ReviewCurrent cardiology reports2021
Atrial Fibrillation Genomics: Discovery and Translation.
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.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
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.
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What Socratic holds
Registered trials
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.