Opportunistic Screening Using Low-Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study

Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT

PURPOSE

To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).

METHODS

This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV). Fully automated algorithms for quantifying CT number (HU) in abdominal fat (subcutaneous and visceral), skeletal muscle, bone, calcium (abdominal Agatston score), and organ size (area or volume) were applied. Biomarker median difference relative to 70 keV and interquartile range were reported by energy level to characterize variation. Linear regression was performed to calibrate non-70 keV data and to estimate their equivalent 70 keV biomarker attenuation values.

RESULTS

Relative to 70 keV, absolute median differences in attenuation-based biomarkers (excluding Agatston score) ranged 39-358, 12-102, 5-48, 9-75 HU for 40, 55, 85, 100 keV, respectively. For area-based biomarkers, differences ranged 6-15, 3-4, 2-7, 0-5 cm2 for 40, 55, 85, 100 keV. For volume-based biomarkers, differences ranged 12-34, 8-68, 12-52, 1-57 cm3 for 40, 55, 85, 100 keV. Agatston score behavior was more spurious with median differences ranging 70-204 HU. In general, VMI < 70 keV showed more variation in median biomarker measurement than VMI > 70 keV.

CONCLUSION

This study characterized the behavior of a fully automated AI CT biomarker toolkit across varying VMI levels obtained with DECT. The data showed relatively little biomarker value change when measured at or greater than 70 keV. Lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70 keV, a level considered equivalent to conventional 120 kVp exams.

Does the deep learning-based iterative reconstruction affect the measuring accuracy of bone mineral density in low-dose chest CT?

OBJECTIVES

To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low-dose chest CT.

METHODS

Phantom and patient studies were separately conducted in this study. The same low-dose protocol was used for phantoms and patients. All images were reconstructed with filtered back projection, hybrid iterative reconstruction (HIR) (KARL®, level of 3,5,7), and deep learning-based iterative reconstruction (artificial intelligence iterative reconstruction [AIIR], low, medium, and high strength). The noise power spectrum (NPS) and the task-based transfer function (TTF) were evaluated using phantom. The accuracy and the relative error (RE) of BMD were evaluated using a European spine phantom. The subjective evaluation was performed by 2 experienced radiologists. BMD was measured using quantitative CT (QCT). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), BMD values, and subjective scores were compared with Wilcoxon signed-rank test. The Cohen's kappa test was used to evaluate the inter-reader and inter-group agreement.

RESULTS

AIIR reduced noise and improved resolution on phantom images significantly. There were no significant differences among BMD values in all groups of images (all P > 0.05). RE of BMD measured using AIIR images was smaller. In objective evaluation, all strengths of AIIR achieved less image noise and higher SNR and CNR (all P < 0.05). AIIR-H showed the lowest noise and highest SNR and CNR (P < 0.05). The increase in AIIR algorithm strengths did not affect BMD values significantly (all P > 0.05).

CONCLUSION

The deep learning-based iterative reconstruction did not affect the accuracy of BMD measurement in low-dose chest CT while reducing image noise and improving spatial resolution.

ADVANCES IN KNOWLEDGE

The BMD values could be measured accurately in low-dose chest CT with deep learning-based iterative reconstruction while reducing image noise and improving spatial resolution.

Comparative effectiveness of four techniques for identifying vertebral fragility fractures among elderly patients

OBJECTIVE

The incidence of vertebral fragile fractures peaked among the elderly population, and identifying individuals at high risk of vertebral fractures and promptly instituting preventions are of critical importance. This study aims to determine the efficacy and values of Hounsfield unit (HU) values, vertebral bone quality (VBQ) scores, bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA), and quantitative computed tomography (QCT) to discriminate between patients with and without vertebral fractures.

METHODS

A thorough search was conducted across PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, and Wan Fang Database to identify potential studies that met the eligibility criteria. Studies that evaluated the utility of HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD in discriminating vertebral fractures were qualified.

RESULTS

The combined results showed that there were significant differences in HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD between the fracture and non-fracture groups. Moreover, the pooled sensitivity, specificity, and AUC of HU values were 0.82, 0.67, and 0.76, respectively; the pooled sensitivity, specificity, and AUC of VBQ scores were 0.70, 0.75, and 0.78; the pooled sensitivity, specificity, and AUC of QCT-measured BMD were 0.85, 0.76 and 0.88.

CONCLUSION

All four methods, namely HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD can effectively distinguish between patients with and without vertebral fragile fractures. Among these, QCT-measured BMD exhibited a relatively high efficacy in discriminating vertebral fractures. VBQ scores and HU values demonstrated comparable efficacy for discriminating vertebral fractures among elderly patients.

KEY POINTS

Question Can four different imaging modalities effectively discriminate vertebral fragility fracture status among elderly patients? Findings These methods can effectively distinguish vertebral fractures status among elderly patients, and quantitative computed tomography (QCT)-measured bone mineral density (BMD) exhibited a relatively high efficacy. Clinical relevance The clinical applications of Hounsfield unit values, vertebral bone quality scores, and BMD measured by dual-energy X-ray absorptiometry and QCT show promising outcomes in identifying individuals at high risk of vertebral fractures.

Crosstalk between kidney and bones: New perspective for modulating osteoporosis

Growing evidence indicates an interesting interplay between kidney and bone. The pathophysiological condition of the skeletal system is intricately associated with the normal functioning of the kidneys. This relationship is modulated by various factors, including calcium and phosphate, 1-α-hydroxylase, erythropoietin (EPO), klotho, fibroblast growth factor 23 (FGF23), bone morphogenetic protein-7 (BMP-7), and extracellular vesicles (EVs). These interactions are notably evident in conditions such as chronic kidney disease with bone mineral density (CKD-BMD), renal osteodystrophy (ROD), and osteoporosis (OP). Furthermore, innovative methodologies such as cell co-culture, organ-on-a-chip, single-cell sequencing, and spatial transcriptomics are highlighted as instrumental in advancing the study of inter-organ interactions. This review, grounded in the pathogenesis, diagnostic and therapeutic modalities, and pharmacological treatments of OP, synthesizes evidence from molecular biology to clinical perspectives. It aims to establish a foundation for the development of more complex and physiologically relevant in vitro models and to propose potential therapeutic strategies.

Deep Learning-enhanced Opportunistic Osteoporosis Screening in Ultralow-Voltage (80 kV) Chest CT: A Preliminary Study

RATIONALE AND OBJECTIVES

To explore the feasibility of deep learning (DL)-enhanced, fully automated bone mineral density (BMD) measurement using the ultralow-voltage 80 kV chest CT scans performed for lung cancer screening.

MATERIALS AND METHODS

This study involved 987 patients who underwent 80 kV chest and 120 kV lumbar CT from January to July 2024. Patients were collected from six CT scanners and divided into the training, validation, and test sets 1 and 2 (561: 177: 112: 137). Four convolutional neural networks (CNNs) were employed for automated segmentation (3D VB-Net and SCN), region of interest extraction (3D VB-Net), and BMD calculation (DenseNet and ResNet) of the target vertebrae (T12-L2). The BMD values of T12-L2 were obtained using 80 and 120 kV quantitative CT (QCT), the latter serving as the standard reference. Linear regression and Bland-Altman analyses were used to compare BMD values between 120 kV QCT and 80 kV CNNs, and between 120 kV QCT and 80 kV QCT. Receiver operating characteristic curve analysis was used to assess the diagnostic performance of the 80 kV CNNs and 80 kV QCT for osteoporosis and low BMD from normal BMD.

RESULTS

Linear regression and Bland-ltman analyses revealed a stronger correlation (R2=0.991-0.998 and 0.990-0.991, P<0.001) and better agreement (mean error, -1.36 to 1.62 and 1.72 to 2.27 mg/cm3; 95% limits of agreement, -9.73 to 7.01 and -5.71 to 10.19mg/cm3) for BMD between 120 kV QCT and 80 kV CNNs than between 120 kV QCT and 80 kV QCT. The areas under the curve of the 80 kV CNNs and 80 kV QCT in detecting osteoporosis and low BMD were 0.997-1.000 and 0.997-0.998, and 0.998-1.000 and 0.997, respectively.

CONCLUSION

The DL method could achieve fully automated BMD calculation for opportunistic osteoporosis screening with high accuracy using ultralow-voltage 80 kV chest CT performed for lung cancer screening.

Opportunistic screening for bone mineral density deficits in pediatric patients from abdominal computed tomography scans obtained for other indications: a cross-sectional study

BACKGROUND

Quantitative computed tomography (QCT) has been widely applied for opportunistic screening for osteoporosis in adults, but it is rarely used in pediatric patients.

OBJECTIVE

This study aimed to investigate the feasibility of QCT and the prevalence of bone mineral density (BMD) deficits at the spine in pediatric patients undergoing an abdominal computed tomography (CT) for other indications.

MATERIALS AND METHODS

Pediatric patients who underwent a clinical abdominal CT scan from October 2018 to November 2020 were recruited for this retrospective cross-sectional study. Lumbar trabecular BMD was evaluated by QCT. The relationships of treatment variables and other potential risk factors with low BMD were analyzed via the signed-rank test and logistic regression analysis.

RESULTS

A total of 748 pediatric patients were included. The QCT scans revealed low lumbar BMD (Z-score ≤ -2) in 86 (11.5%) patients. A history of chemotherapy or radiation therapy (β = 3.925, P < 0.001), a history of chronic corticosteroid exposure (β = 4.852, P < 0.001), older age (10.0-17.9 years vs. 1.0-9.9 years; β = 2.306, P = 0.001), and short stature (height ≤ the 3rd percentile vs. height > the 3rd percentile; β = 1.920, P = 0.047) were significantly associated with low BMD in pediatric patients.

CONCLUSION

QCT could be applied for opportunistic screening for low BMD in pediatric patients. Patients with cancer or renal disease, or those with a history of chemotherapy, radiation therapy, or chronic corticosteroid exposure, have a greater incidence of low BMD and constitute high-risk populations for BMD loss.

