Journal of Bone Metabolism最新文献

Classifying patients with osteoporosis according to fracture risk and establishing adequate treatment strategies is crucial to effectively treat osteoporosis. The Korean Society for Bone and Mineral Research has issued a position statement regarding appropriate treatment strategies for postmenopausal osteoporosis. According to previous fragility fracture history, bone mineral density (BMD) test results, fracture risk assessment tool, and several clinical risk factors, fracture risk groups are classified into low, moderate, high, and very-high-risk groups. In high-risk groups, bisphosphonates (BPs) and denosumab are recommended as first-line therapies. Sequential BP treatment after denosumab discontinuation is required to prevent the rebound phenomenon. In the very high-risk group, anabolic drugs (teriparatide or romosozumab) are recommended as a first-line therapy; sequential therapy with antiresorptive agents is required to maintain BMD gain and reduce fracture risk. Fracture risk was reassessed annually, and the treatment plan was determined based on the results, according to the osteoporosis treatment algorithm for fracture risk.

Background: Osteoporosis can be delayed by providing accurate and adequate information to people at risk. Therefore, we aimed to determine the knowledge, attitude, and behavior levels of women in the postmenopausal period, which is the largest group at risk.

Methods: The study was conducted in a tertiary Training and Research Hospital between 1 December 2018 and 1 May 2019 in 225 postmenopausal women who applied to the Family Medicine outpatient clinic and bone mineral density (BMD) outpatient clinics for BMD measurement or had previously had this measurement at least once. A questionnaire evaluating the knowledge, attitudes, and behavior levels related to osteoporosis was applied to all patients included in the study.

Results: The mean age was 58.05±9.1 years. The median osteoporosis knowledge score was 7 out of 19 points. A total of 119 (52.9%) had low knowledge scores and 106 (47.1%) had higher knowledge scores. Of the individuals with high scores, 40 (37.7%) were smoking, 64 (60.4%) did not sunbathe, 89 (84%) did not consume the recommended daily amount of calcium, and 58 (54.7%) were not exercising in the recommended time. It was seen that those who were university graduates, who had previously learned about osteoporosis from a health professional, and who had a family history of osteoporosis had higher knowledge levels.

Conclusions: Even in postmenopausal women who are aware that they are in the risk group and that they should have BMD, their knowledge, attitude, and behavior levels on osteoporosis were found to be quite low.

Orthodontic tooth movement (OTM) is achieved by the simultaneous activation of bone resorption by osteoclasts and bone formation by osteoblasts. When orthodontic forces are applied, osteoclast-mediated bone resorption occurs in the alveolar bone on the compression side, creating space for tooth movement. Therefore, controlling osteoclastogenesis is the fundamental tenet of orthodontic treatment. Orthodontic forces are sensed by osteoblast lineage cells such as periodontal ligament (PDL) cells and osteocytes. Of several cytokines produced by these cells, the most important cytokine promoting osteoclastogenesis is the receptor activator of nuclear factor-κB ligand (RANKL), which is mainly supplied by osteoblasts. Additionally, osteocytes embedded within the bone matrix, T lymphocytes in inflammatory conditions, and PDL cells produce RANKL. Besides RANKL, inflammatory cytokines, such as interleukin-1, tumor necrosis factor-α, and prostaglandin E2 promote osteoclastogenesis under OTM. On the downside, excessive osteoclastogenesis activation triggers orthodontically-induced external root resorption (ERR) through pro-osteoclastic inflammatory cytokines. Therefore, understanding the mechanisms of osteoclastogenesis during OTM is essential in reducing the adverse effects of orthodontic treatment. Here, we review the current concepts of the mechanisms underlying osteoclastogenesis in OTM and orthodontically induced ERR.

Background: Treating osteoporosis in patients with a distal radius fracture (DRF) became paramount at the Fracture Liaison Service. Spinal sagittal imbalance emerged as a risk factor for subsequent fractures. Therefore, here we investigated the spinal profile of patients with DRF to investigate its association with a history of falls and prevalent vertebral fractures.

Methods: We reviewed the cases of 162 women presenting with DRF and 162 age-matched women without fracture who underwent an osteoporosis evaluation including bone mineral density (BMD) and lateral spine imaging. We compared the incidence of prevalent vertebral fracture and sagittal vertical axis (SVA) to measure spinal sagittal imbalance. We also performed a regression analysis of the risks of prevalent vertebral fracture, such as age, body mass index (BMI), BMD, and SVA.

