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USDA/Agricultural Research Service Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030
* To whom correspondence should be addressed. E-mail: nbutte{at}bcm.edu.
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ABSTRACT |
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 TOP ABSTRACT IntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Introduction |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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To understand the pathophysiology of childhood obesity and to develop effective treatment interventions, it is important to know if energy metabolism is altered in the obese child. To investigate alterations in energy metabolism, appropriate normalization of EE data for age, sexual maturation, sex, and body size or body composition is critical. Basal metabolism declines throughout childhood and approaches adult values late in adolescence. The decline in basal metabolism relative to body weight is secondary to the differential in growth rates of organs with high metabolic rates relative to those with lower metabolic rates (14,15) and changes in the metabolic rates of individual organs and tissues (16). In a previous publication (17), we demonstrated that a curvilinear power function model between EE and body weight or fat free mass (FFM) was physiologically and empirically superior to simple linear models.
In this study, energy metabolism and its metabolic determinants were investigated in a large cohort of boys and girls across a wide range of ages, sexual maturation, and BMI status using 24-h room respiration calorimetry. We hypothesized that differences in energy metabolism between nonoverweight and overweight children are attributable to differences in body size and composition, circulating thyroid hormones, sympathetic nervous system (SNS), and adrenomedullary activity. The specific aims of this study were to: 1) test for differences in 24-h TEE and sleeping EE (SEE), basal EE (BEE), awake sedentary EE (SEDEE), and cycling EE (CEE) and walking EE (WEE), cycling efficiency, and substrate utilization between nonoverweight and overweight children controlling for age, sex, and sexual maturation; 2) determine the contributions of body size and composition to EE, energetic efficiency, and substrate utilization; and 3) test for independent effects of metabolites, thyroid hormones, SNS, and adrenomedullary activity on EE and substrate utilization.
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Subjects and Methods |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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Subjects were from 319 Hispanic families participating in the VIVA LA FAMILIA Study, which was designed to identify genetic and environmental factors affecting childhood obesity in the Hispanic population (18). As part of the study design, 836 of the 1030 children participated in 24-h room respiration calorimetry. To qualify for the study, Hispanic families were required to have at least 1 overweight child between the ages 4–19 y; overweight was defined as BMI 
95th percentile (19) and fat mass (FM) >85th percentile (20,21). All children and their parents gave written informed consent or assent. The protocol was approved by the Institutional Review Board for Human Subject Research for Baylor College of Medicine and Affiliated Hospitals and the Southwest Foundation for Biomedical Research.
Anthropometry and body composition
Body weight to the nearest 0.1 kg was measured with a digital balance and height to the nearest 1 mm was measured with a stadiometer. BMI was calculated as weight/height2 (kg/m2). We measured waist circumference at a level midway between the inferior border of the rib cage and superior border of the iliac crest with a nonstretchable tape measure. Total body estimates of FFM, the sum of lean tissue and bone mineral content, and FM were measured by dual-energy X-ray absorptiometry (DXA) using a Hologic Delphi-A whole-body scanner (Delphi-A, Hologic). We measured subjects in light clothing without shoes and in the fed state.
Room respiration calorimetry
TEE was measured by room respiration calorimetry for 24 h using a standardized protocol. Oxygen consumption and carbon dioxide production were measured continuously in an 18- or 30-m3-room calorimeter. The operation, calibration, and performance of the calorimeters (22) and the reproducibility in children (23) have been described previously. Calorimeter temperature was 22.8 ± 0.4°C and relative humidity averaged 44.9 ± 5.2%. Heart rate was recorded by telemetry (DS-3000; Fukuda Denshi) and physical activity was monitored by a Doppler microwave sensor (D9/50; Microwave Sensors). A 24-h urine collection was obtained for nitrogen and catecholamine measurements while the subjects were in the calorimeter. Urine samples were acidified and refrigerated during collection and subsequently frozen at –80°C. TEE, respiratory quotient (RQ), nonprotein EE (NPEE), nonprotein RQ (NPRQ), and net substrate utilization were computed from 24-h oxygen consumption, carbon dioxide production, and urinary nitrogen excretion according to Livesey and Elia (24). Diet consisted of 15% protein, 30% fat, and 55% carbohydrate as analyzed by the Nutrition Data System software. Three meals were served at 0830, 1200, and 1730 and a snack at 1500; no food or drinks, besides water, were consumed after 1900. Food was provided to achieve energy balance based on a multiple of the subject's calculated basal metabolic rate (25). All food and beverages consumed were weighed before and after consumption. Net energy balance was computed as measured energy intake minus TEE.
