Background: Osteoarthritis (OA) is a common chronic degenerative joint disease and a major contributor to disability and elevated morbidity rates worldwide. This study assesses the epidemiological trends of OA from 1990 to 2021, analyzing data by sex, affected joint sites, and Socio-Demographic Index (SDI) across global, regional, and national levels.Methods: Data on OA were obtained from the (Global Burden of Disease Study) 2021. The age-standardized rates (ASR) for OA were computed, and the estimated annual percentage changes (EAPC) in ASR were determined to evaluate trends in incidence and disability-adjusted life years (DALYs) over the past three decades. Pearson’s correlation analysis was conducted to explore the relationship between ASR and the Socio-Demographic Index (SDI). Additionally, Joinpoint regression software and age-period-cohort (APC) analysis were applied for a comprehensive examination of the OA data.Results: From 1990 to 2021, the global burden of OA has markedly increased. In 2021, there were approximately 466.3 million new OA cases, with an ASR of incidence (ASIR) of around 535 per 100,000 population. The prevalence of OA reached about 606.9 million cases, and DALYs rose to approximately 213 million. The burden of OA is significantly higher in women compared to men, as reflected by higher ASR of incidence, prevalence, and DALYs associated with OA. In 2021, the ASR of incidence was positively associated with the SDI regions. Globally, knee osteoarthritis (KOA) remains the most common form of OA. Among the various risk factors, high body mass index (BMI) emerged as the most critical contributor to OA.Conclusion: From 1990 to 2021, the global burden of OA has steadily increased, leading to a significant decline in health and overall quality of life. The global prevalence of OA indicates higher incidence rates among women and in countries with a higher SDI. Governments and policy makers globally must prioritize increasing awareness of the risk factors and consequences related to OA, promote early diagnostic and therapeutic services, and implement targeted interventions to mitigate the growing burden of OA.

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.

Falls represent the second leading cause of injury-related mortality among older adults globally. The occurrence of falls is the consequence of the interaction of numerous complex risk factors. The objective of this study was to develop a validated fall risk prediction model for the Chinese older individuals. The study used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Thirty-eight indicators including biological factors, behavioral factors and health status were analyzed in this study. The study cohort was randomly divided into the training set (70%) and the validation set (30%). Variables were screened using LASSO regression analysis, the best predictive model based on 10-fold cross-validation, logistic regression model was applied to explore the correlates of fall risk in the older individuals, a nomogram was constructed to develop the predictive model, calibration curves were applied to assess the accuracy of the nomogram model, and predictive performance was assessed by area under the receiver operating characteristic curve and decision curve analysis. A total of 4,913 cases from the 2015 CHARLS database for people aged 60 years and older were ultimately included, and a total of 1,082 (22.02%) of the older individuals had experienced a fall within two years. Multivariate logistic regression analysis showed that Sleeping time, Hearing, Grip strength, ADL score, Cognition, Depression, Health, KD, and Pain DRUG were predictors of fall risk in the older individuals. These factors were used to construct nomogram models that showed good agreement and accuracy. The AUC value for the predictive model was 0.644 (95% CI = 0.621–0.666), with a specificity of 0.695 and a sensitivity of 0.522. For the internal validation set, the AUC value was 0.644 (95% CI = 0.611–0.678), with a specificity of 0.629 and a sensitivity of 0.577. The Hosmer-Lemeshow test value of the model for the training set is p = 0.9368 and for the validation set is p = 0.8545 (both > 0.05). The calibration curves show a more significant agreement between the nomogram model and the actual observations. The ROC and DCA indicate a better predictive performance of the nomogram. The comprehensive nomogram constructed in this study is a promising and convenient tool for assessing the risk of falls in the Chinese older individuals and to help older adults understand the risk level of falls, avoid and eliminate modifiable risk factors, and reduce the incidence of falls. Not applicable.

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.

Background: Osteonecrosis of the femoral head (ONFH) is a common, refractory and disabling disease of orthopedic department, which is one of the common causes of hip pain and dysfunction. Recent studies have shown that much progress has been made in the research of programmed cell death (PCD) in ONFH. However, there is no bibliometric analysis in this research field. This study aims to provide a comprehensive overview of the knowledge structure and research hot spots of PCD in ONFH through bibliometrics.Method: The literature search related to ONFH and PCD was conducted on the Web of Science Core Collection (WoSCC) database from 2002 to 2021. The VOSviewers, “bibliometrix” R package and CiteSpace were used to conduct this bibliometric analysis.Results: In total, 346 articles from 27 countries led by China and USA and Japan were included. The number of publications related to PCD in ONFH is increasing year by year. Shanghai Jiao Tong University, Xi An Jiao Tong University, Wuhan University and Huazhong University of Science and Technology are the main research institutions. Molecular Medicine Reports is the most popular journal in the field of PCD in ONFH, and Clinical Orthopaedics and Related Research is the most cocited journal. These publications come from 1882 authors among which Peng Hao, Sun Wei, Zhang Chang-Qing, Zhang Jian and Wang Kun-zheng had published the most papers and Ronald S Weinstein was cocited most often. Apoptosis, osteonecrosis, osteonecrosis of the femoral head, glucocorticoid and femoral head appeared are the main topics the field of PCD in ONFH. Autophagy was most likely to be the current research hot spot for PCD in ONFH.Conclusion: This is the first bibliometric study that comprehensively summarizes the research trends and developments of PCD in ONFH. This information identified recent research frontiers and hot directions, which will provide a reference for scholars studying PCD in ONFH.

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.