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Application of combined omics platforms to accelerate biomedical discovery in diabesity
Author(s) -
Kurland Irwin J.,
Accili Domenico,
Burant Charles,
Fischer Steven M.,
Kahn Barbara B.,
Newgard Christopher B.,
Ramagiri Suma,
Ronnett Gabriele V.,
Ryals John A.,
Sanders Mark,
Shambaugh Joe,
Shockcor John,
Gross Steven S.
Publication year - 2013
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/nyas.12116
Subject(s) - omics , obesity , diabetes mellitus , metabolomics , bioinformatics , computational biology , medicine , type 2 diabetes , data science , biology , computer science , endocrinology
Diabesity has become a popular term to describe the specific form of diabetes that develops late in life and is associated with obesity. While there is a correlation between diabetes and obesity, the association is not universally predictive. Defining the metabolic characteristics of obesity that lead to diabetes, and how obese individuals who develop diabetes different from those who do not, are important goals. The use of large‐scale omics analyses (e.g., metabolomic, proteomic, transcriptomic, and lipidomic) of diabetes and obesity may help to identify new targets to treat these conditions. This report discusses how various types of omics data can be integrated to shed light on the changes in metabolism that occur in obesity and diabetes.