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Nutritional status and body fat mass: Determinants of sarcopenia in community-dwelling older adults
Experimental Gerontology 122 (2019) 67–73
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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Nutritional status and body fat mass: Determinants of sarcopenia in community-dwelling older adults ⁎
Nasrin Nasimia, Mohammad Hossein Dabbaghmaneshb, , Zahra Sohrabia, a b
T
⁎
Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran Shiraz Endocrinology and Metabolism Research Center, Nemazee Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
A R T I C LE I N FO
A B S T R A C T
Section Editor: M Masternak
Background: Sarcopenia is defined as the old age syndrome characterized by profound decline in muscle mass and function. This study aimed to investigate the prevalence of sarcopenia and its risk factors in older adults. Methods: Totally, 501 older people aged 65 years and older were recruited. Sarcopenia was defined according to the criteria of the Asian Working Group for Sarcopenia (AWGS). For obtaining Skeletal Muscle mass Index (SMI), body composition was evaluated using Bioelectrical Impedance Analysis (BIA). Muscle strength and physical performance were measured by Handgrip Strength (HGS) and Gait Speed (GS), respectively. Nutritional status, physical activity level, and biochemical indicators were assessed, as well. Results: The prevalence of sarcopenia was 20.8%. Multiple logistic regression models of the predictors of decline in the components of sarcopenia showed that older age, low Body Mass Index (BMI), and serum albumin level were associated with a higher risk of low SMI. Low serum albumin level and older age were also predictive of low HGS. Besides, old age, high body fat mass, and low BMI were the risk factors of low GS. Conversely, increased Calf Circumference (CC) was protective against low SMI and GS. Finally, older age, male gender, low BMI, decreased mini-nutritional assessment score, low serum albumin level, and high body fat were associated with a higher risk of sarcopenia, whereas higher CC reduced its risk. Conclusion: The prevalence of sarcopenia is high among elderly individuals. This study underlined that sarcopenia might develop in older adults with impaired nutritional status and high body fat mass. Further studies could evaluate the effects of appropriate nutritional interventions on sarcopenia management and prevention.
Keywords: Sarcopenia Prevalence Nutritional status Body fat Older adults
1. Introduction Sarcopenia is defined as an age-related muscle reduction syndrome, which was originally described by Rosenberg in the 1980s (Rosenberg, 1989). Parallel to the development of researches in Europe and Asia, definition and diagnostic criteria for sarcopenia have evolved in the last two decades (Rolland et al., 2011). International research groups tried to clarify the practical clinical definition of sarcopenia to elucidate the parameters. In 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) asserted that sarcopenia is a condition characterized by progressive decline in the skeletal muscle mass in combination with low muscle function (decreased muscle strength or/and physical performance) (Cruz-Jentoft et al., 2010). In 2014, the Asian Working Group for Sarcopenia (AWGS) suggested a similar approach to
estimate sarcopenia based on Asian countries (Chen et al., 2014). The recently updated sarcopenia definition by EWGSOP “focuses on low muscle strength as a key characteristic of sarcopenia, uses detection of low muscle quantity and quality to confirm the sarcopenia diagnosis, and identifies poor physical performance as indicative of severe sarcopenia” (Cruz-Jentoft et al., 2018). Sarcopenia is a prevalent condition that is associated with major clinical problems in public health (Chen et al., 2016). Studies have shown that the morbidity rate of sarcopenia was 5–13% in the elderly individuals between 60 and 70 years old, and this rate increased to 11–50% in those older than 80 years (von Haehling et al., 2010). Its adverse outcomes for the elderly were closely related to the functional decline (Bastiaanse et al., 2012), high risk of falls and fractures (Yu et al., 2014), disability (Bravo-José et al., 2018), poor quality of life
Abbreviations: AWGS, Asian Working Group for Sarcopenia; BMI, body mass index; CC, calf circumference; FFM, fat-free mass; VFA, visceral fat area; BMC, bone mineral content; SMI, skeletal muscle mass index; HGS, hand grip strength; GS, gait speed; MNA, mini-nutritional assessment; IPAQ, International Physical Activity Questionnaire ⁎ Corresponding authors. E-mail addresses: [email protected] (M.H. Dabbaghmanesh), [email protected] (Z. Sohrabi). https://doi.org/10.1016/j.exger.2019.04.009 Received 10 November 2018; Received in revised form 7 April 2019; Accepted 17 April 2019 Available online 22 April 2019 0531-5565/ © 2019 Published by Elsevier Inc.
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2.2. Clinical characteristics
(Beaudart et al., 2015a), increased hospitalization rates (Gariballa and Alessa, 2013), and mortality (Batsis et al., 2014). Therefore, identifying the features and pathogenesis of this condition is vital to reduce its onset and development as well as to find better ways for its management. The underlying pathophysiological pathways of sarcopenia might be pertinent to various factors, such as reduction of anabolic hormones, abnormal protein metabolism, oxidative stress, chronic inflammations, nutritional deficiencies, and physical inactivity (Walrand et al., 2011; Evans, 2010; Fielding et al., 2011). Sarcopenia is an important area of public and geriatric health. However, results on its prevalence are controversial due to the variety of definitions and methodologies used to assess sarcopenia parameters. Indeed, its prevalence widely varied from 3% to 52% (Fielding et al., 2011). In subsequent studies, the mean prevalence of sarcopenia was reported to be 5–13% in individuals aged 60–70 years and 11–50% in those older than 80 years (Morley et al., 2014). With a rapid rise in the elderly population and the increase in life expectancy in the recent decades, sarcopenia has become a growing concern worldwide (Wu et al., 2016). Despite numerous studies on sarcopenia in the East Asian countries, few studies have been conducted in the Middle East (Gariballa and Alessa, 2013), especially Iran (Hashemi et al., 2016). In the recent years, Iran has encountered a rapid growth in its elderly population, and 29.9% of the population is expected to reach over 65 years old by 2050 (Afshar et al., 2016). Therefore, it is predictable that the elderly population will most likely be afflicted by sarcopenia, and more attention is warranted for assessing sarcopenia by global medical community. However, there are still limited studies in this area. This study aims to evaluate the prevalence of sarcopenia and severe sarcopenia according to the AWGS guideline in order to identify the associated factors among community-dwelling elderly population in southern Iran.
The participants were interviewed and requested to complete a general questionnaire including demographic and habitual variables including age, smoking habit, medical history, and drug consumption history. Medical history included hypertension, hyperlipidemia, myocardial infraction, known cardiomyopathy, osteoarthritis, osteoporosis, thyroid problems, cancer, diabetes, and cerebrovascular accidents. The participants were classified as former smokers if they had smoked 100 times in their life time and were classified as current smokers if they smoked some days or every day (Klein et al., 2013). Body weight, height, and calf circumference were measured by standard methods. 2.2.1. Nutritional assessment The participants' nutritional status was evaluated using MiniNutritional Assessment (MNA) questionnaire, Body Mass Index (BMI), and serum albumin level. The MNA-SF is a validated method for screening malnutrition, including 18 items in four categories as follows: anthropometric measurements, general state, dietary pattern, and selfassessment. In this questionnaire, scores < 17, between 17 and 23.5, and equal to or above 24 indicated malnutrition, risk of malnutrition, and well-nourished status, respectively (Mahdavi et al., 2015). BMI was calculated as weight (in kilogram) divided by square of height (in meter). The cutoff of 20 was considered for BMI, which seems to better distinguish underweight from normal weight in the elderly population (Veronese et al., 2013). 2.2.2. Physical activity assessment Validated International Physical Activity Questionnaire (IPAQ) was used to assess physical activity. The intensity of physical activity was described by Metabolic Equivalents (MET). Regarding IPAQ guideline, the total physical activity level was calculated as the sum of MET coefficient × minutes of activity per day × days per week for each item. Accordingly, the participants' physical activity levels were classified as high, moderate, and low based on scores > 3000, between 600 and 3000, and < 600 MET-min/week, respectively (Moghaddam et al., 2012).
2. Method 2.1. Study design and population The present study was a geriatric health examination survey for assessing the frequency of sarcopenia and its determinants among Iranian elderly individuals. This cross-sectional, population-based study was conducted in Shiraz, southern Iran with the total population of 1,565,572 people. The sample size was estimated to include 501 participants selected via multi-stage sampling. At first, three main health sectors providing health services to general public and private Primary Health Care (PHC) centers in the city were considered as three strata in the stratification sampling approach. In each strata, cluster sampling method was applied based on geographical locations of the PHC centers. Then, four clusters were randomly selected and the number of participants in each cluster was determined proportional to its size. The participants were randomly selected and invited to voluntarily participate in the survey. The data were collected during six months from August 2017 to February 2018. The study population consisted of community-dwelling older people whose health data, such as diseases, drug history, and phone number, were recorded in the Iranian electronic health system. The inclusion criteria were aging 65 years or older, living in the society, having the ability to walk independently, and not having any artificial limbs or limb prostheses. The exclusion criteria were having a history of severe cardiac, pulmonary, or musculoskeletal diseases, severe cognitive impairment, and co-morbidities related to higher risk of falls including Parkinson's or stroke. Elderlies with tumor and uncontrolled endocrine or metabolic diseases, such as diseases associated with thyroid gland and type II diabetes, and those who were currently institutionalized were also excluded from the study.
