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
To examine the relationship of sex steroid hormones with osteopenia in a nationally representative sample of men in the USA. Data on bone mineral density (BMD), serum sex hormones, dairy consumption, smoking status, and body composition were available for 806 adult male participants of the cross-sectional National Health and Nutrition Examination Survey (NHANES, 1999-2004). We estimated associations between quartiles of total and estimated free oestradiol (E2) and testosterone (T) and osteopenia (defined as 1 to 2.5 SD below the mean BMD for healthy 20- to 29-year-old men) by applying sampling weights and using multivariate-adjusted logistic regression. We then estimated the association between serum hormone concentrations and osteopenia by percentage of body fat, frequency of dairy intake, cigarette smoking status, age, and race/ethnicity.Aims
Methods
Up to 10% of fractures result in undesirable outcomes, for which female sex is a risk factor. Cellular sex differences have been implicated in these different healing processes. Better understanding of the mechanisms underlying bone healing and sex differences in this process is key to improved clinical outcomes. This study utilized a macrophage–mesenchymal stem cell (MSC) coculture system to determine: 1) the precise timing of proinflammatory (M1) to anti-inflammatory (M2) macrophage transition for optimal bone formation; and 2) how such immunomodulation was affected by male A primary murine macrophage-MSC coculture system was used to demonstrate the optimal transition time from M1 to M2 (polarized from M1 with interleukin (IL)-4) macrophages to maximize matrix mineralization in male and female MSCs. Outcome variables included Alizarin Red staining, alkaline phosphatase (ALP) activity, and osteocalcin protein secretion.Objectives
Methods
The October 2015 Spine Roundup360 looks at: Traumatic spinal cord injury under the spotlight; The odontoid peg nonunion; Driving and spinal surgery; Drains and antibiotics post-spinal surgery; Vertebroplasty and kyphoplasty equally effective; Who will benefit from steroid injections?; Back pain following lumbar discectomy