Integration of Spinal Musculoskeletal System Parameters for Predicting OVCF in the Elderly: A Comprehensive Predictive Model

Study DesignSystematic literature review.ObjectivesTo develop a predictive model for osteoporotic vertebral compression fractures (OVCF) in the elderly, utilizing current tools that are sensitive to bone and paraspinal muscle changes.MethodsA retrospective analysis of data from 260 patients from October 2020 to December 2022, to form the Model population. This group was split into Training and Testing sets. The Training set aided in creating a nomogram through binary logistic regression. From January 2023 to January 2024, we prospectively collected data from 106 patients to constitute the Validation population. The model's performance was evaluated using concordance index (C-index), calibration curves, and decision curve analysis (DCA) for both internal and external validation.ResultsThe study included 366 patients. The Training and Testing sets were used for nomogram construction and internal validation, while the prospectively collected data was for external validation. Binary logistic regression identified nine independent OVCF risk factors: age, bone mineral density (BMD), quantitative computed tomography (QCT), vertebral bone quality (VBQ), relative functional cross-sectional area of psoas muscles (rFCSAPS), gross and functional muscle fat infiltration of multifidus and psoas muscles (GMFIES+MF and FMFIES+MF), FMFIPS, and mean muscle ratio. The nomogram showed an area under the curve (AUC) of 0.91 for the C-index, with internal and external validation AUCs of 0.90 and 0.92. Calibration curves and DCA indicated a good model fit.ConclusionsThis study identified nine factors as independent predictors of OVCF in the elderly. A nomogram including these factors was developed, proving effective for OVCF prediction.

Nonlinear association between liver fat content and lumbar bone mineral density in overweight and obese individuals: evidence from a large-scale health screening data in China

BACKGROUND

The impact of fatty liver disease on lumbar bone mineral density (BMD) represents an intriguing area of study, particularly in light of established research linking obesity to bone metabolism. However, there remains limited investigation into the correlation between quantifying liver fat content (LFC) and lumbar BMD among overweight and obese populations, particularly within the Chinese demographic. This study aims to accurately quantify LFC and investigate its association with lumbar BMD in overweight or obese individuals.

METHODS

This cross-sectional study was conducted at the Health Management Center of Henan Provincial People's Hospital from January 2019 to February 2023, involving 6996 participants with a body mass index (BMI) of 24 kg/m² or higher. LFC and lumbar BMD were assessed using computed tomography. The study utilized one-way ANOVA, subgroup analysis, multifactor regression analysis, smooth curve fitting, and threshold and saturation effect analysis to explore the relationship between LFC and lumbar BMD. Furthermore, inflammatory cell analysis was included to investigate the potential mediating role of inflammatory cells in the association between LFC and lumbar BMD.

RESULTS

After adjusting for confounding variables, multivariate regression analysis revealed a significant negative association between LFC and lumbar BMD (β = -0.323, 95% CI: -0.464 to -0.183, P < 0.001). Particularly, participants in the highest baseline LFC quartile (Q4 group) exhibited a more pronounced negative impact on lumbar BMD compared to those in the lowest quartile (Q1 group) (β = -5.026, 95% CI: -7.040 to -3.012, P < 0.001). Threshold saturation effect analysis identified a turning point in the LFC-BMD relationship (K = 5.4). Below this point, LFC showed a positive correlation with lumbar BMD (β = 0.962, 95% CI: 0.016-1.907, P < 0.05), whereas above it, LFC was significantly negatively correlated with lumbar BMD (β = -0.405, 95% CI: -0.558 to -0.253, P < 0.001). Additionally, mediation analysis indicated that leukocytes and monocytes potentially mediated the association between LFC and lumbar BMD, with mediation ratios of -5.78 and -6.68%, respectively.

CONCLUSION

Among individuals categorized as overweight or obese, elevated levels of LFC were associated with reduced lumbar BMD, particularly noticeable above a threshold of 5.4%. Additionally, various types of inflammatory cells are presumed to exert a substantial mediating influence on the correlation between LFC and lumbar BMD.

Comparative accuracy of osteoporosis risk assessment tools in postmenopausal women: A systematic review and network meta-analysis

BACKGROUND

The Fracture Risk Assessment Tool (FRAX, threshold ≥9.3 %), Osteoporosis Risk Assessment Instrument (ORAI, ≥9), Osteoporosis Index of Risk (OSIRIS, <1), Osteoporosis Self-Assessment Tool (OST, <2), and Simple Calculated Osteoporosis Risk Estimation (SCORE, ≥6) have been endorsed by the US Preventive Services Task Force for evaluating the need for bone mineral density measurement by dual-energy X-ray absorptiometry in postmenopausal women.

OBJECTIVE

To systematically compare the sensitivity and specificity of the five osteoporosis risk assessment tools for detecting bone mineral density-defined osteoporosis.

METHODS

A systematic search was conducted across the Cochrane Library, Embase, PubMed and Web of Science databases up to January 29, 2024, to identify observational studies that evaluated comparative accuracy of these tools in postmenopausal women. The Quality Assessment of Diagnostic Accuracy Studies-2 and its comparative extension were utilized to evaluate the risk of bias and applicability. Pooled odds ratios (ORs) and 95 % confidence intervals (CIs) for relative sensitivity and specificity were calculated using a multivariate random-effects model, with tool rankings determined by Surface Under the Cumulative Ranking (SUCRA).

RESULTS

17 studies were included, involving 9669 postmenopausal women with bone mineral density-defined osteoporosis and 34,143 without the condition. The SCORE (OR = 12.11, 95 % CI [4.46-32.86]) exhibited significantly higher sensitivity than FRAX, followed by ORAI (OR = 7.01, 95 % CI [2.84-17.31]) and OST (OR = 6.90, 95 % CI [3.07-15.52]). Compared to OSIRIS, higher sensitivity was observed for SCORE (OR = 4.92, 95 % CI [2.41-10.05]), ORAI (OR = 2.85, 95 % CI [1.63-4.99]), and OST (OR = 2.80, 95 % CI [1.58-4.97]). However, specificity was lower for SCORE (OR = 0.16, 95 % CI [0.08-0.33]), ORAI (OR = 0.26, 95 % CI [0.13-0.51]), and OST (OR = 0.28, 95 % CI [0.15-0.53]) compared to FRAX. Similarly, SCORE (OR = 0.25, 95 % CI [0.15-0.41]), ORAI (OR = 0.40, 95 % CI [0.26-0.62]), and OST (OR = 0.44, 95 % CI [0.27-0.69]) showed significantly lower specificity than OSIRIS. Based on SUCRA values, SCORE (98.2 %) ranked as the most sensitive tool, followed by ORAI (64.2 %) and OST (62.6 %), whereas FRAX (96.7 %) was the most specific, followed by OSIRIS (78.3 %).

CONCLUSIONS

The risk assessment tools for identifying postmenopausal women with bone mineral density-defined osteoporosis, endorsed by the US Preventive Services Task Force, can be categorized into two groups. SCORE (≥6), ORAI (≥9), and OST (<2) offer higher sensitivity, identifying more osteoporosis patients, whereas FRAX (≥9.3 %) and OSIRIS (<1) provide higher specificity, identifying those without the condition more accurately.

REGISTRATION

PROSPERO (CRD42024507532).

BMI-stratified cutoff values for spinal sarcopenia in Chinese adults based on CT measures: a multicentre study

BACKGROUND

Decreased size and mass of paraspinal muscles are associated with lower vertebral bone mineral density, more postoperative complications, increased mortality, and spinal sagittal imbalance. However, it is difficult to determine muscle loss in older adults with overweight and obesity. This study aimed to investigate the effects of body mass index (BMI) and central obesity on paraspinal muscle aging and to determine cutoff values for low paraspinal muscle mass/quality in Chinese community populations.

METHODS

In this nationwide cross-sectional study, abdominal CT scans and basic information were collected and analyzed from 4,305 community-dwelling adults from twelve representative cities in China between 2013 and 2017. Psoas and posterior paraspinal muscle index (PMI and PSMI) and density (PMD and PSMD) at the L3 level were measured using OsiriX software. Correlation analysis, multiple linear regression, and one-way ANOVA were performed for statistical analysis. Commonly used cutoff value calculations were applied to define low muscle index and density (Mean-2SD, 5th percentile in young people, and 20th percentile in older people) in the general population and individuals with different BMIs.

RESULTS

Correlation analysis showed that the paraspinal muscle index and density were primarily correlated with sex, BMI, and age. Multiple linear regression analysis indicated that the paraspinal muscle index (PSMI and PMI) was primarily influenced by sex (β=-0.391 and - 0.599, p < 0.001) and BMI (β = 0.442 and 0.371, p < 0.001), followed by age and waist circumference. In contrast, muscle density (PSMD and PMD) was mainly associated with sex (β=-0.405 and - 0.317, p < 0.001) and age (β=-0.409 and - 0.429, p < 0.001), with a slight influence from WC and BMI. Considering the significant effect of BMI on muscle mass, we calculated BMI-stratified cutoffs for PSMI (as 12.3/10.6, 15.0/11.7, and 15.2/11.9 cm2/m2 in normal, overweight, and obese men/women using M-2SD), PMI (as 3.8/2.9, 5.0/3.4, and 4.9/3.9 cm2/m2 in normal, overweight and obese men/women using M-2SD), and unstratified cutoffs for PSMD (as 36.3 and 31.1 HU in men and women) and PMD (as 40.1 and 36.9 HU in men and women).

CONCLUSIONS

This study found that sex and BMI were key determinants of paraspinal muscle mass, with BMI influencing paraspinal muscle number more than age. In contrast, muscle density was primarily influenced by sex and age. This study provided BMI-stratified and non-stratified cutoff values for low paraspinal muscle index and density, which aided in the identification of spinal sarcopenia in individuals with different BMIs.

