Dual-energy X-ray Absorptiometry Images Research Articles

Association between Trunk Muscle Strength, Lumbar Spine Bone Mineral Density, Lumbar Scoliosis Angle, and Skeletal Muscle Volume and Locomotive Syndrome in Elderly Individuals: A Dual-Energy X-ray Absorptiometry Study.

Introduction The present study aimed to investigate the association between trunk muscle strength, lumbar spine bone mineral density (BMD), lumbar scoliosis angle (LSA), and appendicular skeletal muscle mass index (ASMI) and the severity locomotive syndrome (LS) using dual-energy X-ray absorptiometry (DXA) technology in elderly individuals. Methods In this cross-sectional study, we enrolled 168 individuals aged >60 years. We measured their trunk muscle strength (flexion and extension) and BMD, LSA, and ASMI using DXA. We defined degenerative lumbar scoliosis (DLS) as LSA ≥ 10° by the Cobb method using the DXA image. The locomotor function was evaluated using the timed up-and-go (TUG) test and the 25-question Geriatric Locomotive Function Scale (GLFS-25) score. Normal locomotor function, LS-1, and LS-2 were defined as a GLFS-25 score of <7, ≥7 and <16, and ≥16, respectively. We compared the three groups, analyzing the associations between all variables and the locomotor function using univariate and multivariate analyses. Results Although there was no significant difference in sex ratio, BMD, ASMI, and trunk-flexor strength, significant differences were observed in age (p < 0.01), the prevalence of DLS (p = 0.02), trunk-extensor strength (p < 0.01), and trunk-extensor/flexor strength ratio (p < 0.01) among the three groups. In multiple regression analyses, the significant risk factors of the TUG test were age (β = 0.26), body mass index (β = 0.36), LSA (β = 0.15), ASMI (β = −0.30), and trunk-extensor strength (β = −0.19), whereas the significant factor of the GLFS-25 score was trunk-extensor strength (β = −0.31). Conclusions The results indicate that it is clinically important for LS to pay careful attention not only to BMD but also to lumbar scoliosis when DXA examination of the lumbar spine is routinely conducted. Moreover, it is essential to note that trunk-extensor strength is more important than trunk-flexor strength in maintaining locomotor function in elderly individuals.

Assessment of BMD and Statistical Analysis for Osteoporosis Detection

Biomedical engineering is one of the promising disciplines in engineering that deals with technology advancement in human health. Osteoporosis is a common metabolic disease categorized by decreased bone mass and increased liability to fractures. Bone densitometry is a broad term comprising the art and science of measuring the bone mineral content (BMC) and bone mineral density (BMD) of particular skeletal sites or the whole body. There are various methods to measure bone mineral density which differs based on the differential absorption of ionizing radiation or the sound waves. The methods are SPA (Single Photon Absorptiometry), DPA (Dual Photon Absorptiometry), SEXA (Single Energy X ray Absorptiometry), DEXA (Dual Energy X ray Absorptiometry), QCT (Quantitative Computed Tomography), QUS (Quantitative Ultra Sound) and RA (Radiographic absorptiometry). The DEXA test can measure the whole body but usually the lower spine and hips. A major disadvantage of DEXA is that currently there is a lack of standardization in bone and soft tissue measurements. Furthermore, for a given manufacturer, results may vary by the model of the instrument, the mode of operation or the version of the software used to analyze the data. In addition to that, DEXA scan images are only for the confirmation of correct positioning of the patient and correct placement of the regions of interest (ROI). Motivated by the above issues, this paper can pave a way for analysis in the measurement of BMD, measurement of T-score, and Z-score from the DEXA scan images. This proposed methodology includes segmentation algorithms such as k means clustering &amp; mean –shift algorithm and comparison of the accuracy of algorithms. Also in addition, a novel mathematical analysis is also proposed to measure the T–score values in DEXA images with a new parameter ‘S’ from BMD values in order to detect the osteoporosis condition accurately.

