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Prevalence of sarcopenia and associated factors in the healthy older adults of the Peruvian Andes
Archives of Gerontology and Geriatrics 68 (2017) 49–54
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Prevalence of sarcopenia and associated factors in the healthy older adults of the Peruvian Andes Alessandra Tramontanoa , Nicola Veronesea,d,* , Giuseppe Sergia , Enzo Manzatoa,b , Diana Rodriguez-Hurtadoc, Stefania Maggib , Caterina Trevisana , Francesca De Zaiacomoa , Valter Giantina a
Department of Medicine (DIMED), Geriatrics Section, University of Padova, Italy National Research Council, Aging Branch, Neuroscience Institute, Padova, Italy c Faculty of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru d Institute for Clinical Research and Education in Medicine (IREM), Padova, Italy b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 5 May 2016 Received in revised form 6 September 2016 Accepted 7 September 2016 Available online 12 September 2016
Aim: To assess the prevalence of sarcopenia and associated factors in a population of older people living in a rural area of the Peruvian Andes. Materials and methods: The study concerned 222 people aged 65 years. Sarcopenia was diagnosed on the basis of skeletal muscle mass, measured using bioimpedance analysis, and gait speed, measured with the 4-m walking test, as recommended by the International Working Group on sarcopenia. Self-reported physical activity, the Short Physical Performance Battery, and the Six-Minute Walking Test also contributed information on participants’ physical performance status. Disabilities were investigated by assessing participants’ self-reported difficulties in performing one or more basic or instrumental activities of daily living. Results: The prevalence of sarcopenia was 17.6%. Compared with participants without sarcopenia, individuals who were found sarcopenic were significantly older, female and were less frequently farmers, had fewer children, had a worse nutritional status, a significantly lower physical performance, and higher levels of disability in the instrumental activities of daily living. After adjusting for potential confounders, age, female sex, a low body mass index, a self-reported low physical activity level, a worse Six-Minute Walking Test scores, and a low number of children were significantly associated with sarcopenia. Conclusion: The prevalence of sarcopenia seems to be quite high among community-dwelling older subjects in the Peruvian Andes. Age, female sex, a low body mass index, little physical activity, a poor SixMinute Walking Test scores, and a low number of children could be associated with this condition. ã 2016 Elsevier Ireland Ltd. All rights reserved.
Keywords: Activities of daily living Body mass index Instrumental activities of daily living Motor activity Sarcopenia
1. Introduction The International Working Group on Sarcopenia (IWGS) defined sarcopenia as “the age-associated loss of skeletal muscle mass and function” (Fielding et al., 2011). Sarcopenia is a complex syndrome
Abbreviations: ADL, activities of daily living scale; ASMM, appendicular skeletal muscle mass; ASMMI, appendicular skeletal muscle mass index; BIA, bioimpedance analysis; BMI, body mass index; CI, confidence interval; EWGSOP, European Working Group on Sarcopenia in Older People; IADL, instrumental activities of daily living scale; ISI, International Sarcopenia Initiative; MNA, Mini Nutritional Assessment; OR, odds ratio; R, resistance; RI, resistive index; SPPB, Short Physical Performance Battery; VIF, variance inflation factor; Xc, reactance; 6MWT, SixMinute Walking Test. * Corresponding author at: Department of Medicine—DIMED, Geriatrics Division, University of Padova, Via Giustiniani, 2-35128 Padova, Italy. E-mail address: [email protected] (N. Veronese). http://dx.doi.org/10.1016/j.archger.2016.09.002 0167-4943/ã 2016 Elsevier Ireland Ltd. All rights reserved.
that is associated with muscle mass loss alone or in conjunction with increased fat mass. The causes of sarcopenia are multifactorial and can include disuse, changing endocrine function, chronic diseases, inflammation, insulin resistance, and nutritional deficiencies. While cachexia may be a component of sarcopenia, the two conditions are not the same (Fielding et al., 2011). A similar definition was provided by the European Working Group on Sarcopenia in Older People (EWGOP) (Cruz-Jentoft et al., 2010), and the agreement of IWGS and EWGSOP was only fair (Lee, Liu, Peng, Lin, & Chen, 2013). A simultaneous deficiency of muscle mass and muscle function is therefore necessary for a diagnosis of sarcopenia. Several studies have investigated the prevalence of sarcopenia in elderly people. In 2014, for instance, the International Sarcopenia Initiative (ISI) (Cruz-Jentoft et al., 2014) reported a prevalence of sarcopenia from 1% to 33% across different elderly
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populations. An important weakness of this line of research lies, however, in that all these studies were conducted in developed countries. In the developed world, the prevalence of sarcopenia depends on well-known factors, such as age, gender, malnutrition and scarce physical activity (Ishii et al., 2014; Landi et al., 2012; Lin et al., 2013; Rolland et al., 2008). For community-dwelling subjects living in a rural Andean region, on the other hand, factors such as poverty, difficulty in accessing primary care, the Andean diet, and hostile environmental conditions might be associated with sarcopenia (Ministry of Economy and Finance of Peru, 2010; Ministry of Health of Peru, 2013). To our knowledge, no data on the prevalence of sarcopenia and associated factors are available for Peru, and especially for the rural Andean region. In the absence of any data relating directly to Peru, the prevalence of sarcopenia in Latin America as a whole has been estimated to range from 6.1% to 36.6% among community-dwelling elderly people (Barbosa-Silva, Bielemann, Gonzalez, & Menezes, 2016; Da Silva, Duarte, Santos, Wong, & Lebrão, 2014; Pereira, Ferreira Leite, & de Paula, 2015; Pagotto & Silveira, 2014), and even higher in some settings. In the light of the above considerations, it seems important to explore the peculiar and modifiable factors influencing this condition, also in view of the predicted exponential growth in the elderly population in years to come, and in the negative consequences of aging. The knowledge of these factors could be of importance because sarcopenia is preventable if appropriately treated and since this condition is associated with several negative outcomes in the elderly. Moreover, these factors could be different from the other South American countries. The aim of this study was therefore to assess the prevalence of sarcopenia and associated factors in a population of old people living in a rural area of the Peruvian Andes.
cardiovascular diseases, diabetes, dyslipidemia, liver diseases, gastrointestinal diseases, osteoarthritis, osteoporosis, respiratory diseases, stroke, dementia, depression, degenerative brain diseases, renal diseases, genito-urinary diseases, fractures, or cancer with a 5-year recurrence-free follow-up was recorded. Drug intake was also recorded and categorized as vs. <3, since in the elderly taking more than 3 medications is a good proxy for polypharmacotherapy (Hanlon, Schmader, Ruby, & Weinberger, 2001). Participants’ weight and height were measured with subjects wearing light clothing and no shoes, using a balance equipped with a stadiometer (estimated error 0.1 kg for weight, and 0.1 cm for height). Arm circumference was measured at the midpoint from the acromion process of the scapula to the tip of the olecranon process of the mid-elbow; and calf circumference was measured at the widest point, with subjects in a supine position with their knee and ankle at right angles.
