Abstract
OBJECTIVE: Abdominal aortic aneurysms (AAAs) arise through complex pathogenesis, and good methods of risk stratification have proved elusive. Further, the lack of medical options short of surgery for treatment, and the requirement for dedicated imaging for identification, result in delayed diagnosis and hamper patient outcomes. Application of circulating biomarkers to effectively assess disease presence and predict progression would improve clinical management and support patient well-being. Exploration for suitable circulating biomarkers of AAAs is still very much in process; however, no disease-specific biomarker has yet been established for effective diagnosis and prognosis. This review aims to contribute enhanced tools for utilizing biomarkers for risk stratification and management of AAA disease. METHODS: Utilizing MEDLINE/PubMed, we summarize 44 recent publications covering circulating AAA biomarkers. The biomarkers were categorized and tiered into six subgroups by study design, with prospective studies tiered higher than retrospective observational studies. The classification system separately describes a list of post-interventional monitoring biomarkers. Part of the review also deals with recent approaches to identifying potential AAA biomarkers by genetic inference. RESULTS: Forty individual circulating biomarkers, two plasma protein panels (consisting of 9 or 7 proteins), one plasma-multiomic study, and two micro-RNA (miR) panels revealed correlations to AAA disease risk. Among those, many have already been established as biomarkers for other cardiovascular diseases, meaning feasibility has been proven but disease specificity is lacking. CONCLUSIONS: Multiple circulating proteins and miRs have been investigated for their utility as AAA-specific diagnostic or prognostic biomarkers. This work may ultimately identify not only novel AAA biomarkers that are specific for cell type, proteins, metabolites, genetic polymorphisms, and miRNA, but permit framing of comprehensive networks of disease-participating molecules. More robust data with higher disease sensitivity and specificity are needed, along with more multi-centered longitudinal clinical studies with large sample sizes.