Modern concepts of type 2 diabetes mellitus phenotypes
https://doi.org/10.21518/ms2025-396
Abstract
In the context of the rapid increase in the prevalence of type 2 diabetes mellitus, the study of the phenotypic heterogeneity of the disease, caused by the variability of pathophysiological processes, the impact of environmental factors and individual genetic predisposition, is of particular importance. The traditional division into diabetes mellitus types 1 and 2 does not reflect the entire complexity of the pathogenesis and the diversity of clinical subtypes of type 2 diabetes mellitus. Modern studies reveal stable subgroups of patients with different clinical trajectories of disease development, which in the future will require a revision of diagnostic and therapeutic approaches. This review article analyzes studies devoted to the stratification of patients with type 2 diabetes mellitus. The key features of the most reproducible phenotypes, including severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild age-related diabetes (MARD), and severe autoimmune diabetes (SAID), are disclosed, their pathophysiological characteristics, clinical features, therapeutic strategies, and approaches to the prevention of diabetic complications in various subgroups are given. Independent cohort studies have confirmed stable associations of the identified phenotypes with key clinical outcomes, including the degree of glycemic control, the incidence of microvascular and macrovascular complications, and mortality rates, which can be used in the future to develop personalized patient management strategies. However, further studies are required to validate and optimize the methods for subclassifying type 2 diabetes mellitus for the justified implementation of new treatment and diagnostic algorithms in everyday clinical practice.
Keywords
About the Authors
T. A. KiselevaРоссия
Tatiana A. Kiseleva, Cand. Sci. (Med.), Associate Professor of the Department of Endocrinology
49, Butlerov St., Kazan, 420012, Russia
F. V. Valeeva
Россия
Farida V. Valeeva, Dr. Sci. (Med.), Professor, Head of the Department of Endocrinology
49, Butlerov St., Kazan, 420012, Russia
D. R. Islamova
Россия
Diana R. Islamova, Postgraduate Student of the Department of Endocrinology
49, Butlerov St., Kazan, 420012, Russia
R. M. Nabiullina
Россия
Rosa M. Nabiullina, Cand. Sci. (Med.), Assistant Professor of the Department of Biochemistry and Clinical Laboratory Diagnostics
49, Butlerov St., Kazan, 420012, Russia
References
1. Magliano DJ, Boyko EJ, Ali MK, Anstey K, Booth G, Duncan BB et al. IDF Diabetes Atlas. 11th ed. Brussels: International Diabetes Federation; 2025. 130 p. Available at: https://diabetesatlas.org/media/uploads/sites/3/2025/04/IDF_Atlas_11th_Edition_2025-1.pdf.
2. Kim DS, Gloyn AL, Knowles JW. Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar. J Am Coll Cardiol. 2021;78(5):496–512. https://doi.org/10.1016/j.jacc.2021.03.346.
3. Redondo MJ, Hagopian WA, Oram R, Steck AK, 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.
4. Lu X, Xie Q, Pan X, Zhang R, Zhang X, Peng G et al. Type 2 diabetes mellitus in adults: pathogenesis, prevention and therapy. Signal Transduct Target Ther. 2024;9(1):262. https://doi.org/10.1038/s41392-024-01951-9.
5. Herder C, Roden M. A novel diabetes typology: towards precision diabetology from pathogenesis to treatment. Diabetologia. 2022;65(11):1770–1781. https://doi.org/10.1007/s00125-021-05625-x.
6. Ahlqvist E, Storm P, Käräjämäki 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.
7. Misra S, Wagner R, Ozkan B, Schön M, Sevilla-Gonzalez M, Prystupa K et al. Precision subclassification of type 2 diabetes: a systematic review. Commun Med. 2023;3(1):138. https://doi.org/10.1038/s43856-023-00360-3.
8. Deutsch AJ, Ahlqvist E, Udler MS. Phenotypic and genetic classification of diabetes. Diabetologia. 2022;65(11):1758–1769. https://doi.org/10.1007/s00125-022-05769-4.
9. Gouda P, Zheng S, Peters T, Fudim M, Randhawa VK, Ezekowitz J et al. Clinical Phenotypes in Patients With Type 2 Diabetes Mellitus: Characteristics, Cardiovascular Outcomes and Treatment Strategies. Curr Heart Fail Rep. 2021;18(5):253–263. https://doi.org/10.1007/s11897-021-00527-w.
