Bone Mineral Density Estimation Research Articles

Maternal dietary patterns associated with bone density in Chinese lactating women and infants at 6 months postpartum: a prospective study using data from 2018–2019

This cohort study was designed to explore the relationship between maternal dietary patterns (DPs) and bone health in Chinese lactating mothers and infants. We recruited 150 lactating women at 1-month postpartum. The estimated bone mineral density (eBMD) of subjects’ calcanei and the information on dietary intake were collected. After 5-month follow-up, the eBMD of mothers and their infants were measured again. Factor analysis was applied to determine maternal DPs. General linear models were used to evaluate the association between maternal DPs and maternal eBMD loss or infants’ eBMD. With all potential covariates adjusted, Factor 2 (high intake of whole grains, tubers, mixed beans, soybeans and soybean products, seaweeds, and nuts) showed a positive association with the changes of maternal eBMD (β = 0.16, 95 % CI: 0.005, 0.310). Factor 3 (high intake of soft drinks, fried foods, and puffed foods) was inversely correlated with the changes of maternal eBMD (β = –0. 22, 95 % CI: -0.44, 0.00). The changes of maternal eBMD were positively associated with 6-month infants’ eBMD (β = 0.34, 95 % CI: 0.017, 0.652). In conclusion, Factor 2 might contribute to the maintenance of eBMD in lactating women, while Factor 3 could exacerbate maternal eBMD loss. Additionally, the changes of maternal eBMD presented a positive correlation with 6-month infants’ eBMD.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol). The best-performing models achieved mean absolute errors of 0.15-0.16g/cm2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) - 0.80 (radiographs) for the classification of T-scores lower than - 1, and 0.80 (MRIs) - 0.65 (radiographs) for T-scores lower than - 2.5. The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.

Opportunistic CT screening demonstrates increased risk for peri-articular fractures in osteoporotic patients

BackgroundUnderdiagnosis or undertreatment of osteoporosis consequently impacts individual morbidity and mortality, as well as on healthcare systems and communities as a whole. Dual-energy x-ray absorptiometry (DXA) is the gold standard method for identifying osteoporosis, however, opportunistic CT screening is capable of precisely estimating bone mineral density (BMD) in abdominopelvic imaging with no additional cost, radiation exposure or inconvenience to patients. This study uses opportunistic CT screening to determine the prevalence of osteoporosis and anatomic distribution patterns in patients presenting with lower extremity fractures at our institution. HypothesisTrauma patients with low bone mineral density (BMD) are more likely to present with peri-articular versus shaft fractures. Patients and methodsWe conducted a retrospective review of 721 patients presenting as trauma activations to the emergency department (ED) of a Level 1 Trauma Center with lower extremity fractures. Patients were excluded if under the age of 18 or lacking a CT scan upon arrival in the ED. Hounsfield Units (HU) were measured at the L1 vertebral level on CT scans to determine bone mineral density. Values of ≤100 HU were consistent with osteoporosis, whereas 101–150 HU were consistent with osteopenia. ResultsThe final cohort included 416 patients, with mean age of 49 ± 21 years. Average bone density was 203.9 ± 73.4 HU. 15.9% of patients were diagnosed as osteopenic and 9.9% as osteoporotic. 64.2% of fractures were peri-articular, 25.7% were shaft, and 10.1% were a combination. Peri-articular fractures were significantly more likely to have lower average BMD than shaft fractures (189 ± 74.7 HU vs. 230.6 ± 66.1 HU, p < 0.001). DiscussionOur study demonstrates a significant relationship between low bone mineral density and lower extremity fracture pattern, however, likely influenced by other factors such as sex. Opportunistic CT screening for osteoporosis in trauma settings provides ample opportunity for early detection of low BMD and implementation of highly effective lifestyle modification and pharmacotherapy intervention. Reduction in the overall incidence of peri-articular fracture with widespread adoption of opportunistic CT screening may lessen the morbidity, mortality, and total cost currently afflicting patients, healthcare systems, and communities. Level of evidenceIII, therapeutic

Asynchronous calibration of a CT scanner for bone mineral density estimation: sources of error and correction.

