Dual-energy X-ray absorptiometry: fat estimation errors
due to variation in soft tissue hydration
ANGELO PIETROBELLI,1 ZIMIAN WANG,1 CARMELO FORMICA,2
AND STEVEN B. HEYMSFIELD1
1Obesity Research Center, St. Luke’s-Roosevelt Hospital, Columbia University,
College of Physicians and Surgeons, New York 10025; and 2Regional Bone
Center, Helen Hayes Hospital, West Haverstraw, New York 10993
body composition; physical model; fluid compartments
permits quantification of multiple whole body and
regional components, including bone mineral, fat, and
lean soft tissue (10, 18, 33). As a result, DXA is gaining
international acceptance as a body composition reference method (37).
Although the DXA method has many important
features that contribute to its growing application in
animal and human biological research, an important,
incompletely resolved question is the influence of hydration on DXA soft tissue component estimates. Reports
indicate that DXA makes no assumptions related to
tissue hydration, and eight earlier reports in humans
indicate that acutely altering fluid balance or distribution has no measurable influence on DXA or related
dual-photon absorptiometry fat estimates (1, 8, 12, 20,
25, 26, 29, 47). However, other earlier reports suggest
that physical principles and models on which DXA
relies may be influenced by tissue hydration (8, 20, 33).
The magnitude and clinical significance of these potential hydration effects have not been studied systematically. Because many experimental animal models and
human subjects in whom DXA is applied have altered
fluid balance, the unresolved question of DXA hydration effects is of major significance in the field of body
composition research.
The aim of the present study was twofold: to develop
and validate a DXA hydration physical model, and to
use the developed model to quantitatively evaluate the
various influences of fluid balance change on DXA soft
tissue composition estimates.
METHODS
is fundamental to the
study of biological processes in animals and humans
(7). Approaches developed over the past several decades
now allow noninvasive assessment of over 30 distinct
components in vivo (45).
Although there are many available body composition
methods, only a few are sufficiently accurate for quantifying components in the research laboratory. A relatively new method, and one that also has clinical
applicability, is dual-energy X-ray absorptiometry (DXA)
(13, 16, 28). Whereas some methods are costly (4),
require highly trained staff for their operation and
implementation (23, 36), depend in part on subject
participation (11), or expose human subjects to moderate radiation levels (4, 24, 36, 39), DXA systems are
affordable, practical, require no active subject involvement, and impose minimal risk (42). Moreover, unlike
most other body composition methods that are designed
to quantitate a single whole body component (44), DXA
EVALUATING BODY COMPOSITION
E808
Study Design
The study aims were accomplished in a series of linked
experiments. The goal was in vitro simulation of errors
arising in DXA fat estimates as a result of changes in soft
tissue hydration. Earlier in vivo human studies were incapable of producing systematic changes, particularly of large
magnitude, in the fluid concentration of lean soft tissues.
Simulation experiments, with the assumption that they
represent in vivo effects, overcome the barriers posed by
human experimentation.
Accordingly, the validity of the simulation model was
established as follows. A DXA physical model was developed
that links tissue elemental content with photon attenuation.
The first experiment was designed to evaluate the validity of
this physical model by use of a commercially available DXA
scanner.
In the next stage, the physical model was extended to
describe photon attenuation changes anticipated when predefined amounts of two components of known composition are
thoroughly mixed, as would occur with overhydration of
0193-1849/98 $5.00 Copyright r 1998 the American Physiological Society
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.1 on June 16, 2017
Pietrobelli, Angelo, Zimian Wang, Carmelo Formica,
and Steven B. Heymsfield. Dual-energy X-ray absorptiometry: fat estimation errors due to variation in soft tissue
hydration. Am. J. Physiol. 274 (Endocrinol. Metab. 37):
E808–E816, 1998.—Dual-energy X-ray absorptiometry (DXA)
is rapidly gaining acceptance as a reference method for
analyzing body composition. An important and unresolved
concern is whether and to what extent variation in soft tissue
hydration causes errors in DXA fat estimates. The present
study aim was to develop and validate a DXA physical
hydration model and then to apply this model by simulating
errors arising from hypothetical overhydration states. The
DXA physical hydration model was developed by first linking
biological substance elemental content with photon attenuation. The validated physical model was next extended to
describe photon attenuation changes anticipated when predefined amounts of two known composition components are
mixed, as would occur when overhydration develops. Two
overhydration models were developed in the last phase of
study, formulated on validated physical models, and error
was simulated for fluid surfeit states. Results indicate that
systematic errors in DXA percent fat arise with added fluids
when fractional masses are varied as a percentage of combined fluid 1 soft tissue mass. Three independent determinants of error magnitude were established: elemental content
of overhydration fluid, fraction of combined fluid 1 soft tissue
as overhydration fluid, and initial soft tissue composition.
Small but systematic and predictable errors in DXA soft
tissue composition analysis thus can arise with fluid balance
changes.
