Resting Energy Expenditure and Organ-Tissue Body Composition 5 Years After Bariatric Surgery.

INTRODUCTION: Bariatric surgery-induced weight loss may reduce resting energy expenditure (REE) and fat-free mass (FFM) disproportionately thereby predisposing patients to weight regain and sarcopenia. METHODS: We compared REE and body composition of African-American and Caucasian Roux-en-Y gastric bypass (RYGB) patients after surgery with a group of non-operated controls (CON). REE by indirect calorimetry; skeletal muscle (SM), trunk organs, and brain volumes by MRI; and FFM by DXA were measured at post-surgery visits and compared with CON (N = 84) using linear regression models that adjusted for relevant covariates. Ns in RYGB were 50, 42, and 30 for anthropometry and 39, 27, 17 for MRI body composition at years 1, 2, and 5 after surgery, respectively. RESULTS: Regression models adjusted for age, weight, height, ethnicity, and sex showed REE differences (RYGB minus CON; mean ± s.e.): year 1 (43.2 ± 34 kcal/day, p = 0.20); year 2 (- 27.9 ± 37.3 kcal/day, p = 0.46); year 5 (114.6 ± 42.3 kcal/day, p = 0.008). Analysis of FFM components showed that RYGB had greater trunk organ mass (~ 0.4 kg) and less SM (~ 1.34 kg) than CON at each visit. REE models adjusted for FFM, SM, trunk organs, and brain mass showed no between-group differences in REE (- 15.9 ± 54.8 kcal/day, p = 0.8; - 46.9 ± 64.9 kcal/day, p = 0.47; 47.7 ± 83.0 kcal/day, p = 0.57, at years 1, 2, and 5, respectively). CONCLUSIONS: Post bariatric surgery patients maintain a larger mass of high-metabolic rate trunk organs than non-operated controls of similar anthropometrics. Interpreting REE changes after weight loss requires an accurate understanding of fat-free mass composition at both the organ and tissue levels. CLINICAL TRIAL REGISTRATION: Long-term Effects of Bariatric Surgery (LABS-2) NCT00465829.

Brain and high metabolic rate organ mass: contributions to resting energy expenditure beyond fat-free mass.

BACKGROUND: The degree to which interindividual variation in the mass of select high metabolic rate organs (HMROs) mediates variability in resting energy expenditure (REE) is unknown. OBJECTIVE: The objective was to investigate how much REE variability is explained by differences in HMRO mass in adults and whether age, sex, and race independently predict REE after adjustment for HMRO. DESIGN: A cross-sectional evaluation of 55 women [30 African Americans aged 48.7 +/- 22.2 y (mean +/- SD) and 25 whites aged 46.4 +/- 17.7 y] and 32 men (8 African Americans aged 34.3 +/- 18.2 y and 24 whites aged 51.3 +/- 20.6 y) was conducted. Liver, kidney, spleen, heart, and brain masses were measured by magnetic resonance imaging, and fat and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry. REE was measured by indirect calorimetry. RESULTS: REE estimated from age (P = 0.001), race (P = 0.006), sex (P = 0.31), fat (P = 0.001), and FFM (P < 0.001) accounted for 70% (adjusted (2)) of the variability in REE. The addition of trunk HMRO (P = 0.001) and brain (P = 0.006) to the model increased the explained variance to 75% and rendered the contributions of age, sex, and race statistically nonsignificant, whereas fat and FFM continued to make significant contributions (both P < 0.05). The addition of brain to the model rendered the intercept (69 kcal . kg(-1) . d(-1)) consistent with zero, which indicated zero REE for zero body mass. CONCLUSIONS: Relatively small interindividual variation in HMRO mass significantly affects REE and reduces the role of age, race, and sex in explaining REE. Decreases in REE with increasing age may be partly related to age-associated changes in the relative size of FFM components.

Do skinfold measurements provide additional information to body mass index in the assessment of body fatness among children and adolescents?

