Prevalence and association of sarcopenic obesity with locomotive syndrome based on the 2024 Consensus Statement of the Japanese Working Group on Sarcopenic Obesity in community-dwelling Japanese women

Abstract

[Purpose] Sarcopenia, obesity, and sarcopenic obesity (SO) impair physical function and quality of life in older women and are associated with locomotive syndrome (LS). However, the relationship between SO, as defined by the new 2024 algorithm from the Japanese Working Group on Sarcopenic Obesity (JWGSO2024), and LS remains insufficiently investigated. This study aimed to clarify the prevalence of LS and its association with physical function across four groups classified by the JWGSO2024 criteria: normal, sarcopenia, obesity, and SO. [Participants and Methods] We studied 178 community-dwelling women aged 57–91 years. Sarcopenia was diagnosed using the Japanese Strength, Ambulation, Rising from a chair, Stair climbing and history of Falling (SARC-F) questionnaire, handgrip strength, the Five Times Sit-to-Stand test, and appendicular lean mass adjusted to body mass index (ALM/BMI). Obesity was defined based on waist circumference, BMI, and body fat percentage. LS was assessed using the stand-up and two-step tests. We compared the prevalence of LS and differences in physical function among the four diagnostic groups. [Results] The SO prevalence was 3.9–6.2%. All participants in the SO group had LS. This group showed the lowest performance on the two-step test, indicating the highest risk of LS among groups. [Conclusion] SO, as defined by the JWGSO2024 criteria, was strongly associated with LS. These findings provide a scientific basis for developing LS prevention and intervention strategies for patients with SO.

INTRODUCTION

Japan has the most rapidly aging population in the world, with people aged ≥65 years accounting for 29.3% of the total population as of 2024¹⁾. Against this backdrop, extending the healthy life expectancy of older people and supporting their independence have become important public health issues²⁾. Sarcopenia (age-related muscle loss) and obesity are major health issues associated with aging, particularly influencing the independence and quality of life (QOL) of older people. Sarcopenia is characterized by a decrease in muscle mass and strength and is associated with mobility impairments, falls, and reduced QOL³⁾. While previous studies have reported that sarcopenia and obesity are each individually associated with lifestyle-related diseases, cardiovascular diseases, and increased mortality rates⁴⁾, sarcopenic obesity (SO), where both conditions coexist, has been shown to carry a higher risk than either sarcopenia or obesity alone⁵⁾. In particular, among older women, the risk of SO increases sharply when body fat percentage (BFP) exceeds 26.0–34.6%⁶⁾, and SO is significantly associated with cardiovascular events, atrial fibrillation, and heart failure^7,8,9), with the highest mortality risk^{10, 11)}.

Obesity is an important risk factor for locomotive syndrome (LS), and in obese older women, the coexistence of excessive body fat and muscle weakness in the state of SO is strongly associated with the progression of LS¹²⁾. A large-scale cross-sectional study of Japanese older individuals also reported that LS is significantly associated with obesity and metabolic syndrome, with a higher prevalence in women¹³⁾. Moreover, frailty is used as a framework for comprehensively evaluating functional decline in older people, with sarcopenia and LS being its primary components¹⁴⁾. LS was proposed by the Japanese Orthopedic Association as a concept indicating impaired mobility due to musculoskeletal disorders and increased future care risk¹⁵⁾. Early screening for locomotive syndrome is effective not only in the elderly but also in younger age groups, highlighting the importance of determining the appropriate age to initiate such assessments^{16, 17)}. Evaluation methods such as the stand-up test and two-step test have been deemed valid to assess LS^{18, 19)}. The prevalence of LS increases with age, particularly among older women^{13, 20)}. The coexistence of LS and sarcopenia significantly increases the risk of falls and fractures, and their interaction is suggested to exacerbate the risk of requiring care^21,22,23,24).

