Clinic Rev Bone Miner Metab (2016) 14:45–49 DOI 10.1007/s12018-016-9206-4
ASSESSMENT OF BONE HEALTH
Evaluation of Sarcopenia by DXA John Shepherd1
Published online: 10 February 2016 Ó Springer Science+Business Media New York 2016
Abstract There are four body composition phenotypes widely used to describe older adults: normal, sarcopenic, obese, and sarcopenic obese. In this paper, we will discuss how DXA can be used to quantify body composition and how DXA can identify patients with sarcopenia and sarcopenic obesity. Keywords Sarcopenia Dual-energy X-ray absorptiometry Body composition Sarcopenic obesity
What Is DXA? Dual-energy X-ray absorptiometry (DXA) is an X-ray imaging technique primarily used to derive the mass of one material in the presence of another through knowledge of their unique X-ray attenuation at different energies. Two images are made from the attenuation of low and high average X-ray energy. DXA is a special imaging modality that is not typically available on general use X-ray systems because of the need for special beam filtering and nearperfect spatial registration of the two attenuations. Dedicated commercial DXA systems were first available in the late 1980s [1]. DXA’s primary commercial application has been to measure body mineral density as an assessment of fracture risk and to diagnose osteoporosis, and the X-ray energies used are optimized for bone density assessment. The whole body can also be scanned to measure whole
& John Shepherd
[email protected] 1
Department of Radiology and Biomedical Imaging, University of California, San Francisco, 1 Irving Street, Suite A-C109, San Francisco, CA 94143, USA
body bone mass and soft tissue body composition [2, 3] although DXA can only solve for two body composition compartments. However, in image areas that contain only soft tissue, the ratio of the attenuation values provides an estimate of lipid and lean tissue [4], from which percent lipid mass can be calculated, while areas that contain bone use an estimated percent lipid from the surrounding tissue [5]. Reference populations have been scanned and defined by sex, ethnicity, and age. Diagnosis of disease is typically made by comparing individuals to their peer group or to a young healthy population. Currently, there are estimated to be over 50,000 whole body DXA systems in use worldwide. DXA defines the composition of the body as three materials having specific X-ray attenuation properties: bone mineral, lipid (essential and non-essential), and lipidfree soft tissue. Fat is essentially the non-essential lipid or triglyceride component of adipocytes. The non-lipid soft tissue mass is the sum of body water, protein, glycerol, and soft tissue mineral masses. Note that DXA is not quantifying organ composition (skin, muscle, adipose, etc.), but reporting a composite of component masses regardless what organ it originated. For each pixel in a DXA image, these three mass components are being quantified. However, the distribution of the lipid, bone mineral, and nonlipid soft tissue within the volume projected onto the image pixel is not known. The model forces all tissue types into these three components. For example, the distinction between subcutaneous and visceral adipose tissue is lost for trunk measurements when both are projected in the same pixels. The same is true for skin, visceral non-adipose tissue, and muscle when all are projected in the same pixels. This limitation is true for most composition models that cannot represent the body as a true three-dimensional volume.
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Fig. 1 DXA body composition report for a healthy 56-year-old male prior to an exercise program. Height 67.500 , weight 223#, BMI 34.7. Report includes body composition results for individual extremities
and trunk as well as both adipose and lean indices compared to young normals (YN) and age-matched controls (AM). Image courtesy of L. Jankowski, Illinois Bone and Joint Institute, Morton Grove IL
Why Use DXA Instead of Other Body Composition Methods?
Other whole body methods include hydrodensitometry, neutron activation, and anthropometry. However, these techniques are difficult to use for regional measures. Only imaging methods such as DXA, CT, and MRI can estimate regional bone, fat, and soft tissue lean distributions. DXA is low dose in comparison with whole body CT scanning and inexpensive compared to MRI. DXA also has the advantage of subdividing the body into subregions including arms, legs, trunk, and head (Fig. 1). The trunk contains lean mass of viscera and muscle, while the arms and legs contain lean of only muscle mass. Sarcopenia is typically defined from the appendicular (arms ? legs) lean mass (ALM) only. DXA is a very precise measure of ALM.
There are many techniques to measure body composition, so why use DXA? The criterion measure of total body water (TBW) assessment uses the dilution of stable isotopes in body water to derive the total. This is a relatively inexpensive technique that quantifies total body water mass [6]. Bioimpedance analysis relates electrical resistance and reactance to intra- and extracellular water. Since most extracellular water is in lean tissue and fat is for the most part extracellular water free, the percent body fat can be modeled when the height, weight, age, and sex are known.
