Bioelectrical Impedance Analysis Versus Quantitative Computer Tomography and Anthropometry for The Assessment of Body Composition Parameters in China

Abstract Objectives: The study evaluated the bioelectrical impedance analysis (BIA) device against the body composition parameters measured by anthropometry and quantitative computer tomography (QCT) to assess its reliability and accuracy among Chinese adults.Methods: Body composition parameters (waist circumstance [WC], body weight, body mass index [BMI] and visceral fat area [VFA]) were measured in 1,379 subjects (20-81 years old), both manually and by BIA, and in 1,317 of 1,379 subjects by QCT. The correlation coefficients were calculated between these measurements. Linear regression models were used to estimate each parameter based on the BIA measurements. Multivariate linear regression models were applied to calculate the correlation among VFA, WC and BMI. The concordance correlation coefficient from the Bland-Altman plots were calculated for VFA between QCT and BIA. Results: High correlation was observed for WC, weight and BMI (adjusted R2=0.78, 0.99 and 0.99) between BIA and anthropometry, and for VFA between BIA and QCT in both sex (adjusted R2=0.549 and 0.462). The multivariate regression models were established for the accurate prediction of QCT-VFA using WC and BMI (adjusted R2=0.603). In addition, a strong consistency of VFA measurement was found between BIA and QCT.Conclusion: Body composition parameters could be accurately determined in clinic using simple measurements of BIA. WC is more reliable as a predictor of visceral fat in the metabolic syndrome. Being non-invasive, accurate and free of radiation, BIA can be used as a safe and convenient tool in scientific research and clinical practice for the quick measurement of anthropometric parameters.

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Linear Regression Models for Total Body Water Measurement Based on Bioelectrical Impedance Analysis

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) ◽

10.1109/icasert.2019.8934698 ◽

2019 ◽

Author(s):

Farzana Khatun ◽

Md. Sohel Rana ◽

Ajay Krishno Sarkar

Keyword(s):

Linear Regression ◽

Total Body Water ◽

Regression Models ◽

Bioelectrical Impedance Analysis ◽

Bioelectrical Impedance ◽

Impedance Analysis ◽

Body Water ◽

Linear Regression Models ◽

Total Body ◽

Water Measurement

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Linear Regression Models for Fat Free Mass Measurement Based on Bioelectrical Impedance Analysis

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) ◽

10.1109/ccece.2019.8861576 ◽

2019 ◽

Author(s):

Tahasin Ahmed Fahim ◽

Md. Shamim Anower ◽

Mst. Farzana Khatun ◽

Syed Tauhid Zuhori

Keyword(s):

Linear Regression ◽

Regression Models ◽

Bioelectrical Impedance Analysis ◽

Mass Measurement ◽

Bioelectrical Impedance ◽

Impedance Analysis ◽

Fat Free Mass ◽

Linear Regression Models

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Measurement of total and visceral fat mass in young adult women: a comparison of MRI with anthropometric measurements with and without bioelectrical impedance analysis

British Journal of Radiology ◽

10.1259/bjr.20190874 ◽

2020 ◽

Vol 93 (1110) ◽

pp. 20190874

Author(s):

Matthias F. Froelich ◽

Marina Fugmann ◽

Charlotte Lütke Daldrup ◽

Holger Hetterich ◽

Eva Coppenrath ◽

...

Keyword(s):

