Abstract
Purpose: Sarcopenia is a geriatric syndrome, and it is closely related to the prevalence of type 2 diabetes mellitus (T2DM). Until now, the diagnosis of sarcopenia requires Dual Energy X-ray Absorptiometry (DXA) scanning. This study aims to make risk assessment of sarcopenia with support vector machine (SVM) and random forest (RF) when DXA is not available.Methods: Firstly, we recruited 132 patients aged over 65 and diagnosed with T2DM in Changchun, China. Clinical data were collected for predicting sarcopenia. Secondly, we selected 3, 5, and 7 features out of over 40 features of patient's data with backward selection, respectively, to train SVM and RF classification models and regression models. Finally, to evaluate the performance of the models, we performed leave one out and 5-fold cross validation.Results: When training the model with 5 features, the sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were favorable, and it was better than the models trained with 3 features and 7 features. Area under the receiver operating characteristic (ROC) curve (AUC) were over 0.7, and the mean AUC of SVM models was higher than that of RF.Conclusions: Using SVM and RF to make risk assessment of sarcopenia in the elderly is an option in clinical setting. Only 5 features are needed to input into the software to run the algorithm for a primary assessment. It cannot replace DXA to diagnose sarcopenia, but is a good tool to evaluate sarcopenia.
Highlights
Sarcopenia is an age-related geriatric syndrome, it is characterized by loss of muscle mass, decrease of muscle strength and decline of physical performance
A total of 132 subjects were included in this study, and they were classified into the sarcopenia group and the non-sarcopenia group based on Asian Working Group for Sarcopenia (AWGS)
The sarcopenia group was significantly older than the non-sarcopenia group (P < 0.05)
Summary
Sarcopenia is an age-related geriatric syndrome, it is characterized by loss of muscle mass, decrease of muscle strength and decline of physical performance. Quantitative and qualitative changes in skeletal muscle structure and function are involved in the aging-related loss of muscle function [1]. Patients with T2DM are subjected to a higher risk of sarcopenia due to diabetic complications and insulin resistance [3, 4]. A study conducted in China has reported that participants with T2DM have 1.56 times higher risk of sarcopenia than healthy people [5]. Patients with T2DM should concern more about sarcopenia
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