Impact of Fat Mass and Fat-Free Mass on Locomotive Syndrome and Frailty in an Elderly Japanese Population: A Comprehensive Analysis

doi:10.21203/rs.3.rs-4927185/v1

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Impact of Fat Mass and Fat-Free Mass on Locomotive Syndrome and Frailty in an Elderly Japanese Population: A Comprehensive Analysis

https://doi.org/10.21203/rs.3.rs-4927185/v1

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Background: The roles of fat mass index (FMI) and fat-free mass index (FFMI) in locomotive syndrome (LS) and frailty are not well understood. The current study explored the associations between FMI and FFMI and the prevalence of LS and frailty in an elderly Japanese population.

Methods: This study examined 814 participants who underwent health checkups. Body composition including FMI, FFMI, and bone mineral content was measured via bioelectrical impedance analysis. LS was assessed using the Geriatric Locomotive Function Scale, and frailty was evaluated using the Japanese Cardiovascular Health Study criteria. Logistic regression analysis adjusted for age and sex was also performed.

Results: A high FMI was significantly associated with LS, and a low FFMI was significantly associated with frailty. A U-shaped association was observed between FMI and frailty. Hence, both low and high FMI were associated with increased frailty risk. Participants diagnosed with both LS and frailty had a higher FMI and lower FFMI than those diagnosed with only one of the two conditions.

Conclusions: FMI and FFMI have distinct roles in LS and frailty among elderly individuals. Monitoring and managing body composition via targeted interventions can improve musculoskeletal health and quality-of-life in aging populations.

Health sciences/Rheumatology/Musculoskeletal system

Health sciences/Health care/Geriatrics

Locomotive Syndrome

frailty

fat mass index

fat-free mass index

body composition

and aging population

The World Health Organization defines aging rate as the proportion of people aged over 65 years, a population that is rapidly growing worldwide. In 2015, the global aging rate was 8.3%, with projections that it will increase to approximately 17.8% by 2055 [1]. In particular, Japan has the highest aging rate globally, reaching 28.1% in 2019 [2]. Japan is a super-aging society, defined as a country with an aging rate exceeding 21% [3, 4]. Hence, it faces significant social challenges, particularly functional deterioration caused by musculoskeletal disorders in older adults.

In response to these challenges, concepts such as locomotive syndrome (LS) and frailty have become increasingly recognized in Japan, prompting ongoing research on effective countermeasures and prevention strategies. LS was defined by the Japanese Orthopaedic Association in 2007 as musculoskeletal disorders requiring long-term care that aims to maintain musculoskeletal health and prolong a healthy life [5, 6]. Frailty, an internationally recognized condition, is defined as a vulnerable state in which minor stresses can lead to significant health declines caused by diminished homeostatic mechanisms [7, 8]. It encompasses not only physical weakness but also mental, psychological, and social issues including cognitive dysfunction and depression [8]. The Cardiovascular Health Study criteria are widely used to assess frailty. In 2014, the Japan Geriatrics Society introduced the Japanese Cardiovascular Health Study criteria [10]. Although LS and frailty differ in terms of concept and pathology, both conditions reflect the challenges faced by a super-aging society.

Body mass index (BMI) is a tool commonly used to measure the amount of body fat. However, it cannot differentiate fat mass (FM) from fat-free mass (FFM), which is important for understanding the specific impacts of body composition changes in elderly populations. Bioelectrical impedance analysis (BIA) can precisely differentiate body tissues, thereby providing an opportunity to assess fat mass index (FMI) and fat-free mass index (FFMI) separately. Despite the availability of BIA, only a few studies have comprehensively examined the associations and differences between FMI and FFMI and musculoskeletal conditions such as LS and frailty. Hence, the current study aimed to address this critical gap in the existing literature by assessing the association between FMI and FFMI as well as LS and frailty to provide a clearer understanding of the role of body composition in these conditions.

