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Experimental Gerontology 48 (2013) 985–991
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Experimental Gerontology
j ourna l homepage: www.e lsev ie r .com/ locate /expgero
Assessment of regional adipose tissue depots: A DXA and CTcomparison in cadavers of elderly persons
Aldo Scafoglieri a,⁎, Rudi Deklerck b,c, Jonathan Tresignie a, Johan De Mey d,Jan Pieter Clarys a, Ivan Bautmans e
a Department of Experimental Anatomy, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgiumb Department of Electronics and Information Processing, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgiumc Department of Future Media and Imaging, iMinds, Universiteit Gent, Gaston Crommenlaan 8, 9050 Ghent, Belgiumd Department of Medical Imaging and Physical Sciences, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussels, Belgiume Frailty in Ageing Research Department, Vrije Universiteit Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
⁎ Corresponding author at: Experimental Anatomy, VriJette, Laarbeeklaan 103, Building B-037, 1090 Brussels,fax: +32 24774421.
E-mail addresses: [email protected] (A. [email protected] (R. Deklerck), [email protected] (J. De Mey), [email protected]@vub.ac.be (I. Bautmans).
0531-5565/$ – see front matter © 2013 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.exger.2013.07.005
a b s t r a c t
a r t i c l e i n f oArticle history:
Received 11 December 2012Received in revised form 16 April 2013Accepted 8 July 2013Available online 17 July 2013Section Editor: Andrzej Bartke
Keywords:Dual energy X-ray absorptiometryComputer tomographyTissueSamplingDensity
Objective: Since dual energy X-ray absorptiometry (DXA) cannot distinguish between different adipose tissue(AT) deposits, it remains unclear how DXA-derived body composition variables relate to anatomical tissue(sub)compartments. The aim of the present study was to compare and relate regional DXA variables with ab-solute tissue masses obtained by computer tomography (CT) scanning of the lower limbs in elderly persons.Methods: Eleven well-preserved white Caucasian adults (seven male and four female cadavers) with a medi-an age of 79.0 years (ranging from 68 to 96 years) were fully scanned with DXA and CT. Separate densities ofskin tissue, AT, muscle tissue and bone were obtained by hydrostatic weighing.Results: The leg DXA-variables were significantly related (rho-values between 0.60 and 0.98, P b 0.01) toCT-derived tissue counterparts, but showed significant systematic differences except for subcutaneous ATmass (P = 0.773). After controlling for other AT depots, fat as measured by DXA (fatDXA) related only tointermuscular AT (rho = 0.82, P b 0.01) in males and to subcutaneous AT (rho = 0.84, P b 0.05) in females.Conclusion: Although significantly interrelated, DXA and CT variables should not be used interchangeablysince they have different quantitative and physiological significance. Our results suggest that fatDXA repre-
sents different parts of AT depots in elderly men and women. Since DXA is not appropriate for assessing tis-sue variability cautious clinical interpretation is warranted.© 2013 Elsevier Inc. All rights reserved.
1. Introduction
Despite the fact that there is still some controversy whether thetotal amount of adipose tissue (AT) or its distribution confers a great-er risk for developing cardiovascular disease (CVD) (Sheldon, 2008),it has become increasingly clear that excess deep AT (e.g. visceral)as opposed to subcutaneous AT is most related to CVD risk (Canoyet al., 2006; Demerath et al., 2008; Stevens, 1995). The deposition ofAT beneath the muscle fascia between skeletal muscle bundles andaround blood vessels, called intermuscular AT, is recently gaining atten-tion as potential contributor to insulin resistance, metabolic syndrome,muscle function impairment, and disability (Goss and Gower, 2012;
je Universiteit Brussel, CampusBelgium. Tel.: +32 24774423;
oglieri),[email protected] (J. Tresignie),(J.P. Clarys),
rights reserved.
Marcus et al., 2010; Tuttle et al., 2012; Yim et al., 2008; Zoico et al.,2010).
In older persons the age-related accumulation of intermuscular ATin the legs is believed to be pro-atherogenic, while subcutaneous AT isassociated with favorable metabolic risk factors (Goodpaster et al.,2005). Aging is also accompanied by a loss of muscle mass andmusclefatty infiltration in the lower extremities, both of which are associat-ed with physical performance decline (Visser et al., 2002). Recently, ithas been hypothesized that a connection between intermuscular AT,inflammation, and muscle wasting might exist (Marcus et al., 2010).The accurate quantification of regional AT sub-depots and musclemay therefore provide important insights into metabolic and CVDrisk, and physical function in elderly persons.
The in vivo quantification of anatomical tissue compartments re-quires expensive imagingmethods, such asmagnetic resonance imaging(MRI) or computed tomography (CT), not accessible to the majority ofclinicians and researchers. Dual energy X-ray absorptiometry (DXA)has been proposed as a more accessible and reliable alternative forwhole body and regional (i.e. segmental) tissue estimation (Bauer etal., 2012; Kim et al., 2004; Shih et al., 2000). However, there are still
986 A. Scafoglieri et al. / Experimental Gerontology 48 (2013) 985–991
some methodological concerns regarding its validity, since the conver-sion of DXA-derived masses, which are based on a 3-compartmentchemical concept of body composition (BC) (i.e. fatDXA = lipids,leanDXA = proteins plus water and BMCDXA = minerals), to anatomicaltissue masses (e.g. AT, muscle, skin and bone) is susceptible to potentialerror (Clarys et al., 2010). Since DXA cannot distinguish between differ-ent AT and AT free sub-depots, the question raises how the assumedinter-individual variability in (regional) AT and muscle distribution istranslated to DXA outputs. Recent evidence suggests that DXA-derivedfatmass relates to AT sub-depots in children and adolescents, suggestingit to be an advantageous technique for the assessment of regional tissuecomposition (Bauer et al., 2012).
