References

emcardio

Неотложная кардиология и кардиоваскулярные риски

Emergency Cardiology and Cardiovascular Risks journal

2616-633X

Белорусский государственный медицинский университет

emcardio-220

Research Article

Мастер-класс

Master-class

Роль персонализированной медицины в профилактике сахарного диабета 2 типа

Personalized medicine and its role in type 2 diabetes prevention

Салухов

В. В.

Salukhov

V. V.

Ромашевский

Б. В.

Romashevskiy

B. V.

ФГБВОУ ВО «Военно-медицинская академия им. С.М. Кирова»Russian Federation

2019

11112025

654–665

2025

Салухов В.В., Ромашевский Б.В.

Salukhov V.V., Romashevskiy B.V.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://emcardio.bsmu.by/jour/article/view/220

Персонализированная медицина (ПМ) является безопасным и эффективным способом профилактики и лечения сахарного диабета 2 типа (СД2). Основной стратегией ПМ является адаптация различных методов профилактики и лечения к индивидуальным характеристикам пациентов, включая последовательность их генома, состава микробиома, анамнеза жизни и заболевания, диетических предпочтений. В статье освещены перспективы использования персонализированных методов профилактики СД2 основанных на результатах исследований в области геномики, метаболомики, технологий микробиома кишечника, фармакогенетики и фармакогеномики. Продемонстрированы возможности и преимущества использования мобильных приложений и технологий анализа больших объемов данных («Big Data») в структуре ПМ. Приведены данные о роли фармакогенетики и фармакогеномики в выборе эффективных и безопасных лекарственных средств для лечения СД2. В заключении было отмечено о целесообразности проведения популяционных исследований, подтверждающих эффективность, рентабельность и преимущества ПМ по сравнению с традиционными методами профилактики и лечения СД2.

Personalized medicine (PM) is a safe and effective way to prevent and treat type 2 diabetes mellitus (T2DM). The basic strategy of PM is to adapt various prevention and treatment methods to individual characteristics of patients, including their genome sequence, microbiome composition, life, case history and dietary preferences. The article highlights the prospects of personalized methods application for T2DM prevention based on the results of research in the field of genomics, metabolomics, intestinal microbiome technologies, pharmacogenetics and pharmacogenomics. The potential and advantages of mobile applications and technologies for large amounts of data assessment (“Big Data”) in the PM structure are demonstrated. The findings on the role of pharmacogenetics and pharmacogenomics in the selection of effective and safe drugs for T2DM treatment are presented. In conclusion, it was noted, that it would be feasible to conduct population –based studies confirming the effectiveness, profitability and advantages of PM compared to traditional T2DM prevention and treatment methods.

персонализированная медицинапрофилактикасахарный диабет 2 типагеномикаметаболомикамикробиомфармакогенетикафармакогеномика

personalized medicinepreventiontype 2 diabetes mellitusgenomicsmetabolomicsintestinal microbiomepharmacogeneticspharmacogenomics

References1

American Diabetes Association (ADA). Prevention or Delay of Type 2 Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care, 2018, vol. 41, suppl. 1, pp. 51–54.

Profilaktika razvitiya saharnogo diabeta tipa 2: Rol’ i mesto metformina [Prevention of type 2 diabetes: the Role and place of Metformin]. E`ndokrinologiya: Novosti, mneniya, obuchenie, 2017, no. 1, pp. 77–87. (in Russian).

International Diabetes Federation (IDF). IDF Diabetes Atlas [electronic resource], 8th edn. Brussels; Belgium: IDF, 2017. Available at: http://www.diabetesatlas.org. (accessed 28.10.2019).

Sergienko I.V., Boycov S. A, Shestakova M.V., Ansheles A.A., Halimov YU.SH., Saluhov V.V., Ty’renko V.V., Agafonov P.V. Kardiologicheskie aspekty’ saharnogo diabeta 2 tipa [Cardiological aspects of type 2 diabetes]: ucheb. posobie. SPb.: Foliant, 2018, 64 s. (in Russian).

Saluhov V.V., Halimov YU.SH., SHustov S.B., Kadin D.V. Snijenie kardiovaskulyarnogo riska u pacientov s saharny’m diabetom 2 tipa: obzor osnovny’h strategiy i klinicheskih issledovaniy [Reducing cardiovascular risk in patients with type 2 diabetes: a review of key strategies and clinical studies]. Saharny’y diabet, 2018, vol. 21, no. 3, pp. 193–205. (in Russian).