2.3. Diagnostic measures for sarcopenia Sarcopenia was defined using the suggested diagnostic algorithm by AWGS, which encompasses reduction in muscle mass, muscle strength, and/or physical function. Based on this definition, individuals with low skeletal muscle mass and low muscle strength or low physical performance were considered to have sarcopenia, and cases with low skeletal muscle mass and both low muscle strength and low physical performance were considered to have severe sarcopenia (Chen et al., 2014). 2.3.1. Skeletal muscle mass Body composition data were measured using a segmental multifrequency Bioelectrical Impedance Analysis (BIA) InBody S10 analyzer (BioSpace Co., Ltd., South Korea), which measured Skeletal Lean Mass (SLM), segmental lean mass of arms, trunk, and legs, Fat-Free Mass (FFM), Body Fat Mass (BFM), Visceral Fat Area (VFA), Bone Mineral Content (BMC), and protein content. Appendicular Skeletal Muscle mass (ASM) was obtained as the sum of segmental muscle mass values of the legs and arms. Skeletal Muscle mass Index (SMI) was defined as ASM divided by the square of height (in meter) for defining low skeletal muscle mass and sarcopenia. The reference values of < 7.0 kg/m2 and < 5.7 kg/m2 were adopted as SMI cutoff points for males and females, respectively (Chen et al., 2014). 2.3.2. Muscle strength Muscle strength was defined by Handgrip Strength (HGS). The participants squeezed a hydraulic hand dynamometer (model MSD, Sihan, Korea) in both hands three times with 15-second pauses in seated 68
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position. The maximum value was used for further analyses (Beaudart et al., 2015b). HGS values < 18 kg for females and < 26 kg for males were considered as cutoff points for the definition of sarcopenia (Chen et al., 2014).
participants diagnosed with sarcopenia were more at risk of malnutrition (51.9% vs. 20.2%) or malnourished (4.8% vs 3.0%) based on MNA results (p < 0.0001). Considering biochemical indicators, there was a significant difference between sarcopenic and non-sarcopenic participants with regard to the levels of albumin (p = 0.001) and TG (p = 0.009).
2.3.3. Physical performance The participants' muscle performance was determined using the usual Gait Speed (GS) over a distance of four meters. Each participant was asked to walk the distance without any help and then, the time was recorded in seconds by a chronometer (Beaudart et al., 2015b). GS < 0.8 m/s was determined as a low muscle performance indicator (Chen et al., 2014).
3.2. Prevalence of sarcopenia According to the AWGS cut-off, 33.1% of males (n = 84) and 14.6% of females (n = 36) had low SMI. The prevalence rates of low HGS and GS were respectively 6.3% and 52% among males and 6.1% and 64.4% among females. Among the participants with low SMI, low HGS values (sarcopenia) were observed in 17.8% (n = 89) and HGS and GS values below the cutoff point (severe sarcopenia) in 3% (n = 15). Therefore, the prevalence of sarcopenia and severe sarcopenia were respectively 23.6% and 3.9% in males and 11.8% and 2% in females. Overall, 20.8% of the study participants (n = 104) were classified as sarcopenic. In both genders, an age-dependent increase was found in the prevalence of sarcopenia (p = 0.003 for males and p = 0.008 for females) (Table 2). Such significant trend was also observed for all sarcopenia criteria (low SMI, HGS, and GS) in all age groups (65–69, 70–74, 78–79, and > 80 years) in both males and females. In all age groups, lower SMI, HGS, and GS and sarcopenia appeared to be more common among males.
2.4. Biochemical indicators Blood samples were collected after an overnight fasting in hormone laboratory of the endocrinology ward at Nemazee Hospital (an educational hospital affiliated to Shiraz University of Medical Sciences). Serum samples were separated, packed in two Eppendorf tubes, and stored at −70 °C. Then, the blood test, including serum albumin, BUN, creatinine, Fasting Blood Sugar (FBS), and lipid profile (Triglyceride (TG), Low-Density Lipoprotein (LDL), High-Density Lipoprotein (HDL), and total cholesterol), was analyzed by calorimetric assays using Biosystem SA auto-chemistry analyzer (DIRUI CS-T240, Spain). Serum albumin level ≤ 4.0 g/dl was considered as the cutoff point (Uemura et al., 2018).
3.3. Sarcopenia and the associated factors 2.5. Statistical analysis Statistical analysis was done by IBM SPSS Statistics, version 19.0. The prevalence rates of sarcopenia, severe sarcopenia, and no sarcopenia were determined using descriptive analyses. The data were expressed as mean ± SD and percentage for continuous and categorical variables, respectively. The comparisons between continuous variables were analyzed using independent t-test or Mann-Whitney U test and comparisons between categorical variables were analyzed using chisquare test or Kruskal-Wallis H test. Additionally, Cochran-Armitage Trend test was used to assess sarcopenia components based on age groups. Factors associated with sarcopenia with p ≤ 0.2 in univariate analysis were entered in the multivariate model. Multiple logistic regression models were used to analyze the anthropometric and biochemical factors associated with the components included in the definition of sarcopenia (decline in SMI, HGS, and GS). The independent variables associated with sarcopenia were also tested using multivariate regression model. p-Values ≤0.05 were considered to be statistically significant.
The multivariate analysis of different sarcopenia components has been presented in Table 3. Increasing age (OR = 1.11, p = 0.004), low BMI (OR = 24.18, p = 0.004), and low serum albumin level (OR = 2.85, p = 0.012) were the consistent risk factors of low SMI, whilst increased calf circumference (OR = 0.62, p < 0.0001) was protective against low SMI. Moreover, older age (OR = 1.15, p = 0.003) and decreased serum albumin level (OR = 9.26, p = 0.039) were significantly associated with a higher risk of low HGS. Furthermore, increased calf circumference was protective against low GS (OR = 0.82, p = 0.001), whereas old age (OR = 1.10, p = 0.002), low BMI (OR = 5.22, p = 0.016), and increased body fat mass (OR = 1.14, p = 0.014) enhanced the risk of low GS significantly (Table 3). Analysis of the independent variables associated with sarcopenia revealed that older age, male gender, low BMI, decreased MNA, low serum albumin level, and increased body fat mass enhanced the risk of sarcopenia significantly. Conversely, increased calf circumference reduced the risk of sarcopenia. However, no significant association was observed between smoking habit and sarcopenia (Table 4).
3. Results
4. Discussion
3.1. Participants
This cross-sectional study was the first to evaluate the prevalence and associated factors of sarcopenia in elderly population in southern Iran. According to AWGS cutoff point, 27.5% of males and 13.8% of females participating in this study had sarcopenia. Overall, 20.8% of the participants were sarcopenic. The prevalence of sarcopenia has been estimated up to 29% of the elderly in community-dwelling population and 14–33% in those requiring long-term care worldwide (Fuggle et al., 2017). It is noteworthy to state that the prevalence of sarcopenia varies across various studies due to the different diagnosis criteria, measurement tools, racial characteristics, study populations, and elderly's age groups, genders, dietary regimens, and quality of life (Beaudart et al., 2015b; Pagotto and Silveira, 2014a). Pagotto and Silveira showed that sarcopenia prevalence varied from 6.1% to 36.6% depending on the diagnostic criteria and tools used for its detection (Pagotto and Silveira, 2014b). In addition, the latest meta-analysis indicated that the sarcopenia prevalence was lower in Asian countries compared to non-Asian ones (Shafiee et al., 2017).
