ISSN 2413-1032 WORLD SCIENCE ISSN 2413-1032 http://ws-conference.com/ № 10(50), Vol.1, October 2019 1 WORLD SCIENCE № 10(50) Vol.1, October 2019 DOI: https://doi.org/10.31435/rsglobal_ws Copies may be made only from legally acquired originals. A single copy of one article per issue may be downloaded for personal use (non-commercial research or private study). Downloading or printing multiple copies is not permitted. Electronic Storage or Usage Permission of the Publisher is required to store or use electronically any material contained in this work, including any chapter or part of a chapter. Permission of the Publisher is required for all other derivative works, including compilations and translations. Except as outlined above, no part of this work may be reproduced, stored in a retrieval system or transmitted in any form or by any means without prior written permission of the Publisher. 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WORLD SCIENCE ISSN 2413-1032 2 № 10(50), Vol.1, October 2019 http://ws-conference.com/ CHIEF EDITOR Laputyn Roman PhD in transport systems, Associate Professor, Department of Transport Systems and Road Safety, National Transport University, Ukraine EDITORIAL BOARD: Nobanee Haitham Associate Professor of Finance, Abu Dhabi University, United Arab Emirates Almazari Ahmad Professor in Financial Management, King Saud University-Kingdom of Saudi Arabia, Saudi Arabia Lina Anastassova Full Professor in Marketing, Burgas Free University, Bulgaria Mikiashvili Nino Professor in Econometrics and Macroeconomics, Ivane Javakhishvili Tbilisi State University, Georgia Alkhawaldeh Abdullah Professor in Financial Philosophy, Hashemite University, Jordan Mendebaev Toktamys Doctor of Technical Sciences, Professor, LLP "Scientific innovation center "Almas", Kazakhstan Yakovenko Nataliya Professor, Doctor of Geography, Ivanovo State University, Shuya Mazbayev Ordenbek Doctor of Geographical Sciences, Professor of Tourism, Eurasian National, University named after L.N.Gumilev, Kazakhstan Sentyabrev Nikolay Professor, Doctor of Sciences, Volgograd State Academy of Physical Education, Russia Ustenova Gulbaram Director of Education Department of the Pharmacy, Doctor of Pharmaceutical Science, Kazakh National Medical University name of Asfendiyarov, Kazakhstan Harlamova Julia Professor, Moscow State University of Railway Transport, Russia Kalinina Irina Professor of Chair of Medicobiological Bases of Physical Culture and Sport, Dr. Sci.Biol., FGBOU VPO Sibirsky State University of Physical Culture and Sport, Russia Imangazinov Sagit Director, Ph. D, Pavlodar affiliated branch "SMU of Semei city", Kazakhstan Dukhanina Irina Professor of Finance and Investment Chair, Doctor of Sciences, Moscow State Medical Dental University by A. I. Evdokimov of the Ministry of health of the Russian Federation, Russian Federation Orehowskyi Wadym Head of the Department of Social and Human Sciences, Economics and Law, Doctor of Historical Sciences, Chernivtsi Trade-Economic Institute Kyiv National Trade and Economic University, Ukraine Peshcherov Georgy Professor, Moscow State Regional University, Russia Mustafin Muafik Professor, Doctor of Veterinary Science, Kostanay State University named after A. Baitursynov Ovsyanik Olga Professor, Doctor of Psychological Science, Moscow State