Comparison of body weight and composition measured by two different dual energy X-ray absorptiometry devices and three acquisition modes in obese women

doi:10.1016/j.clnu.2005.11.002

Clinical Nutrition

Volume 25, Issue 3, June 2006, Pages 428-437

https://doi.org/10.1016/j.clnu.2005.11.002 Get rights and content

Summary

Background & aims

Weight measured by dual-energy X-ray (DXA) was shown to be increasingly underestimated in subjects over 75 kg compared to an electronic scale. This study compares body weight and composition measured by balance beam scale and three DXA acquisition modes in obese subjects.

Methods

In 39 obese, body weight was measured by balance beam scale, and body weight and composition by DXA Hologic QDR4500A^® in normal (NPM) and high power mode (HPM) (Enhanced v8.26 and v8.26* softwares) and DXA GE-Lunar Prodigy^® (v6.5 software). To ensure linearity of body weight and composition measured by the different DXA acquisitions, we also measured 13 women with a body mass index (BMI) of 25–30 kg/m².

Results

While QDR4500A HPM overestimates scale weight by about 2 kg over the whole BMI spectrum, QDR4500A NPM underestimates scale weight as a weight-dependent response (−1.7±1.8 kg overall, −4.1±1.6 kg in morbidly obese women). These results suggest switching from one mode to the other at a specific threshold, i.e. in our study a weight of 90 kg or a BMI of 34 kg/m². Prodigy gives weight about similar to scale (+0.5±0.8 kg). Both Hologic acquisition modes underestimate fat mass but overestimate lean body mass compared to Prodigy.

Conclusions

The QDR4500A NPM is inappropriate in women over 90 kg. Unfortunately, the QDR4500A HPM overestimates body weight in the range of 90–150 kg. The difference between scale and Prodigy weight remains stable throughout weight ranges. To better assess their accuracies in terms of body composition, QDR4500A NPM, HPM and Prodigy should be tested against phantoms or in vivo multi-compartment models.

Introduction

In clinical practice, practitioners use body mass index (BMI) as an estimation of fat mass (FM) or determine body composition by bedside techniques such as bio-impedance analysis or skinfold measurement. The formulae used to derive fat and lean body (LBM) masses from these techniques are validated either against multi-compartment models or against a single method such as dual-energy X-ray absorptiometry (DXA), hydrostatic weighing, total body potassium or in vivo neutron activation analysis.⁴

DXA is the preferred technique to measure whole body bone mineral content (BMC), FM and LBM. It is however not yet considered a gold standard for determining body composition. In some studies, it overestimates FM and underestimates LBM in healthy and ill subjects compared to multi-compartment models.1, 2 Compared to underwater weighing, Hologic and Lunar DXA give accurate measurements in healthy young subjects. Compared to total body potassium counting, DXA FM is overestimated by 5.3% in post-partum women, but underestimated by 0.5% in obese women. These results demonstrate the need for further research on the accuracy of body composition methods.⁴

Furthermore, the results differ according to the devices used. Presently, the three most common DXA manufacturers are Hologic Inc., GE-Lunar Inc. and Norland Medical Systems. While their most recent devices have not been compared, former Hologic devices measured lower % FM than the Lunar (−3.7%) and Norland devices (−6.3%).³ Body composition measurements also slightly vary between older and newer devices within the same company because of changes in the underlying technology and software.4, 5 For instance, the QDR 4500 A^® by Hologic underestimates %FM compared to the older QDR 2000^®,⁶ as does the DPX-L^® of Lunar relative to the older DPX^®.⁷ These inter- and intra-manufacturer differences demonstrate the need of constant validation of new DXA devices in vitro and/or in vivo against multi-compartment models. However, our preliminary work has shown that an in vivo validation is difficult to perform in obese people because they often do not fit in the scanning area.⁸ Besides technology, the thickness of the measured subjects also influences DXA results. In vitro, DXA underestimates fat tissue compared to direct chemical analysis at a meat thickness up to 26 cm. At a thickness over 26 cm, fat measured by Hologic QDR 1000^® increased markedly.⁹ Economos et al.¹⁰ used phantoms combined with various thicknesses of soft-tissue overlays and noted that bone mineral content measured by Lunar DPX^®, Norland XR^® and Hologic QDR 2000^® decreased as thickness increases from 20.5 to 26.0 cm. While the in vivo effect of abdominal thickness on body composition measured by recent DXA devices is yet unknown, a former abstract showed that weight, measured by Hologic QDR 4500 A^® in normal mode, was increasingly underestimated in subjects over 75 kg compared to an electronic scale.¹¹ The high power mode software has been designed especially for obese subjects, in order to correct this underestimation.