CONTROVERSIES AND CHALLENGES IN DEFINING SARCOPENIC OBESITY

Sarcopenia is defined as the loss of muscle mass, strength and physical function with aging and in conjunction with obesity, leads to incrementally adverse outcomes. There is no current consensus for defining this disease entity, making an accurate evaluation challenging in both the research and clinical settings. We will review the definitions put forth by the Sarcopenia Definition and Outcomes Consortium and the European Working Group for the Study of Sarcopenia. We will present data highlighting the different diagnostic criteria of sarcopenic obesity and the diagnostic accuracy of common anthropometric measures in measuring adiposity using data from the National Health and Nutrition Examination surveys. The advantages and disadvantages of the different modalities of assessing body composition will be discussed, including body impedance analysis, dual energy x-ray absorptiometry, computer tomography and magnetic resonance imaging in addition to simple, novel clinical screening tools for obesity sarcopenia will be presented.

Bone metabolism parameters and inactive matrix Gla protein in patients with obstructive sleep apnea†.

The aim of this study was to investigate differences in dual-energy X-ray absorptiometry (DXA) parameters, trabecular bone score (TBS), bone turnover markers and inactive matrix Gla protein (dp-ucMGP) between patients with obstructive sleep apnea (OSA) and healthy controls. This study enrolled 53 male patients diagnosed with OSA, and 50 age- and body mass index (BMI)-matched control subjects. All participants underwent DXA imaging, TBS assessment and blood sampling for biochemical analysis of bone metabolism markers. Mean apnea-hypopnea index (AHI) score of OSA patients was 43.8 ± 18.8 events/h. OSA patients had significantly higher plasma dp-ucMGP levels in comparison to controls (512.7 ± 71.9 vs. 465.8 ± 50.9 pmol/L, p < 0.001). OSA and control group did not significantly differ regarding standard DXA results, while TBS values were significantly lower in the OSA group (1.24 ± 0.17 vs. 1.36 ± 0.15, p < 0.001). AHI score was a significant independent correlate of plasma dp-ucMGP levels (β ± SE, 1.461 ± 0.45, p = 0.002). In addition, TBS retained a significant relationship with dp-ucMGP values (β ± SE, -93.77 ± 38.1, p = 0.001). dp-ucMGP levels are significantly higher in patients with OSA and correlate with disease severity. In addition, TBS values in OSA patients are lower in comparison with the control group and decrease with disease severity.

Prevalent vertebral fracture is dominantly associated with spinal microstructural deterioration rather than bone mineral density in patients with type 2 diabetes mellitus.

BackgroundAn assessment of bone strength based on bone mineral density (BMD) underestimates the risk of fracture in patients with diabetes mellitus (T2DM). However, using the trabecular bone score (TBS) for estimating bone microarchitecture, previous studies showed that bone fragility is associated with deterioration of the microstructure concomitantly with decreased BMD. This study was conducted to clarify which of these skeletal-related factors had a more prominent relationship with bone fragility.Research design and methodsA retrospective cross-sectional study was performed at Shimane University Hospital. A total of 548 Japanese patients with T2DM [257 postmenopausal women and 291 men aged over 50 years] were included. TBS of the spine was computed from dual-energy X-ray absorptiometry images obtained from BMD measurements.ResultsVertebral fractures (VFs) were identified in 74 (28.8%) women and 115 (39.5%) men. A relationship between BMD and VFs was observed in the limited subgroup of women with a BMD T-score ≤-1.0. According to multivariate logistic regression analysis, low TBS was significantly correlated with prevalent VFs, independent of BMD in both genders, except for men with a BMD T-score > -1.0. The decision tree showed that the priority factor for determining VFs was TBS, not BMD.ConclusionSpinal microarchitecture represented by TBS was a more dominant skeletal factor for bone fragility than the decrease in bone mass, independent of BMD, in patients with T2DM. This observation suggests that loss of structural bone quality was crucial underlying pathogenesis for bone brittleness in these populations, regardless of gender. An integrated assessment of bone strength by BMD and TBS would help diagnose diabetic osteoporosis.