Barry E., Roberts S., Oke J., Vijayaraghavan S., Normansell R., Greenhalgh T. Efficacy and effectiveness of screen and treat policies in prevention of type 2 diabetes: Systematic review and meta-analysis of screening tests and interventions. BMJ, 2017, vol. 356, pp. i6538. doi: 10.1136/bmj. i6538.

Saluhov B.V., Romashevskiy B.V. Sovremenny’e aspekty’ preventivnoy terapii saharnogo diabeta 2-go tipa [Modern aspects of preventive therapy of type 2 diabetes]. Medicinskiy sovet, 2019, no. 4, pp. 6–13. doi: ht tps://doi.org/10.21518/2079-701X-2019-4-6-13. (in Russian).

Florez J.C. Precision medicine in diabetes: is it time? Diabetes Care, 2016, vol. 39, pp. 1085–1088. Терапевтическая стратегия профилактики СД2 должна проводится с оценкой клинической картины заболевания, этнической принадлежности, наследственности, антропометрии, маркеров сердечно-сосудистых заболеваний и микробиоты ЖКТ. Идентификация фенотипа предиабета позволит верифицировать пациентов с высоким риском развития СД2 и разработать персональные рекомендации по улучшению образа жизни и медикаментозной профилактики диабета. (Адаптировано из Samocha-Bonet D., et all. Prevention and Treatment of Type 2 Diabetes, 2018 [65]).

Dedov I.I., Shestakova M.V. Personalizirovannaya terapiya saharnogo diabeta: put’ ot bolezni k bol’nomu [Personalized diabetes therapy: the path from disease to patient]. Ter. Arhiv, 2014, no. 10, pp. 4–9. (in Russian).

Collins C.D., Purohit S., Podolsky R.H., Zhao H.S., Schatz D., Eckenrode S.E., Yang P., Hopkins D., Muir A., Hoffman M., McIndoe R.A., Rewers M., She J.X. The application of genomic and proteomic technologies in predictive, preventive and personalized medicine. Vascul Pharmacol, 2006, vol. 45, no. 5, pp. 258–267.

Semiz S., Dujic T., Causevic A. Pharmacogenetics and personalized treatment of type 2 diabetes. Biochemia Medica, 2013, vol. 23, iss. 2, pp. 154-171. doi:10.11613/bm.2013.020

Fuchsberger C., Flannick J., Teslovich T.M., Mahajan A., Agarwala V., Gaulton K.J., Ma C., Fontanillas P., Moutsianas L., McCarthy D.J., Rivas M.A., Perry J.R.B. [et al.] The genetic architecture of type 2 diabetes. Nature, 2016, vol. 536, pp. 41–47.

Florez J.C., Udler M.S., Hanson R.L. Diabetes in America [electronic resource]. 3rd edn. Bethesda: National Institutes of Health, 2016. Available at: https://www.niddk.nih.gov/about-niddk/strategic-plans-reports/diabetes-in-america-3rd-edition. (accessed 28.10.2019).

Emwas A.H. The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods in Mol. Biol., 2015, vol. 1277, pp. 161–193.

Afshin A., Babalola D., Mclean M., Yu Z., Ma W., Chen C.Y., Arabi M., Mozaffarian D. Information technology and lifestyle: a systematic evaluation ofiInternet and mobile interventions for improving diet, physical activity, obesity, tobacco, and alcohol use. J. Am. Heart. Assoc., 2016, vol. 5, no. 9, pp. e003058.

Laakso M. Biomarkers for type 2 diabetes. Mol. Metab., 2019, vol. 27S, pp. S139–S146. doi: 10.1016/j.molmet.2019.06.016.

Ligthart S., Vaez A., Võsa U., Stathopoulou M.G, de Vries P.S., Prins B.P., Van der Most P.J., Tanaka T., Naderi E., Rose L.M., Wu Y. [et al.] Genome analyses of >200,000 individuals identify 58 loci for chronic inflammation and highlight pathways that link inflammation and complex disorders. J. Hum. Genet, 2018, vol. 103, no. 5, pp. 691–706. doi: 10.1016/j.ajhg.2018.09.009.

Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., [et al.]. Genetic studies of body mass index yield new insights for obesity biology. Nature, 2015, vol. 518, no. 7638, pp. 197–206.

Castaner O., Corella D., Covas M.I., Sorlí J.V., Subirana I., Flores-Mateo G., Nonell L., Bulló M., de la Torre R., Portolés O., Fitó M. In vivo transcriptomic profile after a Mediterranean diet in high-cardiovascular risk patients: a randomized controlled trial. J. Clin. Nutr., 2013, vol. 98, no. 3, pp. 845–853.

