Abstract
ABSTRACTBackgroundEnergy 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.ObjectiveWe 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.MethodsBody 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.ResultsThe 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 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.ConclusionsWe 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.
Highlights
Predicting resting energy expenditure (REE) can be useful in, for example, assessing nutritional energy intake requirements in healthy subjects or patients, in circumstances where expertise or facilities to measure it accurately are not available
The most recent published equations based on body composition measurements relevant to the healthy childhood age range include those of Muller et al [5], which derive coefficients from fat-free mass (FFM), fat mass (FM), and sex and explain 72% of the Supported by the National Institute for Health Research (NIHR) Cambridge Clinical Research Facility, Cambridge, United Kingdom, Wellcome Trust grant 095564/Z/11/Z and the NIHR Cambridge Biomedical Centre, and Medical Research Council Elsie Widdowson Laboratory program numbers Physiological Modelling of Metabolic Risk, MC_UP_A090_1005, and Nutrition, Surveys and Studies, MC_U105960384
There were no significant differences in age, height, weight, BMI, FM, lean soft tissue (LST), bone mass (BM), and REE between the model and validation data sets
Summary
Predicting resting energy expenditure (REE) can be useful in, for example, assessing nutritional energy intake requirements in healthy subjects or patients, in circumstances where expertise or facilities to measure it accurately are not available. The most common published prediction equations used in a pediatric setting are by Schofield [1], Henry [2], Harris and Benedict [3], and Molnar et al [4] These equations are based on characteristics such as age, sex, height, and weight and are derived from large diverse cohorts, often with pooled data [5, 6]. 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 enables change in REE after a clinical intervention (e.g., thyroxine treatment of RTHα) to be monitored.
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