Predictors of genetic polymorphisms in patients with arterial hypertension and ischemic heart disease.

Objective. This study was aimed to develop a mathematical model for predicting the presence of pathological alleles of the angiotensin II receptor type 1 (AGTR1) (C1166C) and endothelial nitric oxide synthase (eNOS) (T786C) genes in patients with arterial hypertension using readily available clinical and demographic parameters without the need for laboratory genetic analysis. Methods. The study included 86 patients with arterial hypertension, 45 of whom had also clinical signs of ischemic heart disease and 30 healthy controls. Clinical and demographic data, including age, sex, body mass index (BMI), smoking and alcohol habits, and systolic and diastolic blood pressure, were collected. A predictive model of genetic polymorphism was constructed using a multivariate regression analysis in Statistica 10.0. Model quality was assessed via residual analysis (histogram, normal probability plot, scatter plot), ANOVA, and the Nagelkerke coefficient of determination (R²). Results. Eight significant predictors of pathological gene variants were identified: age, sex, disease duration, BMI, smoking, alcohol consumption, systolic blood pressure, and diastolic blood pressure. The regression model explained 98.42% of the variance in gene polymorphism (R²=0.9842). Residuals were normally distributed and randomly scattered confirming the model adequacy. ANOVA analysis demonstrated high statistical significance (p<0.001) indicating that the model performed significantly better than predictions based on the mean values. The model allows inference of the probable presence of C alleles in AGTR1 and eNOS genes using simple clinical information. Conclusions. The proposed mathematical model demonstrates high predictive capability and statistical stability providing a cost-effective tool for preliminary genetic screening in patients with hypertension. It enables identification of individuals at risk for carrying pathological gene variants and supports the implementation of a personalized approach in clinical practice. Further validation on independent datasets is recommended.