E809
DXA HYDRATION ERRORS
Physical Model
The DXA method assumes that nonosseous tissue consists
of two distinct components, fat and lean soft tissue (33). The
lean soft tissue component is the difference between body
weight and the sum of fat and bone mineral ash. Fat and lean
components are quantified over regions devoid of bone. The
measured attenuation of DXA’s two main energy peaks is
used to estimate each pixel’s fraction of fat and lean according
to the following series of physical models (33).
A monoenergetic photon beam with incident intensity I0
passing across soft tissues is attenuated, and diminished
beam intensity (I) is recorded in the detector (41). Fractional
lowering of beam intensity is proportional to the substance’s
linear attenuation coefficient (µ) and path length (L)
2d(I/I0) 5 µ 3 dL
(1)
Integration of this equation results in the classical attenuation formula
I 5 I0 3 e2µ3L
(2)
As the linear attenuation coefficient is density (r) dependent,
a convenient practice when working with tissues that differ in
physical density is to calculate the mass attenuation coefficient (µm ) as µ/r (34). Attenuation of monoenergetic photons
using a substance’s mass attenuation coefficient can be
calculated as
I 5 I0 3 eo(2f i3µmi3M )
o(2f 3 µ
i
mi
3 M)
µm 5
(4)
o( f 3 µ
mi )
i
(5)
An element’s mass attenuation coefficient is constant and
known at any photon energy from classical experimental
studies (21, 22, 35, 46).
Two photon energies are used with DXA systems, and these
polyenergetic beams are generated either by pulsing the
X-ray beam or by rare-earth filtration (14). The attenuation of
each beam (i.e., I/I0) by soft tissues can be measured as the
ratio (R) of low to high energy calculated as
R 5 ln(I/I0)L / ln(I/I0)H
o(2f 3 µ 3 M ) /o(2f 3 µ
5 o[ f 3 (µ ) ]/o[ f 3 (µ ) ]
5
i
mi
i
L
mi L
i
i
mi
3 M )H
(6)
mi H
where L and H are low and high effective energies, respectively. Each element has a characteristic R value at specific
energies, with increasing atomic number associated with
higher R values (Table 1) (21, 22, 35, 46).
Elemental and complex absorber R values for DXA systems
producing energies L and H can be calculated using Eq. 6 and
known mass attenuation coefficients. For a two-component
soft tissue mixture, Eq. 6 can be expressed as
R5
[ f1 3 (µm1)L 1 f2 3 (µm2)L]
(7a)
[ f1 3 (µm1)H 1 f2 3 (µm2)H]
A simplified R value formula can be derived, as in earlier
reports (27, 28, 34), which assumes that µm values for the two
soft tissue components at the higher energy are approximately equal (e.g., at 70 keV, µm values for protein, glycogen,
water, extracellular fluid, and intracellular fluid are 0.183,
0.183, 0.194, 0.195, and 0.196, respectively)
R 5 f1 3 R1 1 f2 3 R2
(7b)
In the first experiment, we compared theoretical to measured
R values for five simple compounds, including water, ethanol,
glycine, alanine, and sucrose. Each compound was scanned
Table 1. Mass attenuation coefficients at 40 keV and 70
keV and R value for 13 elements found in humans
Element
(3)
where f i is the mass fraction of the ith component as
heterogeneous absorber. This equation indicates that photon
attenuation in a heterogeneous absorber such as human soft
tissues is a function of incident photon intensity and each
component’s fractional mass, mass attenuation coefficient,
and mass per unit area (M). As pixel area in DXA systems is
constant and known, the mass per unit area represents total
volume element or voxel mass.
Equation 3 can be rearranged, and attenuation can be
expressed as the measurable transmitted-to-incident photon
ratio
ln(I/I0) 5
For a mixture of n components, the mass attenuation
coefficient for heterogeneous mixtures can be calculated from
fractional mass and mass attenuation coefficient of components present as
Hydrogen
Carbon
Nitrogen
Oxygen
Sodium
Magnesium
Phosphorus
Sulfur
Chlorine
Potassium
Calcium
Iron
Total Body Atomic
Amount, kg*
No.
7.0
16.0
1.8
43.0
0.1
0.019
0.58
0.14
0.095
0.14
1.0
0.0042
1
6
7
8
11
12
15
16
17
19
20
26
µm
Atomic
Wt
40 keV
70 keV
R
1.008
12
14
16
23
24.3
31
32.1
35.5
39.1
40.1
56
0.3458
0.2047
0.2246
0.2533
0.3851
0.4704
0.7784
0.9507
1.100
1.484
1.792
3.601
0.3175
0.1678
0.1722
0.1788
0.2022
0.2244
0.2839
0.3258
0.3491
0.4297
0.5059
0.8944
1.0891
1.2199
1.3043
1.4167
1.9045
2.0963
2.7418
2.9180
3.1510
3.4536
3.5422
4.0162
Information from Refs. 21, 22, 33, 35, and 46. µm , Mass attenuation
coefficient; R, ratio of µm at 40 keV/70 keV. * Amount reported in
Snyder et al. (40).
