Abstract
Background
Complicated testing procedures, high costs and heavy clinical workloads limit routine sarcopenia screening. Patients with different sarcopenia stages present distinct clinical manifestations, so early screening and timely intervention are essential. This study aimed to explore the screening efficiency of phase angle (PhA) and calf circumference (CC) for staged sarcopenia in maintenance hemodialysis (MHD) patients and develop a simple scoring tool.
Methods
This prospective cross-sectional study was conducted from September to December 2023 at the Wenjiang Hemodialysis Center, Department of Nephrology, West China Hospital, Sichuan University, Chengdu, China. Sarcopenia staging followed the 2019 Asian Working Group for Sarcopenia (AWGS) criteria. Receiver operating characteristic (ROC) curve analysis was used to calculate the area under the curve. A simple scoring tool was established based on the optimal cut-off points of CC and PhA. In addition to sensitivity and specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR−) were computed for each screening marker to fully characterize diagnostic efficiency.
Results
For possible sarcopenia, the area under the receiver operating characteristic curve (AUC) of PhA was 0.680 (95% confidence interval [CI]: 0.613–0.747), with a sensitivity of 64.29% and specificity of 66.17% (cut-off value: 5.6 °). The AUC of the simple scoring tool was 0.598 (95%CI: 0.531–0.664), with a sensitivity of 53.57% and specificity of 67.67% (cut-off value: 1 point). For sarcopenia, the AUC of CC was 0.771 (95%CI: 0.605–0.937), with a sensitivity of 66.67% and specificity of 81.36% (cut-off value: 29 cm). The AUC of the simple scoring tool was 0.789 (95%CI: 0.658–0.920), with a sensitivity of 66.67% and specificity of 83.90% (cut-off value: 1 point). For severe sarcopenia, the AUC of CC was 0.880 (95%CI: 0.809–0.950), with a sensitivity of 93.33% and specificity of 73.04% (cut-off value: 30 cm). The AUC of PhA was 0.865 (95%CI: 0.773–0.957), with a sensitivity of 86.67% and specificity of 78.26% (cut-off value: 4.9 °). The AUC of the simple scoring tool was 0.911 (95%CI: 0.865–0.958), with a sensitivity of 80.00% and specificity of 90.00% (cut-off value: 1 point).
Conclusions
The simple scoring tool showed improved screening performance as sarcopenia severity increased, with a consistent cut-off of 1 point. CC and PhA can be used for staged sarcopenia screening in MHD outpatients. With high negative predictive values, these indicators are suitable for preliminary exclusion screening at the bedside rather than definitive diagnosis. However, given the limited number of sarcopenia cases in this single-center study, this scoring tool should be considered exploratory and requires validation in larger independent cohorts before routine clinical implementation.
Trial registration
Chinese Clinical Trial Registry (ChiCTR2100051111), registered on 2021−09−13.
Keywords: Maintenance hemodialysis patients, Sarcopenia, Phase angle, Calf circumference
Introduction
As of 2023, an estimated 788 million individuals aged 20 years and older worldwide were living with chronic kidney disease (CKD), with the highest burdens observed in China (152 million cases) and India (138 million cases) [1]. MHD represents one of the reliable therapeutic strategies for acute and chronic renal failure, enabling survival for millions of affected patients [2]. Nevertheless, MHD constitutes a lifelong renal replacement therapy associated with a long and arduous treatment trajectory and abundant complications [3]. Compared with the general population, patients undergoing MHD face a substantially higher risk of sarcopenia, driven by progressive renal deterioration, persistent systemic microinflammation, and long-term dialysis-induced protein-energy wasting [4, 5]. Sarcopenia, also termed skeletal muscle loss syndrome, is a degenerative condition defined by progressive loss of skeletal muscle mass and reduced muscle strength [6]. As a common complication in patients receiving MHD, it increases the likelihood of multiple adverse events such as falls, fractures, and worsening physical function [7–9]. The global pooled prevalence of sarcopenia reaches as high as 25% among patients with CKD [10], and this proportion can range from 20% to 55% in individuals with kidney failure (CKD stage 5) [11].
Both PhA and CC have promising predictive value for sarcopenia screening among MHD patients [12–16], yet they differ markedly in clinical operability: CC measurement is low-cost, rapid and feasible for routine bedside screening without extra equipment or professional training, while PhA testing relies on standardized bioimpedance calibration and trained operators. Despite increased awareness of sarcopenia, screening and assessment are still infrequently performed in clinical practice. Complex diagnostic procedures, additional examination costs, radiation exposure, and increased clinical workload severely limit the recognition of sarcopenia in clinical settings and place high demands on human, material, and administrative resources. Moreover, the different stages of sarcopenia exhibit varying clinical manifestations, making early assessment and intervention particularly important.
Currently, few studies have separately characterized the screening performance of PhA and CC across distinct sarcopenia stages in MHD patients, and no prior work has established a simple scoring tool integrating these two markers for staged sarcopenia screening. This study aims to identify a scientific and feasible screening tool for sarcopenia that can facilitate the rapid screening of high-risk patients at different disease stages, streamline the diagnosis of sarcopenia, reduce clinical staff workload, and thus promote the assessment of sarcopenia.
Materials and methods
Study design and participants
This prospective cross-sectional study was conducted from September to December 2023 at the Wenjiang Hemodialysis Center, Department of Nephrology, West China Hospital, Sichuan University, Chengdu, China.
Inclusion and exclusion criteria
The inclusion criteria were: (1) clear consciousness and intact cognitive function; (2) receiving MHD for no less than 3 months; (3) age ≥ 18 years; (4) voluntary participation in the survey.
The exclusion criteria were: (1) any contraindications to bioelectrical impedance analysis (BIA), such as having a pacemaker, cardiovascular stent, or artificial joint; (2) pregnancy or lactation; (3) significant health changes in the past three months; (4) limb deformity or amputation; (5) cognitive impairment or psychiatric disorders.