The estimation of BMD with CT scans requires a calibration method, usually based on a phantom. In asynchronous calibration, the phantom is scanned separately from the patient. A standardized acquisition protocol must be used to avoid variations between patient and phantom. However, variations can still be induced, for example, by temporal fluctuations or patient characteristics. Based on the further use of 739 forensic and 111 clinical CT scans, this study uses the proximal femur BMD value ("total hip") to assess asynchronous calibration accuracy, using in-scan calibration as ground truth. It identifies the parameters affecting the calibration accuracy and quantifies their impact. For time interval and table height, the impact was measured by calibrating the CT scan twice (once using the phantom scan with closest acquisition parameters and once using a phantom scan with standard values) and comparing the calibration accuracy. For other parameters such as body weight, the impact was measured by computing a linear regression between parameter values and calibration accuracy. Finally, this study proposes correction methods to reduce the effect of these parameters and improve the calibration accuracy. The BMD error of the asynchronous calibration, using the phantom scan with the closest acquisition parameters, was -1.2 ± 1.7% for the forensic and - 1.6 ± 3.5% for the clinical dataset. Among the parameters studied, time interval and body weight were identified as the main sources of error for asynchronous calibration, followed by table height and reconstruction kernel. Based on these results, a correction method was proposed to improve the calibration accuracy.

Estimated bone mineral density and white matter hyperintensities: A bidirectional Mendelian randomization study

PurposeGreater white matter hyperintensities (WMH) in older adults have been associated with reduced bone mineral density (BMD) and increased fractures and falls. However, it is unclear whether there is a causal relationship between BMD reduction and WMH. In this study, Mendelian randomization (MR) was used to find the causality between WMH and estimated BMD (eBMD). MethodsWe performed a two-sample bidirectional MR analysis using statistical data obtained from publicly available genome-wide association studies (GWAS). The main method of MR analysis is the inverse-variance weighted (IVW) method. To identify and account for the impact of horizontal pleiotropy, we also employed MR-Egger regression, MR pleiotropy residual sum, and outlier (MR-PRESSO). ResultsMR analysis found a causal relationship between eBMD and WMH (IVW OR = 0.938, 95 % CI: 0.889–0.990, p = 0.020). Our causal estimates are unlikely to be distorted by horizontal pleiotropy according to heterogeneity test (both p > 0.05) and MR-Egger regression (p > 0.05). However, in the reverse MR analysis, there was no evidence that WMH was causally correlated with eBMD (IVW OR = 0.979, 95 % CI: 0.954–1.005, p = 0.109). ConclusionOur results suggest that low eBMD increased the risk of WMH; conversely, no evidence that WMH causally affects eBMD was found.

Shared genetic basis connects smoking behaviors and bone health: insights from a genome-wide cross-trait analysis.

Although the negative association of tobacco smoking with osteoporosis is well-documented, little is known regarding the shared genetic basis underlying these conditions. In this study, we aim to investigate a shared genetic architecture between smoking and heel estimated bone mineral density (eBMD), a reliable proxy for osteoporosis. We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci and causal relationship of smoking with eBMD, leveraging summary statistics of the hitherto largest genome-wide association studies conducted in European ancestry for smoking initiation (Nsmoker = 1 175 108, Nnonsmoker = 1 493 921), heaviness (cigarettes per day, N = 618 489), cessation (Ncurrent smoker = 304 244, Nformer smoker = 843 028), and eBMD (N = 426 824). A significant negative global genetic correlation was found for smoking cessation and eBMD (${r}_g$ = -0.051, P = 0.01), while we failed to identify a significant global genetic correlation of smoking initiation or heaviness with eBMD. Partitioning the whole genome into independent blocks, we observed 6 significant shared local signals for smoking and eBMD, with 22q13.1 showing the strongest regional genetic correlation. Such a genetic overlap was further supported by 71 pleiotropic loci identified in the cross-trait meta-analysis. Mendelian randomization identified no causal effect of smoking initiation (beta = -0.003g/cm2, 95% CI = -0.033 to 0.027) or heaviness (beta = -0.017g/cm2, 95% CI = -0.072 to 0.038) on eBMD, but a putative causal effect of genetic predisposition to being a current smoker was associated with a lower eBMD compared to former smokers (beta = -0.100g/cm2, 95% CI = -0.181 to -0.018). Our study demonstrates a pronounced biological pleiotropy as well as a putative causal link between current smoking status and eBMD, providing novel insights into the primary prevention and modifiable intervention of osteoporosis by advocating individuals to avoid, reduce or quit smoking as early as possible.

