Assessment of Body Composition by Bioelectrical Impedance, in Adults Diagnosed or Not with Sarcopenia

The term sarcopenia was first described in the late 1980s as a disease characterized by progressive loss of skeletal muscle mass and function [1-3], associated with aging. It comes from the Greek terms ‘sarx’, meaning flesh and ‘penia’, deficiency. Since 2016, the World Health Organization (WHO) includes Sarcopenia as a disease in its International Classification of Diseases and Related Health Problems (ICD) with the code ICD-10-CM (M62.84) [4,5], recognized as a significant geriatric disease [4]. It is a pathology of high prevalence in the older adult population. Its etiology is multifactorial, influenced by nutrition, lifestyle and hormonal factors [5,6], as well as the existence of multiple concurrent processes [4-6]. This disease and its complications impact the older adult and, logically, their quality of life [5]. The parameters to consider in the diagnosis of sarcopenia are the amount of muscle and its functionality. The quantifiable variables are mass, strength and physical performance, for which there are various measurement techniques to assess the loss of muscle mass and the impact of this loss on functional capacity [6-8]. Sarcopenia can also contribute to an increased risk of chronic diseases such as osteoporosis, diabetes and sarcopenic obesity [9]. Sarcopenia is classified into primary sarcopenia, associated with the loss of mass and function of skeletal muscle that occurs during the aging process and secondary sarcopenia, due to the presence of an underlying disease or medication [3].

There are different methods to determine body composition, in addition to those previously mentioned, which include densitometry, bioelectrical impedance analysis, or anthropometric measurements Calf Circumference [CC] and mid-arm muscle circumference, among others [3-10]. Bioimpedance analysis is a quick, inexpensive, non-invasive, safe and painless examination that allows for knowing a person’s body composition, mainly to evaluate the percentage of fat, lean mass (muscle) and variations in body water, considering the individual’s age and sex [11-15]. In this study, it was used to diagnose the risk and stages of sarcopenia, by estimating body composition parameters, such as skeletal muscle mass, fat mass and lean mass, to make comparisons by age group and sex of older adults with decreased skeletal mass and those within the normal range, according to the cutoff point used. Since there are few studies in Cuba to determine the risks and suffering of sarcopenia through Electrical Bioimpedance, this study aimed to understand the relationships between some variables.

This study is longitudinal and analytical. It included 150 apparently healthy older adults from grandfather circles, from popular councils of the Santiago de Cuba municipality, belonging to age groups of 60 to 69, 70 to 79 and 80 to 89, of both sexes and the necessary sample size was calculated using equation (1). The result was 74. The inclusion criteria were: older adults between 60 and 89 and willingness to participate in the research and the exclusion criteria were: intake of plenty of water or food two hours before the measurement, consumption of alcohol or coffee (eight) hours before the measurement, inability to empty the bladder before the application of the BIA, physical activity. This study is longitudinal and analytical. A total of 150 apparently healthy elderly adults from the grandparents’ circles, coming from popular councils in the Municipality of Santiago de Cuba, belonging to age groups 60 to 69, 70 to 79 and 80 to 89, of both sexes were selected and the number of necessary samples was calculated using equation 1. The result was 74. The inclusion criteria were: elderly adults between 60 and 89 and voluntary participation in the research, while the exclusion criteria included: consumption of abundant water or food two hours prior to measurement, consumption of alcohol or coffee (eight) hours before measurement, inability to empty the bladder before the BIA application, intense physical activity prior to the analysis (24 hours before), subjects with prostheses or metal implants, intake of medications such as diuretics, corticosteroids, etc., that could alter body composition (at least 7 days prior to the test) and premenstrual syndrome (due to possible liquid retention that would alter the BIA) possibility of pregnancy and breastfeeding.

where:

N=Total population
Zα=safety coefficient
p= expected proportion (5%=0.95)
q=1-p (in this case 1-0.05=0.95)
d= precision (in this research, the 5% was used)

The safety coefficient Zα varies like this:
A. If the confidence interval were 90%, the coefficient would be 1.645.
B. If the confidence interval were 95%, the coefficient would be 1.96.
C. If the confidence interval were 97.5%, the coefficient would be 2.24.
D. If the confidence interval were 99%, the coefficient would be 2.57.

