Supplementary Tables 1–12.

Circulating plasma proteins play key roles in measuring and reflecting states of disease and health. Developments in protein metrology allow for over 2,900 proteins to be quantified in a single sample. In major epidemiological studies, this allows for profound insights into protein expression in liquid biopsies, mortality, and morbidity. Here, we have investigated the relationship between peripheral blood protein profiles and non-accident all-cause mortality within 5- and 10-year timeframes, using data on 38,150 participants from the UK Biobank. Adjusting for lifestyle and health covariates, we identified 392 proteins associated with an increase in risk for death within 5-years, and 377 associated with an increase in risk within 10-years. Proteomic signatures of cause-specific mortality (cardiovascular, cancer, all-other causes) were also identified, with 19 proteins found to overlap across those. Using logistic regression modelling, we constructed a parsimonious predictive protein panel for each respective all-cause mortality timeframe, including markers such as adrenomedullin, SERPINA1 and PLAUR. When compared to models inclusive of standalone traditional risk criteria, such as demographic and lifestyle factors, models utilising the protein panels modestly improve prediction for 5 and 10-year mortality (from AUC 0.49–0.57 to AUC of 0.62–0.68). Our results demonstrate the potential of the plasma proteome in risk stratification for all-cause mortality.循环血浆蛋白在疾病与健康状态的评估与反映中发挥关键作用。蛋白质计量技术的发展已实现单样本中对2900余种蛋白质的定量检测。在大型流行病学研究中，该技术可助力深入解析液体活检中的蛋白质表达、死亡率与发病率特征。本研究依托英国生物库(UK Biobank)中38150名参与者的数据，探究了外周血蛋白质组谱与5年及10年随访期内非意外全因死亡率之间的关联。在校正生活方式与健康协变量后，本研究共鉴定出392种与5年内死亡风险升高相关的蛋白质，以及377种与10年内死亡风险升高相关的蛋白质。研究同时鉴定出特定死因（心血管疾病、癌症及其他所有死因）的蛋白质组特征，其中19种蛋白质在各类死因特征中均存在重叠。本研究采用逻辑回归建模方法，针对各全因死亡率随访时长构建了简约预测蛋白质组面板，其中包含肾上腺髓质素、丝氨酸蛋白酶抑制剂A1(SERPINA1)以及尿激酶型纤溶酶原激活物受体(PLAUR)等标志物。与仅包含人口统计学与生活方式因素等传统独立风险标准的模型相比，采用蛋白质组面板的模型可小幅提升5年及10年死亡率的预测性能（AUC从0.49~0.57提升至0.62~0.68）。本研究结果证实了血浆蛋白质组在全因死亡率风险分层中的应用潜力。