A DXA-Based Mathematical Model Predicts Midthigh Muscle Mass ...

The Journal of Nutrition Methodology and Mathematical Modeling

A DXA-Based Mathematical Model Predicts Midthigh Muscle Mass from Magnetic Resonance Imaging in Typically Developing Children but Not in Those with Quadriplegic Cerebral Palsy1,2 Christopher M. Modlesky,3* Matthew L. Cavaiola,3 Jarvis J. Smith,3 David A. Rowe,4 David L. Johnson,3 and Freeman Miller5 3 Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE 19716; 4School of Psychological Sciences and Health, University of Strathclyde, Glasgow G13 1PP, UK; and 5Department of Orthopedics, AI duPont Hospital for Children, Wilmington, DE 19803

Abstract Valid methods for assessing regional muscle mass in children are needed. The aim of this study was to determine whether dual-energy X-ray absorptiometry (DXA) can accurately estimate midthigh muscle mass from MRI (muscleMRI) in typically developing children and children with quadriplegic cerebral palsy (CP). A mathematical model predicting muscleMRI from midthigh, fat-free soft tissue mass from DXA (FFSTDXA) was developed using 48 typically developing children (6–13 y) and was validated using the leave-one-out method. The model was also tested in children with quadriplegic CP (n = 10). The model produced valid estimates of midthigh muscle mass (muscleDXA) in typically developing children, as indicated by a very strong relationship between muscleDXA and muscleMRI (r2 = 0.95; SEE = 68 g; P , 0.001), no difference in muscleDXA and muscleMRI (P = 0.951), and visual examination using a Bland-Altman plot. MuscleDXA was very strongly related to muscleMRI in children with CP (r2 = 0.96; SEE = 54 g; P , 0.001); however, muscleDXA overestimated muscleMRI by 15% (P = 0.006). The overestimation of muscleMRI by muscleDXA was strongly related to the lower ratio of muscleMRI to FFSTDXA (muscleMRI/FFSTDXA) in children with CP (r2 = 0.75; P = 0.001). The findings suggest that the DXA-based mathematical model developed in the current study can accurately estimate midthigh muscle mass in typically developing children. However, a population-specific model that takes into account the lower muscleMRI/FFSTDXA is needed to estimate midthigh muscle mass in children with quadriplegic CP. J. Nutr. 140: 2260–2265, 2010.

Introduction Accurate quantification of body tissues is important for the assessment of growth, development, and nutritional status. Poor accretion of skeletal muscle is a sign of malnutrition (1) and may impair a child’s immediate and long-term physical performance and overall health (2). Most studies that have attempted to quantify skeletal muscle mass in children have relied on gross estimates from girth and skinfold measurements (3) or whole body estimates of lean soft tissue mass (4). A paucity of studies have assessed skeletal muscle mass using the most advanced methodologies, such as MRI and computed tomography because of their excessive cost, limited availability, and time-consuming processing procedures, as well as the considerable radiation

exposure associated with computed tomography (5). Therefore, more accessible techniques that yield accurate estimates of muscle mass are needed. Dual-energy X-ray absorptiometry (DXA)6 has been shown to accurately estimate muscle mass in healthy populations (6–8). Furthermore, DXA has a substantially lower expense and much shorter data collection and processing procedures than MRI and computed tomography. Few studies have tested the accuracy of muscle mass estimates by DXA in children using MRI or computed tomography (9) and studies testing its accuracy in populations that commonly experience malnutrition and poor muscle accretion, such as children with cerebral palsy (CP), are lacking. Moreover, few studies have focused on specific regions of the body, such as

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Supported by NIH grant no. HD050530 and the National Osteoporosis Foundation. 2 Author disclosures: C. M. Modlesky, M. L. Cavaiola, J. J. Smith, D. A. Rowe, D. L. Johnson, and F. Miller, no conflicts of interest. * To whom correspondence should be addressed. E-mail: [email protected].

