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Appendicular skeletal muscle mass: Measurement by dual-photon absorptiometry

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Dual-photon absorptiometry (DPA) allows separation of body mass into bone mineral, fat, and fat-free soft tissue. This report evaluates the potential of DPA to isolate appendages of human subjects and to quantify extremity skeletal muscle mass (limb fat-free soft tissue). The method was evaluated in 34 healthy adults who underwent DPA study, anthropometry of the limbs, and estimation of whole-body skeletal muscle by models based on total body potassium (TBK) and nitrogen (TBN) and on fat-free body mass (FFM). DPA appendicular skeletal muscle (22.0 +/- 3.1 kg, mean +/- SD) represented 38.7% of FFM, with similar proportions in males and females. There were strong correlations (all p less than 0.001) between limb muscle mass estimated by DPA and anthropometric limb muscle areas (r = 0.82-0.92), TBK (r = 0.94), and total-body muscle mass based on TBK-FFM (r = 0.82) and TBK-TBN (r = 0.82) models. Appendicular skeletal muscle mass estimated by DPA is thus a potentially practical and accurate method of quantifying human skeletal muscle mass in vivo.
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214 Am J C/in Nuir I990;52:214-8. Printed in USA. © 1990 American Society for Clinical Nutrition
Appendicular skeletal muscle mass: measurement
by dual-photon absorptiometry”2
Steven B Heymsfield, Rebecca Smith, Mary Aulet, Brooke Bensen,
Steven Lichtman, Jack Wang, andRichardNPierson, Jr
ABSTRACT Dual-photon absorptiometry (DPA) allows
separation of body mass into bone mineral, fat, and fat-free
soft tissue. This report evaluates the potential ofDPA to isolate
appendages ofhuman subjects and to quantify extremity skele-
tal muscle mass(limb fat-free soft tissue). The method was eval-
uated in 34 healthy adults who underwent DPA study, anthro-
pomety of the limbs, and estimation of whole-body skeletal
muscle by models based on total body potassium (TBK) and
nitrogen (TBN) and on fat-free body mass (FFM). DPA appen-
dicular skeletal muscle (22.0 ±3. 1 kg, I ± SD) represented
38.7% of FFM, with similar proportions in males and females.
There were strong correlations (all p<0.00 1)between limb
muscle mass estimated by DPA and anthropometric limb mus-
cle areas (r =0.82-0.92), TBK (r =0.94), and total-body mus-
cle mass based on TBK-FFM (r =0.82) and TBK-TBN (r
=0.82) models. Appendicular skeletal muscle mass estimated
by DPA is thus a potentially practical and accurate method of
quantifying human skeletal muscle mass in vivo. Am J
Clin Nutr 1990;52:2 14-8.
KEY WORDS Body composition, dual-photon absorpti-
ometry, skeletal muscle mass, neutron-activation analysis
Introduction
Skeletal muscle represents the largest fraction of fat-free
body mass. Depending on gender, age, and health status, be-
tween one-third and one-half of total body protein is within
skeletal muscle (1).
Despite the obvious significance ofskeletal muscle to physi-
ology and nutrition, methods ofquantification in vivo remain
limited. Although two metabolic end products released from
myocytes, creatinine and 3-methyihistidine, have been used to
estimate whole-body muscle mass, their application is beset
with problems (2, 3). Long urine-collection intervals, the need
for appropriate dietary intake, and concerns related to the met-
abolic origin and distribution of 3-methylhistidine and creati-
nine limit the use ofboth ofthese methods.
At present the most widely accepted methods of evaluating
skeletal muscle mass involve computerized axial tomography,
magnetic-resonance imaging, and ultrasonography performed
in multiple sections ofthe body. Although these methods repre-
sent a technological advance, expense, radiation exposure, urn-
ited instrument access, and concerns for accuracy are often
cited as limitations ofone or the other techniques (4).
The recent development of dual-photon absorptiometry
(DPA) presents a new opportunity to quantify skeletal muscle
mass in vivo. Long recognized for its effectiveness at measuring
bone density, the newly appreciated ability ofDPA to measure
fat and lean components presents an equally significant prom-
ise to the field of body composition research. Because of the
growing number of available whole-body instruments, ex-
tremely low radiation exposure, and ability to define total ap-
pendicular skeletal muscle and bone mass with high precision
(5, 6), these measurements will be widely applicable. Accord-
ingly, in this report we describe the theory behind the use of
DPA in estimating appendicular skeletal muscle mass, the cali-
bration data, and the results ofinitial patient studies.
Methods
Model
Whole-body DPA partitions body weight into two fractions,
bone ash (calcium hydroxyapatite) and soft tissue, by measur-
ing differential attenuation of photons at two energy levels.
These photons may be produced by ‘53Gd or by an x-ray
source, and the underlying principle is identical in both cases.
The DPA algorithm includes a measure of soft-tissue attenua-
tion at the two energy levels referred to as the R. The R.,.
correlates linearly with the proportion of soft tissue as fat (or
lean). The fat content ofscanned soft tissue in vivo can be esti-
mated by means of a calibration equation (6). This is accom-
plished by first scanning phantoms of known fat content and
establishing the prediction equation for percent fat based on
R5T .Chemically analyzed beefphantoms are used for this pur-
pose. Typical regression lines during calibration are r=0.96-
0.98 (6, 7). In an earlier study we described this calibration pro-
cedure and demonstrated excellent agreement between fat esti-
mated by DPA in healthy nonobese subjects and fat deter-
mined by such conventional methods as hydrodensitometry
CFrom the Department of Medicine, Obesity Research Center, St
Luke’s-Roosevelt Hospital Center, Columbia University College of
Physicians and Surgeons, New York.
2Address reprint requests to SB Heymsfield, Weight Control Unit,
4 1 1 West 1 14th Street, New York, NY 10025.
ReceivedJune 16, 1989.
Accepted for publication October 1 1, 1989.
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SKELETAL MUSCLE MEASUREMENT 215
(r =0.94, SEE =1 .82 kg) and neutron-activation analysis
(r =0.95, SEE =1 .68 kg) (6). Hence a given DPA scan, or any
subregion ofa scan, can be analyzed for bone ash, nonosseous
lean tissue, and fat.