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 () was computed on the basis of principal strains criteria. The identified was then compared to that derived from the CT-based models developed in a previous study. The 2D and 3D 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 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.

The Correlation between Trabecular Bone Score and Lumbar Spine Bone Mineral Density in Patients with Normal and High Body Mass Index.

Background:Trabecular bone score (TBS) measures the underlying quality of bone texture using dual-energy X-ray absorptiometry (DXA) images. The present study aimed to investigate the correlation between lumbar spine bone mineral density (BMD) and TBS, and subsequently determine whether the association varies with the body mass index (BMI). Methods:Data from 548 patients were collected and categorized into three groups according to the relationship between BMD and age. BMD of the lumbar spine (LS) using DXA and TBS from DXA images were measured. Pearson’s correlation coefficient (SPSS software, version 24.0) was used to investigate the association between LS-BMD and TBS, as well as the effect of BMI and age on these parameters. P<0.05 was considered statistically significant. Results:The total mean TBS was 1.31±0.12. LS-BMD and TBS values significantly decreased with age in both sexes. A statistically significant correlation was found between TBS and LS-BMD (r=0.601). An increase in BMI was associated with a higher LS-BMD score and a lower TBS level. The correlation coefficient between LS-BMD and TBS reduced as the BMI increased. By comparing TBS with BMD, the majority of the patients with osteopenia and osteoporosis had fully degraded and partially degraded TBS, respectively. Conclusion:TBS was positively correlated with LS-BMD and decreased with age. Moreover, the extent of the correlation varied with respect to BMI.

Describing the application of statistical shape modelling to DXA images to quantify the shape of the proximal femur at ages 14 and 18 years in the Avon Longitudinal Study of Parents and Children.

Bones are complex objects with considerable variation in the shape and structure often attributed to anatomical, environmental or genetic differences. In addition, bone shape has been of interest in relation to its associations with disease processes. Hip shape is an important determinant of hip osteoarthritis and osteoporotic hip fracture; however, its quantification is difficult. While previous studies largely focused on individual geometrical indices of hip geometry such as neck-shaft angle or femoral neck width, statistical shape modelling offers the means to quantify the entire contour of the proximal femur, including lesser trochanter and acetabular eyebrow. We describe the derivation of independent modes of variation (hip shape mode scores) to characterise variation in hip shape from dual-energy X-ray absorptiometry (DXA) images in the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring, using statistical shape modelling. ALSPAC is a rich source of phenotypic and genotypic data which provides a unique opportunity to investigate the environmental and genetic influences on hip shape in adolescence, as well as comparison with adult hip shape.

Factors associated with potentially serious incidental findings and with serious final diagnoses on multi-modal imaging in the UK Biobank Imaging Study: A prospective cohort study.

