GRAPHICAL ABSTRACT

INTRODUCTION

Sarcopenia, characterized by the age-related loss of muscle mass and function, is increasingly recognized as a significant concern in patients with Parkinson’s disease (PD) [1]. Patients with PD and sarcopenia face a greater risk of functional deterioration, a higher likelihood of falls and fractures, loss of independence, reduced quality of life, and increased mortality [1,2]. Therefore, early detection of sarcopenia and subsequent intervention are crucial for improving healthy life expectancy in patients with PD.

According to the Asian Working Group for Sarcopenia (AWGS), the diagnosis of sarcopenia is determined by the presence of low muscle mass combined with either low muscle strength or low physical performance [2-4]. Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are considered the gold standards for assessing muscle mass [2,3,5]. However, their effectiveness can be affected by factors such as device specificity, the presence of classic PD motor symptoms, and hydration status [2]. While MRI and CT scans can accurately assess both the quantity and quality of skeletal muscle, they are not practical for routine clinical use due to high costs, radiation exposure, time constraints, and lack of portability [6,7]. These limitations underscore the need for an effective and practical method to detect sarcopenia early in this at-risk population.

To address these challenges, this study investigated the potential of muscle ultrasonography (US) as a diagnostic tool for assessing sarcopenia in patients with PD. US offers advantages such as portability, cost-effectiveness, and the ability to assess muscle quantity and quality without radiation [5-8]. Decreased muscle thickness (MT) and cross-sectional area (CSA) of specific muscles, as measured by US, have been associated with sarcopenia and impaired physical function [5,7,9]. Furthermore, shear wave elastography (SWE), a relatively new functional ultrasound imaging technique, is being increasingly adopted in various fields due to its noninvasive nature and ability to provide real-time quantitative assessments of tissue properties. Several studies have shown its effectiveness in measuring muscle stiffness and rigidity in patients with PD [10,11]. While previous studies have explored various imaging modalities, few have focused on the application of US for assessing sarcopenia in this specific population.

We hypothesized that US could reliably identify sarcopenia in patients with PD through measurements of MT, CSA, and shear wave velocity (SWV). By comparing these US-derived metrics between sarcopenic and nonsarcopenic patients, we aimed to establish a diagnostic model that clinicians could employ in routine practice.

MATERIALS & METHODS

Study subjects

This prospective, single-center study received approval from the Kaohsiung Chang Gung Memorial Hospital ethics committee (protocol number: 202200096A3), and all participants provided written informed consent. Patients with PD were consecutively recruited from the Neurology Unit at Kaohsiung Chang Gung Memorial Hospital between June 2022 and August 2024. The inclusion criteria for patients with PD were age older than 20 years and a clinical diagnosis of idiopathic PD according to the UK Brain Bank criteria [12]. The exclusion criteria were the following: 1) newly diagnosed PD or a follow-up duration of less than 6 months, 2) comorbidities or trauma leading to muscle atrophy, 3) previous shoulder, hip, or knee joint surgery, 4) inability to complete all examinations, and 5) pregnancy. Initially, 89 participants were enrolled. Four patients with PD were excluded: two due to comorbidities or trauma causing muscle atrophy, and two because they were unable to complete all examinations. Ultimately, 85 patients with PD were eligible and completed all clinical and imaging tests.

Clinical assessment of PD

Demographic information such as age, sex, disease duration, height, weight, body mass index (BMI), and levodopa equivalent daily dose (LEDD) [13] was recorded. The severity of PD was assessed using the Unified Parkinson’s Disease Rating Scale (UPDRS) and the modified Hoehn and Yahr (H&Y) staging scale [14]. Clinical assessments and imaging tests were conducted while the patients were in the “ON” state.

