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Simple method for estimating low muscle mass in persons with bioelectrical impedance analysis measurement
Hirohito Sasaki1, Osamu Yamamura2, Hidenori Onishi2
1Department of Neurology, Faculty of Medical Sciences, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui 910-1104, Japan.
Journal of Clinical Biochemistry and Nutrition
|August 8, 2025
Summary
This study identifies reliable cutoff values for low skeletal muscle mass index in sarcopenia diagnosis using body fat percentage prediction equations, offering an alternative to bioelectrical impedance analysis for assessing muscle mass.
Area of Science:
- Gerontology
- Nutritional Science
- Biometrics
Background:
- Bioelectrical impedance analysis (BIA) is commonly used for body composition assessment but has limitations in certain populations.
- Accurate measurement of skeletal muscle mass is crucial for diagnosing sarcopenia, a condition characterized by age-related muscle loss.
- Developing alternative methods to BIA for sarcopenia screening is essential for broader applicability.
Purpose of the Study:
- To establish cutoff values for low skeletal muscle mass index (SMI) for sarcopenia diagnosis.
- To evaluate the utility of fat-free mass index (FFMI) estimated via body fat percentage (BF%) prediction equations as an alternative to BIA-derived SMI.
- To compare the performance of different BF% prediction equations in estimating low SMI.
Main Methods:
- A cross-sectional study involving 564 elderly residents (mean age 76.0 ± 7.1 years).
- Body composition was assessed using BIA.
- Three established BF% prediction equations (Ito et al., Deurenberg et al., Gallagher et al. for Asians) were applied to estimate FFMI.
- Receiver operating characteristic (ROC) curve analysis was used to determine optimal FFMI cutoff values for low SMI.
Main Results:
- The Ito et al. BF% prediction equation demonstrated the highest area under the curve (AUC = 0.83) for estimating low SMI in men.
- In women, the Ito et al. and Gallagher et al. equations showed comparable performance, with AUCs of 0.779.
- The FFMI estimated using these BF% prediction equations showed significant utility in screening for low SMI.
Conclusions:
- FFMI estimated from BF% prediction equations can serve as a viable alternative to BIA for identifying individuals with low SMI.
- These findings provide practical tools for sarcopenia screening in populations where BIA may be unreliable.
- Further validation in diverse populations is warranted to confirm the generalizability of these FFMI-based cutoff values.
