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Quantifying energy expenditure in childhood: utility in managing pediatric metabolic disorders.
Watson, Laura P E; Carr, Katherine S; Venables, Michelle C; Acerini, Carlo L; Lyons, Greta; Moran, Carla; Murgatroyd, Peter R; Chatterjee, Krishna.
Afiliação
  • Watson LPE; National Institute for Health Research (NIHR) Cambridge Clinical Research Facility, Addenbrooke's Hospital, Cambridge, United Kingdom.
  • Carr KS; National Institute for Health Research (NIHR) Cambridge Clinical Research Facility, Addenbrooke's Hospital, Cambridge, United Kingdom.
  • Venables MC; Nutrition Surveys and Studies, Medical Research Council (MRC) Elsie Widdowson Laboratory, Cambridge, United Kingdom.
  • Acerini CL; NIHR Biomedical Research Centre Nutritional Biomarker Laboratory, University of Cambridge, Cambridge, United Kingdom.
  • Lyons G; Department of Pediatrics, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
  • Moran C; University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.
  • Murgatroyd PR; University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.
  • Chatterjee K; National Institute for Health Research (NIHR) Cambridge Clinical Research Facility, Addenbrooke's Hospital, Cambridge, United Kingdom.
Am J Clin Nutr ; 110(5): 1186-1191, 2019 11 01.
Article em En | MEDLINE | ID: mdl-31410443
BACKGROUND: Energy expenditure prediction equations are used to estimate energy intake based on general population measures. However, when using equations to compare with a disease cohort with known metabolic abnormalities, it is important to derive one's own equations based on measurement conditions matching the disease cohort. OBJECTIVE: We aimed to use newly developed prediction equations based on a healthy pediatric population to describe and predict resting energy expenditure (REE) in a cohort of pediatric patients with thyroid disorders. METHODS: Body composition was measured by DXA and REE was assessed by indirect calorimetry in 201 healthy participants. A prediction equation for REE was derived in 100 healthy participants using multiple linear regression and z scores were calculated. The equation was validated in 101 healthy participants. This method was applied to participants with resistance to thyroid hormone (RTH) disorders, due to mutations in either thyroid hormone receptor ß or α (ß: female n = 17, male n = 9; α: female n = 1, male n = 1), with deviation of REE in patients compared with the healthy population presented by the difference in z scores. RESULTS: The prediction equation for REE = 0.061 * Lean soft tissue (kg) - 0.138 * Sex (0 male, 1 female) + 2.41 (R2 = 0.816). The mean ± SD of the residuals is -0.02 ± 0.44 kJ/min. Mean ± SD REE z scores for RTHß patients are -0.02 ± 1.26. z Scores of -1.69 and -2.05 were recorded in male (n = 1) and female ( n = 1) RTHα patients. CONCLUSIONS: We have described methodology whereby differences in REE between patients with a metabolic disorder and healthy participants can be expressed as a z score. This approach also enables change in REE after a clinical intervention (e.g., thyroxine treatment of RTHα) to be monitored.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estado Pré-Diabético / Metabolismo Energético / Doenças Metabólicas Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Adolescent / Child / Female / Humans / Male Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estado Pré-Diabético / Metabolismo Energético / Doenças Metabólicas Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Adolescent / Child / Female / Humans / Male Idioma: En Ano de publicação: 2019 Tipo de documento: Article