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Przewidywanie ryzyka kardiometabolicznego u pacjentów z niealkoholową stłuszczeniową chorobą wątroby w połączeniu z subkliniczną niedoczynnością tarczycy
Języki publikacji
Abstrakty
One of the most common diseases of our time is non-alcoholic fatty liver disease (NAFLD). Recently published research results indicate that patients with NAFLD along with traditional risk factors for cardiovascular diseases (CVD) have "new"risk factors such as endothelial dysfunction (ED), carotid intima-media thickness (CIMT), an increase in the CRP level, as well as risk factors combined into the Framingham scale. It is also knownthat combination of NAFLD with subclinical hypothyroidism (SH) forms an abnormal metabolic phenotype, which is associated with cardiometabolicrisk factors. The study of cardiometabolic predictors and vascular markers in patients with NAFLD in combination with SH willprovide an opportunityto improve the strategy of cardiovascular events prevention in such comorbid patients.
Niealkoholowe stłuszczenie wątroby (NAFLD) jest jedną z najczęstszych chorób naszych czasów. Ostatnio opublikowane wyniki badań sugerują, że pacjenci z NAFLD, wraz z tradycyjnymi czynnikami ryzyka chorób sercowo-naczyniowych (CVD), mają "nowe" czynniki ryzyka, takie jak dysfunkcja śródbłonka (ED), grubość błony wewnętrznej i środkowej tętnicy szyjnej (CIMT), podwyższony poziom CRP i czynniki ryzyka połączone w skali Framingham. Wiadomo również, że połączenie NAFLD z subkliniczną niedoczynnością tarczycy (SH) tworzy nieprawidłowy fenotyp metaboliczny związany z czynnikami ryzyka kardiometabolicznego. Badanie predyktorów kardiometabolicznych i markerów naczyniowych u pacjentów z NAFLDw połączeniu z SH pozwoli na ulepszenie strategii zapobiegania zdarzeniom sercowo-naczyniowym u takich współistniejących pacjentów.
Słowa kluczowe
Rocznik
Tom
Strony
64--68
Opis fizyczny
Bibliogr. 20 poz., tab., wykr.
Twórcy
autor
- Government Institution "L. T. Malaya Therapy Institute of the National Academy of Medical Science of Ukraine", Kharkiv, Ukraine
autor
- National AerospaceUniversity"Kharkiv Aviation Institute", Kharkiv, Ukraine, 3Ivano-Frankivsk National Medical University, Ivano-Frankivsk, Ukraine
autor
- Government Institution "L. T. Malaya Therapy Institute of the National Academy of Medical Science of Ukraine", Kharkiv, Ukraine
autor
- Government Institution "L. T. Malaya Therapy Institute of the National Academy of Medical Science of Ukraine", Kharkiv, Ukraine
autor
- National AerospaceUniversity"Kharkiv Aviation Institute", Kharkiv, Ukraine, 3Ivano-Frankivsk National Medical University, Ivano-Frankivsk, Ukraine
autor
- Vinnytsia Mychailo Kotsiubynskyi State Pedagogical University, Vinnytsia,Ukraine
- Vinnytsia Mychailo Kotsiubynskyi State Pedagogical University, Vinnytsia,Ukraine
autor
- Institute of Information and Computational Technologies, Almaty, Kazakhstan
autor
- Gymnasium No. 159 named after Y. Altynsarin, Almaty, Kazakhstan
Bibliografia
- [1] Bano A., Chaker L., Plompen E. P. C., et al.: Thyroid Function and the Risk of Nonalcoholic Fatty Liver Disease: The Rotterdam Study. J Clin Endocrinol Metab. 101(8), 2016, 3204–3211.
- [2] Belyalov F. I.: Prognozirovaniye i shkaly v kardiologii 2ye-izd. MEDPRESS-inform, Moscow 2018.
- [3] Belialov F. I.: Risk prediction scores of diseases. Complex Issues of Cardiovascular Diseases 7(1), 2018, 84–93 [http://doi.org/10.17802/2306-1278-2018-7-1-84-93].
- [4] Georgiyants M., Khvysyuk O., Boguslavskaуa N. et al.: Development of a mathematical model for predicting postoperative pain among patients with limb injuries. Eastern-European Journal of Enterprise Technologies 2, N4(86), 2017, 4–9 [http://doi.org/10.15587/1729-4061.2017.95157].
