Integrated analysis of Single-cell RNA-seq,Mendelian randomization and eQTL reveals immune cell-related nomogram model and subtypes in periodontitis Running title: Immune Cell Subtypes and Nomogram Model in Periodontitis

Background Periodontitis is a prevalent chronic inflammatory disease characterized by immune cell dysregulation and tissue destruction. This study integrates single-cell RNA sequencing (scRNA-seq), Mendelian randomization (MR), and expression quantitative trait loci (eQTL) analyses to uncover immune cell subtypes, causal genes, and develop a predictive nomogram model for periodontitis. Methods We analyzed scRNA-seq data to identify differentially expressed genes (DEGs) and immune cell subtypes in periodontitis. MR analysis was conducted to determine causal relationships between immune cell gene expression and periodontitis risk, utilizing eQTL data. Gene ontology (GO) and pathway enrichment analyses were performed to understand functional implications. Additionally, CellChat trajectory analysis explored intercellular communication. A nomogram model was constructed to predict periodontitis risk based on immune-related DEGs. Results The integrated analysis identified 23 distinct immune cell clusters and seven hub genes (ANXA1, ARL4C, CD79B, LRRC25, NKG7, SLC11A1, and VIM) that were causally linked to periodontitis. Pathway enrichment analysis revealed their involvement in key immune regulatory mechanisms. A robust nomogram model based on these DEGs was developed and validated, demonstrating high predictive accuracy for periodontitis risk. Immune subtypes were further characterized, revealing distinct roles in disease progression. Conclusion This study highlights the crucial role of immune cell subpopulations and hub genes in the pathophysiology of periodontitis. The nomogram model offers a novel tool for predicting periodontitis risk and identifying potential therapeutic targets. These findings provide valuable insights into immune-related mechanisms and potential interventions for periodontitis.