Computational Modelling of Atherosclerosis

TL;DR

This paper reviews the current state of computational modelling of atherosclerosis, highlighting recent advances, existing gaps, and future research opportunities in understanding this complex cardiovascular disease.

Contribution

It provides a comprehensive overview of recent modelling efforts, synthesizes current knowledge, and identifies key gaps for future exploration in atherosclerosis research.

Findings

Growing interest in computational models of atherosclerosis

Identification of key factors influencing disease progression

Highlighting gaps and future directions in modelling

Abstract

Atherosclerosis is one of the principle pathologies of cardiovascular disease with blood cholesterol a significant risk factor. The World Health Organisation estimates that approximately 2.5 million deaths occur annually due to the risk from elevated cholesterol with 39% of adults worldwide at future risk. Atherosclerosis emerges from the combination of many dynamical factors, including haemodynamics, endothelial damage, innate immunity and sterol biochemistry. Despite its significance to public health, the dynamics that drive atherosclerosis remain poorly understood. As a disease that depends on multiple factors operating on different length scales, the natural framework to apply to atherosclerosis is mathematical and computational modelling. A computational model provides an integrated description of the disease and serves as an in silico experimental system from which we can learn about the disease and develop therapeutic hypotheses. Although the work completed in this area to-date has been limited, there are clear signs that interest is growing and that a nascent field is establishing itself. This paper discusses the current state of modelling in this area, bringing together many recent results for the first time. We review the work that has been done, discuss its scope and highlight the gaps in our understanding that could yield future opportunities.