Prediction of incident hip fracture with the estimated femoral strength by finite element analysis of DXA Scans in the study of osteoporotic fractures

A 2-yr Randomized Controlled Trial on Creatine Supplementation during Exercise for Postmenopausal Bone Health

PURPOSE

Our purpose was to examine the effects of 2 yr of creatine monohydrate supplementation and exercise on bone health in postmenopausal women.

METHODS

Two hundred and thirty-seven postmenopausal women (mean age, 59 yr) were randomized to receive creatine (0.14 g·kg -1 ·d -1 ) or placebo during a resistance training (3 d·wk -1 ) and walking (6 d·wk -1 ) program for 2 yr. Our primary outcome was the femoral neck bone mineral density (BMD), with lumbar spine BMD and proximal femur geometric properties as the secondary outcomes.

RESULTS

Compared with placebo, creatine supplementation had no effect on BMD of the femoral neck (creatine: 0.725 ± 0.110 to 0.712 ± 0.100 g·cm -2 ; placebo: 0.721 ± 0.102 to 0.706 ± 0.097 g·cm -2 ), total hip (creatine: 0.879 ± 0.118 to 0.872 ± 0.114 g·cm -2 ; placebo: 0.881 ± 0.111 to 0.873 ± 0.109 g·cm -2 ), or lumbar spine (creatine: 0.932 ± 0.133 to 0.925 ± 0.131 g·cm -2 ; placebo: 0.923 ± 0.145 to 0.915 ± 0.143 g·cm -2 ). Creatine significantly maintained section modulus (1.35 ± 0.29 to 1.34 ± 0.26 vs 1.34 ± 0.25 to 1.28 ± 0.23 cm 3 (placebo), P = 0.0011), predictive of bone bending strength, and buckling ratio (10.8 ± 2.6 to 11.1 ± 2.2 vs 11.0 ± 2.6 to 11.6 ± 2.7 (placebo), P = 0.011), predictive of reduced cortical bending under compressive loads, at the narrow part of the femoral neck. Creatine reduced walking time over 80 m (48.6 ± 5.6 to 47.1 ± 5.4 vs 48.3 ± 4.5 to 48.2 ± 4.9 s (placebo), P = 0.0008) but had no effect on muscular strength (i.e., one-repetition maximum) during bench press (32.1 ± 12.7 to 42.6 ± 14.1 vs 30.6 ± 10.9 to 41.4 ± 14 kg (placebo)) and hack squat (57.6 ± 21.6 to 84.4 ± 28.1 vs 56.6 ± 24.0 to 82.7 ± 25.0 kg (placebo)). In the subanalysis of valid completers, creatine increased lean tissue mass compared with placebo (40.8 ± 5.7 to 43.1 ± 5.9 vs 40.4 ± 5.3 to 42.0 ± 5.2 kg (placebo), P = 0.046).

CONCLUSIONS

Two years of creatine supplementation and exercise in postmenopausal women had no effect on BMD; yet, it improved some bone geometric properties at the proximal femur.

The Bone Strain Index: An Innovative Dual X-ray Absorptiometry Bone Strength Index and Its Helpfulness in Clinical Medicine

Bone strain Index (BSI) is an innovative index of bone strength that provides information about skeletal resistance to loads not considered by existing indexes (Bone Mineral Density, BMD. Trabecular Bone Score, TBS. Hip Structural Analysis, HSA. Hip Axis Length, HAL), and, thus, improves the predictability of fragility fractures in osteoporotic patients. This improved predictability of fracture facilitates the possibility of timely intervention with appropriate therapies to reduce the risk of fracture. The development of the index was the result of combining clinical, radiographical and construction-engineering skills. In fact, from a physical point of view, primary and secondary osteoporosis, leading to bone fracture, are determined by an impairment of the physical properties of bone strength: density, internal structure, deformation and fatigue. Dual X-ray absorptiometry (DXA) is the gold standard for assessing bone properties, and it allows measurement of the BMD, which is reduced mainly in primary osteoporosis, the structural texture TBS, which can be particularly degraded in secondary osteoporosis, and the bone geometry (HSA, HAL). The authors recently conceived and developed a new bone deformation index named Bone Strain Index (BSI) that assesses the resistance of bone to loads. If the skeletal structure is equated to engineering construction, these three indexes are all considered to determine the load resistance of the construct. In particular, BSI allows clinicians to detect critical information that BMD and TBS cannot explain, and this information is essential for an accurate definition of a patient’s fracture risk. The literature demonstrates that both lumbar and femoral BSI discriminate fractured osteoporotic people, that they predict the first fragility fracture, and further fragility fractures, monitor anabolic treatment efficacy and detect patients affected by secondary osteoporosis. BSI is a new diagnostic tool that offers a unique perspective to clinical medicine to identify patients affected by primary and, specially, secondary osteoporosis. This literature review illustrates BSI’s state of the art and its ratio in clinical medicine.

The bone strain index predicts fragility fractures. The OFELY study

Recently, the bone strain index (BSI), a new index of bone strength based on a finite element model (FEA) from dual X-ray absorptiometry (DXA), has been developed. BSI represents the average equivalent strain inside the bone, assuming that a higher strain level (high BSI) indicates a condition of higher risk. Our study aimed to analyze the relationship between BSI and age, BMI and areal BMD in pre- and postmenopausal women and to prospectively investigate fracture prediction (Fx) by BSI in postmenopausal women. Methods. At the 14th annual follow-up of the OFELY study, BSI was measured at spine (Spine BSI) and femoral scans (Neck and Total Hip BSI), in addition to areal BMD with DXA (Hologic QDR 4500) in 846 women, mean (SD) age 60 yr (15). The FRAX® (fracture risk assessment tool) for major osteoporotic fractures (MOF) was calculated with FN areal BMD (aBMD) at baseline; incident fragility fractures were annually registered until January 2016. Results. In premenopausal women (n = 261), Neck and Total Hip BSI were slightly negatively correlated with age (Spearman r = -0.13 and -0.15 respectively, p = 0.03), whereas all BSIs were positively correlated with BMI (r = +0.20 to 0.37, p < 0.01) and negatively with BMD (r = -0.69 to -0.37, p < 0.0001). In postmenopausal women (n = 585), Neck and Total Hip BSI were positively correlated with age (Spearman r = +0.26 and +0.31 respectively, p < 0.0001), whereas Spine BSI was positively correlated with BMI (r = +0.22, p < 0.0001) and all BSIs were negatively correlated with BMD (r = -0.81 to -0.60, p < 0.0001). During a median [IQ] 9.3 [1.0] years of follow-up, 133 postmenopausal women reported an incident fragility Fx, including 80 women with a major osteoporotic Fx (MOF) and 26 women with clinical vertebral Fx (VFx). Each SD increase of BSI value was associated with a significant increase of the risk of all fragility Fx with an age-adjusted HR of 1.23 for Neck BSI (p = 0.02); 1.27 for Total Hip BSI (p = 0.004) and 1.35 for Spine BSI (p < 0.0001). After adjustment for FRAX®, the association remained statistically significant for Total Hip BSI (HR 1.24, p = 0.02 for all fragility Fx; 1.31, p = 0.01 for MOF) and Spine BSI (HR 1.33, p < 0.0001 for all fragility Fx; 1.33, p = 0.005 for MOF; 1.67, p = 0.002 for clinical VFx). In conclusion, spine and femur BSI, an FEA DXA derived index, predict incident fragility fracture in postmenopausal women, regardless of FRAX®.

