A mathematical model of atherosclerosis with reverse cholesterol transport and associated risk factors

Area of Science:

• Cardiovascular Science
• Mathematical Biology
• Pharmacology

Background:

• Atherosclerosis is a leading cause of death, characterized by arterial plaque buildup.
• Blood concentrations of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) are key risk factors.
• Previous mathematical models have explored plaque growth based on LDL and HDL levels.

Purpose of the Study:

• To refine a mathematical model of atherosclerosis plaque growth by incorporating reverse cholesterol transport.
• To explore the efficacy of potential drug therapies for plaque regression in mice and extrapolate to humans.
• To investigate the impact of oxidative stress, hypertension, and smoking on atherosclerosis risk.

Main Methods:

• Development and refinement of a mathematical model for plaque growth.
• Inclusion of reverse cholesterol transport mechanisms in the model.
• Simulation-based exploration of plaque regression in mice and analysis of risk factors.

Main Results:

• Model simulations suggest that drugs effective in mice may also slow plaque growth in humans.
• The study explored the influence of oxidative stress, antioxidant deficiency, high blood pressure, and smoking.
• Strategies for risk reduction in individuals with specific (LDL, HDL) concentrations were proposed.

Conclusions:

• Refined mathematical models can provide insights into atherosclerosis progression and regression.
• Therapeutic interventions tested in mice may hold promise for human application.
• Understanding and mitigating risk factors like hypertension and smoking are crucial for managing atherosclerosis.