Predicting fat-free mass index and sarcopenia: a pilot study in community-dwelling older adults

Abstract

Age-related muscle loss, termed sarcopenia, has been linked to an increased risk of falls, disability, and mortality. The purpose of this study was to develop a predictive measurement tool to estimate normalized fat-free mass index (FFMI), a means of identifying sarcopenia, in community-dwelling older adults. Functionally relevant measurements including mobility tests, food records, circumference measures, balance, and gait variables were included to ensure this model was comprehensive and accessible to clinicians. Eighty-five community-dwelling older adults (42 male) aged 75.2 ± 5.7 years participated. Each completed two questionnaires regarding general health and physical activity levels. Anthropometric, strength, balance, gait, nutrition, and body composition tests were then conducted. A fat-free mass value, determined by bioelectrical impedance analysis, was normalized by height (FFMI). FFMI along with grip strength and gait speed was used to classify sarcopenia. FFMI was significantly correlated with all circumference measures (waist, arm, calf, and thigh) and body mass index (BMI), but no nutritional parameters. In males, maximum grip strength and a novel quiet balance measure, time outside of a 95% confidence ellipse (TOE), were both positively correlated to FFMI. In females, age and double-support time correlated to FFMI. The prediction equation that accounted for the most variability of FFMI included the independent variables: sex, step time, BMI, and TOE (adjusted R(2) = 0.9272). The proposed linear regression model can successfully predict FFMI values to a high level of accuracy in men and women. With this information, sarcopenia can be predicted by clinicians, and early interventions can be planned and implemented.

Fig. 1

Acquired prediction model using multiple linear regression analysis with the variables BMI, sex, step time, and time outside 95 % confidence ellipse; the population was divided into a prediction group (circles; adj. R ² = 0.9272) to make the model and a validation group (plus signs; adj. R ² = 0.8271) to verify the predictability of the proposed model