Aims. Despite the interest in the association of
Aims. The effect of the
We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature.Aims
Methods
Osteoporosis (OP) is a chronic metabolic bone disease characterized by the decrease of bone tissue per unit volume under the combined action of genetic and environmental factors, which leads to the decrease of bone strength, makes the bone brittle, and raises the possibility of bone fracture. However, the exact mechanism that determines the progression of OP remains to be underlined. There are hundreds of trillions of symbiotic bacteria living in the human gut, which have a mutually beneficial symbiotic relationship with the human body that helps to maintain human health. With the development of modern high-throughput sequencing (HTS) platforms, there has been growing evidence that the gut microbiome may play an important role in the programming of bone metabolism. In the present review, we discuss the potential mechanisms of the gut microbiome in the development of OP, such as alterations of bone metabolism, bone mineral absorption, and immune regulation. The potential of gut microbiome-targeted strategies in the prevention and treatment of OP was also evaluated. Cite this article:
The metabolic variations between the cartilage of osteoarthritis (OA) and Kashin-Beck disease (KBD) remain largely unknown. Our study aimed to address this by conducting a comparative analysis of the metabolic profiles present in the cartilage of KBD and OA. Cartilage samples from patients with KBD (n = 10) and patients with OA (n = 10) were collected during total knee arthroplasty surgery. An untargeted metabolomics approach using liquid chromatography coupled with mass spectrometry (LC-MS) was conducted to investigate the metabolomics profiles of KBD and OA. LC-MS raw data files were converted into mzXML format and then processed by the XCMS, CAMERA, and metaX toolbox implemented with R software. The online Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the metabolites by matching the exact molecular mass data of samples with those from the database.Aims
Methods
There is an increasing concern of osteoporotic fractures in the ageing population. Low-magnitude high-frequency vibration (LMHFV) was shown to significantly enhance osteoporotic fracture healing through alteration of osteocyte lacuno-canalicular network (LCN). Dentin matrix protein 1 (DMP1) in osteocytes is known to be responsible for maintaining the LCN and mineralization. This study aimed to investigate the role of osteocyte-specific DMP1 during osteoporotic fracture healing augmented by LMHFV. A metaphyseal fracture was created in the distal femur of ovariectomy-induced osteoporotic Sprague Dawley rats. Rats were randomized to five different groups: 1) DMP1 knockdown (KD), 2) DMP1 KD + vibration (VT), 3) Scramble + VT, 4) VT, and 5) control (CT), where KD was performed by injection of short hairpin RNA (shRNA) into marrow cavity; vibration treatment was conducted at 35 Hz, 0.3 g; 20 minutes/day, five days/week). Assessments included radiography, micro-CT, dynamic histomorphometry and immunohistochemistry on DMP1, sclerostin, E11, and fibroblast growth factor 23 (FGF23). In vitro, murine long bone osteocyte-Y4 (MLO-Y4) osteocyte-like cells were randomized as in vivo groupings. DMP1 KD was performed by transfecting cells with shRNA plasmid. Assessments included immunocytochemistry on osteocyte-specific markers as above, and mineralized nodule staining.Aims
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