eng Enviro Research Publishers Current Research in Nutrition and Food Science Journal 2347-467X 2322-0007 2025-11-20 13 3 1380 1393 10.12944/CRNFSJ.13.3.26 24516 article Yaxi Xu 2 Shan Li 3 Ze Chen 4 Jiuchen Wang 1 Department of Reproductive Medical Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, China Department of Pulmonary and Critical Care Medicine, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China Department of Cardiology, Zhongnan Hospital, Wuhan University, Wuhan, China Institute of Myocardial Injury and Repair, Wuhan University, Wuhan, China 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. https://www.foodandnutritionjournal.org/volume13number3/diagnostic-prediction-of-progressive-atherosclerosis-in-high-fat-meal-consumers-and-their-common-pathological-mechanisms/ Diagnostic biomarkers Differentially expressed genes (DEGs) High-fat meal (HFM) Machine-learning Progressive atherosclerosis