Relationship between physical functional status indicators and bone mineral density in older women

Abstract

Background

Osteoporosis significantly predisposes patients to fragility fractures and a reduced quality of life. Therefore, osteoporosis prevention plays an important role in extending healthy life expectancy. The purpose of this study was to identify whether physical functional status was associated with low bone mineral density, and to determine cut-off values of physical status indicators for osteoporosis.

Methods

This cross-sectional study evaluated 343 women aged 60 years or older who were able to walk independently. The measured variables were the body mass index, lumbar and total hip bone mineral density, grip strength, 5-m normal walking speed, one-leg standing time, timed up-and-go test, and skeletal muscle mass using bioelectrical impedance analysis. The associations between physical status indicators and low bone mineral density were analyzed and the cut-off values for detecting osteoporosis were calculated using receiver operating characteristic curve analyses.

Results

The prevalence of osteoporosis was 29.2 %. All measured variables significantly differed between the osteoporotic and non-osteoporotic groups (p < 0.05). Multivariate logistic regression analysis showed that the factors associated with osteoporosis were the skeletal muscle mass index, walking speed, and body mass index. In the receiver operating characteristic curve analysis, the cut-off values of the skeletal muscle mass index, walking speed, and body mass index associated with osteoporosis were 6.31 kg/m², 1.29 m/s, and 22.6 kg/m², respectively.

Conclusions

Older women with low bone mineral density have lower skeletal muscle mass, slower walking speed, and lower body mass index. Measuring the skeletal muscle mass index, walking speed, and body mass index might be useful for daily exercise guidance or osteoporosis screening.

Highlights

• •Older women with low BMD have lower skeletal muscle mass, slower walking speed, and lower BMI than those with a normal BMD.
• •The cut-off values of the SMI, walking speed, and BMI for detecting osteoporosis were 6.31 kg/m², 1.29 m/s, and 22.6 kg/m².
• •Mmeasuring the SMI, BMI, and walking speed may be useful for osteoporosis screening, and the identification of cut-off values may be applicable in setting targets for daily exercise guidance to prevent osteoporosis.

1. Introduction

The older adult population in Japan is increasing, with a prevalence of 29.1 % in 20,23.¹ In a super-aging society, it is important to extend healthy life expectancy. Osteoporosis is a skeletal disorder characterized by compromised bone strength that predisposes patients to fragility fractures of the extremities and trunk.² Osteoporosis significantly reduces an individual's physical functions and quality of life and increases their risk of mortality in the long term.3, 4, 5 Therefore, prevention and early intervention for osteoporosis play an important role in extending healthy life expectancy.

The gold standard method for diagnosing osteoporosis is to measure the bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA), in addition to imaging studies such as radiography. Previous studies have reported relationships between BMD and measures of physical performance such as the walking speed, one-leg standing time with eyes open (OLS), and grip strength (GS) .⁶ Furthermore, low skeletal muscle mass has been reported to contribute to low BMD and osteoporosis.7, 8, 9 Therefore, improved physical performance and increased skeletal muscle mass are important for preventing osteoporosis, and appropriate exercise guidance and the identification of appropriate target setting are important in daily practice.

Despite the evidence of a relationship between physical functional status and osteoporosis, few studies have reported the threshold values of physical functional status for predicting osteoporosis. We hypothesized that low BMD is associated with specific physical functional status and that there are threshold values for the performance measures that indicate an increased risk of osteoporosis. We believe that the identification of these threshold values will be useful as one of the indicators in daily exercise guidance to prevent osteoporosis. Therefore, the purpose of this study was to identify physical functional status associated with low BMD, and to determine the threshold values of functional status associated with osteoporosis.

2. Materials and methods

2.1. Participants

This cross-sectional study was conducted from April 2021 to June 2023. The study participants were women older than 60 years who were able to walk independently without any support and without a T-cane. These participants were attending an orthopedic outpatient clinic and had received diagnoses of degenerative joint and/or cartilage disease in the extremities or spine. The exclusion criteria were¹: acute fractures (less than 6 months after injury),² neuromuscular diseases or internal comorbidities affecting motor function,³ cognitive or mental dysfunction requiring medication,⁴ skeletal dysfunction that had a negative impact on walking.

This study was conducted based on the guidelines laid down in the Helsinki Declaration and comparable ethical standards. This study was reviewed and approved by the ethics committee of the authors’ institution. Informed consent was obtained from all individual participants included in the study.

2.2. Measurement of bone mineral density

The BMD of the lumbar spine (L1–4) and total hip were measured by DXA using a Lunar Prodigy Primo densitometer (GE Healthcare, Chicago, IL, USA). Osteoporosis was defined as T-score ≤2.5 in accordance with the World Health Organization criterion. DXA was performed within 30 days of the date of physical function assessment.