We hypothesized that the measurements of body weight and composition differ between the DXA devices and acquisitions, and that these differences appear predominantly in obese subjects. The study aim was to compare the body weight and composition measured by three different DXA acquisition modes, the Hologic QDR 4500 A^® in normal and high power mode and the Lunar Prodigy^®, in obese women (body mass index (BMI) ⩾30 kg/m²). To ensure that the mode of acquisitions has little influence on body composition of non-obese subjects, and to ensure the linearity of our results, these measurements were also performed in subjects with a BMI between 25.0 and 29.9 kg/m². To our knowledge, this is the first time that body composition and weight between these DXA acquisitions have been compared in obese subjects.

Section snippets

Subjects and methods

This study included 39 Caucasian women with a BMI ⩾30 kg/m² (BMI=weight (kg)/height² (m)) who were hospitalized at the Geneva University Hospital for a future gastroplasty or recruited at the outpatient obesity clinics. To ensure the linearity of body weight and composition measured by different DXA acquisitions in lower BMI ranges, we included additional 13 healthy Caucasian women with a BMI between 25 and 29.9 kg/m². All subjects signed a written informed consent. This protocol was accepted by

Results

Fifty-two women were included in the study. Age was similar between BMI categories (49.5±11.3, 48.8±15.6, 52.1±17.2 and 46.1±12.3 yr for the categories 25–30, 30–35, 35–40 and ⩾40 kg/m², respectively). Women of the BMI category 30–35 kg/m² were significantly taller (167.1±7.5 cm) than women of the group 25–30 kg/m² (161.2±4.1 cm, $P = 0.0 26$ ), 35–40 kg/m² (156.8±9.6 cm, $P = 0.0 22$ ) and ⩾40 kg/m² (159.0±6.6 cm, $P = 0.0 07$ ). Height between the other BMI categories was not significantly different.

All included women

Discussion

Body composition is an important parameter for the follow-up of obesity treatment but 40% of our subjects with a BMI over 30 kg/m² did not fit within the scanning area of the Prodigy table. Our preliminary data suggested that half-body scans can overcome this limitation.⁸ This study highlights the difficulty of accurately measuring body composition in obese women.

Acknowledgements

The authors thank the Foundation Nutrition 2000Plus for financial support as well as Giulio Conicella and Nadine Maisonneuve who helped for the measurements of body composition and Vincent Wazner who helped for scientific input.

References (22)

K.V. Haderslev et al.
Comparison of dual-energy X-ray absorptiometry to four other methods to determine body composition in underweight patients with chronic gastrointestinal disease
Metabolism
(2000)
L. Genton et al.
Dual-energy X-ray absorptiometry and body composition: differences between devices and comparison with reference methods
Nutrition
(2002)
Y. Nakata et al.
Body composition measurements by dual-energy X-ray absorptiometry differ between two analysis modes
J Clin Densitom
(2004)
C.M. Modlesky et al.
Comparison of body composition and bone mineral measurements from two DXA instruments in young men
Am J Clin Nutr
(1996)
Z.R. Cordero-MacIntyre et al.
Reproducibility of DXA in obese women
J Clin Densitom
(2002)
Z.M. Wang et al.
Six-compartment body composition model: inter-method comparisons of total body fat measurement
Int J Obes Relat Metab Disord
(1998)
P. Tothill et al.
Comparisons between Hologic, Lunar and Norland dual-energy X-ray absorptiometers and other techniques used for whole-body soft tissue measurements
Eur J Clin Nutr
(1994)
K.J. Ellis et al.
Bone mineral and body composition measurements: cross-calibration of pencil-beam and fan-beam dual-energy X-ray absorptiometers
J Bone Miner Res
(1998)
B. Oldroyd et al.
Comparison of in-vivo body composition using two Lunar dual-energy X-ray absorptiometers
Eur J Clin Nutr
(1998)
L. Genton et al.
Half-body dual-energy X-ray absorptiometry (DXA) predicts whole body composition (WBC) in obese patients
Clin Nutr
(2003)

S.A. Jebb et al.

