Computational Lipidology: Predicting Lipoprotein Density Profiles in Human Blood Plasma
Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing tog...
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| Published in | PLoS computational biology Vol. 4; no. 5; p. e1000079 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
01.05.2008
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1553-7358 1553-734X 1553-7358 |
| DOI | 10.1371/journal.pcbi.1000079 |
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| Abstract | Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia. |
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| AbstractList |
Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia. Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia. Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond “bad” and “good” cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia. Lipids such as cholesterol and triglycerides, which are synthesized in the body or taken up by food, are indispensable for each cell of the human body. They are transported in blood plasma among the various tissues by so-called lipoproteins, which differ in size as well as in their composition of lipids and proteins. Changes in the amount of certain lipoprotein fractions are considered a major risk factor for atherosclerosis and cardiovascular diseases (CVD)—the main cause of death in the western states. To identify patients at risk for CVD, major lipoprotein classes (“bad” LDL, “good” HDL) are routinely monitored in clinical practice (which equals the lipoprotein profile). In this paper, we present a mathematical model that allows us to calculate lipoprotein profiles by computer and to simulate how the numerous biochemical processes involved in the metabolism of plasma lipoproteins influence the lipoprotein profile. Our simulations successfully reproduce clinically measured lipoprotein profiles of healthy subjects and patients with a defined lipid disorder (dyslipidemia). Calculation of a lipoprotein profile that fits best to the profile measured in individual patients opens the possibility of diagnosing potential molecular causes for dyslipidemia. The results of our model calculations also suggest the existence of lipoprotein sub-fractions which hitherto remained unrecognized in routinely performed separation of lipoprotein fractions. If this finding could be validated in forthcoming experimental investigations, it might help to define better patient-specific risk predictors of CVD. Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia.Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia. Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good" cholesterol are needed to precisely predict individual lipid disorders. Our work contributes to this aim by bringing together experiment and theory. We developed a novel computer-based model of the human plasma lipoprotein metabolism in order to simulate the blood lipid levels in high resolution. Instead of focusing on a few conventionally used predefined lipoprotein density classes (LDL, HDL), we consider the entire protein and lipid composition spectrum of individual lipoprotein complexes. Subsequently, their distribution over density (which equals the lipoprotein profile) is calculated. As our main results, we (i) successfully reproduced clinically measured lipoprotein profiles of healthy subjects; (ii) assigned lipoproteins to narrow density classes, named high-resolution density sub-fractions (hrDS), revealing heterogeneous lipoprotein distributions within the major lipoprotein classes; and (iii) present model-based predictions of changes in the lipoprotein distribution elicited by disorders in underlying molecular processes. In its present state, the model offers a platform for many future applications aimed at understanding the reasons for inter-individual variability, identifying new sub-fractions of potential clinical relevance and a patient-oriented diagnosis of the potential molecular causes for individual dyslipidemia. doi:10.1371/journal.pcbi.1000079 |
| Audience | Academic |
| Author | Holzhütter, Hermann-Georg Hübner, Katrin Winkler, Karl Reich, Jens-Georg Schwager, Thomas |
| AuthorAffiliation | 1 Computational Systems Biochemistry, Institute of Biochemistry, Charité-Universitätsmedizin Berlin, Germany University of California San Diego, United States of America 3 Department of Clinical Chemistry, University Hospital Freiburg, Germany 2 Bioinformatics Group, Max Delbrück Center for Molecular Medicine Berlin-Buch, Germany |
| AuthorAffiliation_xml | – name: 2 Bioinformatics Group, Max Delbrück Center for Molecular Medicine Berlin-Buch, Germany – name: 1 Computational Systems Biochemistry, Institute of Biochemistry, Charité-Universitätsmedizin Berlin, Germany – name: University of California San Diego, United States of America – name: 3 Department of Clinical Chemistry, University Hospital Freiburg, Germany |
| Author_xml | – sequence: 1 givenname: Katrin surname: Hübner fullname: Hübner, Katrin – sequence: 2 givenname: Thomas surname: Schwager fullname: Schwager, Thomas – sequence: 3 givenname: Karl surname: Winkler fullname: Winkler, Karl – sequence: 4 givenname: Jens-Georg surname: Reich fullname: Reich, Jens-Georg – sequence: 5 givenname: Hermann-Georg surname: Holzhütter fullname: Holzhütter, Hermann-Georg |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/18497853$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2008 Public Library of Science Hübner et al. 2008 2008 Hübner et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Hübner K, Schwager T, Winkler K, Reich J-G, Holzhütter 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 |
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| Keywords | Lipoproteins Blood Chemical Analysis Models, Cardiovascular Computer Simulation Humans Statistical Distributions Models, Statistical |
| Language | English |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Current address: BIOQUANT, Modeling Biological Processes, Institute of Zoology, University of Heidelberg, Heidelberg, Germany. Performed the experiments: KW. Analyzed the data: KH. Contributed reagents/materials/analysis tools: TS KW. Wrote the paper: KH TS HH. Data-mined the literature; contributed to the model design; performed model building, implementations, and simulations; conceived and performed in-silico experiments and analyses: KH. Provided stochastic modeling experience and model implementations and contributed to the model design, simulations and analyses: TS. Provided experimental work and clinical experience in lipid diagnostics: KW. Provided mathematical modeling experience and lipoprotein metabolism knowledge: JR. Contributed to the model design and provided mathematical modeling experience, model implementation, and simulation: HH. |
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| Snippet | Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and "good"... Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond “bad” and “good”... Monitoring cholesterol levels is strongly recommended to identify patients at risk for myocardial infarction. However, clinical markers beyond "bad" and... |
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| SubjectTerms | Apolipoproteins Biochemistry Biochemistry/Macromolecular Assemblies and Machines Biochemistry/Theory and Simulation Blood Chemical Analysis - methods Cardiovascular Disorders Cholesterol Computational Biology Computational Biology/Systems Biology Computer Simulation Humans Lipids Lipoproteins - blood Medical research Models, Cardiovascular Models, Statistical Plasma Statistical Distributions Studies |
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| Title | Computational Lipidology: Predicting Lipoprotein Density Profiles in Human Blood Plasma |
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