The effects of lipid location on non-invasive estimates of body composition using EM-SCAN technology
Department of Biology, United States Air Force Academy, CO 80841, USA
* Author for correspondence (e-mail: tom.unangst{at}usafa.af.mil)
Accepted 16 July 2002
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Summary |
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Key words: lipid, electromagnetic scanning (EM-SCAN), total body electrical conductivity (TOBEC), body composition
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Introduction |
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The accuracy and reliability of the body-composition estimates with EM-SCAN
can be affected by hydration, body temperature, position and shape of the
sample, and gastrointestinal tract contents if not properly controlled
(Bachman, 1994;
Bell et al., 1994
;
Voltura and Wunder, 1998
;
Walsberg, 1988; Zuercher et al.,
1997
). Estimation accuracy can also be improved by deriving
species-specific equations (Unangst and
Wunder, 2001
) or specific-condition equations
(Wunder et al., 2000
).
Comparisons of estimates for ML and
MFF in species with very similar morphology (meadow vole
Microtus pennsylvanicus and prairie vole Microtus
ochrogaster) showed a threefold improvement in error estimates in
equations derived for a particular species
(Unangst and Wunder, 2001
).
These results suggest that each species might deposit fat differently or in
different body locations, therefore affecting the EM-SCAN device output
(Unangst and Wunder, 2001
).
Previously frozen specimens can also be analyzed accurately with EM-SCAN, with
error estimates for lipid of 0.5 g, but this necessitates different
calibration equations from live specimens
(Wunder et al., 2000
). Thus,
we tested whether the location of lipid deposition can influence the
disturbance of the EM field and the device output, even when lipid mass is
constant. In addition, we derived predictive equations for both lean and lipid
masses for different lipidlocation conditions and examined the estimation
accuracy of these predictive equations.
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Materials and methods |
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Experiment 1: lipid location effects on device output
In our first experiment, a total of 44 `small mammal' models were used.
Groups consisted of one lean group (without additional fat; 93% lean) and four
lipid-location groups (with additional fat, 82% lean), with fat either being
added in the head, tail or midsection regions of the mold or being mixed
homogenously throughout the mold. Each group contained 11 specimens, with each
individual model weighing approximately 78 g and a pooled body fat of 7% in
the lean group and 18% in each lipid-location group (later confirmed by
Sohxlet ether extraction). To control for dehydration, each model was wrapped
in plastic wrap and held in a freezer at -20°C. In preparation for EM-SCAN
measures, all specimens remained in the freezer for 72 h and were then removed
and placed in a cold room (at 7.4°C) for an additional 24 h to thaw. Each
specimen, in turn, was removed from the cold room and allowed to warm to room
temperature (23°C), weighed to the nearest 0.01 g (Ohaus E400D) and
measured for length (to the nearest mm). Specimen temperature was measured
with a digital thermocouple. Immediately upon warming to 23°C, the
specimen was unwrapped to control dehydration and then centered on the EM-SCAN
insertion platform for placement within the EM-SCAN SA-2 (EM-SCAN Inc.,
Springfield, IL, USA) chamber. Consistent with Unangst and Wunder
(2001) and Voltura and Wunder
(1998
), we took seven readings
for each specimen, omitted the highest and lowest value and averaged the
remaining five values to calculate our EMavg value.
Comparisons of the EMavg value between lipid-location
groups were performed to evaluate the similarity of values across groups.
Because absolute lipid mass was constant between groups, the EM-SCAN device
output should not vary if lipid location has no effect on EM-field
disturbance. After completion of EM-SCAN measures, each specimen was rewrapped
and returned to the cold room. To control for temperature deviations of
>4°C (Walsberg 1988), no specimen was subjected to room temperature for
>10 min during the measurement procedure. Specimen temperature was
reconfirmed immediately upon measurement completion using a digital
thermocouple.
For the head and tail lipid-location groups, the same specimen was used for EM-SCAN measures, with the lipid introduced anteriorly to simulate the head and posteriorly to simulate the tail. We alternated the samples having the `head' first or `tail' first to reduce any effect of time for measurement. For example, the first specimen (no. 1) was scanned with the lipid-end inserted into the scanning chamber first (head), then turned around and scanned with the lipid-end inserted last (tail). Then, the next specimen (no. 2) was scanned lipid-end last (tail), then reversed and scanned lipid-end first (head). This alternating pattern was repeated for all 11 specimens in the head and tail lipid-location groups.
Statistical comparisons of the EMavg value between groups were performed using analysis of variance (ANOVA). Because the head and tail lipid-location groups used the same specimens, a pair-wise t-test comparison was done. The level of significance in all statistical tests was set at P=0.05.
