Page 1
Accepted Manuscript
Title: Measuring body composition in dogs using multifrequency bioelectrical
impedance analysis and dual energy x-ray absorptiometry
Author: L.S. Rae, D.M. Vankan, J.S. Rand, E.A. Flickinger, L.C. Ward
PII: S1090-0233(16)30029-6
DOI: http://dx.doi.org/doi: 10.1016/j.tvjl.2016.04.007
Reference: YTVJL 4800
To appear in: The Veterinary Journal
Accepted date: 11-4-2016
Please cite this article as: L.S. Rae, D.M. Vankan, J.S. Rand, E.A. Flickinger, L.C. Ward,
Measuring body composition in dogs using multifrequency bioelectrical impedance analysis and
dual energy x-ray absorptiometry, The Veterinary Journal (2016), http://dx.doi.org/doi:
10.1016/j.tvjl.2016.04.007.
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1
Measuring body composition in dogs using multifrequency bioelectrical impedance analysis 1
and dual energy X-ray absorptiometry 2 3
L.S. Rae a, D.M. Vankan
a, J.S. Rand
a, E.A. Flickinger
b, L.C. Ward
c,* 4
5 a School of Veterinary Science, The University of Queensland, Gatton, Queensland 4343 6
Australia 7 b Procter and Gamble Pet Care, FEI Products Research, Mason, OH 45040 USA 8
c School of Chemistry and Molecular Bioscience, The University of Queensland, St Lucia, 9
Queensland 4072 Australia 10
11 * Corresponding author. Tel.: +61 7 3365 4633. 12
E-mail address: [email protected] (L.C. Ward). 13
14
Highlights 15
16
Body composition of dogs was measured using multifrequency bioimpedance. 17
Reference body composition was measured by dual-energy X-ray absorptiometry.. 18
When cross-validated bioimpedance predicted mean fat-free mass within 1.5% of measured 19
values. 20
Abstract 21
Thirty-five healthy, neutered, mixed breed dogs were used to determine the ability of 22
multifrequency bioelectrical impedance analysis (MFBIA) to predict accurately fat-free mass 23
(FFM) in dogs, using dual energy X-ray absorptiometry (DXA)-measured FFM as reference. A 24
second aim was to compare MFBIA predictions with morphometric predictions. 25
26
MFBIA-based predictors provided an accurate measure of FFM, within 1.5% when 27
compared to DXA-derived FFM, in normal weight dogs. FFM estimates were most highly 28
correlated with DXA-measured FFM when the prediction equation included resistance quotient, 29
bodyweight, and body condition score. At the population level, the inclusion of impedance as a 30
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predictor variable did not add substantially to the predictive power achieved with morphometric 31
variables alone; in individual dogs, impedance predictors were more valuable than morphometric 32
predictors. These results indicate that, following further validation, MFBIA could provide a 33
useful tool in clinical practice to objectively measure FFM in canine patients and help improve 34
compliance with prevention and treatment programs for obesity in dogs. 35
36
Keywords: Dog; Bioelectrical impedance; DXA; Body composition; Fat-free mass; Obesity 37
38
Introduction 39
Excess body fat is the most common nutritional disorder of dogs in Western countries, 40
with an estimated prevalence 33 to 44% or higher (German, 2006; Gossellin et al., 2007; Zoran, 41
2010; Laflamme, 2012). Obesity is known to induce insulin resistance, oxidative stress and a 42
chronic, low-grade inflammatory state thought to contribute to the development of osteoarthritis 43
and other diseases (Zoran, 2010; Laflamme, 2012), or osteoarthritis and reduced lifespan (Kealy 44
et al., 2002). A moderately high fat diet has been shown to increase visceral fat two-fold in dogs, 45
with minimal increases in bodyweight (BW; Kim et al., 2003) conducive to the development of 46
insulin resistance; insulin resistance increases with adiposity, even if BW is stable. Prevention of 47
obesity is more effective than its subsequent treatment and is best instituted while animals are 48
just beginning to gain weight (Zoran, 2010; La Flamme, 2012), yet veterinarians often neglect to 49
formally diagnose and discuss an increase in BW (Lund et al., 2005). Once obesity is established 50
it is much more difficult to implement successful weight loss strategies (Gossellin et al 2007; 51
Zoran 2010). For these reasons, some authors have stressed the importance of assessing adiposity 52
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per se rather than simply BW (LaFlamme, 2012), as the latter does not necessarily reflect body 53
