Integrated transcriptomic profiling combined with

Objective: Periodontitis (PD) is a prevalent chronic inflammatory disorder in adults, and moderate-to-severe PD (Stage II-III/IV) may accelerate brain aging and neurodegenerative changes via the peripheral-central immune-neural axis, although the molecular connections and mechanisms of interaction have yet to be fully elucidated. This study sought to identify senescence-associated molecules potentially shared by PD and Alzheimer's disease (AD) using integrated transcriptomic analysis, machine learning, and Methods: Transcriptomic datasets related to PD and AD were retrieved from the GEO database, and differential gene expression analysis was performed following batch effect correction; shared aging-associated genes were subsequently identified by combining weighted gene co-expression network analysis (WGCNA) with aging gene databases (HAGR and aging Atlas). Four machine learning algorithms, namely random forest (RF), support vector machine (SVM), generalized linear model (GLM), and extreme gradient boosting (XGB), were further applied to identify key genes, and their diagnostic value was assessed using receiver operating characteristic (ROC) analysis and nomogram models. DSigDB was used to predict candidate small-molecule compounds. In the Results: Seven aging-related genes common to PD and AD were identified, and comprehensive analysis using multiple algorithms selected TMEM140, TIMP1, and ALDH2 as key genes. Notably, TMEM140 was upregulated in PD and downregulated in AD, showed significant correlations with plasma cell and γδ T-cell infiltration, and single-cell analysis further revealed its cell type-specific expression in distinct brain cell subsets. Conclusion: Through integrated transcriptomic analysis together with