AbstractFor many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81–6,038 cases).

摘要对于许多疾病，由于缺乏疾病发作的客观生物标志物，诊断延迟。在这里，来自英国生物银行制药蛋白质组学项目的41931名个体中，我们将约3000种血浆蛋白的测量结果与临床信息相结合，得出218种常见和罕见疾病（81-6038例）10年发病率的稀疏预测模型。

We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02–0.31).

。包括少至5至20种蛋白质的稀疏模型的预测性能优于使用67种病理多样性疾病的基本临床信息开发的模型的性能（中位数δC指数=0.07；范围=0.02-0.31）。

Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins.

稀疏蛋白质模型进一步优于使用基本信息结合52种疾病的临床检测数据开发的模型，包括多发性骨髓瘤，非霍奇金淋巴瘤，运动神经元疾病，肺纤维化和扩张型心肌病。对于多发性骨髓瘤，新诊断患者骨髓中的单细胞RNA测序表明，五种预测蛋白中有四种在浆细胞中特异性表达，这与这些蛋白的强大预测能力一致。

External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases..

EPIC-Norfolk研究中稀疏蛋白质模型的外部复制显示出对所测试的六种疾病的预测具有良好的普遍性。这些发现表明，稀疏的血浆蛋白质特征，包括疾病特异性蛋白质和几种疾病共有的蛋白质预测因子，为常见和罕见疾病提供了临床有用的预测。。

MainA central challenge in precision medicine is the development of clinically useful tools for identifying individuals at high risk, which may enable timely diagnosis, early initiation of treatment and improved patient outcomes1. Clinically recommended tools for predicting the risk of onset of diseases are used widely for heart attack and stroke (for example, the American College of Cardiology/American Heart Association 10-year risk equation)2 but for very few other diseases.

精准医学面临的一个主要挑战是开发临床上有用的工具来识别高危人群，这可以及时诊断，尽早开始治疗并改善患者预后1。临床推荐的预测疾病发作风险的工具广泛用于心脏病发作和中风（例如，美国心脏病学会/美国心脏协会10年风险方程）2，但很少用于其他疾病。

Across diverse disease pathologies, diagnostic delays of months or years are reported from the initial onset of symptoms3,4,5. Over the last decades, single plasma proteins have become established as specific, diagnostic assays for a small number of diseases, including B-type natriuretic peptide (BNP) for heart failure, troponins for acute coronary syndromes and ubiquitin C-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) in traumatic brain injury6.Broad capture plasma proteomics allows estimation of thousands of proteins and agnostic discovery studies not confined to a single disease of interest and represents a promising technology to accelerate progress towards this challenge.

在不同的疾病病理学中，从症状的初始发作开始，诊断延迟数月或数年3,4,5。在过去的几十年中，单一血浆蛋白已被确立为少数疾病的特异性诊断检测方法，包括用于心力衰竭的B型利钠肽（BNP），用于急性冠状动脉综合征的肌钙蛋白和泛素C末端水解酶L1（UCH-L1）和创伤性脑损伤中的胶质纤维酸性蛋白（GFAP）[6]。广泛捕获血浆蛋白质组学可以估计数千种蛋白质和不可知发现研究，这些研究不仅限于一种感兴趣的疾病，而且代表了加速朝着这一挑战取得进展的有前途的技术。

Plasma proteomic signatures capture health behaviors and current health status7, and may integrate the risk of ‘static’ genetic8,9 and dynamic environmental determinants of disease. Translatable, parsimonious models have been described. For example, a sparse protein signature, containing as few as three proteins, improved identification of a high-risk group for diabetes that is currently missed by screening strategies10.Whether plasma proteomics may offer clinically useful predictive or mechanistic information across a wide range of diseases, alone or in combination, is unknown for several reasons.