2. Methods
2.3. Physical performance tests
This study involved older adults (65 years) living in the district of Yanama (Yungay province, in the Ancash region of Peru), a rural area of the Peruvian Andes approximately 3400 mt above sea level. The National Institute of Statistics and Informatics (INEI) of Peru report a population of 472 older people for this district (30 June 2013) (INEI, 2013), equal to 6.7% of the whole population. Data were collected from June 2014 to March 2015. All tests were conducted at an affiliated walk-in clinic at the ‘Mama Ashu’ Hospital, and all community-dwelling individuals aged 65 years or more were invited to go to the ambulatory. Subjects who met any of the following exclusion criteria were not enrolled: symptomatic cardiovascular or pulmonary diseases, evidence of severe renal impairment (requiring dialysis), acute infections requiring hospitalization, uncontrolled metabolic diseases (diabetes, anemia or thyroid disease), skeleton-deforming diseases or severe collagenopathies or rheumatic disease, agitation, dementia or psychosis, use of drugs or supplements capable of modifying body composition (e.g. corticosteroids, anabolic hormones), or a history or confirmed diagnosis of cancer in the previous 5 years. Among the 302 individuals initially screened, 80 were excluded in the light of the above-mentioned inclusion/ exclusion criteria; the study sample thus included 222 subjects. All participants were fully informed of the nature and object of the study and gave their consent. The study was conducted in accordance with the Declaration of Helsinki.
To assess their physical performance, participants were questioned about their daily physical activity (during the interview)and they completed the Short Physical Performance Battery (SPPB) and the Six-Minute Walking Test (6MWT). Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) developed by the WHO (World Health Organization) for physical activity surveillance in countries (Armstrong & Bull, 2006). It collects information on physical activity participation in three settings (Activity at work, travel to and from places, recreational activities) as well as sedentary behavior, comprising 16 questions. Physical activity was categorized as low or recommended (less or more than 150 min/week of moderate-intense and/or 75 min/week of vigorous-intense aerobic physical activity), as suggested by the WHO (2010). The SPPB (Guralnik et al., 1994) includes:
2.1. Clinical data Social and demographic data were obtained by means of faceto-face interviews with the participants. Profession was categorized as farmer vs. others. A positive clinical history of
2.2. Nutritional status The body mass index (BMI) and the Mini-Nutritional Assessment (MNA) were used to assess nutritional status. More specifically, BMI was calculated as the subject’s weight in kilograms divided by the square of their height in meters; the variable was categorized as taking 20 as cut-off since this value seems to better discriminate underweight to normo-weight in the elderly (Veronese et al., 2013). The MNA is an internationally validated method based on 18 items that include anthropometric measures, health status, dietary patterns and subjective assessments of an individual’s nutritional status (Guigoz, Lauque, & Vellas, 2002); this variable was categorized as <24 vs. 24, being this cut-off the value for dividing well-nourished from at risk of and malnourished (Vellas et al., 2006).
Standing balance. (a) Side-by-side stands: participants were asked to remain standing with their feet as close together as possible for 10 s; they scored 1 if the test was completed successfully, and 0 otherwise. (b) Semi-tandem test: participants were asked to remain standing with the ankle of one foot directly behind and in contact with the other for 10 s; they scored 1 if the test was completed successfully, and 0 otherwise. (c) Tandem test: participants were asked to remain standing with the ankle of one foot directly behind and in contact with the other foot for 10 s; they scored 0 if they remained in this position for 2 s, 1 for 3–9 s, and 2 for 10 s. Gait speed. Participants were asked to cover a distance of 4 m at their usual pace, and the time taken to do so was recorded. The use of a cane or walker was permitted. This test was completed twice and the shorter time was used for the analysis. The score
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was: 1 if the walk lasted 5.7 s; 2 for 4.1–5.6 s; 3 for 3.2–4 s; and 4 for 3.1 s. Timed chair stands test. Participants were asked to fold their arms, placing each hand on the opposite shoulder so that they crossed at the wrists, and then stand up and sit down five times as quickly as possible. The time they took to do so was recorded. This test was only administered if participants demonstrated their ability to rise from the chair without using their arms. The score was: 1 if they took 16.7 s; 2 for 13.7–16.6 s; 3 for 11.2– 13.6 s; and 4 for 11.1 s. The sum of all the SPPB subscores ranges from a maximum of 12 to a minimum of 0, higher scores reflecting a better physical performance level. In the 6MWT, participants were asked to walk as far as possible in 6 min and the distance they covered was recorded. They were allowed to use a walking stick. The path was delimited by using two chairs as turning points (Bennell, Dobson, & Hinman, 2011). This variable was categorized as proposed by Steffen et al. in community-dwelling older people in low and high performance according to gender and age specific cut-offs (Steffen, Hacker, & Mollinger, 2002). 2.4. Disability The Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) scales (Katz, Downs, Cash, & Grotz, 1970; Lawton & Brody, 1969) were used to assess participants’ degree of disability. Disability in ADL, evaluated by Katz index, coincides with difficulty in one or more of the following items: dressing, bathing, using the toilet, eating, transferring, and incontinence (Katz et al., 1970). Disability in IADL, evaluated by Lawton index, is diagnosed when an individual finds it difficult or impossible to perform the following activities: housework, preparing meals, doing laundry, using means of transportation, shopping, managing money, using the telephone, and taking medicines (Lawton & Brody, 1969). If a subject had never performed some of the above-mentioned activities, said activities were not considered. These disability variables were classified as disability in ADL (loss of one ADL) and in IADL. 2.5. Body composition Single-frequency tetrapolar bioimpedance analysis (BIA) was performed using a 400 mA (50 kHz) alternating current. BIA was conducted with subjects supine, their arms spread apart from their body and their legs apart, after fasting for at least 2 h, and bladder voiding. All BIA measurements were taken by the same trained investigator. The signal input and output electrodes were placed on the back of the right hand and foot, and the recording electrodes in standard positions on the wrist and ankle (Lukaski, Johnson, Bolonchuk, & Lykken, 1985). Total body resistance (R) and reactance (Xc) were measured in Ohm using an ImpediMed DF50 single-frequency device (ImpediMed DF50 Akern srl). Skeletal muscle mass was calculated by BIA using the following validated equation (Sergi et al., 2015): ASMM (kg) = 3.964 + (0.227*RI) + (0.095*weight) + (1.384*sex) + (0.064*Xc) where: ASMM is the appendicular skeletal muscle mass in kg; RI is the resistive index in cm2/V; weight is in kg; sex = 1 for men and 0 for women; and Xc is the reactance in V. The appendicular skeletal muscle mass index was calculated as follows (Baumgartner et al., 1998):
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Fig. 1. Diagnosis of sarcopenia visualized as a combination of BIA parameters and slow gait speed (<1.0 m/s) (IWGS (Fielding et al., 2011)). Abbreviation: BIA: bioimpedance analysis.
