knuttgen HG. Changes in exercise performance and hormonal concentrations over a big ten soccer season in starters and nonstarters. J Strength Cond Res 18: 121–128, 2004.
71. Kraemer WJ, Häkkinen K, Newton RU, Nindl BC, Volek JS, McCormick M, Gotshalk LA, Gordon SE, Fleck SJ, Campbell WW, Putukian M, Evans WJ. Effects of heavy-resistance training on hormonal response patterns in younger vs. older men. J Appl Physiol (1985) 87: 982–992, 1999.
72. Kraemer WJ, Ratamess NA. Hormonal responses and adaptations to resistance exercise and training. Sports Med 35: 339–361, 2005.
73. LaManca JJ, Haymes EM. Effects of iron repletion on VO2max, endurance, and blood lactate in women. Med Sci Sports Exerc 25: 1386–1392, 1993.
74. Li Y, Oosting M, Deelen P, Ricaño-Ponce I, Smeekens S, Jaeger M, Matzaraki V, Swertz MA, Xavier RJ, Franke L, Wijmenga C, Joosten LA, Kumar V, Netea MG. Inter-individual variability and genetic influences on cytokine responses to bacteria and fungi. Nat Med 22: 952–960, 2016.
75. Lindholm C, Hirschberg AL, Carlström K, von Schoultz B. Hormone anabolic/catabolic balance in female endurance athletes. Gynecol Obstet Invest 36: 176–180, 1993.
76. Lippi G, Montagnana M, Salvagno GL, Franchini M, Guidi GC. Glycaemic control in athletes. Int J Sports Med 29: 7–10, 2008.
77. Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: Osteoarthritis. Am J Sports Med 35: 1756–1769, 2007.
78. Lohmander LS, Ostenberg A, Englund M, Roos H. High prevalence of knee osteoarthritis, pain, and functional limitations in female soccer players twelve years after anterior cruciate ligament injury. Arthritis Rheum 50: 3145–3152, 2004.
79. Lukaski HC. Vitamin and mineral status: Effects on physical performance. Nutrition 20: 632–644, 2004.
80. MacKinnon LT. Special feature for the Olympics: Effects of exercise on the immune system: Overtraining effects on immunity and performance in athletes. Immunol Cell Biol 78: 502–509, 2000.
81. McClung JP, Gaffney-Stomberg E, Lee JJ. Female athletes: A population at risk of vitamin and mineral deficiencies affecting health and performance. J Trace Elem Med Biol 28: 388–392, 2014.
82. McClung JP, Karl JP, Cable SJ, Williams KW, Nindl BC, Young AJ, Lieberman HR. Randomized, double-blind, placebo-controlled trial of iron supplementation in female soldiers during military training: Effects on iron status, physical performance, and mood. Am J Clin Nutr 90: 124–131, 2009.
83. McKenzie AL, Munoz CX, Armstrong LE. Accuracy of urine color to detect equal to or greater than 2% body mass loss in men. J Athl Train 50: 1306–1309, 2015.
84. Mendias CL, Lynch EB, Davis ME, Sibilsky Enselman ER, Harning JA, Dewolf PD, Makki TA, Bedi A. Changes in circulating biomarkers of muscle atrophy, inflammation, and cartilage turnover in patients undergoing anterior cruciate ligament reconstruction and rehabilitation. Am J Sports Med 41: 1819–1826, 2013.
85. Morita E, Kunie K, Matsuo H. Food-dependent exercise-induced anaphylaxis. J Dermatol Sci 47: 109–117, 2007.
86. Mougios V. Reference intervals for serum creatine kinase in athletes. Br J Sports Med 41: 674–678, 2007.
87. Munoz-Torres M, Mezquita-Raya P, Lopez-Rodriguez F, Torres-Vela E, de Dios Luna J, Escobar-Jimenez F. The contribution of IGF-I to skeletal integrity in postmenopausal women. Clin Endocrinol (Oxf) 55: 759–766, 2001.
88. Muñoz C, Rios E, Olivos J, Brunser O, Olivares M. Iron, copper and immunocompetence. Br J Nutr 98(Suppl 1): S24–S28, 2007.
89. Muñoz CX, Johnson EC, Demartini JK, Huggins RA, McKenzie AL, Casa DJ, Maresh CM, Armstrong LE. Assessment of hydration biomarkers including salivary osmolality during passive and active dehydration. Eur J Clin Nutr 67: 1257–1263, 2013.
90. Mussack T, Dvorak J, Graf-Baumann T, Jochum M. Serum S-100B protein levels in young amateur soccer players after controlled heading and normal exercise. Eur J Med Res 8: 457–464, 2003.