Data collection and procedures
Three researchers co-designed and finalized the study protocol, with unified criteria, measurement standards and data validation procedures predefined to ensure rigorous and repeatable research. Two standardized trained researchers completed all on-site measurements and data collection; their training covered questionnaires, BIA testing and anthropometric protocols. Each participant provided written informed consent prior to structured face-to-face interviews for demographic and baseline clinical data acquisition. The questionnaire collected the following information: age, sex, dialysis duration, and history of comorbidities including diabetes and hypertension. These data were used to describe the baseline characteristics of the study population and to adjust for potential confounders in the analyses. Laboratory indicators including hemoglobin (HGB), albumin (ALB), pre-dialysis urea (Pre-URE), pre-dialysis creatinine (Pre-CRE), uric acid (UA), triglycerides (TG), total cholesterol (CHOL), high-density lipoprotein (HDL), low-density lipoprotein (LDL), serum inorganic phosphorus (S-IP), serum iron (S-I), and C-reactive protein (CRP) were extracted from the Laboratory Information System (LIS) of the Hemodialysis Center, Department of Nephrology, West China Hospital, Sichuan University. Waist circumference (WC, cm), hip circumference (HC, cm), and CC (cm) were measured using a flexible tape within two hours after hemodialysis to reduce the interference of tissue edema. All 245 questionnaires were collected and verified on site, with a 100% valid response rate and no invalid questionnaires.
Bioelectrical impedance analysis (BIA)
The InBody S10 multifrequency bioimpedance analyzer (Heugam-gil, Ipjang-myeon, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31025, Republic of Korea) was used to conduct BIA. All BIA measurements were performed within 15 min to 2 h after the completion of hemodialysis to avoid the acute fluid shift interference right after ultrafiltration. Additionally, all participants were required to fast for at least 2 h before testing to eliminate the confounding effects of food and liquid intake [17]. Participants removed all metallic accessories and stood barefoot on an insulated mat in lightweight clothing. Arms were placed in a natural relaxed position, with legs separated at shoulder width. The Skeletal Muscle Index (SMI), an indicator of muscle mass, was calculated as muscle mass divided by height squared (kg/m²) [18]. PhA was derived via the formula: PhA (°) = arctan(Xc/R) × (180° / π), where Xc represents reactance and R represents resistance [19]. Body mass index (BMI) was automatically calculated by the BIA device based on the input height and weight.
Handgrip strength (HGS, kg)
Muscle strength was evaluated among MHD patients via HGS [18]. All HGS tests were conducted before dialysis using an electronic handgrip dynamometer (Zhongshan Camry Electronics Co., Ltd., Zhongshan, China) to avoid physical fatigue induced by dialysis. Participants stood upright with arms relaxed at the sides and elbows fully extended, and performed maximal grip contractions using their non-fistula arm. Three repeated measurements were performed with a 1-minute rest between each trial, and the maximum HGS value was recorded [20]. Among all participants, 209 completed testing with their dominant non-fistula arm, and the remaining 36 used their non-dominant non-fistula arm.
Short physical performance battery (SPPB)
The SPPB scale was adopted to evaluate lower-extremity physical performance among MHD patients. The total SPPB score ranged from 0 to 12 points, and a total score ≤ 9 points indicated poor physical performance [18]. The scale includes three subtests: (1) Chair stand test: Participants sat on a standard chair with arms crossed over the chest. They were instructed to complete five consecutive full stand-ups as rapidly as possible. The stopwatch started when the participant lifted from the seated position and stopped immediately after the fifth standing position was completed. (2) Gait speed test: Participants traversed a flat 4-meter straight track at their self-selected usual walking speed. Two separate timed trials were performed; the shorter elapsed time was recorded for gait speed computation. (3) Balance tests: Three standing balance tasks were performed sequentially: side-by-side stand, semi-tandem stand, and full tandem stand. Each posture was held for up to 10 s. The test was terminated early if the participant shifted foot position or required external support to maintain balance. All tests were carried out strictly following the standardized NIH protocol [21].
Diagnostic criteria for different stages of sarcopenia
Sarcopenia was diagnosed in accordance with the 2019 AWGS consensus criteria [18]. Although this guideline was originally formulated for community-dwelling older adults, it has been widely applied to MHD patients in multiple recent nephrology studies [12–14, 16, 19]. The three core stage-based diagnostic cutoffs were defined as follows: (1) Low muscle mass (SMI): < 7.0 kg/m² for males and < 5.7 kg/m² for females. (2) Low muscle strength (HGS): < 28.0 kg for males and < 18.0 kg for females. (3) Impaired physical performance (SPPB): total score ≤ 9 points (Table 1).
Table 1.
Classification of sarcopenia subtypes
| Different stages | Diagnostic criteria |
|---|---|
| Non-sarcopenic | Fails to satisfy the criteria for possible sarcopenia, sarcopenia, or severe sarcopenia |
| Possible sarcopenia | Meets criterion 2 and/or criterion 3 (reduced muscle strength or impaired physical performance, with normal SMI) |
| Sarcopenia | Meets criterion 1 plus criterion 2 or criterion 3 |
| Severe sarcopenia | Meets all three criteria (1, 2, and 3) |
Development of the simple scoring tool
ROC curve analysis was performed to determine the optimal cut-off values of PhA and CC based on the maximum Youden index. A simple scoring tool was developed using these thresholds: 1 point was awarded when the measured value of each parameter fell below its cut-off value, whereas 0 point was assigned otherwise. The total possible score ranged from 0 to 2.
Statistical analysis
Data analysis was performed with SPSS version 27.0. The Kolmogorov-Smirnov test was used to test the normality of continuous data. Normally distributed data were expressed as mean ± standard deviation (SD), while non-normal data were presented as median (P25, P75). Categorical data were summarized as counts and percentages. According to variable distributions, intergroup comparisons among sarcopenia subgroups were performed using the chi-square test, Fisher’s exact test, independent Student’s t-test, or Kruskal-Wallis H-test. Ordered logistic regression was applied to explore influencing factors of sarcopenia in MHD patients, and odds ratios (ORs) together with 95% confidence intervals (CIs) were calculated. Spearman’s correlation analysis was used to analyze the correlations among CC, PhA and sarcopenia-related indicators. The correlation strength was classified as follows: very weak (r < 0.15), weak (0.15–0.25), moderate (0.25–0.4), strong (0.4–0.75), and very strong (r > 0.75) [22]. ROC curves were plotted using MedCalc 23.3.4 to evaluate the diagnostic efficiency of CC and PhA for different stages of sarcopenia. Based on the optimal cut-off values of CC and PhA, we established a simple scoring tool to screen different stages of sarcopenia in MHD patients. The DeLong method was adopted to calculate AUC values and corresponding 95% CIs. The optimal cut-off point was determined according to the Youden index. Sensitivity, specificity, PPV, NPV, LR+, and LR− were calculated at the best threshold. The significance value adopted was P < 0.05.