Clinical assessment of balance and fall risk in patients with PD

The Tinetti balance and gait assessment, the Berg Balance Scale, and the three-step fall prediction tool (3-test tool) were used to evaluate balance and the risk of falls in individuals with PD [15-17]. The Tinetti assessment includes two sections: a balance score (from 0 to 16) and a gait score (from 0 to 12), with a maximum combined score of 28 points. The Berg Balance Scale allows for a maximum score of 56, with score ranges indicating fall risk as follows: 41–56 represents low risk, 21–40 indicates medium risk, and 0–20 signifies high risk. The 3-test tool assesses fall history, freezing of gait, and a comfortable gait speed of less than 1.1 m/s, offering a personalized evaluation of fall risk for patients with PD. According to this simple 3-test tool, the likelihood of falling in the next 6 months is 17% for individuals at low risk, 51% for those at medium risk, and 85% for those at high risk.

Skeletal muscle mass measurement

The participants’ overall and regional body compositions were assessed using a whole-body DXA scan (Lunar iDXA, GE Healthcare). No specific preparation, fasting, or dietary changes were needed before the examination. Participants lay flat on the scanning bed, and specialized software was used to estimate their lean body mass. All scans were conducted with the patients in the “ON” state to minimize the impact of PD motor symptoms. An experienced investigator (L.S.L.) reviewed all the DXA results. The Appendicular Skeletal Muscle Mass Index (ASMI) was determined by adding the skeletal muscle mass of the limbs and dividing that total by the square of the height.

Handgrip strength

Handgrip strength was evaluated using a handheld dynamometer (digital hand dynamometer-EH10117, CAMRY). The participants were seated with their elbows bent at 90 degrees. Two trials were conducted for each hand, and the maximum value from the stronger hand was documented.

Six-meter walking test

Participants were asked to walk at a normal pace, beginning in motion and keeping their speed steady without slowing down. The use of assistive devices, such as a cane, was permitted. The average result from at least two trials was recorded.

Diagnosis of sarcopenia

Sarcopenia was diagnosed on the basis of the 2019 AWGS criteria [3]. Evaluations included measuring the ASMI using DXA, assessing muscle strength with handgrip dynamometry, and testing physical performance with the 6-meter walking test. Participants were diagnosed with sarcopenia if they met the following conditions: 1) low ASMI (men: <7.0 kg/m²; women: <5.4 kg/m²) and 2) either low handgrip strength (men: <28 kg; women: <18 kg) or low physical performance (6-meter walk speed <1.0 m/s).

Sonographic assessment of sarcopenia

Muscle sonography was performed by a radiologist (S.Y.L. with 5 years of experience in musculoskeletal imaging) who was blinded to the clinical information and test results of the participants. After a 30-minute rest period, participants sat in a relaxed position with their elbows, hips, and knees bent at 90°. To reduce the impact of sports or overuse injuries, which typically affect the dominant side, the nondominant side was examined. The muscles studied included the anterior tibialis (AT), rectus femoris (RF), and biceps brachii (BB) in a relaxed state. The scanning locations were 1) AT: proximal third from the lateral condyle of the tibia to the inferior aspect of the medial malleolus, 2) RF: midway between the greater trochanter and the apex of the patella, and 3) BB: midway from the acromioclavicular joint to the elbow crease (Figure 1).

MT and CSA were measured manually using B-mode US with an ACUSON Sequoia Ultrasound System and an 18L6 linear array probe (Siemens Medical Solutions). Each measurement was taken twice per muscle, and the average values were recorded in millimeters and square millimeters. To evaluate muscle stiffness, SWV was measured with a 9L4 linear array probe (Siemens Medical Solutions). The probe was placed lightly on the skin and aligned with the muscle fibers. The region of interest (ROI) was set at 10 mm. SWV was calculated by averaging values from eight ROIs in the target muscle, with the results recorded in meters per second (m/s) (Figure 2). SWV measurements were repeated twice per muscle. The scan was limited to the nondominant side since limb dominance did not significantly affect muscle stiffness [18]. Ten patients were reassessed within three days by the original radiologist (S.Y.L). Additionally, another group of ten participants was examined by both the original radiologist (S.Y.L) and another general radiologist (N-N.K). These data were used to assess the intra- and interrater reliability for MT, CSA, and SWV of the BB, RF, and AT muscles.

