Osteoporosis International最新文献

The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in November 2023 across multiple databases, including PubMed, Scopus, Web of Science, and Google Scholar. The research targeted studies using AI models for OP diagnosis from dental radiographs. The main outcomes were the sensitivity and specificity of AI models regarding OP diagnosis. The "meta" package from the R Foundation was selected for statistical analysis. A random-effects model, along with 95% confidence intervals, was utilized to estimate pooled values. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was employed for risk of bias and applicability assessment. Among 640 records, 22 studies were included in the qualitative analysis and 12 in the meta-analysis. The overall sensitivity for AI-assisted OP diagnosis was 0.85 (95% CI, 0.70-0.93), while the pooled specificity equaled 0.95 (95% CI, 0.91-0.97). Conventional algorithms led to a pooled sensitivity of 0.82 (95% CI, 0.57-0.94) and a pooled specificity of 0.96 (95% CI, 0.93-0.97). Deep convolutional neural networks exhibited a pooled sensitivity of 0.87 (95% CI, 0.68-0.95) and a pooled specificity of 0.92 (95% CI, 0.83-0.96). This systematic review corroborates the accuracy of AI in OP diagnosis using dental images. Future research should expand sample sizes in test and training datasets and standardize imaging techniques to establish the reliability of AI-assisted methods in OP diagnosis through dental images.

Designing appropriate diagnostic and treatment methods to reduce fall risk and improve quality of life, as well as reduce the cost of care in elderlies. Our findings have potential for early diagnosis of those with a high probability of falling based on fairly simple clinical measures of hyperkyphosis, forward head, and lordosis.

Introduction: Poor balance is an underlying cause of falling in the elderly, for which a change in the natural curvature of the spine plays a major role. Little is known about the relationship between spinal curvatures and fall incidence in this population. We primarily aimed to investigate the relationship between sagittal plane spinal curvatures and fall incidence over 1 year among nursing facility residents. Secondarily, we aim to determine associations of sagittal plane spinal curvatures with participants' perception of fall risk and balance capability.

Methods: Participants (100 residents mean age 70.17 ± 6.01 years) underwent standing measures of sagittal plane spinal curvatures (flexible ruler technique) and forward displacement of the head relative to the cervical spine. The Tinetti Performance Oriented Mobility Assessment (POMA) and Fall Efficacy Scale assessed participants' perception of balance and fear of falling. Incident falls were self-reported monthly and tracked across 1 year. Spearman's correlations and logistic regression evaluated associations between fall incidence and spinal curvature. Predictive performance of spinal curvature and fall risk was determined by the corresponding ROC for defining a cut-off for variables of spinal curvature and fall risk indicators.

Results: Predictive performance of spinal curvature and fall risk factors indicated 84% and 77% of participants were correctly classified using models of kyphosis and head angle, respectively.

Conclusions: Our study adds new data on spinal curvatures and incident falls among nursing facility residents. Efforts are needed to intervene to counter progression of spinal curvatures and improve fall prevention practices.

Purpose: To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death.

Methods: We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses.

Results: During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index =  - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index =  - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk.

Conclusion: Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.

Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR.

Purpose: Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model.

Methods: Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX.

Results: We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX.

Conclusion: Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates.

Nonalcoholic fatty liver disease (NAFLD) has recently been renamed metabolic dysfunction-associated fatty liver disease (MAFLD) by international consensus. Both MAFLD and osteoporosis are highly prevalent metabolic diseases. Recent evidence indicates that NAFLD increases the risk of low bone mineral density and osteoporosis, likely mediated by obesity. NAFLD has a close association with obesity and other metabolic disorders. Although obesity was previously thought to protect against bone loss, it now heightens osteoporotic fracture risk. This overview summarizes current clinical correlations between obesity, NAFLD, and osteoporosis, with a focus on recent insights into potential mechanisms interconnecting these three conditions. This study reviewed the scientific literature on the relationship between obesity, nonalcoholic fatty liver disease, and osteoporosis as well as the scientific literature that reveals the underlying pathophysiologic mechanisms between the three. Emerging evidence suggests obesity plays a key role in mediating the relationship between NAFLD and osteoporosis. Accumulating laboratory evidence supports plausible pathophysiological links between obesity, NAFLD, and osteoporosis, including inflammatory pathways, insulin resistance, gut microbiota dysbiosis, bone marrow adiposity, and alterations in insulin-like growth factor-1 signaling. Adiposity has important associations with NAFLD and osteoporosis, the underlying pathophysiologic mechanisms between the three may provide new therapeutic targets for this complex patient population.

This study aimed to evaluate the correlation between measuring proton-density fat fraction (PDFF) in bone marrow using multi-echo chemical shift-encoded MRI and osteoporosis, assessing its effectiveness as a biomarker for osteoporosis. A systematic review was conducted by two independent researchers using Cochrane, PubMed, EMBASE, and Web of Science databases up to December 2023. Quality assessments were evaluated using the Cochrane risk of bias tool and the Agency for Healthcare Research and Quality (AHRQ) checklist. Fourteen studies involving 1495 patients were analyzed. The meta-analysis revealed a significant difference in PDFF values between the osteoporosis/osteopenia group and the normal control group, with a mean difference of 11.04 (95% CI: 9.17 to 12.92, Z=11.52, P < 0.00001). Measuring PDFF via MRI shows potential as an osteoporosis biomarker and may serve as a risk factor for osteoporosis. This insight opens new avenues for future diagnostic and therapeutic strategies, potentially improving osteoporosis management and patient care.

