Arterial Growth and Remodeling in Layered and Toroidal Geometries Using Constrained Mixture Theory.

Area of Science:

• Biomechanics
• Computational Biology
• Vascular Biology

Background:

• Arterial walls remodel in response to mechanical and biological signals, which can lead to pathological changes like aneurysms.
• Computational modeling is crucial for understanding these complex arterial G&R processes.

Purpose of the Study:

• To present a computational framework for simulating arterial growth and remodeling (G&R) in the thoracic aorta.
• To investigate aneurysm mechanics in the ascending and descending aorta using this framework.

Main Methods:

• Developed a computational framework based on constrained mixture theory for arterial G&R.
• Incorporated multilayered geometries and diverse tissue constituents (elastic fibers, collagens, smooth muscle cells, GAGs).
• Simulated G&R in both ascending and descending thoracic aorta models, including parameter studies on curvature and material properties.

Main Results:

• The multilayered model showed distinct G&R patterns between the media and adventitia in the descending aorta.
• Glycosaminoglycans (GAGs) significantly impacted mechanical responses, despite low abundance in healthy tissue.
• Vessel curvature was found to strongly influence the spatial distribution and extent of remodeling, especially along the outer curvature.

Conclusions:

• The computational framework effectively simulates region-specific arterial adaptation and disease progression in complex aortic geometries.
• This modeling platform can advance mechanistic studies on layered vessel structures and constituent-specific dysfunction in aneurysm development.
• The findings highlight the importance of GAGs and vessel curvature in vascular biomechanics and aneurysm pathogenesis.