SEARCH

SEARCH BY CITATION

Keywords:

  • prevalence;
  • sarcopenia;
  • sarcopenic obesity;
  • skeletal muscle mass;
  • very elderly

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Disclosure statement
  9. References

Aim

Sarcopenia and sarcopenic obesity are significant associative factors for functional impairment related to aging. The main aim of the present study was to investigate the prevalence of sarcopenia and sarcopenic obesity, and their associations with functional status among men aged 80 years and older in Beijing.

Methods

A total of 75 young healthy volunteers, and 101 older men aged 80 years and older participated in the present study. Demographic characteristics, anthropometry, skeletal muscle mass measured by dual X-ray absorptiometry (DXA), 6-m gait speed and handgrip strength were collected. Relative appendicular skeletal muscle index (RASM) and percentage skeletal muscle index (SMI) were obtained.

Results

Overall, the prevalence of sarcopenia was 45.7% by using RASM. By the weight-adjusted skeletal muscle index definition (SMI), the prevalence of sarcopenia was 53.2%. The prevalence of sarcopenic obesity was lower by using RASM than SMI (4.9% vs 11.5%, P < 0.05). When we compared the sarcopenia prevalence (%) in obese participants, it was also remarkably lower by using RASM (40.0%) than SMI (95.0%). By using either RASM or SMI, gait speed was of no significant difference among the pure sarcopenia group, pure obese group and sarcopenic obesity group (0.76 ± 0.27 vs 0.82 ± 0.37 vs 0.82 ± 0.27 m/s, P > 0.05, by RASM; 0.75 ± 0.25 vs 0.92 ± 0.27 vs 0.82 ± 0.35 m/s, P > 0.05 by SMI), respectively. Multiple linear regression analyses showed that thigh skeletal muscle mass was positively correlated with gait speed independently (β = 0.221, P = 0.011), and total body fat (β = −0.216, P = 0.002) and age (β = −0.524, P = 0.000) were negatively correlated with gait speed independently.

Conclusions

The prevalence of sarcopenia is high either based on RASM or SMI among Chinese men aged 80 years and older. Functional limitations were significantly associated with older age, skeletal muscle mass and total body fat. Geriatr Gerontol Int 2014; 14 (Suppl. 1): 29–35.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Disclosure statement
  9. References

It has been reported that age-related changes of body composition, decreased muscle mass (MM) and increased fat mass (FM), were closely associated with functional deterioration in older people.[1] Traditionally, the age-associated loss of MM was described as sarcopenia,[2-4] which can result in loss of physical function, disabilities and frailty.[5-7] Furthermore, sarcopenia was also associated with a higher risk of poor quality of life and mortality.[3, 8] The prevalence of sarcopenia among those aged 60–70 years ranges from 5% to 13%,[9] and could increase to 11–50% among people aged 80 years and older.[9] In a Japanese study, the prevalence of sarcopenia and related disability (defined by instrumental activities of daily living) were both higher in men than that in women.[10] Another study from older Americans showed that 53.1% men and 21.9% women had a moderate degree of sarcopenia.[11] Baumgartner et al.[12] defined sarcopenia of the elderly as appendicular skeletal muscle mass (kg)/height2 (m2) being less than two standard deviations below the mean of a young reference group, and the prevalence of sarcopenia increased from 13–24% in persons less than 70 years-of-age to >50% in persons aged 80 years and older.[12, 13] Because the relationship between MM and muscle strength was always non-linear,[11] the European Working Group on Sarcopenia in Older People (EWGSOP) recommended using both low muscle mass and low muscle function (strength or performance) to define sarcopenia. Currently, two skeletal muscle indices are most commonly used in sarcopenia research, which are height-adjusted skeletal muscle index (relative skeletal muscle index [RASM])[12] and the weight-adjusted skeletal muscle mass (percentage skeletal muscle index [SMI]).[14] However, Woods et al. argued that the two commonly used muscle indices of sarcopenia eventually defined different populations.[15, 16] Furthermore, ethnic differences in measurements of MM, strength or physical performance might exist in the diagnosis of sarcopenia.[16] Although sarcopenia has attracted extensive research attention around the world, little is known regarding comparisons of using RASM[12] and SMI[14] in sarcopenia diagnosis among Chinese octogenarians.