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Abbreviations used: d, Cohen’s d; CP, cerebral palsy; DXA, dual-energy X-ray absorptiometry; fatDXA, midthigh fat mass from DXA; FFSTDXA, midthigh fat-free soft tissue mass from DXA; muscleDXA, midthigh muscle mass estimated using a mathematical model based on FFSTDXA (model 1); muscleMRI, midthigh muscle mass from MRI; muscleMRI/FFSTDXA, ratio of muscleMRI to FFSTDXA.

ã 2010 American Society for Nutrition. Manuscript received May 26, 2010. Initial review completed July 21, 2010. Revision accepted September 23, 2010. First published online October 27, 2010; doi:10.3945/jn.110.126219.

the midthigh. Regional assessment of muscle mass is important because changes in muscle mass are often site specific (10). Moreover, the effect of muscle on other tissues, such as bone, is also site specific (11). It is particularly important to study the thigh musculature. The thigh muscles attach to the proximal femur, the most lethal fracture site in the general population (12), as well as to the middle femur, distal femur, and proximal tibia, the sites of very poor bone development (13–15) and .80% of all fractures in children with CP (16,17). Assessment of thigh muscle mass is also important because it represents the largest proportion of total body skeletal muscle (18). The aim of this study was to determine whether DXA can accurately estimate midthigh muscle mass from MRI (muscleMRI) in typically developing children and in children with quadriplegic CP. Based on prior studies (4,6,8,9,19), it was hypothesized that a DXA-based mathematical model developed using typically developing children would: 1) accurately estimate muscleMRI in typically developing children, and 2) yield midthigh muscle mass values that very strongly correlate with muscleMRI but overestimate muscleMRI in children with quadriplegic CP. It was also hypothesized that the expected overestimation of muscleMRI in children with CP would be related to a lower ratio of midthigh muscle mass:midthigh fat-free soft tissue mass than in typically developing children. A lower ratio of muscle mass:fat-free soft tissue mass has been observed in adults with neurological disorders associated with very low muscle mass, such as spinal cord injury (19).

Methods Participants and protocol. Typically developing children with no history of chronic medication use and nonambulatory children with quadriplegic CP (all 6–13 y) were recruited from the AI duPont Hospital for Children, the University of Delaware, and the Newark, Delaware community. Anthropometrics, pubertal development, gross motor function, midthigh muscle mass, and midthigh fat-free soft tissue mass were assessed within a 2-wk period. The study was approved by the Institutional Review Boards at the University of Delaware and the AI duPont Hospital for Children. Parents gave written consent and children gave written assent, if able, before any testing was conducted. Anthropometrics. Height and body mass were assessed while the children were in shorts and a t-shirt and without shoes or braces. Height of typically developing children was measured to the nearest 0.1 cm using a stadiometer. Height of children with CP was estimated using forearm length, as previously described (20). Body mass of all children was assessed using a digital wheelchair scale to the nearest 0.2 kg. Normative graphs published by the Centers for Disease Control and Prevention (21) were used to determine height, body mass, and BMI percentiles. Tanner staging. Pubertal development was assessed by a physician assistant using the Tanner staging technique (22). Pubic hair and testicular/penis development were assessed in boys. Pubic hair and breast development were assessed in girls. The rating system ranges from 1 to 5. A 1 indicates no signs of pubertal development and a 5 indicates full development. Gross motor function classification. The gross motor function of children with CP was assessed by a physician using the gross motor function classification scale (23). The scale ranges from 1 to 5, with 3–5 reflecting a lack of independent mobility. DXA. The total body was scanned using DXA (Delphi W, version 11.2, Pediatric Whole Body Analysis; Hologic). To limit participant motion, children with CP were secured from the waist down using a modified version of the BodyFIX (Medical Intelligence). The system consists of: 1)