血浆蛋白质组学特征捕获健康行为和当前健康状况7，并可能整合“静态”遗传风险8,9和疾病的动态环境决定因素。已经描述了可翻译的简约模型。例如，含有少至三种蛋白质的稀疏蛋白质特征改善了目前筛查策略所遗漏的糖尿病高危人群的鉴定10。血浆蛋白质组学是否可以提供广泛的临床有用的预测或机制信息疾病，单独或联合使用，由于几个原因尚不清楚。

Data availability

数据可用性

All proteomic, phenotypic and EHR data used in this study are available from UKB upon application (https://www.ukbiobank.ac.uk). The EPIC-Norfolk data can be requested by bona fide researchers for specified scientific purposes via the study website (https://www.mrc-epid.cam.ac.uk/research/studies/epic-norfolk/).

本研究中使用的所有蛋白质组学、表型和EHR数据均可从UKB获得(https://www.ukbiobank.ac.uk)。(https://www.mrc-epid.cam.ac.uk/research/studies/epic-norfolk/)。

Data will either be shared through an institutional data sharing agreement or arrangements will be made for analyses to be conducted remotely without the need for data transfer. Data from the Human Protein Atlas is publicly available (https://www.proteinatlas.org/). KEGG (https://www.genome.jp/kegg/) and REACTOME (https://reactome.org/) pathway data is also publicly available.

数据将通过机构数据共享协议共享，或者将安排在不需要数据传输的情况下远程进行分析。人类蛋白质图谱的数据是公开的(https://www.proteinatlas.org/)。KEGG公司(https://www.genome.jp/kegg/)和REACTOME(https://reactome.org/)路径数据也可公开获得。

scRNA-seq data are available at the European Genome-Phenome Archive under accession number EGAS00001006980. To accelerate the use and translational potential of our findings, we generated an open-access interactive web resource that enables the scientific community to easily visualize post-test probabilities based on derived LRs across all diseases (https://omicscience.org/apps/protpred)..

scRNA-seq数据可在欧洲基因组-现象组档案馆获得，登录号为EGAS0001006980。为了加速我们研究结果的使用和转化潜力，我们生成了一个开放获取的交互式网络资源，使科学界能够根据所有疾病的派生LR轻松可视化测试后概率(https://omicscience.org/apps/protpred)。。

Code availability

代码可用性

Associated code and scripts for the analysis can be found in the following GitHub repository: https://github.com/comp-med/Sparse-proteomic-prediction-of-common-and-rare-diseases.git.

分析的相关代码和脚本可以在以下GitHub存储库中找到：https://github.com/comp-med/Sparse-proteomic-prediction-of-common-and-rare-diseases.git.

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Download referencesAcknowledgementsWe would like to acknowledge the UKB participants for their dedication to participating in ongoing research and EHR linkage. This work and the incredible work of other UKB researchers would not have been possible without their dedication to science.

下载参考文献致谢我们要感谢UKB参与者致力于参与正在进行的研究和EHR链接。如果没有他们对科学的奉献，这项工作和其他UKB研究人员令人难以置信的工作是不可能的。

All UKB data were accessed in accordance with GlaxoSmithKline’s UKB Application no. 20361 and the UKB-PPP Consortium Application no. 65851. We thank the EPIC-Norfolk investigators, the Study Co-ordination team, the Epidemiology Field, Data and Laboratory teams. The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC_UU_00006/1 to N.J.W.) and Cancer Research UK (C864/A14136 to N.J.W.).

所有UKB数据均根据葛兰素史克的UKB申请号20361和UKB-PPP财团申请号65851进行访问。我们感谢EPIC诺福克研究人员，研究协调团队，流行病学领域，数据和实验室团队。EPIC诺福克研究(https://doi.org/10.22025/2019.10.105.00004)已获得医学研究委员会（MR/N003284/1和MC\U UU\U 00006/1至新泽西州）和英国癌症研究（C864/A14136至新泽西州）的资助。

Proteomics measurements in EPIC-Norfolk were supported by an MRC Rapid Call (MC_PC_21036, to C.L. and N.J.W.). J.C.-Z. was supported by a 4-year Wellcome Trust PhD Studentship and the Cambridge Trust. H.H. is supported by Health Data Research UK and the National Institute for Health Research University College London Hospitals Biomedical Research Centre.

