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COMPARISON OF AGREEMENT BETWEEN SEVERAL DIAGNOSTIC CRITERIA OF SARCOPENIA IN COMMUNITY-DWELLING OLDER ADULTS

N. Sosowska, M. Pigłowska, A. Guligowska, B. Sołtysik, T. Kostka

Department of Geriatrics Healthy Ageing Research Centre, Medical University of Lodz, Lodz, Poland

Corresponding Author: Natalia Sosowska, Department of Geriatrics Healthy Ageing Research Centre, Medical University of Lodz, Lodz, Poland, natalia.sosowska@umed.lodz.pl
J Frailty Aging 2021;in press
Published online April 12, 2021, http://dx.doi.org/10.14283/jfa.2021.13

 


Abstract

Purpose: Several diagnostic algorithms exist to detect sarcopenia in older adults. We compared the prevalence of sarcopenia according to the selected diagnostic algorithms.
Methods: This cross-sectional study compared the European Working Group of Sarcopenia in Older People (EWGSOP) 2010, updated EWGSOP 2019, the Foundation for National Institutes of Health (FNIH) and the International Working Group on Sarcopenia (IWGS) criteria in 778 outpatients of the Geriatric Clinic aged 60 to 89 years. Bioimpedance analysis (BIA) to estimate muscle mass, hand-held hydraulic dynamometer to measure muscle strength, the TUG test and gait speed to assess physical function were used.
Results: The prevalence of sarcopenia varied from 0% to 6.43% depending on the algorithm. For the majority of associations between the different definitions of sarcopenia the agreement was null or fair (Cohen’s kappa between 0.2 and 0.4). Moderate agreement (Cohen’s kappa between 0.4 and 0.6) was found for only three relationships. Nevertheless, for these three relationships, McNemar’s test has given different results, indicating that even in the moderately agreeing algorithms, the shared diagnoses of sarcopenia concerned only part of subjects.
Conclusions: According to diagnostic algorithms the prevalence of sarcopenia is low in independent community-dwelling older adults. The agreement between the different definitions is poor.

Key words: Sarcopenia; walking speed, Timed Up and Go, handgrip, BIA.


 

Introduction

Sarcopenia is a disease affecting skeletal muscles. The term refers to the age-related muscle mass loss, deterioration of muscle function, and consequently physical capacity decline (1). In 1989 the term «sarcopenia» was used for the first time by Rosenberg as a combination of two words: Greek «sarx» for body (meat) and «penia» for loss, what refers to a noticeable decrease in muscle mass with age (2). The pathophysiology of sarcopenia includes decreasing number of muscle fibres, physical inactivity, hormonal changes, concomitant diseases and other potential reasons like a role of the gut microbiome (3).
The prevalence of sarcopenia is similar in women and men and sarcopenia is widespread especially amongst older, physically weak patients with osteoporosis and deteriorated health conditions (4, 5). The main and the most important manifestation of the problem is daily activity disfunction and recurring fall incidents (6). Increasing life expectancy and consecutive older population growth make the problem of sarcopenia more important than ever before (7). Exercise intervention, especially resistance training, is the best established method of the prevention and treatment of sarcopenia (8).
In 2010 The European Working Group of Sarcopenia in Older People (EWGSOP) created the operational diagnostic criteria for identification of people with sarcopenia (9). Then, in 2019 the document was updated, and the new definition was launched [10]. In the meantime, the Foundation for National Institutes of Health (FNIH) (11) and the International Working Group on Sarcopenia (IWGS) (6) presented their own definitions. Such a variety of different diagnostic criteria creates a problem of choosing the most sensitive and specific algorithm.
Several studies comparing different diagnostic criteria were performed (12-18). However, most of them do not include all definitions of sarcopenia mentioned above. Therefore, the aim of the present study was to compare the prevalence of sarcopenia according to selected diagnostic algorithms in a large population of older subjects.

 

Material and methods

Subjects

The study was performed in 778 outpatients of the Geriatric Clinic of the Medical University of Lodz, Poland, aged 60 to 89 years who volunteered to participate in the study. The inclusion criteria were age 60 years and over, living in the community, logical contact that allows to understand the instructions, ability to walk, and written consent to participate in the study. We excluded patients who were not able or refused to perform necessary tests, not able to stand, with serious psycho-cognitive impairment, post recent hand surgery or inflammation in this location as well as those with an implemented pacemaker. The study flow chart has been presented in the Figure 1.

Figure 1. The flow chart of the study

 

The study was approved by the Bioethics Committee of the Medical University of Lodz and complies with the Declaration of Helsinki and Good Clinical Practice Guidelines.

