Background: Osteomyelitis (OM) is a severe bone infection with rising incidence rates, particularly among elderly and diabetic patients. The immune–inflammatory axis is implicated in OM pathogenesis, but its complex interplay remains poorly defined. This study aims to explore the causal relationships between immunophenotypes, plasma inflammatory proteins, and OM using Mendelian randomization (MR) and bioinformatics. Methods: Utilizing publicly available genetic data, we undertook a series of quality control measures to identify instrumental variables (IVs) associated with exposure. Subsequently, we conducted MR using inverse variance weighting to explore the causal relationships between immunophenotypes, plasma inflammatory proteins, and OM. Bioinformatics tools were applied to explore the functional enrichment and protein–protein interaction networks of the implicated genes. Results: The MR analysis identified 13 immune cell phenotypes and 2 plasma inflammatory proteins associated with risk of OM. Notably, higher levels of HLA DR on plasmacytoid dendritic cells, memory B cell absolute counts, and CD8dim T cell percentages were associated with increased OM risk. Additionally, elevated levels of CD6 and IL‐12 subunit B were correlated with OM risk. Bioinformatics analysis revealed the enrichment of related genes in immune‐related pathways and highlighted the complex interaction networks of the implicated proteins. Conclusions: This study provides novel insights into the immune–inflammatory axis in OM and identifies potential biomarkers for risk assessment. The findings warrant further validation in diverse populations and pave the way for developing targeted preventive and therapeutic strategies for OM.

Objective. To study the role of bacterial biofilm (BBF) in the formation of chronic osteomyelitis and its prevention and treatment. Methods. In this paper, a large amount of relevant literature was searched for analysis and summary, and the key words chronic osteomyelitis, bacterial biofilm, infection, and debridement were searched in databases, mainly CNKI, Wanfang, and Wipu. The search was conducted until December 2020. The role of bacterial biofilm formation in chronic osteomyelitis and its prevention were analyzed. Results. Chronic osteomyelitis is formed mainly due to poor blood supply and drug-resistant bacteria, of which cellular biofilm is the most important cause. BBF forms on the surface of necrotic soft tissue and bone tissue, which has a protective effect on bacteria and greatly enhances their resistance to antibiotics, leading to difficulties in complete bacterial clearance and recurrent infections in osteomyelitis. Conclusion. Through an in-depth study of the molecular biology and signal transduction of osteomyelitis biofilm, antibiotic biofilm treatment strategies and surgical debridement remain the focus of clinical translation of chronic osteomyelitis.

Background: Osteomyelitis (OM) is a serious infection of the bone, often resulting from diabetic foot ulcers, trauma, or surgical interventions. Given its complex pathophysiology and recurrent nature, identifying reliable risk factors is essential for early diagnosis and intervention. This study identifies independent OM risk factors through a systematic review and meta-analysis method. Methods: A search was conducted across several databases up to January 2025. Data were extracted independently, and study quality was assessed using the Newcastle–Ottawa Scale. Statistical analysis was performed with RevMan 5.3 and Stata 15.0 software, using univariate and multivariate analyses. Heterogeneity was assessed by methods of subgroup analysis and sensitive analysis, and publication bias was evaluated with Egger’s test and funnel plots. Results: A total of 4019 potential articles were systematically reviewed, with 27 studies (n = 11 941 participants) were included. These studies were analyzed to identify risk factors across five categories: demographic features, medical history, clinical features, laboratory findings, and bacterial characteristics. Medical history factors, such as history of foot ulcers/foot disease (OR = 2.520), operation duration time (>3 hours, OR = 1.740), and incision length >10 cm (OR = 6.530), were significant. Clinical features including inflamed ulcer (OR = 6.200), fever (OR = 2.200), and ulcer size >4/5 cm ² (OR = 2.740) also increased OM risk. Laboratory findings such as elevated HbA1c (OR = 1.140), hypoalbuminemia (OR = 1.740), anemia (OR = 1.540), and microbiological perspective such as polymicrobial infections (OR = 2.580) were also recognized as independent risk factors of OM. Subgroup analysis accounted for heterogeneity arising from the type of OM and different risk factor characteristics. Sensitivity analysis confirmed the reliability of the results. Additionally, both funnel plots and Egger tests showed no evidence of publication bias. Conclusions: This is the first meta-analysis to integrate univariate and multivariate evidence on OM risk factors. It establishes clinically preventable risk factors across OM subtypes, enabling early targeted interventions to reduce amputations and healthcare burdens.

