Evidence map›Paper›PMID 31945804›Full record

ArticleJournal of cellular and molecular medicine2020

Metabolomics reveals metabolite changes of patients with pulmonary arterial hypertension in China.

Chenyang Chen, Fei Luo, Panyun Wu, Yiyuan Huang, Avash Das, Shenglan Chen, Jingyuan Chen, Xinqun Hu, Fei Li, Zhenfei Fang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 2 pooled it
6.3field-weighted citation impact, top 3% 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

64 citing papers in PubMed, 2 syntheses or guidelines pooled it, 92 citations in OpenAlex.

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4 more citing papers are in PubMed but not listed here.

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

11 authors at 3 institutions in 2 countries.

Chenyang ChenDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Fei LuoDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Panyun WuDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Yiyuan HuangDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Avash DasDepartment of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Shenglan ChenDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Jingyuan ChenDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Xinqun HuDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Fei LiKunming Institute of Botany, Chinese Academy of Sciences, Kunming, China.
Zhenfei FangDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.ORCID 0000-0001-9125-5348
Shenhua ZhouDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Central South University · CNKunming Institute of Botany · CNThe University of Texas Southwestern Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The specific mechanism of pulmonary arterial hypertension (PAH) remains elusive. The present study aimed to explore the underlying mechanism of PAH through the identity of novel biomarkers for PAH using metabolomics approach. Serum samples from 40 patients with idiopathic PAH (IPAH), 20 patients with congenital heart disease-associated PAH (CHD-PAH) and 20 healthy controls were collected and analysed by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS). Orthogonal partial least square-discriminate analysis (OPLS-DA) was applied to screen potential biomarkers. These results were validated in monocrotaline (MCT)-induced PAH rat model. The OPLS-DA model was successful in screening distinct metabolite signatures which distinguished IPAH and CHD-PAH patients from healthy controls, respectively (26 and 15 metabolites). Unbiased analysis from OPLS-DA identified 31 metabolites from PAH patients which were differentially regulated compared to the healthy controls. Our analysis showed dysregulation of the different metabolic pathways, including lipid metabolism, glucose metabolism, amino acid metabolism and phospholipid metabolism pathways in PAH patients compared to their healthy counterpart. Among these metabolites from dysregulated metabolic pathways, a panel of metabolites from lipid metabolism and fatty acid oxidation (lysophosphatidylcholine, phosphatidylcholine, perillic acid, palmitoleic acid, N-acetylcholine-d-sphingomyelin, oleic acid, palmitic acid and 2-Octenoylcarnitine metabolites) were found to have a close association with PAH. The results from the analysis of both real-time quantitative PCR and Western blot showed that expression of LDHA, CD36, FASN, PDK1 GLUT1 and CPT-1 in right heart/lung were significantly up-regulated in MCT group than the control group.

Indexed as

AdultAnimalsBiomarkersCase-Control StudiesChinaChromatography, High Pressure LiquidDiscriminant AnalysisFamilial Primary Pulmonary HypertensionFatty AcidsFemaleHumansLipid MetabolismMaleMass SpectrometryMetabolic Networks and PathwaysMetabolomicsBiomarkersFatty AcidsMonocrotalinelipid metabolismmetabolomicspulmonary arterial hypertension

Identifiers

PMID31945804
PMCPMC7028857
OpenAlexW2998908283

What Socratic holds

Textmetadata
LicenceCC BY
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.