Improved strength prediction combining clinically available measures of skeletal muscle mass and quality.

BACKGROUND: Measures of skeletal muscle function decline at a faster rate with ageing than do indices of skeletal muscle mass. These observations have been attributed to age-related changes in muscle quality, another functional determinant separate from skeletal muscle mass. This study tested the hypothesis that improved predictions of skeletal muscle strength can be accomplished by combining clinically available measures of skeletal muscle mass and quality.

METHODS: The participants included 146 healthy adult (age ≥ 18 years, range 18-77 years; X ± SD 47 ± 17 years and body mass index 16.5-51.8 kg/m2 ; 27.7 ± 6.2 kg/m2 ) men (n = 60) and women (n = 86) in whom skeletal muscle mass was estimated as appendicular lean soft tissue (LST) measured by dual-energy X-ray absorptiometry and skeletal muscle quality as bioimpedance analysis-derived phase angle and B-mode-evaluated echogenicity of mid-thigh skeletal muscle. Strength of the right leg and both arms was quantified as knee isokinetic extension and handgrip strength using dynamometers. The statistical significance of adding phase angle or echogenicity to strength prediction multiple regression models that included extremity-specific LST and other covariates (e.g. age and sex) was evaluated to test the study hypothesis.

RESULTS: Right leg LST mass alone was significantly (P < 0.0001) correlated with isokinetic right leg strength (R2  = 0.57). The addition of segmental phase angle measured in the right leg at 50 kHz increased the R2 of this model to 0.66 (P < 0.0001); other phase angle frequencies (5 and 250 kHz) did not contribute significantly to these models. Results were similar for both right and left arm handgrip strength prediction models. Adding age and sex as model covariates increased the R2 values of these models further (e.g. right leg strength model R2 increased to 0.71), but phase angle continued to remain a significant (all P < 0.01) predictor of extremity strength. Similarly, when predicting isokinetic right leg strength, mid-thigh skeletal muscle echogenicity added significantly (P < 0.0001) to right leg LST, increasing R2 from 0.57 to 0.64; age was a significant (P < 0.0001) covariate in this model, increasing R2 further to 0.68.

CONCLUSIONS: The hypothesis of the current study was confirmed, strongly supporting and extending earlier reports by quantifying the combined independent effects of skeletal muscle mass and quality on lower-body and upper-body measures of strength. These observations provide a clinically available method for future research aimed at optimizing sarcopenia and frailty risk prediction models.