Anthropometric indices in older adults with and without Locomotive Syndrome

Abstract

Background

Locomotive syndrome is a major challenge for older adults, and anthropometric indices can greatly affect the musculoskeletal system. This study aimed to compare the anthropometric indices between older adults with and without locomotive syndrome.

Methods

This descriptive-analytical study was performed on 211 older adults using random cluster sampling from a population of over-60 individuals covered by comprehensive health service centers in Sari in Autumn, 2021. Participants were divided into two groups: with and without locomotive syndrome. Locomotive syndrome was assessed via the 25-item Geriatric Locomotive Function Scale questionnaire, and the level of anthropometric indices was determined through the Bioelectrical Impedance Analysis device. Data were analyzed by independent t-test, Mann-Whitney U, Chi-Square, and Binary logistic regression in SPSS, Version 23 (P < 0.05).

Results

The average age of older adult participants was 78.4 ± 6.6 years. In older adults with locomotive syndrome, the average percentage of fat-free mass (P < 0.001) was significantly lower while the average percentage of fat mass and fat mass index (P < 0.001) was significantly higher. Older adults with locomotive syndrome were shorter in height (P < 0.001) and had a higher body mass index (P < 0.05). Fat-free mass percentage (OR = 0.59) and body mass index (OR = 1.4) predicted the incidence of locomotive syndrome in the subjects. Significant differences such as chronic diseases, chronic pain, falls, age, and gender were observed between the two groups (P < 0.05).

Conclusion

Anthropometric indices can play an important role in the occurrence of locomotive syndrome in older adults. Evaluation of anthropometric indices and management of body composition can help prevent locomotive syndrome and improve the quality of life of older adults.

Background

As a consequence of aging, musculoskeletal disorders can lead to compromised walking ability, increased frequency of falls, fractures, chronic pain, and limited physical activity [1]. According to the World Health Organization (WHO), physical inactivity is prevalent among older adults [2]. Furthermore, older adults may not engage in physical activities according to the standards of the WHO [3]. Musculoskeletal disorders are associated with healthy longevity and account for 25% of the need for nursing care [4, 5]. Therefore, it is essential to identify and prevent mobility issues to improve the quality of life for older adults and reduce the burden of nursing care costs.

Locomotive Syndrome was first introduced in Japan in 2007 to raise public awareness and facilitate early identification of at-risk older adults [6]. Findings from a study in Iran indicated that 58% of the participants were affected by Locomotive Syndrome [7]. This syndrome results from impairment in one or more organs (bones, joints, and muscles) in the locomotor system and may hinder motor functions like standing, sitting, and walking in older adults, or leads to age-related impairments [6]. A systematic study showed that the worldwide prevalence of falls among older adults is 26.5% [8]. Identifying factors associated with locomotive organ disorders can assist in the development of appropriate strategies for preventing Locomotive Syndrome.

Body composition alterations are major factors in the emergence of musculoskeletal disorders in older adults [9, 10]. Anthropometric characteristics change with increasing age and affect health in various ways [11]. Larger fat mass at older ages [12] and higher overweight levels are associated with an elevated risk of physical disability, functional limitations, and impaired mobility [13]. On the other hand, the loss of skeletal muscle mass is a factor contributing to balance disorders [1]. Osteoporosis fractures pose considerable risks for complications and mortality [14], leading to significant personal and societal consequences.

Existing evidence suggests an association between locomotive syndrome (LS) and anthropometric indices. Muramoto’s study reported the usefulness of waist circumference for the assessment of the risk of LS in women [15]. The results of studies by Nakamura and Ishihara indicated an association of LS with BMI and body fat (BF) [16, 17]. Most previous research has focused on older adults in Japan. Therefore, it is important to study the relationship between LS and anthropometric indices in the older adult population in Iran, as ethnic, national, cultural, and lifestyle characteristics of each country can have different impacts on the health of citizens, especially older adults. To develop preventive measures for LS and improve the health and quality of life for older adults, this study first aimed to examine the demographic and anthropometric indices in two groups of older adults, with and without LS, and second, to determine the predictive factors of locomotive syndrome.

