Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Low muscle mass and mortality risk later in life: A 10-year follow-up study

  • Cristina Camargo Pereira,

    Roles Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

    Affiliation Postgraduate Program in Health Sciences, Medical School, Federal University of Goiás (UFG), Goiania, Brazil

  • Valéria Pagotto,

    Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing

    Affiliation Faculty of Nursing, Postgraduate Program in Nursing, Federal University of Goiás (UFG), Goiania, Brazil

  • Cesar de Oliveira,

    Roles Funding acquisition, Writing – review & editing

    Affiliation Department of Epidemiology & Public Health, University College London, London, United Kingdom

  • Erika Aparecida Silveira

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing

    Affiliations Postgraduate Program in Health Sciences, Medical School, Federal University of Goiás (UFG), Goiania, Brazil, Department of Epidemiology & Public Health, University College London, London, United Kingdom



Little is known about the impact of low muscle mass (MM) assessed by calf circumference (CC), arm circumference (AC), arm muscle circumference (AMC), and corrected arm muscle circumference (CAMC)—on mortality risk later in life. We aimed to investigate the impact of low MM assessed by CC, AC, AMC and, CAMC on all-cause, cardiovascular, and cancer mortality risk.


Data came from 418 older adults who participated in a 10-year follow-up prospective cohort study. Low MM was defined as a CC < 33 cm for women and < 34 cm for men and by the lowest tertile of AC, AMC, and CAMC stratified by sex. The log rank test, Kaplan-Meier curves, and Cox regression were used.


There were 147 deaths: 49 related to CVD and 22 to cancer. A small CC (HR = 1.57, 95% CI, 1.12–2.20), AMC (HR = 1.61, 95% CI, 1.13–2.30) and CAMC (HR = 1.45, 95% CI, 1.03–2.04) were associated with all-cause mortality. A small CAMC was a protective factor for CVD mortality (HR = 0.46, 95% CI, 0.22–0.98). In the Kaplan-Meier analysis, older adults with LMM presented low all-cause mortality survival, with AC (p < 0.05), AMC (p < 0.005), CAMC (p < 0.002), and CC (p < 0.001). Cancer mortality was associated with low CAMC (p < 0.020).


Low MM assessed by anthropometric measures (AC, AMC, CAMC and CC) increased the all-cause mortality risk. A small CAMC decreased the CVD mortality.


The number of older adults is increasing rapidly globally, and by 2050 one in six individuals worldwide will be aged 65 and over [1]. Advancing age increases the occurrence of cardiovascular diseases (CVD), diabetes mellitus (DM), cancer, and respiratory diseases [2,3] which are the leading causes of death in this age group [2].

Prospective studies showed that typical health conditions in older adults such as functional disability, limited mobility, morbidity, and low physical performance have a significant impact on their mortality risk [49]. The health conditions that increase mortality risk are closely associated with reduced muscle mass (MM) that occurs with aging [1012]. MM maintenance can decrease all-cause mortality risk and chronic diseases [10], improve sensitivity to insulin [10,13], increase energy expenditure [14], cardiorespiratory capacity and mobility in adults and older adults [15]. Therefore, MM seems to play an important role in the prevention and treatment of chronic diseases prevalent later in life such as CVD [16,17]. Its maintenance is also a strategy to minimize the adverse effects of chemotherapy such as fatigue, body composition, anxiety, strength and quality of life in people with cancer [12,18,19].

MM can be measured by different methods such as dual x-ray absorptiometry, bioelectrical impedance, ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) [12,19]. However, these methods are expensive and require some previous specific procedures that limit their use in the clinical context [20,21]. Considering that clinical practice scenarios, either public or private, represent the entryway to treatments or health care actions [22], it is important to use less costly, practical, and reproducible MM assessment methods, such as anthropometric measurements of arm circumference (AC) and calf circumference (CC) [2327].

Small AC and CC have been previously associated with a lower survival rate in older institutionalized adults [28]. However, it is still unclear whether low MM, measured by anthropometric measurements, affects the all-cause, CVD, and cancer mortality risk in community-dwelling older adults. Therefore, the main aim of this study was to evaluate whether MM measured by different anthropometric measurements i.e. calf circumference (CC), arm circumference (AC), arm muscle circumference (AMC), and corrected arm muscle circumference (CAMC), impact on all-cause, CVD, and cancer mortality risk later in life using data from a ten-year follow-up cohort.

