An evaluation of four non-destructive methods for predicting body composition in a small rodent

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1 International Journal of Body Composition Research 2007 Vol. 5 No. 4: Smith-Gordon Submitted 21 September 2007 accepted after revision 23 November 2007 An evaluation of four non-destructive methods for predicting body composition in a small rodent Aberdeen Centre for Energy Regulation and Obesity (ACERO), School of Biological Sciences, University of Aberdeen, Aberdeen AB24 2TZ, Scotland, UK. Objective. Body composition in the bank vole (Myodes glareolus) was predicted using four non-destructive methods: body morphometrics, total body electrical conductivity (TOBEC), isotope dilution and dualenergy x-ray absorptiometry (DXA). Methods. For each method, a validation equation was generated for total body fat (TBF), lean body mass (LBM), and where applicable total body water (TBW). A cross-validation method was then used to generate a predicted value for each individual and this was compared to actual values generated from chemical analysis, using Soxhlet apparatus. The errors associated with each method were then assessed. Results. There was a strong inverse correlation between percentage fat mass and percentage body water (male: r=0.94; female: r=0.92), and lean body mass best explained TBW in both sexes (r=0.99). DXA generated the lowest mean absolute errors for TBF (male: 0.47±0.22 g; female: 0.32±0.17 g) and lean body mass (male: 0.45±0.20 g; female: 0.38±0.20 g). TOBEC generated the largest mean absolute errors for TBF (male: 0.71±0.58 g; female: 1.36±0.50 g) and lean body mass (male: 1.60±0.62 g; female: 1.64±0.56 g). Conclusion. Validation equations from TOBEC measured with voles anaesthetized were more reliable than those generated from conscious voles. Isotope dilution equations using deuterium were more reliable than using 18 oxygen for predicting body fat. For female voles, equations generated from body morphometrics produced lower mean absolute errors than either TOBEC or isotope dilution, however, for male voles this method was less reliable. Key words: TOBEC, DXA, isotope dilution, morphometrics, bank vole Introduction Body composition is often used as an indicator of reproductive condition and fitness in wild animals [1-3]. It is generally assumed that in a mature animal, lean body mass is relatively stable over time whereas, fat storage may vary daily, seasonally or monthly. The amount of fat an animal stores is dependent on both environmental factors such as resource availability [4,5] and physiological factors, for example the ability to use daily torpor [6] or hibernation [7]. Fat is primarily stored during times when food is plentiful so that it can be utilised when resources become scarce, for example, in winter, or during periods of intense energy expenditure, such as migration or reproduction [8,9]. The most accurate way to assess body composition is directly, using chemical analysis [10]. However, as well as important ethical questions raised concerning the use and sacrifice of animals for scientific study, chemical analysis is not practical in many instances. These include longitudinal studies following changes in body composition of individuals over time; ecological studies that may be disrupted by the removal of individuals from wild populations or studies that require the subsequent use of organs for molecular analysis. Over the past decade, there has been much work to develop and validate non-destructive methods for predicting body composition [11-13], and methods developed for human studies, for example, dual-energy x-ray absorptiometry (DXA), are being modified for use with small mammals in both biomedical and ecological settings [14]. Non-destructive methods range from those that involve morphological measurements (skinfold thickness, body condition indices), to those that rely on variations in chemical (total body potassium, isotope dilution), and physical properties (DXA, ultrasound, computed tomography, gas dilution, bioimpedance analysis, TOBEC) of the tissues. Details of these and other methods used can be found in two reviews Human body composition [15] and Body composition analysis of animals [16]. Body morphometrics relies on external measurements of body components to assess body composition. These include skin-fold thickness, fat scores, body mass, body length, tarsus length, bill length etc, and rely on the production of morphological indices Address for correspondence: Wendy L. Tidhar, Western EcoSystems Technology Inc., 2003 Central Ave., Cheyenne, WY82001, USA Tel: (307) Fax: (307) wtidhar@west-inc.com

