Alastruey, Jordi, Charlton, Peter H, Bikia, Vasiliki, Paliakaite, Birute, Hametner, Bernhard, Bruno, Rosa Maria, Mulder, Marijn P, Vennin, Samuel, Piskin, Senol, Khir, Ashraf W, Guala, Andrea, Mayer, Christopher C, Mynard, Jonathan, Hughes, Alun D, Segers, Patrick, Westerhof, Berend E, Institut Català de la Salut, [Alastruey J] Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom. [Charlton PH] Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. [Bikia V] Division of Vascular Surgery, School of Medicine, Stanford University, Stanford, California, United States. Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland. [Paliakaite B] Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania. [Hametner B] AIT Austrian Institute of Technology, Center for Health and Bioresources, Medical Signal Analysis, Vienna, Austria. [Bruno RM] INSERM, U970, Paris Cardiovascular Research Center, Universite de Paris, Hopital Europeen Georges Pompidou – APHP, Paris, France. [Guala A] Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. CIBER-CV, Instituto de Salud Carlos III, Madrid, Spain, Vall d'Hebron Barcelona Hospital Campus, and American physiological society
Aging; Arteriosclerosis; Hemodynamics Envelliment; Arteriosclerosi; Hemodinàmica Envejecimiento; Arteriosclerosis; Hemodinámica Arterial pulse waves (PWs) such as blood pressure and photoplethysmogram (PPG) signals contain a wealth of information on the cardiovascular (CV) system that can be exploited to assess vascular age and identify individuals at elevated CV risk. We review the possibilities, limitations, complementarity, and differences of reduced-order, biophysical models of arterial PW propagation, as well as theoretical and empirical methods for analyzing PW signals and extracting clinically relevant information for vascular age assessment. We provide detailed mathematical derivations of these models and theoretical methods, showing how they are related to each other. Finally, we outline directions for future research to realize the potential of modeling and analysis of PW signals for accurate assessment of vascular age in both the clinic and in daily life. This article is based upon work from COST Action “Network for Research in Vascular Ageing” (VascAgeNet, CA18216), supported by COST (European Cooperation in Science and Technology, www.cost.eu). This work was supported by British Heart Foundation Grants PG/15/104/31913 (to J.A. and P.H.C.), FS/20/20/34626 (to P.H.C.), and AA/18/6/34223, PG/17/90/33415, SPG 2822621, and SP/F/21/150020 (to A.D.H.); Kaunas University of Technology Grant INP2022/16 (to B.P.); European Research Executive Agency, Marie-Sklodowska Curie Actions Individual Fellowship Grant 101038096 (to S.P.); Istinye University, BAP Project Grant 2019B1 (to S.P.); “la Caixa” Foundation Grant LCF/BQ/PR22/11920008 (to A.G.); and National Institute for Health and Care Research Grant AI AWARD02499 and EU Horizon 2020 Grant H2020 848109 (to A.D.H.).