Unfortunately, validation studies of accuracy and precision of DXAfor the assessment of regional AT sub-depots in older adults are lack-ing. Since it remains unclear how DXA-derived BC variables relate toanatomical tissue (sub)compartments in general, and in elderly spe-cifically, the aim of the present study was to compare and relate re-gional DXA variables with absolute tissue masses obtained by CTscanning in the legs of cadavers of elderly persons. In the presentstudy, fatDXA is compared to total AT including its sub-depots, in par-ticular subcutaneous AT, intermuscular AT and yellow bone marrow.leanDXA is compared to total soft tissue and its sub-depots, muscle,skin and red bone marrow. Bone mineral content is compared tobone tissue.
2. Material and methods
2.1. Subjects
Eleven well-preserved white Caucasian adult cadavers (sevenmale and four female) with a median age of 79.0 (interquartilerange 70.0–89.0) years were selected for this study. All data werecollected at the Medical Campus of the Vrije Universiteit Brussel,Brussels. In order to restore postmortem tissue dehydration, all bod-ies were treated immediately upon receipt with an embalming solu-tion containing equal proportions of phenol (1 L), alcohol 96% (1 L)and formaldehyde 37% (1 L) mixed with glycerine (250 cc), thymol(20 g), potassium nitrate (250 g), magnesium sulfate (125 g), sodi-um sulfate (125 g) and chloral hydrate (250 g) diluted with waterto a volume of 10 L. The density of the embalming solution wasfound to be 1.042 g/cm3, which approximates the density of bloodin supine position (density 1.047 g/cm3) (Hinghofer-Szalkay andGreenleaf, 1987). The solution was infused by gravity flow throughthe right femoral or carotid artery. An average volume of 7.8 ± 2.5 Lof embalming fluid (of which ~0.8 ± 0.5 L in the leg) was retainedin the body tissues as a replacement for the blood plasma and bodywater lost pre- and postmortem. The use of preserved specimens isnot believed to introduce a significant bias in the BC results obtained(Clarys et al., 2006; Dempster, 1955; Ward and Lieber, 2005). Heightwas measured with the cadaver supine on a horizontal surface, usinga custom-made anthropometer. Body weight was measured with adigital hang scale (Kern Hus-150K50, Kern & Sohn GmBH, Balingen,Germany) accurate to 50 g. After embalming the subjects were placedinto plastic body bags to prevent dehydration and stored at 4 °C untilmedical imaging, at which time they were reweighed. Body massindex was calculated as weight divided by height squared (kg/m2).All applicable institutional, governmental and legal regulationsconcerning the ethical approval of human volunteers were followedduring the study. In this particular situation all subjects donatedtheir body for the purpose of scientific research through a handwrittentestament according to Belgian Law.
2.2. Dual energy X-ray absorptiometry (DXA)
Whole body scans of the cadavers were taken with a QDR 4500upgraded to Discovery Hologic DXA device (Hologic, Waltham, MA).
The DXA machine was calibrated daily using the anthropometricspine phantom supplied by the manufacturer. The densitometer wasalso calibrated weekly for BC using a step phantom, which allowsfor correction of sources related to skin thickness (such as beam hard-ening). The bodies were scanned twice on the same day with reposi-tioning to determine intra-observer reproducibility of DXA results.The regions of interest were delineated using software provided bythe manufacturer (version 12.3.4). All DXA scans were analyzed bytwo experienced observers using a standard protocol described else-where to determine inter-observer reproducibility of DXA results(Scafoglieri et al., 2012). Regional data for fatDXA, leanDXA and BMCDXAwere recorded in grams (g). Intra- and inter-observer reproducibilityof all DXA variables showed ICCs ≥0.98 and CVs b2.7%.
2.3. Computer tomography, 3D reconstruction and segmentation oftissues
Whole body scans of the cadavers were taken with a CT scanner(Discovery CT750HD, GE Healthcare,Waukesha, USA) in helical acqui-sition using the following settings: 120 kVp, 400 mAs, pitch 1, beamcollimation 64 × 0.625 mm, reconstructed slice width 2.5 mm, fieldof view of 50 cm and using the gsi_mict_plus.32 software. After scan-ning whole body 3D images were made from the series of 2D DICOMimages inMatlab (TheMathWorks Inc., Natick, United States) forWin-dows. Frontal images were used for manual segmentation of the bod-ies into anatomical regions comprising the head, left and right arms,trunk, and left and right legs using a 3D image segmentation programfor Windows XP (Deklerck et al., 2006). Identical landmarks (i.e. anangle perpendicular to the femoral neck) were selected for separatingthe leg from the trunk based on the standard protocol for DXA scananalysis. Each transverse image of the legs (~400 per subject) wasmanually segmented by an anatomist on the fascia generalis, separat-ing the skin plus subcutaneous AT from the underlying tissues. Afterhistogram analysis of the legs (Fig. 1), tissues were classified basedon Hounsfield Units (HU). Soft tissues (subcutaneous AT, skin,intermuscular AT and muscle) were determined by measuring thenumber of voxels within the range of −200 HU to +200 HU(Gargiulo et al., 2012). Skin was determined by removing the outervoxel layer (~1 mm) of the air–tissue interface and by adding adjacentvoxels with a CT number higher than 56 HU, to take into account skinfolds. Intermuscular AT was determined within the range −200 HU to−6 HU, while skeletal muscle was measured within the range −5 HUto +200 HU. A semi-automatic segmentation procedure was used todelineate the skeleton, subdivided into yellow bone marrow, redbone marrow and bone tissue. Yellow bone marrow was determinedin the range of−200 HU to−6 HU, red bonemarrow in the range of−5 HU to +200 HU, while trabecular and cortical bone fell in therange of +201 to +1988 HU.