A total of 501 participants, including 254 males (50.7%), with the mean age of 70.35 ± 4.60 years were recruited in this study. Comparison of the participants with and without sarcopenia with respect to anthropometrics and biochemical variables has been shown in Table 1. Compared to the participants without sarcopenia, those with sarcopenia were significantly older (p < 0.0001) and current or former smokers (p = 0.002). Considering body composition characteristics, the sarcopenic participants had significantly lower weight, BMI, FFM, ASM, BFM, VFA, protein, BMC, and calf circumference (p < 0.0001 for all comparisons). Similarly, the participants with (versus without) sarcopenia had lower SMI, HGS and GS (p < 0.0001 for all comparisons). However, no significant difference was found between the two groups concerning the level of physical activity reported by IPAQ score (p = 0.593). The majority of the participants had low physical activity (61.7%) and only 4.8% of them were more active. Additionally, the 69
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Table 1 Comparison of the sarcopenic and non-sarcopenic participants regarding baseline characteristics. Variables
Total (n = 501)
Male sex, n (%) Age, years Age category, n (%) 65–69 years 70–74 years 75–79 years > 80 years Smoking, n (%) Never (%) Former (%) Current (%) Anthropometrics Weight, kg BMI, kg/m2 CC, cm FFM, kg Total body fat, kg VFA, cm2 Protein, kg BMC, kg Sarcopenia criteria ALM, kg SMI, kg/m2 HGS, kg GS, m/s MNA, n (%) Well-nourished Risk of malnutrition Malnutrition IPAQ, n (%) Low Moderate High Biochemical Albumin, g/dl Creatinine, mg/dl BUN, mg/dl FBS, mg/dl Total cholesterol, mg/dl LDL-C, g/dl HDL-C, mg/dl Triglycerides, mg/dl
Sarcopenic (n = 104)
254 (50.7) 70.3 ± 4.6
Non-sarcopenic (n = 397)
70 (67.3) 72.6 ± 5.3
184 (46.3) 69.7 ± 4.1
(26.9) (40.4) (20.2) (12.5)
213 131 39 14
p-Value
< 0.0001 < 0.0001 < 0.0001
241 (48.1) 173 (34.5) 60 (12.0) 27 (5.4)
28 42 21 13
(53.7) (33.0) (9.8) (3.5)
341 (38.1) 58 (11.6) 102 (20.4)
56 (53.8) 21 (20.2) 27 (26.0)
285 (71.8) 37 (9.3) 75 (18.9)
69.3 ± 12.3 27.2 ± 4.7 35.2 ± 3.6 44.0 ± 8.9 24.9 ± 9.0 95.2 ± 29.0 8.8 ± 2.2 2.4 ± 0.4
57.7 ± 7.7 23.1 ± 3.4 31.5 ± 2.1 38.2 ± 7.0 19.0 ± 6.9 76.6 ± 25.5 7.6 ± 1.1 2.1 ± 0.3
72.4 ± 11.5 28.3 ± 4.4 36.1 ± 3.3 45.5 ± 8.8 26.4 ± 9.0 100.1 ± 27.9 9.1 ± 2.4 2.4 ± 0.4
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
18.4 ± 4.4 7.1 ± 1.0 42.4 ± 18.2 0.84 ± 0.66
15.7 ± 3.1 6.2 ± 0.6 33.3 ± 12.7 0.67 ± 0.13
19.2 ± 4.4 7.3 ± 0.9 44.8 ± 18.6 0.88 ± 0.73
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
361 (72.1) 134 (26.7) 6 (1.2)
45 (43.3) 54 (51.9) 5 (4.8)
316 (79.6) 80 (20.2) 1 (3.0)
309 (61.7) 168 (33.5) 24 (4.8)
64 (61.5) 38 (36.5) 2 (1.9)
245 (61.7) 130 (32.7) 22 (5.5)
3.9 ± 0.2 0.88 ± 0.20 14.7 ± 5.1 99.8 ± 31.4 182.7 ± 40.9 100.8 ± 33.1 51.1 ± 12.1 143.3 ± 71.4
3.8 ± 0.2 0.90 ± 0.13 14.8 ± 5.0 97.8 ± 40.7 178.6 ± 40.0 103.4 ± 34.1 51.4 ± 13.4 127.7 ± 64.4
3.9 ± 0.2 0.88 ± 0.22 14.7 ± 5.1 100.4 ± 28.4 183.8 ± 41.1 100.2 ± 32.8 51.1 ± 11.7 147.3 ± 72.6
0.002
0.822
0.001 0.507 0.940 0.520 0.324 0.441 0.926 0.009
BMI, body mass index; CC, calf circumference; FFM, fat-free mass; VFA, visceral fat area; BMC, bone mineral content; ALM, appendicular lean muscle mass; SMI, skeletal muscle mass; HGS, hand grip strength; GS, gait speed; MNA, mini-nutritional assessment; IPAQ, International Physical Activity Questionnaire. p-value < 0.05 is significant. Table 2 Components and prevalence of sarcopenia by age groups. Males
Low SMI Low hand grip Low gait speed Sarcopenia prevalence
Total
65–69 years
70–74 years
75–79 years
> 80 years
p for trend
n = 254
n = 99
n = 97
n = 42
n = 16
84 (33.1) 16 (6.3) 132 (52.0) 70 (27.6)
23 (23.2) 1 (1.0) 31 (31.3) 16 (16.2)
36 (37.1) 8 (8.2) 63 (64.9) 29 (29.9)
18 (42.9) 6 (14.3) 28 (66.7) 17 (40.5)
7 (43.8) 1 (6.3) 10 (62.5) 8 (50.0)
0.022* 0.016* < 0.001* 0.003*
Total
65–69 years
70–74 years
75–79 years
> 80 years
p for trend
n = 247
n = 142
n = 76
n = 18
n = 11
36 15 86 34
14 (9.9) 4 (2.8) 75 (52.8) 12 (8.5)
14 (18.4) 7 (9.2) 58 (76.3) 13 (17.1)
4 (22.2) 3 (16.7) 15 (83.3) 4 (22.2)
4 (36.4) 1 (9.1) 11 (100.0) 5 (45.5)
Females
Low SMI Low hand grip Low gait speed Sarcopenia prevalence
(14.6) (6.1) (35.6) (13.8)
Values are in n (%). * p < 0.05 70
0.020* 0.045* < 0.001* 0.008*
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Table 3 Multiple logistic regression models of the predictors of decline in muscle mass index, handgrip strength, and gait speed. Independent variables
Low SMI OR (95% CI)
Low HGS OR (95% CI)
Age, per 1 y BMI < 20 kg/m2 Calf circumference, per 1 cm increase Body fat, per 1 unit VFA, per 1 unit FFM, per 1 unit Protein, per 1 unit BMC, per 1 unit Albumin ≤ 4.0 g/dl TG, per 1 unit
1.11 (1.02–1.19)⁎ 24.18 (2.75–212.5)⁎ 0.62 (0.51–0.76)⁎ 0.89 (0.78–1.02) 1.03 (0.99–1.06) 0.97 (0.86–1.09) 0.54 (0.21–1.42) 1.74 (0.12–23.69) 2.85 (1.25–6.48)⁎ 0.99 (0.99–1.00)
1.15 4.69 1.00 1.06 0.98 0.96 0.85 1.31 9.26 1.00
⁎
(1.05–1.26)⁎ (0.90–24.26) (0.79–1.26) (0.84–1.34) (0.92–1.05) (0.88–1.05) (0.37–1.97) (0.04–35.65) (1.11–76.92)⁎ (0.99–1.00)
Low GS OR (95% CI) 1.10 5.22 0.82 1.14 0.98 0.98 0.62 2.35 1.48 1.00
(1.03–1.17)⁎ (1.35–20.12)⁎ (0.72–0.92)⁎ (1.02–1.27)⁎ (0.95–1.01) (0.89–1.08) (0.31–1.24) (0.41–13.45) (0.88–2.46) (0.99–1.00)
p < 0.05.
albumin level as a nutritional laboratory parameter (Curcio et al., 2016) in low SMI and HGS as well as high prevalence of sarcopenia. A previous prospective study also revealed that sarcopenia and low serum albumin level were important risk factors for disability in older people (Uemura et al., 2018). Additionally, low serum albumin level was correlated to poor nutritional status. Hence, it could cause a reduction in muscle mass and function due to the devastating effects of malnutrition on protein synthesis (Vandewoude et al., 2012). In this study, increased body fat mass was identified as a risk factor for sarcopenia as well as low GS. This finding suggested that fat mass might play a role in sarcopenia development. Fat accumulation in muscles might happen due to high total fat mass, which could negatively affect skeletal muscle mass through the elevated pro-inflammatory cytokines (Meng et al., 2014). The interplay between low SMI (sarcopenia) and high body fat mass (obesity) that is called sarcopenic obesity is associated with increased mortality risk in older adults (Batsis et al., 2014). The current study findings expressed the importance of assessing the balance between SMI and body fat mass instead of assessing BMI alone to manage sarcopenia. As suggested in the recent studies, the results of the present research confirmed that calf circumference could be used as a valuable measurement by clinicians to assess sarcopenia (Kim et al., 2015; Landi et al., 2014). The results showed a strong association between calf circumference and sarcopenia criteria. Therefore, calf circumference could provide worthy information on muscle mass and physical function, which could predict the risk of sarcopenia progression. Evidence has shown a relationship between sarcopenia and lifestyle factors, such as inactivity and smoking habits. Although the present sarcopenic participants had more smoking habits, the results of logistic regression analysis revealed no significant association between sarcopenia and smoking. This might be attributed to the simultaneous entrance of dependent variables, such as male gender, MNA, and BMI, in to the regression model, causing smoking to lose its power. In the same line, some previous studies expressed that smoking might not be an important risk factor for sarcopenia (Shaw et al., 2017). Although many studies have reported a relationship between low physical activity and sarcopenia, the present study findings revealed no significant difference between the participants with and without sarcopenia regarding physical activity level. This might be due to the inappropriate classification of activities as well as the self-report format of IPAQ. Therefore, it seems that IPAQ is not appropriate for assessment of physical activity among older adults.
