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American Journal of Clinical Nutrition, Vol. 83, No. 5, 1047-1054, May 2006
© 2006 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Evaluation of Lunar Prodigy dual-energy X-ray absorptiometry for assessing body composition in healthy persons and patients by comparison with the criterion 4-component model1,2,3

Jane E Williams, Jonathan CK Wells, Catherine M Wilson, Dalia Haroun, Alan Lucas and Mary S Fewtrell

1 From the MRC Childhood Nutrition Research Centre, Institute of Child Health, London, United Kingdom (JEW, JCKW, DH, AL, and MSF), and the Radiology Department, Great Ormond Street Hospital, London, United Kingdom (CMW)

2 The Institute of Child Health and Great Ormond Street Hospital for Children received funding from the NHS Executive.

3 Address reprint requests to JE Williams, MRC Childhood Nutrition Research Centre, Institute of Child Health, 30 Guilford Street, London WC1N 1EH, United Kingdom. E-mail: jane.williams{at}ich.ucl.ac.uk.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Dual-energy X-ray absorptiometry (DXA) is widely used to assess body composition in research and clinical practice. Several studies have evaluated its accuracy in healthy persons; however, little attention has been directed to the same issue in patients.

Objective: The objective was to compare the accuracy of the Lunar Prodigy DXA for body-composition analysis with that of the reference 4-component (4C) model in healthy subjects and in patients with 1 of 3 disease states.

Design: A total of 215 subjects aged 5.0–21.3 y (n = 122 healthy nonobese subjects, n = 55 obese patients, n = 26 cystic fibrosis patients, and n = 12 patients with glycogen storage disease). Fat mass (FM), fat-free mass (FFM), and weight were measured by DXA and the 4C model.

Results: The accuracy of DXA-measured body-composition outcomes differed significantly between groups. Factors independently predicting bias in weight, FM, FFM, and percentage body fat in multivariate models included age, sex, size, and disease state. Biases in FFM were not mirrored by equivalent opposite biases in FM because of confounding biases in weight.

Conclusions: The bias of DXA varies according to the sex, size, fatness, and disease state of the subjects, which indicates that DXA is unreliable for patient case-control studies and for longitudinal studies of persons who undergo significant changes in nutritional status between measurements. A single correction factor cannot adjust for inconsistent biases.

Key Words: Body composition • fat mass • fat-free mass • dual-energy X-ray absorptiometry • DXA • obesity • clinical practice


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Assessment of body composition is increasingly used to direct the clinical management of patients. First, an abnormal body composition (eg, high or low amounts of body fat) is often a major symptom of diseases such as obesity, with changes in body components reflecting the relative success of treatment. Second, the association of body composition with the risk of diseases, such as coronary heart disease, broadens its clinical significance. Third, body composition can also be used as the basis of requirements for fluids, nutrition, and dosages of drugs and dialysis. Despite increasing awareness of the value of such information, its measurement in routine practice has remained constrained by the lack of appropriate technology.

Dual-energy X-ray absorptiometry (DXA), first developed for assessment of bone mass, provides information on total fat mass (FM) and fat-free mass (FFM) and their distribution in the trunk and upper and lower limbs (1). Over the past decade, DXA has been increasingly used to assess body composition in research and clinical practice, including applications to direct treatment (2-4). Its rapid uptake can be attributed to its ease of use, availability, and low radiation exposure. However, although the precision of the technique for body-composition outcomes is well-established, insufficient attention has been paid to accuracy. Many validation studies have used as the reference method a technique that itself has unknown accuracy, thereby limiting confidence in the findings.

In the absence of chemical analysis of body composition, the ideal reference method is a multicomponent model of body composition, which minimizes the need for theoretical assumptions of biological constancy in tissues (5). A recent study evaluated Hologic Inc (Waltham, MA) DXA instrumentation against the 4-component (4C) model for estimating FM in a group of girls and adolescent females. A large bias and large limits of agreement were found between the 2 methods that could not be attributed to age, ethnicity, or fatness, but that could cause a person's FM to be under- or overestimated by 28% (6). These authors proposed that the bias could be addressed by a correction factor.

