A Two-Phase Model to Investigate LDL Toxicity in Early Atherosclerosis

Authors: Abdush Salam Pramanik, Bibaswan Dey, G. P. Raja Sekhar

arXiv: 2304.02587v1 - DOI (q-bio.TO)

Abstract: Atherosclerosis is a chronic inflammatory cardiovascular disease in which fatty plaque is built inside an artery wall. Early atherosclerotic plaque development is typically characterised by inflammatory tissues primarily consisting of foam cells and macrophages. We present a multiphase model that explores early plaque growth to emphasise the role of cytokines (in particular, Monocyte Chemoattractant Protein-1) and oxidised low-density lipoprotein (oxLDL) in monocyte recruitment and foam cell production, respectively. The plaque boundary is assumed to move at the same speed as the inflammatory tissues close to the periphery. This study discusses oxLDL-induced toxic environment towards macrophages and foam cells such that they start to die beyond a threshold limit of oxLDL concentration owing to excessive consumption. Our findings reveal that initially, the plaque evolves rapidly, and the growth rate subsequently reduces with time because of oxLDL-induced toxicity. In addition, it is observed that the saturation of inflammatory cell volume fraction becomes independent of oxLDL concentration beyond the threshold limit, referred to as the oxLDL toxicity limit. Our study manifests that a higher flux of oxLDL leads to plaque growth decay, whereas an increase in cytokines flux is favourable for enhanced plaque growth. Detailed analysis of the model presented in this article unfolds critical insights into the various biochemical and cellular mechanisms behind early plaque development.

Submitted to arXiv on 05 Apr. 2023

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.