ASMMI (kg/m2) = ASMM/height2 2.6. Sarcopenia Sarcopenia was diagnosed on the strength of BIA and physical function criteria, adopting ASMMI thresholds of 7.23 for men and 5.67 kg/m2 for women, combined with a gait speed of 1 m/s as proposed by IWGS (Fielding et al., 2011). 2.7. Statistical analysis Participants’ characteristics were summarized using means SDs for continuous variables, and counts and percentages for categorical variables. For the continuous variables, normal distributions were tested using the Shapiro-Wilk test. Means and proportions were compared between participants grouped by presence or absence of sarcopenia using an independent t-test or chi-square test, respectively. All factors significantly associated with sarcopenia on univariate analysis (p 0.20) were included in the multivariate analysis, using a logistic regression backward strategy to identify the most appropriate set of covariates associated with sarcopenia. Collinearity was assessed using the variance inflation factor (VIF): adopting a cutoff of 2, none of the covariates were excluded from the models. Using these criteria, the following covariates were initially included: age (as a continuous variable), gender (women vs. men), physical activity level (low vs. recommended), BMI (20 vs. >20), 6MWT categorized as proposed by Steffen et al. (2002), disability in ADL, disability in IADL, MNA (<24 vs. 24), number of children, number of drugs (3 vs. <3), number of chronic diseases. In all analyses, a p value < 0.05 was considered statistically significant. All analyses were completed using SPSS 21.0. 3. Results In our study population of 222 subjects, 69 (31.1%) of the whole sample had only low muscle mass from their BIA parameters, 90 (40.5%) had only slow gait speed, and 39 participants (17.6% of the sample) presented both criteria – and, were, therefore, sarcopenic (Fig. 1). Conversely, 24 subjects did not have any of both conditions cited. Participants’ characteristics, by presence or absence of sarcopenia are shown in Table 1. The sarcopenic subjects were significantly older and women (p < 0.0001). Concerning their social and demographic characteristics, the sarcopenic subjects had fewer children (p = 0.002) and they were less frequently farmers (p < 0.0001). As regards their physical performance, the sarcopenic group was significantly slower in gait speed and the 6MWT, scored lower in the SPPB, took longer to complete the chair stands test, and reported lower levels of physical activity (p < 0.0001 for all comparisons). They were also significantly more disabled in terms of IADL scores (p = 0.01). The sarcopenic
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Table 1 Participants’ characteristics by presence or absence of sarcopenia. Numbers are mean values (and standard deviations) or percentages (%), as appropriate. Sarcopenic (n = 39)
Not sarcopenic (n = 183)
P value
Age (years) Female sex (n)
77.4 7.1 97.4 (38)
73.3 6.7 44.8 (82)
<0.0001 <0.0001
Social and demographic characteristics Number of children Farmer (n)
3.7 2.1 64.1 (25)
5.1 2.6 81.4 (149)
0.002 <0.0001
Physical performance Low physical activity level (n) SPPB (points) Gait speed (m/s) Timed chair stands test (s) 6MWT (mt)
51.3 (20) 7.9 1.6 0.7 0.2 14.1 4.4 248.7 69.8
15.8 (29) 10.1 2.0 1.1 0.4 11.6 3.2 340.5 83.4
<0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Disability ADL (points) IADL (points)
5.7 0.9 4.9 2.5
5.9 0.7 5.9 2.0
0.21 0.01
Nutritional status ASMMI (kg/m2) BMI (kg/m2) MNA (points)
5.2 0.3 21.1 2.3 19.2 3.6
6.5 0.8 24.0 4.2 22.9 3.2
<0.0001 <0.0001 <0.0001
Medical conditions Number of chronic diseases (n) 3 drugs
1.87 1.69 97.4 (38)
1.92 1.80 91.8 (168)
0.87 0.32
Abbreviations: SPPB: Short Physical Performance Battery; 6MWT: Six-Minute Walking Test; ADL: Activities of Daily Living scale; IADL: Instrumental Activities of Daily Living scale; ASMMI: Appendicular Skeletal Muscle Mass Index; MNA: Mini Nutritional Assessment; BMI: Body Mass Index.
group’s nutritional status was significantly worse too, in terms of both BMI and MNA score (p < 0.0001 for both comparisons). Finally, as regards medical conditions, no significant differences emerged for number of chronic diseases reported (p = 0.87) or in the percentage of people taking more than 3 drugs/daily (p = 0.32) (Table 1). Table 2 shows the results of the multivariate analysis on the significant predictors of sarcopenia emerging from the univariate analysis. Increasing age (OR = 1.2; 95%CI: 1.01–1.3; p = 0.02), female sex (OR = 13.5, 95%CI: 1.3–108.5; p = 0.01), low physical activity level (OR = 3.3, 95%CI: 1.2–10.9; p = 0.01) were significant predictors of sarcopenia. Other factors associated with sarcopenia were: low 6MWT performance (OR = 5.6, 95%CI: 1.0–35.0; p = 0.048), BMI lower than 20 kg/m2 (OR = 3.3, 95%CI: 1.0–11.0; p = 0.048), whilst the number of children was a protective factor, since each child reduced the risk of the presence of sarcopenia of 30% (OR = 0.7, 95%CI: 0.6-0.9; p = 0.004) (Table 2).
Taken together, these factors explained 58.3% of the variance for sarcopenia. Other factors inserted in the multivariate model were not associated with the presence of sarcopenia (Table 2). Replacing the categorization of the number of drugs, with the number of drugs as continuous did not significantly modify our findings (OR for the number of drugs = 0.57; 95%CI: 0.3–1.3; p = 0.17). The fully-adjusted model included: age (as a continuous variable), gender (women vs. men), physical activity level (low vs. recommended), BMI (20 vs. >20), 6MWT categorized as proposed by Steffen et al. (2002), disability in ADL, disability in IADL, MNA (< 24 vs. 24), number of children, number of drugs (3 vs. <3), number of chronic diseases. 4. Discussion The present study investigated the prevalence and the factors significantly associated with sarcopenia in a cohort of
Table 2 Significant predictors of sarcopenia in the sample as a whole. Variable
Reference group
OR (95%CI)
p-value
Age Women Low physical activity levels Low 6MWT performance Number of children BMI 20
Continuous Men Recommended physical activity levels High 6MWT performance Continuous BMI > 20
1.2 (1.01–1.3) 13.5 (1.3–108.5) 3.8 (1.3–10.9) 5.6 (1.0–35.0) 0.7 (0.6–0.9) 3.3 (1.0–11.0)
0.02 0.01 0.01 0.048 0.004 0.048
ADL disability IADL disability MNA < 24 Drugs 3 Number of diseases
ADL ability IADL ability MNA 24 Drugs < 3 Continuous
0.5 0.9 3.2 0.4 0.9
0.50 0.83 0.09 0.54 0.52
(0.0–5.9) (0.2–3.2) (0.8–12.4) (0.0–6.8) (0.7–1.2)
Abbreviations: BMI: Body Mass Index; 6MWT: Six-Minute Walking test, ADL: Activities of Daily Living scale; IADL: Instrumental Activities of Daily Living scale; MNA: Mini Nutritional Assessment. Notes: Unless otherwise specified, data are presented as odds ratios with 95% confidence intervals. Backward logistic regression was applied to obtain the best set of significant predictors of sarcopenia. In Italic factors not associated with sarcopenia presence: ORs are reported at the last step in which the covariate appears.