Volkov P., Bacos K., Ofori J.K., Esguerra J.L., Eliasson L., Rönn T., Ling C. Wholegenome bisulfite sequencing of human pancreatic islets reveals novel differentially methylated regions in type 2 diabetes pathogenesis. Diabetes, 2017, vol. 66, no. 4, pp. 1074–1085.

Dayeh T., Volkov P., Salö S., Hall E., Nilsson E., Olsson A.H., Kirkpatrick C.L., Wollheim C.B., Eliasson L., Rönn T., Bacos K., Ling C. Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet, 2014, vol. 10, no. 3, pp. e1004160.

Nicholson J.K., Wilson I.D. Opinion: understanding “global” systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug. Discov., 2003, vol. 2, no. 8, pp. 668–676. doi:10.1038/nrd1157.

Park J.E., Lim H.R., Kim J.W., Shin K.H. Metabolite changes in risk of type 2 diabetes mellitus in cohort studies: a systematic review and meta-analysis. Diabet. Res. Clin. Pract., 2018, vol. 140, pp. 216–227.

Vangipurapu J., Stancáková A., Smith U., Kuusisto J., Laakso M. Nine amino acids are associated with decreased insulin secretion and elevated glucose levels in a 7.4-year follow-up study of 5,181 Finnish men. Diabetes, 2019, vol. 68, no. 6, pp. 1353–1358.

Hayashi Y., Seino Y. Regulation of amino acid metabolism and α-cell proliferation by glucagon. J. Diabet. Investig., 2018, vol. 9, no. 3, pp. 464–472.

Vaishya S., Sarwade R.D., Seshadri V. MicroRNA, Proteins, and Metabolites as Novel Biomarkers for Prediabetes, Diabetes, and Related Complications. 2018. Front. Endocrinol., 2018, vol. 9, pp. 1–12. doi:10.3389/fendo.2018.00180.

Arora T., Backhed F. The gut microbiota and metabolic disease: current understanding and future perspectives. J. Intern. Med., 2016, vol. 280, no. 4, pp. 339–349.

Pedersen H.K., Guðmundsdóttir V., Nielsen H.B., Hyotylainenet T. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature, 2016, vol. 535, no. 7612, pp. 376–381.

Turnbaugh P.J, Bäckhed F., Fulton L., Gordon J.I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell. Host. Microbe, 2008, vol. 3, no. 4, pp. 213–223.

Smits L.P, Bouter KEC, de Vos W.M, Borody T.J, Nieuwdorp M. Therapeutic potential of fecal microbiota transplantation. Gastroenterology, 2013, vol. 145, no. 5, pp. 946–953.

Guertin K.A, Moore S.C, Sampson J.N., Huang W.Y., Xiao Q., Stolzenberg-Solomon R.Z., Sinha R., Cross A.J. Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover dietdisease relations in populations. J. Clin. Nutr. 2014, vol. 100, no. 1, 208–217.

Cheung W., Keski-Rahkonen P., Assi N., Ferrari P., Freisling H., Rinaldi S., Slimani N., Zamora-Ros R., Rundle M., Frost G., Gibbons H., Carr E., Brennan L. A metabolomic study of biomarkers of meat and fish intake. J. Clin. Nutr, 2017, vol. 105, no. 3, pp. 600–608.

Garcia-Perez I., Posma J.M., Gibson R., Chambers E.S. Objective assessment of dietary patterns by use of metabolic phenotyping: a randomized, controlled, crossover trial. Lancet Diabet. Endocrin, 2017, vol. 5, no. 3, pp. 184–195.

Floegel A., von Ruesten A., Drogan D., Schulze M.B., Prehn C., Adamski J., Pischon T., Boeing H. Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. Eur. J. Clin. Nutr., 2013, vol. 67, no. 10, pp. 1100–1108.

Vazquez-Fresno R., Llorach R., Urpi-Sarda M., Lupianez-Barbero A., Estruch R., Corella D., Fitó M., Arós F., Ruiz-Canela M., Salas-Salvadó J., Andres-Lacueva C. Metabolomic pattern analysis after mediterranean diet intervention in a nondiabetic population: a 1- and 3-year follow-up in the PREDIMED study. J. Proteome Res., 2015, vol. 14, no. 1, pp. 531–540.

Floegel A., Wientzek A., Bachlechner U., Jacobs S., Drogan D., Prehn C., Adamski J., Krumsiek J., Schulze M.B., Pischon T., Boeing H. Linking diet, physical activity, cardiorespiratory fitness and obesity to serum metabolite networks: findings from a population-based study. Int. J. Obes (Lond), 2014, vol. 38, no. 11, pp. 1388–1196.

