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Background: In professional soccer, the use of external measures such as GPS data and functional performance screenings to control load, recovery and injury prevention is well established. However, internal biomarkers have so far only been used to a limited extent, as their collection is usually invasive, cost-intensive and difficult to implement at high-frequency. This work aims to present a first practical approach for high-frequency, saliva-based multi-omics biomarker monitoring in professional soccer and explores potential applications. Methods: Twenty-six male first-team players were monitored over an entire season, providing 3-5 saliva samples per week, resulting in 2,554 samples analyzed for 92 biomarkers. Samples were collected using Salivette® Cortisol swabs and analyzed via liquid chromatography-tandem mass spectrometry (intra-assay CV <5%). Individual reference ranges were calculated using a Bayesian inference model with adaptive temporal weighting and outlier detection, combining individual and population data to derive 95% confidence intervals. The analyses focused on selected biomarkers related to hydration, metabolism, stress, and inflammation. Results: Four representative cases showed marked deviations from their individual 95% reference ranges, each linked to distinct physiological conditions: temporary dehydration (total protein: team average 0.49±0.38 mg/ml; individual range 0.33-2.27 mg/mL), incorrect use of dietary supplements (melatonin: 0.28±5.17 ng/mL; 0.001-123.016 ng/mL), elevated stress (cortisol: 3.76±3.39 ng/mL; 0.004-41.836 ng/ml) and medication influences (cortisone: 9.53±5.82 ng/ml; 0.002-10.737 ng/ml). Conclusion: This study represents a first promising attempt to implement saliva-based biomarker assessment in professional soccer, which can support medical staff, nutritionists, and coaches in identifying early signs of maladaptation, optimizing recovery strategies, and enabling timely, individualized interventions throughout the season. Further research is needed to validate these findings and investigate their scalability to other sports.
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Zimmer et al. (2026) studied this question.
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