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Journal of the American College of Nutrition, Vol. 22, No. 5, 408-414 (2003)
Published by the American College of Nutrition

Original Research

Fan Beam Dual Energy X-Ray Absorptiometry Body Composition Measurements in Piglets

Sonia Chauhan, MBBS, Winston W. K. Koo, MBBS, FACN, Mouhanad Hammami, MD and Elaine M. Hockman, PhD

Departments of Pediatrics, Obstetrics and Gynecology (S.C., W.W.K.K., M.H.), Wayne State University, Detroit, Michigan
Departments of Computing and Information Technology (E.M.H.), Wayne State University, Detroit, Michigan

Address correspondence to: Dr. Winston Koo, Department of Pediatrics, Hutzel Hospital, 4707 St Antoine Blvd, Detroit, MI 48201. E-mail: wkoo{at}wayne.edu


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 REFERENCES
 
Objectives: A piglet model was used to validate and cross validate the fan-beam (FB) dual energy X-ray absorptiometry (DXA) software vKH6 and to determine the predictive values of physiologic parameters (weight, length, age and gender) on body composition.

Methods: Nineteen piglets (Group A: 600 to 21100 g) were used to validate the FB-DXA measurements of body composition based on chemical analysis of the carcass. An additional 22 piglets (Group B: 640 g to 17660 g) had FB-DXA measurements, and these values were compared to the predicted values generated from regression equations computed from group A piglets. Body composition for bone mass, lean mass and fat mass was based on ash weight, nitrogen and fat measured from three aliquots of homogenate from each carcass. Data from all piglets (n = 41) were used to determine the variations in body composition. Data analysis used regression, t test and analysis of variance.

Results: Duplicate DXA (total weight TW, bone mineral content BMC, bone area BA, bone mineral density BMD, lean mass LM and fat mass FM) measurements were highly correlated (r = 0.98 to 1.00, p < 0.001 for all comparisons) and were not significantly different. No significant differences were found in the residuals from predicted versus measured DXA values between the larger and the smaller (<1.6 kg) piglets from Group A. For Group B piglets, the DXA measured TW of 5666 ± 5692 g (mean ± SD), LM (5063 ± 5048 g), FM (465 ± 510 g), BMC (138 ± 139 g), BA (486 ± 365 cm2) and BMD (0.235 ± 0.071 g/cm2) were highly significantly correlated with (r = 0.94 to 1.00, p < 0.001 for all comparisons) and were not significantly different from the predicted values. Data from all piglets (n = 41) showed that weight is the dominant predictor of whole body and regional body composition. Length, age or gender contributed to <2% of the variability of body composition.

Conclusion: Body composition measurements using the FB DXA software vKH6 is highly reproducible. The software vKH6 is validated for use in a wide range of body weights and body composition, and cross-validated using a separate group of animals. Body weight is the dominant predictor of body composition in immature piglets.

Key words: body composition, dual energy X-ray absorptiometry, piglet


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 REFERENCES
 
Measurement of body composition provides insight into the regulation of normal development as well as the interactions among nutritional, physiological and biochemical functions. In growing humans and animals, dual energy X-ray absorptiometry (DXA) has been independently validated by multiple investigators [1–3] and is currently the preferred method for the non-invasive measurement of multiple components of body composition simultaneously [4]. With advances in DXA technology, the newer generation of fan beam (FB) DXA has been validated for use in small subjects [5] and offers the opportunity for faster scan acquisition and improved accuracy in body composition measurements. These advantages of FB-DXA promise to displace the older pencil beam DXA as the technique of choice for body composition measurement in growing subjects. However, there are limited data on the use of FB-DXA in small subjects, and there is no report of cross validation for the current software.

Piglets are frequently used as a model for the study of nutritional and non-nutritional intervention on human infant body composition [6–9] because of their similarity in body size to infants and young children, adaptability to nutritional manipulation and relatively rapid growth permits the determination of the biological effect over a relative short period and because they are freely available commercially. Piglets are also useful as a model for conditions that are not possible to study in human infants such as direct measurement of biomechanical properties involving tissue destruction [10]. However, there are limited data to determine whether certain physiologic parameters such as body weight, length, age and gender that can predict body composition in human infants are also applicable to piglets. Knowledge in this area, therefore, would provide additional information to optimize the use of piglets as a model for clinical studies.