Laboratory-based Biomarkers for Risk Prediction, Auxiliary Diagnosis and Post-operative Follow-up of Osteoporotic Fractures

PURPOSE OF REVIEW

Osteoporosis (OP) is characterized by degraded bone microstructure, loss of bone mass and increased risk of fragility fractures. Currently, T-score determined by dual-energy X-ray absorptiometry (DEXA) measurements has been regarded as the gold standard for the diagnosis of osteoporosis. However, multiple factors have indicated that the T-score is insufficient to identify individuals with osteoporosis at a potentially high risk of fracture, or accurately detect those who require treatment, or continuously monitor the risk of re-fracture and clinical outcomes after treatment. This review covers publications in a range of ten years and comprehensively summarizes the studies in laboratory-based biomarkers for osteoporotic fractures (OF), aiming to provide physicians and surgeons with an update of clinical research in identification, verification and application of these tools, and to provide useful information for the design of future clinical studies.

RECENT FINDINGS

It was found that bone formation markers (such as PINP, BGP, ECM1 and SOST), bone resorption markers (such as β-CTX, TRAcP5b, osteocalcin, RANKL, RANKL/OPG ratio, and t-PINP/β-CTX), hormonal biomarkers (such as IGF- 1, PTH, leptin, adiponectin and AMH), indicators of inflammation and oxidative stress (SII, IL- 6, LTL, FlOP_360, FlOP_400, and GGT), microRNAs (such as miR- 21, miR- 320a- 3p, miR- 491 - 5p, miR- 485 - 3p, miR- 19b- 1- 5p, miR- 203a, miR- 31 - 5p, miR- 502 - 3p, miR- 4739, miR- 497, miR- 19b, and miR- 107), other biomarkers (SAF-AGEs and glycine), adipocytokines (irisin and Omentin- 1), senescence biomarkers (RDW), and lncRNAs (MIAT) may be useful biomarkers for clinical practice. Further validation of these biomarkers and a better understanding of the underlying molecular mechanisms may help in the development and application of these biomarkers for risk prediction of OF, differential diagnosis among OP, OF and healthy individuals, as well as post-operative monitoring of re-fracture risk and treatment outcomes.

Correlation analysis of BMD in different regions of the vertebrae determined by QCT and DXA on pedicle screw loosening

Low bone mineral density (BMD) impairs the stability of the bone-screw interface, which leads to screw loosening after spinal instrumentation. Quantitative computed tomography (QCT) and dual-energy X-ray absorptiometry (DXA) were applied to measure BMD of screw trajectories and other regions of the vertebrae in this paper, and the aim was to analyze the best effective tool and BMD of the best appropriate vertebral site to predict pedicle screw loosening after lumbar fusion surgery. 186 patients who underwent lumbar interbody fusion and pedicle screws placement were analyzed retrospectively. Patients were divided into 2 groups according to whether there was screw loosening: fissure is greater than or equal to 1 mm around the screws at follow-up CT scans. The volumetric BMD (vBMD) was measured by QCT in the central vertebral body, pedicle, and screw trajectory region, and DXA was applied for the lumbar spine and hip area BMD (aBMD). The overall pedicle screw loosening rate was 33.9% (63/186). Demographic data, health history, and the lumbar aBMD were not significantly different between the two groups. Multivariate analysis revealed showed that the hip aBMD, vBMD in the central vertebral body, pedicle, and screw trajectory regions were independent risk factors for screw loosening. Additionally, Receiver operating characteristic curve revealed the screw trajectory vBMD had the greatest area under the curve for predicting screw loosening. The screw trajectory vBMD using QCT had a stronger predictive value than the vBMD in other regions of the vertebrae and the hip aBMD, and had a more representative bone quality measurement in the bone-screw binding region.

Dietary riboflavin (vitamin B2) intake and osteoporosis in U.S. female adults: unveiling of association and exploration of potential molecular mechanisms

BACKGROUND

Osteoporosis characterized by deteriorating bone loss is becoming one of the serious health problems globally. Vitamin B2, also known as riboflavin, exhibiting multiple prominent physiological traits such as antioxidant effects, reducing lipid peroxidation and regulating glutathione redox cycle, allows it to be a potential agent to improve bone loss. However, the relationship between dietary vitamin B2 intake and osteoporosis remains unelucidated. The objective of this study was to explore the association between the dietary intake of vitamin B2 and bone loss in the U.S. female adults using the National Health and Nutrition Examination Survey (NHANES) database.

METHODS

Female participants with complete information on dietary vitamin B2 intake, dual-energy X-ray absorptiometry, and other essential covariates from NHANES database were included in the current study. Multivariable logistic regression and linear regression analyses were conducted to assess the relationships of dietary vitamin B2 intake with osteoporosis and bone mineral density (BMD) levels, respectively. Subgroup analyses, interaction tests, and restricted cubic spline (RCS) regression analyses were further used to verify the stability, robustness and potential nonlinearity of the association. Mediation analysis was performed to probe the role of serum alkaline phosphatase (ALP) in the aforementioned relationship, and the network pharmacology analysis was also conducted to determine the potential pathways and key targets for vitamin B2 regulating bone health.

RESULTS

A total of 4, 241 female participants from four NHANES cycles were included in this study. After multivariate adjustment, the intake of vitamin B2 was beneficially associated with reduced risk for femur osteoporosis (ORQ4 vs. Q1=0.613; 95%CI: 0.454-0.829). A higher intake of vitamin B2 (quartile 4) was significantly correlated with decreased risk of reduced femoral BMD levels, with the β being 0.020 (95%CI: 0.007-0.033), 0.015 (95%CI: 0.002-0.027), 0.020 (95%CI: 0.009-0.031) and 0.022 (95%CI: 0.006-0.037) for the BMD of total femur, femoral neck, trochanter, and intertrochanter, respectively (all P value < 0.05). Covariate total MET was found to modify the association between vitamin B2 intake and osteoporosis (P interaction = 0.0364), with the aforementioned relationship being more pronounced in the subgroup of insufficiently active individuals. Furthermore, RCS analysis revealed that vitamin B2 intake was positively and linearly associated with reduced risk for femoral OP and increased BMD levels of total femur, trochanter and intertrochanter, while positively and nonlinearly correlated with increased BMD level of femoral neck. Additionally, the association between vitamin B2 intake, osteoporosis and BMD levels was mediated by ALP, with a mediation proportion of 12.43%, 7.58%, 12.17%, 7.64%, and 6.99% for OP, total femur, femoral neck, trochanter, and intertrochanter BMD, respectively. Finally, network pharmacology analysis indicated that vitamin B2 regulating bone health mainly through pathways like HIF-1 signaling pathway, longevity regulating pathway, p53 signaling pathway, etc. CONCLUSIONS: Higher intake of vitamin B2 is positively associated with reduced risks for femoral osteoporosis and bone loss. Vitamin B2 may represent a modifiable lifestyle factor for the prevention and management of osteoporosis.

The effect of hypersensitive C-reactive protein to albumin ratio on the risk of fragility fracture in the Chinese male population

This study explored the association between the hypersensitive C-reactive protein to albumin ratio (CAR) and fragility fractures in Chinese males. Results show that elevated levels of CAR were associated with an increased risk of fragility fractures and that this association was robust to adjustment for multiple potential confounders.

PURPOSE

This study investigates the relationship between the hypersensitive C-reactive protein to albumin ratio (CAR) and fragility fractures in a Chinese male population.

METHODS

A total of 48,186 male participants (age range 18-98 years old, average age 53.92 years) at baseline were recruited from the Kailuan Study and followed up for outcomes until 2022. The Cox proportional hazards model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident fragility fractures. The dose response between CAR and fracture risk was analyzed using restricted cubic splines. Additionally, the concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI) were utilized to assess the incremental predictive value of various indicators for the discrimination of fragility fractures.

RESULTS

During an average follow-up of 11.17 years, 728 incident fragility fractures occurred among the 48,186 participants. Compared to participants in the second quartile of CAR, those in the highest quartile had a 49% increased risk of fragility fractures (HR = 1.49, 95% CI = 1.21-1.84) after adjusting for risk factors. Restricted cubic spline analysis showed a nonlinear relationship between CAR and the risk of fragility fractures. The C-index, continuous NRI, and IDI for predicting the risk of fragility fractures were 61.142%, 0.089 (p < 0.05), and 0.00009 (p < 0.05), respectively, which were higher than those of hs-CRP (C-index 0.6137, NRI 0.086, IDI 0.000074) and albumin (C- index 0.6116, NRI 0.068, IDI - 0.000004).

CONCLUSION

Elevated levels of CAR were associated with an increased risk of fragility fractures and that this association was robust to adjustment for multiple potential confounders.

Artificial intelligence for opportunistic osteoporosis screening with a Hounsfield Unit in chronic obstructive pulmonary disease patients

INTRODUCTION

To investigate the accuracy of an artificial intelligence (AI) prototype in determining bone mineral density (BMD) in chronic obstructive pulmonary disease (COPD) patients using chest computed tomography (CT) scans.

METHODOLOGY

This study involved 1276 health checkups and 1877 COPD patients who underwent chest CT scans from April 2020 to December 2021. Automated identification, segmentation, and Hounsfield Unit (HU) measurement of the thoracic vertebrae were performed using the musculoskeletal module of the AI-Rad Companion Chest CT (Siemens Healthineers, Er langen, Germany). Patients were divided into three groups: normal BMD, osteopenia, and osteoporosis, with quantitative CT (QCT) as the standard for analysis. The correlation between the HU and BMD values from T8 to T12 and T11-T12 vertebrae was analyzed using Linear regression analysis. The diagnostic performance of the HU values from T8 to T12 and T11-T12 vertebrae for osteoporosis was evaluated using the receiver operating characteristic curve.

RESULTS

The HU values strongly correlated with BMD values in health checkups and COPD patients (R2=0.881‒0.936 and 0.863‒0.927, P < 0.001). The Box-and-Whisker plot showed significant differences between HU and BMD values for T11-T12 vertebrae in normal BMD, osteopenia, and osteoporosis groups in two datasets (P < 0.001). The AUC was 0.970-0.982 and 0.944-0.961 in health checkups and COPD patients for detecting osteoporosis, with a sensitivity of 92.27 %‒97.42 % and 79.48 %‒90.24 % and a specificity of 86.35 %‒92.69 % and 82.81 %‒90.94 %. The optimal thresholds were 99.5‒120.5 HU and 104.5‒123.5 HU, respectively.

