An integrative model of cardiometabolic traits identifies two types of metabolic syndrome
Abstract
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. high LDL- cholesterol and low free cholesterol in small HDL particles) and attenuated cytokine responses of PBMCs 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.
Data availability
Both the obesity cohort and the normal BMI cohort were part of the Human Functional Genomics Project (www.humanfunctionalgenomics.org) and has been previously published.The coronary-atherosclerosis cohort was collected as part of the HORIZON 2020 European Research Program - "REPROGRAM: Targeting epigenetic REPROGRamming of innate immune cells in Atherosclerosis Management and other chronic inflammatory diseases".SLE sequencing public datasets used in our analysis: GSE65391, GSE49454.
Article and author information
Author details
Funding
European Commission (637885)
- Amit Frishberg
- Irit Gat-Viks
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Edward D Janus, University of Melbourne, Australia
Publication history
- Received: August 2, 2020
- Accepted: January 27, 2021
- Accepted Manuscript published: January 28, 2021 (version 1)
- Version of Record published: February 25, 2021 (version 2)
Copyright
© 2021, Frishberg et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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