BackgroundFeedback of potentially serious incidental findings (PSIFs) to imaging research participants generates clinical assessment in most cases. Understanding the factors associated with increased risks of PSIFs and of serious final diagnoses may influence individuals’ decisions to participate in imaging research and will inform the design of PSIFs protocols for future research studies. We aimed to determine whether, and to what extent, socio-demographic, lifestyle, other health-related factors and PSIFs protocol are associated with detection of both a PSIF and a final diagnosis of serious disease.Methods and findingsOur cohort consisted of all UK Biobank participants who underwent imaging up to December 2015 (n = 7334, median age 63, 51.9% women). Brain, cardiac and body magnetic resonance, and dual-energy x-ray absorptiometry images from the first 1000 participants were reviewed systematically by radiologists for PSIFs. Thereafter, radiographers flagged concerning images for radiologists’ review. We classified final diagnoses as serious or not using data from participant surveys and clinical correspondence from GPs up to six months following imaging (either participant or GP correspondence, or both, were available for 93% of participants with PSIFs). We used binomial logistic regression models to investigate associations between age, sex, ethnicity, socio-economic deprivation, private healthcare use, alcohol intake, diet, physical activity, smoking, body mass index and morbidity, with both PSIFs and serious final diagnoses. Systematic radiologist review generated 13 times more PSIFs than radiographer flagging (179/1000 [17.9%] versus 104/6334 [1.6%]; age- and sex-adjusted OR 13.3 [95% confidence interval (CI) 10.3–17.1] p<0.001) and proportionally fewer serious final diagnoses (21/179 [11.7%]; 33/104 [31.7%]). Risks of both PSIFs and of serious final diagnoses increased with age (sex-adjusted ORs [95% CI] for oldest [67–79 years] versus youngest [44–58 years] participants for PSIFs and serious final diagnoses respectively: 1.59 [1.07–2.38] and 2.79 [0.86 to 9.0] for systematic radiologist review; 1.88 [1.14–3.09] and 2.99 [1.09–8.19] for radiographer flagging). No other factor was significantly associated with either PSIFs or serious final diagnoses. Our study is the largest so far to investigate the factors associated with PSIFs and serious final diagnoses, but despite this, we still may have missed some associations due to sparsity of these outcomes within our cohort and small numbers within some exposure categories.ConclusionRisks of PSIFs and serious final diagnosis are substantially influenced by PSIFs protocol and to a lesser extent by age. As only 1/5 PSIFs represent serious disease, evidence-based PSIFs protocols are paramount to minimise over-investigation of healthy research participants and diversion of limited health services away from patients in need.

ASSOCIATION OF BONE MINERAL DENSITY WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN POSTMENOPAUSAL WOMEN.

Osteoporosis (OP) is common in patients with chronic obstructive pulmonary disease (COPD). The relationship between OP and COPD has been primarily studied in male patients, and few reports are available in postmenopausal women. The purpose of this study was to investigate the association between bone mineral density (BMD) and COPD in postmenopausal women. This cross-sectional study included 133 clinically stable female ex-smokers with confirmed COPD, and 31 age-matched "ex-smoker" female controls. We analyzed groups according to their airway obstruction category. BMD was measured on dual-energy X-ray absorptiometry images of the left femoral neck. Patients with COPD had lower T-scores and higher prevalence of osteopenia/OP than the control group. In the COPD group, the airway obstruction category was significantly associated with the T-score after adjustment for confounders. Multivariate logistic regression analysis showed COPD was an independent marker for increased risk of osteopenia/OP in postmenopausal women. COPD and airway obstruction category were strongly related to BMD. Postmenopausal women with COPD, especially those with severe airway obstruction, had a higher prevalence rate and a higher risk of osteopenia and OP than female controls without COPD.

Regional fat depot masses are influenced by protein-coding gene variants.

Waist-to-hip ratio (WHR) is a prominent cardiometabolic risk factor that increases cardio-metabolic disease risk independently of BMI and for which multiple genetic loci have been identified. However, WHR is a relatively crude proxy for fat distribution and it does not capture all variation in fat distribution. We here present a study of the role of coding genetic variants on fat mass in 6 distinct regions of the body, based on dual-energy X-ray absorptiometry imaging on more than 17k participants. We find that the missense variant CCDC92S70C, previously associated with WHR, is associated specifically increased leg fat mass and reduced visceral but not subcutaneous central fat. The minor allele-carrying transcript of CCDC92 is constitutively more highly expressed in adipose tissue samples. In addition, we identify two coding variants in SPATA20 and UQCC1 that are associated with arm fat mass. SPATA20K422R is a low-frequency variant with a large effect on arm fat only, and UQCC1R51Q is a common variant reaching significance for arm but showing similar trends in other subcutaneous fat depots. Our findings support the notion that different fat compartments are regulated by distinct genetic factors.

