Equation to estimate resting energy expenditure in adolescents with sickle cell anemia

¹ From the Center for Nutrition and Department of Family and Community Medicine, Meharry Medical College, Nashville, TN (MSB); the Department of Medicine (MSB and KYC) and General Clinical Research Center (DB), Vanderbilt University Medical Center, Nashville, TN; and St Jude Hospital, Memphis (WCW).

² Supported in part by NIH grant HBL 003530 (to MSB), General Clinical Research Center grants RR-00095 (to Vanderbilt University) and RR-1179204 (to Meharry Medical College), Clinical Nutrition Research Unit grant DK-26657 (to Vanderbilt University), and a Meharry Center of Excellence grant (to MSB).

³ Address reprint requests to MS Buchowski, Center for Nutrition, Meharry Medical College, 1005 DB Todd Boulevard, Nashville, TN 37208. E-mail: maciej.buchowski{at}mcmail.vanderbilt.edu.

ABSTRACT  
Background: Basal energy requirements are higher in adolescents with sickle cell anemia (SCA) than in healthy control subjects. However, no equation is available to accurately predict their energy needs.

Objective: Our objective was to develop a clinically useful equation to estimate resting energy expenditure (REE) in adolescents with SCA.

Design: REE and other components of total energy expenditure were measured in adolescents with SCA (n = 37) and in control subjects (n = 23) for 24 h in a whole-room indirect calorimeter. Multiple linear regression analysis was used to describe the relations of REE with independent variables such as sex, weight, height, fat-free mass, fat mass, age, and hemoglobin concentration in adolescents with SCA. The Bland-Altman comparison technique was used to compare values predicted by existing equations with measured REE values.

Results: Mean (±SD) measured REEs were 7746 ± 974 and 6332 ± 869 kJ/d in the male and female subjects with SCA, respectively, and these values were 16% higher than those in the healthy control subjects. Standard equations underestimated REE by 12% (P 0.001) in adolescents with SCA and overestimated REE by 4% in control subjects (P = 0.01–0.29). Several REE regression equations for SCA were developed. The most parsimonious equation for males was REE (kJ/d) = 5461 + 77.7·weight (kg) - 233.2·hemoglobin (g/dL), or REE (kcal/d) = 1305 + 18.6·weight (kg) - 55.7·hemoglobin (g/dL). For females, the most parsimonious equation was REE (kJ/d) = 4603 + 55.6·weight (kg) - 126.2·hemoglobin (g/dL), or REE (kcal/d) = 1100 + 13.3·weight (kg) - 30.2·hemoglobin (g/dL).

Conclusion: The new equations have better agreement with the basal metabolic needs of adolescents with SCA than do standard predictive equations.

Key Words: Resting energy expenditure • sickle cell anemia • prediction equations • adolescents • African Americans

INTRODUCTION  
Defining and then meeting requirements for energy and nutrients are important for the optimal clinical management of growing adolescents with sickle cell anemia (SCA). Previously we (1, 2) and others (3–6) reported that resting energy expenditure (REE) is higher in persons with sickle cell disease than in healthy control subjects. Higher rates of whole-body protein breakdown and synthesis and higher cardiac output in adolescents with SCA than in healthy subjects probably contribute to this finding. We showed that 50% of the higher resting metabolic rate in patients with SCA could be accounted for by the enhanced energy cost of total protein turnover (7), including 20% caused by higher bone protein turnover (2). The clinical consequences of this hypermetabolic state are the slower growth rate and delayed sexual development often seen in children and adolescents with SCA (8). Therefore, it is important that individual requirements are determined and that an optimal amount of energy is provided to children and adolescents with SCA.

In 1981, the FAO/WHO/UNU Committee on Protein and Energy Requirements recommended that daily energy requirements be estimated by using measurements of energy expenditure (9). This recommendation substantially underscores the importance of measuring REE, which is defined as the energy required to sustain the body’s functions at rest and which accounts for 65% of total daily energy expenditure (10). REE is calculated from the resting metabolic rate, which equals the basal metabolic rate only if the measurement is performed in the postabsorptive state and under precisely specified conditions, which include the position of the subject during measurement, temperature, humidity, and degree of light and sound in the room at the time of measurement, duration and quality of sleep or rest interval before the measurement period, and duration of the measurement (10). Such rigorous conditions are not required in the measurement of REE, which is adequate in most clinical applications. Moreover, as pointed out by others, accurate measurement of total daily energy requirements requires that REE be recorded over a protracted period (11–13). Nevertheless, measurement of REE requires specialized equipment, and it is not practical to obtain an REE measurement on every patient. Therefore, several equations and modified equations were developed to predict age- and sex-adjusted basal or resting energy expenditure for children and adolescents by using variables such as sex, age, height, weight, and body surface area (14–17). Numerous studies showed that the major factor explaining the variation in metabolic rate in humans is fat-free mass (FFM), a heterogeneous component that can be partitioned into muscle mass and nonmuscle mass (10). It was also shown that FFM is lower in children and adolescents with sickle cell disease than in healthy control subjects (4, 6). Because the predictive equations commonly used in clinical practice were generated by using healthy populations and did not take into consideration the hypermetabolic state observed in SCA and differences in body composition, it is reasonable to assume that these equations would not precisely predict REE in subjects with SCA. Kopp-Hoolihan et al (18) showed that standard equations are unreliable in patients with SCA and proposed that clinically useful formulas for predicting REE in patients with SCA should be developed.

