Abstract Body (Do not enter title and authors here): Background: Cellular senescence, a hallmark of aging and heart failure with preserved ejection fraction (HFpEF), is implicated in myocardial fibrosis, inflammation, and metabolic remodeling. Recent studies suggest that senescence-associated gene expression profiles may predict response to sodium-glucose cotransporter 2 (SGLT2) inhibitors. We conducted a systematic review and transcriptomic meta-analysis to evaluate the relationship between senescence gene signatures and SGLT2i treatment efficacy in HFpEF.

Methods: Following PRISMA guidelines, we searched PubMed, GEO, and ArrayExpress through April 2024 for studies that: (1) reported RNA-sequencing or microarray data from HFpEF myocardium or peripheral blood, (2) assessed senescence-associated gene expression (e.g., CDKN2A, SERPINE1, IL6, CXCL8), and (3) evaluated treatment response to SGLT2 inhibitors. For eligible datasets, differential gene expression (DEG) and gene set enrichment analyses (GSEA) were performed. Meta-analysis of log2 fold-change (FC) in responder vs non-responder groups was conducted. Forest plot and heatmaps visualized pooled transcriptomic trends.

Results: Six datasets (total n = 482 patients with HFpEF, 228 on SGLT2i) were included. Senescence pathway genes (CDKN2A, TP53, IL6) were significantly upregulated in non-responders (pooled log2FC: +1.42, 95% CI: 1.10–1.73, p<0.001). Responder groups demonstrated enrichment of mitochondrial oxidative phosphorylation genes and lower SASP (senescence-associated secretory phenotype) scores. Subgroup analysis of peripheral blood samples confirmed the predictive pattern (AUC = 0.81). Heatmap clustering distinguished SGLT2i responders based on senescence-low vs senescence-high phenotypes. No evidence of publication bias was found.

Conclusion: Elevated expression of senescence-related genes is associated with poor response to SGLT2 inhibition in HFpEF. These findings support the use of transcriptomic biomarkers to stratify patients prior to therapy and may inform precision-guided treatment algorithms in HFpEF management.