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pISSN 2288-6982
eISSN 2288-7105

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Original Article

Phys. Ther. Korea 2021; 28(3): 177-185

Published online August 20, 2021

https://doi.org/10.12674/ptk.2021.28.3.177

© Korean Research Society of Physical Therapy

Risk Factors for Sarcopenia, Sarcopenic Obesity, and Sarcopenia Without Obesity in Older Adults

Seo-hyun Kim1 , PT, BPT, Chung-hwi Yi2 , PT, PhD, Jin-seok Lim1 , PT, BPT

1Department of Physical Therapy, The Graduate School, Yonsei University, 2Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Korea

Correspondence to: Chung-hwi Yi
E-mail: pteagle@yonsei.ac.kr

Received: June 24, 2021; Accepted: July 8, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults.
Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models.
Methods: This machine-learning study used the 2008–2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors.
Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease.
Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.

Keywords: Aging, Body mass index,Exercise, Machine learning, Sarcopenia