Predicting fat-free mass index and sarcopenia in assisted-living older adults

Age-related muscle loss, termed sarcopenia, has been linked to functional deficits and an increased risk of falling. Such risk is of alarming concern due to the high disability and mortality rates associated with falling in older adults. Our laboratory recently developed a prediction model for fat-free mass index (FFMI) and, subsequently, sarcopenia within a community-dwelling older adult population using functional measures that are easily accessible to clinicians. The purpose of this study was to (1) determine how our prediction model performed in an older and less mobile assisted-living population, and if performance of the model was poor; (2) to improve and modify our previous prediction model using data acquired from this unique population. Forty assisted-living older adults (10 males) aged 86.1 ± 6.2 years participated in the study. Each completed four questionnaires to examine their mental and physical health status and anxiety levels related to falling. Anthropometric, balance, strength, and gait tests were conducted. Fat-free mass values, determined by bioelectrical impedance analysis, were normalized by height to obtain FFMI. Using an algorithm proposed by the European Working Group on Sarcopenia in Older People, FFMI along with grip strength and gait speed were used to identify sarcopenic individuals. FFMI was significantly correlated with sex, body mass index (BMI), circumference measures, handgrip strength, gait velocity, and measures of gait variability. The percentage of the variable variation explained by our previous model was reduced for a population of assisted-living older adults (R(2) of 0.6744 compared to the reported R(2) of 0.9272 for community-dwelling older adults; McIntosh et al. Age (Dordrecht, Netherlands), 2013). The prediction equation that accounted for the greatest variability of FFMI for the assisted living group included the independent variables of forearm circumference, BMI, handgrip strength, and variability of the double support time during gait (adjusted R(2) = 0.7950). This prediction model could be used by clinicians working in an assisted-living facility to identify individuals with reduced muscle mass and, once identified, aid with the planning and implementation of appropriate intervention strategies to attenuate the progression of additional muscle loss and improve quality of life.