Reduced phase angle as a potential indicator of mild cognitive impairment

Abstract

In this study, we investigated the relationship between phase angle (PhA), measured using bioelectrical impedance analysis (BIA), and mild cognitive impairment (MCI). A cross-sectional analysis was conducted on 290 community residents (83.7% female, average age 74.9 years). Cognitive function was assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J), with MCI defined as a score of ≤25. Body composition, including PhA, was measured using BIA. Multiple regression analysis was used to examine the association between PhA and MCI presence, adjusting for potential confounders. MCI was found in 168 participants. The PhA (leg) was significantly lower in those with MCI than in those without (p = 0.013). A significant association between leg PhA and MCI was identified in the regression model (β = 0.103, p = 0.015), with an adjusted R² value of 0.50. These findings suggested that PhA may serve as an indicator of MCI. Longitudinal and intervention studies are needed to explore the potential of PhA in dementia prevention strategies. In addition, future research should focus on developing dementia prevention strategies that utilize PhA through longitudinal and interventional studies.

Introduction

As the population ages, there is an increasing need for early interventions to delay the onset of dementia and improve the well-being of individuals with mild cognitive impairment (MCI), who are considered to be in the pre-dementia stage, and their caregivers.⁽¹⁾ Cognitive decline affects physical activity,⁽²⁾ suggesting that reduced physical activity in elderly individuals precedes cognitive decline.⁽³⁾ Muscle mass and function are important risk factors for cognitive decline and dementia. Physical health plays a crucial role in the prevention.⁽⁴⁾

In recent years, scholarly attention has increasingly focused on the association between cognitive impairment and sarcopenia, a condition characterized by a reduction in skeletal muscle mass, strength, and function.⁽⁵⁾

Sarcopenia is defined as a decline in muscle mass and strength, and assessment of muscle quality is recommended.⁽⁶⁾ Muscle quality was evaluated based on muscle strength and/or power output per unit of muscle mass.⁽⁷⁾ Additionally, an index based on luminosity uses ultrasound technology to assess fat deposition and fibrosis within the muscles.⁽⁸⁾ An index based on the phase angle (PhA), representing the electrical resistance generated in the cell membranes through bioelectrical impedance analysis (BIA), is expressed as an angle.⁽⁹⁾ Previous research has reported a relationship between muscle quality and dynapenia utilizing upper limb muscle mass and grip strength measured using a BIA method.⁽¹⁰⁾ Screening tools such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are commonly used to evaluate dementia.^(11,12) The MoCA has been reported to exhibit greater sensitivity than the MMSE in identifying MCI and is frequently used in MCI studies.⁽¹²⁾ Muscle quality, defined as the ratio of grip strength to arm lean mass, has been associated with cognitive impairment as measured using the MMSE (score ≤24).⁽¹³⁾ Although this study⁽¹³⁾ suggests that muscle quality influences cognitive function, research directly connecting muscle quality and cognitive function in the early stages of mild dementia is lacking, highlighting the need for further investigation.

While previous research has provided insights into the relationships among physical activity, muscle mass, muscle strength, and cognitive decline,^(2–4) the association between muscle quality and cognitive function, particularly during MCI, remains unclear. Further research is required to determine whether phase differences determined using the BIA method, which is easily quantifiable, can serve as valuable indicators in diagnosing mild dementia.

In this study, we investigated the relationship between MCI and phase differences, particularly in terms of muscle quality. The aim of this study was to clarify using phase-difference measurement in diagnosing mild dementia by integrating muscle quality assessment through anthropometric methods and cognitive function evaluation using the Montreal Cognitive Assessment-Japanese version (MoCA-J). The goal of this study was to establish a novel physical indicator for early detection of cognitive decline, enabling timely intervention.

Materials and Methods

Methods for participant recruitment and analysis in frailty prevention group exercise classes

Group exercise classes aimed at preventing frailty and related issues were conducted concurrently at multiple locations for elderly community residents in Sakai City, Katsuyama City, and Awara City, Fukui Prefecture, using remote and real-time methods. The exercise class participants underwent regular physical assessments. Local governments facilitated participant recruitment through various promotional strategies and residents responded to invitations to join the program. Between July 2022 and July 2024, 309 individuals participated in the study. After excluding 19 individuals due to incomplete anthropometric data, incomplete medical interviews, use of cardiac pacemakers, or difficulties in assessing cognitive function during the initial physical measurement, the analysis included 290 participants (47 males and 243 females) with a mean age of 74.9 ± 6.0 years.