Children's physical activity while in the calorimeter was controlled; the children adhered to a set schedule including meal times, cycling on a stationary bicycle for 15 min in the morning and afternoon, free time, and a set bedtime.
Components of 24-h TEE. TEE was defined as the energy expended during 24 h in a room respiration calorimeter under a standardized protocol. The components of 24 h TEE were defined as follows: SEE, energy expended through nighttime sleep, verified by the motion sensor and heart rate monitoring; BEE, energy expended under thermoneutral conditions upon awakening after 12 h food deprivation; children were asked to remain still, but awake, for 30 min while being monitored both visually and by the motion sensor to confirm that they were lying still (<50 activity counts per minute) for the entire measurement; resting EE, energy expended 2 h after lunch while sitting quietly watching a movie for 20 min; SEDEE, nonexercise energy expended during the awake period computed as TEE minus EE during sleep and cycling; CEE, energy expended during 2 15-min sessions while cycling on a stationary bike first at 20 watts (light level) and then at a mean of 57 watts (range 20–160 watts), set at 60% of the child's maximal oxygen consumption (moderate level); net mechanical energetic efficiency (percent) was computed as external work performed divided by CEE minus resting EE.
Computed physical activity variables. Physical activity level (PAL) is the ratio of TEE:BEE; activity EE (AEE) is the energy expended during the awake period above BEE accounting for the thermic effect of feeding taken as 10% of TEE, computed as TEE/d – BEE/d – (0.1 x TEE/d); nonexercising activity thermogenesis (NEAT) is the energy expended during the awake period above BEE, computed as AEE minus the energy expended during cycling.
Measurements obtained after the 24-h period in the room calorimeter were defined as follows: WEE is the energy expended while walking on a treadmill at 4.0 km/h (2.5 mph) at 0% grade (Model Q55, Quinton Instrument) in the exercise laboratory using a metabolic measurement cart (Model 2900, SensorMedics).
Biochemistries
A blood sample was drawn in the morning after 12-h of fasting. Serum samples were obtained from whole blood after clotting. We measured fasting serum concentrations of glucose, triacylglycerol, and nonesterified fatty acids (NEFA) by enzymatic-colorimetric techniques using the GM7 Analyzer (Analox Instruments) and Microquant Platereader (Biotek Instruments). Glucose was assayed using glucose oxidase (CV = 2.4%). Triacylglycerol was assayed enzymatically using lipase, glycerol kinase, glycerol phosphate oxidase, and peroxidase supplied by Thermo Electron (CV = 2.1%). NEFA were determined using acyl-CoA synthetase and acyl-CoA oxidase supplied by Wako Chemicals (CV = 2.7%).
