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Open Access Research article

DXA, bioelectrical impedance, ultrasonography and biometry for the estimation of fat and lean mass in cats during weight loss

Naida C Borges1*, Ricardo S Vasconcellos2, Aulus C Carciofi2, Karina N V Gonçalves2, Francisco J A Paula3, Daniel E Faria Filho4 and Júlio C Canola2

Author Affiliations

1 Veterinary Hospital, Veterinary and Zootechny School, Federal University of Goiás, Goias State, Brazil

2 College of Agrarian and Veterinarian Sciences, University of Jaboticabal, São Paulo State, Brazil

3 Faculty of Medicine of Ribeirão Preto, University of São Paulo, São Paulo State, Brazil

4 Department of Zootechny, Federal University of Minas Gerais, Minas Gerais State, Brazil

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BMC Veterinary Research 2012, 8:111  doi:10.1186/1746-6148-8-111

Published: 10 July 2012

Abstract

Background

Few equations have been developed in veterinary medicine compared to human medicine to predict body composition. The present study was done to evaluate the influence of weight loss on biometry (BIO), bioimpedance analysis (BIA) and ultrasonography (US) in cats, proposing equations to estimate fat (FM) and lean (LM) body mass, as compared to dual energy x-ray absorptiometry (DXA) as the referenced method. For this were used 16 gonadectomized obese cats (8 males and 8 females) in a weight loss program. DXA, BIO, BIA and US were performed in the obese state (T0; obese animals), after 10% of weight loss (T1) and after 20% of weight loss (T2). Stepwise regression was used to analyze the relationship between the dependent variables (FM, LM) determined by DXA and the independent variables obtained by BIO, BIA and US. The better models chosen were evaluated by a simple regression analysis and means predicted vs. determined by DXA were compared to verify the accuracy of the equations.

Results

The independent variables determined by BIO, BIA and US that best correlated (p < 0.005) with the dependent variables (FM and LM) were BW (body weight), TC (thoracic circumference), PC (pelvic circumference), R (resistance) and SFLT (subcutaneous fat layer thickness). Using Mallows’Cp statistics, p value and r2, 19 equations were selected (12 for FM, 7 for LM); however, only 7 equations accurately predicted FM and one LM of cats.

Conclusions

The equations with two variables are better to use because they are effective and will be an alternative method to estimate body composition in the clinical routine. For estimated lean mass the equations using body weight associated with biometrics measures can be proposed. For estimated fat mass the equations using body weight associated with bioimpedance analysis can be proposed.