Low skeletal muscle mass index is associated with function and nutritional status in residents in a Turkish nursing home

Abstract

Introduction: To determine the prevalence of low muscle mass (LMM) and the relationship between LMM with functional and nutritional status as defined using the LMM evaluation method of European Working Group on Sarcopenia in Older People (EWGSOP) criteria among male residents in a nursing home.

Methods: Male residents aged >60 years of a nursing home located in Turkey were included in our study. Their body mass index (BMI) kg/m², skeletal muscle mass (SMM-kg) and skeletal muscle mass index (SMMI-kg/m²) were calculated. The participants were regarded as having low SMMI if they had SMMI <9.2 kg/m² according to our population specific cut-off point. Functional status was evaluated with Katz activities of daily living (ADL) and Lawton Instrumental Activities of Daily Living (IADL). Nutritional assessment was performed using the Mini Nutritional Assessment (MNA). The number of drugs taken and chronic diseases were recorded.

Results: One hundred fifty-seven male residents were enrolled into the study. Their mean age was 73.1 ± 6.7 years with mean ADL score of 8.9 ± 2.0 and IADL score of 8.7 ± 4.6. One hundred twelve (71%) residents were aged >70 years. Thirty-five men (23%) had low SMMI in group aged >60 years, and twenty-eight subjects (25%) in the group aged >70 years. MNA scores were significantly lower in residents with low SMMI compared with having normal SMMI (17.1 ± 3.4 versus 19.6 ± 2.5, p = 0.005). BMI was significantly lower in the residents with low SMMI compared with normal SMMI (19.6 ± 2.7 versus 27.1 ± 4.1, p< 0.001). ADL scores were significantly different between residents with low SMMI and normal SMMI in those aged >70 years (8.1 ± 2.6 versus 9.1 ± 1.6, p = 0.014). In regression analyses, the only factor associated with better functional status was the lower age (p = 0.04) while the only factor associated with better nutrition was higher SMMI (p = 0.01).

Conclusions: Low SMMI detected by LMM evaluation method of EWGSOP criteria is prevalent among male nursing home residents. There is association of low SMMI with nutritional status and probably with functional status within the nursing home setting using the EWGSOP criteria with Turkish normative reference cut-off value.

• EWGSOP
• low skeletal muscle mass
• male
• nursing home
• nutritional status

Introduction

Sarcopenia is a prevalent syndrome in geriatric populations [1]. It is associated with increased risk of falls, impaired ability to perform activities of daily living, loss of independence, and increased risk of death. Until recently, different definitions of sarcopenia has been used in the literature. In order to correct this concept confusion, in 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) published a consensus report in which they recommended the presence of both low muscle mass and low muscle function (strength or performance) for the diagnosis of sarcopenia [2–4].

There are different definitions of skeletal muscle mass in the literature. Baumgartner et al. defined sarcopenia as reduction in appendicular skeletal muscle mass (ASM) divided by height squared (ASM/height²) of two standard deviations (SD) or more below the normal means for a younger reference group measured using dual X-ray absorptiometry [5]. Janssen et al. estimated muscle mass from bioimpedance analysis measurements and expressed it as the skeletal muscle mass index (SMI = skeletal muscle mass/body mass × 100) [6]. Masanes et al. preferred to use the kg/body surface area formula [7]. In the literature, some studies suggested a negative association of low muscle mass (LMM) with functional status [6,8] and some others failed to show an association [9]. The reason for this could be the use of different units and measurement methods for the definition of LMM. In our previous study in 2010, no significant association between low LMM and functional status was found among nursing home residents using the definition of LMM with kg/body surface area [10].

EWGSOP defined low muscle mass as skeletal muscle mass index (SMMI), which is calculated as skeletal muscle mass/height² – with the corresponding muscle mass unit of “kg/m²” that is two standard deviation below the mean SMMI of young male and female reference groups and recommended the use of normative data of the study population rather than other predictive reference populations [2–4]. Very recently, SMMI cut-off points according to the EWGSOP definition were defined in the Turkish population by use of the corresponding thresholds designated as two standard deviation below the mean SMMI of young male and female reference groups [11].

The objective of this study was to determine the prevalence of low SMMI and the relationship between low SMMI with functional and nutritional status as defined using the EWGSOP criteria in previously-evaluated nursing home men.

Materials and methods

Subjects and measurements

Male residents aged >60 years in a nursing home in Istanbul, Turkey, who were not bed-ridden were included in our study cross-sectionally. Participants were enrolled on September 2009. Male residents aged ≥70 years were also assessed as a subgroup. Height and weight were measured then body mass index (BMI) was calculated as weight (kilograms) divided by squared (meters²).

