Implications of Integrating Nutrigenomic Aspects on Training Ability and Recovery in Athletes: A Literature Review
Bibliographic record
Abstract
Introduction: Nutrition impacts sports performance significantly, influencing each athlete differently. Factors such as age, ethnicity, and genetics affect how athletes respond to nutrition. This review investigates how incorporating nutrigenomics enhances training and recovery in athletes. Methods: A systematic search following PRISMA 2020 guidelines was conducted to find relevant studies that examine the impact of genetic variations on nutritional effects related to athletic performance and recovery. The included articles are non-observational studies selected based on specific inclusion and exclusion criteria and were qualitatively analyzed. Results: The review included nine studies, primarily clinical and randomized controlled trials, conducted in Iran, Brazil, and Canada. Most studies focused on male athletes aged 15 to 40 years. The studies suggest that caffeine and vitamin D effects on performance may vary based on individual genotypes, but specific genes do not consistently influence these effects. Conclusion: Adopting genetic-based nutrition and supplementation approaches holds promising potential for optimizing athletic performance by tailoring nutrient intake to individual genetic profiles.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".