Metabolomics Applied to Diabetes Research

Abstract

Type 2 diabetes is caused by a complex set of interactions between genetic and environmental factors. Recent work has shown that human type 2 diabetes is a constellation of disorders associated with polymorphisms in a wide array of genes, with each individual gene accounting for <1% of disease risk (1). Moreover, type 2 diabetes involves dysfunction of multiple organ systems, including impaired insulin action in muscle and adipose, defective control of hepatic glucose production, and insulin deficiency caused by loss of β-cell mass and function (2). This complexity presents challenges for a full understanding of the molecular pathways that contribute to the development of this major disease. Progress in this area may be aided by the recent advent of technologies for comprehensive metabolic analysis, sometimes termed “metabolomics.” Herein, we summarize key metabolomics methodologies, including nuclear magnetic resonance (NMR) and mass spectrometry (MS)-based metabolic profiling technologies, and discuss “nontargeted” versus “targeted” approaches. Examples of the application of these tools to diabetes and metabolic disease research at the cellular, animal model, and human disease levels are summarized, with a particular focus on insights gained from the more quantitative targeted methodologies. We also provide early examples of integrated analysis of genomic, transcriptomic, and metabolomic datasets for gaining knowledge about metabolic regulatory networks and diabetes mechanisms and conclude by discussing prospects for future insights. In principal, metabolomics can provide certain advantages relative to other “omics” technologies (genomics, transcriptomics, proteomics) in diabetes research: 1 ) Estimates vary, but one current source, the Human Metabolome Database (HMDB)-Canada (3), currently lists ∼6,500 discrete small molecule metabolites, significantly less than the estimate of 25,000 genes, 100,000 transcripts, and 1,000,000 proteins. 2 ) Metabolomics measures chemical phenotypes that are the net result of genomic, transcriptomic, and proteomic variability, therefore providing the most integrated profile of biological status. 3 ) Metabolomics is in …