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Clinical Investigation Meets Computer Simulation To Analyze Risk Factor Of Heart Disease

Date:
May 27, 2008
Source:
Public Library of Science
Summary:
Researchers have developed a novel, computer-based strategy to study plasma lipoprotein profiles considered a major predictor of cardiovascular disease. Lipoproteins are the "container ships" in our blood that transport lipids (fats) such as cholesterol and triglycerides to various tissues; they differ largely in size and "cargo" composition. Abnormalities in the amount of certain lipoprotein fractions are considered a major risk factor for atherosclerosis and CVD.

German researchers at the Charité -- Universitätsmedizin Berlin and the Max-Delbrück-Center Berlin have developed a novel, computer-based strategy to study plasma lipoprotein profiles considered a major predictor of cardiovascular disease (CVD).

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Lipoproteins are the "container ships" in our blood that transport lipids (fats) such as cholesterol and triglycerides to various tissues; they differ largely in size and "cargo" composition. Abnormalities in the amount of certain lipoprotein fractions are considered a major risk factor for atherosclerosis and CVD. To identify patients at risk for CVD, selected lipoprotein fractions - "bad" Low Density Lipoproteins (LDL) and "good" High Density Lipoproteins (HDL) - are routinely monitored in clinical practice (lipoprotein profile).

The decrease of LDL cholesterol is a principal target in cardiovascular preventive strategies. Growing evidence claims that evaluating the lipoprotein profile in greater detail (e.g. looking at subfractions of LDL and HDL) may provide more reliable prognostic information than routine measurement of LDL cholesterol levels, but this needs elaborate and expensive work.

Katrin Hübner and colleagues, therefore, conceived of designing a mathematical model to provide computer calculations of lipoprotein profiles which take into account the entire "fleet" of lipoproteins in blood plasma by simulating every single lipoprotein ("ship"). This way, studying lipoprotein profiles in any desired detail is possible. The model may also be broadly applied to infer relationships between a patient's lipoprotein profile and the underlying biochemical processes.

The calculations were verified by comparing them with clinically measured lipoprotein profiles of healthy subjects and pathological cases of known lipid disorders. The researchers show that more detailed lipoprotein profiles can reveal possibly clinically-relevant abnormalities in the lipid values which would remain undetected by evaluating only LDL and HDL.

Together with independent information on diet and genetic variations this increases the potential for patient-oriented diagnosing of molecular causes for observed abnormal lipoprotein profiles.

 This work was in part associated to the German Systems Biology Initiative HepatoSys. Clinical data of lipoprotein profiles were generated in collaboration with the University Medical Center Freiburg.


Story Source:

The above story is based on materials provided by Public Library of Science. Note: Materials may be edited for content and length.


Journal Reference:

  1. Hübner K, Schwager T, Winkler K, Reich J-G, Holzhu¨ tter H-G (2008) Computational Lipidology: Predicting Lipoprotein Density Profiles in Human Blood Plasma. PLoS Comput Biol 4(5): e1000079. doi:10.1371/journal.pcbi.1000079 [link]

Cite This Page:

Public Library of Science. "Clinical Investigation Meets Computer Simulation To Analyze Risk Factor Of Heart Disease." ScienceDaily. ScienceDaily, 27 May 2008. <www.sciencedaily.com/releases/2008/05/080522210021.htm>.
Public Library of Science. (2008, May 27). Clinical Investigation Meets Computer Simulation To Analyze Risk Factor Of Heart Disease. ScienceDaily. Retrieved March 30, 2015 from www.sciencedaily.com/releases/2008/05/080522210021.htm
Public Library of Science. "Clinical Investigation Meets Computer Simulation To Analyze Risk Factor Of Heart Disease." ScienceDaily. www.sciencedaily.com/releases/2008/05/080522210021.htm (accessed March 30, 2015).

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