2.4. Estimation of CT tissue masses
Each right leg was dissected into its various components expressedon the tissue-system level i.e. skin, adipose tissue, muscle and bone.The density of subcutaneous AT, skin,muscle and bonewas determinedby hydrostatic weighing. Tissues were weighed twice in air, and inwater of known density and temperature with an electronic high preci-sion scale accurate to 0.01 g (Mettler-Toledo Excellence XS model40025, Mettler-Toledo GmBH, Greifensee, Switzerland). If floating(e.g. subcutaneous AT) a small calibration weight was placed directlyon the tissue sample allowing submersion. The average of twomeasureswas used for calculating tissue density using the following classicalequation:
ρtissue ¼ Wair·ρH2Oð Þ= Wair–WH2Oð Þ
with ρ = density and W = average weight of two measures.
Fig. 1. Histogram analysis of individual tissues obtained from ~400 slices of the right leg of an 80 year old male. Hounsfield unit ranges according to Gargiulo et al. (2012) were usedfor tissue estimation: subcutaneous AT [−200, +200], skin [−200, +200], muscle [−5, +200], intermuscular AT [−200,−6], yellow bone marrow [−200,−6], red bone marrow[−5, +200] and bone [+201, +3071]. AT = adipose tissue; N = number of voxels; HU = Hounsfield units.
987A. Scafoglieri et al. / Experimental Gerontology 48 (2013) 985–991
The CVs for tissue density in air and under water were b0.9%.Median tissue densities for the sample are shown in Table 1. Forintermuscular AT and yellow and red bone marrow, densities of re-spectively 0.92 g/cm3, 0.98 g/cm3 and 1.03 g/cm3 were assumed asdescribed by Snyder et al. (1975).
Tissue volumes were calculated by multiplying the total number ofvoxels by their respective size (0.977 × 0.977 × 2.5 mm). For eachcadaver separately tissue density was multiplied with tissue volume tocalculate total tissue weight for the following tissues: subcutaneous AT,skin, muscle and bone. In all previous studies involving the calculationof tissue mass derived by CT, constant densities for AT, muscle and/orbone tissue were assumed ignoring inter-individual variability in tissuedensity. Thus, this study is unique for the fact that human biologicalvariability in tissue density was taken into account for the estimation ofCT leg tissuemasses. Total ATmass was also calculated as the sum of sub-cutaneous AT mass, intermuscular AT mass and yellow bone marrow;total soft tissue mass as the sum of skin mass, muscle mass and red
Table 1Tissue density values of the legs obtained by hydrostatic weighing.
Males (n = 7)Median (range)
Females (n = 4)Median (range)
Skin (g/cm3) 1.070 (1.060–1.091) 1.066 (1.039–1.070)Subcutaneous AT (g/cm3) 0.941 (0.935–0.964) 0.937 (0.928–0.943)Muscle (g/cm3) 1.039 (1.023–1.062) 1.037 (1.020–1.059)Bone (g/cm3) 1.456 (1.347–1.485) 1.437 (1.370–1.473)
AT = adipose tissue.
bone marrow with total leg mass being the sum of total AT, soft tissueand bone tissue mass. Repeated measurements on 2 separate days bythe same observer in our laboratory showed CVs for subcutaneous AT,muscle and intermuscular AT mass b1%.
2.5. Statistical analysis
Statistical Package for Social Sciences (version 20 for Windows,SPSS Inc., Chicago, USA) and MedCalc (version 12 for Windows,MedCalc Software, Mariakerke, Belgium) were used for the data anal-ysis. Data are expressed as median (interquartile range). Genderdifferences in general characteristics and BC were calculated usingMann–Whitney U tests.
Leg composition data for the total sample were compared usingSpearman correlation coefficients, Wilcoxon signed rank tests, BlandAltman analysis and Passing and Bablok regression analysis. Genderspecific relationships were explored using Spearman simple andpartial correlations. Significance was set at the P b 0.05 level.
3. Results
The characteristics of the sample are presented in Table 2. Thetotal sample of eleven subjects ranged in age and BMI from 68 to96 years and from 22.6 to 30.5 kg/m2 respectively. No significant gen-der differences in general characteristics were found (P N 0.05). Al-though total leg AT and its sub-depots did not differ between sexes,the ratio of intermuscular AT to subcutaneous AT was significantly
Table 2Body composition of eleven elderly subjects.
