Is low phase angle a risk indicator for frailty and pre-frailty among community-dwelling older adults?

1. Introduction

Frailty is currently considered one of the main syndromes associated with aging[1] and is characterized by energy decline resulting from neuromuscular, neuroendocrine and immunological changes that occur with the increase in age.[2]Frailty is a predictor of adverse health outcomes, exerts a negative impact on the quality of life of older people and their families and is associated with a greater demand for social and healthcare services, leading to a significant increase in the cost of care.[3]Frailty is not the same as disability; it is a condition involving a high risk of falls, dependence, morbidities, hospitalization, slow recovery, and mortality.[4,5] The manifestations of frailty are slow gait speed, involuntary weight loss, fatigue, reduced muscle strength and low physical activity level.[6]

Despite the existence of several conceptual models and the lack of a universal consensus on the definition of frailty, the criteria proposed by Fried et al,[7] which assesses unintentional weight loss, self-reported fatigue, low muscle strength, insufficient physical activity, and slow gait speed, constitute one of the most widely accepted methods for identifying the condition. This low-cost, noninvasive method does not require extensive training on the part of the healthcare provider. However, it has limitations when applied to individuals with gait or balance disorders, cognitive impairment or deficiencies of the upper or lower limbs that would compromise the determination of strength and physical performance.[5] One way to overcome this limitation is the use of the phase angle (PA).

Bioelectrical impedance analysis (BIA) is a practical, fast, low-cost, noninvasive method used for evaluation of body composition.[8] The PA is one of the most important clinical variables of BIA and is the relation between reactance (Xc) (resistance of the cell membrane) and the resistance (intracellular and extracellular) that body tissues offer.[9] The PA is considered an indicator of the integrity of the cell membrane and reflects muscle mass stocks. It is commonly used in nutritional assessments and a low PA is associated with a risk for different diseases, a worse prognosis and a poorer overall health status.[8,10,11]

Frailty syndrome can co-occur with other chronic comorbidities that further debilitate affected individuals. Scientific evidence suggests that this syndrome can be reversed if identified and treated early.[12,13] As few studies have investigated the association between the PA and frailty, the aim of the present study was to explore this association as well as evaluate demographic and clinical characteristics in community-dwelling older people enrolled in an open university program.

2. Materials and methods 2.1. Study design and participants

A cross-sectional study was conducted with 51 female and male older adults (≥ 60 years of age) enrolled in an open university program for old people in the northeast of Brazil. The sample was selected by convenience through spontaneous adherence following the announcement of the study to all individuals enrolled in the program, recruitment period from November 2019 to March 2020.

The exclusion criteria were being unable to walk, use of a wheelchair, amputated upper and/or lower limbs (inability to perform gait speed and muscle strength tests), use of a pacemaker, metal parts in the body (e.g., plates or screws) and cognitive impairment evaluated using the Mini Mental State Examination.[14]

2.2. Variables 2.2.1. Frailty assessment

Individuals fulfilling at least 3 of the 5 items described below were considered frail. Those fulfilling 1 or 2 items were considered pre-frail and those fulfilling none of the items were considered non-frail.

2.2.2. Unintentional weight loss

This component of frailty was investigated based on the participant’s answer to the following question: “In the past year, have you perceived that you lost ≥ 4.5 kg or ≥ 5% of your body weight without intending to do so, such as through diet or exercise?” Individuals who answered positively were considered to have fulfilled this frailty criterion.

2.2.3. Self-reported fatigue

This component was investigated based on a score of 3 or 4 on at least one of the following 2 items of the center for epidemiological studies depression scale: Item 7 (I felt that everything I did was an effort) and Item 20 (I could not get “going”)[15] based on the frequency in the previous week (rarely or none of the time = 1, sometimes = 2, most of the time = 3, always = 4).[7,16]

2.2.4. Low muscle strength

This component was investigated considering grip strength, which was measured using a Saehan hydraulic hand dynamometer (model SH5001) and recorded in kilogram-force. Three trials were performed using the dominant hand with the elbow flexed at 90° and a 1-minute rest interval between trials. The mean of the 3 trials was calculated and the cutoff points proposed by Da Silva et al[17] were adopted. Individuals with mean grip strength below the 20th percentile of the distribution, adjusted for sex and body mass index (BMI), were considered to have fulfilled this frailty criterion.

2.2.5. Insufficient physical activity

This component was investigated using the “CuritibAtiva physical activity level questionnaire for older people” validated by Rauchbach and Wendling.[18] This questionnaire has 20 items distributed among 3 subscales: household activities (7 items), physical activities (7 items) and subsistence/social/rest activities (6 items). The score is converted, enabling the following classification: inactive (0–32 points); minimally active (33–82 points); moderately active (83–108 points); active (109–133 points); very active (≥134 points). Participants classified as inactive or minimally active were considered to have fulfilled this frailty criterion.

