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The association of functional polymorphisms in genes encoding growth factors for endothelial cells and smooth muscle cells with the severity of coronary artery disease

Overview of attention for article published in BMC Cardiovascular Disorders, November 2016
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Title
The association of functional polymorphisms in genes encoding growth factors for endothelial cells and smooth muscle cells with the severity of coronary artery disease
Published in
BMC Cardiovascular Disorders, November 2016
DOI 10.1186/s12872-016-0402-4
Pubmed ID
Authors

Tadeusz Osadnik, Joanna Katarzyna Strzelczyk, Andrzej Lekston, Rafał Reguła, Kamil Bujak, Martyna Fronczek, Marcin Gawlita, Małgorzata Gonera, Jarosław Wasilewski, Bożena Szyguła-Jurkiewicz, Marek Gierlotka, Mariusz Gąsior

Abstract

Despite the important roles of vascular smooth muscle cells and endothelial cells in atherosclerotic lesion formation, data regarding the associations of functional polymorphisms in the genes encoding growth factors with the severity of coronary artery disease (CAD) are lacking. The aim of the present study is to analyze the relationships between functional polymorphisms in genes encoding basic fibroblast growth factor (bFGF, FGF2), epidermal growth factor (EGF), insulin-like growth factor-1 (IGF-1), platelet derived growth factor-B (PDGFB), transforming growth factor-β1 (TGF-β1) and vascular endothelial growth factor A (VEGF-A) and the severity of coronary atherosclerosis in patients with stable CAD undergoing their first coronary angiography. In total, 319 patients with stable CAD who underwent their first coronary angiography at the Silesian Centre for Heart Diseases in Zabrze, Poland were included in the analysis. CAD burden was assessed using the Gensini score. The TaqMan method was used for genotyping of selected functional polymorphisms in the FGF2, PDGFB, TGFB1, IGF1 and VEGFA genes, while rs4444903 in the EGF gene was genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. The associations between the selected polymorphisms and the Gensini were calculated both for the whole cohort and for a subgroup of patients without previous myocardial infarction (MI). There were no differences in the distribution of the Gensini score between the genotypes of the analyzed polymorphisms in FGF2, EGF, IGF1, PDFGB, and TGFB1 in the whole cohort and in the subgroup of patients without previous MI. The Gensini score for VEGFA rs699947 single-nucleotide polymorphism (SNP) in patients without previous myocardial infarction, after correction for multiple testing, was highest in patients with the A/A genotype, lower in heterozygotes and lowest in patients with the C/C genotype, (p value for trend = 0.013, false discovery rate (FDR) = 0.02). After adjustment for clinical variables, and correction for multiple comparisons the association between the VEGFA genotype and Gensini score remained only nominally significant (p = 0.04, FDR = 0.19) under the dominant genetic model in patients without previous MI. We were unable to find strong association between analyzed polymorphisms in growth factors and the severity of coronary artery disease, although there was a trend toward association between rs699947 and the severity of CAD in patients without previous MI.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 35%
Student > Bachelor 3 15%
Student > Doctoral Student 3 15%
Student > Master 2 10%
Professor > Associate Professor 1 5%
Other 0 0%
Unknown 4 20%
Readers by discipline Count As %
Medicine and Dentistry 10 50%
Biochemistry, Genetics and Molecular Biology 3 15%
Psychology 1 5%
Unknown 6 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 October 2017.
All research outputs
#10,512,624
of 11,862,957 outputs
Outputs from BMC Cardiovascular Disorders
#634
of 741 outputs
Outputs of similar age
#229,918
of 272,701 outputs
Outputs of similar age from BMC Cardiovascular Disorders
#16
of 17 outputs
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