Determinants of resting energy expenditure in young children

Goran, Michael I.; Kaskoun, Mary; Johnson, Rachel K.

doi:10.1016/s0022-3476(05)83277-9

Cited by 104 publications

(82 citation statements)

References 17 publications

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“…The paucity of BMR data from children was previously highlighted by Hayter & Henry (1994). Since 1985, many studies have investigated BMR in children (Bandini et al, 1990;Katch et al, 1990Goran et al, 1994, 1995. Unfortunately, most of these have concentrated on subgroups of the general population, in particular the obese.…”

Section: Introductionmentioning

confidence: 99%

New equations to estimate basal metabolic rate in children aged 10–15 years

1999

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Objective: To develop new equations for the estimation of basal metabolic rate in children aged 10 ± 15 years, and to evaluate the impact of including pubertal stage into the equations. Design: Mixed longitudinal. Setting: The children were recruited from schools in Oxford, and the measurements were made in the schools. Subjects: 195 school children, aged 10 ± 15 years, were recruited in three cohorts. The gender distribution of the subjects was 40% boys and 60% girls. Methods: Basal metabolic rate (BMR) was measured, by indirect calorimetry, at 6-monthly intervals for 3 years. Anthropometric data, height, weight, body breadths and skinfold measurements (biceps, triceps, subscapular, suprailiac and medial calf) were collected on each occasion. Fat and fat-free mass was calculated from the skinfold measurements. Pubertal development was also assessed on annually by paediatricians. Pubic hair (PH) and gonad (G) development was assessed in boys and breast (B) development in girls. The girls were questioned about menarche. Stepwise multiple regression analysis was used to develop and assess new formulae for BMR that also incorporate pubertal development. 554). Weight was the most important factor in developing the regression equations for the calculation of BMR in both sexes (R 2 0.61 and 0.52 for boys and girls, respectively). Stepwise multiple regression analyses, with independent variables such as gender, weight, height, puberty stage and skinfolds, allowed several BMR regression equations to be developed. The inclusion of the menarche status in the regression equations signi®cantly (P`0.05) improved BMR estimation in the pre-menarche girls. Boys, pubertal stage as assessed by Pubic Hair (PH) and Gonadal Stage (G) did not contribute to a signi®cant improvement in BMR estimation, except for 11-year-olds. Conclusions: The inclusion of pubertal stage afforded only minor improvements in the derivation of regression equations for the estimation of BMR of children aged between 10 and 15 years.

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Section: Introductionmentioning

confidence: 99%

New equations to estimate basal metabolic rate in children aged 10–15 years

1999

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“…Indirect calorimetry is considered as a reference and an accurate technique for assessing REE as it has been validated in children with rigorous regard to procedure (Maffeis et al, 1993;Goran et al, 1994;Molnar et al, 1995;Sentongo et al, 2000). At the time of this study, the population did not benefit from nutritional support or medical treatment that could interfere with the uptake of oxygen.…”

Section: Discussionmentioning

confidence: 99%

“…Children with chronic lung disease of prematurity present malnutrition with a decrease in FM and in low metabolic FFM (Goran et al, 1994;Ashley et al, 2001;Atkinson, 2001;Fok et al, 2001). REE, for these reasons, prediction equation should be precisely correlated with age in the population with BPD.…”

Section: Discussionmentioning

confidence: 99%

Comparison of resting energy expenditure in bronchopulmonary dysplasia to predicted equation

et al. 2006

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Objective: Children with bronchopulmonary dysplasia (BPD) often suffer from growth failure because of disturbances in energy balance with an increase of resting energy expenditure (REE). Evaluation of REE is a useful tool for nutritional management. Indirect calorimetry is an elective method for measuring REE, but it is time consuming and requires rigorous procedure. The objective of this study was to test accuracy of prediction equation to evaluate REE in BPD children. Patients and methods: Fifty-two children aged 4-10 years with BPD (30 boys and 22 girls) and 30 healthy lean children (20 boys and 10 girls) were enrolled. In this study, indirect calorimetry was compared to four prediction equations (Schoffield-W, Schoffield-HW, Harris-Benedict and Food and Agriculture Organization equation) using Bland-Altman pair wise comparison. Results: The Harris-Benedict equation was the best equation to predict REE in children with BPD, and Schoffield-W was the best in healthy children. For the children with chronic lung disease of prematurity the Harris-Benedict equation showed the lowest mean predicted REEÀREE measured by indirect calorimetry difference (difference ¼ 15 kcal/day; limits of agreement À266 and 236 kcal/day; 95% confidence interval for the bias À207 to 177 kcal/day), and graphically, the best agreement. For the group of healthy children, it was the Schofield-W equation (À2.9 kcal/day; limits of agreement À275 and 269 kcal/day; 95% confidence interval for the bias À171 to 165 kcal/day), and graphically, the best agreement. Conclusion: Differences in prediction equation are minimal compared to calorimetry. Prediction equation could be useful in the management of children with BPD.

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“…It has been shown in children (Goran et al, 1994) and adults Tataranni & Ravussin, 1995) that BMR is influenced by body size, body composition and gender but these factors only explain 60 -80% of the total between subject variability. Familial studies have also indicated a genetic contribution to BMR in adults .…”

Section: Discussionmentioning

confidence: 99%

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life: evidence of a bias changing with increasing metabolic rate

et al. 2002

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Objective: To compare measurements of sleeping metabolic rate (SMR) in infancy with predicted basal metabolic rate (BMR) estimated by the equations of Schofield. Methods: Some 104 serial measurements of SMR by indirect calorimetry were performed in 43 healthy infants at 1.5, 3, 6, 9 and 12 months of age. Predicted BMR was calculated using the weight only (BMR-wo) and weight and height (BMR-wh) equations of Schofield for 0 -3-y-olds. Measured SMR values were compared with both predictive values by means of the Bland -Altman statistical test. Results: The mean measured SMR was 1.48 MJ=day. The mean predicted BMR values were 1.66 and 1.47 MJ=day for the weight only and weight and height equations, respectively. The Bland -Altman analysis showed that BMR-wo equation on average overestimated SMR by 0.18 MJ=day (11%) and the BMR-wh equation underestimated SMR by 0.01 MJ=day (1%). However the 95% limits of agreement were wide: 7 0.64 to þ 0.28 MJ=day (28%) for the former equation and 7 0.39 to þ 0.41 MJ=day (27%) for the latter equation. Moreover there was a significant correlation between the mean of the measured and predicted metabolic rate and the difference between them. Conclusions: The wide variation seen in the difference between measured and predicted metabolic rate and the bias probably with age indicates there is a need to measure actual metabolic rate for individual clinical care in this age group.

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Determinants of resting energy expenditure in young children

Cited by 104 publications

References 17 publications

New equations to estimate basal metabolic rate in children aged 10–15 years

New equations to estimate basal metabolic rate in children aged 10–15 years

Comparison of resting energy expenditure in bronchopulmonary dysplasia to predicted equation

Comparison of measured sleeping metabolic rate and predicted basal metabolic rate during the first year of life: evidence of a bias changing with increasing metabolic rate

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