Accuracy augmentation of body composition measurement by bioelectrical impedance analyzer in elderly population

Abstract

Bioelectrical impedance analysis (BIA) is currently the most commonly used method in clinical practice to measure body composition. However, the bioelectrical impedance analyzer is not designed according to different countries, races, and elderly populations. Because different races may have different body compositions, a prediction model for the elderly population in Taiwan should be developed to avoid population bias, thereby improving the accuracy of community evaluation surveys. Dual energy X-ray absorptiometry (DXA) was used as a standard method for comparison, and impedance analysis was used for the development of a highly accurate predictive model that is suitable for assessing the body composition of elderly people. This study employed a cross-sectional design and recruited 438 elderly people who were undergoing health examinations at the health management center in the Tri-Service General Hospital as study subjects. Basic demographic variables and impedance analysis values were used in four predictive models, namely, linear regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, to predict DXA body composition. The data from 354 study subjects were used to develop the predictive model, while the data from 84 study subjects were used to validate the accuracy of the predictive model. The body composition of elderly people as estimated by InBody 720 was highly correlated with that estimated by DXA. The correlation coefficient between InBody 720 and DXA for muscle mass was 0.969, and that for fat mass was 0.935. Consistency analysis results showed that InBody 720 tends to underestimate muscle mass and fat mass. A comparison of the accuracy of the linear regression, random forest, SVM, and XGBoost models showed that the linear regression has the highest accuracy. The correlation coefficient between the new model and DXA for muscle mass and fat mass were 0.977 and 0.978, respectively. The new predictive model can be used to monitor the nutrition status of elderly people and identify people with sarcopenia in the community.

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

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High Salt Diet Impacts the Risk of Sarcopenia Associated with Reduction of Skeletal Muscle Performance in the Japanese Population

“…Weight and body compositions were then measured using bioelectrical impedance analysis (Inbody 770; Inbody, Tokyo, Japan). The body composition measurement included skeletal muscle mass index (SMI; kg/m 2 ), body fat percentage (BFP; %), and mass of muscle of upper limbs (ULMM; kg) and lower limbs (LLMM; kg) [ 43 , 44 ]. Then we calculated the body mass index (BMI; kg/m 2 )…”

Methods of assessing body composition and anthropometric measurements – a review of the literature

“…Bioelectrical impedance analysis is a safe and easy to carry out method that allows us to determine the amount of water in the body, and then its composition by analyzing the resultant electrical resistance of the body to the current of low intensity and high frequency passed through it. Bioelectrical impedance is the resultant electrical resistance of the body, the total "obstacle" that body tissues pose to the current flowing through them, expressed in ohms [5,8,9]. It is based on the difference in electric current conductivity in the water and fat compartments [1].…”

“…It is based on the difference in electric current conductivity in the water and fat compartments [1]. BIA is a non-invasive, cheap, and reproducible method for measuring body composition not only in healthy people, but also in bedridden and not able to walk patients [8,9,10]. It is used in both sexes, in people of all age categories, regardless of their health condition [6].…”

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