The objective of our study was to develop clinically useful equations for the estimation of REE in adolescents with SCA. The null hypothesis was that in adolescents with SCA, REE calculated by using existing standard equations would not differ from REE measured in a previously validated whole-room indirect calorimeter under strictly controlled conditions. To test this hypothesis, we measured REE and other components of total energy expenditure in 37 adolescents with SCA and in 23 control subjects during a 24-h stay in a room calorimeter and compared the results with basal energy expenditure values calculated from standard equations (9, 19, 20). Furthermore, we assumed that if the null hypothesis were incorrect, our experimental data would be sufficient to generate a new simple equation for predicting REE in adolescents with SCA. Simple potential predictive factors such as weight, FFM, fat mass, height, age, and hemoglobin concentration were chosen for testing in this study.

SUBJECTS AND METHODS
Subjects
A group of 37 African American adolescents (14–18 y of age) with homozygous SCA were identified and screened for participation in the study at the sickle cell clinics of the Comprehensive Sickle Cell Center at Meharry Medical College in Nashville, TN, and of the Pediatric Hematology Center of Memphis. The group included 18 males and 19 females. Each participant’s hemoglobin genotype was determined by using standard electrophoretic methods (21) to confirm the diagnosis of SCA, in which both ß globin genes code for hemoglobin S (22). Additionally, 23 African American adolescents who did not carry the hemoglobin S gene or have any other hemoglobinopathy were matched for sex, age, FFM, and fat mass.

Before participating in the study, subjects provided a medical history and underwent a complete physical examination. The participants were free of any metabolic, skeletal, hepatic, or renal dysfunction as confirmed by blood tests. They were not taking drugs known to affect energy metabolism and were nonsmokers. Female subjects were not pregnant as determined by a serum pregnancy test and were studied between days 3 and 12 after the onset of menses (follicular phase) to eliminate the influence of menstrual function on energy expenditure (23, 24). Subjects with SCA were studied in the steady state; ie, they were not experiencing a sickle cell crisis during the study and had not experienced a painful crisis for 28 d before the study.

Prospective participants received written and verbal information about the nature and purpose of the study, and those who agreed to participate signed an informed consent form. The consent form was approved by the Institutional Review Boards of both Meharry Medical College and Vanderbilt University School of Medicine for procedures to be performed at the Vanderbilt University General Clinical Research Center.

Study protocol
All participants reported to the General Clinical Research Center after a 10-h overnight fast. After initial admission, they were transferred to the whole-room indirect calorimeter, where they were asked to engage in a 24-h protocol involving standard daily activities and a 2-h nonintensive exercise protocol. Oxygen consumption (CO₂) and the flow rate, temperature (inside and ambient), barometric pressure, and humidity of the air were sampled 60 times/s and integrated at the end of each minute to calculate energy expenditure ( Methods
Anthropometry
Body weight was measured to the nearest 0.05 kg with a digital scale. Height was measured to the nearest 1 cm with a stadiometer. All anthropometric measurements were performed by one investigator (MSB).

Body composition
Fat mass and FFM were determined by hydrodensitometry. The subjects were weighed underwater, and their residual lung volumes were measured by using the nitrogen dilution technique while the subjects were submerged in water to chest level (26). Percentage of body fat was calculated from body density by using Schutte’s equation (27), and fat mass and FFM were calculated from body mass.

Resting energy expenditure
REE (kJ/min) was defined as the average baseline energy expenditure excluding physical activity and sleeping during a 24-h stay in a room calorimeter. Physical activity was detected by a large precision force platform, and energy expenditure above baseline during periods of physical activity was subtracted for REE calculation (28). An automatic algorithm in data processing was used to detect the baseline level of energy expenditure during these nonsleeping, nonexercising, and nonmoving periods. The lowest 60-min period selected by a computer program by sorting was used to obtain the REE. The detailed methodology of the room calorimeter was previously reported (25, 28). Regression models of REE based on the predictors FFM and fat mass were used rather than division of REE by FFM because division by FFM does not account for nonzero intercepts typically observed in these regressions (29).

