关键词: 种植体周围炎, 结核病, 孟德尔随机化, 生物信息学, 免疫细胞, 口腔种植

Abstract: BACKGROUND: There is a correlation between peri-implantitis and tuberculosis, but the specific causal relationship and immunological mechanisms remain unclear.
OBJECTIVE: To explore the association between peri-implantitis and tuberculosis using Mendelian randomization and bioinformatics methods.
METHODS: (1) Four groups of expression profile data of peri-implantitis (the study has been approved by the relevant institutional review board) were obtained from the Gene Expression Omnibus (GEO) database (an open database developed by the National Center for Biotechnology Information (NCBI) in the United States, which collects high-throughput gene expression data, chip data, sequencing data, and other information from around the world, providing a wealth of resources for biomedical research). After batch correction, differential analysis, Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. (2) The most significant pathway from the results of the KEGG enrichment analysis was extracted as the exposure factor for the study. Data on tuberculosis and peri-implantitis (the study has been approved by the relevant institutional review board) were obtained from the Genome-Wide Association Study (GWAS) database (an open-access database developed by the MRC Integrative Epidemiology Unit at the University of Bristol, which integrates GWAS summary data from large cohorts such as UK Biobank and FinnGen, covering a wide range of human phenotypes and disease-related outcomes) as outcome factors for Mendelian randomization analysis. The inverse variance weighting method, MR-Egger regression method, weighted median method, simple mode, and weighted mode were used to evaluate the causal relationship between peri-implantitis and tuberculosis. Heterogeneity analysis, horizontal pleiotropy test, and leave-one-out sensitivity analysis were conducted to assess the reliability of the results of the Mendelian randomization analysis. (3) Immunocyte infiltration analysis was carried out to explore the correlation between tuberculosis pathway genes and immune cells. The differential expression of pathway genes was verified through differential analysis in the validation group.
RESULTS AND CONCLUSION: (1) GO enrichment analysis revealed that peri-implantitis was significantly associated with biological functions such as leukocyte-mediated immunity, lymphocyte-mediated immunity, regulation of monocyte proliferation, and phagocytosis. KEGG enrichment analysis revealed that peri-implantitis was primarily associated with tuberculosis, phagosomes, cytokine-cytokine receptor interactions, Staphylococcus aureus infection, alcoholic liver disease, and COVID-19. The most significant pathway obtained from the KEGG enrichment analysis was tuberculosis. (2) The results of the Mendelian randomization analysis showed that when tuberculosis was regarded as the exposure factor, there was a significant positive correlation between tuberculosis and peri-implantitis (inverse variance weighting: odds ratio=1.490, 95% confidence interval=1.218-1.823, P < 0.001). The results of the weighted median method were consistent with those of the inverse variance weighting method. The results of the heterogeneity analysis indicated no heterogeneity. The results of the horizontal pleiotropy test showed that there was no horizontal pleiotropy in this study. The results of the leave-one-out sensitivity analysis showed that removing any single nucleotide polymorphism individually would not affect the results of the Mendelian randomization. (3) Correlation analysis between immune cells showed that tuberculosis may increase the risk of peri-implantitis through immunosuppressive effects and promoting alveolar bone resorption. To conclusion, there is a causal association between tuberculosis and peri-implantitis, and tuberculosis may increase the risk of peri-implantitis. This study provides a new theoretical perspective for the prevention and treatment of diseases in the field of dental implantation, and is expected to promote the improvement of the risk assessment system for implant treatment in tuberculosis patients in clinical practice, and provide an important reference for reducing the incidence of peri-implantitis and optimizing treatment strategies.

Key words: peri-implantitis, tuberculosis, Mendelian randomization, bioinformatics, immune cells, dental implantation