Genetic Polymorphism of the Adenosine A <sub>2A</sub> Receptor Affects Habitual Caffeine Consumption
Bibliographic record
Abstract
Caffeine is the most widely consumed stimulant in the world and individual differences in response to its stimulating effects may explain some of the variability in caffeine consumption within a population. We examined whether genetic variability in caffeine metabolism ( CYP1A2 ‐163A>C) or the major target of caffeine action ( ADORA2A 1083C>T) affects habitual caffeine consumption. Subjects (n=2735) were participants from a study of gene‐diet interactions and risk of myocardial infarction who did not have a history of hypertension. Genotype frequencies were examined among individuals who were categorized according to their self‐reported daily caffeine intake as assessed using a validated food‐frequency questionnaire. ADORA2A , but not CYP1A2 , genotype was associated with different levels of caffeine intake. Compared to individuals consuming <100 mg/d of caffeine, the odds ratios (95% confidence intervals) for having the ADORA2A TT genotype were 0.74 (0.53–1.03), 0.63 (0.48–0.83) and 0.57 (0.42–0.77) for those consuming 100–200, >200–400, and >400 mg/d, respectively. The association was more pronounced among current smokers than among nonsmokers. Individuals with the ADORA2A TT genotype were also more likely to consume less caffeine (i.e. <100 mg/d) compared to carriers of the C allele [P= 0.011 (nonsmokers), P=0.008 (smokers)]. Our findings demonstrate that the probability of having the ADORA2A 1083 TT genotype decreases as the level of habitual caffeine consumption increases. This observation provides a biological basis for caffeine consumption behavior and suggests that individuals with this genotype might be less vulnerable to caffeine dependence. Funded by CIHR (MOP‐53147) and NIH (HL 60692,HL 071888).
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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) | 0.002 | 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".