Maladaptive Health Factors as Mediators of the Association Between Posttraumatic Stress Disorder and Cardiovascular Disease: A Sex-Stratified Analysis in the U.S. Adult Population
Bibliographic record
Abstract
Objectives: Despite a higher prevalence of posttraumatic stress disorder (PTSD) and greater severity and chronicity of cardiovascular disease (CVD) in females than males, sex-differences in explanations for the PTSD-CVD relationship are lacking. This study examined sex-differences in the role of health risk factors in mediating the relationship between PTSD and CVD.Methods: Data were analyzed from the 2012–2013 National Epidemiological Survey on Alcohol and Related Conditions, which surveyed a nationally representative sample of 36,309 U.S. adults. Natural effect models and logistic regression analyses were conducted to evaluate the potential effects of each health risk factor (smoking, low physical activity, high body mass index [BMI], binge eating, multiple health risk factors) in mediating the relation between PTSD and CVD. Parallel analyses of each model were conducted in females and males.Results: High BMI independently mediated the PTSD-CVD relationship in females (indirectAOR = 1.05, 95% CI = 1.02, 1.07) though not males. Binge eating, smoking and low physicalactivity were not found to mediate this relationship in either sex. The number of health risk factors also mediated this relationship in both sexes (indirect AOR = 1.14, 95% CI = [1.08,1.19] for females; indirect AOR = 1.16, 95% CI = [1.07, 1.26] for males).Conclusions: The results may inform the development of secondary prevention strategies, such as screening for health risk factors to mitigate the adverse effect of PTSD on CVD risk. Further, the findings may inform early intervention strategies designed to reduce risk of PTSD and CVD, such as addressing high BMI in females and the cumulative burden of health risk behaviours inboth sexes through psychological treatments.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".