Dual-energy X-ray absorptiometry measurements of body composition: effects of depth and tissue thickness, including comparisons with direct analysis

Clin Sci (Lond)

(1995)

Cited by (25)

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It appears that DXA overestimates the fat content of pigs with a high percentage of body fat (>20%) and underestimates the fat content in lean pigs (Mitchell et al., 1998b). Similar findings have also been reported in humans (Genton et al., 2006). Because CP in the present study was directly calculated from N, the precision and error of the prediction equations for NDXA are analogous to the ones for CPDXA from lean mass.
Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements benefit from gathering exact measurements of body composition or body nutrient contents. Those are acquired by standardized dissection or by grinding the body followed by wet chemical analysis, respectively. The two methods do not result in the same type of information, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Among imaging techniques, dual-energy X-ray absorptiometry (DXA) is particularly promising. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. Several calibration regressions have been published, but comparative studies of those regression equations and whether they are applicable to different data sets are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (BW range 20–100 kg) as determined by both DXA and chemical analyses, with R² ranging between 0.89 for ash and 0.99 for water and CP. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses, with the prediction error ranging between 4.3% for CP and 12.6% for ash. Finally, we compare existing prediction equations for pigs of a similar range of BWs with the equations derived from our DXA measurements and evaluate their fit with our chemical analysis data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.
Cross-Calibration of Prodigy and Horizon A Densitometers and Precision of the Horizon A Densitometer
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In contrast, total body mass on the Horizon A scanner was 3.58 kg greater than body weight measured on a digital scale (equivalent to a 6% overestimate in a 60 kg adult). It is a greater overestimate than the 2 kg overestimate reported for the Hologic QDR 450 when compared with a Prodigy (12). In a study comparing total body mass on a Prodigy and a Hologic Discovery, total body mass was 0.18 kg lower on the Hologic Discovery than on the Prodigy (7); these values were not compared with body weight measured on a scale.
We performed this study to enable a reliable transition for clinical study participants and patients from a GE Lunar Prodigy to a Hologic Horizon A dual-energy X-ray absorptiometry (DXA) scanner and to assess the reproducibility of measurements made on the new DXA scanner. Forty-five older adults had one spine, hip, and total body scan on a Prodigy dual-energy X-ray absorptiometry (DXA) scanner and 2 spine, hip, and total body scans, with repositioning, on a new Hologic Horizon A DXA scanner. Linear regression models were used to derive cross calibration equations for each measure on the 2 scanners. Precision (group root-mean-square average coefficient of variation) of bone mineral density (BMD) of the total hip, femoral neck, and lumbar spine (L1-L4), and total body fat, bone, and lean mass, appendicular lean mass, and trabecular bone score (TBS) was assessed using the International Society of Clinical Densitometry's (ISCD's) Advanced Precision Calculation Tool. Correlation coefficients for the BMD and body composition measures on the 2 scanners ranged from 0.94 to 0.99 (p<0.001). When compared with values on the Prodigy, mean BMD on the Horizon A was lower at each skeletal site (0.136 g/cm² lower at the femoral neck and 0.169 g/cm² lower at the lumbar spine (L1-4)), fat mass was 0.47 kg lower, and lean mass was 4.50 kg higher. Precision of the Horizon A scans was 1.60% for total hip, 1.94% for femoral neck, and 1.25% for spine (L1-4) BMD. Precision of TBS was 1.67%. Precision of total body fat mass was 2.16%, total body lean mass was 1.26%, appendicular lean mass was 1.97%, and total body bone mass was 1.12%. The differences in BMD and body composition values on the 2 scanners illustrate the importance of cross-calibration to account for these differences when transitioning clinical study participants and patients from one scanner to another.
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It also allows the measurement of segmental body composition, such as visceral fat mass and appendicular skeletal muscle mass. There is variability between different devices and manufacturers [44,45], and hydration status influences results as well [46]. Bioelectrical impedance analysis (BIA) uses hand to foot surface electrodes, with several methods available of either single or multiple frequencies.
The year 2019 marked the centenary of the publication of the Harris and Benedict equations for estimation of energy expenditure. In October 2019 a Scientific Symposium was organized by the European Society for Clinical Nutrition and Metabolism (ESPEN) in Vienna, Austria, to celebrate this historical landmark, looking at what is currently known about the estimation and measurement of energy expenditure.
Current evidence was discussed during the symposium, including the scientific basis and clinical knowledge, and is summarized here to assist with the estimation and measurement of energy requirements that later translate into energy prescription.
In most clinical settings, the majority of predictive equations have low to moderate performance, with the best generally reaching an accuracy of no more than 70%, and often lead to large errors in estimating the true needs of patients. Generally speaking, the addition of body composition measurements did not add to the accuracy of predictive equations. Indirect calorimetry is the most reliable method to measure energy expenditure and guide energy prescription, but carries inherent limitations, greatly restricting its use in real life clinical practice.
