The mu opioid receptor A118G gene polymorphism moderates effects of trait anger-out on acute pain sensitivity
Bibliographic record
Abstract
Both trait anger-in (managing anger through suppression) and anger-out (managing anger through direct expression) are related to pain responsiveness, but only anger-out effects involve opioid mechanisms. Preliminary work suggested that the effects of anger-out on postoperative analgesic requirements were moderated by the A118G single nucleotide polymorphism of the mu opioid receptor gene. This study further explored these potential genotypexphenotype interactions as they impact acute pain sensitivity. Genetic samples and measures of anger-in and anger-out were obtained in 87 subjects (from three studies) who participated in controlled laboratory acute pain tasks (ischemic, finger pressure, thermal). McGill Pain Questionnaire (MPQ) Sensory and Affective ratings for each pain task were standardized within studies, aggregated across pain tasks, and combined for analyses. Significant anger-outxA118G interactions were observed (p's<.05). Simple effects tests for both pain measures revealed that whereas anger-out was nonsignificantly hyperalgesic in subjects homozygous for the wild-type allele, anger-out was significantly hypoalgesic in those with the variant G allele (p's<.05). For the MPQ-Affective measure, this interaction arose both from low pain sensitivity in high anger-out subjects with the G allele and heightened pain sensitivity in low anger-out subjects with the G allele relative to responses in homozygous wild-type subjects. No genetic moderation was observed for anger-in, although significant main effects on MPQ-Affective ratings were noted (p<.005). Anger-in main effects were due to overlap with negative affect, but anger-outxA118G interactions were not, suggesting unique effects of expressive anger regulation. Results support opioid-related genotypexphenotype interactions involving trait anger-out.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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) | 0.000 | 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 teacher head, 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".