Toward Sportomics: Shifting From Sport Genomics to Sport Postgenomics and Metabolomics Specialties. Promises, Challenges, and Future Perspectives
Bibliographic record
Abstract
Together with experience, training, dietary intake, and other environmental factors, the biological and genetic makeup of an athlete play a major role in exercise physiology in terms of performance and outcomes.1 Sport genomics has shown that some DNA singlenucleotide polymorphisms can be associated with athlete level and performance (such as elite/world-class athletic status), having an impact on physical activity-related variables like endurance; strength; sprint; power; speed; flexibility; energetic expenditure; neuromuscular coordination; and respiratory, metabolic, and cardiorespiratory fitness, among others.Moreover, single-nucleotide polymorphisms have been shown to correlate with other parameters, including psychological traits. 2 The athletic phenotype is extremely complex and multifactorial, depending on the combination of different features and characteristics.3 On this basis, sport performance is a "complex science," like that of metadata and multiomics profiles.Several ambitious projects (like the Exercise at the Limit-Inherited Traits of Endurance [ELITE], GAMES, Gene Skeletal Muscle Adaptive Response to Training or Gene SMART, GEN-ATHLETE, Genetics of Elite Status in Sport or GENESIS, 1000 Athlomes, Super-Athletes, and POWERGENE trials) are aimed at discovering genomics-based biomarkers with an adequate predictive power.4 These projects are aimed at overcoming the major drawbacks that plagued previous investigations, generally relying on small and rather heterogeneous cohorts of athletes.Sport genomics could enable researchers, athletes, sport scientists, and coaches/managers to optimize and maximize physical performance and identify prevention strategies in the field of individual risk of sport-related injuries (like Achilles tendinopathy or rotator cuff pathologies).3 However, the athlete genome is only a pebble in the mosaic of sport physiology.3 Exercise has a profound impact also on the human proteome, for instance, finely tuning ATP-related pathways and mitochondrial protein synthesis, as well as proteins belonging to inflammation, antioxidation, anticoagulation, and iron.5 Moreover, exercise modulates transcription patterns and epigenetics, as well as metabolic profiles.All these different omics specialties (like sport genomics, epigenomics, transcriptomics, proteomics, and metabolomics/metabonomics) converge in a unique approach termed as "sportomics."3,6 Introduced for the first time by Brazilian scientist Cameron and colleagues, the word "metabolomics" can be defined as a holistic and top-down framework, characterizing all non-hypothesis-framed but data-driven research for systematically uncovering an individual's biomolecular changes during exercise and sport.6 Sportomics includes both genomics and postgenomics specialties and, comprehensively relying on the "athlete's biological passport" or profile, would enable the systematic study of sportinduced responses and adaptations at any level (genome, transcriptome, proteome, etc).3 This is the ambitious goal of the large collaborative initiative "Athlome Project Consortium," as stated in
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".