Genetically Predicted Sex Hormone-Binding Globulin and Bone Mineral Density: A Mendelian Randomization Study.

Previous observational studies have identified various risk factors associated with the development of osteoporosis, including sex hormone-binding globulin (SHBG). The aim of this study was to determine the potential causal effects of circulating SHBG concentrations on bone mineral density (BMD). Two-sample Mendelian randomization (MR) approach was applied in analyses. From summary-level data of genome-wide association studies (GWAS), we selected 11 single-nucleotide polymorphisms (SNPs) associated with SHBG levels as instrumental variable, and used summary statistics for BMD at forearm (FA) (n = 8143), femoral neck (FN) (n = 32,735), lumbar spine (LS) (n = 28,498) and heel (HL) (n = 394,929), and total-body BMD of different age-stages (15 or less, 15-30, 30-45, 45-60, 60 or more years old) (n = 67,358). Inverse causal associations was observed between SHBG levels and FA BMD (Effect = - 0.26; 95% CI - 0.49 to - 0.04; P = 0.022), HL eBMD (Effect = - 0.09; 95% CI - 0.12 to - 0.06; P = 3.19 × 10-9), and total-body BMD in people aged 45-60 years (Effect = - 0.16; 95% CI - 0.31 to - 2.4 × 10-3; P = 0.047) and over 60 years (Effect = - 0.19; 95% CI - 0.33 to - 0.05; P = 0.006). Our study demonstrates that circulating SHBG concentrations are inversely associated with FA and HL eBMD, and total-body BMD in people aged over 45 years, suggesting that the role of SHBG in the development of osteoporosis might be affected by chronological age of patients and skeletal sites.

Identification of subgroups and development of prognostic risk models along the glycolysis-cholesterol synthesis axis in lung adenocarcinoma.

Lung cancer is one of the most dangerous malignant tumors affecting human health. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Both glycolytic and cholesterogenic pathways play critical roles in metabolic adaptation to cancer. A dataset of 585 LUAD samples was downloaded from The Cancer Genome Atlas database. We obtained co-expressed glycolysis and cholesterogenesis genes by selecting and clustering genes from Molecular Signatures Database v7.5. We compared the prognosis of different subtypes and identified differentially expressed genes between subtypes. Predictive outcome events were modeled using machine learning, and the top 9 most important prognostic genes were selected by Shapley additive explanation analysis. A risk score model was built based on multivariate Cox analysis. LUAD patients were categorized into four metabolic subgroups: cholesterogenic, glycolytic, quiescent, and mixed. The worst prognosis was the mixed subtype. The prognostic model had great predictive performance in the test set. Patients with LUAD were effectively typed by glycolytic and cholesterogenic genes and were identified as having the worst prognosis in the glycolytic and cholesterogenic enriched gene groups. The prognostic model can provide an essential basis for clinicians to predict clinical outcomes for patients. The model was robust on the training and test datasets and had a great predictive performance.

Comparative Efficacy of Pharmacological Therapies for Low Back Pain: A Bayesian Network Analysis.

Low back pain (LBP) is a common problem, but the efficacy of pharmacological therapies remains controversial. Therefore, we aimed to comprehensively evaluate and quantitatively rank various pharmacological therapies for patients with low back pain. Two meta-analyses were performed: an initial pair-wise meta-analysis, followed by network meta-analysis using a random-effects Bayesian model. We included randomized controlled trials comparing placebos, non-steroidal anti-inflammatory drugs, opioids, skeletal muscular relaxants, pregabalin (or gabapentin), and some drug combinations. The primary and secondary outcomes were pain intensity and physical function. Eighty-eight eligible trials with 21,377 patients were included. Here, we show that only skeletal muscle relaxants significantly decreased the pain intensity of acute (including subacute) low back pain. Several kinds of drugs significantly decreased the pain of chronic low back pain, but only opioids and cyclo-oxygenase 2-selective non-steroidal anti-inflammatory drugs effectively reduced pain and improved function. Pregabalin (or gabapentin) seemed to be an effective treatment to relieve pain, but it should be used with caution for low back pain.

Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm.

Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.

Peiminine Suppresses RANKL-Induced Osteoclastogenesis by Inhibiting the NFATc1, ERK, and NF-κB Signaling Pathways.

