ReviewTrends in cardiovascular medicine2016
Gene scanning and heart attack risk.
Review in Trends in cardiovascular medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.Current protocols in human genetics · 2019Pooled it
- Analysis of the differences in immune-related genes and immune cell subtypes in acute myocardial infarction.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2024Article
- Mapping gene and gene pathways associated with coronary artery disease: a CARDIoGRAM exome and multi-ancestry UK biobank analysis.Scientific reports · 2021Article
- Genetic Markers for Coronary Artery Disease.Medicina (Kaunas, Lithuania) · 2018Review
- Gene polymorphisms of fibronectin rs2289202 and fibrillin 2 rs331069 associate with vascular disease, the TAMRISK study.Biomedical reports · 2018Article
- Bioinformatic Analysis of Coronary Disease Associated SNPs and Genes to Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis.Journal of proteomics and genomics research · 2017Article
- Ischemia/Reperfusion.Comprehensive Physiology · 2016Review
- Article
- Differential gene expression profiles in peripheral blood in Northeast Chinese Han people with acute myocardial infarction.Genetics and molecular biologyArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Coronary heart disease remains the leading cause of death in the Western World. The advent of microarray and next-generation sequencing technologies has generated multi-dimensional data sets, allowing for new pathophysiological insights into this complex disease. To date, genome-wide association studies (GWAS) have identified 152 associated loci and 320 candidate genes, contributing to the genetic risk of coronary artery disease (CAD) and acute myocardial infarction (AMI). The majority of single nucleotide polymorphisms (SNPs) mediate their risk by unknown mechanisms. A functional analysis based on Gene Ontology and KEGG pathways of candidate genes that are associated with CAD/AMI-SNPs showed the strongest evidence for genes regulating cholesterol metabolism. Additional clusters were significantly enriched for pathways, which play prominent roles during AMI and the development of atherosclerotic plaques in vascular tissue, including focal adhesion/extracellular matrix interaction, TGF-β signaling, apoptosis, regulation of vascular smooth muscle contraction, angiogenesis, calcium ion binding, and transcription factors. A systems genetics approach, which incorporates genetic risk with gene expression data, metabolomic data, and protein biochemistry into genome-wide network studies, holds promise to elucidate the complex interplay between genetic risk and environmental factors for coronary artery disease.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.