medsovetМедицинский СоветMeditsinskiy sovet = Medical Council2079-701X2658-5790REMEDIUM GROUP Ltd.10.21518/2079-701X-2022-16-10-46-56medsovet-6907Research ArticleСАХАРНЫЙ ДИАБЕТDIABETES MELLITUSВозможные направления клинического применения знаний о генетике сахарного диабета 2-го типаPossible clinical applications of knowledge about the genetics of type 2 diabeteshttps://orcid.org/0000-0001-6385-540XДемидоваТ. Ю.DemidovaT. Yu.

Демидова Татьяна Юльевна, доктор медицинских наук, профессор, заведующая кафедрой эндокринологии лечебного факультета 

117997, Москва, ул. Островитянова, д. 1

Tatiana Y. Demidova, Dr. Sci. (Med.), Professor, Head of the Department of Endocrinology, Faculty of Medicine

1, Ostrovityanov St., Moscow, 117997

t.y.demidova@gmail.comhttps://orcid.org/0000-0003-3826-0935ПлахотняяВ. М.PlakhotnyayaV. M.

Плахотняя Виктория Михайловна, клинический ординатор кафедры эндокринологии лечебного факультета 

117997, Москва, ул. Островитянова, д. 1

Viktoria M. Plakhotnyaya, Resident of the Department of Endocrinology, Faculty of Medicine

1, Ostrovityanov St., Moscow, 117997

vplahotnyaya@gmail.comРоссийский национальный исследовательский медицинский университет имени Н.И. ПироговаРоссияPirogov Russian National Research Medical UniversityRussian Federation2022180620220104656Copyright © Демидова Т.Ю., Плахотняя В.М., 20222022Демидова Т.Ю., Плахотняя В.М.Demidova T.Y., Plakhotnyaya V.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.med-sovet.pro/jour/article/view/6907

Сахарный диабет 2-го типа (СД2) является полигенным заболеванием, для развития которого необходимо взаимодействие наследственной предрасположенности и факторов внешней среды. В большинстве случаев предрасположенность к развитию СД2 связана с наследованием определенных аллелей «здоровых» генов. На сегодняшний день описано более 100 полиморфных вариантов генов, повышающих риск развития СД2. Наиболее хорошо изученным является ряд генов, предрасполагающих к развитию β-клеточной дисфункции и инсулинорезистентности (ИР). Кроме того, предполагается участие в формировании генетической предрасположенности к развитию СД2 генов, влияющих на липидный обмен и пищевое поведение, а также генов, кодирующих некоторые цитокины. Данная статья посвящена обзору наиболее перспективных потенциальных направлений применения знаний о генетике СД2 в клинической практике для уточнения классификации и стратификации СД2 на подклассы, индивидуальной оценки риска развития СД2 и его осложнений, а также для прогнозирования эффективности назначаемой терапии. Прогностические модели риска развития СД2 на настоящий момент недостаточно точны для широкого применения в клинической практике, однако сейчас идет активная работа по повышению их точности и эффективности. В рамках настоящего обзора также затрагивается тема эндофенотипов метаболических заболеваний, которая предполагает наличие определенных патогенных общих звеньев патогенеза развития ИР, ожирения, СД2, сердечно-сосудистых заболеваний, неалкогольной жировой болезни печени  и хронической болезни почек, в основе которых лежат определенные полиморфные варианты генов. Новейшие исследования в области генетики СД2 открывают множество новых возможностей персонализированного подхода к ведению этого заболевания.

