Introduction

In elite sport, physiotherapists play a pivotal role in bridging the gap between clinical expertise, performance optimization, and injury prevention. A key element of this role involves developing a deeper understanding of athletes’ physiological responses to training loads. Traditional monitoring tools—such as heart rate variability, rate of perceived exertion, or external load tracking systems—provide valuable insights into training stress, yet they often fail to capture the full complexity of the athlete’s internal load. As highlighted in the reviewed article, achieving optimal performance while minimizing injury risk requires balancing training load (TL) and recovery through accurate and individualized monitoring.

Biochemical and hormonal markers, including creatine kinase, cortisol, and salivary immunoglobulin-A, have emerged as promising factors to evaluate internal load and identify early signs of maladaptation, fatigue, or increased susceptibility to illness. For physiotherapists, integrating biomarkers for monitoring athletes’ fatigue into practice—often in collaboration with sports physicians, strength and conditioning coaches, and head coaches—can enhance the detection of overtraining risks and guide interventions. This is particularly relevant when interpreting preseason blood tests, where subtle deviations may reflect the cumulative stress of training and competition.

This systematic review contributes to the evolving field of sports science by synthesizing the current evidence on the most effective biomarkers for monitoring athletes’ fatigue in professional team sports. By contextualizing these findings within physiotherapy practice, the article underscores the importance of interdisciplinary collaboration and objective monitoring tools in tailoring training loads to athletes’ physiological profiles. For physiotherapists, these insights represent an opportunity not only to refine injury prevention strategies but also to actively support performance optimization throughout the season.

Methods

This systematic review followed the PRISMA protocol. Four electronic databases were searched: PubMed, Scopus, SportDiscus, and Web of Science. The search combined terms related to elite/professional team sports, physiological, immunological, biochemical, or hormonal markers, and fatigue, performance, recovery, stress, or wellness. Reference lists of included studies were also screened. Study selection was performed independently by two investigators, with disagreements resolved by consensus or a third reviewer.

Inclusion criteria

required studies to focus on elite or professional male team sport athletes, reporting at least one biomarker related to hormones, muscle damage, immunity, oxidative stress, or inflammation. Eligible studies also needed to provide a clear description of biomarker acquisition methods (sample type, timing, and analytical technique), collect data during official matches or training sessions, and adopt a longitudinal design or include more than one competitive or training exposure.

Exclusion criteria

included studies on amateur or youth athletes, laboratory-based or simulated exercise protocols, or studies lacking adequate details on biomarker measurement. Single time-point measurements, studies focusing solely on biomarkers unrelated to fatigue or recovery (e.g., nutritional markers), and non-primary sources such as books, or other reviews were also excluded. Only studies published from 2000 onwards were considered.

Screening and study selection 

The review followed PRISMA guidelines, with one investigator conducting the database searches, identifying relevant studies, and extracting data in a standardized manner. Articles were organized in Microsoft Excel, duplicates removed, and titles and abstracts screened for relevance. Full texts were examined when necessary to ensure compliance with eligibility criteria, resulting in 28 selected articles. Extracted data were tabulated by sport type (soccer, basketball, volleyball, handball), event type (matches or training), and biomarker category (physiological, immunological, biochemical, or hormonal).

Studies quality 

Study quality and risk of reporting bias were assessed independently by two authors using the MINORS checklist, which scores methodological quality from 0–16 for non-comparative studies and 0–24 for comparative studies. Higher scores indicate better methodological quality and lower risk of bias.

Results

The initial search identified 504 studies (496 from databases, 8 from other sources). After removing duplicates, 385 unique studies were screened by title and abstract, yielding 53 potentially eligible studies. Full-text assessment excluded 25 studies that did not meet the criteria, resulting in 28 studies included in the review.

Regarding methodological quality, of the 28 studies, 13 were comparative (maximum 24 points) and 15 non-comparative (maximum 16 points). Nineteen studies were rated as low risk of bias, while four comparative studies had a high risk of bias. The most common methodological weaknesses were lack of neutral evaluations (item 5) and, in comparative studies, absence of a control group with a gold standard intervention (item 8).