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community-dwelling older people in rural areas of the Peruvian Andes (in the district of Yanama). The prevalence of sarcopenia, defined according to IWGS criteria, was 17.6%. Little is known about the prevalence of sarcopenia and possible associated conditions among South America’s community-dwelling elderly. Pereira et al. (2015) found sarcopenia in 10.1% of a cohort of Brazilian older people. There may be several reasons for the difference between the prevalence identified in our study and in the Brazilian one. First, our sample was older (mean age 73.8 vs. 68.3 years for the Brazilian subjects), and had a lower mean BMI (23.5 vs. 25.4) both factors associated with a higher likelihood of sarcopenia (Dodds, Roberts, Cooper, & Sayer, 2015; Fielding et al., 2011; Landi et al., 2012). Second, the Pereira’s study was conducted in a coastal area of Brazil, whereas ours enrolled people living in a mountain region – such different environmental conditions could play a part in the different prevalence of sarcopenia. For instance, the harsher mountainous conditions could limit the feasibility of physical activity, with negative consequences on muscle mass and function, and a greater risk of sarcopenia. Third, our different results could also depend on the different methods used to assess body composition and muscle function (Beaudart et al., 2014; Pagotto & Silveira, 2014). Another study on community-dwelling Brazilians found that the prevalence of sarcopenia varied between 6.1% and 36.6%, depending on the tools and diagnostic criteria used for its identification (Pagotto & Silveira, 2014). On univariate analyses, our non sarcopenic participants were more like to be farmer than the sarcopenic group; maybe this kind of work might need to better physical preformance compared to others typical Andean works (shepherd, dealer, housewife). Our sarcopenia participants also had more disabled in IADL than the non sarcopenic group, while no such significant association with sarcopenia emerged for ADL. Several studies show sarcopenia is correlated with functional disability (Fielding et al., 2011), however our sample population still achieved high score for ADL, and this could hide any significant association between sacopenia and ADL. After adjusting the various factors vis-à-vis each other, age, female sex, a low BMI, a self-reported low physical activity level, a poor 6MWT scores, and a low number of children seem to be independently associated with sarcopenia. There is a well known association between age and sarcopenia and the aging process is one of the mechanisms involved in the development of sarcopenia (Cruz-Jentoft et al., 2010). The currently-available literature is unclear regarding genderrelated differences in the prevalence of sarcopenia (Fielding et al., 2011). Like us, Gao et al. (2015) found female sex independently associated with sarcopenia in community-dwelling Chinese elderly adults. Volpato et al. (2014) also found subjects diagnosed with sarcopenia more likely to be female in a sample of 730 Italian community-dwelling elderly. On the other hand, Landi et al. (2012) reported a higher risk of sarcopenia for males in a sample of Italian nursing home residents, and suggested that setting also has an important role in explaining gender-related differences in the prevalence of sarcopenia. Other studies showed that women generally had relatively less muscle mass than men, and a sharp decline in strength and muscle mass around the time of menopause, compared with the more gradual loss of strength by men of similar age (Gallagher et al., 1997; Hughes, Frontera, Roubenoff, Evans, & Singh, 2002; Tiidus, 2011). However in most studies that reported gender differences, there was no significant association with sarcopenia prevalence (Cruz-Jentoft et al., 2014). An inadequate nutritional status is believed to contribute to the onset and progression of sarcopenia (Cruz-Jentoft et al., 2010). Nutritional status (expressed in terms of BMI and MNA) was significantly worse in the sarcopenic group in our sample too, but
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on logistic regression analysis only a low BMI remained associated with sarcopenia. This is in line with other studies, confirming a close link between BMI and skeletal muscle mass and function (Figueiredo et al., 2014; Iannuzzi-Sucich, Prestwood, & Kenny, 2002). Other factors, such as poverty and a shortage of food, may have a fundamental role in the nutritional status, and any onset of sarcopenia, among the people of the Andes. Further studies are needed to confirm our findings. As expected, we found that sarcopenic subjects were less active physically and they performed less well in physical tests than those without sarcopenia. Our analysis also showed, however, that sarcopenic individuals are likely to have lower 6MWT results, suggesting that sarcopenia may affect physical performance indicators not normally considered in the standard definition of sarcopenia. Conversely, higher number of children is a protective factor for the presence of sarcopenia. We can hypothesize that children could support these older individuals, also in terms of economic burden. Further research is needed to confirm the association between the number of children and sarcopenia. Our findings should be considered within the limitations of our study, the most important of which is its cross-sectional nature, meaning that we cannot rule out a reverse causality. It may be difficult to identify the direction of the cause-effect relationship between sarcopenia and the factors associated with the condition. A second weakness of our study lies in that all our tests were conducted at a walk-in clinic, so subjects living far away or with motor difficulties could not take part, and this could generate a selection bias. This selection bias could be amplified by the fact that healthy subjects might not need to go to a walk-in clinic for being visited as well as subjects who are very ill can't go to a walkin clinic. However, how this bias could affect our results is very difficult to say. Third, limitations lie in the limited size of our sample. Finally, the equation that we have used for assessing ASMMI is derived from European older people and, due to anthropometric differences between South Americans and Europeans, this equation could be not appropriate. Unfortunately, to the best of our knowledge, no equation for the estimation of ASMMI specific for older South American people exists. In conclusion, the prevalence of sarcopenia identified among community-dwelling older people of the Peruvian Andes was high, approximately 18%. Age, female sex, low physical activity levels, a low BMI, a poor 6MWT scores, and a low number of children were found significantly associated with the risk of sarcopenia. Further research in similar settings is needed to confirm our findings. Conflict of interest statement The authors have no conflicts of interest to disclose. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements We thank our study collaborators. References Armstrong, T., & Bull, F. (2006). Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). Journal of Public Health, 14, 66– 70. Barbosa-Silva, T. G., Bielemann, R. M., Gonzalez, M. C., & Menezes, A. M. B. (2016). Prevalence of sarcopenia among community-dwelling elderly of a medium-
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Ishii, S., Tanaka, T., Akishita, M., Ouchi, Y., Tuji, T., & Iijima, K. (2014). Metabolic syndrome, sarcopenia and role of sex and age: Cross-sectional analysis of Kashiwa Cohort Study. PLoS One, 9, e112718. Katz, S., Downs, T. D., Cash, H. R., & Grotz, R. C. (1970). Progress in development of the index of ADL. Gerontologist, 10, 20e30. Landi, F., Liperoti, R., Fusco, D., Mastropaolo, S., Quattrociocchi, D., Proia, A., et al. (2012). Prevalence and risk factors of sarcopenia among nursing home older residents. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 67, 48–55. Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist, 9, 179–186. Lee, W. J., Liu, L. K., Peng, L. N., Lin, M. H., Chen, L. K., & ILAS Research Group (2013). Comparisons of sarcopenia defined by IWGS and EWGSOP criteria among older people: Results from the I-lanlongitudinal aging study. Journal of the American Medical Directors Association, 14(528), e1–e7. Lin, C. C., Lin, W. Y., Meng, N. H., Li, C. I., Liu, C. S., Lin, C. H., et al. (2013). Sarcopenia prevalence and associated factors in an elderly Taiwanese metropolitan population. Journal of the American Geriatrics Society, 61, 459–462. Lukaski, H. C., Johnson, P. E., Bolonchuk, W. W., & Lykken, G. I. (1985). Assessment of fat-free mass using bioelectrical impedance measurements of the human body. The American Journal of Clinical Nutrition, 41, 810–817. Ministry of Economy and Finance of Peru (2010). Consultorı´a para GTZ y ministerio de economı´a y finanzas disen˜o de un programa estrate´gico para la seguridad alimentaria. http://www.mef.gob.pe/contenidos/presu_publ/documentac/ PE0021_Seguridad_alimentaria.pdf. Ministry of Health of Peru (2013). Plan Nacional por la persona adulta mayores 2013– 2017. . http://www.minsa.gob.pe/portada/especiales/2011/saludfamiliar/ archivos/DOCUMENTOS/PLAN_AM_2013-2017.pdf. Pagotto, V., & Silveira, E. A. (2014). Applicability and agreement of different diagnostic criteria for sarcopenia estimation in the elderly. Archives of Gerontology and Geriatrics, 59, 288–294. Pereira, F. B., Ferreira Leite, A. F., & de Paula, A. P. (2015). Relationship between presarcopenia, sarcopenia and bone mineral density in elderly men. Archives of Endocrinology and Metabolism, 59, 59–65. Rolland, Y., Czerwinski, S., Abellan Van Kan, G., Morley, J. E., Cesari, M., Onder, G., et al. (2008). Sarcopenia: Its assessment, etiology, pathogenesis, consequences and future perspectives. The Journal of Nutrition Health and Aging, 12, 433–450. Sergi, G., De Rui, M., Veronese, N., Bolzetta, F., Berton, L., Carraro, S., et al. (2015). Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free-living Caucasian older adults. Clinical Nutrition, 34, 667–673. Steffen, T. M., Hacker, T. A., & Mollinger, L. (2002). Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test and gait speeds. Physical Therapy, 82, 128–137. Tiidus, P. M. (2011). Benefits of estrogen replacement for skeletal muscle mass and function in post-menopausal females: Evidence from human and animal studies. Eurasian Journal of Medicine, 43, 109–114. Vellas, B., Villars, H., Abellan, G., Soto, M. E., Rolland, Y., Guigoz, Y., et al. (2006). Overview of the MNA—Its history and challenges. Journal of Nutrition Health and Aging, 10, 463–465. 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. Journal of the American Medical Directors Association, 14(1), 53–57. Volpato, S., Bianchi, L., Cherubini, A., Landi, F., Maggio, M., Savino, E., et al. (2014). Prevalence and clinical correlates of sarcopenia in community-dwelling older people: Application of the EWGSOP definition and diagnostic algorithm. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 69, 438– 446. WHO (2010). Global recommendations on physical activity for health. . http://www. who.int/dietphysicalactivity/factsheet_recommendations/en/.
