A Systematic Review of Interleukins as Diagnostic and Prognostic Biomarkers for Peripheral Artery Disease
Bibliographic record
Abstract
Background: Peripheral artery disease (PAD) involves atherosclerosis of the lower extremity arteries and is a major contributor to limb loss and death worldwide. Several studies have demonstrated that interleukins (ILs) play an important role in the development and progression of PAD; however, a comprehensive literature review has not been performed. Methods: A systematic review was conducted and reported according to PRISMA guidelines. MEDLINE was searched from inception to 5 December 2022, and all studies assessing the association between ILs and PAD were included. Results: We included 17 studies from a pool of 771 unique articles. Five pro-inflammatory ILs (IL-1β, IL-2, IL-5, IL-6, and IL-8) and one pro-atherogenic IL (IL-12) were positively correlated with PAD diagnosis and progression. In contrast, two anti-inflammatory ILs (IL-4 and IL-10) were protective against PAD diagnosis and adverse limb events. Specifically, IL-6 and IL-8 were the most strongly associated with PAD and can act as potential disease biomarkers to support the identification and treatment of PAD. Conclusions: Ongoing work to identify and validate diagnostic/prognostic inflammatory biomarkers for PAD has the potential to assist clinicians in identifying high-risk patients for further evaluation and management which could reduce the risk of adverse cardiovascular and limb events.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".