Evidence map›Paper›PMID 39920375›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2025

Identification of potential biomarkers for coronary slow flow using untargeted metabolomics.

Yunxian Chen, Jiarong Liang, Sujuan Chen, Baofeng Chen, Fenglei Guan, Xiangying Liu, Xiangyang Liu, Yuanlin Zhao, Liangqiu Tang

Abstract read
PubMed Publisher
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

9 authors.

Yunxian ChenDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Jiarong LiangDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Sujuan ChenDepartment of Neurology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Baofeng ChenDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Fenglei GuanDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Xiangying LiuDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Xiangyang LiuDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Yuanlin ZhaoDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China.
Liangqiu TangDepartment of Cardiology, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, China. Rangochan99@hotmail.com.

Funding

Shaoguan Municipal Bureau of Science and Technology, China 211201096570195
6 · The paper itself

Abstract

backgroundCoronary slow flow (CSF) is associated with poor cardiovascular prognosis. However, its pathogenesis is unclear. This study aimed to identify potential characteristic biomarkers in patients with CSF using untargeted metabolomics.

methodsWe prospectively enrolled 30 patients with CSF, 30 with coronary artery disease (CAD), and 30 with normal coronary arteries (NCA), all of whom were age-matched, according to the results of coronary angiography. Serum metabolomics were analyzed using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Differentially expressed metabolites were identified through orthogonal partial least squares-discriminant analysis (OPLS-DA) combined with univariate fold-change and VIP value analysis. Pathway enrichment of these metabolites was performed using the KEGG database, and ROC curves were plotted to assess the diagnostic value of the metabolites in CSF patients.

resultsCompared to the CAD and NCA groups, 256 metabolites showed specific expression in CSF, with 18 meeting stringent screening criteria (VIP > 1, FC ≥ 2, or FC ≤ 0.5, and P < 0.05). Seven metabolites demonstrated high diagnostic value for CSF: inositol 1,3,4-trisphosphate (AUC: 1.0), Cer (d24:1/18:0 (2OH)) (AUC: 0.984), Creosol (AUC: 0.976), Chaps (AUC: 0.904), Arg-Thr-Lys-Arg (AUC: 0.929), Ser-Tyr-Arg (AUC: 0.912), and Methyl Indole-3-Acetate (AUC: 0.909). Pathway analysis highlighted the HIF-1 signaling pathway as the most significant metabolic pathway.

conclusionsWe identified seven metabolites that may serve as serum biomarkers for predicting and diagnosing CSF through untargeted metabolomics. The HIF-1 signaling pathway appears to be crucial in the development of CSF.

Indexed as

BiomarkersCoronary Artery DiseaseCoronary CirculationMetabolomicsAgedFemaleHumansMaleMiddle AgedProspective StudiesTandem Mass SpectrometryBiomarkersBiomarkersCoronary slow flowMetabolism pathwayUntargeted metabolomics

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

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.