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"Big Data" Approaches for Prevention of the Metabolic Syndrome.

ABSTRACT: Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these "big data", the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of "big data" applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

SUBMITTER: Jiang X

PROVIDER: S-EPMC9095427 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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"Big Data" Approaches for Prevention of the Metabolic Syndrome.

Jiang Xinping X Yang Zhang Z Wang Shuai S Deng Shuanglin S

Frontiers in genetics 20220427

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these "big data", the preven ...[more]

PMID: 35571045

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