Background: Periodontitis is a common chronic inflammatory disease. However, drug‐related risks and underlying molecular mechanisms remain underexplored from large real‐world data. Methods: We first mined the US Food and Drug Administration Adverse Event Reporting System (FAERS) database to identify drugs disproportionately associated with periodontitis, using four signal detection algorithms and logistic regression for confounder adjustment. Identified drugs were then mapped to their protein targets via DrugBank, followed by pathway enrichment and protein–protein interaction (PPI) network analysis to explore biological relevance. To assess potential causality, we conducted Mendelian randomisation (MR) using cis‐pQTLs from UKB‐PPP and deCODE cohorts. Finally, we used single‐cell RNA sequencing (scRNA‐seq) data from gingival tissue and peripheral blood of periodontitis patients to evaluate cell type–specific expression of candidate causal genes. Results: Five drugs (actonel, aclasta, aredia, amlodipine and avastin) were significantly positively associated with periodontitis based on FAERS. VEGFA showed an association with disease risk (OR = 1.043, p = 0.049) after meta‐analysis of two cohorts. scRNA‐seq data identified high VEGFA expression in monocytes in both gingival and blood samples of periodontitis patients. Conclusion: This study uncovered the association between drug and periodontitis and highlighted VEGFA as a potential molecular mediator. Further studies are needed to confirm causality.

Dental caries is a localized deterioration of hard tissues caused by acidic by-products of bacterial fermentation of dietary carbohydrates. However, except for fluoride, there were few studies focusing on the direct association between other trace elements. This study aimed to explore how other trace elements affect dental caries. Data from 10,426 individuals were collected from the National Health and Nutrition Examination Survey (NHANES) during the 2010–2016 cycle. The sample was restricted to individuals aged 20 years or older who had complete clinical oral examination data, and who had also completed blood (N = 10,086), serum (N = 5,152), or urine tests (N = 5,297). These three groups were weighted to represent multi-year data. The effects of 20 trace elements on dental caries experience were investigated using quartiles of continuous variables and weighted multivariate logistic regression models. The analysis revealed significant differences in trace elements (lead, cadmium, mercury, molybdenum, tin, and tungsten) between individuals with no dental caries experience and those with dental caries experience (P < 0.05). Adjusted results from the weighted multivariate logistic regression model showed that the odds ratios (OR) for following trace elements were significantly positively associated with dental caries experience: lead (OR = 1.0105; 95% CI 1.0030–1.0181; P = 0.0077), cobalt (OR = 1.0048; 95% CI 1.001–1.0087; P = 0.0154), cesium (OR = 1.0028; 95% CI 1.0011–1.0046; P = 0.0027), and arsenic (OR = 1.0002; 95% CI 1-1.0004; P = 0.0244), while iron (OR = 0.9974; 95% CI 0.9954–0.9994; P = 0.0116) showed the negative association. The results of this cross-sectional study conducted on the U.S. population identified a statistically significant positive association between dental caries and trace elements, including lead, cobalt, cesium, and arsenic, while a negative association was observed with iron.

Objective This study aims to explore shared key genes between head and neck neoplasm (HNN) and aging. Methods Using single-cell RNA sequencing data of peripheral blood from HNSCC patients, aging individuals, and healthy controls, we identified cross-group co-expressed, downregulated cell subpopulations as core targets. Integrated pseudotime trajectory analysis and intercellular communication modeling were employed to investigate the dynamic evolution and functional interaction patterns of these subpopulations. Differentially expressed genes were identified, followed by Mendelian randomization (MR) analysis to assess their causal associations with HNN. Co-localization analysis was performed using GWAS data for HNN and expression quantitative trait loci (eQTL) datasets. Key genes were further subjected to metabolic pathway enrichment analysis. Results T cell subsets were found to be represented in both HNN and aging. Among them, CD4_naive T cells were down-regulated in both groups, leading to the identification of 24 differentially expressed genes. MR studies have shown that CCR, LEF1, NOSIP and FHIT have causal relationships with HNN. In the validation phase, however, only FHIT was retained, for which co-localization analysis revealed limited evidence of a shared causal variant between the GWAS and eQTL signals (H4 = 0.01). The metabolic enrichment highlighted metabolic pathways associated with these genes. Conclusions This study identified CD4_naive T cells down-regulation as a shared feature of HNN and aging and highlighted FHIT as potential molecular links. These findings may provide novel insights into the intersection of aging and tumorigenesis based on MR and single-cell analysis, offering potential targets for combined therapeutic strategies.