91. Nattiv A, Loucks AB, Manore MM, Sanborn CF, Sundgot-Borgen J, Warren MP; American College of Sports Medicine. American College of Sports Medicine position stand. The female athlete triad. Med Sci Sports Exerc 39: 1867–1882, 2007.
92. Neselius S, Brisby H, Theodorsson A, Blennow K, Zetterberg H, Marcusson J. CSF-biomarkers in Olympic boxing: Diagnosis and effects of repetitive head trauma. PLoS One 7: e33606, 2012.
93. Nielsen BW, Bjerke T, Damsgaard TM, Herlin T, Thestrup-Pedersen K, Schiøtz PO. Hyperosmolarity selectively enhances IgE-receptor-mediated histamine release from human basophils. Agents Actions 35: 170–178, 1992.
94. Nindl BC, Alemany JA, Tuckow AP, Kellogg MD, Sharp MA, Patton JF. Effects of exercise mode and duration on 24-h IGF-I system recovery responses. Med Sci Sports Exerc 41: 1261–1270, 2009.
95. Otto M, Holthusen S, Bahn E, Söhnchen N, Wiltfang J, Geese R, Fischer A, Reimers CD. Boxing and running lead to a rise in serum levels of S-100B protein. Int J Sports Med 21: 551–555, 2000.
96. Peeling P, Blee T, Goodman C, Dawson B, Claydon G, Beilby J, Prins A. Effect of iron injections on aerobic-exercise performance of iron-depleted female athletes. Int J Sport Nutr Exerc Metab 17: 221–231, 2007.
97. Peeling P, Dawson B, Goodman C, Landers G, Trinder D. Athletic induced iron deficiency: New insights into the role of inflammation, cytokines and hormones. Eur J Appl Physiol 103: 381–391, 2008.
98. Peeling P, Dawson B, Goodman C, Landers G, Wiegerinck ET, Swinkels DW, Trinder D. Training surface and intensity: Inflammation, hemolysis, and hepcidin expression. Med Sci Sports Exerc 41: 1138–1145, 2009.
99. Papa L, Ramia MM, Edwards D, Johnson BD, Slobounov SM. Systematic review of clinical studies examining biomarkers of brain injury in athletes after sports-related concussion. J Neurotrauma 32: 661–673, 2015.
100. Phillips PA, Rolls BJ, Ledingham JG, Forsling ML, Morton JJ. Osmotic thirst and vasopressin release in humans: A double-blind crossover study. Am J Physiol 248: R645–R650, 1985.
101. Pimstone BL, Barbezat G, Hansen JD, Murray P. Studies on growth hormone secretion in protein-calorie malnutrition. Am J Clin Nutr 21: 482–487, 1968.
102. Pollock N, Grogan C, Perry M, Pedlar C, Cooke K, Morrissey D, Dimitriou L. Bone-mineral density and other features of the female athlete triad in elite endurance runners: A longitudinal and cross-sectional observational study. Int J Sport Nutr Exerc Metab 20: 418–426, 2010.
103. Refsum HE, Gjessing LR, Stromme SB. Changes in plasma amino acid distribution and urine amino acids excretion during prolonged heavy exercise. Scand J Clin Lab Invest 39: 407–413, 1979.
104. Risser WL, Lee EJ, Poindexter HB, West MS, Pivarnik JM, Risser JM, Hickson JF. Iron deficiency in female athletes: Its prevalence and impact on performance. Med Sci Sports Exerc 20: 116–121, 1988.
105. Roecker L, Meier-Buttermilch R, Brechtel L, Nemeth E, Ganz T. Iron-regulatory protein hepcidin is increased in female athletes after a marathon. Eur J Appl Physiol 95: 569–571, 2005.
106. Rowland TW, Deisroth MB, Green GM, Kelleher JF. The effect of iron therapy on the exercise capacity of nonanemic iron-deficient adolescent runners. Am J Dis Child 142: 165–169, 1988.
107. Sawka MN, Cheuvront SN, Kenefick RW. Hypohydration and human performance: Impact of environment and physiological mechanisms. Sports Med 45(Suppl 1): S51–S60, 2015.
108. Sawka MN, Burke LM, Eichner ER, Maughan RJ, Montain SJ, Stachenfeld NS; American College of Sports Medicine. American College of Sports Medicine position stand. Exercise and fluid replacement. Med Sci Sports Exerc 39: 377–390, 2007.
109. Schoene RB, Escourrou P, Robertson HT, Nilson KL, Parsons JR, Smith NJ. Iron repletion decreases maximal exercise lactate concentrations in female athletes with minimal iron-deficiency anemia. J Lab Clin Med 102: 306–312, 1983.
110. Selby GB, Eichner ER. Endurance swimming, intravascular hemolysis, anemia, and iron depletion. New perspective on athlete's anemia. Am J Med 81: 791–794, 1986.