2 138 or ratios, which can be correlated with lean or fat mass [3]. Body condition indices are widely used in field studies, where transporting equipment to a field location may be an important consideration and electricity may not be available. Unfortunately, in many instances, these methods are used without validation [3]. Isotope dilution has also been widely used in field studies [16-19]. This method relies on the fact that adipose tissue contains less water than lean tissue, hence the more adipose tissue an individual has, the lower the body water is when expressed as a percentage of total body mass. A known amount of labelled water (deuterium, tritium or 18 oxygen) is introduced into the body and following an equilibration period, during which time the labelled water diffuses into the total body water pool, a blood or urine sample is taken and its isotope enrichment analysed [20]. By knowing the background enrichment of isotope present in the individual, the mass and enrichment of isotope delivered and the enrichment following equilibration, the number of mols of water present in the body, and thus total body water can be calculated. Estimated body water can then be used to predict body composition [21]. The main disadvantages of this method are the relatively invasive methods of introduction and sampling of the isotope, the cost of isotope and the equipment to measure it and the reliance of a relationship between body water and fat. A slightly less invasive technique that also relies on the differing water contents of lean and fat tissue is total body electrical conductivity (TOBEC). The underlying principle of this method is that, due to differences in electrolyte concentration, lean tissue is twenty times more conductive than fat or bone [22]. Therefore, when an individual is placed within an electromagnetic field, a measure of conductivity is produced based on its lean body mass. This measure, the TOBEC index (TI), can then be used to predict lean or fat mass. TOBEC also has its own problems, primarily that a correction equation must be produced which is not only species-specific [23,24], but may be population-specific [25,26]. This requires a relatively large number of animals to be sacrificed and a comparison made of TOBEC indices with known levels of lean and fat mass from chemical analysis, thus undermining its claim to be non-destructive. Furthermore, sex [23], body temperature [11,27], body shape [28], hydration [27], gut contents [29] and location of fat stores [26] have all been shown to affect the TOBEC index, and should therefore be considered before producing or using a correction equation. A method that has recently become available to small animal biologists is dual energy x-ray absorptiometry (DXA; [14,30]. The equipment measures the attenuation of two x-ray beams of different energies and relies on the differences in the ratio of mass attenuation coefficients of these two energy beams between body tissues [31]. This is a fast but a relatively expensive system compared to TOBEC or morphometrics, though less expensive than start-up costs associated with isotope dilution. Due to the nature of the measurement the animal must be anaesthetized during the scan and as with the TOBEC machine, a correction equation is required for more accurate body composition estimates [32]. In this study morphometrics, TOBEC (conscious and anaesthetized), isotope dilution (using deuterium and 18 oxygen) and DXA were compared to chemical analysis to determine their accuracy, precision and reliability for predicting body composition in the bank vole (Myodes glareolus), a small (10-30 g) arvicoline rodent. Materials and methods Animals and experimental design Thirty-eight bank voles (19 males and 19 females) were selected from a laboratory breeding colony and divided into four single-sex groups of nine or ten animals. Four methods of predicting body composition were carried out on a single day, according to the following schedule: h body morphometrics, h TOBEC (conscious), h isotope dilution (deuterium and 18oxygen), h TOBEC (anaesthetized), h DXA. Voles were kept in their boxes and supplied with their usual food and water between measurements, to ensure they did not become dehydrated. Work was carried out under UK Home Office licence PPL 60/2060. Following DXA, voles were killed by cervical dislocation while still anaesthetized, and carcasses placed in an oven at 60 C for two weeks until constant dry mass was obtained. Total body water (TBW) was calculated by subtracting dry mass