Concordance of Changes in Metabolic Pathways Based on Plasma Metabolomics and Skeletal Muscle Transcriptomics in Type 1 Diabetes

Abstract

Insulin regulates many cellular processes, but the full impact of insulin deficiency on cellular functions remains to be defined. Applying a mass spectrometry–based nontargeted metabolomics approach, we report here alterations of 330 plasma metabolites representing 33 metabolic pathways during an 8-h insulin deprivation in type 1 diabetic individuals. These pathways included those known to be affected by insulin such as glucose, amino acid and lipid metabolism, Krebs cycle, and immune responses and those hitherto unknown to be altered including prostaglandin, arachidonic acid, leukotrienes, neurotransmitters, nucleotides, and anti-inflammatory responses. A significant concordance of metabolome and skeletal muscle transcriptome–based pathways supports an assumption that plasma metabolites are chemical fingerprints of cellular events. Although insulin treatment normalized plasma glucose and many other metabolites, there were 71 metabolites and 24 pathways that differed between nondiabetes and insulin-treated type 1 diabetes. Confirmation of many known pathways altered by insulin using a single blood test offers confidence in the current approach. Future research needs to be focused on newly discovered pathways affected by insulin deficiency and systemic insulin treatment to determine whether they contribute to the high morbidity and mortality in T1D despite insulin treatment.

Application of metabolomics in autoimmune diseases: Insight into biomarkers and pathology

“…330 plasma metabolites representing 33 metabolic pathways during 8-h insulin deprivation were altered in type 1 diabetic individuals (Dutta et al, 2012). These pathways included glucose, amino acid and lipid metabolism, Krebs cycle, and immune responses, as well as prostaglandin, arachidonic acid, leukotrienes, neurotransmitters, nucleotides, and antiinflammatory responses (Dutta et al, 2012). Importantly, this research found that although insulin replacement normalized plasma glucose and many other metabolites, there were 71 metabolites and 24 pathways that differed between nondiabetes and insulin-treated type 1 diabetes (Dutta et al, 2012).…”

“…A nontargeted UPLC-MS-based metabolomics approach was applied to determine the effects of insulin deficiency on metabolites and pathways in T1D individuals (Dutta et al, 2012). 330 plasma metabolites representing 33 metabolic pathways during 8-h insulin deprivation were altered in type 1 diabetic individuals (Dutta et al, 2012). These pathways included glucose, amino acid and lipid metabolism, Krebs cycle, and immune responses, as well as prostaglandin, arachidonic acid, leukotrienes, neurotransmitters, nucleotides, and antiinflammatory responses (Dutta et al, 2012).…”

“…Therefore, metabolite fingerprint should provide a direct specific method for clarifying it. A nontargeted UPLC-MS-based metabolomics approach was applied to determine the effects of insulin deficiency on metabolites and pathways in T1D individuals (Dutta et al, 2012). 330 plasma metabolites representing 33 metabolic pathways during 8-h insulin deprivation were altered in type 1 diabetic individuals (Dutta et al, 2012).…”

Metabolomics and Diabetes: Analytical and Computational Approaches

“…To determine the effect of insulin treatment on diabetes-associated metabolic changes, Dutta et al (85) examined differences in the plasma metabolome of controls, type 1 diabetic patients treated with insulin, and the same type 1 diabetic patients following 8-h insulin withdrawal. Untargeted metabolomics identified that whereas many of the metabolites associated with insulin deficiency were normalized with insulin treatment, not all metabolites were restored to control levels.…”

Diabetes Subphenotypes and Metabolomics: The Key to Discovering Laboratory Markers for Personalized Medicine?

“…Although such knowledge is not essential for the discovery of diagnostics, it can be invaluable for identifying new targets for individual interventions; functional metabolomics provides optimal possibilities, particularly when stable-isotope-assisted metabolomics or lipidomics approaches are applied (14 ). Sophisticated functional metabolomics studies may give important impetus to translational diabetes research, particularly when they are combined with data from proteomics and/or transcriptomics investigations (14,15 ).…”