Results
Participant characteristics
A total of 245 subjects were enrolled, including 109 non-sarcopenic patients, 112 patients with possible sarcopenia, 9 patients with sarcopenia, and 15 patients with severe sarcopenia. The cohort included 130 males and 115 females, with a mean age of 53.8 ± 14.1 years and a median dialysis duration of 60 (24, 84) months. The prevalence of sarcopenia in MHD patients in this study was 9.8%, 6.2% in male MHD patients and 13.9% in female MHD patients. Such sarcopenia prevalence features of this cohort explain the high NPV and low PPV observed for CC and PhA in diagnostic analyses. Univariate analysis revealed that DM, age, dialysis duration, WC, HC, CC, BMI, ALB, Pre-CRE, HDL, S-IP, S-I, CRP, and PhA differed significantly across sarcopenia subgroups (all P < 0.05). With the progression of sarcopenia severity, both CC and PhA gradually declined. The median (P25, P75) of CC was 33.0 (31.0, 34.0), 32.0 (30.0, 34.0), 27.0 (26.0, 31.0), and 26.0 (25.0, 30.0) in the four subgroups. The mean ± SD of PhA was 6.35 ± 1.02, 5.33 ± 1.05, 5.49 ± 0.74, and 4.09 ± 1.07 accordingly (Table 2).
Table 2.
Characteristics of study population
| Characteristics | Non-sarcopenic (n = 109) |
Possible sarcopenia (n = 112) | Sarcopenia (n = 9) | severe sarcopenia (n = 15) | P value |
|---|---|---|---|---|---|
| Sex, n (%) | |||||
| Male | 66(60.6%) | 56(50.0%) | 3(33.3%) | 5(33.3%) | 0.084 |
| Female | 43(39.4%) | 56(50.0%) | 6(66.7%) | 10(66.7%) | |
| HTN, n (%) | |||||
| No | 18(16.5%) | 13(11.6%) | 3(33.3%) | 3(20.0%) | 0.214 |
| Yes | 91(83.5%) | 99(88.4%) | 6(66.7%) | 12(80.0%) | |
| DM, n (%) | |||||
| No | 96(88.1%) | 76(67.9%) | 9(100.0%) | 10(66.7%) | < 0.001 |
| Yes | 13(11.9%) | 36(32.1%) | 0(0.0%) | 5(33.3%) | |
| Age, years, mean ± SD | 45.3 ± 10.2 | 60.0 ± 12.3 | 56.0 ± 14.4 | 68.7 ± 14.7 | < 0.001 |
| Dialysis duration, months, median (P25, P75) | 56.0(24.0,84.0) | 53.0(24.0,84.0) | 96.0(84.0,108.0) | 60.0(24.0,108.0) | 0.008 |
| WC, cm, mean ± SD | 84.8 ± 12.0 | 88.5 ± 11.6 | 77.6 ± 11.2 | 79.5 ± 9.9 | 0.001 |
| HC, cm, median (P25, P75) | 93.0(89.0,98.0) | 96.0(90.0,100.0) | 81.0(80.0,90.0) | 88.0(82.0,93.0) | < 0.001 |
| CC, cm, median (P25, P75) | 33.0(31.0,34.0) | 32.0(30.0,34.0) | 27.0(26.0,31.0) | 26.0(25.0,30.0) | < 0.001 |
| BMI, kg/m², mean ± SD | 22.8 ± 3.1 | 23.1 ± 3.0 | 18.8 ± 2.5 | 18.9 ± 2.4 | < 0.001 |
| HGB, g/L, median (P25, P75) | 110.0(100.0, 118.0) | 109.0(96.5, 118.0) | 118.0(101.0, 131.0) | 109.0(94.0, 119.0) | 0.529 |
| ALB, g/L, median (P25, P75) | 42.7(40.0, 44.9) | 41.1(38.5, 43.8) | 43.9(42.3, 44.8) | 39.6(35.1, 43.1) | 0.004 |
| Pre-URE, mmol/L, median (P25, P75) | 22.4(19.8,26.5) | 22.0(18.1,27.4) | 23.1(19.27.0) | 21.9(13.0,27.1) | 0.868 |
| Pre-CRE, mean ± SD | 1077.1 ± 273.7 | 897.1 ± 261.8 | 895.4 ± 232.7 | 703.1 ± 245.8 | < 0.001 |
| UA, µmol/L, median (P25, P75) | 418.0(347.0,491.0) | 392.0(311.0,475.0) | 442.0(315.0,514.0) | 393.0(326.0,456.0) | 0.516 |
| TG, mmol/L, median (P25, P75) | 1.36(0.98,2.13) | 1.44(1.01,1.98) | 1.70(1.15,2.14) | 1.26(0.86,1.66) | 0.770 |
| CHOL, mmol/L, median (P25, P75) | 3.69(3.14,4.44) | 3.76(3.21,4.45) | 3.91(3.78,14.50) | 3.89(3.34,5.09) | 0.553 |
| HDL, mmol/L, median (P25, P75) | 1.04(0.83,1.24) | 1.07(0.87,1.29) | 1.17(1.16,1.20) | 1.33(1.21,1.54) | 0.002 |
| LDL, mmol/L, median (P25, P75) | 1.76(1.31,2.45) | 1.78(1.38,2.30) | 1.78(1.54,2.21) | 1.73(1.37,2.31) | 0.987 |
| S-IP, mmol/L, median (P25, P75) | 2.03(1.77, 2.31) | 1.88(1.65, 2.29) | 1.79(1.56, 1.98) | 1.79(1.32, 1.88) | 0.008 |
| S-I, µg/L, median (P25, P75) | 12.40(9.30,15.40) | 10.85(8.75,14.15) | 10.00(7.90,10.80) | 9.50(8.20,10.40) | 0.005 |
| CRP, µg/L, median (P25, P75) | 2.32(1.02, 5.58) | 3.95(1.85, 10.07) | 4.25(1.31, 6.36) | 2.51(0.53, 11.50) | 0.025 |
| SMI, kg/m², median (P25, P75) | 7.8(7.0,8.6) | 7.5(6.5,8.1) | 5.5(5.4,6.6) | 5.5(5.1,6.1) | < 0.001 |
| HGS, kg, median (P25, P75) | 30.9(24.36.0) | 21.3(16.8,26.0) | 17.8(17.5,23.5) | 12.6(9.3,15.1) | < 0.001 |
| SPPB, points, median (P25, P75) | 12.00(12.00,12.00) | 8.00(8.00,9.00) | 11.00(11.00,12.00) | 4.00(3.00,6.00) | < 0.001 |
| PhA, °, mean ± SD | 6.35 ± 1.02 | 5.33 ± 1.05 | 5.49 ± 0.74 | 4.09 ± 1.07 | < 0.001 |