from wet mass. Total body fat (TBF) was determined by lipid extraction, using Soxhlet apparatus and diethyl ether as the solvent. Lean body mass (LBM) was then calculated by subtracting TBF from body mass. Body morphometrics Body mass was recorded (±0.01 g, Sartorius), and body length measured from the tip of the nose to the base of the tail (±0.1 cm). Total body electrical conductivity (TOBEC) TOBEC measurements were taken using an ACAN-2 small animal body composition analyser (Jagmar, Poland). Voles were always placed in the holding tube with their ventral surface downward and their head pointing forward, as position may affect the reading [27]. A plastic strip was used to restrain the animal but did not alter its body shape. The animal was placed within the machine for 6-8 readings (depending on conditions described below i.e. urination, escape, stress). The minimum and maximum readings were then excluded and the average of the remaining values taken as the TOBEC index (TI). Unlike birds, which can be sufficiently restrained by holding the wings, voles are extremely adept at escaping from the holding tube through the smallest of holes; this coupled by the frequent urination and defecation, which can affect the reading [33], meant that the process was quite time-consuming with the voles conscious. For

3 Body composition: evaluation of non-destructive methods 139 this reason, we took the decision to use anaesthetic after the 10 individuals had been completed. If at any point the procedure appeared too stressful, or the animal would not settle, the process was discontinued. Voles were anaesthetized with a 0.1 ml intraperitoneal injection of 2:1 (Vetalar:Rompun, W and J Dunlop) diluted 1:1 with phosphate buffer solution. This induced anaesthesia long enough for both TOBEC and DXA to be completed before the voles were killed by a schedule one method prior to recovery. When voles were anaesthetized, 7 readings were taken, the minimum and maximum values were removed and the average of the remaining values was the TOBEC index. The numbers of individuals for which TOBEC readings were taken are as follows: Concious: 15 females, 10 males; anaesthetized 10 females, 10 males. Four females would not settle in the holder when conscious and so the process was discontinued, the first group of 10 females were not anaesthetized and due to technical problems with the TOBEC machine the final group of 10 males could not be scanned either conscious or anaesthetized. Isotope dilution To determine background levels of isotopes for each vole, a µl blood sample was collected, by tail tipping, the day before the above schedule was performed. Blood samples were immediately flamesealed into 50 µl pipettes (Vitrex, Camlab Ltd.) until analysis. The following day, individuals were labelled with an intraperitoneal injection of approximately 0.2 g of water containing enriched deuterium and 18 oxygen. The syringe was weighed before and after the injection (± g, Ohaus Analytical Plus) to provide an accurate measurement of the amount of isotope injected. After one hour, the time generally assumed for the isotope to reach equilibrium within the body [34,35], a second blood sample ( µl) was collected. Blood samples were vacuum-distilled into glass Pasteur pipettes (Volac, John Poulten Ltd.; [35]) and the distillates used for mass spectrometric analysis of stable isotopes. Mass spectrometric analysis of deuterium was performed using H2 gas, produced by reacting water, distilled from the blood, with LiAlH4 [36]. For analysis of 18 oxygen enrichment, the water distilled from the blood was equilibrated with CO2 gas using the small sample equilibration technique [37]. The number of mols of water in the body was calculated following [38] Equation 4. Finally, the number of mols of water was converted to total body water (g) by multiplying by the molecular mass of body water. We have estimates of total body water from isotope dilution for all 19 female voles but only 17 males due to sampling or analysis problems. Dual-energy x-ray absorptiometry (DXA) DXA measurements were taken on a GE (previously Lunar) PIXIMUS2 series densitometer installed with software version (GE Medical Systems Ultrasound and BMD, Bedford, UK). Anaesthetized voles (see TOBEC) were placed ventral surfacedownwards on a holding tray with their head to the left and limbs extended. The imaging area (80 x 65 mm) was not large enough to accommodate the whole length of the animal; therefore the tail and most of the head were outwith the scan. However, the software provided allows a region of interest to be selected