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<p>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 <i>P</i> &lt; 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.</p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 发表日期：

    2026年6月22日

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

摘要

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.

Graphical Abstract