Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense C, Denmark.
Center for Individualized Medicine in Arterial Diseases, Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense C, Denmark.
Shock. 2021 Jan 1;55(1):41-47. doi: 10.1097/SHK.0000000000001595.
Acute myocardial infarction (AMI) remains a major cause of mortality and morbidity, and cardiogenic shock (CS) a major cause of hospital mortality after AMI. Especially for ST elevation myocardial infarction (STEMI) patients, fast intervention is essential.Few proteins have proven clinically applicable for AMI. Most proposed biomarkers are based on a priori hypothesis-driven studies of single proteins, not enabling identification of novel candidates. For clinical use, the ability to predict AMI is important; however, studies of proteins in prediction models are surprisingly scarce.Consequently, we applied proteome data for identifying proteins associated with definitive STEMI, CS, and all-cause mortality after admission, and examined the ability of the proteins to predict these outcomes.
Proteome-wide data of 497 patients with suspected STEMI were investigated; 381 patients were diagnosed with STEMI, 35 with CS, and 51 died during the first year. Data analysis was conducted by logistic and Cox regression modeling for association analysis, and by multivariable LASSO regression models for prediction modeling.Association studies identified 4 and 29 proteins associated with definitive STEMI or mortality, respectively. Prediction models for CS and mortality (holding two and five proteins, respectively) improved the prediction ability as compared with protein-free prediction models; AUC of 0.92 and 0.89, respectively.
The association analyses propose individual proteins as putative protein biomarkers for definitive STEMI and survival after suspected STEMI, while the prediction models put forward sets of proteins with putative predicting ability of CS and survival. These proteins may be verified as biomarkers of potential clinical relevance.
急性心肌梗死(AMI)仍然是发病率和死亡率的主要原因,心源性休克(CS)是 AMI 后医院死亡率的主要原因。特别是对于 ST 段抬高型心肌梗死(STEMI)患者,快速干预至关重要。很少有蛋白质被证明在临床上适用于 AMI。大多数提出的生物标志物都是基于对单个蛋白质的先验假设驱动研究,无法识别新的候选物。对于临床应用,预测 AMI 的能力很重要;然而,在预测模型中研究蛋白质的情况却出人意料地很少。因此,我们应用蛋白质组数据来识别与明确的 STEMI、CS 和入院后全因死亡率相关的蛋白质,并检查这些蛋白质预测这些结果的能力。
对 497 例疑似 STEMI 患者的蛋白质组数据进行了研究;381 例患者被诊断为 STEMI,35 例为 CS,51 例在第一年死亡。通过逻辑回归和 Cox 回归模型进行关联分析,通过多变量 LASSO 回归模型进行预测建模,对蛋白质组数据进行分析。关联研究确定了与明确 STEMI 或死亡率相关的 4 种和 29 种蛋白质。与无蛋白预测模型相比,CS 和死亡率(分别包含两种和五种蛋白质)的预测模型提高了预测能力;AUC 分别为 0.92 和 0.89。
关联分析提出了单个蛋白质作为明确 STEMI 和疑似 STEMI 后存活的潜在蛋白质生物标志物,而预测模型则提出了具有潜在预测 CS 和存活能力的蛋白质集。这些蛋白质可能被验证为具有潜在临床相关性的生物标志物。