https://orcid.org/0000-0002-1036-3927
Figures
Abstract
Aim
Patients with osteoporosis who also have sarcopenia are at a high risk for falls and fractures. Early detection of sarcopenia is crucial for these patients. This study aimed to compare the effectiveness of SARC-F, SARC-CalF, and calf circumference (CC) as screening tools for sarcopenia in patients with osteoporosis.
Methods
This cross-sectional study was retrospectively conducted on patients who attended the outpatient clinic for Osteoporosis and Sarcopenia at Kyoto Medical Center. Sarcopenia was determined based on low skeletal muscle mass and weak handgrip strength. Sensitivity and specificity analyses were conducted on SARC-F, SARC-CalF, and CC. The diagnostic utility of these three tools was compared using the receiver-operating characteristic (ROC) curves and the area under the ROC curves (AUC).
Results
A total of 225 patients (men/women: 33/192) with a median age of 69.0 years (interquartile range: 61.0‒75.0) were enrolled. The prevalence of sarcopenia was found to be 11.6%. CC had the highest sensitivity (80.8%), while SARC-F had the highest specificity (93.0%) for detecting sarcopenia. ROC analysis revealed that all three tools had significant potential for sarcopenia diagnosis, with SARC-CalF having the highest AUC compared to SARC-F and CC (0.753 vs. 0.619 and 0.700). A multivariate logistic regression, incorporating other confounders as explanatory variables, revealed that SARC-CalF was independently related to sarcopenia (odds ratio: 14.80, 95% confidence interval: 3.83–57.30, p < 0.001).
Citation: Ishihara Y, Kusakabe T, Yasoda A, Kitamura T, Nanba K, Tsuiki M, et al. (2024) Comparison of the utility of SARC-F, SARC-CalF, and calf circumference as screening tools for sarcopenia in patients with osteoporosis. PLoS ONE 19(10): e0310401. https://doi.org/10.1371/journal.pone.0310401
Editor: Hiroto Minamino, Graduate School of Medicine, Kyoto University, JAPAN
Received: March 27, 2024; Accepted: August 30, 2024; Published: October 22, 2024
Copyright: © 2024 Ishihara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This work was supported by Grant-in-Aid for Scientific Research (C) to Y.I. (grant number JP22K11795), T.Kusakabe. (grant number JP21K11691), T.T. (grant number JP18K11093), and a grant from the Smoking Research Foundation to T.Kusakabe. (2020T007). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
In an aging society, the extension of healthy life expectancy is an urgent issue, necessitating early detection and treatment of diseases that cause elderly people to require nursing care [1]. Both osteoporosis and sarcopenia are age-related diseases that significantly increase the risk of falls and fractures, resulting in a decline in activities of daily living and quality of life [2].
Osteoporosis is characterized by the loss of bone mineral density (BMD) and the micro-architectural deterioration of bone structures, resulting in an increased risk of fractures [3]. In Japan, the prevalence of osteoporosis is 3.4% in men and 19.2% in women at the lumber spine (L2–L4) and 12.4% in men and 26.5% in women at the femoral neck [4]. On the other hand, sarcopenia refers to the accelerated decline in skeletal muscle mass plus muscle strength and/or physical performance associated with aging or chronic disease [5, 6]. Recently, the Global Leadership Initiative in Sarcopenia (GLIS) consensus has stated that muscle mass, muscle strength, and muscle-specific strength were accepted as components of sarcopenia, and impaired physical performance was accepted as an outcome rather than a component of sarcopenia [7]. The prevalence of sarcopenia in the Asian region ranges from 5.5% to 25.7% [6]. Sarcopenia is associated with a variety of significant adverse health outcomes, leading to an increased risk of falls and fractures, impaired physical performance, and mortality [5–7].