2.2.6. Slow gait speed

This component was investigated using a stopwatch to measure the time in seconds required to walk a distance of 4.6 meters at 1’s usual pace. Two trials were performed and the mean was calculated, adjusting for sex and height. The cutoff points proposed by Da Silva et al[17] were adopted.

2.3. Determination of phase angle

The PA was determined by measurements of resistance [R] and Xc obtained by BIA using the portable tetrapolar Sanny equipment (model BIA 1010), which administers a current of 500 to 800 μA with a single frequency of 50 Hz. The procedures were performed based on Kyle et al[19] The PA was calculated as Xc (Ω)/ Resistance (Ω) and the result was multiplied by 180/π for transformation into degrees.[20] Due to the absence of specific cutoff points, the distribution of the PA was divided into terciles and values below the first tercile were considered low.

2.4. Anthropometric data

The anthropometric data evaluated were weight and height, according to technical norms,[21] for the subsequent calculation of the BMI, which was determined and classified based on the criteria proposed by the World Health Organization.[22] Weight was measured on a duly calibrated scale (Welmy) with a maximum capacity of 200 kg and height was measured using a stadiometer coupled to the scale with a capacity for 200 cm.

Muscle mass was determined using BIA. The appendicular skeletal muscle mass index (ASMMI) was calculated using the equation ASMMI = ASMM/height², in which ASMM is appendicular skeletal muscle mass. ASMM was calculated using the formula proposed by Janssen et al[23]: ASMM (kg) = [(height²/ resistance) × 0.401] + (sex × 3.825) + (age × −0.071) + 5.102, in which height is measured in centimeters [cm], weight is measured in kilograms [kg], resistance [R] in measured in ohms, sex is 0 for women and 1 for men and age is measured in years. The cutoff point for low muscle mass was ASMMI < 8.87 kg/m2 for men and < 6.42 kg/m2 for women.[23]

2.5. Other variables

Other clinical (sarcopenia and sarcopenic obesity) and demographic (sex and age) data were collected. The diagnosis of sarcopenia was based on the cutoff points established by the European Working Group on Sarcopenia in Older People.[24] Obesity was evaluated based on the percentage of body fat (% BF) determined by BIA, using cutoff points of ≥ 28% for men and ≥ 40% for women.[25] Low grip strength plus high % BF (adjusted for sex) was classified as sarcopenic obesity.

2.6. Statistical methods

The data were analyzed with the aid of the Statistical Package for the Social Sciences (SPSS) version 25.0 (SPSS Inc., Chicago, IL). The Kolmogorov-Smirnov test was used to determine the distribution of the continuous variables. As all variables had normal distribution, the data were expressed as mean and standard deviation. The dependent variable was treated as a continuous variable and associations were tested using a parametric test (Student t test for unpaired data). Pearson correlation coefficients were calculated to test correlations between variables. Multiple linear regression analysis was performed to identify potential independent predictors for PA. The model includes the variables associated with the PA in the bivariate analysis (P < .05). The significance level for the rejection of the null hypothesis was 5% (P < .05) on all statistical tests.

2.7. Ethical aspects

This study protocol was conducted in accordance with the ethical criteria established in the Declaration of Helsinki and received approval from the local Human Research Ethics Committee from Oswaldo Cruz Hospital Complex and Federal University of Pernambuco (certificate numbers: 17029319.1.0000.5192 and 17029319.1.3001.5208.2019). All participants signed a statement of informed consent.

3. Results

The sample was composed of 51 older people, 88.2% of whom were women. No significant differences were found between sexes with regards to age, BMI or the frequency of frailty and pre-frailty. The mean PA was significantly higher in men (P = .044) (Table 1), with a high effect size calculated using Cohen d[26] (d = 0.73).

Table 1 - Demographic characteristics, phase angle and frailty profile per sex among older people. Variables Men (n = 6) Women (n = 45) P value* Mean ± standard deviation Mean ± standard deviation Age (yr) 69.8 ± 3.8 69.7 ± 6.3 .963 BMI (Kg/ m2) 27.7 ± 2.8 27.2 ± 4.2 .793 PA (o) 7.4 ± 1.7 6.4 ± 0.9 .044 Frailty profile ‡ 83.3% 84.4% .995†

BMI = body mass index, PA = phase angle.

*Parametric Student t test.

†Fisher exact test.

‡Frequency of frailty and pre-frailty.

Regarding nutritional status, 43.1% of the participants had excess weight according to the BMI and 49% had a low ASMMI. The prevalence of frailty/pre-frailty was 84.3%, 31.3% were considered sarcopenic and 11.7% had sarcopenic obesity. The value of the first tercile for PA was 5.86º and any value below this cutoff point threshold was considered low.