CONCLUSIONS

The AI software achieved high accuracy for automatic opportunistic osteoporosis screening in COPD patients, which may be a complementary method for quickly screening the population at high risk of osteoporosis.

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography

BACKGROUND

Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP).

PURPOSE

This study investigated the feasibility of using deep learning to establish a system for vBMD prediction and OP classification based on LDCT scans.

METHODS

This study included 551 subjects who underwent both LDCT and QCT examinations. First, the U-net was developed to automatically segment lumbar vertebrae from single 2D LDCT slices near the mid-vertebral level. Then, a prediction model was proposed to estimate vBMD, which was subsequently employed for detecting OP and osteopenia (OA). Specifically, two input modalities were constructed for the prediction model. The performance metrics of the models were calculated and evaluated.

RESULTS

The segmentation model exhibited a strong correlation with manual segmentation, achieving a mean Dice similarity coefficient (DSC) of 0.974, sensitivity of 0.964, positive predictive value (PPV) of 0.985, and Hausdorff distance of 3.261 in the test set. Linear regression and Bland-Altman analysis demonstrated strong agreement between the predicted vBMD from two-channel inputs and QCT-derived vBMD, with a root mean square error of 8.958 mg/mm3 and an R2 of 0.944. The areas under the curve for detecting OP and OA were 0.800 and 0.878, respectively, with an overall accuracy of 94.2%. The average processing time for this system was 1.5 s.

CONCLUSION

This prediction system could automatically estimate vBMD and detect OP and OA on LDCT scans, providing great potential for the osteoporosis screening.

Bicarbonate-Rich Mineral Water Mitigates Hypoxia-Induced Osteoporosis in Mice via Gut Microbiota and Metabolic Pathway Regulation

Background: High-altitude hypoxia is known to adversely affect bone health, leading to accelerated bone loss and metabolic alterations. Recent studies suggest that factors such as bicarbonate and gut microbiota may play key roles in bone health. Mineral water, rich in bicarbonate, may influence bone health and the gut-bone axis under such conditions. Methods: Mice were exposed to hypoxia and treated with different concentrations of drinking water. Bone-related parameters were assessed using dual-energy X-ray absorptiometry (DXA) and Micro-CT. Bone health was assessed using the measurement of serum biomarkers. Additionally, Untargeted Metabolomics was employed to analyze differential metabolites between groups, while gut microbiota composition was analyzed using 16S rRNA sequencing. Results: BMW consumption increased bone mineral density (BMD) and helped alleviate the damage to the microstructure of bones caused by hypoxia and delayed the progression of osteoporosis. Additionally, BMW was shown to enhance probiotics such as Akkermansia and Dubosiella and regulate the longevity-regulating pathway as well as the PI3K/AKT/mTOR (PAM) signaling pathway. This study also discovered changes in metabolic products due to BMW intervention, predominantly in pathways such as the amino acid, prostaglandin, and purine metabolisms, with correlation analysis further exploring the relationships between gut microbiota and these differential metabolites. Conclusions: Long-term exposure to high-altitude hypoxic conditions affects the structure of gut microbiota and bone metabolism in mice. The consumption of BMW improves the structure of gut microbiota and regulates the metabolic pathways to maintain bone health under high-altitude hypoxia.

The correlation between TyG-BMI and the risk of osteoporosis in middle-aged and elderly patients with type 2 diabetes mellitus

Background and objectives

Osteoporosis (OP) has emerged as one of the most rapidly escalating complications associated with diabetes mellitus. However, the potential risk factors contributing to OP in patients with type 2 diabetes mellitus (T2DM) remain controversial. The aim of this study was to explore the relationship between triglyceride glucose-body mass index (TyG-BMI), a marker of insulin resistance calculated as Ln [triglyceride (TG, mg/dL) × fasting plasma glucose (mg/dL)/2] × BMI, and the risk of OP in T2DM patients.

Methods

This retrospective cross-sectional study enrolled 386 inpatients with T2DM, comprising both male and postmenopausal female participants aged 40 years or older. Individuals with significant medical histories or medications known to influence bone mineral density were excluded. Machine learning algorithms were employed to rank factors affecting OP risk. Logistic regression analysis was performed to identify independent influencing factors for OP, while subgroup analysis was conducted to evaluate the impact of TyG-BMI on OP across different subgroups. Restricted cubic spline (RCS) analysis was used to explore the dose-response relationship between TyG-BMI and OP. Additionally, the receiver operating characteristic (ROC) curve was utilized to assess the predictive efficiency of TyG-BMI for OP.

Results

Machine learning analysis identified TyG-BMI as the strongest predictor for type 2 diabetic osteoporosis in middle-aged and elderly patients. After adjusting for confounding factors, multivariate logistic regression analysis revealed that age, osteocalcin, and uric acid were independent influencing factors for OP. Notably, TyG-BMI also emerged as an independent risk factor for OP (95%CI 1.031-1.054, P < 0.01). Subgroup analysis demonstrated a consistent increase in OP risk with higher TyG-BMI levels across all subgroups. RCS analysis indicated a threshold effect, with the risk of OP gradually increasing when TyG-BMI exceeded 191.52. Gender-specific analysis showed increasing the risk of OP when TyG-BMI surpassed 186.21 in males and 198.46 in females, with a more pronounced trend observed in females. ROC suggested that TyG-BMI index has significant discriminative power for type 2 diabetic osteoporosis.

Conclusion

TyG-BMI has been identified as a robust predictive biomarker for assessing OP risk in middle-aged and elderly populations with T2DM.

Intermuscular adipose tissue affected muscle density more than intramuscular adipose tissue content with opportunistic screening at abdominal CT

This study aimed to determine whether intermuscular adipose tissue (IMAT) or intramuscular adipose tissue content (IMAC) has a greater effect on skeletal muscle density (SMD) and to explore the underlying mechanisms. We recruited 292 inpatients without musculoskeletal system disease, all of whom underwent abdominal CT. Muscle parameters, including skeletal muscle area (SMA), skeletal muscle index (SMI), SMD, IMAC, and IMAT, as well as fat parameters-subcutaneous fat area (SFA) and subcutaneous fat density (SFD) in the abdominal wall-were measured by two musculoskeletal radiologists using ImageJ software at the third lumbar vertebra (L3) level. One-way ANOVA with LSD (chi-square test for group comparisons where p > 0.05) or Dunnett's T3 test (p < 0.05) was employed to compare muscle parameters between genders and across age groups. The relationship between SMD and muscle measurements was analyzed using Spearman's correlation coefficient. Multiple regression analysis identified and compared factors influencing SMD. SMD was highly correlated with IMAT and IMAC (p < 0.05), moderately correlated with gender, age, and SFA (p < 0.05). Multiple linear regression analysis indicated that IMAC, IMAT, and age significantly affected SMD (p < 0.05), with the order of influence being IMAT (β = -0.616), IMAC (β = -0.429), and age (β = -0.098). SFA and gender did not significantly affect SMD (p > 0.05). The findings revealed that age, IMAT, and IMAC influence SMD, with IMAT exerting the most significant impact.

A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification

This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation with quantitative computed tomography (QCT) results. The proposed models offer potential for screening patients at a high risk of osteoporosis and reducing unnecessary radiation and costs.

PURPOSE

To explore the feasibility of using a hybrid deep learning framework (HDLF) to establish a model for BMD prediction and classification based on BPX images. This study aimed to establish an automated tool for screening patients at a high risk of osteoporosis.

METHODS

A total of 906 BPX scans from 453 subjects were included in this study, with QCT results serving as the reference standard. The training-validation set:independent test set ratio was 4:1. The L1-L3 vertebral bodies were manually annotated by experienced radiologists, and the HDLF was established to predict BMD and diagnose abnormality based on BPX images and clinical information. The performance metrics of the models were calculated and evaluated.

RESULTS

TheR 2 values of the BMD prediction regression model in the independent test set based on BPX images and multimodal data (BPX images and clinical information) were 0.77 and 0.79, respectively. The Pearson correlation coefficients were 0.88 and 0.89, respectively, with P-values < 0.001. Bland-Altman analysis revealed no significant difference between the predictions of the models and QCT results. The classification model achieved the highest AUC of 0.97 based on multimodal data in the independent test set, with an accuracy of 0.93, sensitivity of 0.84, specificity of 0.96, and F1 score of 0.93.

CONCLUSION

This study demonstrates that deep learning neural networks applied to BPX images can accurately predict BMD and perform classification diagnoses, which can reduce the radiation risk, economic consumption, and time consumption associated with specialized BMD measurement.

Photon-counting CT Spectral Localizer Radiographs for Lumbar Areal Bone Mineral Density Quantification: A Clinical Study on Accuracy, Reliability, and Diagnostic Performance for Osteoporosis

RATIONALE AND OBJECTIVES

To explore the accuracy, reliability, and diagnostic performance of photon-counting CT (PCCT) spectral localizer radiographs (SLRs) for quantifying lumbar areal bone mineral density (BMD) and detecting osteoporosis (T-score ≤-2.5).

MATERIALS AND METHODS

This prospective study recruited consecutive participants from April to July 2024. Participants each underwent a dual-energy X-ray absorptiometry (DXA) examination serving as the gold-standard reference for aBMD (aBMDDXA) and a PCCT scan to obtain SLR. The SLRs were reconstructed into hydroxyapatite (HA) and water maps. Lumbar vertebrae (L1 to L4) and soft tissue were blindly and semiautomatically segmented on HA and water maps to calculate aBMDSLR. The agreement and relative absolute error (RAE) between aBMDSLR and aBMDDXA were calculated. Factors that might influence the RAE were evaluated. Using DXA results as the reference, the diagnostic performance of PCCT-SLRs for osteoporosis was assessed.