Regional University, Russian Federation Suprun Elina Professor, Doctor of Medicine, National University of Pharmacy, Ukraine Kuzmenkov Sergey Professor at the Department of Physics and Didactics of Physics, Candidate of Physico-mathematical Sciences, Doctor of Pedagogic Sciences, Kherson State University Safarov Mahmadali Doctor Technical Science, Professor Academician Academia Science Republic of Tajikistan, National Studies University "Moscow Power Institute" in Dushanbe Omarova Vera Professor, Ph.D., Pavlodar State Pedagogical Institute, Kazakhstan Koziar Mykola Head of the Department, Doctor of Pedagogical Sciences, National University of Water Management and Nature Resources Use, Ukraine Tatarintseva Nina Professor, Southern Federal University, Russia Sidorovich Marina Candidate of Biological Sciences, Doctor of Pedagogical Sciences, Full Professor, Kherson State University Polyakova Victoria Candidate of Pedagogical Sciences, Vladimir Regional Institute for Educational Development Name L. I. Novikova, Russia Issakova Sabira Professor, Doctor of Philology, The Aktyubinsk regional state university of K. Zhubanov, Kazakhstan Kolesnikova Galina Professor, Taganrog Institute of Management and Economics, Russia Utebaliyeva Gulnara Doctor of Philological Science, Al- Farabi Kazakh National University, Kazakhstan Uzilevsky Gennady Dr. of Science, Ph.D., Russian Academy of National Economy under the President of the Russian Federation, Russian Federation Krokhmal Nataliia Professor, Ph.D. in Philosophy, National Pedagogical Dragomanov University, Ukraine Chornyi Oleksii D.Sc. (Eng.), Professor, Kremenchuk Mykhailo Ostrohradskyi National University Pilipenko Oleg Head of Machine Design Fundamentals Department, Doctor of Technical Sciences, Chernigiv National Technological University, Ukraine Nyyazbekova Kulanda Candidate of pedagogical sciences, Kazakhstan Cheshmedzhieva Margarita Doctor of Law, South-West University "Neofit Rilski", Bulgaria Svetlana Peneva MD, dental prosthetics, Medical University - Varna, Bulgaria Rossikhin Vasiliy Full dr., Doctor of Legal Sciences, National Law University named after Yaroslav the Wise, Ukraine Pikhtirova Alina PhD in Veterinary science, Sumy national agrarian university, Ukraine Temirbekova Sulukhan Dr. Sc. of Biology, Professor, Federal State Scientific Institution All-Russia Selection- Technological Institute of Horticulture and Nursery, Russian Federation Tsymbaliuk Vitalii Professor, Doctor of Medicine, The State Institution Romodanov Neurosurgery Institute National Academy of Medical Sciences of Ukraine http://ws-conference.com/ WORLD SCIENCE ISSN 2413-1032 http://ws-conference.com/ № 10(50), Vol.1, October 2019 3 CONTENTS ENGINEERING SCIENCES Grigol Khelidze, Lena Shatakishvili, Bachana Pipia QUANTITATIVE ASSESSMENT OF HYDROPOWER POTENTIAL BY THE IMPACTS OF CLIMATE TRANSFORMATION ON THE EXAMPLE OF THE RIVERS OF GEORGIA……. 4 ARCHITECTURE AND CONSTRUCTION Ксеневич М. Я. ОСНОВНІ ЗАСАДИ МЕТОДИКИ ПРОЕКТУВАННЯ СТАЛОГО РОЗВИТКУ МІСТ- ЦЕНТРІВ АГЛОМЕРАЦІЙ (рекомендації на прикладі України)……………………………... 10 BIOLOGY Н. М. Рзаева, А. И. Дмитренко, А. Н. Нуруллаева, Э. Н. Панахова, М. Х. Ализаде РОЛЬ КОМПЕНСАТОРНЫХ МЕХАНИЗМОВ СЕТЧАТКИ В УСЛОВИЯХ ПИГМЕНТНОЙ ДИСТРОФИИ…………..………………………………………….…............... 