RESUMO
A retrospective case-control study assessing the association of DXA-derived 3D measurements with osteoporosis-related vertebral fractures was performed. Trabecular volumetric bone mineral density was the measurement that best discriminates between fracture and control groups. INTRODUCTION: The aim of the present study was to evaluate the association of DXA-derived 3D measurements at the lumbar spine with osteoporosis-related vertebral fractures. METHODS: We retrospectively analyzed a database of 74 postmenopausal women: 37 subjects with incident vertebral fractures and 37 age-matched controls without any type of fracture. DXA scans at the lumbar spine were acquired at baseline (i.e., before the fracture event for subjects in the fracture group), and areal bone mineral density (aBMD) was measured. DXA-derived 3D measurements, such as volumetric BMD (vBMD), were assessed using a DXA-based 3D modeling software (3D-SHAPER). vBMD was computed at the trabecular, cortical, and integral bone. Cortical thickness and cortical surface BMD were also measured. Differences in DXA-derived measurements between fracture and control groups were evaluated using unpaired t test. Odds ratio (OR) and area under the receiver operating curve (AUC) were also computed. Subgroup analyses according to fractured vertebra were performed. RESULTS: aBMD of fracture group was 9.3% lower compared with control group (p < 0.01); a higher difference was found for trabecular vBMD in the vertebral body (- 16.1%, p < 0.001). Trabecular vBMD was the measurement that best discriminates between fracture and control groups, with an AUC of 0.733, against 0.682 for aBMD. Overall, similar findings were observed within the subgroup analyses. The L1 vertebral fractures subgroup had the highest AUC at trabecular vBMD (0.827), against aBMD (0.758). CONCLUSION: This study showed the ability of cortical and trabecular measurements from DXA-derived 3D models to discriminate between fracture and control groups. Large cohorts need to be analyzed to determine if these measurements could improve fracture risk prediction in clinical practice.