The Relationship between the Risk of Sarcopenia in the Elderly and Autonomic Nervous System Balance ^†

Abstract

Elderly people with sarcopenia experience symptoms related to dysfunction in the autonomic nervous system (ANS), including rapid or irregular heartbeat, dizziness, and fluctuations in blood pressure. These symptoms may be a result of immune system activation and the impact of the disease, leading to alterations in certain aspects of ANS regulation. There is limited research that discusses the relationship between these factors. Therefore, we investigated the correlation between the risk of sarcopenia and the balance of the autonomic nervous system. The participants included 87 females and 25 males aged between 60 and 79 years old, who were assessed for sarcopenia risk including walking speed, grip strength, muscle mass, and the Appendicular Skeletal Muscle Mass Index (ASM). The collected data were analyzed using descriptive statistics, t-tests, one-way ANOVA, and Pearson correlations. The results showed the following: (1) Individuals at a high risk of sarcopenia displayed a slightly greater degree of autonomic nervous system imbalance compared to those at low risk. (2) In the time-domain analysis, sympathetic nervous system activity was higher in males than in females, whereas parasympathetic nervous system activity exhibited the opposite trend. In the frequency-domain analysis, heart rate variability was higher in females than in males, and both genders met the criteria for sarcopenia risk assessment. (3) There were no significant differences in heart rate variability indices among different sarcopenia risk groups. (4) Different levels of sarcopenia risk showed a significant correlation with ANS. Individuals at a higher risk demonstrated an imbalance, while those at a lower risk displayed a more balanced state. Overall, the results of this study confirmed the correlation between sarcopenia risk and heart rate variability among the elderly, further emphasizing the importance of the autonomic nervous system in sarcopenia risk.

1. Introduction

Global aging is a significant social issue faced by countries worldwide. By the end of 2021, those of Taiwan’s population aged 65 years old and above exceeded 16.59% of the total population, officially indicating an aging society. After 2025, it is projected to become a “Super Aged Society”, with the population aged 65 years old and above surpassing 20% of the total population. By 2040, it is estimated to exceed 30%, surpassing the aging rate of countries in Europe and America, and by 2050, it is expected to surpass Japan, becoming the fastest-aging country globally. The challenges associated with aging, particularly issues related to disability in the elderly, have gradually become a concern for most countries around the world.

As people age, their body composition changes, including a gradual reduction in skeletal muscle fibers, a decrease in muscle mass, lower strength, and a decline in muscle endurance and metabolic capacity. These physiological changes contribute to the onset of chronic diseases, cognitive impairments, and diminished physical function. Consequently, this declines in overall mobility and the loss of muscle mass. According to research in the United States and Europe, the prevalence of sarcopenia among the elderly aged 60 to 70 years old is approximately 5 to 13%, while for those aged 80 years old and above, it ranges from about 11 to 50%. Meanwhile, studies in Taiwan indicated that the prevalence of sarcopenia among individuals aged 65 and above is approximately 3.9 to 7.3% (females: 2.5 to 6.5%; males: 5.4 to 8.2%) [1]. According to the European Working Group on Sarcopenia in Older People (EWGSOP), the diagnosis of sarcopenia involves assessing muscle mass, muscle strength, and physical performance, aligning with the understanding of sarcopenia [2]. The Asian Working Group for Sarcopenia (AWGS) adopted a similar approach to diagnosing sarcopenia with differences from EWGSOP. AWGS recommends screening tests that involve the simultaneous measurement of muscle strength (grip strength) and physical performance (gait speed) [3].

We adopted the sarcopenia screening process proposed by AWGS. Individuals with normal grip strength and walking speed are defined to have a low risk for sarcopenia. For those with at least one of either grip strength or walking speed below the normal range, a measurement of skeletal muscle mass is conducted. If muscle mass is normal, it is defined as moderate risk for sarcopenia; otherwise, it is considered high risk. This classification is applied to individuals aged 60 years and above to explore and provide evidence for the early prevention or delay of sarcopenia in the elderly.

Heart Rate Variability (HRV) refers to the variations in the time intervals between each heartbeat. This phenomenon is attributed to the reciprocal interactions of the Sympathetic Nervous System (SNS) and Parasympathetic Nervous System (PNS). It serves as an indicator used to assess the functioning of the Autonomic Nervous System (ANS). The analysis of HRV is conducted through the measurement and observation of continuous electrocardiographic signals. HRV time series are assessed for autonomic nervous system (ANS) activity and quantified using specialized analysis methods. Currently, the most widely used analysis methods are time-domain analysis and frequency-domain analysis. time-domain analysis is conducted using continuous electrocardiogram recordings as the baseline data, offering the advantages of simplicity and speed in calculations. The analysis involves detecting the intervals between each QRS complex wave in the continuous electrocardiogram. The adjacent R waves represent the cycle of heartbeats, and the interval between these cycles is measured in milliseconds (ms), referred to as the R-R interval. Frequency-domain analysis is a common method in HRV that involves decomposing the time series of HRV into composite waveforms containing different frequency components. Currently, the most widely used frequency-domain analysis methods include Fast Fourier Transform and Autoregressive Model Estimation (AR model).