Knowledge, Attitudes, Practices, and Awareness Levels Among Indian Postmenopausal Women About Osteoporosis and Its Relationship With Sociodemographic Factors: A Cross-Sectional Study From Northern India.

Background Osteoporosis is a silent disease and can be prevented by providing correct and appropriate information to the individuals at risk. Therefore, we aim to find out the levels of knowledge, attitudes, and behaviors of postmenopausal women, the highest-risk group. Methods Between May 2021 and December 2023, a cross-sectional study was done in the Military Hospital in Ambala, India, in 1326 postmenopausal women of age between 45 and 70 years who visited the outpatient department of orthopedics and who previously had a measure of bone mineral density (BMD) or estimation of BMD. All patients participating in the study received a questionnaire that assessed the level of knowledge, attitudes, and behaviors related to osteoporosis. The Osteoporosis Knowledge Assessment Tool (OKAT) was used to assess these parameters. Results The mean age was 60±3.1 years. The mean score for osteoporosis awareness was 6/20 points. A total of 983 (73.13%) had no knowledge about osteoporosis, and 221 (16.66%) had higher knowledge. Knowledge about osteoporosis was low with a mean knowledge score of 7.44±3.16 and a median of 7.2.It was found that those who graduated had some knowledge regarding osteoporosis with the help of either a hospital staff or a family member who had a history of osteoporosis. Conclusions Even among postmenopausal women who know they are at risk and should have BMD, knowledge, attitudes, and behaviors about osteoporosis were found to be quite low. In addition, education and mass communications are needed to increase awareness among women of this age about improving bone health.

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals.

Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

Comparison of Singh’s index in view of osteoporosis in patient with in tertrochanteric fracture and intracapsular fracture

Background: Osteoporosis is a progressive, systemic, skeletal disease with low bone mass. Diagnosing osteoporosis in practice is a major challenge, where resources are limited and costly. Measurement of osteoporosis through Singh’s index in proximal femur fracture patients is consistent with Bone Mineral Density estimation like DEXA scan. Singh’s index is a reliable, reproducible and easily available investigation for osteoporosis. The aim of this study is to evaluate the Singh’s index of the patients with inter-trochanteric and intra-capsular fracture. Methods: A retrospective observational study of 20 IT and IC fracture each. Singh’s index evaluated from the X-rays at time of fracture presentation. Other required information collected at presentation. Results: Out of 40 Patients, 6 had Singh's Index Grade 1 (15%), 11 had Grade 2 (27.5%), 10 had Grade 3 (25%), 7 had Grade 4 (17.5%), 6 had Grade 5 (15%), 0 Patients had Singh’s index Grade 6. There was no noticeable significant variation in relation of gender with the type of fracture. Patients with higher age tend to have a lower grade of Singh’s index. Conclusions: Singh’s Index indeed represents bone quality and a fall in the grades predisposes to fractures. Inter trochanteric fractures are common in the older age group with poor Singh’s index (&lt;3) compared to the relatively younger age group with Singh’s index around 3. Gender, exercise, history of hypertension and diabetes, sunlight exposure, etc. has no significant impact on Singh's Index. Age is deciding factor for Singh’s Index.

Systemic Inflammatory Regulators Associated with Osteoporosis: A Bidirectional Mendelian Randomization Study.

