Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities

Abstract

Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P<0.005). These results highlight the specificities of each muscle quality metric to LEF biometrics, SCHOL, and BMI, and particularly highlight the value of the connective tissue regime in this regard.

Fig 1. Diagram depicting the three components of the trimodal radiodensitometric distribution utilized in this study.

This figure illustrates the location and skewnesses of each PDF, with tissue types as follows: I) Fat [-200 to -10 HU], II) Water Equivalent and Loose Connective Tissue [-9 to 40 HU], and III) Muscle [41 to 200 HU].

Fig 2. Current CT standard analyses against A) LEF biometrics and B) SCHOL and BMI.

Note that each population bin is shown with colored circles ranging from the unhealthiest (red) to the healthiest group (green), as defined by each parameter. Regression lines are likewise shown for each data series, along with their respective coefficients of determination (R²).

Fig 3. NTRA parameters against LEF biometrics.

Each population bin is shown with colored circles ranging from the unhealthiest LEF value (red) to the healthiest group (green). Regression lines and their corresponding R² values are analogously shown for each data series.

Fig 4. NTRA parameters against SCHOL and BMI.

Population bins are shown with colored circles ranging from the unhealthiest measurement (red) to the healthiest group (green), along with their regression lines and R² values.

Fig 5. Assembly of NTRA parameters with high correlation fidelity.

Parameters were selected for yielding LEF and SCHOL/BMI correlation coefficients greater than 0.85.

Fig 6. NTRA and standard CT analyses against age and sex.

Each age bin depicts the differences between male (blue) and female (red) subjects. A) Shows each NTRA parameter against subject age for fat (circles) and muscle (squares). In addition, B) shows NTRA parameters associated with loose connective tissue, and C) shows standard CT analyses. Regression lines and their respective coefficients of determination are depicted here for each data series.