The 28 included studies were published between 2008 and 2023, with over 70% appearing after 2015, this trend reflects the growing research interest in identifying and validating reliable biomarkers for monitoring athletes’ fatigue. The studies involved elite athletes from a variety of team sports, most frequently basketball (n=7) and soccer (n=6), followed by handball, futsal, rugby, Australian football, volleyball, rugby union, netball, and water polo.

Regarding study context, 8 studies analyzed responses to official matches, 8 focused on training sessions, and 12 examined both. Matches were generally shown to impose greater physiological stress than training.

The biomarkers most commonly investigated were hormonal indicators such as testosterone and cortisol (n = 15). These were followed by muscle damage markers including creatine kinase and lactate dehydrogenase (n = 9), immunological measures such as immunoglobulin A and immune cell function (n = 8), oxidative stress markers like reactive oxygen species and antioxidant capacity (n = 6), and finally, inflammatory markers such as C-reactive protein and cytokines (n = 4).

Hormonal Markers

Fifteen studies examined the relationship between training and competition loads and hormonal responses, consistently reporting alterations in testosterone, cortisol, and the testosterone/cortisol (T/C) ratio across the season. These changes provide valuable insight for monitoring athletes, particularly since the T/C ratio has emerged as a sensitive indicator of training stress and fatigue. While cortisol alone shows limitations due to its variability, combining it with testosterone values yields a more reliable index of physiological stress. Evidence also suggests that hormonal responses vary by playing position, game time, and sport discipline, reinforcing the complexity of their interpretation. Overall, using T, C, and especially the T/C ratio helps capture the balance between anabolic and catabolic processes. However, these markers should not be considered in isolation; integrating them with other physiological measures enables more precise adjustments in training and recovery, ultimately supporting performance optimization and fatigue management.

Muscle Damage Markers

Creatine kinase (CK) is the most widely studied muscle damage marker, with consistent evidence showing post-exercise elevations linked to fatigue and muscle damage. The review confirmed this pattern, with significant increases observed up to 24–72 hours after training or competition. These elevations were greater than the athletes’ coefficients of variation, supporting CK’s sensitivity as a marker of acute load. However, CK values demonstrate substantial day-to-day fluctuations and circadian variation (peaking in the morning), which complicates their interpretation, particularly for monitoring chronic load.

Despite these limitations, studies show that CK, along with lactate dehydrogenase (LDH), can track muscle damage over the course of a season. Higher values are typically observed during preseason (when training loads are elevated) and during congested match periods or playoffs, while reductions in CK and LDH accompany deliberate decreases in training load to enhance performance. Thus, CK—particularly when measured 24–48 h post-match or training—remains a valuable tool to detect muscle stress and guide load management and recovery strategies.

Immunological Markers

s-IgA (salivary immunoglobulin A) is one of the most important immune markers for athletes. It acts like a first line of defense in the respiratory tract, preventing viruses and bacteria from sticking to the mucosa.

Research shows that when training intensity increases, s-IgA levels often drop, which increases the risk of upper respiratory tract infections (URTI). Several studies reviewed here tested how s-IgA changes during training cycles (pre-season, overload, tapering, etc.) and whether these changes predict illness.

• Link with illness: Some studies found that lower s-IgA was correlated with more frequent URTI symptoms. For example, in one study, during a 4-week block of intense training, players had falling s-IgA levels and more colds and sore throats, especially in the last week. Another study showed that if s-IgA dropped by more than 65%, the risk of getting sick within 2 weeks was much higher.
• Mixed results: Not all studies found a strong statistical link, but athletes with more illnesses generally had lower s-IgA than healthier teammates. Some differences also depended on the player’s role/position, suggesting individual variability.
• Training load effect: Across studies, a common pattern emerged: heavier training loads led to lower s-IgA. For example, one study reported that salivary IgA (s-IgA) measurement may represent a useful tool to monitor excessive training load in athletes. Conversely, another research group did not observe a statistically significant correlation; however, they noted that increases in workload often preceded decreases in s-IgA. Taken together, these findings suggest that appropriate recovery strategies and careful load management may help mitigate immune suppression.

