On molecular genetic predictors of preeclampsia

Objective. To study the molecular genetic characteristics of pregnant women with preeclampsia, to identify probable predictors of this pathology. Material and methods. A prospective cohort study was conducted using the continuous sam-pling method: 29 pregnant women without preeclampsia, 66 with preeclampsia (32 observations of moderate preeclampsia and 34 observations of severe preeclampsia). The anamnesis of pa-tients, pregnancy outcomes, polymorphisms of genes of hemostasis (FII, FV, FVII, F13, FGB, ITGA2, ITGB3, PAI-1), renin-angiotensin-aldosterone system (AGT, AGTR1, AGTR2, CYP11B2), nitric oxide metabolism (NOS3), angiogenesis (VEGFA), adducin-1-alpha (ADD1), G-protein beta-3 (GNB) were studied. Statistical methods (SPSS Statistica for Windows 17.0): Pearson chi-square test, odds ratio (OR) with 95% confidence interval (CI), multivariate dimen-sionality reduction (MDR), and multiple logistic regression (p<0.05) were used. Results. The frequency of diagnosis of the 4G allele (79%) rs1799889 of the PAI-1 gene (OR 4.17; 95% CI 1.52—11.1; p<0.001), the A allele (72.6%) rs1403543 of the AGTR2 gene (OR 2.44; 95% CI 0.93-6.25; p=0.003) in women with preeclampsia was higher than in the control group. The C/C genotype of rs2010963 of the VEGFA gene was more common in severe preeclampsia (21.9% versus 4%; p=0.039). In the control group, a statistically significant increase in the fre-quency of the T allele of rs1799998 of the CYP11B2 gene (88% versus 75.8%; OR 0.54; 95% CI 0.22—1.33; p=0.007) and the T allele of rs4961 of the ADD1 gene (60% versus 43.5%; OR 0.52; 95% CI 0.2—1.32; p=0.03) was noted. Based on the MDR results, “Cluster 1” was formed: VEG-FA G(–634) C, ITGB3 T1565C (pronounced synergism was registered); «Cluster 2» — PAI-1 5G(–675)4G, AGTR1 A1166C (characterized by pronounced antagonism), CYP11B2 C(-344)T. The pairs of alleles 4G rs1799889 of the PAI-1 gene/T rs1799998 of the CYP11B2 gene and 4G rs1799889 of the PAI-1 gene/C rs5186 of the AGTR1 gene were associated with an increased likelihood of developing preeclampsia. A prognostic model of preeclampsia was proposed, the specificity of which was 84%, sensitivity — 95.2%, accuracy — 92%. Conclusion. The use of molecular genetic technologies allows timely identification of pregnant women at high risk of developing preeclampsia and ensure its early prevention.