The extremities consist primarily of three components-
skeleton, skeletal muscle, and fat. The skeleton or bone shaft
contains a small amount of marrow, which for simplicity may
be ignored in the evaluation of limb composition. We also as-
sume that skin and associated subcutaneous tissue are negligi-
ble in mass relative to the skeletal-muscle component. The de-
fatted and marrow-free bone is a mixture ofwater, protein, and
minerals (ash). Under usual circumstances ash represents 55%
ofwet skeletal weight, with mineral content decreasing slightly
(50-52%) in osteoporotic patients (8, 9). A reasonable assump-
tion, therefore, is that wet bone weight equals bone ash divided
by 0.55 or multiplied by 1.82.
Limb fat is estimated through use ofthe R5T and beef phan-
toms as described above. The actual fat mass in the limb is
determined as percent fat multiplied by soft-tissue mass. Skele-
tal muscle mass is then equal to total limb mass minus the sum
oflimb fat and bone mass.
Upon completion ofthe scan, the DPA software generates an
image ofthe subject’s skeleton (Fig I). Using specific anatomic
landmarks and a cursor, the DPA operator isolates the legs and
arms as shown in Figure 1.Once isolated, the system software
provides the total mass, RST, and bone ash for the identified
region. Wet bone mass (bone ash X 1.82) is next subtracted
from total limb mass, followed by subtraction offat mass calcu-
lated from the RST calibration line. This result then represents
skeletal muscle mass either separately for each limb or for the
summed upper- and lower-limb muscle masses.
Protocol
The DPA muscle-mass method was evaluated in 34 healthy
subjects who underwent DPA, anthropometry, whole-body
counting for total body potassium (TBK), and prompt ‘y-neu-
tron-activation analysis for total body nitrogen (TBN). Four
of the subjects underwent the DPA study on 4 (n =2) or 5
(n =2) consecutive days to establish the between-day coeffi-
cient of variation (CV) for estimates of limb composition. A
single trained observer (RS) read all 34 initial DPA scans and
the serial CV studies were interpreted by investigator MA. A
portion of this database is presented in two earlier unrelated
protocols (6, 10). Each volunteer signed an informed consent
before the study, which was approved by the institutional re-
view boards at St Luke’s-Roosevelt Hospital and at Brookha-
yen National Laboratory.
Dual-photon absorptiometrv. Awhole-body DPA scanner
(DP4, Lunar Radiation, Madison, WI) was used to evaluate
each patient’s total and regional bone ash, RST, and soft-tissue
mass. Each head-to-toe scan required -‘-55 mm. For calibra-
tion, seven frozen beef phantoms of known fat content were
scanned and the results were used to relate R5T to percent fat
(6). The aforementioned procedures were then used to derive
whole-body and appendicular bone ash, fat, and skeletal mus-
cle. The between-day CV for bone ash and percent fat are 1.0%
and 1 .7%, respectively (6). The radiation exposure is 0.02 mGy
per scan.
Total bodi’ potassium. Whole-body #{176}Kcounting was used
to derive TBK ( 1 1). The Brookhaven system consists of 54 so-
dium iodide detectors placed above and below the patient. The
FIG I.Reconstruction of DPA scan demonstrating landmarks that
subdivide body into six regions. The neck cut is made just below the
chin. The rib cuts are made as close to, but not touching, the spine. The
arms are isolated by running a line through the humeral head. The
pelvis cut is placedjust above the pelvic brim and the system computer
automatically draws the lower pelvic lines. The spine cut is placed just
below the last pair ofribs coming out ofT 12.
CV for TBK whole-body counting is 2.4%. The system opera-
tion was described in detail previously (1 1).
Total body nitrogen. Prompt -y-neutron-activation analysis
was used to estimate TBN. This system, described by Vartsky
et al (I 2), uses a plutonium-beryllium source of neutrons that
activates ‘4N nuclei. The 1 735 0prompt y decay of activated
nitrogen is then detected by two sodium iodide crystals that are
mounted above the patient. The present system has a CV of
2.4% in nitrogen-containing phantoms. Additional details of
prompt gamma TBN analysis are reviewed in references 12
and 13.
The absolute TBK and TBN were correlated directly against
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216 HEYMSFIELD ET AL
5i±SD.
TABLE I
Subject characteristics and baseline body composition results*
Age Weight Body mass
index Fat
3. kg kg/rn? %
Females(n =16) 56.3 ±22.6 59.7 ± 10.2 22.4 ± 2.9 31.7 ±6.8
Males(n= 18) 48.7± 15.9 72.7± 10.3 23.7±2.9 21.4±6.5
Total(n =34) 52.3 ±19.7 66.6 ±12.1 23.1 ±2.8 26.3 ±8.4
S  SD.
muscle mass estimated by DPA. In addition, total-body skele-
tal muscle mass was calculated by two equations proposed by
Burkinshaw (14, 15). In the first model, skeletal muscle mass
(5MM, kg) is determined by simultaneous measurement of
TBK (mmol) and TBN (g) by use ofthe following relation:
5MM =(TBK -1.33 TBN)/51.2
This model assumes that K/N in skeletal muscle and nonskele-
tal muscle lean tissue is 9 1 and 47 mmol/kg, respectively. The
second model replaces TBN with fat-free body mass(FFM, kg).
Our approach in this study was to use the equation
SMM - (TBK - 48 FFM)/43
in which FFM was calculated as body weight minus DPA total
body fat.
Anthropometric measurements. The anthropometnc mus-
cle-plus-bone area was calculated for upper midarm and mid-
thigh from respective circumference and skinfold measure-
ments. A single trained observer made all ofthe measurements
on the right side of standing patients (14). Midarm and mid-
thigh were identified as being halfway between the acromial
and olecranon processes ofthe scapula and the inferior margin
ofthe ulna and the inguinal crease and proximal border of the
patella, respectively. A calibrated tape measure was used to es-
tablish limb circumferences at each location. The triceps skin-
fold was then measured at the posterior aspect of the upper
midarm, and the results ofthree trials were averaged. The thigh
skinfold was measured at the circumference site in the midsag-
gital plane on the anterior aspect of the thigh. Limb muscle-
plus-bone area was then calculated as
[(circumference) - (ir X skinfoid)]2/4ir
where all units are in centimeters (16).