2.3. Evaluation of physical function

The participants performed physical performance tests including GS, 5-m normal walking speed, OLS, and timed up-and-go test (TUG). The GS of the dominant hand was measured using a digital dynamometer (T.K.K.5401 GRIP D, Takei Scientific Instruments Co., Ltd., Niigata, Japan). Measurements were taken twice, and the maximum value was used in the analysis. In the 5-m walking speed test, the participants walked at normal speed along an 11-m measurement section comprising an initial 3-m acceleration section, a central 5-m timed section, and a final 3-m deceleration section. The walking time of the central 5-m section was measured, and the walking speed (m/second) was calculated. The OLS was assessed using each participant's preferred leg. We measured the time from when the participant raised their leg until the leg was put down on the floor, or up to a maximum of 60 s. Participants performed two trials, with the longer time used in the analysis. The TUG was measured as the time required to stand up from a standard chair, walk a distance of 3-m back and forth, turn around at a corner, walk back to the chair, and sit down. The TUG was performed twice, and the minimum value was used in the analysis.

2.4. Measurement of body composition

The skeletal muscle mass and skeletal muscle mass index (SMI) were measured using bioelectrical impedance analysis with a body composition analyzer (MC-780-A-N, TANITA, Tokyo, Japan). The SMI was calculated by dividing the appendicular skeletal muscle mass by the height in square meters (kg/m²).

2.5. Statistical analysis

First, the participants were divided into the non-osteoporosis and osteoporosis groups, and all measured variables were compared between the two groups using the Mann-Whitney U test. Second, factors with p < 0.05 in the univariate analyses were included as covariates in multivariate logistic regression analysis, which was performed to examine the factors associated with osteoporosis. Finally, receiver operating characteristic (ROC) analysis was performed to determine the cut-off values for indicating osteoporosis based on the Youden index 10. Statistical analyses were performed using SPSS version 29.0 (SPSS Inc., Chicago, IL, USA) and EZR statistical software (Saitama Medical Center, Jichi Medical University, Saitama, Japan).

3. Results

3.1. Basic characteristics

The basic characteristics of the participants are shown in Table 1. Among all participants in the physical function survey, 35 were receiving medical treatment for osteoporosis; of these 35 participants, 15 with a T-score of > −2.5 were excluded from the analysis. Therefore, the study cohort comprised 343 participants with a mean age of 73.6 years (range, 60–92 years). The prevalence of osteoporosis was 29.2 % (100 of 343). There were 89 cases with a history of vertebral fracture and 2 cases with a history of proximal femur fracture, and the group with a history of fragility fractures was significantly older (mean 75.6 years vs 72.9 years, p = 0.005) and had lower total hip BMD(mean 0.741 g/cm² vs 0.787 g/cm², p = 0.002). No adverse events such as falls or worsening of lumbar or joint pain occurred during the measurement period.

Table 1.

Basic participant characteristics.

	N = 343
Age (years)	73.6 ± 7.8
Height (cm)	151.7 ± 6.3
Weight (kg)	54.3 ± 10.4
Body mass index (kg/m2)	23.6 ± 3.9
Lumbar spine BMD (g/cm2)	0.969 ± 0.187
Lumbar spine T-score	−1.2 ± 1.6
Total hip BMD (g/cm2)	0.775 ± 0.122
Total hip T-score	−1.3 ± 1.0
Grip strength (kg)	21.9 ± 4.3
Walking speed (m/s)	1.2 ± 0.3
One-leg standing time (sec)	32.5 ± 23.8
Timed up-and-go (sec)	7.6 ± 2.6
Skeletal muscle mass (kg)	14.9 ± 2.3
Skeletal muscle mass index (kg/m2)	6.5 ± 0.6

Values are shown as the mean ± standard deviation.

Abbreviation: BMD, bone mineral density.

3.2. Association between osteoporosis and assessed variables

The associations between osteoporosis and the assessed variables are shown in Table 2. The osteoporosis group (n = 100) had a significantly older age, lower body mass index (BMI), weaker GS, slower walking speed, shorter OLS time, longer TUG time, and lower SMI than the non-osteoporosis group (n = 243). The covariates included in the adjusted multivariate logistic regression analysis were the age, BMI, GS, walking speed, OLS time, TUG time, and SMI (Table 3). In the adjusted model, the BMI, walking speed, and SMI were significantly associated with osteoporosis (Fig. 1 a, b, c). In the analysis of multicollinearity, variance inflation factors were calculated, which ranged from 1.2 to 2.1 for all items.

