We thank Sim4Life by ZMT, www.zurichmedtech.com for their support. We thank Brian K. Kwon for critically reading the manuscript and his insightful suggestions.

Prevalence of osteoporosis is more than 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 "onesize-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 because of 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 = 5873, 71% female) between 40 and 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 characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas 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.

Context High fracture risk in subjects with low muscle strength is attributed to high risk of fall. Objective To study the association of muscle mass and physical performance with bone microarchitecture decline and risk of fall and nonvertebral fracture in men. Design Prospective 8-year follow-up of a cohort. Setting General population. Participants 821 volunteer men aged ≥60. Interventions Hip areal bone mineral density (aBMD) and appendicular lean mass (ALM) were assessed at baseline by DXA. Lower limb relative ALM (RALM-LL) is ALM-LL/(leg length) 2. The physical performance score reflects ability to perform chair stands and static and dynamic balance. Bone microarchitecture was assessed by high resolution peripheral QCT (HR-pQCT) at baseline, after 4 and 8 years. Statistical analyses were adjusted for shared risk factors. Outcomes Rate of change in the HR-pQCT indices, incident falls and fractures. Results Cortical bone loss and estimated bone strength decline were faster in men with low vs. normal RALM-LL (failure load: -0.74±0.09 vs. -0.43±0.10%/year; p&lt;0.005). Differences were similar between men with poor and those with normal physical performance (failure load: -1.12 ±0.09 vs. -0.40±0.05%/year; p&lt;0.001). Differences were similar between men having poor performance and low RALM-LL and men having normal RALM-LL and performance (failure load: -1.40±0.17 vs. -0.47±0.03%/year; p&lt;0.001). Men with poor physical performance had higher risk of fall (HR=3.52, 95%CI: 1.57–7.90, p&lt;0.05) and fracture (HR=2.68, 95%CI: 1.08–6.66, p&lt;0.05). Conclusion Rapid decline of bone microarchitecture and estimated strength in men with poor physical performance and low RALM-LL may contribute to higher fracture risk.