Age-related changes in ultrasound-assessed muscle composition and postural stability

Abstract

While the simultaneous degradation of muscle composition and postural stability in aging are independently highly investigated due to their association with fall risk, the interplay between the two has received little attention. Thus, the purpose of this study is to explore how age-related changes in muscle composition relate to postural stability. To that aim, we collected posturography measures and ultrasound images of the dominant Vastus Lateralis and Biceps Brachii from 32 young (18-35 year old) and 34 older (65-85 year old) participants. Muscle properties were quantified with echo-intensity and texture-based metrics derived from gray-level co-occurrence matrix analysis, and postural stability with the variability of the center of pressure during bipedal stance tasks. Ultrasound parameters revealed that young muscle possessed lower echo-intensity and higher homogeneity compared to the elderly. Echo-intensity and muscle thickness, and several texture-based parameters possessed outstanding young versus older classification performance. A canonical correlation analysis demonstrated a significant relationship between ultrasound and postural measures only within the young group (r = 0.53, p < 0.002), where those with 'better' muscle composition displayed larger postural sways. Our results indicate that, in older individuals, postural stability and muscle composition, two common fall risk factors, are unrelated. In view of this decoupling, both may contribute independently to fall risk. Furthermore, our data support the view that texture-based parameters provide a robust alternative to echo-intensity in providing markers of muscle composition.

Figure 1

Experimental set-up. Participants were equipped with a 64 channel EEG-cap, 15 bilateral EMG sensors, and stood on a force plate in 4 experimental conditions: either on a hard surface or on foam pads, and with eyes open or closed. EEG and EMG were not analyzed for this study.

Figure 2

Processing of thigh-segment US images. US image of young female shown on left and US image of an older female shown on the right. Vertical white lines represent estimation of muscle thickness (MT) and subcutaneous adipose thickness (SAT). A polygon (in solid yellow) outlines the entire muscle cross-section, which served as the ROI for EI analysis. A rectangle (in dashed yellow) outlines the ROI for texture analysis. Below each US image is the resultant EI histogram. Vertical yellow line indicates mean pixel intensity.

Figure 3

ROC curves for conventional US parameters (a) and texture-based parameters (b). The dotted red line corresponds to the performance of a random classifier. ENT Entropy; CON Contrast; CORR Correlation.

Figure 4

Effect of condition and age on postural stability. Vertical bars and error bars indicate the mean and standard error of the sdCOP_AP for each condition and group (black bars, young group; gray bars, older group). Horizontal lines indicate the p-value for the comparisons of normalized sdCOP_AP between conditions.

Figure 5

Association between US-derived muscle properties and postural sway (sdCOP_AP). (a) Weights of the standardized US parameters (blue) and standardized sdCOP_AP in the 4 conditions (green). (b) Pooled standardized sdCOP_AP across conditions as function of pooled standardized US parameters. Circles indicate individual values, and their linear regression line is in red. Correlation value and associated significance level are indicated in the top left corner. ENT Entropy.