To elucidate the precise upstream and downstream regulatory mechanisms of inflammatory factors in osteoporosis (OP) progression and to establish a causal relationship between inflammatory factors and OP. We conducted bidirectional Mendelian randomization (MR) analyses using data for 41 cytokines obtained from three independent cohorts comprising 8293 Finnish individuals. Estimated bone mineral density (eBMD) data were derived from 426,824 UK Biobank White British individuals (55% female) and fracture data from 416,795 UK Biobank participants of European ancestry. The inverse variance-weighted method was the primary MR analysis approach. We employed other methods as complementary approaches for mutual corroboration. To test for pleiotropy and heterogeneity, we used the MR-Egger regression, MR-pleiotropy residual sum and outlier global test, and the Cochrane Q test. Macrophage inflammatory protein (MIP)-1α and interleukin (IL)-12p70 expression associated negatively and causally with eBMD (β = -0.017 [MIP-1α], β = -0.011 [IL-12p70]). Conversely, tumor necrosis factor-related apoptosis-inducing ligand was associated with a decreased risk of fractures (Odds Ratio: 0.980). Additionally, OP influenced the expression of multiple inflammatory factors, including growth-regulated oncogene-α, interferon-gamma, IL-6, beta nerve growth factor, and IL-2. Finally, we discovered complex bidirectional causal relationships between IL-8, IL-10, and OP. Specific inflammatory factors may contribute to OP development or may be causally affected by OP. We identified a bidirectional causal relationship between certain inflammatory factors and OP. These findings provide new perspectives for early prediction and targeted treatment of OP. Larger cohort studies are necessary in the future to further validate these findings.

Associations of residential greenness with bone mineral density and osteoporosis: the modifying effect of genetic susceptibility

ObjectivesTo investigate the associations of residential greenness with bone mineral density and incident osteoporosis, and further evaluate the potential modifying effect of genetic susceptibility.MethodsWe used the Normalised Difference Vegetation Index...

Comparative Study of BMD in Type 2 Diabetic and Non-diabetic Male Patients

Introduction: Osteoporosis and diabetes are both common human diseases. The prevalence of both is increasing individually and in combination, due to better detection methods and changing definitions. Due to the different pathogenesis of Type 1 and Type 2 diabetes mellitus (T2DM), one of which is a predominant autoimmune process while the other mainly a metabolic disorder, it is not surprising that there is no uniform entity of diabetic bone disease as such, although such term has been proposed in the past but never gained momentum. Paradoxically, an increased risk of osteoporotic fracture in T2DM has been repeatedly demonstrated and this was independent of bone mineral density (BMD). This association with fracture adds uncertainty around the actual association between diabetes mellitus and BMD. This study aims to study the population of diabetes at tertiary care center when they are compared with non-diabetics in terms of BMD. Aims of Study: The aim of this study was to determine the prevalence of osteopenia and osteoporosis in T2DM and non-diabetic male patients using Dual Energy X-ray absorptiometry (DEXA scan). Materials and Methods: Patients for the study included male patients between 40 and 60 years of age group attending outpatient department, health checkup, and admitted in the ward of Saifee Hospital, Department of Medicine. In 200 (100 type 2 diabetic males and 100 non-diabetic males), DEXA Scan was performed in the Department of Imaging, Saifee Hospital from June 2017 to April 2019. Results: Type 2 diabetics were significantly associated with the presence of osteoporosis compared to non-diabetics (P = 0.001). Type 2 diabetics were significantly associated with body mass index (BMI) &gt;25 (P = 0.0) and diabetics had a significantly higher BMI compared to non-diabetics (P = 0.0001). Type 2 diabetics above 50 years of age were significantly associated with osteoporosis (P = 0.000) and diabetics with osteoporosis were significantly older compared to diabetics without osteoporosis (P = 0.0018). Conclusion: The study concluded that there is a correlation between T2DM, increasing age, glycemic control, increased BMI, increased calcium levels, and decreased BMD. Thus, physician treating diabetes must anticipate decreased BMD and rule out or correct all of these factors in patients of diabetes to prevent the complications of decreased BMD in these groups of patients. Therefore, early detection and treatment of osteoporosis/osteopenia by estimation of BMD in Type 2 diabetic males, strict diabetic control with target hemoglobin A1c &lt;6.5, weight control with target BMI &lt;25, supplementation with Vitamin D3 should be advocated. Keywords: Diabetes, osteoporosis, dual-energy X-ray absorptiometry scan.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography.