In the case of this study, Zα=1.96, since the confidence interval is 95%. In this study, ethical principles for medical research in humans were followed, as dictated by the World Medical Association. Exclusion and inclusion criteria were taken into account to carry out the bioimpedance measurements as outlined by the National Institutes of Health Technology Assessment Conference Statement of 1994 and also those of the Spanish Group of the Federation of Sports Medicine from 2009. All selected individuals signed the informed consent beforehand. Then, the elderly participants in the sample were weighed and measured using a T Z 120 HEALH SCALE manufactured in China. Electrical Bioimpedance (BIA) measurements were taken with the BioScan 98 ® and the body composition was determined in terms of Skeletal Muscle Mass (SMM), Free Fat Mass (FMM) and Fat Mass (FM) using predictive multiple regression equations. Skeletal muscle mass [16] was calculated using the Janssen equation [17] which is expressed in equation 2.

where height is expressed in cm; R is resistance in ohms; sex (0: women, 1: men); age in years. Additionally, the comparison of the average values of body composition between women and men, as well as between older adults diagnosed with sarcopenia and those who were not, was carried out using the student’s t-test for independent samples. The multifactorial ANOVA statistical test was applied to the parameters MME, MLG, MG based on the factors age groups and sex in the group of older adults presenting sarcopenia, in order to determine the influence of these factors on the mean values of the referenced parameters.

The fat-free mass (FFM) was estimated, according to the National Health and Nutrition Examination Survey - CDC (NHANES) [18], using equations 3 and 4 for women and men, respectively.

where R is the body’s electrical resistance in Ω.

The body Fat Mass (FM) was calculated from equation 5

The percentage of body fat mass (%BF) was estimated using equation 6

All statistical processing was carried out using the IBM SPSS application, version 23. Table 1 shows the characterization of older adults by age groups, divided by sex. In addition, the comparison of the average values of body composition between women and men, as well as between older adults diagnosed with sarcopenia and those who were not, was performed using the student’s t-test for independent samples. The multifactorial ANOVA statistical test was applied to the parameters SMM, FMM and %FM based on the factors of age groups and sex in the group of older adults who presented with sarcopenia, to determine the influence of these factors on the average values of the referred parameters. Table 1 shows the characterization of older adults by age groups, divided by sex.

Table 1:Characterization of the sample.

Table 1 shows that out of the 74 elderly adults in the sample, 55 (74.0%) are female and 19 (26%) are male. Among those aged 60 to 69, there are 37, of which 29 (78%) are female and 8 (22%) are male. Among those aged 70to79, there are 31, with 22(71%) concentrated in women and 9 in men (29.0%). Among those aged 80 to 89, 4(66.7%) correspond to females and 2(33.3%) to males, for a total of 6. Furthermore, the average age is practically the same for both sexes; regarding weight and height, these are higher in men.

As seen in Figure 1, the highest average response of SMM, FMM was for men and the %FM was for women. Table 2 shows parameters of body composition by age groups of both sexes of older adults with sarcopenia and without sarcopenia.

Figure 1:Response of a) SMM, b) FMM, and c) %FM versus sex factor (0: female; 1: male).

Table 2:Parameters of body composition in both sexes of older adults with sarcopenia and without sarcopenia.

S elderly with sarcopenia; SN elderly without sarcopenia *There are no elderly without sarcopenia in this age group. A third of the sample showed decreased SMM; almost two-thirds were women. SMM decreased in older adults diagnosed with sarcopenia (women aged 60 to 79 and men aged 60 to 89), as well as in women aged 60 to 89 and nonsarcopenic men aged 60 to 79 and it increased in sarcopenic women aged 80 to 89; the %FM increased in both women and men with or without sarcopenia aged 60 to 79. Sex had a greater influence on variations in parameters. FMM increased in healthy men (60-79) and decreased in sarcopenic and healthy women (60-89). SMM and FMM values were higher in men and %FM in women (not significantly).

From the comparison between older adults who presented sarcopenia and those who did not, using the student’s t-test for independent samples, it was inferred that for the age group of 60 to 69 years, the values of FMM and %FM were higher with statistical significance (p<0.05) for women, as well as the values of SMM for men; for women, it was also higher, but not significantly. Between 70 and 79 years, the behavior was similar to that of 60 to 69, whereas between 80 and 89, no comparison was made, since there are no older adults without sarcopenia. In general terms, these parameters were significantly higher (p<0.05) in older adults without sarcopenia, for both sexes. After applying the multifactorial ANOVA statistical test to the parameters SMM, FMM and %FM based on the factors of age groups and sex in the group of older adults who presented sarcopenia, it was observed that the sex factor significantly affects the mean value of the three parameters from a statistical point of view (p<0.05). In the cases of the sex-age group interactions, there are not enough observations for analysis, since in the age group 80 to 89, there are no cases of men without sarcopenia. The responses of SMM, FMM and %FM for sex factor (0: females, 1: males) in major adults are respectively shown in Figure 1. As observed in Figure 1, the highest mean response of SMM and FMM was for males and that of %FM was for females. The responses of SMM (a), FMM (b) and %FM (c) for the factor age groups are shown in Figure 2. It can be noted that the highest mean response of SMM, FMM and %FM was for the age group of 60 to 69 years and the lowest was for the age group of 80 to 89.