(2)
Statistical methods
Correlations between muscle mass and other body composi-
tion estimates were examined by using simple linear-regression
analysis (Statst, Statsoft, Tulsa, OK). All group results are ex-
pressed as mean ± SD.
Results
Subjects
There were 18 male and 16 female subjects (Table I) with
average age for the pooled group of52.3 ±19.7 (i± SD). Over-
all the group was relatively lean, with a body mass index of 23
±2 kg/rn2 and a percentage fat by DPA for men and women
of2l.4 ± 6.5% and 31.7 ± 6.8%, respectively.
DPA limb composition
(1) The repeated studies on four ofthe subjects resulted in CVs
of7.0 ± 2.4%, 2.4 ± 0.5%, and 3.0 ± 1.5% (1± SD) for upper-
extremity, lower-extremity, and combined-limb appendicular
skeletal muscle masses, respectively.
The bone, skeletal muscle, and fat content of the limbs as
estimated by DPA is presented in Table 2. Males had more
bone and skeletal muscle and less fat than did females for both
lower and upper extremities. Males also had more upper-ex-
tremity than lower-extremity skeletal muscle (upper/lower
=0.57) than did females (0.44). The ratio of bone to skeletal
muscle tended to be higher for both upper and lower extremi-
ties in males (0.089 and 0.142) than in females (0.085 and
0. 1 18) and more bone was present relative to skeletal muscle
in lower(pooled value =0. 135) than upper(0.094) extremities.
Skeletal muscle mass
No definitive methods are available for quantifying whole-
body skeletal muscle mass in vivo. DPA was therefore evalu-
ated in relation to available markers of skeletal muscle by
simple linear-regression analysis. The results ofTBK and TBN
estimates are presented in Table 3 along with calculated total-
body skeletal muscle mass (TBK, TBN, and FFM), anthropo-
metric limb muscle areas, and combined (upper and lower)
DPA limb muscle mass.
TBK was highly correlated with DPA extremity muscle mass
(3) fl pooled data for the 34 subjects (r =0.94, p<0.001 ; Table 4
and Fig 2). The correlation between DPA muscle and TBN
was also significant (r =0.78, p<0.001) and ofsimilar magni-
TABLE 2
Limb composition analyses from dual-photon absorptiometry*
Lower extremities Upper extremities
Bone Skeletal muscle Fat Total Bone Skeletal muscle Fat Total
kg kg
Females
Males
Total
1.3±0.3
2.0±0.4
1.7±0.5
11.0±2.2
14.1±1.7
12.6±2.5
5.9±2.0
4.1±1.6
5.0±2.0
18.2±3.5
20.2±2.9
19.3±3.4
0.4±0.1
0.7±0.1
0.6±0.2
4.7±0.9
7.9±1.6
6.4±2.1
3.7± 1.6
3.1±1.5
3.4±1.6
8.8±2.2
11.7±2.7
10.4±2.9
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0Males
.Females
0
2000 3000 4000 5000
TBK
(mmol)
SKELETAL MUSCLE MEASUREMENT 217
*Calculated from TBK and TBN.
tCalculated from TBK and FFM.
TABLE 3
Results ofbody composition studies*
TBK TBN 5MM 1SMM2 AMA TMA AMA +TMA SMM3
mmo/ kg kg kg cm cm2 cm2 kg
Females 2152±394 1.35±0.27 7.0± 5.9 5.0±4.6 31.9± 5.8 136.6±28.6 168.3±31.4 15.7±2.9
Males 3459±578 1.78±0.25 21.4± 8.0 17.1 ±7.1 57.6± 9.5 169.5±28.0 227.1 ±36.3 22.0±3.2
Total 2844±823 1.58±0.34 14.6± 10.1 11.4±8.6 45.5± 15.1 154.0±32.7 199.3±44.9 19.0±4.3
*SMM I, SMM2, and SMM3 are skeletal muscle mass calculated from, respectively, TBK and TBN (Eq 1), TBK and FFM (Eq 2), and the sum
ofupper and lower extremity 5MM estimated by DPA (Eq 3). AMA is arm muscle-plus-bone area; TMA is thigh muscle-plus-bone area.
tude to the correlation between DPA muscle and body weight
(r =0.80, p<0.001).
Total body estimates of skeletal muscle mass (Eqs 1 and 2)
were on average smaller than limb muscle mass estimated by
DPA (I 4.6 and 1 1 .4 kg, respectively, vs 19.0 kg). Although sev-
eral negative values were observed in the former, both calcu-
lated muscle estimates were significantly correlated with DPA
skeletal muscle (both r=0.82, p<0.00 1; Table 4).
Anthropometric muscle-plus-bone areas were significantly
(p <0.001) correlated with DPA extremity muscle mass, with
r=0.82 for upper limb r=0.88 for lower limb, and r=0.92
for the sum of upper- plus lower-limb muscle-plus-bone areas.
Thus two ofthe indirect markers ofskeletal muscle mass, TBK
and anthropometric muscle-plus-bone areas, were highly cor-
related with DPA muscle mass. Significant but weaker associa-
tions were observed between DPA muscle and whole-body
skeletal muscle derived by the TBK-TBN and TBK-FFM
models.
Discussion
Despite the obvious physiological relevance of quantifying
skeletal muscle mass, no definitive whole-body in vivo method
is yet available. The DPA technique described herein advances
our measurement capability by providing a practical approach
to estimating appendicular skeletal muscle mass. The extrem-
ity muscle per se is of intense interest and, moreover, the ap-
pendages account for a large portion (73-75%) oftotal skeletal
TABLE 4
Correlations between DPA appendicular skeletal muscle and other
body composition estimates
Equation rSEE p
Body weight 0.29x +0.0 0.80 2.7 <0.001
TBK 0.005x +5.01 0.94 1.6 <0.001
TBN l0.06x+ 3.14 0.78 2.8 <0.001
Skeletal muscle I  0.35x +1 3.83 0.82 2.6 <0.001
Skeletal muscle 2t 0.41x +14.33 0.82 2.6 <0.00 1
Arm muscle-plus-bone
area 0.24x +8.25 0.82 2.5 <0.001
Thigh muscle-plus-bone
area 0.l2x+ 1.04 0.88 2.1 <0.001
Arm +thigh muscle-
plus-bonearea 0.09x+ 1.33 0.92 1.8 <0.001
muscle mass (17). The between-day CV for the method (3%
for total extremity muscle) is within the range of other body
composition techniques, such as whole-body counting for po-
tassium. Hence the DPA approach brings within range the ca-
pability of reproducibly estimating all but one-fourth of skele-
tal muscle mass.