15 CHEMISTRY Marina Gurgenishvili, Givi Papava, Ia Chitrekashvili, Nanuli Khotenashvili, Zurab Tabukashvili MODIFICATION OF GRAPHITE BY A FLUORINE-CONTAINING OLIGOMER.................... 22 М. М. Челтонов, С. А. Опарин, Е. Ю. Нестерова, А. Л. Кириченко, Е. Б. Устименко ПОЛУЧЕНИЕ МОДИФИЦИРОВАННЫХ НИТРАМИНОВ ДЛЯ ПРИМЕНЕНИЯ В НЕЭЛЕКТРИЧЕСКИХ СИСТЕМАХ ИНИЦИИРОВАНИЯ..…................................................. 26 MEDICINE Oleg Babak, Anna Bashkirova CLUSTER ANALYSIS OF THE PATHOGENETIC RELATIONSHIPS OF METABOLIC PARAMETERS IN PATIENTS WITH NON-ALCOHOLIC FATTY LIVER DISEASE ON THE BACKGROUND OF HYPERTENSION…………………………….............................................. 30 Valeriy P Ivanov, Mariіa O Kolesnyk, Oleg N Kolesnуk CLINICAL EXPERIENCE IN USING COMBINED FERROTHERAPY WITH L-CARNITINE IN STANDARD TREATMENT OF PATIENTS WITH CHRONIC HEART FAILURE WITH REDUCED LEFT VENTRICLE EJECTION FRACTION WITH CONCOMITANT IRON DEFICIENCY ANEMIA…………………………………………………………………............... 37 Yagubova Samira Mammadhasan ULTRASTRUCTURAL PROPERTIES OF THE ADRENAL GLANDS DURING THE ACUTE HYPOXIA…………………………………………………………….……………………............. 41 Олена Карая ОСОБЛИВОСТІ ЛІКУВАННЯ ХВОРИХ НА ХРОНІЧНИЙ БЕЗКАМ’ЯНИЙ ХОЛЕЦИСТИТ ІЗ СУПУТНЬОЮ ГІПЕРТОНІЧНОЮ ХВОРОБОЮ З УРАХУВАННЯМ ПОРУШЕНЬ В СИСТЕМІ ГОМЕОСТАЗУ…………...………………………………................ 47 PHARMACY I. A. Kostiuk, K. L. Kosyachenko SPECIFIC FEATURES ANALYSIS OF INHALATION MEDICATION USE TO TREAT BRONCHIAL ASTHMA IN CHILDREN………………………………………………................ 51 Liliia Hala PROCESS MODEL OF IMPLEMENTATION OF GOOD PHARMACY PRACTICE IN THE ACTIVITY OF PHARMACIES OF UKRAINE……………...…………………………................ 57 WORLD SCIENCE ISSN 2413-1032 30 № 10(50), Vol.1, October 2019 http://ws-conference.com/ CLUSTER ANALYSIS OF THE PATHOGENETIC RELATIONSHIPS OF METABOLIC PARAMETERS IN PATIENTS WITH NON-ALCOHOLIC FATTY LIVER DISEASE ON THE BACKGROUND OF HYPERTENSION Professor Oleg Babak, PhD student Anna Bashkirova, Kharkiv, Ukraine, Kharkiv National Medical University DOI: https://doi.org/10.31435/rsglobal_ws/31102019/6717 ARTICLE INFO Received: 15 August 2019 Accepted: 20 October 2019 Published: 31 October 2019 ABSTRACT The aim of the study was to conduct a cluster analysis of pathogenetic relationships between metabolic parameters, endothelial lipase levels, the severity of steatosis, and clinical parameters in patients with non-alcoholic fatty liver disease with hypertension. To analyze pathogenetic relationships, a cluster analysis was performed with the distribution of parameters into 4 clusters using the Ward's method. The most dense metabolic link by cluster analysis endothelial lipase forms with NAFLD liver fat score (2.639 cu), HbA1C (2.084 cu), total cholesterol (2.272 cu), and alcohol units (2.797 cu). KEYWORDS NAFLD, hypertension, endothelial lipase, cluster analysis. Citation: Oleg Babak, Anna Bashkirova. (2019) Cluster Analysis of the Pathogenetic Relationships of Metabolic Parameters in Patients with Non-Alcoholic Fatty Liver Disease on the Background of Hypertension. World Science. 