In the comparison of PA means and the variables of interest, no significant differences were found (Table 2 and 3). However, PA was inversely correlated with age (r = −0.210, P = .043) and directly correlated with ASMMI (Resistance = 0.132, P = .029) (Table 4). These variables remained associated with PA after the adjustments for confounding factors (Table 5).

Table 2 - Mean phase angle according to demographic and clinical characteristics of older people. Variables Phase angle n Mean ± standard deviation P value* Age (yr) .973 60 to 69 28 6.54 ± 1.19 70 or older 23 6.54 ± 1.06 Fatigue† .307 Yes 09 6.89 ± 1.42 No 42 6.47 ± 1.04 Sarcopenia† .499 Yes 16 6.73 ± 1.50 No 35 6.45 ± 0.90 Weight loss§ .658 Yes 06 7.12 ± 1.48 No 45 6.47 ± 1.06 Sarcopenic obesity‖ .512 Yes 06 7.10 ± 2.19 No 45 6.46 ± 0.91 Gait speed¶ .413 Slow 13 6.37 ± 1.10 Normal 38 6.26 ± 1.05 Grip strength# .457 Dynapenia 15 6.72 ± 1.46 Normal 36 6.47 ± 0.96 Physical activity** .496 Active 11 6.75 ± 1.08 Inactive 40 6.49 ± 1.13

* Parametric Student t test.

† Fatigue - self-reported physical exhaustion, based on a score of three or four on at least one of the following two items of the Center for Epidemiological Studies Depression Scale (CES-D).

‡ Sarcopenia - Low muscle mass < 8.87 kg/ m2 for males and < 6.42 kg/ m2 for females, and reduction in muscle strength per kilogram/force (kg/f) < 30 kg/f for males and < 20 kg/f for females.

§ Unintentional weight loss in the past year ≥ .

‖ 5 kg; Sarcopenic Obesity - percentage of total body ≥ 28% for males and ≥ 40% for females in association with criteria for sarcopenia.

¶ Gait speed – Time in seconds required to walk a distance of 4.6 meters at one’s usual pace. Slow gait speed for males with height ≤ 1,70m and > 5,31s, > 1,70m and > 5,27s, for females with height ≤ 1,53m and > 5,72s, > 1,53m and > 5,63s.

# Grip strength - Three trials were performed using the dominant hand with the elbow flexed at 90° and a one-minute rest interval between trials. The mean of the three trials was calculated and the cutoff points proposed by Da Silva et al (2011).

** Physical activity - using the “CuritibAtiva physical activity level questionnaire for older people” validated by Rauchbach and Wendling (2009), the score is converted in inactive < 83 points and active ≥ 83 points.


Table 3 - Mean phase angle according to nutritional aspects and frailty among older people. Variables Phase angle n Mean ± standard deviation P value* Body mass index .711 With excess weight 22 6.59 ± 1.12 Without excess weight 29 6.48 ± 1.13 ASMMI .711 Low 25 6.35 ± 1.12 Normal 26 6.52 ± 1.07 % Body fat .687 With excess 28 6.53 ± 1.24 Without excess 23 6.66 ± 0.96 Frailty .700 Frail/pre-frail 43 6.57 ± 1.19 Non-frail 08 6.40 ± 0.68

* Parametric student t test; ASMMI- appendicular skeletal muscle mass index.


Table 4 - Correlation between phase angle and clinical/nutritional aspects in older people Variables r P value* Age (yr) −0.210 .043 Weight (kg) 0.109 .448 Height (m) 0.070 .626 ASMMI (kg/m2) 0.132 .029 Grip strength (kgf) 0.058 .684 BMI (kg/m2) 0.054 .707 Gait speed (m/s) 0.130 .364 Body fat (%) −0.130 .364

ASMMI = appendicular skeletal muscle mass index, BMI = body mass index.

* Pearson correlation (r).


Table 5 - Multiple linear regression analysis of phase angle, age and muscle mass in older people. Variables PA Β Adjusted P value CI 95% Age (yr) −0.211 .043* −0.18 −0.24 ASMMI (kg/m2) 0.219 .037* 0.13 0.308

PA = phase angle, ASMMI = appendicular skeletal muscle mass index, CI = confidence interval.

*<0.05.