10. Tanabe H, Masuzaki H, Shimabukuro M. Novel strategies for glycaemic control and preventing diabetic complications applying the clusteringbased classification of adult-onset diabetes mellitus: A perspective. Diabetes Res Clin Pract. 2021;180:109067. https://doi.org/10.1016/j.diabres.2021.109067.
11. Petersen MC, Shulman GI. Mechanisms of Insulin Action and Insulin Resistance. Physiol Rev. 2018;98(4):2133–2223. https://doi.org/10.1152/physrev.00063.2017.
12. Roden M, Shulman GI. The integrative biology of type 2 diabetes. Nature. 2019;576(7785):51–60. https://doi.org/10.1038/s41586-019-1797-8.
13. Zaharia OP, Strassburger K, Strom A, Bönhof GJ, 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.
14. Pigeyre M, Hess S, Gomez MF, Asplund O, Groop L, Paré G, Gerstein H. Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial. Diabetologia. 2022;65(1):206–215. https://doi.org/10.1007/s00125-021-05567-4.
15. Zaharia OP, Strassburger K, Knebel B, Kupriyanova Y, Karusheva Y, Wolkersdorfer M et al. Role of Patatin-Like Phospholipase Domain-Containing 3 Gene for Hepatic Lipid Content and Insulin Resistance in Diabetes. Diabetes Care. 2020;43(9):2161–2168. https://doi.org/10.2337/dc20-0329.
16. Tanabe H, Saito H, Kudo A, Machii N, Hirai H, Maimaituxun G et al. Factors Associated with Risk of Diabetic Complications in Novel Cluster-Based Diabetes Subgroups: A Japanese Retrospective Cohort Study. J Clin Med. 2020;9(7):2083. https://doi.org/10.3390/jcm9072083.
17. Bello-Chavolla OY, Bahena-López JP, Vargas-Vázquez A, Antonio-Villa NE, Márquez-Salinas A, Fermín-Martínez CA et al. Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach. BMJ Open Diabetes Res Care. 2020;8(1):e001550. https://doi.org/10.1136/bmjdrc-2020-001550.
18. Wagner R, Heni M, Tabák AG, 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.
19. Gallagher EJ, LeRoith D. Hyperinsulinaemia in cancer. Nat Rev Cancer. 2020;20(11):629–644. https://doi.org/10.1038/s41568-020-0295-5.
20. Arnold SE, Arvanitakis Z, Macauley-Rambach SL, Koenig AM, Wang HY, Ahima RS et al. Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol. 2018;14(3):168–181. https://doi.org/10.1038/nrneurol.2017.185.
21. Stefan N, Häring HU, Hu FB, Schulze MB. Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol. 2013;1(2):152–162. https://doi.org/10.1016/S2213-8587(13)70062-7.
22. Iacobini C, Pugliese G, Blasetti Fantauzzi C, Federici M, Menini S. Metabolically healthy versus metabolically unhealthy obesity. Metabolism. 2019;92:51–60. https://doi.org/10.1016/j.metabol.2018.11.009.
23. Sumida Y, Yoneda M, Tokushige K, Kawanaka M, Fujii H, Yoneda M et al. Antidiabetic Therapy in the Treatment of Nonalcoholic Steatohepatitis. Int J Mol Sci. 2020;21(6):1907. https://doi.org/10.3390/ijms21061907.
24. Al-Sofiani ME, Ganji SS, Kalyani RR. Body composition changes in diabetes and aging. J Diabetes Complications. 2019;33(6):451–459. https://doi.org/10.1016/j.jdiacomp.2019.03.007.
25. Дедов ИИ, Шестакова МВ, Сухарева ОЮ (ред.). Алгоритмы специализированной медицинской помощи больным сахарным диабетом. 12-й вып. М.; 2025. 247 с. Режим доступа: https://endoinfo.ru/upload/iblock/f46/vim4elq45pp3r07l8ooep550qiknatul/ALG_CD_20250503_250513_073615.pdf.
26. Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. 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.
27. Donath MY, Dinarello CA, Mandrup-Poulsen T. Targeting innate immune mediators in type 1 and type 2 diabetes. Nat Rev Immunol. 2019;19(12):734–746. https://doi.org/10.1038/s41577-019-0213-9.
