Prediction Of Bone Fracture Risk Research Articles

Morphological human bone features and demography controlling damage accumulation and fracture: a finite element study

Prediction of bone fracture risk is clinically challenging. Computational modeling plays a vital role in understanding bone structure and diagnosing bone diseases, leading to novel therapies. The research objectives were to demonstrate the anisotropic structure of the bone at the micro-level taking into consideration the density and subject demography, such as age, gender, body mass index (BMI), height, weight, and their roles in damage accumulation. Out of 438 developed 3D bone models at the micro-level, 46.12% were female. The age distribution ranged from 23 to 95 years. The research unfolds in two phases: micro-morphological features examination and stress distribution investigation. Models were developed using Mimics 22.0 and SolidWorks. The anisotropic material properties were defined before importing into Ansys for simulation. Computational simulations further uncovered variations in maximum von-Misses stress, highlighting that young Black males experienced the highest stress at 127.852 ± 10.035 MPa, while elderly Caucasian females exhibited the least stress at 97.224 ± 14.504 MPa. Furthermore, age-related variations in stress levels for both normal and osteoporotic bone micro models were elucidated, emphasizing the intricate interplay of demographic factors in bone biomechanics. Additionally, a prediction equation for bone density incorporating demographic variables was proposed, offering a personalized modeling approach. In general, this study, which carefully examines the complexities of how bones behave at the micro-level, emphasizes the need for an enhanced approach in orthopedics. We suggest taking individual characteristics into account to make therapeutic interventions more precise and effective.

Bone density in postmenopausal women with scoliosis is associated with markers of degenerative joint disease.

Bone mineral density (BMD) measured with dual x-ray absorptiometry (DXA) is the clinical standard for the diagnosis of osteoporosis and prediction of bone fracture risk. In the aging skeleton, osteoporosis is often concomitantly present with degenerative joint disease (DJD). In this study, we evaluated tissue-level changes in the differentially loaded concave (CC) and convex (CV) sides of the lumbar spine in a sample of postmenopausal women with scoliosis. We used a cumulative degeneration score to assess osteophyte formation, the severity of sclerotic morphology, and marrow changes as markers of DJD in the lumbar spine and examined the correlation between markers of DJD and BMD. More severe osteophyte growth and sclerosis were present on the CC side of the spine. The degenerative score (DS) was higher on the CC side of the lumbar spine compared with the CV side. While CC BMD was positively correlated with CC DS and marrow, CV DS was not correlated with CV BMD. Marrow changes were correlated with DS on the CC lumbar spine. These results highlight the importance of mechanoadaptive as well as broader inflammatory processes in the manifestation of degenerative changes and local mineral deposition at the lumbar spine. DXA-based BMD measurement of osteoporosis need to be contextualized within the biomechanical and degenerative conditions of a joint rather than using a strict threshold cutoff.

The Effects of Obesity on Bone Turnover Markers in Diabetic Patients with Diabetic Ketosis or Ketoacidosis.

Despite the fact that diabetes individuals are often associated with a higher risk of bone fracture, our previous research demonstrated that Diabetic ketosis (DK) or ketoacidosis (DKA) induced significant alterations in bone biomarkers. It is unknown whether there is a difference in bone metabolism between obese and non-obese diabetic populations while they are in DK or DKA; hence the current study will investigate this further to aid in the prognosis and prediction of bone fracture risk in patients with different BMIs. We categorized patients into four groups based on their BMI utilizing data from our hospital's medical record system from 2018 to 2020 in the Department of Endocrinology: obese DK or DKA patients (OB+DK/DKA, n=41), non-obese DK or DKA patients (DK/DKA, n=201), obese type 2 diabetes patients without DK or DKA (OB+T2D, n=93), and patients with type 2 diabetes only (T2D only, n=304). The comparisons were made on glycosylated hemoglobin (HbA1c), body mass index (BMI), fasting plasma C-peptide (FPCP), and plasma lipids, in addition to bone metabolism indicators such as total 25-OH-VitD3 (25-OH-VitD3), N-terminal middle molecular fragment of osteocalcin (NMID), -C terminal cross-linking telopeptide of type 1 collagen (-CTX), parathyroid hormone (PTH), and blood calcium (Ca2+). The OB+DK/DKA group had a lower average age (p<0.05) than the DK/DKA group, while the DK/DKA group had a significantly lower FPCP (p<0.05). The 25-OH-VitD3 levels of DK/DKA patients were considerably lower than those of the T2D-only group (p< 0.05). In contrast, NMID and Ca2+ levels were significantly lower than those of non-ketosis or acidosis patients (p<0.05), and PTH levels in the DK/DKA group were significantly lower than those of OB+ T2D patients (p<0.05). In contrast, the β-CTX of the DK or DKA group (OB+DK/DKA and DK+DKA) was significantly greater than that of the non-DK or DKA group (p<0.05), although there was no significant difference in blood phosphorus between OB+DK/DKA and DK/ DKA (p>0.05). The levels of thyroid-stimulating hormone (TSH) and free T4 (FT4) did not differ significantly among the four groups (p>0.05); however, the levels of total T3 (TT3), T4 (TT4), and free T3 (FT3) were significantly lower in the DK/DKA group (p<0.05); the ratio of TT3 to TT4 (TT3/TT4) was significantly decreased in the DK/DKA group, whereas the ratio of FT3/FT4 was significantly lower (p<0.05). Obese patients with DK or DKA have a younger onset age, superior pancreatic function, and better blood glucose management than non-obese patients with DK/DKA. Despite having higher bone absorption signals than non-DK/DKA patients, OB+DK/DKA patients have stronger bone formation markers than non-obese DK/DKA patients, according to a recent study. Changes in markers of bone metabolism may be linked to non-thyroidal illness syndrome in cases of DK or DKA.

