• Transcriptomic Profiling of Metformin Response in Type 2 Diabetes Patients Reveals Enrichment of Immune and Viral Pathways
  • Javad Yaghmoorian Khojini,1 Babak Negahdari,2 Mohammad Ali Mazlomi,3,*
    1. 1-Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran 2-Student’s Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
    2. 1- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
    3. 1- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran


  • Introduction: Type 2 Diabetes (T2D) is a complex and progressive metabolic disease characterized by insulin resistance and impaired glucose metabolism. Metformin is the most commonly prescribed first-line therapy for T2D due to its efficacy and safety profile. However, individual variability in glycemic response to metformin remains a major clinical challenge. Despite extensive research, the precise molecular mechanisms underlying this variability are not fully understood. Transcriptomic approaches provide a powerful tool to investigate gene expression differences between responders and non-responders, which may help identify predictive biomarkers and elucidate drug response mechanisms. The present study aimed to perform a transcriptome-wide comparison of metformin responders and non-responders in T2D patients to uncover differentially expressed genes (DEGs) and enriched biological pathways that could explain the variability in therapeutic response.
  • Methods: We utilized publicly available RNA-Seq data from the Gene Expression Omnibus (GEO, accession number GSE153315). The dataset included total RNA profiles from 20 T2D patients, categorized as responders (n=10) and non-responders (n=10) to metformin after three months of therapy, based on changes in HbA1c levels. Additionally, data from 10 healthy individuals were included as controls. All samples were derived from whole blood and sequenced using the Ion Torrent Proton platform. Raw count data were downloaded and preprocessed in R. Low-count genes were filtered, and normalization was performed using the DESeq2 package. The experimental design included group comparison (responder vs non-responder). DEGs were defined based on adjusted p-value < 0.05 and |log₂FoldChange| > 2. Results were visualized using volcano plots and heatmaps of the top 30 DEGs. Subsequently, the list of significant genes was mapped to Entrez Gene IDs using the org.Hs.eg.db annotation package. Functional enrichment analysis was carried out using clusterProfiler, focusing on Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Significance of enrichment was determined using FDR-adjusted p-values.
  • Results: Differential expression analysis identified 110 significant DEGs between responders and non-responders to metformin therapy. The volcano plot revealed a symmetrical distribution of upregulated and downregulated genes, with several highly significant hits (adjusted p < 0.001). Heatmap clustering of the top 30 DEGs showed clear separation between responders, non-responders, and healthy controls, reflecting distinct transcriptional signatures. Gene Ontology enrichment analysis revealed that DEGs were significantly associated with immune-related biological processes, including regulation of leukocyte activation, interferon signaling, and inflammatory response pathways. These findings support the hypothesis that immune modulation may be a key factor in metformin response. Interestingly, KEGG pathway analysis highlighted “Coronavirus disease – COVID-19” as the top significantly enriched pathway (adjusted p = 3.04 × 10⁻⁶, gene count = 16). While seemingly unrelated, this pathway includes genes involved in innate immunity, cytokine signaling, and inflammatory cascades, which are known to interact with metabolic regulation and insulin sensitivity. This suggests that metformin response may be partially mediated through immune mechanisms that overlap with antiviral defense responses. No significant enrichment was observed in traditional T2D-related pathways such as AMPK or insulin signaling, indicating that transcriptional response to metformin in blood may be more strongly associated with immune status than previously appreciated.
  • Conclusion: This study provides transcriptomic evidence that the variability in metformin response among T2D patients may be linked to immune-related gene expression patterns. The enrichment of genes in viral and inflammatory pathways, particularly the COVID-19 KEGG pathway, highlights a potentially novel mechanism by which metformin exerts its effects beyond glucose metabolism. These insights could contribute to the development of predictive biomarkers for metformin responsiveness and support the move toward more personalized treatment approaches in diabetes care. Further experimental validation is warranted to confirm these findings and explore their clinical utility.
  • Keywords: Metformin Type 2 Diabetes RNA-Seq Differential Expression Immune Response