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Published

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

Authors

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

Abstract

Geroscience. 2026 Jul 21. doi: 10.1007/s11357-026-02412-7. Online ahead of print.

ABSTRACT

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.

PMID:42481860 | DOI:10.1007/s11357-026-02412-7

UK DRI Authors

Prof Paul Elliott

Group Leader

Using advanced methods in genetic, epidemiology and metabolic phenotyping to improve understanding of dementias

Prof Paul Elliott