Article Title



Ryan M. Miller1, Eduardo D.S. Freitas1, Aaron D. Heishman1, Japneet Kaur1, Brady S. Brown1, Michael G. Bemben1, FACSM.

1University of Oklahoma, Department of Health and Exercise Science, Norman, Oklahoma.

Reductions in appendicular skeletal mass (ASM) may have a greater detrimental effect than reductions in total lean body mass regarding the onset and progression of sarcopenia. The limited access to modalities that accurately measure ASM results in individuals going undiagnosed and experiencing functional decline. Additionally, since women live longer and have a smaller amount of muscle mass compared to men, women are predisposed to being affected by sarcopenia. PURPOSE: The purpose of this investigation was to determine the ability of functional and neuromuscular measures to predict ASM and classify sarcopenia status in older women. METHODS: Forty-one (57.6±5.8years, 160.1±5.01cm, 64.1±6.2kg) women had their body composition analyzed by dual-energy X-ray absorptiometry (DXA), performed bench press 1-repetition maximum strength (1RM) testing, vertical jump height and power, grip strength, timed up and go, Berg Balance testing, and bench press power testing at 20%, 40%, and 60% 1RM. Sarcopenia was classified by criteria established by the Foundation of the National Institute of Health. Independent samples t-tests were used to examine differences between sarcopenic and non-sarcopenic women. Forward regression analyses examined if functional measures could provide an ASM prediction model. Significant models were compared to DXA derived ASM measures using t-tests and Pearson’s correlation with an a priori significance level of p<0.05. RESULTS: Women classified as sarcopenic (n=15; 58.2±6.4years, 158.1±4.7cm, 62.6±6.7kg) performed significantly worse than non-sarcopenic women for each functional measure (p=0.01 - p<0.001). Regression analyses revealed models accounting for 93.8%, 91.1% and 86.4% of the variance in DXA derived ASM, with the most robust model incorporating jump power, body weight and grip strength. Paired t-tests revealed no difference between model derived and DXA derived ASM for each model (p>0.05) and Pearson’s correlations revealed significant correlations for each model (r=0.94-0.96, p<0.001). CONCLUSIONS: The present data indicate that ASM can be predicted with high precision through jump power, body weight, and grip strength in older women. These models could be used to identify and classify sarcopenia, ultimately allowing the implementation of interventions aimed at attenuating the effects of sarcopenia.

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