Osteoporosis International最新文献

This study evaluated trabecular bone score (TBS) for assessing vertebral fractures and spinal deformity in children and adolescents with osteogenesis imperfecta (OI). TBS showed superior performance in identifying vertebral fractures compared to areal bone mineral density (aBMD), especially in patients without densitometric osteoporosis, suggesting its potential for monitoring vertebral fractures and spinal deformity risk.

Background: TBS, derived from a textural greyscale analysis of lumbar spine dual-energy X-ray absorptiometry (DXA) images, offers a non-invasive and indirect evaluation of bone microarchitecture. This method potentially enhances the assessment of skeletal phenotypes beyond the scope of aBMD. We aim to explore the utility of TBS in assessing vertebral fractures and spinal deformity in children and adolescents with OI.

Methods: In this cross-sectional study, 153 children and adolescents with OI were enrolled. DXA was used to measure TBS and aBMD, and their Z-scores were calculated based on reference values for BMD and TBS in normal children and adolescents with the same age and sex. Lateral thoracolumbar films were used to evaluate vertebral fractures and calculate the spine deformity index (SDI). The accuracy of TBS and aBMD for identifying vertebral compression fractures (VCFs) was assessed using area under the curve (AUC).

Results: TBS Z-score was negatively correlated with the age of children with OI (r =  - 0.435, P < 0.001) and was positively correlated to aBMD Z-score at the lumbar spine and femoral neck (both P < 0.01), even after adjusting for confounding factors. TBS Z-score was as effective as lumbar spine aBMD Z-score in discriminating VCFs (AUC, 0.667 vs 0.666, P > 0.05). Notably, in patients without densitometric osteoporosis, TBS Z-score demonstrated superior discriminative power for VCFs compared to lumbar spine aBMD Z-score (AUC, 0.719 vs 0.545, P < 0.05). In this population, only the TBS Z-score (r =  - 0.358, P < 0.05), rather than the lumbar spine aBMD Z-score, was negatively correlated with the SDI.

Conclusion: TBS has a close correlation with bone mineral density in children and adolescents with OI. In patients without densitometric osteoporosis, the Z-score of TBS is more effective than that of bone mineral density in assessing VCFs and spinal deformity, highlighting the potential of TBS in evaluating the risk of VCFs and monitoring the progression of spinal deformity.

Background: The prevalence of patients living with joint replacements is increasing. Nearly two-thirds of patients undergoing elective arthroplasty procedures have low bone mineral density (LBMD), defined as osteopenia in 38.5% and osteoporosis in 24.8%; among those with osteoporosis, only 32.8% received treatment at the time of surgery.

Materials and methods: A group of 7 national societies (FEMECOT, AMMOM, ACOMM, SCCOT, SECOT, SEFRAOS, SEIOMM) developed a joint statement position on the diagnosis of osteoporosis and perioperative bone optimization in candidates for arthroplasty "Arthroplasty Bone Optimization." We performed a scoping review of the available literature, followed by a systematic review and meta-analysis. Subsequently, a Delphi-modified method was used to gather the different positions.

Results: After analyzing the literature, we came up with five recommendations: (1) Patients scheduled for elective arthroplasty should undergo a bone health assessment (BHA). (2) If poor bone quality is observed during surgery and a bone health assessment has not been conducted promptly, a complete BHA, including a DXA scan, is imperative. (3) In the arthroplasty candidate, if LBMD or osteoporosis are noticed, bone loss-related factors should be corrected, and appropriate treatment for osteoporosis should be started before or right after arthroplasty. The use of anti-resorptive and bone anabolic agents has been shown to reduce periprosthetic bone loss, complications, and non-septic revision rates after joint arthroplasty. (4) In arthroplasty candidates, the diagnosis of osteoporosis or low bone mineral density (LBMD) should not delay the surgery. (5) Monitoring central and periprosthetic bone mineral density through DXA protocols can help identify bone loss in central and periprosthetic areas in patients with risk factors or osteoporosis.

Conclusions: Perioperative bone optimization should be considered in all patients who are candidates for arthroplasty. The orthopedic surgeon and multidisciplinary team should be encouraged to diagnose and treat the arthroplasty candidates' bone by screening for bone loss-related factors and diagnosing osteoporosis and starting treatment according to the current international guidelines. Following these recommendations could reduce periprosthetic bone loss, complications, and aseptic revision rates following arthroplasty surgery. More research is needed to understand the implications of osteoporosis and its treatment for joint replacement outcomes and long-term survival.

Dietary quality may be a factor in the progression of non-communicable, chronic diseases. This analysis of NHANES data demonstrates association between consumption of UPF and prevalence of osteoporosis and osteopenia in adults 50 years and older. UPF intake is an important consideration when recommending dietary patterns for optimum bone health PURPOSE: Declining bone mineral density in older adults can result in osteoporosis, leading to decreased physical function, quality of life, and increased risk of mortality. Poor dietary quality may contribute to the progression of this disease. This study explores the association between the consumption of ultra-processed foods (UPF) and the prevalence of osteoporosis and osteopenia in adults aged 50 years and older.