- [5] Graham I., Atar D., Borch-Johnsen K. et al.: European guidelines on cardiovascular disease prevention in clinical practice: full text. Eur. J. Cardiovasc. Prev. Rehabil. 14, 2007, S1–S113.
- [6] Kojuri J., Boostani R., Dehghani P., Nowroozipour F., Saki N.: Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. Journal of Cardiovascular Disease Research. 6(2), 2015, 51–60.
- [7] Kolesnikova E. V.: Sovremennyy patsiyent s zabolevaniyem pechenii patologiyey serdechno-sosudistoy sistemy: kakoy vybor sdelat? Contemporary gastroenterology 2(76), 2014, 85–94.
- [8] Kolesnikova O. V., Nemtsova V. D.: Effect of preventive measures for major metabolic parameters in patients with non-alcoholic fatty liver disease and cardiovascular risk. The ESC Textbook of Preventive Cardiology. Comprehensive, practical, and the official textbook of the European Association for Cardiovascular Prevention and Rehabilitation. Oxford University press, 2015.
- [9] Koval S. M., Snihurska I. O., Vysotska O. et al.: Prognosis of essential hypertension progression in patients with abdominal obesity. Wójcik W., Pavlov S., Kalimoldayev, M. (Eds.): Information Technology in Medical Diagnostics II. Taylor & Francis Group, CRC Press, Balkema book, London 2019.
- [10] Krak I. V., Kryvonos I. G., Kulias A. I.: Applied aspects of the synthesis and analysis of voice information. Cybernetics and Systems Analysis 49(4), 2013, 589–596.
- [11] Krak I., Kondratiuk S.: Cross-platform software for the development of sign communication system: Dactyl language modelling. 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies – CSIT, 2017, 1,8098760, 167–170
- [12] Ludwig U., Holzner D., Denzer C. et al.: Subclinical and clinical hypothyroidism and non-alcoholic fatty liver disease: a cross-sectional study of a random population sample aged 18 to 65 years. BMC Endocr Disord. 15. 2015, 41.
- [13] Ross D. S., Burch G. B, Cooper D. S. et al.: American Thyroid Association Guidelines for Diagnosis and Management of Hyperthyroidism and other causes of Thyrotoxicosis. Thyroid 26(10), 2016, 1343–1421.
- [14] Sinn D. H., Cho S. J., Gu S. et al.: Persistent Nonalcoholic Fatty Liver Disease Increases Risk for Carotid Atherosclerosis. Gastroenterology 151(3), 2016, 481–488.
- [15] Strashnenko A. N., Vysotskaya E. V., Demin Y. A. et al.: A method for prognosis of primary open-angle glaucoma. International Review on Computers and Software 8, 2013, 1943–1949.
- [16] Weiwei He, Xiaofei An, Ling Li et al.: Relationship between Hypothyroidism and Non-Alcoholic Fatty Liver Disease: A Systematic Review and Meta-analysis. Front Endocrinol (Lausanne) 8, 2017, 335.
- [17] Weng S. F., Reps J., Kai J., Garibaldi J. M., Qureshi N.: Can machinelearning improve cardiovascular risk prediction using routine clinical data? PLOS ONE 12(4), 2017, e0174944 [http://doi.org/10.1371/journal.pone.0174944].
- [18] Wójcik W., Pavlov S., Kalimoldayev M.: Information Technology in Medical Diagnostics II. Taylor & Francis Group, CRC Press, Balkema book, London 2019.
- [19] Yakubovska S., Vуsotska O., Porvan A. et al.: Developing a method for prediction of relapsing myocardial infarction based on interpolation diagnostic polynomial. Eastern-European Journal of Enterprise Technologies 5(9(83)), 2016, 41–49 [http://doi.org/10.15587/1729-4061.2016.81004].
- [20] Yasnitsky L. N., Cherepanov F. M.: Neyroekspertnaya sistema diagnostiki, prognozirovaniya i upravleniya riskami serdechno-sosudistykh zabolevaniy. Prikladnaya matematika i voprosy upravleniya 3, 2018, 107–126.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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