Review of biologic and behavioral risk factors linking depression and peripheral artery disease
Bibliographic record
Abstract
The incidence of depression has been rising rapidly, and depression has been recognized as one of the world's leading causes of disability. More recently, depression has been associated with an increased risk of symptomatic atherosclerotic disease as well as worse perioperative outcomes in patients with cardiovascular disease. Additionally, recent studies have demonstrated an association between depression and peripheral artery disease (PAD), which has been estimated to affect more than 200 million people worldwide. These studies have identified that depression is associated with poor functional and surgical outcomes in patients with PAD. Although the directionality and specific mechanisms underlying this association have yet to be clearly defined, several biologic and behavioral risk factors have been identified to play a role in this relationship. These factors include tobacco use, physical inactivity, medical non-adherence, endothelial and coagulation dysfunction, and dysregulation of the hypothalamic-pituitary-adrenal axis, autonomic system, and immune system. In this article, we review these potential mechanisms and the current evidence linking depression and PAD, as well as future directions for research and interventional strategies. Understanding and elucidating this relationship may assist in preventing the development of PAD and may improve the care that patients with PAD and comorbid depression receive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".