Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/29720
Title: Towards a personalised approach in exercise-based cardiovascular rehabilitation: How can translational research help? A ‘call to action’ from the Section on Secondary Prevention and Cardiac Rehabilitation of the European Association of Preventive Cardiology
Authors: Gevaert, Andreas B.
Adams, Volker
Bahls, Martin
Bowen, T. Scott
Cornelissen, Veronique
Dörr, Marcus
HANSEN, Dominique 
Kemps, Hareld M.C.
Leeson, paul
Van Craenenbroeck, Emeline M.
Kränkel, Nicolle
Issue Date: 2020
Publisher: SAGE PUBLICATIONS LTD
Source: European journal of preventive cardiology (Print), 27 (13), p. 1369-1385
Abstract: The benefit of regular physical activity and exercise training for the prevention of cardiovascular and metabolic diseases is undisputed. Many molecular mechanisms mediating exercise effects have been deciphered. Personalised exercise prescription can help patients in achieving their individual greatest benefit from an exercise-based cardiovascular rehabilitation programme. Yet, we still struggle to provide truly personalised exercise prescriptions to our patients. In this position paper, we address novel basic and translational research concepts that can help us understand the principles underlying the inter-individual differences in the response to exercise, and identify early on who would most likely benefit from which exercise intervention. This includes hereditary, non-hereditary and sex-specific concepts. Recent insights have helped us to take on a more holistic view, integrating exercise-mediated molecular mechanisms with those influenced by metabolism and immunity. Unfortunately, while the outline is recognisable, many details are still lacking to turn the understanding of a concept into a roadmap ready to be used in clinical routine. This position paper therefore also investigates perspectives on how the advent of 'big data' and the use of animal models could help unravel inter-individual responses to exercise parameters and thus influence hypothesis-building for translational research in exercise-based cardiovascular rehabilitation.
Notes: Krankel, N (reprint author), Charite Univ Med Berlin, Dept Cardiol, Campus Benjamin Franklin,Hindenburgdamm 30, D-12203 Berlin, Germany. nicolle.kraenkel@charite.de The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: TSB is supported by a Medical Research Council UK New Investigator award (MR/S025472/1). EMVC is supported by Fund for Scientific Research Flanders (Senior Clinical Investigator fellowship) and Koning Boudewijnstichting (Fund Joseph Oscar Waldmann-Berteau 2015). NK receives project-specific funding from the German Centre for Cardiovascular Research (DZHK; 81X2100238 and 81X2100243), the German Foundation of Heart Research (F/39/17) and the German Diabetes Foundation (FP-0421-2018). ABG, VA, MB, VC, MD, DH, HMCK and PL do not receive funding pertinent to this work.
Keywords: Cardiovascular rehabilitation;exercise;personalised medicine;responders;non-responders;immune system;machine learning;big data;animal models
Document URI: http://hdl.handle.net/1942/29720
ISSN: 2047-4873
e-ISSN: 2047-4881
DOI: 10.1177/2047487319877716
ISI #: WOS:000491786700001
Rights: The European Society of Cardiology 2019
Category: A1
Type: Journal Contribution
Validations: ecoom 2020
Appears in Collections:Research publications

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