Commercial radioimmunoassay kits were used to measure fasting serum concentrations of insulin (CV = 7.1%), leptin (CV = 4.6%), and adiponectin (CV = 6.9%) (Linco Research). Serum thyroid stimulating hormone (TSH) (CV = 5.1%), total and free thyroxine (CV = 8.1%, 9.0%), and total and free triiodothyronine (CV = 9.5%, 6.8%) were analyzed using solid phase immunoradiometric assays (Diagnostic Products). Urinary concentrations of free norepinephrine, epinephrine, and dopamine were measured using commercial enzyme immunoassay kits from Rocky Mountain Diagnostics. Norepinephrine and epinephrine were extracted using a cis-diol specific affinity gel, then acylated to N-acylnorepinephrine and N-acylepinephrine and converted enzymatically during the detection procedure into N-acylnormetanephrine and N-acylmetanephrine, respectively. Dopamine was extracted using a cis-diol specific affinity gel, then acylated to N-acyldopamine and converted enzymatically during the detection procedure into N-acyl-3-methoxytyramine. Concentrations of free norepinephrine (CV = 8.5%), epinephrine (13.2%), and dopamine (15.9%) were then determined by enzyme immunoassay competitive binding. Urinary nitrogen concentration was determined by Kjeldahl digestion (Kjeltec Auto Analyzer 1030; Tecator) and a phenol-hypochlorite colorimetric reaction (26).
Statistical methods
We used ACCESS (version 9, Microsoft) for database management and STATA (version 9.1, STATA) for descriptive statistics, generalized estimating equations (GEE), and generalized least squares random effects regression. Determinants of EE and substrate utilization were examined using GEE population-averaged panel data models. To account for correlated data within families, family identification number was used as the cluster variable. Models were adjusted for age, age2, sex (coded boys = 1, girls = 2), and Tanner stage (1–5). BMI status was coded as nonoverweight = 0 and overweight = 1. Preliminary graphical analysis indicated that EE increased nonlinearly with age and thus a quadratic term was needed. Transformation to normality was performed as appropriate. The GEE model, with Gaussian family, identity link function, and exchangeable correlation structure, is asymptotically equivalent to a random intercept model and thus provides almost identical estimates of the parameters. A random-effects linear regression model also was fitted to the data just to obtain R-squares for the final model.
In a previous publication, we demonstrated that an exponential model best described the relationship between EE and body weight or FFM and FM (17). Therefore, the following basic model was used to investigate the effect of BMI status on EE:
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where 
denotes the value of the response measured for the jth child within the ith family; ß0 is the mean coefficient common to all observations; 
are the predictors such as age, age2, gender, BMI status, lnFFM, lnFM, biochemical predictors, calorimeter, and appropriate interaction terms such as those between the classification variables such as sex and BMI status and other predictors. 
are the regression parameters and 
are random errors. The backward stepwise regression procedure and Mallow's 
were used for selection of predictor variables. Regression diagnostics and residual analyses, such as variance inflation factors, the Q-Q plot, Shapiro-Wilk normality test, and Kolmogorov-Smirnov and Anderson-Darling goodness-of-fit tests were used to assess the fitted model. Significance was set at a P-value of 0.05. Values are presented as means ± SD.
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Results |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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PAL did not differ by BMI status, but the absolute differences between TEE and BEE, that is AEE and NEAT, were higher in the overweight children than in the nonoverweight children (P = 0.001), controlling for family membership, calorimeter room, age, age2, Tanner stage, and sex. Further adjusting for body size or body composition, the computed activity variables did not differ by BMI status.
The 24-h substrate utilization in the nonoverweight and overweight children was determined while in the calorimeter (Table 4). Controlling for family membership and energy balance (Model 1), neither 24-h RQ nor NPRQ differed by age, age2, sex, Tanner stage, or BMI status. Protein oxidation, as a percentage of TEE, was slightly lower in the overweight children than in the nonoverweight subjects (P = 0.01); however, net carbohydrate and fat utilization did not differ by BMI status. There was some evidence that the overweight children had slightly lower RQ during basal metabolic rate (P = 0.001), sleep (P = 0.001), and moderate cycling (P = 0.02) than the nonoverweight children. Further controlling for body size (Model 2) or body composition (Model 3), protein oxidation and RQ did not differ by BMI status. In Model 3, rates of protein oxidation adjusted for body composition were 12.63% for the nonoverweight children and 12.76% for the overweight children.