Statistical analysis

The Kolmogorov–Smirnov test was used to assess the normality of continuous variables. These variables were compared using Mann–Whitney U tests and are reported as medians with interquartile ranges. Categorical variables were analyzed with the chi-square test or Fisher’s exact test. To address age and BMI in nonparametric comparisons of shoulder parameters between groups, we transformed the data and utilized a general linear model (GLM). Logarithmic transformations were initially applied to achieve better normality, enabling parametric analysis. The GLM included the group variable as a factor and adjusted for covariates such as age and BMI. This approach facilitated the examination of differences between groups while controlling for these variables. Additionally, a partial Spearman rank correlation was conducted, adjusting for age and BMI, to explore the relationships between functional measures and ultrasound features. The intraclass correlation coefficient (ICC) was calculated to assess the intra- and inter-rater reliability of MT, CSA, and SWV measurements for the BB, AT, and RF muscles. Agreement was categorized as poor (ICC, 0.00–0.20), fair (ICC, 0.20–0.40), good (ICC, 0.40–0.75), or excellent (ICC, >0.75). Key patient characteristics and statistically significant variables from the univariate analysis were included in binary logistic regression to develop a prediction model for sarcopenia. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, represented by the area under the ROC curve (AUC). The optimal cutoff value was determined with the Youden index (maximum sensitivity+specificity-1). The Z test was used to compare the diagnostic power of individual measures with regression equations and to assess the clinical applicability of different univariate models. Statistical analyses were conducted using SPSS software, version 22.0 (SPSS Inc.) and MedCalc statistical software (MedCalc Software Ltd.). A p value of less than 0.05 was considered to indicate statistical significance for all analyses.

RESULTS

Comparison of clinical characteristics and US parameters between patients with PD with and without sarcopenia

Table 1 summarizes the clinical characteristics of patients with PD with and without sarcopenia. The sarcopenic group was older (p<0.001) and had a lower BMI (p=0.002) than the non-sarcopenic group. There were no significant differences between the groups in terms of sex ratio (p=0.983), UPDRS score, H&Y stage, or mean LEDD (all p>0.05).

Comparison of US parameters between patients with PD with and without sarcopenia after adjustment for age and BMI

In the US assessment of sarcopenia, compared with the non-sarcopenic group, the sarcopenic group exhibited significantly lower MT in the BB and AT, along with reduced CSA in the AT (all p<0.05). However, no significant differences were found in the SWV of the muscles between the two groups (Table 2).

Correlation between functional measures and ultrasound parameters after adjustment for age and BMI

Table 3 presents the results of the correlation analysis, highlighting the relationship between US features and functional measures. The analysis revealed significant associations of MT_BB, MT_AT, CSA_BB, CSA_RF, and CSA_AT with handgrip strength, 6-meter walk speed, and ASMI (all p<0.05). Additionally, MT_BB, CSA_BB, and CSA_AT were positively correlated with the Tinetti score, whereas MT_BB and CSA_AT were negatively correlated with the 3-test tool score in patients with PD (all p<0.05). Furthermore, only SWV_RF was significantly negatively correlated with ASMI (r=-0.266, p<0.05).

Intra- and inter-rater reliability of US muscle measurements

For intrarater reliability, both radiologists (L.S.Y. and N-N. K.) demonstrated strong agreement across all measures, with ICCs ranging from 0.859 to 0.985 and 0.864 to 0.983, respectively. Interrater reliability also showed high consistency, with ICCs ranging from 0.841 to 0.974 (Supplementary Table 1 in the online-only Data Supplement).

Integration of clinical and ultrasound parameters for the development of a sarcopenia prediction model

Binary logistic regression analysis was conducted to evaluate independent predictors of sarcopenia based on key patient characteristics (age, sex, and BMI) and ultrasound parameters (MT_BB, MT_AT, and CSA_AT). The analysis revealed age (odds ratio [OR]: 1.16; 95% confidence interval [CI] 1.05–1.27; p=0.002), MT_BB (OR: 0.88; 95% CI 0.80–0.98; p=0.019), and BMI (OR: 0.80; 95% CI 0.67–0.95; p=0.011) as independent predictors (Table 4). A logistic regression equation was formulated using these significant predictors:

LogitY=−2.615+0.146×age−0.226×BMI−0.123×MTBB.

