AIM: To measure the relationship of resting energy expenditure (REE) and body composition, and to compare REE data calculated from anthropometric parameters using published equations with measurements obtained by indirect calorimetry (IC) in a population of obese paediatric patients. METHODS: The study included 82 healthy obese paediatric subjects (49 boys, 33 girls; body mass index 29.6 +/- 5.0 kg/m , age 1 1.4 +/- 2.6 y, weight 72.4 +/- 20.9 kg, height 155 +/- 14 cm). REE was measured by IC, body composition was determined by dual energy X-ray absorptiometry (DXA). Bootstrap analysis was performed to validate the step-down linear regression analysis results. RESULTS: Lean body mass (LBM) and weight were identified as the most significant determinants of REE. LBM was the best single predictor (r = 0.78; p < 0.001) for REE. Regression equations are given in the text. Prediction of REE on the basis of published anthropometric formulas was strongly dependent from the equation used. Some equations tend to underestimate REE in the population studied with a considerable systematic error. CONCLUSION: In the present paper we show that (1) the published equations to predict REE in obese subjects yield scattered data and some are even biased by a systematic error, and that (2) the inclusion of DXA-derived LBM improves accuracy and precision of predicted REE in boys and girls aged from 4 to 10 y and in boys from 11 to 15 y.
AIM: To measure the relationship of resting energy expenditure (REE) and body composition, and to compare REE data calculated from anthropometric parameters using published equations with measurements obtained by indirect calorimetry (IC) in a population of obese paediatric patients. METHODS: The study included 82 healthy obese paediatric subjects (49 boys, 33 girls; body mass index 29.6 +/- 5.0 kg/m , age 1 1.4 +/- 2.6 y, weight 72.4 +/- 20.9 kg, height 155 +/- 14 cm). REE was measured by IC, body composition was determined by dual energy X-ray absorptiometry (DXA). Bootstrap analysis was performed to validate the step-down linear regression analysis results. RESULTS: Lean body mass (LBM) and weight were identified as the most significant determinants of REE. LBM was the best single predictor (r = 0.78; p < 0.001) for REE. Regression equations are given in the text. Prediction of REE on the basis of published anthropometric formulas was strongly dependent from the equation used. Some equations tend to underestimate REE in the population studied with a considerable systematic error. CONCLUSION: In the present paper we show that (1) the published equations to predict REE in obese subjects yield scattered data and some are even biased by a systematic error, and that (2) the inclusion of DXA-derived LBM improves accuracy and precision of predicted REE in boys and girls aged from 4 to 10 y and in boys from 11 to 15 y.
Authors: David A White; Vincent S Staggs; Veronica Williams; Trent C Edwards; Robin Shook; Valentina Shakhnovich Journal: Child Obes Date: 2019-03-19 Impact factor: 2.992
Authors: Catherine J Klein; Stephan A Villavicencio; Amy Schweitzer; Joel S Bethepu; Heather J Hoffman; Nazrat M Mirza Journal: J Am Diet Assoc Date: 2011-08
Authors: Sarah T Henes; Doyle M Cummings; Robert C Hickner; Joseph A Houmard; Kathryn M Kolasa; Suzanne Lazorick; David N Collier Journal: Nutr Clin Pract Date: 2013-08-06 Impact factor: 3.080
Authors: Jimena Fuentes-Servín; Azalia Avila-Nava; Luis E González-Salazar; Oscar A Pérez-González; María Del Carmen Servín-Rodas; Aurora E Serralde-Zuñiga; Isabel Medina-Vera; Martha Guevara-Cruz Journal: Front Pediatr Date: 2021-12-06 Impact factor: 3.418
Authors: J Van Schaik; M Burghard; M H Lequin; E A van Maren; A M van Dijk; T Takken; L B Rehorst-Kleinlugtenbelt; B Bakker; L Meijer; E W Hoving; M Fiocco; A Y N Schouten-van Meeteren; W J E Tissing; H M van Santen Journal: Endocr Connect Date: 2022-07-21 Impact factor: 3.221