Abstract:Femoral neck areal bone mineral density (FN aBMD) is a key determinant of fracture risk in older adults; however, the majority of individuals who have a hip fracture are not considered osteoporotic according to their FN aBMD. This study uses novel tools to investigate the characteristics of bone microarchitecture that underpin bone fragility. Recent hip fracture patients (n = 108, 77% female) were compared with sex-and age-matched controls (n = 216) using high-resolution peripheral quantitative computed tomogr… Show more
“…Image data captured in vivo with human participants were aggregated from four separate studies performed in our lab exploring bone microarchitecture by HR-pQCT, which were: a normative study [17], a hip fracture study [18], and two studies with high-performance athletes [19], [20]. All images were obtained using HR-pQCT (XtremeCT II, Scanco Medical AG, Brütisellen, Switzerland) with the standard fixed offset in vivo scan protocol at standard scan sites for the distal radius and tibia.…”
Section: Methodsmentioning
confidence: 99%
“…Turning to bone volume fraction, we first note that this parameter is defined as the ratio of bone volume to total volume within a compartment. We hypothesize that within a unit volume, this ratio will scale as some power of the ratio of the trabecular thickness to the sum of trabecular thickness and separation: Image data captured in vivo with human participants were aggregated from four separate studies performed in our lab exploring bone microarchitecture by HR-pQCT, which were: a normative study [17], a hip fracture study [18], and two studies with high-performance athletes [19], [20]. All images were obtained using HR-pQCT (XtremeCT II, Scanco Medical AG, Brütisellen, Switzerland) with the standard fixed offset in vivo scan protocol at standard scan sites for the distal radius and tibia.…”
Standard microarchitectural analysis of bone using micro-computed tomography produces a large number of parameters that quantify the structure of the trabecular network. Analyses that perform statistical tests on many parameters are at elevated risk of making Type I errors. However, when multiple testing correction procedures are applied, the risk of Type II errors is elevated if the parameters being tested are strongly correlated. In this article, we argue that four commonly used trabecular microarchitectural parameters (thickness, separation, number, and bone volume fraction) are interdependent and describe only two independent properties of the trabecular network. We first derive theoretical relationships between the parameters based on their geometric definitions. Then, we analyze these relationships with an aggregated in vivo dataset with 2987 images from 1434 participants and a synthetically generated dataset with 144 images using principal component analysis (PCA) and linear regression analysis. With PCA, when trabecular thickness, separation, number, and bone volume fraction are combined, we find that 92% to 97% of the total variance in the data is explained by the first two principal components. With linear regressions, we find high coefficients of determination (0.827 to 0.994) and fitted coefficients within expected ranges. These findings suggest that to maximize statistical power in future studies, only two of trabecular thickness, separation, number and bone volume fraction should be used for statistical testing.
“…Image data captured in vivo with human participants were aggregated from four separate studies performed in our lab exploring bone microarchitecture by HR-pQCT, which were: a normative study [17], a hip fracture study [18], and two studies with high-performance athletes [19], [20]. All images were obtained using HR-pQCT (XtremeCT II, Scanco Medical AG, Brütisellen, Switzerland) with the standard fixed offset in vivo scan protocol at standard scan sites for the distal radius and tibia.…”
Section: Methodsmentioning
confidence: 99%
“…Turning to bone volume fraction, we first note that this parameter is defined as the ratio of bone volume to total volume within a compartment. We hypothesize that within a unit volume, this ratio will scale as some power of the ratio of the trabecular thickness to the sum of trabecular thickness and separation: Image data captured in vivo with human participants were aggregated from four separate studies performed in our lab exploring bone microarchitecture by HR-pQCT, which were: a normative study [17], a hip fracture study [18], and two studies with high-performance athletes [19], [20]. All images were obtained using HR-pQCT (XtremeCT II, Scanco Medical AG, Brütisellen, Switzerland) with the standard fixed offset in vivo scan protocol at standard scan sites for the distal radius and tibia.…”
Standard microarchitectural analysis of bone using micro-computed tomography produces a large number of parameters that quantify the structure of the trabecular network. Analyses that perform statistical tests on many parameters are at elevated risk of making Type I errors. However, when multiple testing correction procedures are applied, the risk of Type II errors is elevated if the parameters being tested are strongly correlated. In this article, we argue that four commonly used trabecular microarchitectural parameters (thickness, separation, number, and bone volume fraction) are interdependent and describe only two independent properties of the trabecular network. We first derive theoretical relationships between the parameters based on their geometric definitions. Then, we analyze these relationships with an aggregated in vivo dataset with 2987 images from 1434 participants and a synthetically generated dataset with 144 images using principal component analysis (PCA) and linear regression analysis. With PCA, when trabecular thickness, separation, number, and bone volume fraction are combined, we find that 92% to 97% of the total variance in the data is explained by the first two principal components. With linear regressions, we find high coefficients of determination (0.827 to 0.994) and fitted coefficients within expected ranges. These findings suggest that to maximize statistical power in future studies, only two of trabecular thickness, separation, number and bone volume fraction should be used for statistical testing.