The prediction model demonstrated 80.6% sensitivity, 79.6% specificity, 80.0% accuracy, and an AUC of 0.857 (cutoff value=0.337). This model showed good diagnostic value for identifying sarcopenia and was more accurate at predicting sarcopenia than age (cutoff=70 years), BMI (cutoff=21.3), or MT_BB (cutoff=22.5) alone (p<0.001) (Figure 3). Table 5 displays the ROC curves, AUCs, sensitivity, specificity, and accuracy for age, BMI, MT_BB and the prediction model.

DISCUSSION

The results of the current study demonstrated that muscle US is a reliable and effective tool for predicting sarcopenia in individuals with PD. Our findings indicated that compared with their nonsarcopenic counterparts, patients with PD with sarcopenia exhibited reduced US-derived CSA and MT. Specifically, measurements of MT and CSA for the BB, RF, and AT were correlated with functional measures and the ASMI. Notably, the correlations between MT_BB, CSA_BB, and CSA_AT with balance and fall risk assessment tests underscore the potential of these parameters in assessing fall risk among patients with PD. Conversely, there were no significant differences in muscle stiffness between the sarcopenic and nonsarcopenic groups, which may reflect the complex interactions between PD-related factors and age-related muscle changes.

Our study aligned with previous research indicating that in vivo ultrasound measurements of muscle architecture are key factors in determining muscle force, velocity, and power output [19,20]. Age-related decreases in muscle mass significantly contribute to muscle weakness and reduced endurance in older adults [21]. These changes strongly negatively affect overall functional performance and play a role in the onset of physical disabilities. Ultrasound has been shown to be a promising method for quantifying muscle mass and function across various clinical populations [22-24]. Our findings indicated that a decrease in muscle mass, as evidenced by lower MT_BB in sarcopenic patients, serves as a strong predictor of sarcopenia and is associated with a higher risk of falls. This finding reinforces the utility of US as an effective tool for the early diagnosis of sarcopenia in patients with PD.

SWE, a cutting-edge ultrasound technology, is widely used to measure tissue stiffness. Several studies have shown the efficacy of SWE in quantifying rigidity and muscle stiffness in patients with PD [10,11]. From a histological standpoint, age-related sarcopenia can lead to a gradual loss of muscle fibers and an increase in intramuscular fat [25,26], which corresponds to a decrease in skeletal muscle stiffness [27]. Notably, our SWE findings differed from those in the established literature, as we detected no significant differences in the SWV of muscles between the sarcopenic and nonsarcopenic groups. This discrepancy may be due to the unique characteristics of muscle rigidity in PD, which could disrupt typical age-related trends in muscle stiffness. Previous studies have indicated that individuals with PD experience increased muscle stiffness in both the upper and lower limbs [11,28], which is attributed to changes in neuromuscular drive and rigidity [29,30]. This increased stiffness may mask the expected decline associated with sarcopenia. Additionally, factors such as motor symptom asymmetry, structural muscle adaptations due to parkinsonian rigidity [31], and diurnal fluctuations in symptoms may influence muscle stiffness in patients with PD. Together, these complex interactions likely diminish the sensitivity of SWE in detecting sarcopenic changes within this population.

US parameters have previously been utilized to assess sarcopenia in patients with PD. Chen et al. [32] reported that male sex, low BMI, and reduced gastrocnemius MT were significant predictors of low ASMI, as measured by BIA. In this study, we created a model to detect sarcopenia using three variables: age, BMI, and MT_BB. Among these individual factors, older age and lower BMI were recognized as risk factors, but they showed low diagnostic value for sarcopenia (AUC <0.8). While these factors can be useful for screening at-risk populations, they are insufficient for the early detection and diagnosis of sarcopenia. US is the most direct method for detecting muscle mass decline, as MT can effectively represent muscle mass. By incorporating both clinical and ultrasound predictors that are easily identifiable in clinical settings into our model, we can improve the diagnostic accuracy for sarcopenia. Our prediction model exhibited a higher diagnostic value (AUC=0.857) than individual factors, underscoring its enhanced diagnostic capability and potential for clinical use.