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.1 on June 16, 2017
normal soft tissue. The validity of these models was tested in
the second experiment by comparing the mixture’s expected
photon attenuation with that measured by the DXA scanner.
Once validated, the concepts advanced in the DXA physical
model section then formed the basis of subsequent hydration
error simulations.
Three stages then followed in developing hydration-error
estimates. In the first stage, we derived fat and lean attenuation values for our DXA system by scanning beef phantoms of
known composition. These ‘‘constants’’ are employed in DXA
systems to predict soft tissue fat fraction from measured
attenuation properties. The fat and lean attenuation values
are required in the error simulation model. In the next stage,
the attenuation values for two representative hydration
fluids that differ in elemental composition were experimentally established with the DXA scanner. In the third and final
stage of analysis, two new ‘‘overhydration’’ models were
developed that allowed simulation of errors in DXA fat
estimates that arise with systematic variation in three independent variables: the elemental content of excess fluid, the
fractional amount of excess fluid, and the fat-to-lean composition of soft tissue.
E810
DXA HYDRATION ERRORS
with a DXA system three times, and the measured R value
results were averaged. These results were then compared
with theoretical R values calculated by using Eqs. 6 and 7b
with energy-specific R value data provided in Refs. 21 and 22.
The equations gave equivalent results, and only data for Eq.
7b are provided in RESULTS. A similar protocol was then
completed for four chemically more complex substances,
including lean beef, lard, saline, and Ringer lactate solution.
The composition of saline and Ringer lactate was established
from manufacturers’ specifications. The elemental contents
(H, C, N, O, P, Ca, Na, K, and Cl) of lean beef and lard were
measured in triplicate on duplicate aliquots.
Error simulation. Measured R values can be used to
estimate the fractional ( f ) mass of each component in a
two-component mixture. Because f1 1 f2 5 1, then
(8)
f2 5 (R1 2 R)/(R1 2 R2)
(9)
and
where R is total R value consisting of two components with
known R values R1 and R2. Equations 8 and 9 are often used
in DXA systems for quantifying two-component mixtures
(e.g., fat 1 lean) from measured R. These formulas will also be
used in developing the overhydration models. In preparing a
model that predicts the effects of hydration changes on fat
estimates, we assume that Eqs. 8 and 9 are employed by the
DXA system to estimate fat and lean fractions from measured
soft tissue R.
The most common clinical situation is overhydration secondary to edema, ascites, and other forms of fluid accumulation,
which can account for up to 20% of body weight. Dehydration
is also common in clinically evaluated patients, but relative
fluid loss is less than with overhydration. Because the model
concepts are similar with all hydration changes, in the
present report we develop only overhydration models. The
outline of the model development that follows is presented in
Fig. 1.
Assume the subject has normally hydrated soft tissue with
known fat ( fFAT ) and lean (i.e., 1 2 fFAT ) fractions. The soft
tissue R value can then be calculated using Eq. 7b with
known RFAT and RLEAN values. Overhydration then develops,
leading to a three-component mixture of fat, lean, and added
fluid (e.g., normal saline or water). The R value of the added
fluid is known from measurement or is based on chemical
Sample Preparation
Fig. 1. Two study overhydration (OH) models.
Water was triple distilled and ethanol was US Pharmacopeia Grade (Pharmco, Brookfield, CT). Glycine and alanine
were .99% pure by thin-layer chromatography (Sigma Chemical, St. Louis, MO). Sucrose was obtained from the hospital
pharmacy.
Lean beef and lard were obtained from a local market.
Normal saline had, per 100 ml, 0.9 g NaCl as defined by the
manufacturer (McGaw, Kendall, Ontario, Canada). Ringer
lactate solution, according to the manufacturer (McGaw,
Kendall, Ontario, Canada), had the following composition per
100 ml: sodium chloride, 0.60 g; sodium lactate, 0.31 g;
potassium chloride, 0.030 g; and calcium chloride, 0.020 g.
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.1 on June 16, 2017
f1 5 (R 2 R2)/(R1 2 R2)
analysis, and therefore the composite R value of the threecompartment mixture can be calculated using Eq. 6. Alternatively, the overhydrated soft tissue can be considered as two
new two-component models, soft tissue 1 added fluid and
overhydrated lean (i.e., added fluid 1 lean) and fat (Fig. 1).
Under normal circumstances, the subject is scanned, and
lean soft tissue and fat fractions are established using the
standard system calibration. This, however, potentially results in an estimation error when additional fluid is present.
