Exploratory bibliometric analysis and text mining to reveal research trends in cardiac aging.

Objectives: We conducted a text mining analysis of 40 years of literature on cardiac aging from PubMed to investigate the current understanding on cardiac aging and its mechanisms. This study aimed to embody what most researchers consider cardiac aging to be. Methods: We used multiple text mining and machine learning tools to extract important information from a large amount of text. Results: Analysis revealed that the terms most frequently associated with cardiac aging include "diastolic," "hypertrophy," "fibrosis," "apoptosis," "mitochondrial," "oxidative," and "autophagy." These terms suggest that cardiac aging is characterized by mitochondrial dysfunction, oxidative stress, and impairment of autophagy, especially mitophagy. We also revealed an increase in the frequency of occurrence of "autophagy" in recent years, suggesting that research on autophagy has made a breakthrough in the field of cardiac aging. Additionally, the frequency of occurrence of "mitophagy" has increased significantly since 2019, suggesting that mitophagy is an important factor in cardiac aging. Conclusions: Cardiac aging is a complex process that involves mitochondrial dysfunction, oxidative stress, and impairment of autophagy, especially mitophagy. Further research is warranted to elucidate the mechanisms of cardiac aging and develop strategies to mitigate its detrimental effects. [ABSTRACT FROM AUTHOR]