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<p>Understanding the links between metabolism, ageing, and age-related phenotypes may clarify the role of ageing in disease onset and improve risk prediction. We conducted a cross-cohort assessment of biological age using broad-spectrum LC–MS metabolomics of 3,686 plasma samples in 2,295 participants, aged 20–89, from the UK Airwave study (<i>N</i> = 960) and the Irish Longitudinal Study of Ageing (<i>N</i> = 1,335). The nucleoside N<sub>2</sub>,N<sub>2</sub>-dimethylguanosine, C-glycosyltryptophan, bile acid glucuronides, and the antioxidant zeta-carotene were associated with chronological age, frailty, and mortality. The noradrenergic metabolite 3-methoxy-4-hydroxyphenylglycol sulphate and the oligosaccharide sialyllactose were strongly associated with both age and mortality. We developed a metabolomic clock that was highly predictive of chronological age (<i>r</i> = 0.92) in test samples. Metabolomic age acceleration was strongly correlated between study visits (<i>r</i> &gt; 0.6). Each standard deviation increase in metabolomic age acceleration (~ 5 years) was associated with 43% higher mortality risk, 27% higher risk of mild cognitive impairment, and 10% increased risk of a higher frailty score in fully adjusted models. The metabolites identified here may link ageing and age-related vulnerability and should be further investigated in mechanistic studies. The metabolomic clock has potential for translational applications, including as a prognostic and response marker of generalised age-related disease risk.</p>

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A metabolomic clock of population ageing: cross-cohort validation and associations with frailty and cognitive function 

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Chung-Ho E. Lau,
  • Elena Chekmeneva,
  • Rui Pinto,
  • Aisling M. O’Halloran,
  • Daniel K. H. Chu,
  • Abbas Dehghan,
  • Ioanna Tzoulaki,
  • Paul Elliott,
  • Rose Anne Kenny,
  • Cathal McCrory,
  • Oliver Robinson

摘要

Understanding the links between metabolism, ageing, and age-related phenotypes may clarify the role of ageing in disease onset and improve risk prediction. We conducted a cross-cohort assessment of biological age using broad-spectrum LC–MS metabolomics of 3,686 plasma samples in 2,295 participants, aged 20–89, from the UK Airwave study (N = 960) and the Irish Longitudinal Study of Ageing (N = 1,335). The nucleoside N2,N2-dimethylguanosine, C-glycosyltryptophan, bile acid glucuronides, and the antioxidant zeta-carotene were associated with chronological age, frailty, and mortality. The noradrenergic metabolite 3-methoxy-4-hydroxyphenylglycol sulphate and the oligosaccharide sialyllactose were strongly associated with both age and mortality. We developed a metabolomic clock that was highly predictive of chronological age (r = 0.92) in test samples. Metabolomic age acceleration was strongly correlated between study visits (r > 0.6). Each standard deviation increase in metabolomic age acceleration (~ 5 years) was associated with 43% higher mortality risk, 27% higher risk of mild cognitive impairment, and 10% increased risk of a higher frailty score in fully adjusted models. The metabolites identified here may link ageing and age-related vulnerability and should be further investigated in mechanistic studies. The metabolomic clock has potential for translational applications, including as a prognostic and response marker of generalised age-related disease risk.