Recently, bone and muscle have been recognized as interacting tissues due to their close proximity and integrated function for locomotion, and osteoporosis and sarcopenia are thought to share common pathophysiological factors such as physical impairment, release of tissue-specific molecules, and increased inflammatory cytokine activity [8]. It has been reported that sarcopenia is significantly associated with low regional or whole-body BMD [9]. Another previous study provided evidence that the prevalence of sarcopenia was higher in subjects with osteoporosis (29.7%) than those with osteopenia (17.8%) and normal BMD (9.0%) among Japanese women [10]. Therefore, osteoporosis and sarcopenia are likely to occur simultaneously. To convey this close relationship between sarcopenia and osteoporosis, “osteosarcopenia” has recently been defined as a geriatric syndrome characterized by the combined occurrence of osteoporosis or osteopenia and sarcopenia [11]. In older people, osteosarcopenia has been reported to significantly increase the risk of falls, fractures, and death compared with osteoporosis or sarcopenia alone [12, 13]. Therefore, early detection of sarcopenia in patients with osteoporosis is particularly important.
To enable early identification and intervention for people with, or at risk for, sarcopenia, the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) recommends the use of a 5-point self-administered questionnaire known as SARC-F [5]. This questionnaire assesses strength, assistance in walking, rising from a chair, climbing stairs, and falls [14]. Additionally, the 2019 Asian Working Group for Sarcopenia (AWGS 2019) recommends the use of either SARC-F, calf circumference (CC), or their combination (SARC-CalF) for assessment [6].
There have been recent studies comparing the predictive accuracy of these screening tools among community-dwelling older people [15–17]. In terms of overall screening ability, CC has been found to be superior to SARC-F and SARC-CalF for sarcopenia in community-dwelling older people, regardless of age, gender, and cognitive function [15], and regardless of whether the AWGS 2014 or 2019 criteria have been used for sarcopenia diagnosis [16]. However, a scoping review suggests that when SARC-F is combined with CC, its sensitivity is enhanced, leading to an improvement in overall diagnostic performance [17]. Therefore, the results across studies have been inconsistent. On the other hand, Xu et al. reported that SARC-CalF appears to be the most appropriate screening tool for sarcopenia in adult patients with type 2 diabetes mellitus [18]. This suggests that different diseases may require different screening tools for predicting sarcopenia. Therefore, the evaluation of screening tools for sarcopenia should be conducted within the context of specific diseases. So far, there are no studies comparing the utility of SARC-F, SARC-CalF, and CC as screening tools for sarcopenia in patients with osteoporosis.
In this study, we aimed to compare the effectiveness of SARC-F, SARC-CalF, and CC as screening tools for sarcopenia in patients with osteoporosis. The findings of this study are anticipated to contribute to the healthy life expectancy of patients with osteoporosis.
Methods
Study subjects and design
In this study, consecutive patients who attended the outpatient clinic for Osteoporosis and Sarcopenia at National Hospital Organization (NHO) Kyoto Medical Center (KMC) between July 2019 and June 2021 were retrospectively studied. Patients with artificial hip joints were excluded because the body composition analyzer cannot accurately measure body composition. In addition, patients with missing data on skeletal muscle mass, handgrip strength (HGS), SARC-F, CC, and BMD were excluded. Additionally, patients without a diagnosis of osteoporosis or osteopenia were excluded. According to the previous reports, we estimated the prevalence of the sarcopenia to be 10% [6, 10, 19]. Based on a previous report [20], when the prevalence of sarcopenia is estimated at 10%, a minimum sample size of 200 patients (including 20 patients having sarcopenia) will be required to achieved a minimum power of 80% to detect a change in the percentage value of sensitivity from 50% to 80%, based on a target significance level of 0.05. This minimum sample size is also sufficient to detect a change in the value of specificity from 80% to 90% which will only require a minimum sample of 119 patients (including 12 patients having sarcopenia). Therefore, we collected patients so that at least 200 patients would be included in the analysis. Oral consent to participate was obtained from all patients before study inclusion. Written consent was not acquired because this study is a retrospective study. All patients’ records and information were anonymized and de-identified prior to analysis. This study was approved by the Institutional Ethics Committee of the NHO KMC (approval number 18–076), and this study conformed to the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Diagnosis of osteopenia and osteoporosis
BMD was assessed at the lumber spine (L2–L4) and femoral neck using dual-energy X-ray absorptiometry (Discovery WI, Hologic, Waltham, MA, USA). In this study, the presence of osteoporosis and osteopenia was determined based on low BMD at the lumber or femoral neck and/or the occurrence of a fragility fracture, following the guidelines of the Japanese Society for Bone and Mineral Research and the World Health Organization criteria [21, 22]. Briefly, osteoporosis was diagnosed when the BMD was equal to or below 70% or −2.5 standard deviation (SD) of the young adult mean (YAM), or in the presence of a fragility fracture at the lumber spine or proximal femur, or in the presence of another fragility fracture with a BMD below 80% of YAM. Osteopenia was diagnosed when the BMD was equal to or below −1.0 SD but above −2.5 SD of YAM, without meeting the criteria for osteoporosis.
