{"title":"多器官代谢组生物学年龄与心脏代谢状况和死亡风险有关。","authors":"Filippos Anagnostakis, Sarah Ko, Mehrshad Saadatinia, Jingyue Wang, Christos Davatzikos, Junhao Wen","doi":"10.1038/s41467-025-59964-z","DOIUrl":null,"url":null,"abstract":"<p><p>Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25<r < 0.42). Crucially, including composite metabolites (e.g. sums or ratios of raw metabolites) results in poor generalizability to independent test data due to multicollinearity. Genome-wide associations identify 405 MetBAG-locus pairs (P < 5 × 10<sup>-8</sup>/5). Using SBayesS, we estimate the SNP-based heritability (0.09< <math> <msubsup><mrow><mi>h</mi></mrow> <mrow><mi>S</mi> <mi>N</mi> <mi>P</mi></mrow> <mrow><mn>2</mn></mrow> </msubsup> </math> < 0.18), negative selection signatures (-0.93 < S < -0.76), and polygenicity (0.001<Pi < 0.003) for the 5 MetBAGs. Genetic correlation and Mendelian randomization analyses reveal potential causal links between the 5 MetBAGs and cardiometabolic conditions (e.g., metabolic disorders and hypertension). Integrating multi-organ and multi-omics features improves disease category and mortality predictions. The 5 MetBAGs extend existing biological aging clocks to study human aging and disease across multiple biological scales. All results are publicly available at https://labs-laboratory.com/medicine/ .</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"16 1","pages":"4871"},"PeriodicalIF":14.7000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk.\",\"authors\":\"Filippos Anagnostakis, Sarah Ko, Mehrshad Saadatinia, Jingyue Wang, Christos Davatzikos, Junhao Wen\",\"doi\":\"10.1038/s41467-025-59964-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25<r < 0.42). Crucially, including composite metabolites (e.g. sums or ratios of raw metabolites) results in poor generalizability to independent test data due to multicollinearity. Genome-wide associations identify 405 MetBAG-locus pairs (P < 5 × 10<sup>-8</sup>/5). Using SBayesS, we estimate the SNP-based heritability (0.09< <math> <msubsup><mrow><mi>h</mi></mrow> <mrow><mi>S</mi> <mi>N</mi> <mi>P</mi></mrow> <mrow><mn>2</mn></mrow> </msubsup> </math> < 0.18), negative selection signatures (-0.93 < S < -0.76), and polygenicity (0.001<Pi < 0.003) for the 5 MetBAGs. Genetic correlation and Mendelian randomization analyses reveal potential causal links between the 5 MetBAGs and cardiometabolic conditions (e.g., metabolic disorders and hypertension). Integrating multi-organ and multi-omics features improves disease category and mortality predictions. The 5 MetBAGs extend existing biological aging clocks to study human aging and disease across multiple biological scales. All results are publicly available at https://labs-laboratory.com/medicine/ .</p>\",\"PeriodicalId\":19066,\"journal\":{\"name\":\"Nature Communications\",\"volume\":\"16 1\",\"pages\":\"4871\"},\"PeriodicalIF\":14.7000,\"publicationDate\":\"2025-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Communications\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41467-025-59964-z\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-025-59964-z","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
跨不同器官系统的多器官生物衰老时钟已被证明可以预测人类疾病和死亡。在这里,我们将这种多器官框架扩展到血浆代谢组学,使用来自274,247名UK Biobank参与者的107种血浆非衍生代谢物开发了5种基于器官特异性代谢组的生物年龄差距(MetBAGs)。我们的年龄预测模型的平均绝对误差约为6年(0.25-8/5)。利用SBayesS,我们估计了基于snp的遗传力(0.09< h SNP 2)
Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk.
Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25-8/5). Using SBayesS, we estimate the SNP-based heritability (0.09< < 0.18), negative selection signatures (-0.93 < S < -0.76), and polygenicity (0.001
期刊介绍:
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.