Search Results (240)

Osteoporosis is a multifactorial, polygenic disease characterized by reduced bone mineral density (BMD) and increased fracture risk. Genome-wide association studies (GWASs) have identified numerous loci associated with BMD and/or bone fractures, but functional characterization of these target genes is essential to understand the biological mechanisms underlying osteoporosis. This review focuses on current methodologies and key examples of successful functional studies aimed at evaluating gene function in osteoporosis research. Functional evaluation typically follows a multi-step approach. In silico analyses using omics datasets expression quantitative trait loci (eQTLs), protein quantitative trait loci (pQTLs), and DNA methylation quantitative trait loci (mQTLs) help prioritize candidate genes and predict relevant biological pathways. In vitro models, including immortalized bone-derived cell lines and primary mesenchymal stem cells (MSCs), are used to explore gene function in osteogenesis. Advanced three-dimensional culture systems provide additional physiological relevance for studying bone-related cellular processes. In situ analyses of patient-derived bone and muscle tissues offer validation in a disease-relevant context, while in vivo studies using mouse and zebrafish models enable comprehensive assessment of gene function in skeletal development and maintenance. Integration of these complementary methodologies helps translate GWAS findings into biological insights and supports the identification of novel therapeutic targets for osteoporosis. Full article

Objectives: The early detection of low bone mineral density (BMD) is essential for preventing osteoporosis and related complications. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its cost and limited availability restrict its use in large-scale screening. This study investigated whether raw bioelectrical impedance analysis (BIA) data combined with explainable machine learning (ML) models could accurately classify osteopenia in women aged 40 to 55. Methods: In a cross-sectional design, 138 women underwent same-day BIA and DXA assessments. Participants were categorized as osteopenic (T-score between −1.0 and −2.5; n = 33) or normal (T-score ≥ −1.0) based on DXA results. Overall, 24.1% of the sample were classified as osteopenic, and 32.85% were postmenopausal. Raw BIA outputs were used as input features, including impedance values, phase angles, and segmental tissue parameters. A sequential forward feature selection (SFFS) algorithm was employed to optimize input dimensionality. Four ML classifiers were trained using stratified five-fold cross-validation, and SHapley Additive exPlanations (SHAP) were applied to interpret feature contributions. Results: The neural network (NN) model achieved the highest classification accuracy (92.12%) using 34 selected features, including raw impedance measurements, derived body composition indices such as regional lean mass estimates and the edema index, as well as a limited number of categorical variables, including self-reported physical activity status. SHAP analysis identified muscle mass indices and fluid distribution metrics, features previously associated with bone health, as the most influential predictors in the current model. Other classifiers performed comparably but with lower precision or interpretability. Conclusions: ML models based on raw BIA data can classify osteopenia with high accuracy and clinical transparency. This approach provides a cost-effective and interpretable alternative for the early identification of individuals at risk for low BMD in resource-limited or primary care settings. Full article

Microelement deficiencies, often termed “hidden hunger”, represent a significant global health challenge. Optimal human health relies on adequate dietary intake of essential microelements, including selenium (Se), zinc (Zn), copper (Cu), boron (B), manganese (Mn), molybdenum (Mo), iron (Fe), nickel (Ni), and chlorine (Cl). In recent years, there has been a growing focus on vitality and longevity, which are closely associated with the sufficient intake of essential microelements. This review focuses on these nine elements, whose bioavailability in the food chain is critically determined by their geochemical behavior in soils. There is a necessity for an understanding of the sources, soil–plant transfer, and plant uptake mechanisms of these microelements, with particular emphasis on the influence of key soil properties, including pH, redox potential, organic matter content, and mineral composition. There is a dual challenge of microelement deficiencies in agricultural soils, leading to inadequate crop accumulation, and the potential for localized toxicities arising from anthropogenic inputs or geogenic enrichment. A promising solution to microelement deficiencies in crops is biofortification, which enhances nutrient content in food by improving soil and plant uptake. This strategy includes agronomic methods (e.g., fertilization, soil amendments) and genetic approaches (e.g., marker-assisted selection, genetic engineering) to boost microelement density in edible tissues. Moreover, emphasizing the need for advanced predictive modeling techniques, such as ensemble learning-based digital soil mapping, enhances regional soil microelement management. Integrating machine learning with digital covariates improves spatial prediction accuracy, optimizes soil fertility management, and supports sustainable agriculture. Given the rising global population and the consequent pressures on agricultural production, a comprehensive understanding of microelement dynamics in the soil–plant system is essential for developing sustainable strategies to mitigate deficiencies and ensure food and nutritional security. This review specifically focuses on the bioavailability of these nine essential microelements (Se, Zn, Cu, B, Mn, Mo, Fe, Ni, and Cl), examining the soil–plant transfer mechanisms and their ultimate implications for human health within the soil–plant–human system. The selection of these nine microelements for this review is based on their recognized dual importance: they are not only essential for various plant metabolic functions, but also play a critical role in human nutrition, with widespread deficiencies reported globally in diverse populations and agricultural systems. While other elements, such as cobalt (Co) and iodine (I), are vital for health, Co is primarily required by nitrogen-fixing microorganisms rather than directly by all plants, and the main pathway for iodine intake is often marine-based rather than soil-to-crop. Full article