Our aims for this study were to determine whether the current software algorithm for the FB-DXA measurement of body composition is applicable for use in piglets smaller than those reported previously, to cross validate its use in another group of piglets and to determine the relative extent of several physiologic parameters including body weight, length, age and gender to predict body composition of piglets.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 REFERENCES
 
Animals
Forty one domestic crossbred (Large White, Yorkshire, Hampshire and Dorak) swine piglets (600 to 21100 g) were obtained commercially (J&M Farms, Lansing, MI). They were studied between birth and 68 days. Nineteen piglets (Group A) weighed between 600 g and 21100 g were used to validate the FB-DXA measurements of body composition based on chemical analysis of 14 larger piglets (1940 to 21100 g) reported elsewhere [5] and an additional five smaller piglets (600 to 1580 g). The data from all piglets in Group A were used to generate the prediction equations for DXA measurements based on physiologic parameters including weight, length and age. An additional 22 piglets (Group B) weighing 640 g to 17660 g had FB-DXA measurements, and these values were compared to the predicted values based on the regression equations generated from group A piglets.

Each animal was weighed using an electronic scale (Seca, Toledo Scale Company, Toledo, Ohio) immediately prior to DXA scanning. Length was measured using an adjustable caliper (Lock-Joint Caliper, L.S. Starret Co., Athol, MA) between tip of the snout and base of the tail. The mean ± standard deviation for weights and lengths of 41 piglets were 6190 ± 5856 g and 50.5 ± 17.5 cm, respectively.

DXA Scans
DXA measurement was performed in duplicate for all piglets. DXA scans were obtained with the same fan-beam densitometer (Hologic QDR 4500A, Hologic Inc., Bedford, MA) operated in the infant whole body mode using a commercial software from the same manufacturer that was further modified and validated as vKH6 [5]. Each piglet was placed on a cotton blanket in the prone position with front and hind limbs extended. The long axis of the animal was positioned at the midline of the scanning table and the snout approximately 5 cm from the cranial end of the scanning table [1,5]. Each piglet was covered with a cotton blanket, and a disposable diaper was used in larger piglets to prevent soiling. The piglets were sedated with pentobarbital and sodium thiopental prior to study. Scan acquisition and analysis were performed by the same trained operator (MH) to insure the technical adequacy of each scan, in particular, the absence of motion artifact [11]. In addition to total body analysis, software delineation of separate regions was also used to measure the body composition of different regions. Regional analyses typically involved the head, each of the four extremities and the trunk.

Chemical Measurements
Three aliquots of dried homogenate from each carcass were used for each measurement. Details of chemical analyses have been reported elsewhere [1,5]. In brief, ash weights were obtained using a computer-controlled temperature-regulated muffle furnace. Nitrogen was measured by micro-Kjeldahl method. Total body content of crude protein was determined as total carcass nitrogen X 6.25, and the lean mass was calculated as the crude protein content plus total body water. The latter was determined as the difference between the weight before and after drying at 105°C. Fat was measured as total lipid extracted by chloroform and methanol. In our laboratory, the average CV of triplicate samples from each piglet was 3.4% for ash measurement, 3.1% for nitrogen determination and 4.4% for lipid measurement. The recovery of nitrogen using the National Institute of Standard and technology certified standard reference material (NIST, U.S. Department of Commerce, Gaithersburg, MD) was 100.9 ± 1.59%, and the recovery of lipid was 98.5 ± 1.46% from commercial vegetable oil.

The study protocol was approved by the Animal Investigation Committee at Wayne State University.

Statistical Analysis
Paired t tests were used to compare the duplicate DXA measurements for all piglets. Based on all piglets with chemical analysis (n = 19), prediction equations of each measured DXA parameters [total weight (TW), bone mineral content (BMC), lean mass (LM), fat mass (FM)] were computed from the scale weight and carcass content of ash weight, lean mass and fat mass respectively. Residuals from the predicted versus the measured DXA values of the five additional piglets were compared to those of the original 14 piglets [5] to determine whether the same DXA algorithm was equally applicable to both the larger (original 14) and the smaller (the additional five) piglets.

Regression analysis was used to generate the predictive equations for each DXA variable based on continuous physiologic parameters of body weight, length and age of Group A piglets. These equations in turn were used to predict the DXA measurements of group B piglets. Paired t test was used to compare the predicted and measured DXA values for Group B piglets. In this study, weight of the pig, rather than the study weight that includes the weight of the covering, was used in the data analysis to minimize the entry of multiple collinear independent weight variables and to be consistent with the practice in clinical studies of using the bare weight of infants [12,13].

DXA measurements for all 41 piglets (Group A & B combined) were used to determine (1) the relationship of each of the physiologic parameters (weight, length, age and gender) for each of the DXA parameters with Pearson correlation, (2) relative predictive values of the continuous physiologic variables (body weight, length, age) on total and regional DXA measured body composition (BMC, LM and FM) with stepwise multiple regression; gender was used as an additional physiological independent variable for the prediction of body composition of total body, (3) the variations in body composition for DXA software delineated region were also determined.

