Personalized haemodynamic modelling for non-invasive quantification of arterial stiffness in the human vasculature.
5 affiliations
- 1Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences , Shenzhen, Guangdong, People's Republic of China.
- 2Shenzhen Institutes for Advanced Study UESTC , Shenzhen, Guangdong, People's Republic of China.
- 3Futian Center for Chronic Disease Control , Shenzhen, People's Republic of China.
- 4Department of Electronics and Mechanical Engineering, Chiba University , Chiba, Japan.
- 5Shenzhen University of Advanced Technology , Shenzhen, Guangdong, People's Republic of China.
Abstract
Arterial stiffness is a pivotal predictor of cardiovascular events, yet its in vivo characterization remains elusive because arterial wall mechanics cannot be directly measured. Conventional inverse approaches require high-resolution imaging to resolve wall motion, restricting their clinical applicability. Here, we present a non-invasive computational framework that infers regional arterial stiffness solely from routinely acquired pressure and blood flow velocity waveforms by leveraging personalized one-dimensional haemodynamic models of the human arterial network. Patient-specific models were constructed for 21 individuals with hypertension using stroke volume, arterial diameters, brachial pressures and carotid flow profiles. Regional stiffness parameters were identified through optimization to reconcile simulated and measured haemodynamics, and validated against brachial-ankle pulse wave velocity. The inferred stiffness demonstrated strong concordance with clinical measurements (r = 0.81; standard error = 279 cm s)-1. Longitudinal evaluation in 13 patients further revealed therapy-dependent remodelling, with calcium channel blockers selectively reducing pathological stiffening while preserving compliant vessels. Together, these results establish model-based inversion of standard haemodynamic data as a practical, scalable approach for quantifying arterial stiffness, enabling individualized cardiovascular risk assessment and treatment monitoring.
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BibTeX
@article{zhang2026personalized,
title = {{Personalized haemodynamic modelling for non-invasive quantification of arterial stiffness in the human vasculature.}},
author = {Xiangdong Zhang and Dan Wu and Fen Miao and Jiajie Zhong and Fan Yu and Xuejiao Zhang and Hao Liu and Ye Li},
journal = {Journal of the Royal Society, Interface},
year = {2026},
month = oct,
doi = {10.1098/rsif.2025.1318},
url = {https://europepmc.org/article/MED/42869458},
note = {CC BY},
}
RIS
TY - JOUR
TI - Personalized haemodynamic modelling for non-invasive quantification of arterial stiffness in the human vasculature.
AU - Xiangdong Zhang
AU - Dan Wu
AU - Fen Miao
AU - Jiajie Zhong
AU - Fan Yu
AU - Xuejiao Zhang
AU - Hao Liu
AU - Ye Li
PY - 2026
DA - 2026/10/01
JO - Journal of the Royal Society, Interface
DO - 10.1098/rsif.2025.1318
UR - https://europepmc.org/article/MED/42869458
AB - Arterial stiffness is a pivotal predictor of cardiovascular events, yet its in vivo characterization remains elusive because arterial wall mechanics cannot be directly measured. Conventional inverse approaches require high-resolution imaging to resolve wall motion, restricting their clinical applicability. Here, we present a non-invasive computational framework that infers regional arterial stiffness solely from routinely acquired pressure and blood flow velocity waveforms by leveraging personalized one-dimensional haemodynamic models of the human arterial network. Patient-specific models were constructed for 21 individuals with hypertension using stroke volume, arterial diameters, brachial pressures and carotid flow profiles. Regional stiffness parameters were identified through optimization to reconcile simulated and measured haemodynamics, and validated against brachial-ankle pulse wave velocity. The inferred stiffness demonstrated strong concordance with clinical measurements (r = 0.81; standard error = 279 cm s)-1. Longitudinal evaluation in 13 patients further revealed therapy-dependent remodelling, with calcium channel blockers selectively reducing pathological stiffening while preserving compliant vessels. Together, these results establish model-based inversion of standard haemodynamic data as a practical, scalable approach for quantifying arterial stiffness, enabling individualized cardiovascular risk assessment and treatment monitoring.
SN - 1742-5689
SN - 1742-5662
ER -