Background: Osteoporosis is a systemic disease characterized by a progressive decrease in bone density and deterioration of the tissue’s microarchitecture. This results in greater structural fragility and a higher risk of fractures. Osteopenia represents the beginning of the process of decreasing bone density and, if left untreated, can lead to osteoporosis. The objective of this study was to validate an experimental model for establishing cases of decreased bone density that allows for the creation of different levels of severity of mineral loss and changes in bone microstructure. Materials and Methods: Twenty female Wistar rats, 12 weeks old and with a body weight ranging from 300 to 400 g, were used in this study. The animals were randomly distributed into five groups (n = 5 per group): a control group (CG), where the animals were not ovariectomized (OVX), and four experimental groups, where the animals were OVX and euthanized at different times: 30 days (G30), 40 days (G40), 60 days (G60), and 80 days (G80). The animals in the experimental groups underwent bilateral ovariectomy to induce mineral loss. The femurs were collected after the periods established for each group and analyzed using microcomputed tomography (μCT) to determine bone density and count the number of trabeculae. Furthermore, the T-score was calculated for each group. Results: There were significant differences in bone density when comparing all groups, with GC > G30 > G40 > G60 > G80. For the number of trabeculae, GC presented more trabeculae than all other groups. More trabeculae were also observed in G30 when compared to G40, G60, and G80; however, there were no differences between G40, G60, and G80. Regarding the calculation of the T-score by group, osteopenia was observed in G30 (T-score: −2.42) and osteoporosis was observed in G40, G60, and G80 (T-scores: −4.38, −6.34, and −7.71, respectively). Conclusions: The results demonstrate that ovariectomy induces progressive changes in bone structure, with the onset of osteopenia 30 days after ovariectomy and osteoporosis after 40 days in this experimental model. These results may aid future investigations that seek to focus on the specific treatment of osteopenia and/or osteoporosis. Full article

Background: Sarcopenia and osteoporosis are common age-related conditions that markedly increase fracture risk and morbidity in the elderly. Leptin, an adipokine secreted by adipose tissue, has been implicated in musculoskeletal health, but its clinical relevance in aging populations remains uncertain. This study aimed to evaluate the associations between serum leptin levels, skeletal muscle mass, muscle strength, bone mineral density (BMD), and fracture risk in elderly women. Methods: This observational analysis included 79 community-dwelling women aged 65 years and older. Participants underwent assessments of body composition, serum leptin concentration, grip strength, and femoral neck BMD. Sarcopenia and obesity were classified based on established criteria. Correlation analyses and binomial logistic regression were performed to examine the relationships among leptin levels, musculoskeletal parameters, and fracture occurrence. Results: Leptin concentrations were significantly associated with fat-related parameters, including BMI, fat index, and total body fat percentage, but showed no significant correlation with skeletal muscle mass (ASM), grip strength, or BMD. Obese participants demonstrated higher leptin levels and fat parameters compared with non-obese participants, but no significant differences were observed in grip strength or BMD. Binomial logistic regression analysis identified femoral neck BMD and grip strength as significant independent predictors of fracture risk, whereas leptin and ASM were not identified as such. Conclusions: In elderly women, serum leptin levels primarily reflect adiposity rather than musculoskeletal health. Leptin is not an independent predictor of spinal fracture risk. These findings highlight the critical importance of maintaining bone density and muscle strength for fracture prevention in aging populations. Full article

