CYP1A2 Genotype Modifies the Effects of Caffeine Compared With Placebo on Muscle Strength in Competitive Male Athletes
Bibliographic record
Abstract
Caffeine is commonly used to improve athletic performance across a variety of sports. Previously, the CYP1A2 gene has been shown to modify the effects of caffeine on endurance performance. The effect of caffeine on strength and power activities is unclear and may differ depending on an individual's CYP1A2 genotype. A randomized controlled trial was used to determine whether caffeine impacts strength and power, determined by the handgrip and vertical jump tests, respectively, and whether CYP1A2 genotype modifies any effects. Competitive male athletes (age = 25 ± 4 years) completed vertical jump (n = 97), and handgrip tests (n = 102) under three conditions: 0 (placebo), 2, or 4 mg of caffeine per kilogram of body mass (in milligrams per kilogram). CYP1A2 (rs762551) genotype was determined from saliva samples. No differences between caffeine doses and placebo were observed for strength or power; however, significant Caffeine × Gene interactions were observed for all exercise tests. Individuals with the CC genotype experienced a 12.8% decrease in handgrip strength with 4 mg/kg of caffeine compared with placebo (53 ± 11 kg vs. 61 ± 17 kg, p = .02). No differences were observed in those with the AC or AA genotypes. Despite observing a significant Caffeine × Gene interaction for vertical jump performance, no differences were observed between caffeine doses and placebo for all genotypes. In summary, caffeine (4 mg/kg) worsened handgrip strength performance in those with the CC genotype, but no differences were observed in those with the AC or AA genotypes. Athletes may want to consider their CYP1A2 genotype prior to using caffeine to improve muscle strength.
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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 | 0.001 |
| 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) | 0.003 | 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".