The latter study highlights important issues; however, further work is still required. First, the results may not apply to other manufacturer's instrumentation, because instruments differ in the way in which tissue masses are quantified. Second, many clinical applications involve extremes of body size and composition; however, the validity of DXA over a wide range of body sizes and health states has yet to be investigated. The aim of this study was to evaluate the level of agreement between DXA and the 4C model reference method when estimating FM, FFM, and weight in a diverse group of healthy and unhealthy adults and children to determine whether biases are consistent between these groups.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
A total of 215 subjects aged 5.0–21.3 y were recruited into studies of body composition in healthy subjects, adults born preterm, and patient groups from Great Ormond Street Children's Hospital and the National Hospital for Neurology and Neurosurgery. The sample consisted of nonobese adults (n = 70), obese adults (n = 16), healthy nonobese children (n = 52), obese children (n = 39), children with cystic fibrosis (CF; n = 26), and children with glycogen storage disease (GSD; n = 12). Obesity was defined as a body mass index (BMI; in kg/m2) above the 95th percentile according to UK 1990 reference data (7). Two obese boys did not complete the protocol because it was not possible to scan their whole body because of the inadequate size of the scanning area. A separate analysis of data from obese men was not possible because of small sample sizes (n = 2). Measurements were conducted over a 90-min visit to the study center at Great Ormond Street Children's Hospital after a light meal. Ethical permission was obtained from the ethical committee of the Institute of Child Health and The National Hospital. Written consent was obtained from adults and parents. Written assent was obtained from children aged ≥11 y, and verbal assent was obtained from children aged <11 y.

Dual-energy X-ray absorptiometry
Bone mineral content (BMC), FM, and FFM were determined by using a Lunar Prodigy whole-body scanner (GE Medical Systems, Madison, WI) in conjunction with Encore 2002 software. The instrument automatically alters scan depth depending on the thickness of the subject, as estimated from age, height, and weight. All scans were performed while the subjects were wearing light indoor clothing and no removable metal objects. The typical scan time was 5 min, depending on height. The radiation exposure per whole-body scan is estimated to be 2 µSv, which is lower than the daily background level. All scans were performed by one operator (CMW). The precision of soft tissue analysis for a Lunar DPX-L instrument (regarded by the manufacturers to be similar to the Lunar Prodigy), established by repeat measurements of humans on 4 successive days, has been reported as 1% for FFM and 2% for FM (8).

Body volume
Body volume (BV) was measured by using Bod Pod Instrumentation (Life Measurement Instruments, Concord, CA) according to the manufacturer's instructions as previously described (9). Measurements were made while the subjects wore a close-fitting swimming costume and hat. The raw volume values that appear transiently on the screen were recorded, and an adjustment for thoracic gas volume and surface area artifact was made to obtain actual BV as described previously (10). To improve precision, the procedure was repeated until 2 values for raw density of within 0.007 kg/L were obtained (11). When it was not possible to achieve 2 such measurements, because of breathing irregularities in 3 of the children with CF, the mean of all raw volume values was used after values ±2 SD were discarded. All measurements were made by 1 of 3 operators (JEW, CMW, or DH).

Anthropometric measurements
Body weight was measured as an integral stage of the Bod Pod procedure to within 0.01 kg. Accuracy was confirmed by the use of 2 solid weights of known mass. Height was measured to within 0.1 cm with a wall-mounted digital display stadiometer (Holtain, Dyfed, United Kingdom). BMI was calculated as weight (kg) divided by the square of height (m). Data on weight, height, and BMI were converted to SD scores (SDS) with the use of UK 1990 reference data (7, 12).

Deuterium dilution
Total body water (TBW) was assessed by deuterium (2H-labeled water) dilution with the use of a dose equivalent to 0.05 g 2H2O/kg body weight. Doses were made up with water to {approx}100 mL for young children and to 150 mL for older children and adults. Saliva samples were taken before the dose was administered and either 4 (for persons of normal body fatness) or 5 (for obese subjects) h after the dose was administered. Absorbent salivettes (Sarstedt, Rommelsdorf, Germany) were used to collect the saliva ≥30 min after the last ingestion of food or drink.

