Evidence map›Paper›PMID 34599422›Full record

ReviewCurrent cardiology reports2021

Atrial Fibrillation Genomics: Discovery and Translation.

David H Yoo, Rolf Bodmer, Karen Ocorr, Christopher J Larson, Alexandre R Colas, Evan D Muse

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current cardiology reports, 2021. 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
–field-weighted citation impact, top 79% 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, 0 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

6 authors at 3 institutions in 1 country.

David H YooScripps Clinic, La Jolla, CA, 92037, USA.
Rolf BodmerSanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
Karen OcorrSanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
Christopher J LarsonSanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
Alexandre R ColasSanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
Evan D MuseScripps Clinic, La Jolla, CA, 92037, USA. emuse@scripps.edu.ORCID http://orcid.org/0000-0002-5288-7462
Sanford Burnham Prebys Medical Discovery Institute · USScripps Clinic · USScripps Research Institute · US

Funding

Scripps Translational Science InstituteUL1TR002550 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2018 to 2022
$28.9M
HEART DEVELOPMENT IN DROSOPHILAR01HL054732 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BODMER, ROLF · 1995 to 2024
$11.3M
NCATS NIH HHS UL1TR002550NHLBI NIH HHS R01 HL054732
6 · The paper itself

Abstract

purpose of reviewOur understanding of the fundamental cellular and molecular factors leading to atrial fibrillation (AF) remains stagnant despite significant advancement in ablation and device technologies. Diagnosis and prevention strategies fall behind that of treatment, but expanding knowledge in AF genetics holds the potential to drive progress. We aim to review how an understanding of the genetic contributions to AF can guide an approach to individualized risk stratification and novel avenues in drug discovery. RECENT

findingsRare familial forms of AF identified monogenic contributions to the development of AF. Genome-wide association studies (GWAS) further identified single-nucleotide polymorphisms (SNPs) suggesting polygenic and multiplex nature of this common disease. Polygenic risk scores accounting for the multitude of associated SNPs that each confer mildly elevated risk have been developed to translate genetic information into clinical practice, though shortcomings remain. Additionally, novel laboratory methods have been empowered by recent genetic findings to enhance drug discovery efforts. AF is increasingly recognized as a disease with a significant genetic component. With expanding sequencing technologies and accessibility, polygenic risk scores can help identify high risk individuals. Advancement in digital health tools, artificial intelligence and machine learning based on standard electrocardiograms, and genomic driven drug discovery may be integrated to deliver a sophisticated level of precision medicine in this modern era of emphasis on prevention. Randomized, prospective studies to demonstrate clinical benefits of these available tools are needed to validate this approach.

Indexed as

Atrial FibrillationArtificial IntelligenceGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsHumansProspective StudiesAtrial fibrillationDrug discoveryGeneticsPolygenic riskTranslation

Identifiers

PMID34599422
OpenAlexW3204098568

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.