Blood-Based Biomarkers for Managing Workload in Athletes: Perspectives for Research on Emerging Biomarkers

Beyond physical exhaustion: Understanding overtraining syndrome through the lens of molecular mechanisms and clinical manifestation

Background

Overtraining Syndrome (OTS) is a condition resulting from excessive physical activity without adequate recovery, predominantly affecting elite athletes and military personnel. While overreaching can be a temporary state, non-functional overreaching may progress to chronic OTS. This review explores various hypotheses regarding the pathogenesis of OTS, including glycogen depletion, dysregulated cytokine response, oxidative stress, and alterations in the autonomic nervous system function. It also highlights the systemic impact of OTS on multiple organ systems, immune function, and overall health, linking the condition to chronic inflammation and an increased disease susceptibility. Additionally, it addresses the role of the gut microbiome in health modulation through physical activity.

Methods

This narrative review was conducted through a structured search of peer-reviewed journal articles in databases such as PubMed, Web of Science, and Google Scholar, focusing on studies involving human participants and published in English.

Results

OTS has systemic effects on multiple organ systems, immune function, and overall health, leading to chronic inflammation and increased disease susceptibility. Athletes with OTS exhibit higher morbidity rates, influenced by factors such as sleep deprivation and stress. The review also emphasizes the role of the gut microbiome as a significant modulator of health through physical activity.

Conclusion

Balanced training and recovery are crucial for preventing OTS and maintaining optimal health and quality of life in physically active individuals. Understanding the complex pathophysiology of OTS is essential for developing effective prevention and treatment strategies.

Pre-analytical pitfalls: How blood collection tubes influence exercise-induced cell-free DNA concentrations

Circulating cell-free DNA (cfDNA) is a promising biomarker for physiological stress, including exercise-induced responses. However, the lack of standardization in blood collection tubes (BCTs) for quantification of cfDNA hampers inter-study comparisons. In this study, we assessed the impact of different BCTs on exercise-induced cfDNA dynamics. Eleven participants [25 (SD 2.3) years of age] performed three different treadmill exercise protocols, including an all-out test and combinations of constant and interval load. Blood samples were collected before, 5 min and 30 min post-exercise using EDTA, lithium-heparin (LH) and serum BCTs. Concentrations of cfDNA were quantified using quantitative PCR. The cfDNA increased significantly across all protocols and BCTs. A significant effect of BCT on cfDNA concentrations (P = 0.034) was found, with serum showing higher concentrations than EDTA and LH. Although absolute differences from pre- to post-exercise were comparable across BCTs (P = 0.476), fold changes differed significantly (P = 0.012), with the highest observed in EDTA and the lowest in serum. Bland-Altman analyses demonstrated better agreement between EDTA and LH compared with serum. Significant correlations of cfDNA with energy expenditure and peak oxygen uptake were found. These correlations were stronger in EDTA and LH than in serum. Our findings highlight the crucial influence of BCT choice on cfDNA measurements in exercise settings. Given that EDTA and LH reflected exercise load better, they could be preferred for exercise physiology research. This work underscores the need to account for the choice of BCT to improve data comparability across studies. Additionally, these findings might have broader implications for clinical settings where cfDNA is used as a biomarker.

Athlete Monitoring Systems in Elite Men's Basketball: Challenges, Recommendations, and Future Perspectives

Athlete monitoring systems (AMSs) provide a centralized platform for integrating, processing, analyzing, and graphing various monitoring data to help coaches manage the rigorous demands of elite men's basketball players, who frequently participate in high-stress games with minimal recovery time. This review synthesizes current challenges in deploying AMSs, underscores their role in injury prevention and performance optimization, and discusses technological advances that could enhance their utility. Key challenges include selecting appropriate monitoring methods based on human and financial resources, accuracy of data collection, real-time data processing, and personalization of training regimens. Due to the weaknesses and limitations of each monitoring method, it is recommended that both objective (e.g., external load data, heart rate measures, and biomarkers) and subjective (athlete-reported outcome measures) monitoring data be integrated into an AMS to provide a holistic insight of the athlete's health and readiness. In addition, decision support systems integrated into an AMS can help coaches quickly gain an overview of their players' current condition and make informed decisions about daily load and recovery management. In this context, future perspectives suggest the potential for AMSs to incorporate predictive analytics and artificial intelligence to further enhance decision-making processes in elite men's basketball. Our findings underscore the need for continued innovation and rigorous validation of AMS technologies to ensure they meet the evolving demands of professional sports environments.

Metabolic Phenotyping from Whole-Blood Responses to a Standardized Exercise Test May Discriminate for Physiological, Performance, and Illness Outcomes: A Pilot Study in Highly-Trained Cross-Country Skiers

BACKGROUND

This study used metabolic phenotyping to explore the responses of highly-trained cross-country skiers to a standardized exercise test, which was part of the athletes' routine testing, and determine whether metabolic phenotyping could discriminate specific physiological, performance, and illness characteristics.

METHODS

Twenty-three highly-trained cross-country skiers (10 women and 13 men) participated in this study. Capillary whole-blood samples were collected before (at rest) and 2.5 min after (post-exercise) a roller-ski treadmill test consisting of 5-6 × 4-min submaximal stages followed by a self-paced time trial (~ 3 min) and analyzed using mass spectrometry. Performance level was defined by International Ski Federation distance and sprint rankings. Illness data were collected prospectively for 33 weeks using the Oslo Sports Trauma Research Center Questionnaire on Health Problems. Orthogonal partial least squares-discriminant analyses (OPLS-DA) followed by enrichment analyses were used to identify metabolic phenotypes of athlete groups with specific physiological, performance, and illness characteristics.