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Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
Prevalence of sarcopenia and associated factors in the healthy older adults of the Peruvian Andes Alessandra Tramontanoa , Nicola Veronesea,d,* , Giuseppe Sergia , Enzo Manzatoa,b , Diana Rodriguez-Hurtadoc, Stefania Maggib , Caterina Trevisana , Francesca De Zaiacomoa , Valter Giantina a
Department of Medicine (DIMED), Geriatrics Section, University of Padova, Italy National Research Council, Aging Branch, Neuroscience Institute, Padova, Italy c Faculty of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru d Institute for Clinical Research and Education in Medicine (IREM), Padova, Italy b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 5 May 2016 Received in revised form 6 September 2016 Accepted 7 September 2016 Available online 12 September 2016
Aim: To assess the prevalence of sarcopenia and associated factors in a population of older people living in a rural area of the Peruvian Andes. Materials and methods: The study concerned 222 people aged 65 years. Sarcopenia was diagnosed on the basis of skeletal muscle mass, measured using bioimpedance analysis, and gait speed, measured with the 4-m walking test, as recommended by the International Working Group on sarcopenia. Self-reported physical activity, the Short Physical Performance Battery, and the Six-Minute Walking Test also contributed information on participants’ physical performance status. Disabilities were investigated by assessing participants’ self-reported difficulties in performing one or more basic or instrumental activities of daily living. Results: The prevalence of sarcopenia was 17.6%. Compared with participants without sarcopenia, individuals who were found sarcopenic were significantly older, female and were less frequently farmers, had fewer children, had a worse nutritional status, a significantly lower physical performance, and higher levels of disability in the instrumental activities of daily living. After adjusting for potential confounders, age, female sex, a low body mass index, a self-reported low physical activity level, a worse Six-Minute Walking Test scores, and a low number of children were significantly associated with sarcopenia. Conclusion: The prevalence of sarcopenia seems to be quite high among community-dwelling older subjects in the Peruvian Andes. Age, female sex, a low body mass index, little physical activity, a poor SixMinute Walking Test scores, and a low number of children could be associated with this condition. ã 2016 Elsevier Ireland Ltd. All rights reserved.
Keywords: Activities of daily living Body mass index Instrumental activities of daily living Motor activity Sarcopenia
1. Introduction The International Working Group on Sarcopenia (IWGS) defined sarcopenia as “the age-associated loss of skeletal muscle mass and function” (Fielding et al., 2011). Sarcopenia is a complex syndrome
Abbreviations: ADL, activities of daily living scale; ASMM, appendicular skeletal muscle mass; ASMMI, appendicular skeletal muscle mass index; BIA, bioimpedance analysis; BMI, body mass index; CI, confidence interval; EWGSOP, European Working Group on Sarcopenia in Older People; IADL, instrumental activities of daily living scale; ISI, International Sarcopenia Initiative; MNA, Mini Nutritional Assessment; OR, odds ratio; R, resistance; RI, resistive index; SPPB, Short Physical Performance Battery; VIF, variance inflation factor; Xc, reactance; 6MWT, SixMinute Walking Test. * Corresponding author at: Department of Medicine—DIMED, Geriatrics Division, University of Padova, Via Giustiniani, 2-35128 Padova, Italy. E-mail address: [email protected] (N. Veronese). http://dx.doi.org/10.1016/j.archger.2016.09.002 0167-4943/ã 2016 Elsevier Ireland Ltd. All rights reserved.
that is associated with muscle mass loss alone or in conjunction with increased fat mass. The causes of sarcopenia are multifactorial and can include disuse, changing endocrine function, chronic diseases, inflammation, insulin resistance, and nutritional deficiencies. While cachexia may be a component of sarcopenia, the two conditions are not the same (Fielding et al., 2011). A similar definition was provided by the European Working Group on Sarcopenia in Older People (EWGOP) (Cruz-Jentoft et al., 2010), and the agreement of IWGS and EWGSOP was only fair (Lee, Liu, Peng, Lin, & Chen, 2013). A simultaneous deficiency of muscle mass and muscle function is therefore necessary for a diagnosis of sarcopenia. Several studies have investigated the prevalence of sarcopenia in elderly people. In 2014, for instance, the International Sarcopenia Initiative (ISI) (Cruz-Jentoft et al., 2014) reported a prevalence of sarcopenia from 1% to 33% across different elderly
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populations. An important weakness of this line of research lies, however, in that all these studies were conducted in developed countries. In the developed world, the prevalence of sarcopenia depends on well-known factors, such as age, gender, malnutrition and scarce physical activity (Ishii et al., 2014; Landi et al., 2012; Lin et al., 2013; Rolland et al., 2008). For community-dwelling subjects living in a rural Andean region, on the other hand, factors such as poverty, difficulty in accessing primary care, the Andean diet, and hostile environmental conditions might be associated with sarcopenia (Ministry of Economy and Finance of Peru, 2010; Ministry of Health of Peru, 2013). To our knowledge, no data on the prevalence of sarcopenia and associated factors are available for Peru, and especially for the rural Andean region. In the absence of any data relating directly to Peru, the prevalence of sarcopenia in Latin America as a whole has been estimated to range from 6.1% to 36.6% among community-dwelling elderly people (Barbosa-Silva, Bielemann, Gonzalez, & Menezes, 2016; Da Silva, Duarte, Santos, Wong, & Lebrão, 2014; Pereira, Ferreira Leite, & de Paula, 2015; Pagotto & Silveira, 2014), and even higher in some settings. In the light of the above considerations, it seems important to explore the peculiar and modifiable factors influencing this condition, also in view of the predicted exponential growth in the elderly population in years to come, and in the negative consequences of aging. The knowledge of these factors could be of importance because sarcopenia is preventable if appropriately treated and since this condition is associated with several negative outcomes in the elderly. Moreover, these factors could be different from the other South American countries. The aim of this study was therefore to assess the prevalence of sarcopenia and associated factors in a population of old people living in a rural area of the Peruvian Andes.
