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Updated: Jun 15, 2025
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
Exploring influencing factors and predictive analysis of sarcopenia in the Chinese population using the body
Shaotian Li1, Jingfeng Zou1, Liping Wang1
1Union Hospital TongJi Medical College, Huazhong University of Science and Technology, Wuhan, China.
Objective:
Employing body composition analysis, this study aims to examine the influencing factors and conduct predictive analysis regarding sarcopenia incidence in the middle-aged and elderly population in China.
Methods:
This study recruited inpatients from the General Medicine Department of Tongji Medical College Affiliated Union Hospital, Huazhong University of Science and Technology, as the subjects for a single-center retrospective study. Diagnosis was conducted according to the 2019 criteria from the Asian Working Group for Sarcopenia. Binary logistic regression analysis was utilized to identify factors influencing sarcopenia, and predictive modeling for sarcopenia occurrence was performed based on the area under the ROC curve (AUC).
Results:
This study comprised 1258 hospitalized patients, of whom 340 were diagnosed with sarcopenia and 918 were not, resulting in a prevalence of 27%. The baseline characteristics showed statistically significant differences between the two groups. Binary logistic regression analysis revealed that low protein, low total body water, low minerals, low basal metabolic rate, and age were risk factors for sarcopenia (OR > 1, P < 0.05). Conversely, being male, having a higher BMI, greater fat-free mass index, and a higher InBody score were identified as protective factors against sarcopenia (OR < 1, P < 0.05). The AUC values for predicting sarcopenia occurrence based on low protein, low total body water, low minerals, low basal metabolic rate, and age were 0.871, 0.846, 0.757, 0.645, and 0.649, respectively, indicating their significance as predictive indicators. Combining these five indicators into a new predictive model for sarcopenia yielded an area under the curve (AUC) value of 0.932, demonstrating excellent sensitivity and specificity concurrently.
Conclusion:
The results of body composition analysis indicate that sarcopenia occurrence in the middle-aged and elderly population in China is associated with factors such as low protein, low total body water, low minerals, low basal metabolic rate, age, gender, BMI, fat-free mass index, and InBody score. The combination of specific body composition indicators facilitates the effective prediction of sarcopenia. Clinical practitioners should proactively identify the risk factors influencing sarcopenia, accurately predict.