In 2024, the Japanese Working Group on Sarcopenic Obesity (JWGSO) proposed a diagnostic algorithm for SO tailored to characteristics of the Japanese population (JWGSO2024)²⁵⁾, adopting many of the Asian Working Group for Sarcopenia 2019 (AWGS2019) criteria²⁶⁾. Screening tools include SARC-F, BMI, waist circumference (WC), and BFP, while diagnostic tools combine grip strength, the Five Times Sit-to-Stand Test (FTSST), and ALM/BMI correction (appendicular lean mass (kg) divided by body mass index (kg/m²))²⁷⁾. This algorithm is expected to serve as an early detection tool for SO in clinical and community health settings. However, there is currently insufficient evidence to establish an association between the JWGSO2024 algorithm and LS. Systematic studies comparing physical function based on SO classification criteria and elucidating the interrelationship with LS are also lacking. Therefore, the present study aimed to apply the four-group classification of SO based on the JWGSO2024 algorithm and clarify the prevalence of LS and its association with physical function in each group.

PARTICIPANTS AND METHODS

Study participants were 178 healthy community-dwelling middle-aged and older women aged 57–91 years (mean age: 75.1 ± 6.3 years). Recruitment methods included inserting advertisements in newspapers and magazines, posting notices on community bulletin boards, and presenting recruitment information at community meetings. Participants were selected from those who contacted the researchers directly via email or telephone. Prior to measurements, participants attended an informational session where the purpose of the study, measurement procedures, and potential risks were explained in writing and verbally. Only those who provided written consent were included as study participants. Exclusion criteria were individuals 1) with secondary obesity due to adrenal disorders, 2) with heart disease or abnormal electrocardiogram findings, 3) with severe liver dysfunction or cirrhosis, 4) with pregnancy or suspected pregnancy, 5) with ongoing orthopedic treatment or physical activity restrictions, 6) requiring assistance in daily living, 7) deemed ineligible for study participation by the study physician due to their condition, and 8) deemed inappropriate for study participation by the principal investigator or co-investigator. This study was approved by the Life Science Ethics Review Committee at Ritsumeikan University’s Biwako-Kusatsu Campus (BKC) (BKC-LSMH-2022-075-2).

Sarcopenia was evaluated using SARC-F, ALM/BMI, HGS, and FTSST in accordance with the JWGSO2024 algorithm²⁵⁾. The SARC-F questionnaire was administered using the Japanese version developed by Kurita et al.²⁷⁾, which assessed muscle strength, walking assistance, standing up from a chair, climbing stairs, and falling. Each item was scored on a scale of 0 to 2, and participants with a total score of 4 or higher were classified as the S group. ALM was measured using bioelectrical impedance analysis (BIA, Inner Scan 50V, RD-804L, manufactured by Tanita Corporation, Tokyo, Japan) and divided by BMI to obtain ALM/BMI. Participants with values <0.512 kg/BMI were classified as the S group. Muscle strength and physical function were evaluated using HGS and FTSST. HGS was measured using a digital grip strength meter (Grip-D, T.K.K.5401, TAKEI, Tokyo, Japan). Measurements were taken twice on each side while standing, and the average of the best values was calculated. Participants with values <18 kg were classified as the S group. FTSST was performed using a chair with a height of 40 cm. Participants were instructed to perform five sit-to-stand movements as quickly as possible, and the time required was measured using a stopwatch. Two measurements were taken, and the faster of the two was adopted. Patients with values ≥12 seconds were classified as the S group.

Obesity was evaluated according to JWGOS2024²⁵⁾ using WC, BMI, and BFP as indicators. WC was measured with a tape measure while standing, using the umbilicus as the reference point; participants with WC ≥90 cm was classified as the O group. BMI was calculated from weight (kg) and height (m²) measured using an automatic height-measuring scale (WB-510, manufactured by Tanita Corporation), and participants with BMI ≥25 kg/m² were classified as the O group. BFP was determined from body fat mass (BFM) measured using the BIA method (Inner Scan 50V RD-804L, manufactured by Tanita Corporation), and participants with BFP ≥30% were classified as the O group.

SO was determined based on SARC-F, ALM/BMI, HGS, FTSST, WC, BMI, and BFP. Following the JWGSO2024 algorithm²⁵⁾, participants were classified into the following four groups: normal group (N group), sarcopenia group (S group), obesity group (O group), and sarcopenic obesity group (SO group).

