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在274241名成年人中绘制血浆代谢组与人类健康和疾病的关联图谱。

Mapping the plasma metabolome to human health and disease in 274,241 adults.

作者信息

You Jia, Cui Xi-Han, Chen Yi-Lin, Wang Yi-Xuan, Li Hai-Yun, Qiang Yi-Xuan, Cheng Ji-Yun, Deng Yue-Ting, Guo Yu, Ren Peng, Zhang Yi, He Yu, He Xiao-Yu, Chen Shi-Dong, Zhang Ya-Ru, Huang Yu-Yuan, Mao Ying, Feng Jian-Feng, Cheng Wei, Yu Jin-Tai

机构信息

Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Academy of Natural Sciences (SANS), Shanghai Medical College, Fudan University, Shanghai, China.

Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.

出版信息

Nat Metab. 2025 Sep 19. doi: 10.1038/s42255-025-01371-1.

Abstract

A systematic characterization of metabolic profiles in human health and disease enhances precision medicine. Here we present a comprehensive human metabolome-phenome atlas, using data from 274,241 UK Biobank participants with nuclear magnetic resonance metabolic measures. This atlas links 313 plasma metabolites to 1,386 diseases and 3,142 traits, with participants being prospectively followed for a median of 14.9 years. This atlas uncovered 52,836 metabolite-disease and 73,639 metabolite-trait associations, where the ratio of cholesterol to total lipids in large low-density lipoprotein percentage was found as the metabolite associated with the highest number (n = 526) of diseases. In addition, we found that more than half (57.5%) of metabolites showed statistical variations from healthy individuals over a decade before disease onset. Combined with demographics, the machine-learning-based metabolic risk score signified the top 30 (around 10%) metabolites as biomarkers, yielding favourable classification performance (area under the curve > 0.8) for 94 prevalent and 81 incident diseases. Finally, Mendelian randomization analyses provided support for causal relationships of 454 metabolite-disease pairs, among which 402 exhibited shared genetic determinants. Additional insights can be gleaned via an accessible interactive resource ( https://metabolome-phenome-atlas.com/ ).

摘要

对人类健康和疾病中的代谢谱进行系统表征可提升精准医学水平。在此,我们利用来自274241名英国生物银行参与者的核磁共振代谢测量数据,呈现了一份全面的人类代谢组-表型图谱。该图谱将313种血浆代谢物与1386种疾病及3142种性状相关联,对参与者进行了为期中位数14.9年的前瞻性随访。此图谱揭示了52836种代谢物-疾病关联和73639种代谢物-性状关联,其中发现大低密度脂蛋白百分比中胆固醇与总脂质的比例是与最多数量(n = 526)疾病相关的代谢物。此外,我们发现超过一半(57.5%)的代谢物在疾病发作前十年就显示出与健康个体的统计学差异。结合人口统计学信息,基于机器学习的代谢风险评分将排名前30(约10%)的代谢物确定为生物标志物,对94种常见疾病和81种新发疾病产生了良好的分类性能(曲线下面积>0.8)。最后,孟德尔随机化分析为454对代谢物-疾病对的因果关系提供了支持,其中402对表现出共同的遗传决定因素。通过一个可访问的交互式资源(https://metabolome-phenome-atlas.com/)可以获得更多见解。

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