Normally distributed continuous data were expressed as mean ± SD. Non-normally distributed continuous data were presented as median (P25, P75). Categorical variables were described as number of cases (percentage). Intergroup comparisons across sarcopenia subgroups were performed using the chi-square test, Fisher’s exact test, independent Student’s t-test, or the Kruskal-Wallis H-test
Abbreviations: HTN hypertension, DM diabetes mellitus, WC waist circumference, HC hip circumference, CC calf circumference, BMI body mass index, HGB hemoglobin, ALB albumin, Pre-URE pre-dialysis urea, Pre-CRE pre-dialysis creatinine, UA uric acid, TG triglycerides, CHOL cholesterol, HDL high-density lipoprotein, LDL low-density lipoprotein, S-IP serum inorganic phosphorus, S-I serum iron, CRP C-reactive protein, PhA phase angle, SMI skeletal muscle index, HGS handgrip strength, SPPB short physical performance battery
Results of the ordered Logistic regression analysis
The parallel lines test yielded χ²= 36.384, P = 0.133 > 0.05, which supported the proportional odds assumption. Thus, ordered logistic regression was appropriate for this analysis. Ordered logistic regression analysis was conducted following univariate comparisons. Independent variables were coded as follows: diabetes status (no = 0, yes = 1); all continuous covariates were entered as raw values. After adjustment for age, DM, dialysis duration, WC, HC, ALB, Pre-CRE, HDL, S-IP, S-I, CRP, and BMI, age, CC and PhA remained independent predictors of sarcopenia in MHD patients (Table 3). The goodness-of-fit test showed that the model fitted the data well: Pearson χ²= 581.724, P = 1.000; Deviance χ²= 361.895, P = 1.000.
Table 3.
Ordered Logistic Regression Analysis of Sarcopenia in MHD Patients
| Variables | B | SE | Wald χ² | P value | OR | OR (95% CI) |
|---|---|---|---|---|---|---|
| Age (years) | 0.075 | 0.014 | 30.444 | < 0.001 | 1.086 | 1.058–1.115 |
| CC (cm) | -0.151 | 0.057 | 7.005 | 0.008 | 0.852 | 0.763–0.952 |
| PhA (°) | -0.411 | 0.185 | 4.929 | 0.026 | 0.834 | 0.720–0.966 |
Abbreviations: CC calf circumference, PhA phase angle, OR odds ratio, CI confidence interval
Correlation analysis of PhA and CC with sarcopenia components
HGS had a moderate positive correlation with CC (r = 0.384, P < 0.001), indicating that higher CC value was associated with greater muscle strength. Similarly, SPPB had a moderate positive correlation with CC (r = 0.253, P < 0.001); SMI had a strong positive correlation with CC (r = 0.562, P < 0.001). PhA exhibited strong positive correlations with HGS (r = 0.572, P < 0.001), SPPB (r = 0.500, P < 0.001), and SMI (r = 0.435, P < 0.001) (Table 4). Notably, PhA and SMI were both measured by BIA, so we cautiously interpret these correlation results to avoid misinterpretation caused by homologous testing. CC was manually measured without such detection bias.
Table 4.
Spearman correlation analysis between PhA, CC with sarcopenia components
| Variables | HGS | SPPB | SMI | P value |
|---|---|---|---|---|
| CC | 0.384 | 0.253 | 0.562 | P < 0.001 |
| PhA | 0.572 | 0.500 | 0.435 | P < 0.001 |
Abbreviations: CC calf circumference, PhA phase angle, HGS handgrip strength, SPPB short physical performance battery, SMI skeletal muscle index
The screening abillity analysis of PhA, CC and the simple scoring tool in screening of possible sarcopenia
The results indicated that for possible sarcopenia screening, the AUC for PhA was 0.680 (95%CI: 0.613–0.747), with a sensitivity of 64.29% and specificity of 66.17% (cut-off value: 5.6 °). The AUC of the simple scoring tool constructed from CC and PhA was 0.598 (95%CI: 0.531–0.664), with a sensitivity of 53.57% and specificity of 67.67% (cut-off value: 1 point). (Table 5; Fig. 1).
Table 5.
Diagnostic indicators of CC, PhA and the simple scoring tool for possible sarcopenia
| Diagnostic methods | AUC (95%CI) | cut-off value | Sensitivity | Specificity | P value | LR+ | LR− | PPV | NPV |
|---|---|---|---|---|---|---|---|---|---|
| CC (cm) | 0.511(0.439–0.584) | 34 | 24.11 | 82.71 | 0.758 | 1.39 | 0.92 | 54.00% | 56.41% |
| PhA (°) | 0.680(0.613–0.747) | 5.6 | 64.29 | 66.17 | < 0.001 | 1.90 | 0.54 | 61.54% | 68.75% |
| simple scoring tool (scores) | 0.598(0.531–0.664) | 1 | 53.57 | 67.67 | 0.004 | 1.66 | 0.69 | 58.25% | 63.38% |
Abbreviations: CC calf circumference, PhA phase angle, AUC area under the receiver operating characteristic curve, CI confidence interval, LR± positive likelihood ratio, LR− negative likelihood ratio, PPV positive predictive value, NPV negative predictive value
Fig. 1.