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

Causal influence of muscle weakness on cardiometabolic diseases and osteoporosis

The causal roles of muscle weakness in cardiometabolic diseases and osteoporosis remain elusive. This two-sample Mendelian randomization (MR) study aims to explore the causal roles of muscle weakness in the risk of cardiometabolic diseases and osteoporosis. 15 single nucleotide polymorphisms (SNPs, P < 5 × 10−8) associated with muscle weakness were used as instrumental variables. Genetic predisposition to muscle weakness led to increased risk of coronary artery disease (inverse variance weighted [IVW] analysis, beta-estimate: 0.095, 95% confidence interval [CI]: 0.023 to 0.166, standard error [SE]:0.036, P-value = 0.009) and reduced risk of heart failure (weight median analysis, beta-estimate: − 0.137, 95% CI − 0.264 to − 0.009, SE:0.065, P-value = 0.036). In addition, muscle weakness may reduce the estimated bone mineral density (eBMD, weight median analysis, beta-estimate: − 0.059, 95% CI − 0.110 to − 0.008, SE:0.026, P-value = 0.023). We found no MR associations between muscle weakness and atrial fibrillation, type 2 diabetes or fracture. This study provides robust evidence that muscle weakness is causally associated with the incidence of coronary artery disease and heart failure, which may provide new insight to prevent and treat these two cardiometabolic diseases.

Bone Mineral Density Derived from Cardiac CT Scans: Using Contrast Enhanced Scans for Opportunistic Screening

Purpose: Osteoporosis is under-diagnosed and often co-exists with other diseases. Very low bone mineral density (BMD) indicates risk of osteoporosis and opportunistic screening for low BMD in CT-scans has been suggested. In a non-contrast enhanced thoracic CT scan, the scan-field-of-view includes vertebrae enabling BMD estimation. However, many CT scans are obtained by administration of contrast material. If the impact of contrast enhancement on BMD measurements could be quantified, considerably more patients are eligible for screening. Methods: This study investigated the impact of intravenous contrast on thoracic BMD measurements in cardiac CT scans pre- and post-contrast, including different contrast trigger levels of 130 and 180 Hounsfield units (HU). BMD was measured using quantitative CT with asynchronous calibration. Results: In 195 participants undergoing cardiac CT (mean age 57±9 years, 37 % females) contrast increased mean thoracic BMD from 116±33 mg/cm3 (non-enhanced CT) to 130±38 mg/cm3 (contrast-enhanced CT) (p<0.001). Using clinical cut-off values for very low (<80 mg/cm3) and low BMD (<120 mg/cm3) showed that 24 % (47/195 participants) were misclassified when BMD was measured on contrast-enhanced CT-scans. Of the misclassified patients, 6 % (12/195 participants) were categorized as having low BMD despite having very low BMD on the non-enhanced images. Contrast-CT using a higher contrast trigger level showed a significant increase in BMD compared to the lower trigger level (119±32 vs. 135±40 mg/cm3, p<0.01). Conclusion: For patients undergoing cardiac CT, using contrast-enhanced images to assess BMD entails substantial overestimation. Contrast protocol trigger levels also affect BMD measurements. Adjusting for these factors is needed before contrast-enhanced images can be used clinically. Mini abstract: Osteoporosis is under-diagnosed. Contrast-enhanced CT made to examine other diseases might be utilized simultaneously for bone mineral density (BMD) screening. These scans, however, likely entails overestimation of BMD due to the effect of contrast. Adjusting for this effect is needed before contrast-enhanced images can be implemented clinically for BMD screening.

Genetic Correlation, Shared Loci, and Causal Association Between Sex Hormone-Binding Globulin and Bone Mineral Density: Insights From a Large-Scale Genomewide Cross-Trait Analysis.

Although the impact of sex hormones on bone metabolism is well-documented, effect of their primary modulator, sex hormone-binding globulin (SHBG), remains inconclusive. This study aims to elucidate the genetic overlap between SHBG and heel estimated bone mineral density (eBMD), a widely-accepted tool for osteoporosis management and fracture risk assessment. Using summary statistics from large-scale genomewide association studies conducted for SHBG (N = 370,125), SHBG adjusted for body mass index (SHBGa, N = 368,929), and eBMD (N = 426,824), a comprehensive genomewide cross-trait approach was performed to quantify global and local genetic correlations, identify pleiotropic loci, and infer causal associations. A significant overall inverse genetic correlation was found for SHBG and eBMD (rg = -0.11, p = 3.34 × 10-10 ), which was further supported by the significant local genetic correlations observed in 11 genomic regions. Cross-trait meta-analysis revealed 219 shared loci, of which seven were novel. Notably, four novel loci (rs6542680, rs8178616, rs147110934, and rs815625) were further demonstrated to colocalize. Mendelian randomization identified a robust causal effect of SHBG on eBMD (beta = -0.22, p = 3.04 × 10-13 ), with comparable effect sizes observed in both men (beta = -0.16, p = 1.99 × 10-6 ) and women (beta = -0.19, p = 2.73 × 10-9 ). Replacing SHBG with SHBGa, the observed genetic correlations, pleiotropic loci and causal associations did not change substantially. Our work reveals a shared genetic basis between SHBG and eBMD, substantiated by multiple pleiotropic loci and a robust causal relationship. Although SHBG has been implicated in preventing and screening aging-related diseases, our findings support its etiological role in osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

The rate of disuse osteopenia in admitted, non-weight-bearing patients.

