The rapid improvement of technology in recent years has made it possible to measure a greater variety of inflammatory biomarkers. It was necessary to conduct a modern analysis using well-established and newly discovered biomarkers of inflammation. A total of 1,090 participants who had a coronary angiography were included in the study. Before beginning the angiography, 24 different inflammatory biomarkers were collected. Cluster analysis conducted using unsupervised machine learning revealed distinctive patterns of inflammatory biomarkers. Cox proportional hazard regression was used to investigate the correlations between inflammatory biomarker clusters and major negative cardiovascular (CV) events (MACE; non-fatal myocardial infarction or stroke, and cardiovascular mortality) during a median follow-up of 3.67 years. It was determined that there were 4 separate clusters. From cluster 1 to cluster 4, there was an incremental rise in inflammatory biomarkers detected. During the follow-up, a total of 263 MACE were identified. When cluster 1 was used as a point of reference, patients in the study who had inflammatory cluster 2 (hazard ratio [HR] 1.55, 95% CI: 1.01-2.37), cluster 3 (HR 1.89, CI: 1.25-2.85), and cluster 4 (HR 2.93, CI: 1.95-4.42) were at an increased risk of MACE. Interleukin (IL)-1α, IL-6, IL-8, IL-10, IL-12, Adhesion molecule-1, high-sensitivity C-reactive protein, ferritin, myeloperoxidase, macrophage inflammatory protein (MIP)-1a, MIP 3, and macrophage colony-stimulating factor-1 were independently related with MACE. It was possible to identify discrete clusters of inflammatory biomarker distributions among patients undergoing coronary angiography procedures. These clusters might have considerable meaning in terms of the prognosis. These findings might help uncover novel therapeutic targets for treating inflammatory diseases like cardiovascular disease.
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