Evidence map›Paper›PMID 37904201›Full record

ArticleHereditas2023

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy.

Wei Yu, Hongli Gao, Tianyang Hu, Xingling Tan, Yiheng Liu, Hongli Liu, Siming He, Zijun Chen, Sheng Guo, Jing Huang

Open access · goldAbstract read
In one paragraph

Article in Hereditas, 2023. 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
0.5field-weighted citation impact, top 32% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  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

10 authors at 3 institutions in 1 country.

Wei Yu *Department of Cardiology, The Yongchuan Hospital of Chongqing Medical University, Chongqing, China.
Hongli Gao *Department of Cardiology, The Yongchuan Hospital of Chongqing Medical University, Chongqing, China.
Tianyang HuPrecision Medicine Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xingling TanDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yiheng LiuDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Hongli LiuDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Siming HeDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Zijun ChenDepartment of Cardiology, The Yongchuan Hospital of Chongqing Medical University, Chongqing, China.
Sheng GuoDepartment of Cardiology, The People's Hospital of Rongchang District, Chongqing, China. 848439581@qq.com.
Jing HuangDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. doctorhuang2015@163.com.ORCID http://orcid.org/0000-0002-3148-6117
Dalian Medical University · CNChongqing Medical University · CNRongcheng City People's Hospital · CN

Funding

National Natural Science Foundation of China 82170445
6 · The paper itself

Abstract

backgroundRNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.

methodsIn the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.

resultsA total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10

conclusionIn conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

Indexed as

Cardiomyopathy, DilatedVentricular Dysfunction, LeftBiomarkersEndothelial CellsHumansInsulin-Like Growth Factor Binding Protein 2RNAStroke VolumeVentricular Function, LeftBiomarkersIGFBP2 protein, humanInsulin-Like Growth Factor Binding Protein 2RNADilated cardiomyopathyIGFBP2Left ventricular ejection fractionMachine learningRNA modifications

Identifiers

PMID37904201
PMCPMC10617082
OpenAlexW4388035649

What OpenQuestion 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 OpenQuestion graph.