Floegel A., Stefan N., Yu Z., Mühlenbruch K., Drogan D., Joost H.G., Fritsche A., Häring H.U., Hrabě de Angelis M., Peters A., Roden M., Prehn C., Wang-Sattler R., Illig T., Schulze M.B., Adamski J., Boeing H., Pischon T. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes, 2013, vol. 62, no. 2, pp. 639–648.

Zheng Y., Ceglarek U., Huang T., Lerong L., Jennifer R., Donna H.R., George A.B., Frank M.S., Dan Schwarzfuchs, Joachim T., Iris S., Lu Q. Weight-loss diets and 2-y changes in circulating amino acids in 2 randomized intervention trials. J. Clin. Nutr., 2016, vol. 103, no. 2, pp. 505–511.

Walford G.A., Ma Y., Clish C., Florez J.C., Wang T.J., Gerszten R.E., Metabolite profiles of diabetes Incidence and intervention response in the Diabetes Prevention Program. Diabetes, 2016, vol. 65, no. 5, pp. 1424–1433.

Ros E. The PREDIMED study. Endocrinol. Diabetes. Nutrición, 2017, vol. 64, no. 2, pp. 63-66. doi: 10.1016/j.endinu.2016.11.003.

David L.A, Maurice C.F., Carmody R.N., Gootenberg D.B., Button J.E., Wolfe B.E., Ling A.V., Devlin A.S., Varma Y., Fischbach M.A., Biddinger S.B., Dutton R.J., Turnbaugh P.J. Diet rapidly and reproducibly alters the human gut microbiome. Nature, 2014, vol. 505, no. 7484, pp. 559–563.

Falony G., Joossens M., Vieira-Silva S., Wang J., Darzi Y., Faust K., Kurilshikov A., Bonder M.J., Valles-Colomer M., Vandeputte D., Tito R.Y., Chaffron S., Rymenans L., Verspecht C., De Sutter L., Lima-Mendez G., D’hoe K., Jonckheere K., Homola D., Garcia R., Tigchelaar E.F., Eeckhaudt L., Fu J., Henckaerts L., Zhernakova A., Wijmenga C., Raes J. Population-level analysis of gut microbiome variation. Science, 2016, vol. 352, no. 6285, pp. 560–564.

Zhernakova A., Kurilshikov A., Bonder M.J., Tigchelaar E.F., Schirmer M., Vatanen T., Mujagic Z., Vila A.V., Falony G., Vieira-Silva S., Wang J., Imhann F., Brandsma E. [et al.]. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science, 2016, vol. 352, no. 6285, pp. 565–559.

Dao M.C, Everard A, Aron-Wisnewsky J., Sokolovska N., Prifti E., Verger E.O., Kayser B.D., Levenez F., Chilloux J., Hoyles L., Dumas M.E., Rizkalla S.W., Doré J., Cani P.D., Clément K. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut, 2016, vol. 65, no. 3, pp. 426–436.

Wu G.D, Compher C., Chen E.Z, Smith S.A., Shah R.D., Bittinger K., Chehoud C., Albenberg L.G., Nessel L., Gilroy E., Star J., Weljie A.M., Flint H.J., Metz D.C., Bennett M.J., Li H., Bushman F.D., Lewis J.D. Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production. Gut, 2014, vol. 65, no. 1, pp. 63–72.

McLoughlin R.F., Berthon B.S., Jensen M.E., Baines K.J., Wood L.G. Short-chain fatty acids, prebiotics, symbiotic, and systemic inflammation: a systematic review and meta-analysis. J. Clin. Nutr., 2017, vol. 106, no. 3, pp. 930–945.

Karnauhov N.S., Il’yuhin R.G. Vozmojnosti tehnologiĭ «BigData» v medicine [Possibilities of “BigData” technologies in medicine]. Vrach i informacionny’e tehnologii, 2019, no. 1, pp. 59–63. (in Russian).

McGloin A.F, Eslami S. Digital and social media opportunities for dietary behavior change. Proc. Nutr. Soc., 2015, vol. 74, no. 2, pp. 139–148.

Obermeyer Z., Emanuel E.J. Predicting the future - big data, machine learning, and clinical medicine. N. Engl. J. Med., 2016, vol. 375, no. 13, pp. 1216–1219.

Wu P.Y., Cheng C.W., Kaddi C.D., Venugopalan J., Hoffman R., Wang M.D. Omic and Electronic Health Record Big Data Analytics for Precision Medicine. IEEE Trans. Biomed. Eng., 2017, vol. 64, no. 2, pp. 263–273.

