DXA-Rectified Appendicular Lean Mass: Development of Ultrasound Prediction Models in Older Adults

Area of Science:

• Gerontology
• Biomedical Engineering
• Body Composition Analysis

Background:

• Dual-energy X-ray absorptiometry (DXA) is standard for diagnosing sarcopenia via appendicular lean soft tissue mass (aLM).
• DXA-derived aLM can be overestimated due to non-skeletal muscle components, particularly in individuals with higher adipose tissue mass.
• B-mode ultrasound offers accurate muscle size measurement in older adults, suggesting its potential for refining body composition assessments.

Purpose of the Study:

• To develop and validate regression-based prediction equations for estimating DXA-rectified appendicular lean tissue mass (aLM minus appendicular fat-free adipose tissue; aLM minus aFFAT).
• To utilize B-mode ultrasound measurements of muscle thickness (MT) in conjunction with demographic data (sex, age) and height for this estimation.
• To provide a more accurate assessment of lean tissue mass, accounting for adipose tissue variations.

Main Methods:

• A cross-sectional study involving 389 Japanese older adults (aged 60-79 years).
• Appendicular lean soft tissue mass (aLM) measured by DXA; muscle thickness (MT) measured by ultrasound at nine sites.
• Ordinary least-squares multiple linear regression models were developed using sex, age, height, and MT (4-site and 1-site models) to predict aLM minus aFFAT.

Main Results:

• Developed prediction models demonstrated high accuracy and stability for estimating aLM minus aFFAT.
• The 4-site MT model yielded R² = 0.902 (adjusted R² = 0.899), and the 1-site MT model yielded R² = 0.868 (adjusted R² = 0.866).
• Bootstrapping confirmed low optimism, with values of 0.0062 for the 4-site model and 0.0036 for the 1-site model.

Conclusions:

• Ultrasound-measured muscle thickness, combined with height, age, and sex, accurately estimates DXA-rectified appendicular lean tissue mass (aLM minus aFFAT) in older Japanese adults.
• The newly developed ultrasound prediction equations offer a valuable tool for population-based studies.
• These equations can improve the assessment of age-related lean tissue mass loss, particularly in contexts where DXA overestimation is a concern.