Inflammatory and Oxidative Stress Markers

Periods of fixture congestion with insufficient recovery often led to cumulative fatigue and greater physiological strain. This is reflected in persistent changes in both inflammatory and oxidative stress biomarkers across consecutive competitions. 

For example, in professional soccer players, large increases were reported in inflammatory cytokines (TNF-α, IL-6) and muscle damage markers (CK, LDH) across the season. Similarly, when players competed in two matches within a week, biomarkers such as CRP, CK, cortisol, and oxidative stress markers showed progressively higher values after the second game, demonstrating the strain caused by limited recovery.

This pattern has been confirmed by other soccer studies. Comparable results were also observed in elite basketball (6-month season) and professional handball (12 weeks), with increases in oxidative stress during intensive phases. These sports showed stronger biochemical disruptions than volleyball, likely because handball and basketball involve greater eccentric loading. Such differences illustrate that the biochemical stress profile varies depending on the sport. However, across all cases, repeated competition and travel without adequate recovery led to unresolved inflammation and redox imbalance, increasing the risk of fatigue and injury.

Mechanistically, sustained oxidative stress can impair muscle contractility and damage cell membranes, while lingering inflammation slows down muscle regeneration and worsens tissue damage. In fact, in elite soccer players, elevated CRP levels after a match were strongly correlated with higher CK levels 24 hours later, highlighting the link between inflammation and secondary muscle damage.

Sex Differences in Chronic Fatigue Monitoring

Most studies focus on male athletes, yet sex differences significantly affect chronic fatigue—from its development to how biomarkers should be interpreted.

In females, the menstrual cycle strongly influences performance, energy use, and recovery. Estrogen may protect muscles against exercise-induced damage, and inflammatory responses differ by sex, with females showing distinct cytokine release patterns (e.g., IL-6, TNF-α). Oxidative stress responses also vary, as females may rely on different antioxidant defenses.

Muscle fiber composition and metabolism further contribute to sex-specific fatigue and recovery patterns. For example, creatine kinase (CK) tends to rise less in females than in males.

Finally, the testosterone/cortisol ratio, widely used in monitoring, is not directly comparable across sexes. Both sexes show acute testosterone increases after exercise, but the rise is much larger in males. This requires sex-specific reference values and careful interpretation.

In summary, monitoring protocols built on male data may not transfer to female athletes. Adapting reference ranges and accounting for hormonal cycles are essential to improve fatigue monitoring in women.

Questions and Thoughts

A key question concerns the practicality of biomarkers for monitoring athletes’ fatigue in sports and clinical settings. Saliva sampling offers a convenient, non-invasive option for field assessments and can be used to measure cortisol, testosterone, and immunological markers such as s-IgA. However, results may be biased by oral lesions, illness, or circadian fluctuations. In contrast, biomarkers of muscle damage (e.g., CK, LDH) and inflammation (e.g., CRP, cytokines, TNF-α), as well as oxidative stress markers, typically require blood or urinary samples and more advanced laboratory methods, which limit their feasibility during the competitive season.

Another challenge lies in interpretation. Some biomarkers, particularly CK, show wide inter-individual variability, making it difficult to define universal cut-off values. Baseline (pre-season) measures are therefore essential for meaningful follow-up.

These biomarkers may provide insight into overtraining syndrome (OTS), but current evidence shows no single biomarker, or hormonal marker can confirm its diagnosis. According to a 2013 consensus, OTS is best defined as a sport-specific and persistent performance decline, accompanied by mood disturbances, that does not resolve despite weeks or months of recovery. Importantly, OTS remains a diagnosis of exclusion, as no laboratory test can definitively rule it in.

Another limitation is the lack of female-specific data on OTS. Female athletes are particularly vulnerable to conditions such as stress fractures and Relative Energy Deficiency in Sport (RED-S). The Female Athlete Triad, as defined in the ACSM position stand —(a) low energy availability (with or without disordered eating), (b) menstrual dysfunction, and (c) low bone mineral density—can overlap with OTS but requires distinct clinical attention. Hormonal factors such as IGF-1 may play a role in bone health, while deficiencies in vitamin D and iron, particularly in endurance athletes, add to the risk. This review highlight that menstrual cycle–related iron losses may further contribute to fatigue and impaired performance. 

