Combining Inflammation and Tissue Turnover in the Modeling of Atherosclerosis Development Following the Outside‐In Disease Approach

Atherosclerosis remains the leading cause of cardiovascular morbidity worldwide. However, its onset and progression are still difficult to predict, and it remains unclear why certain arterial segments develop lesions while others remain unaffected. Recent findings highlight a prominent role of the vasa vasorum (VV)-the small blood vessels embedded within the walls of larger arteries-in driving disease development through an outside-in mechanism. In this view, perfusion deficits caused by vascular dysfunction may trigger chronic inflammation and promote plaque formation. To investigate this mechanism, we propose a novel multi-field model that combines tissue turnover, inflammation, and kinematics-based tissue growth. Perfusion is governed by a diffusion-reaction equation and accounts for VV dysfunction in supplying the outer arterial layers. Inflammation is captured through a phase-field representation that tracks the evolving interface between non-inflamed and inflamed tissue. A multiplicative decomposition of the deformation gradient then combines the inflammation-driven swelling and the homeostasis-driven tissue turnover, which itself is regulated by mechanical stress. The numerical implementation is realized using the standard finite element method. We assess model performance and plausibility through well-designed numerical case studies. The acquired simulation results highlight the coupled interaction among transport of blood-borne factors, inflammation, and mechanics, ultimately emphasizing how compromised VV can initiate a vicious cycle of ischemia, inflammation, and plaque growth in an outside-in fashion. In addition, we show how a moderate increase in blood pressure may result in a progressive increase in peak stress within atherosclerotic plaque tissue. Although our atherosclerosis model yields plausible predictions and allows deep insights into the interaction of mechanics, inflammation and tissue turnover, it is based on multiple modeling approximations, assumptions that would need sound validation in the future.