by the operator and subsequently excluded from the body composition analysis. We ensured the program omitted most of the head but did not exclude the area around the neck where voles can store large amounts of subcutaneous fat. The scanning procedure was completed in less than five minutes per animal, and the computer software program subsequently provided estimations of total tissue mass and percentage fat, from which fat mass (g) and lean mass (g) could be calculated (see [32] for complete description of method). The data file for one individual was corrupt; therefore there are DXA values for only 37 individuals. Statistical analysis Due to potential differences in fat storage between sexes, in terms of amount and location of stores, all data were analysed with males and females separate. Analyses were carried out using Minitab version 11 [39]. Pearson correlations were used to determine relationships between body composition variables from destructive analysis. Regression analyses were carried out for each method of measuring body composition. Where appropriate multiple regression, to determine if variables such as body mass or body length were significant predictors were carried out. Where there was more than one regression for lean body mass, total body fat or total body water, the equation with the highest coefficient of determination (R 2 ) was used. Body morphometrics To determine the strongest relationships between body mass (BM), body length (BL), total body fat and lean body mass, linear regressions were carried out on transformed (square root, arcsinh, log) and untransformed data (BM/BL, 3 BM/BL, BM/BL 3 ). TOBEC (conscious and anaesthetized) Linear and multiple regressions (including body mass and body length as predictors) were carried out to determine the relationship between TOBEC index (TI) and total body fat, lean body mass, and total body water. Although TOBEC is primarily assumed to predict lean mass, the relative errors to predict fat from lean mass are generally high. For this reason, Morton et al. [40] suggest using TI to predict total body fat directly. Therefore, we estimated total body fat both from lean body mass and directly. Differences between TI of anaethetized and conscious voles were analysed using paired t-tests. Isotope dilution (deuterium and 18 oxygen) Linear and multiple regressions (including body mass as a predictor) were used to determine the relation-

4 140 ship between predicted total body water (TBWp) and total body water (from desiccation), total body fat and lean body mass. DXA Linear regressions were used to determine relationships between predicted and observed values of total body fat and lean body mass. Differences between observed values and those predicted by DXA were analysed using paired t-tests. A validation equation was thus generated for each measure of body composition (lean body mass, total body fat, total body water) where applicable, and for each method used. The strength of these equations was then tested using the prediction sums of squares (PRESS) cross-validation procedure. This involves removing each individual value (from sample n), one at a time, and generating a regression equation derived from the n-1 sample. This equation is then used to predict the removed value, and the predicted and observed values compared. The precision and accuracy of the predictive equation was assessed by calculating the PRESS statistic, the root mean square error (RMSE) and the pure error (PE), and comparing each value between methods. In each case, the smaller the value the smaller the predictive error (PRESS), the greater the precision (RMSE) or the greater the accuracy (PE) of the equation. Although a criterion for pure error has not been set, a general rule is that the pure error should be similar to the RMSE of the same equation for the sample used in its development [41]. Finally, the mean percentage error and mean absolute error were generated to determine the usefulness of each method of measuring body composition. Both of these values are included as there is some debate as to the usefulness of one over the other [42]. Error values were determined as follows: a. PRESS statistic (obs pred)2 b. RMSE (obs pred)2 / (n p 1) c. PE (obs pred)2 / n d. Mean percentage error ((obs pred) / obs) * 100 for each individual and expressed as a mean e. Mean absolute error (obs pred) for each individual and expressed as a mean mass and % body water for males and females (Figure 1; r=-0.94 and 0.92, respectively), and lean body mass was highly correlated with total body water in both sexes (Figure 