Abstract
Peripheral artery disease (PAD) is a prevalent atherosclerotic disorder affecting over 113 million individuals worldwide, with its global burden projected to rise substantially amid population aging and increasing metabolic comorbidities, including type 2 diabetes mellitus (T2DM). Conventional diagnostic assessment, including the ankle-brachial index (ABI) as a non-invasive hemodynamic measurement and imaging-based techniques such as duplex ultrasonography, computed tomography angiography, magnetic resonance angiography, and digital subtraction angiography, has limited sensitivity for early disease detection and does not fully capture the multidimensional pathophysiological processes underlying disease progression, and ABI generates false-negative results in elderly and diabetic patients with medial arterial calcification, a critical unaddressed limitation in routine screening. Recent advances in multi-omics technologies have accelerated the discovery of circulating biomarkers that reflect distinct pathological axes-inflammation, oxidative stress, endothelial dysfunction, and metabolic dysregulation-underlying PAD pathogenesis. These novel biomarkers hold considerable promise for enhancing early diagnosis, risk stratification, prognostic assessment, therapeutic monitoring and postoperative restenosis prediction, and individualized precision intervention guidance. This review systematically summarizes the most recent advances in PAD biomarkers across these four pathophysiological categories, elucidating their mechanistic links to disease progression and critically evaluating their clinical utility and translational barriers; We further develop population-specific biomarker strategies for patients with diabetes mellitus, dialysis-dependent kidney disease, and chronic limb-threatening ischemia (CLTI), while considering disease-specific confounding factors and the current limitations of biomarker validation. We further discuss emerging biomarker classes, including extracellular vesicle-derived molecules and microRNAs, and highlight the superior diagnostic and predictive efficacy of multi-biomarker panels compared with single indicators, combined with machine learning modeling frameworks. Large-scale multi-center, multi-ethnic prospective validation studies, standardized pre-analytical and analytical detection protocols, and cost-effectiveness health economic evaluations remain essential prerequisites for full-scale clinical implementation.