Journal
AMERICAN HEART JOURNAL
Volume 252, Issue -, Pages 51-59Publisher
MOSBY-ELSEVIER
DOI: 10.1016/j.ahj.2022.06.004
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Four distinct clusters of inflammatory biomarkers were identified, with higher levels of these biomarkers associated with increased risk of major adverse cardiovascular events. Individuals with high levels of inflammation markers were more likely to experience cardiovascular events.
Background Inflammation, measured by traditional biomarkers such as C-reactive protein, has been linked to cardiovascular (CV) events. Recent technological advancement has allowed for measuring larger numbers of inflammatory biomarkers. A contemporary evaluation with established and novel biomarkers of inflammation is needed. Methods 1,090 individuals who underwent coronary angiography were enrolled. Twenty-four inflammatory biomarkers were collected prior to angiography. Unsupervised machine learning cluster analyses determined unique patterns of inflammatory biomarkers. Cox proportional hazard regression assessed both association of inflammatory biomarker clusters and individual biomarker associations with major adverse cardiovascular events (MACE; non-fatal myocardial infarction or stroke, and CV death) during a median follow-up of 3.67 years. Results Four distinct clusters were recognized. Incremental increases in inflammatory biomarkers were observed from cluster 1 to cluster 4. During follow-up, 263 MACE were ascertained. Considering cluster 1 as a reference, study participants with inflammatory cluster 2 (Hazard ratio [HR] 1.55, 95% confidence interval [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 increased risk of MACE. Interleukin (IL)-1aIL-6, IL-8, IL-10, IL-12, Adhesion molecule-1 high-sensitivity C-reactive protein, ferritin, myeloperoxidase, macrophage inflammatory protein (MIP)-1 alpha, MIP 3, and macrophage colony-stimulating factor-1 were independently associated with MACE. Conclusions Among persons undergoing coronary angiography procedures, distinct clusters of inflammatory biomarker distributions with significant prognostic meaning may be identified. These results may identify unique targets for anti-inflammatory treatments aimed at CV disease.
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