2; r=0.99). Tables 2 and 3 contains the results of the regressions analyses, Tables 4 and 5 compares PRESS, RMSE, PE and mean percentage error, and Tables 6 and 7 compares absolute error and resolution of all methods. Body morphometrics In both sexes, there was a stronger relationship between morphometrics and lean body mass (LBM; R 2 male 0.73; female 0.92) than total body fat (TBF; R 2 male 0.62; female 0.78). The mean absolute errors (± 95 % CI) associated with this method were lower in females than males (LBM: 0.63±0.24 g, TBF: 0.60±0.26 g and LBM: 1.09±0.43 g, TBF: 0.76±0.40 g, respectively), though not significantly (F1,75=3.50, P=0.07). Figure 1. The relationship between fat mass (% body mass) and body water (% body mass) for male and female bank voles. Male r=0.94, female r=0.85. Figure 2. The relationship between lean body mass (g) and total body water (g) for male and female bank voles (r=0.99). Abbreviations: obs=observed variable, pred=predicted variable, n=sample size, p=number of predictors. Means for (d) and (e) were calculated ignoring signs. Results For all calculations, body mass recorded at the start of the day was used. There was a slight increase in body mass during the day by 0.17±0.08 g in females and 0.10±0.11 g in males. This was most likely due to the animals feeding and drinking during the day. Body composition data from destructive analysis are shown in Table 1. There was a significant difference in body mass, lean body mass and % body water between male and female voles (2-sample t-test). There was a strong inverse correlation between % fat

5 Body composition: evaluation of non-destructive methods 141 Table 1. Body composition data of bank voles from desiccation and Soxhlet analysis. P-values relate to comparisons between sexes. Variable Male (n=19) Female (n=19) Range Mean ± sem Range Mean ± sem P-value Body mass (g) ± ± 0.86 <0.05 Body length (cm) ± ± 0.21 ns Lean body mass (g) ± ± 0.60 <0.01 Total body fat (g) ± ± 0.36 ns Body fat (% BM) ± ± 1.30 ns Body water (% BM) ± ± 1.07 <0.01 ns= P>0.05 Table 2. Results of least squares regression analysis of male body composition variables with non-destructive methods of prediction. Method Equation R 2 df F P DXA LBM = 1.12 LBMp , <0.001 TOBEC anaesthetized LBM = 0.43 BM TIa , <0.001 ID deuterium LBM = 0.99 TBWp BM , <0.001 ID 18 oxygen LBM = 0.71 TBWp BM , <0.001 Morphometrics LBM = 30.8 log BM , <0.001 TOBEC conscious LBM = 0.42 TIc , DXA TBF = 1.09 TBFp , <0.001 ID deuterium TBF = 0.66 BM TBWp , <0.001 ID 18 oxygen TBF = 0.62 BM TBWp , <0.001 TOBEC anaesthetized TBF = 0.47 BM 0.15 TIa , Morphometrics Arcsinh TBF = 0.37 (BM/BL) , <0.001 TOBEC conscious TBF = 0.14 TIc , ID deuterium TBW = 1.06 TBWp , <0.001 ID 18 oxygen TBW = 0.88 TBWp , <0.001 TOBEC anaesthetized TBW = 0.26 TIa , <0.001 TOBEC conscious TBW = 0.26 TIc , Abbreviations: Body mass (BM); lean body mass (LBM); lean body mass predicted (LBMp); total body fat (TBF); total body fat predicted (TBFp); total body water (TBW); total body water predicted (TBWp); TOBEC index conscious (TIc); TOBEC index anaesthetized (TIa); isotope dilution (ID). TOBEC LBM and TIa: BM p=0.01, TIa P<0.001; TBF and TIa: BM P<0.001, TIa P=0.01; ID 18 oxygen LBM and TBWp: TBWp P=0.004, BM P=0.007; TBF and TBWp: TBWp P=0.001, BM P<0.001; ID deuterium LBM and TBWp: TBWp P<0.001, BM P=0.006; TBF and TBWp: TBWp P<0.001, BM P<0.001 Table 3. Results of least squares regression analysis of female body composition variables with non-destructive methods of prediction. Method Equation R 2 df F P DXA LBM = 1.11 LBMp , <0.001 Morphometrics LBM = 30.3 log BM , <0.001 ID deuterium LBM = 0.48 TBWp BM , <0.001 ID 18 oxygen LBM = 0.49 TBWp BM , <0.001 TOBEC anaesthetized LBM = 0.37 TIa , <0.001 TOBEC conscious LBM = 0.29 TIc , DXA TBF = 0.94 TBFp , <0.001 Morphometrics TBF = 0.37 BM , <0.001 TOBEC anaesthetized TBF = 0.74 BM 0.25 TIa , ID deuterium TBF = 0.55 TBWp , ID 18 oxygen TBF = 0.63 TBWp , TOBEC conscious TBF = 0.14 TIc , TOBEC anaesthetized TBW = 1.76 BL TIa , <0.001 ID deuterium TBW = 0.87 TBWp , <0.001 ID 18 oxygen TBW = 1.04 TBWp , <0.001 TOBEC conscious TBW = 0.22 TIc , <0.001 Abbreviations: Body mass (BM); lean body mass (LBM); lean body mass predicted (LBMp); total body fat (TBF); total body fat predicted (TBFp); total body water (TBW); total body water predicted (TBWp); TOBEC index conscious (TIc); TOBCEC index anaesthetized (TIa); isotope dilution (ID). TIa and TBF: BM P=0.003, TIa P=0.02; TIa and TBW: BL P=0.007, TIa P <0.001; ID 18 oxygen LBM and TBWp : TBWp P=0.03, BM P<0.001; ID deuterium LBM and TBWp: TBWp P=0.02, BM P<0.001.