Method: Using regression analysis and adjusting for covariates, 24-h recall data from adults 50 years and over in four cycles of NHANES were examined for associations between prevalence of osteoporosis and intakes of UPF as a proportion of daily energy intake.

Results: Mean (SE) intake of UPF as a proportion of total daily energy ranged from 29.5% (0.3) in the lowest quintile to 76.3% (0.3) in the highest. 50.5% of women and 28.0% of men had osteopenia, 8.2% and 1.8%, respectively, had osteoporosis. Increased risk of osteopenia or osteoporosis was observed in the highest quintile of UPF intake compared to that of the lowest: OR 1.52 (95% CI 1.28, 1.79). The odds of self-reported prior fractures at hip, wrist, or spine in women increased by 1.9% for every percentage increase in proportion of UPF intake (95% CI 1.003, 1.035). Increased risk of fracture was not observed among men.

Conclusions: These findings indicate an association between osteoporosis and osteopenia and the intake of UPF as a proportion of total daily energy. Further investigation into the impact of dietary quality on osteoporosis and fracture risk is warranted, particularly in post-menopausal women.

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: The $R^2$ 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.

Most subjects in osteoporosis clinical trials were women with postmenopausal osteoporosis and while bridging studies (BMD endpoint) provide an expectation that osteoporosis medications will reduce fracture risk in men. This real-world study shows direct evidence of fracture risk reduction among men with osteoporosis (36% of hip fracture reduction with denosumab).

Purpose: Direct evidence for fracture risk reduction of medications used among men with osteoporosis is very limited. This study aims to evaluate the real-world effectiveness of denosumab in reducing fracture risk.

Methods: This study included 13,797 men aged ≥ 50 years with osteoporosis who had initiated denosumab in Taiwan. Taiwan's National Health Insurance Research Database includes all Taiwan residents' complete health claim data. We compared incidence rates of clinical fractures between patients on denosumab 60 mg subcutaneously every 6 months (on-treatment) and patients ending therapy after one administration (off-treatment). Propensity score (PS) analysis, adjusting for measured differences at baseline covariates, was used to estimate the adjusted hazard ratio using a Cox proportion hazards model.

Results: During follow-up, 248 hip fracture events occurred. The crude incidence rates of hip fracture were 1.13 events and 1.73 events per 100 person-years in on-treatment and off-treatment cohorts, respectively. After PS inverse probability of treatment weighting, the cohorts achieved balance in all 59 covariates. The hip fracture event rate was lower in on-treatment cohort versus off-treatment cohort by 36% (hazard ratio, 0.64 [95% CI 0.50-0.83]). A similar magnitude of risk reduction was observed in clinical vertebral and nonvertebral fractures. A series of sensitivity analysis, including a validation analysis using a-million individual health records, demonstrated that unmeasured confounders were not suggested to impact study result interpretation.

Conclusion: In this large, real-world study evaluating denosumab treatment among men with osteoporosis, the observed fracture risk reductions were consistent with the available risk reductions demonstrated in clinical trials among women with postmenopausal osteoporosis.

Using data from NHANES for years 2005-2018, we examined temporal trends in osteoporosis prevalence and the proportion of undiagnosed osteoporosis in the United States of America. Our results suggested statistically significant increases in osteoporosis prevalence across several demographic groups. These findings carry profound implications for public health, given increased life expectancy and burden of chronic diseases.

Introduction: This is the first study to assess osteoporosis prevalence trends over time and the proportion of undiagnosed osteoporosis across gender, ethnicity/race, and age groups.

Methods: Observational time trend analyses were conducted using the 2005-2006, 2007-2008, 2009-2010, 2013-2014, and 2017-2018 National Health and Nutrition Examination Survey (NHANES) datasets, along with a descriptive analysis using the 2017-2018 NHANES dataset to capture the proportion of undiagnosed osteoporosis.

Results: The findings showed a statistically significant increase in osteoporosis prevalence among women, non-Hispanic Whites, and all age groups (except for individuals 80 years of age and older) during the study period. A subsequent analysis examining individuals by both gender and ethnicity/race demonstrated a statistically significant increase among Other Hispanic men and non-Hispanic White women. Additional descriptive analyses found that 69.12% of individuals with osteoporosis went undiagnosed. Specifically, 86.88% of men and 84.77% of individuals 50-59 years of age with osteoporosis went undiagnosed, representing the two highest groups.

Discussion and conclusion: The substantial and increasing prevalence among certain groups and sub-groups, along with the lack of diagnostic capture of osteoporosis, highlights existing gaps in public health efforts and care delivery infrastructure. This paper highlights high-risk groups and sub-groups that may benefit most from accelerated initiatives to reduce the burden of illness associated with osteoporosis.