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How to measure and model cardiovascular aging: invited spotlight review

Most acquired cardiovascular diseases are more common in older people, and the biological mechanisms and manifestations of aging provide insight into cardiovascular pathophysiology. Measuring aging within the cardiovascular system may help to better understand risk profiles for specific individuals...

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Bibliographische Detailangaben
Hauptverfasser: Spray, Luke (VerfasserIn) , Richardson, Gavin (VerfasserIn) , Booth, Laura K (VerfasserIn) , Haendeler, Judith (VerfasserIn) , Altschmied, Joachim (VerfasserIn) , Bromage, Daniel I (VerfasserIn) , Wallis, Sienna B (VerfasserIn) , Stellos, Konstantinos (VerfasserIn) , Tual-Chalot, Simon (VerfasserIn) , Spyridopoulos, Ioakim (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: August 2025
In: Cardiovascular research
Year: 2025, Jahrgang: 121, Heft: 10, Pages: 1489-1508
ISSN:1755-3245
DOI:10.1093/cvr/cvaf138
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1093/cvr/cvaf138
Volltext
Verfasserangaben:Luke Spray, Gavin Richardson, Laura K. Booth, Judith Haendeler, Joachim Altschmied, Daniel I. Bromage, Sienna B. Wallis, Konstantinos Stellos, Simon Tual-Chalot, and Ioakim Spyridopoulos
Beschreibung
Zusammenfassung:Most acquired cardiovascular diseases are more common in older people, and the biological mechanisms and manifestations of aging provide insight into cardiovascular pathophysiology. Measuring aging within the cardiovascular system may help to better understand risk profiles for specific individuals and direct targeted preventative therapy. In this review, we explore telomere attrition, cellular senescence, epigenetic modifications, and mitochondrial dysfunction as key molecular mechanisms of aging. These phenomena are associated with cardiovascular disease through endothelial dysfunction and systemic inflammation, which are measurable in clinical practice with a variety of clinical, laboratory, and imaging techniques. Finally, we discuss that the next tools for modelling cardiovascular aging must be capable of incorporating a vast amount of diverse data from a given patient, pointing to recent developments in artificial intelligence and machine learning.
Beschreibung:Online veröffentlicht: 28. August 2025
Gesehen am 04.11.2025
Beschreibung:Online Resource
ISSN:1755-3245
DOI:10.1093/cvr/cvaf138

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