OBJECTIVES: The purpose of this work was to validate the performance of age- and gender-specific BMI, triceps, and subscapular skinfold for the classification of excess of body fat in children and adolescents and to examine how much additional information these 2 skinfold measurements provide to BMI-for-age. METHODS: The receiver operating characteristic curve was used to characterize the sensitivity and specificity of these 3 indices in classifying excess body fat. Percentage of body fat was determined by dual-energy radiograph absorptiometry. Both > or = 85th and > or = 95th percentile of percentage of body fat were used to define excess body fat. Data from the New York Pediatric Rosetta Body Composition Project were examined (n = 1196; aged 5-18 years). RESULTS: For children aged 5 to 18 years, BMI-for-age, triceps skinfold-for-age, and subscapular skinfold-for-age each performed equally well alone in the receiver operating characteristic curves in the identification of excess body fat defined by either the 85th or 95th percentile of percentage of body fat by dual-energy radiograph absorptiometry. However, if BMI-for-age was already known and was > 95th percentile, the additional measurement of skinfolds did not significantly increase the sensitivity or specificity in the identification of excess body fat. CONCLUSIONS: In contrast to the recommendations of expert panels, skinfold measurements do not seem to provide additional information about excess body fat beyond BMI-for-age alone if the BMI-for-age is >95th percentile.

Prediction models for evaluation of total-body bone mass with dual-energy X-ray absorptiometry among children and adolescents.

OBJECTIVE: The performance of dual-energy x-ray absorptiometry (DXA) in identifying children with decreased bone mass is increasing, but there is no consensus regarding how to interpret the results. The World Health Organization diagnostic categories for normal, osteopenia, and osteoporosis, based on T scores, are not applicable to children and adolescents who have not yet reached peak bone mass. The pediatric reference standards provided by DXA manufacturers have been questioned. Bone mineral density determined with DXA is "areal" density (a 2-dimensional measurement of a 3-dimensional structure), and its misleading nature among growing and maturing children is well recognized. Few published pediatric reference values for bone mineral density measured with DXA include factors that are known to affect the results besides age and gender. Our objective was to develop an algorithm for the evaluation of bone mass among children that included known determinants of bone mass and of its measurement with DXA. METHODS: Height, weight, pubertal status, and total-body bone mineral content, total-body bone area, and total-body bone mineral density measured with DXA were recorded for an ethnically diverse group of healthy pediatric subjects (n = 1218; age: 6-18 years). Prediction models for bone measurements were developed and validated with healthy pediatric subjects and then applied to children with medical disorders. RESULTS: There was a significant gender effect, as well as an interaction between gender and ethnicity. Separate models were developed for log total-body bone mineral content, log total-body bone area, and 1/total-body bone mineral density for girls and boys. The variability explained for each measurement increased from level 1, including age and ethnicity (76-86%), to level 2, including age, ethnicity, height, and weight (84-97%), and to level 3, including age, ethnicity, height, weight, and bone area (89-99%). Pubertal stage was an additional significant predictor of bone measurements but increased the explained variability by only 0.1% with height and weight in the models. The values predicted with each model were not different from measured values for the validation group but were different for patients with medical disorders, with different patterns according to the diagnoses. CONCLUSIONS: These models, including known determinants of bone mass and of bone measurements with DXA, provide an evaluation of pediatric bone mass that proceeds in steps from level 1 to level 3. The outcomes were different for patients at risk for compromised bone mass, compared with healthy children, with specific patterns for each medical disorder. We propose an algorithm for evaluation of bone measurements that follows levels 1 to 3. Our findings suggest that application of this algorithm to well-characterized groups of pediatric patients could identify disease-specific features of DXA results. We recommend this approach as a basis for consensus regarding the clinical evaluation of pediatric bone mass, and we suggest that it could lead to meaningful classification of pediatric bone disorders, investigation of pathophysiologic processes, and development of appropriate interventions.