Table 2.

Comparison of measured variables between patients with and without OP.

	non-OP	OP	P-value
Number of participants (%)	243 (70.8 %)	100 (29.2 %)
Age (years)	72.0 [67, 79]	76.0 [69, 73]	<0.01
BMI (kg/m2)	23.6 [21.6, 26.4]	21.3 [19.4, 24.4]	<0.01
Lumbar spine BMD (g/cm2)	1.020 [0.923, 1.339]	0.769 [0.716, 0.807]	<0.01
Total hip BMD (g/cm2)	0.817 [0.753, 0.893]	0.639 [0.602, 0.699]	<0.01
Grip strength (kg)	22.7 [19.7, 25.3]	20.4 [16.9, 23.2]	<0.01
Walking speed (m/s)	1.3 [1.1, 1.5]	1.2 [0.9,1.3]	<0.01
One-leg standing time (sec)	31.4 [10.3, 60.0]	17.6 [5.1,60]	0.035
Timed up-and-go (sec)	6.8 [5.9,8.1]	7.6 [6.0, 9.5]	0.007
SMI (kg/m2)	6.6 [6.2,6.9]	6.2 [5.8, 6.6]	<0.01

Values are shown as median [lower quartile, upper quartile].

Abbreviations: BMI, body mass index; SMI, skeletal muscle mass index; OP, osteoporosis.

Table 3.

Multivariate logistic regression analysis to identify the associated factors of osteoporosis.

	Adjusted OR (95%CI)	P-value
Age	1.020 (0.978–1.070)	0.335
BMI	0.907 (0.836–0.984)	0.019
Grip strength	0.968 (0.891–1.050)	0.435
Walking speed	0.162 (0.045–0.584)	0.005
One-leg standing time (sec)	1.010 (0.991–1.020)	0.480
Timed up-and-go (sec)	0.952 (0.831–1.090)	0.475
SMI	0.418 (0.240–0.726)	0.002

Abbreviations: OR, odds ratio; CI, confidence interval; BMI, body mass index; SMI, skeletal muscle mass index.

Fig. 1.

(a) Comparison of the SMI between participants with and without osteoporosis. (b) Comparison of the walking speed between participants with and without osteoporosis. (c) Comparison of the BMI between participants with and without osteoporosis. The number beside each box indicates the median value.

Abbreviations: SMI, skeletal muscle mass index; BMI, body mass index.

3.3. Cut-off values for indicating osteoporosis

Fig. 2 a, b, c shows the ROC curve for the detection of osteoporosis. The cut-off values for osteoporosis were 6.31 kg/m² for the SMI (p < 0.01; area under the ROC curve (AUC) 0.710; sensitivity 62 %; specificity 72.8 %), 1.29 m/s for the walking speed (p < 0.01; AUC 0.659; sensitivity 74.0 %; specificity 53.9 %), and 22.6 kg/m² for the BMI (p < 0.01; AUC 0.674; sensitivity 66.0 %; specificity 63.4 %).

Fig. 2.

(a) Receiver operating characteristic (ROC) curve of the SMI for osteoporosis. The cut-off value was 6.31 kg/m² calculated from the Youden index (sensitivity 62 %, specificity 72.8 %). (b) ROC curve of the walking speed for osteoporosis. The cut-off value was 1.29 m/s calculated from the Youden index (sensitivity 74.0 %, specificity 53.9 %). (c) ROC curve of the BMI for osteoporosis. The cut-off value was 22.6 kg/m² calculated from the Youden index (sensitivity 66.0 %, specificity 63.%).

4. Discussion

The present study showed that low BMD was related to the SMI, walking speed, and BMI. Furthermore, the present study is the first to report the cut-off values for the detection of osteoporosis using the ROC curve, which were 6.31 kg/m² for the SMI, 1.29 m/s for the walking speed, and 22.6 kg/m² for the BMI.

The present findings are similar to the findings of previous studies reporting associations between the BMD and the skeletal muscle mass, body weight, lean body mass, and walking speed.6, 7, 8, 9, 11 This may be because the body weight acts as a stress on the skeletal system to promote bone formation mechanically by stimulating osteocytes to increase the bone density.¹² In addition, muscle may be important as an extragonadal source of estrogen.¹³ Therefore, nutritional and exercise guidance to maintain body weight may help prevent loss of bone mass.