Type 2 diabetes mellitus (T2DM) is a polygenic disease that develops as a result of the interaction of hereditary predisposition and environmental factors. The predisposition to develop T2DM is associated with the inheritance of certain alleles of “healthy” genes. More than 100 polymorphic variants of genes that increase the risk of developing T2DM have already been described. Today, genes predisposing to the development of β-cell dysfunction and insulin resistance (IR) are the most well studied. In addition, genes that affect lipid metabolism and eating behavior and genes of some cytokines can participate in the formation of a genetic predisposition to the development of T2DM. Our article reviews the most promising potential areas of application of knowledge about the genetics of T2DM in clinical practice. The first direction is to specify the classification and stratification of T2DM into subclasses/clasters. The second one is an individual assessment of the risk of developing T2DM and its complications. Today, predictive models of the risk of developing type 2 diabetes are not accurate enough for widespread use in clinical practice, but now researchers are actively working to improve their accuracy and effectiveness. And finally, knowledge about the genetics of T2DM can help predict the effectiveness of glucose-lowering therapy. In this review, we also discuss the topic of metabolic disease endophenotypes. The concept of endophenotypes suggests the presence of certain pathogenic common links in the pathogenesis of IR, obesity, T2DM, cardiovascular diseases, non-alcoholic fatty liver disease and chronic kidney disease, which are based on certain polymorphic gene variants. The results of research in the field of genetics of T2DM give us new possibilities for a personalized approach to the management of this complex disease.

генетика сахарного диабетаоценка генетического рискаклассификацияэндофенотипыфармакогенетикаgenetics of diabetesgenetic risk assessmentclassificationendophenotypespharmacogenomicsReferencesНосиков В.В. Рекомендации по оформлению статей по молекулярной генетике. Клиническая и экспериментальная тиреоидология. 2007;2(4):3–5. https://doi.org/10.14341/ket2007343-5.Nosikov V.V. Recommendations for the design of articles on molecular genetics. Clinical and Experimental Thyroidology. 2007;3(4):3–5. (In Russ.) https://doi.org/10.14341/ket2007343-5.Авзалетдинова Д.Ш., Моругова Т.В., Шарипова Л.Ф., Кочетова О.В. Ассоциация полиморфных локусов предрасположенности к сахарному диабету 2 типа в различных этнических группах Российской Федерации. Сахарный диабет. 2021;24(3):262–272. https://doi.org/10.14341/DM12531.Avzaletdinova D.S., Morugova T.V., Sharipova L.F., Kochetova O.V. Association of polymorphic loci of susceptibility to diabetes mellitus type 2 in various ethnic groups of the Russian Federation. Diabetes Mellitus. 2021;24(3):262–272. (In Russ.) https://doi.org/10.14341/DM12531.Бондарь И.А., Шабельникова О.Ю. Генетические основы сахарного диабета 2 типа. Сахарный диабет. 2013;16(4):11–16. https://doi.org/10.14341/DM2013411-16.Bondar I.A., Shabelnikova O.Yu. Genetic framework of type 2 diabetes mellitus. Diabetes Mellitus. 