Caffeine, CYP1A2 Genotype, and Endurance Performance in Athletes
Bibliographic record
Abstract
PURPOSE: Many studies have examined the effect of caffeine on exercise performance, but findings have not always been consistent. The objective of this study was to determine whether variation in the CYP1A2 gene, which affects caffeine metabolism, modifies the ergogenic effects of caffeine in a 10-km cycling time trial. METHODS: Competitive male athletes (n = 101; age = 25 ± 4 yr) completed the time trial under three conditions: 0, 2, or 4 mg of caffeine per kilogram body mass, using a split-plot randomized, double-blinded, placebo-controlled design. DNA was isolated from saliva and genotyped for the -163A > C polymorphism in the CYP1A2 gene (rs762551). RESULTS: Overall, 4 mg·kg caffeine decreased cycling time by 3% (mean ± SEM) versus placebo (17.6 ± 0.1 vs 18.1 ± 0.1 min, P = 0.01). However, a significant (P <0.0001) caffeine-gene interaction was observed. Among those with the AA genotype, cycling time decreased by 4.8% at 2 mg·kg (17.0 ± 0.3 vs 17.8 ± 0.4 min, P = 0.0005) and by 6.8% at 4 mg·kg (16.6 ± 0.3 vs 17.8 ± 0.4 min, P < 0.0001). In those with the CC genotype, 4 mg·kg increased cycling time by 13.7% versus placebo (20.8 ± 0.8 vs 18.3 ± 0.5 min, P = 0.04). No effects were observed among those with the AC genotype. CONCLUSION: Our findings show that both 2 and 4 mg·kg caffeine improve 10-km cycling time, but only in those with the AA genotype. Caffeine had no effect in those with the AC genotype and diminished performance at 4 mg·kg in those with the CC genotype. CYP1A2 genotype should be considered when deciding whether an athlete should use caffeine for enhancing endurance 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 | 0.001 | 0.001 |
| 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".