Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements benefit from gathering exact measurements of body composition or body nutrient contents. Those are acquired by standardized dissection or by grinding the body followed by wet chemical analysis, respectively. The two methods do not result in the same type of information, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Among imaging techniques, dual-energy X-ray absorptiometry (DXA) is particularly promising. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. Several calibration regressions have been published, but comparative studies of those regression equations and whether they are applicable to different data sets are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (BW range 20–100 kg) as determined by both DXA and chemical analyses, with R² ranging between 0.89 for ash and 0.99 for water and CP. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses, with the prediction error ranging between 4.3% for CP and 12.6% for ash. Finally, we compare existing prediction equations for pigs of a similar range of BWs with the equations derived from our DXA measurements and evaluate their fit with our chemical analysis data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.