Table 4 Independent risk factors associated with sarcopenia. Independent variables
OR (95% CI)
p-Value
Age, per 1-year increase Gender, male BMI < 20 kg/m2 MNA < 24 Albumin ≤ 4.0 g/dl Body fat, per 1 kg increase Calf circumference, per 1 cm increase Smoking habit
1.10 (1.02–1.18) 3.13 (1.23–7.98) 10.64 (2.20–51.25) 2.29 (1.03–5.09) 3.34 (1.40–7.95) 1.11 (1.03–1.19) 0.50 (0.40–0.62) 1.06 (0.50–2.27)
0.010 0.016 0.003 0.041 0.006 0.003 < 0.0001 0.862
Assessing the factors associated with sarcopenia revealed that sarcopenia prevalence was pertinent to various factors, including age, gender, BMI, MNA, serum albumin level, body fat, and calf circumference. Aging process is the major determinant involved in the progression of sarcopenia (Kim et al., 2016). In this context, the present study findings were in agreement with those of the previous studies, revealing a well-known association between age and sarcopenia in both genders (Kim et al., 2015). The findings demonstrated an age-dependent trend of increase in sarcopenia prevalence and decrease in muscle mass (SMI) and function (HGS, GS). Similar trends were also reported in the previous studies (Zeng et al., 2015; Yamada et al., 2013). There are many contradictions regarding the gender-related differences in the prevalence of sarcopenia in the available literature (Shaw et al., 2017). In confirmation of the previous studies, the current study results showed that males were more prone to sarcopenia. Some studies have reported that the differences in the level of Insulin-like Growth Factor-1 (IGF-1), as the crucial mediator of muscle growth, might justify the gender differences in the prevalence of sarcopenia. Accordingly, IGF-1 level was lower and, consequently, the rate of muscular atrophy was higher among older males compared to females (Albani et al., 2009). Nutritional status might have a great impact on the onset and development of sarcopenia (Hai et al., 2017). The present research findings showed that sarcopenia was related to poor nutritional status (low BMI, undesirable MNA results, and decreased albumin level). Additionally, low BMI was a major predictor of low SMI and GS as well as sarcopenia development. These findings were in line with those of the previous investigations (Tramontano et al., 2017; Senior et al., 2015; Han et al., 2015). Moreover, the prevalence of sarcopenia was significantly higher among the individuals with malnutrition or risk of malnutrition based on MNA results. These results were consistent with those of a previous study expressing low MNA score as a risk factor for sarcopenia (Liguori et al., 2018). Previous studies also indicated that malnourished elderly individuals had an increased risk of sarcopenia due to decreased muscle protein synthesis (Vandewoude et al., 2012). The current study results showed the predictive value of serum
4.1. Limitations of the study The present study had several limitations. Firstly, the study design was cross-sectional and no end-points or clinical outcomes were observed. In spite of the interesting associations, the data did not determine the causal relationships or mechanism of sarcopenia. Therefore, future longitudinal studies are recommended to determine 71
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the predictors leading to sarcopenia or vice a versa, and to confirm the causal relationships. Although the AWGS has considered BIA to be acceptable for muscle measurement, it is not the gold standard method. The recommended method is Dual X-ray Absorptiometry (DXA), but this method is expensive and used in hospitals. Hence, it is almost unreachable for community-dwelling elderly individuals. In the community setting, BIA technique might be regarded as a feasible, wellvalidated, and more practical screening method.
polymorphic variant of the insulin-like growth factor 1 (IGF-1) receptor correlates with male longevity in the Italian population: a genetic study and evaluation of circulating IGF-1 from the “Treviso Longeva (TRELONG)” study. BMC Geriatr. 9 (1), 19. Bastiaanse, L.P., Hilgenkamp, T.I., Echteld, M.A., Evenhuis, H.M., 2012. Prevalence and associated factors of sarcopenia in older adults with intellectual disabilities. Res. Dev. Disabil. 33 (6), 2004–2012. Batsis, J., Mackenzie, T., Barre, L., Lopez-Jimenez, F., Bartels, S., 2014. Sarcopenia, sarcopenic obesity and mortality in older adults: results from the National Health and Nutrition Examination Survey III. Eur. J. Clin. Nutr. 68 (9), 1001. Beaudart, C., Reginster, J.-Y., Petermans, J., Gillain, S., Quabron, A., Locquet, M., et al., 2015a. Quality of life and physical components linked to sarcopenia: the SarcoPhAge study. Exp. Gerontol. 69, 103–110. Beaudart, C., Reginster, J.-Y., Slomian, J., Buckinx, F., Dardenne, N., Quabron, A., et al., 2015b. Estimation of sarcopenia prevalence using various assessment tools. Exp. Gerontol. 61, 31–37. Bravo-José, P., Moreno, E., Espert, M., Romeu, M., Martínez, P., Navarro, C., 2018. Prevalence of sarcopenia and associated factors in institutionalised older adult patients. Clin. Nutr. ESPEN. 27, 113–119. Chen, L.-K., Liu, L.-K., Woo, J., Assantachai, P., Auyeung, T.-W., Bahyah, K.S., et al., 2014. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J. Am. Med. Dir. Assoc. 15 (2), 95–101. Chen, L.-K., Lee, W.-J., Peng, L.-N., Liu, L.-K., Arai, H., Akishita, M., et al., 2016. Recent advances in sarcopenia research in Asia: 2016 update from the Asian Working Group for Sarcopenia. J. Am. Med. Dir. Assoc. 17 (8), 767 (e1-. e7). Cruz-Jentoft, A.J., Baeyens, J.P., Bauer, J.M., Boirie, Y., Cederholm, T., Landi, F., et al., 2010. 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Kim, H., Suzuki, T., Kim, M., Kojima, N., Yoshida, Y., Hirano, H., et al., 2015. Incidence and predictors of sarcopenia onset in community-dwelling elderly Japanese women: 4-year follow-up study. J. Am. Med. Dir. Assoc. 16 (1) (85. e1-. e8). Kim, H., Hirano, H., Edahiro, A., Ohara, Y., Watanabe, Y., Kojima, N., et al., 2016. Sarcopenia: prevalence and associated factors based on different suggested definitions in community-dwelling older adults. Geriatr Gerontol Int 16, 110–122. Klein, H., Sterk, C.E., Elifson, K.W., 2013. Initial smoking experiences and current smoking behaviors and perceptions among current smokers. J. Addict. 2013. Landi, F., Onder, G., Russo, A., Liperoti, R., Tosato, M., Martone, A.M., et al., 2014. Calf circumference, frailty and physical performance among older adults living in the community. Clin. Nutr. 33 (3), 539–544. Liguori, I., Curcio, F., Russo, G., Cellurale, M., Aran, L., Bulli, G., et al., 2018. Risk of malnutrition evaluated by mini nutritional assessment and sarcopenia in noninstitutionalized elderly people. Nutr. Clin. Pract. 33 (6), 879–886. Mahdavi, A.M., Mahdavi, R., Lotfipour, M., Jafarabadi, M.A., Faramarzi, E., 2015. Evaluation of the Iranian mini nutritional assessment short-form in communitydwelling elderly. Health Promot. Perspect. 5 (2), 98. Meng, P., Hu, Y.X., Fan, L., Zhang, Y., Zhang, M.X., Sun, J., et al., 2014. Sarcopenia and sarcopenic obesity among men aged 80 years and older in Beijing: prevalence and its association with functional performance. Geriatr Gerontol Int 14 (S1), 29–35. Moghaddam, M.B., Aghdam, F.B., Jafarabadi, M.A., Allahverdipour, H., Nikookheslat, S.D., Safarpour, S., 2012. The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability. World Appl. Sci. 18 (8), 1073–1080. Morley, J.E., Anker, S.D., von Haehling, S., 2014. Prevalence, incidence, and clinical impact of sarcopenia: facts, numbers, and epidemiology—update 2014. J. Cachexia. Sarcopenia Muscle 5 (4), 253–259. Pagotto, V., Silveira, E.A., 2014a. Methods, diagnostic criteria, cutoff points, and prevalence of sarcopenia among older people. Sci. World J. 2014. Pagotto, V., Silveira, E.A., 2014b. Applicability and agreement of different diagnostic criteria for sarcopenia estimation in the elderly. Arch. Gerontol. Geriatr. 59 (2), 288–294.
