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Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.
RESUMO
AIMS: We aim to investigate the correlation between high levels of the systemic immune-inflammation index (SII) and long-term mortality and major cardiovascular adverse events in advanced chronic heart failure patients with renal dysfunction. METHODS AND RESULTS: Seven hundred seventeen advanced chronic heart failure patients with renal dysfunction, who visited the First affiliated hospital of Zhengzhou University from September 2019 to December 2020, were included. All-cause mortalities (ACM) were selected as primary endpoints and major cardiovascular adverse events (MACEs) as the secondary endpoints. Based on the receiver operating characteristic (ROC) curve and the Youden index, the optimal cut-off values of SII for ACM and MACEs were 1228 and 1406. In the group where ACM were the primary endpoint, patients were categorized into the low-SII group (n = 479) and the high-SII group (n = 238). Patients in the group using MACEs as the secondary endpoint were also categorized into the low-SII groups (n = 514) and the high-SII groups (n = 203). Univariate and multivariate COX regression were used to screen the independent predictors for ACM and MACEs, revealing the relationship between SII levels and endpoints. According to the univariate COX analysis, SII was the risk factor (hazard ratio [HR] = 2.144, 95% confidence interval [CI]: 1.565-2.938, P < 0.001) for the ACM subgroup. It was also the risk factor (HR = 1.625, CI: 1.261-2.905, P < 0.001) for the MACEs subgroup. Multivariate COX regression analysis indicated that the occurrence of ACM and MACEs in high-level SII and low-level SII patients had statistical differences. The incidence of ACM increased by 70.3% (HR = 1.703; 95% CI: 1.200-2.337; P = 0.002) in patients of the high SII level group, the incidence of MACEs increased by 58.3% (HR = 1.583, 95% CI: 1.213-2.065, P = 0.001). Kaplan-Meier (K-M) survival analysis further suggested that patients with a high SII level had an increased risk of having ACM (log-rank P < 0.001) and MACEs (log-rank P < 0.001) within 30 months. SII could be considered as a novel predictor of the occurrence of ACM and MACEs for patients with advanced chronic heart failure and renal dysfunction. CONCLUSIONS: This study suggested that SII is a novel independent predictor of mortality in advanced chronic heart failure patients with renal dysfunction, and it should be considered in current clinical management.