4. Discussion

To the best of our knowledge, this is the first study to investigate the association between the PA and frailty in a sample of older Brazilians. We found no significant association between PA and this clinical condition, but age and the ASMMI were independently associated for PA, as the increase in age was correlated with a low PA and a direct correlation was found for muscle mass. Such findings are important, since these variables are predisposing factors biomarkers for the emergence of frailty.[27]

The advance in age is associated with a reduction in muscle mass, changes in body composition and an increased risk of disorders of the musculoskeletal system. The reduction in muscle mass that occurs naturally with the aging process is associated with a decline in muscle strength and a poorer physical performance, which favors the development of frailty.[27] Once installed, frailty leads to reductions in functional capacity and independence as well as the intensification of multiple morbidities, an increased risk of falls and hospitalization, and even premature death.[28]

A pioneering prospective study conducted with 550 older people in Japan found that a low PA was associated with the risk of frailty and locomotive syndrome.[10] In our results, no difference in PA was found between individuals with and without frailty/pre-frailty. However, the rates of frailty and pre-frailty were high (23.5% and 60.8%, respectively), which is in agreement with data reported in studies conducted in different countries[29] and in Brazil.[1] In the present investigation, no significant difference was found between the sexes with frailty/pre-frailty.

Several methods are used for detection of frailty. We used the criteria proposed by Fried et al,[7] which are widely employed in scientific studies and identified by the Brazilian Frailty Consensus as the most widely used in Brazil. The prevalence of frailty and pre-frailty can vary depending on the criteria employed, the adaptations performed and the evaluation setting.[4]

The high rates of frailty and pre-frailty in our study merit attention. As most participants were independent and autonomous, we expected these frequencies to be lower. Moreover, it is concerning that these clinical conditions could go undetected in the early stages, leaving affected individuals vulnerable to adverse outcomes, such as dependence, falls, hospitalization and even death,[5] due to a lack of early identification and treatment.

Another finding that might be emphasized was the high percentage of physical inactivity (78.4%) among the group studied. Despite being independent older people who performed various activities of daily living, the majority did not practice physical activity in a systematic manner. We opted for a specific physical activity assessment tool that was validated in Brazil,[18] as this instrument addresses the most frequent activities in Brazilian culture and would therefore more faithfully reflect the daily lives of our participants. Physical inactivity can contribute to a reduction in muscle mass and functioning, which can result in the emergence of frailty syndrome in older people and a reduction in the PA. The importance of physical exercises as therapeutic and prevention strategies in patients with pre-frailty and frailty is well established in the literature, considering that such exercises can improve body composition, resulting in an increase in muscle quality, strength and endurance.[30,31]

Due to the lack of a standard cutoff point, PA was categorized in terciles, as done for other clinical conditions and also in a Japanese study on PA and frailty.[9–11,32] The first tercile of the sample was below 6º, suggesting compromised cellular mass and functional capacity. This finding indicates that such a value could be used for the population with frailty and pre-frailty. However, studies with a larger, more heterogeneous sample are needed to confirm this finding. It should be pointed out that several scientific studies have demonstrated the impact of a low PA on the integrity of the cell membrane, the emergence of diseases, muscle quality and functional capacity.[32]

The PA has gained prominence in scientific studies in recent years due to the fact that it is highly predictive of deleterious clinical outcomes and mortality in various diseases.[8–11,32,33] In the present study, the mean PA was significantly higher in the male sex (P = .044), with a high effect size (d = 0.73), which demonstrates the reliability of the result. This finding is compatible with data described by Tanaka et al[10] and may be explained by the fact that men have more muscle mass and better muscle quality than women resulting from the natural action of male hormones.[34,35] Unfortunately, in the present study the number of men was greatly reduced. However, the comparability of PA distribution according to sex was at the limit of statistical significance, when evaluated by probability or by estimating confidence intervals. In addition, for the other parameters, the variability between genders was very discreet. That is, the reduced number of men does not seem to compromise the results of the study.

No significant association was found between PA and nutritional or clinical variables in the present study, although the percentage of sarcopenic individuals was considered high (31.3%) and a high% BF and BMI were also prevalent (54.9% and 43.1%, respectively). The aging process is accompanied by changes in body composition, with a reduction in muscle mass and increase in fat mass. Thus, older people are more vulnerable to sarcopenia, which, in turn, is a predisposing factor for frailty. Once installed, frailty has a negative impact on health and quality of life and may co-occur with other chronic comorbidities that further debilitate affected individuals.[27]

Although this study offers relevant data for the follow-up of older people, the small sample size limits the generalization of the results and the strength of the study. The data collection process needed to be interrupted due to the COVID-19 pandemic, which impeded the inclusion of more participants. Another limitation regards the cross-sectional design, which does not enable the establishment of causality. Nonetheless, the findings contribute to explaining the role of the phase angle as a marker of muscle mass, serving as a general health indicator of functioning in older people. Moreover, this study is innovative, as investigations involving the determination of PA in individuals with frailty and pre-frailty are scarce and, to the best of our knowledge, no previous studies of this type have been conducted with the Brazilian population.

5. Conclusion

In the present study, the ASMMI and age were associated with the phase angle. These findings are important, as aging and a reduction in muscle mass are predisposing conditions for the development of frailty. Prospective studies are needed to support these findings and gain a more complete understan

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