Methods

Participants

This descriptive-analytical study was conducted cross-sectionally in the autumn 2021 on older adults over 60 years of age who were covered by comprehensive health service centers in Sari, North of Iran. Participants were selected using a random cluster sampling method. The 12 comprehensive health service centers in Sari were divided into five geographical categories (North, South, West, and center). For the purpose of this study, one health center was randomly selected from each geographical area, and then the sampling was carried out. As there has been no previous study on the target population in Iran, the sample size was determined by conducting a pilot. The effect size of the pilot study was 0.5, hence, the sample size of the current study was calculated as 216 participants assuming a Type I error probability of 5%, test power of 90% and a dropout rate of 25%. Ultimately, due to incomplete records of 5 participants, data from 211 participants were included in the final statistical analysis. Inclusion criteria were; men and women over 60 years of age, had the ability to walk without assistive devices, living at home and being able to perform self-care activities, and willingness to participate in the study. Exclusion criteria were individuals with the following conditions: severe heart, brain, lung, and kidney diseases; cognitive and mental problems; a positive history of lower limb or spinal fractures in the past six months; and current reception of care services for acute injuries. Relevant information was obtained from the participants’ self-report and electronic health records. After explaining the research objectives and obtaining informed consent from the participants, researcher personally reads the questions to each older adult participant. This approach ensured consistent responses from both literate and illiterate individuals and guaranteed that all questions were answered. Researcher then completed the questionnaires. Initially, demographic questionnaires were completed for all the participants. Subsequently, anthropometric indices were measured including height, weight, waist circumference, waist-to-hip ratio, body fat percentage, fat-free mass percentage, fat mass index, and body mass index. Following that, the LS status was assessed. Overall, 211 older adults participated in the study, including 113 women and 98 men (mean age ± standard deviation, 66 ± 7.4 years; range 60 to 84 years). This study was approved by the Ethics Committee of Babol University of Medical Sciences (code IR.MUBABOL.HRI.REC.1400.033). Ethical considerations, including principles of privacy and confidentiality, were followed at every stage, and informed consent was obtained from all the participants.

Measurement of variables

Fat mass and fat-free mass of the participants were measured using the Omron Bioelectrical Impedance Analysis (BIA) model scale with minimal clothing. Participants held the analyzer handles and stood on the platform, ensuring the soles were in contact with two metal plates. This method, based on earlier research, is considered a suitable, reliable, easy, non-invasive, and secure approach for assessing the fat quantity and percentage in older adults [18].

Weight was also measured by a body composition device with minimal clothing. A wall-mounted stadiometer (Seca) was used to measure height, accuracy to 0.1 centimeters, with participants standing without shoes. Waist and hip circumferences were measured via a flexible, non-stretchable tape with an accuracy of 0.5 centimeters. Waist circumference was taken at the narrowest area between the last rib and the upper part of the iliac crest [15]. To determine the waist-to-hip ratio, waist circumference was measured at the narrowest area between the last rib and the upper part of the iliac crest, and hip circumference was measured at its widest point while standing. Measurements were performed after normal expiration, with each measurement repeated twice. If the difference between the two measurements was less than one centimeter, the average was recorded; otherwise, the measurement was repeated. Subsequently, the waist-to-hip ratio was calculated. Body Mass Index (BMI) was calculated by dividing weight (in kilograms) by the square of height (in meters) [19]. Similarly, the Fat Mass Index (FMI), which represents the amount of fat relative to height, was calculated by dividing fat mass (in kilograms) by the square of height (in meters) [20].

Evaluation of LS

The Persian version of the GLFS-25 questionnaire was employed to screen older adults at risk of locomotive syndrome. This functional mobility scale consists of 25 items addressing daily activities, quality of life, pain, social relations, and mental state. Each item is scored on a scale of zero to four. The mobility syndrome questionnaire scores range between zero and 100. In the case of the Iranian older adults, the cut-off point is 16, with a sensitivity of 88.0% and a specificity of 84.0%. As a result, individuals with a score of 16 or higher are categorized into the locomotive syndrome group. The validity and reliability of this tool have been confirmed for Iranian older adults. The test-retest procedures demonstrated a reliability index of 0.84 and a Cronbach’s alpha of 0.93 (P = 0.01) [7].

