ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing

High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique capable of imaging trabecular bone in-vivo. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases, assess epidemiological associations, and evaluate the effects of pharmaceutical interventions. Processing HR-pQCT images has largely been supported using the scanner manufacturer scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The ORMIR_XCT Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows

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, there is much incentive to study how bone responds to muscular contraction forces across the population. Muscles must generate sufficient force close to their points of attachments in order to mobilize the bone lever arm. As such, bone must adapt sufficiently to withstand the high dynamic loads J Musculoskelet Neuronal Interact 2014; 14(3) Abstract Objective: To assess bone-muscle (B-M) indices as risk factors for incident fractures in men. Methods: Participants of the Osteoporotic Fractures in Men (MrOS) Study completed a peripheral quantitative computed tomography scan at 66% of their tibial length. Bone macrostructure, estimates of bone strength, and muscle area were computed. Areal bone mineral density (aBMD) and body composition were assessed with dual-energy X-ray absorptiometry. Four year incident non-spine and clinical vertebral fractures were ascertained. B-M indices were expressed as bone-to-muscle ratios for: strength, mass and area. Discriminative power and hazards ratios (HR) for fractures were reported. Results: In 1163 men (age: 77.2±5.2 years, body mass index (BMI): 28.0±4.0 kg/m 2 , 4.1±0.9 follow-up years, 7.7% of men ≥1 fracture), B-M indices were smaller in fractured men except for bending and areal indices. Smaller B-M indices were associated with increased fracture risk (HR: 1.30 to 1.74) independent of age and BMI. Strength and mass indices remained significant after accounting for lumbar spine but not total hip aBMD. However, aBMD correlated significantly with B-M indices. Conclusion: Mass and bending B-M indices are risk factors for fractures in men, but may not improve fracture risk prediction beyond that provided by total hip aBMD

Osteoporotic fractures and obesity affect frailty progression: a longitudinal analysis of the Canadian multicentre osteoporosis study

Abstract Background Despite knowing better how to screen older adults, understanding how frailty progression might be modified is unclear. We explored effects of modifiable and non-modifiable factors on changes in frailty in community-dwelling adults aged 50+ years who participated in the Canadian Multicentre Osteoporosis Study (CaMos). Methods Rates of change in frailty over 10 years were examined using the 30-item CaMos Frailty Index (CFI). Incident and prevalent low-trauma fractures were categorized by fracture site into hip, clinical vertebral and non-hip-non-vertebral fractures. Multivariable generalized estimating equation models accounted for the time of frailty assessment (baseline, 5 and 10 years), sex, age, body mass index (BMI, kg/m2), physical activity, bone mineral density, antiresorptive therapy, health-related quality of life (HRQL), cognitive status, and other factors for frailty or fractures. Multiple imputation and scenario analyses addressed bias due to attrition or missing data. Results The cohort included 5566 women (mean ± standard deviation: 66.8 ± 9.3 years) and 2187 men (66.3 ± 9.5 years) with the mean baseline CFI scores of 0.15 ± 0.11 and 0.12 ± 0.10, respectively. Incident fractures and obesity most strongly predicted frailty progression in multivariable analyses. The impact of fractures differed between the sexes. With each incident hip fracture, the adjusted mean CFI accelerated per 5 years by 0.07 in women (95% confidence interval [CI]: 0.03 to 0.11) and by 0.12 in men (95% CI: 0.08 to 0.16). An incident vertebral fracture increased frailty in women (0.05, 95% CI: 0.02 to 0.08) but not in men (0.01, 95% CI: -0.07 to 0.09). Irrespective of sex and prevalent fractures, baseline obesity was associated with faster frailty progression: a 5-year increase in the adjusted mean CFI ranged from 0.01 in overweight (BMI: 25.0 to 29.9 kg/m2) to 0.10 in obese individuals (BMI: ≥ 40 kg/m2). Greater physical activity and better HRQL decreased frailty over time. The results remained robust in scenario analyses. Conclusions Older women and men with new vertebral fractures, hip fractures or obesity represent high-risk groups that should be considered for frailty interventions

Additional file 1: Table S1. of Osteoporotic fractures and obesity affect frailty progression: a longitudinal analysis of the Canadian multicentre osteoporosis study

The CaMos Frailty Index. This document presents a table that describes 30 items included in the Camos Frailty Index. Source: Kennedy CC, Ioannidis G, Rockwood K, Thabane L, Adachi JD, Kirkland S, et al. A Frailty Index predicts 10-year fracture risk in adults age 25 years and older: results from the Canadian Multicentre Osteoporosis Study (CaMos). Osteoporos Int. 2014; 25:2825–32. Obtained with copyright permission (November 14,2017). (DOC 100 kb

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