We consider the magnitude of this error as the difference
between actual fat fraction and that calculated using the
standard DXA calibration equation, expressed in percent fat
units. Predicted fat fractions that are less than actual values
will have a negative sign and vice versa. All terms required
for this analysis have algebraic solutions, with the assumption that initial fat fraction, the fraction of soft tissue as
added fluid, and the R values for fat, lean, and fluid are
known. The main clinical condition of interest is when
variable amounts of fluid expand normal soft tissue (model 1
in Fig. 1). Another possibility is when fluid expansion occurs
and remains stable, producing an overhydrated lean compartment, while the proportion of soft tissue fat varies with
energy balance.
The hydration model assumes that mixtures of two components follow predictable rules with respect to R value changes.
That is, when fluid of known R value is added to soft tissue,
the result is a predictable new R value reflecting the overhydrated state.
In the hydration experiment, we first established the
validity of Eq. 7b in predicting the R values for various
mixtures of two components, each with a known R value.
Mixtures of two of the substances evaluated in the first
experiment (wt/wt) were made as follows: ethanol-water,
25:75, 50:50, 75:25; normal (i.e., 0.9%) saline-water, 25:75,
50:50; Ringer lactate-water, 25:75, 50:50, 75:25; lean beefnormal saline, 95:5, 90:10, 80:20; and lean beef-Ringer, 95:5,
90:10, 80:20.
We then experimentally established the RFAT and RLEAN
values for our DXA system. This allowed development of fat
fraction prediction equations based on Eq. 8. Five beef
phantoms of varying fat content were scanned, and R values
were measured. The fat fraction vs. R value regression line
was developed and then solved for fat fractions of 1 and 0 for
estimating RFAT and RLEAN, respectively. Beef fat content was
measured by lipid solvent extraction.
In the last stage of analysis, the soft tissue component R
values and fat prediction equation were used to estimate
errors arising with hypothetical levels of soft tissue overhydration. Water and normal saline were used as the excess fluids,
and their R values were measured in triplicate as noted
below. Hypothetical mixtures of beef and water or normal
saline were then created, and R values were calculated using
Eq. 8, as described above.
E811
DXA HYDRATION ERRORS
Dual-Energy Measurements
Chemical Methods
Chemical composition of lean beef and lard was analyzed
on two sample aliquots in triplicate. Fat was measured by the
method of Folch et al. (6). Hydrogen, carbon, and nitrogen
were determined by combustion at 1,050°C in a constant O2
stream with infrared absorption for CO2 and H2O and thermal conductivity for N2 (Leco CHN 1000, St. Joseph, MI) (2, 5,
32). Oxygen was measured by pyrolysis in a N2 stream at
1,200°C, with detection of resulting CO and CO2 by separate
infrared detectors (Leco RO-478, St. Joseph, MI) (30). Sodium, magnesium, phosphorus, potassium, and calcium were
detected by inductively coupled plasma emission spectroscopy, with a detection limit of 0.01 parts/million (ppm) for Na,
0.85 µg/ml for Mg, 0.1 ppm for P and K, and 3.14 µg/l for Ca
(43). Sulfur was determined with a quantitation limit of 0.08
mg by combustion in atmospheric O2 at 1,350°C and analysis
of resulting SO2 by infrared detector. Chlorine was detected
with a quantification limit of 10 ppm by ion chromatography,
peak area ratio, and conductimetric detection (38).
Statistical Methods
The developed hydration model is based on the assumption
that R values follow physical rules related to photon attenuation and biological substance component proportions. The
conceptual basis of these models was validated by comparison
of theoretically derived with actually measured DXA R values. Simple linear regression analysis and means 6 SD were
used as the basis of these analyses. Our analysis was
designed to explore qualitative R value associations, and
exact theoretical-to-measured R value agreement was not
RESULTS
Physical Model
Theoretical vs. measured R values. The theoretical R
values and their measured counterparts are summarized in Table 2. The group mean theoretical (theor)
and measured (meas) R values were similar (1.314 6
0.055 vs. 1.322 6 0.061), and the two R value estimates
were highly correlated (Fig. 2; theor R 5 0.896 3 meas
R 1 0.129, r2 5 0.956, SEE 5 20.013, P , 0.0001).
These observations support the validity of the overall
theoretical DXA model presented in METHODS.
Hydration Effects
Two-component mixture analysis. The R value results
for two-component mixtures are presented in Table 3.
Four sets of R values are provided along with each
mixture’s elemental content. The R1 column indicates
the theoretical R value calculated using Eq. 6, which is
based on elemental composition.
This computation is identical to the one used earlier
for estimating theoretical R values of the nine biological substances presented in Table 2. The R2 column is
the R value calculated using Eq. 7a, with the assumption of a two-component mixture with known theoretical R values for each component, as presented in Table
2. The R3 column gives the R value calculated using Eq.
7a with measured R values for each of the two components as presented in Table 2. The actual measured R
value for the composite mixtures is given in the fourth
column.
