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NCT Number: NCT07836088

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

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.

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Key information

Conditions

Metabolic Syndrome Atrophy Body Weight Diabetes Mellitus Diabetes Mellitus, Type 2 Endocrine System Diseases Glucose Metabolism Disorders Hyperinsulinism Insulin Resistance Metabolic Diseases Muscular Atrophy Nervous System Diseases Neurologic Manifestations Neuromuscular Manifestations Nutrition Disorders Nutritional and Metabolic Diseases Obesity Overnutrition Overweight Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Sarcopenia Signs and Symptoms Type 2 Diabetes

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Area under the ROC curve (AUC) of multimodal ultrasound for detecting metabolic sarcopenia

    Time frame: At baseline assessment

    Area under the receiver operating characteristic curve (AUC) of ultrasound parameters including muscle thickness, cross-sectional area, echo intensity, shear wave velocity (SWV), and Young's modulus (Emean) for detecting metabolic sarcopenia, using dual-energy X-ray absorptiometry (DXA)-measured appendicular skeletal muscle mass (ASM) as the reference standard.

  2. Sensitivity of multimodal ultrasound for detecting metabolic sarcopenia

    Time frame: At baseline assessment

    Sensitivity (true positive rate) of ultrasound parameters including muscle thickness, cross-sectional area, echo intensity, shear wave velocity (SWV), and Young's modulus (Emean) for detecting metabolic sarcopenia, using dual-energy X-ray absorptiometry (DXA)-measured appendicular skeletal muscle mass (ASM) as the reference standard.

  3. Specificity of multimodal ultrasound for detecting metabolic sarcopenia

    Time frame: At baseline assessment

    Specificity (true negative rate) of ultrasound parameters including muscle thickness, cross-sectional area, echo intensity, shear wave velocity (SWV), and Young's modulus (Emean) for detecting metabolic sarcopenia, using dual-energy X-ray absorptiometry (DXA)-measured appendicular skeletal muscle mass (ASM) as the reference standard.

  4. Positive predictive value of multimodal ultrasound for detecting metabolic sarcopenia

    Time frame: At baseline assessment

    Positive predictive value of ultrasound parameters including muscle thickness, cross-sectional area, echo intensity, shear wave velocity (SWV), and Young's modulus (Emean) for detecting metabolic sarcopenia, using dual-energy X-ray absorptiometry (DXA)-measured appendicular skeletal muscle mass (ASM) as the reference standard.

  5. Negative predictive value of multimodal ultrasound for detecting metabolic sarcopenia

    Time frame: At baseline assessment

    Negative predictive value of ultrasound parameters including muscle thickness, cross-sectional area, echo intensity, shear wave velocity (SWV), and Young's modulus (Emean) for detecting metabolic sarcopenia, using dual-energy X-ray absorptiometry (DXA)-measured appendicular skeletal muscle mass (ASM) as the reference standard.

  6. Correlation between ultrasound parameters and metabolic indicators

    Time frame: At baseline assessment

    Correlation coefficients (Pearson or Spearman) between multimodal ultrasound parameters (muscle thickness, cross-sectional area [CSA], echo intensity, shear wave velocity [SWV], Young's modulus [Emean], microvascular density) and metabolic indicators including fasting blood glucose, glycated hemoglobin (HbA1c), homeostatic model assessment of insulin resistance (HOMA-IR), triglycerides, high-density lipoprotein cholesterol (HDL-C), and systemic inflammatory markers.

  7. Combined diagnostic model for metabolic sarcopenia

    Time frame: At baseline assessment

    Development and validation of a combined diagnostic model integrating multimodal ultrasound parameters and clinical indicators (age, BMI, metabolic components) for diagnosing metabolic sarcopenia, with model performance evaluated by area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis.

  8. Predictive value of baseline ultrasound for 12-month outcomes

    Time frame: 12 months

    Hazard ratios (HRs) from Cox proportional hazards regression models for baseline multimodal ultrasound parameters as predictors of metabolic disease progression, incident sarcopenia, and cardiovascular complications during the 12-month prospective follow-up period. Kaplan-Meier survival curves will be generated to compare complication rates across different ultrasound parameter levels.

Study contacts

Contact information is provided by the study sponsor or research team.

Fahui Lu, Master's

CONTACT

[email protected]

0596-2082027

Wenting Jiang, Master's

CONTACT

[email protected]

13625926205

Sponsors and collaborators

Lead sponsor

Zhangzhou Municipal Hospital

Other

Registry information

Important dates

Study start
2023
Primary completion
2027
Study completion
2027
First posted
Sep 23, 2026
Registry last updated
Sep 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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