Evidence map›Paper›PMID 39549086›Full record

ArticleMetabolic brain disease2024

Untargeted metabolomics to detect and identify plasma metabolic signatures associated with intracranial aneurysm and its rupture.

Siming Gui, Jia Jiang, Dingwei Deng, Dachao Wei, Xiheng Chen, Yudi Tang, Jian Lv, Wei You, Ting Chen, Yang Zhao and 6 more

Abstract read
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In one paragraph

Article in Metabolic brain disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

16 authors.

Siming Gui *Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Jia Jiang *Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Dingwei DengDepartment of Neurosurgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Dachao WeiDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Xiheng ChenDepartment of Neurosurgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Yudi TangDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Jian LvDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Wei YouDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Ting ChenSchool of Biomedical Engineering, Capital Medical University, Beijing, China.
Yang ZhaoDepartment of Neurosurgery, Peking University International Hospital, Peking University, Beijing, China.
Hengwei JinDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Xinke LiuDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Huijian GeDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Peng LiuDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yuhua JiangDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China. jxy_200321@163.com.
Youxiang LiDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China. liyouxiang@mail.ccmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The biological basis for metabolic differences between unruptured and ruptured intracranial aneurysm (UIA and RIA) populations and their potential role in triggering IA rupture remain unclear. The aim of this study was to analyze the plasma metabolic profiles of patients with UIA and RIA using an untargeted metabolomic approach and to develop a model for early rupture classification. Plasma samples were analyzed using an ultra-high-performance liquid chromatography high-resolution tandem mass spectrometry-based platform. Least absolute shrinkage and selection operator regression and random forest machine learning methods were employed for metabolite feature selection and predictive model construction. Among 49 differential plasma metabolites identified, 31 were increased and 18 were decreased in the plasma of RIA patients. Five key metabolites-canrenone, piperine, 1-methyladenosine, betaine, and trigonelline-were identified as having strong potential to discriminate between UIA and RIA patients. This combination of metabolites demonstrated high diagnostic accuracy, with an area under the curve exceeding 0.95 in both the training and validation datasets. Our finding highlights the significance of plasma metabolites as potential biomarkers for early detection of IA rupture risk, offering new insights for clinical practice and future research on IA management.

Indexed as

Aneurysm, RupturedBiomarkersIntracranial AneurysmMetabolomicsAdultAgedFemaleHumansMaleMetabolomeMiddle AgedBiomarkersIntracranial aneurysmMachine learningMetabolomicsSerum biomarkers

Identifiers

PMID39549086

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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 OpenQuestion graph.