Skeletal muscle mass derived by DPA was highly correlated
with other regional (anthropometry) and total-body (whole-
body counting, neutron activation) estimates of muscle mass.
These associations demonstrate the potential of using DPA to
explore other methods ofquantifying muscle. For example, the
sum ofanthropometric limb muscle-plus-bone areas showed a
strong correlation (r =0.92) with DPA total-extremity muscle
mass, suggesting the potential for developing anthropometric
limb-muscle-mass prediction equations. Another example is
the demonstration that the neutron-activation and the whole-
body counting models (Eqs 1 and 2) for partitioning FFM into
muscle and nonmuscle components provides muscle estimates
that on average are too low (1 1-15 kg vs 19 kg for DPA limb
muscle), with negative values observed in some cases. These
models therefore need to be reconsidered and perhaps revised
in light ofthe present findings.
The DPA skeletal-muscle-mass method has several possible
limitations worthy of discussion. At present our gadolinium
system has a long scan time (55 mm), restricting the study to
patients with sufficient endurance. New x-ray-based dual-pho-
ton systems (DEXA) reduce scan time to 15 mm, thus par-
tially alleviating this problem. The minimal radiation doses are
<0.01 of 1% ofannual background, or ‘--2 h background mdi-
30
25
DPA
Skeletal Muscle
(kg) 20
15
10 -
1000
FIG 2. DPA appendicular skeletal muscle mass vs total body potas-
sium (TBK). Thirty-four observations pooled for males (n =18) and
females (n =I6) (DPA skeletal muscle =0.005 TBK +5.0, SEE =I .6
kg. r=0.94, p <0.001).
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218 HEYMSFIELD ET AL
ation. An additional problem is that the method developed in
the present study includes bone marrow and skin in the esti-
mate of limb muscle mass. However, these contributions are
relatively minor because the amount of nonfat extremity mar-
row and skin found in healthy young adults averages 1.2 kg, or
<5% of skeletal muscle as reported in this study (17). Finally,
changes in muscle hydration would alter the ratio between
muscle cell mass and total muscle weight, and this confounding
factor should be considered in the interpretation of results in
patients with edema.
In summary, we describe a promising new approach for esti-
mating the amount of skeletal muscle in the appendages. The
initial results in healthy adults indicate that limb muscle mass
determined by the DPA approach is highly correlated with
muscle estimates established by other available techniques. Fu-
ture studies are needed to evaluate the method’s applicability
in patients with altered body habitus and disease states. The
relative safety and low radiation exposure of the method and
the growing number and accessibility of available instruments
suggest that DPA may be a practical and widely applica-
ble technique of evaluating extremity skeletal muscle mass
invivo. II
References
1. Heymsfield SB, McManus C, Stevens V, Smith J. Muscle mass:
reliable indicator of protein-energy malnutrition severity and out-
come. Am J Clin Nutr l982;35: 1 192-9.
2. Heymsfield SB, Arteaga C, McManus C, et al. Measurement of
muscle mass in humans: validity ofthe 24-hour urinary creatinine
method. Am J Clin Nutr 1983; 37:478-93.
3. Buskirk ER, Mendez J. Lean body tissue assessment, with empha-
sis on skeletal muscle mass. In: Roche AF, ed. Body composition
assessments in youth and adults. Report ofthe Sixth Ross Confer-
ence on Medical Research. Columbus, OH: Ross Laboratories,
1985:59-65.
4. Heymsfield SB. Human body composition: analysis by computer-
ized axial tomography and nuclear magnetic resonance. In: Nor-
gan GN, ed. Proceedings of Euro-Nut Conference. The Hague:
CIP-Gegevens Konenkeijke Bibliotheek, 198:105-12.
S. Mazess RN, Peppler WW, Gibbons M. Total body composition
by dual-photon (‘53Gd) absorptiometry. Am J Clin Nutr l984;40:
834-9.
6. Heymsfield SB, Wang J, Funfar J, Kehayias JJ, Pierson RN. Dual
photon absorptiometry: accuracy of bone mineral and soft tissue
mass measurement in vivo. Am J Clin Nutr 1989;49:1283-9.
7. WangJ, Heymsfield SB, Aulet M, Thornton JC, Pierson RN. Body
fat from body density: underwaterweighing vs dual photon absorp-
tiometry. Am J Physiol 1989;256:E829-34.
8. Woodard HQ. The composition ofhuman corticol bone. Clin Or-
thop 1964;37: 187-93.
9. Burnell JM, Baylink DJ, Chestnut CH III, Mathews MW, Teubner
El. Bone mineral and matrix abnormalities in post menopausal
osteoporosis. Metabolism 1982; 31:1 1 13-20.
10. Heymsfield SB, Wang J, Kehayias JJ, Heshka S, Lichtman S, Pier-
son RN Jr. Chemical determination of human body density in
vivo: relevance to hydrodensitometry. Am J Clin Nutr 1989; 50:
1282-89.
1 1. Cohn SH, Shukle KK, Dombrowski CS, Fairchild RG. Design and
calibration of “broad-beam” 238Pu, Be neutron source for total-
body neutron activation analysis. J Nucl Med 1972; 13:487-92.
12. Vartsky D, Ellis KJ, Cohn SH. In vivo quantification ofbody nitro-
gen by neutron capture prompt gamma-ray analysis. JNucl Med
l979;20:l 158-65.
13. Cohn SH, Vartsky D, Yasumura S, et al. Compartmental body
composition based on total body nitrogen potassium, and calcium.
Am J Physiol 1980;239:E524-30.
14. Burkinshaw L. Measurement ofhuman body composition in vivo.
In: Orton CG, ed. Progress in medical radiation physics. New
York: Plenum, 1985;2:l 13-37.
15. Burkinshaw L. Models ofthe distribution ofprotein in the human
body. In: Ellis KJ, Yasumura S, Morgan WD, eds. In vivo body
composition studies. London: Institute of Physical Sciences in
Medicine, 1987:15-24.
