Complex post-traumatic stress disorder is linked with accelerated epigenetic aging in female Yazidi ex-captives.
Bibliographic record
Abstract
Abstract Previous theories have linked exposure to trauma, and particularly trauma symptoms, to greater physical “wear and tear” that manifests as poorer health and metabolic syndrome. Very recent studies have shown that post-traumatic stress disorder (PTSD, which comprises, according to the ICD-11, three symptom clusters; intrusions, avoidance and arousal) longitudinally leads to more accelerated epigenetic aging. This study examines for the first time how complex-PTSD (CPTSD; which includes PTSD in addition to another three symptom clusters related to disturbances in self-organization; affect dysregulation, negative self-concept, and difficulties in forming and maintaining relationships) is linked with epigenetic accelerated aging. The sample comprised 94 female Yazidi women who had been held as sex slaves by ISIS and emigrated to Europe after their release ad who were resettled in Europe after their release from captivity were sampled (Mage 38.41±11.93, range 18-72.81; 63.2% female). Measures included demographic variables, PTSD and CPTD questionnaires along with salvia from which epigenetic aging was derived. Results show older epigenetic aging in those with CPTSD. Their epigenetic age was 4 years greater than ex-captives who did not display trauma symptoms. The data also revealed shorter telomere length for those with CPTSD. Although preliminary, these results demonstrate how complex trauma is associated with accelerated epigenetic mechanisms and address factors that speed up biological aging. The results have implications for normal aging, especially when one ages in the shadow of stress and trauma.
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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 | 0.000 |
| 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 | 0.000 | 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".