cardiovascular diseases, diabetes, dyslipidemia, liver diseases, gastrointestinal diseases, osteoarthritis, osteoporosis, respiratory diseases, stroke, dementia, depression, degenerative brain diseases, renal diseases, genito-urinary diseases, fractures, or cancer with a 5-year recurrence-free follow-up was recorded. Drug intake was also recorded and categorized as vs. <3, since in the elderly taking more than 3 medications is a good proxy for polypharmacotherapy (Hanlon, Schmader, Ruby, & Weinberger, 2001). Participants’ weight and height were measured with subjects wearing light clothing and no shoes, using a balance equipped with a stadiometer (estimated error 0.1 kg for weight, and 0.1 cm for height). Arm circumference was measured at the midpoint from the acromion process of the scapula to the tip of the olecranon process of the mid-elbow; and calf circumference was measured at the widest point, with subjects in a supine position with their knee and ankle at right angles.
2. Methods
2.3. Physical performance tests
This study involved older adults (65 years) living in the district of Yanama (Yungay province, in the Ancash region of Peru), a rural area of the Peruvian Andes approximately 3400 mt above sea level. The National Institute of Statistics and Informatics (INEI) of Peru report a population of 472 older people for this district (30 June 2013) (INEI, 2013), equal to 6.7% of the whole population. Data were collected from June 2014 to March 2015. All tests were conducted at an affiliated walk-in clinic at the ‘Mama Ashu’ Hospital, and all community-dwelling individuals aged 65 years or more were invited to go to the ambulatory. Subjects who met any of the following exclusion criteria were not enrolled: symptomatic cardiovascular or pulmonary diseases, evidence of severe renal impairment (requiring dialysis), acute infections requiring hospitalization, uncontrolled metabolic diseases (diabetes, anemia or thyroid disease), skeleton-deforming diseases or severe collagenopathies or rheumatic disease, agitation, dementia or psychosis, use of drugs or supplements capable of modifying body composition (e.g. corticosteroids, anabolic hormones), or a history or confirmed diagnosis of cancer in the previous 5 years. Among the 302 individuals initially screened, 80 were excluded in the light of the above-mentioned inclusion/ exclusion criteria; the study sample thus included 222 subjects. All participants were fully informed of the nature and object of the study and gave their consent. The study was conducted in accordance with the Declaration of Helsinki.
To assess their physical performance, participants were questioned about their daily physical activity (during the interview)and they completed the Short Physical Performance Battery (SPPB) and the Six-Minute Walking Test (6MWT). Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) developed by the WHO (World Health Organization) for physical activity surveillance in countries (Armstrong & Bull, 2006). It collects information on physical activity participation in three settings (Activity at work, travel to and from places, recreational activities) as well as sedentary behavior, comprising 16 questions. Physical activity was categorized as low or recommended (less or more than 150 min/week of moderate-intense and/or 75 min/week of vigorous-intense aerobic physical activity), as suggested by the WHO (2010). The SPPB (Guralnik et al., 1994) includes:
2.1. Clinical data Social and demographic data were obtained by means of faceto-face interviews with the participants. Profession was categorized as farmer vs. others. A positive clinical history of
2.2. Nutritional status The body mass index (BMI) and the Mini-Nutritional Assessment (MNA) were used to assess nutritional status. More specifically, BMI was calculated as the subject’s weight in kilograms divided by the square of their height in meters; the variable was categorized as taking 20 as cut-off since this value seems to better discriminate underweight to normo-weight in the elderly (Veronese et al., 2013). The MNA is an internationally validated method based on 18 items that include anthropometric measures, health status, dietary patterns and subjective assessments of an individual’s nutritional status (Guigoz, Lauque, & Vellas, 2002); this variable was categorized as <24 vs. 24, being this cut-off the value for dividing well-nourished from at risk of and malnourished (Vellas et al., 2006).
Standing balance. (a) Side-by-side stands: participants were asked to remain standing with their feet as close together as possible for 10 s; they scored 1 if the test was completed successfully, and 0 otherwise. (b) Semi-tandem test: participants were asked to remain standing with the ankle of one foot directly behind and in contact with the other for 10 s; they scored 1 if the test was completed successfully, and 0 otherwise. (c) Tandem test: participants were asked to remain standing with the ankle of one foot directly behind and in contact with the other foot for 10 s; they scored 0 if they remained in this position for 2 s, 1 for 3–9 s, and 2 for 10 s. Gait speed. Participants were asked to cover a distance of 4 m at their usual pace, and the time taken to do so was recorded. The use of a cane or walker was permitted. This test was completed twice and the shorter time was used for the analysis. The score
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was: 1 if the walk lasted 5.7 s; 2 for 4.1–5.6 s; 3 for 3.2–4 s; and 4 for 3.1 s. Timed chair stands test. Participants were asked to fold their arms, placing each hand on the opposite shoulder so that they crossed at the wrists, and then stand up and sit down five times as quickly as possible. The time they took to do so was recorded. This test was only administered if participants demonstrated their ability to rise from the chair without using their arms. The score was: 1 if they took 16.7 s; 2 for 13.7–16.6 s; 3 for 11.2– 13.6 s; and 4 for 11.1 s. The sum of all the SPPB subscores ranges from a maximum of 12 to a minimum of 0, higher scores reflecting a better physical performance level. In the 6MWT, participants were asked to walk as far as possible in 6 min and the distance they covered was recorded. They were allowed to use a walking stick. The path was delimited by using two chairs as turning points (Bennell, Dobson, & Hinman, 2011). This variable was categorized as proposed by Steffen et al. in community-dwelling older people in low and high performance according to gender and age specific cut-offs (Steffen, Hacker, & Mollinger, 2002). 2.4. Disability The Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) scales (Katz, Downs, Cash, & Grotz, 1970; Lawton & Brody, 1969) were used to assess participants’ degree of disability. Disability in ADL, evaluated by Katz index, coincides with difficulty in one or more of the following items: dressing, bathing, using the toilet, eating, transferring, and incontinence (Katz et al., 1970). Disability in IADL, evaluated by Lawton index, is diagnosed when an individual finds it difficult or impossible to perform the following activities: housework, preparing meals, doing laundry, using means of transportation, shopping, managing money, using the telephone, and taking medicines (Lawton & Brody, 1969). If a subject had never performed some of the above-mentioned activities, said activities were not considered. These disability variables were classified as disability in ADL (loss of one ADL) and in IADL. 2.5. Body composition Single-frequency tetrapolar bioimpedance analysis (BIA) was performed using a 400 mA (50 kHz) alternating current. BIA was conducted with subjects supine, their arms spread apart from their body and their legs apart, after fasting for at least 2 h, and bladder voiding. All BIA measurements were taken by the same trained investigator. The signal input and output electrodes were placed on the back of the right hand and foot, and the recording electrodes in standard positions on the wrist and ankle (Lukaski, Johnson, Bolonchuk, & Lykken, 1985). Total body resistance (R) and reactance (Xc) were measured in Ohm using an ImpediMed DF50 single-frequency device (ImpediMed DF50 Akern srl). Skeletal muscle mass was calculated by BIA using the following validated equation (Sergi et al., 2015): ASMM (kg) = 3.964 + (0.227*RI) + (0.095*weight) + (1.384*sex) + (0.064*Xc) where: ASMM is the appendicular skeletal muscle mass in kg; RI is the resistive index in cm2/V; weight is in kg; sex = 1 for men and 0 for women; and Xc is the reactance in V. The appendicular skeletal muscle mass index was calculated as follows (Baumgartner et al., 1998):
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Fig. 1. Diagnosis of sarcopenia visualized as a combination of BIA parameters and slow gait speed (<1.0 m/s) (IWGS (Fielding et al., 2011)). Abbreviation: BIA: bioimpedance analysis.
