Dual-energy X-ray absorptiometry (DXA) body composition measurements are widely performed in both clinical and research settings, and enable the rapid and noninvasive estimation of total and regional fat and lean mass tissues. DXA upgrading can occur during longitudinal monitoring or study; therefore, cross calibration of old and new absorptiometers is required. We compared soft tissue estimations from the GE Prodigy (GE Healthcare, Madison, WI) with the more recent iDXA (GE Healthcare) and developed translational equations to enable Prodigy values to be converted to iDXA values. Eighty-three males and females aged 20.1–63.3 yr and with a body mass index range of 17.0–34.4 kg/m2 were recruited for the study. Fifty-nine participants (41 females and 18 males) comprised the cross-calibration group and 24 (14 females and 10 males) comprised the validation group. Total body Prodigy and iDXA scans were performed on each subject within 24 h. Predictive equations for total and regional soft tissue parameters were derived from linear regression of the data. Measures of lean and fat tissues were highly correlated (R2 = 0.95–0.99), but significant differences and variability between machines were identified. Bland-Altman analysis revealed significant biases for most measures, particularly for arm, android, and gynoid fat mass (12.3%–22.7%). The derived translational equations reduced biases and differences for most parameters, although limits of agreement exceeded iDXA least significant change. In conclusion, variability in soft tissue estimates between the Prodigy and iDXA were detected, supporting the need for translational equations in longitudinal monitoring. The derived equations are suitable for group analysis but not individual analysis.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)