2013;16(4):11–16. (In Russ.) https://doi.org/10.14341/DM2013411-16.Oliynyk R.T. Future preventive gene therapy of polygenic diseases from a population genetics perspective. Int J Mol Sci. 2019;20(20):5013.https://doi.org/10.3390/ijms20205013.Oliynyk R.T. Future preventive gene therapy of polygenic diseases from a population genetics perspective. Int J Mol Sci. 2019;20(20):5013. https://doi.org/10.3390/ijms20205013.Вахромеева К.А., Суплотова Л.А., Носиков В.В. Полиморфные генетические маркеры сахарного диабета 2 типа в русской популяции. Проблемы эндокринологии. 2016;62(5):10–11. https://doi.org/10.14341/probl201662510-11.Vakhromeeva K.A., Suplotova L.A., Nosikov V.V. Genetic markers of type 2 diabetes in Russian population. Problemy Endokrinologii. 2016;62(5):10–11. https://doi.org/10.14341/probl201662510-11.Sun X., Yu W., Hu C. Genetics of type 2 diabetes: insights into the pathogenesis and its clinical application. Biomed Res Int. 2014;2014:926713. https://doi.org/10.1155/2014/926713.Sun X., Yu W., Hu C. Genetics of type 2 diabetes: insights into the pathogenesis and its clinical application. Biomed Res Int. 2014;2014:926713. https://doi.org/10.1155/2014/926713.Аметов А.С., Камынина Л.Л., Ахмедова З.Г. Клинические аспекты генетики, нутриогенетики и фармакогенетики сахарного диабета 2-го типа. Терапевтический архив. 2015;(8):124–131. https://doi.org/10.17116/terarkh2015878124-131.Ametov A.S., Kamynina L.L., Akhmedova Z.G. Type 2 diabetes mellitus: Clinical aspects of genetics, nutrigenetics, and pharmacogenetics. Terapevticheskii Arkhiv. 2015;(8):124–131. (In Russ.) https://doi.org/10.17116/terarkh2015878124-131.Billings L.K., Florez J.C. The genetics of type 2 diabetes: what have we learned from GWAS? Ann N Y Acad Sci. 2010;1212:59–77. https://doi.org/10.1111/j.1749-6632.2010.05838.x.Billings L.K., Florez J.C. The genetics of type 2 diabetes: what have we learned from GWAS? Ann N Y Acad Sci. 2010;1212:59–77. https://doi.org/10.1111/j.1749-6632.2010.05838.x.Коненков В.И., Шевченко А.В., Прокофьев В.Ф., Климонтов В.В., Королев М.А., Фазуллина О.Н. и др. Ассоциации вариантов гена фактора роста сосудистого эндотелия (VEGF) и генов цитокинов (IL-1B, IL-4, IL-6, IL-10, TNFA) с сахарным диабетом 2 типа у женщин. Сахарный диабет. 2012;15(3):4–10. https://doi.org/10.14341/2072-0351-6079.Konenkov V.I., Shevchenko A.V., Prokofev V.F., Klimontov V.V., Korolev M.A., Fazullina O.N. et al. Associations of vascular endothelial growth factor (VEGF) gene and cytokine (IL-1B, IL-4, IL-6, IL-10, TNFA) genes combinations with type 2 diabetes mellitus in women. Diabetes Mellitus. 2012;15(3):4–10. (In Russ.) https://doi.org/10.14341/2072-0351-6079.Gusti A.M.T., Qusti S.Y., Bahijri S.M., Toraih E.A., Bokhari S., Attallah S.M. et al. Glutathione S-Transferase (GSTT1 rs17856199) and Nitric Oxide Synthase (NOS2 rs2297518) Genotype Combination as Potential Oxidative Stress-Related Molecular Markers for Type 2 Diabetes Mellitus. Diabetes Metab Syndr Obes. 2021;14:1385–1403. https://doi.org/10.2147/DMSO.S300525.Gusti A.M.T., Qusti S.Y., Bahijri S.M., Toraih E.A., Bokhari S., Attallah S.M. et al. Glutathione S-Transferase (GSTT1 rs17856199) and Nitric Oxide Synthase (NOS2 rs2297518) Genotype Combination as Potential Oxidative StressRelated Molecular Markers for Type 2 Diabetes Mellitus. Diabetes Metab Syndr Obes. 2021;14:1385–1403. https://doi.org/10.2147/DMSO.S300525.Осокина И.В., Платонов Ф.