Currently, the frequency-domain analysis of HRV is classified into short-term (3–5 min) and long-term (24–48 h) analyses. The primary indicators for heart rate variability in frequency-domain analysis are as follows:

• Total Power (TP) refers to the magnitude of normal heartbeat interval amplitudes within the frequency range of ≤0.4 Hz for both long-term and short-term HRV time series;
• High-Frequency Power (HF) refers to the magnitude of normal heartbeat interval amplitudes within the frequency range of 0.15 to 0.4 Hz, representing the PNS activity index;
• Low-Frequency Power (LF) refers to the magnitude of normal heartbeat interval amplitudes within the frequency range of 0.04 to 0.15 Hz, representing an index jointly regulated by the SNS and PNS;
• Very Low-Frequency Power (VLF) is typically referenced when analyzing continuous data spanning over 24 h or more;
• Ultra Very Low-Frequency Power (ULF) is valuable when analyzing continuous data for durations exceeding 24 h;
• Normalized Low-Frequency Power Ratio (nLF): This refers to LF/(TP − VLF) × 100, representing an indicator of sympathetic nervous system activity;
• Normalized High-Frequency Power Ratio (nHF): This refers to HF/(TP − VLF) × 100, representing the reciprocal of the parasympathetic nervous system (PNS) activity index;
• Low/High-Frequency Power Ratio (LF/HF): This represents an indicator of SNS and PNS balance or sympathetic nerve regulation.

The above frequency-domain indicators are widely applied in daily life, clinical assessments, and disease monitoring. For example, during the day, there is a predominance of SNS activity, indicated by a higher LF component. Conversely, during nighttime rest, PNS activity relatively increases, characterized by slower breathing, lower blood pressure, and a slower heart rate, with an enhanced HF component [4]. HRV, as one of the indicators for assessing ANS function, has been widely utilized in clinical applications and research. Its time-domain and frequency-domain indicators show correlations with physiological functions in the human body, providing real-time, reliable, and accurate assessment metrics.

2. Methods

2.1. Procedure

We conducted HRV analysis and sarcopenia risk assessments for the elderly in the community. We compared the correlation and differences between the risk levels of sarcopenia (low risk, moderate risk, and high risk) and obtained HRV indicators using time-domain analysis and frequency-domain analysis (Figure 1).

2.2. Research Subject

We recruited participants aged 60 years and above in Taiwan. Exclusion criteria included individuals with limited mobility, undergoing dialysis, having chronic conditions (hypertension, hyperlipidemia, hyperglycemia, and chronic kidney disease), cancer, and those unable to cooperate or participate. A total of 112 participants were recruited, and the AWGS screening process [3] was utilized for assessment.

2.3. Sampling Method

We used purposive sampling, and the testing location was the community care center in Taichung City.

2.4. Research Tools

We investigated the association between sarcopenia risk classification, HRV, and the influence on the balance of the ANS for the elderly in Taiwan. The following are the relevant measurement methods and tools.

• Detection of Sarcopenia Risk: (1) A Four-limb Muscle Mass Index was used to measure the four-limb skeletal muscle mass index using a body composition analyzer, (2) Grip Strength was used to measure the grip strength of the dominant hand using a handgrip dynamometer, and (3) a Walking Speed Assessment was used to measure walking speed using a walking activity recorder.
• HRV Instrument Assessment: The VitalScan ANS PWV instrument was used for the measurement of HRV in time-domain and frequency-domain analysis. Time-domain analysis was conducted to assess HRV by examining the intervals between each consecutive QRS complex in the continuous waveform. Frequency-domain analysis was conducted using the Fast Fourier Transform to decompose the composite waveforms of different frequency ranges in the HRV time series. In the time-domain analysis, the standard deviation of normal-to-normal (SDNN) intervals represents the variability of all normal heartbeats. In the frequency-domain analysis, High-Frequency Power refers to the magnitude of normal heartbeat interval and amplitudes within the frequency range of 0.15 to 0.4 Hz as an indicator of PNS activity. Low-Frequency Power presents the magnitude of normal heartbeat interval amplitudes within the frequency range of 0.04 to 0.15 Hz, serving as an indicator of both the SNS and the PNS regulation. nLF represents the ratio of LF divided by the difference between TP and VLF, multiplied by 100. It serves as an indicator of SNS activity.
• nHF represents the ratio of HF divided by the difference between TP and VLF, multiplied by 100. It is the reciprocal indicator of PNS activity. LF/HF represents an index of the balance between the SNS and PNS or an indicator of sympathetic nerve regulation.

