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

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 p<0,3 in univariate models to rank the contribution of each trait to HTN development.

Results. 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.

1. Kobalava ZhD, Konradi AO, Nedogoda SV, et al. 2024 Clinical practice guidelines for Hypertension in adults. Russian Journal of Cardiology. 2024;29(9):6117. (In Russ.) doi:10.15829/1560-4071-2024-6117. EDN: GUEWLU.

2. Slepukhina AA, Zelenskaya EM, Lifshits GI. Genetic risk factors for vascular aging: molecular mechanisms, polymorphism of candidate genes and gene networks. Russian Journal of Cardiology. 2019;(10):78-85. (In Russ.) doi:10.15829/1560-4071-2019-10-78-85. EDN: IORPLN.

3. Boytsov SA, Drapkina OM, Shlyakhto EV et al. Epidemiology of Cardiovascular Diseases and their Risk Factors in Regions of Russian Federation (ESSE-RF) study. Ten years later. Cardiovascular Therapy and Prevention. 2021;20(5):3007. (In Russ.) doi:10.15829/1728-8800-2021-3007. EDN: ZPGROP.

4. Swets J. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285- 93. doi:10.1126/science.3287615.

5. Kovaleva AYa, Kokh NV, Voronina EN, et al. The relationship of genetic risk factors with the development of arterial hypertension taking into account ethnic differences. Russian Journal of Cardiology. 2019;(10):66-71. (In Russ.) doi:10.15829/1560-4071-2019-10-66-71. EDN: URPPTB.

6. Verweij N, Mahmud H, Leach IM, et al. Genome-wide association study on plasma levels of midregional-proadrenomedullin and C-terminal-pro-endothelin 1. Hypertension. 2013; 61(3):602-8. doi:10.1161/HYPERTENSIONAHA.111.203117.

7. Bulatova IA, Tretyakova YuI, Shchekotov VV, et al. Сatalase gene rs1001179 polymorphism and oxidative stress in patients with chronic hepatitis C and ulcerative colitis. Therapeutic Archive. 2015;87(2):49-53. (In Russ.) doi:10.17116/terarkh201587249-53.

8. Erdman, VV, Nasibullin, TR, Tuktarova, IA, et al. Analysis of FOXO1A and FOXO3A gene allele association with human longevity. Russ J Genet. 2016;52:416-22. doi:10.1134/S1022795416020034.

9. Yu Y, Bhangale TR, Fagerness J, et al. Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration. Hum Mol Genet. 2011;20(18):3699- 709. doi:10.1093/hmg/ddr270.

10. Stasia MJ. CYBA encoding p22(phox), the cytochrome b558 alpha polypeptide: gene structure, expression, role and physiopathology. Gene. 2016;586(1):27-35. doi:10.1016/j.gene.2016.03.050.

11. Soerensen M, Thinggaard M, Nygaard M, et al. Genetic variation in TERT and TERC and human leukocyte telomere length and longevity: a cross-sectional and longitudinal analysis. Aging Cell. 2012;(2):223-7. doi:10.1111/j.1474-9726.2011.00775.x.

12. Torgerson DG, Ampleford EJ, Chiu GY, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43(9):887-92. doi:10.1038/ng.888.

13. Bis JC, DeCarli C, Smith AV, et al. Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nat Genet. 2012;44(5):545-51. doi:10.1038/ng.2237.

14. Moffett SP, Zmuda JM, Cauley JA, et al. Association of the VDR translation start site polymorphism and fracture risk in older women. J Bone Miner Res. 2007;(5):730-6. doi:10.1359/jbmr.070201.

15. Tourniaire F, Gouranton E, von Lintig J, et al. Вeta-сarotene conversion products and their effects on adipose tissue. Genes Nutr. 2009;3:179-87. doi:10.1007/s12263-009-0128-3.

16. Berlanga-Taylor AJ, Knight JC, et al. An integrated approach to defining genetic and environmental determinants for major clinical outcomes involving vitamin D. Mol Diagn Ther. 2014;3:261-72. doi:10.1007/s40291-014-0087-2.

17. Duell EJ, Lujan-Barroso L, Llivina C. Vitamin C transporter gene (SLC23A1 and SLC23A2) polymorphisms, plasma vitamin C levels, and gastric cancer risk in the EPIC cohort. Genes Nutr. 2013;6:549-60. doi:10.1007/s12263-013-0346-6.

18. Mottl AK, Shoham DA, North KE. Angiotensin II type 1 receptor polymorphisms and susceptibility to hypertension: a HuGE review. Genet Med. 2008;10(8):560-74. doi:10.1097/gim.0b013e3181809613.

19. Bustami J, Sukiasyan A, Kupcinskas J, et al. Cholesteryl ester transfer protein (CETP) I405V polymorphism and cardiovascular disease in eastern European Caucasians — a cross-sectional study. BMC Geriatr. 2016;16:144. doi:10.1186/s12877-016-0318 y.