LS was evaluated²⁸⁾ using the stand-up test and two-step test. In the stand-up test, participants sat on a 40-cm-high platform with their arms folded across their chest and their legs shoulder-width apart, and their ability to stand up without momentum and remain standing for 3 seconds was evaluated. Participants who successfully stood up were classified as non-locomotive syndrome (NL), while those who failed were classified as LS. In the two-step test, participants stood with the toes of both feet aligned at the starting line, took two steps forward with as wide a stride as possible, and stopped with both feet aligned. The stride length was measured from the starting point to the tip of the toes at the final landing point, and the two-step test value was calculated as the length of two strides (cm) divided by height (cm). The measurement was performed twice, and the better of the two results was adopted. The cutoff value for LS evaluation was set at ≤1.3.

The prevalence of SO was calculated by determining the number and percentage of participants belonging to each of the four groups. For the prevalence of LS in the N, O, S, and SO groups, χ² tests (Pearson’s χ² test and Fisher’s exact probability test) were used to evaluate differences between groups. For intergroup comparisons of the two-step test results among the LS evaluation indices, the Kruskal–Wallis test was used, and when significant differences were observed, multiple comparisons (Dunn–Bonferroni method) were performed using Bonferroni correction. P<0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics for Macintosh, Version 29.0 (IBM Corp., Armonk, NY, USA).

RESULTS

The prevalence of SO among study participants was shown in Table 1. The prevalence of SO using SARC-F + WC as the screening indicator was 3.9% (n=7), and that using SARC-F + BMI was 6.2% (n=11). The prevalence according to diagnostic evaluation criteria HGS + ALM/BMI + BFP and FTSST + ALM/BMI + BFP were 2.7% (n=4) and 3.9% (n=7), respectively. When using HGS + BFP, FTSST + BFP, and ALM/BMI + BFP alone, the rates were 11.8% (n=21), 6.7% (n=10), and 3.9% (n=7), respectively.

Table 1. Prevalence of sarcopenic obesity classified according to the Diagnostic Algorithm for Sarcopenic Obesity in Japan (2024).

	Sarcopenia assessment	Obesity assessment	n	N	O	S	SO
Screening	SARC-F	WC	178	117	42	12	7
				(65.7%)	(23.6%)	(6.7%)	(3.9%)
	SARC-F	BMI	178	122	37	8	11
				(68.5%)	(20.8%)	(4.5%)	(6.2%)
Diagnosis	HGS	BFP	178	37	106	14	21
				(20.8%)	(59.6%)	(7.9%)	(11.8%)
	FTSST	BFP	149	36	102	1	10
				(24.2%)	(68.5%)	(0.7%)	(6.7%)
	ALM/BMI	BFP	178	50	120	1	7
				(28.1%)	(67.4%)	(0.6%)	(3.9%)
	HGS and ALM/BMI	BFP	178	50	120	1	7
				(28.1%)	(67.4%)	(0.6%)	(3.9%)
	FTSST and ALM/BMI	BFP	149	37	108	0	4
				(24.8%)	(72.5%)	(0.0%)	(2.7%)

Prevalence of sarcopenic obesity based on the Diagnostic Algorithm for Sarcopenic Obesity in Japan (2024) in participants classified into four groups (N: Normal; O: Obesity; S: Sarcopenia; SO: Sarcopenic obesity). Upper row: number of participants with sarcopenic obesity. Lower row: prevalence (%). SARC-F: screening tool for sarcopenia, consisting of the following items: (1) strength, (2) assistance in walking, (3) rising from a chair, (4) climbing stairs, and (5) falls. HGS: handgrip strength; FTSST: five times sit-to-stand test; ALM: appendicular lean mass; BMI: body mass index; ALM/BMI: ALM divided by BMI; WC: waist circumference; BFP: body fat percentage.

The prevalence of sarcopenia was 0.6% (n=1) when classified using the combination of HGS + ALM/BMI + BFP and 0% (n=0) when using the combination of FTSST + ALM/BMI + BFP. When using HGS + BFP, FTSST + BFP, and ALM/BMI + BFP alone, the rates were 7.9% (n=14), 0.7% (n=1), and 0.6% (n=1) respectively.