RESULTS

A total of 159 participants (88 females) with a median age of 66 years (interquartile range [IQR], 55-72 years) were included. The median (IQR) aBMDDXA and aBMDSLR values were 1.095 (0.936-1.261) g/cm2 and 1.086 (0.932-1.255) g/cm2, respectively. There was excellent agreement between the two methods (mean bias=-0.57%). The median (IQR) RAE was 2.65% (1.23-4.07%). The RAE was unaffected by age, body mass index, aBMD, sex, tube voltage, or tube current. The sensitivity and specificity of PCCT-SLRs for osteoporosis diagnosis were 92.31% (12/13) and 98.63% (144/146), respectively.

CONCLUSION

The PCCT-SLR is an accurate and reliable approach for lumbar aBMD quantification in humans, with high diagnostic performance for osteoporosis.

Medical multimodal multitask foundation model for lung cancer screening

Lung cancer screening (LCS) reduces mortality and involves vast multimodal data such as text, tables, and images. Fully mining such big data requires multitasking; otherwise, occult but important features may be overlooked, adversely affecting clinical management and healthcare quality. Here we propose a medical multimodal-multitask foundation model (M3FM) for three-dimensional low-dose computed tomography (CT) LCS. After curating a multimodal multitask dataset of 49 clinical data types, 163,725 chest CT series, and 17 tasks involved in LCS, we develop a scalable multimodal question-answering model architecture for synergistic multimodal multitasking. M3FM consistently outperforms the state-of-the-art models, improving lung cancer risk and cardiovascular disease mortality risk prediction by up to 20% and 10% respectively. M3FM processes multiscale high-dimensional images, handles various combinations of multimodal data, identifies informative data elements, and adapts to out-of-distribution tasks with minimal data. In this work, we show that M3FM advances various LCS tasks through large-scale multimodal and multitask learning.

Patellar Dislocation Patients Had Lower Bone Mineral Density and Hounsfield Unit Values in the Knee Joint Compared to Patients with Anterior Cruciate Ligament Ruptures: A Focus on Cortical Bone in the Tibia

Anterior cruciate ligament (ACL) rupture and patellar dislocation (PD) are common knee injuries. Dual-energy X-ray absorptiometry (DXA) and computed tomography (CT) are widely used clinical diagnostic tools. The aim was to investigate the characteristics of knee bone mineral density (BMD) in patients with ACL rupture and PD and to explore the relationship between BMD and Hounsfield unit (HU) values. This prospective cross-sectional study included 32 ACL rupture and 32 PD patients assessed via DXA and CT. BMD and CT measurements were taken from regions of interest in the femoral and tibial condyles. Statistical analyses included t-tests and mixed-effects models. The results showed that BMD in the PD group was significantly lower than in the ACL group (p < 0.05). The HU values of cortical bone in the femur and tibia differed significantly between the ACL group and the PD group (p < 0.05). The BMD of the femur and tibia showed significant correlations with the HU values of cancellous bone and cortical bone (p < 0.05). The conclusion was that PD patients had lower BMD and HU values in the femur and tibia compared to patients with ACL ruptures, particularly in the cortical bone of the tibia, and there was a strong correlation between HU value and BMD.

Comprehensive Diagnostic Value of Vertebral Bone Quality Scores and Paravertebral Muscle Quality Parameters in Osteoporotic Vertebral Fractures

OBJECTIVE

Both vertebral bone quality (VBQ) scores and paravertebral muscle quality can predict osteoporotic vertebral fractures (OVFs). This study aimed to compare the diagnostic value of opportunistic VBQ scores and sarcopenia for OVF and to determine if their combined use could enhance diagnostic efficacy.

METHODS

A total of 194 patients, matched 1:1 by age and sex, were included. VBQ scores and paravertebral muscle cross-sectional areas (CSAs) were measured from lumbar magnetic resonance imaging. Independent risk factors for OVF were determined using multivariate conditional logistic regression. The predictive value of VBQ and muscle-related parameters for OVF were assessed with receiver operating characteristic curves.

RESULTS

VBQ, CSA, and degree of fat infiltration (DFF) showed significant differences between the fracture and nonfracture groups (P < 0.001). Multivariate analysis identified lower multifidus (MF) CSA, higher MF DFF, and higher VBQ as independent risk factors for OVF. Thresholds of 3.46 for VBQ and 11.83 cm2 for MF CSA yielded area under the curve values of 0.668 and 0.736, respectively, for predicting OVF. Combining VBQ and MF CSA notably enhanced the sensitivity and specificity of OVF diagnosis.

CONCLUSIONS

The predictive value of MF CSA in anticipating OVF was marginally superior to that of VBQ and MF DFF. Furthermore, the concurrent utilization of VBQ and MF CSA substantially enhanced the diagnostic accuracy of OVF. Considering that both VBQ and MF CSA can be opportunistically obtained during routine examinations, individuals with a VBQ ≥3.46 and MF CSA ≤11.83 cm2 should be categorized as high risk for OVF, warranting timely preventive measures.

Application of deep learning model based on unenhanced chest CT for opportunistic screening of osteoporosis: a multicenter retrospective cohort study

INTRODUCTION

A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.

MATERIALS AND METHODS

Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals. These participants were divided into a training set (n = 581), an external test set 1 (n = 229), an external test set 2 (n = 198) and an external test set 3 (n = 118). Five CNN models were constructed based on chest CT images to screen patients with osteoporosis and compared with the SMI model to predict the performance of osteoporosis patients.

RESULTS

All CNN models have good performance in predicting osteoporosis patients. The average F1 score of Densenet121 in the three external test sets was 0.865. The area under the curve (AUC) of Desenet121 in external test set 1, external test set 2, and external test set 3 were 0.827, 0.859, and 0.865, respectively. Furthermore, the Densenet121 model demonstrated a notably superior performance compared to the SMI model in predicting osteoporosis patients.

CONCLUSIONS

The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures.

CRITICAL RELEVANCE STATEMENT

The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures.

KEY POINTS

The application of unenhanced chest CT is increasing. Most people do not consciously use DXA to screen themselves for osteoporosis. A deep learning model was constructed based on CT images from four institutions.

Bone density measurement in patients with spinal metastatic tumors using chest quantitative CT deep learning model

Objective

This study aims to develop a deep learning model using the 3DResUNet architecture to predict vertebral volumetric bone mineral density (vBMD) from Quantitative Computed Tomography (QCT) scans in patients with spinal metastatic tumors, enhancing osteoporosis screening capabilities.

Methods

749 patients with spinal metastatic tumors underwent QCT vertebral vBMD measurements. The dataset was randomly split into training (599 cases) and test sets (150 cases). The 3DResUNet model was trained for vBMD classification and prediction using QCT images processed with automated bone segmentation and ROI extraction.

Results

The deep learning model demonstrated strong performance with Spearman correlation coefficients of 0.923 (training set) and 0.918 (test set) between predicted and QCT-measured vBMD values. Bland-Altman analysis showed a slight bias of -1.42 mg/cm3 (training set) and -1.14 mg/cm3 (test set) between the model predictions and QCT measurements. The model achieved an area under the curve (AUC) of 0.977 (training set) and 0.966 (test set) for diagnosing Osteoporosis based on vBMD.

Conclusion

The developed deep learning model using 3DResUNet effectively predicts vertebral vBMD from QCT scans in patients with spinal metastatic tumors. It provides accurate and automated vBMD measurements, potentially facilitating widespread osteoporosis screening in clinical practice, mainly where DXA availability is limited.

Prediction of Bone Mineral Density based on Computer Tomography Images Using Deep Learning Model

INTRODUCTION

The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of older populations. However, the diagnosis of osteoporosis and people's understanding of it are seriously insufficient. We aim to develop a deep learning model to automatically measure bone mineral density (BMD) and improve the diagnostic rate of osteoporosis.

METHODS

The images of 801 subjects with 2,080 vertebral bodies who underwent chest or abdominal paired computer tomography (CT) and quantitative computer tomography (QCT) scanning was retrieved from June 2020 to January 2022. The BMD of T11-L4 vertebral bodies was measured by QCT. Developing a multistage deep learning-based model to simulate the segmentation of the vertebral body and predict BMD. The subjects were randomly divided into training dataset, validation dataset and test dataset. Analyze the fitting effect between the BMD measured by the model and the standard BMD by QCT. Accuracy, precision, recall and f1-score were used to analyze the diagnostic performance according to categorization criterion measured by QCT.

RESULTS

410 males (51.2%) and 391 females (48.8%) were included in this study. Among them, there were 154 (19.2%) males and 118 (14.7%) females aged 23-44; 182 (22.7%) males and 205 (25.6%) females aged 45-64; 74 (9.2%) males and 68 (8.5%) females aged 65-84. The number of vertebral bodies in the training dataset, the validation dataset, and the test dataset was 1433, 243, 404, respectively. In each dataset, the BMD of males and females decreases with age. There was a significant correlation between the BMD measured by the model and QCT, with the coefficient of determination (R2) 0.95-0.97. The diagnostic accuracy based on the model in the three datasets was 0.88, 0.91, and 0.91, respectively.

CONCLUSION

The proposed multistage deep learning-based model can achieve automatic measurement of vertebral BMD and performed well in the prediction of osteoporosis.

Development and Validation of Objective and Subjective Osteoporosis Knowledge Instruments Among Chinese Orthopaedic Surgeons

BACKGROUND

Clinicians must be knowledgeable about osteoporosis so that they can convey information regarding the prevention of fragility fractures to their patients. The purposes of this study were to develop objective and subjective knowledge instruments for osteoporosis and fragility fractures and then test their reliability and validity among Chinese orthopaedic surgeons.

METHODS

A 2-round procedure was used to develop the objective and subjective knowledge instruments. A cross-sectional online survey was distributed to 293 orthopaedic surgeons; 189 surgeons returned the questionnaires. We examined internal consistency, test-retest reliability, criterion validity, and discriminant validity; we also compared the subjective knowledge level with the objective knowledge level among surgeons.