a plastic-like foil that is placed over the participant while the participant is supine on the DXA table; 2) small stabilizing cushions placed around the participant that minimize movement around the waist and extremities; and 3) a vacuum that draws air from the space between the foil and the participant, which hardens the stabilizing cushions and creates a constant pressure on the participant’s waist and lower extremities. The large deflatable cushion (i.e. Bluebag) that the participant lies on and that creates a mold around the participant during the BodyFIX procedure was not included in the scan, because it slightly elevates fat-free soft tissue mass from DXA (FFSTDXA). On the other hand, the modified BodyFIX procedure has no effect on FFSTDXA (C. M. Modlesky, R. Rawal, and F. Miller, unpublished data). After completion of the scan, midthigh, FFSTDXA and midthigh fat mass from DXA (fatDXA) were determined at the level of the middle third of the nondominant femur using a procedure previously described (19). FFSTDXA is composed primarily of muscle, but it also includes nonmuscle constituents such as skin, connective tissue, and the lean portion of adipose tissue. To assess the test-retest reliability of FFSTDXA and fatDXA, 20 typically developing children 5–13 y of age were tested twice on the same day after repositioning. The CV for repeat measures of FFSTDXA and fatDXA were 0.6 and 0.7%, respectively, and the intraclass correlation coefficients were .0.99. MRI. MRI was used to assess muscleMRI in the nondominant side, as previously described (24). Participants were immobilized from the waist down using the BodyFIX. Axial T1-weighted images of the thigh beginning at the top of the nondominant femoral head and extending to the distal end of the femur (1 cm thick separated by 0.5 cm) were collected on a GE 1.5 Tesla magnetic resonance imager using a phasedarray torso coil (TR = 750, TE = 14, FOV = 16–20, 1 NEX, 512 3 512 matrix) and a 3-plane localizer. Images at the level of the middle 3rd of the femur (7–11 images) were analyzed using custom software designed in-house with Interactive Data Language (Research Systems). The software automatically identifies the images that will be analyzed based on the number of images collected. Image analysis was limited to the level of the middle 3rd of the femur, because it contains the largest portion of the thigh musculature and coverage beyond this region was not possible in the tallest participants using a single scan. Images were filtered and image segmentation was performed with a fuzzy clustering algorithm (25). The software separates muscle from other tissues such as adipose tissue, bone, and skin. Muscle tissue pixels were summed to determine their cross-sectional areas and their volumes were quantified by accounting for image thickness and spacing between images. The inner images were multiplied by 1.5 to account for thickness of each image (1.0 cm) and the separation between images (0.5 cm). Images that included the top and bottom ends of the midthigh were multiplied by an appropriate correction factor (,1.5), so the entire image set reflected the thigh at the level of the middle 3rd of the femur and was aligned with the region analyzed using DXA. MuscleMRI was determined by multiplying muscle volume by 1.04, the assumed density (g/cm3) of muscle (26). Using thigh images from 20 children, the analysis procedure using the in-house program was validated against another program (Sliceomatic, v. 4.20; TomoVision) that has been shown to yield accurate estimates of muscle mass (27). A very small CV (,1.5%) and an intraclass correlation coefficient that was essentially 1.0 (.0.99) indicated excellent agreement between muscleMRI estimates from the in-house program compared with the validated program. To assess the test-retest reliability of muscleMRI, 8 children (2 children with quadriplegic CP and 6 typically developing children) 5–12 y were tested twice on the same day after repositioning or on different days within 2 wk apart. The CV of repeat measures of muscleMRI was 0.5% and the intraclass correlation coefficient was .0.99. Statistics. Data were analyzed using SPSS, version 17.0. In typically developing children, independent t tests were used to determine whether there were gender differences in age, pubertal development, height, body mass, BMI, and body composition. Independent t tests were also used to assess differences in age, pubertal development, height, body mass, BMI, and body composition between typically developing children and children with CP. One-sample t tests were used to determine whether Muscle mass estimates from DXA in children

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the age-based percentiles for height, body mass, and BMI were different from the 50th percentile in typically developing children and children with CP. One-sample t tests were also used to determine whether the ratio of muscleMRI:FFSTDXA (muscleMRI/FFSTDXA) differed from 0.9, the ratio previously observed in adults (19). Pearson correlations were used to identify factors related to muscleMRI/FFSTDXA and r2 is reported. Mathematical prediction models were developed using simple linear regression analysis and data from the typically developing children to determine whether FFSTDXA can accurately estimate muscleMRI in typically developing children and children with quadriplegic CP (28). Age, Tanner Stage, height, and fatDXA were included in models to determine whether they improved the prediction of muscleMRI. Crossvalidation of the models was conducted using the leave-one-out method (29), the same sample of typically developing children, and a BlandAltman plot (30). Cross-validation of the models was also conducted using the sample of children with CP. An a level of 0.05 was used for all significance tests. The magnitude of effects was assessed using Cohen’s d (d), with values of 0.2, 0.5, and 0.8 indicating small, medium, and large effects (31).