4.2. Conclusion In conclusion, approximately one-fifth of the community-dwelling elderly individuals were faced with the threats of sarcopenia. The results showed that sarcopenia was affected by multiple risk factors, including old age, male gender, low BMI, decreased MNA, low serum albumin level, and high body fat mass. On the other hand, calf circumference was the only protective factor against sarcopenia. The findings also revealed the co-occurrence of malnutrition and sarcopenia. Thus, management of sarcopenia should be carried out based on the evaluation of BMI, body fat mass, and calf circumference. Due to the dire consequences of sarcopenia for health and future outcomes in the elderly population, appropriate nutritional assessments and interventions might be main strategies for prevention or management of this disorder in this group. Funding This work was supported by Shiraz University of Medical Sciences (grant No. 8413633) in collaboration with School of Nutrition and Endocrinology and Metabolic Research Institute. Conflict of interest The authors have no conflict of interests to declare. Ethical approval The local Medical Ethics Committee of Shiraz University of Medical Sciences reviewed and approved the study protocol. All participants gave their written informed consents after receiving explanation about the study protocol. Authors' contribution N-N was responsible for the research idea, study design, data acquisition, data interpretation, and writing and preparation of the manuscript. MH-D was responsible for the research idea, study design, data interpretation, and critical revision of the paper. Z-S was responsible for the research idea, study design, data interpretation, and critical revision of the paper. Acknowledgment The authors wish to thank Dr. Z. Bagheri for her invaluable assistance in statistical analysis of the data. They would also like to appreciate Ms. A. Keivanshekouh at the Research Improvement Center of Shiraz University of Medical Sciences for improving the use of English in the manuscript. Finally, the authors are thankful for the participants for their great and kind contribution to the research. References Afshar, P.F., Asgari, P., Shiri, M., Bahramnezhad, F., 2016. A review of the Iran's elderly status according to the census records. Galen Med. J. 5 (1), 1–6. Albani, D., Batelli, S., Polito, L., Vittori, A., Pesaresi, M., Gajo, G.B., et al., 2009. A
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Veronese, N., De Rui, M., Toffanello, E.D., De Ronch, I., Perissinotto, E., Bolzetta, F., et al., 2013. Body mass index as a predictor of all-cause mortality in nursing home residents during a 5-year follow-up. J. Am. Med. Dir. Assoc. 14 (1), 53–57. von Haehling, S., Morley, J.E., Anker, S.D., 2010. An overview of sarcopenia: facts and numbers on prevalence and clinical impact. J. Cachexia. Sarcopenia Muscle 1 (2), 129–133. Walrand, S., Guillet, C., Salles, J., Cano, N., Boirie, Y., 2011. Physiopathological mechanism of sarcopenia. Clin. Geriatr. Med. 27 (3), 365–385. Wu, Y.-H., Hwang, A.-C., Liu, L.-K., Peng, L.-N., Chen, L.-K., 2016. Sex differences of sarcopenia in Asian populations: the implications in diagnosis and management. J. Clin. Gerontol. Geriatr. 7 (2), 37–43. Yamada, M., Nishiguchi, S., Fukutani, N., Tanigawa, T., Yukutake, T., Kayama, H., et al., 2013. Prevalence of sarcopenia in community-dwelling Japanese older adults. J. Am. Med. Dir. Assoc. 14 (12), 911–915. Yu, R., Leung, J., Woo, J., 2014. Incremental predictive value of sarcopenia for incident fracture in an elderly Chinese cohort: results from the Osteoporotic Fractures in Men (MrOs) Study. J. Am. Med. Dir. Assoc. 15 (8), 551–558. Zeng, P., Wu, S., Han, Y., Liu, J., Zhang, Y., Zhang, E., et al., 2015. Differences in body composition and physical functions associated with sarcopenia in Chinese elderly: reference values and prevalence. Arch. Gerontol. Geriatr. 60 (1), 118–123.
Rolland, Y., Dupuy, C., van Kan, G.A., Gillette, S., Vellas, B., 2011. Treatment strategies for sarcopenia and frailty. Med. Clin. 95 (3), 427–438. Rosenberg, I.H., 1989. Summary comments. Am. J. Clin. Nutr. 50 (5), 1231–1233. Senior, H.E., Henwood, T.R., Beller, E.M., Mitchell, G.K., Keogh, J.W., 2015. Prevalence and risk factors of sarcopenia among adults living in nursing homes. Maturitas 82 (4), 418–423. Shafiee, G., Keshtkar, A., Soltani, A., Ahadi, Z., Larijani, B., Heshmat, R., 2017. Prevalence of sarcopenia in the world: a systematic review and meta-analysis of general population studies. J. Diabetes Metab. Disord. 16 (1), 21. Shaw, S., Dennison, E., Cooper, C., 2017. Epidemiology of sarcopenia: determinants throughout the lifecourse. Calcif. Tissue Int. 101 (3), 229–247. Tramontano, A., Veronese, N., Sergi, G., Manzato, E., Rodriguez-Hurtado, D., Maggi, S., et al., 2017. Prevalence of sarcopenia and associated factors in the healthy older adults of the Peruvian Andes. Arch. Gerontol. Geriatr. 68, 49–54. Uemura, K., Doi, T., Lee, S., Shimada, H., 2018. Sarcopenia and low serum albumin level synergistically increase the risk of incident disability in older adults. J. Am. Med. Dir. Assoc. 20 (1), 90–93. Vandewoude, M.F., Alish, C.J., Sauer, A.C., Hegazi, R.A., 2012. Malnutrition-sarcopenia syndrome: is this the future of nutrition screening and assessment for older adults? J. Aging Res. 2012.
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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero
Nutritional status and body fat mass: Determinants of sarcopenia in community-dwelling older adults ⁎
Nasrin Nasimia, Mohammad Hossein Dabbaghmaneshb, , Zahra Sohrabia, a b
T
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Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran Shiraz Endocrinology and Metabolism Research Center, Nemazee Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
A R T I C LE I N FO
A B S T R A C T
Section Editor: M Masternak
Background: Sarcopenia is defined as the old age syndrome characterized by profound decline in muscle mass and function. This study aimed to investigate the prevalence of sarcopenia and its risk factors in older adults. Methods: Totally, 501 older people aged 65 years and older were recruited. Sarcopenia was defined according to the criteria of the Asian Working Group for Sarcopenia (AWGS). For obtaining Skeletal Muscle mass Index (SMI), body composition was evaluated using Bioelectrical Impedance Analysis (BIA). Muscle strength and physical performance were measured by Handgrip Strength (HGS) and Gait Speed (GS), respectively. Nutritional status, physical activity level, and biochemical indicators were assessed, as well. Results: The prevalence of sarcopenia was 20.8%. Multiple logistic regression models of the predictors of decline in the components of sarcopenia showed that older age, low Body Mass Index (BMI), and serum albumin level were associated with a higher risk of low SMI. Low serum albumin level and older age were also predictive of low HGS. Besides, old age, high body fat mass, and low BMI were the risk factors of low GS. Conversely, increased Calf Circumference (CC) was protective against low SMI and GS. Finally, older age, male gender, low BMI, decreased mini-nutritional assessment score, low serum albumin level, and high body fat were associated with a higher risk of sarcopenia, whereas higher CC reduced its risk. Conclusion: The prevalence of sarcopenia is high among elderly individuals. This study underlined that sarcopenia might develop in older adults with impaired nutritional status and high body fat mass. Further studies could evaluate the effects of appropriate nutritional interventions on sarcopenia management and prevention.
Keywords: Sarcopenia Prevalence Nutritional status Body fat Older adults
1. Introduction Sarcopenia is defined as an age-related muscle reduction syndrome, which was originally described by Rosenberg in the 1980s (Rosenberg, 1989). Parallel to the development of researches in Europe and Asia, definition and diagnostic criteria for sarcopenia have evolved in the last two decades (Rolland et al., 2011). International research groups tried to clarify the practical clinical definition of sarcopenia to elucidate the parameters. In 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) asserted that sarcopenia is a condition characterized by progressive decline in the skeletal muscle mass in combination with low muscle function (decreased muscle strength or/and physical performance) (Cruz-Jentoft et al., 2010). In 2014, the Asian Working Group for Sarcopenia (AWGS) suggested a similar approach to
estimate sarcopenia based on Asian countries (Chen et al., 2014). The recently updated sarcopenia definition by EWGSOP “focuses on low muscle strength as a key characteristic of sarcopenia, uses detection of low muscle quantity and quality to confirm the sarcopenia diagnosis, and identifies poor physical performance as indicative of severe sarcopenia” (Cruz-Jentoft et al., 2018). Sarcopenia is a prevalent condition that is associated with major clinical problems in public health (Chen et al., 2016). Studies have shown that the morbidity rate of sarcopenia was 5–13% in the elderly individuals between 60 and 70 years old, and this rate increased to 11–50% in those older than 80 years (von Haehling et al., 2010). Its adverse outcomes for the elderly were closely related to the functional decline (Bastiaanse et al., 2012), high risk of falls and fractures (Yu et al., 2014), disability (Bravo-José et al., 2018), poor quality of life
Abbreviations: AWGS, Asian Working Group for Sarcopenia; BMI, body mass index; CC, calf circumference; FFM, fat-free mass; VFA, visceral fat area; BMC, bone mineral content; SMI, skeletal muscle mass index; HGS, hand grip strength; GS, gait speed; MNA, mini-nutritional assessment; IPAQ, International Physical Activity Questionnaire ⁎ Corresponding authors. E-mail addresses: [email protected] (M.H. Dabbaghmanesh), [email protected] (Z. Sohrabi). https://doi.org/10.1016/j.exger.2019.04.009 Received 10 November 2018; Received in revised form 7 April 2019; Accepted 17 April 2019 Available online 22 April 2019 0531-5565/ © 2019 Published by Elsevier Inc.