Body mass index and diagnosis of sarcopenia
All anthropometric measurements were conducted in the morning by a trained technician to ensure accuracy. Height and body weight values were measured to the nearest 0.1 cm and 0.1 kg, respectively. Body mass index (BMI, kg/m2) was calculated by dividing the body weight (kg) by the square of the height (m2). A multi-frequency segmental body composition analyzer (MC-780A-N, TANITA Co., Ltd., Tokyo, Japan) was used to measure bioelectrical impedance and obtain data on whole body composition. An estimation formula for appendicular skeletal muscle mass in this model has been published, and a previous validation study showed that body composition measured using this device was highly correlated with that measured dual-energy X-ray absorptiometry measurements [23]. The skeletal muscle mass index (SMI, kg/m2) was calculated by dividing the appendicular skeletal muscle mass (kg) by the square of the height (m2). HGS was measured in the standing position with the arms straight down to the sides. The maximum grip strengths of the right and left hands were measured two times, alternatively with rests as necessary, using a Smedley Spring-Type Dynamometer (Grip-D, Takei Scientific Instruments Co., Ltd., Niigata, Japan) to the nearest 0.1 kg [24–26]. The maximum values of these measurements were used for analyses, as they are less likely to be affected by the number of trials than the means [27, 28]. Sarcopenia was diagnosed based on the EWGSOP2 criteria, that is sarcopenia was confirmed by low SMI and weak HGS [5]. The cutoff values for SMI were set at less than 7.0 kg/m2 for men and less than 5.7 kg/m2 for women, while the cutoff values for HGS were set at less than 28 kg for men and less than 18 kg for women [6].
Screening tools for sarcopenia
In the study subjects, the effectiveness of SARC-F, SARC-CalF, and CC as screening tools for sarcopenia was compared. The SARC-F assessment involved a 5-point self-administered questionnaire, with each item having a score range of 0 to 2 and a total score range of 0 to 10 [14]. The Japanese version of SARC-F was used [29], and a score of 4 or higher indicated a positive result for sarcopenia screening [6]. CC was measured to the nearest 0.1 cm in the standing position using a nonelastic tape measure [30]. The tape measure was placed around the calf without compressing the subcutaneous tissue, and the maximal circumference was recorded by moving the tape measure along the length of the calf. The maximum CC values from both legs were used [31], with values below 34 cm for men and 33 cm for women considered positive for sarcopenia screening [6, 30, 31]. SARC-CalF was a composite score combining CC and SARC-F components. It was calculated by adding 10 points to the SARC-F score when the CC measurement fell below the cutoff value [32]. A score of 11 or higher indicated a positive result for sarcopenia screening [6].
Presence of hypertension, diabetes mellitus, dyslipidemia, and cancer
The presence of hypertension, diabetes mellitus, dyslipidemia, and cancer was determined via self-reporting and/or by considering the use of medications for each disease.