Males (n = 7)Median (IQR)
Females (n = 4)Median (IQR)
Pa
Age (year) 80.0 (72.2–87.7) 72.5 (71.0–84.5) 0.777Weight (kg) 66.2 (64.9–74.0) 67.8 (62.7–76.3) 0.850Height (cm) 168.3 (160.6–169.4) 159.4 (156.2–162.1) 0.089BMI (kg/m2) 26.0 (22.8–26.5) 27.6 (24.6–30.2) 0.257
DXA leg compositionfatDXA (g) 2065.2 (1859.8–3485.1) 3198.2 (1755.8–4605.5) 0.413leanDXA (g) 7219.8 (6516.6–7606.9) 5969.4 (5260.4–6980.3) 0.076BMCDXA (g) 465.7 (416.5–592.5) 393.1 (327.7–414.2) 0.014
CT leg compositionTotal adipose tissue (g) 3007.6 (2603.3–3884.4) 4510.8 (2692.4–5423.5) 0.219
Subcutaneous AT (g) 2342.0 (2102.8–2883.7) 3973.7 (2558.4–4897.6) 0.133Intermuscular AT (g) 976.3 (791.1–1243.1) 843.2 (564.2–1037.3) 0.306
Total soft tissue (g) 5891.7 (5186.0–6558.5) 4961.3 (4333.5–5275.2) 0.004Muscle (g) 4582.6 (4093.3–5084.9) 3693.0 (3414.1–3960.7) 0.002Skin (g) 411.3 (383.7–462.3) 412.0 (348.5–463.6) 0.682
Total bone tissue (g) 1246.1 (1062.8–1352.5) 999.5 (886.0–1077.0) 0.029
RatiosIMAT/SAT 0.43 (0.37–0.44) 0.24 (0.17–0.29) 0.012IMAT/muscle 0.20 (0.17–0.32) 0.23 (0.14–0.29) 0.682
Data are presented as median (interquartile range), IQR = interquartile range; BMI =body mass index; DXA = dual energy X-ray absorptiometry; BMC = bone mineralcontent; CT = computer tomography; AT = adipose tissue; SAT = subcutaneous AT;IMAT = intermuscular AT.
a Mann–Whitney U test.
Differen
ceMea
n±
SDPa
ssing–
Bablok
regression
95%LO
APa
Intercep
t(9
5%CI)
Slop
e(9
5%CI)
−66
3.8±
412.0
47.1(−
440.7–
508.4)
0.83
(0.70–
0.95
)−
1736
.0;408
.4b0.00
111
9.6±
751.2
−68
5.0(
−15
33.4–32
8.2)
1.21
(0.85–
1.58
)−
1352
.7;159
2.0
0.77
319
68.5
±10
53.0
−38
3.5(
−35
58.4–75
5.3)
3.17
(2.06–
6.37
)−
95.4;403
2.3
b0.00
128
62.4
±12
40.3
−19
45.8(N
S)11
6.5(
NS)
431.3;52
93.4
b0.00
112
62.8
±57
5.7
−10
46.3(−
3029
.4–11
21.8)
1.43
(0.98–
1.80
)13
4.4;23
91.3
b0.00
124
88.4
±85
1.7
−16
29.4(−
7592
.6–77
5.2)
1.92
(1.34–
3.43
)81
9.0;41
57.8
b0.00
163
30.2
±10
32.8
−73
17.7(N
S)33
.7(N
S)43
05.9;835
4.6
b0.00
163
16.1
±10
98.1
NS(NS)
NS(NS)
4163
.9;846
8.3
b0.00
1−
685.6±
111.8
−97
.8(−
192.1–
4.5)
0.49
(039
–0.58
)−
904.6;−
466.5
b0.00
186
.5±
266.6
300.7(
−36
8.5–
1022
.1)
0.95
(0.88–
1.03
)−
436.1;60
9.2
0.15
8
grap
hy;IQ
R=
interqua
rtile
rang
e;rh
o=
Spea
rman
correlation
coefficien
t;CI
=co
nfide
nceinterval;NS=
not
988 A. Scafoglieri et al. / Experimental Gerontology 48 (2013) 985–991
higher in males (P = 0.012). Males showed also higher soft tissuemass (P = 0.004), muscle mass (P = 0.002), BMCDXA (P = 0.014)and bone mass (P = 0.029) compared to females.
elde
rly(n
=22
).
CT Med
ian(IQR)
rho
5.3)
3567
.9(2
603.3–
5146
.2)
0.92
†
2541
.9(2
102.8–
3964
.1)
0.80
†
912.8(
744.3–
1152
.2)
0.60
‡
48.7(2
7.4–
71.6)
0.15
3.8)
5470
.8(4
954.6–
5988
.9)
0.83
†
4113
.3(3
845.1–
4850
.0)
0.63
‡
411.3(
381.1–
462.3)
0.35
463.2(
363.3–
511.8)
−0.26
8)11
30.3(9
74.7–12
67.5)
0.92
†
59.1)
9990
.5(9
298.2–
1098
3.7)
0.98
†
energy
X-ray
absorptiom
etry;CT
=co
mpu
tertomo
3.1. Comparison between DXA and CT variables
All DXA-values were significantly (P b 0.01) related to theirCT-derived tissue counterparts with r-values ranging between 0.60and 0.98, except the skin and bone marrow mass (P N 0.05)(Table 3). Passing and Bablok regression indicated that DXA-derivedanalogs showed systematic (constant and proportional) differenceswith CT tissue masses, except subcutaneous AT mass (P = 0.773).Dual energy X-ray absorptiometry underestimated total leg AT by~25% and overestimated leg muscle mass by ~30%. The difference be-tween fatDXA and total AT tended to increase with increasing amountsof AT (Fig. 2). The difference between leanDXA and muscle massincreased with increasing amounts of soft tissue (Fig. 3).
Table3
Compa
risonbe
twee
nDXAan
dCT
values
(g)forlegco
mpo
sition
in
Variableco
mpa
rison
DXA
Med
ian(IQR)
DXA
CT
Fat
Totala
dipo
setissue
2605
.3(1
839.7–
427
Subc
utan
eous
AT
Interm
uscu
larAT
Yello
wbo
nemarrow
Lean
Totals
ofttissue
6876
.3(5
837.3–
760
Mus
cle
Skin
Redbo
nemarrow
BMC
Totalb
onetissue
b42
0.8(
374.9–
521.
