Evidence map›Paper›PMID 37332756›Full record

ArticleFrontiers in medicine2023

Serum metabolic signatures for acute pulmonary embolism identified by untargeted metabolomics.

Ming Xie, Yu Liu, Hui Zheng, Xiaoli Gao, Ran Liu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2023. 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
0.6field-weighted citation impact, top 33% of its field
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, 2 citations in OpenAlex.

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

5 authors at 2 institutions in 1 country.

Ming XieNorth China Petroleum Bureau General Hospital, Renqiu, China.
Yu LiuKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Hui ZhengKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Xiaoli GaoNorth China Petroleum Bureau General Hospital, Renqiu, China.
Ran LiuKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Southeast University · CNHangzhou Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: The important metabolic features of acute pulmonary embolism (APE) risk stratification and their underlying biological basis remain elusive. Our study aims to develop early diagnostic models and classification models by analyzing the plasma metabolic profile of patients with APE. Materials and methods: Serum samples were collected from 68 subjects, including 19 patients with confirmed APE, 35 patients with confirmed NSTEMI, and 14 healthy individuals. A comprehensive metabolic assessment was performed using ultra-performance liquid chromatography-mass spectrometry based on an untargeted metabolomics approach. In addition, an integrated machine learning strategy based on LASSO and logistic regression was used for feature selection and model building. Results: The metabolic profiles of patients with acute pulmonary embolism and NSTEMI is significantly altered relative to that of healthy individuals. KEGG pathway enrichment analysis revealed differential metabolites between acute pulmonary embolism and healthy individuals mainly involving glycerophosphate shuttle, riboflavin metabolism, and glycerolipid metabolism. A panel of biomarkers was defined to distinguish acute pulmonary embolism, NSTEMI, and healthy individuals with an area under the receiver operating characteristic curve exceeding 0.9 and higher than that of D-dimers. Conclusion: This study contributes to a better understanding of the pathogenesis of APE and facilitates the discovery of new therapeutic targets. The metabolite panel can be used as a potential non-invasive diagnostic and risk stratification tool for APE.

Indexed as

acute pulmonary embolismbiomarkersdiagnosisLASSOmachine-learningmetabolites

Identifiers

PMID37332756
PMCPMC10272531
OpenAlexW4379233385

What OpenQuestion holds

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LicenceCC BY
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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.