In our methodology, we focused on the BB, RF, and AT muscles for two primary reasons. First, ultrasound parameters for these muscles are highly reproducible and serve as effective indicators of lean muscle mass loss [33]. Second, these muscles are superficial and easily identifiable via ultrasound, which simplifies the learning process for clinicians. The US evaluation of these three muscles can be performed while patients are seated comfortably and in a relaxed state. This method is both practical and convenient, allowing clinicians to efficiently assess sarcopenia in at-risk populations during outpatient visits.

The current study has several limitations. First, factors such as physical activity, nutrition, and comorbidities, which may influence sarcopenia in patients with PD, were not thoroughly examined. Second, the absence of a healthy control group may have obscured US findings that are specific to patients with PD. Third, a significant limitation is the lack of a validation set for the prediction model, primarily due to the small sample size used in this study. Despite these limitations, our findings provide valuable insights into the potential role of muscle sonography in the diagnosis of sarcopenia in patients with PD. Future research should aim to include larger sample sizes to validate these results.

In conclusion, US is an effective tool for assessing sarcopenia in patients with PD, demonstrating significant differences in MT and CSA between sarcopenic and nonsarcopenic individuals. By integrating clinical predictors into a diagnostic model, US increases the accuracy of the detection of sarcopenia in clinical practice.

Supplementary Materials

Notes

Conflicts of Interest

The authors have no financial conflicts of interest.

Funding Statement

This work was supported by Grant CMRPG8M0801, CMRPG8M0802 from Kaohsiung Chang Gung Memorial Hospital, Taiwan.

Acknowledgments

We would like to acknowledge Biostatistics Center, Kaohsiung Chang Gung Memorial Hospital for their support in the statistical analyses of this study.

Author Contributions

Conceptualization: Lay San Lim, Sieh Yang Lee, Cheng-Hsien Lu. Data curation: Yun-Ru Lai, Cheng-Hsien Lu. Formal Analysis: Na-Ning Kan, Chien- Chang Liao. Funding acquisition: Sieh Yang Lee. Investigation: Yun-Ru Lai, Cheng-Hsien Lu. Methodology: Lay San Lim, Sieh Yang Lee, Cheng-Hsien Lu. Project administration: Lay San Lim, Sieh Yang Lee. Resources: Sieh Yang Lee, Yun-Ru Lai, Cheng-Hsien Lu. Software: Sieh Yang Lee, Na-Ning Kan, Chien-Chang Liao. Supervision: Cheng-Hsien Lu, Yun-Ru Lai. Validation: Na-Ning Kan, Chien-Chang Liao. Visualization: Lay San Lim, Sieh Yang Lee. Writing—original draft: Lay San Lim, Sieh Yang Lee. Writing—review & editing: Cheng-Hsien Lu

Figure 1.

Patient posture and US probe positioning during examination. A: Photograph depicting the patient in a relaxed position with elbows, hips, and knees flexed at 90°. B, C: US probe positioning for measuring muscle thickness and cross-sectional area of the BB (a), RF (b), and AT (c) muscles. Shear wave velocity was measured with the probe aligned with the muscle fibers of the BB (d), RF (e), and AT (f). US, ultrasonography; BB, biceps brachii; RF, rectus femoris; AT, anterior tibialis.

Figure 2.

US and SWE assessment of the BB. A: Measurement of muscle thickness and cross-sectional area of BB in transverse plane of B-mode US. B: SWE of the BB in the longitudinal plane. Eight regions of interest were positioned within the muscle, and the average shear wave velocity value was documented. US, ultrasonography; SWE, shear wave elastography; BB, biceps brachii.

Figure 3.

Receiver operating characteristic curves for age, BMI, MTBB, and prediction model. BMI, body mass index; MT, muscle thickness; BB, biceps brachii.

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