ROC curves of CC, PhA and the simple scoring tool for screening possible sarcopenia
The screening abillity analysis of PhA, CC and the simple scoring tool in screening of sarcopenia
The results indicated that for sarcopenia screening, the AUC for CC was 0.771 (95%CI: 0.605–0.937), with a sensitivity of 66.67% and specificity of 81.36% (cut-off value: 29 cm). The AUC of the simple scoring tool constructed from CC and PhA was 0.789 (95%CI: 0.658–0.920), with a sensitivity of 66.67% and specificity of 83.90% (cut-off value: 1 point). (Table 6; Fig. 2).
Table 6.
Diagnostic indicators of CC, PhA and the simple scoring tool for sarcopenia
| Diagnostic methods | AUC (95%CI) | cut-off value | Sensitivity | Specificity | P value | LR+ | LR− | PPV | NPV |
|---|---|---|---|---|---|---|---|---|---|
| CC (cm) | 0.771(0.605–0.937) | 29 | 66.67 | 81.36 | 0.001 | 3.58 | 0.41 | 12.00% | 98.46% |
| PhA (°) | 0.576(0.429–0.723) | 6.6 | 100.00 | 24.15 | 0.310 | 1.32 | 0.00 | 4.79% | 100.00% |
| simple scoring tool (scores) | 0.789(0.658–0.920) | 1 | 66.67 | 83.90 | < 0.001 | 4.14 | 0.40 | 13.64% | 98.51% |
Abbreviations: CC calf circumference, PhA phase angle, AUC area under the receiver operating characteristic curve, CI confidence interval, LR± positive likelihood ratio, LR− negative likelihood ratio, PPV positive predictive value, NPV negative predictive value
Fig. 2.
ROC curves of CC, PhA and the simple scoring tool for screening sarcopenia
The screening abillity analysis of PhA, CC and the simple scoring tool in screening of severe sarcopenia
The results indicated that for severe sarcopenia screening, the AUC for CC was 0.880 (95%CI: 0.809–0.950), with a sensitivity of 93.33% and specificity of 73.04% (cut-off value: 30 cm). The AUC for PhA was 0.865 (95%CI: 0.773–0.957), with a sensitivity of 86.67% and specificity of 78.26% (cut-off value: 4.9 °). The AUC of the simple scoring tool constructed from CC and PhA was 0.911 (95%CI: 0.865–0.958), with a sensitivity of 80.00% and specificity of 90.00% (cut-off value: 1 point). (Table 7; Fig. 3).
Table 7.
Diagnostic indicators of CC, PhA and the simple scoring tool for severe sarcopenia
| Diagnostic methods | AUC (95%CI) | cut-off value | Sensitivity | Specificity | P value | LR+ | LR− | PPV | NPV |
|---|---|---|---|---|---|---|---|---|---|
| CC (cm) | 0.880(0.809–0.950) | 30 | 93.33 | 73.04 | 0.001 | 3.46 | 0.09 | 18.42% | 99.41% |
| PhA (°) | 0.865(0.773–0.957) | 4.9 | 86.67 | 78.26 | < 0.001 | 3.99 | 0.17 | 20.63% | 98.90% |
| simple scoring tool (scores) | 0.911(0.865–0.958) | 1 | 80.00 | 90.00 | < 0.001 | 8.00 | 0.22 | 34.29% | 98.57% |
Abbreviations: CC calf circumference, PhA phase angle, AUC area under the receiver operating characteristic curve, CI confidence interval, LR± positive likelihood ratio, LR− negative likelihood ratio, PPV positive predictive value, NPV negative predictive value
Fig. 3.
ROC curves of CC, PhA and the simple scoring tool for screening severe sarcopenia
Discussion
It is estimated that sarcopenia affects 10% to 16% of older adults worldwide. The prevalence of sarcopenia is higher among patients undergoing MHD compared with the general population [23]. In the present study, the overall sarcopenia prevalence was 9.8% in MHD patients, with a rate of 6.2% in males and 13.9% in females. These stratified figures were highly consistent with the findings reported by Zeng et al. [24]. In contrast, Zhao et al. [25] detected a higher sarcopenia prevalence of 21.82% among MHD patients, which was substantially greater than that identified in our investigation. The relatively low prevalence observed in our sample can be attributed to the fact that all enrolled participants were regular outpatient MHD patients.
Correlation analyses in this study revealed that CC was strongly positively correlated with SMI and moderately positively correlated with SPPB and HGS. Similarly, PhA showed strong positive correlations with SMI, SPPB, and HGS. These findings were consistent with those reported by Zhu et al. [22] and Kim et al. [26]. Changes in PhA and CC can lead to changes in skeletal muscle mass, strength, and function, contributing to the development of sarcopenia [18, 27]. Early recognition of sarcopenia in MHD patients is crucial to prevent adverse outcomes and enhance their quality of life.
Ordinal logistic regression analyses revealed that age, CC and PhA were independently correlated with sarcopenia stages among MHD patients, which was consistent with the findings of several previous hemodialysis cohort studies [24, 28]. The elevated risk of sarcopenia in elderly MHD patients arises from the combined catabolic stress of physiological aging and uremia-specific metabolic disturbances [29]. PhA serves as a direct marker reflecting cell membrane integrity and overall body cell mass [30], and reduced PhA values are closely linked to malnutrition, frailty, adverse clinical outcomes and higher all-cause mortality risk [31, 32]. As a simple anthropometric measurement, CC indirectly reflects skeletal muscle reserve. The World Health Organization recognizes CC as a highly sensitive screening indicator for muscle mass loss; its measurement is simple, low-cost and widely applicable in routine clinical practice [33]. Therefore, regular sarcopenia screening and timely targeted interventions are recommended for patients receiving long-term MHD to slow progressive muscle loss.
