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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Autores principales:	Katrin Hübner, Thomas Schwager, Karl Winkler, Jens-Georg Reich, Hermann-Georg Holzhütter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c7916a01ea8c41c59ad14ef7ee327615
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spelling	oai:doaj.org-article:c7916a01ea8c41c59ad14ef7ee3276152021-11-25T05:41:17ZComputational lipidology: predicting lipoprotein density profiles in human blood plasma.1553-734X1553-735810.1371/journal.pcbi.1000079https://doaj.org/article/c7916a01ea8c41c59ad14ef7ee3276152008-05-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18497853/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Monitoring 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.Katrin HübnerThomas SchwagerKarl WinklerJens-Georg ReichHermann-Georg HolzhütterPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 5, p e1000079 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Katrin Hübner Thomas Schwager Karl Winkler Jens-Georg Reich Hermann-Georg Holzhütter Computational lipidology: predicting lipoprotein density profiles in human blood plasma.
description	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.
format	article
author	Katrin Hübner Thomas Schwager Karl Winkler Jens-Georg Reich Hermann-Georg Holzhütter
author_facet	Katrin Hübner Thomas Schwager Karl Winkler Jens-Georg Reich Hermann-Georg Holzhütter
author_sort	Katrin Hübner
title	Computational lipidology: predicting lipoprotein density profiles in human blood plasma.
title_short	Computational lipidology: predicting lipoprotein density profiles in human blood plasma.
title_full	Computational lipidology: predicting lipoprotein density profiles in human blood plasma.
title_fullStr	Computational lipidology: predicting lipoprotein density profiles in human blood plasma.
title_full_unstemmed	Computational lipidology: predicting lipoprotein density profiles in human blood plasma.
title_sort	computational lipidology: predicting lipoprotein density profiles in human blood plasma.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/c7916a01ea8c41c59ad14ef7ee327615
work_keys_str_mv	AT katrinhubner computationallipidologypredictinglipoproteindensityprofilesinhumanbloodplasma AT thomasschwager computationallipidologypredictinglipoproteindensityprofilesinhumanbloodplasma AT karlwinkler computationallipidologypredictinglipoproteindensityprofilesinhumanbloodplasma AT jensgeorgreich computationallipidologypredictinglipoproteindensityprofilesinhumanbloodplasma AT hermanngeorgholzhutter computationallipidologypredictinglipoproteindensityprofilesinhumanbloodplasma
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Computational lipidology: predicting lipoprotein density profiles in human blood plasma.

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