Association of systemic inflammation with posttraumatic stress disorder after a myocardial infarction
Bibliographic record
Abstract
Background: Adverse mental health conditions including depression, posttraumatic stress disorder (PTSD), and anxiety are prevalent among patients who survive myocardial infarctions (MI) and are associated with adverse outcomes. The mechanisms underlying these associations, however, are not well understood. Inflammatory pathways may mediate the cardiovascular outcomes of patients with mental health disorders. We examined the bidirectional association between PTSD symptoms and inflammatory biomarkers in a young/middle-aged post MI population. We further examined how this association may differ between women and men as well as between Black and non-Black individuals. Methods: Participants included individuals with early onset MI between the ages 25 and 60. Mental health scores for depression, PTSD, perceived stress, and anxiety as well as inflammatory biomarkers, interleukin-6 (IL-6) and high sensitivity C-reactive protein (hsCRP), were collected at baseline and at six-month follow up. We examined the bidirectional changes in mental health symptoms and inflammatory biomarkers between baseline and follow-up. Results: Among 244 patients in the study (mean age: 50.8, 48.4% female, 64.3% Black), the geometric means for IL-6 level and hsCRP at rest were 1.7 pg/mL and 2.76 mg/L, respectively. Mental health scores at baseline did not consistently predict changes in inflammatory biomarkers at follow-up. However, baseline levels of both IL-6 and hsCRP were robustly associated with an increase in re-experiencing PTSD symptoms at 6 months: in adjusted linear mixed models, there was a 1.58-point increase in re-experiencing PTSD symptoms per unit of baseline hsCRP (p = 0.01) and 2.59-point increase per unit of baseline IL-6 (p = 0.02). Once the analysis was stratified by race, the association was only noted in Black individuals. Baseline inflammation was not associated with change in any of the other mental health symptom scores. Conclusion: Markers of inflammation are associated with an increase in post-event PTSD symptoms in younger or middle-aged patients who experienced an MI, especially Black patients. These results suggest a mechanistic link between inflammation and the development of PTSD among individuals with cardiovascular disease.
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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 | 0.002 |
| 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 | 0.001 |
| 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".