Independent of MM, high FM or high percentage body fat as the obesity index also significantly increased the risk of physical dysfunction.[17, 18] Waters and Baumgartner proposed four body composition phenotypes in older adults; that is, normal, sarcopenic, obese, and a combination of sarcopenic and obese.[19] Sarcopenic obesity (SO) is described as sarcopenia co-occuring with an increase in FM. Because of the unanimous criteria to define low MM and obesity,[20] the prevalence of SO varied from 4% to 50% of older populations.[1, 12, 21] SO has been reported to be associated with lower physical function (gait speed) in post-menopausal women,[22] as well as declined functional ability,[23] higher risk of frailty and poorer quality of life,[24] longer hospital stay,[25] and higher mortality risk.[20] On the contrary, the lack of association between SO and functional limitations has been reported elsewhere.[26-28] Kim et al. found that the association of central obesity and functional limitations might have sex-specific differences.[29] However, the association of SO and functional limitations was less commonly evaluated among people aged 80 years and older. Therefore, the main aim of the present study was to evaluate the prevalence of sarcopenia and SO in Chinese men aged 80 years and older in Beijing, and to compare the associations of body composition with gait speed by using different skeletal muscle indices.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Disclosure statement
  9. References

Study participants

The present study was carried out in communities in Beijing, China. The entire study protocol was approved by the institutional review board of PLA General Hospital, and all participants were enrolled after they signed the informed consent. People aged 80 years in these communities were invited to participate in the study by the locality-based community health centers that were affiliated with the PLA General Hospital. Participants with the following conditions were excluded from the study: (i) unable to communicate with research nurses or to grant the informed consent; (i) unable to walk for 6 m within a reasonable period of time; (iii) not likely to live for more than 6 months because of any major illness; and (iv) people who are currently institutionalized. Overall, 101 participants who met the inclusion and exclusion criteria were recruited for the present study.

Anthropometric measurements

Bodyweight and standing height of all participants were measured when they were wearing light indoor clothing and no shoes. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Obesity status was defined by the World Health Organization Asia-Pacific criteria of obesity[30] as follows: underweight (BMI <18.5 kg/m2), normal weight (BMI, 18.5–22.9 kg/m2), overweight (BMI, 23.0–27.4 kg/m2) and obese (BMI >27.5 kg/m2).

Diagnosis of sarcopenia and sarcopenic obesity

In the present study, sarcopenia was defined according to the European Working Group on Sarcopenia in Older People (EWGSOP) criteria.[2] Muscle strength was assessed by handgrip strength by using a dynamometer (Jamar Plus+ digital hand dynamometer, Sammons Preston, Rolyon, Bolingbrook, IL, USA). One trial for each hand was carried out, and the result from the strongest hand was used for the analysis. According to Lee et al's study,[16] low handgrip strength was defined as the limit less than 22.4 kg. Physical performance was evaluated by measuring the usual gait speed (m/s).[16, 31] Participants walked at their usual speed with a static start without deceleration throughout a 6-m straight line in an examination room that was more than 8 m in length. The time was measured by the same trained study nurse. Low physical performance was defined as ≤0.8 m/s according to the EWGSOP definition.

