Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.

开发单域检测方法来识别未来事件高风险个体是预防多种疾病和死亡的首要任务。通过训练神经网络，我们根据 52,006 名英国生物银行参与者测量的 1461 个 Olink 血浆蛋白，开发了疾病/死亡特异性蛋白质组风险评分 (ProRS)。该综合评分对 45 种常见疾病的风险进行了显着分层，包括传染病、血液学、内分泌、精神、神经、感觉、循环、呼吸、消化、皮肤、肌肉骨骼和泌尿生殖系统疾病、癌症和死亡。ProRS 在 10 个终点（例如癌症、痴呆和死亡）方面实现了很高的判别准确率，C 指数超过 0.80。值得注意的是，对于几乎所有终点，ProRS 都比既定的临床指标产生了更好或相当的预测性能。将临床预测因子与 ProRS 相结合可增强大多数终点的预测能力，但与单独使用 ProRS 相比，这种组合仅表现出有限的改进。一些蛋白质，例如GDF15，对各种疾病表现出重要的区分价值。我们还表明，观察到的良好判别性能可以在很大程度上转化为实际的临床效用。总而言之，蛋白质组学谱可以替代复杂的实验室测试或临床测量，以同时完善多种疾病和死亡的综合风险评估。我们的模型在英国生物银行进行了内部验证；因此，在应用于临床之前，需要进一步独立的外部验证来证实我们的研究结果。