Whey Protein Isolate Supplementation While Endurance Training Does Not Alter Cycling Performance or Immune Responses at Rest or After Exercise
Bibliographic record
Abstract
This study examined whey protein isolate supplementation combined with endurance training on cycling performance, aerobic fitness and immune cell responses. Eighteen male cyclists were randomly assigned to either placebo or whey protein supplementation (WS). Both groups completed the identical endurance training program, 4 days per week for 6 weeks. Blood samples were obtained at rest and after 5 and 60 minutes of recovery from a simulated 40 km cycling time trial (TT) and were repeated after training. There were similar improvements in TT performance, peak oxygen uptake and ventilatory thresholds 1 and 2 after training in both groups. White blood cells (WBC) and neutrophils were elevated 5 minutes after the TT and continued to rise after 60 minutes (P<0.05). Training lowered WBC and neutrophils, 5 and 60 minutes after exercise (P<0.05). Lymphocytes increased 5 minutes after the TT and decreased below rest after 60 minutes of recovery (P<0.05). Training also lowered lymphocytes after 60 minutes of recovery. There was no change in natural killer cell activity with exercise, training or between groups. It was concluded that whey protein isolate supplementation while endurance training did not deferentially change cycling performance or the immune response at rest or after exercise. However, endurance training did alter performance, aerobic fitness and some post exercise immune cell response counts.
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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 | 0.000 | 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".