Differences in Hip Geometry Between Female Subjects With and Without Acute Hip Fracture: A Cross-Sectional Case-Control Study

BACKGROUND AND PURPOSE

Although it is widely recognized that hip BMD is reduced in patients with hip fracture, the differences in geometrical parameters such as cortical volume and thickness between subjects with and without hip fracture are less well known.

MATERIALS AND METHODS

Five hundred and sixty two community-dwelling elderly women with hip CT scans were included in this cross-sectional study, of whom 236 had an acute hip fracture. 326 age matched women without hip fracture served as controls. MIAF-Femur software was used for the measurement of the intact contralateral femur in patients with hip fracture and the left femur of the controls. Integral and cortical volumes (Vols) of the total hip (TH), femoral head (FH), femoral neck (FN), trochanter (TR) and intertrochanter (IT) were analyzed. In the FH and FN the volumes were further subdivided into superior anterior (SA) and posterior (SP) as well as inferior anterior (IA) and posterior (IP) quadrants. Cortical thickness (CortThick) was determined for all sub volumes of interest (VOIs) listed above.

RESULTS

The average age of the control and fracture groups was 71.7 and 72.0 years, respectively. The fracture patients had significantly lower CortThick and Vol of all VOIs except for TRVol. In the fracture patients, cortical thickness and volume at the FN were significantly lower in all quadrants except for cortical volume of quadrant SA (p= 0.635). Hip fracture patients had smaller integral FN volume and cross-sectional area (CSA) before and after adjustment of age, height and weight. With respect to hip fracture discrimination, cortical volume performed poorer than cortical thickness across the whole proximal femur. The ratio of Cort/TrabMass (RCTM), a measure of the internal distribution of bone, performed better than cortical thickness in discriminating hip fracture risk. The highest area under curve (AUC) value of 0.805 was obtained for the model that included THCortThick, FHVol, THRCTM and FNCSA.

CONCLUSION

There were substantial differences in total and cortical volume as well as cortical thickness between fractured and unfractured women across the proximal femur. A combination of geometric variables resulted in similar discrimination power for hip fracture risk as aBMD.

Operator-Related Errors and Pitfalls in Dual Energy X-Ray Absorptiometry: How to Recognize and Avoid Them

Dual-energy X-ray absorptiometry (DXA) is the most common modality for quantitative measurements of bone mineral density. Nevertheless, errors related to this exam are still very common, and may significantly impact on the final diagnosis and therapy. Operator-related errors may occur during each DXA step and can be related to wrong patient positioning, error in the acquisition process or in the scan analysis. The aim of this review is to provide a practical guide on how to recognize such errors in spine and hip DXA scan and how to avoid them, also presenting some of the most common artifacts encountered in clinical practice.

On challenges in clinical assessment of hip fracture risk using image-based biomechanical modelling: a critical review

INTRODUCTION

Hip fracture is a common health risk among elderly people, due to the prevalence of osteoporosis and accidental fall in the population. Accurate assessment of fracture risk is a crucial step for clinicians to consider patient-by-patient optimal treatments for effective prevention of fractures. Image-based biomechanical modeling has shown promising progress in assessment of fracture risk, and there is still a great possibility for improvement. The purpose of this paper is to identify key issues that need be addressed to improve image-based biomechanical modeling.

MATERIALS AND METHODS

We critically examined issues in consideration and determination of the four biomechanical variables, i.e., risk of fall, fall-induced impact force, bone geometry and bone material quality, which are essential for prediction of hip fracture risk. We closely inspected: limitations introduced by assumptions that are adopted in existing models; deficiencies in methods for construction of biomechanical models, especially for determination of bone material properties from bone images; problems caused by separate use of the variables in clinical study of hip fracture risk; availability of clinical information that are required for validation of biomechanical models.

RESULTS AND CONCLUSIONS

A number of critical issues and gaps were identified. Strategies for effectively addressing the issues were discussed.

Short-Term Precision Error of Bone Strain Index, a New DXA-Based Finite Element Analysis Software for Assessing Hip Strength

Bone Strain Index (BSI) is a new finite element analysis tool applied to hip dual energy X-ray absorptiometry scans. The aim of this study was to assess the short-term precision error of BSI on the proximal femur, both on a phantom and patients. The International Society for Clinical Densitometry guidelines were followed for short-term precision error assessment. Dual energy X-ray absorptiometry measurements were performed on an anthropomorphic femur phantom that was scanned twice for 30 times, for a total of 60 scans. For the in vivo part, 30 subjects were scanned twice. BSI precision error was compared to that of bone mineral density (BMD). Both for the phantom and the in vivo study BSI reproducibility was lower compared to that of BMD, as the precision error of BSI resulted 3 times higher compared to that BMD. For phantom measurements, the highest precision value was that of total femur (TF) BMD (coefficient of variation [CoV] = 0.63%, reproducibility = 98.24%), while the lowest precision was the femoral neck (FN) BSI (CoV = 3.08%, reproducibility = 91.48%). Similarly, for the in vivo study, the highest precision was found at TF BMD (CoV = 1.36%, reproducibility = 96.22%), while the lowest value of precision was found for FN BSI (CoV = 4.17%, reproducibility = 88.46%). Reproducibility at TF was always better compared to that of the FN. BSI precision error was about 3 times higher compared to BMD, confirming previous results of lumbar spine BSI. The main source of variability of this new software is related to patient positioning.

Finite element analysis informed variable selection for femoral fracture risk prediction

Logistic regression classification (LRC) is widely used to develop models to predict the risk of femoral fracture. LRC models based on areal bone mineral density (aBMD) alone are poor, with area under the receiver operator curve (AUROC) scores reported to be as low as 0.63. This has led to researchers investigating methods to extract further information from the image to increase performance. Recently, the use of active shape (ASM) and appearance models (AAM) have resulted in moderate improvements, but there is a risk that inclusion of too many modes will lead to overfitting. In addition, there are concerns that the effort required to extract the additional information does not justify the modest improvement in fracture risk prediction. This raises the question, are we reaching the limits of the information that can be extracted from an image? Finite element analysis was used in combination with active shape and appearance modelling to select variables to develop LRC models of fracture risk. Active shape and active appearance models were constructed based on a previously reported cohort of 94 post-menopausal Caucasian women (47 with and 47 without a fracture). T-tests were used to identify differences between the two groups for each mode of variation. Femur strength was predicted for two load cases, stance and a fall. Stepwise multi-variate linear regression was used to identify shape and appearance modes that were predictors of strength for the femurs in the training set. Femurs were also synthetically generated to explore the influence of the first 10 modes of the shape and appearance models. Identified modes of variation were then used to generate LRC models to predict fracture risk. Only 6 modes, 4 active appearance and 2 active shape modes, were identified that had a significant influence on predicted fracture strength. Of these, only two active appearance modes were needed to substantially improve the predictive mode performance (ΔAUROC = 0.080). The addition of 3 more modes (1 AAM and two ASM) further improved the performance of the classifier (ΔAUROC = 0.123). Further addition of modes did not result in any further substantial improvements. Based on these findings, it is suggested that we are reaching the limits of the information that can be extracted from an image to predict fracture risk.

Beyond Bone Mineral Density: A New Dual X-Ray Absorptiometry Index of Bone Strength to Predict Fragility Fractures, the Bone Strain Index

For a proper assessment of osteoporotic fragility fracture prediction, all aspects regarding bone mineral density, bone texture, geometry and information about strength are necessary, particularly in endocrinological and rheumatological diseases, where bone quality impairment is relevant. Data regarding bone quantity (density) and, partially, bone quality (structure and geometry) are obtained by the gold standard method of dual X-ray absorptiometry (DXA). Data about bone strength are not yet readily available. To evaluate bone resistance to strain, a new DXA-derived index based on the Finite Element Analysis (FEA) of a greyscale of density distribution measured on spine and femoral scan, namely Bone Strain Index (BSI), has recently been developed. Bone Strain Index includes local information on density distribution, bone geometry and loadings and it differs from bone mineral density (BMD) and other variables of bone quality like trabecular bone score (TBS), which are all based on the quantification of bone mass and distribution averaged over the scanned region. This state of the art review illustrates the methodology of BSI calculation, the findings of its in reproducibility and the preliminary data about its capability to predict fragility fracture and to monitor the follow up of the pharmacological treatment for osteoporosis.