RESULTS

Our results showed that the Subjective Knowledge Scale (SKS) regarding Osteoporosis and Fragility Fractures had a high Cronbach alpha coefficient (0.915), and the objective Osteoporosis Knowledge Test for Clinicians (OKTC) had an adequate Kuder-Richardson 20 coefficient (0.64). Item analyses were conducted, and a short version of the OKTC (the OKTC-SF) was developed. The SKS, the OKTC, and the OKTC-SF all showed good test-retest reliability, criterion validity, and discriminant validity. The percentage of surgeons with a high subjective knowledge level was higher than the percentage of surgeons who selected the correct answer for several corresponding questions related to objective knowledge.

CONCLUSIONS

The SKS, the OKTC, and the OKTC-SF all demonstrated good reliability and validity. However, the orthopaedic surgeons may have overestimated their knowledge level regarding osteoporosis. Targeted continuing medical education that is based on individual knowledge level is needed to improve the undertreatment of osteoporosis among patients with fragility fractures.

Using QCT for the prediction of spontaneous age- and gender-specific thoracolumbar vertebral fractures and accompanying distant vertebral fractures

PURPOSE

To investigate the value and age- and gender-specific threshold values of bone mineral density (BMD) by quantitative computed tomography (QCT) for the prediction of spontaneous thoracolumbar vertebral fractures and thoracolumbar junction fractures accompanying distant vertebral fractures.

METHODS

Among the 556 patients included, 68 patients had thoracolumbar vertebral fractures (12 patients with distant vertebral fractures, 56 patients without distant vertebral fractures) and 488 patients had no vertebral fractures. All patients were grouped by gender and age. According to the principle of Youden index, the threshold values were calculated from receiver operating characteristic (ROC) curves.

RESULTS

The threshold values for predicting thoracolumbar vertebral fractures were 89.8 mg/cm3 for all subjects, 90.1 mg/cm3 for men, and 88.6 mg/cm3 for women. The threshold values for men aged < 60 years old and ≥ 60 years old were 117.4 mg/cm3 and 87.5 mg/cm3, respectively. The threshold values for women aged < 60 years old and ≥ 60 years old were 88.6 and 68.4 mg/cm3, respectively. The threshold value for predicting spontaneous thoracolumbar junction fractures with distant vertebral fractures was 62.7 mg/cm3.

CONCLUSIONS

QCT provides a good ability to predict age- and gender-specific spontaneous thoracolumbar vertebral fractures, and to further predict spontaneous thoracolumbar junction fractures with distant vertebral fractures.

Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model

A multi-feature fusion DCNN model for automated evaluation of lumbar vertebrae L1 on chest combined with clinical information and radiomics permits estimation of volumetric bone mineral density for evaluation of osteoporosis.

PURPOSE

To develop a multi-feature deep learning model based on chest CT, combined with clinical information and radiomics to explore the feasibility in screening for osteoporosis based on estimation of volumetric bone mineral density.

METHODS

The chest CT images of 1048 health check subjects were retrospectively collected as the master dataset, and the images of 637 subjects obtained from a different CT scanner were used for the external validation cohort. The subjects were divided into three categories according to the quantitative CT (QCT) examination, namely, normal group, osteopenia group, and osteoporosis group. Firstly, a deep learning-based segmentation model was constructed. Then, classification models were established and selected, and then, an optimal model to build bone density value prediction regression model was chosen.

RESULTS

The DSC value was 0.951 ± 0.030 in the testing dataset and 0.947 ± 0.060 in the external validation cohort. The multi-feature fusion model based on the lumbar 1 vertebra had the best performance in the diagnosis. The area under the curve (AUC) of diagnosing normal, osteopenia, and osteoporosis was 0.992, 0.973, and 0.989. The mean absolute errors (MAEs) of the bone density prediction regression model in the test set and external testing dataset are 8.20 mg/cm3 and 9.23 mg/cm3, respectively, and the root mean square errors (RMSEs) are 10.25 mg/cm3 and 11.91 mg/cm3, respectively. The R-squared values are 0.942 and 0.923, respectively. The Pearson correlation coefficients are 0.972 and 0.965.

CONCLUSION

The multi-feature fusion DCNN model based on only the lumbar 1 vertebrae and clinical variables can perform bone density three-classification diagnosis and estimate volumetric bone mineral density. If confirmed in independent populations, this automated opportunistic chest CT evaluation can help clinical screening of large-sample populations to identify subjects at high risk of osteoporotic fracture.

3D U-Net Neural Network Architecture-Assisted LDCT to Acquire Vertebral Morphology Parameters: A Vertebral Morphology Comprehensive Analysis in a Chinese Population

To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.

Comparison of Predictive Performance for Pedicle Screw Loosening Between Computed Tomography-Based Hounsfield Units and Magnetic Resonance Imaging-Based Vertebral Bone Quality Score After Lumbar Surgery

OBJECTIVE

To compare predictive performance for pedicle screw loosening between computed tomography (CT)-based Hounsfield units (HU) and magnetic resonance imaging (MRI)-based vertebral bone quality score (VBQ) after lumbar surgery.

METHODS

A retrospective study was conducted on patients who received transforaminal lumbar interbody fusion continuously at our institution from May 2018 to September 2020. On the basis of 12 months' follow-up lumbar radiographs, screw loosening was defined as a clear zone of minimal thickness of ≥1 mm around the pedicle screw on radiography. VBQ score and HU value were measured using preoperative MRI and CT, respectively. Then, we evaluated the predictive performance of these 2 parameters by comparing the receiver operating characteristic curve.

RESULTS

In all patients, area under the curve (AUC) of the VBQ score (AUC = 0.752; 95% confidence interval [CI] 0.663-0.841; P < 0.001) was larger than those of the CT HU value (AUC = 0.652; 95% CI 0.558-0.746; P = 0.005), but there was no significant difference between them (PAUC = 0.076). In patients with lumbar spinal stenosis, AUC of VBQ score (AUC = 0.863; 95% CI 0.764-0.961; P < 0.001) was larger than those of the CT HU value (AUC = 0.673; 95% CI 0.513-0.833; P = 0.043), with significant difference (PAUC = 0.003).

CONCLUSIONS

MRI-based VBQ score and CT-based HU value have similar performance in predicting pedicle screw loosening after lumbar surgery. Furthermore, in patients with lumbar spinal stenosis, VBQ score demonstrated better predictive ability than HU value.

Unraveling the Omega-3 Puzzle: Navigating Challenges and Innovations for Bone Health and Healthy Aging

Omega-3 polyunsaturated fatty acids (ω-3 PUFAs, n-3 PUFAs), including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and alpha-linolenic acid (ALA), are essential polyunsaturated fats primarily obtained from fatty fish and plant-based sources. Compelling evidence from preclinical and epidemiological studies consistently suggests beneficial effects of ω-3 PUFAs on bone health and healthy aging processes. However, clinical trials have yielded mixed results, with some failing to replicate these benefits seen in preclinical models. This contraindication is mainly due to challenges such as low bioavailability, potential adverse effects with higher doses, and susceptibility to oxidation of ω-3 fatty acids, hindering their clinical effectiveness. This review comprehensively discusses recent findings from a clinical perspective, along with preclinical and epidemiological studies, emphasizing the role of ω-3 PUFAs in promoting bone health and supporting healthy aging. Additionally, it explores strategies to improve ω-3 PUFA efficacy, including nanoparticle encapsulation and incorporation of specialized pro-resolving mediators (SPM) derived from DHA and EPA, to mitigate oxidation and enhance solubility, thereby improving therapeutic potential. By consolidating evidence from various studies, this review underscores current insights and future directions in leveraging ω-3 PUFAs for therapeutic applications.

AI-based opportunistic quantitative image analysis of lung cancer screening CTs to reduce disparities in osteoporosis screening

Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115-165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. CLINICALTRIALS.GOV IDENTIFIER: N/A.

The paradox of bone mineral density and fracture risk in type 2 diabetes

Fracture risk in type 2 diabetes (T2D) patients is paradoxically increased despite no decrease in areal bone mineral density (BMD). This phenomenon, known as the "diabetic bone paradox", has been attributed to various factors including alterations in bone microarchitecture and composition, hyperinsulinemia and hyperglycemia, advanced glycation end products (AGEs), and comorbidities associated with T2D. Zhao et al. recently investigated the relationship between T2D and fracture risk using both genetic and phenotypic datasets. Their findings suggest that genetically predicted T2D is associated with higher BMD and lower fracture risk, indicating that the bone paradox is not observed when confounding factors are controlled using Mendelian randomization (MR) analysis. However, in prospective phenotypic analysis, T2D remained associated with higher BMD and higher fracture risk, even after adjusting for confounding factors. Stratified analysis revealed that the bone paradox may disappear when T2D-related risk factors are eliminated. The study also highlighted the role of obesity in the relationship between T2D and fracture risk, with BMI mediating a significant portion of the protective effect. Overall, managing T2D-related risk factors may be crucial in preventing fracture risk in T2D patients.

Cost-Effectiveness of Artificial Intelligence-Based Opportunistic Compression Fracture Screening of Existing Radiographs

PURPOSE

Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software.

METHODS

A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices.

RESULTS

In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened.

CONCLUSIONS

Artificial intelligence-based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective.

Impact of altitude on the development of low bone mineral density and osteoporosis in individuals aged 50 years and older: protocol for a multicentre prospective cohort study

INTRODUCTION

Osteoporotic fractures are a leading cause of disability and contribute significantly to medical care costs worldwide. Variations in bone mineral density and the risk of osteoporosis are notably influenced by altitude. This study aims to longitudinally examine individuals with osteoporosis and low bone mass at three different altitudes (low, high and very high) to understand the effects of high-altitude environments on bone density.

METHODS AND ANALYSIS

This multicentre, prospective cohort study will involve 893 participants divided into three groups based on altitude: low (500-1500 m), high (2500-4500 m) and very high (4500-5500 m). Participants will undergo comprehensive diagnostic assessments, including demographic data collection, structured questionnaires, medical examinations and clinical laboratory tests. Follow-up visits will occur annually for a minimum of 5 years. The primary outcome will be changes in bone mineral density values. Secondary outcomes will include the incidence of osteoporosis and osteoporotic fractures. Cox proportional hazard models will be used to calculate the risk associated with osteoporotic events and related fractures.