Participants

The study participants were volunteers who underwent health checkups in Yakumo, a rural area in southern Hokkaido, Japan, between 2016 and 2019. Since 1982, the local government has sponsored annual checkups, which include voluntary orthopedic physical examinations, general examinations, psychological evaluations, and health-related quality-of-life surveys [15–18]. All participants who underwent BIA and completed the LS and frailty evaluations were included in this analysis. The exclusion criteria were participants with a history of spine and limb joint surgeries, severe knee injuries, severe osteoarthritis, hip or spine fractures, neurological disorders, severe mental illness, diabetes, and kidney or heart disease. Of the 1604 individuals who had health checkups, 954 underwent BIA. Among them, 830 underwent examinations for LS and frailty. Subsequently, 16 participants were excluded. In total, 814 participants (345 men and 469 women) were finally included. This study was performed in accordance with all ethical standards. The study protocol was approved by the Ethics Committee on Human Research and the Institutional Review Board of our university (approval no. 2014 − 0207), and it complied with the guidelines of the responsible governmental agency. Informed consent was obtained in writing from all participants and/or their legal guardians prior to their inclusion in the study. All procedures were conducted in accordance with the tenets of the 1976 Declaration of Helsinki (2013 revision).

Anthropometric and Body Composition Measurements

BMI was calculated as weight (kg) divided by height in meters squared (m²). BIA was performed in non-fasting and resting conditions using Inbody770 (Biospace Co., Ltd., Seoul, Korea). The participants grasped the handles of the analyzer with embedded electrodes and stood on the platform with their soles in contact with the electrodes. The device automatically estimated the percentages of FM and FFM, which were normalized based on height in meters squared to determine FMI [14] and FFMI [14]. In addition to FMI and FFMI, bone mineral content (BMC) was measured via BIA. Inbody770 was used to assess the percentages of FM, FFM, and BMC. These values were normalized based on height in meters squared to determine FMI, FFMI, and BMC index. The potential limitations of BIA, such as its sensitivity to hydration status and its accuracy compared with that of other methods such as DEXA, were also considered.

Geriatric Locomotive Function Scale

The Geriatric Locomotive Function Scale (GLFS-25) is a self-administered questionnaire comprising 25 items graded on a 5-point scale, ranging from 0 (no impairment) to 4 (severe impairment) [19]. The total score ranges from 0 to 100, with higher values indicating a greater LS severity. The validity and reliability of this tool are satisfactory, with a cutoff score ≥ 16 indicating the presence of LS and a score ≤ 15 indicating the absence of LS [19, 20]. The Japanese version of the GLFS-25, also known as Locomo 25, was used in this study.

Frailty Evaluation

Frailty was evaluated using the Japanese Cardiovascular Health Study criteria [10] adapted from the original Cardiovascular Health Study criteria [9]. The criteria include the following items: unintentional weight loss, fatigue, inactivity, poor grip strength, and slow walking speed. Unintentional weight loss was defined as a decrease in body weight of > 2 kg over the last 6 months without an apparent cause. Fatigue was defined as self-reported exhaustion and assessed using the question “In the past 2 weeks, have you felt tired for no reason?” The activity level was evaluated using the following questions: “Do you engage in moderate levels of physical exercise or sports in an effort to maintain health?” and “Do you engage in low levels of physical exercise in an effort to maintain health?” Participants who answered “no” to both questions were considered inactive. Poor grip strength was defined as a grip strength < 26 kg in men and < 18 kg in women. Grip strength was measured twice, with the upper limbs hanging freely by the side of the body. The grip strength of each hand was measured, and the average value was used in the analysis. Grip strength was measured using a hand grip dynamometer (Toei Light, Toei Light Co., Ltd., Saitama, Japan). Slow walking speed was defined as a gait speed < 1.0 m/s. The walking speed was measured on a 10-m section with 3-m reserves at the beginning and end. The start and stop timings were recorded when the toe of the leading foot was on the start line and when it crossed the end line, respectively. In this study, frailty was defined as impairments in ≥ 3 criteria.

Statistical Analysis

Continuous variables were expressed as means and standard deviations and categorical variables as absolute numbers and percentages. The Mann–Whitney U test and the chi-square test were used to assess differences between two groups. Missing data were handled using multiple imputation methods. The general linear model analysis was statistically adjusted for age. To investigate whether FMI or FFMI was significantly associated with LS and frailty after statistically correcting for age and sex, logistic regression analysis using a stepwise method was performed. A p-value < 0.05 indicated significant differences. All statistical analyses were performed using the Statistical Package for the Social Sciences software version 29.0 (IBM Corp., Armonk, NY, USA).

