A combination of strongly associated prothrombotic single nucleotide polymorphisms could efficiently predict venous thrombosis risk - Natae, Shewaye Fituma; Merzah, Mohammed Abdulridha; Sándor, János; Ádány, Róza; Bereczky, Zsuzsanna; Fiatal, Szilvia (Frontiers in Cardiovascular Medicine, 2023)
Background: Venous thrombosis (VT) is multifactorial trait that contributes to the burden of cardiovascular diseases worldwide. Although abundant single nucleotide polymorphisms(SNPs) provoke the susceptibility of an individual to VT, it seems that the five most strongly associated SNPs play the greatest role. We aimed to explore the combined VT risk prediction ability of five SNPs[rs6025(F5 Leiden),rs2066865(FGG),rs2036914 (F11), rs8176719(ABO), and rs1799963(F2)] and well-known non-genetic VT risk factors such as aging and obesity in the Hungarian population.Methods: SNPs were genotyped in clinically confirmed VT patients (n=298) and controls(n=400). Associations were established using standard genetic models. Genetic risk scores (GRS)(unweighted-unGRS, weighted- wGRS) were computed as well. Correspondingly, area under receiver operating characteristic curves (AUCs) for genetic and non-genetic risk factors were estimated to explore their VT risk predictability in the study population.Results: rs6025 is the most prevalent VT risk allele in the Hungarian population. Its risk allele frequency was 3.52-fold higher in the VT group than in the control group (adjusted odds ratio(AOR)=3.52, 95% CI: 2.50; 4.95). The rs6025 and rs2036914 SNPs remained significantly associated with VT disease risk using all genetic models after multiple correction testing was performed. However, rs8176719 only remained statistically significant in the multiplicative (AOR=1.33, 95% CI: 1.07; 1.64) and genotypic models(AOR=1.77, 95% CI: 1.14; 2.73). Additionally, rs2066865 lost its significant association with VT disease risk after multiple testing was used. Conversely, the prothrombin mutation (rs1799963) did not show any significant association. The AUC of Leiden mutation (rs6025) showed better discriminative accuracy than other SNPs(AUC=0.62,95% CI: 0.57; 0.66). The wGRS was a better predictor for VT than the unGRS (AUC=0.67 vs. 0.65). Furthermore, the combination of genetic and non-genetic VT risk factors significantly increased the AUC value to 0.89 with statistically significant differences (Z=3.924, p<0.0001). Conclusions:Our study revealed that five strongly associated SNPs combined with non-genetic factors could efficiently predict individual VT risk susceptibility. The combined model is the best predictor of VT risk, so the stratification of highly vulnerable individuals based on their genetic profiling and well-known non-modifiable VT risk factors is important for the effective and efficient utilization of preventive and control measures of VT risk.