Statistical analysis
Categorical variables were presented as numbers and percentages and analyzed using the χ2 test or Fisher’s exact test, as appropriate. Continuous variables were presented as medians and interquartile range (IQR) and analyzed using the Mann–Whitney U test. The diagnostic utility of the three screening tools to predict the presence of sarcopenia was compared using the receiver-operating characteristic (ROC) curves, the area under the ROC curves (AUC), and 95% confidence interval (CI). The AUCs were compared each other using the DeLong method [33]. A larger AUC indicated better overall diagnostic accuracy [34]. A multivariate logistic regression was performed to assess whether the screening tool with the highest AUC and diagnostic potential among the three remained associated with sarcopenia even after adjusting for other sarcopenia-related factors. Comparing between the sarcopenia and non-sarcopenia groups, factors that might influence sarcopenia screening tools other than indices for sarcopenia diagnosis, such as SMI, HGS, and CC, were identified, and these factors were defined as confounders. These analyses were performed using the EZR software (Saitama Medical Center, Jichi Medical University, Saitama, Japan) [35] and the Statistical Package for the Social Sciences software Version 29.0 (SPSS; IBM Corporation, Armonk, NY, USA). All statistical tests were two-tailed, and a p-value of less than 0.05 was considered statistically significant.
Results
Characteristics of the study population
A total of 307 patients (men/women: 58/249) attended the outpatient clinic for Osteoporosis and Sarcopenia at NHO KMC between July 2019 and June 2021. However, 82 patients who met the exclusion criteria were excluded, and 225 patients (men/women: 33/192) were included in the analysis (Fig 1). The characteristics of the study population are summarized in Table 1. The median age of the patients was 69.0 years (IQR: 61.0–75.0). In this study, the prevalence of sarcopenia was 11.6% (36.4% in men and 7.3% in women). For each screening tool for sarcopenia, the positive rates were 9.8% for SARC-F (12.1% in men and 9.4% in women), 24.4% for SARC-CalF (21.2% in men and 25.0% in women), and 45.3% for CC (45.5% in men and 45.3% in women). A total of 153 patients (68.0%) were receiving osteoporosis treatment (Bisphosphonates/Active vitamin D/Calcium/Selective estrogen receptor modulator/Anti-receptor activator of nuclear factor kappa B ligand antibody/Sclerostin antibody: 44/106/45/26/39/5).
Comparison of patients with and without sarcopenia
The characteristics of patients with or without sarcopenia are summarized and compared in Table 2. Significant differences were found between the two groups in terms of gender (men/women: 12/14 vs. 21/178, p < 0.001), age (76.5 years [IQR: 72.3–80.8] vs. 68.0 years [IQR: 60.5–74.0], p < 0.001), SMI (5.37 kg/m2 [IQR: 5.13–5.97] vs. 6.33 kg/m2 [IQR: 6.04–6.67], p < 0.001), HGS (16.9 kg [IQR: 15.2–23.8] vs. 23.1 kg [IQR: 20.2–26.4], p < 0.001), CC (31.0 cm [IQR: 29.0–32.2] vs. 33.3 cm [IQR: 31.5–35.1], p < 0.001), femoral bone T score (−2.8 [IQR: −3.3,−2.4] vs. −2.3 [IQR: −2.8,−2.0], p = 0.008), and the positive rates for each sarcopenia screening tool, including SARC-F test-positive (30.8% vs. 7.0%, p = 0.001), SARC-CalF test-positive (69.2% vs. 18.6%, p < 0.001), and CC test-positive (80.8% vs. 40.7%, p < 0.001). In addition, significant differences were found between the two groups in terms of the complicated rates of hypertension (53.8% vs. 25.1%, p = 0.005) and diabetes mellitus (38.5% vs. 10.1%, p < 0.001).
Comparison of the studied screening tools for sarcopenia
Table 3 presents the performance characteristics of SARC-F, SARC-CalF, and CC in sarcopenia screening. CC had the highest sensitivity compared to SARC-F and SARC-CalF (80.8% vs. 30.8% and 69.2%). On the other hand, SARC-F had the highest specificity compared to SARC-CalF and CC (93.0% vs. 81.4% and 59.3%). The AUC of SARC-CalF was significantly higher than those of SARC-F and CC in women. When men and women were analyzed together, the AUC of SARC-CalF (0.753, 95% CI: 0.659–0.848) was higher than those of SARC-F (0.619, 95% CI: 0.526–0.711) and CC (0.700, 95% CI: 0.616–0.785), suggesting that SARC-CalF has better diagnostic accuracy for sarcopenia than SARC-F and CC.