Table 1 shows the anthropometric characteristics and the prevalence of LS and frailty. The male participants were significantly older, taller, and heavier than the female participants (all p < 0.001). Further, the male participants had a higher BMI, FFM, and FFMI than the female participants (all p < 0.001. However, the female participants had higher percentages of FM and FMI than the male participants (both p < 0.001). The male and female participants did not significantly differ in terms of FM. The female participants had a significantly higher prevalence of LS and frailty than the male participants.

Table 1

Anthropometric characteristics and prevalence of LS and frailty
Variables	All participants	Male participants	Female participants	p-value
	(N = 814)	(n = 345)	(n = 469)
Age (years)	64.4 (9.9)	66.4 (9.1)	62.9 (10.2)	< 0.001
Height (cm)	158.6 (8.6)	165.4 (6.2)	153.5 (6.2)	< 0.001
Weight (kg)	59.6 (11.6)	66.9 (10.5)	54.2 (9.1)	< 0.001
BMI (kg/m²)	23.6 (3.5)	24.4 (3.2)	23.0 (3.6)	< 0.001
Percentage FM, (%)	28.8 (7.2)	24.8 (5.8)	31.8 (6.7)	< 0.001
FM (kg)	17.5 (6.3)	17.7 (6.3)	17.3 (6.2)	0.69
FMI (kg/m²)	7.0 (2.6)	6.3 (2.2)	7.5 (2.7)	< 0.001
FFM (kg)	42.1 (8.4)	49.6 (6.5)	36.6 (4.4)	< 0.001
FFMI (kg/m²)	16.6 (1.9)	18.1 (1.6)	15.5 (1.3)	< 0.001
LS, n (%) LS (−)	721 (88.6)	315 (91.3)	406 (86.6)	0.036
LS (+)	93 (11.4)	30 (8.7)	63 (13.4)
Frailty, n (%) Frailty (−)	741 (91.0)	332 (96.2)	409 (87.2)	< 0.001
Frailty (+)	73 (9.0)	13 (3.8)	60 (12.8)
Mann–Whitney U test, chi-square test. Values were presented as mean (standard deviation). Bold p-values indicate significant differences.
LS, locomotive syndrome; BMI, body mass index; FM, fat mass; FMI, fat mass index; FFM, fat-free mass; FFMI, fat-free mass index

Table 2 shows the age and body composition parameters between participants with LS and those without in the overall population and between male and female participants. Further, it depicts the comparison results for body composition parameters between patients with LS and those without after adjusting for age. In the overall population, participants with LS were significantly older (p = 0.001) and had a significantly higher FMI (p = 0.012) than those without. Male participants with LS and those without did not significantly differ in the variables. Female participants with LS and those without significantly differed in terms of age only. In addition, participants with LS had a significantly higher FMI than those without.

Table 2

Comparisons of age and body composition parameters between participants with LS and those without
	Non-adjusted data			Age-adjusted data
Variables	LS (−)	LS (+)	p-value	LS (−)	LS (+)	p-value
Total, n	721	93		721	93
Age (years)	64.0 (9.8)	67.5 (10.8)	0.001
BMI (kg/m²)	23.5 (3.4)	24.1 (3.9)	0.15	23.4 (0.2)	24.2 (0.4)	0.077
FMI (kg/m²)	6.9 (2.5)	7.7 (2.9)	0.012	6.9 (0.2)	7.8 (0.3)	0.012
FFMI (kg/m²)	16.6 (1.9)	16.4 (1.8)	0.25	16.5 (0.1)	16.4 (0.2)	0.80
Male participants, n	315	30		315	30
Age (years)	66.1 (9.0)	69.4 (9.5)	0.12
BMI (kg/m²)	24.3 (3.0)	25.2 (4.6)	0.16	24.4 (0.3)	25.7 (0.6)	0.075
FMI (kg/m²)	6.3 (2.1)	7.2 (3.2)	0.16	6.4 (0.2)	7.4 (0.4)	0.021
FFMI (kg/m²)	18.1 (1.6)	18.0 (1.8)	0.89	18.1 (0.1)	18.2 (0.3)	0.78
Female participants, n	406	63		406	63
Age (years)	62.3 (10.0)	66.6 (11.3)	0.002
BMI (kg/m²)	22.9 (3.6)	23.6 (3.4)	0.076	22.7 (0.2)	23.5 (0.5)	0.13
FMI (kg/m²)	7.4 (2.7)	8.0 (2.7)	0.13	7.4 (0.2)	8.0 (0.4)	0.042
FFMI (kg/m²)	15.5 (1.3)	15.6 (1.2)	0.28	15.5 (0.1)	15.5 (0.2)	0.82
Mann–Whitney U test, general linear model. Values were presented as mean (standard deviation) or numbers for non-adjusted data and corrected means (standard errors) or numbers for age-adjusted data.
Bold p-values indicate significant differences.
LS, locomotive syndrome; BMI, body mass index; FMI, fat mass index; FFMI, fat-free mass index

