Literature DB >> 22497714

Predicting resting energy expenditure in boys with Duchenne muscular dystrophy.

Sarah A Elliott1, Zoe E Davidson, Peter S W Davies, Helen Truby.   

Abstract

BACKGROUND: Understanding how best to predict energy needs in Duchenne muscular dystrophy (DMD) is fundamental to weight management in clinical practice; however there is a large gap in the literature regarding information on the most appropriate method. We aimed to ascertain the most valid predictive equation that can be used to predict REE in steroid treated ambulatory boys with DMD.
METHODS: REE was measured in 9 boys with DMD using indirect calorimetry after an overnight fast. REE was predicted using five different equations, based on height, weight, or body composition variables.
RESULTS: Mean measured REE was 5.4 (SD 0.4) MJ/day. The inclusion of fat free mass in the prediction equation provided no benefit over body weight. The exclusion of height, when compared with weight alone, improved predictive performance, as seen with the Schofield equations, in which a minimal bias and root means squared error is seen.
CONCLUSIONS: The most accurate and precise equation was the Schofield weight equation (Bias -0.2 MJ, 95% CI: -1.3-0.9 MJ), which can easily be calculated in a clinical setting and provides a solid foundation from which clinicians can establish energy requirements to support nutritional management in boys with DMD.
Copyright © 2012 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22497714     DOI: 10.1016/j.ejpn.2012.02.011

Source DB:  PubMed          Journal:  Eur J Paediatr Neurol        ISSN: 1090-3798            Impact factor:   3.140


  6 in total

Review 1.  Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and neuromuscular, rehabilitation, endocrine, and gastrointestinal and nutritional management.

Authors:  David J Birnkrant; Katharine Bushby; Carla M Bann; Susan D Apkon; Angela Blackwell; David Brumbaugh; Laura E Case; Paula R Clemens; Stasia Hadjiyannakis; Shree Pandya; Natalie Street; Jean Tomezsko; Kathryn R Wagner; Leanne M Ward; David R Weber
Journal:  Lancet Neurol       Date:  2018-02-03       Impact factor: 44.182

2.  Nutritional status evaluation in patients affected by bethlem myopathy and ullrich congenital muscular dystrophy.

Authors:  Silvia Toni; Riccardo Morandi; Marcello Busacchi; Lucia Tardini; Luciano Merlini; Nino Carlo Battistini; Massimo Pellegrini
Journal:  Front Aging Neurosci       Date:  2014-11-17       Impact factor: 5.750

3.  Resting Energy Expenditure in Adults with Becker's Muscular Dystrophy.

Authors:  Matthew F Jacques; Paul Orme; Jonathon Smith; Christopher I Morse
Journal:  PLoS One       Date:  2017-01-06       Impact factor: 3.240

4.  Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy.

Authors:  Amit Pande; Prasant Mohapatra; Alina Nicorici; Jay J Han
Journal:  JMIR Rehabil Assist Technol       Date:  2016-07-19

5.  Dystropathology increases energy expenditure and protein turnover in the mdx mouse model of duchenne muscular dystrophy.

Authors:  Hannah G Radley-Crabb; Juan C Marini; Horacio A Sosa; Liliana I Castillo; Miranda D Grounds; Marta L Fiorotto
Journal:  PLoS One       Date:  2014-02-19       Impact factor: 3.240

6.  Improved Muscle Function in Duchenne Muscular Dystrophy through L-Arginine and Metformin: An Investigator-Initiated, Open-Label, Single-Center, Proof-Of-Concept-Study.

Authors:  Patricia Hafner; Ulrike Bonati; Beat Erne; Maurice Schmid; Daniela Rubino; Urs Pohlman; Thomas Peters; Erich Rutz; Stephan Frank; Cornelia Neuhaus; Stefanie Deuster; Monika Gloor; Oliver Bieri; Arne Fischmann; Michael Sinnreich; Nuri Gueven; Dirk Fischer
Journal:  PLoS One       Date:  2016-01-22       Impact factor: 3.240

  6 in total

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