MicroRNA-19b predicts widespread pain and posttraumatic stress symptom risk in a sex-dependent manner following trauma exposure
Bibliographic record
Abstract
Posttraumatic widespread pain (PTWP) and posttraumatic stress symptoms (PTSS) are frequent comorbid sequelae of trauma that occur at different rates in women and men. We sought to identify microRNA (miRNA) that may contribute to sex-dependent differences in vulnerability to these outcomes. Monte Carlo simulations (x10,000) identified miRNA in which predicted targeting of PTWP or PTSS genes was most enriched. Expression of the leading candidate miRNA to target PTWP/PTSS-related genes, miR-19b, has been shown to be influenced by estrogen and stress exposure. We evaluated whether peritraumatic miR-19b blood expression levels predicted PTWP and PTSS development in women and men experiencing trauma of motor vehicle collision (n = 179) and in women experiencing sexual assault trauma (n = 74). A sex-dependent relationship was observed between miR-19b expression levels and both PTWP (β = -2.41, P = 0.034) and PTSS (β = -3.01, P = 0.008) development 6 months after motor vehicle collision. The relationship between miR-19b and PTSS (but not PTWP) was validated in sexual assault survivors (β = -0.91, P = 0.013). Sex-dependent expression of miR-19b was also observed in blood and nervous tissue from 2 relevant animal models. Furthermore, in support of increasing evidence indicating a role for the circadian rhythm (CR) in PTWP and PTSS pathogenesis, miR-19b targets were enriched in CR gene transcripts. Human cohort and in vitro analyses assessing miR-19b regulation of key CR transcripts, CLOCK and RORA, supported the potential importance of miR-19b to regulating the CR pathway. Together, these results highlight the potential role that sex-dependent expression of miR-19b might play in PTWP and PTSS development after trauma/stress exposure.
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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".