Linear Regression ◽

Predictive Value ◽

Regression Models ◽

Bioelectrical Impedance Analysis ◽

Bioelectrical Impedance ◽

Impedance Analysis ◽

Anthropometric Measurements ◽

Linear Regression Models ◽

Multivariable Model ◽

Hip Circumference

Objective: MRI is established for measurement of body fat mass (FM) and abdominal visceral adipose tissue (VAT). Anthropometric measurements and bioelectrical impedance analysis (BIA) have been proposed as surrogates to estimation by MRI. Aim of this work is to assess the predictive value of these methods for FM and VAT measured by MRI. Methods: Patients were selected from cohort study PPS-Diab (prediction, prevention and subclassification of Type 2 diabetes). Total FM and VAT were quantified by MRI and BIA together with clinical variables like age, waist and hip circumference and height. Least-angle regressions were utilized to select anthropometric and BIA parameters for their use in multivariable linear regression models to predict total FM and VAT. Bland–Altman plots, Pearson correlation coefficients, Wilcoxon signed-rank tests and univariate linear regression models were applied. Results: 116 females with 35 ± 3 years and a body mass index of 25.1 ± 5.3 kg/m2 were included into the analysis. A multivariable model revealed weight (β = 0.516, p < 0.001), height (β = −0.223, p < 0.001) and hip circumference (β = 0.156, p = 0.003) as significantly associated with total FM measured by MRI. A additional multivariable model also showed a significant predictive value of FMBIA (β = 0.583, p < 0.001) for FM. In addition, waist circumference (β = 0.054, p < 0.001), weight (β = 0.016, p = 0.031) in one model and FMBIA (β = 0.026, p = 0.018) in another model were significantly associated with VAT quantified by MRI. However, deviations reached more than 5 kg for total FM and more than 1 kg for VAT. Conclusion: Anthropometric measurements and BIA show significant association with total FM and VAT. Advances in knowledge: As these measurements show significant deviations from the absolute measured values determined by MRI, MRI should be considered the gold-standard for quantification.

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Comparison of Bioelectrical Impedance Analysis and Computed Tomography on Body Composition Changes Including Visceral Fat After Bariatric Surgery in Asian Patients with Obesity

Obesity Surgery ◽

10.1007/s11695-021-05569-6 ◽

2021 ◽

Author(s):

Ja Kyung Lee ◽

Young Suk Park ◽

Kyuho Kim ◽

Tae Jung Oh ◽

Won Chang

Keyword(s):

Computed Tomography ◽

Bariatric Surgery ◽

Body Composition ◽

Visceral Fat ◽

Bioelectrical Impedance Analysis ◽

Bioelectrical Impedance ◽

Impedance Analysis ◽

Asian Patients ◽

Composition Changes

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Evaluation of the Relationships between Simple Anthropometric Measures and Bioelectrical Impedance Assessment Variables with Multivariate Linear Regression Models to Estimate Body Composition and Fat Distribution in Adults: Preliminary Results

Biology ◽

10.3390/biology10111209 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1209

Author(s):

Danúbia da Cunha de Sá-Caputo ◽

Anelise Sonza ◽

Ana Carolina Coelho-Oliveira ◽

Juliana Pessanha-Freitas ◽

Aline Silva Reis ◽

...

Keyword(s):

Body Composition ◽

Linear Regression ◽

Regression Models ◽

Bioelectrical Impedance ◽

Fat Distribution ◽

Multivariate Linear Regression ◽

Body Fat Percentage ◽

Linear Regression Models ◽

Fat Percentage ◽

Anthropometric Measures

Background: Overweight and obesity are conditions associated with sedentary lifestyle and accumulation of abdominal fat, determining increased mortality, favoring chronic diseases, and increasing cardiovascular risk. Although the evaluation of body composition and fat distribution are highly relevant, the high cost of the gold standard techniques limits their wide utilization. Therefore, the aim of this work was to explore the relationships between simple anthropometric measures and BIA variables using multivariate linear regression models to estimate body composition and fat distribution in adults. Methods: In this cross-sectional study, sixty-eight adult individuals (20 males and 48 females) were subjected to bioelectrical impedance analysis (BIA), anthropometric measurements (waist circumference (WC), neck circumference (NC), mid-arm circumference (MAC)), allowing the calculation of conicity index (C-index), fat mass/fat-free mass (FM/FFM) ratios, body mass index (BMI) and body shape index (ABSI). Statistical analyzes were performed with the R program. Nonparametric Statistical tests were applied to compare the characteristics of participants of the groups (normal weight, overweight and obese). For qualitative variables, the Fisher’s exact test was applied, and for quantitative variables, the paired Wilcoxon signed-rank test. To evaluate the linear association between each pair of variables, the Pearson correlation coefficient was calculated, and Multivariate linear regression models were adjusted using the stepwise variable selection method, with Akaike Information Criterion (p ≤ 0.05). Results: BIA variables with the highest correlations with anthropometric measures were total body water (TBW), body fat percentage (BFP), FM, FFM and FM/FFM. The multiple linear regression analysis showed, in general, that the same variables can be estimated through simple anthropometric measures. Conclusions: The assessment of fat distribution in the body is desirable for the diagnosis and definition of obesity severity. However, the high cost of the instruments (dual energy X-ray absorptiometry, hydrostatic weighing, air displacement plethysmography, computed tomography, magnetic resonance) to assess it, favors the use of BMI in the clinical practice. Nevertheless, BMI does not represent a real fat distribution and body fat percentage. This highlights the relevance of the findings of the current study, since simple anthropometric variables can be used to estimate important BIA variables that are related to fat distribution and body composition.

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