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The LDL-HDL Profile Determines the Risk of Atherosclerosis: A Mathematical Model

Overview of attention for article published in PLOS ONE, March 2014
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Title
The LDL-HDL Profile Determines the Risk of Atherosclerosis: A Mathematical Model
Published in
PLOS ONE, March 2014
DOI 10.1371/journal.pone.0090497
Pubmed ID
Authors

Wenrui Hao, Avner Friedman

Abstract

Atherosclerosis, the leading death in the United State, is a disease in which a plaque builds up inside the arteries. As the plaque continues to grow, the shear force of the blood flow through the decreasing cross section of the lumen increases. This force may eventually cause rupture of the plaque, resulting in the formation of thrombus, and possibly heart attack. It has long been recognized that the formation of a plaque relates to the cholesterol concentration in the blood. For example, individuals with LDL above 190 mg/dL and HDL below 40 mg/dL are at high risk, while individuals with LDL below 100 mg/dL and HDL above 50 mg/dL are at no risk. In this paper, we developed a mathematical model of the formation of a plaque, which includes the following key variables: LDL and HDL, free radicals and oxidized LDL, MMP and TIMP, cytockines: MCP-1, IFN-γ, IL-12 and PDGF, and cells: macrophages, foam cells, T cells and smooth muscle cells. The model is given by a system of partial differential equations with in evolving plaque. Simulations of the model show how the combination of the concentrations of LDL and HDL in the blood determine whether a plaque will grow or disappear. More precisely, we create a map, showing the risk of plaque development for any pair of values (LDL,HDL).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 297 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 296 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 72 24%
Student > Master 35 12%
Student > Ph. D. Student 26 9%
Researcher 20 7%
Student > Postgraduate 18 6%
Other 37 12%
Unknown 89 30%
Readers by discipline Count As %
Medicine and Dentistry 36 12%
Biochemistry, Genetics and Molecular Biology 34 11%
Agricultural and Biological Sciences 33 11%
Nursing and Health Professions 17 6%
Engineering 16 5%
Other 65 22%
Unknown 96 32%