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On-line version ISSN 1980-0037
Rev. bras. cineantropom. desempenho hum. vol.14 no.1 Florianópolis 2012
Cassiano Ricardo RechI; Rodolfo André DellagranaII; Maria de Fátima Nunes MarucciIII; Edio Luiz PetroskiI
IUniversidade Estadual de Ponta Grossa. Departamento de Educação Física. Ponta Grossa, PR. Brasil
IIUniversidade Federal do Paraná. Centro de Pesquisa em Exercício e Esporte. Curitiba, PR. Brasil
IIIUniversidade de São Paulo. Departamento de Epidemiologia. São Paulo, SP. Brasil
IVUniversidade Federal de Santa Catarina. Centro de Desportos. Programa de Pós-Graduação em Educação Física. Florianópolis, SC. Brasil
The objective of this study was to compare skeletal muscle mass (SMM) in older adults as estimated by dual-energy X-ray absorptiometry (DXA) and three predictive equations. A total of 180 older adults (120 women and 60 men) aged 60 to 81 years were studied. Appendicular SMM was measured by DXA and estimated using predictive equations based on anthropometric measures, age, race, and gender. Agreement between methods was evaluated by the paired t-test, Pearsons correlation coefficient, and dispersion error. The prevalence of sarcopenia estimated with the different methods was compared using the kappa coefficient, sensitivity, and specificity. No difference was observed in mean SMM estimated by the predictive equation of Lee et al. and DXA (p>0.05), with a high correlation between methods in men (r=0.90; p<0.001) and women (r=0.86; p<0.001). The prevalence of sarcopenia did not differ between methods (DXA = 33.3% vs. equation = 36.1%), with high agreement between methods (kappa = 0.74; p<0.001) and high specificity (89%) and sensitivity (86%). The results indicate agreement between DXA and the predictive equation of Lee et al. for estimation of SMM and prevalence of sarcopenia in older adults.
Key words: Anthropometry; Elderly; Muscle mass; Sarcopenia.
Skeletal muscle mass (SMM) is a key component of the assessment of health and nutritional status in older adults.1,2 SMM constitutes the metabolically active tissue of the human body, and accounts for much of fat-free body mass (FFM). The amount of SMM is associated with physical fitness and fracture prevention.1,3,4 It is estimated that 15 to 20% o folder adults have deficient levels of SMM; in the presence of reduced muscle strength, this constitutes sarcopenia, which has a prevalence rate of over 50% in persons aged 80 or older.5
Estimation of SMM in vivo can be accomplished by a variety of methods, such as dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI)6,7; these three modalities are considered the gold standard for this purpose, but carry a high financial cost that makes their use in population-based studies unfeasible. The brief radiation exposure and little need for patient cooperation7,8 are appealing advantages of DXA over other methods for assessment of older persons. Despite high agreement between DXA-predicted and MRI-derived estimates of SMM in adults7 and elderly women,8 cost is still a concern.
Several authors7,8 have found that approximately 75% of muscle mass is located in the appendicular regionthat is, the upper and lower limbs (arms and legs respectively). Predictive equations have been developed9-11 for estimation of whole-body skeletal muscle mass on the basis of anthropometric data, which can be collected in a more affordable manner, in an attempt to make SMM calculation easier and enable its use in epidemiological research. However, these equations were developed in other countries, for other populations and age groups, and have yet to be validated for Brazilian older adults.
Gobbo et al.12 investigated the validity of four such equations9-11 in adult males (college students) and found only two to be valid as compared with DXA findings. As sarcopenia jeopardizes the health status of older adults, hinders execution of basic, essential activities of daily living, and decreases quality of life; and in light of the population aging process currently underway in Brazil, with consequent increases in the incidence and prevalence of sarcopenia, the present study sought to: a) assess the validity of predictive equations for estimation of SMM in older adults; and b) evaluate the agreement between predictive equation-based estimates of sarcopenia and DXA-predicted measurements of SMM.
