Agent Based Modeling of Atherosclerosis: A Concrete Help in Personalized Treatments

  • Francesco Pappalardo
  • Alessandro Cincotti
  • Alfredo Motta
  • Marzio Pennisi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5755)


Atherosclerosis, a pathology affecting arterial blood vessels, is one of most common diseases of the developed countries. We present studies on the increased atherosclerosis risk using an agent based model of atherogenesis that has been previously validated using clinical data. It is well known that the major risk in atherosclerosis is the persistent high level of low density lipoprotein (LDL) concentration. However, it is not known if short period of high LDL concentration can cause irreversible damage and if reduction of the LDL concentration (either by life style or drug) can drastically or partially reduce the already acquired risk. We simulated four different clinical situations in a large set of virtual patients (200 per clinical scenario). In the first one the patients lifestyle maintains the concentration of LDL in a no risk range. This is the control case simulation. The second case is represented by patients having high level of LDL with a delay to apply appropriate treatments; The third scenario is characterized by patients with high LDL levels treated with specific drugs like statins. Finally we simulated patients that are characterized by several oxidative events (smoke, sedentary life style, assumption of alcoholic drinks and so on so forth) that effective increase the risk of LDL oxidation. Those preliminary results obviously need to be clinically investigated. It is clear, however, that SimAthero has the power to concretely help medical doctors and clinicians in choosing personalized treatments for the prevention of the atherosclerosis damages.


Foam Cell Virtual Patient Foam Cell Formation Sedentary Life Style Arterial Blood Vessel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francesco Pappalardo
    • 1
    • 2
  • Alessandro Cincotti
    • 3
  • Alfredo Motta
    • 4
  • Marzio Pennisi
    • 2
  1. 1.Institute for Computing Applications ’M. Picone’National Research Council (CNR)RomeItaly
  2. 2.University of CataniaCataniaItaly
  3. 3.School of Information ScienceJapan Advanced Institute of Science and TechnologyJapan
  4. 4.Politecnico di MilanoMilanoItaly

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