А. Изучение генетической предрасположенности к сахарному диабету 2 типа в якутской популяции. Евразийский союз ученых. 2018;4(49):53–57. Режим доступа: https://www.elibrary.ru/item.asp?id=35033816.Osokina I.V., Platonov F.A. Study of genetic susceptibility to type 2 diabetes mellitus in the Yakut population. Evraziyskiy Soyuz Uchenykh. 2018;4(49):53–57. (In Russ.) Available at: https://www.elibrary.ru/item.asp?id=35033816.Бондарь И.А., Филипенко М.Л., Шабельникова О.Ю., Соколова Е.А. Ассоциация полиморфных маркеров rs7903146 гена TCF7L2 и rs1801282 гена PPARG (Pro12Ala) с сахарным диабетом 2 типа в Новосибирской области. Сахарный диабет. 2013;16(4):17–22. https://doi.org/10.14341/DM2013417-22.Bondar I.A., Filipenko M.L., Shabelnikova O.Yu., Sokolova E.A. Rs7903146 variant of TCF7L2 gene and rs18012824 variant of PPARG2 gene (Pro12Ala) are associated with type 2 diabetes mellitus in Novosibirsk population. Diabetes Mellitus. 2013;16(4):17–22. (In Russ.) https://doi.org/10.14341/DM2013417-22.Авзалетдинова Д.Ш., Шарипова Л.Ф., Кочетова О.В., Моргуева Т.В., Эрдман В.В., Сомова Р.Ш., Мустафина О.Е. Анализ ассоциаций полиморфного маркера rs7903146 гена TCF7L2 с сахарным диабетом 2 типа в татарской этнической группе, проживающей в Башкортостане. Сахарный диабет. 2016;19(2):119–124. https://doi.org/10.14341/DM2004138-45.Avzaletdinova D.S., Sharipova L.F., Kochetova O.V., Morugova T.V., Erdman V.V., Somova R.S., Mustafina O.E. The association of TCF7L2 rs7903146 polymorphism with type 2 diabetes mellitus among Tatars of Bashkortostan. Diabetes Mellitus. 2016;19(2):119–124. (In Russ.) https://doi.org/10.14341/DM2004138-45.Валеева Ф.В., Киселева Т.А., Хасанова К.Б., Ахметов И.И., Валеева Е.В., Набиуллина Р.М. Анализ ассоциаций полиморфных маркеров гена TCF7L2 с сахарным диабетом 2-го типа у жителей Республики Татарстан. Медицинский альманах. 2017;6(51):126–129. Режим доступа: https://www.elibrary.ru/item.asp?id=3054806.Valeeva F.V., Kiseleva T.A., Khasanova K.B., Akhmetov I.I., Valeeva E.V., Nabiullina R.M. Analysis of associations of polymorphous markers of TCF7L2 gene with diabetes mellitus of the 2nd type in the case of residents of Republic of Tatarstan. Medical Almanac. 2017;6(51):126–129. (In Russ.) Available at: https://www.elibrary.ru/item.asp?id=3054806.Назарова А.М., Шарафетдинов Х.Х., Плотникова О.А., Пилипенко В.В., Алексеева Р.И., Сорокина Е.Ю. Изучение полиморфного маркера rs266729 гена ADIPOQ у пациентов с сахарным диабетом 2 типа в российской популяции. Вопросы диетологии. 2018;8(3):11–17. Режим доступа: https://www.elibrary.ru/item.asp?id=36709732.Nazarova A.M., Sharafetdinov Kh.Kh., Plotnikova O.A., Pilipenko V.V., Alekseeva R.I., Sorokina E.Yu. A study of the rs266729 polymorphic marcer of the ADIPOQ gene in patients with type 2 diabetes mellitus in Russian population. Nutrition. 2018;8(3):11–17. (In Russ.) https://www.elibrary.ru/item.asp?id=36709732.Авзалетдинова Д.Ш., Шарипова Л.Ф., Кочетова О.В., Мустафина О.Е. Ассоциация аллелей гена адипонектина с сахарным диабетом 2-го типа у жителей Башкортостана. Проблемы эндокринологии. 2019;65(1):31–38. https://doi.org/10.14341/probl9426.Avzaletdinova D.S., Sharipova L.F., Kochetova O.V., Morugova T.V., Mustafina O.E. Association of adiponectin gene alleles with type 2 diabetes mellitus in residents of Bashkortostan. Problemy Endokrinologii. 