Overview
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.
Description
This study will be conducted at Zhangzhou Hospital, Fujian Medical University. Participants in the four groups will be matched for age and sex to ensure comparability. Multimodal ultrasound examinations will be performed by two trained sonographers using a high-end musculoskeletal ultrasound system (GE Logiq E9 or Philips EPIQ 7) with a 10-15 MHz linear array probe. Parameters assessed include B-mode imaging (muscle thickness, fascicle length, pennation angle, cross-sectional area, and echo intensity); shear-wave elastography (shear wave velocity and Young's modulus measured at rest and during isometric contraction); and artificial intelligence (automated region of interest \[ROI\] segmentation and texture analysis for quantification of muscle fat infiltration).
Clinical and biochemical assessments include fasting blood glucose, HbA1c, lipid profile, insulin, HOMA-IR, and liver/kidney function. Muscle function is assessed by handgrip strength, 6-meter gait speed, and SPPB. All assessments follow standardized protocols with quality control measures including inter-observer reproducibility testing (intraclass correlation coefficient \[ICC\] \> 0.85).
Statistical analyses will be performed using SPSS 26.0. Group comparisons will use analysis of variance (ANOVA) or Kruskal-Wallis tests. Correlation analyses will use Pearson or Spearman methods. Receiver operating characteristic (ROC) curve analysis will determine diagnostic thresholds. Logistic regression will be used to construct combined diagnostic models. Cox proportional hazards models and Kaplan-Meier analysis will evaluate prognostic value. A p-value \< 0.05 will be considered statistically significant.
The study is expected to yield diagnostic thresholds of multimodal ultrasound parameters for metabolic sarcopenia; a combined ultrasound-clinical diagnostic model; prognostic prediction models for disease progression and complications; and a standardized protocol for clinical application of multimodal ultrasound in metabolic disease management.
Eligibility
Inclusion Criteria:
- Diagnosis of metabolic syndrome according to the Chinese Consensus on Diagnosis and Management of Metabolic Syndrome (2020 edition), meeting at least 3 of the following 5 criteria: abdominal obesity, hypertension, hyperglycemia, hypertriglyceridemia, and low HDL-C.
- Diagnosis of type 2 diabetes according to the ADA 2023 diagnostic criteria, with disease duration of at least 1 year.
- Body mass index (BMI) \>= 28 kg/m², without hypertension, hyperglycemia, dyslipidemia, or other metabolic abnormalities.
- Healthy volunteers without metabolic diseases, muscle diseases, chronic liver or kidney dysfunction, malignancies, cardiovascular diseases, or neurological disorders, and without long-term use of corticosteroids or other medications affecting muscle metabolism.
- Age and sex matched across all four groups.
- Willing and able to provide written informed consent and comply with all study procedures including ultrasound examination, DXA scan, and follow-up visits.
Exclusion Criteria:
- Severe hepatic or renal failure, malignancy, stroke, severe cardiovascular disease, or autoimmune diseases.
- Long-term use of corticosteroids, muscle relaxants, or other medications affecting muscle metabolism.
- Presence of movement disorders, joint diseases, muscle injury, muscular dystrophy, or other conditions affecting muscle function.
- Pregnancy or lactation.
- Inability to cooperate with ultrasound examination, DXA scanning, or follow-up assessments.
- Any other condition that, in the opinion of the investigator, would interfere with the study objectives or participant safety.