Totalm
ass
Totaltissu
emass
1000
9.4(
9333
.3–10
7
AT=
adiposetissue
;BM
C=
bone
mineral
conten
t;DXA=
dual
suitab
le;LO
A=
limitsof
agreem
ent.
‡Pb0.01
.†Pb0.00
1.a
Wilc
oxon
sign
edrank
test.
bEx
trao
sseo
uscalcification
includ
ed.
3.2. Relationship between DXA and CT-measured leg tissue masses bygender
Spearman simple correlation analysis indicated that fatDXA masswas significantly positively associated with all leg AT depots inmales, but only with total AT and subcutaneous AT in females(P b 0.05) (Table 4). Partial correlation analysis indicated that fatDXAwas significantly positively related to leg intermuscular AT indepen-dent of other AT depots in males. In females fatDXA was significantlypositively related to subcutaneous AT independent of intermuscularAT and yellow bone marrow.
In males leanDXA mass was significantly related to soft tissue massand its sub-depots except to red bone marrow (Table 5). In femalesleanDXA was significantly related only to soft tissue mass. Partial cor-relation analysis showed that leanDXA was positively related to skinmass in males and to soft tissue mass in females, independent ofother soft tissue compartments.
Fig. 2. Bland and Altman plot showing a tendency for increased underestimation of legadipose tissue by DXA at higher adipose tissue quantities in elderly persons. DXA =dual energy X-ray absorptiometry, CT = computer tomography.
989A. Scafoglieri et al. / Experimental Gerontology 48 (2013) 985–991
4. Discussion
The main finding of this study was that fatDXA and subcutaneous ATestimations are not significantly different. Their mutual relationshiphowever seemed to differ in genders. After controlling for inter-muscular AT, the association between fatDXA and subcutaneous ATremained significant in women, but not in men. This suggests thatthere may be sex-differences in the association between fatDXA and ATdepots in elderly persons. Moreover, after controlling for other AT de-posits, intermuscular AT showed a positive and significant relationshipwith fatDXA in males, but not in females. These observations are impor-tant because they address the practical usefulness of DXA in clinicalstudies of human BC. Since it has been shown that leg intermuscularAT and subcutaneous AT have independent and opposite relationshipswith CVD (Yim et al., 2008), the physiological significance of the fatDXAvariablemight be different between sexes. For this reason it is importantto take note of the tissue variability between sexeswhenusing fatDXA datain elderly, as it has been shown thatmales have significant higher propor-tions of intermuscular AT compared to females at any given level of adi-posity. With regard to the underlying mechanism responsible for theaccumulation of intermuscular AT, it has been postulated thatmuscle sat-ellite cells can acquire features of adipocytes, including the abilities toexpress adipocyte-specific genes and accumulate lipids (Vettor et al.,
Fig. 3. Bland and Altman plot showing a tendency for increased overestimation of legmuscle by DXA at higher soft tissue quantities in elderly persons. DXA = dual energyX-ray absorptiometry, CT = computer tomography.
2009). Although the mechanisms by which an increase in adipositywith aging may lead to an increase in AT infiltration in muscle are stillnot understood (Zoico et al., 2010), it is well recognized that womenare more efficient at storing blood lipids into subcutaneous AT as com-pared to deep AT (Karastergiou et al., 2012).
In their longitudinal study, Song et al. (2004) showed that inter-muscular AT increased while subcutaneous AT remained unchangedin a group of healthy elderly women over a period of 2 years follow-up. However in the same study, DXA was not able to quantify thechanges in deep adiposity as fatDXA did not change over time. Theimportance of the intermuscular AT compartmentwith regard to its im-plications on the development of sarcopenia might be underestimatedas most of the studies dealing with loss of muscle and physical functiondid not take into account changes in intermuscular AT. Moreover, sinceDXA detects increased hydration and intramuscular AT as an increase inlean tissue (Proctor et al., 1999), this might imply that loss of skeletalmuscle during aging is even greater than considered this far. Theaccurate quantification of the intermuscular AT compartment mightfurther elucidate some of the contradictory findings in the relationshipbetween sarcopenia, physical function and health related consequences(Janssen, 2010).
In the present study DXA leg variables were strongly related to totaltissuemasses obtained by CT (rho ≥ 0.83, P b 0.001). Several other val-idation studies have shown strong correlation of DXAbody compositionvalues with CT or MRI derived tissues in elderly subjects (Chen et al.,2007; Levine et al., 2000; Salamone et al., 2000; Visser et al., 1999).Dual energy X-ray absorptiometry significantly underestimated totalleg AT and bone tissue, while it overestimated leg soft tissue mass.This finding is supported by previous validationwork in elderly persons(Chen et al., 2007; Levine et al., 2000), but in contrast with recent find-ings of Scherzer et al. (2008) who found that fatDXA overestimatedMRI-derived leg AT inmiddle-aged HIV positive adults and normal con-trols. Although it can be pointed out that these groups present signifi-cantly smaller amounts of intermuscular AT compared to elderlysubjects, the reasons for these contradictory findings remain unclear.It may be argued that that the inclusion of significant amounts (N25%)of non-lipid components in AT such as water and protein (e.g. connec-tive tissue, blood vessels and nerves) in combination with only smallquantities of lipid present in the muscle, skin and bone (Forbes et al.,1956; Moore et al., 1968) may cause a positive bias for AT. Since an ex-planation for the negative bias of DXA in the estimation of regional AT isnot apparent (Lukaski, 2008), it is suggested that different methodolo-gies for AT measurement are likely to account for these differences.Whole limb and/or chemical tissue analysis (e.g. the accurate quantifi-cation of lipid, water, protein and mineral content) might elucidatethe discrepancy in findings.
