Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements necessitate gathering exact measurements of body composition or body nutrient contents. Wet chemical analysis methods or standardized dissection are commonly applied, 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. Dual-energy X-ray absorptiometry (DXA) is particularly promising due to its low acquisition and operating costs, low levels of radiation emission and simple image processing. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. In principle, DXA results should be independent of animal size and body composition, because bone mineral content and the content of fat and lean tissue are derived from the attenuation of X-rays transmitted through the body. Several calibration regressions have been published, but comparative studies 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 for the animal’s breed, age, class or sex. 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, and we present the accuracy of those predictions. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses. This opens up promising possibilities for the reduction of invasive procedures in the course of nutritional studies. Finally, we compare existing prediction equations for pigs of a similar range of body weights with the equations derived from our DXA measurements and evaluate their fit with our chemical analyses 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.