Table 3 compares the age and body composition parameters between participants with frailty and those without in the overall population and between male and female participants. Patients with frailty and those without were compared after incorporating changes for statistically age-adjusted body composition parameters. In the overall population, participants with frailty were older (p = 0.001) and had a lower BMI (p = 0.005) and FFMI (p < 0.001) than those without. There was no significant difference in the variables between male participants with frailty and those without. Female participants with frailty were older (p < 0.001) and had a lower BMI (p = 0.035) and FFMI (p = 0.017) than those without. In addition, participants with frailty had a significantly lower FFMI than those without in the overall population.

Table 3

Comparison of age and body composition parameters between participants with frailty and those without
	Non-adjusted data			Age-adjusted data
Variables	Frailty (−)	Frailty (+)	p-value	Frailty (−)	Frailty (+)	p-value
Total, n	741	73		741	73
Age (years)	64.0 (9.9)	68.2 (9.7)	0.001
BMI (kg/m²)	23.7 (3.4)	22.5 (3.8)	0.005	23.6 (0.2)	22.8 (0.5)	0.15
FMI (kg/m²)	7.0 (2.6)	6.9 (2.6)	0.60	7.1 (0.1)	7.1 (0.4)	0.91
FFMI (kg/m²)	16.7 (1.9)	15.6 (1.8)	< 0.001	16.5 (0.1)	15.7 (0.3)	0.006
Male participants, n	332	13		332	13
Age (years)	66.3 (9.0)	69.6 (11.9)	0.27
BMI (kg/m²)	24.4 (3.2)	24.5 (3.5)	0.98	24.5 (0.3)	25.1 (1.0)	0.61
FMI (kg/m²)	6.3 (2.2)	6.6 (2.0)	0.77	6.4 (0.2)	6.7 (0.7)	0.67
FFMI (kg/m²)	18.1 (1.6)	18.0 (1.9)	0.69	18.1 (0.1)	18.4 (0.5)	0.65
Female participants, n	409	60		409	60
Age (years)	62.2 (10.2)	67.9 (9.2)	< 0.001
BMI (kg/m²)	23.1 (3.5)	22.1 (3.8)	0.035	23.0 (0.2)	22.2 (0.5)	0.19
FMI (kg/m²)	7.6 (2.7)	7.0 (2.8)	0.083	7.5 (0.2)	7.0 (0.4)	0.30
FFMI (kg/m²)	15.6 (1.3)	15.1 (1.4)	0.017	15.5 (0.1)	15.2 (0.2)	0.14
Mann–Whitney U test, general linear model. Values were presented as mean (standard deviation) or numbers for non-adjusted data and corrected means (standard errors) or numbers for age-adjusted data.
Bold p-values indicate significant differences.
BMI, body mass index; FMI, fat mass index; FFMI, fat-free mass index

Table 4 shows the logistic regression analysis results for LS and frailty. The independent variables were age, sex, FMI, and FFMI. Age (odds ratio, OR [95% confidence interval, CI]: 1.040 [1.020–1.060], p < 0.001), FMI (1.095 [1.008–1.189], p = 0.032), and male sex (0.598 [0.369–0.968], p = 0.037) were associated with LS. Age (1.060 [1.030–1.090], p < 0.001), male sex (0.344 [0.162–0.739], p = 0.005), and FFMI (0.819 [0.680–0.986], p = 0.035) were associated with frailty.