Muscle mass

Bioimpedance analysis (BIA) was used to estimate muscle mass with AKERN BIA 101 device (Akern, Italy). The measurements were taken in the morning, with patients who have completed a minimum 4 hours fasting, in a supine position, between the patient’s wrist and ankle, on the right side of the body. Body mass and body height of participants were assessed with the RADWAG personal scale weight (WPT60_150OW) (Radwag Balances and Scales, Radom, Poland). The obtained raw BIA data: resistance (R, Ohm) and reactance (Xc, Ohm), as well as sex, body mass and body height were used to calculate skeletal muscle mass (SM) (19) and skeletal muscle index (SMI) (20), as well as to estimate appendicular skeletal muscle mass (ASM) (21) and appendicular lean mass (ALM) (22).

Muscle strength

The Jamar hand-held hydraulic dynamometer (model F12-0600) was used to measure muscle strength according to the standardized protocol (23). Patient in standing position repeated tests 3 times with each hand. The best score was used for analysis.

Physical performance

To assess physical function, the TUG test and gait speed were used. In TUG test, the time that participant needed to rise from a chair, walk 3 meters, turn around, walk back and sit down on the chair was recorded (24). Gait speed was assessed with the 4-meter Walking Test in 163 subjects (25). TUG test was performed in all the participants. To calculate gait speed values, the TUG test of 163 subjects was correlated with their gait speed (24, 26). For this correlation 0.8 m/s gait speed corresponded with 12.6s in TUG test while gait speed of 1 m/s with TUG of 9.9 s. Thereupon, these cut-off points of TUG test were used in the analysis for all the algorithms. TUG cut-off point of 20 seconds was also used as the performance measure for all the algorithms.

Sarcopenia algorithms

On the basis of the obtained data, the prevalence of sarcopenia was assessed according to different existing definitions: the EWGSOP (2010) (9), EWGSOP2 (2019) (10), FNIH (11) and IWGS (6). Additionally, some algorithms were used in different suggested variants.

The 2010 EWGSOP algorithm

In EWGSOP (2010) definition (9) Janssen formula to assess skeletal muscle mass was used (19), and SMI was calculated on the basis of Janssen et al. (19) The SMI cut-off points suggested by Janssen (20) as well as Chien (27) were used in the algorithm. According to this algorithm (9) patients were diagnosed as “sarcopenic” when they obtained the following results: 1) normal physical function, low muscle strength and low muscle mass; or 2) low physical function and low muscle mass.

The 2019 EWGSOP algorithm

In EWGSOP2 (2019) algorithm [10] ASM was presented in kilograms (kg) and kg divided by squared meters (kg/m2). According to this definition, sarcopenia was diagnosed in patients with low muscle strength and low muscle mass, whereas severe sarcopenia was diagnosed in patients who presented low muscle strength, low muscle mass and poor physical function. Additionally, probable sarcopenia was diagnosed according to the algorithm.

FNIH algorithm

On the basis of the FNIH definition patients were defined as sarcopenic when they presented low muscle strength and low muscle mass (11). As suggested, the ALM was presented as ALM/BMI. The cut-off points suggested by Cawthon et al. (28) were used.

IWGS algorithm

In the IWGS definition (6) the ALM divided by the height squared (ALM/ht²) was used with cut-off points of ≤ 7.23 kg/ m² in men and ≤ 5.67 kg/m² in women (6). According to the IWGS definition sarcopenia is diagnosed amongst patients with poor physical function and low muscle mass (6).
The cut-off points used in selected algorithms of sarcopenia are presented in Table 1. Algorithms for sarcopenia case findings and the number of patients diagnosed with sarcopenia according to different algorithms have been presented in Figures 2, 3, 4 and 5.

Table 1. Cut-off points according to selected algorithms of sarcopenia

Figure 2. EWGSOP 2010 algorithm. The number of patients with the risk of sarcopenia

J – Janssen cut-off points; C – Chien cut-off points

Figure 3. EWGSOP 2019 algorithm. The number of patients with the risk of sarcopenia

Figure 4. FNIH algorithm. The number of patients with the risk of sarcopenia

Figure 5. IWGS algorithm. The number of patients with the risk of sarcopenia

 

Statistical methods

Statistical analysis was performed using the Statistica (13.1) software (StatSoft). Cohen’s kappa and Mc Nemar’s test were used to compare the agreement between the different definitions of sarcopenia. Fleiss kappa was used to assess the overall concordance rate between the nine definitions of sarcopenia. Sample size required for the contingency tables in the category 2×2 to detect a difference between the two Cohen’s kappa coefficient values (K1 and K2) of 0.2 (K1=0.0 vs K2=0.2) is 194, while a difference of 0.3 (K1=0.0 vs K2=0.3), 0.4 (K1=0.0 vs K2=0.4) and 0.5 (K1=0.0 vs K2=0.5) will yield a minimum sample size of 85, 47 and 29, respectively. The level of significance was set at p < 0.05.