Figure 2:Response of the SMM (a), FMM (b) and c) %FM versus the age group factor (6: 60-69; 7: 70-79; 8: 80-89).

The distribution of older adults by age groups agrees with what was reported by Vergara AA [8], where the number of women was greater than that of men and the number of older adults in the different groups decreased as age increased. This is also in accordance, according to the researcher himself, with the statistical data provided by the Pan American Health Organization (PAHO) [18] gland the World Health Organization (WHO) [19], which state that globally among older adults, the female sex prevails over the male, as women have a life expectancy that is 6 to 8 years higher than that of men. In addition, the WHO states that the population decreases with age due to natural aging, during which, according to De Frutos JM [20], Genua Goena M [21], Zenón TG [22] and Gómez Candela [23] a series of biological changes occur that exhibit great interindividual variability, increase vulnerability to disease and impact nutritional status, thus increasing the risk of malnutrition. Vergara AA [8] argues that during aging, physiological, psychological, social and economic changes also occur, with significant repercussions in the genesis of various pathologies and an increase in various disabilities that simultaneously affect the dynamics of families and their socioeconomic structure. Other factors that affect the modification of body composition during aging are cellular oxidation and chronic-degenerative diseases that alter physiology; these changes refer to the decrease in muscle mass, mineral mass and an increase in adipose tissue, among others [3]. The highest percentage of women was found in the age groups of 60 to 69 (50%) and 70 to 79 (41.9%). The results of the differences in the comparison of the mean values of the parameters SMM, % and FMM between women and men, both for older adults with sarcopenia and those without, agree with what was obtained by De Frutos JM [20]. About one third of the older adults in the sample have decreased SMM. Of these, nearly two thirds were women. The average values of SMM in diagnosed women varied between approximately 16 and 17 kg (only decreasing in the age group 70 to 79) and in men between 19 and 25 kg; this latter range is comparable to that of the study by Vergara AA [8], but unlike that study, there is a gradual decrease in SMM as age increases. This can be explained by the fact that in women, the loss of SMM is primarily affected in the first few years after menopause due to the loss of bone density and mineral content and in men after the age of 60. The average values of SMM in undiagnosed women ranged from approximately 16 to 19kg and in men from 28 to 29 kg. The behavior of %FM for both sexes in older adults who did not present sarcopenia is also consistent with what is reported in the literature, as there is unanimity among different researchers that fat mass increases with age and then decreases or remains stable in old age [18]. This can be explained by considering that the aging process brings about important changes in its distribution, as visceral adipose tissue increases while subcutaneous adipose tissue decreases. The behavior of %FM for both sexes in older adults who did not present sarcopenia is also consistent with what is reported in the literature, as there is unanimity among different researchers that fat mass increases with age and then decreases or remains stable in old age [19]. This can be explained by considering that the aging process brings about important changes in its distribution, as visceral adipose tissue increases while subcutaneous adipose tissue decreases. The %FM was higher for women (31.3±5.0%) than for men (22.90±2.6%), which is normal according to the classifications of Bray GA [24] and the Spanish Society for the Study of Obesity (SEEDO) [25].

This parameter, for those diagnosed with sarcopenia of both sexes, decreased from ages 60 to 69 to those of 70 to 79, but there was an increase from 80 to 89 only in older adult women, as no men of those ages were diagnosed with the disease. However, in men, it increased as age increased, starting from 60. Regarding the FMM parameter, it decreased as the age group increased in older adults of both sexes with sarcopenia; those without sarcopenia showed similar results in the evaluated age groups (except between 60 to 79 in men, where there were practically no differences). This is also a consequence of aging [26]. The sex factor is what causes the greatest incidence in the variations of the skeletal muscle mass index and other body composition parameters (FFM, FM and %FM). The age group factor, as well as the interactions between the sex and age group factors, do not significantly impact the aforementioned variations in SMM, FFM and %FM. This is consistent with what Gómez Candela and others proposed in 2004 [23]. This study can be applied to groups of adults over 60 years old.

The body composition showed differences in age groups of older adults diagnosed with sarcopenia and healthy individuals, depending on sex and age group in some cases (%FM and FMM), but not in others (SMM). The study demonstrated that, although factors such as sex and age caused variations in the parameters of body composition, it was sex that had the most significant impact, both in adults diagnosed with sarcopenia and in those who were not.

