Modeling of Brain Metabolism and Pyruvate Compartmentation Using 13C NMR in Vivo: Caution Required

Author:

Jeffrey F Mark12,Marin-Valencia Isaac34,Good Levi B5,Shestov Alexander A6,Henry Pierre-Gilles6,Pascual Juan M357,Malloy Craig R128

Affiliation:

1. Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

2. Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

3. Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

4. Division of Pediatric Neurology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

5. Department of Neurology and Neurotherapeutics, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

6. Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA

7. Department of Physiology, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas, USA

8. Department of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas, USA

Abstract

Two variants of a widely used two-compartment model were prepared for fitting the time course of [1,6-13C2]glucose metabolism in rat brain. Features common to most models were included, but in one model the enrichment of the substrates entering the glia and neuronal citric acid cycles was allowed to differ. Furthermore, the models included the capacity to analyze multiplets arising from 13C spin-spin coupling, known to improve parameter estimates in heart. Data analyzed were from a literature report providing time courses of [1,6-13C2]glucose metabolism. Four analyses were used, two comparing the effect of different pyruvate enrichment in glia and neurons, and two for determining the effect of multiplets present in the data. When fit independently, the enrichment in glial pyruvate was less than in neurons. In the absence of multiplets, fit quality and parameter values were typical of those in the literature, whereas the multiplet curves were not modeled well. This prompted the use of robust statistical analysis (the Kolmogorov-Smirnov test of goodness of fit) to determine whether individual curves were modeled appropriately. At least 50% of the curves in each experiment were considered poorly fit. It was concluded that the model does not include all metabolic features required to analyze the data.

Publisher

SAGE Publications

Subject

Cardiology and Cardiovascular Medicine,Neurology (clinical),Neurology

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