Results: The SVA was significantly smaller (indicating more stable sagittal balance) in patients with a DRF versus controls (16 mm vs. 34 mm, respectively; p<0.001). The incidence of a prevalent vertebral fracture was similar between groups (12% vs. 15%, respectively; p=0.332). In both groups, the SVA was significantly greater in those with versus without a vertebral fracture. The vertebral fracture was significantly associated with age and SVA but not BMI or spinal BMD.

Conclusions: Spinal sagittal balance was superior in DRF patients, yet the frequency of prevalent vertebral fractures was similar. The identification of this unique spinal profile in patients with DRF may increase our understanding of osteoporotic fractures.

Alkaptonuria is an extremely rare autosomal recessive metabolic disorder characterized by dark urine, ochronosis, and arthritis of the spine and major joints. We report a case of ochronotic arthritis observed during total knee replacement surgery in a 65-year-old male patient with no relevant medical history. Based on a literature review, this is the first case of ochronotic arthritis reported in Korea.

Dental pulp stem cells (DPSCs) have garnered significant interest in dental research for their unique characteristics and potential in tooth development and regeneration. While there were many studies to define their stem cell-like characteristics and osteogenic differentiation functions that are considered ideal candidates for regenerating damaged dental pulp tissue, how endogenous DPSCs respond to dental pulp injury and supply new dentin-forming cells has not been extensively investigated in vivo. Here, we review the recent progress in identity, function, and regulation of endogenous DPSCs and their clinical potential for pulp injury and regeneration. In addition, we discuss current advances in new mouse models, imaging techniques, and its practical uses and limitations in the analysis of DPSCs in pulp injury and regeneration in vivo.

Vitamin D (VD) exerts a wide variety of biological actions in addition to its well-known roles in calcium homeostasis. Nutritional VD deficiency induces rachitic abnormalities in growing children and osteomalacia in adults, and it has been proposed to underlie the onset and development of multiple non-communicable chronic diseases. Therefore, the administration of VD or synthetic VD analogues represents a promising therapeutic strategy; indeed, VD and a VD agonist have shown clinical promise in mitigating osteoporosis and symptoms of insufficient calcium intake. However, even though high doses of VD analogues have shown pre-clinical efficacy against several diseases, including cancers, they have not yet had wide-spread clinical success. This difference may be due to limitation of clinical doses in light of the inherent calcemic action of VD. An approach to overcome this problem involves the development of VD analogues with lower calcemic activity, which could be administered in high doses to attenuate the onset and progress of disease. In a similar strategy, selective estrogen receptor modulators have had success as anti-osteoporosis drugs, and they have shown benefit for other estrogen target organs by serving as partial antagonists or agonists of estrogen receptor α. It is thus conceivable to generate synthetic partial antagonists or agonists for the VD receptor (VDR) that would exert beneficial effects on bone and other VD target organs. In this review, we discuss the molecular basis of the development of such synthetic VDR ligands from the viewpoint of roles of VDR in gene regulation.

Background: Iris Koreana NAKAI (IKN) is a flowering perennial plant that belongs to the Iridaceae family. In this study, we aimed to demonstrate the effects of IKN on osteoclast differentiation in vitro and in vivo. We also sought to verify the molecular mechanisms underlying its anti-osteoclastogenic effects.

Methods: Osteoclasts were formed by culturing mouse bone marrow macrophage (BMM) cells with macrophage colony-stimulating factor and receptor activator of nuclear factor-κB ligand (RANKL). Bone resorption assays were performed on dentin slices. mRNA expression levels were analyzed by quantitative polymerase chain reaction. Western blotting was performed to detect protein expression or activation. Lipopolysaccharide (LPS)-induced osteoclast formation was performed using a mouse calvarial model.

Results: In BMM cultures, an ethanol extract of the root part of IKN suppressed RANKL-induced osteoclast formation and bone resorptive activity. In contrast, an ethanol extract of the aerial parts of IKN had a minor effect on RANKL-induced osteoclast formation. Mechanistically, the root part of IKN suppressed RANKL-induced p38 mitogen-activated protein kinase (MAPK) activation, effectively abrogating the induction of c-Fos and nuclear factor of activated T cells 1 (NFATc1) expression. IKN administration decreased LPS-induced osteoclast formation in a calvarial osteolysis model in vivo.

Conclusions: Our study suggested that the ethanol extract of the root part of IKN suppressed osteoclast differentiation and function partly by downregulating the p38 MAPK/c-Fos/NFATc1 signaling pathways. Thus, the root part.

Background: The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database.

Methods: The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used.

Results: In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender.

Conclusions: ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.

Background: Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology.

Methods: The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including "hip fractures" AND "artificial intelligence".

Results: A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%.

Conclusions: We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.