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Keywords:

  • aging;
  • prevalence;
  • sarcopenia

Abstract

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

Aim

Several operative definitions and screening methods for sarcopenia have been proposed in previous studies; however, the opinions of researchers still differ. We compared the prevalence of sarcopenia using two different algorithms: (i) the European working group on sarcopenia in older people (EWGSOP)-suggested algorithm using gait speed as the first step; and (ii) the muscle mass and strength algorithm.

Methods

A population-based, cross-sectional survey of adults aged over 65 years was carried out. Data on a total of 4811 participants were available for analysis. Gait speed, grip strength and appendicular skeletal muscle mass were assessed to determine sarcopenia. Appendicular skeletal muscle mass was estimated from bioimpedance analysis measurements and expressed as skeletal muscle mass index. Grip strength and skeletal muscle mass index were considered to be low if they fell below the threshold of the lowest 20% of values measured in a subset of healthy subjects. We compared the prevalence rates of sarcopenia determined by the two algorithms.

Results

The prevalence rate of sarcopenia in a representative sample of older Japanese adults was 8.2% for men and 6.8% for women based on the EWGSOP algorithm. The two algorithms identified the same participants as sarcopenic, the only difference being the EWGSOP algorithm classified an additional seven participants (0.15%) into sarcopenia compared with the muscle mass and strength algorithm.

Conclusion

It is debatable whether inclusion of gait speed is necessary when screening for sarcopenia in community-dwelling older adults. Future research should examine the necessity of including gait speed in algorithms and the validity of cut-off values. Geriatr Gerontol Int 2014; 14 (Suppl. 1): 46–51.


Introduction

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

Several changes in body composition occur with the aging process (e.g. a decrease in bone and muscle mass, and an increase in the proportion of fat).[1, 2] Lower muscle mass is associated with decreased strength, and might lead to the development of functional limitations and disability in old age.[3-6] Advanced skeletal muscle loss could also potentially have an impact on quality of life, the need for supportive services and ultimately the need for long-term care in older persons.[5] Thus, it is important to develop a valid and feasible method to screen older adults for sarcopenia, and to establish a preventive strategy for sarcopenia in older people.

Although operative definitions and screening methods for sarcopenia have been proposed in previous studies, the opinions of researchers have been conflicting.[3, 7-10] Recently, a European working group on sarcopenia in older people (EWGSOP) published their recommendations for a clinical definition, and consensus diagnostic criteria, for sarcopenia.[10] In that report, the EWGSOP suggested an algorithm using the presence of both low muscle mass and low muscle function, including strength and gait performance, for the diagnosis of sarcopenia. Low gait performance is the first step to identify sarcopenia in the EWSOP algorithm. Thus, it is possible that older adults with high gait performance would not be categorized as sarcopenic, even if they had evident muscle atrophy.

The term “sarcopenia” was coined by Rosenberg in 1989 to refer to the process of age-related loss of skeletal muscle mass.[11] Originally, “sarcopenia” derives from the Greek words sarx (meaning flesh) and penia (meaning loss), and this term is used to refer specifically to the gradual loss of skeletal muscle mass and strength that occurs with advancing age.[12] According to the original meaning, the definition and diagnosis of sarcopenia should be based on the reduction of muscle mass and strength. Furthermore, sarcopenia is a fundamental component of frailty, and it can be seen as one dimension of frailty. Frailty is a geriatric syndrome resulting from age-related cumulative declines across multiple physiological systems, and is characterized by the following five domains: unintended weight loss, self-reported exhaustion, weakness (reduced grip strength), slow gait speed and low levels of physical activity.[13] If sarcopenia patients are screened according to gait speed, sarcopenia becomes roughly synonymous with frailty, and it could confuse interpretation of both sarcopenia and frailty.

The purpose of the present study was to compare the difference in prevalence of sarcopenia determined using two different algorithms: (i) the EWGSOP algorithm, using gait speed as the first step; and (ii) the muscle mass and strength algorithm, and to examine whether gait speed should be a critical component for screening sarcopenia.

Methods

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

Participants

The present study was based on data collected as part of the Obu Study of Health Promotion for the Elderly (OSHPE), carried out in Obu, Aichi, Japan, from August 2011 to February 2012. OSHPE initially sent postal invitations to 14 313 persons aged 65 years and older, resident in the city of Obu. Individuals who had participated in previous studies, were hospitalized and/or in residential care, or were certified as requiring more than level 3 care needing support or care by the Japanese public long-term care insurance system were excluded from participation in OSHPE. A total of 5104 persons responded and agreed to participate in the present study (response rate: 35.7%). The overall survey consisted of face-to-face interviews on health status, physical and cognitive function tests, and body composition, among other items. Major chronic illnesses were assessed by nurses through face-to-face interviews. Chronic illnesses included in the study were hypertension, hyperlipidemia, diabetes mellitus, heart disease, stroke, Parkinson's disease, dementia, clinical depression, cancer, lung disease, osteoporosis and arthritis (rheumatoid and osteoarthritis).

