Application of combined omics platforms to accelerate biomedical discovery in diabesity

 
Thumbnail
View/Open
Author
Kurland, Irwin J
Accili, Domenico
Burant, Charles
Fischer, Steven M
Newgard, Christopher B
Ramagiri, Suma
Ronnett, Gabriele V
Ryals, John A
Sanders, Mark
Shambaugh, Joe
Shockcor, John
Gross, Steven S
Note: Order does not necessarily reflect citation order of authors.
Published Version
https://doi.org/10.1111/nyas.12116
Metadata
Show full item record
Citation
Kurland, I. J., D. Accili, C. Burant, S. M. Fischer, B. B. Kahn, C. B. Newgard, S. Ramagiri, et al. 2013. “Application of combined omics platforms to accelerate biomedical discovery in diabesity.” Annals of the New York Academy of Sciences 1287 (1): 1-16. doi:10.1111/nyas.12116. http://dx.doi.org/10.1111/nyas.12116.
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.
Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709136/pdf/
Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:11855879

Collections