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Abstract
Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.
Investigating metabolic disturbances in coronary artery disease (CAD) may allow the identifications of new prognostic biomarkers. Here the authors perform a metabolomics study to highlight the shared and distinct metabolites characterizing risks of several cardiovascular events in CAD patients.
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1 Qilu Hospital of Shandong University, Department of Emergency Medicine, Jinan, China (GRID:grid.452402.5) (ISNI:0000 0004 1808 3430); Qilu Hospital of Shandong University, Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Jinan, China (GRID:grid.452402.5) (ISNI:0000 0004 1808 3430); Shandong University, Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Jinan, China (GRID:grid.27255.37) (ISNI:0000 0004 1761 1174)
2 Qilu Hospital of Shandong University, Department of Emergency Medicine, Jinan, China (GRID:grid.452402.5) (ISNI:0000 0004 1808 3430); Qilu Hospital of Shandong University, Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Jinan, China (GRID:grid.452402.5) (ISNI:0000 0004 1808 3430); Qilu Hospital of Shandong University, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Jinan, China (GRID:grid.452402.5) (ISNI:0000 0004 1808 3430); Qilu Hospital of Shandong University, Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Jinan, China (GRID:grid.452402.5) (ISNI:0000 0004 1808 3430)
3 Chinese Academy of Sciences, Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Shanghai, China (GRID:grid.9227.e) (ISNI:0000000119573309); Shanghai Key Laboratory of Aging Studies, Shanghai, China (GRID:grid.9227.e)
4 Shandong University, Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Jinan, China (GRID:grid.27255.37) (ISNI:0000 0004 1761 1174); Shandong University, National Institute of Health Data Science, Jinan, China (GRID:grid.27255.37) (ISNI:0000 0004 1761 1174)
5 Chinese Academy of Sciences, Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Shanghai, China (GRID:grid.9227.e) (ISNI:0000000119573309)