Of the 5104 OSHPE participants, we excluded those with missing data on body composition, gait speed or muscle strength. Data on 4811 participants (94.3% of all participants, 2343 men and 2468 women) were available for this analysis. All participants were informed about the study procedures and provided written informed consent before participation. In addition, this study was carried out in accordance with the Helsinki Declaration, and was approved by the ethics committee of the National Center for Geriatrics and Gerontology.

Assessment of appendicular muscle mass

A multifrequency bioelectrical impedance analyzer (MC-980A; Tanita, Tokyo, Japan) was used to measure bioimpedance. This bioelectrical impedance analysis (BIA) instrument uses six electrical frequencies (1 kHz, 5 kHz, 50 kHz, 250 kHz, 500 kHz and 1000 kHz), and we calculated the impedance index, height2 (cm) divided by resistance (Ω). The participants stood barefoot on the analyzer platform, grasping the two handgrips. Eight-point tactile electrodes made contact with the palm and thumb of each hand, and with the anterior and posterior aspects of the sole of each foot. Surface electrodes were placed on the right side of the body, on the dorsal surface of the hands and feet proximal to the metacarpal- and metatarsal-phalangeal joints, respectively, medially between the distal prominences of the radius and ulna, and between the medial and lateral malleoli at the ankle. Measurements were carried out by trained staff, and completed within 30 s.

We estimated appendicular skeletal muscle mass (ASM) using the following equations that were developed for Japanese older adults:[14]

  • display math
  • display math

Skeletal muscle mass index (SMI) was calculated as ASM / height.[2]

Measurement of muscle strength

Maximal voluntary isometric strength of handgrip was measured using a hand dynamometer Grip-D (Takei, Niigata, Japan). The measurement was taken with the dominant hand in a standing position. The muscle strength test was carried out once only. Handgrip strength has been widely used to measure muscle strength and correlates well with most relevant outcomes.[15]

Measurement of gait speed

Participants were asked to walk 6.4 m (divided into two 2.0-m zones at each end, and a 2.4-m middle-zone) at their usual pace. We measured the required time (in seconds) to pass the 2.4-m middle zone to calculate gait speed (m/s). Use of a cane or walker was permitted if participants could not practice the gait test. The gait test was carried out five times, and the average value was used.

Gait speed is a valid and widely used measure of mobility limitation for both healthy and impaired older persons,[16] with high predictive validity for subsequent disability, hospitalization and mortality.[17, 18]

Algorithm and cut-off values to determine sarcopenia

We used the EWGSOP-algorithm as one method to determine the individuals with sarcopenia. We also used the muscle mass and strength algorithm. The EWGSOP recommends use of normative (healthy young adult) rather than other predictive reference populations, with cut-off points (for muscle mass and strength) at two standard deviations below the mean reference value.[10] However, no reference data from a normative Japanese population were available with which to determine cut-off values for grip strength and SMI. In the absence of normative reference populations, previous studies have used healthy older adults as their reference groups (applying cut-off points derived from the lowest sex-specific quartiles[13] or quintiles[9, 19]). To overcome this limitation, we selected a healthy subset of people from our study, and used their sex-specific quintile points (lowest 20%) as cut-off values. This healthy subset was defined as follows: no impairment of activities of daily living, no medical history (stroke, Parkinson's disease, Alzheimer's disease or other serious neurological diagnoses, depression), gait speed ≥1.0 m/s and Mini-Mental State Examination (MMSE) score ≥21. Participants were classified as “low level” when their grip strength or SMI values fell below the cut-off points. In the EWGSOP-algorithm, a gait speed at 0.8 m/s is used as the cut-off value.[10]

Statistical analysis

Differences in age, body mass index (BMI), SMI, gait speed, grip strength, and MMSE score were compared between those with and without sarcopenia using t-tests by sex. The prevalence of major chronic illnesses was also compared between those with and without sarcopenia using χ2-tests. All analysis was carried out using commercially available software, IBM spss statistics (version 19; SPSS, Chicago, IL, USA), and the level of significance was as set at P < 0.05.

Results

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

Determination of the cut-off values for sarcopenia

A total of 3810 (74.6% of all participants, 1848 men and 1962 women, mean age 71.2 ± 4.9 years) were included in the healthy subset of people used to determine cut-off values. Cut-off values of grip strength were set at 28.8 kg and 18.2 kg for men and women, respectively. Similarly, cut-off values of SMI were set at 7.09 kg/m2 in men and 5.91 kg/m2 in women.

