Age-related vascular stiffening: causes and consequences
Open accessAbstract
Arterial stiffening occurs with age and is closely associated with the progression of cardiovascular disease. Stiffening is most often studied at the level of the whole vessel because increased stiffness of the large arteries can impose increased strain on the heart leading to heart failure. Interestingly, however, recent evidence suggests that the impact of increased vessel stiffening extends beyond the tissue scale and can also have deleterious microscale effects on cellular function. Altered extracellular matrix (ECM) architecture has been recognized as a key component of the pre-atherogenic state. Here, the underlying causes of age-related vessel stiffening are discussed, focusing on age-related crosslinking of the ECM proteins as well as through increased matrix deposition. Methods to measure vessel stiffening at both the macro- and microscale are described, spanning from the pulse wave velocity measurements performed clinically to microscale measurements performed largely in research laboratories. Additionally, recent work investigating how arterial stiffness and the changes in the ECM associated with stiffening contributed to endothelial dysfunction will be reviewed. We will highlight how changes in ECM protein composition contribute to atherosclerosis in the vessel wall. Lastly, we will discuss very recent work that demonstrates endothelial cells (ECs) are mechano-sensitive to arterial stiffening, where changes in stiffness can directly impact EC health. Overall, recent studies suggest that stiffening is an important clinical target not only because of potential deleterious effects on the heart but also because it promotes cellular level dysfunction in the vessel wall, contributing to a pathological atherosclerotic state.
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Endothelial cell dysfunction strongly affects arterial stiffness through cellular adhesion molecules, matrix metalloproteinases and collagen molecules.
“EC dysfunction has been shown to strongly affect arterial stiffness via cellular adhesion molecules (CAMs), matrix metalloproteinases (MMPs) and collagen molecules (COLs)67–71.”
From An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging (2021) · cites this paper as [68]
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- Authors
- Kohn, J. C., Lampi, M. C., & Reinhart-King, C. A.
- Affiliations
- Cornell University (US)
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2015-03-30
- Volume
- 06
- Pages
- 112-112
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC4396535
- OpenAlex
- W2048903244
- Cited by
- 440
- References
- 229
- Citations by year
- 2026: 45 · 2025: 36 · 2024: 58 · 2023: 60 · 2022: 46 · 2021: 51 · 2020: 36 · 2019: 29 · 2018: 40 · 2017: 26 · 2016: 11 · 2015: 1 · 2014: 1
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1435755, 2013165170, 2013155462)
- National Heart, Lung, and Blood Institute (R21HL097296, HL097296)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Elasticity and Material Modeling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
- Keywords
- arterial stiffening, aging, extracellular matrix, matrix deposition, pulse wave velocity, endothelial dysfunction, atherosclerosis, cardiovascular disease, heart failure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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