fat content (Stanton et al., 1992). 54
55
Body fat can be accurately measured by various methods (Heymsfield et al., 2005; 56
Gossellin et al., 2007), although many of these, e.g. computed tomography (Purushothaman et 57
al., 2013), quantitative magnetic resonance imaging and dual- energy X ray absorptiometry 58
(DXA; Zanghi et al., 2013) require specialised equipment and/or general anaesthesia, and are not 59
practical or available for many research and clinical applications. Body condition scoring 60
(BCS), morphometric measurements, and bioelectrical impedance analysis (BIA) offer non-61
invasive, practical methods for estimating body composition. BCS, using a validated 62
methodology, offers a semi-quantitative assessment that correlates well with percent body fat 63
(Mawby et al., 2004; Shoveller et al., 2014), but is a somewhat subjective measure, relying on 64
visual appraisal and palpation that requires some level of training for competency. BIA, by 65
contrast, is an objective technique that measures the electrical resistance of body water (TBW) 66
that relates directly to the fat-free mass (FFM) of the body (Stone et al., 2009; German et al., 67
2010). 68
69
This study aimed to evaluate the ability of multifrequency bioelectrical impedance 70
analysis (MFBIA), BCS scoring and morphometric measures to predict FFM in dogs compared 71
with FFM measured by DXA. 72
73
Materials and methods 74
Dogs 75
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Thirty-five neutered (18 male, 17 female) mixed breed research dogs were used. All 76
dogs were clinically healthy based on history, physical examination findings, recent 77
haematological and biochemical blood analyses and were determined to be free from internal 78
parasites by faecal analysis after deworming. The dogs’ ages were unknown but, based on 79
physical characteristics and dentition, all were estimated to be > 7 months of age. Bodyweights 80
were 12.1 - 43.0 kg and BCS was 3 - 4.25 (average of two assessors) on a scale of 1 - 5 (1, 81
emaciated; 5, obese; Table 1). 82
83
The study was performed according to The University of Queensland Animal Ethics 84
Committee’s Policies and Guidelines and study protocols were approved by The University of 85
Queensland Animal Ethics Committee SVS/307/04, approved 1st July 2004 and annually until 86
16th
July 2007. 87
88
Bodyweight and body condition score 89
Bodyweight was measured (to the nearest 10 g using a veterinary scale (SK-Vet-150; 90
Accuweigh) and BCS was determined by two experienced research technicians following 91
standardised assessment protocols (McGreevy et al., 2005). BCS was graded into 0.5 increments 92
as proposed by Baldwin et al. (2010). BCS data were averaged for the two assessors; individual 93
ratings did not vary more than 0.5 unit. BCS has been validated against DXA for assessment of 94
body composition in dogs (LaFlamme, 1997). 95
96
Morphometric measurement – length 97
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In order to generate MFBIA-based prediction equations for body composition, a 98
measurement of the current path is required. Since the precise path is unknown, a surrogate 99
measurement is used, e.g. height in humans (Foster and Lukaski, 1996), or simple linear 100
measurement between the sense electrodes in animals (Ward and Battersby, 2009). After dogs 101
were sedated and placed in left lateral recumbency, body length to the nearest mm was measured 102
from the middle of the right eye to the anus using a flexible tape measure. 103
104
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105
Dual energy X-ray absorptiometry and multifrequency bioelectrical impedance analysis 106
Dogs were fasted for at least 20 h prior to DXA followed by MFBIA measurements, 107
performed on the same day. Dogs were sedated with SC methadone (0.3 mg/kg; Methone 108
Injection, 10 mg/mL, Ceva Animal Health) and acepromazine (0.03 mg/kg; ACP2, 2 mg/mL, 109
Delvet) 30 min prior to anaesthesia with IV alfaxalone (1-2 mg/kg; Alfaxan CD-RTU, 10mg/mL, 110
Jurox). 111
112
Dogs were scanned (Hologic QDR-4500A) and scans were analysed using 113
manufacturer’s software (Hologic). Dogs were positioned in a standardized fashion, aided by 114
gridlines on the scanner bed, in dorsal recumbency with the head extended, forelegs bent and 115
