Plausible ergogenic effects of vitamin D on athletic performance and recovery
Bibliographic record
Abstract
The purpose of this review is to examine vitamin D in the context of sport nutrition and its potential role in optimizing athletic performance. Vitamin D receptors (VDR) and vitamin D response elements (VDREs) are located in almost every tissue within the human body including skeletal muscle. The hormonally-active form of vitamin D, 1,25-dihydroxyvitamin D, has been shown to play critical roles in the human body and regulates over 900 gene variants. Based on the literature presented, it is plausible that vitamin D levels above the normal reference range (up to 100 nmol/L) might increase skeletal muscle function, decrease recovery time from training, increase both force and power production, and increase testosterone production, each of which could potentiate athletic performance. Therefore, maintaining higher levels of vitamin D could prove beneficial for athletic performance. Despite this situation, large portions of athletic populations are vitamin D deficient. Currently, the research is inconclusive with regards to the optimal intake of vitamin D, the specific forms of vitamin D one should ingest, and the distinct nutrient-nutrient interactions of vitamin D with vitamin K that affect arterial calcification and hypervitaminosis. Furthermore, it is possible that dosages exceeding the recommendations for vitamin D (i.e. dosages up to 4000-5000 IU/day), in combination with 50 to 1000 mcg/day of vitamin K1 and K2 could aid athletic performance. This review will investigate these topics, and specifically their relevance to athletic performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".