The whole-body dual-energy X-ray absorptiometry (DXA) scan (GE Lunar, Madison, WI, USA) was used for the measurement of fat-free lean body mass (LBM), percentage of fat mass and bone mineral density of the participants. Appendicular skeletal muscle mass (ASM) was calculated as the sum of LBM from both arms and legs. Relative skeletal muscle mass index (RASM) was defined as ASM divided by height (in meter) squared.[12] Percentage skeletal muscle index (SMI%) was defined as ASM (in kg) / bodyweight (in kg) × 100.[32] A total of 75 healthy young volunteers (men) aged 20–40 years were invited for skeletal muscle measurements as the reference group. Those who had any history of specific diseases, such as diabetes, stroke, coronary artery diseases, thyroid disease, arthritis, tuberculosis, asthma, chronic obstructive lung disease, liver cirrhosis and any cancer, were excluded. Low skeletal muscle mass was defined as RASM or SMI below two standard deviations for the mean of the young reference group. Sarcopenia means low muscle mass plus low muscle strength or low performance or both according to the EWGSOP criteria. Sarcopenic obesity (SO) was considered as the combination of sarcopenia and obesity.

Comprehensive geriatric assessment

All study individuals underwent a comprehensive geriatric assessment (CGA) including medical, functional and neuropsychological assessment. The functional assessment was carried out by assessing the basic activities of daily living. The cognitive assessment was carried out using the Mini-Mental State Examination. Mini Nutritional Assessment (MNA) was carried out to evaluate the nutritional status for each participant.

Statistical analysis

In the present study, the statistical analyses were carried out by using commercial software (spss 16.0, IBM, Chicago, IL, USA). The continuous variables in the text and tables are expressed by mean ± standard deviation, and categorical variables are expressed by number (percentage). Comparisons between continuous variables were analyzed using Student's t-test or one-way anova when appropriate. Comparisons of categorical variables were carried out by χ2-test or Fisher's exact test. Correlation coefficients were calculated using partial correlation analysis on ranks. The independent effect of body composition on gait speed was further tested in multivariate linear regression models. For all tests, a two-way P value <0.05 was considered statistically significant

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Disclosure statement
  9. References

Among 75 young reference adults, the mean RASM (ASM/ht2) was 8.21 kg/m2, and the mean SMI % was 35.0%. The cut-off points of low MM were defined as two standard deviations below the mean for the sex-specific young reference group, which were 6.85 kg/m2 by RASM and 28.0% by SMI for men (Table 1).

Table 1. Characteristics of the young reference group
 Men (n = 75)
  1. Results are expressed as mean ± SD.

Age (years)27.0 ± 4.8
Anthropometric measurements 
Height (cm)174.9 ± 4.3
Weight (kg)72.6 ± 10.0
Body mass index (kg/m2)23.7 ± 3.0
Dual-energy X-ray absorptiometry 
Total body fat (kg)14.5 ± 7.4
Total body fat percentage (%)19.2 ± 7.0
Appendicular skeletal muscle mass (kg)25.1 ± 2.4
Thigh skeletal muscle mass (kg)18.4 ± 2.3
Relative skeletal muscle index (kg/m2)8.21 ± 0.68
Percentage skeletal muscle index (%)35.0 ± 3.5
Cut-off values for height-adjusted definition (kg/m2) 
2 SD below the relative skeletal muscle index (kg/m2)6.85
Cut-off values for weight-adjusted definition (%) 
2 SD below the percentage skeletal muscle index (%)28.0

Table 2 summarizes comparisons of the prevalence of sarcopenia by using RASM and SMI as the indicators of low muscle mass. By using RASM, the prevalence of sarcopenia was 45.7%, and it increased to 53.2% by using SMI (Table 2). Furthermore, the prevalence of SO was lower by using RASM than SMI (4.9% vs 11.5%, P < 0.05). The prevalence of sarcopenia in obese participants was significantly lower by using RASM (40.0%) than SMI (95%; Table 2). RASM was positively correlated with BMI (r = 0.538, P < 0.001), whereas SMI was negative correlated with BMI (r = −0.616, P < 0.001). Cases with SO were more common based on SMI criterion for sarcopenia than that based on RASM criterion, which was consistent with Table 2 (Fig. 1).