Osteoclasts (OCs) play an important role in osteoporosis, a disease that is mainly characterized by bone loss. In our research, we aimed to identify novel approach for regulating osteoclastogenesis and thereby treating osteoporosis. Previous studies have set a precedent for screening traditional Chinese herbal extracts for effective inhibitors. Peiminine is an alkaloid extracted from the bulb of Fritillaria thunbergii Miq that reportedly has anticancer and anti-inflammatory effects. Thus, the potential inhibitory effect of peiminine on OC differentiation was investigated via a series of experiments. According to the results, peiminine downregulated the levels of specific genes and proteins in vitro and consequently suppressed OC differentiation and function. Based on these findings, we further investigated the underlying molecular mechanisms and identified the NF-κB and ERK1/2 signaling pathways as potential targets of peiminine. In vivo, peiminine alleviated bone loss in an ovariectomized mouse model.

Desktop-Stereolithography 3D Printing of a Polyporous Extracellular Matrix Bioink for Bone Defect Regeneration.

INTRODUCTION: Decellularized tendon extracellular matrix (tECM) perfectly provides the natural environment and holds great potential for bone regeneration in Bone tissue engineering (BTE) area. However, its densifying fiber structure leads to reduced cell permeability. Our study aimed to combine tECM with polyethylene glycol diacrylate (PEGDA) to form a biological scaffold with appropriate porosity and strength using stereolithography (SLA) technology for bone defect repair. METHODS: The tECM was produced and evaluated. Mesenchymal stem cell (MSC) was used to evaluate the biocompatibility of PEGDA/tECM bioink in vitro. Mineralization ability of the bioink was also evaluated in vitro. After preparing 3D printed polyporous PEGDA/tECM scaffolds (3D-pPES) via SLA, the calvarial defect generation capacity of 3D-pPES was assessed. RESULTS: The tECM was obtained and the decellularized effect was confirmed. The tECM increased the swelling ratio and porosity of PEGDA bioink, both cellular proliferation and biomineralization in vitro of the bioink were significantly optimized. The 3D-pPES was fabricated. Compared to the control group, increased cell migration efficiency, up-regulation of osteogenic differentiation RNA level, and better calvarial defect repair in rat of the 3D-pPES group were observed. CONCLUSION: This study demonstrates that the 3D-pPES may be a promising strategy for bone defect treatment.

Association between serum high-density lipoprotein cholesterol and bone health in the general population: a large and multicenter study.

This study was a cross-sectional study and enrolled 14,147 participants after excluding. We performed a large number of data analyses to indicate that HDL-C levels were related to bone health. A high HDL-C level is an independent risk factor for bone loss both in males and females. INTRODUCTION: Serum high-density lipoprotein cholesterol (HDL-C), usually called "good" cholesterol, is beneficial for preventing cardiovascular diseases. Previous studies have indicated that HDL-C levels may be related to bone mass. We performed a cross-sectional study to examine the relationship between HDL-C levels and bone mass, both in men and women. METHODS: A total of 14,147 Chinese participants from five medical centers were enrolled in this study. Pearson's correlation analyses, linear regression analyses, one-way ANOVAs, and logistic regression analyses were performed to assess the relationship between HDL-C levels and bone mass in various cohorts. RESULTS: Binary logistic regression analyses (after adjusting the confounding factors) indicated that a higher HDL-C level among males leads to a higher risk of at least osteopenia [OR (95% CI) = 1.807 (1.525, 2.142)] and osteoporosis [OR (95% CI) = 1.932 (1.291, 2.892)]. In the female group, the ORs of HDL-C for at least osteopenia [OR (95% CI) = 1.390 (1.100, 1.757)] and osteoporosis [OR (95% CI) = 1.768 (1.221, 2.560)] were still significant after adjusting for potential confounding factors except BMI. Data-standardized bivariate logistic regression analyses indicated that an increase in age is a stronger risk factor for osteoporosis and at least osteopenia than is higher HDL-C levels in females. CONCLUSIONS: A high HDL-C level is an independent risk factor for bone loss both in males and females. Compared with high HDL-C levels, an increase in age and menopause have a much more negative effect on bone mass in females.