The prevalence of LS was shown in Table 2. The prevalence of LS was calculated for each group using the combinations of SARC-F + WC, SARC-F + BMI, and HGS + BPF, after excluding indicators with a prevalence of SO of 1 or 0 (Table 1) based on their statistical validity and clinical practicality. The prevalence of LS using SARC-F + WC was 44.4% (n=52/117) in the N group, 69.0% (n=29/42) in the O group, 66.7% (n=8/12) in the S group, and 100.0% (n=7/7) in the SO group for the stand-up test, and 65.8% (n=77/117) in the N group, 73.8% (n=31/42) in the O group, 100.0% (n=12/12) in the S group, and 100.0% (n=7/7) in the SO group for the two-step test. The prevalence of LS using SARC-F + BMI was 47.5% (n=58/122) in the N group, 62.2% (n=23/37) in the O group, 50.0% (n=4/8) in the S group, and 100.0% (n=11/11) in the SO group for the stand-up test, and 68.9% (n=84/122) in the N group, 64.9% (n=24/37) in the O group, 100.0% (n=8/8) in the S group, and 100.0% (n=11/11) in the SO group for the two-step test. The prevalence of LS using HGS + BFP was 32.4% (n=12/37) in the N group, 57.5% (n=61/106) in the O group, 42.9% (n=6/14) in the S group, and 81.0% (n=17/21) in the SO group for the stand-up test, and 45.9% (n=17/37) in the N group, 72.6% (n=77/106) in the O group, 92.9% (n=13/14) in the S group, and 95.2% (n=20/21) in the SO group for the two-step test. The prevalence of LS was high in the SO group, ranging from 81% to 100%. Significant differences were observed among the four groups for all combinations (p<0.05, p<0.01, p<0.001).

Table 2. Prevalence of locomotive syndrome based on the Diagnostic Algorithm for Sarcopenic Obesity in Japan (2024).

Assessment				N	O	S	SO	χ2	p-value
Sarcopenia	Obesity	Locomotive syndrome
SARC-F	WC	Stand-Up test	n	52 / 117	29 / 42	8 / 12	7 / 7	14.9	***
				(44.4%)	(69.0%)	(66.7%)	(100.0%)
		Two step test	n	77 / 117	31 / 42	12 / 12	7 / 7	9.5	*
				(65.8%)	(73.8%)	(100.0%)	(100.0%)
SARC-F	BMI	Stand-Up test	n	58 / 122	23 / 37	4 / 8	11 / 11	12.5	**
				(47.5%)	(62.2%)	(50.0%)	(100.0%)
		Two step test	n	84 / 122	24 / 37	8 / 8	11 / 11	8.8	*
				(68.9%)	(64.9%)	(100.0%)	(100.0%)
HGS	BFP	Stand-Up test	n	12 / 37	61 / 106	6 / 14	17 / 21	14.3	**
				(32.4%)	(57.5%)	(42.9%)	(81.0%)
		Two step test	n	17 / 37	77 / 106	13 / 14	20 / 21	20.8	***
				(45.9%)	(72.6%)	(92.9%)	(95.2%)

The prevalence of locomotive syndrome was assessed using the 2024 Diagnostic Algorithm for Sarcopenic Obesity, which combines sarcopenia and obesity indicators. Participants were classified into four groups (N: Normal; O: Obesity; S: Sarcopenia; SO: Sarcopenic obesity), and independence was evaluated using the χ² test. Upper row: number of participants with locomotive syndrome (left) and total number of participants (right). Lower row: prevalence (%). SARC-F: screening tool for sarcopenia, assessing the following items: (1) strength, (2) assistance in walking, (3) rising from a chair, (4) climbing stairs, and (5) falls. HGS: hand grip strength; WC: waist circumference; BMI: body mass index; BFP: body fat percentage. Two-step test: Measures the length of two strides. The total stride length (cm) is divided by height (cm). The results of the chi-square test showed significant differences (*p<0.05, **p<0.01, ***p<0.001).

