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

IF 20.8 1区医学 Q1 ENDOCRINOLOGY & METABOLISM

Nature metabolism Pub Date : 2025-09-19 DOI:10.1038/s42255-025-01371-1

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

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引用次数: 0

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/ ).

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274,241名成人血浆代谢组与人类健康和疾病的关系

对人类健康和疾病中代谢特征的系统描述提高了精准医学。在这里，我们提出了一个全面的人类代谢组-表型图谱，使用来自274,241名英国生物银行参与者的核磁共振代谢测量数据。该图谱将313种血浆代谢物与1,386种疾病和3,142种特征联系起来，参与者的预期随访时间中位数为14.9年。该图谱揭示了52,836种代谢物疾病和73,639种代谢物性状相关，其中胆固醇与总脂质之比在大低密度脂蛋白百分比中被发现是与最高数量（n = 526）疾病相关的代谢物。此外，我们发现超过一半（57.5%）的代谢物在发病前10年与健康个体存在统计学差异。结合人口统计学，基于机器学习的代谢风险评分将前30种（约10%）代谢物作为生物标志物，对94种流行疾病和81种突发疾病产生了有利的分类性能（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。可以通过可访问的交互式资源（https://metabolome-phenome-atlas.com/）收集更多的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature metabolism ENDOCRINOLOGY & METABOLISM-

CiteScore

27.50

自引率

2.40%

发文量

170

期刊介绍： Nature Metabolism is a peer-reviewed scientific journal that covers a broad range of topics in metabolism research. It aims to advance the understanding of metabolic and homeostatic processes at a cellular and physiological level. The journal publishes research from various fields, including fundamental cell biology, basic biomedical and translational research, and integrative physiology. It focuses on how cellular metabolism affects cellular function, the physiology and homeostasis of organs and tissues, and the regulation of organismal energy homeostasis. It also investigates the molecular pathophysiology of metabolic diseases such as diabetes and obesity, as well as their treatment. Nature Metabolism follows the standards of other Nature-branded journals, with a dedicated team of professional editors, rigorous peer-review process, high standards of copy-editing and production, swift publication, and editorial independence. The journal has a high impact factor, has a certain influence in the international area, and is deeply concerned and cited by the majority of scholars.