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Table 1 Current competing definitions of sarcopenia. From Bischoff-Ferrari [8] Definition
DXA lean indices
Cutoff value (kg/m2)
Functional indices
Cutoff values
H or GE
Baumgartner [9]
ALM/Ht2
B7.26 M
None
GE
None
H H
B 5.45 W Delmonico [10]
ALM/Ht2
B7.25 M
Delmonico 2 [11]
ALM = f (height, Fat), Z-score
B-2
None
Cruz-Jentoft [12]
ALM/Ht2
B7.26 M
\0.8 m/s and/or
B5.54 W
Gait speed and/or grip strength
B7.23 M
Gait speed
\1 m/s
H
Gait speed
\1 m/s
H
Gait speed
\0.8 m/s
GE
B5.67 W
Fielding [13]
ALM/Ht2
GE
\30 kg M \20 kg W
B5.67 W Morley [13]
ALM/Ht2
B6.81 M
Muscaritoli [14]
SMI
B37 % M
B 5.18 B28 % W Studenski 1 [15]
ALM/BMI
B0.789 M
H
B0.512 W Studenski 2 [15]
ALM/BMI
B0.789 M B0.512 W
Grip speed
\26 kg M
H
\16 kg W
ALM appendicular lean mass, GE general electric lunar systems, H Hologic, M men, SMI skeletal muscle index = (total lean soft tissue mass)/total mass 9 100, W women
Using a subset of 609 participants from the 1999–2002 NHANES study, with duplicate DXA whole body scans acquired from 3 to 51 days apart (mean, 18.7 ± 8.4 days), Powers et al. [7] found that the precision of the ALM was 391 g (RMS-SD) or 1.66 % (RMS-%CV). The NHANES data were acquired using Hologic DXA systems and analyzed using Apex 3.0 software. Lastly, DXA is easily tied to physical standards that are verifiable in the field, such as steric acid and water, such that cross-calibration and pooling of data across clinical centers are possible.
Use of DXA to Evaluate Sarcopenia Sarcopenia has many definitions that subtly vary from each other. Bischoff-Ferrari et al. [8] compared the performance of nine different definitions using the criteria of the best predictor of falling. Of the nine definitions, all contained a measure of lean body mass using DXA, and five also included a measurement of function (see Table 1). The details of each cutoff value and indices are given in each respective publication. It was found that among the 445 community-dwelling seniors, 231 participants fell over the three year study period (514 falls), and that the prospective rate of falls (sarcopenic vs. non-sarcopenic individuals) was best predicted by the Baumgartner and Cruz-Jentoft definitions. None of the other definitions showed
statistically significant differences in falls between the two groups.
Sarcopenic Obesity and DXA Sarcopenic obesity is sarcopenia in the presence of excess fat mass. Sarcopenic obesity presents a double burden to the afflicted since it combines a poor metabolic profile from obesity and frailty from sarcopenia. Prado et al. [16] suggested a working definition of sarcopenic obesity as being in the lowest 50th percentile of appendicular lean mass index (ALMI, ALM/Ht2) and the top 50th percentile of fat mass index (FMI, fat mass/Ht2) by age and sex. This definition requires age and sex reference values for ALMI and FMI from a large representative population. Prado et al. used the 1999–2004 NHANES DXA dataset made up of 16,383 men and women of mixed ethnicity. Using this definition, it was found that 10 % of the women and 15 % of the men were classified as sarcopenic obese. A further validation of this definition is yet to come. An example of sarcopenic obesity is shown in Fig. 2: 80-year-old man with a BMI in the overweight category (27 kg/m2) fulfills the definition of sarcopenia using several of the definitions in Table 1 (ALMI = RSMI = 7.06 kg/m2). His fat mass is in the 99th percentile with a body fat of 40.7 %. He may be considered to be obese using a %fat standard. Thus, it
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Fig. 2 DXA report for a sarcopenic obese man. Sarcopenic obesity is characterized by a low muscle mass and high fat mass. In this example, an 80-year-old man with a BMI in the overweight category (27 kg/m2) fulfills the definition of sarcopenia using several of the definitions in Table 1 (ALMI = RSMI = 7.06 kg/m2). Even though
the BMI is in the WHO overweight category, the individual’s fat mass is in the 99th percentile with a body fat of 40.7 %. He is obese using a %fat standard and can be classified as sarcopenic obese. Image courtesy of D. Krueger, University of Wisconsin, Madison, WI
would be appropriate to classify this individual as sarcopenic obese
whole body measures. It was found that the whole body BMC was approximately 10 % higher on GE systems compared to Hologic. However, total body lean soft tissue mass was 7.7 % lower and ALM was approximately 3 % lower. Given how sensitive the cutoff values are to defining sarcopenia, these manufacturer differences are critical to keep in mind. For example, all of the ALM cutoff values are within several percent of each other but derived in both GE and Hologic systems. The standardization equations derived for whole body systems imply that these cut points are not interchangeable.
