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

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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.

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Notes

  1. If COPML was less than zero, then π was added to the COPθ calculation.

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Acknowledgments

We would like to thank our participants from the Village by the Arboretum Retirement Community, the Evergreen Seniors Community Centre, the Guelph Wellington Men's Club, and the Colonel John McCrae Memorial Branch 234 Royal Canadian Legion. We would also like to extend our appreciation to statistician Dr. Michelle Edwards, Dr. Andrea Buchholz from The University of Guelph Body Composition and Metabolism Lab, Dr. Alison Duncan and Dr. Janis-Randall Simpson for use of their BIA unit, and Dr. Amanda Wright and Hillary Tulk from the Human Nutraceutical Research Unit. Lastly, we would like to thank Willy de Wit and Upper Canada Analytical Services for their help with data processing as well as laboratory assistants Sigrid Carino, Nina Falak, Natalie Pond, Cassandra Shipp, Chris Dulhanty, and especially Katherine Harrison for their help with data collection and entry. This work was financially supported by the University of Guelph Research Student Assistantship (to K.B.S.), Ontario Neurotrauma Foundation Summer Internship (to E.I.M.; grant # 2010-PREV-INT-854), and the University of Guelph-Humber Faculty Research Award (to L.A.V).

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Authors and Affiliations

  1. Department of Human Health and Nutritional Sciences, University of Guelph, 50 Stone Road East, Guelph, ON, Canada, N1G 2W1

    Emily I. McIntosh, K. Brent Smale & Lori Ann Vallis

  2. Schlegel-UW Research Institute for Aging, 325 Max Becker Dr., Suite 202, Kitchener, ON, Canada, N2E 4H5

    Emily I. McIntosh & Lori Ann Vallis

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  1. Emily I. McIntosh
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  2. K. Brent Smale
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  3. Lori Ann Vallis
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Correspondence to Lori Ann Vallis.

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McIntosh, E.I., Smale, K.B. & Vallis, L.A. Predicting fat-free mass index and sarcopenia: A pilot study in community-dwelling older adults. AGE 35, 2423–2434 (2013). https://doi.org/10.1007/s11357-012-9505-8

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  • DOI: https://doi.org/10.1007/s11357-012-9505-8

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