2.5. Data Analysis

• Sarcopenia Risk Classification Method

We adopted the sarcopenia screening process proposed by AWGS as the risk classification method [3]. According to this process, grip strength and walking speed were measured for the elderly population. If both measurements were normal, individuals were classified into the low-risk group for sarcopenia. If one of the measurements was below the normal range, a further assessment of muscle mass was conducted. If muscle mass was normal, individuals were classified into the moderate-risk group for sarcopenia. If muscle mass was below the normal range, individuals were classified into the high-risk group for sarcopenia.

• Autonomic Nervous System Balance (ANS)

ANS balance is a crucial mechanism for the body to adapt and regulate in response to environmental changes. We referred to the research conducted by Kou and Yang [4] to assess HRV reference values for healthy adult males and females at various age groups in Taiwan. Based on the assumption of a normal distribution, we asserted with a 95% confidence level so that the values of HF, LF, and LF/HF could be distributed within plus or minus two standard deviations from the mean. Therefore, in this study, values falling within this range were considered indicative of ANS balance, while values outside this range were deemed indicative of ANS imbalance.

2.6. Statistical Methods

SPSS version 23 was used for various statistical analyses. The following are the statistical methods employed in this study.

Descriptive Statistical Analysis: Descriptive statistical analysis, including frequency distribution tables, percentages, means, and standard deviations, was employed to explore the current status of age, height, weight, walking speed, grip strength, and four-limb muscle mass index among individuals classified into sarcopenia low, moderate, and high-risk groups.

Independent Samples t-Test: In an independent samples t-test, differences in indicators such as the walking speed, grip strength, and four-limb muscle mass index were examined based on the presence or absence of autonomic nervous system (ANS) balance.

One-Way Analysis of Variance (ANOVA): We employed ANOVA to explore whether there are differences in HRV time-domain analysis and frequency-domain indicators among individuals classified into sarcopenia low, moderate, and high-risk groups.

Pearson Chi-Square Test: We employed the Pearson Chi-Square Test to explore whether there is a correlation between low-, moderate-, and high-risk sarcopenia groups and the presence or absence of autonomic nervous system (ANS) balance.

3. Result and Discussion

3.1. Detection of Sarcopenia Risk

This study included 112 participants with an average age of 72.88 ± 9.27 years, consisting of 25 males and 87 females. The research results indicated differences in sarcopenia risk and HRV indicators among the elderly. Male participants demonstrated better walking speed, grip strength, and four-limb muscle mass index compared to female participants, while female participants exhibited superior HRV indicators compared to male counterparts. These findings provide information for understanding the correlation between sarcopenia risk and autonomic nervous system (ANS) balance in the elderly. Differences were found in sarcopenia risk between elderly males and females, related to gender disparities and physiological decline. In the survey on the distribution of sarcopenia risk that was conducted, 76 participants were classified as low-risk for sarcopenia, 26 as moderate-risk, and 10 as high-risk. In the low-risk sarcopenia group, males constituted 22.4%, and females constituted 77.6%, with an average age of 71.12 years old, an average height of 157.32 cm, and an average weight of 62.41 kg. In the moderate-risk sarcopenia group, males accounted for 26.9%, females accounted for 73.1%, with an average age of 76.58 years, an average height of 153.17 cm, and an average weight of 63.16 kg. In the high-risk sarcopenia group, males comprised 10.0%, females comprised 90.0%, with an average age of 76.60 years, an average height of 148.30 cm, and an average weight of 58.73 kg. We measured grip strength, walking speed, and muscle mass in each group. These results help to understand the sarcopenia situation in different risk groups. The findings of this study provide information on the health status of the elderly and relevant preventive measures.

3.2. Correlation between Sarcopenia Risk and ANS Balance

We investigated the correlation between sarcopenia risk and ANS balance. To mitigate the potential impact of significant variations in measured values, we referred to the values for HRV for classification in different age groups of healthy Taiwanese adults provided by Kuo and Yang [4]. As the reference values were only for the age group of 60−79 years old, we included 81 participants in this study. Assuming that the measured values follow a normal distribution, we considered values within two standard deviations from the mean, accounting for 95% of the distribution, as an indicator of ANS balance. Conversely, values outside this range are classified as indicative of ANS imbalance. However, due to the non-normal distribution of the histogram of measured values, we took the natural logarithm of the values, using ln (ms2) as the unit for more accurate classification.

