The Effect of the <i>CYP1A2</i> −163 C > A Polymorphism on Caffeine Metabolism and Subsequent Cycling Performance
Bibliographic record
Abstract
Introduction: Prior studies from our laboratory suggest the −163 C > A polymorphism of the Cytochrome P450 ( CYP1A2 ) gene influences the ergogenic effect of caffeine. However, serum caffeine and/or metabolites have not been reported in these studies. The purpose of this study was to determine whether CYP1A2 polymorphism affects caffeine metabolism and subsequent exercise performance between the genotypes (AA homozygotes and C allele carriers). Material and Methods: Twenty male subjects participated in two 3 km cycling time trials 60 minutes following placebo (all-purpose flour) and caffeine (6 mg/kg BW anhydrous caffeine) supplementation. “Slow metabolizers” were characterized as possessing a C allele (AC heterozygotes and CC homozygotes), and “fast metabolizers” were homozygous for the A allele. Results: C allele carriers had significantly higher serum caffeine after one hour (C allele carriers = 14.2 ± 1.8 ppm, AA homozygotes = 11.7 ± 1.7 ppm, p = 0.001). There were no significant differences ( p > 0.05) between genetic groups for any measured caffeine metabolite, the metabolite:caffeine ratio, or the paraxanthine:caffeine ratio. While there was a main effect ( p = 0.03) for caffeine ingestion on time trial performance, there was no caffeine x genotype interaction (C allele carriers: placebo = 297.0 ± 20.8 seconds, caffeine = 292.0 ± 20.0 seconds; AA homozygotes: placebo = 318.3 ± 34.5 seconds; caffeine = 307.9 ± 21.9 seconds) ( p > 0.05). Conclusions: Results from this study suggest C allele carriers have higher serum caffeine after one hour than AA homozygotes. However, these findings do not support an influence of the CYP1A2 −163 C > A polymorphism on the ergogenic effect of caffeine in a 3 km cycling time trial.
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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".