Age-related metabolomic signatures and stroke susceptibility in a population-based cohort.

Authors

Yanfeng Jiang, Qingxia Huang, Jincheng Li, Qinsheng Chen, Jialin Li, Zhenqiu Liu, Mei Cui, Chen Suo, Kelin Xu, Li Jin, Huiru Tang, Xingdong Chen

Published in

GeroScience. Jun 22, 2026. Epub Jun 22, 2026.

Abstract

Aging-related metabolic dysregulation and vascular vulnerability contribute substantially to stroke susceptibility, yet subtype-specific metabolic signatures remain incompletely characterized. Employing a nested case-control design within the Taizhou Longitudinal Study, we quantified 296 lipoprotein parameters and 54 metabolites in 1208 stroke-control pairs using nuclear magnetic resonance. Logistic regression estimated subtype-specific associations, and machine learning constructed prediction models for ischemic stroke (IS) and intracerebral hemorrhage (ICH). Distinct metabolic profiles were observed across stroke subtypes. Triglyceride-enriched lipoproteins and several low-molecular-weight metabolites were positively associated with both IS and ICH, whereas apolipoprotein A-related components showed inverse associations, with generally stronger effects observed for IS than for ICH. Age-stratified and interaction analyses revealed age-dependent heterogeneity, especially among histidine and lipoprotein composition measures. To further characterize systemic metabolic vulnerability, we constructed a weighted metabolic risk score (MRS), which was associated with age and statistically accounted for part of the age-stroke association (average causal mediation effects: 0.020 for IS; 0.025 for ICH). MRSs were also positively correlated with age and inflammatory markers, particularly for IS (both P < 0.001). Metabolite-based models improved risk discrimination beyond traditional risk factors for both IS and ICH. These findings identify subtype-specific metabolic signatures of stroke and suggest that circulating metabolomic profiles reflect age-associated metabolic alterations relevant to stroke susceptibility beyond traditional cardiometabolic risk factors.

PMID:
42329535
Bibliographic data and abstract were imported from PubMed on 22 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.