Trabecular bone microarchitecture predicts fragility fractures in postmenopausal women on denosumab treatment.

Abstract

High-resolution peripheral quantitative computed tomography (HR-pQCT) represents a three-dimensional tool for the screening of osteoporosis patients i.e., regarding fracture risk. The purpose of this study was to determine the baseline and follow-up bone microarchitecture in relation to incident fracture risk in postmenopausal women on denosumab treatment. We have retrospectively evaluated data from 182 postmenopausal women treated with denosumab that underwent an initial HR-pQCT scan before the initiation of the treatment; and at least one second HR-pQCT after 12 months. Women were assigned to two groups based on documented fragility fractures for the following 2.9 ± 1.1 years: fracture (n = 22) and no fracture (n = 160). Baseline parameters from DXA, HR-pQCT and bone turnover were compared between the two groups. Furthermore, ROC and multiple regression analyses of the baseline and follow-up data were performed to evaluate the predictive value regarding incident fractures. At baseline, trabecular parameters were significantly reduced in the fracture group and showed the best predictive value for new fractures, while DXA results could not predict fractures. A multiple regression model identified BV/TV and age as the best baseline parameters for incident fracture risk. At 12 months, cortical and trabecular parameters increased in the non-fracture group, while no significant increase was noted in the fracture group. However, no significant differences regarding the changes of these parameters could be detected between the non-fracture and fracture cohort. Trabecular bone microstructure at baseline is crucial for incident fracture risk in postmenopausal women on denosumab treatment, especially in comparison to DXA values. In this context, the microstructural follow-up results seemed to be of lesser importance regarding fracture risk. The results of this exploratory study should be validated in independent populations.