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Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics
Kyle Edmunds1, Magnús Gíslason1, Sigurður Sigurðsson2
1Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.
Plos One
|March 8, 2018
Summary
This study introduces nonlinear trimodal regression analysis (NTRA) for skeletal muscle assessment using CT scans. NTRA parameters show strong correlation with lower extremity function and health metrics, offering a more comprehensive analysis than standard CT metrics.
Area of Science:
- Gerontology and Musculoskeletal Imaging
- Radiology and Medical Imaging Analysis
- Biostatistics and Quantitative Analysis
Background:
- Sarcopenic muscular degeneration is a key mortality risk factor in aging populations.
- Computed tomography (CT) analysis quantifies skeletal muscle volume and composition, but optimal assessment methods are debated.
- Current CT metrics (average Hounsfield units, cross-sectional areas) lack standardization and comparison to advanced distribution analyses.
Purpose of the Study:
- To compare nonlinear trimodal regression analysis (NTRA) parameters of muscle radiodensitometric distributions with standard CT metrics.
- To evaluate the correlation of NTRA parameters with lower extremity function (LEF) biometrics, cholesterol, and BMI.
- To explore the utility of NTRA in assessing muscle quality and its association with aging-related health parameters.
Main Methods:
- Analysis of CT data from 3,162 subjects (aged 66-96 years) from the AGES-Reykjavik Study.
- Application of 1-D k-means clustering and Sturges' Formula for data discretization.
- Linear regression analysis comparing eleven NTRA parameters against standard CT metrics (fat/muscle area, average HU) and correlating with LEF, SCHOL, and BMI.
Main Results:
- NTRA parameters demonstrated strong linear correlation (coefficients > 0.85) with standard CT analyses.
- Multiple regression analysis of correlative NTRA parameters yielded a high correlation coefficient (0.99, P<0.005).
- Specific NTRA parameters showed distinct correlations with LEF, total solubilized cholesterol (SCHOL), and body mass index (BMI), highlighting the connective tissue regime's importance.
Conclusions:
- Nonlinear trimodal regression analysis (NTRA) offers a robust method for detailed skeletal muscle assessment via CT.
- NTRA parameters provide valuable insights into muscle quality, correlating significantly with functional and health-related biometrics.
- This advanced analytical approach surpasses standard CT metrics in comprehensively evaluating muscle composition and its impact on aging-related health outcomes.
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