In the present study leanDXA overestimated significantly musclemass. This is in agreement with previous in vivo findings in similarpopulations (Chen et al., 2007; Visser et al., 1999). This observationsupports the notion that muscle mass is overestimated in elderlybecause of their lower muscle/lean ratio than observed in youngadults. It is suggested that the variability in hydration of the adiposeand soft tissue compartment may have affected the ratio of muscleCTto leanDXA. Increased hydration of adipose and/or soft tissue sub-depots is associated with a higher contribution of fluid to leanDXA,leading to overestimates of muscle. Since DXA assumes a constantwater content of 0.73 for its lean compartment, changes in tissue hy-dration may lead to error in leanDXA (Clarys et al., 2010). As stated inthe Material and methods section the embalming procedure – as usedin the present study – restores normal hydration of tissues. However,one might argue that the postmortem hydration may have caused ahyperhydration of the extra-cellular fluid compartment, which inturn may have led to increasing discrepancy between chemical and an-atomical tissue quantities. Although its influence cannot be completelyruled out, there are no reasons to believe that this might have biasedour results. Indeed, pilot results on one single cadaver in our laboratory
Table 4Spearman zero order and partial correlations between fatDXA mass and CT adipose tissue depots by sex.
Male (n = 14) Female (n = 8)
Zero order rho 1st order rhoa 2nd order rhob Zero order rho 1st order rhoa 2nd order rhob
Total adipose tissue 0.94† 0.88† 0.45 0.91‡ 0.78⁎ 0.78Subcutaneous AT 0.79† 0.45 0.45 0.76⁎ 0.77⁎ 0.84⁎Intermuscular AT 0.93† 0.84† 0.82† 0.50 0.53 −0.14Yellow bone marrow 0.55⁎ 0.35 −0.18 −0.69 −0.79⁎ −0.69
a Adjusted for the largest adipose tissue (AT) sub-depot.b Adjusted for the two largest AT sub-depots; DXA = dual energy X-ray absorptiometry; CT = computer tomography.⁎ P b 0.05.‡ P b 0.01.† P b 0.001.
Table 5Spearman zero order and partial correlations between leanDXA mass and CT soft tissue depots by sex.
Male (n = 14) Female (n = 8)
Zero order rho 1st order rhoa 2nd order rhob Zero order rho 1st order rhoa 2nd order rhob
Total soft tissue 0.78† 0.61⁎ 0.56 0.86‡ 0.84⁎ 0.92‡Muscle 0.62⁎ 0.58⁎ 0.36 0.45 0.45 0.45Skin 0.66‡ 0.62⁎ 0.62⁎ 0.12 −0.10 −0.06Red bone marrow −0.42 0.10 −0.10 −0.10 0.08 −0.03
a Adjusted for the largest soft tissue (ST) sub-depot.b Adjusted for the two largest ST sub-depots; DXA = dual energy X-ray absorptiometry; CT = computer tomography.⁎ P b 0.05.‡ P b 0.01.† P b 0.001.
990 A. Scafoglieri et al. / Experimental Gerontology 48 (2013) 985–991
before and after embalming showed that the fatDXA/AT ratio (0.68 and0.72 respectively) and the leanDXA/muscleCT ratio (1.69 and 1.58 respec-tively) were similar.
The physical density values of leg subcutaneous AT,muscle and bonefound in this in vitro experiment are in agreement with those reportedby Snyder et al. (1975) for the total body, suggesting their proper use forclinical purposes in elderly. In the present study however, both elderlymen and women showed important inter-individual differences in legmuscle and bone tissue density. This biological variability in tissue den-sity may lead to estimation errors when tissue volume is converted totissue mass. In the present study, muscle density varied between1.02 g/cm3 and 1.06 g/cm3 both in elderly men and women. By assum-ing a constant density of 1.04 g/cm3 formuscle (which is common in BCstudies) conversion errors in the range of 4% for muscle mass are to beexpected. For example, in the present study average leg muscle volumewas 3800 cm3. A difference of 0.04 g/cm3 inmuscle densitymay lead toa difference of 152 g in muscle mass, which equals ~3.8% of averagemuscle mass. The same is true for bone tissue mass estimations sincebone tissue density in elderly may vary between 1.35 g/cm3 and1.48 g/cm3, a difference of 0.13 g/cm3 (see Table 1). As bone tissuerepresents a smaller compartment (800 cm3) compared to muscle(3800 cm3), the inter-individual difference leads tomore important es-timation errors. A difference of 0.13 g/cm3 in bone densitymay lead to adifference of 104 g in bone mass, which equals ~9.3% of average bonetissue mass. These two examples show that density determination oftissues may be an important, but unfortunately often overlooked aspectof tissue mass estimation in BC studies. The latter is often ignored incomparative or effect studies and may blur results. Tissue densitieswere linearly related to Hounsfield units as described by Saw et al.(2005), except skin tissuewhich showed significantly lower Hounsfieldunits than expected (data not shown). This observation can primarilybe ascribed to the partial volume effects at the air–skin interface causedby insufficient resolution of the skin image.
There are both strengths and limitations inherent to the presentstudy, wherein the conclusions are only applicable to elderly Cauca-sians. One limitation of this study is the small number of subjects in-cluded. The body weight range (60–80 kg) was therefore limited.