This cross-sectional study was conducted on a convenience sample of older adults from the capital of a Southern Brazilian state. The criteria for exclusion were age <60 years and/or presence of incapacitating medical conditions (paralysis, stroke, cancer, hypertension, hyper- or hypothyroidism, kidney or liver failure), as determined by telephone interviews with the participants, carried out prior to data collection. A total of 180 subjects between the ages of 60 and 81 took part in this investigation, a sample size similar to that of other validation studies with the same design.7,8
Anthropometric parameters (body mass; height; forearm and mid-upper arm circumference; mid-upper thigh girth; leg circumference) were measured15 by trained examiners. After collection of anthropometric data, SMM was estimated using the selected equations (Table 1). The choice of predictive equations for analysis was based on a search of the PubMed, SciELO, and LILACS databases. In order to ascertain the validity of these equations, their results were compared to those obtained by DXA (performed on a LUNAR Prodigy DF + 14319 densitometer, GE, Madison, WI, with DPX-L 7.52.002 software), which was chosen as the gold standard for comparison.
DXA was performed with patients wearing gowns provided by the investigator and free of jewelry, dental appliances, or other metallic objects. The procedure lasted 20 to 30 minutes per subject, and was always performed by the technician in charge of calibrating the densitometer, according to manufacturer instructions.
Total FFM and appendicular FFM (AFFM) values were used for estimation of SMM (kg) according to the Kim et al. equation,(7) which also takes into account age in years and a gender-based score (0 extra points for women and 1 for men):
SMM (kg) = (1.13 × AFFM) (0.02 × age) + (0.61 × gender) + 0.97
SMM was used to calculate the muscle mass index (MMI), expressed as the SMM divided by height (in meters) squared. Descriptive statistics (means, standard deviations, and frequencies) were used to characterize the sample by gender and age.
The paired t-test, Pearsons linear correlation coefficient, and the coefficient of determination (R²), as well as measures of dispersion, were used to assess the validity of the selected equations. Difference and standard error of estimate (SEE) were analyzed, as well as residual plots.13 The kappa coefficient, sensitivity, and specificity were used for comparison of the prevalence of sarcopenia.
The classification system proposed by Janssen et al.14 was used to estimate the prevalence of sarcopenia. The kappa coefficient, sensitivity, and specificity were calculated in the Statistical Package for the Social Sciences (SPSS) 15.0 software environment, with 95% confidence intervals and a level of significance set at 5%.
This study was conducted in accordance with Brazilian National Health Council Resolution no. 196 of 10 October 1996 and was approved by the Universidade Federal de Santa Catarina Human Subject Research Ethics Committee with judgment number 059/05.
The study sample comprised 180 older adults. Most participants (67%) were female. Mean age was 69.3±5.7 years for male subjects and 67.3±5.2 years for female subjects (range, 6081 years). Mean body fat percentage, as estimated by DXA, was 23.1±5.77% in males and 37.3±6.9% in females. Mean BMI was 28±3.75 kg/m2 (range, 18.439.3 kg/m2).
In the present study, the predictive equations proposed by Martin et al.10 and Doupe et al.9 were found invalid for estimation of SMM, as they produced estimates that were significantly different from DXA measurements, despite acceptable correlation coefficients and standard errors15 (Table 2).
SMM estimates obtained through use of the Lee et al. equation11 were not statistically different from DXA-predicted SMM measurements. Correlation between this equation and DXA was high both for male subjects (r=0.90; p<0.05) and for female subjects (r=0.86; p<0.05). In approximately 75% of participants, the difference in SMM as estimated by the two methods was <2 kg.
Scatter diagrams showed no statistically significant differences between DXA-predicted SMM measurements and SMM estimates calculated with the Lee et al. equation.11The mean error of estimate was -0.3 kg (95%CI, -4.2 to 3.6 kg) in male subjects and -0.3 kg (95%CI, -3.6 to 3.0 kg) in female subjects (Figures 1 and 2).
Regarding the prevalence of sarcopenia (Table 3), high levels of agreement were found between DXA-predicted measurement and the Lee et al. equation11 (kappa 0.743; p<0.001), as well as high sensitivity (86%) and specificity (89%), with no differences in prevalence as estimated by DXA measurement or the Lee et al. equation.11
The objective of this study was to ascertain the validity of three anthropometry-based predictive equations for estimation of SMM, some taking into account age or ethnicity, developed by Martin et al.,10 Doupe et al.,9 and Lee et al.11 We found the equation developed by Lee et al.11 to be valid for estimation of SMM in older adults, as compared with DXA-predicted measurement of SMM.
Countless studies have revealed a phenomenon of population aging in developing countries, including Brazil.16 The prevalence of sarcopenia among older adults is on the rise, as reported by Baumgartner et al.,5 who found that over 50% of subjects in a sample of New Mexican adults aged 80 or older had sarcopenia, as identified by self-reported measurements.