Lack of Standardization of DXA Measures Most of the indices used for defining lean mass in sarcopenia definitions use ALM in absolute units of kg/m2. Absolute units are susceptible to calibration differences in the field as well as systemic differences between manufacturers, models, and software versions. It is well known that measures of bone and soft tissue across manufacturers cannot be directly compared and are not interchangeable for the common osteoporosis measurement sites of the hip, spine, and forearm. There have been efforts in the past to standardize the BMD units to a common calibration [17– 19]. Even though conversion equations are available, they are not widely used. Shepherd et al. [20] derived equations to cross-calibrate between Hologic and GE systems for
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Summary DXA is the preferred method for defining loss of lean mass due to aging. All current definitions of sarcopenia include a DXA measure of lean soft tissue mass with or without a functional strength index. DXA is a precise measure of lean
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mass, but further standardization is needed to ensure that cut points are used accurately on all make and model DXA systems.
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Compliance with Ethical Standards Conflicts of interest John Shepherd has received research grants from Amgen, Hologic, GE Healthcare, Merck. Animal and Human Studies This article does not include any studies with human or animal subjects performed by the author.
References 1. Kelly TL, Slovik DM, Neer RM. Calibration and standardization of bone mineral densitometers. J Bone Miner Res. 1989;4:663–9. 2. Laskey M, Phil D. Dual-energy X-ray absorptiometry and body composition. Nutrition. 1996;12:45–51. 3. Kelly TL, Berger N, Richardson TL. DXA body composition: theory and practice. Appl Radiat Isot. 1998;49:511–3. 4. Pietrobelli A, Formica C, Wang Z, Heymsfield SB. Dual-energy X-ray absorptiometry body composition model: review of physical concepts. Am J Physiol. 1996;271:E941–51. 5. Blake GM, Fogelman I. Technical principles of dual energy X-ray absorptiometry. Semin Nucl Med. 1997;27:210–28. 6. Sheng HP, Huggins RA. A review of body composition studies with emphasis on total body water and fat. Am J Clin Nutr. 1979;32:630–47. 7. Powers C, Fan B, Borrud L, Shepherd J. Evaluation of whole body and subregional DXA precision. J Clin Densitom. 2011;14:166. 8. Bischoff-Ferrari H, Orav J, Kanis J, Rizzoli R, Schlo¨gl M, Staehelin H, Willett W, Dawson-Hughes B. Comparative performance of current definitions of sarcopenia against the prospective incidence of falls among community-dwelling seniors age 65 and older. Population. 2015;9:14. 9. Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, Garry PJ, Lindeman RD. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 1998;147:755–63. 10. Delmonico MJ, Harris TB, Lee JS, Visser M, Nevitt M, Kritchevsky SB, Tylavsky FA, Newman AB. Alternative definitions of sarcopenia, lower extremity performance, and functional
12.
13.
14.
15.
16.
17.
18.
19.
20.
impairment with aging in older men and women. J Am Geriatr Soc. 2007;55:769–74. Delmonico MJ, Harris TB, Visser M, Park SW, Conroy MB, Velasquez-Mieyer P, Boudreau R, Manini TM, Nevitt M, Newman AB. Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr. 2009;90:1579–85. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European working group on sarcopenia in older people. Age Age. 2010;39:412–23. Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB, van Kan GA, Andrieu S, Bauer J, Breuille D. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc. 2011;12:249–56. Muscaritoli M, Anker S, Argiles J, Aversa Z, Bauer J, Biolo G, Boirie Y, Bosaeus I, Cederholm T, Costelli P. Consensus definition of sarcopenia, cachexia and pre-cachexia: joint document elaborated by Special Interest Groups (SIG)‘‘cachexia-anorexia in chronic wasting diseases’’ and ‘‘nutrition in geriatrics’’. Clin Nutr. 2010;29:154–9. Studenski SA, Peters KW, Alley DE, Cawthon PM, McLean RR, Harris TB, Ferrucci L, Guralnik JM, Fragala MS, Kenny AM. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol Ser A Biol Sci Med Sci. 2014;69:547–58. Prado CM, Siervo M, Mire E, Heymsfield SB, Stephan BC, Broyles S, Smith SR, Wells JC, Katzmarzyk PT. A populationbased approach to define body-composition phenotypes. Am J Clin Nutr. 2014;99:1369–77. Genant HK, Grampp S, Gluer CC, Faulkner KG, Jergas M, Engelke K, Hagiwara S, Van Kuijk C. Universal standardization for dual x-ray absorptiometry: patient and phantom cross-calibration results. J Bone Miner Res. 1994;9:1503–14. Lu Y, Fuerst T, Hui S, Genant HK. Standardization of bone mineral density at femoral neck, trochanter and Ward’s triangle. Osteoporos Int. 2001;12:438–44. Shepherd JA, Cheng XG, Lu Y, Njeh C, Toschke J, Engelke K, Grigorian M, Genant HK. Universal standardization of forearm bone densitometry. J Bone Miner Res. 2002;17:734–45. Shepherd JA, Fan B, Lu Y, Wu XP, Wacker WK, Ergun DL, Levine MA. A multinational study to develop universal standardization of whole-body bone density and composition using GE Healthcare Lunar and Hologic DXA systems. J Bone Miner Res. 2012;27:2208–16.
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