Prediction equations for basal metabolic rate
The equations formulated by Harris and Benedict (19), FAO/WHO/UNU (9), and Schofield (20) were used to predict the REE of the subjects (Table 1). The predictive power of existing equations was assessed by comparing measured and predicted REE values.

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TABLE 1 . Equations for prediction of basal metabolic rate (BMR)  
Statistical analysis
Sample size was calculated by using data from our preliminary studies showing that REE was 170 and 136 kJ/kg FFM in adolescents with SCA and control adolescents, respectively. Using a two-sided t test with an level of 0.005, we calculated that 10 participants in each sex subgroup would provide 90% statistical power to detect differences between the adolescents with SCA and the control adolescents within each sex. We used a conservative number of 12 adolescents in each SCA sex subgroup because of uncertainty regarding the effect of other variables.

The Bland and Altman method (30) was used to assess agreement between the REE values predicted by the equations and the REE values measured by whole-body indirect calorimetry. Differences between predicted and measured REE were plotted against the averages of predicted and measured REE. Regression analysis was used to test the relation between these differences and means. If the slope relating the differences and means was significant, the 95% limits of agreement were estimated as 2 SD of the estimate around the regression line. Differences between subjects in continuous variables were evaluated by using independent-sample, two-tailed t tests. Multiple linear regression analysis was performed on REE by using a backward elimination approach. The covariates that were considered for inclusion in the modeling were SCA status (0 = healthy control subject, 1 = subject with SCA), sex (0 = male, 1 = female), FFM, fat mass, age, hemoglobin concentration, and plausible interactions among these. A multiple linear model (analysis of variance) was used to test for differences between estimated and measured REE values. Plots of REE estimated from the new equations against measured REE were used to assess the general agreement between paired values of estimated and measured REE. The constancy of estimation across the range of predicted REE values was assessed by plotting these values against residuals. A P value < 0.05 was used for inclusion of terms in the regression and to indicate statistical significance. Statistical analyses were performed with SPSS version 10.0 (SPSS Inc, Chicago).

RESULTS
Subject characteristics
Descriptive data for the study participants are shown in Table 2. Body weight, age, FFM, and fat mass were not significantly different between the adolescents with SCA and the control adolescents. Mean (±SD) hemoglobin concentrations were significantly (P < 0.001) lower in the subjects with SCA than in the control subjects (8.9 ± 1.8 compared with 13.3 ± 1.1 g/dL). Plasma concentrations of albumin and thyroid-stimulating hormone and red blood cell concentrations of folate were not significantly different between the subjects with SCA and the control subjects. There were no significant differences between the subjects with SCA and the control subjects in plasma concentrations of testosterone in the males and of estradiol in the females.

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TABLE 2 . Subject characteristics¹  
Measured resting energy expenditure
Multiple regression analysis of REE showed that FFM was an important predictor of REE, as expected (P < 0.001). After adjustment for FFM and sex, the subjects with SCA had, on average, a higher REE than did the healthy control subjects (difference: 1377 kJ/d; P < 0.0001). Both the males with SCA and the control males had higher REE than did the girls after adjustment for FFM and SCA (difference: 576 kJ/d; P = 0.001). These results are shown in Figure 1.

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FIGURE 1. . Relation between resting energy expenditure (REE) and fat-free mass (FFM) in male (; n = 18; R² = 0.82) and female (; n = 19; R² = 0.77) adolescents with sickle cell anemia (SCA) and in healthy male (; n = 12; R² = 0.60) and female (•; n = 11; R² = 0.41) control adolescents. On average, REE was 1377 kJ/d higher in the adolescents with SCA than in the control adolescents after adjustment for sex and FFM (P < 0.001). On average, REE was 576 kJ/d lower in the females than in the males after adjustment for SCA status and FFM. No interactions involving sex were significant. A positive relation between REE and FFM was observed in both the adolescents with SCA and the healthy control adolescents after adjustment for sex. The overall regression equation (R² = 0.846) was REE (kJ/d) = 2366 + 82.94 · FFM - 576 · sex + 1377 · SCA. The relation between REE and FFM in the male adolescents with SCA (upper solid line) was significantly greater than that in the male control adolescents (lower solid line) (P < 0.001). The relation between REE and FFM in the female adolescents with SCA (upper dashed line) was significantly greater than that in the female control adolescents (lower dashed line) (P < 0.001).