According to the classification results, we set the standards for the LF values for male participants aged 60 to 64 years old as 2.449 to 6.464, HF values as 1.829 to 5.946, and LF/HF values as 0.853 to 1.991. For male participants aged 65 to 69 years old, the LF values were set between 2.050 and 6.553, the HF values between 1.840 and 5.861, and the LF/HF values between −1.144 and 2.044. For those aged 70 to 74 years old, the LF values were set between 2.335 and 5.680, the HF values between 1.615 and 5.592, and the LF/HF values between −1.159 and 1.967. Finally, for male participants aged 75 to 79 years old, the LF values were set between 1.891 and 6.210, the HF values between 1.146 and 6.129, and the LF/HF values between −1.066 and 1.892. If the participant’s LF, HF, and LF/HF values all fell within the standard range, ANS balance was diagnosed; otherwise, if any of these values were below or above the standard range, it was considered ANS imbalance.

The chi-square test results for the association between ANS balance and sarcopenia risk revealed a significant relationship. The distribution of participants across different risk categories indicated that those with low sarcopenia risk were predominantly in ANS balance (80.0%), while individuals with high sarcopenia risk were primarily in ANS imbalance (83.3%). The participants with low sarcopenia risk showed ANS balance, whereas a higher proportion experienced ANS imbalance in the high sarcopenia risk group. Individuals with low sarcopenia risk were in ANS balance (80.0%), while those with medium and high sarcopenia risk were in ANS imbalance (42.9%). The participants with a low sarcopenia risk showed ANS balance. Conversely, in the high sarcopenia risk group, there was a higher proportion experiencing ANS imbalance.

The independent samples t-test results showed no significant difference in walking speed among those in ANS balance. There was no significant difference in walking speed, grip strength, and four-limb muscle mass index among participants, regardless of ANS balance.

Before the measurements, the sample data were not adjusted for participants’ gender and external factors. This caused large differences in HRV values. HRV indicators of too high or too low are considered abnormal. The average values are compared to understand the canceling effect of high and low scores and non-significant differences. The observed results are not attributed to participants’ abnormal HRV indices or other factors. Previous studies suggested a correlation between sarcopenia and ANS balance [5]. To mitigate potential score cancelation, we used a classification approach to explore the relationship between sarcopenia risk and ANS balance. Previous study results indicated that adults with sarcopenia were older than those without sarcopenia [5,6,7]. Additionally, a correlation between sarcopenia in older adults and ANS dysregulation was observed [5]. There was an increasing trend in the proportion of ANS imbalance among individuals at a high risk of sarcopenia.

4. Conclusions

We investigated the association between sarcopenia risk and the autonomic nervous system (ANS) balance of the elderly. In total, 23% of the elderly in Taiwan have a moderate- to high-risk of sarcopenia, and about 25.9% exhibit an imbalance in the ANS. Females show higher PNS activity in time-domain analysis compared to men, while their SNS activity showed the opposite trend. In frequency-domain analysis, females exhibited higher HRV than men. Regarding the three indicators of sarcopenia, males and females had normal values for walking speed, grip strength, and four-limb muscle mass index. In the low-risk sarcopenia group, an ANS balance was observed. In contrast, the moderate- and high-risk sarcopenia groups showed an increase in ANS imbalance. Among the overall participants, ANS was balanced in the low-risk sarcopenia group, while the proportion of ANS imbalance increased in the moderate- and high-risk sarcopenia groups. A correlation between sarcopenia risk in the elderly and ANS balance was observed.

Due to limitations in the number of sample and analysis methods, there are constraints in this study. It is necessary to expand the sample size and employ more precise analysis methods to obtain more accurate results. Additionally, it is suggested to apply the research findings in practical healthcare and health management to aid in the prevention and treatment of sarcopenia in the elderly. It is beneficial to understand the physical fitness performance and health education of the elderly. This involves the implementation of community-based exercise programs, with courses tailored to different age groups and health conditions to equip the elderly with accurate knowledge and a heightened awareness of preventive measures. Then, the onset of sarcopenia can be delayed for a healthy and high-quality life.

In community care centers for the elderly, diverse exercise programs are provided, including those focused on physical health, such as reducing blood pressure and improving insomnia, as well as activities such as yoga and meditation. The activities contribute to enhancing overall well-being. These practices help to regulate somatic reflex function, balance the sympathetic and parasympathetic nervous systems, and promote the effectiveness of parasympathetic activity, ultimately addressing situations of autonomic nervous system imbalance.