Shared genetic architecture of posttraumatic stress disorder with cardiovascular imaging, risk, and diagnoses
Bibliographic record
Abstract
Patients with post-traumatic stress disorder face increased cardiovascular risk. This study examines shared genetic regions between post-traumatic stress disorder and 246 cardiovascular conditions across electronic health records, 82 cardiac imaging, and health behaviors defined by Life’s Essential 8. Post-traumatic stress disorder is genetically correlated with cardiovascular diagnoses in 33 regions, imaging traits in 4 regions, and health behaviors in 44 regions. Potentially shared causal variants between post-traumatic stress disorder and 17 cardiovascular conditions were observed in 11 regions. Subsequent observational analysis in AllofUS cohort showed post-traumatic stress disorder is associated with 13 diagnoses even after accounting for socioeconomic factors and depression. Genetically regulated proteome expression in brain and blood tissues identified 33 blood and 122 brain genes shared between the two conditions, revealing neuronal, immune, metabolic, and calcium-related mechanisms, with several genes as targets for existing drugs. These findings exhibit shared risk loci and genes are involved in tissue-specific mechanisms. Study shows PTSD predisposition shares distinct genes and genomic regions with several cardiovascular conditions. Here the findings reveal neuronal, immune, and metabolic pathways, and repurposed drug targets that further the understanding of the comorbidity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".