Bone strain index as a predictor of further vertebral fracture in osteoporotic women: An artificial intelligence-based analysis

BACKGROUND

Osteoporosis is an asymptomatic disease of high prevalence and incidence, leading to bone fractures burdened by high mortality and disability, mainly when several subsequent fractures occur. A fragility fracture predictive model, Artificial Intelligence-based, to identify dual X-ray absorptiometry (DXA) variables able to characterise those patients who are prone to further fractures called Bone Strain Index, was evaluated in this study.

METHODS

In a prospective, longitudinal, multicentric study 172 female outpatients with at least one vertebral fracture at the first observation were enrolled. They performed a spine X-ray to calculate spine deformity index (SDI) and a lumbar and femoral DXA scan to assess bone mineral density (BMD) and bone strain index (BSI) at baseline and after a follow-up period of 3 years in average. At the end of the follow-up, 93 women developed a further vertebral fracture. The further vertebral fracture was considered as one unit increase of SDI. We assessed the predictive capacity of supervised Artificial Neural Networks (ANNs) to distinguish women who developed a further fracture from those without it, and to detect those variables providing the maximal amount of relevant information to discriminate the two groups. ANNs choose appropriate input data automatically (TWIST-system, Training With Input Selection and Testing). Moreover, we built a semantic connectivity map usingthe Auto Contractive Map to provide further insights about the convoluted connections between the osteoporotic variables under consideration and the two scenarios (further fracture vs no further fracture).

RESULTS

TWIST system selected 5 out of 13 available variables: age, menopause age, BMI, FTot BMC, FTot BSI. With training testing procedure, ANNs reached predictive accuracy of 79.36%, with a sensitivity of 75% and a specificity of 83.72%. The semantic connectivity map highlighted the role of BSI in predicting the risk of a further fracture.

CONCLUSIONS

Artificial Intelligence is a useful method to analyse a complex system like that regarding osteoporosis, able to identify patients prone to a further fragility fracture. BSI appears to be a useful DXA index in identifying those patients who are at risk of further vertebral fractures.

Validation of 3D finite element models from simulated DXA images for biofidelic simulations of sideways fall impact to the hip

Computed tomography (CT)-derived finite element (FE) models have been proposed as a tool to improve the current clinical assessment of osteoporosis and personalized hip fracture risk by providing an accurate estimate of femoral strength. However, this solution has two main drawbacks, namely: (i) 3D CT images are needed, whereas 2D dual-energy x-ray absorptiometry (DXA) images are more generally available, and (ii) quasi-static femoral strength is predicted as a surrogate for fracture risk, instead of predicting whether a fall would result in a fracture or not. The aim of this study was to combine a biofidelic fall simulation technique, based on 3D computed tomography (CT) data with an algorithm that reconstructs 3D femoral shape and BMD distribution from a 2D DXA image. This approach was evaluated on 11 pelvis-femur constructs for which CT scans, ex vivo sideways fall impact experiments and CT-derived biofidelic FE models were available. Simulated DXA images were used to reconstruct the 3D shape and bone mineral density (BMD) distribution of the left femurs by registering a projection of a statistical shape and appearance model with a genetic optimization algorithm. The 2D-to-3D reconstructed femurs were meshed, and the resulting FE models inserted into a biofidelic FE modeling pipeline for simulating a sideways fall. The median 2D-to-3D reconstruction error was 1.02 mm for the shape and 0.06 g/cm³ for BMD for the 11 specimens. FE models derived from simulated DXAs predicted the outcome of the falls in terms of fracture versus non-fracture with the same accuracy as the CT-derived FE models. This study represents a milestone towards improved assessment of hip fracture risk based on widely available clinical DXA images.

Risk Assessment of Hip Fracture Based on Machine Learning

Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.

3D Analysis of the Proximal Femur Compared to 2D Analysis for Hip Fracture Risk Prediction in a Clinical Population

Due to the adverse impacts of hip fractures on patients' lives, it is crucial to enhance the identification of people at high risk through accessible clinical techniques. Reconstructing the 3D geometry and BMD distribution of the proximal femur could be beneficial in enhancing hip fracture risk predictions; however, it is associated with a high computational burden. It is also not clear whether it provides a better performance than 2D model analysis. Therefore, the purpose of this study was to compare the 2D and 3D model reconstruction's ability to predict hip fracture risk in a clinical population of patients. The DXA scans and CT scans of 16 cadaveric femurs were used to create training sets for the 2D and 3D model reconstruction based on statistical shape and appearance modeling. Subsequently, these methods were used to predict the risk of sustaining a hip fracture in a clinical population of 150 subjects (50 fractured, and 100 non-fractured) that were monitored for five years in the Canadian Multicentre Osteoporosis Study. 3D model reconstruction was able to improve the identification of patients who sustained a hip fracture more accurately than the standard clinical practice (by 40%). Also, the predictions from the 2D statistical model didn't differ significantly from the 3D ones (p > 0.76). These results indicated that to enhance hip fracture risk prediction in clinical practice implementing 2D statistical modeling has comparable performance with lower associated computational load.

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 ± 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.

Image-based finite-element modeling of the human femur

Fracture is considered a critical clinical endpoint in skeletal pathologies including osteoporosis and bone metastases. However, current clinical guidelines are limited with respect to identifying cases at high risk of fracture, as they do not account for many mechanical determinants that contribute to bone fracture. Improving fracture risk assessment is an important area of research with clear clinical relevance. Patient-specific numerical musculoskeletal models generated from diagnostic images are widely used in biomechanics research and may provide the foundation for clinical tools used to quantify fracture risk. However, prior to clinical translation, in vitro validation of predictions generated from such numerical models is necessary. Despite adopting radically different models, in vitro validation of image-based finite element (FE) models of the proximal femur (predicting strains and failure loads) have shown very similar, encouraging levels of accuracy. The accuracy of such in vitro models has motivated their application to clinical studies of osteoporotic and metastatic fractures. Such models have demonstrated promising but heterogeneous results, which may be explained by the lack of a uniform strategy with respect to FE modeling of the human femur. This review aims to critically discuss the state of the art of image-based femoral FE modeling strategies, highlighting principal features and differences among current approaches. Quantitative results are also reported with respect to the level of accuracy achieved from in vitro evaluations and clinical applications and are used to motivate the adoption of a standardized approach/workflow for image-based FE modeling of the femur.

Perspectives on the non-invasive evaluation of femoral strength in the assessment of hip fracture risk

We reviewed the experimental and clinical evidence that hip bone strength estimated by BMD and/or finite element analysis (FEA) reflects the actual strength of the proximal femur and is associated with hip fracture risk and its changes upon treatment.

INTRODUCTION

The risk of hip fractures increases exponentially with age due to a progressive loss of bone mass, deterioration of bone structure, and increased incidence of falls. Areal bone mineral density (aBMD), measured by dual-energy X-ray absorptiometry (DXA), is the most used surrogate marker of bone strength. However, age-related declines in bone strength exceed those of aBMD, and the majority of fractures occur in those who are not identified as osteoporotic by BMD testing. With hip fracture incidence increasing worldwide, the development of accurate methods to estimate bone strength in vivo would be very useful to predict the risk of hip fracture and to monitor the effects of osteoporosis therapies.