 

Results

The characteristics of the patients have been presented in Table 2. Mean age of the studied population was 72 years and almost two third were women.

Table 2. Characteristics of the patients

 

The number of patients with sarcopenia and agreement between all analysed algorithms is presented in the Table 3. According to the EWGSOP 2010 definition, more sarcopenic individuals were found while Janssen cut-off points were used (6.43%) in comparison to Chien cut-off points (2.83%). Using the EWGSOP 2019 algorithm, 0.13% (ASM in kg/m²) and 1.54% (ASM in kg) of subjects were diagnosed as sarcopenic. Moreover, 0% (ASM in kg/m²) and 0.64% (ASM in kg) of patients were found as “sarcopenia severe”, but 4.37% as “probable sarcopenia”. The percentage of patients with sarcopenia according to the FNIH definition was 0.77% and according to IWGS criteria was 1.28%.
Cohen’s kappa (standard error for kappa) have been presented in the higher-right part of the Table 3. Number of patients with sarcopenia in a cross-table design (number of patients diagnosed with sarcopenia concomitantly in the two algorithms) and McNemar’s test have been shown in the lower-left part of the Table 3.

Table 3. Number of patients with sarcopenia and agreement between selected algorithms. Cohen’s kappa (standard error for kappa) have been presented in the higher-right part of the Table. Number of patients with sarcopenia in a cross-table design (number of patients diagnosed with sarcopenia concomitantly in the two algorithms) and McNemar’s test have been shown in the lower-left part of the Table

d – different; *p ≤ 0,05; **p ≤ 0,01; ***p ≤ 0,001; ns – not significant or values less than 0 for Cohen’s kappa

 

For the majority of associations the agreement was null (slight) or fair (Cohen’s kappa between 0.2 and 0.4). Moderate agreement (Cohen’s kappa between 0.4 and 0.6) was found for only three relationships. Nevertheless, for these three relationships, McNemar’s test has given different results, indicating that even in the moderately agreeing algorithms, the shared diagnoses of sarcopenia concerned only part of subjects. Lack of difference with McNemar’s test between some of the algorithms was due to the very small number of diagnosed sarcopenia cases. Cohen’s kappa for these associations was null or fair. Furthermore, an overall Fleiss kappa was 0.145 (standard error 0.00598), which means that global concordance rate between the nine definitions of sarcopenia is null (slight).
When TUG cut-off point of 20 seconds was used as the performance measure for all the algorithms, the number of patients with sarcopenia for EWGSOP 2019 algorithm has changed only for “Severity, ASM in kg” option. Likewise, the number of patients with sarcopenia has diminished when applying the TUG 20 seconds cut-off point for EWGSOP 2010 and IWGS algorithms. It did not substantially change the agreement indicators between those algorithms which remained poor.

 

