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Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk |
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| Authors: | Danielle E Whittier Elizabeth J Samelson Marian T Hannan Lauren A Burt David A Hanley Emmanuel Biver Pawel Szulc Elisabeth Sornay-Rendu Blandine Merle Roland Chapurlat Eric Lespessailles Andy Kin On Wong David Goltzman Sundeep Khosla Serge Ferrari Mary L Bouxsein Douglas P Kiel Steven K Boyd |
| Affiliation: | 1. McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Canada;2. Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA;3. Division of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland;4. INSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France;5. Regional Hospital of Orleans, PRIMMO, Orleans, France EA 4708-I3MTO, University of Orleans, Orleans, France;6. Joint Department of Medical Imaging, University Health Network, Toronto, Canada Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada;7. Department of Medicine, McGill University and McGill University Health Centre, Quebec, Canada;8. Kogod Center on Aging and Division of Endocrinology, Mayo Clinic, Rochester, MN, USA;9. Center for Advanced Orthopedic Studies, BIDMC, Boston, MA, USA |
| Abstract: | 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 “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 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. © 2021 American Society for Bone and Mineral Research (ASBMR). |
| Keywords: | BONE HIGH-RESOLUTION PERIPHERAL COMPUTED TOMOGRAPHY PHENOTYPE MACHINE LEARNING OSTEOPOROSIS FRACTURE RISK |