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2.2. Clinical characteristics
(Beaudart et al., 2015a), increased hospitalization rates (Gariballa and Alessa, 2013), and mortality (Batsis et al., 2014). Therefore, identifying the features and pathogenesis of this condition is vital to reduce its onset and development as well as to find better ways for its management. The underlying pathophysiological pathways of sarcopenia might be pertinent to various factors, such as reduction of anabolic hormones, abnormal protein metabolism, oxidative stress, chronic inflammations, nutritional deficiencies, and physical inactivity (Walrand et al., 2011; Evans, 2010; Fielding et al., 2011). Sarcopenia is an important area of public and geriatric health. However, results on its prevalence are controversial due to the variety of definitions and methodologies used to assess sarcopenia parameters. Indeed, its prevalence widely varied from 3% to 52% (Fielding et al., 2011). In subsequent studies, the mean prevalence of sarcopenia was reported to be 5–13% in individuals aged 60–70 years and 11–50% in those older than 80 years (Morley et al., 2014). With a rapid rise in the elderly population and the increase in life expectancy in the recent decades, sarcopenia has become a growing concern worldwide (Wu et al., 2016). Despite numerous studies on sarcopenia in the East Asian countries, few studies have been conducted in the Middle East (Gariballa and Alessa, 2013), especially Iran (Hashemi et al., 2016). In the recent years, Iran has encountered a rapid growth in its elderly population, and 29.9% of the population is expected to reach over 65 years old by 2050 (Afshar et al., 2016). Therefore, it is predictable that the elderly population will most likely be afflicted by sarcopenia, and more attention is warranted for assessing sarcopenia by global medical community. However, there are still limited studies in this area. This study aims to evaluate the prevalence of sarcopenia and severe sarcopenia according to the AWGS guideline in order to identify the associated factors among community-dwelling elderly population in southern Iran.
The participants were interviewed and requested to complete a general questionnaire including demographic and habitual variables including age, smoking habit, medical history, and drug consumption history. Medical history included hypertension, hyperlipidemia, myocardial infraction, known cardiomyopathy, osteoarthritis, osteoporosis, thyroid problems, cancer, diabetes, and cerebrovascular accidents. The participants were classified as former smokers if they had smoked 100 times in their life time and were classified as current smokers if they smoked some days or every day (Klein et al., 2013). Body weight, height, and calf circumference were measured by standard methods. 2.2.1. Nutritional assessment The participants' nutritional status was evaluated using MiniNutritional Assessment (MNA) questionnaire, Body Mass Index (BMI), and serum albumin level. The MNA-SF is a validated method for screening malnutrition, including 18 items in four categories as follows: anthropometric measurements, general state, dietary pattern, and selfassessment. In this questionnaire, scores < 17, between 17 and 23.5, and equal to or above 24 indicated malnutrition, risk of malnutrition, and well-nourished status, respectively (Mahdavi et al., 2015). BMI was calculated as weight (in kilogram) divided by square of height (in meter). The cutoff of 20 was considered for BMI, which seems to better distinguish underweight from normal weight in the elderly population (Veronese et al., 2013). 2.2.2. Physical activity assessment Validated International Physical Activity Questionnaire (IPAQ) was used to assess physical activity. The intensity of physical activity was described by Metabolic Equivalents (MET). Regarding IPAQ guideline, the total physical activity level was calculated as the sum of MET coefficient × minutes of activity per day × days per week for each item. Accordingly, the participants' physical activity levels were classified as high, moderate, and low based on scores > 3000, between 600 and 3000, and < 600 MET-min/week, respectively (Moghaddam et al., 2012).
2. Method 2.1. Study design and population The present study was a geriatric health examination survey for assessing the frequency of sarcopenia and its determinants among Iranian elderly individuals. This cross-sectional, population-based study was conducted in Shiraz, southern Iran with the total population of 1,565,572 people. The sample size was estimated to include 501 participants selected via multi-stage sampling. At first, three main health sectors providing health services to general public and private Primary Health Care (PHC) centers in the city were considered as three strata in the stratification sampling approach. In each strata, cluster sampling method was applied based on geographical locations of the PHC centers. Then, four clusters were randomly selected and the number of participants in each cluster was determined proportional to its size. The participants were randomly selected and invited to voluntarily participate in the survey. The data were collected during six months from August 2017 to February 2018. The study population consisted of community-dwelling older people whose health data, such as diseases, drug history, and phone number, were recorded in the Iranian electronic health system. The inclusion criteria were aging 65 years or older, living in the society, having the ability to walk independently, and not having any artificial limbs or limb prostheses. The exclusion criteria were having a history of severe cardiac, pulmonary, or musculoskeletal diseases, severe cognitive impairment, and co-morbidities related to higher risk of falls including Parkinson's or stroke. Elderlies with tumor and uncontrolled endocrine or metabolic diseases, such as diseases associated with thyroid gland and type II diabetes, and those who were currently institutionalized were also excluded from the study.
2.3. Diagnostic measures for sarcopenia Sarcopenia was defined using the suggested diagnostic algorithm by AWGS, which encompasses reduction in muscle mass, muscle strength, and/or physical function. Based on this definition, individuals with low skeletal muscle mass and low muscle strength or low physical performance were considered to have sarcopenia, and cases with low skeletal muscle mass and both low muscle strength and low physical performance were considered to have severe sarcopenia (Chen et al., 2014). 2.3.1. Skeletal muscle mass Body composition data were measured using a segmental multifrequency Bioelectrical Impedance Analysis (BIA) InBody S10 analyzer (BioSpace Co., Ltd., South Korea), which measured Skeletal Lean Mass (SLM), segmental lean mass of arms, trunk, and legs, Fat-Free Mass (FFM), Body Fat Mass (BFM), Visceral Fat Area (VFA), Bone Mineral Content (BMC), and protein content. Appendicular Skeletal Muscle mass (ASM) was obtained as the sum of segmental muscle mass values of the legs and arms. Skeletal Muscle mass Index (SMI) was defined as ASM divided by the square of height (in meter) for defining low skeletal muscle mass and sarcopenia. The reference values of < 7.0 kg/m2 and < 5.7 kg/m2 were adopted as SMI cutoff points for males and females, respectively (Chen et al., 2014). 2.3.2. Muscle strength Muscle strength was defined by Handgrip Strength (HGS). The participants squeezed a hydraulic hand dynamometer (model MSD, Sihan, Korea) in both hands three times with 15-second pauses in seated 68
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position. The maximum value was used for further analyses (Beaudart et al., 2015b). HGS values < 18 kg for females and < 26 kg for males were considered as cutoff points for the definition of sarcopenia (Chen et al., 2014).
participants diagnosed with sarcopenia were more at risk of malnutrition (51.9% vs. 20.2%) or malnourished (4.8% vs 3.0%) based on MNA results (p < 0.0001). Considering biochemical indicators, there was a significant difference between sarcopenic and non-sarcopenic participants with regard to the levels of albumin (p = 0.001) and TG (p = 0.009).
2.3.3. Physical performance The participants' muscle performance was determined using the usual Gait Speed (GS) over a distance of four meters. Each participant was asked to walk the distance without any help and then, the time was recorded in seconds by a chronometer (Beaudart et al., 2015b). GS < 0.8 m/s was determined as a low muscle performance indicator (Chen et al., 2014).
3.2. Prevalence of sarcopenia According to the AWGS cut-off, 33.1% of males (n = 84) and 14.6% of females (n = 36) had low SMI. The prevalence rates of low HGS and GS were respectively 6.3% and 52% among males and 6.1% and 64.4% among females. Among the participants with low SMI, low HGS values (sarcopenia) were observed in 17.8% (n = 89) and HGS and GS values below the cutoff point (severe sarcopenia) in 3% (n = 15). Therefore, the prevalence of sarcopenia and severe sarcopenia were respectively 23.6% and 3.9% in males and 11.8% and 2% in females. Overall, 20.8% of the study participants (n = 104) were classified as sarcopenic. In both genders, an age-dependent increase was found in the prevalence of sarcopenia (p = 0.003 for males and p = 0.008 for females) (Table 2). Such significant trend was also observed for all sarcopenia criteria (low SMI, HGS, and GS) in all age groups (65–69, 70–74, 78–79, and > 80 years) in both males and females. In all age groups, lower SMI, HGS, and GS and sarcopenia appeared to be more common among males.
2.4. Biochemical indicators Blood samples were collected after an overnight fasting in hormone laboratory of the endocrinology ward at Nemazee Hospital (an educational hospital affiliated to Shiraz University of Medical Sciences). Serum samples were separated, packed in two Eppendorf tubes, and stored at −70 °C. Then, the blood test, including serum albumin, BUN, creatinine, Fasting Blood Sugar (FBS), and lipid profile (Triglyceride (TG), Low-Density Lipoprotein (LDL), High-Density Lipoprotein (HDL), and total cholesterol), was analyzed by calorimetric assays using Biosystem SA auto-chemistry analyzer (DIRUI CS-T240, Spain). Serum albumin level ≤ 4.0 g/dl was considered as the cutoff point (Uemura et al., 2018).
3.3. Sarcopenia and the associated factors 2.5. Statistical analysis Statistical analysis was done by IBM SPSS Statistics, version 19.0. The prevalence rates of sarcopenia, severe sarcopenia, and no sarcopenia were determined using descriptive analyses. The data were expressed as mean ± SD and percentage for continuous and categorical variables, respectively. The comparisons between continuous variables were analyzed using independent t-test or Mann-Whitney U test and comparisons between categorical variables were analyzed using chisquare test or Kruskal-Wallis H test. Additionally, Cochran-Armitage Trend test was used to assess sarcopenia components based on age groups. Factors associated with sarcopenia with p ≤ 0.2 in univariate analysis were entered in the multivariate model. Multiple logistic regression models were used to analyze the anthropometric and biochemical factors associated with the components included in the definition of sarcopenia (decline in SMI, HGS, and GS). The independent variables associated with sarcopenia were also tested using multivariate regression model. p-Values ≤0.05 were considered to be statistically significant.
