There are currently no population-based reference data sets available for volumetric bone mineral density and microarchitecture parameters measured using the second-generation high-resolution peripheral quantitative computed tomography (HR-pQCT), yet the technology is rapidly becoming a standard for studies of bone microarchitecture. Although cross-calibrated data sets from the first-generation HR-pQCT have been reported, they are not suitable for second-generation bone microarchitecture properties because of fundamental differences between scanner generations. This study provides site- and sex-specific centile curves across the adult life span for second-generation HR-pQCT properties. A total of 1236 adult participants (768 female and 468 male) from the Calgary area between the ages of 18 and 90 years were scanned at the distal tibia and radius using the second-generation HR-pQCT. Bone densities, microarchitectural properties, and failure load estimated using finite element analysis were determined using standard in vivo protocol. Site- and sex-specific centile curves were generated using the generalized additive models for location, scale, and shape (GAMLSS) method. These data provide reference curves appropriate for predominantly white male and female adults, which can be used as a tool to assess patient- or cohort-specific bone health. © 2020 American Society for Bone and Mineral Research.

Prevalence of osteoporosis is over 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size fits all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography HR-pQCT provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging due to the complex interactions between the numerous properties measured. In this study we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = of 5,873, 71% female) between 40–96 years of age, we employed fuzzy c-means clustering, an unsupervised machine learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterised the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype while females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD.

Most fracture risk assessment tools use clinical risk factors combined with bone mineral density (BMD) to improve assessment of osteoporosis; however, stratifying fracture risk remains challenging. This study developed a fracture risk assessment tool that uses information about volumetric bone density and three‐dimensional structure, obtained using high‐resolution peripheral quantitative compute tomography (HR‐pQCT), to provide an alternative approach for patient‐specific assessment of fracture risk. Using an international prospective cohort of older adults (n = 6802) we developed a tool to predict osteoporotic fracture risk, called μFRAC. The model was constructed using random survival forests, and input predictors included HR‐pQCT parameters summarizing BMD and microarchitecture alongside clinical risk factors (sex, age, height, weight, and prior adulthood fracture) and femoral neck areal BMD (FN aBMD). The performance of μFRAC was compared to the Fracture Risk Assessment Tool (FRAX) and a reference model built using FN aBMD and clinical covariates. μFRAC was predictive of osteoporotic fracture (c‐index = 0.673,p < 0.001), modestly outperforming FRAX and FN aBMD models (c‐index = 0.617 and 0.636, respectively). Removal of FN aBMD and all clinical risk factors, except age, from μFRAC did not significantly impact its performance when estimating 5‐year and 10‐year fracture risk. The performance of μFRAC improved when only major osteoporotic fractures were considered (c‐index = 0.733,p < 0.001). We developed a personalized fracture risk assessment tool based on HR‐pQCT that may provide an alternative approach to current clinical methods by leveraging direct measures of bone density and structure. © 2023 The Authors.Journal of Bone and Mineral Researchpublished by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).