METHODS

We reviewed experimental and clinical evidence regarding the association between aBMD and/orCT-finite element analysis (FEA) estimated femoral strength and hip fracture risk as well as their changes with treatment.

RESULTS

Femoral aBMD and bone strength estimates by CT-FEA explain a large proportion of femoral strength ex vivo and predict hip fracture risk in vivo. Changes in femoral aBMD are strongly associated with anti-fracture efficacy of osteoporosis treatments, though comparable data for FEA are currently not available.

CONCLUSIONS

Hip aBMD and estimated femoral strength are good predictors of fracture risk and could potentially be used as surrogate endpoints for fracture in clinical trials. Further improvements of FEA may be achieved by incorporating trabecular orientations, enhanced cortical modeling, effects of aging on bone tissue ductility, and multiple sideway fall loading conditions.

Enhancing hip fracture risk prediction by statistical modeling and texture analysis on DXA images

Each year in the US more than 300,000 older adults suffer from hip fractures. While protective measures exist, identification of those at greatest risk by DXA scanning has proved inadequate. This study proposed a new technique to enhance hip fracture risk prediction by accounting for many contributing factors to the strength of the proximal femur. Twenty-two isolated cadaveric femurs were DXA scanned, 16 of which had been mechanically tested to failure. A function consisting of the calculated modes from the statistical shape and appearance modeling (to consider the shape and BMD distribution), homogeneity index (representing trabecular quality), BMD, age and sex of the donor was created in a training set and used to predict the fracture load in a test group. To classify patients as "high risk" or "low risk", fracture load thresholds were investigated. Hip fracture load estimation was significantly enhanced using the new technique in comparison to using t-score or BMD alone (average R² of 0.68, 0.32, and 0.50, respectively) (P < 0.05). Using a fracture cut-off of 3400 N correctly predicted risk in 94% of specimens, a substantial improvement over t-score classification (38%). Ultimately, by identifying patients at high risk more accurately, devastating hip fractures can be prevented through applying protective measures.

A new finite element based parameter to predict bone fracture

Dual Energy X-Ray Absorptiometry (DXA) is currently the most widely adopted non-invasive clinical technique to assess bone mineral density and bone mineral content in human research and represents the primary tool for the diagnosis of osteoporosis. DXA measures areal bone mineral density, BMD, which does not account for the three-dimensional structure of the vertebrae and for the distribution of bone mass. The result is that longitudinal DXA can only predict about 70% of vertebral fractures. This study proposes a complementary tool, based on Finite Element (FE) models, to improve the DXA accuracy. Bone is simulated as elastic and inhomogeneous material, with stiffness distribution derived from DXA greyscale images of density. The numerical procedure simulates a compressive load on each vertebra to evaluate the local minimum principal strain values. From these values, both the local average and the maximum strains are computed over the cross sections and along the height of the analysed bone region, to provide a parameter, named Strain Index of Bone (SIB), which could be considered as a bone fragility index. The procedure is initially validated on 33 cylindrical trabecular bone samples obtained from porcine lumbar vertebrae, experimentally tested under static compressive loading. Comparing the experimental mechanical parameters with the SIB, we could find a higher correlation of the ultimate stress, σULT, with the SIB values (R2adj = 0.63) than that observed with the conventional DXA-based clinical parameters, i.e. Bone Mineral Density, BMD (R2adj = 0.34) and Trabecular Bone Score, TBS (R2adj = -0.03). The paper finally presents a few case studies of numerical simulations carried out on human lumbar vertebrae. If our results are confirmed in prospective studies, SIB could be used-together with BMD and TBS-to improve the fracture risk assessment and support the clinical decision to assume specific drugs for metabolic bone diseases.

Improving the Hip Fracture Risk Prediction Through 2D Finite Element Models From DXA Images: Validation Against 3D Models

Osteoporotic fracture incidence represents a major social and economic concern in the modern society, where the progressive graying of the population involves an highly increased fracture occurrence. Although the gold standard to diagnose osteoporosis is represented by the T-score measurement, estimated from the Bone Mineral Density (BMD) using Dual-energy X-ray Absorptiometry (DXA), the identification of the subjects at high risk of fracture still remains an issue. From this perspective, the purpose of this work is to investigate the role that DXA-based two-dimensional patient-specific finite element (FE) models of the proximal femur, in combination with T-score, could play in enhancing the risk of fracture estimation. With this aim, 2D FE models were built from DXA images of the 28 post-menopausal female subjects involved. A sideways fall condition was reproduced and a Risk of Fracture (RF^) was computed on the basis of principal strains criteria. The identified RF^ was then compared to that derived from the CT-based models developed in a previous study. The 2D and 3D RF^ turned out to be significantly correlated (Spearman's ρ = 0.66, p < 0.001), highlighting the same patients as those at higher risk. Moreover, the 2D RF^ resulted significantly correlated with the T-score (Spearman's ρ = −0.69, p < 0.001), and managed to better differentiate osteopenic patients, drawing the attention to some of them. The Hip Structural Analysis (HSA) variables explaining the majority of the variance of the 2D and 3D fracture risk were the same as well, i.e., neck-shaft angle and narrow neck buckling ratio. In conclusion, DXA-based FE models, developable from currently available clinical data, appear promising in supporting and integrating the present diagnostic procedure.

Bone Mineral Density and Bone Turnover Markers in Postmenopausal Women Subjected to an Aqua Fitness Training Program

The purpose of this study was to analyze the influence of aqua fitness training in deep water on bone tissue. The study was performed with 18 postmenopausal women separated into two groups: training and control groups. Before and after the training program, the hip and spine areal bone mineral density were measured along with the biochemical parameters of serum concentration of osteocalcin (OC) and C-terminal telopeptide of type I collagen (CTX). The most significant effect was found in differences between the two groups of women in terms of femur strength index (p < 0.05) during the period of the training program. The study demonstrated that an aqua fitness training program caused favorable changes in femur strength index in postmenopausal women, and this kind of exercise could be a useful form of physical activity for postmenopausal women.

Fracture Risk Indices From DXA-Based Finite Element Analysis Predict Incident Fractures Independently From FRAX: The Manitoba BMD Registry

OBJECTIVE

Finite element analysis (FEA) is a computational method to predict the behavior of materials under applied loading. We developed a software tool that automatically performs FEA on dual-energy X-ray absorptiometry hip scans to generate site-specific fracture risk indices (FRIs) that reflect the likelihood of hip fracture from a sideways fall. This longitudinal study examined associations between FRIs and incident fractures.

METHODS

Using the Manitoba Bone Mineral Density (BMD) Registry, femoral neck (FN), intertrochanter (IT), and subtrochanter (ST) FRIs were automatically derived from 13,978 anonymized dual-energy X-ray absorptiometry scans (Prodigy, GE Healthcare) in women and men aged 50 yr or older (mean age 65 yr). Baseline covariates and incident fractures were assessed from population-based data. We compared c-statistics for FRIs vs FN BMD alone and fracture risk assessment (FRAX) probability computed with BMD. Cox regression was used to estimate hazard ratios and 95% confidence intervals (95% CIs) for incident hip, major osteoporotic fracture (MOF) and non-hip MOF adjusted for relevant covariates including age, sex, FN BMD, FRAX probability, FRAX risk factors, and hip axis length (HAL).