2019;65(1):31–38. (In Russ.) Available at: https://doi.org/10.14341/probl9426.Kochetova O.V., Avzaletdinova D.S., Morugova T.V., Mustafina O.E. Chemokine Gene Polymorphisms Association with Increased Risk of Type 2 Diabetes Mellitus in Tatar Ethnic Group, Russia. Mol Biol Rep. 2019;46(1):887–896. https://doi.org/10.1007/s11033-018-4544-6.Kochetova O.V., Avzaletdinova D.S., Morugova T.V., Mustafina O.E. Chemokine Gene Polymorphisms Association with Increased Risk of Type 2 Diabetes Mellitus in Tatar Ethnic Group, Russia. Mol Biol Rep. 2019;46(1):887–896. https://doi.org/10.1007/s11033-018-4544-6.Демидова Т.Ю., Зенина С.Г. Молекулярно-генетические особенности развития сахарного диабета и возможности персонализации терапии. Сахарный диабет. 2020;23(5):467–474. https://doi.org/10.14341/DM12486.Demidova T.Yu., Zenina S.G. Molecular genetic features of the diabetes mellitus development and the possibility of precision therapy. Diabetes Mellitus. 2020;23(5):467–474. (In Russ.) https://doi.org/10.14341/DM12486.Kim D.S., Gloyn, A.L., Knowles J.W. Genetics of Type 2 Diabetes: Opportunities for Precision Medicine. J Am Coll Cardiol. 2021;78(5):496–512. https://doi.org/10.1016/j.jacc.2021.03.346.Kim D.S., Gloyn, A.L., Knowles J.W. Genetics of Type 2 Diabetes: Opportunities for Precision Medicine. J Am Coll Cardiol. 2021;78(5):496–512. https://doi.org/10.1016/j.jacc.2021.03.346.Uffelmann E., Huang Q.Q., Munung N.S., Vries J., Okada Y., Martin A.R. et al. Genome-wide association studies. Nat Rev Methods Primers. 2021;1:59. https://doi.org/10.1038/s43586-021-00056-9.Uffelmann E., Huang Q.Q., Munung N.S., Vries J., Okada Y., Martin A.R. et al. Genome-wide association studies. Nat Rev Methods Primers. 2021;1:59. https://doi.org/10.1038/s43586-021-00056-9.Redondo M.J., Hagopian W.A., Oram R., Steck A.K., Vehik K., Weedon M. et al. The clinical consequences of heterogeneity within and between different diabetes types. Diabetologia. 2020;63(10):2040–2048. https://doi.org/10.1007/s00125-020-05211-7.Redondo M.J., Hagopian W.A., Oram R., Steck A.K., Vehik K., Weedon M. et al. The clinical consequences of heterogeneity within and between different diabetes types. Diabetologia. 2020;63(10):2040–2048. https://doi.org/10.1007/s00125-020-05211-7.Li L., Cheng W.-Y., Glicksberg B.S., Gottesman O., Tamler R., Chen R. et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7(311):311ra174. https://doi.org/10.1126/scitranslmed.aaa9364.Li L., Cheng W.-Y., Glicksberg B.S., Gottesman O., Tamler R., Chen R. et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7(311):311ra174. https://doi.org/10.1126/scitranslmed.aaa9364.Ahlqvist E., Storm P., Karajamaki A., Martinell M., Dorkhan M., Carlsson A. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361–369. https://doi.org/10.1016/S2213-8587(18)30051-2.Ahlqvist E., Storm P., Karajamaki A., Martinell M., Dorkhan M., Carlsson A. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361–369. https://doi.org/10.1016/S2213-8587(18)30051-2.Zou X., Zhou X., Zhu Z., Ji L. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol. 2019;7(1):9–11. https://doi.org/10.1016/S2213-8587(18)30316-4.Zou X., Zhou X., Zhu Z., Ji L. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol. 2019;7(1):9–11. https://doi.org/10.1016/S2213-8587(18)30316-4.Tanabe H., Saito H., Kudo A., Machii N., Hirai H., Maimaituxun G. et al. Factors Associated with Risk of Diabetic Complications in Novel ClusterBased Diabetes Subgroups: A Japanese Retrospective Cohort Study. J Clin Med. 2020;9(7):2083. https://doi.org/10.3390/jcm9072083.Tanabe H., Saito H., Kudo A., Machii N., Hirai H., Maimaituxun G. et al. Factors Associated with Risk of Diabetic Complications in Novel ClusterBased Diabetes Subgroups: A Japanese Retrospective Cohort Study. J Clin Med. 2020;9(7):2083. https://doi.org/10.3390/jcm9072083.Zaharia O.P., Strassburger K., Strom A., Bönhof G.J., Karusheva Y., Antoniou S. et al. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 2019;7(9):684–694. https://doi.org/10.1016/S2213-8587(19)30187-1.Zaharia O.P., Strassburger K., Strom A., Bönhof G.J., Karusheva Y., Antoniou S. et al. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 2019;7(9):684–694. https://doi.org/10.1016/S2213-8587(19)30187-1.Dennis J.M., Shields B.M., Henley W.E., Jones A.G., Hattersley A.T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019;7(6):442–451. https://doi.org/10.1016/S2213-8587(19)30087-7.Dennis J.M., Shields B.M., Henley W.E., Jones A.G., Hattersley A.T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019;7(6):442–451. https://doi.org/10.1016/S2213-8587(19)30087-7.Kahkoska A.R., Geybels M.S., Klein K.R., Kreiner F.F., Marx N., Nauck M.A. et al. Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN-6 cardiovascular outcomes trials. Diabetes Obes Metab. 2020;22(9):1537–1547. https://doi.org/10.1111/dom.14063.Kahkoska A.R., Geybels M.S., Klein K.R., Kreiner F.F., Marx N., Nauck M.A. et al. Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN-6 cardiovascular outcomes trials. Diabetes Obes Metab. 2020;22(9):1537–1547. https://doi.org/10.1111/dom.14063.Pigeyre M., Hess S., Gomez M.F., Asplund O., Groop L., Pare G., Gerstein H. Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial. Diabetologia. 2022;65:206–215. https://doi.org/10.1007/s00125-021-05567-4.Pigeyre M., Hess S., Gomez M.F., Asplund O., Groop L., Pare G., Gerstein H. Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial. Diabetologia. 2022;65:206–215. https://doi.org/10.1007/s00125-021-05567-4.Wagner R., Heni M., Tabák A.G., Machann J., Schick F., Randrianarisoa E. et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med. 2021;27(1):49–57. https://doi.org/10.1038/s41591-020-1116-9.Wagner R., Heni M., Tabák A.G., Machann J., Schick F., Randrianarisoa E. et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med. 2021;27(1):49–57. https://doi.org/10.1038/s41591-020-1116-9.Oyelade J., Isewon I., Oladipupo F., Aromolaran O., Uwoghiren E., Ameh F. et al. Clustering Algorithms: Their Application to Gene Expression Data. Bioinform Biol Insights. 2016;10:237–253. https://doi.org/10.4137/BBI.S38316.Oyelade J., Isewon I., Oladipupo F., Aromolaran O., Uwoghiren E., Ameh F. et al. Clustering Algorithms: Their Application to Gene Expression Data. Bioinform Biol Insights. 2016;10:237–253. https://doi.org/10.4137/BBI.S38316.Udler M.S., McCarthy M.I., Florez J.C., Mahajan A. Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine. Endocrine Reviews. 2019;40(6):1500–1520. https://doi.org/10.1210/er.2019-00088.Udler M.S., McCarthy M.I., Florez J.C., Mahajan A. Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine. Endocrine Reviews. 