taped away from the body and the hind legs extended. A single scan (2-3 min duration) was 116
performed by an experienced DXA technician. Tissue quantification was achieved by measuring 117
the differential attenuation by lean, fat and bone mineral of two X-ray beams of different energy 118
levels to provide measurements of whole body lean mass, fat mass (FM) and bone mineral mass 119
(Heymsfield et al., 2005). FFM was calculated as the sum of lean and bone mineral content 120
(BMC). FFM determined by DXA was comparable with that determined from measurement of 121
TBW by tracer dilution (Heymsfield et al., 2005). 122
123
Whole body impedance was measured using a tetrapolar multifrequency bioimpedance 124
spectrometer (SFB7, ImpVet, ImpediMed), which measured resistance (R) and reactance (Xc) at 125
256 frequencies from 3 - 1000 kHz at a constant drive current of 200 µA. Ag-AgCl gel EKG-126
style (24 x 22 mm) skin electrodes (ImpediMed) were used. Hair at the electrode site was clipped 127
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closely to the skin and cleaned with an alcohol wipe. Based on preliminary reproducibility and 128
reliability studies, the following electrode locations were used: voltage sense electrodes were 129
placed cranially at the right stifle and right elbow with current drive electrodes 10 cm distal, 130
similar to the protocol used in other studies (Scheltinga et al., 1991). Measurement time was <1 s 131
and data (10 consecutive readings) were downloaded to a computer for analysis. 132
133
Multifrequency bioelectrical impedance analysis theory and data analysis 134
MFBIA data were uploaded to a computer and analysed (Bioimp software, version 135
4.15.0.0, ImpediMed). The software fitted the recorded resistance and reactance data to a semi-136
circular plot of resistance vs. reactance, after the Cole model of biological impedance (Thomas et 137
al., 1998) that represents the body as a resistor representing the extracellular water (ECW), in 138
parallel with a resistor representing intracellular water and a capacitor representing the cell 139
membranes. According to this circuit model, the resistance measured at infinite frequency, or 140
other high frequency, (Kyle et al., 2004; McGree et al., 2007), is that of the overall conductive 141
volume, i.e. TBW, while the resistance at zero frequency is that of the ECW (Cornish et al., 142
1993). The impedance at the frequency of maximal reactance, the characteristic frequency or fc, 143
had special significance, since at fc current flow is dependent only on the resistances of the water 144
compartments and not on membrane capacitance. Hence the impedance (Zc) at fc should also be 145
an appropriate frequency from which to predict TBW (Cornish et al., 1996). 146
147
TBW volume was related to impedance or resistance and length according to the 148
following equation: 149
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8
where TBW is the volume of TBW, is the specific resistivity of ohm.cm , L is 150
conductive length (cm) and Z or R is impedance or resistance (ohm), respectively (Thomas et al., 151
1998). FFM was readily obtained from TBW by dividing by the hydration constant of FFM, 152
assumed to be 0.732 (Schoeller, 1996); consequently FFM could be substituted for TBW in the 153
above equation. 154
155
Statistical analysis 156
Descriptive data are presented as mean ± standard deviation (SD) with group differences 157
assessed using Students t test following normality testing D’Agostino-Pearson test; MedCalc, 158
version 12.7.0, MedCalc Software). Body composition prediction equations were produced using 159
multiple linear regression techniques (Zar, 1999), using a backward stepwise method (MedCalc, 160
version 12.7.0, MedCalc Software); FFM by DXA was the dependent variable. Independent 161
variables examined were sex, BW, BCS, length (L), and the impedance indices, R50 index 162
(L2/R50); R500 index (L
2/R500); R∞ index (L
2/R∞); Zc index (L
2/Zc). The coefficient of 163
determination adjusted for multiple independent variables (r2 adjusted) and the root mean square 164
error (RMSE) were determined with alpha level of significance set at 0.05. 165
166
A ‘split-group’ cross-validation procedure was used in which prediction equations were 167
generated in a randomly selected by sex ‘prediction’ group (12 males and 11 females). These 168