16. Wright RA, Heymsfield SB. Nutritional assessment. Boston:
Blackwell Scientific Publications, 1984.
17. Snyder WS, Cook Mi, Nasset ES, Karhausen LR, Howells GP,
Tipton IH, eds. Report ofthe task group on reference man. Oxford,
England: International Commission on Radiological Protection,
1984. (ICRP report 23.)
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Sarcopenia, characterized by an age‐related progressive loss of muscle mass and strength, presents significant health concerns. Recommending dietary nutrition emerges as a viable strategy to counteract muscle deterioration. Vitamin A, indispensable throughout the human life cycle and unattainable through endogenous synthesis, necessitates intake via diet. However, the direct correlation between sarcopenia prevalence and vitamin A intake remains unclear. This study systematically investigated the relationship between sarcopenia prevalence and vitamin A intake, including retinol and some carotenoids, across diverse races and genders utilizing multiple statistical analyses. Mixture analysis revealed significant positive correlations between total vitamin A intake and muscle mass among American adult males (Male: OR: 1.019, 95% CI: 1.010–1.027, p < 0.001). We also observed the gender‐specific results, with retinol playing a more significant role in enhancing muscle mass for males, while certain carotenoids were found to be more influential in females. Moreover, inflammation and oxidative stress mediated the relationship between vitamin A intake and sarcopenia prevalence in both genders. There may be a gender‐ and race‐specific relationship between dietary vitamin A intake and sarcopenia. Further prospective studies are imperative to elucidate the association between vitamin A intake and sarcopenia prevalence.
... After performing the scans, the system provided the bone mineral content, fat mass and the mass of lean soft tissue for both, the whole body and specific regions. The appendicular lean soft tissue (ALST) was calculated by the sum of lean soft tissue of upper and lower limbs 43 and SMM was estimated by one of the equations proposed by Kim et al. 25 We used this equation because it is the most widely used equation to estimate SMM from ALST, it was also developed in a general population with a broad age range and BMI < 35 kg/m 2 , and has recently demonstrated great accuracy estimating SMM of a group of 475 healthy Caucasian men and women 44 . ...
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Assessment of skeletal muscle mass (SMM) is essential to monitor physical performance and health status. The most widely used anthropometric equations have repeatedly demonstrated to overestimate or underestimate SMM in different populations. Herein, we developed and cross-validated a new anthropometric regression equation for estimating SMM, using dual-energy X-ray absorptiometry (DXA) as the reference method. A group of 206 healthy Caucasian participants aged 18–65 years were included in the final analysis. Participants underwent a DXA scan, and body mass, stature, four skinfolds (biceps, triceps, subscapular, and supracrestal) and four breadths (femoral, humeral, ankle, and wrist) were assessed by an accredited anthropometrist. Accuracy was assessed by mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland–Altman plots. The proposed equation explained 91.3% of the variance in the DXA-derived SMM percentage, with a low random error (SEE = 1.95%), and a very strong agreement (CCC = 0.94). In addition, it demonstrated no fixed or proportional bias and a relatively low individual variability (3.84%). The new anthropometric equation can accurately predict SMM percentage in a Caucasian population with a wide age range (18–65 years).
... Tissue quantity was reported as cross-sectional area (CSA) and density as mean attenuation, measured in Houns eld Units (HU). Body compartments were identi ed using prede ned HU thresholds of -29 to + 150 HU skeletal muscle (SM), -190 to -30 HU subcutaneous adipose tissue (SAT), -50 to -150 HU visceral adipose tissue (VAT), and − 190 to -30 HU intramuscular adipose tissue (IMAT) (11,19). Two trained assessors (LM and KL) blinded to patient outcomes analysed images; the mean inter-rater coe cient was 0.73%. ...
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Background Low muscle mass, myosteatosis, and excess adiposity are associated with adverse outcomes after oesophagogastric (OG) cancer surgery. There is limited prospective data to evaluate body composition throughout treatment. We aimed to measure longitudinal changes in skeletal muscle and adipose tissue and describe variations according to baseline BMI. Methods This prospective longitudinal study included patients having OG cancer surgery at Alfred Health, Melbourne, Australia. CT images and bioimpedance spectroscopy (BIS) were used to assess body composition at multiple time points up to 12 months postoperatively. Low skeletal muscle, myosteatosis and visceral obesity were defined using published thresholds. BMI groups were defined as ≥ 30kg/m² (obese) and < 30kg/m² (non-obese). Results There were 50 patients. During neoadjuvant treatment, CT-muscle declined (152.7 vs 142.4cm², p<0.001) and adipose tissue was stable. Postoperatively, total adipose tissue reduced (357.7 vs 224.4cm², p<0.001), but muscle did not (142.4 vs 133.6cm², p=0.064). Low CT-muscle prevalence increased during neoadjuvant treatment (diagnosis 33%, restaging 49%, p=0.02) but not at 12 months (54%, p=0.21). Visceral obesity was common and stable between diagnosis and restaging (58% vs 54%, p=1.00) with a marked reduction at 12 months (19%, p<0.001). BIS-muscle declined rapidly early after surgery and did not recover. The proportion of muscle and adipose tissue loss between BMI groups was comparable. Conclusion Weight loss during OG cancer treatment is significant. Skeletal muscle loss occurs during neoadjuvant treatment, while adipose tissue loss is predominant postoperatively. Anticipated changes in body composition should be considered throughout treatment, focusing on early muscle loss.
... The scientific variables most related to the increase in the volume of muscle (kg) tissue are [2] as follows: Fat-free mass (FFM) was calculated as "all that is not fat", subtracting fat weight from body weight, or when the measurements were obtained by dual X-ray absorptiometry was calculated as lean tissue plus bone mineral content [3]. Lean muscle mass (LMM), lean mass, lean body mass, bone-free lean body mass or mineral-free lean mass was calculated as the fat-free mass minus the bone mineral content (DXA) or as fat-free mass minus the estimated weight [4] of the live bone by the equation of Heymsfield, Smith [5]. Skeletal muscle mass (SMM) or skeletal muscle was defined as lean muscle and was calculated by anthropometric equations, by proprietary algorithms when using bioimpedance or by estimates based on dual X-ray absorptiometry data [6]. ...