2019;40(6):1500–1520. https://doi.org/10.1210/er.2019-00088.Udler M.S., Kim J., von Grotthuss M., Bonàs-Guarch S., Cole J.B., Chiou J. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLOS Med. 2018;15(9):e1002654. https://doi.org/10.1371/journal.pmed.1002654.Udler M.S., Kim J., von Grotthuss M., Bonàs-Guarch S., Cole J.B., Chiou J. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLOS Med. 2018;15(9):e1002654. https://doi.org/10.1371/journal.pmed.1002654.Mahajan A., Taliun D., Thurner M., Robertson N.R., Torres J.M., Rayner N.W. et al. Finemapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50(11):1505–1513. https://doi.org/10.1038/s41588-018-0241-6.Mahajan A., Taliun D., Thurner M., Robertson N.R., Torres J.M., Rayner N.W. et al. Finemapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50(11):1505–1513. https://doi.org/10.1038/s41588-018-0241-6.Udler M.S. Type 2 Diabetes: Multiple Genes, Multiple Diseases. Curr Diab Rep. 2019;19:55. https://doi.org/10.1007/s11892-019-1169-7.Udler M.S. Type 2 Diabetes: Multiple Genes, Multiple Diseases. Curr Diab Rep. 2019;19:55. https://doi.org/10.1007/s11892-019-1169-7.Talmud P.J., Hingorani A.D., Cooper J.A., Marmot M.G., Brunner E.J., Kumari M. et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010;340:b4838. https://doi.org/10.1136/bmj.b4838.Talmud P.J., Hingorani A.D., Cooper J.A., Marmot M.G., Brunner E.J., Kumari M. et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010;340:b4838. https://doi.org/10.1136/bmj.b4838.Khera A.V., Chaffin M., Aragam K.G., Haas M.E., Roselli C., Choi S.H. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–1224. https://doi.org/10.1038/s41588-018-0183-z.Khera A.V., Chaffin M., Aragam K.G., Haas M.E., Roselli C., Choi S.H. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–1224. https://doi.org/10.1038/s41588-018-0183-z.Vujkovic M., Keaton J.M., Lynch J.A., Miller D.R., Zhou J., Tcheandjieu C. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multiancestry meta-analysis. Nat Genet. 2020;52(7):680–691. https://doi.org/10.1038/s41588-020-0637-y.Vujkovic M., Keaton J.M., Lynch J.A., Miller D.R., Zhou J., Tcheandjieu C. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multiancestry meta-analysis. Nat Genet. 2020;52(7):680–691. https://doi.org/10.1038/s41588-020-0637-y.Li J.H., Szczerbinski L., Dawed A.Y., Kaur V., Todd J.N., Pearson E.R., Florez J.C. A polygenic score for type 2 diabetes risk is associated with both the acute and sustained response to sulfonylureas. Diabetes. 2021;70(1):293–300. https://doi.org/10.2337/db20-0530Li J.H., Szczerbinski L., Dawed A.Y., Kaur V., Todd J.N., Pearson E.R., Florez J.C. A polygenic score for type 2 diabetes risk is associated with both the acute and sustained response to sulfonylureas. Diabetes. 2021;70(1):293–300. https://doi.org/10.2337/db20-0530Jiang G., Luk A.O., Tam C.H.T., Lau E.S., Ozaki R., Chow E.Y. et al. Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: a cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank. PLOS Med. 2020;17(7):e1003209. https://doi.org/10.1371/journal.pmed.1003209.Jiang G., Luk A.O., Tam C.H.T., Lau E.S., Ozaki R., Chow E.Y. et al. Obesity, clinical, and genetic predictors for glycemic progression in Chinese patients with type 2 diabetes: a cohort study using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank. PLOS Med. 2020;17(7):e1003209. https://doi.org/10.1371/journal.pmed.1003209.Wagner R., Jaghutriz B.A., Gerst F., Oquendo M.B., Machann J., Schick F. et al. Pancreatic steatosis associates with impaired insulin secretion in genetically predisposed individuals. J Clin Endocrinol Metab. 