RESULTS

During mean follow-up of 6 yr, there were 268 subjects with incident hip fractures, 1003 with incident MOF, and 787 with incident non-hip MOF. All FRIs gave significant stratification for hip fracture (c-statistics FN-FRI: 0.76, 95% CI 0.73-0.79, IT-FRI 0.74, 0.71-0.77; ST-FRI 0.72, 0.69-0.75). FRIs continued to predict hip fracture risk even after adjustment for age and sex (hazard ratio per standard deviation FN-FRI 1.89, 95% CI 1.66-2.16); age, sex, and BMD (1.26, 1.07-1.48); FRAX probability (1.30, 1.11-1.52); FRAX probability with HAL (1.26, 1.05-1.51); and individual FRAX risk factors (1.32, 1.09-1.59). FRIs also predicted MOF and non-hip MOF, but the prediction was not as strong as for hip fracture.

SUMMARY

Automatically-derived FN, IT, and ST FRIs are associated with incident hip fracture independent of multiple covariates, including FN BMD, FRAX probability and risk factors, and HAL.

Osteoporosis

Fractures resulting from osteoporosis become increasingly common in women after age 55 years and men after age 65 years, resulting in substantial bone-associated morbidities, and increased mortality and health-care costs. Research advances have led to a more accurate assessment of fracture risk and have increased the range of therapeutic options available to prevent fractures. Fracture risk algorithms that combine clinical risk factors and bone mineral density are now widely used in clinical practice to target high-risk individuals for treatment. The discovery of key pathways regulating bone resorption and formation has identified new approaches to treatment with distinctive mechanisms of action. Osteoporosis is a chronic condition and long-term, sometimes lifelong, management is required. In individuals at high risk of fracture, the benefit versus risk profile is likely to be favourable for up to 10 years of treatment with bisphosphonates or denosumab. In people at a very high or imminent risk of fracture, therapy with teriparatide or abaloparatide should be considered; however, since treatment duration with these drugs is restricted to 18-24 months, treatment should be continued with an antiresorptive drug. Individuals at high risk of fractures do not receive adequate treatment and strategies to address this treatment gap-eg, widespread implementation of Fracture Liaison Services and improvement of adherence to therapy-are important challenges for the future.

Progressive bone impairment with age and pubertal development in neurofibromatosis type I

Bone density impairment represents an established complication in adults with neurofibromatosis type 1, while few data exist in the pediatric population. Age- and gender-adjusted bone mass decreases with age and pubertal development, identifying childhood as the best time frame to introduce prevention strategies aiming at peak bone mass achievement.

PURPOSE

The present study aims at evaluating bone mineral density (BMD) in a population of children with neurofibromatosis type I (NF-1), with particular focus on changes occurring during growth and pubertal development.

METHODS

Bone metabolic markers and bone status [by dual-energy X-ray absorptiometry scans (DXA) of the total body and lumbar spine with morphometric analysis] were assessed in 50 children (33 males; mean age ± SD, 11.6 ± 4 years). Bone mineral apparent density (BMAD), trabecular bone score (TBS), and bone strain (BS) of the lumbar spine (LS) DXA were also obtained.

RESULTS

In our cohort areal BMD (aBMD) Z-score was below the mean in 88% of the patients at LS (70% after correction for bone size) and in 86% considering total body (TB) DXA. However, aBMD Z-score was < - 2 in 12% after correction for bone size at LS and TB, respectively. Lumbar spine aBMD Z-score (r = - 0.54, P < 0.0001), LS BMAD Z-score (r = - 0.53, P < 0.0001), and TB Z-score (r = - 0.39, P = 0.005) showed a negative correlation with growth and pubertal development (P = 0.007, P = 0.02, P = 0.01, respectively), suggesting that patients failed to gain as much as expected for age.

CONCLUSION

Bone density impairment becomes more evident with growth and pubertal development in NF-1 patients, thus identifying childhood as the best time frame to introduce prevention strategies aiming at peak bone mass achievement. TBS and BS, providing bone DXA qualitative information, could be useful during longitudinal follow-up for better characterizing bone impairment in these patients.

Comparison of femoral strength and fracture risk index derived from DXA-based finite element analysis for stratifying hip fracture risk: A cross-sectional study

BACKGROUND

Dual-energy X-ray absorptiometry (DXA)-based finite element analysis (FEA) has been studied for assessment of hip fracture risk. Femoral strength (FS) is the maximum force that the femur can sustain before its weakest region reaches the yielding limit. Fracture risk index (FRI), which also considers subject-specific impact force, is defined as the ratio of von Mises stress induced by a sideways fall to the bone yield stress over the proximal femur. We compared risk stratification for prior hip fracture using FS and FRI derived from DXA-based FEA.

METHODS

The study cohort included women aged ≥65years undergoing baseline hip DXA, with femoral neck T-scores <-1 and no osteoporosis treatment; 324 cases had prior hip fracture and 655 controls had no prior fracture. Using anonymized DXA hip scans, we measured FS and FRI. Separate multivariable logistic regression models were used to estimate odds ratios (ORs), c-statistics and their 95% confidence intervals (95% CIs) for the association of hip fracture with FS and FRI.

RESULTS

Increased hip fracture risk was associated with lower FS (OR per SD 1.36, 95% CI: 1.15, 1.62) and higher FRI (OR per SD 1.99, 95% CI: 1.63, 2.43) after adjusting for Fracture Risk Assessment Tool (FRAX) hip fracture probability computed with bone mineral density (BMD). The c-statistic for the model containing FS (0.69; 95% CI: 0.65, 0.72) was lower than the c-statistic for the model with FRI (0.77; 95% CI: 0.74, 0.80) or femoral neck BMD (0.74; 95% CI: 0.71, 0.77; all P<0.05).

CONCLUSIONS

FS and FRI were independently associated with hip fracture, but there were differences in performance characteristics.

Future Osteoporotic Fracture Risk Related to Lumbar Vertebral Trabecular Attenuation Measured at Routine Body CT

We sought to determine if vertebral trabecular attenuation values measured on routine body computed tomography (CT) scans obtained for a variety of unrelated indications can predict future osteoporotic fractures at multiple skeletal sites. For this Health Insurance Portability and Accountability Act (HIPAA)-compliant and Institutional Review Board (IRB)-approved retrospective cohort study, trabecular attenuation of the first lumbar vertebra was measured in 1966 consecutive older adults who underwent chest and/or abdominal CT at a single institution over the course of 1 year. New pathologic fragility fractures that occurred after a patient's CT study date were identified through an electronic health record database query using International Classification of Diseases (ICD)-9 codes for vertebral, hip, and extremity fractures. Univariate and multivariate Cox proportional hazards regression were performed to determine the effect of L₁ trabecular attenuation on fracture-free survival. Age at CT, sex, and presence of a prior fragility fracture were included as confounders in multivariate survival analysis. Model discriminative capability was assessed through calculation of an optimism-corrected concordance index. A total of 507 patients (mean age 73.4 ± 6.3 years; 277 women, 230 men) were included in the final analysis. The median post-CT follow-up interval was 5.8 years (interquartile range 2.1-11.0 years). Univariate analysis showed that L₁ attenuation values ≤90 Hounsfield units (HU) are significantly associated with decreased fracture-free survival (p < 0.001 by log-rank test). After adjusting for age, sex, prior fracture, glucocorticoid use, bisphosphonate use, chronic kidney disease, tobacco use, ethanol abuse, cancer history, and rheumatoid arthritis history, multivariate analysis demonstrated a persistent modest effect of L₁ attenuation on fracture-free survival (hazard ratio [HR] = 0.63 per 10-unit increase; 95% confidence interval [CI] 0.47-0.85). The model concordance index was 0.700. Ten-year probabilities for major osteoporosis-related fractures straddled the treatment threshold for most subcohorts over the observed L₁ HU range. In conclusion, for patients undergoing body CT scanning for any indication, L₁ vertebral trabecular attenuation is a simple measure that, when ≤90 HU, identifies patients with a significant decrease in fracture-free survival. © 2018 American Society for Bone and Mineral Research.