Free Circulating miRNAs Measurement in Clinical Settings: The Still Unsolved Issue of the Normalization.

Circulating molecules that are released into the circulation in response to specific stimuli are considered potential biomarkers for physiological or pathological processes. Their effective usefulness as biomarkers resides in their stability and high availability in all the biological fluids, combined with the limited invasiveness of intervention. Among the circulating molecules, miRNAs represent a novel class of biomarkers as they possess all the required characteristics such as sensitivity, predictivity, specificity, robustness, translatability, and noninvasiveness.miRNAs are small non-coding RNAs, that act as inhibitors of protein translation, and intervene in the complex network of the post-transcriptional mechanisms finely regulating gene expression.The emerging role of miRNAs as potential biomarkers for clinical applications (e.g., cancer and cardiovascular diseases diagnosis and prediction, musculoskeletal disease diagnosis and bone fracture risk prediction), however, requires the standardization of miRNA processing, from sample collection and sample storage, to RNA isolation, RNA reverse-transcription, and data analyses. Normalization is one of the most controversial issues related to quantitative Real-Time PCR data analysis since no universally accepted normalization strategies and reference genes exist, even more importantly, for circulating miRNA quantification. As it is widely demonstrated that the choice of different normalization strategies influences the results of gene expression analysis, it is important to select the most appropriate normalizers for each experimental set. This review discloses on the different strategies adopted in RT-qPCR miRNA normalization and the concerning issues to highlight on the need of a universally accepted methodology to make comparable the results produced by different studies.

Handling limited datasets with neural networks in medical applications: A small-data approach.

Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.

On the influence of marrow on the mechanical behavior of porcine trabecular bone under dynamic loading: a numerical investigation

The correct prediction of bone fracture risk in bone disease, e.g. osteoporosis, is a key issue for the prevention of traumatic incidents and the reduction of the economic and social burden. Numero...

Influence of the specimen scan condition on the finite element voxel model of human vertebral cancellous bone

Quantitative computed tomography-based finite element (FE) modelling technique is a promising tool for the prediction of bone fracture risk in clinic. However, FE models created from CT image datasets acquired at different scan conditions may yield comparably different results. The aim of this study was to investigate the influence of specimen scan condition on the FE models. Eight thoracolumbar vertebrae (T12) were scanned prior to autopsy (in situ). Afterwards, the segments (T10–L1) were dissected and scanned again under in vitro condition. Both scans were performed with high-resolution scan protocols, and the images were reconstructed with a voxel size of 0.18 mm × 0.18 mm × 0.3 mm. FE models with the cuboid volume of interest from the vertebral cancellous part were created. Inhomogeneous nonlinear bone properties were defined and the axial compression was simulated. The apparent bone mineral density, the modulus and yield strength showed significant differences in the two scan condition groups. In conclusion, the specimen scan condition may have to be considered when transferring the in vitro research knowledge into in vivo clinical applications.

