Improved Strength Prediction Combining MRI Biomarkers of Muscle Quantity and Quality

Muscle strength declines with aging at a faster rate compared with muscle mass, suggesting that not only muscle quantity but also muscle quality and architecture are age-dependent. This study tested the hypothesis that quantitative MRI (qMRI)-derived biomarkers of muscle quality (fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [AD], fat fraction [FF], and T₂ relaxation time) and architecture (fascicle length) could improve the prediction of skeletal muscle strength over muscle mass alone. We recruited 24 adults (12 female, age range 30-79 years). Muscle mass was estimated as the volume and cross-sectional area (CSA) of the quadriceps. FA, RD, and AD parameters, together with fascicle length for the rectus femoris (RF) and vastus lateralis (VL), were derived from diffusion tensor imaging (DTI), and muscle-T₂ was calculated from a multi-echo spin echo sequence. FF was determined using the Dixon approach. CSA values were combined with FF to calculate the lean CSA. Isometric, eccentric, and concentric knee extension torques were measured for the left and right leg using an isokinetic dynamometer. The univariable assessment of torque was performed using a linear regression. The statistical significance of adding qMRI parameters to the torque prediction models was tested using a mixed-effect regression. The best univariable predictor of isometric, eccentric, and concentric torque was lean CSA. Adding FA, RF fascicle length, and VL fascicle length to the model improved the prediction of concentric torque compared with CSA alone. The addition of FA, T₂, RD, RF fascicle length, and VL fascicle length improved the prediction of eccentric torque over CSA alone. The addition of FF was not significant within the model. Our results confirmed the hypothesis that the inclusion of qMRI parameters of muscle composition and architecture leads to higher R² coefficients for the prediction of muscle strength compared with models solely based on muscle quantity. These observations support the utility of qMRI for future research on sarcopenia prediction and management.