Price N.D., Magis A.T., Earls J.C., Glusman G., Levy R., Lausted C., McDonald D.T., Kusebauch U., Moss C.L., Zhou Y., Qin S., Moritz R.L., Brogaard K., Omenn G.S., Lovejoy J.C., Hood L. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 2017, vol. 35, no. 8, pp. 747–756.

Zeevi D., Korem T., Zmora N., Israeli D., Rothschild D., Weinberger A., Ben-Yacov O., Lador D., Avnit-Sagi T., Lotan-Pompan M., Suez J., Mahdi J.A., Matot E., Malka G., Kosower N., Rein M., Zilberman-Schapira G., Dohnalová L., Pevsner-Fischer M., Bikovsky R., Halpern Z., Elinav E., Segal E. Personalized nutrition by prediction of glycemic responses. Cell, 2015, vol. 163, no. 5, pp. 1079–1094.

Wang D. D., Hu F.B. Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabet. Endocrinol., 2018, vol. 6, no. 5, pp. 416–426. doi:10.1016/s2213-8587(18)30037-8.

Moin T., Schmittdiel J.A., Flory J.H., Yeh J., Karter A.J., Kruge L.E., Schillinger D., Mangione C.M., Herman W.H., Walker E.A. Review of Metformin Use for Type 2 Diabetes Prevention. J. Preventive Med., 2018, vol. 55, no, 4, pp. 565–574. doi:10.1016/j.amepre.2018.04.038.

Zhou K., Donnelly L., Yang J., Li M., Deshmukh H., Van Zuydam N., Ahlqvist E., Spencer C.C., Groop L., Morris A.D., Colhoun H.M., Sham P.C., McCarthy M.I., Palmer C.N., Pearson E.R. Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis. Lancet Diabetes Endocrinol., 2014, vol. 2, no. 2, pp. 481–487.

Florez J.C. The pharmacogenetics of metformin. Diabetologia, 2017, vol. 60, pp. 1648–1655.

Sorokina YU.A, Lovcova L.V., Zanozina O.V. Personificirovannoe primenenie metformina s pozicii farmakogenenetiki (obzor) [Personalized use of Metformin from the position of pharmacogenetics]. E`ksperemental’naya i klinicheskaya farmakologiya, 2015, vol. 78, no. 9, pp. 39–44. (in Russian).

Wang D.S., Jonker J.W., Kato Y., Kusuhara H., Schinkel A.H., Sugiyama Y. Involvement of organic cation transporter 1 in hepatic and intestinal distribution of metformin. Mol. Pharmacol., 2003, vol. 63, no. 4, pp. 844–848. http://dx.doi.org/10.1124/jpet.102.034140.

Kimura N., Okuda M., Inui K. Metformin transport by renal basolateral organic cation transporter hOCT2. Pharm Res., 2005, vol. 22, no. 2, pp. 255–259.

Forslund K., Hildebrand F., Nielsen T., Falony G., Chatelier E.L., Sunagawa S., Prifti E., Vieira-Silva S., Guðmundsdóttir V., Pedersen H., Arumugam M., Kristiansen K. [et al.]. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature, 2015, vol. 528, no. 7581, pp. 262–266.

Wu H., Esteve E., Tremaroli V., Khan M.T., Caesar R., Mannerås-Holm L., Ståhlman M., Olsson L.M., Serino M., Planas-Fèlix M., Xifra G., Mercader J.M., Torrents D., Burcelin R., Ricart W., Perkins R., Fernàndez-Real J.M., Bäckhed F. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med., 2017, vol. 23, no. 7, pp. 850–858.

Bauer, P.V. et al. Metformin alters upper small intestinal microbiota that impact a glucose-SGLT1-sensing glucoregulatory pathway. Cell Metab., 2018, vol. 27, no. 1, pp. 101–117.e5.

Semenova E.A., Valeeva E.V., Buly’gina E.A, Gubaydullina S.I., Ahmetov I.I. Primenenie omiksny’h tehnologiy v sisteme sportivnoy podgotovki [Application of omix technologies in the system of sports training]. Ucheny’e zapiski Kazan. un-ta: Estestvenny’e nauki, 2017, vol. 159, no. 2, pp. 232–247. (in Russian).

Samocha-Bonet D., Debs S., Greenfield1 J. Prevention and Treatment of Type 2 Diabetes: A Pathophysiological-Based Approach. Trends in Endocrinology&Metabolism, 2018, vol. 29, no. 6, pp. 370–379.

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