A mathematical model using eight clinical and demographic predictors explained 98.42% of the variance in AGTR1 (C1166C) and eNOS (T786C) gene polymorphisms in patients with arterial hypertension, providing a cost-effective tool for preliminary genetic screening without laboratory genetic analysis.
Key Findings
Results
Eight clinical and demographic variables were identified as significant predictors of pathological alleles in the AGTR1 (C1166C) and eNOS (T786C) genes.
The eight predictors identified were: age, sex, disease duration, body mass index (BMI), smoking, alcohol consumption, systolic blood pressure, and diastolic blood pressure.
All predictors are readily available clinical and demographic parameters that do not require laboratory genetic analysis.
These predictors were identified through multivariate regression analysis performed in Statistica 10.0.
Results
The regression model explained 98.42% of the variance in gene polymorphism presence.
The Nagelkerke coefficient of determination was R²=0.9842.
ANOVA analysis demonstrated high statistical significance (p<0.001), indicating the model performed significantly better than predictions based on mean values.
Residual analysis using histogram, normal probability plot, and scatter plot confirmed that residuals were normally distributed and randomly scattered, confirming model adequacy.
Methods
The study population consisted of 86 patients with arterial hypertension and 30 healthy controls.
Of the 86 hypertension patients, 45 also had clinical signs of ischemic heart disease.
30 healthy individuals served as controls.
Clinical and demographic data collected included age, sex, BMI, smoking and alcohol habits, and systolic and diastolic blood pressure.
Conclusions
The model allows inference of the probable presence of C alleles in AGTR1 and eNOS genes using simple clinical information.
The target genetic variants were the C1166C polymorphism in the angiotensin II receptor type 1 (AGTR1) gene and the T786C polymorphism in the endothelial nitric oxide synthase (eNOS) gene.
The model is proposed as a cost-effective tool for preliminary genetic screening in patients with hypertension.
The authors recommend further validation on independent datasets.
Conclusions
The proposed mathematical model supports a personalized approach in clinical practice by enabling identification of individuals at risk for carrying pathological gene variants.
The model demonstrated 'high predictive capability and statistical stability' according to the authors.
The model is intended as a preliminary screening tool, not a replacement for formal genetic analysis.
The approach targets patients with arterial hypertension specifically.
What This Means
This research suggests that it may be possible to predict whether a patient with high blood pressure carries certain genetic variants linked to cardiovascular disease risk — without performing a genetic test. The researchers studied 86 patients with high blood pressure (45 of whom also had heart disease) and 30 healthy individuals, and built a mathematical model using eight simple pieces of information routinely collected in clinical settings: age, sex, how long the patient has had the disease, body weight (BMI), smoking habits, alcohol use, and blood pressure readings. The model was able to account for about 98% of the variation in whether patients carried risk variants in two genes — AGTR1 and eNOS — that are associated with how the body regulates blood pressure and blood vessel function.
This research suggests that a straightforward statistical model using basic patient information could serve as a low-cost screening tool to flag individuals who are more likely to carry these genetic risk variants, potentially guiding decisions about who should receive more detailed genetic testing. This could be particularly valuable in healthcare settings where genetic testing is expensive or not readily available. The high explanatory power of the model (R²=0.9842) and strong statistical significance (p<0.001) suggest the model performs well in this dataset.
However, the study has notable limitations: it was conducted in a relatively small sample of 86 patients and 30 controls from what appears to be a single center, and the authors themselves recommend validation on independent datasets before the model is used more broadly. This research represents a preliminary but promising step toward using routine clinical data to support personalized cardiovascular care.
Pidruchna S, Yarema N, Kuzmak I, Prokopovych O, Kotsiuba O, Sverstuk A, et al.. (2026). Predictors of genetic polymorphisms in patients with arterial hypertension and ischemic heart disease.. Endocrine regulations. https://doi.org/10.2478/enr-2026-0024