ETHICS AND DISSEMINATION

The study has been approved by the Institutional Review Board of the Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region (No: 2024-70). The acquired insights will be disseminated via academic forums, scholarly articles and stakeholder engagement sessions.

TRIAL REGISTRATIONNUMBER

ChiCTR2300078872.

Epimedin A inhibits the PI3K/AKT/NF-κB signalling axis and osteoclast differentiation by negatively regulating TRAF6 expression

BACKGROUND

Epimedin A (EA) has been shown to suppress extensive osteoclastogenesis and bone resorption, but the effects of EA remain incompletely understood. The aim of our study was to investigate the effects of EA on osteoclastogenesis and bone resorption to explore the corresponding signalling pathways.

METHODS

Rats were randomly assigned to the sham operation or ovariectomy group, and alendronate was used for the positive control group. The therapeutic effect of EA on osteoporosis was systematically analysed by measuring bone mineral density and bone biomechanical properties. In vitro, RAW264.7 cells were treated with receptor activator of nuclear factor kappa-B ligand (RANKL) and macrophage colony-stimulating factor (M-CSF) to induce osteoclast differentiation. Cell viability assays, tartrate-resistant acid phosphatase (TRAP) staining, and immunofluorescence were used to elucidate the effects of EA on osteoclastogenesis. In addition, the expression of bone differentiation-related proteins or genes was evaluated using Western blot analysis or quantitative polymerase chain reaction (PCR), respectively.

RESULTS

After 3 months of oral EA intervention, ovariectomized rats exhibited increased bone density, relative bone volume, trabecular thickness, and trabecular number, as well as reduced trabecular separation. EA dose-dependently normalized bone density and trabecular microarchitecture in the ovariectomized rats. Additionally, EA inhibited the expression of TRAP and NFATc1 in the ovariectomized rats. Moreover, the in vitro results indicated that EA inhibits osteoclast differentiation by suppressing the TRAF6/PI3K/AKT/NF-κB pathway. Further studies revealed that the effect on osteoclast differentiation, which was originally inhibited by EA, was reversed when the TRAF6 gene was overexpressed.

CONCLUSIONS

The findings indicated that EA can negatively regulate osteoclastogenesis by inhibiting the TRAF6/PI3K/AKT/NF-κB axis and that ameliorating ovariectomy-induced osteoporosis in rats with EA may be a promising potential therapeutic strategy for the treatment of osteoporosis.

Automated Opportunistic Osteoporosis Screening Using Low-Dose Chest CT among Individuals Undergoing Lung Cancer Screening in a Korean Population

Opportunistic osteoporosis screening using deep learning (DL) analysis of low-dose chest CT (LDCT) scans is a potentially promising approach for the early diagnosis of this condition. We explored bone mineral density (BMD) profiles across all adult ages and prevalence of osteoporosis using LDCT with DL in a Korean population. This retrospective study included 1915 participants from two hospitals who underwent LDCT during general health checkups between 2018 and 2021. Trabecular volumetric BMD of L1-2 was automatically calculated using DL and categorized according to the American College of Radiology quantitative computed tomography diagnostic criteria. BMD decreased with age in both men and women. Women had a higher peak BMD in their twenties, but lower BMD than men after 50. Among adults aged 50 and older, the prevalence of osteoporosis and osteopenia was 26.3% and 42.0%, respectively. Osteoporosis prevalence was 18.0% in men and 34.9% in women, increasing with age. Compared to previous data obtained using dual-energy X-ray absorptiometry, the prevalence of osteoporosis, particularly in men, was more than double. The automated opportunistic BMD measurements using LDCT can effectively predict osteoporosis for opportunistic screening and identify high-risk patients. Patients undergoing lung cancer screening may especially profit from this procedure requiring no additional imaging or radiation exposure.

Assessing Thoracic Vertebral Bone Mineral Density (T8-T10) for Osteoporosis Diagnosis During CT Lung Cancer Screening in Older Adults

Introduction

Osteoporosis diagnosis often utilizes quantitative computed tomography (QCT). This study explored the validity of applying lumbar bone mineral density (LBMD) standards to thoracic vertebrae (T8-T10) for osteoporosis detection during CT lung cancer screenings. This study investigated the utility of thoracic BMD (BMD-T8-T10) for detecting osteoporosis in older persons during CT lung cancer screening.

Methods

We studied 701 participants who underwent QCT scans for both LBMD and BMD-T8-T10. Osteoporosis was diagnosed using ACR criteria based on LBMD. We determined BMD-T8-T10 thresholds via a receiver operating characteristic (ROC) curve and translated BMD-T8+T9+T10 to LBMD (TTBMD) using linear regression. Kappa test was used to evaluate the accuracy of BMD-T8-T10 thresholds and TTBMD in diagnosing osteoporosis.

Results

Raw BMD-T8-T10 poorly identified osteoporosis (kappa = 0.51). ROC curve analysis identified BMD-T8-T10 thresholds for osteopenia (138 mg/cm3) and osteoporosis (97 mg/cm3) with areas under the curve of 0.97 and 0.99, respectively. We normalized BMD-T8-T10 to TTBMD based on the formula: TTBMD = 0.9 × BMD-T8-T10 - 2.56. These thresholds (kappa = 0.74) and TTBMD performed well in detecting osteoporosis/osteopenia (kappa = 0.74).

Conclusion

Both calculating BMD-T8-T10 threshold (138.0 mg/cm3 for osteopenia and 97 mg/cm3 for osteoporosis) and normalizing BMD-T8-T10 to LBMD demonstrated good performance in identifying osteoporosis in older adults during CT lung cancer screening.

Effect of fully automatic classification model from different tube voltage images on bone density screening: A self-controlled study

PURPOSE

To develop two bone status prediction models combining deep learning and radiomics based on standard-dose chest computed tomography (SDCT) and low-dose chest computed tomography (LDCT), and to evaluate the effect of tube voltage on reproducibility of radiomics features and predictive efficacy of these models.

METHODS

A total of 1508 patients were enrolled in this retrospective study. LDCT was conducted using 80 kVp, tube current ranging from 100 to 475 mA. On the other hand, SDCT was performed using 120 kVp, tube current ranging from 100 to 520 mA. We developed an automatic thoracic vertebral cancellous bone (TVCB) segmentation model. Subsequently, 1184 features were extracted and two classifiers were developed based on LDCT and SDCT images. Based on the diagnostic results of quantitative computed tomography examination, the first-level classifier was initially developed to distinguish normal or abnormal BMD (including osteoporosis and osteopenia), while the second-level classifier was employed to identify osteoporosis or osteopenia. The Dice coefficient was used to evaluate the performance of the automated segmentation model. The Concordance Correlation Coefficients (CCC) of radiomics features were calculated between LDCT and SDCT, and the performance of these models was evaluated.

RESULTS

Our automated segmentation model achieved a Dice coefficient of 0.98 ± 0.01 and 0.97 ± 0.02 in LDCT and SDCT, respectively. Alterations in tube voltage decreased the reproducibility of the extracted radiomic features, with 85.05 % of the radiomic features exhibiting low reproducibility (CCC < 0.75). The area under the curve (AUC) using LDCT-based and SDCT-based models was 0.97 ± 0.01 and 0.94 ± 0.02, respectively. Nonetheless, cross-validation with independent test sets of different tube voltage scans suggests that variations in tube voltage can impair the diagnostic efficacy of the model. Consequently, radiomics models are not universally applicable to images of varying tube voltages. In clinical settings, ensuring consistency between the tube voltage of the image used for model development and that of the acquired patient image is critical.

CONCLUSIONS

Automatic bone status prediction models, utilizing either LDCT or SDCT images, enable accurate assessment of bone status. Tube voltage impacts reproducibility of features and predictive efficacy of models. It is necessary to account for tube voltage variation during the image acquisition.

Reference Values for Paravertebral Muscle Size and Myosteatosis in Chinese Adults, a Nationwide Multicenter Study

RATIONALE AND OBJECTIVES

The paravertebral muscles, characterized by their susceptibility to severe size loss and fat infiltration in old age, lack established reference values for age-related variations in muscle parameters. This study aims to fill this gap by establishing reference values for paravertebral muscles in a Chinese adult population.

MATERIALS AND METHODS

This cross-sectional study utilized the baseline data from the prospective cohort China Action on Spine and Hip (CASH). A total of 4305 community-dwelling participants aged 21-80 years in China were recruited between 2013 and 2017. Pregnant women, individuals with metal implants, limited mobility or diseases/conditions (spinal tumor, infection, etc.) affecting lumbar vertebra were excluded from the study. Psoas and paraspinal muscles were measured in quantitative computed tomography (QCT) images at the L3 and L5 levels using Osirix software. Age-related reference values for muscle area, density, and fat fraction were constructed as percentile charts using the lambda-mu-sigma (LMS) method.

RESULTS

The paravertebral muscles exhibited an age-related decline in muscle area and density, coupled with an increase in muscle fat fraction. Between the ages of 25 and 75, the reductions in psoas and paraspinal muscle cross-sectional area at the L3 level were - 0.47%/yr and - 0.53%/yr in men, and - 0.19%/yr and - 0.23%/yr in women, respectively. Notably, accelerated muscle loss was observed during menopause and postmenopause in women (45-75 years) and intermittently during middle and old age in men (35-55 and 60-75 years). Besides, the age-related decreases in PSMA, PMA, and PSMD and the increases in PSMFF were more pronounced in L5 than in L3 CONCLUSION: This study shows distinct patterns of accelerated muscle loss were identified in menopausal and postmenopausal women and in middle-aged and old men. The findings contribute valuable information for future investigations on paravertebral muscle loss and myosteatosis.

