Previous studies have identified differences between DXA models and software versions; however, few have evaluated differences in young children. The International Society for Clinical Densitometry (ISCD) recommend in vitro cross-calibration when comparing devices of the same model but in vivo cross-calibration when comparing devices from different manufacturers8. A study comparing two models by the same manufacturer found that spine phantom cross-calibration can be inaccurate compared to in vivo calibration9. This is further complicated in body composition studies, as there is a lack of a suitable phantom for cross-calibration of fat and lean masses. Therefore, in our study, we cross-calibrated two GE Lunar DXA systems (Prodigy and iDXA) in vivo among 28 young children and found significant differences between the two devices, even after Prodigy scans were reanalysed with enhanced analysis.
To our knowledge, no previous study has cross-calibrated the Prodigy and iDXA in a cohort of young children (< 5 years). DXA cross-calibration studies in young children are limited; however, a previous study (3–19 years, n = 126) found that FM from the iDXA (v16) was approximately 15% higher in girls and 31% higher in boys in comparison to the GE Lunar DPX-Pro (v9.3). LM was also reduced when measured with the iDXA compared to the DPX-Pro; however, this was only significant in boys10. Other studies have compared single Hologic scans reanalysed with updated software and found differences in FM, FFM, and %BF, but no differences in total mass3,5. In young children, there are clear differences between device types and software versions; however, the contribution of scan versus software has not previously been evaluated.
We observed differences between the two devices in all parameters, with iDXA %BF being approximately 1.5-fold greater than Prodigy measurements, whereas BMD was ~ 20% lower (Table 2). When we reanalysed the Prodigy scans with enhanced analysis, although differences remained in all estimates except for TBLH total mass and BMD, as well as some regional estimates, the percentage differences and biases were substantially reduced (Tables 2 and S1).
In our study, differences between devices were most substantial among children with low %BF. Shypailo et al.5 reanalysed a large number of paediatric scans (n = 1,384) obtained with a Hologic QDR-4500 (v11.2) with updated software (v12.1) and observed greater differences in FM and %BF among younger, smaller subjects, and in girls; although, these results may not be relevant to GE Lunar devices given the differences in technology used in the two scanner types11. A pilot study in 13 women (20–46 years) found that differences between the iDXA and Prodigy were most substantial among women who were least adipose (< 20 kg FM and < 30% BF)12. DXA estimates body composition according to the attenuation of X-ray beams at high and low energy. A limitation of the technology is that DXA can only differentiate between two tissue types simultaneously (i.e., bone vs non-bone, fat vs lean)1. In an adult DXA scan, 40 to 45% of pixels will contain bone, fat, and lean tissue, whereas, in children, this percentage is increased3. Therefore, improvements to the estimation of body composition in bone-containing tissue will have a greater impact in younger, smaller children. This may also explain why in some cross-validation studies, only regional estimates were affected13,14.
Although comparison has not been made between the iDXA and the Prodigy in a cohort of young children, previous studies in adults have found only small differences between the Prodigy and the iDXA, which have not been consistent across body composition parameters and regions, nor in the direction of the difference13–17. The variations in software used may partially explain these conflicting results. The studies used Prodigy scanners with enCORE software versions ranging from 6.10 to 16, while the iDXA scanners used enCORE software version 12.3 to 1713–17.
Watson et al.17,18 evaluated differences between the iDXA and Prodigy following reanalysis of Prodigy files with enhanced analysis in both adults (20–65 years, n = 69) and school-aged children (6–16 years, n = 124). Among their cohort of children, differences were apparent in all parameters except whole-body, leg, and trunk BMC. Similar to our findings, differences were most pronounced for total FM and LM, which were 0.71 kg (6%) higher and 1.07 kg (3.5%) lower with the Prodigy than the iDXA18.
Although they did not compare basic and enhanced analysis in their study of children, among adults, Watson et al.17 noted no differences in whole-body FM and LM when Prodigy scans were analysed with basic compared to enhanced analysis. However, the authors observed differences in total BMC and bone area and regional FM and LM (arm FM and leg LM). This contrasts with our study, where substantial differences were noted between Prodigy scans analysed with the two software versions for all parameters except for leg total and tissue mass. In line with our results, Crabtree et al.11 found differences between basic and enhanced analysis when data was pooled from DXA studies involving children aged 4 to 20 years.
A limitation of our study is that we could not compare our results to a suitable reference method to determine which of the two DXA scans was most accurate. In early childhood, there is no gold-standard method for assessing body composition. A four-compartment (4C) model may be used as a reference since it provides additional clarification about the composition of the FFM compartment19; however, this would have been time- and resource-intensive. Nonetheless, a previous study in adults found that the iDXA aligned more closely with a 4C model than results from the Prodigy, although there was a systematic bias, with FM being overestimated among those with greater FM17. This systematic bias in FM was not observed when iDXA measurements were validated against a 4C model in school-aged children, although mean FM was overestimated by 2 kg18. The authors also found iDXA to underestimate FFM by 1.3 kg, with this increasing as total FFM increased18. Correction of iDXA FFM according to individually measured TBW (i.e., correcting for FFM hydration) resulted in a reduction in limits of agreement and removal of the systematic bias. However, a mean bias of approximately 2 kg remained18. In addition to determining which DXA device is more accurate, we acknowledge the need to replicate the adjustment equations in an independent group of children.
In summary, we have conducted the first cross-calibration study of the GE Lunar Prodigy and iDXA in a cohort of young children. There were substantial differences between the iDXA and the Prodigy, which were attenuated following reanalysis of the Prodigy scans with enhanced software. Thus, the same child scanned by the two devices will yield different results in part due to differences in scan resolution but also due to software differences. However, it is difficult to disentangle these differences and to determine which is a more accurate reflection of true body composition. This highlights a key challenge researchers and clinicians face when collecting longitudinal body composition data in children. As manufacturers upgrade devices and software over the duration of a study or clinical observation, it becomes difficult to determine the true trajectory of body composition. Therefore, researchers and clinicians need to consider the manufacturer, model, and software version when conducting DXA scans as results may not be comparable.