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Discussion |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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In this study, 24-h TEE and its components were clearly higher in the overweight children than in the nonoverweight children; age, age2, sex, Tanner stage, and BMI status accounted for 55–71% of the variance in EE (Model 1). Higher rates of EE, except for light cycling, in the overweight children were explained by height and weight in Model 2, which accounted for 72–91% of the variance in EE. We found it necessary to include height, because weight alone tended to overcorrect, resulting in lower rates of EE in the overweight children. Higher rates of EE in the overweight children similarly were explained by FFM and FM in Model 3, which accounted for 72–93% of the variance in EE. The lower net mechanical energetic efficiency of cycling in the overweight children also was accounted for by differences in body size and composition. In the present analysis, weight and height, or FFM and FM, accounted for the differences in BEE or SEE by BMI status, but main effects of sex and Tanner stages on BEE and SEE were still significant.
Physical activity within the confines of the calorimeter did not differ between the overweight and the nonoverweight children. Despite similar PAL values, the overweight children expended more energy in AEE or NEAT than the nonoverweight children, attributable to their greater body mass. Although free-living physical activity cannot be replicated completely in a calorimeter, it is of interest to observe the children's activity behavior, as reflected in PAL, AEE, and NEAT. The considerable range in PAL (1.11–1.87), AEE [46–1046 kcal/d (192–4376 kJ/d)], and NEAT [6–1009 kcal/d (25–4222 kJ/d)] within the calorimeter under a standardized protocol substantiates the high degree of interindividual variation in spontaneous physical activity that may reflect volitional, free-living physical activity. Similarly, spontaneous physical activity, equivalent to NEAT, amounted to 348 (range 100–700) kcal/d [or 1456 (range 418–2929 kJ/d)] in adult Pima Indians residing in a calorimeter (27). Spontaneous physical activity was highly variable but more similar among Pima siblings than among unrelated individuals, suggesting a genetic influence.
Our GEE model accounted for correlated data within families and indicated that 20–30% of the residual variance in EE was attributable to family membership. This is consistent with our quantitative genetic analysis that indicated significant heritabilities for TEE (h2 = 0.52), SEE (h2 = 0.37), BEE (h2 = 0.40), CEE1 (h2 = 0.60), CEE2 (h2 = 0.35), WEE (h2 = 0.46), PAL (h2 = 0.46), AEE (h2 = 0.45), and NEAT (h2 = 0.43) [(18) and unpublished results]. Significant heritabilities were also seen for RQ (h2 = 0.97), and protein (h2 = 0.59), carbohydrate (h2 = 0.89), and fat (h2 = 0.93) utilization. Although we do not have evidence of altered energy metabolism in the overweight group, this does not preclude the possibility of alterations contributing to obesity in some genetically susceptible individuals.
Controlling for subject characteristics and net energy balance, the overweight children had slightly lower 24-h protein oxidation as a percentage of TEE and lower RQ during basal conditions, sleeping, and cycling. Differences in protein oxidation and RQ were accounted for by differences in body size and composition. We did not observe differences in carbohydrate or fat utilization (%TEE or %NPEE) between the overweight and nonoverweight children in contrast to other reports of increased fat oxidation in obese children during the postabsorptive period (28–30) and postprandial periods (28). As in our study, FM was one of the determinants of fat utilization in children (12). In adult Pima Indians, 24-h RQ was positively correlated with energy balance and negatively correlated with percent FM, insulin, and NEFA (31). In a multiple regression, energy balance, percent FM, and sex explained 18% of the variance in RQ. However, family membership was the greatest determinant of 24-h RQ; the intraclass correlation coefficient (rho) for family membership was 0.28. In our study, age2, sex, FM, energy balance, NEFA, triacylglycerol, adiponectin, leptin, free thyroxine, and free triiodothyronine explained 24% of the variance in NPRQ. The intraclass correlation coefficient for family membership was even higher (rho = 0.57), supporting a genetic influence on fuel utilization.