However, a shortcoming of both CT and DXA is the field of viewwhich limits the size of the subjects to be examined. Another limita-tion might be related to the preservation method of the cadavers. Ithas been shown that the embalming technique restores water con-tent and volume of tissues and does not affect tissue density (Wardand Lieber, 2005). However, it cannot be excluded that the postmor-tem hydration process caused local edema since it is difficult to assesswhether fluid is equally repartitioned over all tissue compartments.Although this may have influenced the magnitude of the differencesbetween methods in the present study, it is assumed not to affectthe direction of the bias (Pietrobelli et al., 1998).
5. Conclusions
Although DXA and CT variables are significantly interrelated, it isadvisable not to use these variables interchangeably, since our datashow that they have different quantitative and physiological signifi-cance. Our results suggest that fatDXA represents different AT depotsin elderly men and women. These findings show that DXA is not anappropriate tool for assessing AT variability between sexes.
Conflict of interest
The authors have no conflicts of interests.
Acknowledgments
The authors wish to thank N Buls and G Van Gompel for their tech-nical assistance in operating the CT device.
This work was supported by grants OZR1402BOF and OZR2095BOFfrom the Vrije Universiteit Brussel.
References
Bauer, J., Thornton, J., Heymsfield, S., Kelly, K., Ramirez, A., Gidwani, S., Gallagher, D.,2012. Dual-energy X-ray absorptiometry prediction of adipose tissue depots inchildren and adolescents. Pediatr. Res. 72, 420–425.
991A. Scafoglieri et al. / Experimental Gerontology 48 (2013) 985–991
Canoy, D., Wareham, N., Luben, R., Welch, A., Bingham, S., Day, N., Khaw, K.T., 2006.Serum lipid concentration in relation to anthropometric indices of central and pe-ripheral fat distribution in 20,021 British men and women: results from the EPIC-Norfolk population-based cohort study. Atherosclerosis 189, 420–427.
Chen, Z., Wang, Z., Lohman, T., Heymsfield, S.B., Outwater, E., Nicholas, J.S., Bassford, T.,LaCroix, A., Sherrill, D., Punyanitya, M., Wu, G., Going, S., 2007. Dual-energy X-rayabsorptiometry is a valid tool for assessing skeletal muscle mass in older women.J. Nutr. 137, 2775–2780.
Clarys, J.P., Provyn, S., Marfell-Jones, M., Van Roy, P., 2006. Morphological and constitu-tional comparison of age-matched in-vivo and post-mortem populations.Morphologie 90, 189–196.
Clarys, J.P., Scafoglieri, A., Provyn, S., Louis, O., Wallace, J.A., De Mey, J., 2010. Macro-quality evaluation of DXA variables using whole dissection, ashing, and computertomography in pigs. Obesity (Silver Spring) 18, 1477–1485.
Deklerck, R., Guo, J., Salomie, A., Suliga, M., Nyssen, E., Tao, L., Baleriaux, D., Cornelis, J.,2006. A method to construct flat maps of the brain. Int. J. Pattern Recognit. Artif.Intell. 20, 679–709.
Demerath, E.W., Reed, D., Rogers, N., Sun, S.S., Lee, M., Choh, A.C., Couch, W.,Czerwinski, S.A., Chumlea, W.C., Siervogel, R.M., Towne, B., 2008. Visceral adiposityand its anatomical distribution as predictors of the metabolic syndrome andcardiometabolic risk factor levels. Am. J. Clin. Nutr. 88, 1263–1271.
Dempster, W.T., 1955. Space requirements of the seated operator. WADC Technical Re-port. Wright-Patterson Air Force Base, OH, pp. 55–159.
Forbes, R.M., Cooper, A.R., Mitchell, H.H., 1956. Further studies on the gross composi-tion and mineral elements of the adult human body. J. Biol. Chem. 223, 969–975.
Gargiulo, P., Helgason, T., Ingvarsson, P., Mayr, W., Kern, H., Carraro, U., 2012. Medicalimage analysis and 3-d modeling to quantify changes and functional restorationin denervated muscle undergoing electrical stimulation treatment. Human-centricComput. Inf. Sci. 2, 10 (Available at http://www.hcis-journal.com/content/2/1/10).
Goodpaster, B.H., Krishnaswami, S., Harris, T.B., Katsiaras, A., Kritchevsky, S.B.,Simonsick, E.M., Nevitt, M., Holvoet, P., Newman, A.B., 2005. Obesity, regionalbody fat distribution, and the metabolic syndrome in older men and women.Arch. Intern. Med. 165, 777–783.
Goss, A.M., Gower, B.A., 2012. Insulin sensitivity is associated with thigh adipose tissuedistribution in healthy postmenopausal women. Metabolism 61, 1817–1823.
Hinghofer-Szalkay, H., Greenleaf, J.E., 1987. Continuous monitoring of blood volumechanges in humans. J. Appl. Physiol. 63, 1003–1007.
Janssen, I., 2010. Evolution of sarcopenia research. Appl. Physiol. Nutr.Metab. 35, 707–712.Karastergiou, K., Smith, S.R., Greenberg, A.S., Fried, S.K., 2012. Sex differences in human
adipose tissues— the biology of pear shape. Biol. Sex Differ. 3, 13. http://dx.doi.org/10.1186/2042-6410-3-13.
Kim, J., Heshka, S., Gallagher, D., Kotler, D.P., Mayer, L., Albu, J., Shen, W., Freda, P.U.,Heymsfield, S.B., 2004. Intermuscular adipose tissue-free skeletal muscle mass: esti-mation by dual-energy X-ray absorptiometry in adults. J. Appl. Physiol. 97, 655–660.
Levine, J.A., Abboud, L., Barry, M., Reed, J.E., Sheedy, P.F., Jensen, M.D., 2000. Measuringleg muscle and fat mass in humans: comparison of CT and dual-energy X-ray ab-sorptiometry. J. Appl. Physiol. 88, 452–456.