Automation of a DXA-based finite element tool for clinical assessment of hip fracture risk

Finite element analysis of medical images is a promising tool for assessing hip fracture risk. Although a number of finite element models have been developed for this purpose, none of them have been routinely used in clinic. The main reason is that the computer programs that implement the finite element models have not been completely automated, and heavy training is required before clinicians can effectively use them. By using information embedded in clinical dual energy X-ray absorptiometry (DXA), we completely automated a DXA-based finite element (FE) model that we previously developed for predicting hip fracture risk. The automated FE tool can be run as a standalone computer program with the subject's raw hip DXA image as input. The automated FE tool had greatly improved short-term precision compared with the semi-automated version. To validate the automated FE tool, a clinical cohort consisting of 100 prior hip fracture cases and 300 matched controls was obtained from a local community clinical center. Both the automated FE tool and femoral bone mineral density (BMD) were applied to discriminate the fracture cases from the controls. Femoral BMD is the gold standard reference recommended by the World Health Organization for screening osteoporosis and for assessing hip fracture risk. The accuracy was measured by the area under ROC curve (AUC) and odds ratio (OR). Compared with femoral BMD (AUC = 0.71, OR = 2.07), the automated FE tool had a considerably improved accuracy (AUC = 0.78, OR = 2.61 at the trochanter). This work made a large step toward applying our DXA-based FE model as a routine clinical tool for the assessment of hip fracture risk. Furthermore, the automated computer program can be embedded into a web-site as an internet application.

Association of incident hip fracture with the estimated femoral strength by finite element analysis of DXA scans in the Osteoporotic Fractures in Men (MrOS) study

Finite element model can estimate bone strength better than BMD. This study used such a model to determine its association with hip fracture risk and found that the strength estimate provided limited improvement over the hip BMDs in predicting femoral neck (FN) fracture risk only.

INTRODUCTION

Bone fractures occur only when it is loaded beyond its ultimate strength. The goal of this study was to determine the association of femoral strength, as estimated by finite element (FE) analysis of DXA scans, with incident hip fracture as a single condition or with femoral neck (FN) and trochanter (TR) fractures separately in older men.

METHODS

This prospective case-cohort study included 91 FN and 64 TR fracture cases and a random sample of 500 men (14 had a hip fracture) from the Osteoporotic Fractures in Men study during a mean ± SD follow-up of 7.7 ± 2.2 years. We analysed the baseline DXA scans of the hip using a validated plane-stress, linear-elastic FE model of the proximal femur and estimated the femoral strength during a sideways fall.

RESULTS

The estimated strength was significantly (P < 0.05) associated with hip fracture independent of the TR and total hip (TH) BMDs but not FN BMD, and combining the strength with BMD did not improve the hip fracture prediction. The strength estimate was associated with FN fractures independent of the FN, TR and TH BMDs; the age-BMI-BMD adjusted hazard ratio (95% CI) per SD decrease of the strength was 1.68 (1.07-2.64), 2.38 (1.57, 3.61) and 2.04 (1.34, 3.11), respectively. This association with FN fracture was as strong as FN BMD (Harrell's C index for the strength 0.81 vs. FN BMD 0.81) and stronger than TR and TH BMDs (0.8 vs. 0.78 and 0.81 vs. 0.79). The strength's association with TR fracture was not independent of hip BMD.

CONCLUSIONS

Although the strength estimate provided additional information over the hip BMDs, its improvement in predictive ability over the hip BMDs was confined to FN fracture only and limited.

Automated DXA-based finite element analysis for hip fracture risk stratification: a cross-sectional study

Fracture risk indices (FRIs) generated from DXA-based finite element analysis were associated with hip fracture independent of FRAX score computed with femoral neck bone mineral density (BMD). Prospective studies are warranted to determine whether FRIs represent an improvement over BMD for predicting incident hip fractures.

INTRODUCTION

The study aims to examine the association between prior hip fracture and FRIs derived from automated finite element analysis (FEA) of DXA hip scans. Femoral neck, intertrochanteric, and subtrochanteric FRIs were calculated as the von Mises stress induced by a sideways fall divided by the bone yield stress over the specified region of interest (ROI).

METHODS

Using the Manitoba Bone Mineral Density Database, we selected women age ≥ 65 years with femoral neck T-scores below - 1 and no osteoporosis treatment. From this population, we identified 324 older women with hip fracture before DXA testing and a random sample of 658 non-fracture controls. FRIs were derived from the anonymized DXA scans. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for the associations between FRIs (per SD increase) and hip fracture.

RESULTS

After adjusting for FRAX score (hip fracture with BMD), femoral neck FRI (OR 1.36, 95% CI 1.13, 1.64), intertrochanteric FRI (OR 1.81, 95% CI 1.44, 2.27), and subtrochanteric FRI (OR 2.09, 95% CI 1.68, 2.60) were associated with hip fracture. Intertrochanteric and subtrochanteric FRIs gave significantly higher c-statistics (all P ≤ 0.05) than femoral neck BMD. Subgroup analyses showed that all FRIs were more strongly associated with hip fracture in women who were younger and had higher body mass index (BMI) or non-osteoporotic BMD (all P _interaction < 0.1).

CONCLUSIONS

FRIs derived from DXA-based FEA were independently associated with prior hip fracture, suggesting that they could potentially improve hip fracture risk assessment.

The role of carboxy-terminal cross-linking telopeptide of type I collagen, dual x-ray absorptiometry bone strain and Romberg test in a new osteoporotic fracture risk evaluation: A proposal from an observational study

The consolidated way of diagnosing and treating osteoporosis in order to prevent fragility fractures has recently been questioned by some papers, which complained of overdiagnosis and consequent overtreatment of this pathology with underestimating other causes of the fragility fractures, like falls. A new clinical approach is proposed for identifying the subgroup of patients prone to fragility fractures. This retrospective observational study was conducted from January to June 2015 at the Nuclear Medicine-Bone Metabolic Unit of the of the Fondazione IRCCS Ca' Granda, Milan, Italy. An Italian population of 125 consecutive postmenopausal women was investigated for bone quantity and bone quality. Patients with neurological diseases regarding balance and vestibular dysfunction, sarcopenia, past or current history of diseases and use of drugs known to affect bone metabolism were excluded. Dual X-ray absorptiometry was used to assess bone quantity (bone mineral density) and bone quality (trabecular bone score and bone strain). Biochemical markers of bone turnover (type I collagen carboxy-terminal telopeptide, alkaline phosphatase, vitamin D) have been measured. Morphometric fractures have been searched by spine radiography. Balance was evaluated by the Romberg test. The data were evaluated with the neural network analysis using the Auto Contractive Map algorithm. The resulting semantic map shows the Minimal Spanning Tree and the Maximally Regular Graph of the interrelations between bone status parameters, balance conditions and fractures of the studied population. A low fracture risk seems to be related to a low carboxy-terminal cross-linking telopeptide of type I collagen level, whereas a positive Romberg test, together with compromised bone trabecular microarchitecture DXA parameters, appears to be strictly connected with fragility fractures. A simple assessment of the risk of fragility fracture is proposed in order to identify those frail patients at risk for osteoporotic fractures, who may have the best benefit from a pharmacological and physiotherapeutic approach.