Prevalence and characteristics of sarcopenia

Data on a total of 4811 participants (94.3% of all participants, 2343 men and 2468 women) were available for analysis. The mean age was 72.2 ± 5.5 years in men and 72.1 ± 5.7 in women. The mean SMI was 7.71 ± 0.79 kg/m2 in men and 6.51 ± 0.70 kg/m2 in women.

According to the EWGSOP-algorithm, 7.5% (n = 360) of all participants were classified as having sarcopenia. The prevalence of sarcopenia was 8.2% for men and 6.8% for women, but this difference was not significant (P = 0.09). The prevalence of sarcopenia increased with age in both men and women, with people aged 80 years and older having the highest prevalence rates (25.0% in men and 12.2% in women, Fig. 1).

figure

Figure 1. The prevalence of sarcopenia by age category and sex.

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The characteristics of normal and sarcopenic participants are summarized in Table 1. Compared with the normal participants, both male and female sarcopenic participants were significantly older (P < 0.01) and had lower BMI (P < 0.01). In addition, there were significant differences in the proportions of participants with hypertension (P < 0.01) and osteoporosis (P < 0.01).

Table 1. Comparison of characteristics of those with and without sarcopenia by sex according to the European working group on sarcopenia in older people algorithm
VariablesMenWomen
NormalSarcopeniaP-valueNormalSarcopeniaP-value
(n = 2,152)(n = 191)(n = 2,299)(n = 169)
  1. Values are mean ± SD or %. ASM, appendicular skeletal muscle mass; BMI, body mass index; MMSE, Mini-Mental State Examination; SMI, skeletal muscle index.

Ageyears71.8 ± 5.276.0 ± 7.2<0.0171.9 ± 5.574.5 ± 7.0<0.01
BMIkg/m224.0 ± 2.719.9 ± 1.6<0.0123.5 ± 3.219.0 ± 1.8<0.01
SMIkg/m27.8 ± 0.76.6 ± 0.4<0.016.6 ± 0.75.5 ± 0.3<0.01
Diagnosis%      
Hypertension49.134.6<0.0145.134.3<0.01
Diabetes mellitus15.817.80.4611.04.70.01
Stroke7.18.40.503.84.10.82
Heart disease19.216.20.3213.914.80.75
Respiratory disease12.820.9<0.019.112.40.16
Cancer11.416.20.058.55.30.15
Osteoporosis1.16.3<0.0119.231.4<0.01
Gait speedm/s1.3 ± 0.21.1 ± 0.2<0.011.3 ± 0.21.2 ± 0.3<0.01
Grip strengthkg33.7 ± 5.824.5 ± 3.2<0.0121.3 ± 4.015.8 ± 2.5<0.01
MMSEscore25.9 ± 2.724.8 ± 3.2<0.0126.5 ± 2.826.1 ± 3.40.13

We also calculated the prevalence of sarcopenia using the muscle mass and strength algorithm, and compared the prevalence of sarcopenia determined using the two methods (Fig. 2). The present results showed that the two algorithms produced similar overall estimates of sarcopenia prevalence (7.5% vs 7.3% using the EWGSOP and muscle mass and strength algorithms, respectively). The same participants were identified by both algorithms, with the exception of seven people (0.15%) who were classified as having sarcopenia using the EWGSOP-algorithm, but who did not have sarcopenia according to the muscle mass and strength algorithm. Conversely, all of the participants (n = 353) classified with sarcopenia by the muscle mass and strength algorithm were also defined as having sarcopenia using the EWGSOP-algorithm.

figure

Figure 2. The prevalence of sarcopenia in the community setting determined using two different algorithms. (a) The European working group on sarcopenia in older people-suggested algorithm of sarcopenia. (b) The algorithm based on muscle strength and muscle mass to determine sarcopenia.

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Discussion

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

The EWGSOP recommends that cut-off values for handgrip strength were 30.0 kg in men and 20.0 kg in women.[10] In a sample of Japanese older adults, Tanimoto et al. reported the cut-off values for low grip strength were 30.3 kg in men and 19.3 kg in women.[20] However, the EWGSOP recommendations were based on results that included non-Japanese participants. Tanimoto et al. recruited regular attendees of welfare centers for the aged or community centers to their study.[20] As a result, the generalizability of their results might be limited, and it may not be appropriate to apply their cut-off values in the present study. The present study, using a similar methodology as several previous studies, applied the lowest quintile of grip strength in a healthy subset of subjects (aged ≥65 years) as the cut-off point. The cut-off values for grip strength determined using this method were slightly lower than those published in previous studies. The validity of the cut-off points used in the present study remains to be determined.