Methodology for participant assessment and cognitive function evaluation in frailty prevention

Written informed consent was obtained from all participants, after which a series of anthropometric measurements were performed. The assessed parameters included height and body composition [weight, body mass index (BMI), skeletal muscle mass index (SMI), muscle mass, lean mass, leg points, estimated bone mass, basal metabolic rate, fat mass, body fat percentage, visceral fat level, body water content, and phase differences]. Medical interviews were conducted to gather data on the age, sex, medical history, and educational background of the patients. Information on smoking and drinking habits was also collected. The assessments were performed using Cognitive Function Tests (MoCA-J). Body composition was measured using body composition analyzers (MC-780A-N and MC-780A; TANITA Co., Tokyo, Japan), which use multi-frequency measurements to enhance precision. SMI was calculated by dividing the limb skeletal muscle mass (kg) by the square of the individual’s height (m). The body fat percentage was defined as the body fat mass to total body weight ratio. BMI was calculated by dividing the body weight by the square of the height in meters. The PhA was determined by calculating the impedance, which is derived from the magnitude of the phase shift (reactance) occurring in the muscle cells when a weak current passes through it, along with the resistance value representing the resistive component. The mean PhA was calculated by averaging measurements from the left and right arms and legs. The leg muscle score quantifies leg muscle mass as a percentage of the total body weight. The MoCA-J was used to evaluate cognitive function.⁽¹⁴⁾ The MoCA-J is designed to detect MCI across six cognitive domains: visuospatial, executive, attention, memory, language, and registration.⁽¹⁴⁾ The MoCA-J test was administered by medical students, nursing students, physicians, laboratory technicians, and nurses, all under the supervision of a neurologist and nurse with at least seven years of experience in neuropsychological testing. Physicians, nurses, and clinical laboratory technicians assisted medical and nursing students. Nurses with more than 7 years of experience verified the results. The MoCA-J is recognized as a valid MCI screening tool; a score of ≥26 indicates a healthy range, and a score of ≤25 indicates 93% sensitivity and 87% specificity.⁽¹⁴⁾ The characteristics of the muscle quality assessment methods applicable in community settings are summarized.^(6–10,15) The relationship between the PhA and the cells in the BIA is depicted in Fig. 1.^(16,17)

Fig. 1.

The relationship between phase angle and cells in bioelectrical impedance analysis. Phase angle (°) = arctan (Xc/R) × (180/n). Resistance: Resistance, often referred to as the resistance component, reflects the impedance of both extracellular and intracellular fluids. It is primarily influenced by the extracellular fluid, with a lesser contribution from the intracellular fluid. Reactance: Reactance, known as the capacitive component, represents the opposition to current caused by the cell membrane, which functions as a capacitor. It is also influenced by the resistive properties of both extracellular and intracellular fluids.

Statistical analyses

Statistical analyses were performed using EZR ver. 1.54 (Saitama Medical Center, Jichi Medical University, Japan).⁽¹⁸⁾ Continuous variables are reported as mean ± SD, while nominal variables are presented as counts and percentages. The Chi-square test was used to compare nominal variables between the two groups, and the Mann–Whitney U test was used for continuous variables. Single and multiple linear regressions were conducted to investigate the relationship between the presence or absence of MCI and PhA (foot) as outcome variables.

Multiple regression analysis was conducted with PhA (foot) as the dependent variable and the presence or absence of MCI as the independent variable. Age, sex, bone mass, leg points, and SMI were included as covariates. The significance level for all tests was set at p<0.05.

Results

Background of subjects

Table 1 presents the demographic characteristics of the participants. The cohort comprised 47 males (mean age 76.1 ± 6.0 years). Of the 290 participants, 168 (30 males, 138 females; 57.9%) were diagnosed with MCI.