1. Sandoval Animas GE (2021) Social service project: Sarcopenia and malnutrition in older adults, an updated literature review. Fundación de Obras Sociales de San Vicente IAP, Mexico.
2. Gainol, KA Galindo G (2018) Electrical bioimpedance, dynamometry and SPPB in older adults in the city of Guayaquil electrical bioimpedance, dynamometry and SPPB in older adults in the city of Guayaquil (Undergraduate thesis). Catholic University of Santiago de Guayaquil, Guayaquil.
3. Cuevas L, Chavarría E, Cuevas, Díaz CV (2021) Urgent need for devices to assess sarcopenia: Methodological, instrumentation, logical, statistical, evidence and epistemological aspects in health Urgent need for devices to assess sarcopenia. Methodology, Instrumentation, Logic, Statistics, Evidence and Epistemology in Health.
4. Cao Li, Morley JE. (2016) Sarcopenia is recognized as an independent condition by an international classification of disease, tenth revision, clinical modification (ICD-10-CM) with the code. J Am Med Dir Assoc 17(8): 675-677.
5. Bermúdez C, Buckcanan A, Buckcanan G (2019) Sarcopenia: Comprehensive approach to the older adult. Revista Médica Sinergia 24-34.
6. Torán F, Navarro M, Sacanella E, Meseguer, López A, et al. (2209) What is sarcopenia? Revista Médica Sinergia.
7. Rodriguez AI, Ruiz MD, Artacho R (2020) Diagnosis and prevention of sarcopenia in elderly care homes. Nutrición Hospitalaria 36(5).
8. Vergara AA (2015) Diagnosis of sarcopenia by gait speed determination and muscle mass index using the bioelectrical impedance method in older adults from Apango Municipality, State of Mexico (Bachelor's thesis). Autonomous University of the State of Mexico, UAEM Amecameca University Center.
9. Gómez CA, Rodríguez GV, Vila Maldonado S, Casajús A, Ara I, et al. (2012) Aging and body composition: Sarcopenic obesity in spain. Nutr Hosp 27(1): 22-30.
10. Geraldo P, Amórtegui I, Rodríguez C, Rojas Y, Santana J, et al. (2018) Anthropometric methods and techniques for calculating body composition. Journal of Materials and Information Sciences 5(10): 61-70.
11. Carvajal W, Deturnell Y, Echavarría IM, Aguilera D, Espósito LR, et al. (2017) Analysis of body composition using bioelectrical parameters in the Cuban sports population. Sports Medicine Archives 34(4): 207-215.
12. Ortega JA, Vázquez FE, Vélez M, Cortes CE, Barrios C, et al. (2018) Comparison of classical anthropometry and bioelectrical impedance methods through body composition determination in university youth. Clinical Nutrition and Hospital Dietetics 38(4): 164-171.
13. Moonen HP, Van Zanten AR (2019) Bioelectric impedance analysis for body composition measurement and other potential clinical applications in critical illness. Current Opinion in Critical Care 27(4): 344-353.
14. Vasold KL, Parks AC, Phelan DM, Pontifex MB, Pivarnik JM, et al. (2019) Reliability and validity of commercially available low-cost bioelectrical impedance analysis. International Journal of Sport Nutrition and Exercise Metabolism 29(4): 406-410.
15. González J (2022) Body composition analysis and its use in clinical practice for people living with obesity. Revista Médica Clínica Las Condes 33(6): 615-622.
16. Villada J, González C, Marulanda F (2018) Provisional cut-off points for the diagnosis of sarcopenia in elderly people from caldas (Colombia). Biomedical. Journal of the National Institute of Health 38(4): 521-526.
17. Janssen I, Baumgartner RN, Ross R, Rosenberg IH, Roubenoff R (2004) Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. American Journal of Epidemiology 159(4): 413-421.
18. Pan American Health Organization. Women, Health and Development Program. Gender and Ageing.
19. World Health Organization (2025) Ageing and life course: Ageing facts.
20. De Frutos JM (2017) Bioelectrical impedance analysis as an indicator of muscle mass and strength in a group of elderly people Trabajo de Fin de Grado, Bachelor’s thesis, Human Nutrition and Dietetics. Autonomous University of Madrid.
21. Genua Goena M (2001) Nutrition and assessment of nutritional status in the elderly. Matia Foundation 1(1): 1-21.
22. Zenón TG, Villalobos Silva JA (2012) Malnutrition in the elderly. Part I: Undernutrition, the old enemy. Med Interna Mexico. 28(1): 57-64.
23. Gómez Candela C, Reuss Fernández JM (2004) Manual of nutritional recommendations in geriatric patients. Medical Editors, Madrid, pp. 21-22.
24. Bray GA (1987) Overweight is risking fate: Definition, classification, prevalence and risk. Ann NY Accad Sci 499: 14-28.
25. Vitonica (2025) Percentage of body fat and its normal ranges according to sex and age.
26. Kyle UG, Genton L, Hans D, Karsegard L, Slosman DO, et al. (2001) Age-related differences in fat-free mass, skeletal muscle, body cell mass and fat mass between 18 and 94 years. Eur J Clin Nutr 55(8): 663-672.