Body composition was assessed with bio impedance analysis (BIA) using a Tanita BC 532 model body analysis monitor [12]. The EWGSOP denoted BIA as the most valid, reliable and feasible method of measuring muscle mass in daily practice. Whole-body bioelectrical impedance analysis measurements were undertaken in a standardised manner, all being in erect position, within the same examination room early in the morning after the subjects completed a minimum 6-h fast and before any significant physical activity. Fat-free mass was measured using BIA and SMM was calculated using the following equation: SMM (kg) = 0.566*FFM (fat-free mass). The skeletal muscle mass index (SMMI) was calculated as skeletal muscle mass (kg)/height² [13].

The study participants were regarded as having low SMMI if they had SMMI <9.2 kg/m² according to our population specific cut-off point [11].

Similar to our previous study, the number of drugs used on a long-term basis, the number of chronic diseases, and also the functional and nutritional status evaluation were recorded by a physician. Functional status was evaluated using the 5-item Katz Activities of Daily Living (ADL) and 7-item Lawton Instrumental Activities of Daily Living (IADL) [14]. Nutritional assessment was performed using the Mini Nutritional Assessment (MNA). Residents with an MNA score <17 were assessed as having malnutrition, an MNA score of 17–23.5 was defined as at risk of malnutrition, and >24 was well nourished [15].

The study was conducted in accordance with the guidelines in the Declaration of Helsinki. All of the male residents meeting our inclusion criteria (male residents, >60 years of age, not bedridden) were invited for participation and those given informed consent (self or or the related conservators) were included. The study was approved by the local ethics committee – ethics committee of Istanbul University Istanbul Medical Faculty. The related legal permission was also provided from the nursing home administration.

Statistical analysis

The variables were investigated to determine whether they were normally distributed. Numerical variables were given as mean ± standard deviation for normally-distributed variables, and as median (minimum–maximum) for skew-distributed continuous variables. Categorical variables were shown as frequencies. The two groups were compared with independent sample t-test or Mann–Whitney U test when necessary. The p values less than 0.05 were accepted as significant. Linear regression analyses were formed to document the factors independently associated with functional and nutritional status. The statistical analysis was performed using the statistical package SPSS for Windows version 21.0. (SPSS Inc., Chicago, IL).

Results

Demographic data, low skeletal muscle mass prevalence, functional and nutritional status

One hundred fifty-seven male residents were enrolled into the study. Their mean age was 73.1 ± 6.7 years with a mean ADL score of 8.9 ± 2.0 and IADL score of 8.7 ± 4.6. The clinical characteristics of the subjects are summarised in Table 1(a).

Table 1a. Demographic data of the male residents > 60 years old (n = 157).

Parameter	Mean ± SD (min–max)	*Median (interquartile range)
Age (years)	[73.1 ± 6.7] (60–90)
Heights (cm)	[162.2 ± 7.7] (145–181)
*Weights (kg)	[67.0 ± 14.1] (43–105)	65.9 (20.3)
*BMI (kg/m^2)	[25.4 ± 5.0] (14.3–38.1)	25.2 (7.1)
*ADL score	[8.9 ± 2.0] (2–10)	10 (2)
*IADL score	[8.7 ± 4.6] (0–14)	9.5 (7)
*No of chronic diseases	[3.7 ± 1.8] (1–9)	4 (3)
*No of drugs	[7.5 ± 4.2] (0–18)	7 (6)
Skeletal muscle mass indexes (kg/m^2)	[10.2 ± 1.4] (6.1–15.1)

All the data are given as [mean ± SD] (min–max).

* Median and interquartile range are given for the skewed variables.

SD: standard deviation; BMI: body mass index; ADL: activities of daily living; IADL: instrumental activities of daily living.

One hundred twelve (71%) residents were aged >70 years. Their related clinical characteristics data are given in Table 1(b). Low SMMI was observed in 35 men (23%) in the group aged > 60 years and 28 men (25%) aged > 70 years.

Table 1b. Demographic data of the male residents > 70 years old (n = 112).

Parameter	Mean ± SD (min–max)	*Median (interquartile range)
Age (years)	[76.5 ± 4.6] (70–90)
Heights (cm)	[161.0 ± 7.7] (146–181)
*Weights (kg)	[66.1 ± 13.7] (41.7–105)	65.9 (20.3)
*BMI (kg/m^2)	[25.3 ± 4.9] (17.3–38.1)	25.2 (7.1)
*ADL score	[8.8 ± 1.9] (2–10)	10 (2)
*IADL score	[8.7 ± 4.5] (0–14)	9.5 (7)
*No of chronic diseases	[3.8 ± 1.8] (1–8)	4 (3)
*No of drugs	[7.5 ± 3.9] (0–16)	7 (6)
Skeletal muscle indexes (kg/m^2)	[10.2 ± 1.3] (7.9–13.6)

*Median and interquartile range are given for the skewed variables.

