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WIREs Mechanisms of Disease
Article . 2021 . Peer-reviewed
License: Wiley Online Library User Agreement
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Modeling cholesterol metabolism and atherosclerosis

Authors: Mc Auley, Mark Tomás;

Modeling cholesterol metabolism and atherosclerosis

Abstract

AbstractAtherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low‐density lipoprotein cholesterol (LDL‐C) remains the gold standard. Cholesterol metabolism at the cellular and whole‐body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future.This article is categorized under: Cardiovascular Diseases > Computational Models

Country
United Kingdom
Related Organizations
Keywords

Cholesterol, LDL, Cardiovascular disease, Atherosclerosis, Cardiovascular Diseases, Risk Factors, cholesterol metabolism, Computational models, Humans, atherosclerosis, 190, mathematical models, Forecasting

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    13
    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Average
Top 10%
Green