Current Prognostic Biomarkers for Peripheral Arterial Disease: A Comprehensive Systematic Review of the Literature
Bibliographic record
Abstract
Background: Peripheral arterial disease (PAD) is a chronic atherosclerotic disease characterized by atheromatous plaque buildup within arteries of the lower limbs. It can lead to claudication, skin ulcerations, and, in severe cases, chronic limb-threatening ischemia, requiring amputation. There are several plasma protein biomarkers that have been suggested as prognostic markers for adverse events, including major adverse cardiovascular and limb events. However, the clinical benefit and ability to clinically adapt these biomarkers remains uncertain due to inconsistent findings possibly related to heterogenous study designs and differences in methodology. Objectives: This review aims to evaluate the current literature on the prognostic value of plasma protein biomarkers for PAD, their predictive ability for PAD-related adverse outcomes, and their potential roles in guiding PAD management. Methods: To address these challenges, we conducted a systematic review of MEDLINE, Embase, and Cochrane CENTRAL libraries of the current literature (2010–2024). Results: We found 55 studies that evaluated the prognostic value of 44 distinct plasma proteins across various pathophysiological processes. These included markers of immunity and inflammation, markers of metabolism, cardiac biomarkers, markers of kidney function, growth factors and hormones, markers of coagulation and platelet function, extracellular matrix and tissue remodeling proteins, and transport proteins. This review summarizes the existing evidence for prognostic protein plasma biomarkers for PAD and their association with adverse events related to PAD. Conclusions: With this review, we hope to provide a comprehensive list of the prognostic markers and their value as prognostic biomarkers to guide clinical decision making in these patients.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".