Quantifying energy expenditure in childhood: utility in managing pediatric metabolic disorders. Issue 5 (13th August 2019) - Watson, Laura P E; Carr, Katherine S; Venables, Michelle C; Acerini, Carlo L; Lyons, Greta; Moran, Carla; Murgatroyd, Peter R; Chatterjee, Krishna
ABSTRACT: 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 β or α (β: 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 ( R 2  = 0.816). The mean ± SD of the residuals is −0.02 ± 0.44 kJ/min. Mean ± SD REE z scores for RTHβ 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.