Plasma lipids, alcohol intake frequency and risk of Osteoarthritis: a Mendelian randomization study.

BACKGROUD: Plasma lipids and alcohol intake frequency have been reported to be associated with the risk of osteoarthritis (OA). However, it remains inconclusive whether plasma lipids and alcohol intake frequency play a role in the development of OA. METHODS: The study employed a comprehensive genome-wide association database to identify independent genetic loci strongly linked to plasma lipids and alcohol intake frequency, which were used as instrumental variables. The causal association between plasma lipids, alcohol intake frequency, and the risk of OA was then analyzed using two-sample Mendelian randomization methods such as inverse variance weighted (IVW), MR-Egger regression, and weighted median estimator (WME), with odds ratios (ORs) as the evaluation criteria. RESULTS: A total of 392 SNPs were included as instrumental variables in this study, including 32 for total cholesterol (TC), 39 for triglycerides (TG), 170 for high-density lipoproteins (HDL), 60 for low-density lipoproteins (LDL), and 91 for alcohol intake frequency. Using the above two-sample Mendelian Randomization method to derive the causal association between exposure and outcome, with the IVW method as the primary analysis method and other MR analysis methods complementing IVW. The results of this study showed that four exposure factors were causally associated with the risk of OA. TC obtained a statistically significant result for IVW (OR = 1.207, 95% CI: 1.018-1.431, P = 0.031); TG obtained a statistically significant result for Simple mode (OR = 1.855, 95% CI: 1.107-3.109, P = 0.024); LDL obtained three statistically significant results for IVW, WME and Weighted mode (IVW: OR = 1.363, 95% CI: 1.043-1.781, P = 0.023; WME: OR = 1.583, 95% CI: 1.088-2.303, P = 0.016; Weighted mode: OR = 1.521, 95% CI: 1.062-2.178, P = 0.026). Three statistically significant results were obtained for alcohol intake frequency with IVW, WME and Weighted mode (IVW: OR = 1.326, 95% CI: 1.047-1.678, P = 0.019; WME: OR = 1.477, 95% CI: 1.059-2.061, P = 0.022; Weighted mode: OR = 1.641, 95% CI: 1.060-2.541, P = 0.029). TC, TG, LDL, and alcohol intake frequency were all considered as risk factors for OA. The Cochran Q test for the IVW and MR-Egger methods indicated intergenic heterogeneity in the SNPs contained in TG, HDL, LDL, and alcohol intake frequency, and the test for pleiotropy indicated a weak likelihood of pleiotropy in all causal analyses. CONCLUSIONS: The results of two-sample Mendelian randomization analysis showed that TC, TG, LDL, and alcohol intake frequency were risk factors for OA, and the risk of OA increased with their rise.

Pyroptosis -related potential diagnostic biomarkers in steroid-induced osteonecrosis of the femoral head.

PURPOSE: Steroid-induced necrosis of the femoral head (SONFH) is a refractory orthopedic hip disease occurring in young and middle-aged people, with glucocorticoids being the most common cause. Previous experimental studies have shown that cell pyroptosis may be involved in the pathological process of SONFH, but its pathogenesis in SONFH is still unclear. This study aims to screen and validate potential pyroptosis-related genes in SONFH diagnosis by bioinformatics analysis to further elucidate the mechanism of pyroptosis in SONFH. METHODS: There were 33 pyroptosis-related genes obtained from the prior reviews. The mRNA expression was downloaded from GSE123568 dataset in the Gene Expression Omnibus (GEO) database, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples. The pyroptosis-related differentially expressed genes involved in SONFH were identified with "affy" and "limma" R package by intersecting the GSE123568 dataset with pyroptosis genes. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the pyroptosis-related differentially expressed genes involved in SONFH were conducted by "clusterProfiler" R package and visualized by "GOplot" R package. Then, the correlations between the expression levels of the pyroptosis-related differentially expressed genes involved in SONFH were confirmed with "corrplot" R package. Moreover, the protein-protein interaction (PPI) network was analysed by using GeneMANIA database. Next, The ROC curve of pyroptosis-related differentially expressed genes were analyzed by "pROC" R package. RESULTS: A total of 10 pyroptosis-related differentially expressed genes were identified between the peripheral blood samples of SONFH patients and non-SONFH patients based on the defined criteria, including 20 upregulated genes and 10 downregulated genes. The GO and KEGG pathway enrichment analyses revealed that these 10 pyroptosis-related differentially expressed genes involved in SONFH were particularly enriched in cysteine-type endopeptidase activity involved in apoptotic process, positive regulation of interleukin-1 beta secretion and NOD-like receptor signaling pathway. Correlation analysis revealed significant correlations among the 10 differentially expressed pyroptosis-related genes involved in SONFH. The PPI results demonstrated that the 10 pyroptosis-related differentially expressed genes interacted with each other. Compared to non-SONFH samples, these pyroptosis-related differentially expressed genes had good predictive diagnostic efficacy (AUC = 1.000, CI = 1.000-1.000) in the SONFH samples, and NLRP1 had the highest diagnostic value (AUC: 0.953) in the SONFH samples. CONCLUSIONS: There were 10 potential pyroptosis-related differentially expressed genes involved in SONFH were identified via bioinformatics analysis, which might serve as potential diagnostic biomarkers because they regulated pyroptosis. These results expand the understanding of SONFH associated with pyroptosis and provide new insights to further explore the mechanism of action and diagnosis of pyroptosis associated in SONFH.