A review of the scientific literature revealed no population-based studies on the prevalence of sarcopenia in Brazil. This absence can be partially attributed to the lack of low-cost methods for estimation of SMM. A study carried out in a sample of older women revealed reductions in SMM as measured by upper arm muscle area.18
In the present study, of the three predictive equations9-11 available for estimation of SMM in older adults of both genders, only the Lee et al. equation,11 developed for a sample of adult men and women (aged 20 to 81), provided estimates that were statistically similar to DXA measurements of SMM. These findings corroborate those reported by Gobbo et al.12 in a study of adult males. Furthermore, the Lee et al. equation11 had a higher predictive value for SMM and a lower standard error of estimate (SEE) in older adults of both genders as compared with the Martin et al.10 and Doupe et al. equations.9
The variation in SMM estimates provided by the Lee et al. equation11 was not high. As shown in Figures 1 and 2, 95% of male subjects had estimate errors within a range of -4.2 kg to 3.6 kg (range, 7.8 kg), a variation lower than that reported in a study of adult male college students12 and in a bioelectrical impedance analysis study of younger and older adults.19 In women, the difference range was 6.6 kg, superior to that reported by Chen et al. (5.2 kg).8 However, the authors validated DXA as a method for SMM estimation in older women, with MRI as the gold standard for comparison. Therefore, the Lee et al. equation11 is valid for estimation of SMM in older adults.
Although the Martin et al.10 and Doupe et al.9 equations are cadaver-based, they did not prove valid for estimation of SMM in our study (Table 2), due to significant differences between SMM predicted by these equations and DXA-predicted values. A similar finding was reported by Gobbo et al.,12 who found that the Martin et al.10 and Doupe et al.9 equations tended to overestimate SMM. It should be stressed that the cadaver embalming process may have led to changes in body composition, thus interfering directly with the SMM estimates used to develop these predictive equations. The equations proposed by Martin et al.10 and Doupe et al.9 were also invalid for women in our sample (Table 2). This, however, may be explained by the fact that both equations were originally developed using male cadavers as a basis.
The sample-wide prevalence of sarcopenia was 36.1% as estimated by the Lee et al. equation11 and 33.1% as predicted by DXA,7 with no statistically significant difference. The use of anthropometric parameters for estimation of SMM has proved valid in several studies.11,20,21 Calculation of upper arm and thigh muscle area suffices for assessment of changes in SMM; however, a more accurate determination of the extent of sarcopenia requires estimation of whole-body SMM.
The findings reported herein suggest that the Lee et al. equation11 is valid for estimation of SMM in older adults. Nevertheless, some limitations should be mentioned. Anthropometric estimation of SMM may not provide an accurate portrayal of changes in the muscle tissue. Advancing age is associated with a trend toward adipose infiltration of muscle fibers, which can only be diagnosed accurately in vivo by means of magnetic resonance imaging.22 The convenience sampling strategy used may have limited estimation of the prevalence of sarcopenia in the study population. Moreover, the sample size was not sufficient for actual stratification of findings by gender and age. However, by presenting these data, we sought to demonstrate the applicability and feasibility of anthropometry-derived estimation of SMM.
Finally, the cutoffs used herein for diagnosis of sarcopenia were obtained from a study carried out in North America, and have yet to be tested in older adults from other locations. Therefore, we suggest that these cutoffs be specifically analyzed in Brazilian older adults so as to determine whether they are associated with reductions in muscle mass and functional disability in this population.
The findings of this study confirm the validity of the Lee et al. equation11 for estimation of SMM in Brazilian older adults, and also confirm the applicability of this equation in studies designed to estimate extent of sarcopenia in elderly populations.