10(50), Vol.1. doi: 10.31435/rsglobal_ws/31102019/6717 Copyright: © 2019 Oleg Babak, Anna Bashkirova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Non-alcoholic fatty liver disease (NAFLD) is the most common cause of impaired liver function in adults and children [1]. NAFLD covers the histological spectrum from simple steatosis to non- alcoholic steatohepatitis (NASH), progressive fibrosis and cirrhosis [2]. Simple steatosis without fibrosis or inflammation in most cases has a benign clinical course, but often leads to an increase in mortality [3]. The possible role of NAFLD as a risk factor for the development of cardiovascular diseases has been discussed for a long time, and only recent data have demonstrated the existing relationship between these conditions [4]. Insulin resistance is often detected in patients with NAFLD, as in patients without obesity and diabetes [5]. NAFLD is often associated with components of the metabolic syndrome, such as type 2 diabetes mellitus (T2DM), obesity, hypertension, and dyslipidemia [7]. However, an increasing number of patients with a normal body mass index (BMI) have been described, with central obesity and latent insulin resistance. [6] Several studies have shown that adopting a healthy lifestyle, reducing weight, and proactively correcting individual components of the metabolic syndrome can help prevent, slow down, or reverse liver damage associated with NAFLD [8]. Endothelial lipase (EL) is a strong determinant of the structural and functional properties of high density lipoproteins (HDL) [9]. EL is a new marker of cardiovascular risk, which is closely associated with dyslipidemia and insulin resistance and has hardly been studied in the presence of NAFLD [10]. Regardless of this, NAFLD increases the risk of premature cardiovascular disease and related mortality, therefore, research and monitoring of the metabolic function of the liver and early detection of accumulation of EL, as well as the relationships between them, are of great importance. MEDICINE http://ws-conference.com/ WORLD SCIENCE ISSN 2413-1032 http://ws-conference.com/ № 10(50), Vol.1, October 2019 31 The purpose of the study was to conduct a cluster analysis of pathogenetic relationships between metabolic parameters, EL levels and clinical parameters in patients with liver steatosis on the background of hypertension. Materials and methods 80 patients have been examined on the basis department of internal medicine №1 of Kharkiv National Medical University and National Institute of Therapy named by L.T. Malaya of National Academy of Medical Sciences of Ukraine. The patients have been divided into three groups according to the severity of liver steatosis. The first group consisted of 16 patients with hypertension without laboratory or instrumental signs of liver steatosis (hypertension group). Patients who, in addition to hypertension, had signs of steatosis during ultrasound and normal level of transaminases (ALT, AST), formed a group with moderate liver steatosis (MLS, n = 20). Patients with hypertension who, in addition to the echoscopic features of hepatic steatosis had increased level of transaminases, were assigned to the group with severe liver steatosis (group SLS, n = 24). The control group consisted of 20 practically healthy individuals. The patients' ages ranged from 45 to 60 years, with an average age of 52.12 + 5.24 years. Among them 28 were female (46.66%) and 32 were male (53.33%). for identification of liver steatosis and its severity we have used liver fat index (NAFLD liver fat score), which includes such indicators as the presence of metabolic syndrome and T2DM, serum