Results Forty-eight typically developing children (n = 23 boys, n = 25 girls) and 10 children with quadriplegic CP (n = 2 boys, n = 8 girls) and a gross motor classification of 3, 4, or 5 (4.4 6 1.0) participated in the study. All children were Caucasian except 1 typically developing girl who was Asian. Physical characteristics of the participants are reported in Table 1. In the typically developing children, there were no significant differences in age (d = 0.35; P = 0.270), height (d = 0.33; P = 0.343), body mass (d = 0.44; P = 0.197), or BMI (d = 0.39; P = 0.211) in boys compared with girls. Height, body mass, and BMI were between the 10th and 90th age-based percentiles for 46 of the 48 participants. Two girls had body masses and BMI . 90th age-based percentiles. Height, body mass, and BMI were not significantly different from the 50th percentiles for age (P . 0.05). Thirtyseven of the typically developing children were prepubertal (Tanner stage = 1) and 11 showed signs of pubertal development (Tanner stage 2–4). The girls had greater pubertal development than the boys (P , 0.05). In children with CP, 5 had a height lower than the 10th percentile for age, 5 had a BMI lower than the 10th percentile for age, and 4 had a weight lower than the 10th percentile for age. There were no significant differences in

TABLE 1

age (d = 0.18; P = 0.617), height (d = 0.57; P = 0.104), body mass (d = 0.24; P = 0.491), or BMI (d = 0.17; P = 0.606) between children with CP and the combined sample of typically developing children. Height in children with CP, however, was lower than the 50th percentile for age (P = 0.005). Measures of soft tissue from DXA and MRI are also reported in Table 1. FFSTDXA was higher than muscleMRI in typically developing children (d = 2.26; P , 0.001) and in children with CP (d = 1.45; P = 0.001). In typically developing children, there were no significant gender differences in FFSTDXA (d = 0.27; P = 0.223) or muscleMRI (d = 0.36; P = 0.360). However, children with CP had much lower FFSTDXA (43%; d = 1.35; P , 0.001) and muscleMRI (49%; d = 1.61; P , 0.001) than the total sample of typically developing children. There was not a significant gender difference in muscleMRI/FFSTDXA in typically developing children (d = 0.44; P = 0.126), but muscleMRI/FFSTDXA was 9% lower in children with CP than the typically developing children (d = 1.35; P , 0.001; Fig. 1). The muscleMRI/FFSTDXA was not related to muscleMRI in typically developing children (r2 = 0.008; P = 0.538) or children with CP (r2 , 0.001; P = 0.925; Fig. 1). FFSTDXA was very highly related to muscleMRI, explaining .95% of the variance in muscleMRI (P , 0.001; Table 2, model 1). There was only a small increase in explained variance and a small decrease in SEE when age was added to the mathematical model (Table 2, model 2) and no change when gender, Tanner stage, or fatDXA were added (Table 2, model 3). Following the leave-one-out cross-validation analysis, midthigh muscle mass estimated using FFSTDXA and model 1 (muscleDXA) and muscleMRI were highly correlated and not significantly (P = 0.951) or meaningfully (d , 0.02) different in typically developing children (Fig. 2). The strong agreement between muscleDXA and muscleMRI is further demonstrated by a Bland-Altman plot in which the mean difference between muscleDXA and muscleMRI is 0 and the variability is small (SD = 64 g; Fig. 3). The same patterns were observed when models 2 and 3 were cross-validated. In children with CP, there was a very strong relationship between muscleDXA and muscleMRI (P , 0.001; Fig. 2); however, muscleDXA overestimated muscleMRI by 15% (71 6 64 g; d = 1.11; P = 0.006; Fig. 3). The overestimation of muscleMRI in children with CP was even greater (28%; 131 6 83 g, d = 1.58; P = 0.001) when age was included with FFSTDXA as a predictor (Table 2,

