The aim of this study was to investigate the global and local impact of fat on bone in obesity by using the diet-induced obese (DIO) mouse model. In this study, we generated a diet-induced mouse model of obesity to conduct lipidomic and 3D imaging assessments of bone marrow fat, and evaluated the correlated bone adaptation indices and bone mechanical properties.Aims
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Previous studies have suggested that selenium as a trace element is involved in bone health, but findings related to the specific effect of selenium on bone health remain inconclusive. Thus, we performed a meta-analysis by including all the relevant studies to elucidate the association between selenium status (dietary intake or serum selenium) and bone health indicators (bone mineral density (BMD), osteoporosis (OP), or fracture). PubMed, Embase, and Cochrane Library were systematically searched to retrieve relevant articles published before 15 November 2022. Studies focusing on the correlation between selenium and BMD, OP, or fracture were included. Effect sizes included regression coefficient (β), weighted mean difference (WMD), and odds ratio (OR). According to heterogeneity, the fixed-effect or random-effect model was used to assess the association between selenium and bone health.Aims
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The involvement of long non-coding RNA (lncRNA) in bone marrow mesenchymal stem cell (MSC) osteogenic differentiation during osteoporosis (OP) development has attracted much attention. In this study, we aimed to disclose how LINC01089 functions in human mesenchymal stem cell (hMSC) osteogenic differentiation, and to study the mechanism by which LINC01089 regulates MSC osteogenesis. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) and western blotting were performed to analyze LINC01089, miR-1287-5p, and heat shock protein family A (HSP70) member 4 (HSPA4) expression. The osteogenic differentiation of MSCs was assessed through alkaline phosphatase (ALP) activity, alizarin red S (ARS) staining, and by measuring the levels of osteogenic gene marker expressions using commercial kits and RT-qPCR analysis. Cell proliferative capacity was evaluated via the Cell Counting Kit-8 (CCK-8) assay. The binding of miR-1287-5p with LINC01089 and HSPA4 was verified by performing dual-luciferase reporter and RNA immunoprecipitation (RIP) experiments.Aims
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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
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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
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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
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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:
Osteoporosis is a systemic bone metabolic disease, which often occurs among the elderly. Angelica polysaccharide (AP) is the main component of angelica sinensis, and is widely used for treating various diseases. However, the effects of AP on osteoporosis have not been investigated. This study aimed to uncover the functions of AP in mesenchymal stem cell (MSC) proliferation and osteoblast differentiation. MSCs were treated with different concentrations of AP, and then cell viability, Cyclin D1 protein level, and the osteogenic markers of runt-related transcription factor 2 (RUNX2), osteocalcin (OCN), alkaline phosphatase (ALP), bone morphogenetic protein 2 (BMP-2) were examined by Cell Counting Kit-8 (CCK-8) and western blot assays, respectively. The effect of AP on the main signalling pathways of phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) and Wnt/β-catenin was determined by western blot. Following this, si-H19#1 and si-H19#2 were transfected into MSCs, and the effects of H19 on cell proliferation and osteoblast differentiation in MSCs were studied. Finally, Objectives
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Advanced glycation end-products (AGEs) are a post-translational modification of collagen that form spontaneously in the skeletal matrix due to the presence of reducing sugars, such as glucose. The accumulation of AGEs leads to collagen cross-linking, which adversely affects bone quality and has been shown to play a major role in fracture risk. Thus, intervening in the formation and accumulation of AGEs may be a viable means of protecting bone quality. An Objectives
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