Materials and methods

Study population

The study was conducted in the city of Goiânia, capital of the state of Goiás, Midwestern Brazil. The procedures used in the Goiânia Older Adult Project cohort that started in 2008, were described in detail in previous publications [2933]. The cohort population comprised of community-dwelling older adults, with a probabilistic sample representative of the municipality, aged 60 years or older (n = 418). All participants included in this research signed an informed consent form and authorized verification of the death certificate. This study was approved by the Research Ethics Committee of the Hospital das Clínicas of the Federal University of Goiás, Brazil (protocol number: 2.500.441).


The participants were followed-up from baseline in 2008 until the date of the last interview held in 2018/2019.

Mortality ascertainment

Mortality information was obtained from the Brazilian Mortality Information System from the baseline in 2008 to March 2019. All deaths were confirmed in home visits. The basic cause of death was coded using the World Health Organization International Classification of Diseases, Tenth Revision (ICD-10) [34]. CVD mortality was defined using the ICD-10 codes I100-I99, and mortality from cancer was defined using the ICD-10 codes C100-D48.

Muscle mass (MM) assessment

MM was measured by the following anthropometric measurements: arm circumference (AC), arm muscle circumference (AMC), corrected arm muscle circumference (CAMC), and calf circumference (CC).

AC was measured at the intermediate point between the lateral projection of the scapular acromion process and the lower margin of the ulna olecranon with the person in an upright position [35]. The TSF was measured to calculate AMC and CAMC. The TSF measurement was performed with a Lange adipometer, with a constant pressure of 10 g/mm2 on the contact surface and an accuracy of 1 mm, with a 0–65 mm ruler. The measurement was taken on the back of the arm and midway between the point of the acromion and olecranon process while the arm was hanging relaxed. Three measurements were taken successively, and the average of three measurement was used [36]. CAMC was estimated using the formulas proposed by Heymsfield et al. [37] to determine muscle tissue reserve, correcting the bone area by sex. Calf circumference (CC) was measured at the point of greatest circumference in relation to the longitudinal line of the right calf [35].

Low MM was defined using the lowest tertile stratified by sex for AC (< 29.1 cm for men and 29.2 cm for women), AMC (< 23.1 cm2 for men and < 23.2 cm2 for women), and CAMC (< 20.1 cm2 for men and < 20.0 cm2 for women). For CC, a previously validated cut-off point was used for this same study population, with dual energy x-ray absorptiometry (DXA) data as the reference, with low MM values for CC being < 34 cm for men and < 33 cm for women [29].

The anthropometric measurements were performed by trained researchers, and validated according to the technique proposed by Habicht [38], with calculations of precision and accuracy verified by performing inter- and intra-technical error analyses to avoid measurement variability. All circumferences were measured twice using an inelastic tape (CESCORF) and averaged. All anthropometric measurements were collected by trained researchers using standardized procedures.

Sociodemographic, lifestyle, and health variables

We also collected the following data: (i) sociodemographic characteristics (age, sex, skin colour, level of education, socioeconomic class, living with partner); (ii) health conditions, number of comorbidities and history of diseases previously diagnosed by physicians (diabetes and hypertension); (iii) lifestyle (physical activity level, smoking status, alcohol consumption and eating habits).

Biomarkers and nutritional status

Weight (in kilograms) and height (in meters) were measured according to standard procedures [35]. Weight was measured using a calibrated portable digital electronic scale (Tanita) with a capacity of up to 150 kg and an accuracy of 100 g. Height was measured using a 2-meter tape measure, with an accuracy of 0.1 cm and fixed on a flat wall without a wall baseboard using a plumb line and a wooden square. Body Mass Index (BMI) was calculated by dividing the weight (kg) by the square of height (m). Systolic (SBP) and diastolic (DBP) blood pressures were obtained with a semi-automatic device (OMRON—HEM 705 CC) according to standardized recommendations [39]. Participants with a systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic (DBP) ≥ 90 mmHg or receiving pharmacological treatment for hypertension were considered hypertensive [39]. For the data on HDL-cholesterol, LDL-cholesterol, triglycerides-TG and glycemia, participants were asked to present the results of their laboratory blood tests performed up to three months before the interview date. Those reporting the use of hypoglycaemic drugs were classified as having diabetes mellitus. The drugs were identified according to medical prescription or possession and classified according to the Anatomical Therapeutic Chemical (ATC) Guidelines. The number of comorbidities was identified by answering the question “What diseases has your doctor said you have?”. The diseases mentioned were further categorized according to the ICD-10 [34].

Statistical analysis

The anthropometric measurements were described according to the three mortality risk groups. The differences between the groups were evaluated using the Student’s t, Chi-square, or Fisher’s exact test.