ROC curve analyses in this study demonstrated that the optimal PhA cut-off values for screening possible sarcopenia and severe sarcopenia were 5.6° and 4.9°, respectively. Zhu et al. [22] reported PhA thresholds of 4.5° for possible sarcopenia, 4.4° for sarcopenia, and 4.1° for severe sarcopenia among community-dwelling older adults. Ding et al. [34] identified a PhA cut-off of 4.67° for sarcopenia screening in MHD patients. The inconsistent threshold values across studies may be attributed to differences in study populations, measuring devices and sarcopenia assessment criteria. The AWGS 2019 consensus proposed the concept of “possible sarcopenia” and advocated early identification and intervention [18]. This condition is frequently overlooked during routine screening due to its mild clinical manifestations. PhA serves as a biomarker of cell membrane integrity. It can detect subtle early injuries at the cellular level and identify abnormalities before gross skeletal muscle atrophy develops [24]. Our findings suggest that PhA may act as a potential screening marker for possible sarcopenia among MHD patients, which could facilitate clinical identification of early-stage sarcopenia and lays a foundation for timely intervention and improved long-term prognosis. However, given that PhA and the reference muscle mass measurements were derived from the same BIA device, this finding should be interpreted with caution. In our study, CC was valid for screening sarcopenia and severe sarcopenia in MHD patients, with corresponding optimal thresholds of 30 cm and 29 cm. CC mainly reflects macroscopic skeletal muscle volume reserves. When sarcopenia progresses to clinically established or severe stages with substantial loss of muscle volume, calf circumference decreases significantly [14]. Özcan et al. [35] proposed a CC threshold of 31 cm for sarcopenia screening in MHD cohorts. Hwang et al. [36] reported sex-specific CC cut-offs of 33 cm for males and 32 cm for females for sarcopenia screening. In a survey of 406 Turkish older adults aged 65–99 years, Bahat et al. [37] also set a unified CC threshold of 33 cm for sarcopenia screening regardless of sex. The AWGS 2019 consensus recommended general screening cut-offs of < 34 cm for men and < 33 cm for women across community and nursing home settings [18]. Collectively, CC critical values for sarcopenia screening vary substantially across different populations, indicating that population-specific thresholds should be determined before implementing CC-based sarcopenia screening.
Recently, modified sarcopenia screening scales such as SARC-F-CalF incorporate CC into simple integer scoring frameworks to enhance diagnostic specificity in routine bedside screening among older adults [38]. Consistent with this practical strategy, our study constructed a straightforward 0–2 point simple scoring tool using CC and PhA cut-off values, which enables convenient rapid sarcopenia screening for MHD patients. The equal-weight assignment was based on the comparable odds ratios of CC (OR = 0.852) and PhA (OR = 0.834) from our ordinal logistic regression analysis (Table 3), supporting similar contributions of both indicators. This study further found that a simple scoring tool developed based on the optimal cut-off values of PhA and CC presented reliable screening performance across all three sarcopenia stages in MHD patients, with its screening capacity gradually elevated as sarcopenia severity increased. Three independent ROC analyses yielded a consistent optimal cut-off score of 1 point; a total score of ≥ 1 point was defined as a positive screening result in all subgroups, which suggests potential stable and convenient clinical applicability of this tool that warrants further investigation.
Limitations
This study has several notable limitations that should be addressed. First, this was a single-center cross-sectional investigation. The small sample of individuals with sarcopenia-related stages (n = 9 for sarcopenia and n = 15 for severe sarcopenia) may lead to unstable AUC and cut-off values in ROC analyses and increase the risk of statistical overfitting. To mitigate overfitting bias, we constructed a simple scoring tool based on PhA and CC cut-off values rather than a complex combined prediction model. In addition, all threshold values were generated and analyzed within the same cohort without any internal (e.g., bootstrap or cross-validation) or external validation in independent populations, which substantially restricts the generalizability of this screening tool. Therefore, our findings should be regarded as preliminary and exploratory, and this scoring tool is not yet ready for direct clinical implementation; prospective multicenter studies with larger sample sizes are urgently needed to validate and refine these results. Second, potential selection bias cannot be excluded. Only ambulatory MHD patients who could complete BIA measurement were enrolled; patients with severe frailty or inability to finish BIA were excluded. Moreover, fluid overload is common among MHD populations, and body hydration status interferes with BIA-derived PhA results and manual CC measurements, inevitably affecting screening performance. Additionally, residual confounding may persist even after adjusting for demographic, nutritional, inflammatory and dialysis-related covariates, as unrecorded factors such as long-term protein intake, regular physical activity and repeated fluid overload could contribute to skeletal muscle loss. Third, Both PhA and CC are closely correlated with muscle mass, the core indicator for sarcopenia staging defined by AWGS 2019. Notably, PhA and muscle mass data were both obtained via BIA using identical equipment, which creates homologous testing bias; therefore, all correlation and screening results of PhA were interpreted cautiously. Fourth, sex-specific and age-stratified screening cut-off values were not established in the present study, which reduces the accuracy and clinical applicability of our screening criteria for male, female and different age subgroups. Furthermore, the enrolled MHD participants were relatively young with a low overall sarcopenia prevalence, so these results cannot be extrapolated to elderly, severely frail MHD patients or non-Asian ethnic groups. In the future, large-sample, multicenter cohorts are required to externally validate the PhA and CC thresholds and the simplified scoring tool. Subsequent studies should establish sex- and age-specific cut-off values, incorporate multiple anthropometric markers, and record detailed lifestyle and dietary data to further eliminate confounding factors and improve the external validity and stability of sarcopenia screening models for MHD populations.