Energy homeostasis is regulated by complex interactions among thyroid hormones, SNS, and adrenomedullary activity (32). Studies on SNS activity in lean and obese individuals are conflicting; however, evidence is increasing that obese individuals actually have heightened SNS activity (33). In adults, muscle SNS activity increased in direct proportion to FM (34). Variability in TEE, RMR, and spontaneous physical activity was associated positively with SNS activity. In our study, urinary norepinephrine and dopamine excretion was higher in the overweight children and correlated positively with TEE. As in our study, 24-h urinary norepinephrine excretion was positively correlated with 24-h EE independent of body size and body composition in Caucasian adults (35). In contrast, Pima Indians had lower SNS activity as assessed by 24-h urinary norepinephrine excretion and their SNS activity did not correlate with resting metabolic rate (36). Urinary excretion of epinephrine was lower in the overweight children. Deficient adrenomedullary function has been related to the development of central obesity (37,38). Diminished activity of adrenal medulla has been shown to contribute to dyslipidemia often associated with obesity and insulin resistance; however, the role of deficient adrenomedullary function in sustaining obesity or promoting its complications is not fully understood. Thyroid hormones are potent modulators of thermogenesis and may play a role in the development of obesity. Consistent with reports in obese adults (39), TSH and total triiodothyronine were higher in the overweight children than in the nonoverweight children. Free triiodothyronine was positively and free thyroxine negatively associated with TEE and SEE, suggesting an increase in de-iodinating enzymes with higher EE. Interestingly, heart rate was also positively correlated with total and free triiodothyronine throughout the 24-h in the calorimeter.
From a scientific point of view, this study provides a definitive analysis on the determinants of EE, energetic efficiency and substrate utilization in a large cohort of overweight and nonoverweight children and adolescents using advanced calorimetry and body composition methodology. Appropriate normalization of EE and substrate utilization resolved discrepancies in the literature. From a clinical point of view, these data are valuable for the clinical management of overweight children, providing an understanding of the determinants of the higher absolute rates of TEE and its components (SEE, BEE, SEDEE, CEE, WEE, AEE, NEAT) seen in overweight children. From a public health point of view, these results would imply that major defects in energy metabolism are not responsible for the current epidemic of childhood obesity; however, this does not preclude the possibility of altered energy metabolism in some genetically susceptible individuals. Further research is needed to determine the genetic variants responsible for variation in EE, physical activity and substrate utilization in children. A limitation of this study was the lack of data on free-living TEE. Data are needed on the determinants of free-living TEE in overweight Hispanic children to better formulate intervention strategies to prevent and treat obesity in this high risk population.
In conclusion, higher EE associated with childhood obesity was largely explained by differences in body size and composition, with minor contributions of thyroid hormones, SNS, and adrenomedullary activity. Alterations in EE, energetic efficiency and substrate utilization were not evident in the overweight children.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Author disclosures: N. F. Butte, M. R. Puyau, F. A. Vohra, A. L. Adolph, N. R. Mehta, and I. Zakeri, no conflicts of interest.
3 Abbreviations used: AEE, activity energy expenditure; BEE, basal energy expenditure; CEE, cycling energy expenditure; DXA, dual-energy X-ray absorptiometry; EE, energy expenditure; FFM, fat free mass; FM, fat mass; GEE, general estimating equation; HR, heart rate; NEAT, nonexercising activity thermogenesis; NEFA, nonesterified fatty acid; NPEE, nonprotein energy expenditure; NPRQ, nonprotein respiratory quotient; PAL, physical activity level; REE, resting energy expenditure; RQ, respiratory quotient; SEDEE, sedentary energy expenditure; SEE, sleeping energy expenditure; SNS, sympathetic nervous system; TEE, total energy expenditure; TSH, thyroid stimulating hormone; WEE, walking energy expenditure.
Manuscript received 26 July 2007. Initial review completed 29 August 2007. Revision accepted 17 September 2007.
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LITERATURE CITED |
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TOPABSTRACTIntroductionSubjects and MethodsResultsDiscussionLITERATURE CITED |
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