Application and reduction of a nonlinear hyperelastic wall model capturing ex vivo relationships between fluid pressure, area, and wall thickness in normal and hypertensive murine left pulmonary arteries (original) (raw)

Haider, Mansoor A., Pearce, Katherine J., Chesler, Naomi C., Hill, Nicholas A. ORCID logoORCID: https://orcid.org/0000-0003-3079-828X and Olufsen, Mette S.(2024) Application and reduction of a nonlinear hyperelastic wall model capturing ex vivo relationships between fluid pressure, area, and wall thickness in normal and hypertensive murine left pulmonary arteries.International Journal for Numerical Methods in Biomedical Engineering, 40(3), e3798. (doi: 10.1002/cnm.3798) (PMID:38214099)

Abstract

Pulmonary hypertension is a cardiovascular disorder manifested by elevated mean arterial blood pressure (>20 mmHg) together with vessel wall stiffening and thickening due to alterations in collagen, elastin, and smooth muscle cells. Hypoxia-induced (type 3) pulmonary hypertension can be studied in animals exposed to a low oxygen environment for prolonged time periods leading to biomechanical alterations in vessel wall structure. This study introduces a novel approach to formulating a reduced order nonlinear elastic structural wall model for a large pulmonary artery. The model relating blood pressure and area is calibrated using ex vivo measurements of vessel diameter and wall thickness changes, under controlled pressure conditions, in left pulmonary arteries isolated from control and hypertensive mice. A two-layer, hyperelastic, and anisotropic model incorporating residual stresses is formulated using the Holzapfel–Gasser–Ogden model. Complex relations predicting vessel area and wall thickness with increasing blood pressure are derived and calibrated using the data. Sensitivity analysis, parameter estimation, subset selection, and physical plausibility arguments are used to systematically reduce the 16-parameter model to one in which a much smaller subset of identifiable parameters is estimated via solution of an inverse problem. Our final reduced one layer model includes a single set of three elastic moduli. Estimated ranges of these parameters demonstrate that nonlinear stiffening is dominated by elastin in the control animals and by collagen in the hypertensive animals. The pressure–area relation developed in this novel manner has potential impact on one-dimensional fluids network models of vessel wall remodeling in the presence of cardiovascular disease.

Item Type: Articles
Additional Information: Supported in part by the US National Science Foundation (DMS-1615820 and DMS-1638521) and by U.K. Research and Innovation (EPSRC EP/N014642/1, EP/S030875/1, and EP/T017899/1), and a Leverhulme Research Fellow-ship (NAH).
Status: Published
Refereed: Yes
Glasgow Author(s) Enlighten ID: Hill, Professor Nicholas
Authors: Haider, M. A., Pearce, K. J., Chesler, N. C., Hill, N. A., and Olufsen, M. S.
College/School: College of Science and Engineering > School of Mathematics and Statistics > Mathematics
Journal Name: International Journal for Numerical Methods in Biomedical Engineering
Publisher: Wiley
ISSN: 2040-7939
ISSN (Online): 2040-7947
Published Online: 12 January 2024
Copyright Holders: Copyright © 2024 The Authors
First Published: First published in International Journal for Numerical Methods in Biomedical Engineering 40(3): e3798
Publisher Policy: Reproduced under a Creative Commons license

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Funder and Project Information

EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancer

Raymond Ogden

EP/N014642/1

M&S - Mathematics

EPSRC Centre for Multiscale soft tissue mechanics with MIT and POLIMI (SofTMech-MP)

Xiaoyu Luo

EP/S030875/1

M&S - Mathematics

The SofTMech Statistical Emulation and Translation Hub

Dirk Husmeier

EP/T017899/1

M&S - Statistics

Deposit and Record Details

ID Code: 316538
Depositing User: Publications Router
Datestamp: 12 Feb 2024 16:13
Last Modified: 29 Oct 2024 12:12
Date of acceptance: 26 November 2023
Date of first online publication: 12 January 2024
Date Deposited: 12 February 2024
Data Availability Statement: Yes