Physical characteristics of 6- to 13-y-old typically developing children and children with quadriplegic CP Typically developing Boys

n Age, y Tanner stage, breast/penis Tanner stage, pubic hair Height, m Body mass, kg BMI, kg/m2 Midthigh FFSTDXA, g muscleMRI, g muscleMRI/FFSTDXA

9.6 1.1 1.1 1.37 32.6 17.0

23 6 1.7 6 0.3a 6 0.3a 6 0.13 6 8.7 6 1.8

Girls 25 10.2 6 1.5 6 1.6 6 1.41 6 36.5 6 18.1 6

Total

48 1.7 9.9 6 1.7 0.9 1.3 6 0.7 1.0 1.3 6 0.8 0.11 1.39 6 0.12 11.8 34.6 6 10.5 3.8 17.6 6 3.0

CP 10 10.3 6 2.3 1.5 6 1.0 1.6 6 1.1 1.31 6 0.18 32.0 6 13.5 18.2 6 5.9

1125 6 313b 1261 6 433b 1196 6 382b,c 672 6 358b 893 6 253 971 6 325 934 6 293c 472 6 235 0.79 6 0.05 0.77 6 0.05 0.78 6 0.05c 0.71 6 0.07

Values are means 6 SD. aDifferent from typically developing girls, P , 0.05; different from muscleMRI, P , 0.001; cdifferent from children with quadriplegic CP, P , 0.001.

1 b

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FIGURE 1 Scatter plot of muscleMRI compared with the ratio of muscleMRI to FFSTDXA in typically developing children and children with quadriplegic CP. The dotted horizontal lines represent the mean muscleMRI/FFSTDXA in typically developing children 6 2 SD. The relationship for typically developing children is represented by the thin solid line. The relationship for children with CP is represented by the thick solid line.

TABLE 2

Mathematical models for predicting muscle mass from MRI in the midthigh developed using 6- to 13-y-old typically developing children

Model

R2

SEE, g

1. FFSTDXA (g) × 0.747 + 40.7 2. FFSTDXA (g) × 0.648 + age (y) × 27.5 – 114.2 3. FFSTDXA (g) × 0.773 – fatDXA (g) × 0.0625 + 33.1

0.95 0.96 0.96

64 58 63

1

All models are significant, P , 0.001; n = 48.

model 2). When fatDXA was included with FFSTDXA as a predictor (Table 2, model 3), the overestimation was reduced to 12% (58 6 59 g), but the difference was still large (d = 0.98) and significant (P = 0.012). There was a strong relationship between the difference in muscleDXA and muscleMRI and muscleMRI/FFSTDXA in typically developing children (P , 0.001) and in children with CP (P = 0.001; Fig. 4). Furthermore, in almost all typically developing children (26 of 27) and in all children with CP (9 of 9) with a muscleMRI/FFSTDXA lower than the mean muscleMRI/FFSTDXA in typically developing children (0.78), muscleDXA overestimated muscleMRI (Fig. 4). On the other hand, in almost all typically developing children (20 of 21) and in the lone child with CP with a muscleMRI/FFSTDXA that was higher than the mean muscleMRI/FFSTDXA in typically developing children, muscleDXA underestimated muscleMRI (Fig. 4).

Discussion The finding that the DXA-based mathematical model developed in the present study using FFSTDXA provided valid estimates of muscleMRI in typically developing children is consistent with studies in adults in which the accuracy of total body and regional muscle mass from MRI was estimated using DXA (4,6,8,9). It is also consistent with a study by Kim et al. (9) whose DXAbased model provided accurate estimates of total body muscle mass from MRI in a group of children and adolescents as determined using the leave-one-out technique and a separate cross-validation sample. In the present study, the mathematical model using FFSTDXA alone explained 95% of the variance in muscleMRI and the SEE (64 g) relative to the mean muscleMRI

FIGURE 2 Scatter plot demonstrating the very strong relationship between muscleDXA and muscleMRI in typically developing children and children with quadriplegic CP. The plot also demonstrates the overestimation of muscleMRI by muscleDXA in children with CP. The dotted diagonal line represents the line of identity. The thin solid line represents the regression line for the typically developing children. The thick line represents the regression line for the children with CP.

