Evidence map›Paper›PMID 42329535›Full record

ArticleGeroScience2026

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

Yanfeng Jiang, Qingxia Huang, Jincheng Li, Qinsheng Chen, Jialin Li, Zhenqiu Liu, Mei Cui, Chen Suo, Kelin Xu, Li Jin and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yanfeng Jiang *Human Phenome Institute, Research and Innovation Center, Shanghai Pudong Hospital, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China. yanfengjiang@fudan.edu.cn.ORCID http://orcid.org/0000-0003-4976-6333
Qingxia Huang *State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, China.
Jincheng Li *Human Phenome Institute, Research and Innovation Center, Shanghai Pudong Hospital, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
Qinsheng ChenState Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, China.
Jialin LiHuman Phenome Institute, Research and Innovation Center, Shanghai Pudong Hospital, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
Zhenqiu LiuHuman Phenome Institute, Research and Innovation Center, Shanghai Pudong Hospital, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
Mei CuiDepartment of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
Chen SuoFudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China.
Kelin XuFudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China.
Li JinHuman Phenome Institute, Research and Innovation Center, Shanghai Pudong Hospital, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
Huiru TangState Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai, China. huiru_tang@fudan.edu.cn.
Xingdong ChenFudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China. xingdongchen@fudan.edu.cn.

Funding

Science and Technology Innovation 2030 Major Projects 2023ZD0510000
6 · The paper itself

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.

Indexed as

BiomarkerLipoproteinMetabolomicsRisk predictionStroke

Identifiers

PMID42329535

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.