Conclusion
In summary, this study performed stratified screening for sarcopenia among patients receiving MHD based on the AWGS 2019 criteria. ROC analyses validated that both PhA and CC acted as effective screening markers for sarcopenia across distinct severity stages in this population, with optimal screening cut-off values established for each stage. A simple scoring tool derived from the respective cut-off values of PhA and CC exhibited consistent screening performance for all three sarcopenia stages, with a total score of ≥ 1 designated as the positive screening threshold; this tool is easy to operate and may be suitable for routine clinical application pending further validation. Ordinal logistic regression further identified age, CC and PhA as independent correlates of progressive sarcopenia severity. Clinicians may consider integrateing age, PhA and CC to implement regular early sarcopenia screening for MHD patients. This simple scoring system could facilitate rapid identification of high-risk individuals, potentially enabling timely nutritional and exercise interventions to slow skeletal muscle wasting and improve long-term clinical prognoses of MHD patients. However, given the exploratory nature of this single-center study, these findings and the proposed scoring tool require confirmation in larger, prospective, multicenter cohorts prior to widespread clinical adoption.
Acknowledgements
The authors would like to thank all of the healthcare providers and patients who participated in this study from Wenjiang Hemodialysis Center in the Department of Nephrology at West China Hospital, Sichuan University, Chengdu, China.
Abbreviations
- ALB
Albumin
- AUC
Area under the receiver operating characteristic curve
- AWGS
Asian Working Group for Sarcopenia
- BIA
Bioelectrical impedance analysis
- BMI
Body mass index
- CC
Calf circumference
- CHOL
Cholesterol
- CI
Confidence interval
- CKD
Chronic kidney disease
- CRP
C-reactive protein
- DM
Diabetes mellitus
- HC
Hip circumference
- HDL
High-density lipoprotein
- HGB
Hemoglobin
- HGS
Handgrip strength
- HTN
Hypertension
- IQR
Interquartile range
- LR+
Positive likelihood ratio
- LR−
Negative likelihood ratio
- LDL
Low-density lipoprotein
- LIS
Laboratory Information System
- MHD
Maintenance hemodialysis
- NPV
Negative predictive value
- OR
Odds ratio
- PhA
Phase angle
- PPV
Positive predictive value
- Pre-CRE
Pre-dialysis creatinine
- Pre-URE
Pre-dialysis urea
- ROC
Receiver operating characteristic
- SD
Standard deviation
- S-I
Serum iron
- S-IP
Serum inorganic phosphorus
- SMI
Skeletal muscle index
- SPPB
Short Physical Performance Battery
- TG
Triglycerides
- UA
Uric acid
- WC
Waist circumference
Authors' contributions
Y.C. participated in the conception and design of the study, conducted the majority of the thesis writing, data extraction, data analysis, and article proofreading. Y.Z. participated in revisions of the research background and discussion sections. P.F. and H.H.Y. organized and coordinated the research efforts. Y.J.Y. was involved in the design of the study. Y.Q. participated in data acquisition. Y.C. was the main contributor to writing the manuscript. All authors reviewed the manuscript, and the final version was read and approved by all authors.
Funding
This study was supported by the following funding:
1.3.5 project for disciplines of excellence from West China Hospital of Sichuan University (ZYGD23015).
Data availability
The datasets generated and/or analysed during the present study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study received approval from the Ethics Committee of Sichuan University (ethical approval number: 2020[1002], Clinical Trial Number: ChiCTR2100051111) and adhered to the principles of the Declaration of Helsinki. All patients provided written informed consent. For illiterate patients, the consent document was read aloud in the presence of a literate family member, and patients affixed their fingerprints on the consent form for participation confirmation. This procedure for informed consent was also approved by the Biomedical Ethics Committee of West China Hospital, Sichuan University.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.GBD 2023 Chronic Kidney Disease Collaborators. Global, regional, and national burden of chronic kidney disease in adults, 1990–2023, and its attributable risk factors: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. 2025;406(10518):2461–82. [DOI] [PubMed] [Google Scholar]
- 2.Flythe JE, Watnick S. Dialysis for Chronic Kidney Failure: A Review. JAMA. 2024;332(18):1559–73. [DOI] [PubMed] [Google Scholar]
- 3.Yan S, Wang Y, Li Z, et al. Dialysis Vintage and Symptom Burden in Hemodialysis: A Comprehensive Analysis. Nurs Health Sci. 2025;27(3):e70226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Beddhu S, Chen X, Wei G, et al. Associations of Protein-Energy Wasting Syndrome Criteria With Body Composition and Mortality in the General and Moderate Chronic Kidney Disease Populations in the United States. Kidney Int Rep. 2017;2(3):390–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Hanna RM, Ghobry L, Wassef O, et al. A Practical Approach to Nutrition, Protein-Energy Wasting, Sarcopenia, and Cachexia in Patients with Chronic Kidney Disease. Blood Purif. 2020;49(1–2):202–11. [DOI] [PubMed] [Google Scholar]
- 6.Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Zhang L, Yang L, Wang X, et al. Effect of nutritional intervention combined with resistance exercise on clinical indicators of patients with sarcopenia in maintenance hemodialysis: a systematic review and meta-analysis. Ren Fail. 2025;47(1):2492365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Zicarelli M, Duni A, Leivaditis K, et al. Comprehensive Insights into Sarcopenia in Dialysis Patients: Mechanisms, Assessment, and Therapeutic Approaches. Med (Kaunas). 2025;61(3):449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Veronese N, Smith L, Barbagallo M, et al. Sarcopenia and fall-related injury among older adults in five low- and middle-income countries. Exp Gerontol. 2021;147:111262. [DOI] [PubMed] [Google Scholar]