The three outcomes, all-cause, CVD, and cancer mortality, were analysed according to the occurrence of low MM in 2008, measured by the following anthropometric variables: AC, AMC, CAMC, and CC, using three Cox regression models for adjustment, as follows: Model 1: sociodemographic variables; Model 2: Model 1 + lifestyle; Model 3: Model 1 + Model 2 + DM, hypertension, number of chronic diseases and biochemical markers (HDL-cholesterol, LDL-cholesterol, TG and glycemia).

The survival curves were plotted using the Kaplan-Meier’s method. The log rank test was used to compare the survival curves of older adults with low MM and adequate MM. For all statistical analyses, significance was determined at p <0.05 level. All analyses were performed using STATA 12.0 software. In cases of loss to follow-up, survival time was censored on October 10, 2018.


The sample characterization was as follows: mean age 70.69 ± 7 years (60 to 98 years), mean BMI = 26.97 ± 5.12 kg/m2, female (66%), white skin colour (46.4%), four years of education (41.2%), social class C (61%), living with a partner (45.2%), former smoker (43.3%), sedentary (35.2%), and not consuming fruits and vegetables on a daily basis (72.3%). The prevalence of diabetes was 23% and that of hypertension was 60% (Table 1). The baseline sample characteristics by mortality cause (all-cause, cardiovascular disease and cancer) are presented in Table 1.

Table 1. Baseline characteristics of participants stratified by all–cause, CVD and cancer mortality.

During the ten-years follow-up, 147 (35.2%) deaths occurred. Of these, 49 (33.3%) died of CVD and 22 (14.9%) of cancer. The all-cause mortality rates were statistically different for low MM values evaluated by CC (p < 0.000), AC (p < 0.027), AMC (p < 0.004), and CAMC (p < 0.002) and the cancer mortality rates were statistically different considering CAMC (p < 0.0036) (Table 2).

Table 2. Anthropometric measurements of participants at baseline stratified by all-cause mortality, CVD and cancer.

In the Cox regression analysis, low MM measured by CC (HR = 1.84; 95% CI, 1.32–2.54), AMC (HR = 1.59; CI95%: 1.15–2.22), and CAMC (HR = 1.67; 95% CI, 1.20–2.32) significantly increased all-cause mortality risk in the ten-year follow-up. Low MM measured by CAMC increased cancer mortality (HR = 2.60; 955 CI, 1.12–6.03) (Table 3).

Table 3. Cox’s crude proportional risk analysis to assess associations of low muscle according to anthropometric measures with all-cause mortality, CVD and cancer.

In the adjusted Cox regression analysis, all-cause mortality risk was significantly associated with the three low MM anthropometric measurements i.e. CC (HR = 1.57; 95% CI, 1.12–2.20), AMC (HR = 1.61; 95% CI, 1.13–2.30), and CAMC (HR = 1.45; 95% CI, 1.03–2.04) (Table 4). A small CAMC was associated with a lower CVD mortality risk (HR = 0.46, 95% CI, 0.22–0.98). There was no significant association for the other low MM parameters (Table 5).

Table 4. Fully adjusted Cox proportional risk analysis to assess the association of low muscle mass according to anthropometric measures with all-cause mortality (n = 147).

Table 5. Fully adjusted to Cox’s proportional risk to assess associations of low muscle according to anthropometric measures with CVD mortality (n = 49) and Cancer (n = 22).

For all-cause mortality, the log rank test showed that participants with low MM measured by all anthropometric measurements (CC: p < 0.001; AC: p < 0.05; AMC: p < 0.005; CAMC: p < 0.002;) had shorter survival times compared to participants with adequate MM (Fig 1A, 1B, 1C and 1D).

Fig 1. Survival curves of the population studied according to the anthropometric measurements ( Normal; Low).

(A) Normal Calf circumference; Low Calf circumference. (B) Normal Mid-Arm circumference; Low Mid-Arm circumference. (C) Normal Mid-Arm muscle circumference; Low Mid-Arm muscle circumference. (D) Normal Corrected arm muscle circumference; Low Corrected arm muscle circumference.

The survival curves for CVD in participants with low and normal MM measured by AC (p = 0.546), AMC (p = 0.231), CAMC (p = 0.364), and CC (p = 0.366) showed no significant differences. With regards to cancer mortality risk, a small CAMC (p < 0.020) reduced survival. Small AC (p = 0.122), AMC (p = 0.625), and CC (p = 0.231) did not change cancer survival.


To the best of our knowledge, this study was the first to analyse the impact of low muscle mass measured by various anthropometric measures on all-cause, CVD, and cancer mortality risk in community-dwelling older adults from a Latin American community. This study used four anthropometric measurements to evaluate muscle mass and our findings showed that having a small AC, AMC, CAMC, and CC increased significantly the all-cause mortality risk after 10 years of follow-up. In addition, older adults with a small CAMC had a shorter survival time for cancer and lower risk of death from CVD.

