BACKGROUND
the body composition is used to describe the percentages of fat, bone and muscle.
therefore, two people of equal height and body weight may look completely different from each other because they have a different body composition.
fat mass  and lean mass  can be estimated and determined by different techniques, varying in precision and accuracy.
methods described thus for dogs and cats include body mass index, body condition score , biometry , dilution methods, bioelectrical impedance analysis , ultrasonography  and dual energy x-ray absorptiometry   <cit> .
many equations are available to estimate the body composition of humans.
due to the convenience of application, bio is the non-invasive method most used to characterize groups and populations  <cit> .
regardless of the method used to predict the body composition, the validity of an equation depends on the degree of precision and accuracy with which the variables are estimated within or outside the population of origin  <cit> .
therefore, most of the equations are specific, and can achieve the highest predictive ability when applied to a population similar to that from which were derived  <cit> .
few equations have been developed in veterinary medicine compared to human medicine to predict body composition.
associating results of bio and bia, different predictive equations for total body water, body protein, lm and fm have been proposed  <cit> .
for dogs and cats, equations for estimation of body composition using chemical analysis  <cit> , biometry  <cit>  and ultrasonography  <cit>  have also been proposed.
the bcs system, like the one proposed by laflamme  <cit>  still is the method most widely used by clinicians to monitor changes in body condition during obesity treatment in cats.
however, practical equations, easy to use that allow a more accurate prediction of lm and fm than the bcs system could be of value during the evaluation and follow-up of nutritional interventions in cats.
equations to predict body composition are very useful in terms of practical aspects, especially during nutritional interventions and evaluation of individual responses to nutritional therapy.
noninvasive reference methods, such as dxa or deuterium isotope dilution, although accurate and sensitive are not available in all research laboratories and are not feasible in most clinical practices.
in these contexts us and bia could represent less expensive and available equipment.
the validity and utility of these instruments and methods, however, depend on finding equations that generate estimates of body composition from the variables generated by them.
such equations must be validated, and their accuracy and precision must be determined.
the objective of the present study was to evaluate the influence of weight loss on biometry, bioelectrical impedance and ultrasonography in cats, proposing equations to estimate fat and lean body mass, as compared to dxa as the referenced method.
RESULTS
dxa, bcs, bio, bia and us
body composition analyzed by dxa, and bcs, bio, bia and us measurement are described in table  <dig>  the independent variables determined by bio, bia and us that best correlated with the dependent variables  were bw, tc, pc, r and sflt .
*f  and *m ;  <dig> according to laflamme .
bw, body weight; bl, body length; tc, thoracic circumference; pc, pelvic circumference; rtl, right thoracic limb length; rtl, right pelvic limb length; r, resistance; xc, reactance; sflt, subcutaneous fat layer thickness.
predictive equations for fm and lm
in order to correct factors that eventually might affect the precision and accuracy of the equations, all results of bio, bia and us were previously tested for homogeneity, linearity and multicolinearity  <cit> .
the stepwise multiple regression analysis produced  <dig> equations,  <dig> for fm estimation and eight for lm estimation.
the  <dig> better models  obtained are listed in tables  <dig> and  <dig>
m, males; f, females; bw, body weight; t <dig>  ; t <dig>  ; t <dig> ; bl, body length; pc, pelvic circumference; tc, thoracic circumference; rtl, right thoracic limb length; r, resistance; sflt, subcutaneous fat layer thickness.
m, males; f, females; bw, body weight;t <dig>  ; t <dig>  ; t <dig> ; bl, body length; pc, pelvic circumference; tc, thoracic circumference; rtl, right thoracic limb length; bl2/r, impedance index; r, resistance; sflt, subcutaneous fat layer thickness.
separating data according to gender, for fm  the association of biometric measurements accounted for 94% of the variation in fm content of males .
for females two equations for fm estimation were chosen, number  <dig>  and number  <dig> .
when data were separated by time of evaluation six equations were proposed , one using bw and r , one using bio, us and bia all together , one using the combination of bio and us , and three using only one measure, which are bia , us  and bio .
finally, analysis independent of gender and obese state  resulted in three equations to estimate fm, one using bio, us and bia , one using bio and bia , and one using bio and us .
for lm prediction , the association of biometric measurements accounted for 92% of the variation for males .
regarding females the better model obtained was described by equation  <dig> .
the analysis independent of gender and obese state  resulted in two equations to predict lm, one with a combination of bw and the impedance index, in other words, bl2/r , and the other with the bw, impedance index and sflt .
the comparisons between dxa results and values predicted by the equations are listed in table  <dig>  for fm, similar results were obtained between dxa and estimations generated by equations  <dig> to  <dig> .
for lm estimation, only the result of equation  <dig> was similar to dxa .
the results of these equations are illustrated in figure  <dig>
m, males; f, females; t <dig> ; t <dig> ; t <dig> ; y = estimated value; x = determined value.
discussion
dxa, bcs, bio, bia and us
the differences in body composition observed between genders and after weight loss confirm data reported by several authors in studies on humans  <cit>  and animals  <cit> .
these data support the need for studies of body composition considering the influence and importance of breed, sex, and sexual condition, allowing a better understanding the influence of these factors on cat body composition.
considering that males had lower fm  them females, we can conclude that tc and pc values do not necessarily reflect the fm  of a particular cat.
these results must consider the gender.
the current study confirmed that the 9-point bcs  <cit>  correlated well with body fat mass estimated by dxa.
the main value of this score system is that it helps clinicians and owners to estimate the ideal body composition for their pets.
however, a high coefficient of variation  between bcs and fm  was verified, demonstration the need for more precise procedures for nutritional studies.
the us measurement of the sflt proved to be a sensitive method since an important reduction of subcutaneous fat was verified during weight loss.
sflt results alone also presented a good correlation with dxa results of fm.
however, the results obtained here could not be compared because this method was not utilized in the consulted bibliography.
ultrasonography is of simple application, is available in clinical practice, and can be used to monitor changes in body composition during nutritional intervention.
however, future studies are necessary to validate it.
predictive equations for fm and lm
adopting the criteria used by macneil  <cit> , guo et al.
<cit>  and freund and littell  <cit>  for the selection of predictive models, i.e., statistical significance of the independent variables , high coefficient of determination , and lower mallows’cp statistics and rmse values,  <dig> equation for fm and lm prediction were selected.
different equations were generated for males and females.
the suggested equation for males used only biometric measurements, while bia and us proved to be valuable for females.
the amount of body fat mass also influenced the equations suggested for fm and lm estimations.
bia was valuable for more obese animals, while biometry was important for less fat animals.
some of the equations generated in the present study, however, are too complex for practical use.
equations  <dig>   <dig> and  <dig>  for example, were selected in the mathematical process but use three types of animal evaluation .
although several equations resulted in fm and lm values statistically similar to that determined by dxa, only the equations  <dig>   <dig>   <dig>  and  <dig> resulted in means that differed less than 5% from the dxa values.
equation  <dig> is very complicated to be used in practice; however, for research purposes it could be sensitive enough to understand changes in body composition during diet or protocols for weight loss evaluations in cats.
equation  <dig> appears to be the most interesting for fm estimations in practice, using simple biometry and bia.
for obese cats, fm could also be estimated with equation  <dig>  again using body weight and bia.
several studies on human beings also reported bia as a good technique for the prediction of fm  <cit> .
for humans, bia is also suggested as a good method for lm  <cit>  estimations, which were not verified for cats in the present study.
for lm estimations, no equation could be found in the present assay .
controversies about the use of bia for lm estimation for humans, however, still exist  <cit> .
the independent variable sflt was used in only one equation for lm estimation, i.e.
equation  <dig>  however, the result obtained was 17% percent lower than the dxa result.
this was surprising because bia, whose results did not change with weight loss, was more correlated with body fm estimations than sflt, which presented a significant reduction during the weight loss.
anyway, the measurement of sflt appears to be an interesting variable to study in other experiments about the prediction of cat body composition.
in the present study the equations were developed and further tested in the same animals to assess their accuracy.
several measurements were made on the same animals along the process of weight loss.
if, on the one hand, this allowed an understanding of the changes in the variables studied regarding the weight loss of cats, we must consider that the use of the same animals may have influenced the results, a fact that should be considered with caution.
another important aspect to consider during the process of development and validation of predictive equations is the introduction of age ranges, weight ranges, height ranges, sexual status, and breeds.
compared with human beings, few studies have been conducted with this purpose in companion animals.
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.

 the body composition is used to describe the percentages of fat, bone and muscle. therefore, two people of equal height and body weight may look completely different from each other because they have a different body composition. fat mass and lean mass can be estimated and determined by different techniques, varying in precision and accuracy. methods described thus for dogs and cats include body mass index, body condition score, biometry, dilution methods, bioelectrical impedance analysis, ultrasonography and dual energy x-ray absorptiometry. many equations are available to estimate the body composition of humans. due to the convenience of application, bio is the non-invasive method most used to characterize groups and populations. regardless of the method used to predict the body composition, the validity of an equation depends on the degree of precision and accuracy with which the variables are estimated within or outside the population of origin. therefore, most of the equations are specific, and can achieve the highest predictive ability when applied to a population similar to that from which were derived. few equations have been developed in veterinary medicine compared to human medicine to predict body composition. associating results of bio and bia, different predictive equations for total body water, body protein, lm and fm have been proposed. for dogs and cats, equations for estimation of body composition using chemical analysis, biometry and ultrasonography have also been proposed. the bcs system, like the one proposed by laflamme still is the method most widely used by clinicians to monitor changes in body condition during obesity treatment in cats. however, practical equations, easy to use that allow a more accurate prediction of lm and fm than the bcs system could be of value during the evaluation and follow-up of nutritional interventions in cats. equations to predict body composition are very useful in terms of practical aspects, especially during nutritional interventions and evaluation of individual responses to nutritional therapy. noninvasive reference methods, such as dxa or deuterium isotope dilution, although accurate and sensitive are not available in all research laboratories and are not feasible in most clinical practices. in these contexts us and bia could represent less expensive and available equipment. the validity and utility of these instruments and methods, however, depend on finding equations that generate estimates of body composition from the variables generated by them. such equations must be validated, and their accuracy and precision must be determined. the objective of the present study was to evaluate the influence of weight loss on biometry, bioelectrical impedance and ultrasonography in cats, proposing equations to estimate fat and lean body mass, as compared to dxa as the referenced method.  dxa, bcs, bio, bia and us body composition analyzed by dxa, and bcs, bio, bia and us measurement are described in table  the independent variables determined by bio, bia and us that best correlated with the dependent variables were bw, tc, pc, r and sflt. *f and *m; according to laflamme. bw, body weight; bl, body length; tc, thoracic circumference; pc, pelvic circumference; rtl, right thoracic limb length; rtl, right pelvic limb length; r, resistance; xc, reactance; sflt, subcutaneous fat layer thickness. predictive equations for fm and lm in order to correct factors that eventually might affect the precision and accuracy of the equations, all results of bio, bia and us were previously tested for homogeneity, linearity and multicolinearity. the stepwise multiple regression analysis produced equations, for fm estimation and eight for lm estimation. the better models obtained are listed in tables  and m, males; f, females; bw, body weight; t; t; t; bl, body length; pc, pelvic circumference; tc, thoracic circumference; rtl, right thoracic limb length; r, resistance; sflt, subcutaneous fat layer thickness. m, males; f, females; bw, body weight;t; t; t; bl, body length; pc, pelvic circumference; tc, thoracic circumference; rtl, right thoracic limb length; bl2/r, impedance index; r, resistance; sflt, subcutaneous fat layer thickness. separating data according to gender, for fm the association of biometric measurements accounted for 94% of the variation in fm content of males. for females two equations for fm estimation were chosen, number and number. when data were separated by time of evaluation six equations were proposed, one using bw and r, one using bio, us and bia all together, one using the combination of bio and us, and three using only one measure, which are bia, us and bio. finally, analysis independent of gender and obese state resulted in three equations to estimate fm, one using bio, us and bia, one using bio and bia, and one using bio and us. for lm prediction, the association of biometric measurements accounted for 92% of the variation for males. regarding females the better model obtained was described by equation. the analysis independent of gender and obese state resulted in two equations to predict lm, one with a combination of bw and the impedance index, in other words, bl2/r, and the other with the bw, impedance index and sflt. the comparisons between dxa results and values predicted by the equations are listed in table  for fm, similar results were obtained between dxa and estimations generated by equations to. for lm estimation, only the result of equation was similar to dxa. the results of these equations are illustrated in figure  m, males; f, females; t; t; t; y = estimated value; x = determined value. discussion dxa, bcs, bio, bia and us the differences in body composition observed between genders and after weight loss confirm data reported by several authors in studies on humans and animals. these data support the need for studies of body composition considering the influence and importance of breed, sex, and sexual condition, allowing a better understanding the influence of these factors on cat body composition. considering that males had lower fm them females, we can conclude that tc and pc values do not necessarily reflect the fm of a particular cat. these results must consider the gender. the current study confirmed that the 9-point bcs correlated well with body fat mass estimated by dxa. the main value of this score system is that it helps clinicians and owners to estimate the ideal body composition for their pets. however, a high coefficient of variation between bcs and fm was verified, demonstration the need for more precise procedures for nutritional studies. the us measurement of the sflt proved to be a sensitive method since an important reduction of subcutaneous fat was verified during weight loss. sflt results alone also presented a good correlation with dxa results of fm. however, the results obtained here could not be compared because this method was not utilized in the consulted bibliography. ultrasonography is of simple application, is available in clinical practice, and can be used to monitor changes in body composition during nutritional intervention. however, future studies are necessary to validate it. predictive equations for fm and lm adopting the criteria used by macneil, guo et al. and freund and littell for the selection of predictive models, i.e., statistical significance of the independent variables, high coefficient of determination, and lower mallows’cp statistics and rmse values, equation for fm and lm prediction were selected. different equations were generated for males and females. the suggested equation for males used only biometric measurements, while bia and us proved to be valuable for females. the amount of body fat mass also influenced the equations suggested for fm and lm estimations. bia was valuable for more obese animals, while biometry was important for less fat animals. some of the equations generated in the present study, however, are too complex for practical use. equations and for example, were selected in the mathematical process but use three types of animal evaluation. although several equations resulted in fm and lm values statistically similar to that determined by dxa, only the equations and resulted in means that differed less than 5% from the dxa values. equation is very complicated to be used in practice; however, for research purposes it could be sensitive enough to understand changes in body composition during diet or protocols for weight loss evaluations in cats. equation appears to be the most interesting for fm estimations in practice, using simple biometry and bia. for obese cats, fm could also be estimated with equation again using body weight and bia. several studies on human beings also reported bia as a good technique for the prediction of fm. for humans, bia is also suggested as a good method for lm estimations, which were not verified for cats in the present study. for lm estimations, no equation could be found in the present assay. controversies about the use of bia for lm estimation for humans, however, still exist. the independent variable sflt was used in only one equation for lm estimation, i.e. equation however, the result obtained was 17% percent lower than the dxa result. this was surprising because bia, whose results did not change with weight loss, was more correlated with body fm estimations than sflt, which presented a significant reduction during the weight loss. anyway, the measurement of sflt appears to be an interesting variable to study in other experiments about the prediction of cat body composition. in the present study the equations were developed and further tested in the same animals to assess their accuracy. several measurements were made on the same animals along the process of weight loss. if, on the one hand, this allowed an understanding of the changes in the variables studied regarding the weight loss of cats, we must consider that the use of the same animals may have influenced the results, a fact that should be considered with caution. another important aspect to consider during the process of development and validation of predictive equations is the introduction of age ranges, weight ranges, height ranges, sexual status, and breeds. compared with human beings, few studies have been conducted with this purpose in companion animals.  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. 
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. the present study was done to evaluate the influence of weight loss on biometry, bioimpedance analysis and ultrasonography in cats, proposing equations to estimate fat and lean body mass, as compared to dual energy x-ray absorptiometry as the referenced method. for this were used gonadectomized obese cats in a weight loss program. dxa, bio, bia and us were performed in the obese state, after 10% of weight loss and after 20% of weight loss. 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. few equations have been developed in veterinary medicine compared to human medicine to predict body composition. stepwise regression was used to analyze the relationship between the dependent variables determined by dxa and the independent variables obtained by bio, bia and us. the independent variables determined by bio, bia and us that best correlated with the dependent variables were bw, tc, pc, r and sflt.
 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, bioimpedance analysis and ultrasonography in cats, proposing equations to estimate fat and lean body mass, as compared to dual energy x-ray absorptiometry as the referenced method. for this were used gonadectomized obese cats in a weight loss program. dxa, bio, bia and us were performed in the obese state, after 10% of weight loss and after 20% of weight loss. stepwise regression was used to analyze the relationship between the dependent variables 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.  the independent variables determined by bio, bia and us that best correlated with the dependent variables were bw, tc, pc, r and sflt. using mallows’cp statistics, p value and r equations were selected; however, only equations accurately predicted fm and one lm of cats.  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. 