Background: Diabetes peripheral neuropathy (DPN) is mainly treated with diabetes as a whole, and there is no targeted treatment. Some studies have reported that adjuvant hyperbaric oxygen therapy (HBOT) for DPN has achieved a good effect, our study aimed to evaluate the clinical efficacy and safety of HBOT for DPN and provide reference for the clinic by using a systematic review and meta-analysis. Methods: A comprehensive search was conducted across several databases, including PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Internet Database, Chinese BioMedical Database, China Scientific Journal Database, and Wanfang Database, for relevant randomized controlled trials published before July 2022. The population, intervention, comparison, outcomes, study design criteria were used to guide the selection of studies. Meta-analysis was performed using RevMan 5.4 and STATA 14.0, with odds ratios and mean differences along with 95% confidence intervals serving as measures of effect size. Results: Fourteen randomized controlled trials were included in the final analysis, comprising 675 patients in the HBOT group and 648 in the standard therapy (ST) group. The HBOT group demonstrated a significantly higher effective treatment rate compared to the ST group (P < .001). Additionally, the HBOT group showed significant improvements in motor nerve conduction velocity (MNCV) and sensory nerve conduction velocity (SNVC) across multiple nerves: median nerve (PMNCV < 0.001, PSNCV = 0.001), ulnar nerve (PMNCV = 0.02, PSNCV < 0.001), peroneal nerve (PMNCV < 0.001, PSNCV < 0.001), and tibial nerve (PMNCV = 0.001, PSNCV = 0.008). Six adverse events were reported in the HBOT group, while no adverse events occurred in the ST group, with no significant difference between the 2 groups. Publication bias was identified in some outcome variables through funnel plots, Begger test, and Egger test. Conclusions: HBOT significantly enhances treatment efficacy and nerve conduction velocity in patients with DPN, with few adverse events, making it a safe and effective adjunctive therapy for DPN.

Limited drug treatment data are available for osteomyelitis (OM), an inflammatory bone condition secondary to infection. Given its genetic characteristics, it is necessary to integrate genetics into drug development for osteomyelitis. This study applied pharmacogenomics to identify new drug targets for osteomyelitis using Mendelian randomization (MR). Following the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization guidelines, expression and protein quantitative trait loci (QTL) analysis was applied to simulate drug exposure. Single nucleotide polymorphisms were selected as instrumental variables for MR analysis using blood QTL data and independent osteomyelitis genome-wide association study datasets from UK Biobank and FinnGen R10. A random-effects model meta-analysis combining the results from two datasets was performed. Bayesian co-localization analysis was conducted to validate the targets. Sensitivity analyses were performed using various MR methods, with MR-Egger regression and Cochran’s Q test being conducted to assess the horizontal pleiotropy and heterogeneity of the instrumental variables. At α = 1 × 10−5, the meta-analysis identified 12 drug target mechanisms. Gene expression of QDPR, TGM1, NTSR1, CBR3, and NEK6 was positively correlated with osteomyelitis risk, whereas HLA-DRB1, LAMC1, LTB4R, MAPK3, FPR1, ABAT, and LTA4H were negatively correlated with this risk. Five potential drug repurposing opportunities and three drugs that may increase osteomyelitis risk were identified. Sensitivity analyses highlighted LTA4H, LAMC1, QDPR, and NEK6 as having the strongest genetic evidence based on MR-Egger regression and protein QTL tests. This study identified 12 new genetically supported drug targets for osteomyelitis, thereby providing a genetic foundation for new drug development, repurposing existing drugs, and personalized treatment.