equations were then used to predict FFM in the remaining one-third of the population (six 169
females and six males), the ‘validation’ group. The validation and prediction groups were not 170
significantly different (P > 0.05) in any characteristics. Predicted FFM was compared to that 171
measured by DXA using the concordance correlation coefficient, rc (Lin, 1989), Pearson 172
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correlation coefficient, rp (Zar, 1999) and agreement between the two methods was assessed 173
using limits of agreement (LOA) analysis (Bland and Altman, 1986). 174
175
Results 176
Dogs enrolled in this study (Table 1) ranged from small to large breed crosses of varying 177
lengths (61 - 98 cm) and weights (12.1 - 43 kg). BCSs, however, were more uniform, with 89% 178
of dogs in ideal condition (BCS 3), 11% classified as overweight (BCS 4) and none classified as 179
obese or underweight (Table 1). Male animals were significantly heavier and had significantly 180
greater lean (P < 0.05), BMC (P < 0.001) and FFM (P < 0.05) than female animals. FM was 181
30% greater in males than females, although this difference was not significant (P = 0.124), 182
reflecting the difference in BW. When expressed as %BW, %FM was 17.7 ± 4.4% and 17.3 ± 183
4.3% in males and females, respectively. 184
185
Fat-free mass (FFM) prediction equations. 186
Seven equations to predict FFM were generated by the regression analyses (Tables 2 and 187
3). Preliminary analyses showed that sex was not a significant predictor in any analysis; 188
therefore, it was removed from all equations. The resistance indices, L2/Zc and L
2/R50 were also 189
not significant, but because L2/R50 was approaching significance (P = 0.057) an equation was 190
generated using this index to provide a point of comparison with previously published studies. 191
An equation was also generated to include only the most significant BIA variable, L2/R∞, and the 192
most significant morphometric measure, BW. Therefore, equations were generated using three 193
BIA resistance indices: L2/R50, L
2/R500 and L
2/R∞. As BW was the most highly significant of all 194
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variables, another equation was generated using only BW and BW combined with the other 195
morphometric variables. 196
197
Testing the predictor in the validation group of dogs 198
Predicted FFM values were calculated from the equations for every dog in the validation 199
group and compared with the FFM measured by DXA. Concordance and Pearson’s correlation 200
coefficients, and bias and LOA are presented in Table 4. 201
202
For every equation, values for rc and rp were very similar and > 0.98 for all equations, 203
indicating that the predicted data were highly correlated and were close to the line of identity 204
with the measured data. For all equations, biases were small (0.6 - 1.3%) and positive, indicating 205
the equations slightly underestimated FFM compared to DXA-measured FFM. Equations that 206
included a resistance index as a predictor variable exhibited the smallest LOA, varying from ± 207
6.9% for equation 3 to ± 7.5% for equation 1 (Figs. 1 and 2). In contrast, morphometric-based 208
predictors generally exhibited smaller biases than those that included an impedance predictor 209
variable but had larger LOA, ranging from ± 8.2% (equation 5) to ± 11.6% (equation 7). 210
Although differences between equations in bias, LOA (Table 4), r2 and RMSE (Table 2) were 211
generally small, equation 3 was deemed to have the best predictive performance for impedance-212
based predictors, while equation 5 was considered to be the best performing morphometric-based 213
predictor. 214
215
Thus, the final predictive equation (Equation 8) based on these variables, BW, BCS and 216
L2/R∞, using the data for all dogs, was determined: 217
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where the measurement units were as follows: FFM (g), BW (g), L (cm), and ∞ (ohm). This 218
equation was used to predict FFM in all dogs and %FM by difference with BW (Fig. 3a). 219
Correlation with DXA-determined %FM was high (rp = 0.789; rc = 0.730; Fig. 3b), although 220
LOA were wide with a significant slope, indicating that the BIA-based prediction overestimated 221
body fat percentage in animals with low %FM, but underestimated above approximately 24% 222
FM (Fig. 3b). 223
224