Evaluation of the capability of the simulated dual energy X-ray absorptiometry-based two-dimensional finite element models for predicting vertebral failure loads

Prediction of the vertebral failure load is of great importance for the prevention and early treatment of bone fracture. However, an efficient and effective method for accurately predicting the failure load of vertebral bones is still lacking. The aim of the present study was to evaluate the capability of the simulated dual energy X-ray absorptiometry (DXA)-based finite element (FE) model for predicting vertebral failure loads.Thirteen dissected spinal segments (T11/T12/L1) were scanned using a HR-pQCT scanner and then were mechanically tested until failure. The subject-specific three-dimensional (3D) and two-dimensional (2D) FE models of T12 were generated from the HR-pQCT scanner and the simulated DXA images, respectively. Additionally, the areal bone mineral density (aBMD) and areal bone mineral content (aBMC) of T12 were calculated. The failure loads predicted by the simulated DXA-based 2D FE models were more moderately correlated with the experimental failure loads (R2 = 0.66) than the aBMC (R2 = 0.61) and aBMD (R2 = 0.56). The 2D FE models were slightly outperformed by the HR-pQCT-based 3D FE models (R2 = 0.71). The present study demonstrated that the simulated DXA-based 2D FE model has better capability for predicting the vertebral failure loads than the densitometric measurements but is outperformed by the 3D FE model. The 2D FE model is more suitable for clinical use due to the low radiation dose and low cost, but it remains to be validated by further in vitro and in vivo studies.

Body Fat Distribution and Systolic Blood Pressure in 10,000 Adults with Whole-Body Imaging: UK Biobank and Oxford BioBank.

ObjectiveThis study aimed to quantify the associations of regional fat mass and fat‐free mass with systolic blood pressure.MethodsThis analysis combined individual participant data from two large‐scale imaging studies: UK Biobank and Oxford BioBank. In both studies, participants were interviewed and measured, and they underwent dual‐energy x‐ray absorptiometry imaging. Linear regression was used to relate systolic blood pressure to anthropometric measures of adiposity (BMI, waist circumference, and waist to hip ratio) and dual‐energy x‐ray absorptiometry–derived measures of body composition (visceral android fat, subcutaneous android fat, subcutaneous gynoid fat, and fat‐free mass).ResultsAmong 10,260 participants (mean age 49; 96% white), systolic blood pressure was positively associated with visceral android fat (3.2 mmHg/SD in men; 2.8 mmHg/SD in women) and fat‐free mass (1.92 mmHg/SD in men; 1.64 mmHg/SD in women), but there was no evidence of an association with subcutaneous android or gynoid fat. Associations of systolic blood pressure with BMI were slightly steeper than those with waist circumference or waist to hip ratio; these associations remained unchanged following adjustment for fat‐free mass, but adjustment for visceral android fat eliminated associations with waist circumference and waist to hip ratio and more than halved associations with BMI.ConclusionsThis analysis indicates that visceral fat is the primary etiological component of excess adiposity underlying the development of adiposity‐related hypertension.

Computer-Aided Detection of Incidental Lumbar Spine Fractures from Routine Dual-Energy X-Ray Absorptiometry (DEXA) Studies Using a Support Vector Machine (SVM) Classifier.