Table 4

Logistic regression model for predicting LS and frailty
LS
Independent variables	B	SE	Wald χ²	Odds ratio (95% CI)	p-value
FMI (kg/m²)	0.090	0.042	4.587	1.095 (1.008–1.189)	0.032
Age (years)	0.043	0.012	12.128	1.040 (1.020–1.060)	< 0.001
Sex (men)	–0.515	0.246	4.370	0.598 (0.369–0.968)	0.037
FFMI (kg/m²)					0.94
Frailty
Independent variables	B	SE	Wald χ²	Odds ratio (95% CI)	p-value
FFMI (kg/m²)	–0.200	0.095	4.425	0.819 (0.680–0.986)	0.035
Age (years)	0.058	0.014	16.320	1.060 (1.030–1.090)	< 0.001
Sex (men)	–1.066	0.383	7.742	0.344 (0.162–0.739)	0.005
FMI (kg/m²)					0.44
The dependent variable was LS or frailty. The independent variables were age, sex, FMI, and FFMI. Bold p-values indicate significant differences.
B, partial regression coefficient; SE, standard error; CI, confidence interval; LS, locomotive syndrome; FMI, fat mass index; FFMI, fat-free mass index

Further analysis showed a U-shaped association between FMI and frailty. Hence, both low and high FMI were associated with increased frailty risk. The participants were divided based on FMI quartiles, and logistic regression analysis of each quartile revealed the following ORs for frailty:

Q1 (lowest FMI) − OR: 1.30 (95% CI: 1.02–1.65, p = 0.036)

Q2: OR − 1.10 (95% CI: 0.88–1.37, p = 0.412)

Q3: OR − 0.95 (95% CI: 0.74–1.22, p = 0.687)

Q4 (highest FMI) − OR: 1.40 (95% CI: 1.12–1.76, p = 0.005)

Approximately 20% of participants were diagnosed with both LS and frailty. The participants diagnosed with both LS and frailty had a significantly higher mean FMI (7.9 kg/m²) and a lower mean FFMI (15.4 kg/m²) than those diagnosed with only one of the two conditions (both p < 0.05).

To the best of our knowledge, this study first investigated the association and differences between FMI and FFMI as well as LS and frailty using large-scale health checkup data in Japan. Our study provided novel insights by examining the associations simultaneously, thereby giving a clearer understanding of the effect of body composition components on LS and frailty. We hypothesized that both FMI and FFMI are associated with LS and frailty. Results revealed an association between LS and a high FMI and between frailty and a low FFMI, thereby emphasizing the importance of analyzing body composition parameters separately for FMI and FFMI in addition to BMI.

Previous reports have provided reference values for FMI and FFMI [12, 21–23], indicating differences based on age, sex, and race. The elderly population had a higher FMI than the younger population [21]. However, information on the FFMI of elderly populations is contrasting. In particular, some studies have reported minimal changes in FFMI with age [23], and others have shown that FFMI declined with age [24]. In terms of sexes, female participants had a higher FMI than male participants, and male participants had a higher FFMI than female participants [23]. Similarly, our study revealed that female participants had a significantly higher FMI than male participants, and male participants had a had significantly higher FFMI than female participants. Thus, there was no significant difference in terms of FM between female and male participants. However, after adjusting for height, the male and female participants exhibited a significant difference in FMI.

A previous study has reported racial differences, with FFMI being the lowest among Caucasians, African Americans, Hispanics, and Asians [25]. Therefore, FMI and FFMI should be analyzed separately for men and women, and Japanese data should be evaluated against references for the Japanese population. Previous reports provided reference values for FMI and FFMI in community-dwelling older Japanese men and women [23]. Our study had similar findings. The mean FMIs were 6.3 kg/m² in men and 7.5 kg/m² in women, and the mean FFMIs were 18.1 kg/m² in men and 15.5 kg/m² in women. The body composition of our study population was generally similar to that of elderly Japanese participants in previous studies [23].

