A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank

Shiyu Zhang; Zheng Wang; Yijing Wang; Yixiao Zhu; Qiao Zhou; Xingxing Jian; Guihu Zhao; Jian Qiu; Kun Xia; Beisha Tang; Julian Mutz; Jinchen Li; Bin Li

doi:10.1038/s41467-024-52310-9

A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank

Shiyu Zhang, Zheng Wang, Yijing Wang, Yixiao Zhu, Qiao Zhou, Xingxing Jian, Guihu Zhao, Jian Qiu, Kun Xia, Beisha Tang, Julian Mutz, Jinchen Li, Bin Li

Social Genetic & Developmental Psychiatry

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate "biomarker - disease" causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.

Original language	English
Pages (from-to)	8081
Number of pages	1
Journal	Nature Communications
Volume	15
Issue number	1
DOIs	https://doi.org/10.1038/s41467-024-52310-9
Publication status	Published - 15 Sept 2024

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abstract = "The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate {"}biomarker - disease{"} causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.",

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AU - Zhang, Shiyu

AU - Wang, Zheng

AU - Wang, Yijing

AU - Zhu, Yixiao

AU - Zhou, Qiao

AU - Jian, Xingxing

AU - Zhao, Guihu

AU - Qiu, Jian

AU - Xia, Kun

AU - Tang, Beisha

AU - Mutz, Julian

AU - Li, Jinchen

AU - Li, Bin

PY - 2024/9/15

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AB - The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate "biomarker - disease" causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.

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A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank

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