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(OR04-03-26) Metabolomic Signatures of Aging and Their Associations with Clinical Biomarkers and Mortality

Saturday, July 25, 2026
8:42 AM - 8:54 AM ET
Location: Maryland Ballroom C
Early Career Research Award Competition

    Presenting Author(s)

  • FW

    Fenglei Wang, PhD

    Assistant Professor
    Peking University
    Beijing, Beijing, China (People's Republic)

    Disclosure(s): No relevant financial relationship(s) with ineligible companies to disclose.

    • Co-Author(s)

  • AT

    Anne-Julie Tessier

    Université de Montréal, Canada

  • AG

    Andrea J. Glenn

    New York University, United States

  • MG

    Marta Guasch-Ferré

    University of Copenhagen, Denmark

  • Frank B. Hu, MD, MPH, PhD (he/him/his)

    Chair, Department of Nutrition; Fredrick J. Stare Professor of Nutrition and Epidemiology
    Harvard T.H. Chan School of Public Health
    Boston, Massachusetts, United States

Disclosure(s):
Fenglei Wang, PhD: No relevant financial relationship(s) with ineligible companies to disclose.
Frank B. Hu, MD, MPH, PhD: No relevant financial relationship(s) with ineligible companies to disclose.
Objectives: Longitudinal metabolomic data characterizing aging-related signatures remain limited. We aimed to identify metabolomic profiles associated with aging and to evaluate their associations with clinical biomarkers and mortality risk.

Methods: We included 10,497 middle-­aged and older adults (mean age: 54.7 years; 84% women) with baseline metabolomic data. Among them, 1,505 women had repeat metabolomic measurements obtained after 10 years. Cross-sectional and longitudinal analyses were conducted to identify age-associated metabolites. An elastic net regression model was applied to the cross-sectional data to construct a metabolomic aging clock. We examined associations of metabolomic age acceleration (predicted minus chronological age) with 10 clinical biomarkers—including blood lipids, C-peptide, and inflammatory biomarkers—as well as with all-cause mortality. Changes in metabolomic aging status over 10 years were further evaluated in relation to subsequent mortality risk.

Results: Of the 440 metabolites examined, 44 were consistently associated with age in both cross-sectional and longitudinal analyses. Metabolites positively associated with age included pseudouridine, 4-acetamidobutanoate, and N2,N2-dimethylguanosine, whereas myristoleic acid, serine, C18:2 LPC, and tryptophan were inversely associated. Metabolite-predicted age was moderately to strongly correlated with chronological age (r = 0.65). Metabolomic age acceleration was associated with higher levels of triacylglycerols, low-density lipoprotein cholesterol, and inflammatory biomarkers, including C-reactive protein, tumor necrosis factor receptors, and adiponectin. Each 5-year increment in age acceleration was associated with higher all-cause mortality risk (HR = 1.17; 95% CI: 1.12–1.22). Over 10 years, compared with individuals who remained slow agers (age acceleration < 0 at both time points), those who transitioned to accelerated aging (HR = 1.33; 95% CI: 1.01–1.76) or remained accelerated agers (HR = 1.51; 95% CI: 1.11–2.05) had higher mortality risk.

Conclusions: A metabolomic aging clock captured biological aging beyond chronological age and predicted mortality. Longitudinal acceleration in metabolomic aging was associated with increased subsequent mortality risk.

Funding Sources: National Institutes of Health