BACKGROUND Many studies have explored whether individual plasma protein biomarkers improve cardiovascular disease risk prediction. We sought to investigate the use of a plasma proteomics-based approach in predicting different cardiovascular outcomes. METHODS Among 51 859 UK Biobank participants (mean age, 56.7 years; 45.5% male) without cardiovascular disease and with proteomics measurements, we examined the primary composite outcome of fatal and nonfatal coronary heart disease, stroke, or heart failure (major adverse cardiovascular events), as well as additional secondary cardiovascular outcomes. An exposome-wide association study was conducted using relative protein concentrations, adjusted for a range of classic, demographic, and lifestyle risk factors. A prediction model using only age, sex, and protein markers (protein model) was developed using a least absolute shrinkage and selection operator-regularized approach (derivation: 80% of cohort) and validated using split-sample testing (20% of cohort). Their performance was assessed by comparing calibration, net reclassification index, and c statistic with the PREVENT (Predicting Risk of CVD Events) risk score. RESULTS Over a median 13.6 years of follow-up, 4857 participants experienced first major adverse cardiovascular events. After adjustment, the proteins most strongly associated with major adverse cardiovascular events included NT-proBNP (N-terminal pro B-type natriuretic peptide; hazard ratio [HR], 1.68 per SD increase), proADM (pro-adrenomedullin; HR, 1.60), GDF-15 (growth differentiation factor-15; HR, 1.47), WFDC2 (WAP four-disulfide core domain protein 2; HR, 1.46), and IGFBP4 (insulin-like growth factor-binding protein 4; HR, 1.41). In total, 222 separate proteins were predictors of all outcomes of interest in the protein model, and 86 were selected for the primary outcome specifically. In the validation cohort, compared with the PREVENT risk factor model, the protein model improved calibration, net reclassification (net reclassification index +0.09), and c statistic for major adverse cardiovascular events (+0.051). The protein model also improved the prediction of other outcomes, including ASCVD (c statistic +0.035), myocardial infarction (+0.023), stroke (+0.024), aortic stenosis (+0.015), heart failure (+0.060), abdominal aortic aneurysm (+0.024), and dementia (+0.068). CONCLUSIONS Measurement of targeted protein biomarkers produced superior prediction of aggregated and disaggregated cardiovascular events. This study represents an important proof of concept for the application of targeted proteomics in predicting a range of cardiovascular outcomes.

中文翻译：

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一种基于蛋白质组学的方法，用于预测不同的心血管疾病和痴呆症。


背景 许多研究探讨了单个血浆蛋白生物标志物是否能改善心血管疾病风险预测。我们试图研究基于血浆蛋白质组学的方法在预测不同心血管结局中的应用。方法 在 51 859 名没有心血管疾病的英国生物样本库参与者 （平均年龄 56.7 岁;45.5% 为男性） 中，我们通过蛋白质组学测量检查了致命性和非致命性冠心病、中风或心力衰竭 （主要不良心血管事件） 的主要复合结局，以及其他次要心血管结局。使用相对蛋白质浓度进行了一项全暴露组关联研究，并根据一系列经典、人口统计学和生活方式风险因素进行了调整。仅使用年龄、性别和蛋白质标记物的预测模型（蛋白质模型）是使用最小绝对收缩和选择运算符正则化方法（推导：队列的 80%）开发的，并使用分样本测试（队列的 20%）进行验证。通过将校准、净重分类指数和 c 统计量与 PREVENT （预测 CVD 事件风险） 风险评分进行比较来评估他们的性能。结果 在中位 13.6 年的随访中，4857 名参与者首次经历了主要不良心血管事件。调整后，与主要不良心血管事件最密切相关的蛋白质包括 NT-proBNP（N 末端 B 型利钠肽前体;风险比 [HR]，每 SD 增加 1.68）、proADM（促肾上腺髓质素;HR，1.60）、GDF-15（生长分化因子-15;HR，1.47）、WFDC2（WAP 四二硫键核心结构域蛋白 2;HR，1.46）和 IGFBP4（胰岛素样生长因子结合蛋白 4;HR，1.41）。 总共有 222 种单独的蛋白质是蛋白质模型中所有感兴趣结果的预测因子，其中 86 种被专门选择用于主要结果。在验证队列中，与 PREVENT 风险因素模型相比，蛋白质模型提高了校准、净重分类（净重分类指数 +0.09）和主要不良心血管事件的 c 统计量 （+0.051）。蛋白质模型还改善了对其他结局的预测，包括 ASCVD （c 统计量 +0.035）、心肌梗死 （+0.023）、中风 （+0.024）、主动脉瓣狭窄 （+0.015）、心力衰竭 （+0.060）、腹主动脉瘤 （+0.024） 和痴呆 （+0.068）。结论 靶向蛋白质生物标志物的测量对聚集和分解的心血管事件产生了卓越的预测。这项研究代表了靶向蛋白质组学在预测一系列心血管结局中的应用的重要概念证明。