Modeling of Brain Metabolism and Pyruvate Compartmentation Using 13C NMR in Vivo: Caution Required. Issue 8 (August 2013)

• Record Type: Journal Article
• Authors: Jeffrey, F Mark; Marin-Valencia, Isaac; Good, Levi B; Shestov, Alexander A; Henry, Pierre-Gilles; Pascual, Juan M; Malloy, Craig R
• Is Part Of: Journal of cerebral blood flow & metabolism. Volume 33:Issue 8(2013)
• Page Start: 1160
• Page End: 1167
• Publication Date: 2013-08
• Subjects: cerebral energy metabolism -- in vivo13C NMR spectroscopy -- mathematical modeling -- rat -- tricarboxylic acid cycle -- [1, 6-13C2]glucose; Cerebral circulation -- Periodicals; Brain -- Metabolism -- Periodicals; Brain -- Blood-vessels -- Periodicals; Cerebrovascular disease -- Periodicals; 612.824
• DOI: 10.1038/jcbfm.2013.67
• Languages: English
• ISSNs: 0271-678X

Abstract

Two variants of a widely used two-compartment model were prepared for fitting the time course of [1, 6- 13 C2 ]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 13 C spin-spin coupling, known to improve parameter estimates in heart. Data analyzed were from a literature report providing time courses of [1, 6- 13 C2 ]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.