figure

Figure 1. The relationship between low muscle mass and body mass index (BMI) is compared by using (a) relative appendicular skeletal muscle index (RASM) and (b) percentage skeletal muscle index (SMI) as the definition. RASM was positively correlated with BMI (r = 0.538, P = 0.000), whereas SMI was negatively correlated with BMI (r = −0.616, P = 0.000). Obesity status was defined by the World Health Organization Asia-Pacific criteria of obesity as follows: underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5–22.9 kg/m2), overweight (BMI 23.0–27.4 kg/m2) and obese (BMI −27.5 kg/m2). The cut-off points of lower muscle mass were defined as two standard deviations below the mean for the sex-specific young reference group, which were 6.85 kg/m2 by RASM and 28.0% by SMI for men.

Download figure to PowerPoint

Table 2. Characteristics of study participants
 Men (n = 101)
  1. Low muscle mass (1) was defined as relative appendicular skeletal muscle index (RASM) or percentage skeletal muscle index (SMI) as below −2 SD for the sex-specific young reference group. Low muscle strength (2) was defined as less than 22.4 kg. Low physical performance (3) was defined as ≤0.8 m/s according to the EWGSOP definition. Sarcopenia means low muscle mass plus low muscle strength or low performance or both (2) and (3) based on the EWGSOP definition. Sarcopenic obesity (SO) was considered as the combination of participants whose RASM or SMI below 2 SD of the young reference group and obesity.

Age (years)88.8 ± 3.7
Anthropometric measurements 
Height (cm)168.2 ± 5.0
Weight (kg)70.4 ± 9.9
Body mass index (kg/m2)24.9 ± 3.2
Dual-energy x-ray absorptiometry 
Total body fat (kg)22.1 ± 6.8
Total body fat percentage (%)31.1 ± 6.7
Appendicular skeletal muscle mass (kg)18.2 ± 2.2
Thigh skeletal muscle mass (kg)13.6 ± 1.7
Relative skeletal muscle index (kg/m2)6.43 ± 0.67
Prevalence of sarcopenia by relative skeletal muscle index only74%
Percentage skeletal muscle index (%)26.1 ± 3.0
Gait speed (m/s)0.80 ± 0.28
Handgrip strength, kg24.1 ± 8.4
Sarcopenia prevalence in all study participants (%) 
Height-adjusted definition 
Sarcopenia45.7%
Sarcopenic obesity4.9%
Weight-adjusted definition 
Sarcopenia53.2%
Sarcopenic obesity11.5%
Sarcopenia prevalence (%) in obese participants 
Height-adjusted definition of sarcopenia40.0%
Weight-adjusted definition of sarcopenia95.0%

However, gait speed was of no significant difference between the sarcopenia group, obesity group and SO group by using either RASM or SMI (0.76 ± 0.27 vs 0.82 ± 0.37 vs 0.82 ± 0.27 m/s, P > 0.05 by RASM; 0.75 ± 0.25 vs 0.92 ± 0.27 vs 0.82 ± 0.35 m/s, P > 0.05 by SMI), respectively. According to Spearman's correlation analysis, gait speed was negatively correlated with age (r = −0.257, P = 0.032), arm fat (r = −0.352, P = 0.003), thigh fat (r = −0.281, P = 0.019), trunk fat (r = −0.249, P = 0.038), total body fat (r = −0.284, P = 0.017) and total body fat percentage (r = −0.349, P = 0.003). In contrast, gait speed was positively correlated with thigh skeletal muscle mass (r = 0.249, P = 0.037) and SMI (r = 0.349, P = 0.003; Table 3). However, when we used gait speed as the dependent variable, results of multiple linear regression analyses showed that only thigh skeletal muscle mass was positively correlated with gait speed (β = 0.221, P = 0.011), but total body fat (β = −0.216, P = 0.002) and age (β = −0.524, P < 0.001) were negatively correlated with gait speed independently. Thigh fat was negatively correlated with gait speed (β = −0.356, P = 0.036) in model 2, but the absolute value β of the former decreased with P > 0.05 when adjusted for age in model 3 (Table 4).

