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dc.contributor.author Kurland, Irwin J. -
dc.contributor.author Accili, Domenico -
dc.contributor.author Burant, Charles -
dc.contributor.author Fischer, Steven M. -
dc.contributor.author Kahn, Barbara B. -
dc.contributor.author Newgard, Christopher B. -
dc.contributor.author Ramagiri, Suma -
dc.contributor.author Ronnett, Gabriele V. -
dc.contributor.author Ryals, John A. -
dc.contributor.author Sanders, Mark -
dc.contributor.author Shambaugh, Joe -
dc.contributor.author Shockcor, John -
dc.contributor.author Gross, Steven S. -
dc.date.available 2017-07-05T09:02:27Z -
dc.date.created 2017-06-29 -
dc.date.issued 2013-05 -
dc.identifier.issn 0077-8923 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/2504 -
dc.description.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. -
dc.language English -
dc.publisher New York Academy of Sciences -
dc.title Application of combined omics platforms to accelerate biomedical discovery in diabesity -
dc.type Article -
dc.identifier.doi 10.1111/nyas.12116 -
dc.identifier.scopusid 2-s2.0-84878437983 -
dc.identifier.bibliographicCitation Annals of the New York Academy of Sciences, v.1287, no.1, pp.1 - 16 -
dc.description.isOpenAccess FALSE -
dc.subject.keywordAuthor omics -
dc.subject.keywordAuthor diabesity -
dc.subject.keywordAuthor diabetes -
dc.subject.keywordAuthor obesity -
dc.subject.keywordAuthor metabolomics -
dc.subject.keywordAuthor proteomics -
dc.subject.keywordAuthor lipidomics -
dc.subject.keywordAuthor metabolism, metabolic profiling -
dc.subject.keywordPlus TANDEM MASS-SPECTROMETRY -
dc.subject.keywordPlus DECREASES FOOD-INTAKE -
dc.subject.keywordPlus INSULIN-RESISTANCE -
dc.subject.keywordPlus GENE-EXPRESSION -
dc.subject.keywordPlus UNTARGETED METABOLOMICS -
dc.subject.keywordPlus ENERGY-EXPENDITURE -
dc.subject.keywordPlus GLUCOSE DISPOSAL -
dc.subject.keywordPlus AMINO-ACIDS -
dc.subject.keywordPlus METABOLISM -
dc.subject.keywordPlus ASSOCIATION -
dc.citation.endPage 16 -
dc.citation.number 1 -
dc.citation.startPage 1 -
dc.citation.title Annals of the New York Academy of Sciences -
dc.citation.volume 1287 -
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