Discussion 225
This is the first study to use and validate MFBIA analysis to estimate body composition 226
in relatively lean dogs. In this cohort of mixed-breed dogs, MFBIA-based predictors provided an 227
accurate (within 1.5%) measurement of FFM when compared to DXA-derived FFM. FFM 228
estimates correlated most highly with DXA-FFM when the prediction equation included 229
resistance quotient, BW, and BCS. Notably, however, the inclusion of impedance as a predictor 230
variable did not add substantially to predictive power compared with prediction based upon 231
simple morphometric measurements, as judged by the magnitude of the correlation coefficient 232
and bias, although LOA were generally smaller, indicating greater predictive accuracy at the 233
individual subject rather than population level. 234
235
Comparisons of our observations with existing published data (Stone et al., 2009; 236
German et al., 2010; Jeusette et al., 2010) were difficult, as different impedance devices were 237
used, and either the prediction equations were not published or cross-validations were not 238
performed. In addition, data were presented as derived FM rather than measured FFM. SFBIA-239
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predicted FM correlated well (r = 0.819) with BCS in 46 dogs (Stone et al., 2009), although 240
agreement was not determined, and comparison against a reference technique such as DXA was 241
not performed. German et al. (2010) compared FM by DXA with that predicted by SFBIA in 24 242
dogs, but found a poor correlation (r2
= 0.44) and poor LOA (-16% to 21%). Both of these 243
studies presented data for body FM only, not FFM, and in neither case was the predictive 244
algorithm or its validation provided. Neither study compared impedance based predictors against 245
simple morphometric measurements. The more recent study of Jeusette et al. (2010), using an 246
SFBIA device, found good correlation of FFM and DXA-FFM (r = 0.84 - 0.87) in a population 247
of mixed breed dogs (n = 19); addition of morphometric measurements (height, BW, length and 248
pelvic circumference) to the regression improved correlation (r = 0.92), although LOA remained 249
poor (-6 - 5 kg; 25 - 21%). MFBIA prediction of FFM in our study provided better predictive 250
performance than previously published single frequency predictions. 251
252
FM was predicted less accurately than FFM. Correlation of FM by DXA and impedance 253
was high (r = 0.79), but LOA were wide; an observation in accord with the observations of 254
Jeusette et al. (2010), where correlations and LOA for FM were weaker than those for FFM. This 255
was not entirely surprising, since impedance is a function of body water content and hence FFM, 256
not FM per se. BIA provides an indirect estimate of FM, as BW-FFM, with consequent 257
propagation of errors (Kyle et al., 2004). In addition, DXA measurement of FM incorporates 258
larger imprecision errors (3 - 4%) than for FFM. 259
260
Our study has both strengths and limitations. Although the population of dogs was large 261
by comparison with previous studies (German et al., 2010; Jeusette et al., 2010), it did not 262
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include any underweight or obese animals or very small or very large dogs, or dogs with unusual 263
shape, such as Dachshunds. In our study, all dogs were young adults; however, obesity is more 264
common in middle aged pet dogs. Future studies are required in dogs with a range of ages, 265
especially middle-aged dogs (German, 2006), since that is when accurate determination of body 266
composition is most important for making recommendations to clients regarding management of 267
canine obesity. Sex was not a significant predictor variable in this cohort. This was not 268
surprising, since all dogs were neutered and representative of the general population of dogs in 269
Australia, where 78% are neutered (Heady, 2006). The effect of sex on prediction of body 270
composition should be determined in a study of intact animals for comparison. Despite the larger 271
number of dogs used in the present study compared to previous studies (Jeusette et al. 2010), our 272
data provided limited scope for cross-validation. Ideally, the prediction equations generated here 273
for FFM and the derived estimates of FM should be validated in a large, independent, mixed 274
