Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

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).     

In memoriam Professor Em. Jan G. van den Tweel (1942–2020)

Wiener Medizinische Wochenschrift -     

Application of the adverse outcome pathway framework to genotoxic modes of action

In May 2017, the Health and Environmental Sciences Institute's Genetic Toxicology Technical Committee hosted a workshop to discuss whether mode of action (MOA) investigation is enhanced through the application of the adverse outcome pathway (AOP) framework. As AOPs are a relatively new approach in genetic toxicology, this report describes how AOPs could be harnessed to advance MOA analysis of genotoxicity pathways using five example case studies. Each of these genetic toxicology AOPs proposed for further development includes the relevant molecular initiating events, key events, and adverse outcomes (AOs), identification and/or further development of the appropriate assays to link an agent to these events, and discussion regarding the biological plausibility of the proposed AOP. A key difference between these proposed genetic toxicology AOPs versus traditional AOPs is that the AO is a genetic toxicology endpoint of potential significance in risk characterization, in contrast to an adverse state of an organism or a population. The first two detailed case studies describe provisional AOPs for aurora kinase inhibition and tubulin binding, leading to the common AO of aneuploidy. The remaining three case studies highlight provisional AOPs that lead to chromosome breakage or mutation via indirect DNA interaction (inhibition of topoisomerase II, production of cellular reactive oxygen species, and inhibition of DNA synthesis). These case studies serve as starting points for genotoxicity AOPs that could ultimately be published and utilized by the broader toxicology community and illustrate the practical considerations and evidence required to formalize such AOPs so that they may be applied to genetic toxicity evaluation schemes. Environ. Mol. Mutagen. 61:114–134, 2020. © 2019 Wiley Periodicals, Inc.