Application of combined omics platforms to accelerate biomedical discovery in diabesity
- Title
- 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.
- Issued Date
- 2013-05
- Citation
- Annals of the New York Academy of Sciences, v.1287, no.1, pp.1 - 16
- Type
- Article
- Author Keywords
- omics ; diabesity ; diabetes ; obesity ; metabolomics ; proteomics ; lipidomics ; metabolism, metabolic profiling
- Keywords
- TANDEM MASS-SPECTROMETRY ; DECREASES FOOD-INTAKE ; INSULIN-RESISTANCE ; GENE-EXPRESSION ; UNTARGETED METABOLOMICS ; ENERGY-EXPENDITURE ; GLUCOSE DISPOSAL ; AMINO-ACIDS ; METABOLISM ; ASSOCIATION
- ISSN
- 0077-8923
- Abstract
- 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. © 2013 New York Academy of Sciences.
- Publisher
- New York Academy of Sciences
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- Appears in Collections:
- ETC 1. Journal Articles
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