population of dogs. Future studies should also investigate whether measurements taken while 275
unsedated dogs are standing (German et al., 2010) are feasible, as this would make the method 276
truly non-invasive and increase its utility in routine clinical practice. 277
278
The present study confirmed good predictive performance of simple morphometric 279
measurements for body composition, possibly because most of the dogs were lean. These data 280
question the value of impedance for body composition assessment. Our results indicate that when 281
monitoring individual dogs, predictors that included impedance in addition to morphometric 282
measurements were most accurate. Bodyweight is a readily determined objective measurement. 283
Measurement of height is more difficult in a conscious animal, but is nonetheless an objective 284
measurement, while BCS involves subjective assessment. In the present study, BCS was the 285
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recorded as the mean score of two experienced assessors. Although individual veterinarians are 286
likely to be consistent in their assessment of BCS, the measurement is subjective and variation 287
between veterinarians can occur. In our study, predictive performance was only slightly 288
worsened when BCS was omitted as a predictor variable. Bodyweight alone provided good 289
prediction of FFM in our population of lean dogs, but this does not necessarily mean that it is 290
acceptable for clinical use, particularly in obese animals. A dog classified as overweight on the 291
basis of BW according to a breed standard may not necessarily have excess body fat. Rather, it 292
could have increased lean body mass as, for example, in athletic and working dogs (Crane, 293
1991). 294
295
Conclusions 296
The impedance technique has the potential to provide a tool that veterinarians could use 297
to routinely assess the body composition of dogs in veterinary practice. It is safe, potentially non-298
invasive, portable, and low-cost compared to techniques such as DXA. It can provide a fast and 299
objective quantitative measure of FM and FFM, which is not possible with subjective 300
assessments such as BCS. Despite this, simple morphometric measurements performed equally 301
well in this population of lean dogs. Whether impedance measurements can improve body 302
composition assessment in the target population of overweight and obese animals requires 303
further study. 304
305
Conflict of interest statement 306
L.C. Ward consults for ImpediMed. E.A. Flickinger is an employee of P and G Pet Care, 307
which partially funded the research. ImpediMed did not have any involvement in the execution 308
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of this study or in the preparation of this manuscript. None of the other authors has any other 309
financial or personal relationships that could inappropriately influence or bias the content of the 310
paper. 311
312
Acknowledgements 313
The authors wish to thank Deanne Waine, Jenny Hall, Libby Jolly, Melita Watkins and 314
veterinary students of The University of Queensland for their assistance with data collection and 315
to Nicole Richards and Linda Oliver for their dedication to the care of the dogs. Our appreciation 316
also goes to Princess Alexander Hospital for their technical assistance with the DXA scans. 317
This work was supported by grant from P and G Pet Care, USA. 318
319
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Figure legends 443
444
Fig. 1. Correlation comparison plots in the validation group of dogs for the equation with the best 445
resistance index, L2/R∞ ([0.57 x BW] + [-1239.593 x BCS] + [75.993 x R infinity index] + 446
4041.392; L, length; R, resistance; equation 3, Table 2) and the morphometric equation ([0.7041 447
x BW] +2045.5622; equation 5, Table 2). Dual energy X-ray absorptiometry -determined FFM is 448
on the x-axis and the predicted FFM on the y-axis. Panel: (a) Equation 3 fat-free mass; (b) 449
Equation 5 fat-free mass. Solid circles, females; solid triangles, males; , 95% confidence 450
interval; , line of identity; ___, line of best fit. 451
452
Fig. 2. Bland and Altman plots in the validation group of dogs for the equation with the best 453
resistance index, L2/Rinfinity (L, length; R, resistance; equation 3) and the morphometric equation 454