A multivariate logistic regression for the prediction of sarcopenia
Comparison of the sarcopenia and non-sarcopenia groups showed that, other than the indices for sarcopenia diagnosis, gender, age, femoral T-score, presence of hypertension, and presence of diabetes mellitus were significantly different (Table 2). Then, using these as explanatory variables, a multivariate analysis was performed to determine whether SARC-CalF, which had the highest AUC, was independently associated with sarcopenia (Table 4). After excluding these confounders, SARC-CalF was found to be independently related to sarcopenia (odds ratio: 14.80, 95% CI: 3.83–57.30, p < 0.001). In addition, we also examined the relationship between SARC-F or CC and sarcopenia. Then, we found that CC was related to sarcopenia even after adjusting for other covariates (odds ratio: 7.37, 95% CI: 2.24–24.30, p < 0.001) but not SARC-F (S1 and S2 Tables).
Discussion
To the best of our knowledge, this study is the first to demonstrate that SARC-CalF is a more effective screening tool for sarcopenia in patients with osteoporosis than SARC-F and CC. Furthermore, SARC-CalF was found to be independently related to sarcopenia, even after accounting for other confounders. The presence of osteosarcopenia has significant implications for overall health outcomes. Therefore, it is crucial to identify screening tools that can easily and accurately detect sarcopenia in patients with osteoporosis. The findings of this study have the potential to improve the healthy life expectancy of patients with osteoporosis.
In this study, the prevalence of sarcopenia in patients with osteoporosis was found to be 11.6%. A review of epidemiological studies conducted in older adults from Asian countries reported a prevalence of sarcopenia ranging from 5.5% to 25.7% [6]. In a study specifically focused on patients with osteoporosis, the prevalence of sarcopenia was reported as 10.8% in patients with low BMD [19]. Another study conducted on Japanese women observed a higher prevalence of sarcopenia in subjects with osteoporosis (29.7%) than in those with osteopenia (17.8%) and normal BMD (9.0%) [10]. Although this study did not examine the prevalence of sarcopenia in individuals without osteoporosis, the prevalence of sarcopenia in patients with osteoporosis appeared to be consistent with previous studies.
In this study conducted on patients with osteopenia and osteoporosis, CC had the highest sensitivity compared to SARC-F and SARC-CalF. In addition, SARC-F had the highest specificity compared to SARC-CalF and CC. A previous study comparing the performance of screening tools for sarcopenia among community-dwelling older adults reported a sensitivity of 17.9% and a specificity of 93.7% for SARC-F, a sensitivity of 47.5% and a specificity of 92.0% for SARC-CalF, and a sensitivity of 81.4% and a specificity of 77.0% for CC [15]. Although the target population in our study was different, our findings were consistent with this previous study.
SARC-F and CC have different characteristics as screening tools for sarcopenia. SARC-F is a self-administered questionnaire that primarily reflects physical ability [14, 29]. However, due to its subjective nature, SARC-F may have limited screening ability to exclude sarcopenia [36]. Especially, men tend to select answers that evaluate their physical ability more favorably and lightly than women [37]. On the other hand, CC is positively correlated with appendicular skeletal muscle mass and SMI [30]. Although CC is an objective indicator, it can be influenced by factors such as obesity and edema, which may mask the presence of sarcopenia [17, 38]. Therefore, caution should be taken when measuring CC in the presence of these conditions. In this study, CC was considered to accurately reflect skeletal muscle mass as there were few obese or edematous patients with osteoporosis. Considering these factors, the combination of SARC-F, which reflects physical ability, and CC, which reflects skeletal muscle mass, in SARC-CalF was deemed to be more useful than using SARC-F and CC alone in patients with osteoporosis.