Table 2. Theoretical and measured R values for materials of known elemental content
Mass Fraction
L-Alanine
Glycine
Sucrose
Ethanol
Water
0.9% NaCl
Ringer Solution
Lean soft tissue
Lard
H
C
N
0.0792
0.0671
0.0648
0.1313
0.111
0.1109
0.1103
0.11883
0.12645
0.4041
0.3197
0.4208
0.5209
0.1571
0.1865
O
0.3592
0.4263
0.5144
0.3473
0.889
0.8802
0.0019
0.8760
0.18724 0.0394 0.64568
0.73886 0.00517 0.12824
Na
Mg
R
P
S
Cl
K
Ca
Theor
1.283
1.301
1.304
1.255
1.357
0.0035
0.0054
1.377
0.0052
0.0063
0.0002 0.00007 1.382
0.000743 0.0000692 0.002014 0.002087 0.000048 0.00381 0.000069 1.345
0.000272 0.000039 0.000172 0.000517 0.000021 0.00019 0.000006 1.220
Meas
1.28060.012
1.28460.009
1.31360.001
1.26460.002
1.36360.001
1.39960.001
1.38960.002
1.37260.001
1.23260.002
Measured R values (means 6 SD) reported are based on 3 repeated scans. Theor, theoretical; Meas, measured. Fractional weight of
substances (compound wt/total wt): L-Alanine: C3H7NO2 ; Glycine: C2H5NO2 ; Sucrose: C12H22O11 ; and Ethanol: C2H5OH.
Downloaded from http://ajpendo.physiology.org/ by 10.220.33.1 on June 16, 2017
A Lunar DPX-L (Lunar, Madison, WI) pencil beam system
with version 1.3z software was used for all studies. The
DPX-L system has a 76.0 kVp X-ray source, and cerium
k-edge filtration is used to generate two photon peaks with
main energies at 40 keV and 70 keV (27). All substances were
scanned in a 4-liter plastic container by use of the fast scan
mode. The height of material in the container was kept
constant for all scans at 10 cm. R values were obtained using
a region of interest that excluded container edges and base.
All scans were run in triplicate, and the R value results were
averaged.
An assumption of the present study is that the DXA system
used to evaluate various biological substances is representative of DXA systems as a whole. Other systems vary in mode
of photon generation, use of various filters, and peak photon
energies, although underlying physical concepts are similar
for all DXA methodologies.
needed for establishing hydration model validity. Additional
tests (e.g., Bland Altman analyses) exploring the statistical
significance of R value differences were therefore not carried
out.
Simple linear regression analysis was used to develop a
DXA fat fraction prediction formula based on R values as the
independent variable. Two of the samples were the lean beef
and lard. Lean beef and lard were then thoroughly mixed to
form three additional mixtures of varying fat fraction. Fat
content was then measured by the method of Folch et al. (6),
as noted in Chemical Methods.
All analyses were carried out using the statistical program
SAS Release 6.10 (Statistical Analysis System, SAS Institute,
Cary, NC).
E812
DXA HYDRATION ERRORS
As for the primary substances evaluated in Table 2,
the theoretical R values based on elemental composition for the two-component mixtures were in close
agreement with the corresponding measured R values
(1.347 6 0.026 vs. 1.365 6 0.029; theor R 5 0.841 3
meas R 1 0.198, r2 5 0.910, SEE 5 20.008, P ,
0.0001).
The two calculated R values based on Eq. 7a, one
with theoretical component R values and the other with
measured component R values, were in good agreement
with actual measured R values [1.346 6 0.026 and
1.362 6 0.028 vs. 1.365 6 0.029, respectively; (Fig. 3)
theor 2 component R 5 0.840 3 meas R 1 0.200, r2 5
0.910, SEE 5 20.008, P , 0.0001 and theor 2 compo-
nent R 5 0.976 3 meas R 2 0.031, r2 5 0.994, SEE 5
20.002, P , 0.0001].
Our analysis up to this point indicates a close concordance between 1) R values calculated with physical
constants and measured R values and 2) R values
calculated with the assumption of a mixture of two
biological substances, each with a known R value, and
measured R values. The next stage of analysis involved
error simulation with changes in soft tissue hydration.
