Rheumatoid Arthritis (RA) is a chronic inflammatory disorder that may increase the susceptibility to depression, yet the molecular basis linking RA to depression remains unclear. This study aims to explore the potential causality between RA and depression and to identify molecular signatures that may serve as predictive markers or therapeutic targets.

Mendelian Randomization (MR) analysis was employed to investigate the causal relationship between RA and depression, complemented by sensitivity analyses to ensure the robustness of the findings. Transcriptome sequencing of peripheral blood samples from 51 patients with RA and depression (RA-D) and 43 healthy controls was analyzed. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify genes associated with RA-D. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify the biological processes and pathways involved. A diagnostic nomogram was developed using 10 hub genes and validated with external datasets. The single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate immune cell infiltration.

MR analysis revealed that genetic predisposition to RA was significantly associated with a decreased risk of depression in Finnish and European populations (odds ratio: 0.95, 95% CI: 0.92-0.98, p=0.006). WGCNA identified 96 genes with a strong positive correlation with RA-D. GO and KEGG analyses highlighted their involvement in platelet activation and immune responses. A diagnostic nomogram based on 10 hub genes showed promising predictive capability, validated using datasets GSE77298 and GSE76826. Single-sample Gene Set Enrichment Analysis (ssGSEA) indicated alterations in immune cell levels, suggesting an intricate immune involvement in RA-D.

This study elucidates the protective effect of genetic predisposition against depression in RA patients, uncovering the shared molecular mechanisms between RA and depression. By integrating Mendelian randomization with transcriptomic analysis, our research offers new insights into the causal relationship and molecular underpinnings of RA and depression. These findings may guide the development of precision medicine approaches and therapeutic interventions, contributing to the advancement of research and clinical strategies for managing RA and depression comorbidity.