In general, a higher AUC indicates better overall performance of a diagnostic test [34]. Therefore, the performance of a screening tool is evaluated using the AUC. In this study conducted on patients with osteoporosis, SARC-CalF had the highest AUC compared to SARC-F and CC (0.753 vs. 0.619 and 0.700; Table 3). On the other hand, in the previous study conducted on community-dwelling older adults, CC had the highest AUC compared to SARC-F and SARC-CalF (0.80 vs. 0.56 and 0.70) [15]. This study excluded people taking medications that affect body composition, such as diuretics and glucocorticoids, and people with clinically visible edema, suggesting that it targeted people with relatively preserved physical ability compared with our osteoporotic patients [13]. Such differences in the target population among studies may cause the discrepancy in the most effective screening tool for sarcopenia. As patients with both osteoporosis and sarcopenia are at a high risk of falls and fractures [12], it may be more appropriate to use SARC-F, which reflects physical ability. Taken together, we assume that SARC-CalF, in combination with SARC-F and CC, provides a better balance and higher AUC than SARC-F or CC alone as a screening tool for sarcopenia in patients with osteoporosis.
In this study, we compared the characteristics of patients with or without sarcopenia and observed significant differences in the presence of diabetes mellitus and hypertension, as well as in each sarcopenia screening tool, gender, age, and femoral bone T score. A previous systematic review and meta-analysis has also demonstrated an association between type 2 diabetes mellitus and an increased risk of sarcopenia, with some pathophysiological mechanisms underlying this association [39]. Furthermore, sarcopenia has been found to be associated with hypertension in another systematic review and meta-analysis [40]. Since the shared mechanism of chronic inflammation and catabolic cytokine production is the most accepted mechanism of sarcopenia and a major risk factor for chronic diseases such as hypertension, sarcopenia and hypertension may be associated through these shared mechanisms [40]. Therefore, the factors associated with sarcopenia in this study are consistent with those reported in previous studies.
This study has several limitations that should be acknowledged. First, it had a retrospective single-center-based design, which may introduce bias in the ratio of men to women and the severity of osteoporosis, and the exact data on the duration of sarcopenia exposure could not be addressed. Second, the history of fragility fractures relied on self-reporting, and other fragility fractures beyond lumber and femoral neck fractures were not assessed, potentially resulting in missing some patients with osteoporosis. Third, in this study, sarcopenia was diagnosed based on the EWGSOP2 criteria [5], so patients with low physical performance, low muscle mass, and high handgrip strength were included in the non-sarcopenia group. If sarcopenia is diagnosed based on the AWGS2019 criteria [6], another analysis will be needed. Fourth, in this study, we excluded patients with artificial hip joints because we could not evaluate body composition accurately. However, patients with artificial hip joints due to fragility fractures are diagnosed as osteoporosis. As for osteoporotic patients with artificial hip joints, another analysis will be need in the future. Fifth, this study did not examine chronic kidney disease, endocrine disease, exercise and nutritional status, or frailty, which may affect sarcopenia and osteoporosis [8, 41–43]. Finally, the effects of medications taken by the study patients were not considered, although certain medications for osteoporosis, hypertension, and diabetes mellitus have been suggested to affect skeletal muscles [44]. Validation studies with larger sample sizes are needed to address these limitations.
Conclusion
In conclusion, while SARC-F, SARC-CalF, and CC are all simple and useful screening tools for the early detection of sarcopenia, SARC-CalF appears to be more effective than SARC-F and CC as a screening tool for sarcopenia in patients with osteoporosis. Further research is needed to validate the utility of SARC-CalF in various populations.
Supporting information
S1 Table. A multivariate logistic regression for the prediction of sarcopenia with SARC-F.
https://doi.org/10.1371/journal.pone.0310401.s001
(DOCX)
S2 Table. A multivariate logistic regression for the prediction of sarcopenia with CC.
https://doi.org/10.1371/journal.pone.0310401.s002
(DOCX)
Acknowledgments
The authors would like to thank Hiromi Kusakabe, Sayo Hasegawa, and Chiharu Ishihara at the NHO KMC for their secretarial assistance and assistance in acquiring the data. The authors would also like to thank Enago (www.enago.jp) for the English language review.
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