Discussion

In this study we have compared several definitions of sarcopenia in the largest so far Central-European population of older adults. Our data indicate that the overall agreement between those algorithms of sarcopenia definition is poor. This makes the message of geriatricians to the general medical community really diffused and hampers further use of sarcopenia as an important geriatric syndrome.
Available data in the literature present inconsistent results on the performance of screening methods for sarcopenia. The prevalence of sarcopenia varied from 5.7% to 16.7% depending on one of the five applied definitions in 306 community-dwelling subjects aged 74.8±5.9 years (29). The prevalence of probable sarcopenia at age 69 was 19% according to EWGSOP 2019 definition in1686 participants of the British National Survey of Health and Development (30). The prevalence of sarcopenia is higher in older and institutionalised subjects. Zeng et al. (15) presented the study about the prevalence of sarcopenia in 277 nursing home residents using 4 diagnostic criteria. The prevalence of sarcopenia was 32.5%,34.3%, 38.3%, and 31.4% according to the EWGSOP 2010, Asia Working Group for Sarcopenia (AWGS), IWGS, and FNIH criteria (15). In 249 older Spanish aged 84.9±6.7 years, 60.1% of the participants had sarcopenia and 58.1% had severe sarcopenia according to the EWGSOP 2019, while 63% had sarcopenia and 61.2%, severe sarcopenia according to the EWGSOP 2010 (14). On the other hand, sarcopenia prevalence according to EWGSOP 2010 (27.7%) was significantly higher than with EWGSOP 2019 (18.1%) in 144 older inpatients (12). In 114 patients with liver cirrhosis, 30.7% suffered from pre-sarcopenia and 36% from sarcopenia based on the EWGSOP 2010 definition, while with the EWGSOP 2019 definition, 3.5% were diagnosed with pre-sarcopenia and 16.7% with sarcopenia (31). The study carried out in patients with gastric cancer after gastrectomy showed similar prevalence of sarcopenia but suggests that sarcopenia defined by EWGSOP 2019 criteria better predicts clinical outcomes than that defined by EWGSOP 2010 criteria (32). In the recent large study, the prevalence of the disease has been shown to be low among the Canadian senior population and the agreement between IWGS, FNIH and EWGSOP 2019 definitions in diagnostic process of sarcopenia was poor (33).
One important issue is that the prevalence of sarcopenia prevalence varies with muscle strength or function definitions, and with population-specific vs. standard cut-off values (34). In 2,099 ambulatory community-dwelling older Korean adults the prevalence of probable sarcopenia (2.2%), confirmed sarcopenia (1.4%) and severe sarcopenia (0.8%) was low according to EWGSOP 2019 criteria (35). Differences in the prevalence of sarcopenia occurred depending on the criterion used, such as indicators of muscle strength or muscle mass (35). Amongst 248 older Turkish patients with endocrinological problems sarcopenia prevalence was 11.7% with EWGSOP 2019, and 41.1% by the use of regional grip strength thresholds for EWGSOP 2019 with body mass index adjustments for SM (36). In the present study, we used original cut-off points of selected algorithms. Nevertheless, adopting different muscle mass, muscle strength or performance criteria would have caused changes in the prevalence of sarcopenia. Furthermore, simplified diagnostic algorithms, e.g. without gait speed, are recently being sought (37).
Available literature shows that the agreement between existing definitions of sarcopenia is rather small. A slight to moderate agreement across five diagnostic definitions of sarcopenia was found, except the substantial agreement (Cohen’s kappa 0.71) observed when comparing the EWGSOP 2010 and IWGS definitions (29). Subsequent study about prevalence of the sarcopenia in 483 Chinese community-dwelling older people has shown a lack of consistency in the application of the EWGSOP 2019 definition in comparison with EWGSOP 2010, AWGS, IWGS and FNIH definitions (16). The prevalence of EWGSOP 2019-defined sarcopenia (men: 6.5%; women: 3.3%) was lower than that defined by the EWGSOP 2010 (men: 22.3%; women 11.7%), AWGS (men: 10.9%; women: 8.0%), and IWGS (men: 24.5%; women: 11.0%) criteria, but higher than FNIH criteria (men: 6.0%; women: 1.7%) (16). Another paper analysed the relationship between the criteria used in the old and the new EWGSOP definitions amongst 127 renal transplantation patients. There was a fair agreement between the two definitions when muscle strength measurement was performed by handgrip test, while the slight agreement was found for the Five Times Sit to Stand Test (13). In the study of Savas et al., the comparison of EWGSOP 2010 versus EWGSOP 2019 was not possible due to lack of sarcopenic patients with height adjustment (36).
In the present study the prevalence of sarcopenia was generally low in our community-dwelling older subjects. Furthermore, the prevalence of sarcopenia differed with EWGSOP 2010 definition depending on the SMI cut-off points and with EWGSOP 2019 criteria depending on algorithm (ASM presented in kg or in kg/m2, severe sarcopenia, probable sarcopenia). Overall agreement between all the selected definitions of sarcopenia was poor, suggesting further prospective studies and search for a more uniform algorithm of diagnosing sarcopenia.
Our study has some limitations. We were not able to conduct tests amongst people with severe dementia or those who due to mobility problems were not able to be present at our clinic and perform all the tests. Therefore, relatively young, high-functioning volunteer outpatients of the geriatric clinic participated in the study and the prevalence of sarcopenia was generally lower than in the literature, especially as compared to institutionalised older adults. Body composition cut-off values are usually based on DEXA, so it is not clear that these cut-off values can also be the same when measured by BIA. The study was conducted in a Central-European population and results may be different in other cultures. Nevertheless, the key strengths of the present study are careful recruitment procedures and large population studied.

 

Conclusion

An important difference in the diagnosis of sarcopenia according to diagnostic algorithms was found. These results support data from several previous studies (12, 13, 16, 17, 31)that there is a substantial mismatch in sarcopenia case finding between different algorithms. Further prospective studies and search for uniform definition are needed in order to present a robust algorithm of this important geriatric syndrome to the general medical community.

Key summary points

Aim: To investigate agreement between several diagnostic criteria of sarcopenia in community-dwelling older adults.
Findings: The prevalence of sarcopenia varied from 0% to 6.43% depending on the algorithm. The agreement between different definitions of sarcopenia was poor for the majority of associations.
Message: In independent community-dwelling older adults, the prevalence of sarcopenia is low and the agreement between the different definitions is poor according to the existing diagnostic algorithms. Further search for uniform definition is needed in order to present a robust algorithm of this important geriatric syndrome to the general medical community.

 

Acknowledgements: This study was supported by Grant 503/6-07701/503-61-002 from the Medical University of Lodz.

Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee: the Ethics Committee of the Medical University of Lodz, and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

 

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