Background/Objectives: Accurate assessment of spinal bone density is essential for evaluating bone health, particularly in preoperative planning. Conventional manual methods for Hounsfield unit (HU) measurements rely on single-slice measurements within the region of interest, limiting their precision and reproducibility in patients with severe vertebral deformities. We hypothesize that a novel deep-reasoning and learning model (DR-AI) can fully automate spinal bone density assessment volumetrically, with high correlations to spinal bone mineral density (BMD) obtained from dual-energy X-ray absorptiometry (DXA), as well as to the T- and Z-scores. Methods: A cross-sectional study was conducted on patients who had BMD assessment of their lumbar spine and lumbar CT scans within 1 year. The fully-automated DR model was utilized to analyze the soft-tissue window of the lumbar vertebrae CT scans. Spearman correlation coefficients were calculated to assess the strength of relationships between the computed volumetric HUs and the BMD, T-, and Z-scores from each vertebra. Results: 84 patients (67 females, mean age 74.1 ± 10.3 years; 17 males, mean age 68.1 ± 12.4 years) meeting inclusion criteria. Correlation analyses for L1 to L4 showed significant positive relationships (p < 0.0001), with the strongest correlation at L2 between HU and BMD (ρ = 0.75). Conclusions: the DR model for automated assessment of volumetric HUs offers a highly reliable, efficient, and precise alternative to DXA measurements. Full article

Background: With the intensification of population aging, osteoporosis has become one of the significant public health issues affecting human health. Currently available medications for treating osteoporosis are associated with various adverse effects and resistance issues. Oligonucleotide drugs show great potential. Effective delivery systems are essential to enhance the stability, bioavailability, and targeting of sRNA drugs. Lipid nanoparticles (LNPs) show promise as alternative osteoporosis therapeutics. This study explores the potential of LNPs as an effective delivery system to treat osteoporosis. Methods: LNPs were prepared using microfluidic techniques with varying lipid compositions, and characterized in terms of size, zeta potential, and entrapment efficiency (EE%). Dynamic light scattering (DLS) was employed to determine the size of the LNPs. The zeta potential was measured using electrophoretic light scattering. The pharmacodynamic effects and safety were then evaluated in a mouse model through intravenous administration. Results: Several lipid nanoparticle (LNP) formulations with different nitrogen/phosphorus ratios and different DMG-PEG2000 ratios were examined, and a lead candidate that supports delivery of sRNA in animal models of osteoporosis was identified. In OVX mice, LNP-sRNA significantly improved bone mineral density (BMD), trabecular microstructure, and biomechanical strength. Safety assessments revealed no systemic toxicity. It is shown that the optimized LNPs can serve as a promising delivery system to mediate sRNA delivery to bone tissue. Conclusions: After comparison of in vitro and in vivo properties, the optimized LNPs demonstrated good comprehensive performance as a delivery system for osteoporosis treatment. These results highlight the potential of the optimized LNPs as an ideal delivery system for osteoporosis, offering improved therapeutic efficacy and reduced systemic side effects. Full article

Naked mole-rats are extremely long-living rodents with a maximum lifespan of 37 years, and their cellular aging and tissue aging are almost nonexistent. Therefore, in this study, we aim to analyze the extracellular matrix of the temporomandibular joint (TMJ) of naked mole-rats at the molecular level and explore the molecules involved in anti-aging and their localization. Micro-computed tomography (CT) scans revealed increased mineral density and wear of the mandibular condyle in aged mice. Conversely, CT scans did not reveal wear of the mandibular condyle in naked mole-rats, and histological analysis did not reveal wear of the articular disk. Using various spectroscopies and artificial intelligence (AI), we found that the articular disk of naked mole-rats is composed of a cartilage-like layer with hyaluronic acid and collagen fibers with varying orientations, which is thought to have relieved mechanical stress and have protected the mandibular condyle. These results suggest that not only the amount, but also the spatial distribution of the extracellular matrix is important for the anti-aging properties of the TMJ, and may contribute to elucidating the pathology of TMJ disorders and other degenerative conditions and developing therapeutic drugs. Full article