6 142 Table 4. Error variables for predictions of body composition in male bank voles. Method PRESS RMSE (g) PE (g) Mean % error (± sem) Lean body mass DXA ± 0.45 TOBEC anaethetized ± 0.94 ID deuterium ± 0.96 ID 18 oxygen ± 1.00 TOBEC conscious ± 1.45 Morphometrics ± 1.08 Total body fat DXA ± 6.42 TOBEC conscious ± 12.0 ID deuterium ± 5.11 TOBEC anaethetized ± 14.4 ID 18 oxygen ± 9.68 Morphometrics ± 14.1 Abbreviations: prediction sums of squares (PRESS); root mean square error (RMSE); pure error (PE); isotope dilution (ID) Table 5. Error variables for predictions of body composition in female bank voles. Method PRESS RMSE (g) PE (g) Mean % error (± sem) Lean body mass DXA ± 0.59 TOBEC anaesthetized ± 1.12 Morphometrics ± 0.72 ID 18 oxygen ± 0.62 ID deuterium ± 0.70 TOBEC conscious ± 2.09 Total body fat DXA ± 4.27 Morphometrics ± 9.63 TOBEC anaesthetized ± 14.6 ID deuterium ± 13.8 TOBEC conscious ± 16.1 ID 18 oxygen ± 16.0 Abbreviations: prediction sums of squares (PRESS); root mean square error (RMSE); pure error (PE); isotope dilution (ID) Table 6. Variables to determine resolution and use of non-destructive methods to determine body composition in male bank voles. Method Mean absolute Differences Range of Range Resolution error (± 95%CI) detectable (g) values (g) (g) (%) Lean body mass DXA 0.45 ± 0.20 > ID deuterium 0.80 ± 0.38 > TOBEC anaesthetized 0.85 ± 0.43 > ID 18 oxygen 1.05 ± 0.42 > Morphometrics 1.09 ± 0.43 > TOBEC conscious 1.60 ± 0.62 > Total body fat DXA 0.47 ± 0.22 > ID deuterium 0.68 ± 0.30 > Morphometrics 0.76 ± 0.40 > ID 18 oxygen 0.81 ± 0.36 > TOBEC conscious 0.71 ± 0.58 > TOBEC anaesthetized 0.80 ± 0.71 > Total body fat DXA 0.42 ± 0.24 > (BM-LBM) ID deuterium 0.65 ± 0.35 > ID 18 oxygen 0.84 ± 0.39 > Morphometrics 0.89 ± 0.40 > TOBEC anaesthetized 0.92 ± 0.44 > TOBEC conscious 1.16 ± 0.56 > Abbreviations: Isotope dilution (ID); body mass (BM); lean body mass (LBM)

7 Body composition: evaluation of non-destructive methods 143 Table 7. Variables to determine resolution and use of non-destructive methods to determine body composition in female bank voles. Method Mean absolute Differences Range of Range Resolution error (± 95%CI) detectable (g) values (g) (g) (%) Lean body mass DXA 0.38 ± 0.20 > Morphometrics 0.63 ± 0.24 > ID 18 oxygen 0.67 ± 0.24 > ID deuterium 0.69 ± 0.24 > TOBEC anaesthetized 0.87 ± 0.35 > TOBEC conscious 1.64 ± 0.56 > Total body fat DXA 0.32 ± 0.17 > Morphometrics 0.60 ± 0.26 > ID deuterium 1.07 ± 0.37 > ID 18 oxygen 1.10 ± 0.39 > TOBEC conscious 1.36 ± 0.50 > TOBEC anaesthetized 1.09 ± 0.80 > Total body fat DXA 0.42 ± 0.23 > (BM-LBM) Morphometrics 0.59 ± 0.25 > ID 18 oxygen 0.59 ± 0.29 > ID deuterium 0.71 ± 0.26 > TOBEC anaesthetized 0.69 ± 0.41 > TOBEC conscious 1.44 ± 0.51 > Abbreviations: Isotope dilution (ID); body mass (BM); lean body mass (LBM) TOBEC TOBEC index was significantly higher for conscious (TIc) compared to anaesthetized (TIa) female (T=5.53, P <0.001) but not male voles (T=0.21, p=0.84). In all respects, the relationships based on anaesthetized animals were better than those for conscious animals. In females, TOBEC index predicted total body water (R 2 TIc 0.61; TIa 0.92) more reliably than lean body mass (R 2 TIc 0.61; TIa 0.78) or total body fat (R 2 TIc 0.30; TIa 0.68), but in males TOBEC index predicted lean body mass (R 2 TIc 0.56; TIa 0.91), more reliably than total body water (R2 TIc 0.41; TIa 0.74) or total body fat (R 2 TIc 0.22; TIa 0.69). Isotope dilution Isotope dilution consistently under-estimated total body water by an average of 21 % ( 18 oxygen) and 