The multivariate analysis of different sarcopenia components has been presented in Table 3. Increasing age (OR = 1.11, p = 0.004), low BMI (OR = 24.18, p = 0.004), and low serum albumin level (OR = 2.85, p = 0.012) were the consistent risk factors of low SMI, whilst increased calf circumference (OR = 0.62, p < 0.0001) was protective against low SMI. Moreover, older age (OR = 1.15, p = 0.003) and decreased serum albumin level (OR = 9.26, p = 0.039) were significantly associated with a higher risk of low HGS. Furthermore, increased calf circumference was protective against low GS (OR = 0.82, p = 0.001), whereas old age (OR = 1.10, p = 0.002), low BMI (OR = 5.22, p = 0.016), and increased body fat mass (OR = 1.14, p = 0.014) enhanced the risk of low GS significantly (Table 3). Analysis of the independent variables associated with sarcopenia revealed that older age, male gender, low BMI, decreased MNA, low serum albumin level, and increased body fat mass enhanced the risk of sarcopenia significantly. Conversely, increased calf circumference reduced the risk of sarcopenia. However, no significant association was observed between smoking habit and sarcopenia (Table 4).
3. Results
4. Discussion
3.1. Participants
This cross-sectional study was the first to evaluate the prevalence and associated factors of sarcopenia in elderly population in southern Iran. According to AWGS cutoff point, 27.5% of males and 13.8% of females participating in this study had sarcopenia. Overall, 20.8% of the participants were sarcopenic. The prevalence of sarcopenia has been estimated up to 29% of the elderly in community-dwelling population and 14–33% in those requiring long-term care worldwide (Fuggle et al., 2017). It is noteworthy to state that the prevalence of sarcopenia varies across various studies due to the different diagnosis criteria, measurement tools, racial characteristics, study populations, and elderly's age groups, genders, dietary regimens, and quality of life (Beaudart et al., 2015b; Pagotto and Silveira, 2014a). Pagotto and Silveira showed that sarcopenia prevalence varied from 6.1% to 36.6% depending on the diagnostic criteria and tools used for its detection (Pagotto and Silveira, 2014b). In addition, the latest meta-analysis indicated that the sarcopenia prevalence was lower in Asian countries compared to non-Asian ones (Shafiee et al., 2017).
A total of 501 participants, including 254 males (50.7%), with the mean age of 70.35 ± 4.60 years were recruited in this study. Comparison of the participants with and without sarcopenia with respect to anthropometrics and biochemical variables has been shown in Table 1. Compared to the participants without sarcopenia, those with sarcopenia were significantly older (p < 0.0001) and current or former smokers (p = 0.002). Considering body composition characteristics, the sarcopenic participants had significantly lower weight, BMI, FFM, ASM, BFM, VFA, protein, BMC, and calf circumference (p < 0.0001 for all comparisons). Similarly, the participants with (versus without) sarcopenia had lower SMI, HGS and GS (p < 0.0001 for all comparisons). However, no significant difference was found between the two groups concerning the level of physical activity reported by IPAQ score (p = 0.593). The majority of the participants had low physical activity (61.7%) and only 4.8% of them were more active. Additionally, the 69
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Table 1 Comparison of the sarcopenic and non-sarcopenic participants regarding baseline characteristics. Variables
Total (n = 501)
Male sex, n (%) Age, years Age category, n (%) 65–69 years 70–74 years 75–79 years > 80 years Smoking, n (%) Never (%) Former (%) Current (%) Anthropometrics Weight, kg BMI, kg/m2 CC, cm FFM, kg Total body fat, kg VFA, cm2 Protein, kg BMC, kg Sarcopenia criteria ALM, kg SMI, kg/m2 HGS, kg GS, m/s MNA, n (%) Well-nourished Risk of malnutrition Malnutrition IPAQ, n (%) Low Moderate High Biochemical Albumin, g/dl Creatinine, mg/dl BUN, mg/dl FBS, mg/dl Total cholesterol, mg/dl LDL-C, g/dl HDL-C, mg/dl Triglycerides, mg/dl
Sarcopenic (n = 104)
254 (50.7) 70.3 ± 4.6
Non-sarcopenic (n = 397)
70 (67.3) 72.6 ± 5.3
184 (46.3) 69.7 ± 4.1
(26.9) (40.4) (20.2) (12.5)
213 131 39 14
p-Value
< 0.0001 < 0.0001 < 0.0001
241 (48.1) 173 (34.5) 60 (12.0) 27 (5.4)
28 42 21 13
(53.7) (33.0) (9.8) (3.5)
341 (38.1) 58 (11.6) 102 (20.4)
56 (53.8) 21 (20.2) 27 (26.0)
285 (71.8) 37 (9.3) 75 (18.9)
69.3 ± 12.3 27.2 ± 4.7 35.2 ± 3.6 44.0 ± 8.9 24.9 ± 9.0 95.2 ± 29.0 8.8 ± 2.2 2.4 ± 0.4
57.7 ± 7.7 23.1 ± 3.4 31.5 ± 2.1 38.2 ± 7.0 19.0 ± 6.9 76.6 ± 25.5 7.6 ± 1.1 2.1 ± 0.3
72.4 ± 11.5 28.3 ± 4.4 36.1 ± 3.3 45.5 ± 8.8 26.4 ± 9.0 100.1 ± 27.9 9.1 ± 2.4 2.4 ± 0.4
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
18.4 ± 4.4 7.1 ± 1.0 42.4 ± 18.2 0.84 ± 0.66
15.7 ± 3.1 6.2 ± 0.6 33.3 ± 12.7 0.67 ± 0.13
19.2 ± 4.4 7.3 ± 0.9 44.8 ± 18.6 0.88 ± 0.73
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
361 (72.1) 134 (26.7) 6 (1.2)
45 (43.3) 54 (51.9) 5 (4.8)
316 (79.6) 80 (20.2) 1 (3.0)
309 (61.7) 168 (33.5) 24 (4.8)
64 (61.5) 38 (36.5) 2 (1.9)
245 (61.7) 130 (32.7) 22 (5.5)
3.9 ± 0.2 0.88 ± 0.20 14.7 ± 5.1 99.8 ± 31.4 182.7 ± 40.9 100.8 ± 33.1 51.1 ± 12.1 143.3 ± 71.4
3.8 ± 0.2 0.90 ± 0.13 14.8 ± 5.0 97.8 ± 40.7 178.6 ± 40.0 103.4 ± 34.1 51.4 ± 13.4 127.7 ± 64.4
3.9 ± 0.2 0.88 ± 0.22 14.7 ± 5.1 100.4 ± 28.4 183.8 ± 41.1 100.2 ± 32.8 51.1 ± 11.7 147.3 ± 72.6
0.002
0.822
0.001 0.507 0.940 0.520 0.324 0.441 0.926 0.009
BMI, body mass index; CC, calf circumference; FFM, fat-free mass; VFA, visceral fat area; BMC, bone mineral content; ALM, appendicular lean muscle mass; SMI, skeletal muscle mass; HGS, hand grip strength; GS, gait speed; MNA, mini-nutritional assessment; IPAQ, International Physical Activity Questionnaire. p-value < 0.05 is significant. Table 2 Components and prevalence of sarcopenia by age groups. Males
Low SMI Low hand grip Low gait speed Sarcopenia prevalence
Total
65–69 years
70–74 years
75–79 years
> 80 years
p for trend
n = 254
n = 99
n = 97
n = 42
n = 16
84 (33.1) 16 (6.3) 132 (52.0) 70 (27.6)
23 (23.2) 1 (1.0) 31 (31.3) 16 (16.2)
36 (37.1) 8 (8.2) 63 (64.9) 29 (29.9)
18 (42.9) 6 (14.3) 28 (66.7) 17 (40.5)
7 (43.8) 1 (6.3) 10 (62.5) 8 (50.0)
0.022* 0.016* < 0.001* 0.003*
Total
65–69 years
70–74 years
75–79 years
> 80 years
p for trend
n = 247
n = 142
n = 76
n = 18
n = 11
36 15 86 34
14 (9.9) 4 (2.8) 75 (52.8) 12 (8.5)
14 (18.4) 7 (9.2) 58 (76.3) 13 (17.1)
4 (22.2) 3 (16.7) 15 (83.3) 4 (22.2)
4 (36.4) 1 (9.1) 11 (100.0) 5 (45.5)
Females
Low SMI Low hand grip Low gait speed Sarcopenia prevalence
(14.6) (6.1) (35.6) (13.8)
Values are in n (%). * p < 0.05 70
0.020* 0.045* < 0.001* 0.008*
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Table 3 Multiple logistic regression models of the predictors of decline in muscle mass index, handgrip strength, and gait speed. Independent variables
Low SMI OR (95% CI)
Low HGS OR (95% CI)
Age, per 1 y BMI < 20 kg/m2 Calf circumference, per 1 cm increase Body fat, per 1 unit VFA, per 1 unit FFM, per 1 unit Protein, per 1 unit BMC, per 1 unit Albumin ≤ 4.0 g/dl TG, per 1 unit
1.11 (1.02–1.19)⁎ 24.18 (2.75–212.5)⁎ 0.62 (0.51–0.76)⁎ 0.89 (0.78–1.02) 1.03 (0.99–1.06) 0.97 (0.86–1.09) 0.54 (0.21–1.42) 1.74 (0.12–23.69) 2.85 (1.25–6.48)⁎ 0.99 (0.99–1.00)
1.15 4.69 1.00 1.06 0.98 0.96 0.85 1.31 9.26 1.00
⁎
(1.05–1.26)⁎ (0.90–24.26) (0.79–1.26) (0.84–1.34) (0.92–1.05) (0.88–1.05) (0.37–1.97) (0.04–35.65) (1.11–76.92)⁎ (0.99–1.00)
Low GS OR (95% CI) 1.10 5.22 0.82 1.14 0.98 0.98 0.62 2.35 1.48 1.00
(1.03–1.17)⁎ (1.35–20.12)⁎ (0.72–0.92)⁎ (1.02–1.27)⁎ (0.95–1.01) (0.89–1.08) (0.31–1.24) (0.41–13.45) (0.88–2.46) (0.99–1.00)
p < 0.05.