Disuse osteopenia is a well-recognized consequence of prolonged physical inactivity, but its rate after orthopaedic injuries necessitating non-weight-bearing is not well studied. The purpose of this study was to estimate the rate of disuse osteopenia at the lumbar spine and proximal femur in patients with lower extremity trauma admitted to the hospital. We performed a retrospective chart review of patients with lower extremity trauma with a period of strict non-weight-bearing between completion of two computed tomography (CT) scans. The radiodensity of the proximal femur or lumbar vertebrae was measured from the earliest and latest available CT scans within the non-weight-bearing timeframe. The change in estimated bone mineral density (eBMD) was calculated as a proxy for disuse osteopenia. A total of 189,111 patients were screened, with 17 patients in the proximal femur group and 15 patients in the lumbar spine group meeting inclusion and exclusion criteria. The average rate of change in eBMD of the proximal femur was a decrease of 7.54 HU/day, 95% confidence interval(CI) [3.65, 11.43]. The average rate of change in eBMD of the lumbar spine was an increase of 1.45 HU/day, 95% CI [-3.15, 6.06]. In admitted, non-weight-bearing orthopaedic trauma patients, our novel study suggests that the proximal femur experiences disuse osteopenia during periods of non-weight-bearing, although this finding was not observed at the lumbar spine. The clinical significance of this data underscores the important consideration of disuse osteopenia by all physicians when caring for patients that may require non-weight-bearing restrictions.

Spina bifida and cardiorespiratory profile: the impact of leisure sport activities on physical fitness.

The aim of the present study is to evaluate a population of young patients affected by Spina Bifida (SB) to describe their cardiorespiratory function and bone mineral density profile, analyzing any differences between people performing and those who do not perform sports activity. The study also aimed to rule out possible congenital heart disease associated with spina bifida, considering the common origin of certain cardiac structures with those found to be altered in SB patients. Thirty-four young patients, aged between 12 and 22years, diagnosed with spinal dysraphism (SD), have been clinically described and, in order to evaluate their physical fitness, functional capacity and bone mass, almost all of them underwent a complete cardiorespiratory assessment, including electrocardiogram (ECG), echocardiogram, Cardiopulmonary Exercise Test (CPET), body composition analysis using bioimpedance analysis (BIA) and Dual Energy X-ray Absorptiometry (DEXA), as well as the estimation of bone mineral density (BMD) with Computerized Bone Mineralometry (CBM). Collected data demonstrated that only 35% of the subjects practiced physical activity during the week. BMI and percentage FM values were pathological in at least 50% of the population. On cardiological investigations (ECG and echocardiogram), no significant alterations were found. In all patients who performed CPET (79.4%), pathological values of the main functional capacity parameters were revealed, especially peak oxygen consumption (VO2 peak), even when corrected for BCM or FFM estimated at BIA and DEXA, respectively. In the CBM analysis, out of 27 patients in whom the femoral T-score was evaluated, a condition of osteopenia was revealed in 40.7% of the patients (11/27) and osteoporosis in 18.5% (5/27); out of 27 patients in whom the lumbar T-score was evaluated, 37% of the patients showed osteopenia (10/27) and 29.6% osteoporosis (8/27). When the comparison between exercising and non-exercising patients was performed, the only statistically significant difference that emerged was the median lumbar T-score value, which appeared lower in the group not performing physical activity (p = 0,009). The extensive cardiorespiratory evaluation, including CPET, of our cohort of spina bifida patients showed altered values of the main parameters related to cardiorespiratory fitness and is the only study in the literature that analysed bone mineralization values in physically active and sedentary spina bifida patients and demonstrated a statistically significant difference. Furthermore, it is the only study to date that investigated the possible association of congenital heart diseases with SD, without demonstrating the existence of pathological conditions.