1. Rolland Y, Lauwers-Cances V, Cristini C, Abellan van Kan G, Janssen I, Morley JE, et al. Difficulties with physical function associated with obesity, sarcopenia, and sarcopenic-obesity in community-dwelling elderly women: the EPIDOS (EPIDemiologie de l'OSteoporose) Study. Am J Clin Nutr 2009;89:1895-900. [ Links ]
2. Lang T, Streeper T, Cawthon P, Baldwin K, Taaffe DR ,Harris TB. Sarcopenia: etiology, clinical consequences, intervention, and assessment. Osteoporos Int 2010;21:543-59. [ Links ]
3. Stephen WC ,Janssen I. Sarcopenic-obesity and cardiovascular disease risk in the elderly. J Nutr Health Aging 2009;13:460-6. [ Links ]
4. Aubertin-Leheudre M, Lord C, Labonte M, Khalil A, Dionne IJ. Relationship between sarcopenia and fracture risks in obese postmenopausal women. J Women Aging 2008;20:297-308. [ Links ]
5. Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 1998;147:755-63. [ Links ]
6. Bouchard DR, Dionne IJ ,Brochu M. Sarcopenic/Obesity and Physical Capacity in Older Men and Women: Data From the Nutrition as a Determinant of Successful Aging (NuAge)-the Quebec Longitudinal Study. Obesity (Silver Spring) 2009;17:2082-8. [ Links ]
7. Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr 2002;76:378-83. [ Links ]
8. Chen Z, Wang Z, Lohman T, Heymsfield SB, Outwater E, Nicholas JS, et al. Dual-energy X-ray absorptiometry is a valid tool for assessing skeletal muscle mass in older women. J Nutr 2007;137:2775-80. [ Links ]
9. Doupe MB, Martin AD, Searle MS, Kriellaars DJ, Giesbrecht GG. A new formula for population-based estimation of whole body muscle mass in males. Can J Appl Physiol 1997;22:598-608. [ Links ]
10. Martin AD, Spenst LF, Drinkwater DT, Clarys JP. Anthropometric estimation of muscle mass in men. Med Sci Sports Exerc 1990;22:729-33. [ Links ]
11. Lee RC, Wang Z, Heo M, Ross R, Janssen I, Heymsfield SB. Total-body skeletal muscle mass: development and cross-validation of anthropometric prediction models. Am J Clin Nutr 2000;72:796-803. [ Links ]
12. Gobbo LA CE, Petroski EL, Cardoso JR, Carvalho FO, Romanzini M, Avelar A. Validation of anthropometric equations for the estimation of muscular mass by dual energy x-ray absorptiometry in male college students. Rev Bras Med Esporte 2008;14:376-80. [ Links ]
13. Bland JM ,Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10. [ Links ]
14. Janssen I, Baumgartner RN, Ross R, Rosenberg IH ,Roubenoff R. Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol 2004;159:413-21. [ Links ]
15. Lohman TG, Roche AF, Martorell R. Antropometric standardization reference manual. Champaign: Human Kinetics; 1988. [ Links ]
16. Silva DAS, Pelegrini A, Pires-Neto CS, Vieira MFS, Petroski EL. The anthropometrist in the search for more reliable data. Rev Bras Cineantropom Desempenho Hum 2011,13(1):82-85 [ Links ]
17. Veras R. Envelhecimento populacional contemporâneo: demandas, desafios e inovações. Rev Saúde Pública 2009;43:548-54. [ Links ]
18. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412-23. [ Links ]
19. Menezes TN, Marucci MFN. [Anthropometry of elderly people living in geriatric institutions, Brazil.]. Rev Saude Publica 2005;39:169-75. [ Links ]
20. Janssen I, Heymsfield SB, Baumgartner RN ,Ross R. Estimation of skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol 2000;89:465-71. [ Links ]
21. Reid IR, Evans MC, Ames R. Relationships between upper-arm anthropometry and soft-tissue composition in postmenopausal women. Am J Clin Nutr 1992;56:463-6. [ Links ]
22. Kuriyan R, Thomas T, Kurpad AV. Total body muscle mass estimation from bioelectrical impedance analysis & simple anthropometric measurements in Indian men. Indian J Med Res 2008;127:441-6. [ Links ]
23. Kim J, Heshka S, Gallagher D, Kotler DP, Mayer L, Albu J, et al. Intermuscular adipose tissue-free skeletal muscle mass: estimation by dual-energy X-ray absorptiometry in adults. J Appl Physiol 2004;97:655-60. [ Links ]
Address for correspondence Received: 19 Juny 2011
Cassiano Ricardo Rech
Universidade Estadual de Ponta Grossa
Departamento de Educação Física
Carlos Cavalcanti, 4748
Av. Uvaranas - Ponta Grossa, Paraná, Brasil
CEP: 84030 900
Accepted: 09 August 2011
Address for correspondence
Received: 19 Juny 2011