To assess whether application of a support vector machine learning algorithm to ancillary data obtained from posterior-anterior dual-energy X-ray absorptiometry (DEXA) studies could identify patients with lumbar spine (L1-L4) vertebral body fractures without additional DEXA imaging or radiation. Three hundred seven patients (199 without any fractures of the spine, and 108 patients with at least one fracture of the L1, L2, L3, or L4 vertebral bodies) who had DEXA studies were evaluated. Ancillary data from DEXA output was analyzed. The dataset was split into training (80%) and test (20%) datasets. Support vector machines (SVMs) with 10-fold cross-validation and different kernels were used to identify the best kernel based on the greatest area under the curve (AUC) and the best training vectors in the training dataset. The SVM with the best kernel was then applied to the test dataset to assess the accuracy of the SVM. Receiver operating characteristic (ROC) curves of the SVMs using different kernels in the test dataset were compared using DeLong's test. The SVM classifier with the linear kernel had the greatest AUC in the training dataset (AUC = 0.9258). The AUC of the SVM classifier with the linear kernel in the test dataset was 0.8963. The SVM classifier with the linear kernel had an overall average accuracy of 91.8% in the test dataset. The sensitivity, specificity, positive predictive value, and negative predictive of the SVM classifier with the linear kernel to detect lumbar spine fractures were 81.8%, 97.4%, 94.7%, and 90.5%, respectively. The SVM classifier with the linear kernel ROC curve had a significantly better AUC than the SVM classifier with the cubic polynomial kernel (P = 0.034) for discriminating between patients with lumbar spine fractures and control patients, but not significantly different from the SVM classifier with a radial basis function (RBF) kernel (P = 0.317) or the SVM classifier with a sigmoid kernel (P = 0.729). All fractures identified by the SVM classifiers were not prospectively identified by the radiologist. SVM analysis of ancillary data obtained from routine DEXA studies can identify lumbar spine fractures without the use of vertebral fracture assessment (VFA) DEXA imaging or radiation, and identify fractures missed by radiologists.

The differences in the relationship between obstructive sleep apnea severity and trabecular bone score in men and women with type 2 diabetes

AimsType 2 diabetes mellitus (T2DM) and obstructive sleep apnea (OSA) may adversely affect bone. Gender is a well-established factor influencing bone health. We investigated the impact of OSA on bone mineral density (BMD) and trabecular bone score (TBS) in T2DM. MethodsEighty-one T2DM patients [33 men and 48 women] participated. OSA was diagnosed using an overnight monitor, with its severity assessed by an apnea hypopnia index (pAHI). The measurements of hypoxia, including the percentage of total sleep time in which oxygen saturation remains below 90% (pT90), the oxygen desaturation index (pODI) and minimum O2 (min O2), were reported. Lumbar spine (L1-4) and femoral neck (FN) BMD were measured using dual-energy X-ray absorptiometry (DXA). TBS was computed from DXA images. ResultsSixty-five patients (80.2%) had OSA. pAHI, pT90, pODI and min O2 were not correlated to L1-4 BMD, FN BMD or TBS in all participants by multiple regression analyses adjusting for age, gender and BMI. However, an interaction between gender and pAHI, and gender and pODI were significantly associated with TBS (b = 0.003, p = 0.034 and b = 0.004, p = 0.046, respectively). We therefore reassessed an association between pAHI or pODI and TBS separately between men and women. After adjusting for age and BMI, more severe OSA (higher pAHI) and higher pODI significantly associated with lower TBS (b = −0.002, p = 0.034 and b = −0.003, p = 0.021, respectively) in men. On the other hand, higher pAHI non-significantly associated with better trabecular microarchitecture as indicated by higher TBS (b = 0.002, p = 0.059) in women. When considered only postmenopausal (n = 33), higher pAHI and higher pODI were significantly associated with higher TBS (b = 0.004, p = 0.003 and b = 0.004, p = 0.008, respectively). ConclusionsIn T2DM patients, there is a complex interrelationship among OSA severity, gender and TBS. More severe OSA predicted lower TBS in men, but predicted higher TBS in postmenopausal women.