Results of the two-step test using the combinations of SARC-F + WC, SARC-F + BMI, and HGS + BPF were shown in Fig. 1A–1C, respectively. When SARC-F + WC was used, numerical values (mean ± standard deviation) were 1.17 ± 0.18 in the N group (n=117), 1.13 ± 0.17 in the O group (n=42), 0.95 ± 0.19 in the S group (n=12), and 0.90 ± 0.11 in the SO group (n=7), with the SO group having the lowest values (p<0.001). When SARC-F + BMI was used, numerical values were 1.16 ± 0.18 in the N group (n=122), 1.16 ± 0.19 in the O group (n=37), 0.99 ± 0.16 in the S group (n=8), and 0.89 ± 0.17 in the SO group (n=11), with the SO group having the lowest values (p<0.001). When HGS + BFP was used, numerical values were 1.22 ± 0.20 in the N group (n=37), 1.16 ± 0.16 in the O group (n=106), 1.02 ± 0.20 in the S group (n=14), and 0.96 ± 0.18 in the SO group (n=21), with the SO group showing the lowest values (p<0.001). Significant differences were observed between the N and O groups (p<0.01), N and SO groups (p<0.001), and O and SO groups (p<0.01) when using SARC-F + WC; between the N and SO groups (p<0.001) and O and SO groups (p<0.001) when using SARC-F + BMI; and between the N and O groups (p<0.01), N and S groups (p<0.001), and N and SO groups (p<0.001) when using HGS + BFP.

Fig. 1.

Comparison of two-step test results in four groups.

The results of the two-step test are shown. According to the Japanese Working Group on Sarcopenic Obesity (JWGSO2024) algorithm, the combinations SARC-F + WC (A), SARC-F + BMI (B), and HGS + BFP (C) were used to classify participants into four groups (N: Normal; O: Obesity; S: Sarcopenia; SO: Sarcopenic obesity). SARC-F: screening tool for sarcopenia, assessing the following items: (1) strength, (2) assistance in walking, (3) rising from a chair, (4) climbing stairs, and (5) falls. HGS: hand grip strength; WC: waist circumference; BMI: body mass index; BFP: body fat percentage. Two-step test: Measures the length of two strides. The total stride length (cm) is divided by height (cm). L1, L2, L3, and the dotted lines in the figure indicate the locomotive syndrome risk levels. For intergroup comparisons of two-step test results, the Kruskal–Wallis test was used, and when significant differences were observed, multiple comparisons using Bonferroni correction (Dunn-Bonferroni method) were performed.

DISCUSSION

This study classified participants into four groups according to the JWGSO2024 algorithm and clarified the prevalence of SO and that of LS in each group, as well as their relationship with physical function. To the best of our knowledge, this is the first study to examine the relationship between LS and physical function using the JWGSO2024 classification algorithm.

The prevalence of sarcopenia and SO varied greatly depending on the combination of diagnostic indicators used. At the screening stage, a certain number of cases of sarcopenia and SO were detected using the combination of SARC-F and obesity indicators (WC and BMI). However, at the diagnostic stage, when multiple indicators—particularly FTSST and ALM/BMI—were used in combination, the detection rates for sarcopenia and SO were found to significantly decrease. For example, when using FTSST + ALM/BMI, prevalence rates for the S and SO groups were 0% and 2.7%, respectively, showing a significant discrepancy from values at the screening stage. These results suggest that the use of strict criteria for sarcopenia evaluation may make it difficult to diagnose both sarcopenia and SO^{29, 30)}. Since individuals who do not meet any of the criteria for muscle strength (HGS), physical function (FTSST), or muscle mass (ALM/BMI) are excluded from the diagnosis, some at-risk individuals may be overlooked. Previous reports have also indicated that including physical function indicators such as FTSST may lead to inclusion of cases where criteria are temporarily not met due to factors other than aging (e.g., knee pain, lack of exercise habits)³¹⁾. Therefore, there may be a gap between the strictness of diagnosis and actual risk. On the other hand, the SO group showed the highest prevalence (11.8%, n=21) when HGS + BFP was used. This may be because the HGS + BFP classification does not include criteria for muscle mass or physical function, and thus, many cases of obesity with slightly reduced muscle strength might have been classified as SO. However, HGS reflects muscle strength without being influenced by obesity indicators, and it has been reported to be useful for detecting potential sarcopenia in obese individuals³²⁾. While the issue of accuracy requires further verification, this classification may allow for detection of SO with a certain sensitivity, even without conducting muscle mass evaluations.

