
doi: 10.1002/wsbm.1546
pmid: 34931487
handle: 10034/626608 , 10034/626884 , 10034/626615 , 10034/627130
doi: 10.1002/wsbm.1546
pmid: 34931487
handle: 10034/626608 , 10034/626884 , 10034/626615 , 10034/627130
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
Cholesterol, LDL, Cardiovascular disease, Atherosclerosis, Cardiovascular Diseases, Risk Factors, cholesterol metabolism, Computational models, Humans, atherosclerosis, 190, mathematical models, Forecasting
Cholesterol, LDL, Cardiovascular disease, Atherosclerosis, Cardiovascular Diseases, Risk Factors, cholesterol metabolism, Computational models, Humans, atherosclerosis, 190, mathematical models, Forecasting
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