Table 3. Correlations of gait speed with characteristics of body composition among Chinese men aged 80 years and older
 Gait speed
rp
  1. RASM, relative appendicular skeletal muscle index; SMI, percentage skeletal muscle index.

Age−0.2570.032
Height0.1050.392
Arm fat−0.3520.003
Thigh fat−0.2810.019
Trunk fat−0.2490.038
Total body fat−0.2840.017
Arm skeletal muscle mass0.0820.498
Thigh skeletal muscle mass0.2490.037
Trunk skeletal muscle mass0.0670.580
Appendicular skeletal muscle mass0.2140.075
Total body skeletal muscle mass0.1240.306
Total body fat percentage−0.3490.003
RASM0.1880.122
SMI0.3490.003
Table 4. Linear regression analyses of gait speed with age, body composition for Chinese men aged 80 years and older
CharacteristicModel 1Model 2Model 3§
  1. The dependent variable was gait speed. Adjusted for height, total body skeletal muscle mass. Adjusted for height, total body skeletal muscle mass, total body fat percentage, thigh fat, relative appendicular skeletal muscle mass (RASM) and percentage skeletal muscle index (SMI). §Adjusted for height, total body skeletal muscle mass, total body fat percentage, thigh fat, RASM, SMI and age.

Thigh skeletal muscle mass

β = 0.554, t = 7.333

P = 0.000

B = 0.522, t = 6.862

P = 0.000

β = 0.221, t = 2.594

P = 0.011

Total body fat

β = −0.325, t = −4.301

P = 0.000

B = −0.003, t = −0.018

P = 0.985

β = −0.216, t = −3.171

P = 0.002

Thigh fat

B = −0.356, t = −2.118

P = 0.036

β = −0.163, t = −1.079

P = 0.283

Age

β = −0.524, t = −6.166

P = 0.000

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Disclosure statement
  9. References

In the present study, the prevalence of sarcopenia of Chinese octogenarian men differed greatly from 45.7 to 74% according to Baumgartner's approach or the EWGSOP definition. Furthermore, the prevalence of SO was lower by using RASM than SMI (4.9% vs 11.5%). However, there was no significant difference in gait speed between the sarcopenia group, obesity group and SO group by using either RASM or SMI. Gait speed was independently associated with thigh skeletal muscle mass, total body fat and age. Although detecting sarcopenia and SO was important for intervention program development, little is known about the prevalence and demographic characteristics of sarcopenia and SO in Chinese men aged 80 years and older.

According to the EWGSOP criteria, either by using the height-adjusted or weight-adjusted skeletal muscle index, the prevalence of sarcopenia and SO in the present study was slightly different from that in other studies. The prevalence of sarcopenia in the present study was significantly higher than that from a study from Taiwan (45.7% vs 5.8%),[16] which could be caused by the older age of the present study participants (88.8 ± 3.7 and 74.4 ± 6.1 years, respectively). Similarly, both the gait speed and handgrip strength of the present study participants were far lower than that showed by Lee et al.[16] because of the age difference of the two studies. Prado et al. assessed the prevalence and clinical implications of SO in patients with cancer aged from 35 to 88 years, which disclosed that the prevalence of SO was 15%, and SO (muscle mass was measured by computed tomography scan and obesity was defined by the BMI of 30 kg/m2) was an independent predictor for poorer functional status and shorter survival.[20] Kim et al. reported the prevalence of SO among Korean older people was 7.6% in men (mean age 73.9 years) and 9.1% in women (mean age 72.9 years) by using weight-adjusted skeletal muscle mass index and waist circumference.[29] Siervo et al. reported the prevalence of SO ranged from 0 to 67% in women aged younger than 60 years, and from 49 to 90% among those aged ≥60 years by using bioimpedance analysis for muscle mass measurement and BMI for adiposity.[21] The results of these reports suggested the need for a universal approach for sarcopenia and SO based on the differences of age, sex and ethnicity.

