Diagnostic Prediction of Progressive Atherosclerosis in High-Fat Meal Consumers and Their Common Pathological Mechanisms

The occurrence of high-fat meals among the general population is on the rise, and ample evidence indicates their involvement in the progression of atherosclerosis (AS). Nevertheless, it is imperative to investigate vital research inquiries concerning the shared pathological processes, associations, and means of foretelling the susceptibility to AS in individuals who consume meals potentially rich in fat. We obtained high-fat meal (HFM) and progressive coronary artery disease (PCAD) gene expression profiles from the database of gene expression. Following the identification of the genes with differential expression common to HFM and PCAD, we performed a series of bioinformatics analyses, including machine learning and immune infiltration, to further our understanding of this issue. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) validated the critical genes in murine serum. Using a combination of 20 widely used machine learning algorithms, we successfully created a reliable diagnostic predictor to evaluate the vulnerability for PCAD development in a particular HFM. Additionally, an in-depth exploration of their mutual connections within domains such as immunology enabled us to recognize possible mechanisms underlying the co-onset of HFM and PCAD and find the critical genes in the progressive AS model group. In this investigation, we employed machine learning to anticipate the pathogenesis and examine the mechanisms of two correlated conditions: HFM and PCAD. Our discoveries present innovative perspectives on the mechanistic comprehension of HFM and PCAD, which may prove beneficial for advancing forthcoming preemptive, individualized, and precise medical strategies.