Low muscle mass and all-cause mortality

This study showed that the lowest AMC and CAMC tertiles and small CC increased the risk of death. The results from the survival curves analyses showed that individuals with low muscle mass measured by AC, AMC, CAMC, and CC have lower survival rates after a ten-year follow-up. These results corroborate previous studies [4053] in older community-dwelling adults that evaluated the impact of low muscle mass assessed by anthropometric measurements on mortality risk. However, these studies were mostly conducted in European and Asian countries. A study [52] conducted with 1,298 fragile older Mexican adults showed that a CC < 31 cm was a risk factor for mortality after a 14-year follow-up. Findings from the Longitudinal Aging Study Amsterdam (LASA) cohort with community-dwelling individuals aged 65 or older showed that a AC < 30 cm increased the risk of death in men by 1.8 times and in women by up to 2.3 times after a 15-year follow-up [47]. Another study, using data from community-dwelling older Japanese adults, showed that a small AMC was a risk factor for mortality after a two-year follow-up [48]. In British older adults, higher AMC values were associated with a reduction of up to 15% in all-cause mortality after a 15-year follow-up [40].

Interestingly, in a community-dwelling sample aged 70 and older, a European study [51] showed that CAMC was not associated with all-cause mortality after six years, but a study in Australia [52] showed that a CAMC lower than ≤ 21.4 cm2 in men and ≤ 21.6 cm2 in women increased the risk of death after an eight-year follow-up. Data from a cohort with a six-month follow-up showed that the risk of death has been reduced by up to 5% for each unit of increased CAMC [42].

Decreased anthropometric measurements may indicate low muscle mass, being associated with worse health conditions and a higher likelihood of death [54,55]. Therefore, the use of anthropometric measures is relevant to evaluate and monitor older adults in health settings where the most effective methods to evaluate body composition are expensive and difficult to access [30].

The most used sophisticated methods to assess body composition, such as dual x-ray absorptiometry and bioelectrical impedance, despite having a good precision in the measurement of body compartments, are not routinely used in clinical practice, due to the need for trained personnel, high costs and time spent for their performance [12,19]. Anthropometric measurements are valid alternatives for the assessment of body composition, since they are relatively fast, inexpensive and a large number of people can be examined in a short period of time [2327]. In addition, they can be widely used to estimate body composition in several clinical conditions, such as cardiovascular disease, cancer and sarcopenia later in life.

Low muscle mass and CVD mortality

Previous findings on the association between anthropometric measures and cause-specific mortality in older adults living in the community are limited [50,51]. Furthermore, the available evidence from the studies [4951,56] that evaluated the impact of muscle mass on CVD and cancer mortality using anthropometric measures remains inconclusive.

The findings of the present study showed that a small CAMC reduces the risk of CVD mortality by 54%. Most previous investigations on the impact of low muscle mass on CVD mortality used only AMC measurement [49,50,56]. One study evaluating 1,061 older European adults living in the community aged 70 to 77 years showed that CAMC was not associated with CVD mortality [51]. However, in this same study, increased AC was associated with a higher risk of CVD mortality [51].

Data from the Bangladesh Health Effects of Arsenic Longitudinal Study (HEALS) cohort, which followed 1,975 individuals aged 18 and older [41] for 8 years showed that a small AC was a risk factor for CVD in people with low BMI. In the NHANES III, with 11,958 people aged 20 to 90 years, small AC was a risk factor for CVD death after a 14-year follow-up [49]. In the Charleston Heart Study [56], low AC was a risk factor for CVD mortality in black men, but not in white men.

Since the CAMC calculation involves triceps skinfold (DCT) and AC with bone mass correction, the decrease in CAMC probably reflects a gain in subcutaneous fat mass. The associations of each body fat deposit in the risk of CVD vary, since the upper and lower parts of the body contain divergent fat deposits with different biological functions [56].

Regional differences in the severity of adipose inflammation, storage and renewal of lipids, release of adipokines and endocrine effects are among the mechanisms potentially responsible for the aforementioned proposed associations [5658].

Even for similar types of fat, subcutaneous adipose tissue in the arm was considered less susceptible to unregulated release of free fatty acids compared to abdominal subcutaneous adipose tissue [59]. A study on postmenopausal older women showed that trunk fat was associated with an increased risk of CVD, while fat in the extremities (arms and legs) was not [56].

On the other hand, CAMC is considered a surrogate marker to indicate the muscle mass used to determine a muscle tissue reserve, reflecting the sarcopenia that is associated with mortality in the older adults [46]. The mechanism underlying the relationship of sarcopenia with all-cause mortality in community-dwelling older adults is not fully elucidated [10]. However, sarcopenia is closely associated with physical function, conferring greater risk to fractures, disability, dependence, recurrent hospitalization and mortality [12]. Therefore, considering changes in body composition with ageing it is important to evaluate the changes using different anthropometric measurements to predict mortality.

