Cross-Calibration of iDXA and pQCT Scanners at Rural and Urban Research Sites in The Gambia, West Africa

Abstract

Between-scanner differences in measures of bone and body composition can obscure or exaggerate physiological differences in multi-site studies or the magnitude of changes in longitudinal studies. We conducted a cross-calibration study at two bone imaging centres in The Gambia, West Africa where DXA (dual-energy X-ray absorptiometry) and pQCT (peripheral Quantitative-Computed Tomography) are routinely used. Repeat scans were obtained from 64 Gambian adults (58% Male) aged Mean(SD) 30.9 (13.5) years with Mean(SD) body mass index (BMI) 21.7 (4.0) kg/m², using DXA (GE Lunar iDXA, whole body [WB], total hip [TH], lumbar spine [LS]) and pQCT (Stratec XCT2000L/XCT2000, tibia 4%, 50% sites). Between-scanner differences were tested using paired t tests (p < 0.05). Between-scanner correlation was explored with linear regression, and cross-calibration equations derived. Bland-Altman analysis investigated machine trend/bias. When differences were detected (p < 0.05), cross-calibration equations were applied to urban values, with t tests and Bland Altman analysis repeated. Between-scanner differences exceeded the predefined level of statistical significance (p < 0.05) for WB aBMD and BA; all pQCT measures vBMD, BMC, cortical cross-sectional area (CSA) and stress-strain index (SSI). Between-scanner correlation was high (R²:0.92-0.99), except pQCT Mu.Den (R² = 0.51). Bland Altman plots indicated bias increased with increasing BMD. Cross-calibration equations attenuated all between-scanner differences and systematic bias. Cross-calibration, particularly of pQCT scanners, is an important consideration in multi-site studies particularly where between population comparisons are intended. Our experiences and findings may be generalisable to other resource-limited settings where the logistics of sourcing parts and in-country repair may result in lengthy scanner downtime.

Keywords: Body composition; Cross-calibration; DXA; Densitometry; pQCT.

Fig. 1

Correlation between rural and urban scanners for a) DXA WB aBMD, b) pQCT Tot.vBMD, c) pQCT Tb.vBMD, and d) pQCT Ct.vBMD. Adjusted R² shown from linear model where the independent variable was the outcome measure from the rural site, and the dependent variable was the same outcome measure from the urban site. DXA Dual-energy X-ray absorptiometry; pQCT peripheral quantitative-computed tomography; WB aBMD whole body areal bone mineral density; Tot.vBMD total volumetric bone mineral density; Tb.vBMD trabecular volumetric bone mineral density; Ct.vBMD cortical volumetric bone mineral density

Fig. 2

Correlation between rural and urban scanners for a) DXA WB LM, b) DXA WB FM, c) pQCT CSMA, d) pQCT Mu.Den. Adjusted R² shown from linear model where the independent variable was the outcome measure from the rural site, and the dependent variable was the same outcome measure from the urban site. DXA Dual-energy X-ray absorptiometry; pQCT peripheral quantitative-computed tomography; WB LM whole body lean mass; WB FM whole body fat mass; CSMA cross-sectional muscle area; Mu.Den muscle density

Fig. 3

Bland–Altman plots for a) DXA WB aBMD, b) pQCT Tot.vBMD, c) pQCT Tb.vBMD, d) pQCT Ct.vBMD before the application of cross-calibration equations. DXA, Dual-energy X-ray absorptiometry; pQCT peripheral quantitative-computed tomography; WB aBMD whole body areal bone mineral density; Tot.vBMD total volumetric bone mineral density; Tb.vBMD trabecular volumetric bone mineral density; Ct.vBMD, cortical volumetric bone mineral density

Fig. 4

Bland–Altman plots for a) DXA WB LM, b) DXA WB FM, c) pQCT CSMA, d) pQCT Mu.Den. before the application of cross-calibration equations. Dual-energy X-ray absorptiometry; pQCT, peripheral quantitative-computed tomography; WB LM, whole body lean mass; WB FM, whole body fat mass; CSMA, cross-sectional muscle area; Mu.Den, muscle density