The method of obesity evaluation could also affect the prevalence of SO. The prevalence of SO in the SO group was 3.9% when WC was used and 6.2% when BMI was used at the screening stage, and 11.8% when BFP was used at the diagnosis stage. These results suggest that different definitions of obesity affect the classification of SO, consistent with previous reports^{33, 34)}.

The O group had the highest percentage of obesity at 72.5%; percentages in the S and SO groups were 0% and 2.4%, respectively, particularly when using FTSST + ALM/BMI. This is because all obese individuals who did not meet the criteria for sarcopenia were classified as obese. Since both FTSST and ALM/BMI are used together, even obese individuals whose values fall below the threshold for either measure are not classified as having sarcopenia. As a result, individuals who may actually have muscle strength or mass decline are classified as obese³⁵⁾; this phenomenon could lead to a bias in prevalence rates that do not reflect the actual situation.

This study confirmed that the prevalence of sarcopenia and SO varies significantly depending on the combination of diagnostic criteria used, and that the frequency of occurrence is greatly influenced by the definition of obesity and sarcopenia adopted. Adopting strict criteria may lead to the omission of high-risk groups, while adopting lenient criteria may raise concerns about overdiagnosis. The JWGSO²⁵⁾ has proposed Japan-specific diagnostic criteria but noted that further validation with large-scale data is necessary. Going forward, it will be essential to carefully evaluate how these criteria function in clinical practice and epidemiological studies, as well as which indicator combinations possess the highest clinical validity.

In this study, we used SARC-F and HGS as sarcopenia assessment indicators, WC, BMI, and BFP as obesity assessment indicators, and stand-up test and two-step test results as LS assessment indicators to examine the prevalence of LS. The prevalence of LS in the SO group was 81–100%, which was significantly higher compared with other groups. This strongly reflects the marked decline in lower limb muscle strength and balance ability in SO. Baumgartner reported that SO is strongly associated with fall risk and physical function decline, consistent with the results of this study³⁶⁾. Moreover, SO is reportedly associated with a higher risk of activities of daily living (ADL) decline, frailty, and LS onset, compared with sarcopenia or obesity alone^{37, 38)}. This suggests that SO is strongly associated with LS.

There were significant differences in the prevalence of LS when assessed using the three combinations (SARC-F + WC, SARC-F + BMI, and HGS + BFP) in the stand-up test or two-step test. The prevalence of LS was higher in the two-step test than in the stand-up test across all four groups. Specifically, 92.9% to 100% of participants in the S group were found to have LS when evaluated based on the results of the two-step test. The two-step test is useful for detecting early declines in walking ability and mobility^{13, 39, 40)} and thus may contribute to identifying functional decline in all groups, regardless of their status (i.e., N, O, S, or SO).

When HGS + BFP was used, the prevalence of LS was 81.0% in the stand-up test and 95.2% in the two-step test for the SO group. Although these values were lower compared with other groups, it is noteworthy that the combination captured a certain degree of functional decline. SARC-F is a subjective questionnaire, and although it is considered an excellent assessment tool for primary care, its values may vary depending on the health status (disease status) of participants^{41, 42)} and thus may not adequately capture early muscle weakness or changes in mobility. On the other hand, HGS and BFP, both objective assessments, are also used for the diagnosis of SO²⁵⁾. Obesity is known to impair motor function through lower limb muscle weakness, increased joint load, and chronic inflammation⁴³⁾. Especially in older people, the combination of muscle weakness and obesity accelerates physical function decline⁴⁴⁾. Moreover, there are reports that HGS decreases, and BFP increases, in LS^{45, 46)}. While HGS alone is not used as an indicator to diagnose sarcopenia in the algorithm, classification using these indicators may be useful for detecting LS.