Table 1.

Background of subjects

	Total (n = 290)	male (n = 47)	female (n = 243)
Age (years)	74.9 ± 6.0	76.1 ± 6.0	74.7 ± 6.0
Height (cm)	153.3 ± 7.1	163.5 ± 6.2	151.3 ± 5.4
Lifestyle
Smoking, n (%)	5 (1.7)	1 (2.1)	4 (1.6)
Drinking alcohol, n (%)	76 (26.2)	24 (51.1)	52 (21.4)
Current medical history (with medication)
Hypertension, n (%)	132 (45.5)	29 (61.7)	103 (42.4)
Diabetes mellitus, n (%)	45 (15.5)	12 (25.5)	33 (13.6)
Dyslipidemia, n (%)	53 (18.2)	3 (6.4)	50 (20.6)
Cardiac disease, n (%)	30 (10.3)	6 (12.8)	24 (9.9)
Years of education	12.6 ± 2.2	13.2 ± 2.6	12.5 ± 2.1
Scale
MoCA-J	24.1 ± 3.7	23.0 ± 4.5	24.3 ± 3.5
MoCA-J; 25 points or less	168 (57.9)	30 (63.8)	138 (56.8)

n (%), mean ± SD (unit). Continuous variables: Mann–Whitney U test. Nominal variables: χ² test (including Yates continuity correction).

Comparison of the robust and MCI groups in the BIA method

Table 2 presents a comparative analysis of the robust and MCI groups using BIA. There were significant differences between the groups in terms of age, muscle mass, lean body mass, SMI, bone mass, leg points, PhA (leg), and basal metabolic rate.

Table 2.

Comparison of the robust and mild cognitive impairment (MCI) groups using the bioelectrical impedance analysis (BIA) method

	Total (n = 290)	robust (n = 122) (42.0%)	MCI (n = 168) (57.9%)	p value
Sex (male/female)	47/243	17/105	30/138	0.422
Age (years)	74.9 ± 6.0	72.1 ± 5.3	76.9 ± 5.7	<0.001
Body weight (kg)	53.2 ± 8.9	54.1 ± 9.0	52.5 ± 8.8	0.208
BMI (kg/m2)	22.5 ± 3.2	22.6 ± 3.2	22.5 ± 3.2	0.946
Body fat mass (kg)	16.2 ± 5.7	16.5 ± 5.9	15.9 ± 5.6	0.631
Visceral fat rating (level)	6.7 ± 3.3	6.7 ± 3.4	6.7 ± 3.3	0.503
Body fat% (%)	29.9 ± 7.5	30.0 ± 7.4	29.8 ± 7.7	0.961
Muscle mass (kg)	34.9 ± 5.5	35.5 ± 5.5	34.5 ± 5.5	0.058
Lean body mass (kg)	36.9 ± 5.8	37.5 ± 5.8	36.5 ± 5.8	0.057
SMI (kg/m2)	6.57 ± 0.73	6.68 ± 0.71	6.50 ± 0.74	0.017
Bone mass (kg)	1.9 ± 0.3	2.05 ± 0.34	1.96 ± 0.34	0.028
Leg muscle score (point)	89.3 ± 9.2	90.9 ± 9.2	88.1 ± 9.1	0.02
Phase angle - arm (°)	5.1 ± 0.5	5.2 ± 0.5	5.1 ± 0.5	0.548
Phase angle - leg (°)	4.2 ± 0.6	4.3 ± 0.6	4.1 ± 0.6	0.013
BMR (kcal)	1,048.3 ± 150.3	1,068.7 ± 149.9	1,033.5 ± 149.3	0.029
Total body water mass (kg)	27.1 ± 4.3	27.5 ± 4.4	26.7 ± 4.3	0.071

n (%), mean ± SD (unit). BMI, body mass index; BMR, basal metabolic rate; SMI, skeletal muscle mass index. Continuous variables: Mann–Whitney U test. Nominal variables: χ² test (including Yates continuity correction).

Relationship between PhA (leg) and MCI (presence or absence) using linear regression models

A significant association was found between PhA (leg) and MCI in the single regression model (β = −0.123, p = 0.035). Table 3 presents the multiple regression model, PhA (leg) was also significantly associated with MCI (β = 0.103, p = 0.015). The adjusted R² value for the multiple regression model was set at 0.5.