Low muscle mass and cancer mortality

In the present study, the ten-year survival was lower in individuals with low muscle mass compared to those with normal muscle mass using CAMC. To date, there are no studies on the association between CAMC and the risk of death from cancer in older adults living in the community. A study evaluating cancer mortality in obese and non-obese people in the US used AC to measure muscle mass and reported no association between AC and cancer mortality risk [45].

The relationship between low muscle mass and cancer mortality later in life also remains uncertain. A greater understanding of the underlying mechanisms of muscle loss is needed. However, it is known that maintaining muscle mass can improve the metabolism and increase energy reserves, increasing, consequently, the chances of older adults coping with disease [60]. Therefore, low muscle mass estimated by anthropometric measurements may constitute indicators of poor prognosis in the face of cancer diagnosis at advanced ages.

This study demonstrates that anthropometric measures such as CC, AMC and CAMC used as indicators of low muscle mass can predict mortality in community-dwelling older adults. Knowing that CC is more affected by edema than AC [61] and considering that TSF and AC data are accessible and easy to measure in community-dwelling older adults, we recommend the use of AC and TSF in the nutritional assessment to calculate AMC and CAMC.

Overall, there are no consistent results between anthropometric measurements and the risk of death from specific causes. It should be noted that most of the studies were conducted on young adults in European countries.

The most important limitation of this study include the lack of muscle strength measurement and tests that evaluate muscle function (such as usual gait speed). It is recognized that strength is better than mass in predicting adverse outcomes, particularly mortality [12]. Another potential limitation of this study relates to its low statistical power to analyse the association with cancer mortality due to the number of deaths from this cause in the follow-up period. This study has strengths such as the analysis of non-institutionalized or hospitalized older adults. Most of the evidence on the studied topic comes from these groups. Another positive aspect is the ten-year follow-up period, especially in Brazil. Finally, this study used a validated CC cut-off point [25] in the same population, in which the DXA was used as reference.

These results have relevant implications for clinical practice in gerontology and geriatrics, as well as for planning public health actions. The measurement of these anthropometric measures should be incorporated into health care practices for older adults, as they are more practical and cheaper than other methods to measure muscle mass and, consequently, helping the development of mortality prevention measures. Anthropometric evaluations are effective as the first step in screening older patients to identify those most at risk of death and to target interventions to prevent loss of muscle mass. The early detection of low muscle mass can reduce disability and, in turn, increase the survival rate later in life. In additional, future studies should investigate the relationship of these anthropometric measurements by CVD type and primary site of cancer.


Low muscle mass evaluated by the anthropometric measurements i.e. AC, AMC, CAMC and CC increased the all-cause mortality risk in older community-dwelling adults but not for cancer and CVD mortality. Except for a small CAMC, which increased the risk of cancer mortality and reduced the risk of CVD mortality.


We thank all participants and their families. The authors also would like to thank the Universidade Federal de Goiás for providing a working space and an examination room.