Background: Osteoporosis is a chronic metabolic condition characterized by progressive loss of bone mass and disruption of the bone spatial architecture. Pathological changes are influenced by multiple factors, including genetic predispositions. Identifying risk factors for osteoporosis is crucial for recognizing at-risk populations, implementing preventive strategies, and supporting diagnostics. Type I collagen, composed of two chains—α1(I) and α2(I), encoded by the COL1A1 and COL1A2 genes, respectively—plays a key role in the mechanical strength of tissues, including bones. The aim of this study was to assess the effect of the rs17166249 and rs412777 polymorphisms in the COL1A2 gene on bone mineral density (BMD) in postmenopausal women. Methods: The study included 570 unrelated women: 119 diagnosed with osteoporosis, 96 with osteopenia, and 355 healthy controls. Polymorphisms in the COL1A2 gene were analyzed using real-time PCR with specific primers and TaqMan probes. Results: The results showed no significant differences in the distribution of genotypes and alleles of rs412777 between the groups. However, the rs17166249 T allele was found to be more prevalent in the osteoporosis group, although the association was not statistically significant after adjusting for confounders. Furthermore, no significant correlations were observed between the genotypes of either SNP and BMD parameters such as T-score, Z-score, and BMD measurements. Conclusion: These findings suggest that while the COL1A2 gene may have a modest influence on bone health, its role in osteoporosis risk remains inconclusive, highlighting the need for further studies to explore additional genetic and environmental factors. Full article

Background: Diabetes induces osteoporosis primarily by impairing osteoblast function. Intracellular zinc homeostasis, which is controlled by zinc transporters, plays a significant role in osteoblast differentiation. In the present study, we aimed to explore the role of zinc homeostasis in the pathogenesis of diabetic bone loss using a diabetic mouse model. Methods: Streptozotocin (STZ)-induced diabetic female mice were used for in vivo experiments. In vitro, the effects of zinc transporter knockdown using small interfering RNA was investigated in MC3T3E1 pre-osteoblastic cells. Results: STZ-induced diabetic mice exhibited severe bone loss and decreased expression of osteogenic genes, as well as a decrease in zinc content and the expression of several zinc transporters localized in the cellular membrane, including Zip6, Zip9, and Zip10 in the tibia. Moreover, the messenger RNA (mRNA) levels of Zip6, Zip9, and Zip10 were positively correlated with trabecular bone mineral density in the tibiae of diabetic mice. This in vitro study, using MC3T3E1 pre-osteoblastic cells, revealed that knockdown of Zip6 reduced the expression of osteogenic genes in pre-osteoblastic cells. Additionally, Zip6 knockdown downregulated protein levels of phosphorylated p38 mitogen-activated protein kinase (p38MAPK) in pre-osteoblastic cells, and this change was observed in the tibiae of diabetic mice. Conclusions: Our data suggest that the downregulation of zinc transporters localized in the cellular membrane, such as Zip6, may be involved in the impairment of osteoblastic differentiation through the inhibition of p38 MAPK signaling, leading to osteoporosis under diabetic conditions. Maintaining zinc homeostasis in bone tissues may be vital for preventing and treating diabetic bone loss, and zinc transporters may serve as novel therapeutic targets for diabetic osteoporosis. Full article

