In the randomized, placebo-controlled FREEDOM study of women aged 60 to 90 years with postmenopausal osteoporosis, treatment with denosumab once every 6 months for 36 months significantly reduced hip and new vertebral fracture risk by 40% and 68%, respectively. To gain further insight into this efficacy, we performed a nonlinear finite element analysis (FEA) of hip and spine quantitative computed tomography (QCT) scans to estimate hip and spine strength in a subset of FREEDOM subjects (n = 48 placebo; n = 51 denosumab) at baseline, 12, 24, and 36 months. We found that, compared with baseline, the finite element estimates of hip strength increased from 12 months (5.3%; p < 0.0001) and through 36 months (8.6%; p < 0.0001) in the denosumab group. For the placebo group, hip strength did not change at 12 months and decreased at 36 months (-5.6%; p < 0.0001). Similar changes were observed at the spine: strength increased by 18.2% at 36 months for the denosumab group (p < 0.0001) and decreased by -4.2% for the placebo group (p = 0.002). At 36 months, hip and spine strength increased for the denosumab group compared with the placebo group by 14.3% (p < 0.0001) and 22.4% (p < 0.0001), respectively. Further analysis of the finite element models indicated that strength associated with the trabecular bone was lost at the hip and spine in the placebo group, whereas strength associated with both the trabecular and cortical bone improved in the denosumab group. In conclusion, treatment with denosumab increased hip and spine strength as estimated by FEA of QCT scans compared with both baseline and placebo owing to positive treatment effects in both the trabecular and cortical bone compartments. These findings provide insight into the mechanism by which denosumab reduces fracture risk for postmenopausal women with osteoporosis.

High-resolution peripheral quantitative computed tomography (HR-pQCT) has recently been introduced as a clinical research tool for in vivo assessment of bone quality. The utility of this technology to address important skeletal health questions requires translation to standardized multicenter data pools. Our goal was to evaluate the feasibility of pooling data in multicenter HR-pQCT imaging trials. Reproducibility imaging experiments were performed using structure and composition-realistic phantoms constructed from cadaveric radii. Single-center precision was determined by repeat scanning over short-term (<72 hours), intermediate-term (3-5 months), and long-term intervals (28 months). Multicenter precision was determined by imaging the phantoms at nine different HR-pQCT centers. Least significant change (LSC) and root mean squared coefficient of variation (RMSCV) for each interval and across centers was calculated for bone density, geometry, microstructure, and biomechanical parameters. Single-center short-term RMSCVs were <1% for all parameters except cortical thickness (Ct.Th) (1.1%), spatial variability in cortical thickness (Ct.Th.SD) (2.6%), standard deviation of trabecular separation (Tb.Sp.SD) (1.8%), and porosity measures (6% to 8%). Intermediate-term RMSCVs were generally not statistically different from short-term values. Long-term variability was significantly greater for all density measures (0.7% to 2.0%; p < 0.05 versus short-term) and several structure measures: cortical thickness (Ct.Th) (3.4%; p < 0.01 versus short-term), cortical porosity (Ct.Po) (15.4%; p < 0.01 versus short-term), and trabecular thickness (Tb.Th) (2.2%; p < 0.01 versus short-term). Multicenter RMSCVs were also significantly higher than short-term values: 2% to 4% for density and micro-finite element analysis (µFE) measures (p < 0.0001), 2.6% to 5.3% for morphometric measures (p < 0.001), whereas Ct.Po was 16.2% (p < 0.001). In the absence of subject motion, multicenter precision errors for HR-pQCT parameters were generally less than 5%. Phantom-based multicenter precision was comparable to previously reported in in vivo single-center precision errors, although this was approximately two to five times worse than ex vivo short-term precision. The data generated from this study will contribute to the future design and validation of standardized procedures that are broadly translatable to multicenter study designs.