Unless stated otherwise, all values were shown as mean ± standard deviation. All statistical tests were performed using SPSS 11.5 for Windows (SPSS Inc, Chicago, IL), and a p value of 0.05 was used to judge the significance.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
REFERENCES
 
Of the 82 scans performed for this study, one scan was inadvertently deleted during scan analysis, but the data from 81 scans were available for analysis. For each DXA parameter (TW, BMC, BA, BMD, LM, FM), the values from duplicate total body scans were highly significantly correlated and were not significantly different (Table 1). The same relationships exist for duplicate regional (head and four extremities) measurements as delineated from the total body scan, although the correlation values were between 0.860 to 1.000. All subsequent analyses were based on the average of the duplicate scans except for the one piglet with the single scan.


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  		Table 1. Duplicate Fan Beam Dual Energy X-Ray Absorptiometry (DXA) Measurements in 40 Piglets

 
Data on chemical analysis of 19 piglets in group A including the data of 14 piglets [5], are shown in Fig. 1. There were no significant differences in the residuals from predicted versus measured DXA values between the 14 larger piglets and the five smaller piglets.



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  		Fig. 1. Relationship of fan-beam dual energy X ray absorptiometry (DXA) measurements to scale weight and chemical analysis. Open circles represent 14 larger piglets, and asterisks represent five smaller piglets. Regression line and r2 value represent the relationship for all 19 piglets.

 
For Group B piglets, the measured and predicted (based on regression equation from group A piglets) DXA values were highly significantly correlated (Fig. 2) and not significantly different.



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  		Fig. 2. Scatter plot of 22 piglets with measured versus predicted fan-beam dual energy X ray absorptiometry (DXA) measurements for total weight, bone area, bone mineral content, bone mineral density, lean mass and fat mass. Lines indicated mean regression and 95% confidence interval for the mean. Number in parentheses = adjusted r2 and standard error of estimate, respectively.

 
For all piglets (n = 41), correlation analyses show that each continuous physiological parameters (weight, length, age) is significantly related to BA, BMC, BMD, LM and FM. Gender was not related to any DXA measurements (Table 2).


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  		Table 2. Pearson Correlation between Physiologic Parameters with Each Parameter from Fan Beam Dual Energy X-Ray Absorptiometry Measurements in 41 Piglets

 
Stepwise multiple regression analyses show that body weight was consistently the best predictor of body composition of whole piglets. It alone accounted for >98% of the variance of BMC, FM and LM. Length, age and gender had minimal additional contribution to the prediction of body composition of piglets and contributed to <2% to the prediction for any aspect of body composition. Body weight remains the dominant predictor for BMC even if the soft tissue components (LM and FM) were added to the regression model as additional independent predictors. Body weight was also the best predictor of regional BMC, LM and FM, and accounted for >95%, >93% and >97% of the variance for these body composition parameters respectively. With an increase in body weight from 3.5 kg to 10.5 kg, the predicted changes in regional body composition at the extremities and trunk are shown in Table 3.


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  		Table 3. Predicted Percentage Increase in Regional Body Composition with a Two Hundred Percent Increase of Body Weight from 3.5 and 10.5 kg

 
Left versus right sided regional DXA measurements were highly significantly correlated (r = 0.72 to 1.00, p < 0.001) and not significantly different.


   DISCUSSION
 
The major advantages of the FB-DXA over the older pencil beam DXA in the measurement of bone mass and body composition in infants and young children include a significant reduction in scanning time from about 15 minutes in older infants to less than 3 minutes [5]. This in turn results in a less potential for movement artifact [11]. We have demonstrated that FB DXA technique has a much greater dynamic range of body composition measurement compared to the older PB DXA technique [1–3].

Our findings of excellent reproducibility of FB-DXA measurements with almost identical values in repeated scan is consistent with previous reports of excellent reproducibility of DXA measurements in animals [1–5] and human infants [4,12,13]. The slightly lower correlation from duplicated regional measurements is most likely a reflection of the ability of the software algorithm to clearly demarcate the different regions of interest, and the proportional error is always higher for subregion versus regional, or regional versus total body measurements in animals [14] and human infants [15]. Thus, care should be taken in performing regional analysis since the exact separation between different regions can be limited by the position of the subject and the ability of the software to clearly demarcate the region of interest. Nevertheless, our data demonstrated that regional measurements are sufficiently precise to be useful as a screening tool or for demonstrating trends in body composition variations.