ASMMI (kg/m2) = ASMM/height2 2.6. Sarcopenia Sarcopenia was diagnosed on the strength of BIA and physical function criteria, adopting ASMMI thresholds of 7.23 for men and 5.67 kg/m2 for women, combined with a gait speed of 1 m/s as proposed by IWGS (Fielding et al., 2011). 2.7. Statistical analysis Participants’ characteristics were summarized using means SDs for continuous variables, and counts and percentages for categorical variables. For the continuous variables, normal distributions were tested using the Shapiro-Wilk test. Means and proportions were compared between participants grouped by presence or absence of sarcopenia using an independent t-test or chi-square test, respectively. All factors significantly associated with sarcopenia on univariate analysis (p 0.20) were included in the multivariate analysis, using a logistic regression backward strategy to identify the most appropriate set of covariates associated with sarcopenia. Collinearity was assessed using the variance inflation factor (VIF): adopting a cutoff of 2, none of the covariates were excluded from the models. Using these criteria, the following covariates were initially included: age (as a continuous variable), gender (women vs. men), physical activity level (low vs. recommended), BMI (20 vs. >20), 6MWT categorized as proposed by Steffen et al. (2002), disability in ADL, disability in IADL, MNA (<24 vs. 24), number of children, number of drugs (3 vs. <3), number of chronic diseases. In all analyses, a p value < 0.05 was considered statistically significant. All analyses were completed using SPSS 21.0. 3. Results In our study population of 222 subjects, 69 (31.1%) of the whole sample had only low muscle mass from their BIA parameters, 90 (40.5%) had only slow gait speed, and 39 participants (17.6% of the sample) presented both criteria – and, were, therefore, sarcopenic (Fig. 1). Conversely, 24 subjects did not have any of both conditions cited. Participants’ characteristics, by presence or absence of sarcopenia are shown in Table 1. The sarcopenic subjects were significantly older and women (p < 0.0001). Concerning their social and demographic characteristics, the sarcopenic subjects had fewer children (p = 0.002) and they were less frequently farmers (p < 0.0001). As regards their physical performance, the sarcopenic group was significantly slower in gait speed and the 6MWT, scored lower in the SPPB, took longer to complete the chair stands test, and reported lower levels of physical activity (p < 0.0001 for all comparisons). They were also significantly more disabled in terms of IADL scores (p = 0.01). The sarcopenic
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Table 1 Participants’ characteristics by presence or absence of sarcopenia. Numbers are mean values (and standard deviations) or percentages (%), as appropriate. Sarcopenic (n = 39)
Not sarcopenic (n = 183)
P value
Age (years) Female sex (n)
77.4 7.1 97.4 (38)
73.3 6.7 44.8 (82)
<0.0001 <0.0001
Social and demographic characteristics Number of children Farmer (n)
3.7 2.1 64.1 (25)
5.1 2.6 81.4 (149)
0.002 <0.0001
Physical performance Low physical activity level (n) SPPB (points) Gait speed (m/s) Timed chair stands test (s) 6MWT (mt)
51.3 (20) 7.9 1.6 0.7 0.2 14.1 4.4 248.7 69.8
15.8 (29) 10.1 2.0 1.1 0.4 11.6 3.2 340.5 83.4
<0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Disability ADL (points) IADL (points)
5.7 0.9 4.9 2.5
5.9 0.7 5.9 2.0
0.21 0.01
Nutritional status ASMMI (kg/m2) BMI (kg/m2) MNA (points)
5.2 0.3 21.1 2.3 19.2 3.6
6.5 0.8 24.0 4.2 22.9 3.2
<0.0001 <0.0001 <0.0001
Medical conditions Number of chronic diseases (n) 3 drugs
1.87 1.69 97.4 (38)
1.92 1.80 91.8 (168)
0.87 0.32
Abbreviations: SPPB: Short Physical Performance Battery; 6MWT: Six-Minute Walking Test; ADL: Activities of Daily Living scale; IADL: Instrumental Activities of Daily Living scale; ASMMI: Appendicular Skeletal Muscle Mass Index; MNA: Mini Nutritional Assessment; BMI: Body Mass Index.
group’s nutritional status was significantly worse too, in terms of both BMI and MNA score (p < 0.0001 for both comparisons). Finally, as regards medical conditions, no significant differences emerged for number of chronic diseases reported (p = 0.87) or in the percentage of people taking more than 3 drugs/daily (p = 0.32) (Table 1). Table 2 shows the results of the multivariate analysis on the significant predictors of sarcopenia emerging from the univariate analysis. Increasing age (OR = 1.2; 95%CI: 1.01–1.3; p = 0.02), female sex (OR = 13.5, 95%CI: 1.3–108.5; p = 0.01), low physical activity level (OR = 3.3, 95%CI: 1.2–10.9; p = 0.01) were significant predictors of sarcopenia. Other factors associated with sarcopenia were: low 6MWT performance (OR = 5.6, 95%CI: 1.0–35.0; p = 0.048), BMI lower than 20 kg/m2 (OR = 3.3, 95%CI: 1.0–11.0; p = 0.048), whilst the number of children was a protective factor, since each child reduced the risk of the presence of sarcopenia of 30% (OR = 0.7, 95%CI: 0.6-0.9; p = 0.004) (Table 2).