Bone fragility beyond strength and mineral density: Raman spectroscopy predicts femoral fracture toughness in a murine model of rheumatoid arthritis

Clinical prediction of bone fracture risk primarily relies on measures of bone mineral density (BMD). BMD is strongly correlated with bone strength, but strength is independent of fracture toughness, which refers to the bone's resistance to crack initiation and propagation. In that sense, fracture toughness is more relevant to assessing fragility-related fracture risk, independent of trauma. We hypothesized that bone biochemistry, determined by Raman spectroscopy, predicts bone fracture toughness better than BMD. This hypothesis was tested in tumor necrosis factor-transgenic mice (TNF-tg), which develop inflammatory-erosive arthritis and osteoporosis. The left femurs of TNF-tg and wild type (WT) littermates were measured with Raman spectroscopy and micro-computed tomography. Fracture toughness was assessed by cutting a sharp notch into the anterior surface of the femoral mid-diaphysis and propagating the crack under 3 point bending. Femoral fracture toughness of TNF-tg mice was significantly reduced compared to WT controls (p=0.04). A Raman spectrum-based prediction model of fracture toughness was generated by partial least squares regression (PLSR). Raman spectrum PLSR analysis produced strong predictions of fracture toughness, while BMD was not significantly correlated and produced very weak predictions. Raman spectral components associated with mineralization quality and bone collagen were strongly leveraged in predicting fracture toughness, reiterating the limitations of mineralization density alone.

DISTRIBUTION OF NANOELASTICITY IN OSTEONAL LEVEL AFFECTS BONE FRACTURE TOUGHNESS BEHAVIOUR

The prediction of bone fracture risk in individual patients based on Bone Mineral Density (BMD) measurements is of limited accuracy. This is due to the fact that bone is a complex hierarchically structured material consisting of more than four different hierarchical levels which span from the nanoup to the macro-scale [Fratzl; 2007]. Therefore the mechanical properties of bone derive from the behaviour that each individual hierarchical level exhibits during loading. Furthering our understanding of bone’s structure-function relationship [Uzel; 2010], i.e. the origin of bone material properties, is the first step towards more accurate fracture risk estimation. While several studies have dealt with the nanoand the micro-scale mechanical behaviour of the bone, little is known about the so-called sub-osteonal level. This level extends from some hundred of nanometres up to some tens of microns and includes bone single lamellae, the interlamellar areas and the cement lines. The aim of this study is to correlate nanomechanical properties of human cortical bone samples with more conventionally measured fracture toughness parameters. Using cantileverbased nanoindentation we measured cortical bone’s nanoelasticity in a spatially resolved fashion, and its distribution within the various sub-osteonal features (i.e. lamellar and interlamellar areas). First results from this study suggest a strong relationship between nanoelasticity distribution and fracture toughness within human cortical bone.

Load transfer analysis of the distal radius from in-vivo high-resolution CT-imaging

Prevention of osteoporotic bone fractures requires accurate diagnostic methods to detect the increase in bone fragility at an early stage of osteoporosis. However, today's bone fracture risk prediction, primarily based on bone density measurement, is not sufficiently precise. There is increasing evidence that, in addition to bone density, also the bone microarchitecture and its mechanical loading conditions are important factors determining the fracture risk. Recently, it has been shown that new high-resolution imaging techniques in combination with new computer modeling techniques based on the finite-element (FE) method can account for these additional factors. These techniques might provide information that is more relevant for the prediction of bone fracture risk. So far, however, these new imaged-based FE techniques have not been feasible in-vivo. The objectives of this study were to quantify the load transfer through the trabecular network in a distal radius using a computer model based on in-vivo high-resolution images and to determine if common regions of fractures can be explained as a result of high tissue loading in these regions. The left distal radius and the two adjacent carpal bones of a healthy volunteer were imaged using a high-resolution three-dimensional CT system providing an isotropic resolution of 165 μm. The bone representation was converted into a FE-model that was used to calculate stresses and strains in the trabecular network. The two carpal bones were loaded using different load ratios (for each load case 1000 N in total) representing impact forces on the hand either in near-neutral position or ulnar/radial deviation. The load transfer through the trabecular network of the radius was characterized by the tissue strain energy density (SED) distribution for all load cases. It was found that the distribution of the tissue loading depends on the ratio of the forces acting on the carpal bones. For all load cases the higher SED values (on average: 0.02±0.08 (S.D.) N mm −2) are found in a 10 mm region adjacent to the articular surface which corresponds well with the region where Colles- or Chauffeur-fractures occur. We expect that, eventually, this new approach can lead to a better prediction of the fracture risk than methods based on bone density alone since it accounts for the bone microstructure as well as its loading conditions.