By using Baumgartner's approach for sarcopenia definition (ASM / ht2 <2 SD below the mean in the young reference group), 74% of the study participants would be sarcopenic. When sarcopenia was defined as the presence of low skeletal muscle mass plus low physical performance or low muscle strength (EWGSOP definition), the prevalence of sarcopenia dropped sharply to 45.7% (by RASM) or and 53.2% (SMI), respectively. A recent study showed that handgrip strength and gait speed did not play the same role in mortality prediction for older people.[33] The different predictive effect of sarcopenia defined by low skeletal muscle mass plus low handgrip strength or slow walking speed deserves further studies for clarification. The prevalence of SO was remarkably lower when using RASM than SMI (4.9% vs 11.5%), and the phenomenon was similar among obese participants (40.0% by using RASM, and 95.0% by using SMI). It has been reported that height-adjusted and weight-adjusted skeletal muscle index eventually categorized different populations. Therefore, it is hard to conclude which skeletal muscle index defines sarcopenia and SO better. Domiciano et al. reported that RASM might underestimate the prevalence of sarcopenia in overweight and obese people.[34] Kim et al. reported that nearly no patients were classified as SO by using a height-adjusted definition,[29] which was in accordance with the present study. Estrada et al. suggested that relative sarcopenia with ASM adjusted for body mass was a better mobility predictor,[14] which was not supported by a large prospective cohort study showing that RASM was a better predictor for mobility.[35]

As physical performance decline has been the main adverse effect of sarcopenia on older people,[36] both EWGSOP and International Working Group on Sarcopenia emphasized physical performance in the diagnosis of sarcopenia. In the present study, we showed the relationship of gait speed and body composition. As both sarcopenia and obesity can lead to decline in physical function, in theory, older people with SO would walk slower than those with only sarcopenia or obesity. However, gait speed was not significantly different among the sarcopenia group,[37] obesity group and SO group. Bouchard et al. found that SO individuals did not present with lower physical capacity compared with obese individuals,[38] which supported our findings. Abe et al. reported that there were no significant correlations between the anterior/posterior muscle thickness ratio, maximum and normal walking speeds, but an age-related loss of adductor/quadriceps muscles could be associated with a decrease in a relatively difficult task performance, such as zig-zag walking.[39] Therefore, further study focused on a comprehensive functional ability test that includes balance, strength, and mobility performance measures to evaluate the effects on sarcopenia and SO diagnosis is required.

The present study evaluated the relationship between regional body composition and gait speed, and we found that gait speed was positively correlated with thigh skeletal muscle mass, but negatively associated with age, arm fat, thigh fat, trunk fat, total body fat and total body fat percentage. Nevertheless, only thigh muscle mass, total body fat and age were independently correlated with gait speed. These findings suggested that gait speed was affected by multifactorial determinants, and that total fatness might play some role in gait speed. Total fatness could lead to fat accumulation in the muscle, which is negatively associated with skeletal muscle mass through the increase of pro-inflammatory cytokines.[17, 40]

There were some limitations in the present study. First, the sample size was relatively small, that might not be sufficiently extrapolated to the general population in China. Furthermore, the cross-sectional study design limited the ability of predicting clinical outcomes of sarcopenia and SO. Second, data were collected by the community health centers, which was not a population-based sampling process. Therefore, most participants were otherwise healthy community-dwelling older people, which might underestimate the prevalence of sarcopenia in the general population, because frail or disabled elderly might not be included for study. Third, SO in the present study was defined by low muscle mass plus high BMI, and more adiposity indices might be considered to evaluate the clinical impacts of SO in the future. In conclusion, sarcopenia is a prevalent condition among community-dwelling Chinese octogenarian men in Beijing. Thigh skeletal muscle mass, total body fat and age were all associated with functional limitation, and further longitudinal study is required to evaluate the clinical impact of sarcopenia and SO in older Chinese men.