  1. 1. Nations United. World Population Prospects 2019: Highlights. ST/ESA/SER. Department of Economic and Social Affairs, editor. Nations United. New York: Population Division; 2019. 39 p.
  2. 2. Dicker D, Nguyen G, Abate D, Abate KH, Abay SM, Abbafati C, et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1684–735. pmid:30496102
  3. 3. Silveira EA, Kliemann N, Noll M, Sarrafzadegan N, De Oliveira C. Visceral obesity and incident cancer and cardiovascular disease: An integrative review of the epidemiological evidence. Obes Rev [Internet]. 2021 [cited 2021 Jul 21];22(1):1–17. Available from: pmid:32692447
  4. 4. Landi F, Calvani R, Tosato M, Martone AM, Bernabei R, Onder G, et al. Impact of physical function impairment and multimorbidity on mortality among community-living older persons with sarcopaenia: Results from the ilSIRENTE prospective cohort study. BMJ Open. 2016;6(e008281):1–7. pmid:27456324
  5. 5. Hirani V, Naganathan V, Blyth F, Le Couteur DG, Seibel MJ, Waite LM, et al. Longitudinal associations between body composition, sarcopenic obesity and outcomes of frailty, disability, institutionalisation and mortality in community-dwelling older men: The Concord Health and Ageing in men project. Age Ageing. 2017;46(3):413–20. pmid:27932368
  6. 6. Cereda E, Zagami A, Vanotti A, Piffer S, Pedrolli C. Geriatric Nutritional Risk Index and overall-cause mortality prediction in institutionalised elderly: A 3-year survival analysis. Clin Nutr. 2008;27(5):717–23. pmid:18774626
  7. 7. Timmermans EJ, Hoogendijk EO, Broese Van Groenou MI, Comijs HC, Van Schoor NM, Thomése FCF, et al. Trends across 20 years in multiple indicators of functioning among older adults in the Netherlands. Eur J Public Health. 2019;29(6):1096–102. pmid:31008512
  8. 8. Hoogendijk EO, Deeg DJH, Poppelaars J, van der Horst M, Broese van Groenou MI, Comijs HC, et al. The Longitudinal Aging Study Amsterdam: cohort update 2016 and major findings. Eur J Epidemiol. 2016;31(9):927–45. pmid:27544533
  9. 9. St John PD, Tyas SL, Menec V, Tate R. Multimorbidity, disability, and mortality in community-dwelling older adults. Can Fam Physician. 2014;60(5):e272–80. pmid:24829022; PMCID: PMC4020665.
  10. 10. Landi F, Camprubi-Robles M, Bear DE, Cederholm T, Malafarina V, Welch AA, et al. Muscle loss: The new malnutrition challenge in clinical practice. Clin Nutr. 2019;38(5):2113–20. pmid:30553578
  11. 11. JafariNasabian P, Inglis JE, Reilly W, Kelly OJ, Ilich JZ. Aging human body: Changes in bone, muscle and body fat with consequent changes in nutrient intake. J Endocrinol. 2017;234(1):37–51. pmid:28442508
  12. 12. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16–31. pmid:30312372
  13. 13. Kim G, Kim JH. Impact of Skeletal Muscle Mass on Metabolic Health. Endocrinol Metab. 2020;35(1):1. pmid:32207258
  14. 14. Petersen MC, Shulman GI. Mechanisms of insulin action and insulin resistance. Physiol Rev. 2018;98(4):2133–223. pmid:30067154
  15. 15. Vogiatzis I. Strategies of muscle training in very severe COPD patients. Eur Respir J. 2011;38(1):971–5. pmid:21737548
  16. 16. Srikanthan P, Horwich TB, Tseng CH. Relation of Muscle Mass and Fat Mass to Cardiovascular Disease Mortality. Am J Cardiol. 2016;117(8):1355–60. pmid:26949037
  17. 17. Pin F, Couch ME, Bonetto A. Preservation of muscle mass as a strategy to reduce the toxic effects of cancer chemotherapy on body composition. Curr Opin Support Palliat Care. 2018;12(4):420–6. pmid:30124526
  18. 18. Silveira EA, Roosevelt Da R, Filho S, Claudia M, Spexoto B, Haghighatdoost F, et al. Molecular Sciences The Role of Sarcopenic Obesity in Cancer and Cardiovascular Disease: A Synthesis of the Evidence on Pathophysiological Aspects and Clinical Implications. Int J Mol Sci [Internet]. 2021;22(9):4339. Available from:
  19. 19. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis. Age Ageing. 2010;39(4):412–23. pmid:20392703
  20. 20. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, et al. Bioelectrical impedance analysis—Part I: Review of principles and methods. Clin Nutr. 2004;23(5):1226–43. pmid:15380917
  21. 21. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, et al. Bioelectrical impedance analysis—Part II: Utilization in clinical practice. Clin Nutr. 2004;23(6):1430–53. pmid:15556267