。J、 C.-Z.得到了为期4年的惠康基金会博士生和剑桥基金会的支持。H、 H.得到了英国健康数据研究所和英国国立卫生研究院大学伦敦学院医院生物医学研究中心的支持。

We acknowledge Julian Hoffmann Anton, Werner Römisch-Margl and Gabi Kastenmüller for their support designing and implementing the associated webserver.Author informationAuthor notesThese authors contributed equally: Julia Carrasco-Zanini, Maik Pietzner, Jonathan Davitte, Nicholas J. Wareham, Harry Hemingway, Robert A.

我们感谢Julian Hoffmann Anton、Werner Römisch Margl和Gabi Kastenmüller对设计和实现相关Web服务器的支持。作者信息作者注意到这些作者做出了同样的贡献：茱莉亚·卡拉斯科·扎尼尼，梅克·皮埃茨纳，乔纳森·戴维特，尼古拉斯·J·沃勒姆，哈里·海明威，罗伯特·A。

Scott, Claudia Langenberg.Authors and AffiliationsHuman Genetics and Genomics, GSK Research and Development, Stevenage, UKJulia Carrasco-Zanini, Praveen Surendran, Daniel Freitag, Joanna C. Betts & Robert A. ScottMRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UKJulia Carra.

斯科特，克劳迪亚·兰根伯格（ClaudiaLangenberg Scott）。葛兰素史克研究与发展（GSK Research and Development）的作者和附属机构舒曼遗传学和基因组学（Shuman Genetics and Genomics），史蒂夫纳格（Stevenage），乌克朱莉娅·卡拉斯科·扎尼尼（UKJulia Carrasco Zanini），普拉文·苏伦德兰（Praveen Surendran），丹尼尔·弗雷塔格（Daniel Freitag），乔安娜·贝茨（JoannaC.Betts。

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PubMed Google ScholarContributionsJ.C.-Z., N.J.W., H.H., R.A.S. and C.L. conceptualized the project and designed the analysis. J.C.-Z., M.P., J.D., P.S., D.C.C.-C., T.K., D.F., S.D., J.C.B., H.H., R.A.S. and C.L. drafted the manuscript. J.C.-Z. and J.D. performed the analyses.

PubMed谷歌学术贡献。C、 -Z.，N.J.W.，H.H.，R.A.S.和C.L.对项目进行了概念化并设计了分析。J、 C.Z.，M.P.，J.D.，P.S.，D.C.C.-C.，T.K.，D.F.，S.D.，J.C.B.，H.H.，R.A.S.和C.L.起草了手稿。J、 C.-Z.和J.D.进行了分析。

J.C.-Z., P.S. and D.C.C.-C. designed the data visualization. J.D., P.S., D.C.C.-C., C.R., S.G. and F.Z. performed QC and prepared proteomic data and disease definition in UKB. A.T., C.T., C.Y., N.F., S.D. and H.H. developed the phenotyping algorithm to derive refined disease definitions. N.J.W. is Principal Investigator of the EPIC-Norfolk cohort.

J、 C.-Z.，P.S.和D.C.C.-C.设计了数据可视化。J、 D.，P.S.，D.C.C.-C.，C.R.，S.G.和F.Z.进行了QC，并在UKB中准备了蛋白质组学数据和疾病定义。A、 T.，C.T.，C.Y.，N.F.，S.D.和H.H.开发了表型算法，以得出精确的疾病定义。N、 J.W.是EPIC诺福克队列的首席研究员。

F.G. and S.H. performed scRNA-seq experiments and analyses. All authors contributed to the interpretation of the results and critically reviewed the manuscript.Corresponding authorsCorrespondence to.

