Heartbeats and hidden codes: Decoding cardiac aging with multi-omics and computational approaches.

Aging is a major risk factor for cardiovascular disease, yet the mechanisms driving age-related cardiac decline remain incompletely defined. Although traditional omics approaches have identified global signatures of inflammation and metabolic shifts, bulk analyses often mask cell-type-specific heterogeneity. Recent single-cell multi-omics innovations address this gap by enabling simultaneous profiling of gene expression and chromatin accessibility. These high-resolution analyses reveal that fibroblasts and macrophages exhibit particularly pronounced age-associated remodeling, marked by heightened senescence, disrupted intercellular communication, and reduced chromatin accessibility, relative to other cardiac lineages. Looking ahead, the integration of machine-learning-based and emerging language-model-assisted approaches is becoming essential to enhance annotation and accelerate the extraction of biological insights from the vast single-cell datasets. Together, these experimental and computational tools provide a framework for prioritizing candidate cell-type-specific vulnerabilities for subsequent mechanistic study. Ultimately, we emphasize that, while single-cell datasets generate robust hypotheses, animal models remain indispensable for validating these targets and capturing the in vivo interactions that underlie age-associated cardiac dysfunction.