Objective: This study aims to assess the correlation between measuring proton-density fat fraction (PDFF) in bone marrow using multi-echo chemical shift-encoded MRI and osteoporosis, evaluating its effectiveness as a biomarker for osteoporosis.

Materials and methods: This systematic review was carried out by two independent researchers using Cochrane, PubMed, EMBASE, and Web of Science databases up to December 2023. Quality assessments were evaluated using the Cochrane risk of bias tool and the Agency for Healthcare Research and Quality (AHRQ) checklist.

Results: Fourteen studies involving 1495 patients were analyzed. The meta-analysis revealed a significant difference in PDFF values between the osteoporosis/osteopenia group and the normal control group, with a (MD = 11.04, 95% CI: 9.17 to 12.92, Z = 11.52, P < 0.00001). Subgroup analyses indicated that diagnostic methods, gender, and echo length did not significantly impact the PDFF-osteoporosis association.

Conclusion: PDFF measurement via MRI shows potential as an osteoporosis biomarker and may serve as a risk factor for osteoporosis. This insight opens new avenues for future diagnostic and therapeutic strategies, potentially improving osteoporosis management and patient care.

Identifying dysregulated plasma proteins in osteoporosis (OP) progression offers insights into prevention and treatment. This study found 8 such proteins associated with OP, suggesting them as therapy targets. This discovery may cut drug development costs and improve personalized treatments.

Purpose: This study aims to identify potential therapeutic targets for OP using summary data-based Mendelian randomization (SMR) and colocalization analysis methods. Furthermore, we seek to explore the biological significance and pharmacological value of these drug targets.

Methods: To identify potential therapeutic targets for OP, we conducted SMR and colocalization analysis. Plasma protein (pQTL, exposure) data were sourced from the study by Ferkingstad et al. (n = 35,559). Summary statistics for bone mineral density (BMD, outcome) were obtained from the GWAS Catalog (n = 56,284). Additionally, we utilized enrichment analysis, protein-protein interaction (PPI) network analysis, drug prediction, and molecular docking to further analyze the biological significance and pharmacological value of these drug targets.

Results: In the SMR analysis, while 20 proteins showed significance, only 8 potential drug targets (GCKR, ERBB3, CFHR1, GPN1, SDF2, VTN, BET1L, and SERPING1) received support from colocalization (PP.H4 > 0.8). These proteins are closely associated with immune function in terms of biological significance. Molecular docking also demonstrated favorable binding of drugs to proteins, consistent with existing structural data, further substantiating the pharmacological value of these targets.

Conclusions: The study identified 8 potential drug targets for OP. These prospective targets are believed to have a higher chance of success in clinical trials, thus aiding in prioritizing OP drug development and reducing development costs.

Objectives: Previous evidence suggests that bisphosphonates (BPs) may lower the risk of recurrent fractures and enhance functional recovery in patients with fractures. However, there has been controversy regarding the optimal timing of treatment initiation for patients with fragility fractures. We conducted a meta-analysis to evaluate the available evidence on the use of BPs during the perioperative period and compared it to both non-perioperative periods and non-usage.

Methods: Electronic searches were performed using PubMed, EMBASE, Web of Science and the Cochrane Library published before February 2023, without any language restrictions. The primary outcomes included fracture healing rate, healing time, and new fractures. We also examined a wide range of secondary outcomes. Random effects meta-analysis was used.

Results: A total of 19 clinical trials involving 2543 patients were included in this meta-analysis. When comparing patients with non-perioperative BPs use in 4-6 weeks and approximately 10-12 weeks post-surgically, the overall risk ratios (RRs) of perioperative BPs use for healing rate were 1.06 (95% CI: 0.81, 1.38, p=0.69) and 1.02 (95% CI: 0.94, 1.11, p=0.65), respectively, suggesting no difference in healing rate between perioperative and non-perioperative BP initiation. For healing time, the overall mean difference between perioperative and non-perioperative periods was -0.19 week (95% CI: -1.03, 0.64, p=0.65) at approximately 10-12 weeks, indicating no significant impact of perioperative BP initiation on healing time. In terms of new fractures, the overall RR with BP use was 0.35 (95% CI: 0.17-0.73, p=0.005), when compared to patients without BPs use. This suggests a protective impact of BP use against new fractures compared to patients without BP use. Perioperative BP use was associated with a markedly higher likelihood of having adverse experiences, including fever (RR: 23.78, 95% CI: 8.29, 68.21, p< 0.001), arthralgia (RR: 10.20, 95% CI: 2.41, 43.16, p=0.002), and myalgia (RR: 9.42, 95% CI: 2.54, 34.87, p< 0.001), compared with non-BPs use.

Conclusions: Treatment with BP during the perioperative period does not affect the healing process and has positive effects on therapy for patients with fragility fractures. These compelling findings underscore the potential efficacy of BP use during the perioperative period as a viable treatment option for patients with fragility fractures.