Abstract
OPINION article Front. Genet., 07 December 2021Sec. Computational Genomics https://doi.org/10.3389/fgene.2021.783930
Highlights
Metabolic syndrome (MetS) is a group of conditions that happen concurrently which increases the risks of stroke, cardiovascular diseases, and type 2 diabetes
The exposome concept was first introduced in 2005, which aims to investigate the relationships between the external exposures and internal -omes in the progression and occurrence of the disease, which can be an essential complement to the conventional etiology studies which primarily have focused on genetics (Wild, 2005)
The results showed the adults in the low-stress group were 45% less likely to have MetS compared with the adults in the highstress group with the controls of known covariates
Summary
Metabolic syndrome (MetS) is a group of conditions that happen concurrently which increases the risks of stroke, cardiovascular diseases, and type 2 diabetes. The exposome concept was first introduced in 2005, which aims to investigate the relationships between the external exposures and internal -omes in the progression and occurrence of the disease, which can be an essential complement to the conventional etiology studies which primarily have focused on genetics (Wild, 2005). EWAS usually applied advanced analytic methodologies to find the significant associations between phenotypes and the exposome (Patel, 2019) It is a data-driven approach that can fit the application of both population health and individual differences so that the complex relationship between external and internal environments as well as the life-course environmental health can be systematically explored (Chung et al, 2019). This article aims to outline the feasibility of leveraging the interdisciplinary EWAS to understand MetS in more depth, focusing on the interactions between different types of environmental exposures and internal physiological factors, and underlying mechanisms in the pathogenesis of MetS and its components such as cardiovascular disease (Juarez et al, 2020)
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