13 % (deuterium). However, the R 2 values for the relationships between total body water predicted and observed ranged from (mean 0.79) across sex and isotope. Relationships based on total body water predictions from deuterium had greater R 2 values than those for 18 oxygen. For both isotopes and sexes, total body water predicted lean body mass more reliably than total body fat. DXA DXA significantly underestimated lean body mass by approximately 15 % and total tissue mass (TTM) by approximately 7 % in both males and females (p<0.001). Total body fat was significantly overestimated P <0.001) by approximately 49 %. There were, however, strong relationships between machine-predicted and observed values for all three of these variables (R 2 : male TTM 0.97, LBM 0.94, TBF 0.88; female TTM 0.99, LBM 0.96, TBF 0.92), thus allowing more accurate equations to be generated and used. All error values presented for DXA are based these correction equations. Discussion This study evaluated the precision and accuracy of four methods of predicting body composition, body morphometrics, TOBEC, isotope dilution and DXA, by comparing them with chemical analysis. DXA predicted both lean and fat mass with less error, percentage and absolute, than all other methods. Estimates obtained directly from the PIXIMUS2 machine significantly underestimated lean body mass and total tissue mass and overestimated total body fat, this is in accordance with other studies [14]. However, strong associations between DXA values and those obtained through chemical analysis in this and other studies [14,30], allow accurate measurements to be obtained from correction equations. The underestimate of total tissue mass was most likely due to the scanning area not being able to encompass all of the head and tail. In general, TOBEC gave the highest errors, although predictions were more reliable when the voles were anaesthetized. This discrepancy is most likely due to the animal s ability to move slightly when conscious and that the restrainer may alter the body shape and affect the reading [28]. In addition, voles did not urinate or defecate when anaesthetized which allowed the process to be more rapid, as the holding tube did not need cleaned between measures. TOBEC index is often used to predict lean body mass, and total body fat determined indirectly from subtracting lean mass from body mass. This process can lead to relatively large errors and negative fat values if lean mass is over-estimated [25,43]. In an attempt to eliminate these problems, Morton et al [40] suggests using a direct equation from TOBEC index to total body fat. Unangst and Wunder [24] found that directly estimating lipid mass produced smaller errors than doing so via lean mass and body mass. We

8 144 found, for male voles, this was also the case, however, for females it was dependent on whether the voles were anaesthetized or not. TOBEC may have performed worse than other methods because the range of body fat in males and females used in the analysis were 3.0 g and 0.4 g lower than those used for other methods. This was because only 10 voles were used compared to 19 in other methods, and highlights one reason why the widest range of variables possible should be used for to generate validation equations. We tested this suggestion with a subsample of voles (n=10) and found that although the accuracy of the other methods was reduced, TOBEC generally retained the highest errors (data not shown). Previous studies have shown that predictions incorporating TOBEC did not always perform better than