ArticleFrontiers in cardiovascular medicine2023
A combination of strongly associated prothrombotic single nucleotide polymorphisms could efficiently predict venous thrombosis risk.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Investigation of Prothrombin G20210A andGenes · 2025Article
- Impact of Gene-Smoking Interaction on Risk of Atherosclerosis: Molecular Study of Prothrombin (F2) Gene rs1799963 G/A Polymorphism in Atherosclerotic Patients.Cardiovascular toxicology · 2025Article
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6 authors.
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Abstract
Background: Venous thrombosis (VT) is multifactorial trait that contributes to the global burden of cardiovascular diseases. Although abundant single nucleotide polymorphisms (SNPs) provoke the susceptibility of an individual to VT, research has found that the five most strongly associated SNPs, namely, rs6025 ( Methods: SNPs were genotyped in the VT group ( Results: rs6025 was the most prevalent VT risk allele in the Hungarian population. Its risk allele frequency was 3.52-fold higher in the VT group than that in the control group [adjusted odds ratio (AOR) = 3.52, 95% CI: 2.50-4.95]. Using all genetic models, we found that rs6025 and rs2036914 remained significantly associated with VT risk after multiple correction testing was performed. However, rs8176719 remained statistically significant only in the multiplicative (AOR = 1.33, 95% CI: 1.07-1.64 Conclusions: Our study revealed that the five strongly associated SNPs combined with non-genetic factors could efficiently predict individual VT risk susceptibility. The combined model was the best predictor of VT risk, so stratifying high-risk individuals based on their genetic profiling and well-known non-modifiable VT risk factors was important for the effective and efficient utilization of VT risk preventive and control measures. Furthermore, we urged further study that compares the VT risk predictability in the Hungarian population using the formerly discovered VT SNPs with the novel strongly associated VT SNPs.
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