Socio-demographic questionnaire

The socio-demographic questionnaire collected information on age, gender, marital status, education level, occupation, and history of chronic diseases (including hypertension, diabetes, cardiovascular diseases, cancer, respiratory, renal, orthopedic diseases, neurological disorders, psychological conditions, and ocular, auricular, and hepatic conditions). It also gathered data on the history of chronic pain (defined as pain lasting six months or longer that does not respond to treatment) and falls within the past year.

Statistical analysis

Participants were classified into two groups based on their GLFS-25 scores: LS (≥ 16) and non-LS (< 16). Independent variables were then compared between these groups. Data were presented as mean ± standard deviation, counts, and percentages. An independent t-test was used for normally distributed data, the Mann-Whitney U test for non-normally distributed data, and the Chi-Square test for categorical data. Additionally, binary logistic regression analysis was applied to identify the predictor variables of locomotive syndrome, and adjusted regression was used to control for confounding factors. All statistical analyses were conducted using SPSS, version 23, with a significance level of p < 0.05.

Results

In this study, 211 older adults were divided into two groups: those with locomotive syndrome (79 individuals) with a mean age of 67.2 ± 4 years, and those without locomotive syndrome (139 individuals) with a mean age of 65.3 ± 5.7 years; they were covered by comprehensive health centers in Sari. Anthropometric Indices were then measured. The majority were females (53.6%), retired (57.3%), and had educational levels below a high school diploma (38.9%). The average locomotive syndrome score in older adult participants was 14.5 ± 10.4.

Table 1 Comparison of Demographic Components in older adults with and without Locomotive Syndrome (LS)

According to Table 1, a statistically significant difference was observed in demographic ccomponents between the two groups (p < 0.05). Individuals aged over 75 years in the group with locomotive syndrome and those without locomotive syndrome comprised 64.3% and 35.7% of the sample, respectively. Males made up 21.4% of the locomotive syndrome group and 78.6% of the non-locomotive syndrome group. The percentages of single individuals were 36.6% and 36.4%, while retirees constituted 24% and 76% in the respective groups. Participants with tertiary education were 19.3% in the locomotive syndrome group and 80.7% in the non-locomotive syndrome group. The prevalence of chronic pain in older adults with locomotive syndrome (56.3%) was significantly higher than in those without locomotive syndrome (43.7%), showing a statistically significant difference (p < 0.001). Overall, 74.9% of participants had a history of chronic diseases, and 35.1% reported falls in the last 6 months, indicating a significant difference in the prevalence of chronic diseases and falls between the two groups (p < 0.001). Among the various chronic diseases, musculoskeletal disorders showed the most significant difference between the two groups (p < 0.001).

Table 2 Comparison of Anthropometric indices in older adults with and without Locomotive Syndrome (LS)

According to Table 2, the mean fat-free mass percentage in the locomotive syndrome group was 26.6 (± 4), while it was 29.8 (± 5) in the non-locomotive syndrome group, showing a statistically significant difference (p < 0.001). The mean body fat percentage in the locomotive syndrome group was 37.6 (± 9), compared to 31.8 (± 10.5) in the non-locomotive syndrome group, with a statistically significant difference (p < 0.001). The mean fat mass index in the locomotive syndrome group was 11.4 (± 3.9) kg/m², while in the non-locomotive syndrome group was 3.9 (± 4) kg/m², indicating a statistically significant difference (p < 0.001). The mean height in the locomotive syndrome group was 158.9 cm (± 9) compared to 164.8 cm (± 9.3) in the non-locomotive syndrome group, which were significantly different (p < 0.001). No statistically significant differences were found between the two groups regarding weight, waist circumference, and waist-to-hip ratio.

Table 3 Predictive factors of Locomotive Syndrome in the Elderly based on anthropometric indices

According to Table 3, four independent variables associated with locomotive syndrome were entered into the model using the enter method. Two variables successfully predicted the status of locomotive syndrome. With each unit increase in fat-free mass percentage (95% CI: 0.38–0.93, OR = 0.59), the odds of developing locomotive syndrome decreased by 0.41 (1–0.59). With each unit increase in body mass index (95% CI: 1.07–1.91, OR = 1.4), the odds of developing locomotive syndrome increased by 1.4 times.

