Deng Yue-Ting, You Jia, He Yu, Zhang Yi, Li Hai-Yun, Wu Xin-Rui, Cheng Ji-Yun, Guo Yu, Long Zi-Wen, Chen Yi-Lin, Li Ze-Yu, Yang Liu, Zhang Ya-Ru, Chen Shi-Dong, Ge Yi-Jun, Huang Yu-Yuan, Shi Le-Ming, Dong Qiang, Mao Ying, Feng Jian-Feng, Cheng Wei, Yu Jin-Tai
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
Cell. 2025 Jan 9;188(1):253-271.e7. doi: 10.1016/j.cell.2024.10.045. Epub 2024 Nov 22.
Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here, we provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://proteome-phenome-atlas.com/) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets.
大规模蛋白质组学研究能够深化我们对健康与疾病的理解,并推动精准医学的发展。在此,我们提供了一份详细的图谱,涵盖了英国生物银行中53026名个体(中位随访时间:14.8年)的2920种与疾病相关的血浆蛋白(406种常见疾病和660种新发疾病)以及986种与健康相关的性状,这代表了迄今为止最全面的蛋白质组概况。该图谱揭示了168100种蛋白质与疾病的关联以及554488种蛋白质与性状的关联。超过650种蛋白质在至少50种疾病中共享,超过1000种表现出性别和年龄异质性。此外,蛋白质在疾病鉴别方面显示出有前景的潜力(183种疾病的曲线下面积[AUC]>0.80)。最后,整合蛋白质数量性状位点数据确定了474种因果蛋白质,提供了37个药物再利用机会和26个具有良好安全性的有前景靶点。这些结果提供了一个开放获取的综合蛋白质组-表型组资源(https://proteome-phenome-atlas.com/),以帮助阐明疾病的生物学机制,并加速疾病生物标志物、预测模型和治疗靶点的开发。