Polygenic Risk Associations of Psychosocial Influences for Alcohol Use Disorder (AUD): The Role of Social Stresses
Bibliographic record
Abstract
Objectives We examined the effect of exposure to Stressful Life Events (SLE) on risk of developing past-year DSM-5 Alcohol Use Disorder (pyAUD5). We evaluated if PRS moderated the relationship between SLE exposure and AUD development. Methods This is a secondary analysis of the National Epidemiologic Survey on Alcohol and Related Conditions III (NESARC III; N=36,309), and the newly available NESARC-III genomic dataset (N=22,848). Results. Subjects with high SLE exposure (N=4436) were at greater odds of developing AUD (OR: 1.8 95% CI: 1.5-2). We report statistically significant associations between PRS and pyAUD5 in matched data, before and after adjustment for potential confounders [before adjustment (OR: 1.07; 95%CI: 1.051.09), after adjustment (OR: 1.03; 95% CI: 1.0031.05)]. PRS moderated the relationship between SLE exposure and AUD development whereas PRS significantly interacted with stress exposure category and change in probability of pyAUD5 (p=0.04, SE: 211.84). Conclusions Our findings are consistent with literature indicating the role of stress in the development of substance use disorder, and preliminary in suggesting the clinical utility of PRS. We illustrate the interactive effect of PRS in moderating genetic vulnerability to pyAUD5.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".