30years of DXA technology innovations

As the successor of Dual Photon Absorptiometry (DPA), Dual X-ray Absorptiometry (DXA) has seen 30years of continuous technological innovations. Implementation of measures for standardization and quality assurance made DXA a reliable and clinically useful approach. Its use in clinical multicenter drug studies in osteoporosis lead to general acceptance as the standard technique of bone densitometry. The limitations of DXA are well established. As a measure of areal bone mineral density (aBMD) it depends on bone size and is biased by overlaying soft tissue and calcified structures. To some extent these errors can be reduced by estimation of bone depth and/or lateral imaging. DXA based aBMD can be supplemented by additional information obtainable from DXA scans: geometric indices such as hip axis length or complex models like 2-D finite element analysis have been developed and tested. Given the drastic improvement in image quality current DXA scans can be used for Vertebral Fracture Analysis (VFA) or grading of Abdominal Aortic Calcifications. A textural measure, Trabecular Bone Score (TBS) provides independent information on fracture risk. DXA devices can also be used for assessments beyond bone density. Periprosthetic aBMD changes can be monitored to study the mechanical fitting of bone implants. Total body composition measurements are increasingly being used in studies on nutrition, obesity, and sarcopenia. 30years after its inception DXA is the undisputed standard imaging technique for the assessment of osteoporotic fracture risk with new applications beyond bone densitometry adding to its value.

Non-BMD DXA measurements of the hip

Hip fracture is one of the most serious complications of osteoporosis. More than 50% of hip and other fractures occur in patients without densitometric osteoporosis. Therefore, areal bone mineral density (aBMD) may not be the best way to assess fracture risk. In order to improve assessment of fracture risk, many other approaches have been taken. At the present time, the Fracture Risk Algorithm (FRAX©) is one of the most notable ways to improve assessment of fracture risk. However, since early in the initiation of the dual energy x-ray absorptiometry (DXA) era, several non-BMD DXA approaches to the assessment of hip fracture risk have been proposed. This review will cover some of those methodologies, including hip-axis length (HAL), hip-structural analysis (HSA), finite element analysis (FEA) by DXA, and body composition of the thigh by DXA (BCT). These methods have been utilized in models of hip fracture occurrence and in pharmacological clinical trials. How they should be used in clinical practice or if they should be used in clinical practice is more of an issue. In addition, we will discuss the recent proposal of the use of Long Femur Scan Field in the effort to diagnose atypical femoral fractures.

Fat Mass Is Positively Associated with Estimated Hip Bone Strength among Chinese Men Aged 50 Years and above with Low Levels of Lean Mass

This study investigated the relationships of fat mass (FM) and lean mass (LM) with estimated hip bone strength in Chinese men aged 50-80 years (median value: 62.0 years). A cross-sectional study including 889 men was conducted in Guangzhou, China. Body composition and hip bone parameters were generated by dual-energy X-ray absorptiometry (DXA). The relationships of the LM index (LMI) and the FM index (FMI) with bone phenotypes were detected by generalised additive models and multiple linear regression. The associations between the FMI and the bone variables in LMI tertiles were further analysed. The FMI possessed a linear relationship with greater estimated hip bone strength after adjustment for the potential confounders (p < 0.05). Linear relationships were also observed for the LMI with most bone phenotypes, except for the cross-sectional area (p < 0.05). The contribution of the LMI (4.0%-12.8%) was greater than that of the FMI (2.0%-5.7%). The associations between the FMI and bone phenotypes became weaker after controlling for LMI. Further analyses showed that estimated bone strength ascended with FMI in the lowest LMI tertile (p < 0.05), but not in the subgroups with a higher LMI. This study suggested that LM played a critical role in bone health in middle-aged and elderly Chinese men, and that the maintenance of adequate FM could help to promote bone acquisition in relatively thin men.

Ethical use of animal models in musculoskeletal research

The use of animals in research is under increasing scrutiny from the general public, funding agencies, and regulatory authorities. Our ability to continue to perform in-vivo studies in laboratory animals will be critically determined by how researchers respond to this new reality. This Perspectives article summarizes recent and ongoing initiatives within ORS and allied organizations to ensure that musculoskeletal research is performed to the highest ethical standards. It goes on to present an overview of the practical application of the 3Rs (reduction, refinement, and replacement) into experimental design and execution, and discusses recent guidance with regard to improvements in the way in which animal data are reported in publications. The overarching goal of this review is to challenge the status quo, to highlight the absolute interdependence between animal welfare and rigorous science, and to provide practical recommendations and resources to allow clinicians and scientists to optimize the ways in which they undertake preclinical studies involving animals. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:740-751, 2017.

DXA-Based Measurements in Diabetes: Can They Predict Fracture Risk?

In the absence of a fragility fracture, osteoporosis is usually diagnosed from bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA). Osteoporosis is an increasingly prevalent disease, as is diabetes [in particular type 2 diabetes (T2D)], in part due to aging populations worldwide. It has been suggested that an increased risk of fracture may be another complication ensuing from longstanding diabetes. The purpose of this review is to concentrate on skeletal parameters and techniques readily available from DXA scanning, and their utility in routine clinical practice for predicting fracture risk. In addition to BMD, other applications and measures from DXA include trabecular bone score (TBS), skeletal geometry and DXA-based finite-element analysis, vertebral fracture assessment, and body composition. In type 1 diabetes (T1D), BMD and FRAX^R (when secondary osteoporosis is included without BMD) only partially account for the excess risk of fracture in T1D. Consistent data exist to show that BMD and FRAX^R can be used to stratify fracture risk in T2D, but do not account for the increased risk of fracture. However, several adjustments to the FRAX score can be made as proxies for T2D to inform the use of FRAX by primary care practitioners. Examples include using the rheumatoid arthritis input (as a proxy for T2D), lumbar spine TBS (to adjust FRAX probability) or an altered hip T-score (lowered by 0.5 units). These adjustments can improve fracture risk prediction in T2D and help to avoid systematically underestimating the risk of osteoporosis-related fractures in those with diabetes.

Clinical Evaluation of Bone Strength and Fracture Risk

PURPOSE OF REVIEW

This paper seeks to evaluate and compare recent advances in the clinical assessment of the changes in bone mechanical properties that take place as a result of osteoporosis and other metabolic bone diseases and their treatments.

RECENT FINDINGS

In addition to the standard of DXA-based areal bone mineral density (aBMD), a variety of methods, including imaging-based structural measurements, finite element analysis (FEA)-based techniques, and alternate methods including ultrasound, bone biopsy, reference point indentation, and statistical shape and density modeling, have been developed which allow for reliable prediction of bone strength and fracture risk. These methods have also shown promise in the evaluation of treatment-induced changes in bone mechanical properties. Continued technological advances allowing for increasingly high-resolution imaging with low radiation dose, together with the expanding adoption of DXA-based predictions of bone structure and mechanics, as well as the increasing awareness of the importance of bone material properties in determining whole-bone mechanics, lead us to anticipate substantial future advances in this field.