Association of hip shapes with knee osteoarthritis outcomes in older-adults

Purpose: Various hip shapes may be important as a risk factor for development and progression of knee osteoarthritis, potentially due to the biomechanical link between the two joints. Therefore, this study aims to identify the relationship between hip morphology and structural and clinical osteoarthritis outcomes in the knee, in older-adults. Methods: 377 community-dwelling older-adults aged 50-80 years were studied. At baseline, dual-energy X-ray absorptiometry images of the left hip were obtained and hip shapes were described using mode scores from an 85-point statistical shape model. MRI scans were conducted at baseline and a mean follow-up of 10.7(SD:0.67) years later, to assess right knee tibial cartilage volume and bone-marrow lesions(BMLs). Knee pain was assessed using Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC). Knee replacement(KR) data were obtained by data linkage to the Australian Orthopaedic Association National Joint Replacement Registry. Linear mixed-effects, log-binomial models and survival analysis were used to investigate associations between hip shape modes and knee osteoarthritis outcomes adjusting for potential confounders. Results: Ten hip shape modes were identified, describing 78% of the total shape variance in descending order from mode 01 (31% variance) to mode 10 (1.82% variance). Hip shapes with a larger greater trochanter (mode 07) were associated with less knee cartilage volume loss (β: 2.14, 95% CI: 0.07,4.21), while a shorter and narrower femoral neck shape (mode 09) was related to increased volume loss (β: -3.86, 95% CI: -6.16, -1.56). Hip shapes with a non-spherical femoral head (mode 04) were associated with an increased risk of incident BMLs (RR: 1.19, 95% CI:1.07, 1.34). Those with a longer, wider femoral neck and a larger femoral head (mode 01) had an increased risk of worsening knee pain (RR: 1.33, 95% CI: 1.09, 1.61), whereas those with a smooth curving upper femoral neck (mode 09) had a lower risk of worsening knee pain (RR: 0.78, 95% CI: 0.67, 0.90). A larger greater trochanter and wider femoral neck shape (mode 08) was associated with an increased risk of KR (RR: 1.73, 95% CI: 1.18, 2.52), while increasing acetabular coverage (mode 10) was associated with a lower risk of KR (RR: 0.54, 95% CI: 0.36,0.8). Conclusions: Hip shape variations were associated with significant MRI-based and clinical outcomes in the knee over 10.7 years, possibly due to biomechanical, lifestyle or other factors related to both joints. These results suggest that hip shape may play an important role in the onset and progression of knee osteoarthritis over time.

Assessment of trabecular bone score, an index of bone microarchitecture, in HIV positive and HIV negative persons within the HIV UPBEAT cohort.

IntroductionIncreased prevalence of low bone mineral density (BMD) and increased fracture incidence are observed in persons living with HIV (PLWH). The trabecular bone score (TBS) is a novel index of bone microarchitecture which improves fracture prediction independent of BMD.MethodsThe HIV UPBEAT study is a single centre, prospective cohort study that enrolled subjects with and without HIV from similar sociodemographic backgrounds for annual assessments of bone health. TBS was derived from lumbar spine (LS) dual-energy X-ray absorptiometry images. Univariate and multivariable linear regression was used to assess relationships between baseline TBS, BMD, sociodemographic and clinical factors.Results463 subjects (201 HIV positive) were included; PLWH were younger and more likely male, of non-African ethnicity and current smokers. HIV was associated with a mean reduction of 0.037 [-0.060, -0.013] (p = 0.002) in TBS. Lower TBS was also associated with male gender, non-African ethnicity, current smoking status and lower LS BMD. HIV remained associated with lower TBS after adjustment for LS BMD, age, gender and ethnicity. However, adjustment for current smoking significantly attenuated the association between HIV and TBS, with further adjustment for higher bone turnover markers largely explaining any residual association. Among the sub-group of PLWH, exposure to protease inhibitors and lower nadir CD4+ T-cell counts were both predictors of lower TBS.ConclusionsPLWH have lower TBS independent of LS BMD. However, this is largely explained by higher current smoking rates and higher bone turnover in those with HIV. Exposure to PI, but not tenofovir disproxil fumarate, also contributed to lower TBS in those with HIV.