DXA fat fraction prediction equation. The fat fraction
coefficients of variation (CV) within and between aliquots for the five beef-lard mixtures were 1.52 and
1.54%, respectively. The between-measurement DXA R
value CV for the five phantoms was 0.2%. Measured R
values for the five beef-lard mixtures were highly
Table 3. Theoretical and measured R values for two-component mixtures
Mass Fraction
Mixture,
wt/wt
Ethanol1
H2O
25:75
50:50
75:25
0.9% NaCl1
H2O
25:75
50:50
Ringer sol1
H2O
25:75
50:50
75:25
Lean10.9%
NaCl
95:5
90:10
80:20
Lean1
Ringer sol
95:5
90:10
80:20
H
C
N
O
Na
Mg
Theor
P
S
Cl
K
Ca
R1
R2
Meas
R3
R
0.1161 0.1302
0.1212 0.2605
0.1262 0.3907
0.7536
0.6182
0.4827
1.332 1.332 1.338 1.34460.001
1.307 1.306 1.314 1.31660.001
1.281 1.281 1.289 1.28860.001
0.1109
0.1109
0.8868 0.00088
0.8846 0.00176
0.00136
0.00272
0.1108 0.00048
0.1106 0.00096
0.1105 0.00143
0.8857 0.00131
0.8825 0.00317
0.8792 0.00392
0.00158 0.000075 0.000032 1.364 1.363 1.370 1.37660.002
0.00317 0.00010 0.000035 1.370 1.369 1.376 1.37960.017
0.00475 0.00015 0.000053 1.376 1.375 1.383 1.38460.001
1.363 1.362 1.372 1.37860.001
1.368 1.367 1.381 1.38460.002
0.1184 0.1779
0.1180 0.1685
0.1172 0.1498
0.0374 0.6574 0.0008
0.0354 0.6691 0.0010
0.0315 0.6926 0.0013
0.000066 0.0019 0.0019 0.0003
0.00006 0.0018 0.0019 0.0006
0.00006 0.0016 0.0017 0.0011
0.0036
0.0034
0.0030
0.00006
0.00006
0.00005
1.347 1.346 1.374 1.37560.002
1.348 1.348 1.375 1.37660.001
1.351 1.351 1.378 1.37860.002
0.1184 0.1779
0.1179 0.1687
0.1171 0.1502
0.0374 0.6572 0.0003
0.0355 0.6687 0.0012
0.0315 0.6917 0.0016
0.00007
0.00006
0.00006
0.0037
0.0035
0.0031
0.00007
0.00007
0.00006
1.347 1.347 1.374 1.37760.002
1.349 1.348 1.374 1.37760.002
1.352 1.352 1.376 1.38060.001
0.0019 0.0019 0.0004
0.0018 0.0019 0.0007
0.0016 0.0017 0.0013
Measured R values (means 6 SD) reported are based on 3 repeated scans. Theoretical R values were calculated using (R1 ) Eq. 6 with
elemental content, (R2 ) Eq. 7a with theoretical R values for each of 2 components, and (R3) Eq. 7a with measured R values as given in Table 2
for each of 2 components.
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Fig. 2. Theoretical (theor) vs. dual-energy X-ray absorptiometry
(DXA)-measured (meas) ratio (R) values for 9 biological substances
presented in Table 2. R values were calculated using Eq. 7a (theor
R 5 0.896 3 meas R 1 0.129, r2 5 0.956, SEE 5 20.013, P , 0.0001).
Fig. 3. Theoretical vs. DXA-measured R values for 14 two-component
mixtures. R values were calculated using Eq. 7a with theoretical R
values for each of the two components (theoretical R2 in Table 3)
(theor 2-component R 5 0.840 3 meas R 1 0.200, r2 5 0.910, SEE 5
20.0081. P , 0.0001).
DXA HYDRATION ERRORS
Fig. 4. Chemically measured fat fraction vs. DXA-measured R value
for 5 beef-lard mixtures (ffat 5 9.418 2 6.815 3 R, r2 5 0.998, SEE 5
0.09, and P , 0.001).
Fig. 5. Simulated fat fraction error, expressed in percent fat units,
for overhydration model 1. Soft tissue baseline composition is assumed 25% fat and 75% lean. Negative and positive errors represent
under- and overestimates of actual percent fat, respectively.
maximum (e.g., for 0.9% NaCl 5 211.6%) when added
fluid fraction 5 1.
With model 2, we begin again with a base soft tissue
composition of 25 and 75% fat and lean fractions,
respectively. The soft tissue is then overhydrated to a
level of 0.10 with 0.9% NaCl. The soft tissue R value
increases from 1.345 to 1.351 with overhydration, and
the lean R value increases from 1.382 to 1.384. The
overhydrated lean R value of 1.384 for 0.10 fluid
fraction is now maintained constant, and the fat fraction is then varied as might be found in representative
men (0.05, 0.15) and women (0.25, 0.35). The simulated
error increases from a minimum of 20.97% at fat
fraction 5 0.35 to a maximum of 21.42% at fat fraction 5 0.05 (Fig. 6). A trend opposite in sign is observed
when water is the overhydration fluid. Error in this
model is at a maximum (e.g., for 0.9% NaCl 5 21.5%)
when fat fraction is zero and is at a minimum (i.e., 0%)
when fat fraction is 1.
DISCUSSION
Unlike previous investigations (1, 8, 12, 20, 25, 26,
29, 47), the present study results clearly demonstrate a
bias error in DXA soft tissue composition estimates as a
function of tissue hydration. Specifically, the present
study results indicate the existence of a small magnitude bias error in fat fraction with changes in soft
tissue hydration. Our findings are supported at two
levels, one theoretical and the other experimental.