While the limitations of predictive equations are clear, their use is still the mainstay in clinical practice. It is imperative to recognize specific patient populations for whom a specific equation should be preferred. When available, the use of indirect calorimetry is advised in a variety of clinical settings, aiming to avoid under-as well as overfeeding.
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Body compartments, such as FFM, fat mass and body water, can be measured quantitatively. Numerous methods of body composition measurement have been developed through time: anthropometry, including the 4-skinfold method,39 hydrodensitometry,40 the measurements of mid-arm muscle circumference,40 in vivo neutron activation analysis,33–35 anthropogammametry from total body 40Potassium,41 nuclear magnetic resonance (NMR),42 dual-energy X-ray absorptiometry (DEXA),43,44 BIA,30,31,45 and more recently, computerized tomography (CT).27,46 The technical approaches and targets of the different methods are well summarized by the five-level model of Wang et al. (Fig. 3).47
Body composition reflects nutritional intakes, losses and needs over time. Undernutrition, i.e. fat-free mass (FFM) loss, is associated with decreased survival, worse clinical outcome and quality of life, as well as increased therapy toxicity in cancer patients. In numerous clinical situations, such as sarcopenic obesity and chronic diseases, the measurement of body composition with available methods, such as dual-X ray absorptiometry, computerized tomography and bioelectrical impedance analysis, quantifies the loss of FFM, whereas body weight loss and body mass index only inconstantly reflect FFM loss. The measurement of body composition allows documenting the efficiency of nutrition support, tailoring the choice of disease-specific and nutritional therapies and evaluating their efficacy and putative toxicity. Easy-to-use body composition methods integrated to the routine of care allow sequential measurements for an initial nutritional assessment and objective patients follow-up. By allowing an earlier and objective management of undernutrition, body composition assessment could contribute to reduce undernutrition-induced morbidity, worsening of quality of life, and global health care costs by a timely nutrition intervention.
Comparison of multifrequency bioelectrical impedance analysis and dual-energy X-ray absorptiometry assessments in outpatient hemodialysis patients
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Malnutrition is common in hemodialysis patients and closely related to increased morbidity and mortality. As such, simple, reliable, and easily available methods of determining nutritional status and recognition of short-term changes in body composition are desirable for routine clinical practice.
Diagnostic test study.
53 stable adult hemodialysis patients attending for thrice-weekly outpatient hemodialysis in a university tertiary hospital dialysis center.
Comparison of dual-energy x-ray absorptiometry (DEXA) and multifrequency bioelectrical impedance analysis (BIA) using a tetrapolar 8-point tactile electrode system as 2 index tests of body composition.
None.
Assessment of whole-body composition showed that lean body mass measured using the 2 techniques correlated highly, with good method agreement shown using a Bland-Altman plot (r = 0.92; P < 0.001; bias, +1 g [95% CI, −1,173 to 1,175]), as did fat mass (r = 0.93; P < 0.001; bias, −157 g [95% CI, −1,251 to 937]). Similarly, segmental analysis of lean body mass showed strong correlations between lean body mass of the trunk and right and left legs with small bias (r = 0.85, 0.89, and 0.86, respectively; P < 0.001; Bland-Altman bias, −859, +364, and +552 g, respectively), but weaker correlations for lean body mass for the right and left arm (r = 0.69 and 0.75, respectively; P < 0.001; Bland-Altman bias, −240 and +12 g, respectively). Bone mineral content derived using multifrequency BIA overestimated that measured using DEXA (r = 0.77; P < 0.001; bias, +530 g [95% CI, 422-638]).
Retrospective study in a healthy ambulant outpatient cohort.
Compared with DEXA, multifrequency BIA appears to be a robust tool for measuring and monitoring total-body fat and lean body mass in hemodialysis patients; however, there is less agreement in bone mineral content assessment between the 2 methods.
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ORIGINAL ARTICLEComparison of body weight and composition measured by two different dual energy X-ray absorptiometry devices and three acquisition modes in obese women

Summary

Background & aims

Methods

Results

Conclusions

Introduction

Section snippets

Subjects and methods

Results

Discussion

Acknowledgements

Metabolism

Nutrition

J Clin Densitom

Am J Clin Nutr

J Clin Densitom

Six-compartment body composition model: inter-method comparisons of total body fat measurement

Int J Obes Relat Metab Disord

Comparisons between Hologic, Lunar and Norland dual-energy X-ray absorptiometers and other techniques used for whole-body soft tissue measurements

Eur J Clin Nutr

Bone mineral and body composition measurements: cross-calibration of pencil-beam and fan-beam dual-energy X-ray absorptiometers

J Bone Miner Res

Comparison of in-vivo body composition using two Lunar dual-energy X-ray absorptiometers

Eur J Clin Nutr

Half-body dual-energy X-ray absorptiometry (DXA) predicts whole body composition (WBC) in obese patients

Clin Nutr

Dual-energy X-ray absorptiometry measurements of body composition: effects of depth and tissue thickness, including comparisons with direct analysis

Clin Sci (Lond)

ORIGINAL ARTICLE
Comparison of body weight and composition measured by two different dual energy X-ray absorptiometry devices and three acquisition modes in obese women