Comparing musculoskeletal and connective tissue disorder risks of teriparatide and abaloparatide in osteoporosis: an analysis based on FDA adverse event reporting system (FAERS).

BACKGROUND: Osteoporosis (OP), characterized by low bone mass and increased fracture risk, is a prevalent skeletal disorder. Teriparatide (TP) and abaloparatide (ABL) are anabolic agents that may reduce fracture incidence, but their impact on musculoskeletal and connective tissue disorders (MCTD) risk is uncertain. RESEARCH DESIGN AND METHODS: A retrospective, observational disproportionality analysis was conducted utilizing FAERS data from Q1 2004 to Q3 2023, where TP or ABL was identified as the primary suspect drug. Multiple data mining algorithms, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS), were employed to detect MCTD safety signals. RESULTS: A total of 366,747 TP-related and 422,377 ABL-related cases were identified, predominantly among female patients aged ≥45 years. The top specific AEs involved musculoskeletal, connective tissue, and administration site disorders. Comparative analysis revealed a higher frequency of AEs related to the nervous, cardiovascular, and gastrointestinal systems for ABL compared to TP. Both drugs exhibited strong signals for arthralgia, limb pain, back pain, muscle spasms, bone pain, muscle pain, and muscle weakness. CONCLUSION: The analysis suggests a potential MCTD risk with TP and ABL treatment in OP patients, highlighting the need for AE monitoring and management in clinical practice. This contributes to a better understanding of the safety profiles of these anabolic medications.

Indications and adverse events of teriparatide: based on FDA adverse event reporting system (FAERS).

Background: Teriparatide is approved for osteoporosis. Post-marketing surveillance is critical given its widespread use. Objective: To investigate adverse events (AEs) associated with teriparatide using the FAERS database, compare association strengths for key AEs, and explore potential applications to provide clinical reference. Methods: FAERS data from 2004 to 2023 were analyzed. Reports where teriparatide was the primary suspect drug were included. Adverse events were mapped to System Organ Classes and Preferred Terms. Disproportionality analysis using ROR, PRR, BCPNN and EBGM algorithms was conducted to detect safety signals. Results: Out of 107,123 reports with teriparatide as the primary suspect, key AEs identified included pain in extremity (PRR: 4.54), muscle spasms (PRR: 5.11), fractures (PRR range: 17.67-552.95), and increased calcium levels (PRR: 50.73). Teriparatide exhibited a stronger association with increased calcium levels (PRR: 50.73) compared to fractures (PRR range: 17.67-552.95). Notably, only 10.86% of AE reports were submitted by physicians and another 10% by other health professionals. Subset analyses showed a higher consistency of reported AEs from health professionals compared to the general dataset. Off-label uses were noted in conditions such as arthritis (0.57%) and cancer (0.12%). For osteoporosis, main AEs were pain (18.2%), fractures (12.4%), muscle spasms (7.7%), and nausea (6.5%), while glucocorticoid-induced osteoporosis AEs included fractures (24.1%), pain (13.2%), decreased bone density (9.8%), and nausea (5.1%). Conclusion: Our findings provide real-world safety data on teriparatide, revealing key AEs and their association strengths. The low proportion of reports by healthcare professionals suggests the need for cautious interpretation. Continuous vigilance and further research are imperative to guide teriparatide's clinical use.

