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An integrative model of cardiometabolic traits identifies two types of metabolic syndrome

Human diseases arise in a complex ecosystem composed of disease mechanisms and the whole-body state. However, the precise nature of the whole-body state and its relations with disease remain obscure. Here we map similarities among clinical parameters in normal physiological settings, including a lar...

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Autores principales: Frishberg, Amit, van den Munckhof, Inge, ter Horst, Rob, Schraa, Kiki, Joosten, Leo AB, Rutten, Joost HW, Iancu, Adrian C, Dregoesc, Ioana M, Tigu, Bogdan A, Netea, Mihai G, Riksen, Niels P, Gat-Viks, Irit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906604/
https://www.ncbi.nlm.nih.gov/pubmed/33507147
http://dx.doi.org/10.7554/eLife.61710
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author Frishberg, Amit
van den Munckhof, Inge
ter Horst, Rob
Schraa, Kiki
Joosten, Leo AB
Rutten, Joost HW
Iancu, Adrian C
Dregoesc, Ioana M
Tigu, Bogdan A
Netea, Mihai G
Riksen, Niels P
Gat-Viks, Irit
author_facet Frishberg, Amit
van den Munckhof, Inge
ter Horst, Rob
Schraa, Kiki
Joosten, Leo AB
Rutten, Joost HW
Iancu, Adrian C
Dregoesc, Ioana M
Tigu, Bogdan A
Netea, Mihai G
Riksen, Niels P
Gat-Viks, Irit
author_sort Frishberg, Amit
collection PubMed
description Human diseases arise in a complex ecosystem composed of disease mechanisms and the whole-body state. However, the precise nature of the whole-body state and its relations with disease remain obscure. Here we map similarities among clinical parameters in normal physiological settings, including a large collection of metabolic, hemodynamic, and immune parameters, and then use the mapping to dissect phenotypic states. We find that the whole-body state is faithfully represented by a quantitative two-dimensional model. One component of the whole-body state represents ‘metabolic syndrome’ (MetS) – a conventional way to determine the cardiometabolic state. The second component is decoupled from the classical MetS, suggesting a novel ‘non-classical MetS’ that is characterized by dozens of parameters, including dysregulated lipoprotein parameters (e.g. low free cholesterol in small high-density lipoproteins) and attenuated cytokine responses of immune cells to ex vivo stimulations. Both components are associated with disease, but differ in their particular associations, thus opening new avenues for improved personalized diagnosis and treatment. These results provide a practical paradigm to describe whole-body states and to dissect complex disease within the ecosystem of the human body.
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spelling pubmed-79066042021-02-26 An integrative model of cardiometabolic traits identifies two types of metabolic syndrome Frishberg, Amit van den Munckhof, Inge ter Horst, Rob Schraa, Kiki Joosten, Leo AB Rutten, Joost HW Iancu, Adrian C Dregoesc, Ioana M Tigu, Bogdan A Netea, Mihai G Riksen, Niels P Gat-Viks, Irit eLife Computational and Systems Biology Human diseases arise in a complex ecosystem composed of disease mechanisms and the whole-body state. However, the precise nature of the whole-body state and its relations with disease remain obscure. Here we map similarities among clinical parameters in normal physiological settings, including a large collection of metabolic, hemodynamic, and immune parameters, and then use the mapping to dissect phenotypic states. We find that the whole-body state is faithfully represented by a quantitative two-dimensional model. One component of the whole-body state represents ‘metabolic syndrome’ (MetS) – a conventional way to determine the cardiometabolic state. The second component is decoupled from the classical MetS, suggesting a novel ‘non-classical MetS’ that is characterized by dozens of parameters, including dysregulated lipoprotein parameters (e.g. low free cholesterol in small high-density lipoproteins) and attenuated cytokine responses of immune cells to ex vivo stimulations. Both components are associated with disease, but differ in their particular associations, thus opening new avenues for improved personalized diagnosis and treatment. These results provide a practical paradigm to describe whole-body states and to dissect complex disease within the ecosystem of the human body. eLife Sciences Publications, Ltd 2021-01-28 /pmc/articles/PMC7906604/ /pubmed/33507147 http://dx.doi.org/10.7554/eLife.61710 Text en © 2021, Frishberg et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Frishberg, Amit
van den Munckhof, Inge
ter Horst, Rob
Schraa, Kiki
Joosten, Leo AB
Rutten, Joost HW
Iancu, Adrian C
Dregoesc, Ioana M
Tigu, Bogdan A
Netea, Mihai G
Riksen, Niels P
Gat-Viks, Irit
An integrative model of cardiometabolic traits identifies two types of metabolic syndrome
title An integrative model of cardiometabolic traits identifies two types of metabolic syndrome
title_full An integrative model of cardiometabolic traits identifies two types of metabolic syndrome
title_fullStr An integrative model of cardiometabolic traits identifies two types of metabolic syndrome
title_full_unstemmed An integrative model of cardiometabolic traits identifies two types of metabolic syndrome
title_short An integrative model of cardiometabolic traits identifies two types of metabolic syndrome
title_sort integrative model of cardiometabolic traits identifies two types of metabolic syndrome
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7906604/
https://www.ncbi.nlm.nih.gov/pubmed/33507147
http://dx.doi.org/10.7554/eLife.61710
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