LAUSR

In a cross-sectional cohort of 340 healthy Brazilian men aged 20 to 92 years, data on density, structure, and strength of the distal radius and tibia were obtained using high-resolution peripheral quantitative computed tomography (HR-pQCT) to develop age- and site-specific reference curves. Age-dependent changes differed between the sites and bone compartments (trabecular and cortical). The aim of this study was to establish age-related reference curves for bone densities, microarchitectural properties, and estimated failure load measured by HR-pQCT (distal radius and tibia) in men. Also, to correlate bone stiffness with the other HR-pQCT parameters, areal bone mineral density (BMD) by DXA and trabecular bone score (TBS). Healthy Brazilian men (n = 340) between the ages of 20 and 92 years were recruited. Non-dominant radius and left tibia were scanned using HR-pQCT (Xtreme CT I). Standard and automated segmentation methods were performed, and bone strength estimated by FE analysis. Bone mineral density at lumbar spine, total hip, femoral neck, and TBS were measured using DXA (Hologic, QDR4500). Age-related reference curves were constructed at the distal radius and tibia for volumetric bone density, morphometry, and estimated bone strength parameters. There was a linear relationship with age only for thickness measurements of distal radius (trabecular: R2 0.108, p<0.001; cortical: R2 0.062, p=0.002) and tibia (trabecular: R2 0.109, p<0.001; cortical: R2 0.063, p=0.010), and bone strength at distal radius (R2 0.157, p<0.001). The significant correlations (p <0.05) found by Pearson’s correlations (r) between bone stiffness and all other variables measured by HR-pQCT and DXA showed to be stronger at the tibia site than the distal radius. The current study expands the HR-pQCT worldwide database and presents an adequate methodology for the construction of reference data in other populations. Moreover, the correlation of bone strength estimated by FEA with other bone microstructural parameters provided by HR-pQCT helps to determine the contribution of each of these variables to fracture risk prediction in men.