Acknowledgement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Disclosure statement
  9. References

This study was supported by Military Healthcare Grants of China (12BJZ40).

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgement
  8. Disclosure statement
  9. References
  • 1
    Baumgartner RN. Body composition in healthy aging. Ann N Y Acad Sci 2000; 904: 437448.
  • 2
    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 Ageing 2010; 39: 412423.
  • 3
    Fielding RA, Vellas B, Evans WJ et al. 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: 249256.
  • 4
    Wang C, Bai L. Sarcopenia in the elderly: basic and clinical issues. Geriatr Gerontol Int 2012; 12: 388396.
  • 5
    Marsh AP, Rejeski WJ, Espeland MA et al. Muscle strength and BMI as predictors of major mobility disability in the Lifestyle Interventions and Independence for Elders pilot (LIFE). J Gerontol A Biol Sci Med Sci 2011; 66: 13761383.
  • 6
    Dufour AB, Hannan MT, Murabito JM, Kiel DP, McLean RR. Sarcopenia definitions considering body size and fat mass are associated with mobility limitations: the Framingham Study. J Gerontol A Biol Sci Med Sci 2013; 68: 168174.
  • 7
    Hida T, Ishiguro N, Shimokata H et al. High prevalence of sarcopenia and reduced leg muscle mass in Japanese patients immediately after a hip fracture. Geriatr Gerontol Int 2013; 13: 413420.
  • 8
    Morley JE, Abbatecola AM, Argiles JM et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc 2011; 12: 403409.
  • 9
    Rolland Y, Czerwinski S, Van Kan GA et al. Sarcopenia: its assessment, etiology, pathogenesis, consequences and future perspectives. J Nutr Health Aging 2008; 12: 433450.
  • 10
    Tanimoto Y, Watanabe M, Sun W et al. Association between sarcopenia and higher-level functional capacity in daily living in community-dwelling elderly subjects in Japan. Arch Gerontol Geriatr 2012; 55: e913.
  • 11
    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: 413421.
  • 12
    Baumgartner RN, Koehler KM, Gallagher D et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 1998; 147: 755763.
  • 13
    Tanimoto Y, Watanabe M, Sun W et al. Association between muscle mass and disability in performing instrumental activities of daily living (IADL) in community-dwelling elderly in Japan. Arch Gerontol Geriatr 2012; 54: e230e233.
  • 14
    Estrada M, Kleppinger A, Judge JO, Walsh SJ, Kuchel GA. Functional impact of relative versus absolute sarcopenia in healthy older women. J Am Geriatr Soc 2007; 55: 17121719.
  • 15
    Woods JL, Iuliano-Burns S, King SJ, Strauss BJ, Walker KZ. Poor physical function in elderly women in low-level aged care is related to muscle strength rather than to measures of sarcopenia. Clin Interv Aging 2011; 6: 6776.
  • 16
    Lee WJ, Liu LK, Peng LN, Lin MH, Chen LK. Comparisons of sarcopenia defined by IWGS and EWGSOP criteria among older people: results from the I-Lan longitudinal aging study. J Am Med Dir Assoc 2013; 14: 528.
  • 17
    Rolland Y, Lauwers-Cances V, Cristini C 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: 18951900.
  • 18
    Ochi M, Tabara Y, Kido T et al. Quadriceps sarcopenia and visceral obesity are risk factors for postural instability in the middle-aged to elderly population. Geriatr Gerontol Int 2010; 10: 233243.
  • 19
    Waters DL, Baumgartner RN. Sarcopenia and obesity. Clin Geriatr Med 2011; 27: 401421.
  • 20
    Prado CM, Lieffers JR, McCargar LJ et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 2008; 9: 629635.
  • 21
    Prado CM, Wells JC, Smith SR, Stephan BC, Siervo M. Sarcopenic obesity: a Critical appraisal of the current evidence. Clin Nutr 2012; 31: 583601.