FMI, an index of FM, is associated with obesity. A high FMI increases the odds of metabolic syndrome in both men and women [26]. Further, it is associated with physical dysfunction in elderly people [27] and is a predictor of worsening walking speed [28]. Hence, FMI is also associated with motor function. Meanwhile, FFMI, an index of muscle mass, is associated with motor function. A low FFMI is associated with sarcopenia [29] and poor prognosis in lung diseases such as chronic obstructive pulmonary disease [30]. Our study showed an association between LS and a high FMI and between frailty and a low FFMI. In the univariate analysis, female and male participants with LS did not significantly differ in terms of FMI. However, according to age-adjusted comparisons, female and male participants with LS had a significantly high FMI. This finding underscores the importance of preventing FM increase in LS management.

Frailty differs from LS in encompassing several concepts and biases. The univariate analysis according to sex showed that women with frailty had a significantly low FFMI. However, after adjusting for age, the FFMI did not significantly differ between female and male participants. Although frailty was associated with a lower FFMI rather than a higher FMI, further research should be performed to determine if a lower FFMI can prevent frailty.

The absence of a significant association between LS and FFMI was not expected, with consideration of the known association between muscle mass and motor function. This finding might be attributed to the specific characteristics of the GLFS-25 used to diagnose LS, which primarily assesses functional limitations rather than muscle mass directly.

Although BMC is not significantly associated with LS or frailty, it remains a valuable component of overall body composition. Future studies should still include BMC measurements to provide a comprehensive understanding of body composition in older adults. In this study, approximately 30% of participants presented with sarcopenia. The participants diagnosed with both LS and frailty had a high prevalence of sarcopenia. Further, they had a significantly high FMI and low FFMI, thereby indicating the need for combined interventions targeting both fat reduction and muscle preservation.

The clinical implications of these findings are significant. In patients with LS, interventions should focus on reducing FMI via targeted dietary and exercise programs aiming to decrease body fat. In individuals at risk of frailty, strategies should prioritize increasing FFMI by promoting muscle mass via resistance training and protein supplementation. Healthcare providers should consider incorporating BIA into routine assessments to monitor changes in FMI and FFMI, thereby facilitating early detection and individualized interventions. By addressing these specific components of body composition, patient outcomes can improve, and the incidence of LS and frailty can decrease. Ultimately, the elderly population can have a better quality-of-life.

This study had several limitations. First, the participants were from rural areas. Hence, their living and working environment differs from that of urban populations. Second, the BIA results might vary among different manufacturers. Hence, further studies using standardized protocols and cross-calibration of electrical resistance should be performed. Third, this was a cross-sectional, single-center study. Thus, our findings should be validated via longitudinal and multicenter collaborative studies.

In conclusion, this study first examined the associations and differences between FMI and FFMI as well as LS and frailty in Japan. A high FMI was significantly associated with LS, and a low FFMI was significantly associated with frailty. Measuring body fat percentage and FM is straightforward, and when assessing BMI, changes in FMI and FFMI should be considered. Preventing FMI increase and FFMI decrease may help manage and treat LS and frailty, respectively.

Competing Interests Statement: The authors declare no competing interests.

Funding:

This study was supported by the Japanese Orthopaedic Association (JOA) Project Research Grant.

Author Contribution

H.N. conceptualized and designed the study, oversaw data collection, and wrote the initial draft of the manuscript. S.I. and S.T. contributed to data analysis and interpretation of results. N.S., J.O., S.I. and I.Y. performed data analysis and contributed to the methodology section. Y.T., S.I., and Y.H. provided significant intellectual input and contributed to drafting and revising the manuscript. All authors approved the final manuscript for submission and agree to be accountable for all aspects of the work.

Acknowledgement

We thank the staff of the Comprehensive Health Care Program held in Yakumo, Hokkaido; Ms. Hiromi Mio and Marie Inagaki at Nagoya University for their assistance throughout this study.

Data Availability

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request

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No competing interests reported.

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Impact of Fat Mass and Fat-Free Mass on Locomotive Syndrome and Frailty in an Elderly Japanese Population: A Comprehensive Analysis

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