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Bone & Joint Research
Vol. 10, Issue 9 | Pages 619 - 628
27 Sep 2021
Maestro-Paramio L García-Rey E Bensiamar F Saldaña L

Aims. To investigate whether idiopathic osteonecrosis of the femoral head (ONFH) is related to impaired osteoblast activities. Methods. We cultured osteoblasts isolated from trabecular bone explants taken from the femoral head and the intertrochanteric region of patients with idiopathic ONFH, or from the intertrochanteric region of patients with osteoarthritis (OA), and compared their viability, mineralization capacity, and secretion of paracrine factors. Results. Osteoblasts from the intertrochanteric region of patients with ONFH showed lower alkaline phosphatase (ALP) activity and mineralization capacity than osteoblasts from the same skeletal site in age-matched patients with OA, as well as lower messenger RNA (mRNA) levels of genes encoding osteocalcin and bone sialoprotein and higher osteopontin expression. In addition, osteoblasts from patients with ONFH secreted lower osteoprotegerin (OPG) levels than those from patients with OA, resulting in a higher receptor activator of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) ligand (RANKL)-to-OPG ratio. In patients with ONFH, osteoblasts from the femoral head showed reduced viability and mineralized nodule formation compared with osteoblasts from the intertrochanteric region. Notably, the secretion of the pro-resorptive factors interleukin-6 and prostaglandin E. 2. as well as the RANKL-to-OPG ratio were markedly higher in osteoblast cultures from the femoral head than in those from the intertrochanteric region. Conclusion. Idiopathic ONFH is associated with a reduced mineralization capacity of osteoblasts and increased secretion of pro-resorptive factors. Cite this article: Bone Joint Res 2021;10(9):619–628


Bone & Joint Research
Vol. 11, Issue 8 | Pages 548 - 560
17 Aug 2022
Yuan W Yang M Zhu Y

Aims

We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism.

Methods

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.