RESULTS

Blood metabolite phenotypes were significantly different after the standardized exercise test compared to rest for metabolites involved in energy, purine, and nucleotide metabolism (all OPLS-DA p < 0.001). Acute changes in the metabolic phenotype from rest to post-exercise could discriminate athletes with: (1) higher vs. lower peak blood lactate concentrations; (2) superior vs. inferior performance levels in sprint skiing, and (3) ≥ 2 vs. ≤ 1 self-reported illness episodes in the 33-week study period (all p < 0.05). The most important metabolites contributing to the distinction of groups according to (1) post-exercise blood lactate concentrations, (2) sprint performance, and (3) illness frequency were: (1) inosine, hypoxanthine, and deoxycholic acid, (2) sorbitol, adenosine monophosphate, and 2-hydroxyleuroylcarnitine, and (3) glucose-6-phosphate, squalene, and deoxycholic acid, respectively.

CONCLUSION

Metabolic phenotyping discriminated between athlete groups with higher vs. lower post-exercise blood lactate concentrations, superior vs. inferior sprint skiing performance, and more vs. less self-reported illnesses. While the biological relevance of the identified biomarkers requires validation in future research, metabolic phenotyping shows promise as a tool for routine monitoring of highly-trained endurance athletes.

Exploring sex differences in blood-based biomarkers following exhaustive exercise using bioinformatics analysis

This study examined the acute effects of exercise testing on immunology markers, established blood-based biomarkers, and questionnaires in endurance athletes, with a focus on biological sex differences. Twenty-four healthy endurance-trained participants (16 men, age: 29.2± 7.6 years, maximal oxygen uptake (V ˙ O 2 max ): 59.4 ± 7.5 ml · min-1 · kg-1; 8 women, age: 26.8 ± 6.1 years,V ˙ O 2 max : 52.9 ± 3.1 ml · min-1 · kg-1) completed an incremental submaximal exercise test and a ramp test. The study employed exploratory bioinformatics analysis: mixed ANOVA, k-means clustering, and uniform manifold approximation and projection, to assess the effects of exhaustive exercise on biomarkers and questionnaires. Significant increases in biomarkers (lymphocytes, platelets, procalcitonin, hemoglobin, hematocrit, red blood cells, cell-free DNA (cfDNA)) and fatigue were observed post-exercise. Furthermore, differences pre- to post-exercise were observed in cytokines, cfDNA, and other blood biomarkers between male and female participants. Three distinct groups of athletes with differing proportions of females (Cluster 1: 100% female, Cluster 2: 85% male, Cluster 3: 37.5% female and 65.5% male) were identified with k-means clustering. Specific biomarkers (e.g., interleukin-2 (IL-2), IL-10, and IL-13, as well as cfDNA) served as primary markers for each cluster, potentially informing individualized exercise responses. In conclusion, our study identified exercise-sensitive biomarkers and provides valuable insights into the relationships between biological sex and biomarker responses.

A Narrative Review on Adipose Tissue and Overtraining: Shedding Light on the Interplay among Adipokines, Exercise and Overtraining

Lifestyle factors, particularly physical inactivity, are closely linked to the onset of numerous metabolic diseases. Adipose tissue (AT) has been extensively studied for various metabolic diseases such as obesity, type 2 diabetes, and immune system dysregulation due to its role in energy metabolism and regulation of inflammation. Physical activity is increasingly recognized as a powerful non-pharmacological tool for the treatment of various disorders, as it helps to improve metabolic, immune, and inflammatory functions. However, chronic excessive training has been associated with increased inflammatory markers and oxidative stress, so much so that excessive training overload, combined with inadequate recovery, can lead to the development of overtraining syndrome (OTS). OTS negatively impacts an athlete's performance capabilities and significantly affects both physical health and mental well-being. However, diagnosing OTS remains challenging as the contributing factors, signs/symptoms, and underlying maladaptive mechanisms are individualized, sport-specific, and unclear. Therefore, identifying potential biomarkers that could assist in preventing and/or diagnosing OTS is an important objective. In this review, we focus on the possibility that the endocrine functions of AT may have significant implications in the etiopathogenesis of OTS. During physical exercise, AT responds dynamically, undergoing remodeling of endocrine functions that influence the production of adipokines involved in regulating major energy and inflammatory processes. In this scenario, we will discuss exercise about its effects on AT activity and metabolism and its relevance to the prevention and/or development of OTS. Furthermore, we will highlight adipokines as potential markers for diagnosing OTS.

Increase in Serum Interleukin-1 Receptor Antagonist (IL-1ra) Levels after Wheelchair Half Marathon Race in Male Athletes with Spinal Cord Injury

Exercise increases the serum level of interleukin-6 (IL-6), which in turn stimulates the production of various inflammatory cytokine antagonists, such as interleukin-1 receptor antagonist (IL-1ra). Individuals with cervical spinal cord injury (CSCI) are at high risk of inflammatory conditions. This study compared the effects of wheelchair half marathon on the immune system of male athletes with CSCI and those with thoracic/lumber spinal cord injury (SCI). Neutrophil count, IL-1ra, IL-6, and various endocrine parameters were measured before, immediately and 1 h after the race in five CSCI and six SCI who completed the wheelchair marathon race. The percentage of neutrophils was significantly higher in CSCI immediately and 1 h after the race, compared with the baseline, and significantly higher in SCI at 1 h after the race. IL-6 was significantly higher immediately and 1 h after the race in SCI, whereas no such changes were noted in IL-6 in CSCI. IL-1ra was significantly higher at 1 h after the race in both SCI and CSCI. The race was associated with an increase in IL-1ra in both CSCI and SCI. These findings suggest wheelchair half marathon race increases IL-1ra even under stable IL-6 status in male CSCI individuals, and that such post-race increase in IL-1ra is probably mediated through circulatory neutrophils.

Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months

The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO2max: 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.