COMPREHENSIVE EVALUATION OF VARIABILITY IN ARTERIAL HYPERTENSION WITH DIFFERENT BODY WEIGHT

Psarova V. G.

CLINICAL AND EXPERIMENTAL MEDICINE

Scentific article

The study aimed to build a diagnostic model of formation of a group of hypertensive patients with different bodyweight based on the results of the evaluation of the variability of quantitative and qualitative indicators of influence. 300 patients with AH aged 45 to 55 who gave informed written consent to participate in the study and met the inclusion criteria were examined. Group 1 consisted of 200 patients with AH and obesity I-II classes, group 2 – 50 patients with AH and normal body weight, group 3 – 50 patients with AH and overweight. In the factor analysis of the group of hypertensive patients 4 factors were found. Those factors explained 23.32% of the variability of the indicators: «anthropometric-metabolic-endothelial factor», «systolic factor», «hemodynamic factor» and «insulin-resistance-diastolic factor». The data of the logistic regression method showed that the formation of a group of hypertensive patients with different body weight was influenced by anthropometric indicators (BMI), metabolic indicators (HOMA index, adiponectin), RAAS activity (aldosterone and ARC), systolic (EF and SV) and diastolic cardiac function (MV E, e f and E/e), as well as ADIPOQ genetic polymorphism.

hypertension, obesity, diagnostic model, factor analysis, logistic regression

1.  Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. European Heart Journal. 2018;39(33):3021-104. DOI: 10.1093/eurheartj/ehy339
2. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Heart disease and stroke statistics-2015 update: a report from the american heart association [Internet]. Circulation. 2015;131(4):e29-e322. Available from: http://circ.ahajournals. org/content/131/4/e29. full.pdf+html DOI: 10.1161/CIR.0000000000000152 [cited 2015 Dec 07].
3. Kovalenko VM, Karnatskyi VM. Khvoroby systemy krovoobihu yak medyko-sotsialna i suspilno-politychna problema. Analitychno-statystychnyi posibnyk. Kyiv; 2014. 279 s. [in Ukrainian].
4. Ushakov AV, Ivanchenko VS, Gagarina AA. Patogeneticheskie mehanizmyi formirovaniya stoykoy arterialnoy gipertenzii pri hronicheskom psihoemotsionalnom napryazhenii. Arterialnaya gipertenziya. 2016;22(2):128-43. [in Russian].
5. Rebrova OYu. Statisticheskiy analiz meditsinskih dannyih. Moskva: MediaSfera; 2002. 70 s. [in Russian].
6. Dyuk VA, Emmanuel VL. Informatsionnyie tehnologii v medikobiologicheskih issledovaniyah. SPb: Piter; 2003. 528 s. [in Russian].
7. Gaydyishev I. Analiz i obrabotka dannyih: spetsialnyiy spravochnik. SPb: Piter; 2001. 752 s. [in Russian].
8. David W. Hosmer, Stanley Lemeshow. Applied Logistic Regression. 2nd ed. New York, Chichester, Wiley; 2002. 392 p.
9. Davis J, Goadrich M. The Relationship Between Precision-Recall and ROC Curves. In: Proc. of 23rd International Conference on Machine Learning, Pittsburgh, PA; 2006.
10. Fawcett T. ROC Graphs: Notes and Practical Considerations for Researchers. Kluwer Academic Publishers; 2004.

«Bulletin of problems biology and medicine» Issue 4 Part 1 (153), 2019 year, 148-152 pages, index UDK 616.12-008.331.1:613.25

10.29254/2077-4214-2019-4-1-153-148-152