Application of combined omics platforms to accelerate biomedical discovery in diabesity

Irwin J. Kurland, Domenico Accili, Charles Burant, Steven M. Fischer, Barbara B. Kahn, Christopher B. Newgard, Suma Ramagiri, Gabriele V. Ronnett, John A. Ryals, Mark Sanders, Joe Shambaugh, John Shockcor, Steven S. Gross

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalAnnals of the New York Academy of Sciences
Volume1287
Issue number1
DOIs
StatePublished - May 2013

Keywords

  • Diabesity
  • Diabetes
  • Lipidomics
  • Metabolism, metabolic profiling
  • Metabolomics
  • Obesity
  • Omics
  • Proteomics

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • History and Philosophy of Science

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