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The present chapter delves into the topic of muscle hypertrophy in detail, focusing on defining what muscle hypertrophy is, the types of hypertrophy, the mechanisms, and the relationship with resistance training, as well as the variables affecting hypertrophy such as nutrition, rest, exercise selection, training volume, and training frequency, among others. The importance of mechanical tension, metabolic stress, and muscle damage as triggers for muscle hypertrophy is emphasized. Various types of muscle hypertrophy are explored, including connective tissue hypertrophy and sarcoplasmic and myofibrillar hypertrophy. The text also delves into how hypertrophy mechanisms relate to resistance training, highlighting the significance of mechanical tension and metabolic stress as stimuli for muscle hypertrophy. In a practical point of view, the text also discusses factors like nutrition and recovery, highlighting the importance of maintaining a positive energy balance and adequate protein intake to promote muscle growth optimally. Training variables such as exercise selection, exercise order, intensity, volume, frequency, and tempo of execution are discussed in detail, outlining their impact on muscle hypertrophy. The text provides a comprehensive overview of muscle hypertrophy, analyzing various factors that influence the ability to increase muscle mass. It offers detailed information on the biological mechanisms, types of hypertrophy, training strategies, and nutritional and recovery considerations necessary to achieve optimal results in terms of muscle hypertrophy.
... lipids in cellular membranes are included in LBM [24]. According to Heymsfield et al, LBM excludes bone mass, focusing instead on muscles and organ tissues [25]. However in published literature, LBM may include Bone Mineral Content (BMC) or exclude it [26,27]. ...
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This study addresses the pressing need for improved methods to predict lean mass in adults, and in particular lean body mass (LBM), appendicular lean mass (ALM), and appendicular skeletal muscle mass (ASMM) for the early detection and management of sarcopenia, a condition characterized by muscle loss and dysfunction. Sarcopenia presents significant health risks, especially in populations with chronic diseases like cancer and the elderly. Current assessment methods, primarily relying on Dual-energy X-ray absorptiometry (DXA) scans, lack widespread applicability, hindering timely intervention. Leveraging machine learning techniques, this research aimed to develop and validate predictive models using data from the National Health and Nutrition Examination Survey (NHANES) and the Action for Health in Diabetes (Look AHEAD) study. The models were trained on anthropometric data, demographic factors, and DXA-derived metrics to accurately estimate LBM, ALM, and ASMM normalized to weight. Results demonstrated consistent performance across various machine learning algorithms, with LassoNet, a non-linear extension of the popular LASSO method, exhibiting superior predictive accuracy. Notably, the integration of bone mineral density measurements into the models had minimal impact on predictive accuracy, suggesting potential alternatives to DXA scans for lean mass assessment in the general population. Despite the robustness of the models, limitations include the absence of outcome measures and cohorts highly vulnerable to muscle mass loss. Nonetheless, these findings hold promise for revolutionizing lean mass assessment paradigms, offering implications for chronic disease management and personalized health interventions. Future research endeavors should focus on validating these models in diverse populations and addressing clinical complexities to enhance prediction accuracy and clinical utility in managing sarcopenia.
... A multi-frequency electrical impedance body composition analysers, the BCA-2A uses a quadrupole 8-point haptic electrode system with ve different measurement frequencies (5, 50, 100, 250 and 500 kHz). Skeletal muscle content (ASM) of extremities is the sum of skeletal muscle content of both upper and lower limbs [7] . Skeletal muscle mass normalized for height (RASM, ASM/Ht 2 ) is de ned as the ratio of ASM (kg) to height square (m 2 ) [8] . ...
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Background: Sarcopenia is a syndrome of loss of muscle mass and decreased skeletal muscle function with impaired ability in the activities of daily life and cause some adverse consequences in the elderly. In China, where the aging trend is obvious, the incidence of sarcopenia is increasing. Exploring potential biomarkers for sarcopenia may lead to early screening and intervention for sarcopenia.This study investigated the prevalence and potential biomarkers of sarcopenia in older adult living in rural community in Wuhan,China. Methods: This cross-sectional study involved 236 older participants (age ≥65 years) who received a health examination that included body composition and 23 circulating biomarkers.Sarcopenia was defined by the Asian Working Group for Sarcopenia revised in 2019 (AWGS2019). We divided the participants into a non-sarcopeniagroup and a sarcopenia group. The correlation between biomarkers and sarcopenia was analyzed by independent sample t-test, and then the significant variables of the t-test (p < 0.05) were included in the multivariate logistic regression model to determine the independent factors associated with sarcopenia. Results: Among the 236 participants, 92 were men and 144 were females, with a mean age of 70.6 ± 4.4years. The prevalence of sarcopenia in rural community was 25.4%(men 20.7%, women 28.5%). Analyses were conducted using multivariate logistic regression,growth differentiation factor 11(GDF11), was an independent risk factor for sarcopenia [Exp (B) 1.031, 95% CI: 1.010-1.052, p=0.003]. However, body mass index, albumin(ALB), fibroblast growth factor 19(FGF19), and tumour necrosis factor alpha(TNF-α ) were independent protective factors for sarcopenia [BMI: Exp (B) 0.007, 95% CI: 0.000-0.244, p=0.006;ALB: Exp (B) 0.490, 95% CI: 0.281-0.853,p=0.012; FGF19: Exp(B) 0.804, 95% CI: 0.683-0.946, p=0.009; TNF-α: Exp (B) 0.379, 95% CI: 0.194-0.742, p=0.005]. Conclusions: About a quarter of elderly people in rural Chinese communities are at risk of sarcopenia. Lower BMI, lower serum ALB, FGF19, TNF-α, and higher circulating GDF11 are associated with sarcopenia.
... According to EWGSOP2 recommendations (7), key assessments for identifying sarcopenia in clinical practice and research encompass: (1) Questionnaire screening (21). (2) Muscle strength evaluation, including grip strength measurement (22), chair stand test, etc. (3) Quantitative assessment of muscle mass, utilizing techniques like DXA for ASM measurement (23)(24)(25) and bioelectrical impedance analysis (BIA) for predicting total body skeletal muscle mass (SMM) or ASM (26)(27)(28)(29). (4) Physical performance assessment, evaluating gait speed and conducting a 400-meter walk test to gauge walking ability and endurance (22), among others. ...