Prediction of femoral strength using 3D finite element models reconstructed from DXA images: validation against experiments

Computed tomography (CT)-based finite element (FE) models may improve the current osteoporosis diagnostics and prediction of fracture risk by providing an estimate for femoral strength. However, the need for a CT scan, as opposed to the conventional use of dual-energy X-ray absorptiometry (DXA) for osteoporosis diagnostics, is considered a major obstacle. The 3D shape and bone mineral density (BMD) distribution of a femur can be reconstructed using a statistical shape and appearance model (SSAM) and the DXA image of the femur. Then, the reconstructed shape and BMD could be used to build FE models to predict bone strength. Since high accuracy is needed in all steps of the analysis, this study aimed at evaluating the ability of a 3D FE model built from one 2D DXA image to predict the strains and fracture load of human femora. Three cadaver femora were retrieved, for which experimental measurements from ex vivo mechanical tests were available. FE models were built using the SSAM-based reconstructions: using only the SSAM-reconstructed shape, only the SSAM-reconstructed BMD distribution, and the full SSAM-based reconstruction (including both shape and BMD distribution). When compared with experimental data, the SSAM-based models predicted accurately principal strains (coefficient of determination >0.83, normalized root-mean-square error <16%) and femoral strength (standard error of the estimate 1215 N). These results were only slightly inferior to those obtained with CT-based FE models, but with the considerable advantage of the models being built from DXA images. In summary, the results support the feasibility of SSAM-based models as a practical tool to introduce FE-based bone strength estimation in the current fracture risk diagnostics.

Study of sex differences in the association between hip fracture risk and body parameters by DXA-based biomechanical modeling

There is controversy about whether or not body parameters affect hip fracture in men and women in the same way. In addition, although bone mineral density (BMD) is currently the most important single discriminator of hip fracture, it is unclear if BMD alone is equally effective for men and women. The objective of this study was to quantify and compare the associations of hip fracture risk with BMD and body parameters in men and women using our recently developed two-level biomechanical model that combines a whole-body dynamics model with a proximal-femur finite element model. Sideways fall induced impact force of 130 Chinese clinical cases, including 50 males and 80 females, were determined by subject-specific dynamics modeling. Then, a DXA-based finite element model was used to simulate the femur bone under the fall-induced loading conditions and calculate the hip fracture risk. Body weight, body height, body mass index, trochanteric soft tissue thickness, and hip bone mineral density were determined for each subject and their associations with impact force and hip fracture risk were quantified. Results showed that the association between impact force and hip fracture risk was not strong enough in both men (r=-0.31,p<0.05) and women (r=0.42,p<0.001) to consider the force as a sole indicator of hip fracture risk. The correlation between hip BMD and hip fracture risk in men (r=-0.83,p<0.001) was notably stronger than that in women (r=-0.68,p<0.001). Increased body mass index was not a protective factor against hip fracture in men (r=-0.13,p>0.05), but it can be considered as a protective factor among women (r=-0.28,p<0.05). In contrast to men, trochanteric soft tissue thickness can be considered as a protective factor against hip fracture in women (r=-0.50,p<0.001). This study suggested that the biomechanical risk/protective factors for hip fracture are sex-specific. Therefore, the effect of body parameters should be considered differently for men and women in hip fracture risk assessment tools. These findings support further exploration of sex-specific preventive and protective measurements to reduce the incidence of hip fractures.

The Initial Slope of the Variogram, Foundation of the Trabecular Bone Score, Is Not or Is Poorly Associated With Vertebral Strength

Trabecular bone score (TBS) rests on the textural analysis of dual-energy X-ray absorptiometry (DXA) to reflect the decay in trabecular structure characterizing osteoporosis. Yet, its discriminative power in fracture studies remains incomprehensible because prior biomechanical tests found no correlation with vertebral strength. To verify this result possibly owing to an unrealistic setup and to cover a wide range of loading scenarios, the data from three previous biomechanical studies using different experimental settings were used. They involved the compressive failure of 62 human lumbar vertebrae loaded 1) via intervertebral discs to mimic the in vivo situation ("full vertebra"); 2) via the classical endplate embedding ("vertebral body"); or 3) via a ball joint to induce anterior wedge failure ("vertebral section"). High-resolution peripheral quantitative computed tomography (HR-pQCT) scans acquired from prior testing were used to simulate anterior-posterior DXA from which areal bone mineral density (aBMD) and the initial slope of the variogram (ISV), the early definition of TBS, were evaluated. Finally, the relation of aBMD and ISV with failure load (F(exp)) and apparent failure stress (σexp) was assessed, and their relative contribution to a multilinear model was quantified via ANOVA. We found that, unlike aBMD, ISV did not significantly correlate with F(exp) and σexp , except for the "vertebral body" case (r(2) = 0.396, p = 0.028). Aside from the "vertebra section" setup where it explained only 6.4% of σexp (p = 0.037), it brought no significant improvement to aBMD. These results indicate that ISV, a replica of TBS, is a poor surrogate for vertebral strength no matter the testing setup, which supports the prior observations and raises a fortiori the question of the deterministic factors underlying the statistical relationship between TBS and vertebral fracture risk.

A biomechanical sorting of clinical risk factors affecting osteoporotic hip fracture

Osteoporotic fracture has been found associated with many clinical risk factors, and the associations have been explored dominantly by evidence-based and case-control approaches. The major challenges emerging from the studies are the large number of the risk factors, the difficulty in quantification, the incomplete list, and the interdependence of the risk factors. A biomechanical sorting of the risk factors may shed lights on resolving the above issues. Based on the definition of load-strength ratio (LSR), we first identified the four biomechanical variables determining fracture risk, i.e., the risk of fall, impact force, bone quality, and bone geometry. Then, we explored the links between the FRAX clinical risk factors and the biomechanical variables by looking for evidences in the literature. To accurately assess fracture risk, none of the four biomechanical variables can be ignored and their values must be subject-specific. A clinical risk factor contributes to osteoporotic fracture by affecting one or more of the biomechanical variables. A biomechanical variable represents the integral effect from all the clinical risk factors linked to the variable. The clinical risk factors in FRAX mostly stand for bone quality. The other three biomechanical variables are not adequately represented by the clinical risk factors. From the biomechanical viewpoint, most clinical risk factors are interdependent to each other as they affect the same biomechanical variable(s). As biomechanical variables must be expressed in numbers before their use in calculating LSR, the numerical value of a biomechanical variable can be used as a gauge of the linked clinical risk factors to measure their integral effect on fracture risk, which may be more efficient than to study each individual risk factor.

Proceedings of the 2015 Santa Fe Bone Symposium: Clinical Applications of Scientific Advances in Osteoporosis and Metabolic Bone Disease

The 2015 Santa Fe Bone Symposium was a venue for healthcare professionals and clinical researchers to present and discuss the clinical relevance of recent advances in the science of skeletal disorders, with a focus on osteoporosis and metabolic bone disease. Symposium topics included new developments in the translation of basic bone science to improved patient care, osteoporosis treatment duration, pediatric bone disease, update of fracture risk assessment, cancer treatment-related bone loss, fracture liaison services, a review of the most significant studies of the past year, and the use of telementoring with Bone Health Extension for Community Healthcare Outcomes, a force multiplier to improve the care of osteoporosis in underserved communities.

Choosing the tool for osteoporosis risk prediction

PURPOSE OF REVIEW

Predicting fracture risk is a major challenge because it allows the prevention of major osteoporotic fracture in high-risk populations. With the aging of the population, this matter will become of even greater importance. In recent years, novel clinical, biochemical, and imaging tools have been developed to improve the assessment of fracture risk.

RECENT FINDINGS

The present review summarizes novel clinical strategies, Dual energy X-ray absorptiometry (DXA)-derived tools, imaging techniques, and biochemical markers that have been developed recently to improve fracture risk prediction.

SUMMARY

DXA and clinical fracture risk prediction tools are preferential markers of fracture risk. Clinical fracture risk alone might be used if DXA facilities are unavailable. The fracture risk assessment tool may be used in osteoporosis consultation in many countries. Other tools may be used soon after more studies are performed, particularly trabecular bone score, quantitative ultrasound, bone turnover markers. Specific factors for example falls, hip axis length, vertebral fracture assessment could be used in individual patients. This may significantly improve the clinical decision-making.