FIGURE 3 Bland-Altman plot demonstrating the strong agreement between muscleDXA and muscleMRI in typically developing children and the overestimation of muscleMRI by muscleDXA in children with quadriplegic CP. The dotted lines indicate the mean difference between muscleDXA and muscleMRI 6 2 SD for the typically developing children.

(934 g) in typically developing children was 6.9%. In the study of children and adolescents by Kim et al. (9), the mathematical model explained 98% of the variance in total body muscle mass from MRI and the SEE (0.57 kg) relative to the mean total body muscle mass (12 kg) was 4.7%. A unique aspect of the present study compared with the study by Kim et al. (9) is that the validity of muscle mass estimates from DXA was evaluated in a specific region of the body. One reason it is important to accurately assess regional muscle mass is because the site-specific effects of muscle on bone can be studied. It is particularly important to study the thigh musculature. Larger thigh muscles may foster better growth and development of the femur, which may reduce immediate fracture risk in children with limited mobility, such as children with quadriplegic CP, and long-term fracture risk in typically developing children. The findings in the present study are in line with a study by Bridge et al. (32) in which a strong relationship between FFSTDXA and muscleMRI was observed in healthy peripubertal boys and girls aged 11–14 y (r2 = 0.96; P , 0.001). A strength of the present study was the use of validated software to estimate muscleMRI. An additional strength was that the DXA-based mathematical model was cross-validated in a group of typically developing children. A notable difference between the present study and the study by Bridge et al. (32) is that the DXA machines used to estimate FFSTDXA were manufactured by different companies. It has been shown that DXA software, model, and manufacturer differences can affect soft tissue and bone mineral estimates (33). Therefore, the mathematical models developed in the current study are specific to the DXA software and instruments that were used. To our knowledge, this is the first study to assess the validity of muscle mass estimates from a DXA-based mathematical model in a group of children with extremely low muscle mass for their age. Muscle mass in the midthigh was ~50% lower in children with CP than in typically developing children. The strong relation between muscleDXA and muscleMRI but the low muscleMRI/FFSTDXA observed in children with CP is similar to the pattern observed in men with spinal cord injury (19), another group with very low muscle mass. Although a DXA-based mathematical model was not created in the study of men with spinal cord injury and healthy controls due to the small sample size, it was inferred that overestimates of muscle mass by DXA would occur if their lower muscleMRI/FFSTDXA in the men with Muscle mass estimates from DXA in children

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FIGURE 4 Scatter plot of muscleMRI/FFSTDXA compared with the difference between muscleDXA and muscleMRI in typically developing children and children with quadriplegic CP. The dashed horizontal line indicates the point where the difference between muscleDXA and muscleMRI is zero. The dashed vertical line indicates the mean muscleMRI/FFSTDXA in typically developing children (0.78). The thin solid line represents the regression line for the typically developing children. The thick line represents the regression line for the children with CP.

spinal cord injury was not taken into account. The present study supports the notion that muscle mass is overestimated in children with quadriplegic CP because of their lower muscleMRI/FFSTDXA than observed in typically developing children. This is demonstrated by the strong relationship between the difference in muscleDXA and muscleMRI and muscleMRI/ FFSTDXA in children with CP and in typically developing children. Furthermore, muscleDXA underestimated muscleMRI when muscleMRI/FFSTDXA was lower than the mean ratio observed in the typically developing children (0.78) and it overestimated muscleMRI when the ratio was higher. Two observations suggest that the 15% overestimation of muscleMRI by muscleDXA is substantial. First, the effect size representing the difference between muscleDXA and muscleMRI was large (d . 0.8). Second, the mean difference between muscleDXA and muscleMRI in children with CP (75 g) was higher than the SEE of the mathematical model (64 g). Moreover, 5 of the 10 children with CP had differences that were greater than the SEE and 1 child had a difference that was more than twice the SEE. Variability in skin and connective tissue may have affected muscleMRI/FFSTDXA; however, these tissues were not evaluated in the present study because they are difficult to assess using MRI. Another factor that may have affected muscleMRI/FFSTDXA is the degree of adiposity. Adipocytes contain protein and water (i.e. lean) as well as fat. If increased adiposity is associated with a higher degree of lean mass from adipose tissue and a lower muscleMRI/FFSTDXA, it should lead to overestimates of muscle mass by a mathematical model that relies on FFSTDXA. However, the addition of fatDXA to the DXA-based mathematical model did not increase the explained variance in muscleMRI in typically developing children. Although it reduced the degree of overestimation of midthigh muscle mass in children with CP, a 12% overestimation of muscle mass still remained. One limitation associated with using fatDXA, or adipose tissue mass from MRI or computed tomography, to test the effect of the lean component of adipose tissue mass on estimates of muscle mass is the considerable variability in the lean and fat portions of adipose tissue. A wide range in the ratio of fat to adipose tissue (0.54– 0.85) has been reported in cadavers (34). 2264