Theoretical Error Basis
A theoretical basis strongly supports the hypothesis
that DXA fat estimation errors occur secondary to soft
tissue hydration changes. The underlying concept of
DXA soft tissue composition analysis is that fat and
lean have different but stable elemental proportions
(14). Fat, which is mainly triglyceride, consists of stable
proportions of the three relatively low-R elements H, C,
and O (3, 15). Lean soft tissue also has H, C, and O,
with additional amounts of higher-R elements such as
Na, K, Cl, Ca, S, Mg, and Fe (3, 17, 19, 40). Although not
Fig. 6. Simulated fat fraction error, expressed in percent fat units, as
a function of soft tissue fat fraction for overhydration model 2.
Baseline soft tissue composition is assumed 25% fat and 75% lean,
with 10% 0.9% NaCl overhydration. Negative and positive errors
represent under- and overestimates of actual percent fat, respectively.
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correlated with fraction of soft tissue as fat: ffat 5
9.418 2 6.815 3 R, r2 5 0.998, SEE 5 0.09, and P ,
0.001 (Fig. 4). Solving this equation for fat and lean R
values (i.e., ffat 5 1.0 and 0) gives RFAT and RLEAN of
1.235 and 1.382, respectively.
Error simulations. The respective measured R values
for H2O and 0.9% NaCl were 1.363 6 0.001 and 1.399 6
0.001 (Table 2), respectively. Four levels of hydration
were considered as a fraction of combined fluid plus soft
tissue mass, 0.01, 0.05, 0.10, and 0.20. A base soft tissue
composition was selected arbitrarily as 25% fat and
75% lean. If we assume RFAT and RLEAN values of 1.235
and 1.382, respectively, this soft tissue mixture has an
R value of 1.345 based on Eq. 7a.
With model 1, as normal saline is added, the actual
fat fraction progressively declines and the soft tissue R
value increases, as predicted with Eq. 7a. The error
arising when the standard DXA fat fraction model (i.e.,
ffat 5 9.418 2 6.815 3 measured R) is applied to the
overhydrated soft tissue is shown in Fig. 5. The error is
relatively small, predicted 0.11% less than actual fat
fraction with soft tissue fluid fraction of 0.01, and rises
to 22.3% with fluid fraction of 0.20. An error of smaller
magnitude and opposite in sign is produced by adding
water to the soft tissue mixture (added fluid fraction of
0.01 5 0.13% and 0.20 5 2.6%). Error is at a minimum
(i.e., 0%) when no fluid is added and increases to a
E813
E814
DXA HYDRATION ERRORS
Experimental Error Basis
The experimental focus of the present investigation
was a systematic attempt to support each step of the
theoretical model development. We first showed that
our DXA system provides measured R values in very
close agreement with theoretically derived R values for
substances ranging from simple molecules to complex
animal tissues. In the next step, we demonstrated that
various two-component mixtures produce R values also
very similar to predicted R values based solely on
elemental content of each component or the component’s actual measured R value. These first two experiments provide strong support for developing models
that predict changes in soft tissue and lean R values
with addition of various amounts of known R value
fluids.
In the hydration study phase, we went on to confirm
the previously reported (33) strong association between
measured R value and fraction of soft tissue as fat, an
experiment that allowed us to develop R value constants for fat and lean components. Last, these fat and
lean R values for our DXA system were used to develop
two different soft tissue hydration models that allowed
error simulation.
DXA Hydration Error
Results clearly show that DXA fat estimation errors
occur as a function of added fluid R value, fraction of
added fluid, and soft tissue composition. Overall, DXA
fat estimation errors increased with larger deviations
between assumed lean R and added fluid R. In effect, if
the accumulating fluid has the same R value as lean
and there is no R value difference, no fat estimation
errors occur no matter how much extra fluid is added. If
the hydration fluid and lean R values do differ, then
greater relative fluid accumulation is associated with
larger fat estimation errors. Finally, if we assume that
overhydration results in a higher and stable new lean
R, fat estimation error diminishes as the fat fraction
increases.
Although the present study demonstrates a potential
fat estimation bias error with soft tissue hydration
changes, the magnitude of these errors is small when
considered in the context of the physiological range of
accumulated fluid compatible with life. Hydration,
including fluid and electrolyte balance, is maintained
remarkably stable in health (9). Simulated experiments suggest DXA fat errors of ,1% with hydration
changes of 1–5% (Figs. 5 and 6). Such changes are
below the usual detection threshold of DXA systems
unless subject samples are large and measurements
are repeated and averaged to minimize error. The
between-measurement technical error for most DXA
systems is ,1% fat units. The possibility does exist,
however, for fat estimation errors in the range of
several percent when soft tissue overhydration is severe, perhaps in the range of 20–25% of total soft tissue
mass. Profound overhydration in this range is not
common clinically, although regional DXA fat estimates
might be affected by large local accumulations such as
occur with pedal edema or ascites. On the other hand,
some small between-subject variability in DXA fat
estimation accuracy can be anticipated as hydration
varies with fluid intake and other physiological processes throughout the day and with varying conditions
such as prior exercise and season of the year.