Deuterium samples were analyzed by Iso-Analytical Ltd (Sandbach, United Kingdom) by using the equilibration method of Scrimgeour et al (13). Briefly, 0.3 mL liquid, along with a vial of 5% platinum on alumina powder (Sigma-Aldrich, Poole, United Kingdom), was placed in a septum sealed container (Labco, High Wycombe, United Kingdom) and flushed for 2 min with hydrogen. Low-enrichment and high-enrichment standard waters were similarly prepared to normalize data against SMOW-SLAP (Standard Mean Ocean Water/Standard Light Arctic Precipitation) standards. Samples were equilibrated at room temperature for 3 d before analysis. The head spaces in the containers were then analyzed for deuterium enrichment with a continuous-flow isotope ratio mass spectrometer (Geo20-20; Europa Scientific, Crewe, United Kingdom). The accuracy of the analyses was checked by measuring an intermediate water standard within each batch of samples. All samples were prepared and analyzed in duplicate. The mean SD of deuterium analyses by the equilibration technique in the laboratory is <2.5{per thousand}.

2H dilution space was assumed to overestimate TBW by a factor of 1.044 (14), and a correction was made for fluid intake during the equilibrium period to derive actual body water.

Four-component model
The 4C model uses values of BMC, body weight (BW), BV, and TBW to derive values for mineral, water, fat, and protein as described previously (5). Assumed densities of the 4 components were accounted for when calculating fat mass from the measurements.

Formula 1(1)
FFM was calculated as the difference between weight and FM. We previously propagated error for FFM and FM on the basis of precision of the raw measurements and obtained values of 0.5 kg FM or FFM in children (15) and adults (5).

Statistical analysis
Comparisons for anthropometric and body-composition variables between groups were made by using analysis of variance (ANOVA). Data for males and females were pooled, unless a significant interaction between group and sex was present. Pairwise post hoc comparisons between groups were made where appropriate by using Tukey's test.

The method of Bland and Altman (16) was used to assess agreement between techniques. The mean difference between techniques (bias) and the ±2 SDs of the difference between techniques (limits of agreement) were calculated. The bias was then tested for significance from zero by using a paired t test. The correlation between the bias and the mean of the measured values was also determined. To express the bias as a percentage of the mean, the bias of the individual natural log value multiplied by 100 was calculated (17, 18). We used analysis of covariance (ANCOVA) to examine factors predicting bias in FM, FFM, weight, and percentage body fat, including age, sex, disease state, and either BMI SDS, FM, or percentage body fat as independent variables. All analyses were performed by using the Statistical Package for Social Sciences (version 11.0; SPSS Inc, Chicago, IL), and a P value <0.05 was considered significant.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Subject characteristics and body composition
The population encompassed a wide range of body sizes and nutritional status. The characteristics of the adults (n = 84) are shown in Table 1Go. The young adults (19–22 y) of normal weight were representative of the UK population in height and weight. There were no significant differences in age and weight, height, and BMI SDS between the sexes in the nonobese group. The obese women were significantly shorter and, by definition, heavier than the UK reference data.


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  		TABLE 1. Characteristics of the adults1

 
Characteristics of the children (n = 127) are shown in Table 2Go. The subjects were deliberately chosen to represent a range of body sizes and shapes and, as expected, ANOVA showed significant differences in weight, height, and BMI SDS between groups. Compared with the UK reference data, the nonobese boys were representative of the general population in terms of height and weight SDS, but the nonobese girls were significantly heavier than expected (P < 0.05). The obese girls were significantly taller (P < 0.001), whereas the patient groups showed a wide range of body sizes: the children with CF were significantly shorter and the children with GSD were significantly shorter and heavier.


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  		TABLE 2. Characteristics of the children1

 
The body-composition data for adults are shown in Table 3Go. ANOVA showed significant differences between groups for all variables, and post hoc testing indicated that these significant differences were present between all groups for all variables except the BMC and FFM of obese and nonobese women and the density of FFM in the nonobese men and women.


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  		TABLE 3. Body composition of the adults1

 
Data for the children's body composition are given in Table 4Go. ANOVA showed significant differences between groups for all variables, except for the density of FFM (P = 0.06). Mean percentage fat varied widely between groups, averaging 42% in obese girls and 19% in nonobese boys. FFM also varied widely between groups. Hydration was significantly higher (1.6%; P < 0.01) in the obese children than in the nonobese children.