(equation 5). (a) Equation 3 fat-free mass; (b) Equation 5 fat-free mass. Solid circles, females; 455
solid triangles, males; , limits of agreement (1.96 × standard deviation); , zero mean 456
difference; , regression line; ___, mean of data. 457
458
Fig. 3. Comparison of measured body fat percentage and predicted body fat percentage for all 459
dogs using the final prediction equation (Equation 8). (a) Box plot. Solid circles, females; solid 460
triangles, males; box, 25th to 75th
percentile and median; I, minium to maximum values (b) 461
Correlation plot. Solid circles, females; solid triangles, males; , 95% confidence interval; , 462
line of identity; ___, line of best fit. (c) Limits of agreement plot. Solid circles, females; solid 463
triangles, males; , limits of agreement (1.96 × standard deviation); , zero mean difference; 464
, regression line; ___, mean of data. 465
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20
Table 1. Mean ± standard deviation data for enrolled dogs. 466
Parameter Neutered males Neutered females All
n 18 17 35
Scale weight (kg) 24.2 ± 7.5 19.1 ± 4.4b 21.6 ± 6.8
Length (cm) 80.4 ± 8.8 74.1 ± 7.5 77.4 ± 8.6
Body composition
BCS 3.2 ± 0.37 3.2 ± 0.38 3.2 ± 0.4
Lean mass (kg) 18.5 ± 5.0a 14.8 ± 3.4
b 16.8 ± 4.6
Fat mass (kg) 4.5 ± 2.3
3.4 ± 1.5 3.9 ± 2.0
Bone mineral content (g) 702.9 ± 199.9a 525.8 ± 129.1
c 616.8 ± 189.4
Fat-free mass (kg) 19.2 ± 5.2a
15.4 ± 3.5b 17.3 ± 4.8
DXA weight (kg) 23.5 ± 7.2 18.8 ± 4.8b 21.3 ± 6.5
DXA weight (% scale
weight)
98.4 ± 0.9 98.4 ± 1.2 98.4 ± 1.1
Whole body impedance
R50 (ohm) 136.3 ± 13.6 138.3 ± 10.6 137.3 ± 12.1
R500 (ohm) 101.7 ± 10.8 102.3 ± 8.0 102.0 ± 9.4
∞ ohm 94.9 ± 10.3 95.7 ± 7.5 95.3 ± 8.9
Zc (ohm) 140.2 ± 15.2 140.4 ± 12.2 140.3 ± 13.6
BCS, body condition score; DXA, dual energy X-ray absorptiometry; R, resistance 467 a Neutered males, P <0.05 468
b Neutered females, P <0.05 469
c Neutered females, P<0.001 470
471 472
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Table 2. Equations and corresponding adjusted coefficient of determination (r2) and root mean 473
square error (RMSE) for predicting fat-free mass using bioelectrical impedance and 474
morphometric variables in the prediction group of 23 dogs. 475
Equation
number
Equations for fat-free mass prediction (kg) r2
(adjusted)
RMSE
(g)
1 (0.624 x BW) + (-1.579 x BCS) + (0.079 x R50 index) + 5.319 0.973a 0.724
2 (0.578 x BW) + (-1.306 x BCS) + (0.078 x R500 index) + 4.306 0.977 a 0.659
3 (0.570 x BW) + (-1. 39 x CS + 0.076 x ∞ index + 4.041 0.979 a 0.634
4 (0.46 x BW) + 0.098 x ∞ index + 0.905 0.975 a 0.692
5 (0.7041 x BW) +2.046 0.955 a 0.952
6 (0.793 x BW) + (-2.090 x BCS) + 6.895 0.969 a 0.778
7 (0.649 x BW) + (-1.786 x BCS) + (0.114 x Length) + 0.188 0.975 a 0.691
BW, bodyweight; R, resistance; BCS, body condition score 476 a P<0.001; 477
478
Table 3. Significance (P value; variance inflation factor indicated in parentheses) of each 479
variable included in the prediction equations presented in Table 2. 480
481
Equation
Variables 1 2 3 4 5 6 7
Body condition
score
0.021
(2.9)
0.038
(2.4)
0.040
(2.4)
- - 0.003
(1.9)
0.005
(2.1)
Bodyweight
<0.0001
(12.2)
<0.0001
(11.7)
<0.0001
(10.9)
<0.0001
(6.2)
<0.0001
(1.9)
<0.0001
(7.8)
Resistance index
(l2/R)
0.057
(9.7)
0.008
(8.2)
0.003
(7.6)
0.0002
(6.3)
- - -
Length - - - - - - 0.021
(5.8)
482
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Table 4. Concordance (rc and Pearson’s rp) correlation coefficients, bias, limits of agreement, 483
and significance for the regression slope for all prediction equations (Table 2) for fat-free mass 484
for the validation group of dogs (n = 12). 485
Equation rc rp a Bias
(g)
Limits of agreement
(g)
P for slope
Impedance-based predictors
1 0.993
0.993 95.0
(0.6%)
-1225.1 to 1415.0
(6.9% to 8.2%)
0.975
2 0.993 0.994 153.7
(0.8%)
-1121.7 to 1429
(6.2% to 7.7%)
0.405
3 0.992 0.994 202.7
(1.0%)
-1068.0 to 1473.3
(5.7% to 7.8%)
0.342
4 0.989 0.993 291.4
(1.3%)
-1175.3 to 1758.0
(5.6% to 8.2%)
0.087
Morphometric only (bodyweight, length body condition score)-based predictors
5 0.991 0.992 46.8
(0.8%)
-1300.9 to 1570.9
(-7.4% to 9.1%)
0.830
6 0.992 0.992 39.6
(0.5%)
-1422.0 to 1501.2
(-7.9% to 8.9%)
0.335
7 0.985 0.986 113.7
(1.2%)
-1806.5 to 2033.8
(10.4% to 12.8%)
0.460
a Comparison, dual energy X-ray absorptiometry-predicted 486
487
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