Significant differences were observed in the relationship between each classification method (SARC-F + WC, SARC-F + BMI, HGS + BFP) and the two-step test. The SO group consistently had the lowest two-step test values, suggesting that SO is associated with the most significant decline in mobility (p<0.001). When SARC-F + WC was used (Fig. 1A), significant differences were observed between the O and SO groups (p<0.01), suggesting that obesity exacerbates the severity of sarcopenia. When SARC-F + BMI was used (Fig. 1B), no difference was observed between the S and SO groups, but a significant difference was observed between the O and SO groups (p<0.001). Since the S group had lower values than the O group, the combination of obesity and sarcopenia may contribute to the worsening of LS. When HGS + BFP was used (Fig. 1C), no significant differences were observed among the O, S, and SO groups. However, significant differences were observed between the N group and O, S, and SO groups (p<0.01, p<0.001), suggesting that either obesity or sarcopenia may influence the progression of LS severity.

The results of the two-step test revealed a significant difference between the N and SO groups for all three classifications (SARC-F + WC, SARC-F + BMI, HGS + BFP) (p<0.001). This finding objectively demonstrates decreases in two-step test values in the SO group, since the N group, which is composed of individuals whose muscle mass, muscle strength, and obesity levels are within normal ranges, is considered a healthy group⁴⁷⁾.

The results of the two-step test were categorized according to the severity classification defined by JOA: LS risk level 1 (initial decline in mobility function), LS risk level 2 (progressive decline in mobility function), and LS risk level 3 (significant mobility impairment and social participation difficulties)²⁸⁾. In all categories, mean values for the SO group fell within the range corresponding to LS risk levels 2–3. Previous studies have reported that high BFP and muscle weakness are associated with the progression of LS. Both fat accumulation and muscle weakness independently contribute to impaired musculoskeletal function, but in SO, their coexistence results in the most pronounced functional impairment^{12, 13)}. The present findings are consistent with these reports and suggest that the use of SARC-F and HGS to assess sarcopenia and WC, BMI, and BFP to assess obesity may be useful for evaluating LS risk. In SO, the coexistence of muscle weakness and excessive fat accumulation synergistically exacerbates mobility impairment, which significantly increases the risk of progression to severe musculoskeletal dysfunction such as LS risk level 2 or 3. This synergistic effect indicates that SO increases the risk of rapid decline in physical function and progression to severe LS more than sarcopenia or obesity alone.

This study has several limitations. First, we calculated the prevalence of SO by combining existing screening indicators and diagnostic criteria items. While this method is useful for understanding the prevalence of SO in the target population, it may not necessarily correspond to the prevalence rate obtained through a rigorous diagnostic process. Second, this study employed a cross-sectional design, which limits the ability to establish causal relationships, and the temporal association between SO and LS was not confirmed. Third, since the study population was limited to health checkup participants, groups at higher risk for SO, such as those with underlying conditions or older individuals requiring care, may not be adequately represented. Finally, the number of cases in specific subgroups (such as middle-aged individuals and those with severe obesity) was insufficient, limiting the accuracy of stratified analysis. The relatively small sample size in the SO group should also be acknowledged as a limitation, as it reduces the statistical power to detect differences and may restrict the generalizability of our findings. Moreover, potential confounding factors such as age, physical activity, and comorbidities were not adjusted for in the present analysis. These unaccounted variables may have influenced the associations and observed prevalence of SO, and thus should be considered when interpreting the results. Such biases could contribute to changes in the prevalence of SO. To address these limitations, future challenges include conducting algorithm-based studies, diversifying the target population, and standardizing evaluation protocols. From a clinical perspective, possible intervention strategies such as regular exercise, nutritional management, and weight control may play important roles in the prevention and management of SO. Future research should evaluate the effectiveness of such interventions in diverse populations.

In conclusion, this study demonstrated a significant association between SO and LS in community-dwelling middle-aged and older women based on the JWGSO2024 algorithm. These findings may serve as scientific evidence to support LS prevention and intervention programs for individuals with SO.

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.