Taken together, these factors explained 58.3% of the variance for sarcopenia. Other factors inserted in the multivariate model were not associated with the presence of sarcopenia (Table 2). Replacing the categorization of the number of drugs, with the number of drugs as continuous did not significantly modify our findings (OR for the number of drugs = 0.57; 95%CI: 0.3–1.3; p = 0.17). The fully-adjusted model included: age (as a continuous variable), gender (women vs. men), physical activity level (low vs. recommended), BMI (20 vs. >20), 6MWT categorized as proposed by Steffen et al. (2002), disability in ADL, disability in IADL, MNA (< 24 vs. 24), number of children, number of drugs (3 vs. <3), number of chronic diseases. 4. Discussion The present study investigated the prevalence and the factors significantly associated with sarcopenia in a cohort of
Table 2 Significant predictors of sarcopenia in the sample as a whole. Variable
Reference group
OR (95%CI)
p-value
Age Women Low physical activity levels Low 6MWT performance Number of children BMI 20
Continuous Men Recommended physical activity levels High 6MWT performance Continuous BMI > 20
1.2 (1.01–1.3) 13.5 (1.3–108.5) 3.8 (1.3–10.9) 5.6 (1.0–35.0) 0.7 (0.6–0.9) 3.3 (1.0–11.0)
0.02 0.01 0.01 0.048 0.004 0.048
ADL disability IADL disability MNA < 24 Drugs 3 Number of diseases
ADL ability IADL ability MNA 24 Drugs < 3 Continuous
0.5 0.9 3.2 0.4 0.9
0.50 0.83 0.09 0.54 0.52
(0.0–5.9) (0.2–3.2) (0.8–12.4) (0.0–6.8) (0.7–1.2)
Abbreviations: BMI: Body Mass Index; 6MWT: Six-Minute Walking test, ADL: Activities of Daily Living scale; IADL: Instrumental Activities of Daily Living scale; MNA: Mini Nutritional Assessment. Notes: Unless otherwise specified, data are presented as odds ratios with 95% confidence intervals. Backward logistic regression was applied to obtain the best set of significant predictors of sarcopenia. In Italic factors not associated with sarcopenia presence: ORs are reported at the last step in which the covariate appears.
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community-dwelling older people in rural areas of the Peruvian Andes (in the district of Yanama). The prevalence of sarcopenia, defined according to IWGS criteria, was 17.6%. Little is known about the prevalence of sarcopenia and possible associated conditions among South America’s community-dwelling elderly. Pereira et al. (2015) found sarcopenia in 10.1% of a cohort of Brazilian older people. There may be several reasons for the difference between the prevalence identified in our study and in the Brazilian one. First, our sample was older (mean age 73.8 vs. 68.3 years for the Brazilian subjects), and had a lower mean BMI (23.5 vs. 25.4) both factors associated with a higher likelihood of sarcopenia (Dodds, Roberts, Cooper, & Sayer, 2015; Fielding et al., 2011; Landi et al., 2012). Second, the Pereira’s study was conducted in a coastal area of Brazil, whereas ours enrolled people living in a mountain region – such different environmental conditions could play a part in the different prevalence of sarcopenia. For instance, the harsher mountainous conditions could limit the feasibility of physical activity, with negative consequences on muscle mass and function, and a greater risk of sarcopenia. Third, our different results could also depend on the different methods used to assess body composition and muscle function (Beaudart et al., 2014; Pagotto & Silveira, 2014). Another study on community-dwelling Brazilians found that the prevalence of sarcopenia varied between 6.1% and 36.6%, depending on the tools and diagnostic criteria used for its identification (Pagotto & Silveira, 2014). On univariate analyses, our non sarcopenic participants were more like to be farmer than the sarcopenic group; maybe this kind of work might need to better physical preformance compared to others typical Andean works (shepherd, dealer, housewife). Our sarcopenia participants also had more disabled in IADL than the non sarcopenic group, while no such significant association with sarcopenia emerged for ADL. Several studies show sarcopenia is correlated with functional disability (Fielding et al., 2011), however our sample population still achieved high score for ADL, and this could hide any significant association between sacopenia and ADL. After adjusting the various factors vis-à-vis each other, age, female sex, a low BMI, a self-reported low physical activity level, a poor 6MWT scores, and a low number of children seem to be independently associated with sarcopenia. There is a well known association between age and sarcopenia and the aging process is one of the mechanisms involved in the development of sarcopenia (Cruz-Jentoft et al., 2010). The currently-available literature is unclear regarding genderrelated differences in the prevalence of sarcopenia (Fielding et al., 2011). Like us, Gao et al. (2015) found female sex independently associated with sarcopenia in community-dwelling Chinese elderly adults. Volpato et al. (2014) also found subjects diagnosed with sarcopenia more likely to be female in a sample of 730 Italian community-dwelling elderly. On the other hand, Landi et al. (2012) reported a higher risk of sarcopenia for males in a sample of Italian nursing home residents, and suggested that setting also has an important role in explaining gender-related differences in the prevalence of sarcopenia. Other studies showed that women generally had relatively less muscle mass than men, and a sharp decline in strength and muscle mass around the time of menopause, compared with the more gradual loss of strength by men of similar age (Gallagher et al., 1997; Hughes, Frontera, Roubenoff, Evans, & Singh, 2002; Tiidus, 2011). However in most studies that reported gender differences, there was no significant association with sarcopenia prevalence (Cruz-Jentoft et al., 2014). An inadequate nutritional status is believed to contribute to the onset and progression of sarcopenia (Cruz-Jentoft et al., 2010). Nutritional status (expressed in terms of BMI and MNA) was significantly worse in the sarcopenic group in our sample too, but
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on logistic regression analysis only a low BMI remained associated with sarcopenia. This is in line with other studies, confirming a close link between BMI and skeletal muscle mass and function (Figueiredo et al., 2014; Iannuzzi-Sucich, Prestwood, & Kenny, 2002). Other factors, such as poverty and a shortage of food, may have a fundamental role in the nutritional status, and any onset of sarcopenia, among the people of the Andes. Further studies are needed to confirm our findings. As expected, we found that sarcopenic subjects were less active physically and they performed less well in physical tests than those without sarcopenia. Our analysis also showed, however, that sarcopenic individuals are likely to have lower 6MWT results, suggesting that sarcopenia may affect physical performance indicators not normally considered in the standard definition of sarcopenia. Conversely, higher number of children is a protective factor for the presence of sarcopenia. We can hypothesize that children could support these older individuals, also in terms of economic burden. Further research is needed to confirm the association between the number of children and sarcopenia. Our findings should be considered within the limitations of our study, the most important of which is its cross-sectional nature, meaning that we cannot rule out a reverse causality. It may be difficult to identify the direction of the cause-effect relationship between sarcopenia and the factors associated with the condition. A second weakness of our study lies in that all our tests were conducted at a walk-in clinic, so subjects living far away or with motor difficulties could not take part, and this could generate a selection bias. This selection bias could be amplified by the fact that healthy subjects might not need to go to a walk-in clinic for being visited as well as subjects who are very ill can't go to a walkin clinic. However, how this bias could affect our results is very difficult to say. Third, limitations lie in the limited size of our sample. Finally, the equation that we have used for assessing ASMMI is derived from European older people and, due to anthropometric differences between South Americans and Europeans, this equation could be not appropriate. Unfortunately, to the best of our knowledge, no equation for the estimation of ASMMI specific for older South American people exists. In conclusion, the prevalence of sarcopenia identified among community-dwelling older people of the Peruvian Andes was high, approximately 18%. Age, female sex, low physical activity levels, a low BMI, a poor 6MWT scores, and a low number of children were found significantly associated with the risk of sarcopenia. Further research in similar settings is needed to confirm our findings. Conflict of interest statement The authors have no conflicts of interest to disclose. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements We thank our study collaborators. References Armstrong, T., & Bull, F. (2006). Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). Journal of Public Health, 14, 66– 70. Barbosa-Silva, T. G., Bielemann, R. M., Gonzalez, M. C., & Menezes, A. M. B. (2016). Prevalence of sarcopenia among community-dwelling elderly of a medium-
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