IRF8 and its related molecules as potential diagnostic biomarkers or therapeutic candidates and immune cell infiltration characteristics in steroid-induced osteonecrosis of the femoral head.

PURPOSE: Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people, but the pathogenesis of SONFH remained unclear. We aimed to identify the potential genes and screen potential therapeutic compounds for SONFH. METHODS: The microarray was obtained for blood tissue from the GEO database, and then it identifies differentially expressed genes (DEGs). The DEGs were analyzed to obtain the differences in immune cell infiltration. The gene functional enrichment analysis of SONFH was analyzed. The PPI of DEGs was identified through the STRING database, and the cluster modules and hub genes were ascertained using MCODE and CytoHubba, and the ROC curve of hub genes was analyzed, and the tissues distribution of hub genes was understood by the HPA, Bgee and BioGPS databases. The hub genes and target miRNAs and corresponding upstream lncRNAs were predicted by TargetScan, miRDB and ENCORI database. Subsequently, we used CMap, DGIdb and L1000FWD databases to identify several potential therapeutic molecular compounds for SONFH. Finally, the AutoDockTools Vina, PyMOL and Discovery Studio were employed for molecular docking analyses between compounds and hub genes. RESULTS: The microarray dataset GSE123568 was obtained related to SONFH. There were 372 DEGs including 197 upregulated genes and 175 downregulated genes by adjusted P value < 0.01 and |log2FC|> 1. Several significant GSEA enrichment analysis and biological processes and KEGG pathway associated with SONFH were identified, which were significantly related to cytoskeleton organization, nucleobase-containing compound catabolic process, NOD-like receptor signaling pathway, MAPK signaling pathway, FoxO signaling pathway, neutrophil-mediated immunity, neutrophil degranulation and neutrophil activation involved in immune response. Activated T cells CD4 memory, B cells naïve, B cells memory, T cells CD8 and T cells gamma delta might be involved in the occurrence and development of SONFH. Three cluster modules were identified in the PPI network, and eleven hub genes including FPR2, LILRB2, MNDA, CCR1, IRF8, TYROBP, TLR1, HCK, TLR8, TLR2 and CCR2 were identified by Cytohubba, which were differed in bone marrow, adipose tissue and blood, and which had good diagnostic performance in SONFH. We identified IRF8 and 10 target miRNAs that was utilized including Targetsan, miRDB and ENCORI databases and 8 corresponding upstream lncRNAs that was revealed by ENCORI database. IRF8 was detected with consistent expression by qRT-PCR. Based on the CMap, DGIdb and L1000FWD databases, the 11 small molecular compounds that were most strongly therapeutic correlated with SONFH were estradiol, genistein, domperidone, lovastatin, myricetin, fenbufen, rosiglitazone, sirolimus, phenformin, vorinostat and vinblastine. All of 11 small molecules had good binding affinity with the IRF8 in molecular docking. CONCLUSION: The occurrence of SONFH was associated with a "multi-target" and "multi-pathway" pattern, especially related to immunity, and IRF8 and its noncoding RNA were closely related to the development of SONFH. The CMap, DGIdb and L1000FWD databases could be effectively used in a systematic manner to predict potential drugs for the prevention and treatment of SONFH. However, additional clinical and experimental research is warranted.

The Effect of the Hip Flexion Angle in Osteonecrosis of the Femoral Head Based on China-Japan Friendship Hospital Classification - A Finite Element Study.

