Abstract
Acute myocardial infarction (AMI) is a leading cause of mortality worldwide, with about 7 million deaths annually from cardiovascular disease. Current diagnostic and therapeutic approaches face challenges, highlighting the need for new biomarkers and diagnostic methods. Abnormalities in glucose metabolism and circadian rhythm genes are linked to cardiovascular conditions, and understanding these mechanisms may reveal new therapeutic targets. This study aimed to identify differentially expressed genes related to glucose metabolism and circadian rhythms (GMCRRDEGs) in AMI. Bioinformatics techniques, including differential expression analysis, enrichment analysis, and machine learning models, were used to find prospective biomarkers for early diagnosis and therapeutic intervention. Twelve GMCRRDEGs were found, with six key genes (JUN, EPAS1, IL1B, ADRB2, FOS, CD36) incorporated into the diagnostic model, showing high accuracy in both the training set (AUC = 0.93) and validation set (AUC = 0.91). Enrichment analysis linked GMCRRDEGs to biological processes related to NO synthesis and tumor necrosis factor signaling pathways. Immune infiltration analysis showed significant changes in immune cell abundance, especially in mast cells and neutrophils, among high-risk patients. This study highlights GMCRRDEGs as AMI biomarkers, emphasizing their role in disease mechanisms and immune responses. Further research should validate these findings in larger cohorts to enhance early detection for AMI.