A Clinical Study on Using Multimodal Ultrasound to Assess Muscle Mass in Metabolic Diseases

Registry record from ClinicalTrials.gov (NCT07836088). This is a study registration, not published results. Lead sponsor: Zhangzhou Municipal Hospital. Recruitment status: RECRUITING. Study type: OBSERVATIONAL. Enrollment: 320 participants (ESTIMATED). Conditions: Metabolic Syndrome, Obesity, Sarcopenia, Type 2 Diabetes. Primary outcome measures: Area under the ROC curve (AUC) of multimodal ultrasound for detecting metabolic sarcopenia , At baseline assessment; Sensitivity of multimodal ultrasound for detecting metabolic sarcopenia , At baseline assessment; Specificity of multimodal ultrasound for detecting metabolic sarcopenia , At baseline assessment; Positive predictive value of multimodal ultrasound for detecting metabolic sarcopenia , At baseline assessment; Negative predictive value of multimodal ultrasound for detecting metabolic sarcopenia , At baseline assessment; Correlation between ultrasound parameters and metabolic indicators , At baseline assessment; Combined diagnostic model for metabolic sarcopenia , At baseline assessment; Predictive value of baseline ultrasound for 12-month outcomes , 12 months. Brief summary: This study aims to evaluate the clinical value of multimodal ultrasound for assessing muscle mass in patients with metabolic diseases, including metabolic syndrome, type 2 diabetes, and simple obesity. Skeletal muscle is the largest metabolic organ in the human body and plays a critical role in glucose metabolism. Muscle mass reduction is common in patients with metabolic diseases and is associated with insulin resistance, poor disease control, and increased risk of complications. Currently available methods for muscle assessment, such as dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI), have limitations including high cost, radiation exposure, or poor portability, making them unsuitable for routine bedside monitoring. Multimodal ultrasound combines B-mode imaging, shear-wave elastography, superb microvascular imaging, and artificial intelligence analysis to provide a comprehensive evaluation of muscle morphology, stiffness, microcirculation, and quality. This non-invasive, radiation-free, and portable technique may serve as an ideal tool for muscle assessment in clinical practice. This prospective observational study will enroll 320 participants divided into four groups: metabolic syndrome (n=80), type 2 diabetes (n=80), simple obesity (n=80), and healthy controls (n=80). All participants will undergo baseline assessments including clinical data collection, biochemical tests, muscle function tests (handgrip strength, gait speed, Short Physical Performance Battery \[SPPB\]), multimodal ultrasound examination (muscle thickness, cross-sectional area, echo intensity, shear wave velocity, Young's modulus, microvascular density), and DXA measurement as the reference standard. The three metabolic disease groups will be followed prospectively for 12 months with repeat assessments at 6 and 12 months. The primary objectives are to determine diagnostic thresholds of multimodal ultrasound parameters for detecting metabolic sarcopenia; to establish correlation between ultrasound parameters and metabolic indicators (blood glucose, glycated hemoglobin \[HbA1c\], homeostatic model assessment of insulin resistance \[HOMA-IR\], lipids); to develop a combined diagnostic model integrating ultrasound and clinical parameters; and to evaluate the predictive value of baseline ultrasound parameters for 12-month disease progression and complications. The findings will provide a non-invasive, convenient, and widely applicable tool for early screening, risk stratification, and therapeutic monitoring of muscle abnormalities in patients with metabolic diseases.