{"title":"心肌梗死后院外心源性猝死的新蛋白生物标志物","authors":"Maomao Zhang, Zhonghua Tong, Naixin Wang, Kaiyang Lin, Yafei Zhang, Dongni Wang, Xiaoqi Wang, Penghe Wang, Qiannan Yang, Yingjin Kong, Mengdi Wang, Jingxuan Cui, Zhuozhong Wang, Muhua Cao, Lulu Li, Ying Liu, Zhaoying Li, Shaohong Fang, Fan Zhang, Zhenwei Pan, Jinwei Tian, Bo Yu","doi":"10.1161/CIRCEP.124.013217","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Early identification of out-of-hospital high-risk sudden cardiac death (SCD) after acute myocardial infarction is crucial for timely therapeutic interventions. However, left ventricular ejection fraction as a standalone clinical stratification tool has major limitations, necessitating improved risk stratification strategies.</p><p><strong>Methods: </strong>Mass spectrometry measured 6592 peptides and 522 proteins, from which targeted proteomics identified the optimal protein combination to assess out-of-hospital SCD risk. ELISA validated its predictive value by comparing it with a clinical stratification tool (left ventricular ejection fraction ≤35%) and 2 reported models (risk score and out-of-hospital cardiac arrest score) in 3 case-control cohorts nested within diverse contemporary postinfarction populations.</p><p><strong>Results: </strong>In the discovery cohort (105 SCD cases and 105 survivors), mass spectrometry discovered 44 differential proteins associated with SCD, unveiling early circulating features characterized by inflammatory response and complement activation in out-of-hospital SCD cases. Targeted proteomics identified the optimal SCD-warning 3-protein combination, including coronin-1A, haptoglobin, and CFD (complement factor D), to assess out-of-hospital SCD risk. An ELISA-based SCD-warning 3-protein combination model significantly outperformed left ventricular ejection fraction alone (C statistic: 0.752 versus 0.548; <i>P</i><0.001) and improved their performance (ΔC statistic, 0.281; categorical net reclassification improvement, 0.095; continuous net reclassification improvement, 0.952; integrated discrimination improvement, 0.291). Similar incremental discrimination metrics were observed in 2 reported stratification models (risk score and out-of-hospital cardiac arrest score), particularly within the left ventricular ejection fraction-preserved population. These findings were repeatably validated in 2 independent cohorts (n=234 and 48, respectively). CFD inhibition protection for mortality and pro-malignant arrhythmias in acute myocardial infarction mice supported the biological plausibility of the critical protein in SCD-warning 3-protein combination.</p><p><strong>Conclusions: </strong>In high-risk individuals for out-of-hospital SCD, the SCD-warning 3-protein combination may contribute to enhanced early identification for timely intensive management. These findings suggest pivotal proteins for improving understanding SCD pathophysiology.</p>","PeriodicalId":10319,"journal":{"name":"Circulation. 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However, left ventricular ejection fraction as a standalone clinical stratification tool has major limitations, necessitating improved risk stratification strategies.</p><p><strong>Methods: </strong>Mass spectrometry measured 6592 peptides and 522 proteins, from which targeted proteomics identified the optimal protein combination to assess out-of-hospital SCD risk. ELISA validated its predictive value by comparing it with a clinical stratification tool (left ventricular ejection fraction ≤35%) and 2 reported models (risk score and out-of-hospital cardiac arrest score) in 3 case-control cohorts nested within diverse contemporary postinfarction populations.