insulin level, AST and the ratio AST/ALT and is calculated by the formula [11]: NAFLD liver fat score= - 2.89+1.18×metabolic syndrome (yes=1/no=0)+ 0.45×type 2 diabetes (yes=2/no=0)+0.15× fasting serum Insulin (mU/L)+ 0.04 × fasting serum АSТ(U/L) – 0.94 × АSТ/АLТ. The FIB-4 index has been used to identify liver fibrosis, which includes indicators such as AST, ALT, platelet count, and is calculated by the formula [12]: FIB4 = Age (years) ×AST (IU/L)/platelet count (×109/L)×√ALT (IU/L) Serum endothelial lipase (EL) concentration was determined by enzyme-linked immunosorbent assay using Aviscera Bioscience INC reagent kit (USA) using a Labline 90 enzyme immunoassay analyser. For excluding the alcoholic genesis of NAFLD all patients have been interviewed to determine alcohol units. This test has international standardization and allows detecting alcohol abuse by the formula: Alcohol units = amount (liters) × alcoholic strength (%) × 0.789 Alcohol abuse was eliminated by less than 14 units per week regardless of gender [13]. In order to monitor the implementation of dietary recommendations, we have used a questionnaire designed by the original questionnaire, which asked patients about the consumption of 15 basic foods that are not recommended for overweight, carbohydrate metabolism disorders and liver steatosis. The statistical processing of the survey data has been performed using Microsoft Exel and Statistica 7.0 using standard methods of virion statistics. Results and discussion. Results of studies are presented in table 1. Table 1. Anthrometric, laboratory and surrogate ratios indicating the severity of liver steatosis Parameter Control, n=20 Hypertension group, n=16 MLS group, n=20 SLS group, n=24 Significance of difference, P 0 1 2 3 Mean SD Mean SD Mean SD Mean SD 1 2 3 4 5 6 7 8 9 10 AST/ALT, U 0.73 0.34 0,95 0,26 1,19 0,38 1,75 0,77 12, 23, 13 ALT, U/L 18,15 7,26 22,44 6,29 24,65 7,19 62,54 40,78 23, 13 АSТ, U/L 11,45 3,78 24,56 7,84 22,90 10,18 38,71 20,70 23, 13 AP, mmol/l 1334,90 464,11 1422,50 302,46 1457,13 384,29 1834,06 690,13 13, 23 NAFLD liver fat score -1,93 0,65 -0,308 1,14 2,308 2,43 4,48 3,21 For all groups < 0,001 Fib-4 0,43 0,16 1,07 0,36 1,14 0,72 1,36 0,63 With control - all groups< 0.0001 13, 23 BMI, kg/m2 21,44 1,57 25,91 3,42 30,00 2,79 29,04 5,44 01, 02, 03, 12 WC, cm 75,50 6,83 79,31 8,58 98,08 10,53 104,10 8,67 02, 03, 12, 13, 23 WC/height, U 0,44 0,03 0,47 0,04 0,57 0,05 0,60 0,04 01, 02, 03, 12, 13, 23 WORLD SCIENCE ISSN 2413-1032 32 № 10(50), Vol.1, October 2019 http://ws-conference.com/ Continuation of table 1 1 2 3 4 5 6 7 8 9 10 SBP, mm Hg 116,00 4,17 161,56 17,77 163,89 17,54 169,17 22,20 01, 02, 03 DBP, mm Hg 73,50 5,16 101,56 7,47 102,78 8,26 101,46 9,94 01, 02, 03 Cholesterol, mmol/l 3,85 0,77 5,25 1,47 5,74 0,85 5,80 1,42 01, 02, 03 Triglycerides, mmol/l 0,92 0,16 1,13 0,38 1,70 0,83 0,33 1,96 0,67 0,37 12, 13, 23 HDL, mmol/l 1,77 0,28 1,47 0,42 1,42 0,30 1,20 0,27 13, 23 LDL, mmol/l 2,36 0,46 3,45 1,41 3,34 0,85 3,75 1,25 VLDL, mmol/l 0,38 0,05 0,56 0,16 0,70 0,40 0,92 0,31 13, 23 EL, ng / ml 8,23 2,47 10,54 2,69 13,21 3,59 13,71 3,71 01, 02, 03, 12, 13 Diet 2,36 0,81 2,57 0,53 2,64 1,15 2,08 0,86 Alcohol units 4,26 2,27 4,29 1,82 6,39 2,99 6,62 2,98 02, 03, 12, 13 Fasting glucose, mmol/l 4,36 0,72 5,01 0,60 6,32 1,75 5,73 0,91 12, 13 