  22. 22. Landi F, Martone AM, Calvani R, Marzetti E. Sarcopenia risk screening tool: A new strategy for clinical practice. J Am Med Dir Assoc. 2014;15(9):613–4. pmid:25023830
  23. 23. Hsu WC, Tsai AC, Wang JY. Calf circumference is more effective than body mass index in predicting emerging care-need of older adults—Results of a national cohort study. Clin Nutr. 2016;35(3):735–40. pmid:26093536
  24. 24. Landi F, Onder G, Russo A, Liperoti R, Tosato M, Martone AM, et al. Calf circumference, frailty and physical performance among older adults living in the community. Clin Nutr. 2014;33(3):539–44. pmid:23948128
  25. 25. Pagotto V, Santos KF Dos, Malaquias SG, Bachion MM, Silveira EA. Calf circumference: clinical validation for evaluation of muscle mass in the elderly. Rv Bras Enferm [Internet]. 2018;71(2):322–8. pmid:29412289
  26. 26. Barbosa-Silva TG, Bielemann RM, Gonzalez MC, Menezes AMB. Prevalence of sarcopenia among community-dwelling elderly of a medium-sized South American city: Results of the COMO VAI? Study. J Cachexia Sarcopenia Muscle. 2016;7(4):136–43. pmid:27493867
  27. 27. Chao YP, Kao TW, Chang YW, Peng TC, Chen WL, Wu LW. Utilization of anthropometric parameters as a novel tool for detection of insulin resistance. Clin Nutr [Internet]. 2020;39(8):2571–2579. Available from: pmid:31812468
  28. 28. Tsai ACH, Lai MC, Chang TL. Mid-arm and calf circumferences (MAC and CC) are better than body mass index (BMI) in predicting health status and mortality risk in institutionalized elderly Taiwanese. Arch Gerontol Geriatr. 2012;54(3):443–7.
  29. 29. Pagotto V, Silveira EA. Applicability and agreement of different diagnostic criteria for sarcopenia estimation in the elderly. Arch Gerontol Geriatr. 2014;59(2):288–94. pmid:24935822
  30. 30. Pagotto V, Silveira EA. Methods, diagnostic criteria, cutoff points, and prevalence of sarcopenia among older people. Sci World J. 2014;2014(1):1–11.
  31. 31. de Araújo NC, Silveira EA, Mota BG, Mota JPN, de Camargo Silva AEB, Guimarães RA, et al. Potentially inappropriate medications for the elderly: Incidence and impact on mortality in a cohort ten-year follow-up. PLoS One. 2020;15(10):e0240104. pmid:33112864
  32. 32. Silveira EA, Dalastra L, Pagotto V. Polypharmacy, chronic diseases and nutritional markers in community-dwelling older. Rev Bras Epidemiol. 2014;17(4):818–29. pmid:25388483
  33. 33. Vieira EC, Peixoto M do RG, Silveira EA da. Prevalence and factors associated with Metabolic Syndrome in elderly users of the Unified Health System. Rev Bras Epidemiol. 2014;17(4):805–71. pmid:25388482
  34. 34. Organização Mundial da Saúde. CID-10 Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde. 10th ed. Universidade de São Paulo, editor. Edusp. São Paulo; 1997. 1200 p.
  35. 35. Lohman TJ, Roache AF, Martorell R. Anthropometric Standardization Reference Manual. 1st ed. Human Kinetics Books, editor. Medicine & Science in Sports & Exercise. Champaign, IL; 1988. 90 p.
  36. 36. Frisancho AR. Triceps skin fold and upper arm muscle size norms for assessment of nutritional status. Am J Clin Nutr. 1974;27(10):1052–8. pmid:4419774
  37. 37. Heymsfield SB, McManus C, Smith J, Stevens V, Nixon DW. Anthropometric measurement of muscle mass: Revised equations for calculating bone-free arm muscle area. Am J Clin Nutr. 1982;36(4):680–90. pmid:7124671
  38. 38. Onis M, Habicht JP. Anthropometric reference data for international use: recommendations from a World Health Organization Expert Committee. Am J Clin Nutr [Internet]. 1996[cited 2019 Feb 14];64:650–8. Available from: content/64/4/650.abstract. pmid:8839517
  39. 39. Malachias MVB, Souza WKSB, Plavnik FL, Rodrigues CIS, Brandão AA, Neves MFT, et al. 7ª Diretriz Brasileira de Hipertensão Arterial. Arq Bras Cardiol 2016; 107(3Supl.3):1–83.
  40. 40. Bates CJ, Hamer M, Mishra GD. A study of relationships between bone-related vitamins and minerals, related risk markers, and subsequent mortality in older British people: The National Diet and Nutrition Survey of People Aged 65 Years and over. Osteoporos Int. 2012;23(2):457–66. pmid:21380638
  41. 41. Chen Y, Ge W, Parvez F, Bangalore S, Eunus M, Ahmed A, et al. A prospective study of arm circumference and risk of death in Bangladesh. Int J Epidemiol. 2014;43(4):1187–96. pmid:24713183