We also used sex-specific quintile points (lowest 20%) as the cut-off values for SMI, and these values were similar to previously reported cut-off points of >2 standard deviations less than the mean value for young Japanese adults (7.0 kg/m2 in men and 5.8 kg/m2 in women).[20] These results suggest that the lowest 20% of SMI in Japanese older adults could be a useful substitute for the value two standard deviations below the sex-specific mean SMI of young adults.

Using the EWGSOP-algorithm, 7.5% of all participants were classified as having sarcopenia. The prevalence of sarcopenia in older adults has been widely investigated in European and American countries, and most of these values ranged from 10% to 30%.[3, 5, 21, 22] Reports published on the prevalence of sarcopenia in older adults in Asian countries have tended to show a lower prevalence of sarcopenia in Japan (11.3% and 10.7% in men and women, respectively),[20] Korea (12.1% and 11.9% in men and women, respectively),[23] Hong Kong (12.3% and 7.6% in Chinese men and women, respectively)[24] and Taiwan (23.6% and 18.6% in men and women, respectively).[25] The present study found a similarly low prevalence of sarcopenia. Differences in the prevalence rate of sarcopenia between studies might be as a result of real differences between races and regions. However, because of differences in the operative definitions and screening methods used to detect sarcopenia, we could not directly compare our results with other studies. In addition, the cut-off values for grip strength that we used were slightly lower than those of previous studies. This might lead to an underestimation of the prevalence rate of sarcopenia in our sample. Additional studies are required not only to confirm the validity of cut-off points, but also to determine the standardized definition of sarcopenia.

We tested two screening methods for determining sarcopenia in the present study: (i) the EWGSOP-suggested algorithm using gait speed as the first step; and (ii) the muscle mass and strength algorithm. The resulting prevalence rates of sarcopenia corresponded closely. Although the EWGSOP-algorithm uses a measurement of gait speed as the first step with a cut-off point of 0.8 m/s, there were few people whose gait speed was below 0.8 m/s in our sample of community-dwelling older adults. In addition, most participants categorized as slow (gait speed <0.8 m/s) also had muscle weakness. In fact, Buchner et al. reported that the relationship between muscle strength and gait speed was non-linear, and small changes in muscle strength could have substantial effects on gait speed in frail adults, whereas large changes in muscle strength have little or no effect in healthy adults.[26] The EWGSOP report does not specifically recommend a method for measuring gait speed, and variations in methodology exist (e.g. walking courses may or may not include acceleration and deceleration phases). Differences in the methodology used to measure gait speed could be one reason why a cut-off point of 0.8 m/s was too low for the present study. In any case, we consider that a cut-off value of 0.8 m/s will be too slow if the acceleration and deceleration phases are excluded from the measurement of gait speed. It is debatable whether gait speed is necessary for screening sarcopenic participants in community-dwelling older adults. Future research should examine the necessity of including gait speed in algorithms and the validity of cut-off values.

The present study had several limitations that should be recognized. First, the response rate to postal invitation was 35.7%, and as a result, it is possible that our study suffered from selection bias. Second, we estimated the appendicular skeletal muscle mass by BIA methods. Although BIA is reported to be a highly reliable and accurate method of assessing muscle mass, the accuracy of BIA measurement can be affected by factors such as hydration status, food intake and exercise.[27] Older adults in particular can often have disturbances in water balance and/or extracellular water retention (e.g. edema). Yamada et al. suggested that extracellular water might mask actual muscle atrophy.[28] More precise methods (dual-energy X-ray absorptiometry or magnetic resonance image) should be used in future to assess muscle mass. Third, we used pragmatic cut-off points for determining sarcopenia. It is currently unclear whether the sex-specific lowest 20% was the best value for screening sarcopenic participants. Additional longitudinal studies will be required to confirm the predictive validity of the cut-off values in the future.

The present study showed that the prevalence of sarcopenia in a representative sample of older Japanese adults was 8.2% for men and 6.8% for women based on the EWGSOP-algorithm. When compared with the muscle mass and strength algorithm, the EWSOP-algorithm classified seven additional people (0.15%) into sarcopenia. Future research should examine the necessity of including gait speed in algorithms and the validity of cut-off values.

Acknowledgments

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

This study was supported in part by a Health Labor Sciences Research Grant (Comprehensive Research on Aging and Health: H23-Choju-Ippan-001) and the Research Finding for Longevity Sciences (22-16) from National Center for Geriatric and Gerontology, Japan. We thank the Obu City Office for help with participant recruitment.

References

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