Although our in vitro modeling experiments afford
distinct advantages over more-complex-to-interpret in
vivo studies, there are some limitations of the present
study approach that should be recognized. First, added
fluids such as water and normal saline may reflect
conditions that are not physiological. Second, our selected models assumed stable volumes with changing
fluid and fat fractions. Other model variations are
possible, and these should be considered in future
studies. Last, our hypothetical soft tissue system was
devoid of bone minerals and leaves unanswered the
magnitude and importance of errors arising in the
broader context of in vivo pixel analysis.
Previous Study Linkage
The present study results should be considered in the
context of earlier DXA hydration investigations. As a
representative example, Horber et al. (20) investigated
the effects of water ingestion (0.8–2.4 liters) and hemodialysis in six healthy subjects and seven patients with
chronic renal failure, respectively. Changes in fluid
balance did not significantly affect either DXA fat or
bone mineral mass estimates. Chronic renal failure per
se results in fluid accumulation and presumably also
alters the lean soft tissue R value. Errors in fat
estimation, although small in magnitude, may already
thus be present even before initiation of hemodialysis.
Ingested water and fluid removed with hemodialysis
would have different R values and would, accordingly,
be expected to have different effects on DXA fat estimation errors. With respect to water ingestion, in healthy
subjects, water is cleared rapidly by renal mechanisms,
and it appears within a short time interval in the
bladder. The pubic bone is anterior to the bladder, and
DXA cannot directly evaluate soft tissue R values in
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a distinct chemical moiety as is triglyceride, lean
composition and elemental proportions are by necessity
relatively stable as part of overall homeostasis maintenance. Accordingly, DXA soft tissue analysis is founded
on a two-component model in which fat and lean are
assumed to have constant and different R values. A
linear association between soft tissue fat fraction and R
value can be demonstrated, leading to the potential for
composition prediction equation development.
The theoretical links developed in the present report
unequivocally show that, with changes in soft tissue
hydration, there occurs alteration in lean tissue elemental proportions and R value. Addition of a high-R fluid
such as normal saline would by necessity increase lean
R in relation to the relative amount added. Lower-R
hydration fluids, such as water or ethanol, would cause
corresponding reductions in lean R values. Any change
in the assumed constant lean R value would lead to soft
tissue composition estimation errors.
DXA HYDRATION ERRORS
Hydration Error in Other Methods
Other body composition methods are also based on
two-component models and are prone to fat estimation
errors as well. For example, the classic two-compartment total body water method assumes a constant
fat-free body mass hydration of 0.732 (31). Error for the
total body water method can also be simulated if we
assume for theoretical purposes that fat-free body mass
and lean soft tissue mass are equivalent compartments. Using 0.25 fat and 0.75 lean soft tissue fractions, water fraction of total soft tissue is 0.732 3
0.75 5 0.549. If total soft tissue mass is now increased
by 10% with water addition, actual fat fraction decreases to 0.225 and total water fraction increases to
0.594. Assuming a lean soft tissue hydration level of
0.732, one arrives at a fat fraction of 0.188, an error of
20.036 or 23.6% fat. The corresponding DXA error, as
shown in Fig. 6, was 1.16%, which is smaller in
magnitude than the corresponding two-compartment
total body water method error.
Potential Error Correction
Overall, the simulated fat estimation error is small
in magnitude in the context of normal, or even pathologi-
cal, variation in hydration. Nevertheless, an important
question is whether or not hydration errors are correctable. One approach available on DXA systems that
provide R value information is to measure the actual R
value of evacuated fluid (e.g., with paracentesis) and
then correct the measured whole body soft tissue R
value accordingly. Large fluid evacuations such as with
ascites removal might be studied in this manner.
Conclusion
The present investigation provides strong evidence
that the widely used body composition method, DXA, is
prone to fat estimation errors related to variation in soft
tissue hydration. The magnitude of this potential error
source is a function of two main independent variables,
the fractional amount and the elemental content of the
lost or gained fluid. Under normal or even most clinical
conditions, the anticipated magnitude of this error is
small and should not pose any substantial limitations
to the accuracy of the DXA technique.
The authors gratefully acknowledge Dr. Carol Boozer and the
Galbraith Laboratories in Knoxville, TN, for assistance in analyzing
chemical composition of biological materials and Dr. Oksana Duda
and Martha Paszek for technical contributions in carrying out the
DXA studies.
This study was supported by National Institute of Diabetes and
Digestive and Kidney Diseases Grant RO1-DK-42618.
Address for reprint requests: A. Pietrobelli, Weight Control Unit,
Obesity Research Center, 1090 Amsterdam Ave, Floor 14, New York,
NY 10025.
Received 4 August 1997; accepted in final form 28 January 1998.
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