 
Resting energy expenditure by prediction equations
Absolute differences between predicted and measured REE in the subjects with SCA and in the control subjects are shown in Table 3. The prediction equations underestimated measured REE in the SCA group and overestimated it in the control group. Detailed comparisons between values predicted by using 3 standard equations and measured values are shown in Figure 2. The mean (±SD) bias in REE in adolescents with SCA was negative for all equations and ranged from -771 ± 479 kJ/d for the FAO/WHO/UNU equation by weight (Figure 2A) to -930 ± 521 kJ/d for the Harris-Benedict equation (Figure 2B). For the FAO/WHO/UNU equation by weight and height and the Schofield equation by weight, the mean biases were -834 ± 467 kJ/d (P = 0.101, r = -0.274) and -724 ± 482 kJ/d (P = 0.627, r = -0.083), respectively. In the control subjects, the mean bias was positive for all equations and ranged from 171 ± 367 kJ/d for the FAO/WHO/UNU equation by weight to 82 ± 360 kJ/d for the Harris-Benedict equation. Ratios of predicted to measured REE expressed in percentages are shown in Table 4. On average, the prediction equations underestimated the measured values for adolescents with SCA by 11.30%, and the differences in REE values were significant (P < 0.001) in both the males and the females for all equations. A comparison between measured REE and REE obtained by using the FAO/WHO/UNU equation by weight is shown in Figure 3 (top). Overestimation of REE in the control subjects ranged from 1.8% in the Harris-Benedict equation to 5.4% in the Schofield equation by weight. Detailed analysis showed that the bias was significant in the female control subjects for the FAO/WHO/UNU equation by weight (P = 0.008; t test), the Schofield equation by weight (P = 0.003; t test), the Schofield equation by weight and height (P = 0.035; t test), and the Harris-Benedict equation by weight (P = 0.036; t test). No significant bias was found in the male control subjects (P > 0.05 for all; t test).

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TABLE 3 . Absolute differences between resting energy expenditure (REE) predicted by standard equations and measured REE in adolescents with sickle cell anemia (SCA) and in control adolescents¹  

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FIGURE 2. . Bias in resting energy expenditure (REE; REE predicted by an equation - REE measured for 24 h with whole-room indirect calorimetry) in control adolescents () and adolescents with sickle cell anemia (SCA) () plotted against the mean of predicted and measured REE values. The solid line in each panel represents the mean difference between predicted and measured REE values. The 2 dashed lines in each panel represent the upper and lower limits of agreement, which were calculated as the mean ± 2 SD. P values are for the slope relating the difference between predicted and measured REE values to average REE, and r values are Pearson’s correlation coefficients between measured and average REE. The equations were A) the FAO/WHO/UNU (9) equation by weight (basal metabolic rate = 12.1[weight (kg)] + 499; P = 0.085 (r = -0.367) and P = 0.264 (r = -0.189) in the control adolescents and the adolescents with SCA, respectively], B) the Harris-Benedict (19) equation (basal metabolic rate = 9.6[weight (kg)] + 1.9[height (cm)] - 4.7[age (y)] + 655; P = 0.791 (r = 0.059) and P = 0.869 (r = -0.028) in the control adolescents and the adolescents with SCA, respectively], and C) the Schofield (20) equation by weight and height (basal metabolic rate = 8.4[weight (kg)] + 4.7[height (cm)] + 200; P = 0.136 (r = -0.321) and P = 0.287 (r = -0.180) in the control adolescents and the adolescents with SCA, respectively].

 

View this table:
TABLE 4 . Ratio of resting energy expenditure (REE) predicted by equations to REE measured by using whole-body indirect calorimetry in adolescents with sickle cell anemia (SCA) and in control adolescents¹  

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FIGURE 3. . Comparison between measured resting energy expenditure (REE) and REE predicted either with the FAO/WHO/UNU (9) equation by weight (top) or with the new proposed equations (bottom) in adolescents with sickle cell anemia (n = 37). The solid line in each panel is the line of identity (ie, measured REE = predicted REE), and the dashed line in each panel represents the relation between measured REE and predicted REE. R² = 0.828 for the FAO/WHO/UNU equation by weight, and R² = 0.893 for the new equations.

 
New regression equations
Our data were used to derive new equations for predicting REE in adolescents with SCA aged 14–18 y. A regression matrix between REE and simple, potentially predictive factors for REE such as sex, weight, height, FFM, fat mass, and age is shown in Table 5. FFM and body weight were the most important alternative variables in developing regression equations for calculation of REE in males (R² = 0.805 and 0.702, respectively) and females (R² = 0.757 and 0.825, respectively).

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TABLE 5 . Multiple regression model results for resting energy expenditure in adolescents with sickle cell anemia  
Multiple regression analyses with independent variables such as sex, weight, height, FFM, fat mass, age, and hemoglobin concentration allowed several REE regression equations for SCA to be developed. The most accurate overall equation (P 0.0001, overall R² = 0.879) was

ACKNOWLEDGMENTS  
We are indebted to our volunteers and their families for their enthusiasm for research and participation in this study. We also thank the staff of the General Clinical Research Center at Vanderbilt University for help with this project, Karen Townsend for technical help and expertise, and LeMonica Lewis for help in preparing this manuscript.

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