4.7 Review

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Journal

Publisher

MDPI
DOI: 10.3390/ijms22073601

Keywords

peripheral arterial disease; biomarkers; inflammation; coagulation; extracellular vesicles; microRNAs; machine learning

Funding

  1. Instituto de Salud Carlos III-FEDER
  2. Fondo de Investigaciones Sanitarias [PI18/01195]
  3. CIBERCV [CB16/11/00371, CB16/11/00483]
  4. Foundation for Applied Medical Research, Universidad de Navarra (Spain)

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Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly caused by atherosclerosis and associated with traditional cardiovascular risk factors. It is highly prevalent in subjects over 65 years old and is expected to increase significantly with the aging population, posing a severe socioeconomic issue in the future. Poor prognosis of PAD may lead to impaired walking function and increased risk of cardiovascular events. Reliable biomarkers and machine learning methods have the potential to improve identification and personalized treatment of PAD patients.
Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly of atherosclerotic aetiology, associated to traditional cardiovascular (CV) risk factors. It is one of the most prevalent CV conditions worldwide in subjects >65 years, estimated to increase greatly with the aging of the population, becoming a severe socioeconomic problem in the future. The narrowing and thrombotic occlusion of the lower limb arteries impairs the walking function as the disease progresses, increasing the risk of CV events (myocardial infarction and stroke), amputation and death. Despite its poor prognosis, PAD patients are scarcely identified until the disease is advanced, highlighting the need for reliable biomarkers for PAD patient stratification, that might also contribute to define more personalized medical treatments. In this review, we will discuss the usefulness of inflammatory molecules, matrix metalloproteinases (MMPs), and cardiac damage markers, as well as novel components of the liquid biopsy, extracellular vesicles (EVs), and non-coding RNAs for lower limb PAD identification, stratification, and outcome assessment. We will also explore the potential of machine learning methods to build prediction models to refine PAD assessment. In this line, the usefulness of multimarker approaches to evaluate this complex multifactorial disease will be also discussed.

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