Computer-aided osteoporosis detection from DXA imaging

Background and objectiveOsteoporosis is a skeletal disease caused by a high rate of bone tissue loss, and it is a major cause of bone fracture. In contemporary society, osteoporosis is more common than cancer and stroke and results in a higher rate of morbidity and mortality in the human population. Osteoporosis can conclusively be diagnosed with dual energy X-ray absorptiometry (DXA). In this study, we propose a computer-aided osteoporosis detection (CAOD) technique that automatically measures bone mineral density (BMD) and generates an osteoporosis report from a DXA scan. MethodsThe CAOD model denoise and segments DXA images using a non-local mean filter, Machine learning pixel label random forest respectively, and locates regions of interest with higher accuracy. Pixel label random forest classifies a pixel either bone or soft tissue; then contours are extracted from binary image to locate regions of interest and calculate BMD from bone and soft tissues pixels. Mean standard deviation and correlation coefficients statistical analysis were used to evaluate the consistency and accuracy of BMD measurements. ResultsDuring a consistency test of BMD measurements using three consecutive scans from Computerized Imaging Reference Systems’ Bona Fide Phantom (CIRS-BFP) for the spine, the CAOD model showed an averaged standard deviation of 0.0029 while the standard deviation from manual measurements on the same data set by three different individuals was recorded as 0.1199. During another correlation study of BMD measurements evaluating real human scan images by the CAOD model versus manual measurement, the model scored a correlation coefficient of R2 = 0.9901 while the CIRS-BFP study scored a correlation coefficient of R2 = 0.9709. ConclusionsThe CAOD model increases the preciseness and accuracy of BMD measurements. This CAOD method will help clinicians, untrained DXA operators, and researchers (medical scientists, doctors, and bone researchers) use the DXA system with reliable accuracy and overcome workload challenges. It will also improve osteoporosis diagnosis from DXA systems and increase system performance and value.

Assessment of hip fracture risk by cross-sectional strain-energy derived from image-based beam model

BackgroundClinicians have been looking for a simple and effective biomechanical tool for the assessment of hip fracture risk. Dual-energy X-ray absorptiometry (DXA) is currently the primary bone imaging modality in clinic, and the engineering beam is the simplest model for a mechanical analysis. Therefore, we developed a DXA-based beam model for the above purpose. MethodsA beam model of the proximal femur was constructed from the subject's hip DXA image and denoted DXA-beam. Femur stiffness was calculated at cross-sections of interest using areal bone-mineral-density profile. Impact force induced in a sideways fall was applied as a critical loading. Fracture risk index at a cross-section was defined as the ratio of strain-energy induced by the impact force to the allowable strain-energy. A clinic cohort was used to study the discriminability of DXA-beam, which was measured by the area under the curve and odds ratio, both with 95% confidential interval. FindingsFracture risk measured by DXA-beam model at the femoral neck [odds ratio 2.23, 95% confidence interval (1.83, 2.57)], inter-trochanter [2.49, (2.14, 3.25)] and sub-trochanter [2.82, (2.38, 3.51)] were strongly associated with hip fracture. The area under the curve by DXA-beam at the femoral neck [0.74, 95% confidence interval (0.70, 0.76)], inter-trochanter [0.77, (0.75, 0.82)] and sub-trochanter [0.76, (0.74, 0.81)] were higher than that by femoral neck bone mineral density [0.71, (0.65, 0.78)]. InterpretationThe DXA-beam model is a simple and yet effective mechanical model. It had promising performance in discrimination of fracture cases from controls.

Using statistical shape modelling of DXA images to quantify the shape of the proximal femur at ages 14 and 18 years in the Avon Longitudinal Study of Parents and Children

Hip shape is an important determinant of hip osteoarthritis and osteoporotic hip fracture; however, little is known about its development in childhood and adolescence. While previous studies largely focused on individual geometrical indices of hip geometry such as neck-shaft angle or femoral neck width, statistical shape modelling offers the means to quantify the entire contour of the proximal femur, including lesser trochanter and acetabular eyebrow. We describe the derivation of independent modes of variation (hip shape mode scores) to characterise variation in hip shape from dual-energy X-ray absorptiometry (DXA) images in the Avon Longitudinal Study of Parents and Children (ALSPAC) offspring, using statistical shape modelling. ALSPAC is a rich source of phenotypic and genotypic data which provides a unique opportunity to investigate the environmental and genetic influences on hip shape in adolescence, as well as comparison with adult hip shape.