“…For example, three bone microarchitecture phenotypes associated with different levels of osteoporotic fracture risk were identified using data from the BoMIC cohort. 15 In addition, a recent study 16 suggested that heterogeneous microarchitectural deterioration leading to the formation of void spaces may play a role in the determination of bone fragility, on top of low bone mineral density.…”
High-resolution peripheral quantitative computed tomography (HR-pQCT) is a low-dose three-dimensional imaging technique, originally developed for in vivo assessment of bone microarchitecture at the distal radius and tibia in osteoporosis. HR-pQCT has the ability to discriminate trabecular and cortical bone compartments, providing densitometric and structural parameters. At present, HR-pQCT is mostly used in research settings, despite evidence showing that it may be a valuable tool in osteoporosis and other diseases. This review summarizes the main applications of HR-pQCT and addresses the limitations that currently prevent its integration into routine clinical practice. In particular, the focus is on the use of HR-pQCT in primary and secondary osteoporosis, chronic kidney disease (CKD), endocrine disorders affecting bone, and rare diseases. A section on novel potential applications of HR-pQCT is also present, including assessment of rheumatic diseases, knee osteoarthritis, distal radius/scaphoid fractures, vascular calcifications, effect of medications, and skeletal muscle. The reviewed literature seems to suggest that a more widespread implementation of HR-pQCT in clinical practice would offer notable opportunities. For instance, HR-pQCT can improve the prediction of incident fractures beyond areal bone mineral density provided by dual-energy X-ray absorptiometry. In addition, HR-pQCT may be used for the monitoring of anti osteoporotic therapy or for the assessment of mineral and bone disorder associated with CKD. Nevertheless, several obstacles currently prevent a broader use of HR-pQCT and would need to be targeted, such as the small number of installed machines worldwide, the uncertain cost-effectiveness, the need for improved reproducibility, and the limited availability of reference normative datasets.
“…This study suggests that certain mechanisms of microarchitectural deterioration (e.g., cortical versus trabecular deterioration) may be more detrimental for a specific phenotype, as they may not be able to compensate mechanically through adaptation of other traits. A retrospective study of hip fracture patients further verified a strong relationship between fractures at the hip, a major osteoporotic fracture site, and the low-density phenotype, but highlighted sex-specific differences in terms of distribution between male and female hip fracture patients across the microarchitectural phenotypes [ 37 •]. Fig.…”
Section: Bone Phenotypes and Their Role In Fracture Risk Assessmentmentioning
confidence: 99%
“…The left figure of each example depicts trabecular (green) and cortical (grey) microarchitecture and the right figures of each example depict porosity (red) in the cortical compartment (transparent grey). Examples are from population-based cohorts courtesy of the Bone Imaging Lab, Calgary, Canada [ 37 , 48 ]. B Three-dimensional HR-pQCT reconstructions of distal tibia scans showing examples of the variability of bone phenotypes in females with type I osteogenesis imperfecta [ 67 ] …”
Section: Metabolic and Rare Bone Diseases In The Context Of Bone Phen...mentioning
Purpose of Review
Summarize the recent literature that investigates how advanced medical imaging has contributed to our understanding of skeletal phenotypes and fracture risk across the lifespan.
Recent Findings
Characterization of bone phenotypes on the macro-scale using advanced imaging has shown that while wide bones are generally stronger than narrow bones, they may be more susceptible to age-related declines in bone strength. On the micro-scale, HR-pQCT has been used to identify bone microarchitecture phenotypes that improve stratification of fracture risk based on phenotype-specific risk factors. Adolescence is a key phase for bone development, with distinct sex-specific growth patterns and significant within-sex bone property variability. However, longitudinal studies are needed to evaluate how early skeletal growth impacts adult bone phenotypes and fracture risk. Metabolic and rare bone diseases amplify fracture risk, but the interplay between bone phenotypes and disease remains unclear. Although bone phenotyping is a promising approach to improve fracture risk assessment, the clinical availability of advanced imaging is still limited. Consequently, alternative strategies for assessing and managing fracture risk include vertebral fracture assessment from clinically available medical imaging modalities/techniques or from fracture risk assessment tools based on clinical risk factors.
Summary
Bone fragility is not solely determined by its density but by a combination of bone geometry, distribution of bone mass, microarchitecture, and the intrinsic material properties of bone tissue. As such, different individuals can exhibit distinct bone phenotypes, which may predispose them to be more vulnerable or resilient to certain perturbations that influence bone strength.
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