This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes.Aims
Methods
To investigate the effects of senescent osteocytes on bone homeostasis in the progress of age-related osteoporosis and explore the underlying mechanism. In a series of in vitro experiments, we used tert-Butyl hydroperoxide (TBHP) to induce senescence of MLO-Y4 cells successfully, and collected conditioned medium (CM) and senescent MLO-Y4 cell-derived exosomes, which were then applied to MC3T3-E1 cells, separately, to evaluate their effects on osteogenic differentiation. Furthermore, we identified differentially expressed microRNAs (miRNAs) between exosomes from senescent and normal MLO-Y4 cells by high-throughput RNA sequencing. Based on the key miRNAs that were discovered, the underlying mechanism by which senescent osteocytes regulate osteogenic differentiation was explored. Lastly, in the in vivo experiments, the effects of senescent MLO-Y4 cell-derived exosomes on age-related bone loss were evaluated in male SAMP6 mice, which excluded the effects of oestrogen, and the underlying mechanism was confirmed.Aims
Methods
Osteoporosis is characterized by decreased trabecular bone volume, and microarchitectural deterioration in the medullary cavity. Blood and femoral bone marrow suspension Aims
Methods
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
Aims. The distal radius is a major site of osteoporotic bone loss resulting in a high risk of
Despite the interest in the association of gut microbiota with bone health, limited population-based studies of gut microbiota and bone mineral density (BMD) have been made. Our aim is to explore the possible association between gut microbiota and BMD. A total of 3,321 independent loci of gut microbiota were used to calculate the individual polygenic risk score (PRS) for 114 gut microbiota-related traits. The individual genotype data were obtained from UK Biobank cohort. Linear regressions were then conducted to evaluate the possible association of gut microbiota with L1-L4 BMD (n = 4,070), total BMD (n = 4,056), and femur total BMD (n = 4,054), respectively. PLINK 2.0 was used to detect the single-nucleotide polymorphism (SNP) × gut microbiota interaction effect on the risks of L1-L4 BMD, total BMD, and femur total BMD, respectively.Aims
Methods
Bone is a dynamic tissue with a quarter of the trabecular and a fifth of the cortical bone being replaced continuously each year in a complex process that continues throughout an individual’s lifetime. Bone has an important role in homeostasis of minerals with non-stoichiometric hydroxyapatite bone mineral forming the inorganic phase of bone. Due to its crystal structure and chemistry, hydroxyapatite (HA) and related apatites have a remarkable ability to bind molecules. This review article describes the accretion of trace elements in bone mineral giving a historical perspective. Implanted HA particles of synthetic origin have proved to be an efficient recruiting moiety for systemically circulating drugs which can locally biomodulate the material and lead to a therapeutic effect. Bone mineral and apatite however also act as a waste dump for trace elements and drugs, which significantly affects the environment and human health. Cite this article:
During the last decades, several research groups have used bisphosphonates for local application to counteract secondary bone resorption after bone grafting, to improve implant fixation or to control bone resorption caused by bone morphogenetic proteins (BMPs). We focused on zoledronate (a bisphosphonate) due to its greater antiresorptive potential over other bisphosphonates. Recently, it has become obvious that the carrier is of importance to modulate the concentration and elution profile of the zoledronic acid locally. Incorporating one fifth of the recommended systemic dose of zoledronate with different apatite matrices and types of bone defects has been shown to enhance bone regeneration significantly
This study aimed to assess the effect of age and osteoporosis on the proliferative and differentiating capacity of bone-marrow-derived mesenchymal stem cells (MSCs) in female rats. We also discuss the role of these factors on expression and migration of cells along the C-X-C chemokine receptor type 4 (CXCR-4) / stromal derived factor 1 (SDF-1) axis. Mesenchymal stem cells were harvested from the femora of young, adult, and osteopenic Wistar rats. Cluster of differentiation (CD) marker and CXCR-4 expression was measured using flow cytometry. Cellular proliferation was measured using Alamar Blue, osteogenic differentiation was measured using alkaline phosphatase expression and alizarin red production, and adipogenic differentiation was measured using Oil red O. Cells were incubated in Boyden chambers to quantify their migration towards SDF-1. Data was analyzed using a Student’s Objectives
Methods