- 10.Duarte MP, Almeida LS, Neri SGR, et al. Prevalence of sarcopenia in patients with chronic kidney disease: a global systematic review and meta-analysis. J Cachexia Sarcopenia Muscle. 2024;15(2):501–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wilkinson TJ, Miksza J, Yates T, et al. Association of sarcopenia with mortality and end-stage renal disease in those with chronic kidney disease: a UK Biobank study. J Cachexia Sarcopenia Muscle. 2021;12(3):586–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Alirezaei A, Miladipour A, Asgari N, et al. Association of phase angle with sarcopenia in patients undergoing maintenance hemodialysis: A case-control study. J Res Med Sci. 2024;29:40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Zhang J, Wang N, Li J, et al. The Diagnostic Accuracy and Cutoff Value of Phase Angle for Screening Sarcopenia: A Systematic Review and Meta-Analysis. J Am Med Dir Assoc. 2024;25(11):105283. [DOI] [PubMed] [Google Scholar]
- 14.Shen Y, Qin H, Liu X, et al. Calf circumference predicts sarcopenia and all-cause mortality in older patients undergoing maintenance hemodialysis: A prospective cohort study. Nutr Clin Pract. 2026;41(1):235–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Vanitcharoenkul E, Unnanuntana A, Chotiyarnwong P, et al. Evaluating SARC-F, SARC-CalF, and calf circumference as diagnostic tools for sarcopenia in Thai older adults: results from a nationwide study. BMC Geriatr. 2024;24(1):1043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Wang Y, Hu Y, Zhang M, et al. Bioelectrical impedance analysis-derived phase angle predicts sarcopenia in patients on maintenance hemodialysis. Nutr Clin Pract. 2023;38(4):881–8. [DOI] [PubMed] [Google Scholar]
- 17.Ozkok S, Bahat G. Measuring Muscle Mass to Identify Sarcopenia Via Bioelectrical Impedance Analysis in Hemodialysis: Importance of Timing and Equipment Choice. Turkish J Nephrol. 2024;33(2):140–4. [Google Scholar]
- 18.Chen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020;21(3):300–e307302. [DOI] [PubMed] [Google Scholar]
- 19.Shin J, Kim H, Jeon HE, et al. Optimizing hemodialysis management using bioelectrical impedance analysis: emphasizing the significance of the phase angle. Ren Fail. 2025;47(1):2567037. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lim SK, Beom J, Lee SY, et al. Standardized Measurement of Muscle Strength and Physical Performance for Sarcopenia: An Expert-Based Delphi Consensus. Ann Geriatr Med Res. 2025;29(2):185–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49(2):M85–94. [DOI] [PubMed] [Google Scholar]
- 22.Zhu X, Dong X, Wang L, et al. Screening efficacy of PhA and MNA-SF in different stages of sarcopenia in the older adults in community. BMC Geriatr. 2023;23(1):13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Yuan S, Larsson SC. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism. 2023;144:155533. [DOI] [PubMed] [Google Scholar]
- 24.Zeng Y, Chen Y, Yang Y, et al. Bioelectrical impedance analysis-derived phase angle predicts possible Sarcopenia in patients on maintenance hemodialysis: a retrospective study. BMC Nephrol. 2024;25(1):357. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zhao Q, Zhu Y, Zhao X, et al. Prevalence and risk factors of sarcopenia in patients on maintenance hemodialysis: a retrospective cohort study. BMC Musculoskelet Disord. 2024;25(1):424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kim S, Kim M, Lee Y, et al. Calf Circumference as a Simple Screening Marker for Diagnosing Sarcopenia in Older Korean Adults: the Korean Frailty and Aging Cohort Study (KFACS). J Korean Med Sci. 2018;33(20):e151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Tomeleri CM, Cavalcante EF, Antunes M, et al. Phase Angle Is Moderately Associated With Muscle Quality and Functional Capacity, Independent of Age and Body Composition in Older Women. J Geriatr Phys Ther. 2019;42(4):281–6. [DOI] [PubMed] [Google Scholar]
- 28.Zhang Y, Zhang Z, Cao Z, et al. Clinical and novel insights into risk factors for sarcopenia in dialysis patients: a systematic review and meta-analysis. BMC Musculoskelet Disord. 2025;26(1):58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Watanabe H, Kitazato Y, Hirashima S, et al. Organ Crosstalk in CKD-Associated Frailty: Mechanistic Insights, Translational Opportunities, and Clinical Implications. Biol Pharm Bull. 2026;49(6):896–909. [DOI] [PubMed] [Google Scholar]
- 30.Costa Pereira JPD, Rebouças AS, Prado CM, et al. Phase angle as a marker of muscle quality: A systematic review and meta-analysis. Clin Nutr. 2024;43(12):308–26. [DOI] [PubMed] [Google Scholar]
- 31.Kyle UG, Soundar EP, Genton L, et al. Can phase angle determined by bioelectrical impedance analysis assess nutritional risk? A comparison between healthy and hospitalized subjects. Clin Nutr. 2012;31(6):875–81. [DOI] [PubMed] [Google Scholar]
- 32.Tanaka S, Ando K, Kobayashi K, et al. A low phase angle measured with bioelectrical impedance analysis is associated with osteoporosis and is a risk factor for osteoporosis in community-dwelling people: the Yakumo study. Arch Osteoporos. 2018;13(1):39. [DOI] [PubMed] [Google Scholar]
- 33.Sato R, Sawaya Y, Hirose T, et al. Anthropometrically estimated calf muscle circumference is a marker for early detection of muscle mass decline in older adults: Second report. J Nutr Health Aging. 2026;30(1):100729. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Ding Y, Chang L, Zhang H, et al. Predictive value of phase angle in sarcopenia in patients on maintenance hemodialysis. Nutrition. 2022;94:111527. [DOI] [PubMed] [Google Scholar]
- 35.Özcan B, Güner M, Ceylan S, et al. Calf circumference predicts sarcopenia in maintenance hemodialysis. Nutr Clin Pract. 2024;39(1):193–201. [DOI] [PubMed] [Google Scholar]
- 36.Hwang AC, Liu LK, Lee WJ, et al. Calf Circumference as a Screening Instrument for Appendicular Muscle Mass Measurement. J Am Med Dir Assoc. 2018;19(2):182–4. [DOI] [PubMed] [Google Scholar]
- 37.Bahat G, Tufan A, Tufan F, et al. Cut-off points to identify sarcopenia according to European Working Group on Sarcopenia in Older People (EWGSOP) definition. Clin Nutr. 2016;35(6):1557–63. [DOI] [PubMed] [Google Scholar]
- 38.Yilmaz O, Kucukdagli P, Seker N, et al. Comparison of diagnostic value of the SARC-F and its two modified versions (SARCF-EBM and SARCF-calF) in older adults in Turkey. Geriatr Nurs. 2025;65:103572. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analysed during the present study are available from the corresponding author upon reasonable request.