Targeted metabolomic profiling and machine learning-based prediction models for persistent atrial fibrillation: a multi-center observational study.

Cellular senescence refers to persistent and irreversible cell cycle arrest triggered by diverse forms of cellular stress, accompanied by macromolecular damage, secretory features, and altered metabolism []. Cellular senescence is typically promoted by multiple factors, including DNA damage, oxidative stress, and ionizing radiation [], and is widely recognized as a hallmark of organismal aging that contributes to the decline in physical function [,]. During aging, senescent cells (SnCs) progressively accumulate across multiple organs. SnCs adopt an enlarged and flattened shape, often displaying an accumulation of lysosomal and mitochondrial contents []. Beyond their morphological changes, SnCs exhibit a pro-inflammatory secretory profile known as the senescence-associated secretory phenotype (SASP) []. SASP factors comprise not only inflammatory cytokines and chemokines but also enzymatic regulators, such as matrix metalloproteinases (MMPs) and serine/cysteine proteinase inhibitors (SERPINs) []. This secretory repertoire is highly context-dependent, with its molecular signatures tailored to the cell type and stress source. Sustained secretion of SASP molecules induces inflammaging, which disrupts tissue remodeling, the immune system, and homeostatic control []. Furthermore, once stem cells and progenitor cells undergo cell cycle arrest, they fail to replenish damaged tissues, thereby impairing the regenerative capacity of the organs []. Thus, cellular senescence plays a leading role in the onset and progression of various age-related diseases, such as cardiovascular disease, obesity, type 2 diabetes, pulmonary fibrosis, and sarcopenia [,].