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  		TABLE 4. Body composition of the children1

 
Comparison between DXA and the 4C model
Results of the Bland-Altman analyses are given in Table 5Go. These results show the variable bias in FM, FFM, body weight, and percentage fat in the different subject groups. DXA-measured weight was significantly underestimated in obese women and children, except the nonobese and obese boys, and was significantly overestimated in nonobese men. FM was significantly overestimated in all adults and obese boys and was significantly underestimated in nonobese boys. However, the bias in FFM did not mirror that in FM, as might have been expected. This finding was accounted for by variable bias in weight between groups. Correlation analyses showed that the magnitude of the bias was related to the magnitude of the variable in several categories (Table 5Go). DXA measurement significantly underestimated percentage fat in nonobese boys, showed no significant bias in nonobese girls and children with CF or GSD, and overestimated it in obese children and all 3 categories of adults.


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  		TABLE 5. Bland-Altman analysis of mean bias in fat mass (FM), fat-free mass (FFM), weight, and percentage fat measured by dual-energy X-ray absorptiometry (DXA) compared with the 4-component model1

 
Factors predicting bias between DXA and the 4C model, by ANCOVA, are shown in Table 6Go. FM bias was significantly positively associated with both age and BMI SDS. FFM bias was significantly associated with age, sex, BMI SDS, and CF, and there was a significant interaction between age and BMI SDS, which indicated that BMI SDS was associated with decreased bias with increasing age. Weight bias was significantly associated with age, sex, and BMI SDS. The use of an alternative model, in which BMI SDS was replaced with FM or percentage fat, showed that weight bias was associated with age, sex, and either FM or percentage fat. Bias in percentage fat was associated with age, sex, BMI SDS, and CF, and there was a significant interaction between age and sex, which indicated that the effect of being female on percentage fat bias was greater with increasing age. When waist circumference (as an alternative proxy for depth of scan) was substituted for BMI SDS in the ANCOVA, the results for all 4 body-composition biases were similar to those for BMI SDS (data not shown).


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  		TABLE 6. Analysis of factors predicting for bias between dual-energy X-ray absorptiometry and the 4-component (4C) model by analysis of covariance1

 
Propagation of error analysis indicated that methodologic imprecision was equivalent to {approx}0.6 kg of bias. Biases of ≤0.6 kg could be attributed to imprecision, but larger biases could be attributed to the combination of imprecision and inaccuracy. For biases >1 kg, most of the bias could be attributed to the inaccuracy of one or other method.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
DXA was designed for the measurement of bone mass and has been shown to be both accurate and precise when used for this purpose. However, this method is increasingly being used for the measurement of body composition. Our study was the first to examine the validity of DXA in groups differing in body size, fatness, and (due to the effects of disease states) the chemical composition of FFM. We showed that the bias of DXA varied according to body size, body fatness, sex, and disease state. These findings indicate that caution is necessary when DXA is used to compare patients with control subjects or to assess changes in body composition in persons whose relative weight changes significantly between measurements.

Many studies have investigated the accuracy of DXA. Animal carcass studies have shown systematic biases in younger age groups, which required a correction factor to be generated (19) and applied (20, 21). However, most studies in humans have used reference methods that may not have been accurate. Two-component techniques, such as hydrodensitometry, rely on assumed constant properties of FM and FFM. We showed previously that this is not the case in healthy adults (5) or children (15), and the issue is of even greater importance when measuring patients in whom body-composition variability, especially FFM composition, is most extreme.

Recently, several studies have assessed DXA in relation to the 4C model. These studies are summarized in Table 7Go, and they highlight 2 issues: 1) the bias varies according to several factors, including subject age and instrumentation, and 2) the vast majority of work has been conducted in healthy adults and children. Demonstration of the validity of DXA in healthy subjects is not sufficient justification for its application in patients, and our findings are highly relevant to this issue.