  • 22
    Oliveira RJ, Bottaro M, Junior JT, Farinatti PT, Bezerra LA, Lima RM. Identification of sarcopenic obesity in postmenopausal women: a cutoff proposal. Braz J Med Biol Res 2011; 44: 11711176.
  • 23
    Bouchard DR, Janssen I. Dynapenic-obesity and physical function in older adults. J Gerontol A Biol Sci Med Sci 2010; 65: 7177.
  • 24
    Baumgartner RN, Wayne SJ, Waters DL, Janssen I, Gallagher D, Morley JE. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res 2004; 12: 19952004.
  • 25
    Kyle UG, Pirlich M, Lochs H, Schuetz T, Pichard C. Increased length of hospital stay in underweight and overweight patients at hospital admission: a controlled population study. Clin Nutr 2005; 24: 133142.
  • 26
    Davison KK, Ford ES, Cogswell ME, Dietz WH. Percentage of body fat and body mass index are associated with mobility limitations in people aged 70 and older from NHANES III. J Am Geriatr Soc 2002; 50: 18021809.
  • 27
    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: 20822088.
  • 28
    Zoico E, Di FV, Guralnik JM et al. Physical disability and muscular strength in relation to obesity and different body composition indexes in a sample of healthy elderly women. Int J Obes Relat Metab Disord 2004; 28: 234241.
  • 29
    Kim JH, Choi SH, Lim S et al. Sarcopenia and obesity: gender-different relationship with functional limitation in older persons. J Korean Med Sci 2013; 28: 10411047.
  • 30
    Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157163.
  • 31
    Munoz-Mendoza CL, Cabanero-Martinez MJ, Millan-Calenti JC, Cabrero-Garcia J, Lopez-Sanchez R, Maseda-Rodriguez A. Reliability of 4-m and 6-m walking speed tests in elderly people with cognitive impairment. Arch Gerontol Geriatr 2011; 52: e67e70.
  • 32
    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: 378383.
  • 33
    Chen LY, Liu LK, Liu CL et al. Predicting functional decline of older men living in veteran homes by minimum data set: implications for disability prevention programs in long term care settings. J Am Med Dir Assoc 2013; 14: 309.e9309.13.
  • 34
    Figueiredo CP, Domiciano DS, Lopes JB et al. Prevalence of sarcopenia and associated risk factors by two diagnostic criteria in community-dwelling older men: the Sao Paulo Ageing & Health Study (SPAH). Osteoporos Int 2013; PMID: 23892584 [Epub ahead of print].
  • 35
    Delmonico MJ, Harris TB, Lee JS et al. Alternative definitions of sarcopenia, lower extremity performance, and functional impairment with aging in older men and women. J Am Geriatr Soc 2007; 55: 769774.
  • 36
    Tanimoto Y, Watanabe M, Sun W et al. Association of sarcopenia with functional decline in community-dwelling elderly subjects in Japan. Geriatr Gerontol Int 2013; 13: 958963.
  • 37
    Liu LK, Lee WJ, Liu CL et al. Age-related skeletal muscle mass loss and physical performance in Taiwan: implications to diagnostic strategy of sarcopenia in Asia. Geriatr Gerontol Int 2013; 13: 964971.
  • 38
    Barwell ND, Malkova D, Leggate M, Gill JM. Individual responsiveness to exercise-induced fat loss is associated with change in resting substrate utilization. Metabolism 2009; 58: 13201328.
  • 39
    Abe T, Ogawa M, Loenneke JP, Thiebaud RS, Loftin M, Mitsukawa N. Relationship between site-specific loss of thigh muscle and gait performance in women: the HIREGASAKI study. Arch Gerontol Geriatr 2012; 55: e21e25.
  • 40
    Yudkin JS, Kumari M, Humphries SE, Mohamed-Ali V. Inflammation, obesity, stress and coronary heart disease: is interleukin-6 the link. Atherosclerosis 2000; 148: 209214.