2020;105(11): 3518–3525. https://doi.org/10.1210/clinem/dgaa435.Wagner R., Jaghutriz B.A., Gerst F., Oquendo M.B., Machann J., Schick F. et al. Pancreatic steatosis associates with impaired insulin secretion in genetically predisposed individuals. J Clin Endocrinol Metab. 2020;105(11): 3518–3525. https://doi.org/10.1210/clinem/dgaa435.Srinivasan S., Jablonski K.A., Knowler W.C., Dagogo-Jack S., Kahn S.E., Boyko E.J. et al. A polygenic lipodystrophy genetic risk score characterizes risk independent of BMI in the Diabetes Prevention Program. J Endocr Soc. 2019;3(9):1663–1677. https://doi.org/10.1210/js.2019-00069.Srinivasan S., Jablonski K.A., Knowler W.C., Dagogo-Jack S., Kahn S.E., Boyko E.J. et al. A polygenic lipodystrophy genetic risk score characterizes risk independent of BMI in the Diabetes Prevention Program. J Endocr Soc. 2019;3(9):1663–1677. https://doi.org/10.1210/js.2019-00069.Stettler C., Christ E., Diem P. (eds.) Novelties in Diabetes. Endocr Dev. Basel, Karger. 2016;31:I–VI. https://doi.org/10.1159/000441863.Stettler C., Christ E., Diem P. (eds.) Novelties in Diabetes. Endocr Dev. Basel, Karger. 2016;31:I–VI. https://doi.org/10.1159/000441863.Lyssenko V., Laakso M. Genetic Screening for the Risk of Type 2 Diabetes: worthless or valuable? Diabetes Care. 2013;36(Suppl. 2):S120–S126. https://doi.org/10.2337/dcS13-2009.Lyssenko V., Laakso M. Genetic Screening for the Risk of Type 2 Diabetes: worthless or valuable? Diabetes Care. 2013;36(Suppl. 2):S120–S126. https://doi.org/10.2337/dcS13-2009.Billings L.K., Florez J.C. The genetics of type 2 diabetes: what have we learned from GWAS? Ann N Y Acad Sci. 2010;1212:59–77. https://doi.org/10.1111/j.1749-6632.2010.05838.xBillings L.K., Florez J.C. The genetics of type 2 diabetes: what have we learned from GWAS? Ann N Y Acad Sci. 2010;1212:59–77. https://doi.org/10.1111/j.1749-6632.2010.05838.xFlorez J.C., Jablonski K.A., Baylay N., Pollin T.I., de Bakker P.I.W., Shuldiner A.R. et al. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med. 2006;355(3):241–250. https://doi.org/10.1056/NEJMoa062418.. 46. Florez J.C., Jablonski K.A., Baylay N., Pollin T.I., de Bakker P.I.W., Shuldiner A.R. et al. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med. 2006;355(3):241–250. https://doi.org/10.1056/NEJMoa062418.Grant R.W., Hivert M., Pandiscio J.C., Florez J.C., Nathan D.M., Meigs J.B. The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia. 2009;52(11):2299–2305. https://doi.org/10.1007/s00125-009-1512-7.Grant R.W., Hivert M., Pandiscio J.C., Florez J.C., Nathan D.M., Meigs J.B. The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia. 2009;52(11):2299–2305. https://doi.org/10.1007/s00125-009-1512-7.Srinivasan S., Yee A.W., Giacomini K.M. Pharmacogenetics of Antidiabetic Drugs. Adv Pharmacol. 2018;83:361–389. https://doi.org/10.1016/bs.apha.2018.04.005.Srinivasan S., Yee A.W., Giacomini K.M. Pharmacogenetics of Antidiabetic Drugs. Adv Pharmacol. 2018;83:361–389. https://doi.org/10.1016/bs.apha.2018.04.005.

The authors declare that there are no conflicts of interest present.