Analysis of hypertension predictors using multivariate models through the lens of aging processes

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Abstract
Aim. To identify independent genetic and non-genetic predictors of hypertension (HTN) and rank their contribution to disease progression, as well as to identify potential new mechanisms that may influence the hypertension development. Material and methods. This cross-sectional observational study included 610 patients, including 142 with HTN. All participants completed a questionnaire, blood pressure (BP) measurements, biometric measurements, and molecular genetic testing. HTN predictors were identified using logistic regression models. Using singlefactor models, individual HTN predictors were determined. A multivariate logistic regression model was created using covariates with a significance level of pResults. A mathematical model was constructed using non-genetic and genetic markers to assess the risk of HTN, better classifying individuals with a low genetic risk for HTN. Genetic predictors were more significant for optimal calculations of HTN probability in the logistic regression model, while non-genetic traits were not included in the final model. Possible mechanisms that may lead to HTN, based on the identified genetic predictors, are considered. In addition, the concept of the contribution of a protective combination of genetic variants is also explored. Conclusion. A logistic regression model based on molecular genetic testing results is optimal for identifying individuals with a low HTN risk. Thus, for patients with a low genetic risk, lifestyle factors are more significant, and lifestyle modification is especially important for them to prevent HTN.

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