Background/Objectives: Osteoporosis is a major public health concern, due to its high risk of fractures and disability and associated medical costs. Romosozumab, an anabolic agent, has been approved for the treatment of osteoporosis in postmenopausal women at high risk of fractures. However, limited data exist on its long-term effects in the Korean population, particularly regarding its impact on bone mineral density (BMD), bone turnover markers, and body composition. This study aimed to evaluate the 12-month effects of romosozumab treatment on BMD, bone turnover markers, and body composition in postmenopausal Korean women with high-fracture-risk osteoporosis (T-scores ≤ −3.0). Additionally, the impact of concomitant postmenopausal hormone therapy (MHT) on BMD changes was assessed. Methods: This multicenter, retrospective observational study included 50 postmenopausal women diagnosed with osteoporosis (T-scores ≤ −3.0) who received 12 monthly doses of romosozumab (210 mg) at two hospitals in Korea. Changes in BMD in the lumbar spine, femoral neck, and total hip were assessed using dual-energy X-ray absorptiometry (DXA). Bone turnover markers, including procollagen type 1 N-terminal propeptide (P1NP) and C-terminal telopeptide of type 1 collagen (CTX), were measured at baseline and at 3, 6, and 12 months. Changes in body composition, including the skeletal muscle index (SMI), body mass index (BMI), and visceral adipose tissue (VAT), were also analyzed. Results: After 12 months of romosozumab treatment, BMD significantly increased at the lumbar spine (14.65%), femoral neck (6.58%), and total hip (4.19%) (p < 0.05). P1NP levels increased significantly at 3 months (+37.9%), but returned to baseline at 6 months, while CTX levels continuously decreased (−27.8%) over 12 months. No significant changes were observed in SMI or BMI, but the VAT showed a slight decreasing trend (p < 0.05). Additionally, patients receiving concomitant MHT demonstrated a significantly greater increase in lumbar spine BMD compared to those receiving romosozumab alone (p < 0.05), while no significant differences were observed in femoral neck and total hip BMD. Conclusions: This study demonstrated that 12 months of romosozumab treatment significantly improved BMD and bone turnover markers in postmenopausal Korean women with severe osteoporosis. The combination of romosozumab and MHT further enhanced lumbar spine BMD gains. These findings support the use of romosozumab as an effective treatment for high-risk osteoporotic fractures in postmenopausal Korean women, and suggest potential benefits of a combined therapeutic approach. Full article

Background: Advanced platelet-rich fibrin + (A-PRF+) represents a third generation of autologous platelet derivatives. Appropriate centrifugation conditions cause the formation of a clot containing platelets, which slowly release growth factors that influence healing. The objective of this article was to undertake a review of the available literature on the effectiveness of A-PRF+ use in hard tissue procedures. Materials and methods: In order to ensure the most accurate and relevant results, only randomized clinical trials regarding bone regeneration techniques/bone healing that compared the effect of the A-PRF+ addition in dentistry were included in this study. Articles taken into consideration for the review were published between the beginning of 2014 and 31 December 2024. The search of manuscripts for the review was conducted using the PubMed, Scopus, Google Scholar, and Cochrane databases. For this study, 10 articles focusing on A-PRF+ were qualified. Results: A-PRF+ was found to increase the post-surgical vertical and horizontal alveolar ridge dimensions. The bone formed in the surgical site presented a higher volume of vital and non-vital bone and a more optimal bone composition, at the same time providing a lower percentage of connective tissue inclusions. When combined with other grafting biomaterials, A-PRF+ enhanced their performance and integration. A-PRF+ did not have any significant effect on the mineral bone density compared with other grafting materials. Compared with PRF and other blood derived plasmas rich in growth factors, the performance of A-PRF+ was generally better, but often with no statistical significance. The treatment of periodontal defects measured by the reduction in pocket depth and clinical attachment level also fared better with the A-PRF+ addition, although there was no differences noted between A-PRF+ and biphasic calcium phosphate and xenograft. Finally, the A-PRF+ addition improved the primary implant stability in the evaluated studies. Conclusions: The A-PRF+ addition to the surgical protocols significantly enhanced the healing of the bone and when combined with biomaterials improved their integration and increased the implant insertion torque, improving the primary and secondary stability. It may be a viable alternative for patients that express their concern towards human- and animal-derived biomaterials. Full article

