C957T polymorphism of the dopamine D <sub>2</sub> receptor (DRD <sub>2</sub> ) gene is associated with caffeine‐induced mood elevation in males
Bibliographic record
Abstract
Caffeine is the most widely consumed stimulant in the world and is associated with elevated mood in some individuals. This effect may be related to an increase in dopamine release. Caffeine consumption also leads to sexually dimorphic patterns of DRD 2 gene expression in mice, but differences in behavioral responses between men and women are not clear. A single nucleotide polymorphism (C957T) in the DRD 2 affects its binding potential and has been associated with certain mood disorders. The objective of this study was to investigate the association between DRD 2 genotypes and self‐reported behavioral responses to caffeine. Men and women (n=400) aged 20–29 years were asked to indicate the type and intensity of effects they experience up to 12 hrs after consuming a caffeinated beverage. DNA was isolated from blood samples and genotyped by real‐time PCR. Genotype frequencies were compared using χ 2 tests. Females were more likely than males to report an elevation in mood following caffeine consumption (51% vs. 37%, P=0.01). However, a significant association between DRD 2 genotype and mood was observed only among males. The proportion of male subjects who reported experiencing an elevation in mood was higher for those with the T/T (53%) genotype than those with the C/T (44%) or C/C (23%) genotypes (P=0.02). Among females, the proportion of subjects who reported an elevation in mood were 61%, 43% and 54% for those with T/T, C/T and C/C genotypes, respectively (P=0.11). These findings suggest that the DRD 2 gene mediates caffeine‐induced mood elevation in males. (Supported by AFMNet)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".