Effects of macro- and micronutrients on exercise-induced hepcidin response in highly trained endurance athletes
Bibliographic record
Abstract
Iron deficiency has ergolytic effects on athletic performance. Exercise-induced inflammation impedes iron absorption in the digestive tract by upregulating the expression of the iron regulatory protein, hepcidin. Limited research indicates the potential of specific macro- and micronutrients on blunting exercise-induced hepcidin. Therefore, we investigated the effects of postexercise supplementation with protein and carbohydrate (CHO) and vitamins D3 and K2 on the postexercise hepcidin response. Ten highly trained male cyclists (age: 26.9 ± 6.4 years; maximal oxygen uptake: 67.4 ± 4.4 mL·kg–1·min–1 completed 4 cycling sessions in a randomized, placebo-controlled, single-blinded, triple-crossover study. Experimental days consisted of an 8-min warm-up at 50% power output at maximal oxygen uptake, followed by 8 × 3-min intervals at 85% power output at maximal oxygen uptake with 1.5 min at 60% power output at maximal oxygen uptake between each interval. Blood samples were collected pre- and postexercise, and at 3 h postexercise. Three different drinks consisting of CHO (75 g) and protein (25 g) with (VPRO) or without (PRO) vitamins D3 (5000 IU) and K2 (1000 μg), or a zero-calorie control drink (PLA) were consumed immediately after the postexercise blood sample. Results showed that the postexercise drinks had no significant (p ≥ 0.05) effect on any biomarker measured. There was a significant (p < 0.05) increase in hepcidin and interleukin-6 following intense cycling intervals in the participants. Hepcidin increased significantly (p < 0.05) from baseline (nmol·L–1: 9.94 ± 8.93, 14.18 ± 14.90, 10.44 ± 14.62) to 3 h postexercise (nmol·L–1: 22.27 ± 13.41, 25.44 ± 11.91, 22.57 ± 15.57) in VPRO, PRO, and PLA, respectively. Contrary to our hypothesis, the drink compositions used did not blunt the postexercise hepcidin response in highly trained athletes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".