Experiment 2: calibration equation and estimate accuracy
In our second experiment, a total of 65 different `small mammal' models was
constructed. Groups consisted of one lean group (without additional fat; 93%
lean; N=5) and four lipid-location groups (head, tail, midsection or
homogenous distribution; N=20 in each group). Within each
lipid-location group, five specimens with either 10%, 15%, 20% or 25% body
fat, respectively, were made. As in experiment 1, the head and tail groups
used identical specimens for EM-SCAN measures. Each model weighed
approximately 80 g, with a pooled lipid mass of approximately 14 g (18% body
fat) within each lipid-location group.
EM-SCAN procedures were identical to those previously described. Once
measured, each specimen was dried in a convection-drying oven at 70°C
until it reached constant mass. The specimens were then homogenized, and
chemical lipid extractions were performed using a modified Soxhlet procedure
at a contracted laboratory at the University of Western Ontario, USA.
Estimation equations for MFF and ML by
lipid-location group were completed using multiple-regression procedures
(Unangst and Wunder, 2001;
Voltura, 1997
;
Voltura and Wunder, 1998
). To
account for body-size effects, we incorporated a conductive index
(CI; Fiorotto et al.,
1987
), defined as:
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Results |
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Calibration equation and estimate accuracy
Body composition and body length for each lipid-location group were not
statistically different (Table
2). The EM-SCAN was able to estimate both ML
and MFF very well, with r2 values
ranging from 0.80 to 0.87 for each body-composition component
(Table 3). The average error in
estimates for ML was similar across lipid locations,
averaging approximately 1.5 g or 9-13% lipid
(Table 4). Error estimates for
MFF ranged from 1.3 g to 2.2 g, with a smaller percent
error rate of approximately 3% (Table
4). This improved performance for MFF was
expected, because the pooled samples contained approximately 82% lean
tissue.
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Discussion |
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In mammalian hibernators, energy reserves are often met by stored energy in
the form of body fat. Increases in body mass exceeding 30% (primarily fat) are
common in pre-hibernating mammals (Lyman
et al., 1982), as documented for bears
(Hilderbrand et al., 2000
),
bats (Kunz et al., 1998
;
Serra-Cobo et al., 2000
) and
other small mammals (Arnold,
1993
; Buck and Barnes,
1999
; Lehmer and Van Horne,
2001
; Pulawa and Florant,
2000
). In hibernators and many non-hibernators, brown adipose
tissue (BAT) increases are found primarily in the cervical, interscapular and
thoracic regions to provide energy via non-shivering thermogenesis
(Hayward and Lyman, 1967
;
Lyman et al., 1982
; Nedergard
et al., 1993; Smalley and Dryer,
1967
; Trayhurn,
1993
). Increased body fat is also found in many mammalian, avian
and reptilian species prior to reproduction to meet gestation and lactation
demands (Bronson, 1989
;
Meier and Burns, 1976
).
Finally, studies involving rodent obesity models differ widely in the type and
extent of obesity and warrant such consideration for estimation-model
specificity (Tschop and Heiman,
2001
).
As demonstrated in several studies
(Castro et al., 1992;
Unangst and Wunder, 2001
;
Voltura, 1997
;
Voltura and Wunder, 1998
;
Wunder et al., 2000
), the
EM-SCAN estimates MFF accurately and performs well in
estimates of ML in relatively fatter individuals. Our data
from experiment 2 clearly show that the EM-SCAN allows good estimation of body
composition in relatively fat specimens (18% lipid) even when lipid-deposition
locations vary (Table 3). However, a specific calibration equation for each lipid location was necessary
to achieve a higher degree of estimation accuracy (Tables
3,
4). In our design, the pooled
relative body fat used in equation derivation was confirmed by chemical lipid
extraction to be approximately 18%; thus, the accuracy in error estimates was
improved over conditions with very lean individuals
(Voltura and Wunder, 1998
).
Estimate errors of approximately 1.5 g represent a 10% error as a percentage
of the total body lipid. Because lean mass averaged 82% of the total body
mass, the 2 g error in MFF estimates equates to a 3% error
rate of total body lean tissue.
Thus, we suggest that body composition can be estimated most accurately in species that vary lipid location and amounts by deriving body-composition estimation equations specific to the expected condition. In hibernators, where significant seasonal changes in both white and brown fat amounts and deposition in specific locations occur, using an estimation equation derived during lean body-composition periods may not be appropriate. The significant preparatory fat deposition associated with reproduction in some mammalian, avian and reptilian species is another possible condition where different estimation equations may be warranted. Overall, derivation of body-composition estimation equations most representative of the physiological state of the specimen will improve both the reliability and accuracy of body-composition estimates using non-invasive methods such as EM-SCAN.
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Acknowledgments |
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References |
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