Table 3.

Relationship between phase angle (leg) and mild cognitive impairment (MCI) (presence or absence) using linear regression models

	Multiple regression model
	B	β	95% CI lower–Upper	p value
Presence or absence of MCI	0.141	0.103	0.026–0.255	0.015

B: partial regression coefficient, β: standardized partial regression coefficient, CI: confidence interval. Multiple regression model: depenent variable, phase angle (leg); independent variable, MCI (presence or absence); adjusted variables, age, sex, bone mass, leg muscle score, SMI, adjusted R² = 0.50.

Characteristics of muscle quality assessment methods

The characteristics of the muscle evaluation methods are summarized in Table 4.^(6–10,16) Each method has its advantages and disadvantages. PhA measured using the body composition analyzer is user-friendly, requiring only the subject to step onto the device; it also assesses muscle mass. In contrast, assessing strength, muscle mass, and power requires measuring both muscle mass and strength, or instantaneous force. Ultrasound is highly accurate and evaluates actual muscle mass but requires careful technical considerations.

Table 4.

Methods of assessing muscle quality that can be used in the community

Evaluation method	Indicator	Characteristics/ Explanation	Advantages	Disadvantages	Ease of use
Body composition analyzer using bioelectrical impedance analysis (BIA)	Phase angle	An indicator of muscle quality calculated from the reactance and resistance of bioelectrical impedance	Simple, easy to measure	Measurement accuracy varies by model; not available for pacemaker users	◎
Indicator per muscle mass	Strength/ Muscle mass	Evaluate muscle strength per muscle mass	Simplified, muscle strength can be evaluated	Ascertaining muscle strength and volume	△～〇
	Power/ Muscle mass	Assess the instantaneous power output relative to muscle mass, such as during extension movements in relation to muscle mass	Capable of evaluating instantaneous force, etc.	Requires measuring equipment, might be more burdensome for the elderly, understanding of muscle power and volume
Ultrasonography	Muscle echo intensity	Evaluation of muscle tissue structure (uniformity of echo brightness)	Non-invasive, real-time measurements possible	Depends on skills of evaluators; analysis methods need to be standardized	△

Discussion

In this study, we found that the PhA (leg) of the lower extremities, measured using BIA, was significantly associated with MCI. These findings suggested that the PhA reflects body composition and is a potential early marker of cognitive decline.

PhA has been shown to correlate with SMI and muscle quality.⁽¹⁹⁾ Ther results confirmed that the MCI group exhibits lower PhA and SMI. Individuals with cognitive impairment exhibit significantly lower fat-free mass (FFM) and fat-free mass index (FFMI) values than those with normal cognitive function.⁽²⁰⁾ A longitudinal correlation has been identified between reduced appendicular lean soft tissue mass (ALM) and cognitive decline associated with aging.⁽²¹⁾ Previous research indicated a significant correlation between the reduction of muscle mass and cognitive decline, and the findings of the present study corroborated this association. The primary mechanisms responsible for age-related muscle mass loss, atrophy, and diminished growth response are multifaceted. These mechanisms involve alterations in several processes, including protein homeostasis, mitochondrial function, extracellular matrix remodeling, and neuromuscular junction function.⁽²²⁾ Skeletal muscle atrophy is characterized by weakening, shrinkage, and reduction in muscle mass and fiber cross-sectional area at the histological level, leading to decreased strength, increased fatigue, reduced mobility, and diminished quality of life. Oxidative stress and inflammation have been suggested as mechanisms in this process.⁽²³⁾ Although PhA is recognized as an indicator of cellular health, it has also been proposed as a potential marker of oxidative and inflammatory processes.^(24,25) Oxidative and inflammatory processes were associated with MCI in this study. It has been proposed that skeletal muscle atrophy induces the secretion of hemopexin, which subsequently migrates to the brain via the bloodstream, thereby accelerating the onset of dementia through neuroinflammation.⁽²⁶⁾ Cognitive alterations are more prevalent among the elderly, with increased cerebral inflammation identified as a significant factor contributing to and exacerbating cognitive decline in this population.⁽²⁷⁾ The correlations among oxidative stress, age-related cognitive decline, and neurological disorders associated with aging are well-established.⁽²⁸⁾ Given that PhA is a potential marker of oxidative and inflammatory processes, the diminished muscle quality observed in the MCI group in this study might be linked to elevated levels of systemic oxidative stress and inflammation. The correlation between diminished muscle quality and cognitive impairment, as assessed using PhA, indicates that oxidative stress and chronic inflammation may play a role.