Activation of the osteoblastic HIF-1α pathway partially alleviates the symptoms of STZ-induced type 1 diabetes mellitus via RegIIIγ

The hypoxia-inducible factor-1α (HIF-1α) pathway coordinates skeletal bone homeostasis and endocrine functions. Activation of the HIF-1α pathway increases glucose uptake by osteoblasts, which reduces blood glucose levels. However, it is unclear whether activating the HIF-1α pathway in osteoblasts can help normalize glucose metabolism under diabetic conditions through its endocrine function. In addition to increasing bone mass and reducing blood glucose levels, activating the HIF-1α pathway by specifically knocking out Von Hippel‒Lindau (Vhl) in osteoblasts partially alleviated the symptoms of streptozotocin (STZ)-induced type 1 diabetes mellitus (T1DM), including increased glucose clearance in the diabetic state, protection of pancreatic β cell from STZ-induced apoptosis, promotion of pancreatic β cell proliferation, and stimulation of insulin secretion. Further screening of bone-derived factors revealed that islet regeneration-derived protein III gamma (RegIIIγ) is an osteoblast-derived hypoxia-sensing factor critical for protection against STZ-induced T1DM. In addition, we found that iminodiacetic acid deferoxamine (SF-DFO), a compound that mimics hypoxia and targets bone tissue, can alleviate symptoms of STZ-induced T1DM by activating the HIF-1α-RegIIIγ pathway in the skeleton. These data suggest that the osteoblastic HIF-1α-RegIIIγ pathway is a potential target for treating T1DM.

The quality of bone and paraspinal muscle in the fragility osteoporotic vertebral compression fracture: a comprehensive comparison between different indicators

PURPOSE

To evaluate the value of five indicators in predicting OVCF through a retrospective case-control study, and explore the internal correlation of different indicators.

METHOD

We retrospectively enrolled patients over 50 years of age who had been subjected to surgery for fragility OVCF at China Japan Friendship Hospital from January 2021 to September 2023. Demographic characteristics, T-score based on dual-energy X-ray absorptiometry (DXA), CT-based Hounsfield unit (HU) value, vertebral bone quality (VBQ) score based on magnetic resonance imaging (MRI), relative cross-sectional area (rCSA) and the rate of fat infiltration (FI) of paraspinal muscle were collected. A 1:1 age- and sex-matched, fracture-free control group was established from patients admitted to our hospital for lumbar spinal stenosis or lumbar disk herniation.

RESULTS

A total of 78 patients with lumbar fragility OVCF were included. All the five indicators were significantly correlated with the occurrence of OVCFs. Logistic regression analysis showed that average HU value and VBQ score were significantly correlated with OVCF. The area under the curve (AUC) of VBQ score was the largest (0.89). There was a significantly positive correlation between average T-score, average HU value and average total rCSA. VBQ score was significantly positive correlated with FI.

CONCLUSION

VBQ score and HU value has good value in predicting of fragility OVCF. In addition to bone mineral density, we should pay more attention to bone quality, including the fatty signal intensity in bone and the FI in paraspinal muscle.

Prevalence rate of primary osteoporosis in China: a meta-analysis

BACKGROUND

Primary osteoporosis (POP) is recognized as a "silent disease" and often ignored. This meta-analysis aimed to determine the prevalence of POP in the Chinese population over the past 20 years to raise awareness of the disease's epidemiology, which is hoped to help prevent and treat the condition better.

METHODS

Eight English and three Chinese language databases were searched systematically from January 2002 to December 2023. Relevant data were analysed using Stata 16.0. Meta-regression and subgroup analyses were performed to explore causes of heterogeneity. A funnel plot was further drawn in combination with Egger and Begg tests to determine publication bias.

RESULTS

A total of 45 studies (241,813 participants) were included. The meta-analysis revealed that the overall prevalence of POP in the Chinese population was 18.2% (95% CI: 14.7-21.7%), showing a positive correlation with age. Specifically, prevalence rates were 23.4% (18.3-28.5%) in women and 11.5% (9.1-13.9%) in men. A notable increase was observed within the span of 20 years (16.9% before 2010 and 20.3% in 2011-2020). Notably, regional variations were observed, with southern China reporting a lower prevalence of 16.4% compared to 20.2% in northern China. Meta-regression suggested that sample size significantly influenced the estimation of point prevalence (P = 0.037).

CONCLUSIONS

Over the past two decades, there has been an increase in the prevalence of POP within the Chinese population. The growing prevalence of older individuals and women further highlights the urgency for tailored disease prevention and control measures.

Trabecular bone mineral density as measured by thoracic vertebrae predicts incident hip and vertebral fractures: the multi-ethnic study of atherosclerosis

We evaluated the relationship of bone mineral density (BMD) by computed tomography (CT), to predict fractures in a multi-ethnic population. We demonstrated that vertebral and hip fractures were more likely in those patients with low BMD. This is one of the first studies to demonstrate that CT BMD derived from thoracic vertebrae can predict future hip and vertebral fractures.

PURPOSE/INTRODUCTION

Osteoporosis affects an enormous number of patients, of all races and both sexes, and its prevalence increases as the population ages. Few studies have evaluated the association between the vertebral trabecular bone mineral density(vBMD) and osteoporosis-related hip fracture in a multiethnic population, and no studies have demonstrated the predictive value of vBMD for fractures.

METHOD

We sought to determine the predictive value of QCT-based trabecular vBMD of thoracic vertebrae derived from coronary artery calcium scan for hip fractures in the Multi-Ethnic Study of Atherosclerosis(MESA), a nationwide multicenter cohort included 6814 people from six medical centers across the USA and assess if low bone density by QCT can predict future fractures. Measures were done using trabecular bone measures, adjusted for individual patients, from three consecutive thoracic vertebrae (BDI Inc, Manhattan Beach CA, USA) from non-contrast cardiac CT scans.

RESULTS

Six thousand eight hundred fourteen MESA baseline participants were included with a mean age of 62.2 ± 10.2 years, and 52.8% were women. The mean thoracic BMD is 162.6 ± 46.8 mg/cm3 (95% CI 161.5, 163.7), and 27.6% of participants (n = 1883) had osteoporosis (T-score 2.5 or lower). Over a median follow-up of 17.4 years, Caucasians have a higher rate of vertebral fractures (6.9%), followed by Blacks (4.4%), Hispanics (3.7%), and Chinese (3.0%). Hip fracture patients had a lower baseline vBMD as measured by QCT than the non-hip fracture group by 13.6 mg/cm3 [P < 0.001]. The same pattern was seen in the vertebral fracture population, where the mean BMD was substantially lower 18.3 mg/cm3 [P < 0.001] than in the non-vertebral fracture population. Notably, the above substantial relationship was unaffected by age, gender, race, BMI, hypertension, current smoking, medication use, or activity. Patients with low trabecular BMD of thoracic vertebrae showed a 1.57-fold greater risk of first hip fracture (HR 1.57, 95% CI 1.38-1.95) and a nearly threefold increased risk of first vertebral fracture (HR 2.93, 95% CI 1.87-4.59) compared to normal BMD patients.

CONCLUSION

There is significant correlation between thoracic trabecular BMD and the incidence of future hip and vertebral fracture. This study demonstrates that thoracic vertebrae BMD, as measured on cardiac CT (QCT), can predict both hip and vertebral fractures without additional radiation, scanning, or patient burden. Osteopenia and osteoporosis are markedly underdiagnosed. Finding occult disease affords the opportunity to treat the millions of people undergoing CT scans every year for other indications.

Menopause modified the association of blood pressure with osteoporosis among gender: a large-scale cross-sectional study

Purpose

This study aimed to assess the potential association between blood pressure and osteoporosis in a rural population with limited resources. Existing evidence on this association is limited, particularly in such settings.

Methods

Data from 7,689 participants in the Henan Rural Cohort study were analyzed. Four blood pressure indicators [systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and pulse pressure (PP)] were measured. The logistic regression model and restricted cubic spline plots were used to assess the relationship between blood pressure indicators and osteoporosis prevalence.

Results

Positive trends were noted between blood pressure indicators and osteoporosis prevalence in the entire group and women (P trend < 0.05 for SBP, MAP, and PP). Women with higher SBP and PP exhibited elevated odds of osteoporosis compared with those with the lowest SBP and PP (ORs ranging from 1.15 to 1.5 for SBP and 1.06 to 1.83 for PP). No such associations were found in men. These relationships were only evident in postmenopausal women. Dose-response analysis confirmed these findings. Excluding participants taking hypertension medication did not alter the results.

Conclusion

In resource-limited settings, higher SBP and PP are associated with the increased prevalence of osteoporosis in women, potentially influenced by menopause-related factors. This indicates that potential gender-based differences and social inequalities may affect bone health.

Clinical trial registration

The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699) http://www.chictr.org.cn/showproj.aspx?proj=11375.

Visual scoring of osteoporosis on low-dose CT in lung cancer screening population

OBJECTIVES

The risk factors for lung cancer screening eligibility, age as well as smoking history, are also present for osteoporosis. This study aims to develop a visual scoring system to identify osteoporosis that can be applied to low-dose CT scans obtained for lung cancer screening.

MATERIALS AND METHODS

We retrospectively reviewed 1000 prospectively enrolled participants in the lung cancer screening program at the Mount Sinai Hospital. Optimal window width and level settings for the visual assessment were chosen based on a previously described approach. Visual scoring of osteoporosis and automated measurement using dedicated software were compared. Inter-reader agreement was conducted using six readers with different levels of experience who independently visually assessed 30 CT scans.

RESULTS

Based on previously validated formulas for choosing window and level settings, we chose osteoporosis settings of Width = 230 and Level = 80. Of the 1000 participants, automated measurement was successfully performed on 774 (77.4 %). Among these, 138 (17.8 %) had osteoporosis. There was a significant correlation between the automated measurement and the visual score categories for osteoporosis (Kendall's Tau = -0.64, p < 0.0001; Spearman's rho = -0.77, p < 0.0001). We also found substantial to excellent inter-reader agreement on the osteoporosis classification among the 6 radiologists (Fleiss κ = 0.91).

CONCLUSIONS

Our study shows that a simple approach of applying specific window width and level settings to already reconstructed sagittal images obtained in the context of low-dose CT screening for lung cancer is highly feasible and useful in identifying osteoporosis.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images

Background

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs).

Methods

In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated.

Results

In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively.

Conclusions

The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.