Discussion

According to the findings of the present research, older adults with locomotive syndrome had a higher average body fat percentage, fat mass index, and body mass index compared to those without LS. Additionally, they had lower average body fat-free percentage. These findings are consistent with previous research. Studies by Imamura (body fat percentage) [21], Chang (body fat percentage, muscle mass, and skeletal muscle mass) [22], Nakamura (body fat percentage and BMI) [16], and Ishihara (body fat percentage and BMI) [17] all demonstrated the association of anthropometric indices with locomotive syndrome. With age, BFM and BMI increase, while FFM (including skeletal structure, muscles, and body water) decreases [23]. Obesity is associated with changes in body shape and disorders of joints and intervertebral discs [24], as well as pain in the lower limbs and spine [25]. Additionally, extra fat causes inflammation and places stress on the joints, that can lead to osteoarthritis and sarcopenic obesity. The combination of muscle weakness and obesity worsens mobility issues and increases the risk of falls among older adults [26, 27].

Based on the findings of this study, it is evident that the likelihood of developing locomotive syndrome in older adults correlates with fat-free mass percentage and body mass index. Recent research has indicated that greater lean body mass can prevent musculoskeletal disorders [28], improve physical performance, and reduce the risk of sarcopenia [29]. Chang et al.‘s study revealed that a decrease in muscle mass was a significant risk factor for locomotive syndrome [22]. Furthermore, the results of the study by Momoki et al. demonstrated that the proportion of participants with locomotive syndrome in the sarcopenia group was significantly higher than the non-sarcopenia group [30]. These findings are largely consistent with the results of our study. Similar to the present research, Nakamura et al.‘s findings indicated that BMI could potentially serve as a useful screening tool for locomotive syndrome [16]. Individuals with an increased body mass index may experience muscle weakness and deformities in soft tissues as they age, which can interfere with the correct performance of daily movements [31].

The findings of this study revealed a significant age and gender disparity between the two groups. These results are consistent with the findings of Sadeghi, Akune, Matsumoto, and Arbex, which also identified age and female gender as significant factors [7, 32,33,34]. Accordingly, with more chronic diseases and aging, the clinical performance of the musculoskeletal system changes, may necessitate surgeries [24]. The observed gender differences in the two study groups may be explained by distinct body composition patterns and greater muscular mass in men. Moreover, the higher prevalence of multiple concurrent diseases and obesity in women compared to men can justify such discrepancies [35]. This study demonstrated a significant difference in chronic disease variables, chronic pain, and falls between the two groups. The findings of this research are consistent with the studies of Matsumoto et al. (falls) [33], Sadeghi and Akahane (chronic disease and chronic pain) [6, 7], and Sasaki, Silva, and Nakamura (chronic disease) [16, 36, 37].

There were few limitations in this study. First, no causal relationship could be established between body composition changes and locomotive syndrome due to the cross-sectional design. Second, because of the relatively small sample size, the findings for some variables (such as waist circumference and WHR) cannot be generalized across all Iranian older adults. Third, due to pain and limited mobility from musculoskeletal disorders like osteoarthritis and spinal stenosis, not all individuals with locomotive syndrome participated in the study. Therefore, our study focused on older adults visiting comprehensive health service centers. Future studies might consider sampling older people living in their own home. Moreover, further studies need to be conducted with larger sample size in different Iranian sociocultural settings and ethnicities. On top of that, devices sufficiently more accurate than BIA can be used to assess body composition.

Conclusion

It was revealed in the present study that an increase in fat mass, body mass index, body fat index, plus a decrease in fat-free mass are associated with higher incidence of locomotive syndrome in older adults. In addition, fat-free mass and body mass index were found to be the predictive factors for locomotive syndrome. Clinical interventions aimed at managing fat mass, preserving and increasing fat-free mass, and maintaining a normal body mass index may reduce the likelihood of locomotive syndrome in older adults. Health caregivers and physicians can help reduce locomotive syndrome and dependency among seniors by screening anthropometric indices, identifying abnormalities, and educating seniors. (e.g., appropriate diet, regular exercise, and muscle strengthening exercises).