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Background Sarcopenia frequently occurs as a comorbidity in individuals with COPD. However, research on the impact of Appendicular Skeletal Muscle Mass (ASM) on survival in COPD patients is scarce. Moreover, there is a lack of research on the association between dietary pro-inflammatory capacity and sarcopenia in COPD. Methods We analyzed data from the National Health and Nutrition Examination Survey (NHANES) covering the years 1999 to 2006 and 2011 to 2018. We aimed to investigate the relationship between the Dietary Inflammatory Index (DII) and sarcopenia prevalence among adults diagnosed with COPD in the United States. Furthermore, we sought to explore the relationship between sarcopenia, ASMI, and all-cause mortality. The study included a total of 1,429 eligible adult participants, divided into four groups based on quartiles of DII, with adjustments for sample weights. Methodologically, we used multivariable logistic regression analyses and to examine the association between DII and sarcopenia. Additionally, we used restricted cubic spline (RCS) tests to evaluate potential non-linear relationships. To assess the effect of sarcopenia on overall all-cause mortality, we used Kaplan–Meier models and Cox proportional hazards models. Moreover, we used RCS analyses to investigate potential non-linear relationships between ASMI and all-cause mortality. Subgroup analyses were conducted to confirm the reliability of our study findings. Results In our COPD participant cohort, individuals with higher DII scores were more likely to be female, unmarried, have lower educational attainment, and show lower ASMI. Using multivariable logistic regression models, we found a positive association between the highest quartile of DII levels and sarcopenia incidence [Odds Ratio (OR) 2.37; 95% Confidence Interval (CI) 1.26–4.48; p = 0.01]. However, analysis of RCS curves did not show a non-linear relationship between DII and sarcopenia. Throughout the entire follow-up period, a total of 367 deaths occurred among all COPD patients. Kaplan–Meier survival curves showed a significantly higher all-cause mortality rate among individuals with concurrent sarcopenia (p < 0.0001). Cox proportional hazards model analysis showed a 44% higher risk of all-cause mortality among COPD patients with sarcopenia compared to those without sarcopenia [Hazard Ratio (HR): 1.44; 95% CI 1.05–1.99; p < 0.05]. Additionally, our final RCS analyses revealed a significant non-linear association between ASMI levels and all-cause mortality among COPD patients, with a turning point identified at 8.32 kg/m². Participants with ASMI levels above this inflection point had a 42% lower risk of all-cause mortality compared to those with ASMI levels below it (HR 0.58; 95% CI 0.48–0.7). Conclusion We observed a significant association between concurrent sarcopenia and an increased risk of all-cause mortality in COPD patients within the United States. Moreover, ASMI demonstrated a non-linear association with all-cause mortality, with a critical threshold identified at 8.32 kg/m². Our findings also revealed an association between DII and the presence of sarcopenia. Consequently, further investigations are warranted to explore the feasibility of dietary DII adjustments as a means to mitigate muscle wasting and enhance the prognosis of COPD.
Article
Background Low muscle mass (MM) predicts unfavorable outcomes in cancer. Protein intake supports muscle health, but oncologic recommendations are not well characterized. The objectives of this study were to evaluate the feasibility of dietary change to attain 1.0 or 2.0 g/kg/day protein diets, and the preliminary potential to halt MM loss and functional decline in patients starting chemotherapy for stage II-IV colorectal cancer. Patients and methods Patients were randomized to the diets and provided individualized counseling. Assessments at baseline, 6 weeks, and 12 weeks included weighed 3-day food records, appendicular lean soft tissue index (ALSTI) by dual-energy X-ray absorptiometry to estimate MM, and physical function by the Short Physical Performance Battery (SPPB) test. Results Fifty patients (mean ± standard deviation: age, 57 ± 11 years; body mass index, 27.3 ± 5.6 kg/m²; and protein intake, 1.1 ± 0.4 g/kg/day) were included at baseline. At week 12, protein intake reached 1.6 g/kg/day in the 2.0 g/kg/day group and 1.2 g/kg/day in the 1.0 g/kg/day group (P = 0.012), resulting in a group difference of 0.4 g/kg/day rather than 1.0 g/kg/day. Over one-half (59%) of patients in the 2.0 g/kg/day group maintained or gained MM compared with 44% of patients in the 1.0 g/kg/day group (P = 0.523). Percent change in ALSTI did not differ between groups [2.0 g/kg/day group (mean ± standard deviation): 0.5% ± 4.6%; 1.0 g/kg/day group: −0.4% ± 6.1%; P = 0.619]. No differences in physical function were observed between groups. However, actual protein intake and SPPB were positively associated (β = 0.37; 95% confidence interval 0.08-0.67; P = 0.014). Conclusion Individualized nutrition counselling positively impacted protein intake. However, 2.0 g/kg/day was not attainable using our approach in this population, and group contamination occurred. Increased protein intake suggested positive effects on MM and physical function, highlighting the potential for nutrition to attenuate MM loss in patients with cancer. Nonetheless, muscle anabolism to any degree is clinically significant and beneficial to patients. Larger trials should explore the statistical significance and clinical relevance of protein interventions.
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A chemical approach to establishing human body density in vivo was developed by combining recently developed noninvasive methods. Four compartments were measured: protein (P; prompt-gamma neutron activation), water (A; 3H2O dilution), mineral (M; dual-photon absorptiometry and delayed-gamma neutron activation), and fat (F; dual-photon absorptiometry). By this model body weight is equal to P + A + M + F. This approach was applied to 13 healthy adults (8 females and 5 males). The four compartments accounted for greater than 97% actual body weight. Calculated density based upon composition agreed within 0.6 +/- 0.4% (mean +/- SD) with density (D) measured by hydrodensitometry [calculated D (g/cc) = 0.86 measured D +0.15; r = 0.94, p less than 0.001]. The average calculated lean (P + A + M) density of 1.096 +/- 0.007 g/cc agreed closely with three classic human cadaver studies (1.100 g/cc). This multicompartment approach provides a new opportunity to estimate human body density in vivo and to refine body composition methods based upon an assumed but inadequately validated constant lean density.