The SMI and walking speed, which were identified as factors associated with a low BMD in the present study, are key variables in the diagnostic criteria for sarcopenia.¹⁴ Therefore, the present results confirm the association between sarcopenia and osteoporosis that has been reported in several studies.15, 16, 17, 18 Women with sarcopenia reportedly have 4.34–12.9-times higher odds of having osteoporosis in comparison to women without sarcopenia.15, 16 Furthermore, the prevalence of sarcopenia is higher in women with osteopenia (32.0 %) and osteoporosis (43.6 %) than in normal individuals (15.3 %) ,¹⁷ and sarcopenia is more frequent in osteoporotic (62.7 %) versus osteopenic (47.7 %) participants.¹⁸ These interrelationships may be explained by the mechanostat hypothesis that suggests that the action of muscle contraction provides direct mechanical stimulus to bone, which promotes osteogenesis.¹⁹ The skeletal muscles also secrete insulin-like growth factor-1 and fibroblast growth factor-2 that promote bone formation .¹⁷

The present study is the first to indicate cut-off values for each variable for indicating osteoporosis using the ROC curve. However, previous studies have indicated cut-off values of the SMI for predicting osteoporosis. One study reported a cut-off SMI value of 6.5 kg/m² for predicting osteoporosis in elderly women,²⁰ which is compatible with the cut-off value (6.31 kg/m²) reported in the present study. Another study reported a cut-off SMI value of 5.94 kg/m² for predicting osteoporosis in patients with type 2 diabetes.²¹ Regarding physical function, although Lee et al.²⁰ reported a cut-off GS value of 23.1 kg for predicting osteoporosis, no other studies have reported cut-off values for other variables. Therefore, the present study is the first to show that a cut-off walking speed of 1.29 m/s may indicate the presence of osteoporosis. The cut-off values of the SMI and walking speed for osteoporosis in the present study were high compared with the Asian Working Group for Sarcopenia diagnostic criteria for sarcopenia a.¹⁴ This suggests that osteoporosis may occur earlier than sarcopenia, and that exercises performed to increase the skeletal muscle mass and walking speed are also important for the prevention of osteoporosis.

The early diagnosis of osteoporosis is difficult because there are few subjective symptoms until a bone fragility fracture occurs. The gold standard method for the diagnosis of osteoporosis is to measure the BMD using DXA, in addition to imaging studies such as radiography. However, BMD measurements are only made when an individual visits a medical facility, and asymptomatic individuals are unlikely to do so. In contrast, the SMI and BMI can be easily measured with a body composition analyzer, and the walking speed can be measured without special tools or a visit to a medical facility. Since walking speed is a diagnostic criterion not only for sarcopenia,¹⁴ as mentioned above, but also for physical frailty,²² it is likely to be an important indicator of osteoporosis. Therefore, measuring the SMI, BMI, and walking speed may be useful for osteoporosis screening, and the identification of cut-off values may be applicable in setting targets for daily exercise guidance to prevent osteoporosis.

The present study has some limitations. First, as this was a cross-sectional study, the casual relationships are not clear. Longitudinal and interventional studies are required to evaluate the causal relationships. Second, the study participants were outpatients with musculoskeletal diseases, which might affect physical performance. However, as the participants did not need support to walk, the impact of their disease status on their physical performance seemed to be negligible or small. Despite these limitations, the present study is first to comprehensively evaluate the relationships between BMD and various physical function indicators and indicate cut-off values of these variables for the detection of osteoporosis. Therefore, we believe that the present results will contribute to the early diagnosis of osteoporosis and will be useful as one of the indicators in daily exercise guidance to prevent osteoporosis in a super-aging society. Nutritional and exercise guidance to maintain body weight and improve skeletal muscle mass and walking speed might help prevent the development of osteoporosis.

In conclusion, older women with low BMD have lower skeletal muscle mass, slower walking speed, and lower BMI than those with a normal BMD. The cut-off values of the SMI, walking speed, and BMI for detecting osteoporosis were 6.31 kg/m², 1.29 m/s, and 22.6 kg/m², respectively.

Funding

This work was partially supported by the Japanese Clinical Orthopedic Association under Grant 2021. The funder played no role in the design, methods, subject recruitment, data collection, analysis or preparation of the paper.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Institutional ethical committee approval

The local institutional review board approved this study. All patients provided informed consent.

CRediT authorship contribution statement

Ikuko Takahashi: Material preparation and data collection were performed, Writing – original draft, Formal analysis. Kei Watanabe: Formal analysis, all authors commented on previous versions of the manuscript, All authors read and approved the final manuscript, All authors contributed to the study conception and design. Hideo Noguchi: Material preparation and data collection were performed. Junko Sato: Material preparation and data collection were performed. Yoshinori Ishii: Material preparation and data collection were performed.