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  		TABLE 7. Summary of studies that assessed dual-energy X-ray absorptiometry against a 4-component model

 
Many factors may contribute to the differences between studies. First, DXA assumes a constant value for FFM hydration; however, this may not apply to all categories of subjects and, hence, may represent an inappropriate bias that introduces error (22, 28-32). One study, however, suggested that the magnitude of this bias is likely to be small (30). Second, subject size may influence bias through the effect of tissue depth (29), with increasing tissue depth associated with greater bias by DXA (33). Third, some DXA instruments have no algorithms specific to children. Fourth, accuracy may differ between pencil- and fan-beam DXA instruments (34). Fifth, fat distribution may influence accuracy, because pixels containing bone (approximately one-third of the total) extrapolate soft tissue composition from adjacent regions, which may have a fat content different from the region overlying the bone (29). Sixth, most of the head soft tissue composition is not calculated. Seventh, DXA instruments vary in the approach used to estimate the fat content of bone, which leads to generic differences between manufacturers. Finally, it is possible that 4C models differ according to whether BV is measured by underwater weighing or plethysmography, although most studies have shown good agreement between these methods (35). Note that our own findings apply only to Lunar Prodigy instrumentation.

The results of our study highlight variable bias in the measurement of FM, FFM, and weight by DXA according to several key characteristics of subjects. Clearly, variable bias between patients and healthy subjects presents difficulties for case-control studies. Bias in weight has particular relevance to longitudinal studies because it may confound the estimation of changes in body composition (36). The sex-difference in bias has implications for studies intending to derive body composition cutoffs for overweight and obesity (37, 38). The difference in bias between obese and nonobese persons indicates that DXA may be unsuitable for assessing changes in body composition during weight loss, as was reported in several studies (24, 39). However, body size and fatness did not fully account for the variability in bias between groups: children with GSD, though fatter than nonobese children, did not have biases similar to those of obese children. Because the bias was inconsistent, it would be difficult to derive a simple single correction factor, as has been proposed by others working on more homogenous samples (6).

The magnitude of mean biases found in our study was ≤2 kg of FFM and FM in groups, equivalent to ≤2% fat. In individuals, the limits of agreement were ≤3 kg of FFM and FM in adults and ≤2 kg in children, equivalent to 4–6% fat depending on age and group. This range of bias is smaller than that reported by Wong et al (6), but remains a serious issue because it is potentially of the order of magnitude of difference that might be expected after treatment in an individual or between groups. Factors including size, age, sex, and disease state all showed an independent effect in ANCOVA, which suggests that qualitatively different factors contribute to DXA bias. Our data indicate that DXA has limitations for measurement of body composition in clinical practice, but our findings are also important in the context of research studies. The literature contains increasing numbers of studies using DXA to undertake clinical research intended to provide evidence appropriate as the basis for clinical practice. Our findings challenge the validity of this approach and suggest that other approaches, such as multicomponent models, are preferable.

The main limitation of our study, which is common to all studies comparing DXA with the 4C model, was that DXA provides data for both measurements. The measurement of BMC is integral to DXA calculations of FFM and FM, and the same data are also used in the 4C model. However, we believe that our study is not adversely affected by this scenario. First, we calculated that BMC would need to be measured with >30% error to induce a 2% error in percentage fat. Thus, we think it highly unlikely that our finding of variable bias between category of subjects can be entirely attributed to an effect of BMC error on both methods. Second, we reran our Bland-Altman calculations using the 3-component model, which incorporates no data from DXA and is therefore fully independent, and obtained similar results. We chose the 4C model as the reference because only this method can address the variability in mineralization that occurs within and between groups. Although the results are limited to the groups being studied, it is sufficient to highlight that the accuracy with which DXA measures body composition varies depending on several factors.

In conclusion, our study suggests that caution is required in the application of this instrumentation in the measurement of body composition in medical research and clinical practice. Our findings may be particularly challenging for randomized controlled trials, in which differences in body composition at follow-up may induce inconsistent accuracy between 2 groups. We suggest that multicomponent models remain the best existing method for underpinning the evidence base for body-composition studies.


   ACKNOWLEDGMENTS
 
We thank the children, their families, and the adults who participated in this study and P Lee and A Jaffe for assistance with recruitment.

JCKW and MSF conceived the study. JEW, CMW, and DH measured the subjects and modeled the body-composition data. JEW, JCKW, and MSF conducted the statistical analyses. JEW wrote the first draft of the manuscript. All authors contributed to the revision of the manuscript. None of the authors had a conflict of interest.


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 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication May 23, 2005. Accepted for publication January 17, 2006.




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