Oxidative stress plays a significant role in the pathophysiological process following MI. Myocardial ischemia and hypoxia after MI can lead to an increase in reactive oxygen species (ROS), and the imbalance between its production and the antioxidant system induce sustained oxidative stress, further aggravating myocardial damage and cardiac remodeling, thereby causing post-MI complications and even heart failure. Thus, assessing oxidative stress-related biomarkers could enhance our understanding of cardiac remodeling post-MI and help stratify patients’ risk for post-MI complications. Besides, these biomarkers could serve as novel therapeutic targets for MI intervention. Previous observational studies have shown that antioxidant enzymes such as glutathione peroxidase9 and heme oxygenase-1 (HO-1) in the blood could predict the prognosis of MI. Regarding treatment, various therapeutic approaches aim to regulate oxidative stress-related cell signaling pathways to effectively treat MI. For instance, melatonin reduces post-MI injury by activating the Notch1/Mfn2 pathway, thereby decreasing oxidative stress. Early administration of melatonin after percutaneous coronary intervention significantly reduces infarct size. Other compounds like wogonin, hirudin, and dapsone could alleviate oxidative stress post-MI and preserve normal myocardial function by targeting the NRF2/HO-1 pathway. Stem cell therapies involving overexpression of HO-1 in stem cells have also shown promise in improving stem cells’ tolerance to hypoxia and oxidative stress, thereby enhancing myocardial function in infarcted myocardium. However, numerous antioxidant enzymes and signaling pathways have not been extensively studied experimentally and clinically. This suggests that the currently monitored oxidative stress biomarkers in post-MI treatment are limited, and related therapies are not fully developed, leaving potential biomarkers unexplored. This study aimed to determine if there were causal relationships between MI and oxidative stress though the mendelian randomization (MR) approach.