Fasting insulin, mU/l 7, 91 3,71 17,77 6,86 24,51 9,49 33,28 13,82 12, 13, 23 HOMA-IR 1,55 0,85 3,61 1,80 7,02 4,76 8,35 5,25 12, 13 HbA1C, % - - 5,40 0,63 6,64 1,76 5,79 0,49 12, 23, 13 For the analysis of pathogenetic relationships, a cluster analysis was performed. The graph of parameter merging using Ward's method showed that it is advisable to distribute data into 3-4 clusters (Fig. 1). Fig. 1. Cluster aggregation of data parameters The first and second cluster illustrates the existence of offline hypertension and the close relationship of hyperinsulinism with BMI. The third cluster covered liver steatosis associated with alcohol consumption, compensation for carbohydrate metabolism (by HbA1C) and the level of total cholesterol and EL. The fourth cluster demonstrates the connection of lipid profile parameters with the patient's nutritional preferences (Table 2). Plot of Linkage Distances across Steps Euclidean distances Linkage Distance 0 2 4 6 8 10 12 14 Step -500 0 500 1000 1500 2000 2500 3000 L in k a g e D is ta n c e http://ws-conference.com/ WORLD SCIENCE ISSN 2413-1032 http://ws-conference.com/ № 10(50), Vol.1, October 2019 33 Table 2. Clustering of model components containing parameters of lipid-carbohydrate metabolism with hypertension and liver steatosis Cluster 1 Cluster 2 Cluster 3 Cluster 4 Parameter Mean Parameter Mean Parameter Mean Parameter Mean SBP 33,04 Fasting insulin 8,520 HbA1C 2,084 Triglycerides 0,840 DBP 33,04 BMI 8,520 EL 5,386 HDL 1,231 Cholesterol 2,272 LDL 1,864 Alcohol units 2,797 VLDL 1,470 NAFLD liver fat score 2,639 Diet 1,306 WC/height 1,472 Grouping the results of the examination of patients allowed us to identify 4 main clusters. Cluster 1 - overweight patients with abdominal fat distribution and moderate hypertension, moderate hyperinsulinism with prediabetic levels of HbA1C, increased levels of total cholesterol, LDL and borderline HDL, moderate steatosis (table 3). Table 3. Basic statistical analysis of clinical and laboratory parameters Cluster 1 Parameters Valid N Mean Minimum Maximum Variance Std.Dev. Coef.Var. WC/height 13 0,5391 0,3593 0,6221 0,00544 0,073755 13,68212 Fasting insulin 13 25,0715 15,7000 46,0500 93,55990 9,672637 38,58015 HbA1C 13 6,1092 4,3300 10,3000 2,36916 1,539207 25,19477 EL 13 12,0465 7,0665 17,4200 9,18047 3,029928 25,15198 Cholesterol 13 5,9615 4,0200 7,7900 1,40600 1,185748 19,88996 Triglycerides 13 1,4846 0,7900 4,1600 0,73911 0,859715 57,90828 HDL 13 1,4046 0,6900 2,0000 0,13968 0,373734 26,60755 LDL 13 3,7492 2,2800 5,3500 0,86937 0,932402 24,86917 VLDL 13 0,6277 0,1900 1,8700 0,16587 0,407270 64,88377 SBP 13 142,6923 130,0000 150,0000 52,56410 7,250111 5,08094 DBP 13 94,6154 80,0000 100,0000 56,08974 7,489309 7,91553 BMI 13 27,5394 16,8525 32,4500 17,56233 4,190744 15,21728 Alcohol units 13 6,4231 2,0000 9,2000 5,46692 2,338145 36,40226 Diet 13 2,7692 1,0000 4,0000 0,85897 0,926809 33,46809 NAFLD liver fat score 13 2,1543 -0,8161 6,1263 3,98455 1,996134 92,65719 Cluster 2 included patients with grade 1 obesity with severe abdominal fat distribution and severe hypertension, a pre-diabetic level of HBA1C, an increase in total cholesterol, triglycerides, LDL and a distinct decrease in HDL and severe liver steatosis (Table 4). WORLD SCIENCE ISSN 2413-1032 34 № 10(50), Vol.1, October 2019 http://ws-conference.com/ Table 4. Basic statistical analysis of clinical and laboratory indicators of representatives of Cluster 2 Parameters Valid N Mean Minimum Maximum Variance Std.Dev. Coef.Var. WC/height 11 0,5934 0,5058 0,7317 0,0038 0,06164 10,38688 Fasting insulin 11 53,0082 35,3300 73,3500 172,3410 13,12787 24,76574 HbA1C 11 6,0109 4,8600 7,4400 0,9214 0,95992 15,96965 EL 11 13,6655 7,7600 19,7200 21,8332 4,67261 34,19283 Cholesterol 11 6,4600 4,1500 8,5500 1,4985 1,22414 18,94954 Triglycerides 11 2,5227 0,9700 5,6900 1,8036 1,34298 53,23534 HDL 11 1,0718 0,7600 1,5600 0,0545 0,23340 21,77624 LDL 11 4,2218 1,9400 6,4000 1,8807 1,37138 32,48310 VLDL 11 1,0982 0,3700 2,5600 0,4095 0,63995 58,27362 SBP 11 175,9091 160,0000 180,0000 54,0909 7,35465 4,18094 DBP 11 105,0000 100,0000 120,0000 45,0000 6,70820 6,38877 BMI 11 30,0625 22,9481 48,3343 51,2784 7,16089 23,82002 Alcohol units 11 6,1909 1,5000 10,0000 8,6669 2,94396 47,55298 Diet 11 2,9091 1,0000 4,0000 1,0909 1,04447 35,90352 NAFLD liver fat score 11 6,8779 0,9228 10,9828 11,5463 3,39799 49,40435 Cluster 3 was composed of overweight patients with abdominal fat distribution, a slight increase in insulin at normal HbA1C, a slight increase in total cholesterol and LDL, normal triglycerides and marginal HDL (Table 5). Table 5. Basic statistical analysis of clinical and laboratory indicators of representatives of Cluster 3 Parameters Valid N Mean Minimum Maximum Variance Std.Dev. Coef.Var. WC/height 23 0,5448 0,4606 0,6875 0,00439 0,066239 12,1574 Fasting insulin 23 18,1643 4,8600 37,1000 85,67632 9,256150 50,9578 HbA1C 23 5,6678 4,3700 7,2800 0,61653 0,785192 13,8535 EL 23 11,2209 5,3300 18,9900 10,31109 3,211089 28,6171 Cholesterol 23 5,3009 2,8400 6,9900 1,65284 1,285627 24,2531 Triglycerides 23 1,3804 0,7500 2,6900 0,29569 0,543770 39,3912 HDL 22 1,3318 0,8000 2,3000 0,11832 0,343977 25,8276 LDL 22 3,2109 1,0000 5,1600 1,38308 1,176042 36,6265 VLDL 23 0,6991 0,3400 1,5000 0,10728 0,327538 46,8493 SBP 23 168,2609 150,0000 180,0000 53,65613 7,325034 4,3534 DBP 23 103,0435 100,0000 120,0000 31,22530 5,587960 5,4229 BMI 23 28,1174 22,6003 37,1255 13,17259 3,629407 12,9080 Alcohol units 23 5,3000 1,5000 11,5000 8,36909 2,892938 54,5837 Diet 23 3,0000 1,0000 4,0000 0,90909 0,953463 31,7821 NAFLD liver fat score 23 0,8359 -2,7277 4,9742 4,40681 2,099239 251,1388 Cluster 4 is the least numerically representative, but the most unexpected. It included patients with severe abdominal obesity, diabetic levels of HbA1C, moderate hyperinsulinism, with an increase in total cholesterol, TG, LDL and a decrease in HDL (Table 6). http://ws-conference.com/ WORLD SCIENCE ISSN 2413-1032 http://ws-conference.com/ № 10(50), Vol.1, October 2019 35 Table 6. Basic statistical analysis of clinical and laboratory indicators of representatives of Cluster 4 Parameters Valid N Mean Minimum Maximum Variance Std.Dev. Coef.Var. WC/height 5 0,5782 0,4821 0,6180 0,0032 0,05692 9,84298 Fasting insulin 5 21,9720 16,1700 27,5400 26,5516 5,15283 23,45179 HbA1C 5 6,6280 5,4200 9,9600 4,8687 2,20652 34,86912 EL 5 13,9380 9,2300 18,2800 14,5143 3,80977 27,33367 Cholesterol 5 5,6600 4,6000 6,7600 0,7388 0,85951 15,18561 Triglycerides 5 2,2500 1,2300 3,2900 0,8322 0,91222 40,54322 HDL 5 1,1440 0,7800 1,6100 0,1012 0,31817 27,81177 LDL 5 3,5060 2,4400 4,5000 0,6001 0,77468 22,09585 