predictions using only body mass [13,44]; we concluded that the resolution of body mass and/or body length to predict lean body mass in females and total body fat in both sexes, was approximately 50 % better than that of TOBEC, conscious or anaesthetized. There was a significant inverse relationship between % fat mass and % body water, similar to other reports (Figure 1) [12,45]. There was also a strong correlation between lean body mass and total body water (Figure 2; r=0.99). This relationship is the basis of the isotope dilution method and in accordance; Layton et al. [21] found a very strong relationship between total body water predicted from dilution space and lean body mass (R 2 =0.998). Although we found a good correlation between lean body mass and total body water predicted from isotope dilution (mean R 2 =0.88 ( )) there was a large discrepancy between body water predicted and that observed from desiccation, suggesting a problem with the assumed enrichment of the solution. Furthermore, deuterium consistently predicted all variables better than 18 oxygen. To determine whether an estimation of body composition is useful, one must ascertain whether the prediction errors are smaller than the variation in the variable that is being estimated [42]. For a small mammal, such as a bank vole, the inter-individual range of TBF is relatively small (6.8 g). From a previous study, we found that seasonal changes in TBF in the bank vole, in response to photoperiod, equate to an average difference of 0.35 g. From this example, DXA is the only method that has smaller prediction errors than the change in total body fat (0.32 g). In contrast, from the same study, voles increased their lean body mass by 1.61 g in response to photoperiod, which is within the absolute error range of all methods described. In conclusion, DXA proved most reliable in the prediction of lean body mass and total body fat in the bank vole, where the amount of fat is extremely small and thus accuracy is more important. For animals that are larger in size, other methods may prove more accurate and the one chosen will depend on financial costs as well as time and practicality. DXA may be reliable, but it is relatively expensive and has, to our knowledge, only been used in the laboratory setting. TOBEC, though cheaper than DXA or isotope dilution, can be somewhat unreliable and requires anaesthesia for more accurate results. For field studies, morphometric measurements may be more practical, as long as indices have been adequately validated. Acknowledgements We would like to thank the animal house staff from the Zoology department for the help with the care taking of the voles; Peter Thomson for technical assistance with mass spectrometry analysis; and Sarah Johnston and Shona Fleming for assistance with DXA measurements. This work was carried out with the support of a BBSRC studentship. References 1. Krebs CJ, Singleton GR. Indices of condition for small mammals. Aust J Zool 1993; 41: Brown ME. Assessing body composition in birds. Current Ornithology 1996; 13: Hayes JP, Shonkwiler JS. Morphometric indicators of body condition: worthwhile or wishful thinking? In Speakman JR, ed. Body composition analysis of animals: a handbook of non-destructive methods. Cambridge: Cambridge University Press, 2001: Rogers CM. Predation risk and fasting capacity: do wintering birds maintain optimal mass? Ecology 1987; 68: Rogers CM, Smith JNM. Life-history theory in the nonbreeding period: trade-offs in avian fat reserves? 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