  42. 42. Weng CH, Tien CP, Li CI, L’Heureux A, Liu CS, Lin CH, et al. Mid-upper arm circumference, calf circumference and mortality in Chinese long-term care facility residents: A prospective cohort study. BMJ Open. 2018;8(5):10–2. pmid:29743327
  43. 43. Wijnhoven HA, van Bokhorst-de van der Schueren MA, Heymans MW, de Vet HC, Kruizenga HM, Twisk JW, et al. Low mid-upper arm circumference, calf circumference, and body mass index and mortality in older persons. J Gerontol A Biol Sci Med Sci. 2010 Oct;65(10):1107–14. pmid:20547497; PMCID: PMC3304296.
  44. 44. Wu LW, Lin YY, Kao TW, Lin CM, Liaw FY, Wang CC, et al. Mid-arm muscle circumference as a significant predictor of all-cause mortality in male individuals. PLoS One. 2017;12(2):1–11. pmid:28196081
  45. 45. Wu LW, Lin YY, Kao TW, Lin CM, Wang CC, Wang GC, et al. Mid-Arm Circumference and All-Cause, Cardiovascular, and Cancer Mortality among Obese and Non-Obese US Adults: The National Health and Nutrition Examination Survey III. Sci Rep. 2017;7(1):1–8.
  46. 46. De Hollander EL, Bemelmans WJE, De Groot LCPGM. Associations Between Changes in Anthropometric Measures and Mortality in Old Age: A Role for Mid-Upper Arm Circumference? J Am Med Dir Assoc. 2013;14(3):187–93. pmid:23168109
  47. 47. Easton JF, Stephens CR, Román-Sicilia H, Cesari M, Pérez-Zepeda MU. Anthropometric measurements and mortality in frail older adults. Exp Gerontol. 2018;110(Sep):61–9. pmid:29775746
  48. 48. Enoki H, Kuzuya M, Masuda Y, Hirakawa Y, Iwata M, Hasegawa J, et al. Anthropometric measurements of mid-upper arm as a mortality predictor for community-dwelling Japanese elderly: The Nagoya Longitudinal Study of Frail Elderly (NLS-FE). Clin Nutr. 2007;26(5):597–604. pmid:17669559
  49. 49. Gueresi P, Miglio R, Cevenini E, Gualdi Russo E. Arm measurements as determinants of further survival in centenarians. Exp Gerontol. 2014;58(1):230–4. pmid:25172624
  50. 50. Ho SC, Wang JY, Kuo HP, et al. Mid-arm and calf circumferences are stronger mortality predictors than body mass index for patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2016;11:2075–2080. Published 2016 Aug 31. pmid:27621613
  51. 51. Landi F, Russo A, Liperoti R, Pahor M, Tosato M, Capoluongo E, et al. Midarm muscle circumference, physical performance and mortality: Results from the aging and longevity study in the Sirente geographic area (ilSIRENTE study). Clin Nutr. 2010;29(4):441–7. pmid:20116909
  52. 52. Miller MD, Crotty M, Giles LC, Bannerman E, Whitehead C, Cobiac L, et al. Corrected arm muscle area: An independent predictor of long-term mortality in community-dwelling older adults? J Am Geriatr Soc. 2002;50(7):1272–7. pmid:12133024
  53. 53. Schaap LA, Quirke T, Wijnhoven HAH, Visser M. Changes in body mass index and mid-upper arm circumference in relation to all-cause mortality in older adults. Clin Nutr. 2018;37(6):2252–9. pmid:29195733
  54. 54. Alam N, Wojtyniak B, Rahaman MM. Anthropometric indicators and risk of death. Am J Clin Nutr. 1989;49(5):884–8. pmid:2718923
  55. 55. Sánchez-García S, García-Peñ C, Duque-López MX, Juárez-Cedillo T, Cortés-Núñez AR, Reyes-Beaman S. Anthropometric measures and nutritional status in a healthy elderly population. BMC Public Health. 2007;7(2):1–9.
  56. 56. Stevens J, Keil JE, Rust PF, Verdugo RR, Davis CE, Tyroler HA, et al. Body mass index and body girths as predictors of mortality in black and white men. Am J Epidemiol. 1992;135(10):1137–46. pmid:1632424
  57. 57. Karpe F, Pinnick KE. Biology of upper-body and lower-body adipose tissue—Link to whole-body phenotypes. Nat Rev Endocrinol. 2015;11(2):90–100. pmid:25365922
  58. 58. Tchkonia T, Thomou T, Zhu Y, Karagiannides I, Pothoulakis C, Jensen MD, et al. Mechanisms and metabolic implications of regional differences among fat depots. Cell Metab. 2013;17(5):644–56. pmid:23583168
  59. 59. Stefan N, Schick F, Häring HU. Causes, Characteristics, and Consequences of Metabolically Unhealthy Normal Weight in Humans. Cell Metab. 2017;26(2):292–300. pmid:28768170
  60. 60. Van Pelt RE, Evans EM, Schechtman KB, Ehsani AA, Kohrt WM. Contributions of total and regional fat mass to risk for cardiovascular disease in older women. Am J Physiol—Endocrinol Metab. 2002;282(5):E1023–8. pmid:11934666
  61. 61. Ishida Y, Maeda K, Nonogaki T, Shimizu A, Yamanaka Y, Matsuyama R, et al. Impact of edema on length of calf circumference in older adults. Geriatr Gerontol Int. 2019;19(10):993–998. pmid:31397070