The cross validation data in this study show that the measured and predicted body composition measurements were highly correlated and not significantly different. The 95% confidence interval between the measured and predicted DXA BMD was greater than that for other DXA parameters. This is not surprising since other validation studies [1–3,5], as well as this report, are based on carcass ash and calcium content and not bone density for the validation of DXA bone measurements in small subjects. In any case, even if BMD was used in the piglet or the human studies, the error in the prediction for BMD would be small compared to the changes in BMD of about 40% in this study of piglets and >50% for humans during infancy [12]. Furthermore, this issue is of little clinical importance since DXA BMD is an areal density, i.e., not true volumetric density, and subjected to multiple errors associated with different projection, and is not recommended for use in growing organisms [16,17].

Genetic traits are well known to affect the rate and quantity of tissue accretion, although it was not our intention to study the physiologic predictor of body composition of each strain of pigs. Furthermore, almost all domestic commercial strains of piglets are cross breeds aimed to achieve consistent quality and efficiency in pork production, and they are also less sensitive to environmental changes and stressors than inbred genetic groups [18,19]. Thus our data is likely to be applicable to most species of pigs for commercial or research use. The role of genetic trait as a predictor of body composition is analogous to the racial influence on body composition in human neonates and infants, in which body weight is by far the strongest predictor in comparison [12,13].

Numerous mathematical models of growth and carcass body composition of pigs have been developed which quantified the accretion rates of protein, lipid, moisture and ash. They generally focus on the most rapid means of achieving the desired body composition at body weights >30 kg with the greatest commercial value. Our data in immature piglets is consistent with the general agreement that body weight has a very highly significant correlation with body composition of pigs across all weight ranges [20–22], although interaction among weight, genetic trait and gender can affect growth and body composition to varying extent [22]. This is also consistent with the reports in human neonates and infants that body weight is the best predictor of body composition among different races and genders [12,13,23,24]. The use of LM and FM as additional independent variable did not improve the predictive ability of body weight for bone mass and is also consistent with the report in infants [13].

Age is generally a good surrogate for weight. However, the rate of growth and tissue accretion may differ among different reports such that the oldest and heaviest pigs may have different body composition due to a continued increase in body fat but not for nitrogen or lean tissue [19,22]. Few studies report the length of pigs, and there is a tendency to limit the measurement to the trunk. Presumably length is of less importance than weight or body composition to determine the commercial value of the pigs, and it is difficult to measure accurately in unsedated pigs. Our data in immature piglets demonstrated that weight and length are colinear and that weight can be used as a surrogate for all measures of body composition. This is also consistent with the reports in human neonates and infants [13,15,22].

Details of hormonal control of growth and body composition in pigs are limited, although differences in the rate of growth and quantities and ratios of tissue accretion, or body composition between boars and gilts, and between boars and barrows are well known [18,25]. However, our data indicated any independent effect of gender on the prediction of body composition in immature piglets was minimal. This is consistent with the observation in piglets [26], and human neonates and infants [13].

Variations in body composition occurs at different regions of the pig carcass based on commercial cuts of the pork [22,25], or from software algorithm specific for the DXA instrument [14,21]. However, the ability of the DXA software algorithm to clearly demarcate each region of interest may affect the accuracy and precision of this relationship [14,15]. In the current study, body weight is also the dominant predictor of body composition in DXA delineated regions. In the current study, the increase in total body bone, fat and lean masses are fairly proportional to body weight gain. This is in contrast to a much greater proportional gain in total body fat of >400% in human infants over the same amount of total weight gain [13]. For piglets, the body composition gains varied depending on the region measured as have been reported for infants [12,13,23]. With increasing body weight, the trunk to extremities ratio of lean mass became higher than that for fat or bone mass in piglets, which is consistent with that reported for human infants [12,13]. In contrast, the trunk—extremities ratio for fat is stable in growing piglets but decreases in the growing human infant [12]. It seems reasonable to use domestic crossbred piglets as a model for human infant body composition studies although certain differences exists between them; in particular, these piglets have relatively slower proportional gain in total body fat and proportionally less fat in the trunk.

We conclude that the current modified fan beam DXA software vKH6 for use in body composition measurements is highly reproducible and is applicable over a wide weight range comparable to that of the human preterm newborn to toddler. Our cross-validation study shows the software algorithm is robust and applicable to different group of subjects. Consistent with the data in human infants, body weight is the best predictor for the total and regional body composition in growing piglets. There are many similarities in body composition between piglets and human infants, although differences may exist for the proportion of tissue gained during growth and in regional distribution of various tissues.

Received December 16, 2002. Accepted April 4, 2003.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 REFERENCES
 

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