A prospective study of genetic factors, human laboratory phenotypes, and heavy drinking in late adolescence
Bibliographic record
Abstract
Subjective responses to alcohol are considered candidate endophenotypes for alcohol use disorder and appear to anticipate future consumption. However, prospective studies have been rare, and laboratory research has typically examined subjective responses absent measures of self-administration. This study examined the association of subjective responses with subsequent laboratory self-administration, also evaluating laboratory phenotypes in relation to putative genetic risk factors [family history (FH) of alcohol dependence and OPRM1 genotype] and subsequent heavy drinking. Participants (N = 61, M = 19.89 years, SD = 0.86) completed laboratory sessions involving intravenous alcohol challenge (Session 1) and free-access intravenous self-administration (Session 2), followed by prospective assessments. Multilevel modeling showed that higher reported stimulation and lower sedation during Session 1 independently predicted greater alcohol self-administration during Session 2. Although self-administration did not differ by FH group, participants with the OPRM1 118G allele evidenced steeper breath alcohol concentration (BrAC) trajectories and greater peak BrAC relative to 118A homozygous participants. Prospective analyses supported significant indirect associations between Session 1 subjective responses and 6-month heavy drinking via peak BrAC in Session 2. Additionally, significant indirect associations of FH (via Session 1 stimulation and Session 2 peak BrAC) and OPRM1 (via peak BrAC) with follow-up heavy drinking were observed. These results further support the utility of human laboratory phenotypes in prospective studies of alcohol use disorder risk and highlight the potential role of self-administration phenotypes in longitudinal research.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".