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Other limitations associated with the study must be considered. First, it is unclear if the mathematical models developed in the current study can be used to monitor changes in muscle mass due to normal growth, dietary changes, surgery, or pharmacological intervention. Marked changes in diet, such as occurs with the use of enteral feeding via a gastrostomy tube in children with CP, can have a profound effect on growth (35). In addition, surgical procedures and pharmacological treatment can appreciably alter muscle size (36,37), which may or may not be accurately detected by DXA. Second, DXA-based mathematical models cannot quantify the individual muscles in the thigh or other regions of the body. Third, muscle mass estimates from MRI assume that the density of muscle is known and the same in typically developing children and in children with CP. There is evidence that intramyocellular lipid content is elevated in individuals with neurological disorders such as CP (38). Moreover, some studies (39), but not all (40), suggest that lean mass in individuals with CP is hypohydrated. Increased lipid and/or water in muscle would reduce the muscle’s density and alter estimates of muscle mass by MRI. However, whether muscle concentrations of lipid and/or water are higher in children with quadriplegic CP requires further investigation. Furthermore, if the density of muscle were lower in children with CP, the overestimation of muscle by the DXA-based mathematical model would be even greater in children with CP. For example, if the density of muscle in children with CP was assumed to be 1.00 vs. 1.04 g/cm3, the model would overestimate muscle mass by 20% rather than 15%. On the other hand, the density of muscle would need to be 1.196 g/cm3 in children with quadriplegic CP for the difference in muscle estimates from MRI and the DXA-based model developed in the present study to agree. Fourth, DXA-based mathematical models cannot assess muscle quality, which is reflected by intermuscular adipose tissue mass and intramyocellular lipid content. Elevated intermuscular adipose tissue mass and intramyocellular lipid content are associated with insulin resistance (41) and have been reported in individuals with neurological disorders, such as CP (24,38). Lastly, in the present study, we developed and tested only DXAbased mathematical models to assess muscle mass in the midthigh. If there is an interest in quantifying muscle mass in the entire lower extremity, other regions of the body or the entire body, additional mathematical models must be developed for the DXA machine and software used in the present study. In summary, the findings suggest that the DXA-based mathematical models developed in the present study provide accurate estimates of midthigh muscle mass in typically developing children that have not reached pubertal maturity. Although the DXA-based model with FFSTDXA alone yielded midthigh muscle mass estimates that correlated very strongly with muscleMRI in children with quadriplegic CP, the estimates from the DXA-based model were 15% higher. A reduction in the bias resulted when fatDXA was taken into account, but significant differences still remained. A follow-up study with a larger sample size is needed to develop an appropriate DXA-based model for children with CP. Studies are also needed to determine whether DXA-based models can detect changes in muscle mass such as occurs with normal growth, malnutrition, exercise, injury, or rehabilitation. Acknowledgments We thank Patty Groves for assistance with data collection. C.M.M. designed research; C.M.M., M.L.C., D.L.J., and F.M. conducted research; C.M.M. and D.A.R. analyzed data; C.M.M., M.L.C., and J.J.S. wrote the paper; and C.M.M. had primary

responsibility for final content. All authors read and approved the final manuscript.

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