People engaged in various activities in cold environments—such as those living in cold climates, polar workers, cold storage workers, and athletes engaged in winter sports—are frequently affected by cold environments. Therefore, it is of great significance to explore the modelling and remodelling of bones in cold environments. Cold environments can shorten the length of bones, thin the thickness of bones, decrease bone mineral density (BMD), change the biomechanical properties of bones, and lead to bone loss. In addition, cold directly affects the bone microenvironment. Exposure to cold causes spindle-like and fibroblast-like changes in bone marrow mesenchymal stem cells (BMSCs) and decreases their proliferation, and cold exposure promotes the osteogenic differentiation of BMSCs partly through the p38 MAPK pathway. Cold also alters the dendritic differentiation of OBs by reducing the transmembrane glycoprotein E11/podoplanin and damages endothelial cells (ECs) by elevating levels of VEGF, resulting in a reduced blood supply and thus fewer OBs. In addition, cold promotes lipolysis of marrow adipose tissue (MAT), but in combination with exercise, it can promote the differentiation of BMSCs into MAT. Cold environments interfere with angiogenesis and inhibit bone growth by affecting factors such as platelet-derived growth factor type BB (PDGF-BB), slit guidance ligand 3 (SLIT3), Notch, and VEGF. In addition, cold environments may promote bone resorption by activating sympathetic nerves to activate β-adrenergic receptors and regulating leptin secretion, and regulate bone metabolism by activating the p38 MAPK signalling pathway and increasing the synthesis of brown fat, which ultimately inhibit bone formation and enhance bone resorption. In this paper, we describe the effects of cold environments on bones in the locomotor system in terms of bone structure, bone mass, biomechanical properties, and various skeletal cells, bone blood vessels, and bone fat systems in the bone microenvironment. Full article

Background/Objectives: Bone maturation and development are crucial for growth and development, especially in children and adolescents; however, some qualitative methods, such as Greulich & Pyle, do not provide accurate data. Our aim is to verify whether skeletal age (SA) can predict and correlate with bone mineral content (BMC), bone mineral density (BMD), and body composition (BC). Methods: A cross-sectional study was conducted on 115 male adolescents (ages 12.1–15.8 years). Skeletal age was assessed using the Tanner–Whitehouse 3 (TW3) method, while BMC, BMD, and BC were measured using full-body DXA. Anthropometric data, including height and body mass, were also recorded. Statistical analysis included descriptive methods and bivariate correlation coefficients. Results: SA was significantly correlated with stature (r = 0.598, p = 0.001) and body mass (r = 0.517, p = 0.001), showing a stronger association than chronological age (CA) for these variables. Body composition variables, including lean mass (LM) (r = 0.521, p = 0.001) and fat tissue (FT) (r = 0.522, p = 0.001), also showed a stronger correlation with SA than CA. However, associations between SA and bone parameters were weaker: BMC (r = 0.103, p = 0.275) and BMD (r = 0.161, p = 0.086) did not reach statistical significance. When stratified by SA/CA tertiles, individuals in the highest tertile exhibited slightly greater BMC (1439 ± 108.32 g) and BMD (1.028 ± 0.127 g/cm²), though without a significant effect. These findings suggest a dynamic but complex relationship between skeletal age and bone development. Conclusions: SA demonstrates a stronger association with anthropometric and body composition variables than CA, highlighting its potential as a predictor of growth used in conjunction with LM and FM. However, its relationship with BMD and BMC remains inconclusive, warranting further longitudinal research, considering limitations regarding nutritional intake. Full article

Osteoporosis is a metabolic bone disorder characterized by the progressive loss of bone mass, which significantly increases the risk of fractures. While dual-energy X-ray absorptiometry is the standard technique for assessing bone mineral density, its use is limited in high-risk female populations. Additionally, quantitative computed tomography offers three-dimensional evaluations of bone mineral density but is costly and prone to motion artifacts. To overcome these limitations, this study proposes a hybrid model integrating U-Net and artificial neural networks, specifically focusing on abdominal X-ray images in the anteroposterior view for detailed skeletal analysis and improved accuracy in L2 vertebra mineral density measurement. The model targets female patients, who are at a higher risk for spinal disorders and osteoporosis. The U-Net model is employed for image preprocessing to reduce background noise and enhance bone tissue features, followed by analysis with the artificial neural network model to predict bone mineral density through nonlinear regression. The performance of the model, demonstrated by a high correlation coefficient of 0.77 and a low mean absolute error of 0.08 g per square centimeter, highlights its significance and effectiveness, particularly in comparison to dual-energy X-ray absorptiometry. Full article