About this Research Topic
Metabolic syndrome affects 23 % of adults, and it places individuals at risk of heart diseases, diabetes, stroke, and even cancer. Metabolic syndrome is diagnosed when an individual has multiple metabolic conditions, including abdominal obesity, insulin resistance, hypertension, and hyperlipidemia. Like many other diseases, metabolic syndrome is caused by both the genome and exposome. An individual's genome contains a complete set of the person's genetic material. The variants in this entire six billion base pair collection of individuals' DNA determine the risk baseline of developing metabolic disorders. Additionally, other factors, including diet, stress, lifestyles, behaviors, living environments, and medication, further interact with genetic factors and eventually shape an individual's metabolic health. Not until recently, we started to peek into these highly diverse and dynamic interactions via quantifying their consequences on an individual's health by molecular profiling at the cellular level and tracking the physiomes of the individual.
With the recent advance of molecular biology, the reduced cost of next-generation sequencing technology, the digitalization of health records, and the rapid development of new technologies (e.g., wearable devices), we can now perform deep profiling of various aspects of gene-environmental interactions and link them to metabolic syndrome. The multi-omics profiles, including genome, epigenome, transcriptome, proteome, metabolome, microbiome, as well as physiome and activities, have been widely exploited in cellular systems, animal models, and human studies to investigate health-disease transition, drug development, biomarker discovery, and many other fields related to metabolic syndrome. Furthermore, machine learning techniques have been broadly applied to integrate information across multiple omics layers to predict risk, assist therapy, and derive biological insights. The multi-omics, big data approaches have become a fundamental methodology in biomedical research to decode the extensive interplay between genetic and environmental factors and reveal the complexity underlying Metabolic syndrome.
We would like to showcase the most recent advance in this field and provide future perspectives for the relevant research community.
With the recent advance of molecular biology, the reduced cost of next-generation sequencing technology, the digitalization of health records, and the rapid development of new technologies (e.g., wearable devices), we can now perform deep profiling of various aspects of gene-environmental interactions and link them to metabolic syndrome. The multi-omics profiles, including genome, epigenome, transcriptome, proteome, metabolome, microbiome, as well as physiome and activities, have been widely exploited in cellular systems, animal models, and human studies to investigate health-disease transition, drug development, biomarker discovery, and many other fields related to metabolic syndrome. Furthermore, machine learning techniques have been broadly applied to integrate information across multiple omics layers to predict risk, assist therapy, and derive biological insights. The multi-omics, big data approaches have become a fundamental methodology in biomedical research to decode the extensive interplay between genetic and environmental factors and reveal the complexity underlying Metabolic syndrome.
We would like to showcase the most recent advance in this field and provide future perspectives for the relevant research community.
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