Opportunistic CT-Based Hounsfield Units Strongly Correlate with Biomechanical CT Measurements in the Thoracolumbar Spine.

Retrospective cohort study. Hounsfield units (HUs) are known to correlate with clinical outcomes, but no study has evaluated how they correlate with biomechanical computed tomography (BCT) and dual-energy x-ray absorptiometry (DXA) measurements. Low bone mineral density (BMD) represents a major risk factor for fracture and poor outcomes following spine surgery. DXA can provide regional BMD measurements but has limitations. Opportunistic HUs provide targeted BMD estimates; however, they are not formally accepted for diagnosing osteoporosis in current guidelines. More recently, BCT analysis has emerged as a new modality endorsed by the International Society for Clinical Densitometry for assessing bone strength. Consecutive cases from 2017 to 2022 at a single institution were reviewed for patients who underwent BCT in the thoracolumbar spine. BCT-measured vertebral strength, trabecular BMD, and the corresponding American College of Radiology Classification were recorded. DXA studies within three months of the BCT were reviewed. Pearson Correlation Coefficients were calculated, and receiver-operating characteristic curves were constructed to assess the predictive capacity of HUs. Threshold analysis was performed to identify optimal HU values for identifying osteoporosis and low BMD. Correlation analysis of 114 cases revealed a strong relationship between HUs and BCT vertebral strength ( r =0.69; P <0.0001; R2 =0.47) and trabecular BMD ( r =0.76; P <0.0001; R2 =0.58). However, DXA poorly correlated with opportunistic HUs and BCT measurements. HUs accurately predicted osteoporosis and low BMD (Osteoporosis: C =0.95, 95% CI 0.89-1.00; Low BMD: C =0.87, 95% CI 0.79-0.96). Threshold analysis revealed that 106 and 122 HUs represent optimal thresholds for detecting osteoporosis and low BMD. Opportunistic HUs strongly correlated with BCT-based measures, while neither correlated strongly with DXA-based BMD measures in the thoracolumbar spine. HUs are easy to perform at no additional cost and provide accurate BMD estimates at noninstrumented vertebral levels across all American College of Radiology-designated BMD categories.

Estimation of Bone Mineral Density in the Femoral Neck and Lumbar Spine using Texture Analysis of Chest and Pelvis Computed Tomography Hounsfield Unit.

This study aimed to establish an academic basis for using a computed tomography (CT) model for predicting osteoporosis in the clinical setting by illustrating the effectiveness of morphometric texture analysis. We introduce texture analysis and quantitative approaches using CT Hounsfield units (HU) to screen osteoporosis. From March 6th, 2013, to August 11th, 2020, a total of 4,333 cases (1,766 patients) were included in the study. After applying exclusion criteria concerning the patient status and scan interval between CT and DXA, we selected only 1,647 samples (736 patients) and analyzed both their CT and DXA bone mineral density (BMD) results. BMD was measured in the femoral neck and L1 spine body. A region of interest (ROI) was extracted from each patient's CT as the maximum trabecular area of the L1 spine body and femoral neck. A total of 45 texture features were extracted from every ROI using gray-level co-occurrence matrices. Machine-learning techniques, including linear regression (LR) and artificial neural network (ANN), were applied to predict BMD. We assigned samples to (1) Set 1 (857 lumbar spine samples in chest model, L1 spine DXA BMD), (2) Set 2 (392 lumbar spine samples in lumbar spine CT model, L1 spine DXA BMD), (3) Set 3 (1,249 lumbar spine samples in both chest and lumbar spine CT model, L1 spine DXA BMD), (4) Set 4 (398 femoral neck samples in hip and pelvis CT model, femoral neck DXA BMD), and (5) Set 5 (a total of 1,647 samples). When we applied LR, the correlation coefficients between estimated and reference values for Sets 1, 2, 3, and 4 were 0.783, 0.784, 0.757, and 0.652, respectively. For total samples (Set 5), LR and ANN provided correlation coefficients of 0.707 and 0.782, respectively. The modality using morphometric texture analysis with CT HU can be an additional diagnostic tool for osteoporosis and an alternative for DXA.