In this study, the lower extremity PhA showed a stronger association with MCI than the upper extremity PhA. Previous research has highlighted site-specific muscle loss in the upper and lower extremities and the trunk.⁽²⁹⁾ Additionally, an analysis of the impact of muscle strength on sarcopenia assessment outcomes revealed that weakness in the lower extremity muscles significantly influenced the results of these assessments.⁽³⁰⁾ These studies suggest that lower extremity muscles are more vulnerable to aging than upper extremity muscles. Lower extremity motor function has been linked to an increased risk of dementia, whereas grip strength (indicative of upper extremity muscle strength) has not.⁽³¹⁾ The present findings showing that MCI is associated with a decline in the lower limb PhA supported these results. This finding suggested that reduced muscle quality contributes to the development of MCI.

Although the PhA is commonly used to evaluate muscle quality, it is also a useful indicator of inflammation and oxidative stress. Its simplicity and ease of measurement, requiring only standing on a body composition analyzer, make it more accessible for use in local communities than other techniques (Table 4). The present study suggested a correlation between the PhA and MCI, highlighting its potential as an indicator of cognitive decline. Therefore, anthropometry-based indicators can be readily used daily by public health nurses and administrative officials. This would enable the early detection of conditions, such as frailty, sarcopenia, and dementia, allowing for timely intervention and improvement in the health and quality of life of residents.

This study has several limitations. Because this was a cross-sectional study, causal relationships could not be estimated. Future longitudinal studies are required to determine how variations in PhA influence the risk of developing MCI. Additionally, the pathogenesis of MCI involves various factors, requiring a comprehensive analysis that includes psychosocial elements and lifestyle factors such as exercise and diet. Furthermore, because of the inherent characteristics of BIA, variations in the body water content may have affected the measurements. Therefore, future studies should use more precise techniques, such as intramuscular fat evaluation via magnetic resonance imaging or computed tomography, along with the BIA method.

Conclusions

This results of this study demonstrated that PhA (leg), measured using BIA, might be a valuable indicator of MCI. These findings supported the hypothesis that diminished muscle quality contributes to cognitive decline and suggested the potential for early detection of MCI and intervention through simple body composition measurements. Future research should focus on establishing dementia prevention strategies that utilize PhAs through longitudinal and interventional studies.

Author Contributions

OY and HOnishi contributed significantly to the conceptualization of the study; MH, HOnishi, YM, MK, YN, TI, HOhama, TT, SS, FM, AK, YS, OM, NK, AM, MI, HH, and OY contributed significantly to data acquisition; MH, HOnishi, and OY contributed significantly to data analysis and interpretation; MH, HOnishi, and OY contributed to manuscript preparation. All authors critically reviewed and revised the manuscript and approved its final version.

Funding

This research was financially supported by the University of Fukui Fund for the promotion of Collaborative Research “Reff”, 2022–2023, and by the Japan Society for the Promotion of Science through a JSPS KAKENHI Grant-in-Aid for Scientific Research Project/Grant-in-Aid for Early-Career Scientists, Number 24K18638. The authors declare no conflicts of interest.

Ethics Approval

This study was approved by the University of Fukui Medical Research Ethics Review Committee (Approval No. 20220048). All researchers involved in this study complied with the Ethical Guidelines for Medical and Biological Research Involving Human Subjects (MEXT/MHLW/METI Notification No. 1 March 23, 2021) and the Declaration of Helsinki.

Conflict of Interest

HOnishi signed a non-disclosure agreement with Novel METS Inc. and MacNica Inc. The authors declare no conflict of interest.