Evidence map›Paper›PMID 33842614›Full record

ArticleAnnals of translational medicine2021

Biomarkers and key pathways in atrial fibrillation associated with mitral valve disease identified by multi-omics study.

Ming-Yang Li, Huan-Xin Chen, Hai-Tao Hou, Jun Wang, Xiao-Cheng Liu, Qin Yang, Guo-Wei He

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.7field-weighted citation impact, top 9% 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

13 citing papers in PubMed, 18 citations in OpenAlex.

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  3. Recent advances in omics-based research of mitral valve disease.Frontiers in cardiovascular medicine · 2026
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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

7 authors at 2 institutions in 2 countries.

Ming-Yang LiCenter for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School, Peking Union Medical College, Beijing, China.
Huan-Xin ChenCenter for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School, Peking Union Medical College, Beijing, China.
Hai-Tao HouCenter for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School, Peking Union Medical College, Beijing, China.
Jun WangCenter for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School, Peking Union Medical College, Beijing, China.
Xiao-Cheng LiuCenter for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School, Peking Union Medical College, Beijing, China.
Qin YangCenter for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School, Peking Union Medical College, Beijing, China.
Guo-Wei HeCenter for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School, Peking Union Medical College, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNOregon Health & Science University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMitral valve disease (MVD)-associated atrial fibrillation (AF) is one of the most common arrhythmias with an increased risk of thromboembolic events. This study aimed to identify the molecular mechanisms and possible biomarkers for chronic AF in MVD by using multi-omics methods.

methodsThis prospective study enrolled patients with MVD (n=100) undergoing mitral valve replacement surgery. The patients were allocated into chronic AF and sinus rhythm (SR) groups. Plasma samples were collected preoperatively. Proteomics was performed with isobaric tags for relative and absolute quantitation (iTRAQ) to identify differential proteins (DPs) between the two groups. The selected DPs were then validated in a new cohort of patients by enzyme-linked immunosorbent assay (ELISA). A gas chromatography-mass spectrometer was used in the metabolomics study to identify differential metabolites (DMs). Bioinformatics analyses were performed to analyze the results.

resultsAmong the 447 plasma proteins and 322 metabolites detected, 57 proteins and 55 metabolites, including apolipoprotein A-I (ApoA-I), apolipoprotein A-II (ApoA-II), LIM domain only protein 7 (LMO7), and vitronectin (VN) were differentially expressed between AF and SR patients. Bioinformatics analyses identified enriched pathways related to AF, including peroxisome proliferator-activated receptor alpha (PPARα), the renin angiotensin aldosterone system (RAAS), galactose, biosynthesis of unsaturated fatty acids, and linoleic acid metabolism.

conclusionsUsing integrated multi-omics technologies in MVD-associated AF patients, the present study, for the first time, revealed important signaling pathways, such as PPARα, as well as possible roles of other signaling pathways, including the RAAS and galactose metabolism to understand the molecular mechanism of MVD-associated AF. It also identified a large number of DPs and DMs. Some identified proteins and metabolites, such as ApoA-I, ApoA-II, LMO7, and VN, may be further developed as biomarkers for MVD-associated AF.

Indexed as

Atrial fibrillation (AF)metabolomicsmitral valve disease (MVD)proteomics

Identifiers

PMID33842614
PMCPMC8033373
OpenAlexW3139393811

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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.