Data are given as [Mean ± SD] (min–max).

SD: standard deviation; BMI: body mass index; ADL: activities of daily living; IADL: instrumental activities of daily living.

Association of low skeletal muscle mass index with functional and nutritional status

• Subjects aged >60 years:

  The ADL scores and IADL scores were lower in residents who have low SMMI, but this was not statistically significant (9.0 ± 1.7 versus 8.3 ± 2.6; p = 0.057, and 9.0 ± 4.5 versus 7.8 ± 5.1; p = 0.18, respectively). The MNA scores were significantly lower in residents who have low SMMI compared with residents having normal SMMI (17.1 ± 3.4 versus 19.6 ± 2.5, p = 0.005).

  BMI was significantly lower in residents who have low SMMI compared with residents having normal SMMI (19.6 ± 2.7 versus 27.1 ± 4.1, p < 0.001). The related data are given in Table 2(a) .

• Subjects aged >70 years:

  The ADL scores were significantly different between residents who have low SMMI compared with residents having normal SMMI (8.1 ± 2.6 versus 9.1 ± 1.6, p = 0.014). IADL was lower in the residents who have low SMMI, but this was not statistically significant (7.7 ± 5.2 versus 9.1 ± 4.3, p= 0.20).

  The MNA scores were significantly lower in the residents who have low SMMI compared with residents having normal SMMI (17.3 ± 2.7 versus 19.3 ± 2.3, p= 0.022). BMI was significantly lower in the residents who have low SMMI compared with residents having normal SMMI (20 ± 2 versus 27.3 ± 4.1, p < 0.001). The related data are given in Table 2(b).

  Among residents having low SMMI, 8 residents (28.5%) were malnourished, 13 residents (46.4%) were at risk of malnutrition and 7 residents (25%) were well nourished. There were two malnourished residents having normal SMMI (2.3%), 13 residents (15.4%) were at risk of malnutrition and 69 residents (82.1%) were well nourished.

Table 2a. The comparison of MNA, BMI, ADL scores and IADL scores among ≥ 60 years old residents with low SMMI versus normal SMMI (n = 157).

Parameter	Low SMMI residents (n = 122; 77%)	Normal SMMI residents (n = 35; 23%)	p
ADL score	9.0 ± 1.7	8.3 ± 2.6	0.057
IADL score	9.0 ± 4.5	7.8 ± 5.1	0.18
MNA scores	19.6 ± 2.5	17.1 ± 3.4	0.005*
BMI (kg/m^2)	27.1 ± 4.1	19.6 ± 2.7	<0.001*

MNA: Mini Nutritional Assessment Test; BMI: body mass index; ADL: activities of daily living; IADL: instrumental activities of daily living.

Data are given as mean + standard deviation.

* Statistically significant p values.

Table 2b. The comparison of MNA, BMI, ADL scores and IADL scores among ≥ 70 years old residents with low SMMI versus normal SMMI (n = 112).

Parameter	Low SMMI residents (n = 84; 75%)	Normal SMMI residents (n = 28; 25%)	p
ADL score	9.1 ± 1.6	8.1 ± 2.6	0.014*
IADL score	9.1 ± 4.3	7.7 ± 5.2	0.20
MNA scores	19.3 ± 2.3	17.3 ± 2.7	0.022*
BMI (kg/m^2)	27.3 ± 4.1	20 ± 2	<0.001*

MNA: Mini Nutritional Assessment Test; BMI: body mass index; ADL: activities of daily living; IADL: instrumental activities of daily living.

Data are given as mean + standard deviation.

* Statistically significant p values.

We performed regression analyses to detect the factors that have independent association with functional and nutritional status. In the first linear regression analysis, ADL score was the dependent factor and age, MNA score, SMMI, number of diseases and number of drugs were the independent variables. The only factor associated with better functional status was the lower age (p = 0.04). In the second linear regression analysis, MNA score was the dependent factor and age, ADL score, SMMI, number of diseases and number of drugs were the independent variables. The only factor associated with better nutrition was higher SMMI (p = 0.01).

Discussion

In the present study, using the recommendations of the EWGSOP criteria we found the prevalence of LMM in men aged >60 years and >70 years as 23% and 25%, respectively. Using the same criteria, there was a strong association with malnutrition and LMM in both age groups. There was an association at the significance border between LMM and functional status in the group aged >60 years; while a significant correlation was detected in those aged >70 years.

