Research Topic

Using Big Data Approaches to Understand Metabolic Syndrome

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.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

25 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

25 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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