Associations of <scp><i>OPRM1</i> A118G</scp> and alcohol sensitivity with intravenous alcohol self‐administration in young adults
Bibliographic record
Abstract
Human laboratory and animal models implicate variation in the μ-opioid receptor gene (OPRM1) as relevant for alcohol-related reward. OPRM1 is associated with alcohol self-administration in non-human primate studies, but the relevance of this finding to human models is unclear. This study used computer-assisted self-infusion of ethanol (CASE) to examine associations among OPRM1 A118G genotype, subjective responses to alcohol and intravenous alcohol self-administration in young heavy drinkers (n = 40, mean age = 19.95 years, SD = 0.82). Participants completed a 2-hour CASE session comprising a priming phase followed by ad libitum self-administration in a free-access paradigm. Participants achieved a mean peak breath alcohol concentration (BrAC) of 81.18 mg% (SD = 24.96). Those with the OPRM1 118G variant (GA or GG genotypes) achieved significantly higher peak BrAC (M = 94.90 mg%, SD = 16.56) than those with the AA genotype (M = 74.46 mg%, SD = 25.36), reflecting a significantly greater number of alcohol requests among GA/GG participants. Eighty percent of GA/GG participants surpassed a threshold defining a laboratory analog of heavy alcohol exposure (80 mg%) compared with 46 percent of AA participants. Results indicated significant associations between subjective measures of alcohol sensitivity and CASE outcomes, although the pattern of findings differed across self-report measures. Subjective responses did not differ by OPRM1 status. These results offer further support for the feasibility of the CASE paradigm and provide initial evidence for an association of OPRM1 with alcohol self-administration in a human laboratory context.
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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".