The effects of aerobic exercise and heat stress on the unbound fraction of caffeine
Bibliographic record
Abstract
Introduction The fraction of drug circulating in the blood that is not bound to plasma proteins (fu) is considered pharmacologically active since it readily binds to its receptor. In vitro evidence suggests that changes in temperature and pH affect the affinity of drug binding to plasma proteins, resulting in changes in fu. In light of the well-established effects of exercise on body temperature and blood pH, we investigated whether an increase in blood temperature and decrease in pH facilitated through passive heating and exercise translated to a change in the fu of caffeine. Methods Ten healthy participants (4 females and 6 males; age: 21.9 ± 2.7 years [means ± SD]) ingested 3 mg/kg of anhydrous caffeine on two separate occasions comprised of a control trial involving 105 min of rest, and an experimental trial involving 10 min of passive heating, followed by 20 min of cycling at 55% V˙O2peak , and then 10 sprint intervals at 90% V˙O2peak . Venous blood was sampled and the plasma was processed via ultrafiltration to quantify the fu of caffeine and its major metabolite, paraxanthine. Results The exercise protocol resulted in maximal increases in core temperature of 1.37°C ± 0.27°C and lactate of 10.34 ± 3.33 mmol/L, and a decrease in blood pH of 0.12 ± 0.051 (all p < 0.05), which did not affect the fu of caffeine (baseline: 0.86 vs post-exercise: 0.75; p = 0.30) or paraxanthine (baseline: 0.59 vs. post-exercise: 0.70; p = 0.11). Furthermore, the rate of metabolism of caffeine assessed through the metabolic ratio ([paraxanthine]/[caffeine]) did not differ between resting and exercise trials. Discussion Therefore, the changes in blood temperature and pH in this study did not affect the fu of caffeine or paraxanthine.
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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) | 0.000 | 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) | 0.003 | 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".