111. Shahim P, Tegner Y, Wilson DH, Randall J, Skillbäck T, Pazooki D, Kallberg B, Blennow K, Zetterberg H. Blood biomarkers for brain injury in concussed professional ice hockey players. JAMA Neurol 71: 684–692, 2014.
112. Siles RI, Hsieh FH. Allergy blood testing: A practical guide for clinicians. Cleve Clin J Med 78: 585–592, 2011.
113. Sinclair LM, Hinton PS. Prevalence of iron deficiency with and without anemia in recreationally active men and women. J Am Diet Assoc 105: 975–978, 2005.
114. Smith LL. Cytokine hypothesis of overtraining: A physiological adaptation to excessive stress? Med Sci Sports Exerc 32: 317–331, 2000.
115. Smith DJ, Norris SR. Changes in glutamine and glutamate concentrations for tracking training tolerance. Med Sci Sports Exerc 32: 684–689, 2000.
116. Snow CM, Rosen CJ, Robinson TL. Serum IGF-I is higher in gymnasts than runners and predicts bone and lean mass. Med Sci Sports Exerc 32: 1902–1907, 2000.
117. Sorichter S, Puschendorf B, Mair J. Skeletal muscle injury induced by eccentric muscle action: Muscle proteins as markers of muscle fiber injury. Exerc Immunol Rev 5: 5–21, 1999.
118. Spriet LL, Whitfield J. Taurine and skeletal muscle function. Curr Opin Clin Nutr Metab Care 18: 96–101, 2015.
119. Stalnacke BM, Ohlsson A, Tegner Y, Sojka P. Serum concentrations of two biochemical markers of brain tissue damage S-100B and neurone specific enolase are increased in elite female soccer players after a competitive game. Br J Sports Med 40: 313–316, 2006.
120. Stalnacke BM, Sojka P. Repeatedly heading a soccer ball does not increase serum levels of S-100B, a biochemical marker of brain tissue damage: An experimental study. Biomark Insights 3: 87–91, 2008.
121. Stalnacke BM, Tegner Y, Sojka P. Playing ice hockey and basketball increases serum levels of S-100B in elite players: A pilot study. Clin J Sport Med 13: 292–302, 2003.
122. Stalnacke BM, Tegner Y, Sojka P. Playing soccer increases serum concentrations of the biochemical markers of brain damage S-100B and neuron-specific enolase in elite players: A pilot study. Brain Inj 18: 899–909, 2004.
123. Stiefel P, Ruiz-Gutierrez V, Gajón E, Acosta D, García-Donas MA, Madrazo J, Villar J, Carneado J. Sodium transport kinetics, cell membrane lipid composition, neural conduction and metabolic control in type 1 diabetic patients. Changes after a low-dose n-3 fatty acid dietary intervention. Ann Nutr Metab 43: 113–120, 1999.
124. Strohbach CA, Scofield DE, Nindl BC, Centi AJ, Yanovich R, Evans RK, Moran DS. Female recruits sustaining stress fractures during military basic training demonstrate differential concentrations of circulating IGF-I system components: A preliminary study. Growth Horm IGF Res 22: 151–157, 2012.
125. Sugimoto T, Nishiyama K, Kuribayashi F, Chihara K. Serum levels of insulin-like growth factor (IGF) I, IGF-binding protein (IGFBP)-2, and IGFBP-3 in osteoporotic patients with and without spinal fractures. J Bone Miner Res 12: 1272–1279, 1997.
126. Suzuki K, Nakaji S, Yamada M, Totsuka M, Sato K, Sugawara K. Systemic inflammatory response to exhaustive exercise. Cytokine kinetics. Exerc Immunol Rev 8: 6–48, 2002.
127. Svoboda SJ, Harvey TM, Owens BD, Brechue WF, Tarwater PM, Cameron KL. Changes in serum biomarkers of cartilage turnover after anterior cruciate ligament injury. Am J Sports Med 41: 2108–2116, 2013.
128. Tanskanen MM, Kyröläinen H, Uusitalo AL, Huovinen J, Nissilä J, Kinnunen H, Atalay M, Häkkinen K. Serum sex hormone-binding globulin and cortisol concentrations are associated with overreaching during strenuous military training. J Strength Cond Res 25: 787–797, 2011.
129. Tartibian B, Maleki BH, Abbasi A. The effects of ingestion of omega-3 fatty acids on perceived pain and external symptoms of delayed onset muscle soreness in untrained men. Clin J Sport Med 19: 115–119, 2009.
130. Todd J, Simpson P, Estis J, Torres V, Wub AH. Reference range and short- and long-term biological variation of interleukin (IL)-6, IL-17A and tissue necrosis factor-alpha using high sensitivity assays. Cytokine 64: 660–665, 2013.