VLDL 5 1,0000 0,5000 1,4800 0,1811 0,42562 42,56172 SBP 5 203,0000 190,0000 240,0000 445,0000 21,09502 10,39164 DBP 5 114,0000 100,0000 130,0000 130,0000 11,40175 10,00154 BMI 5 30,1405 24,8016 33,9100 12,8248 3,58118 11,88161 Alcohol units 5 6,8800 4,5000 11,5000 10,6970 3,27063 47,53818 Diet 5 2,6000 2,0000 3,0000 0,3000 0,54772 21,06625 NAFLD liver fat score 5 1,7530 0,2018 2,9370 1,5633 1,25032 71,32395 For better visualization, we have reduced the average values of the parameters for the clusters into a common table (Table 7), which allows us to compare trends. Table 7. The average values of clinical and laboratory indicators with the distribution of clusters Parameters Cluster 1 Cluster 2 Cluster 3 Cluster 4 WC/height 0,54 0,59 0,54 0,58 Fasting insulin 25,07 53,01 18,16 21,97 HbA1C 6,11 6,01 5,67 6,33 EL 12,05 13,67 11,22 13,94 Cholesterol 5,96 6,46 5,30 5,66 Triglycerides 1,48 2,52 1,38 2,25 HDL 1,40 1,07 1,33 1,14 LDL 3,75 4,22 3,21 3,51 VLDL 0,63 1,10 0,70 1,00 SBP 142,69 175,91 168,26 203,00 DBP 94,62 105,00 103,04 114,00 BMI 27,54 30,06 28,12 30,14 Alcohol units 6,42 6,19 5,30 6,88 Diet 2,77 2,91 3,00 2,60 NAFLD liver fat score 2,15 6,88 0,84 1,75 The lowest variability of characteristics in the first cluster is inherent in indicators of blood pressure, BMI and insulin concentration, and the largest - in the severity of liver steatosis. The lowest variability in the second cluster is inherent in blood pressure and anthropometric parameters, as well as indicators of carbohydrate metabolism. The variability of the severity of steatosis is half that of the previous group. Cluster 3 from cluster 1 is distinguished by lower numbers of blood pressure, less severe liver steatosis and less alcohol abuse. The indicated group is determined by the relative stability of lipid WORLD SCIENCE ISSN 2413-1032 36 № 10(50), Vol.1, October 2019 http://ws-conference.com/ profile parameters, the stability of carbohydrate metabolism compensation, but the high variability of NAFLD. Thus, it is understood that the formation of steatosis is not latent even under conditions of a moderate shift in metabolic parameters. Cluster 4 from cluster 2 is distinguished by pronounced hypertension, low insulin values, less compensation for carbohydrate metabolism, but also less severity of liver steatosis. In addition, alcohol abuse is the highest in this group, and the lowest adherence to dietary recommendations. The fact that dyslipidemia is isolated is also obvious, which is confirmed by the data of a large population study under the auspices of NHANES, which included more than 23 thousand Americans, in patients with hepatic pathology with high levels of transaminases lipid profiles with low LDL and high HDL can be recorded, which may be caused by a defect in the synthesis of lipoproteins or a violation of the synthetic function of the liver and a marker of latent hepatopathies [14]. Conclusions. 1. Clustering of patient examination results demonstrates a reliable distribution of groups according to the severity of liver steatosis. 2. In case of non-compliance with dietary recommendations and the use of alcohol even within acceptable limits, the progression of liver steatosis occurs even against the background of minimal metabolic disturbances 3. 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