Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet

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Abstract

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.

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Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.

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Charalambos Vlachopoulos, Konstantinos Aznaouridis, Christodoulos Stefanadis (2010)

The purpose of this study was to calculate robust quantitative estimates of the predictive value of aortic pulse wave velocity (PWV) for future cardiovascular (CV) events and all-cause mortality by meta-analyses of longitudinal studies. Arterial stiffness is increasingly recognized as a surrogate end point for CV disease. We performed a meta-analysis of 17 longitudinal studies that evaluated aortic PWV and followed up 15,877 subjects for a mean of 7.7 years. The pooled relative risk (RR) of clinical events increased in a stepwise, linear-like fashion from the first to the third tertile of aortic PWV. The pooled RRs of total CV events, CV mortality, and all-cause mortality were 2.26 (95% confidence interval: 1.89 to 2.70, 14 studies), 2.02 (95% confidence interval: 1.68 to 2.42, 10 studies), and 1.90 (95% confidence interval: 1.61 to 2.24, 11 studies), respectively, for high versus low aortic PWV subjects. For total CV events and CV mortality, the RR was significantly higher in high baseline risk groups (coronary artery disease, renal disease, hypertension) compared with low-risk subjects (general population). An increase in aortic PWV by 1 m/s corresponded to an age-, sex-, and risk factor-adjusted risk increase of 14%, 15%, and 15% in total CV events, CV mortality, and all-cause mortality, respectively. An increase in aortic PWV by 1 SD was associated with respective increases of 47%, 47%, and 42%. Aortic stiffness expressed as aortic PWV is a strong predictor of future CV events and all-cause mortality. The predictive ability of arterial stiffness is higher in subjects with a higher baseline CV risk.

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Author and article information

Journal

Journal ID (nlm-ta): Am J Physiol Heart Circ Physiol

Journal ID (iso-abbrev): Am J Physiol Heart Circ Physiol

Journal ID (publisher-id): AJPHEART

Title: American Journal of Physiology - Heart and Circulatory Physiology

Publisher: American Physiological Society (Rockville, MD )

ISSN (Print): 0363-6135

ISSN (Electronic): 1522-1539

Publication date (Print): 1 July 2023

Publication date (Electronic): 31 March 2023

Publication date PMC-release: 31 March 2023

Volume: 325

Issue: 1

Pages: H1-H29

Affiliations

[1] ¹Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London , London, United Kingdom

[2] ²Department of Public Health and Primary Care, University of Cambridge , Cambridge, United Kingdom

[3] ³Division of Vascular Surgery, School of Medicine, Stanford University , Stanford, California, United States

[4] ⁴Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology , Lausanne, Switzerland

[5] ⁵Biomedical Engineering Institute, Kaunas University of Technology , Kaunas, Lithuania

[6] ⁶AIT Austrian Institute of Technology, Center for Health and Bioresources, Medical Signal Analysis , Vienna, Austria

[7] ⁷INSERM, U970, Paris Cardiovascular Research Center, Université de Paris, Hopital Europeen Georges Pompidou–APHP , Paris, France

[8] ⁸Cardiovascular and Respiratory Physiology, TechMed Centre, University of Twente , Enschede, The Netherlands

[9] ⁹Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Istinye University , Istanbul, Turkey

[10] ¹⁰Department of Engineering, Durham University , Durham, United Kingdom

[11] ¹¹Vall d’Hebron Institut de Recerca , Barcelona, Spain

[12] ¹²CIBER-CV, Instituto de Salud Carlos III , Madrid, Spain

[13] ¹³Heart Research, Murdoch Children’s Research Institute , Parkville, Victoria, Australia

[14] ¹⁴Department of Paediatrics, University of Melbourne , Parkville, Victoria, Australia

[15] ¹⁵Department of Biomedical Engineering, University of Melbourne , Parkville, Victoria, Australia

[16] ¹⁶MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London , London, United Kingdom

[17] ¹⁷Institute for Biomedical Engineering and Technology, Ghent University , Ghent, Belgium

[18] ¹⁸Department of Pulmonary Medicine, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

[19] ¹⁹Department of Neonatology, Radboud University Medical Center, Radboud Institute for Health Sciences, Amalia Children’s Hospital , Nijmegen, The Netherlands

Author notes

Correspondence: J. Alastruey ( jordi.alastruey-arimon@ 123456kcl.ac.uk ).

Author information

Jordi Alastruey https://orcid.org/0000-0003-3742-5259

Peter H. Charlton https://orcid.org/0000-0003-3836-8655

Vasiliki Bikia https://orcid.org/0000-0002-4660-1560

Birute Paliakaite https://orcid.org/0000-0002-4831-6587

Bernhard Hametner https://orcid.org/0000-0003-2048-1019

Rosa Maria Bruno https://orcid.org/0000-0002-6107-3356

Marijn P. Mulder https://orcid.org/0000-0003-0318-0046

Samuel Vennin https://orcid.org/0000-0001-7615-2690

Senol Piskin https://orcid.org/0000-0002-8799-9472

Andrea Guala https://orcid.org/0000-0002-5006-8164

Christopher C. Mayer https://orcid.org/0000-0002-5612-5481

Jonathan Mynard https://orcid.org/0000-0002-5692-2106

Alun D. Hughes https://orcid.org/0000-0001-5432-5271

Patrick Segers https://orcid.org/0000-0003-3870-3409

Berend E. Westerhof https://orcid.org/0000-0003-4753-2461

Article

Publisher ID: H-00705-2022 Publisher-manuscript ID: H-00705-2022

DOI: 10.1152/ajpheart.00705.2022

PMC ID: 7614613

PubMed ID: 37000606

SO-VID: 080ab1f0-05fb-483c-9693-cbd32a49154a

License:

Licensed under Creative Commons Attribution CC-BY 4.0. Published by the American Physiological Society.

History

Date received : 15 December 2022

Date revision received : 27 March 2023

Date accepted : 27 March 2023

Funding

Funded by: EU Horizon 2020, doi 10.13039/100010661;

Award ID: H2020 848109

Award Recipient : Alun D. Hughes

Funded by: Kaunas University of Technology, doi 10.13039/501100016248;

Award ID: INP2022/16

Award Recipient : Birute Paliakaite

Funded by: European Commission | European Research Executive Agency, doi 10.13039/501100000780;

Award ID: 101038096

Award Recipient : Senol Piskin