131. Tom A, Nair KS. Assessment of branched-chain amino Acid status and potential for biomarkers. J Nutr 136(1 Suppl): 324S–330S, 2006.
132. Tourville TW, Johnson RJ, Slauterbeck JR, Naud S, Beynnon BD. Relationship between markers of type II collagen metabolism and tibiofemoral joint space width changes after ACL injury and reconstruction. Am J Sports Med 41: 779–787, 2013.
133. Urhausen A, Gabriel H, Kindermann W. Blood hormones as markers of training stress and overtraining. Sports Med 20: 251–276, 1995.
134. Urhausen A, Kindermann W. Diagnosis of overtraining: What tools do we have? Sports Med 32: 95–102, 2002.
135. Vallee BL, Falchuk KH. The biochemical basis of zinc physiology. Physiol Rev 73: 79–118, 1993.
136. Vandenbogaerde TJ, Hopkins WG. Effects of acute carbohydrate supplementation on endurance performance: A meta-analysis. Sports Med 41: 773–792, 2011.
137. Velloso CP. Regulation of muscle mass by growth hormone and IGF-I. Br J Pharmacol 154: 557–568, 2008.
138. Vervoorn C, Quist AM, Vermulst LJ, Erich WB, de Vries WR, Thijssen JH. The behaviour of the plasma free testosterone/cortisol ratio during a season of elite rowing training. Int J Sports Med 12: 257–263, 1991.
139. Volek JS, Freidenreich DJ, Saenz C, Kunces LJ, Creighton BC, Bartley JM, Davitt PM, Munoz CX, Anderson JM, Maresh CM, Lee EC, Schuenke MD, Aerni G, Kraemer WJ, Phinney SD. Metabolic characteristics of keto-adapted ultra-endurance runners. Metabolism 65: 100–110, 2016.
140. Volfinger L, Lassourd V, Michaux JM, Braun JP, Toutain PL. Kinetic evaluation of muscle damage during exercise by calculation of amount of creatine kinase released. Am J Physiol 266: R434–R441, 1994.
141. von Porat A, Roos EM, Roos H. High prevalence of osteoarthritis 14 years after an anterior cruciate ligament tear in male soccer players: A study of radiographic and patient relevant outcomes. Ann Rheum Dis 63: 269–273, 2004.
142. Walsh NP, Laing SJ, Oliver SJ, Montague JC, Walters R, Bilzon JL. Saliva parameters as potential indices of hydration status during acute dehydration. Med Sci Sports Exerc 36: 1535–1542, 2004.
143. Wang W, Wu Z, Dai Z, Yang Y, Wang J, Wu G. Glycine metabolism in animals and humans: Implications for nutrition and health. Amino Acids 45: 463–477, 2013.
144. Ward RJ, Francaux M, Cuisinier C, Sturbois X, Witte P. Changes in plasma taurine levels after different endurance events. Amino Acids 16: 71–77, 1999.
145. Warren MP, Perlroth NE. The effects of intense exercise on the female reproductive system. J Endocrinol 170: 3–11, 2001.
146. Webster CC, Noakes TD, Chacko SK, Swart J, Kohn TA, Smith JA. Gluconeogenesis during endurance exercise in cyclists habituated to a long-term low carbohydrate high fat diet. J Physiol 594: 4389–4405, 2016.
147. Yanovich R, Evans RK, Friedman E, Moran DS. Bone turnover markers do not predict stress fracture in elite combat recruits. Clin Orthop Relat Res 471: 1365–1372, 2013.
148. Young VR, El-Khoury AE, Raguso CA, Forslund AH, Hambraeus L. Rates of urea production and hydrolysis and leucine oxidation change linearly over widely varying protein intakes in healthy adults. J Nutr 130: 761–766, 2000.
149. Zetterberg H, Hietala MA, Jonsson M, Andreasen N, Styrud E, Karlsson I, Edman A, Popa C, Rasulzada A, Wahlund LO, Mehta PD, Rosengren L, Blennow K, Wallin A. Neurochemical aftermath of amateur boxing. Arch Neurol 63: 1277–1280, 2006.
150. Zetterberg H, Tanriverdi F, Unluhizarci K, Selcuklu A, Kelestimur F, Blennow K. Sustained release of neuron-specific enolase to serum in amateur boxers. Brain Inj 23: 723–726, 2009.
151. Zhu YI, Haas JD. Altered metabolic response of iron-depleted nonanemic women during a 15-km time trial. J Appl Physiol (1985) 84: 1768–1775, 1998.
152. Zoller H, Vogel W. Iron supplementation in athletes–first do no harm. Nutrition 20: 615–619, 2004.