Abstract
Objectives
This article aimed to develop and validate an anthropometric equation based on the
least absolute shrinkage and selection operator (LASSO) regression, a machine learning
approach, to predict appendicular skeletal muscle mass (ASM) in 60-70-year-old women.
Design
A cross-sectional study.
Setting and participants
Community-dwelling women aged 60-70 years.
Methods
A total of 1296 community-dwelling women aged 60-70 years were randomly divided into
the development or the validation group (1:1 ratio). ASM was evaluated by bioelectrical
impedance analysis (BIA) as the reference. Variables including weight, height, body
mass index (BMI), sitting height, waist-to-hip ratio (WHR), calf circumference (CC),
and 5 summary measures of limb length were incorporated as candidate predictors. LASSO
regression was used to select predictors with 10-fold cross-validation, and multiple
linear regression was applied to develop the BIA-measured ASM prediction equation.
Paired t test and Bland-Altman analysis were used to validate agreement.
Results
Weight, WHR, CC, and sitting height were selected by LASSO regression as independent
variables and the equation is ASM = 0.2308 × weight (kg) – 27.5652 × WHR + 8.0179 ×
CC (m) + 2.3772 × Sitting height (m) + 22.2405 (adjusted R2 = 0.848, standard error of the estimate = 0.661 kg, P < .001). Bland-Altman analysis showed a high agreement between BIA-measured ASM and
predicted ASM that the mean difference between the 2 methods was −0.041 kg, with the
95% limits of agreement of −1.441 to 1.359 kg.
Conclusions and Implications
The equation for 60-70-year-old women could provide an available measurement of ASM
for communities that cannot equip with BIA, which promotes the early screening of
sarcopenia at the community level. Additionally, sitting height could predict ASM
effectively, suggesting that maybe it can be used in further studies of muscle mass.
Keywords
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Article info
Publication history
Published online: October 07, 2022
Footnotes
Jianan Shi and Qiang He share first authorship.
This work was supported by the Shandong Provincial Social Science Planning Project (grant 18CSHJ07), Shandong Provincial Natural Science Foundation (grant ZR2021QH211), and the Fundamental Research Funds of Shandong University (grant 2020HW034).
The authors declare no conflicts of interest.
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© 2022 AMDA - The Society for Post-Acute and Long-Term Care Medicine.