Lukaski, H.C., 2008. In search of a practical tool to assess regional body composition.Am. J. Clin. Nutr. 88, 875–876.
Marcus, R.L., Addison, O., Kidde, J.P., Dibble, L.E., Lastayo, P.C., 2010. Skeletal muscle fat in-filtration: impact of age, inactivity, and exercise. J. Nutr. Health Aging 14, 362–366.
Moore, F.D., Lister, J., Boyden, C.M., Ball, M.R., Sullivan, N., Dagher, F.J., 1968. Theskeleton as a feature of body composition. Values predicted by isotope dilu-tion and observed by cadaver dissection in an adult human female. Hum.Biol. 40, 135–188.
Pietrobelli, A., Wang, Z., Formica, C., Heymsfield, S.B., 1998. Dual-energy X-ray absorp-tiometry: fat estimation errors due to variation in soft tissue hydration. Am.J. Physiol. 274, E808–E816.
Proctor, D.N., O'Brien, P.C., Atkinson, E.J., Nair, K.S., 1999. Comparison of techniques toestimate total body skeletal muscle mass in people of different age groups. Am.J. Physiol. Endocrinol. Metab. 277, E489–E495.
Salamone, L.M., Fuerst, T., Visser, M., Kern, M., Lang, T., Dockrell, M., Cauley, J.A., Nevitt,M., Tylavsky, F., Lohman, T.G., 2000. Measurement of fat mass using DEXA: a vali-dation study in elderly adults. J. Appl. Physiol. 89, 345–352.
Saw, C.B., Loper, A., Komanduri, K., Combine, T., Huq, S., Scicutella, C., 2005. Determina-tion of CT-to-density conversion relationship for image-based treatment planningsystems. Med. Dosim. 30, 145–148.
Scafoglieri, A., Tresignie, J., Provyn, S., Marfell-Jones, M., Reilly, T., Bautmans, I., Clarys,J.P., 2012. Prediction of segmental lean mass using anthropometric variables inyoung adults. J. Sports Sci. 30, 777–785.
Scherzer, R., Shen, W., Bacchetti, P., Kotler, D., Lewis, C.E., Shlipak, M.G., Punyanitya, M.,Heymsfield, S.B., Grunfeld, C., 2008. Study of fat redistribution metabolic change inHIV infection. Comparison of dual-energy X-ray absorptiometry and magnetic res-onance imaging-measured adipose tissue depots in HIV-infected and control sub-jects. Am. J. Clin. Nutr. 88, 1088–1096.
Sheldon, E.L., 2008. Which measures of obesity best predict cardiovascular risk? J. Am.Coll. Cardiol. 52, 616–619.
Shih, R., Wang, Z., Heo, M., Wang, W., Heymsfield, S.B., 2000. Lower limb skeletal mus-cle mass: development of dual-energy X-ray absorptiometry prediction model.J. Appl. Physiol. 89, 1380–1386.
Snyder, W.S., Cook, M.J., Nasset, E.S., Karhausen, L.R., Howells, G.P., Tipton, I.H., 1975.Report of the Task Group on Reference Man. Pergamon Press, Oxford.
Song, M.-Y., Ruts, E., Kim, J., Janumala, I., Heymsfield, S., Gallagher, D., 2004. Sarcopeniaand increased adipose tissue infiltration of muscle in elderly African Americanwomen. Am. J. Clin. Nutr. 79, 874–880.
Stevens, J., 1995. Obesity, fat patterning and cardiovascular risk. Adv. Exp. Med. Biol.369, 21–27.
Tuttle, L.J., Sinacore, D.R., Mueller, M.J., 2012. Intermuscular adipose tissue is muscle spe-cific and associated with poor functional performance. J. Aging Res. 2012, 172957.
Vettor, R., Milan, G., Franzin, C., Sanna, M., De Coppi, P., Rizzuto, R., Federspil, G., 2009.The origin of intermuscular adipose tissue and its pathophysiological implications.Am. J. Physiol. Endocrinol. Metab. 297, E987–E998.
Visser, M., Fuerst, T., Lang, T., Salamone, L., Harris, T.B., 1999. Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass.Health, aging, and body composition study — dual-energy X-ray absorptiometryand body composition working group. J. Appl. Physiol. 87, 1513–1520.
Visser, M., Kritchevsky, S.B., Goodpaster, B.H., Newman, A.B., Nevitt, M., Stamm, E.,Harris, T.B., 2002. Leg muscle mass and composition in relation to lower extremityperformance in men and women aged 70 to 79: the health, aging and body compo-sition study. J. Am. Geriatr. Soc. 50, 897–904.
Ward, S.R., Lieber, R.L., 2005. Density and hydration of fresh and fixed human skeletalmuscle. J. Biomech. 38, 2317–2320.
Yim, J.E., Heshka, S., Albu, J.B., Heymsfield, S., Gallagher, D., 2008. Femoral–gluteal sub-cutaneous and intermuscular adipose tissues have independent and opposing rela-tionships with CVD risk. J. Appl. Physiol. 104, 700–707.
Zoico, E., Rossi, A., Di Francesco, V., Sepe, A., Olioso, D., Pizzini, F., Fantin, F., Bosello, O.,Cominacini, L., Harris, T.B., Zamboni, M., 2010. Adipose tissue infiltration in skeletalmuscle of healthy elderly men: relationships with body composition, insulin resis-tance, and inflammation at the systemic and tissue level. J. Gerontol. A Biol. Sci.Med. Sci. 65, 295–299.
