MULTI-TISSUE MULTI-TRACER METABOLIC PHENOTYPING OF DIABETES WITH PRE-CLINICAL PET

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DESCRIPTION (provided by applicant): Type 2 Diabetes (T2D) is a result of systemic disturbances in metabolism characterized mainly by impaired insulin action in peripheral tissues such as liver, muscle, and adipose tissue. In light of the highly interconnected and coordinated nature of substrate metabolism in health, and its failure in T2D, current research necessitates an integrated in-vivo "systems biology" strategy to investigate in-vivo multi-tissue metabolic alterations in the etiology of insulin resistance in obesity and diabetes. Pre-clinical PET is unique in that multiple tissues are in the field-of-view (FOV). This realization affords the opportunity to perform non-invasive multi-tissue quantitative imaging and metabolic phenotyping through multi-tracer experiments coupled with mathematical models of tracer kinetics. With that in mind and given our experience in quantifying myocardial substrate metabolism in rodents, we hypothesize that employing a similar approach will provide quantitative measures of substrate metabolism in liver, muscle, and adipose tissue. The metabolic tracers considered in this proposal include [11C]Palmitate for FA oxidation and triglycerides synthesis (storage), 18FTHA for fatty acid oxidation; [11C]Glucose for glucose oxidation and glycogen synthesis, 18FDG for glucose utilization, and [11C]Lactate as a potential imaging marker for gluconeogenesis. Given the dual-input function to the liver, in Specific Aim 1 we will validate and optimize an algorithm to reconstruct the liver dual-input function in multi- tracer imaging of hepatic substrate metabolism. With the liver dual input function at hand, in Specific Aim 2 we construct and validate compartmental models of 18FDG, [11C]Glucose, [11C]Lactate, [11C]Palmitate, [11C]Acetate, and 18FTHA metabolism through interventions that enhance the dynamic range of metabolic response. Having constructed and validated compartmental models for the tracers in this proposal, we assess and characterize metabolic disturbances in the pathogenesis of T2D by performing multi-tissue multi-tracer time-course metabolic phenotyping. In-vivo metabolic phenotyping will be coupled with expression array analysis to correlate genomics alterations to metabolic disturbances. We anticipate that successful completion of the proposed work will provide an integrated in-vivo metabolic phenotyping platform linking genomic alterations in transgenetic/knockout animal models of disease to multi-tissue metabolic disturbances in the study of T2D. In addition, the proposed work will facilitate characterization of the interplay between T2D and cardiovascular disease, among others highlighted in the proposal. Equally important, we anticipate that strategy and insights derived from this work will be translated to clinical applications. PUBLIC HEALTH RELEVANCE: Type 2 Diabetes (T2D) is a complex disease affecting more than 150 million people worldwide, and a large increase in these numbers is expected within the coming years. Recent epidemiological and experimental evidence suggests that there is a close link between the etiology of obesity and T2D, thus confounding the bleak outlook for T2D. A common feature to both obesity and T2D is insulin resistance. In the setting of insulin resistance, multiple-tissues including liver, muscle, and adipose tissue, fail to regulate glucose and fatty acid metabolism. Given this coordinated failure in regulating metabolism, new strategies are needed to characterize substrate metabolism non-invasively in multiple systems. We propose to develop and validate an integrated strategy to perform metabolic imaging in multi-tissues using multiple metabolic tracers in conjunction with pre-clinical Positron Emission Tomography (PET). Pre-clinical PET is unique in that multiple tissues are in the field-of-view (FOV). This realization affords the opportunity to perform non-invasive multi-tissue quantitative imaging to assess metabolism through multi-tracer experiments coupled with mathematical models to quantify metabolism. The tracers we will consider in this proposal include tracers involved in fatty acid metabolism, glucose metabolism, and lactate metabolism. We anticipate that the proposed strategy will aid in characterizing the progression of T2D, the study of the interplay between diabetes and cardiovascular disease-a leading cause of death among diabetic patients-as well as other diseases and conditions highlighted in the proposal.