</p><p><strong>Results: </strong>In the discovery cohort (105 SCD cases and 105 survivors), mass spectrometry discovered 44 differential proteins associated with SCD, unveiling early circulating features characterized by inflammatory response and complement activation in out-of-hospital SCD cases. Targeted proteomics identified the optimal SCD-warning 3-protein combination, including coronin-1A, haptoglobin, and CFD (complement factor D), to assess out-of-hospital SCD risk. An ELISA-based SCD-warning 3-protein combination model significantly outperformed left ventricular ejection fraction alone (C statistic: 0.752 versus 0.548; <i>P</i><0.001) and improved their performance (ΔC statistic, 0.281; categorical net reclassification improvement, 0.095; continuous net reclassification improvement, 0.952; integrated discrimination improvement, 0.291). Similar incremental discrimination metrics were observed in 2 reported stratification models (risk score and out-of-hospital cardiac arrest score), particularly within the left ventricular ejection fraction-preserved population. These findings were repeatably validated in 2 independent cohorts (n=234 and 48, respectively). 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引用次数: 0
摘要
背景:早期识别急性心肌梗死后院外高危心源性猝死(SCD)对于及时干预治疗至关重要。然而,左室射血分数作为独立的临床分层工具有很大的局限性,需要改进风险分层策略。方法:质谱测定6592个多肽和522个蛋白,通过靶向蛋白质组学鉴定出院外SCD风险评估的最佳蛋白组合。ELISA通过将其与临床分层工具(左室射血分数≤35%)和2个报告模型(风险评分和院外心脏骤停评分)在3个病例对照队列中进行比较,验证了其预测价值,这些队列嵌套在不同的当代梗死后人群中。结果:在发现队列(105例SCD病例和105例幸存者)中,质谱分析发现了44种与SCD相关的差异蛋白,揭示了院外SCD病例中以炎症反应和补体激活为特征的早期循环特征。靶向蛋白质组学确定了最佳的SCD预警3蛋白组合,包括冠状蛋白- 1a、接触珠蛋白和补体因子D,以评估院外SCD风险。基于elisa的SCD-warning 3蛋白组合模型显著优于单独左心室射血分数(C统计值:0.752 vs 0.548;结论:在院外SCD高危人群中,SCD预警3蛋白组合可能有助于加强早期识别,及时进行强化管理。这些发现提示了提高对SCD病理生理的理解的关键蛋白。
Novel Protein-Based Biomarkers of Out-of-hospital Sudden Cardiac Death After Myocardial Infarction.
Background: Early identification of out-of-hospital high-risk sudden cardiac death (SCD) after acute myocardial infarction is crucial for timely therapeutic interventions. However, left ventricular ejection fraction as a standalone clinical stratification tool has major limitations, necessitating improved risk stratification strategies.
Methods: Mass spectrometry measured 6592 peptides and 522 proteins, from which targeted proteomics identified the optimal protein combination to assess out-of-hospital SCD risk. ELISA validated its predictive value by comparing it with a clinical stratification tool (left ventricular ejection fraction ≤35%) and 2 reported models (risk score and out-of-hospital cardiac arrest score) in 3 case-control cohorts nested within diverse contemporary postinfarction populations.
Results: In the discovery cohort (105 SCD cases and 105 survivors), mass spectrometry discovered 44 differential proteins associated with SCD, unveiling early circulating features characterized by inflammatory response and complement activation in out-of-hospital SCD cases. Targeted proteomics identified the optimal SCD-warning 3-protein combination, including coronin-1A, haptoglobin, and CFD (complement factor D), to assess out-of-hospital SCD risk. An ELISA-based SCD-warning 3-protein combination model significantly outperformed left ventricular ejection fraction alone (C statistic: 0.752 versus 0.548; P<0.001) and improved their performance (ΔC statistic, 0.281; categorical net reclassification improvement, 0.095; continuous net reclassification improvement, 0.952; integrated discrimination improvement, 0.291). Similar incremental discrimination metrics were observed in 2 reported stratification models (risk score and out-of-hospital cardiac arrest score), particularly within the left ventricular ejection fraction-preserved population. These findings were repeatably validated in 2 independent cohorts (n=234 and 48, respectively). CFD inhibition protection for mortality and pro-malignant arrhythmias in acute myocardial infarction mice supported the biological plausibility of the critical protein in SCD-warning 3-protein combination.
Conclusions: In high-risk individuals for out-of-hospital SCD, the SCD-warning 3-protein combination may contribute to enhanced early identification for timely intensive management. These findings suggest pivotal proteins for improving understanding SCD pathophysiology.
期刊介绍:
Circulation: Arrhythmia and Electrophysiology is a journal dedicated to the study and application of clinical cardiac electrophysiology. It covers a wide range of topics including the diagnosis and treatment of cardiac arrhythmias, as well as research in this field. The journal accepts various types of studies, including observational research, clinical trials, epidemiological studies, and advancements in translational research.