scholarly journals Estimation of body composition and water data depends on the bioelectrical impedance device

2018 ◽  
Vol 9 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Natália T. Bellafronte ◽  
Marina R. Batistuti ◽  
Nathália Z. dos Santos ◽  
Héric Holland ◽  
Elen A. Romão ◽  
...  

Abstract Overweight, obese and chronic kidney disease patients have an altered and negative body composition being its assessment important. Bioelectrical impedance analysis is an easy-to-operate and low-cost method for this purpose. This study aimed to compare and correlate data from single- and multi-frequency bioelectrical impedance spectroscopy applied in subjects with different body sizes, adiposity, and hydration status. It was a cross-sectional study with 386 non-chronic kidney disease volunteers (body mass index from 17 to 40 kg/m2), 30 patients in peritoneal dialysis, and 95 in hemodialysis. Bioelectrical impedance, body composition, and body water data were assessed with single- and multi-frequency bioelectrical impedance spectroscopy. Differences (95% confidence interval) and agreements (Bland-Atman analyze) between devices were evaluated. The intraclass correlation coefficient was used to measure the strength of agreement and Pearson’s correlation to measure the association. Regression analyze was performed to test the association between device difference with body mass index and overhydration. The limits of agreement between devices were very large. Fat mass showed the greatest difference and the lowest intraclass and Pearson’s correlation coefficients. Pearson’s correlation varied from moderate to strong and the intraclass correlation coefficient from weak to substantial. The difference between devices were greater as body mass index increased and was worse in the extremes of water imbalance. In conclusion, data obtained with single- and multi-frequency bioelectrical impedance spectroscopy were highly correlated with poor agreement; the devices cannot be used interchangeably and the agreement between the devices was worse as body mass index and fat mass increased and in the extremes of overhydration.

Author(s):  
Nancy M. Rodig ◽  
Jennifer Roem ◽  
Michael F. Schneider ◽  
Patricia W. Seo-Mayer ◽  
Kimberly J. Reidy ◽  
...  

2011 ◽  
Vol 21 (6) ◽  
pp. 455-461 ◽  
Author(s):  
Abdelrahman Khedr ◽  
Essam Khedr ◽  
Andrew A. House

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Samuel Chan ◽  
Anne Cameron ◽  
Zaimin Wang ◽  
Sree K. Venuthurupalli ◽  
Ken S. Tan ◽  
...  

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Akiko Toda ◽  
Shigeko Hara ◽  
Hiroshi Tsuji ◽  
Yasuji Arase

Abstract Background and Aims Obesity is a risk factor for chronic kidney disease (CKD), but the effect of reducing body mass index (BMI) on the prevention of CKD is controversial. One of reasons for this disagreement is that part of patients with a BMI decrease may have an unfavourable health status. In such cases, the BMI decrease could be a risk factor for the development of CKD. Therefore, by analysing the data of annual health check-ups, we examined an association between BMI change and CKD development to determine whether BMI reduction helps prevent CKD development. Method We analysed the data of 6,959 subjects who underwent annual health check-ups in both 2013 and 2018. By a multivariate logistic regression analysis, we investigated a relationship between BMI change and CKD development within the 5 years between 2013 and 2018. The percent change in the BMI (ΔBMI) was calculated using the following equation: {(BMI in 2018 − BMI in 2013)/BMI in 2013} ×100. For analyses, we classified the subjects into five groups based on their ΔBMI value: (i) severe BMI decrease (ΔBMI <−2.5%); (ii) moderate BMI decrease (ΔBMI ≥−2.5% but <0%); (iii) maintained BMI (ΔBMI ≥0% but <2.5%); (iv) moderate BMI increase (ΔBMI ≥2.5% but <5%); (v) severe BMI increase (ΔBMI ≥5%). For further analysis, we divided the subjects into non-obesity category (basal BMI <25 Kg/m2) and obesity category (basal BMI ≥25 Kg/m2). Subjects with an estimated glomerular filtration rate <60 mL/min./1.73 m2 were defined as having a CKD. Results After adjusting several covariates, compared with the maintained BMI group, the severe BMI decrease group showed a significantly low risk of CKD development (odds ratio (OR) 0.70, 95% confidence intervals (CI) 0.54-0.91, p <0.01) and the severe BMI increase group had a significantly high risk (OR 1.40, CI 1.08-1.81, p = 0.01). A farther analysis revealed that the OR of CKD development for the severe BMI increase group in the obesity category was higher than that in the non-obesity category (OR 1.75 vs. 1.29). Conclusion In subjects who underwent annual health check-ups, BMI reduction had a significant effect on the prevention of CKD development, whereas an increase in the BMI was a risk factor for CKD development. Moreover, by severe increase in the BMI, obesity subjects showed higher risk of CKD development than non-obesity subjects.


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