OBJECTIVE: The alteration in the mechanical environment of the necrotic area is the primary cause of the collapse observed in osteonecrosis of the femoral head (ONFH). This study aims to evaluate the biomechanical implications of the China-Japan Friendship Hospital (CJFH) classification system and hip flexion angles on the necrotic area in ONFH using finite element analysis (FEA). The goal is to provide valuable guidance for hip preservation treatments and serve as a reference for clinical diagnosis and therapeutic interventions. METHODS: Hip tomography CT scan data from a healthy volunteer was used to create a 3D model of the left hip. The model was preprocessed and imported into Solidworks 2018, based on the CJFH classification. Material parameters and boundary conditions were applied to each fractal model in ANSYS 21.0. Von Mises stresses were calculated, and maximum deformation values were obtained to evaluate the biomechanical effects of the load on the necrotic area and post-necrotic femur, as well as assess each fractal model's collapse risk. RESULTS: (1) At the same hip flexion angle, maximum deformation followed this order: M Type < C Type < L Type. The L3 type necrotic area experienced the most significant deformation at 0, 60, and 110° angles (1.121, 1.7913, and 1.8239 mm respectively). (2) Under the same CJFH classification, maximum deformation values increased with hip flexion angle (0 < 60 < 110°), suggesting a higher risk of collapse at larger angles. (3) Von Mises stress results showed that the maximum stress was not located in the necrotic area but near the inner and outer edge of the femoral neck, indicating decreased stiffness and strength of the subchondral bone after osteonecrosis. CONCLUSION: The study found that femoral head collapse risk was higher when the necrotic area was located in the lateral column under the same stress load and flexion angle. Mechanical properties of the necrotic area changed, resulting in decreased bone strength and stiffness. Large-angle hip flexion is more likely to cause excessive deformation of the necrotic area; thus, ONFH patients should reduce or avoid large-angle hip flexion during weight-bearing training in rehabilitation activities.

Identification of potential autophagy-related genes in steroid-induced osteonecrosis of the femoral head via bioinformatics analysis and experimental verification.

PURPOSE: Steroid-induced osteonecrosis of the femoral head (SONFH) is a refractory orthopaedic hip joint disease that occurs in young- and middle-aged people. Previous experimental studies have shown that autophagy might be involved in the pathological process of SONFH, but the pathogenesis of autophagy in SONFH remains unclear. We aimed to identify and validate the key potential autophagy-related genes involved in SONFH to further illustrate the mechanism of autophagy in SONFH through bioinformatics analysis. METHODS: The GSE123568 mRNA expression profile dataset, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples, was downloaded from the Gene Expression Omnibus (GEO) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). The autophagy-related genes involved in SONFH were screened by intersecting the GSE123568 dataset with the set of autophagy genes. The differentially expressed autophagy-related genes involved in SONFH were identified with R software. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially expressed autophagy-related genes involved in SONFH were conducted by using R software. Then, the correlations between the expression levels of the differentially expressed autophagy-related genes involved in SONFH were confirmed with R software. Moreover, the protein-protein interaction (PPI) network was analysed by using the Search Tool for the Retrieval of Interacting Genes (STRING), significant gene cluster modules were identified with the MCODE Cytoscape plugin, and hub genes among the differentially expressed autophagy-related genes involved in SONFH were screened by using the CytoHubba Cytoscape plugin. Finally, the expression levels of the hub genes of the differentially expressed autophagy-related genes involved in SONFH were validated in hip articular cartilage specimens from necrotic femur heads (NFHs) by using the GSE74089 dataset and further verification by qRT-PCR. RESULTS: A total of 34 differentially expressed autophagy-related genes were identified between the peripheral blood samples of SONFH patients and non-SONFH patients based on the defined criteria, including 25 upregulated genes and 9 downregulated genes. The GO and KEGG pathway enrichment analyses revealed that these 34 differentially expressed autophagy-related genes involved in SONFH were particularly enriched in death domain receptors, the FOXO signalling pathway and apoptosis. Correlation analysis revealed significant correlations among the 34 differentially expressed autophagy-related genes involved in SONFH. The PPI results demonstrated that the 34 differentially expressed autophagy-related genes interacted with each other. Ten hub genes were identified by using the MCC algorithms of CytoHubba. The GSE74089 dataset showed that TNFSF10, PTEN and CFLAR were significantly upregulated while BCL2L1 was significantly downregulated in the hip cartilage specimens, which was consistent with the GSE123568 dataset. TNFSF10, PTEN and BCL2L1 were detected with consistent expression by qRT-PCR. CONCLUSIONS: Thirty-four potential autophagy-related genes involved in SONFH were identified via bioinformatics analysis. TNFSF10, PTEN and BCL2L1 might serve as potential drug targets and biomarkers because they regulate autophagy. These results expand the autophagy-related understanding of SONFH and might be useful in the diagnosis and prognosis of SONFH.