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Measuring muscle mass is an important component of the nutritional assessment examination and a suggested index of this body space is the 24-h urinary excretion of creatinine. The method originated from studies in a variety of animal species in whom early workers found a parallelism between total body creatine and urinary excretion of creatinine. Assuming that nearly all creatine was within muscle tissue, that muscle creatine content remained constant and that creatinine was excreted at a uniform rate, an obvious "corollary" was that urinary creatinine was proportional to muscle mass. The so-called "creatinine equivalence" (kg muscle mass/g urinary creatinine) ranged experimentally from 17 to 22. One of the limiting factors in firmly establishing this constant and its associated variability was (and is) the lack of another totally acceptable noninvasive technique of measuring muscle mass to which the creatinine method could (or would) be compared. An improved understanding of creatine metabolism and a variety of clinical studies in recent years has tended to support the general validity of this approach. However, specific conditions have also been established in which the method becomes either inaccurate or invalid. While creatinine excretion may serve as a useful approximation of muscle mass in carefully selected subjects, there remains a need for accurate and practical indices of muscle mass for use in the individuals in whom the method cannot be reliably applied.
Chapter
Until almost the middle of this century, the study of human body composition was the province of the anatomist and pathologist, whose methods of investigation were dissection and analysis of tissues postmortem. Two developments during the 1940s first made practicable the quantitative study of human body composition in vivo. The first was the enunciation by Behnke and his colleagues of the idea that the body is made up of two components, fat and lean tissue, whose proportions in an individual can be deduced from the measured density of the body.(1) The second was the increasing availability of radioactive isotopes, which can serve as tracers to determine the masses of body compartments by dilution. Behnke’s method required the subject to be weighed under water, and it was therefore applicable only to people willing and able to be immersed; the dilution method made no such demands on the subject and opened up the study of the body’s composition in patients with a variety of diseases.
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The techniques of prompt gamma neutron-activation analysis for the measurement of total-body nitrogen and whole-body counting for the measurement of total-body potassium were used to determine the mass of muscle and nonmuscle lean tissue and their protein content in 135 normal male and female subjects, 20-80 yr of age. Age-related changes in the size of the muscle and nonmuscle compartments and their protein content provide basic data for the investigation of protein metabolism in aging subjects and in individuals with various metabolic disorders, particularly wasting diseases such as cancer. Significant age-related changes in the size of various body compartments were noted. The loss of muscle mass and its protein content contrasts with the relative constancy of the nonmuscle lean tissue and suggests that skeletal muscle is particularly vulnerable to the aging process.
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This study extended initial observations that indicated the potential of dualphoton absorptiometry (DPA) to measure total-body bone mineral (TBBM) and fat in vivo. DPA-derived TBBM and fat were compared with established methods in 13 subjects (aged 24-94 y) who underwent measurement of body density (Db), total-body water (TBW), potassium (TBK), calcium (TBCa, delayed-gamma neutron activation), and nitrogen (prompt-gamma neutron activation). TBBM was highly correlated with TBCa (r = 0.95, p less than 0.001) and the slope of TBCa vs TBBM (0.34) was similar to Ca content of ashed skeleton (0.34-0.38). DPA-measured fat (means +/- SD, 16.7 +/- 4.9 kg) correlated significantly (r = 0.79-0.94; p less than 0.01-0.001) with fat established by Db (16.3 +/- 5.4 kg), TBW (16.0 +/- 4.3 kg), TBK (17.7 +/- 4.6 kg), combined TBW-neutron activation (17.6 +/- 5.9 kg), and means of all four methods (16.9 +/- 4.8 kg). DPA thus offers a new opportunity to study human skeleton in vivo and to quantify fat by a method independent from the classical assumption that bone represents a fixed fraction of fat-free body mass.
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The lean-fat composition (%FATR) of soft tissue and the mineral mass of the skeleton were determined in vivo using dual-photon (153Gd) absorptiometry (dose under 2 mrem). A rectilinear raster scan was made over the entire body in 18 subjects (14 female, 4 male). Single-photon absorptiometry (125I) measured bone mineral content on the radius. Percentage fat (%FATD) was determined in the same subjects using body density (from underwater weighing with correction for residual lung volume). Lean body mass (LBM) was determined using both %FATR and %FATD. Percentage fat from absorptiometry and from underwater density were correlated (r = 0.87). The deviation of %FATD from %FATR was due to the amount of skeletal mineral as a percentage of the LBM (r = 0.90). Therefore, skeletal variability, even in normal subjects, where mineral ranges only from 4 to 8% of the LBM, essentially precludes use of body density as a composition indicator unless skeletal mass is measured. Anthropometry (fatfolds and weight) predicted %FATR and LBM at least as well as did underwater density. The predictive error of %FATR from fatfolds was 5% while the predictive error in predicting LBM from anthropometry was 2 to 3 kg (3%).
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Iliac crest biopsies from 56 postmenopausal osteoporotic females with spontaneous compression fractures and decreased total body Ca were compared to similar tissue from 48 normal controls. Biopsies were analyzed for bone density, Na, Ca, Mg, P, Co3, and hydroxyproline (OH-P). From the results OH-P/matrix, % mineral, and the ion content of the mineral were calculated. osteoporotic subjects showed decreased bone density, % mineral in bone, and OH-P in the bone matrix. Within the mineral, CO3 and Ca/P were decreased, while Na and Mg were increased. Statistical analysis showed that matrix OH-P and % mineral varied independently, and therefore the patients were separated into 4 subgroups: Group Ia: decreased matrix OH-P with normal % mineral (n = 9), Group Ib: decreased matrix OH-P with decreased % mineral (n = 5), Group IIa: normal matrix OH-P with normal % mineral (n = 33), Group IIb: normal matrix OH-P with decreased % mineral (n = 9). Decreased % mineral was associated with decreased bone density and an increase in Na and Mg in the mineral, which suggests skeletal Ca deficiency. Decreased matrix OH-P was associated with decreased bone density and, in the low % mineral group, with decreased mineral CO3 and Ca/P, suggesting a mineral of decreased mean crystal size. When both abnormalities coexisted (Group Ib), the greatest reduction in total body Ca was seen. Patients with normal matrix and normal % mineral (Group IIa) still had decreased bone density. The results suggest that in a large, clinically homogeneous population of postmenopausal osteoporotic women, 4 subgroups can be identified by differences in chemical composition of iliac crest biopsies.