Literature DB >> 26260782

Computational modeling to predict nitrogen balance during acute metabolic decompensation in patients with urea cycle disorders.

Erin L MacLeod1, Kevin D Hall2, Peter J McGuire3.   

Abstract

Nutritional management of acute metabolic decompensation in amino acid inborn errors of metabolism (AA IEM) aims to restore nitrogen balance. While nutritional recommendations have been published, they have never been rigorously evaluated. Furthermore, despite these recommendations, there is a wide variation in the nutritional strategies employed amongst providers, particularly regarding the inclusion of parenteral lipids for protein-free caloric support. Since randomized clinical trials during acute metabolic decompensation are difficult and potentially dangerous, mathematical modeling of metabolism can serve as a surrogate for the preclinical evaluation of nutritional interventions aimed at restoring nitrogen balance during acute decompensation in AA IEM. A validated computational model of human macronutrient metabolism was adapted to predict nitrogen balance in response to various nutritional interventions in a simulated patient with a urea cycle disorder (UCD) during acute metabolic decompensation due to dietary non-adherence or infection. The nutritional interventions were constructed from published recommendations as well as clinical anecdotes. Overall, dextrose alone (DEX) was predicted to be better at restoring nitrogen balance and limiting nitrogen excretion during dietary non-adherence and infection scenarios, suggesting that the published recommended nutritional strategy involving dextrose and parenteral lipids (ISO) may be suboptimal. The implications for patients with AA IEM are that the medical course during acute metabolic decompensation may be influenced by the choice of protein-free caloric support. These results are also applicable to intensive care patients undergoing catabolism (postoperative phase or sepsis), where parenteral nutritional support aimed at restoring nitrogen balance may be more tailored regarding metabolic fuel selection.

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Year:  2015        PMID: 26260782      PMCID: PMC4713290          DOI: 10.1007/s10545-015-9882-0

Source DB:  PubMed          Journal:  J Inherit Metab Dis        ISSN: 0141-8955            Impact factor:   4.982


  17 in total

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Authors:  Peter J McGuire; Tatiana N Tarasenko; Tony Wang; Ezra Levy; Patricia M Zerfas; Thomas Moran; Hye Seung Lee; Brian J Bequette; George A Diaz
Journal:  Dis Model Mech       Date:  2013-11-21       Impact factor: 5.758

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