albumin level as a nutritional laboratory parameter (Curcio et al., 2016) in low SMI and HGS as well as high prevalence of sarcopenia. A previous prospective study also revealed that sarcopenia and low serum albumin level were important risk factors for disability in older people (Uemura et al., 2018). Additionally, low serum albumin level was correlated to poor nutritional status. Hence, it could cause a reduction in muscle mass and function due to the devastating effects of malnutrition on protein synthesis (Vandewoude et al., 2012). In this study, increased body fat mass was identified as a risk factor for sarcopenia as well as low GS. This finding suggested that fat mass might play a role in sarcopenia development. Fat accumulation in muscles might happen due to high total fat mass, which could negatively affect skeletal muscle mass through the elevated pro-inflammatory cytokines (Meng et al., 2014). The interplay between low SMI (sarcopenia) and high body fat mass (obesity) that is called sarcopenic obesity is associated with increased mortality risk in older adults (Batsis et al., 2014). The current study findings expressed the importance of assessing the balance between SMI and body fat mass instead of assessing BMI alone to manage sarcopenia. As suggested in the recent studies, the results of the present research confirmed that calf circumference could be used as a valuable measurement by clinicians to assess sarcopenia (Kim et al., 2015; Landi et al., 2014). The results showed a strong association between calf circumference and sarcopenia criteria. Therefore, calf circumference could provide worthy information on muscle mass and physical function, which could predict the risk of sarcopenia progression. Evidence has shown a relationship between sarcopenia and lifestyle factors, such as inactivity and smoking habits. Although the present sarcopenic participants had more smoking habits, the results of logistic regression analysis revealed no significant association between sarcopenia and smoking. This might be attributed to the simultaneous entrance of dependent variables, such as male gender, MNA, and BMI, in to the regression model, causing smoking to lose its power. In the same line, some previous studies expressed that smoking might not be an important risk factor for sarcopenia (Shaw et al., 2017). Although many studies have reported a relationship between low physical activity and sarcopenia, the present study findings revealed no significant difference between the participants with and without sarcopenia regarding physical activity level. This might be due to the inappropriate classification of activities as well as the self-report format of IPAQ. Therefore, it seems that IPAQ is not appropriate for assessment of physical activity among older adults.
Table 4 Independent risk factors associated with sarcopenia. Independent variables
OR (95% CI)
p-Value
Age, per 1-year increase Gender, male BMI < 20 kg/m2 MNA < 24 Albumin ≤ 4.0 g/dl Body fat, per 1 kg increase Calf circumference, per 1 cm increase Smoking habit
1.10 (1.02–1.18) 3.13 (1.23–7.98) 10.64 (2.20–51.25) 2.29 (1.03–5.09) 3.34 (1.40–7.95) 1.11 (1.03–1.19) 0.50 (0.40–0.62) 1.06 (0.50–2.27)
0.010 0.016 0.003 0.041 0.006 0.003 < 0.0001 0.862
Assessing the factors associated with sarcopenia revealed that sarcopenia prevalence was pertinent to various factors, including age, gender, BMI, MNA, serum albumin level, body fat, and calf circumference. Aging process is the major determinant involved in the progression of sarcopenia (Kim et al., 2016). In this context, the present study findings were in agreement with those of the previous studies, revealing a well-known association between age and sarcopenia in both genders (Kim et al., 2015). The findings demonstrated an age-dependent trend of increase in sarcopenia prevalence and decrease in muscle mass (SMI) and function (HGS, GS). Similar trends were also reported in the previous studies (Zeng et al., 2015; Yamada et al., 2013). There are many contradictions regarding the gender-related differences in the prevalence of sarcopenia in the available literature (Shaw et al., 2017). In confirmation of the previous studies, the current study results showed that males were more prone to sarcopenia. Some studies have reported that the differences in the level of Insulin-like Growth Factor-1 (IGF-1), as the crucial mediator of muscle growth, might justify the gender differences in the prevalence of sarcopenia. Accordingly, IGF-1 level was lower and, consequently, the rate of muscular atrophy was higher among older males compared to females (Albani et al., 2009). Nutritional status might have a great impact on the onset and development of sarcopenia (Hai et al., 2017). The present research findings showed that sarcopenia was related to poor nutritional status (low BMI, undesirable MNA results, and decreased albumin level). Additionally, low BMI was a major predictor of low SMI and GS as well as sarcopenia development. These findings were in line with those of the previous investigations (Tramontano et al., 2017; Senior et al., 2015; Han et al., 2015). Moreover, the prevalence of sarcopenia was significantly higher among the individuals with malnutrition or risk of malnutrition based on MNA results. These results were consistent with those of a previous study expressing low MNA score as a risk factor for sarcopenia (Liguori et al., 2018). Previous studies also indicated that malnourished elderly individuals had an increased risk of sarcopenia due to decreased muscle protein synthesis (Vandewoude et al., 2012). The current study results showed the predictive value of serum
4.1. Limitations of the study The present study had several limitations. Firstly, the study design was cross-sectional and no end-points or clinical outcomes were observed. In spite of the interesting associations, the data did not determine the causal relationships or mechanism of sarcopenia. Therefore, future longitudinal studies are recommended to determine 71
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the predictors leading to sarcopenia or vice a versa, and to confirm the causal relationships. Although the AWGS has considered BIA to be acceptable for muscle measurement, it is not the gold standard method. The recommended method is Dual X-ray Absorptiometry (DXA), but this method is expensive and used in hospitals. Hence, it is almost unreachable for community-dwelling elderly individuals. In the community setting, BIA technique might be regarded as a feasible, wellvalidated, and more practical screening method.
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4.2. Conclusion In conclusion, approximately one-fifth of the community-dwelling elderly individuals were faced with the threats of sarcopenia. The results showed that sarcopenia was affected by multiple risk factors, including old age, male gender, low BMI, decreased MNA, low serum albumin level, and high body fat mass. On the other hand, calf circumference was the only protective factor against sarcopenia. The findings also revealed the co-occurrence of malnutrition and sarcopenia. Thus, management of sarcopenia should be carried out based on the evaluation of BMI, body fat mass, and calf circumference. Due to the dire consequences of sarcopenia for health and future outcomes in the elderly population, appropriate nutritional assessments and interventions might be main strategies for prevention or management of this disorder in this group. Funding This work was supported by Shiraz University of Medical Sciences (grant No. 8413633) in collaboration with School of Nutrition and Endocrinology and Metabolic Research Institute. Conflict of interest The authors have no conflict of interests to declare. Ethical approval The local Medical Ethics Committee of Shiraz University of Medical Sciences reviewed and approved the study protocol. All participants gave their written informed consents after receiving explanation about the study protocol. Authors' contribution N-N was responsible for the research idea, study design, data acquisition, data interpretation, and writing and preparation of the manuscript. MH-D was responsible for the research idea, study design, data interpretation, and critical revision of the paper. Z-S was responsible for the research idea, study design, data interpretation, and critical revision of the paper. Acknowledgment The authors wish to thank Dr. Z. Bagheri for her invaluable assistance in statistical analysis of the data. They would also like to appreciate Ms. A. Keivanshekouh at the Research Improvement Center of Shiraz University of Medical Sciences for improving the use of English in the manuscript. Finally, the authors are thankful for the participants for their great and kind contribution to the research. References Afshar, P.F., Asgari, P., Shiri, M., Bahramnezhad, F., 2016. A review of the Iran's elderly status according to the census records. Galen Med. J. 5 (1), 1–6. Albani, D., Batelli, S., Polito, L., Vittori, A., Pesaresi, M., Gajo, G.B., et al., 2009. A
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