F、 G.和S.H.进行了scRNA-seq实验和分析。所有作者都为结果的解释做出了贡献，并批判性地审查了手稿。通讯作者通讯。

Julia Carrasco-Zanini, Robert A. Scott or Claudia Langenberg.Ethics declarations

朱莉娅·卡拉斯科·扎尼尼、罗伯特·斯科特或克劳迪亚·兰根伯格，《道德宣言》

Competing interests

相互竞争的利益

J.D., P.S., D.C.C.-C., C.R., T.K., S.G., D.F., F.Z., J.B. and R.A.S. are all employees of and/or shareholders of GSK. The remaining authors declare no competing interests.

J、 D.，P.S.，D.C.C.-C.，C.R.，T.K.，S.G.，D.F.，F.Z.，J.B.和R.A.S.都是葛兰素史克的员工和/或股东。其余作者声明没有利益冲突。

Peer review

同行评审

Peer review information

同行评审信息

Nature Medicine thanks Jochen Schwenk, Stefanie Hauck and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Michael Basson, in collaboration with the Nature Medicine team.

Nature Medicine感谢Jochen Schwenk，Stefanie Hauck和另一位匿名审稿人对这项工作的同行评审做出的贡献。主要处理编辑：Michael Basson，与Nature Medicine团队合作。

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Overview of the study design in the context of the UK biobank Pharma Proteomics Project (UKB-PPP).a, Study design used for 193 diseases for which only participants from the randomly selected subset were included in the analysis.

。扩展数据扩展数据图1英国生物银行制药蛋白质组学项目（UKB-PPP）背景下的研究设计概述。a，用于193种疾病的研究设计，其中只有随机选择的子集的参与者被包括在分析中。

b, Study design used for 25 less common diseases were incident cases within 10 years of follow-up for the specific disease under study were included in the analysis. Created with BioRender.com.Extended Data Fig. 2 Example of the improvement from proteomically informed screening strategies for coeliac disease.We present two scenarios, in which screening is performed in 1) the general population and 2) a high-risk population (individuals with other autoimmune conditions).

b、 研究设计用于25种不太常见的疾病，分析中包括了所研究特定疾病随访10年内的事件病例。使用BioRender.com.Extended Data创建图2蛋白质组学信息筛选乳糜泻策略的改进示例。我们提出了两种情况，其中筛查是在1）普通人群和2）高危人群（患有其他自身免疫疾病的个体）中进行的。

According to their predicted risk, individuals are classified as ‘positive’ (those predicted to develop coeliac disease within the next 10 years) or ‘negatives’ (not predicted at risk of coeliac disease). We illustrate the number of true positives, false positives, true negative and false negative that would be obtained according to the detection rate we estimated for coeliac disease in UK biobank at a 10% false positive rate.

根据他们的预测风险，个人被分类为“阳性”（那些预计在未来10年内发展为乳糜泻的人）或“阴性”（未预测有乳糜泻风险）。我们说明了根据我们在英国生物库中估计的乳糜泻检出率以10%的假阳性率获得的真阳性，假阳性，真阴性和假阴性的数量。

We further represent the pre-test probability, likelihood ratio (LR) and post-test probability in the two different scenarios (general population and high-risk population). Created with BioRender.com.Extended Data Fig. 3 Predictive performance is not related with the number of incident cases.a, Predictive performance (C-index) of protein-based models, across 67 diseases for which these outperformed clinical models, was not correlated with the number of incident cases wi.

我们进一步表示了两种不同情景（一般人群和高风险人群）中的测试前概率，似然比（LR）和测试后概率。使用BioRender.com.Extended Data创建图3预测性能与事件病例数无关。a，基于蛋白质的模型的预测性能（C指数）与67种疾病的预测性能（C指数）无关，这些疾病优于临床模型，与事件病例数wi。

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Reprints and permissionsAbout this articleCite this articleCarrasco-Zanini, J., Pietzner, M., Davitte, J. et al. Proteomic signatures improve risk prediction for common and rare diseases.

转载和许可本文引用本文Carrasco Zanini，J.，Pietzner，M.，Davidte，J。等人。蛋白质组学特征可改善常见和罕见疾病的风险预测。

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