Recent evidence also suggests that the menstrual cycle may influence performance capacity, although findings remain inconclusive regarding the extent to which different phases affect physical abilities.

Talk nerdy to me

This study followed PRISMA guidelines, which is a strong choice because it ensures transparency, reproducibility, and minimizes selection bias. The use of multiple sport-specific databases (PubMed, Scopus, SportDiscus, Web of Science) also reduces the risk of missing key literature. 

The inclusion criteria were clearly defined, targeting only elite or professional male team athletes and requiring longitudinal data collected across matches or training sessions. This enhances ecological validity, as the results reflect actual competitive demands. However, the scope is quite narrow: by excluding women, amateur athletes, and laboratory-based studies, the review prioritizes specificity over breadth. As a result, the conclusions cannot be generalized to female athletes or non-elite athlete populations. Moreover, the review encompassed a variety of sports, each characterized by distinct internal loads that naturally lead to different adaptations. To achieve greater accuracy, these differences should have been considered and explored through subgroup analyses.

Another strength is the detailed requirement for biomarkers for monitoring athletes’ fatigue acquisition methods, including sample type, timing, and analytical techniques. This helps standardize comparisons across studies. Still, variability remains: biomarker responses are highly time-dependent, and the methods of collection (e.g., saliva vs. blood, morning vs. evening sampling) differ substantially across studies. This heterogeneity reduces the comparability of results and may blur biomarker trends. Furthermore, the authors point out that the timing of data acquisition varied considerably across studies. For example, creatine kinase (CK) levels were measured at different times of day. While these analyses could have been adjusted to account for circadian fluctuations, the authors contend that the consistent CK elevation observed 24 to 48 hours post-training likely mitigates the impact of such timing discrepancies.

Finally, the review relied on a single primary investigator for search and extraction, with arbitration only when disagreements arose. This introduces potential bias: even unintentional preferences during screening might affect study inclusion. A dual independent review would have increased reliability.

Take-home messages

Hormonal Monitoring (Testosterone & Cortisol):

• The T/C ratio is more reliable than either hormone alone for assessing training stress and fatigue.
• Hormonal responses vary by sex, playing position, game time, and sport discipline → interpretation must be individualized.
• Use saliva sampling for easy field monitoring, but be aware of circadian variation.

Muscle Damage Markers (CK, LDH):

• CK peaks 24–72h post-exercise and is useful for monitoring acute load and recovery.
• High preseason or congested schedules = ↑ CK/LDH → indicates need for tailored recovery strategies.
• Always compare to individual baseline values (large day-to-day fluctuations).

Immunological Markers (s-IgA):

• ↓ s-IgA = ↑ risk of respiratory illness (especially with intense training/competition).
• Track trends over time rather than single values to guide recovery and prevent illness.
• Salivary measures are practical and can serve as an early warning signal.

Inflammatory & Oxidative Stress Markers (CRP, cytokines, ROS):

• Elevated during congested match periods → indicates unresolved fatigue and ↑ injury risk.
• Persistent inflammation and oxidative stress impair recovery and muscle regeneration.
• Regular monitoring can help guide load reduction and recovery planning.

Sex-Specific Considerations:

• Female athletes show different responses in biomarkers for monitoring athletes’ fatigue due to factors like the menstrual cycle, estrogen effects, and muscle fiber composition.
• CK rises less in females, and the T/C ratio cannot be interpreted the same as in males.
• Monitoring protocols must include sex-specific reference ranges and menstrual cycle tracking.

This open-access study provides an exhaustive overview of the current research on biomarkers for monitoring athletes’ fatigue and their application in sport performance.

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Reference

Soler-López, A., Moreno-Villanueva, A., Gómez-Carmona, C. D., & Pino-Ortega, J. (2024). The Role of Biomarkers in Monitoring Chronic Fatigue Among Male Professional Team Athletes: A Systematic Review. Sensors, 24(21), 6862.