Effect of Caffeine on Endurance Performance in Athletes May Depend on HTR2A and CYP1A2 Genotypes
Bibliographic record
Abstract
ABSTRACT: Guest, NS, Corey, P, Tyrrell, PN, and El-Sohemy, A. Effect of caffeine on endurance performance in athletes may depend on HTR2A and CYP1A2 genotypes. J Strength Cond Res 36(9): 2486-2492, 2022-This investigation determined whether variation in the HTR2A (serotonin receptor) gene modifies the ergogenic effects of caffeine on endurance and further modifies performance by the CYP1A2 genotype. Male athletes ( n = 100; 25 ± 4 years) completed 10-km cycling time trials under 3 conditions as follows: 0, 2, or 4 mg of caffeine per kg body mass. Using a randomized, double-blinded, placebo-controlled design, data were analyzed using analysis of covariance to compare changes in cycling time between placebo (0 mg·kg -1 ) and each caffeine dose and adjusted for the placebo trial and order of treatment. A significance of ρ ≤ 0.05 was used. Subjects were genotyped for HTR2A (rs6313) and CYP1A2 (rs762551). A significant caffeine- HTR2A interaction ( p = 0.003) was observed; however, after adjustment for placebo trials, the interaction was no longer significant ( p = 0.37). Because of the strong caffeine- CYP1A2 interaction ( p < 0.0001) previously reported in these subjects, where the 4-mg dose resulted in divergent effects (slower and faster) on the 10-km cycling time, we conducted a simplified model to examine these same factors by the HTR2A genotype. The post hoc analysis excluded HTR2A CT heterozygotes and 2-mg·kg -1 caffeine trials. Among CYP1A2 fast metabolizers alone, a significant difference (1.7 minutes; p = 0.006) was observed when comparing (4- vs. 0-mg·kg -1 caffeine trials) between the HTR2A CC ( n = 16; 2.4 minutes) and TT ( n = 7; 0.7 minutes) genotypes. Our results show that 4-mg·kg -1 caffeine improves performance in individuals with the HTR2A CC genotype but only in those who are also CYP1A2 AA fast metabolizers. This study was registered with clinicaltrials.gov (NCT02109783).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".