Understanding and assessing diabetic metabolism is vital for monitoring disease progression and improving treatment of patients. In vivo assessments, using MRI and MRS, provide non-invasive and accurate measurements, and the development of hyperpolarized 13 C spectroscopy in particular has been demonstrated to provide valuable metabolic data in real time. Until now, studies have focussed on individual organs. However, diabetes is a systemic disease affecting multiple tissues in the body. Therefore, we have developed a technique to simultaneously measure metabolism in both the heart and liver during a single acquisition. A hyperpolarized 13 C MRS protocol was developed to allow acquisition of metabolic data from the heart and liver during a single scan. This protocol was subsequently used to assess metabolism in the heart and liver of seven control male Wistar rats and seven diabetic rats (diabetes was induced by three weeks of high-fat feeding and a 30 mg/kg injection of streptozotocin). Using our new acquisition, we observed decreased cardiac and hepatic pyruvate dehydrogenase flux in our diabetic rat model. These diabetic rats also had increased blood glucose levels, decreased insulin, and increased hepatic triglycerides. Decreased production of hepatic [1-13 C]alanine was observed in the diabetic group, but this change was not present in the hearts of the same diabetic animals. We have demonstrated the ability to measure cardiac and hepatic metabolism simultaneously, with sufficient sensitivity to detect metabolic alterations in both organs. Further, we have non-invasively observed the different reactions of the heart and liver to the metabolic challenge of diabetes.
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cardiac, diabetes, hepatic, hyperpolarized, spectroscopy, Alanine, Algorithms, Animals, Bicarbonates, Carbon-13 Magnetic Resonance Spectroscopy, Computer Systems, Diabetes Mellitus, Lactic Acid, Liver, Machine Learning, Male, Metabolic Flux Analysis, Molecular Imaging, Myocardium, Pyruvic Acid, Rats, Rats, Wistar, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted