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

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

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

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

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 ^{2}, 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.

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 (FM) and lean mass (LM) 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 (BCS), biometry (BIO), dilution methods, bioelectrical impedance analysis (BIA), ultrasonography (US) and dual energy x-ray absorptiometry (DXA)

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

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

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

**Variables**

**Animals**

**T0**

**T1**

**T2**

*F (n = 8) and *M (n = 8); ^{1} according to Laflamme (1997).

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.

BCS^{1}

F*

8.7(0.5)

8.0 (0.8)

6.7 (1.1)

M*

8.6 (0.5)

7.9 (0.7)

6.0 (0.8)

TBM (g)

F

4576.0 (914.7)

4005.0 (873.2)

3581.0 (770.0)

M

5258.0 (1175.1)

4617.0 (1145.0)

4105.0 (1055.9)

FM(g)

F

1929.0 (540.3)

1430.0 (541.6)

1026.0 (371.5)

M

1968.0 (675.0)

1426.0 (670.4)

1077.0 (534.3)

FM (%)

F

41.6 (4.0)

34.9 (7.4)

28.0 (4.9)

M

36.7 (4.6)

29.7 (6.5)

25.3 (6.3)

LM (g)

F

2538.0 (383.1)

2474.0 (431.2)

2458.0 (425.0)

M

3153.0 (512.9)

3063.0 (488.5)

2902.0 (586.7)

LM (%)

F

56.0 (4.0)

62.6 (7.3)

69.3 (4.8)

M

60.7 (4.5)

67.5 (6.4)

71.6 (6.1)

BW (g)

F

4781.3 (839.3)

4228.8 (868.8)

3788.8 (769.8)

M

5431.3 (1174.4)

4881.3 (1125.8)

4366.3 (1036.4)

BL (cm)

F

47.5 (4.0)

47.5 (4.0)

47.1 (3.6)

M

50.5 (2.1)

50.8 (2.1)

49.9 (2.3)

TC (cm)

F

39.6 (2.4)

36.2 (4.4)

34.0 (3.2)

M

41.3 (3.1)

38.0 (3.8)

35.6 (3.8)

PC (cm)

F

42.3 (3.3)

38.5 (4.9)

36.3 (4.1)

M

46.5 (4.9)

39.8 (5.2)

36.9 (4.9)

RTL (cm)

F

16.9 (1.8)

16.9 (1.1)

16.7 (1.3)

M

17.8 (1.7)

18.2 (1.6)

17.3 (1.8)

RPL (cm)

F

18.6 (1.8)

19.0 (1.6)

18.8 (1.6)

M

19.3 (1.6)

19.7 (1.4)

19.3 (1.0)

SFLT (cm)

F

0.065 (0.02)

0.048 (0.01)

0.023 (0.01)

M

0.071 (0.03)

0.048 (0.02)

0.020 (0.02)

R (Ω)

F

176.9 (14.3)

180.7 (13.2)

185.8 (21.5)

M

167.3 (17.4)

162.9 (16.0)

182.1 (23.6)

Xc (Ω)

F

27.0 (3.5)

28.2 (3.1)

28.3 (4.0)

M

28.5 (2.0)

23.3 (6.2)

24.9 (4.3)

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

**Equations for fat mass estimation (kg)**

**Equation number**

^{2}

**Cp**

**p**

**RMSE (kg)**

M, males; F, females; BW, body weight; T0, (time zero – obese animals); T1, (time one −10% reduction in BW); T2 (time two – 20% reduction in BW); BL, body length; PC, pelvic circumference; TC, thoracic circumference; RTL, right thoracic limb length; R, resistance; SFLT, subcutaneous fat layer thickness.

M (n = 24)

0.7BW + 3.22PC/TC–0.005BW/RTL-4

1

0.94

1.23

=0.04

0.19

F (n = 24)

−0.07BL + 0.9BW + 0.008R - 0.60

2

0.93

1.97

=0.01

0.17

0.4BW + 11.50 SFLT-0.69

3

0.94

3.46

=0.01

0.16

M and F (n = 48)

0.4BW + 0.006R + 9.67 SFLT-1.84

4

0.90

1.25

=0.009

0.21

−0.05BL + 0.7BW + 0.007R - 0.60

5

0.88

1.95

=0.009

0.24

0.3BW + 9.97 SFLT-0.57

6

0.88

2.90

=0.009

0.23

T0 (n = 16)

0.5BW + 0.007R - 1.88

7

0.86

6.50

=0.05

0.25

T1 (n = 16)

0.4BW + 0.01R + 16.13 SFLT-2.71

8

0.93

1.20

=0.009

0.17

0.6BW + 0.01R-2.84

9

0.86

1.01

=0.04

0.24

0.3BW + 15.49 SFLT-0.69

10

0.86

0.75

=0.04

0.24

T2 (n = 16)

0.5BW + 3.55PC/TC–0.005BW/RTL + 8.48 SFLT-3.74

11

0.94

5.00

=0.02

0.12

−0.022BL + 0.7BW + 4.24PC/TC–0.006BW/RTL-3.65

12

0.94

4.50

=0.02

0.13

**Equations for lean mass estimation (kg)**

**Equation number**

^{2}

**Cp**

**p**

**RMSE (kg)**

M, males; F, females; BW, body weight;T0, (time zero – obese animals); T1, (time one – 10% reduction in BW); T2 (time two – 20% reduction in BW); BL, body length; PC, pelvic circumference; TC, thoracic circumference; RTL, right thoracic limb length; BL^{2}/R, impedance index; R, resistance; SFLT, subcutaneous fat layer thickness.

M (n = 24)

0.3BW + 3.0PC/TC – 0.003BW/RTL + 3.51

13

0.92

2.31

=0.04

0.16

F (n = 24)

−0.003BW/RTL + 0.11BL^{2}/R + 0.40

14

0.83

0.45

=0.04

0.17

M and F (n = 48)

0.2BW + 0.09BL^{2}/R + 0.25

15

0.85

4.72

=0.0001

0.21

0.04 BW + 0.07 BL^{2}/R – 3.7SFLT + 0.17

16

0.87

2.65

=0.05

0.20

T0 (n = 16)

0.4BW + 0.08BL^{2}/R - 0.05

17

0.86

4.23

=0.05

0.22

T1 (n = 16)

0.3BW + 0.08BL^{2}/R + 0.20

18

0.87

6.40

=0.02

0.17

T2 (n = 16)

0.3BW – 2.71PC/TC – 0.004BW/RTL + 0.05BL^{2}/R + 2.73

19

0.97

3.09

=0.02

0.10

Separating data according to gender, for FM (Table ^{2} = 0.93; Cp = 1.97; p < 0.01) and number 3 (^{2} = 0.94; Cp = 3.46; p < 0.01).

When data were separated by time of evaluation six equations were proposed (Table ^{2} = 0.86, equation 7), one using BIO, US and BIA all together (equation 8), one using the combination of BIO and US (^{2} = 0.94, equation 11), and three using only one measure, which are BIA (^{2} = 0.9486, equation 9), US (^{2} = 0.86, equation 10) and BIO (^{2} = 0.94, equation 12). Finally, analysis independent of gender and obese state (^{2} =0.90, equation 4), one using BIO and BIA (^{2} = 0.87, equation 5), and one using BIO and US (^{2} = 0.88, equation 6).

For LM prediction (Table ^{2} = 0.83; Cp = 0.45; p = 0.04). The analysis independent of gender and obese state (^{2}/R (^{2} = 0.85, equation 15), and the other with the BW, impedance index and SFLT (^{2} = 0.87, equation 16).

The comparisons between DXA results and values predicted by the equations are listed in Table

**DXA (kg)**

**Estimated values (kg)**

**Equation number**

**p value (****-Test)**

**Linear regression**

^{2}

M, males; F, females; T0 (before weight loss [WL]); T1 (after 10% of WL); T2 (after 20% of WL); y = estimated value; x = determined value.

**FM**

M (

1.49 (0.71)

2.88 (0.94)

1

<0.0001

y = 0.728x - 0.605

0.93

F (

1.46 (0.60)

1.37 (0.57)

2

= 0.60

y = 1.014x + 0.069

0.92

1.53 (0.59)

3

= 0.67

y = 0.976x - 0.036

0.94

M and F (

1.48 (0.65)

1.49 (0.62)

4

= 0.90

y = 1.002x - 0.019

0.91

1.40 (0.59)

5

= 0.55

y = 1.034x + 0.028

0.88

1.26 (0.56)

6

= 0.09

y = 1.086x + 0.105

0.88

T0 (

1.95 (0.59)

1.89 (0.52)

7

= 0.78

y = 1.042x - 0.023

0.85

T1 (

1.43 (0.45)

1.60 (0.61)

8

= 0.43

y = 0.932x - 0.058

0.93

1.61 (0.58)

9

= 0.38

y = 0.945x - 0.094

0.85

1.41 (0.54)

10

= 0.94

y = 1.004x + 0.010

0.86

T2 (

1.05 (0.45)

2.21 (0.61)

11

<0.0001

y = 0.691x - 0.473

0.89

2.59 (0.68)

12

<0.0001

y = 0.612x - 0.535

0.87

**LM**

M (

3.04 (0.52)

8.19 (0.48)

13

<0.0001

y = 0.882x - 4.179

0.67

F (

2.49 (0.40)

1.79 (0.27)

14

<0.0001

y = 1.299x + 0.168

0.79

M and F (

2.76 (0.53)

1.59 (0.25)

15

<0.0001

y = 1.868x - 0.202

0.75

1.15 (0.19

16

<0.0001

y = 2.047x + 0.402

0.52

T0 (

2.85 (0.54)

3.13 (0.55)

17

= 0.15

y = 0.925x - 0.051

0.87

T1 (

2.77 (0.54)

1.68 (0.36)

18

<0.0001

y = 2.008x - 0.210

0.78

T2 (

2.68 (0.54)

0.44 (0.15)

19

<0.0001

y = 2.651x + 1.509

0.56

Regression analysis of determinate (by DXA) vs. predicted values by the equations 2, 3, 4, 5, 6, 7, 8, 9 and 10 for fat mass (FM) and equation 17 for lean mass (LM) in cats.

**Regression analysis of determinate (by DXA) vs. predicted values by the equations 2, 3, 4, 5, 6, 7, 8, 9 and 10 for fat mass (FM) and equation 17 for lean mass (LM) in cats.**

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

The current study confirmed that the 9-point BCS

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 ^{2}), and lower Mallows’Cp statistics and RMSE values, 19 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 4, 8 and 16, for example, were selected in the mathematical process but use three types of animal evaluation (biometry, BIC and US).

Although several equations resulted in FM and LM values statistically similar to that determined by DXA, only the equations 4, 5, 7, and 10 resulted in means that differed less than 5% from the DXA values. Equation 4 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 5 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 7, again using body weight and BIA.

Several studies on human beings also reported BIA as a good technique for the prediction of FM

The independent variable SFLT was used in only one equation for LM estimation, i.e. equation 16. 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.

Methods

Animals and experimental design

All experimental protocols were approved by the animal welfare and use committee of the College of Agrarian and Veterinarian Sciences, São Paulo State University, Brazil.

Sixteen gonadectomized mongrel adult cats, eight males and eight females, were selected. The average of body weight (BW) was 4.8 ± 0.8 kg and 5.5 ± 1.1 kg, respectively, for females and males. Obesity was considered to be present when cats had a BCS of at least 8 on a 9 points scale ^{2}) for exercise and socialization. Food was offered at 18:00 h and any remaining food was removed at 08:00 h. Throughout the study, mean ambient temperature was 21.75 ± 0.8 °C and a 12 h dark:12 h light cycle was provided.

The diet fed for weight loss presented, per M Joule of metabolizable energy: 28.4 g crude protein, 6.4 g of ether extract, 9.7 g of total dietary fiber, 16.1 g of starch, 4.6 g of ash, 0.9 g of calcium, and 0.7 g of phosphorus. To achieve weight loss, cats were fed 60% of their estimated maintenance energy requirements, calculated according to the NRC equation for obese cats ^{0.4}. Cats were weighed weekly, at the same time of day using a digital scale (Digital Weight Scales, model LC 50, Marte, São Paulo, SP). Under this management, all cats lost approximately 20% of their initial body weight in 24.6 ± 0.25 weeks.

During the study cats were evaluated at three times: obese state (T0; obese animals), middle of weight loss (T1; body weight [BW] 10% lower than T0), and end of weight loss (T2; BW 20% lower than T0). The results obtained at the three times were analyzed statistically considering three possibilities: cats independently of gender and obese state (

Body measures

Biometry (BIO), BCS, BIA, US and DXA exams were performed in triplicate at T0, T1 and T2. Before each exam, cats were fasted for 12 hours and then anesthetized with a combination of levomepromazine (Neozine 5 mg/ml, Aventis Pharma LTDA, São Paulo, SP), tiletamine and zolazepam hydrochloride (Zoletil 50 mg/ml, Virbac do Brasil Indústria e Comércio LTDA, São Paulo, SP) administered intramuscularly at respective doses of 0.5, 2.5 and 2.5 mg/kg. After loss of the postural reflex, cats were positioned according to the requirements of each technique.

Body composition was determined by DXA (QDR 4500 Elite Windows®, Guide Hologic Inc. 35, Bedford, MA, USA) with three consecutive scans without repositioning the animals between scans, as described by Lauten et al.

The cat’s BCS and BIO classification during the study was done by the same trained veterinarian (VASCONCELLOS, R.S) ^{th} intercostals space

The BIA exam was performed using a monofrequential Bioimpedance Analyzer (Quantum II, model RJL, RJL Systems Inc., Clinton, MI, USA). Four acupuncture needles (0.40 x 15 mm) with a spiral head covered with copper coupled to the connection clamp were used as electrodes. The positioning of the animals and the points of needle insertion were based on the methodology described by Stanton et al.

Ultrasonography was performed with ultrasound equipment (Pie Medical Scanner 200 C, model 41480, Can Medical, Kingston, ON, Canada) using a multifrequential linear arrangement transducer of 6 to 8 MHz, with electronic scanning being carried out at the highest frequency. The transducer was covered with gel on its transmitting and receiving surfaces, inserted into a specifically made silicone cushion and positioned transversely on the region corresponding to the seventh lumbar vertebra, as described by Morooka et al.

The fat layer, a hypoechoic line between two hyperechoic lines (skin and subcutaneous connective tissue) was measured in centimeters. The probe was first placed on the top of the spinous process of L7 and then was moved horizontally to the right and left (Figure

Ultrasonogram of the measurement of fat layer

**Ultrasonogram of the measurement of fat layer.** (a) Transversal sonogram demonstrates the places (arrows) to measurements of fat layer. (sp) spinous process of 7th lumbar vertebrae (L7), (R) right side, (L) left side. (b) Image in detail with the use of standoff (so). The fat layer is a hypoechoic line between two hyperechoic lines (arrows). (sp) spinous process

Statistical analysis

DXA, BCS, BIO, BIC, and US results are reported as means plus standard deviation (

Stepwise multiple regressions were used to elaborate equations to estimate the dependent variables FM and LM, according to method proposed by Freund and Littell ^{2}/R was included as an independent variable as recommended by Kushner et al.

The general linear regression model used was y = β _{0} + β _{1*} x_{1} + … + β _{m*}x_{m} + ϵ, where y represents the dependent variable; β _{0,} β _{1},… β _{m} represent the unknown parameters; x_{1}_{2} + … + x_{m} the prediction variables; and ϵ the random error

The better models obtained were selected according to the criteria cited by MacNeil ^{2}), lowest Mallows’Cp statistic and the association of these parameters with the practical quality of the model. The Mallows’Cp statistic was calculated according to the following mathematical model: Cp = _{e}_{e} is the residual sum of squares,

After choosing the better models for FM and LM predictions, the average results of DXA were compared with the average predicted results obtained by the equations generated by the model. These comparisons was made using the

Abbreviations

BCS, Body condition score; BIA, Bioimpedance analysis; BIO, Biometry; BL, Body length; BL2/R, Impedance index; BW, Body weight; Cp, Mallows’Cp statistic; CV, Coefficient of variation; DXA, Dual energy x-ray absorptiometry; F, Females; FM, Fat mass; LM, Lean mass; M, Males; p, Statistical significance; PA, Phase angle; PC, Pelvic circumference; R, Resistance; r2, Coefficient of determination; RMSE, Root mean square error; RTL, Right thoracic limb length; SFLT, Subcutaneous fat layer thickness; s, Standard deviation; T0, Time zero – obese animals; T1, Time one – animals after 10% reduction in BW; T2, Time two – animals after 20% reduction in BW; TC, Thoracic circumference; US, Ultrasonography; Xc, Reactance.

Competing interests

The author(s) declare that they have no competing interests.

Authors’ contributions

NCB and RSV planned and designed the study, performed the experiments and drafted the manuscript; the nutrition part of the experiment was designed by ACC, who have also, performed the experiments, drafted the manuscript and coordinated the study; KNVG helped performing the experiments; FJAP read, analyzed and performed the DXA; DEFF performed the procedures and the statistical analysis; JCC coordinated the study and helped to draft the manuscript. All the authors read and approved the final manuscript.

Acknowledgements

Research supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), São Paulo-Brazil (process 04/15416-9) and Mogiana Alimentos S.A. (Guabi), Campinas, Brazil.