In the literature, there are different definitions of LMM [5–7]. Studenski et al. accepted LMM as BMI-adjusted low lean mass – designated as <0.789 – in the Foundation for the National Institutes of Health (FNIH) sarcopenia project [16]. Kim et al. used appendicular skeletal muscle mass (ASM; kg), which was defined as the sum of the muscle mass in the arms and legs, assuming that all non-fat and non-bone tissues were skeletal muscles. ASM measurements were divided by weight and values that were 1 standard deviation below the mean of the sex-specific healthy reference group were considered as LMM [17]. Byeon et al. recently measured ASM adjusted for body weight [18]. Different definitions resulted in different prevalence rates. In our 2010 study, before the recommendation of EWGSOP, where we used the definition of LMM with kg/body surface area index, we detected the prevalence as 87.5%, which is much more higher than the prevalence detected in the present study [10]. Our results show the importance of using a standardised definition to make comparisons in international studies.

The prevalence of LMM differs between community-dwelling and institutionalised elders. In population-based surveys, LMM was found more prevalent in nursing home residents [19]. In the literature, there are few prevalence studies among nursing homes in which EWGSOP LMM definitions are used [20–22]. In these studies, only one reported the prevalence of low SMMI itself, but reported EWGSOP definition of sarcopenia as a whole. Obviously the participants reported to have sarcopenia had all low SMMI. Thus, the sarcopenia prevalence noted in these studies might give an opinion on low SMMI to be able to make some comparison. Among these studies, Senior et al. diagnosed 40.2% of residents as being sarcopenic among nursing home residents in which male participants had a mean SMMI of 8.9 ± 1.9 kg/m² [20]. Landi et al. identified 32.8% of residents as affected by sarcopenia [21]. The only study presenting low SMMI as itself is another Turkish study reported from a different city – Ankara. Presarcopenia – that is isolated low SMMI – was found in 18.4% and sarcopenia was detected in 29%, overall summing up to prevalence of 47.4% prevalence of low SMMI in residents aged over 65 years. Sarcopenia was also reported as associated with malnutrition [22]. Our prevalence of LMM in residents aged >60 years was 23%, and 25% in those aged >70 years is lower than these studies. In all those studies, the mean age was higher (> or about 80 years). One more point is that, in this study, we excluded bedridden residents, which probably resulted in an underestimation of LMM prevalence.

In our previous study in 2010, no significant association between LMM and functional status was found [10]. In contrast, in the present study, ADL scores were statistically significant between residents having low muscle mass and normal muscle mass in >70 years subgroup and at significance border in >60 years group. This shows that the EWGSOP definition of LMM correlates well with functional status using the national normative cut offs. Also, this definition shows that it works well. Our results also indicate that the correlation of LMM with functional status becomes more evident by aging which is in accordance with the former studies [22]. Like in our study, Yalcin et al. showed participants with sarcopenia had low scores for activities of daily living [22]. Malnutrition and malnutrition risk were more frequent in participants with sarcopenia in the present study (28.5% and 46.4%). In all, the aforementioned nursing home studies, malnutrition was reported more common in the sarcopenic residents [20–22]. On the other hand, the regression analysis revealed that ADL was not independently associated with low SMMI but malnutrition is. This is also in accordance with studies reported by Senior et al. and Landi et al. [20,21].

The present study has some limitations. We included 157 subjects limited to a single nursing home which may somewhat be judged as quite small and local. However, an important point is that there is significantly less older residents in the nursing homes of developing countries. This is both due to lower expected survival times in the developing countries and also to the cultural background. Only the male residents are the subject of the study and subjects who were bedridden were excluded for proper measurements. In addition, this nursing home is the largest one in Turkey, accepting submissions from all around the country. Thus, we believe that our study population shall serve as a significant data base for Turkey’s male nursing home residents. Also, the number of our study participants is much more higher than the number of male participants of the aforementioned studies while also higher than the their total study participant number [20–22]. The cross-sectional design prevented investigating causality while this was also valid for the other nursing home studies [20–23]. We excluded nursing home residents who were bedridden, which may have underestimated the prevalence of LMM in the study. In addition, we used only low muscle mass to have a view on sarcopenia because we reassessed the previous data set. Gait speed and handgrip strength were not used in the study.

In conclusion, this is the first study to evaluate the prevalence and association of low SMMI with functional and nutritional status within the nursing home setting using the EWGSOP LMM criteria with Turkish normative reference cut-off value. Our results also emphasise the probable better use of EWGSOP LMM index in predicting functional status in older people. This may provide a rationale for the development of interventions about LMM awareness in nursing homes.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.