28. Lawler PR, Bhatt DL, Godoy LC, Lüscher TF, Bonow RO, Verma S, Ridker PM. Targeting cardiovascular inflammation: next steps in clinical translation. Eur Heart J. 2021;42(1):113–131. https://doi.org/10.1093/eurheartj/ehaa099.
29. Veelen A, Erazo-Tapia E, Oscarsson J, Schrauwen P. Type 2 diabetes subgroups and potential medication strategies in relation to effects on insulin resistance and beta-cell function: A step toward personalised diabetes treatment? Mol Metab. 2021;46:101158. https://doi.org/10.1016/j.molmet.2020.101158.
30. Perreault L, Skyler JS, Rosenstock J. Novel therapies with precision mechanisms for type 2 diabetes mellitus. Nat Rev Endocrinol. 2021;17(6):364–377. https://doi.org/10.1038/s41574-021-00489-y.
31. Xing L, Peng F, Liang Q, Dai X, Ren J, Wu H et al. Clinical Characteristics and Risk of Diabetic Complications in Data-Driven Clusters Among Type 2 Diabetes. Front Endocrinol. 2021;12:617628. https://doi.org/10.3389/fendo.2021.617628.
32. Ross SA, Dzida G, Vora J, Khunti K, Kaiser M, Ligthelm RJ. Impact of Weight Gain on Outcomes in Type 2 Diabetes. Curr Med Res Opin. 2011;27:1431–1438. https://doi.org/10.1185/03007995.2011.585396.
33. LeRoith D, Biessels GJ, Braithwaite SS, Casanueva FF, Draznin B, Halter JB et al. Treatment of Diabetes in Older Adults: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2019;104(5):1520–1574. https://doi.org/10.1210/jc.2019-00198.
34. Zhou JB, Tang X, Han M, Yang J, Simó R. Impact of antidiabetic agents on dementia risk: A Bayesian network meta-analysis. Metabolism. 2020;109:154265. https://doi.org/10.1016/j.metabol.2020.154265.
35. Kalaitzoglou E, Fowlkes JL, Popescu I, Thrailkill KM. Diabetes pharmacotherapy and effects on the musculoskeletal system. Diabetes Metab Res Rev. 2019;35(2):e3100. https://doi.org/10.1002/dmrr.3100.
36. Lipska KJ, Krumholz H, Soones T, Lee SJ. Polypharmacy in the Aging Patient: A Review of Glycemic Control in Older Adults With Type 2 Diabetes. JAMA. 2016;315(10):1034–1045. https://doi.org/10.1001/jama.2016.0299.
37. Rawshani A, Rawshani A, Franzén S, Eliasson B, Svensson AM, Miftaraj M et al. Mortality and Cardiovascular Disease in Type 1 and Type 2 Diabetes. N Engl J Med. 2017;376(15):1407–1418. https://doi.org/10.1056/nejmoa1608664. .
38. Gao H, Wang K, Zhao W, Zhuang J, Jiang Y, Zhang L et al. Cardiorenal Risk Profiles Among Data-Driven Type 2 Diabetes Sub-Phenotypes: A Post-Hoc Analysis of the China Health and Nutrition Survey. Front Endocrinol. 2022;13:828403. https://doi.org/10.3389/fendo.2022.828403.
39. Correa-de-Araujo R, Addison O, Miljkovic I, Goodpaster BH, Bergman BC, Clark RV et al. Myosteatosis in the Context of Skeletal Muscle Function Deficit: An Interdisciplinary Workshop at the National Institute on Aging. Front Physiol. 2020;11:963. https://doi.org/10.3389/fphys.2020.00963.
40. Stefan N. Causes, consequences, and treatment of metabolically unhealthy fat distribution. Lancet Diabetes Endocrinol. 2020;8(7):616–627. https://doi.org/10.1016/S2213-8587(20)30110-8.
41. Stidsen JV, Henriksen JE, Olsen MH, Thomsen RW, Nielsen JS, Rungby J et al. Pathophysiology-based phenotyping in type 2 diabetes: A clinical classification tool. Diabetes Metab Res Rev. 2018;34(5):e3005. https://doi.org/10.1002/dmrr.3005.
42. Lee PG, Halter JB. The Pathophysiology of Hyperglycemia in Older Adults: Clinical Considerations. Diabetes Care. 2017;40(4):444–452. https://doi.org/10.2337/dc16-1732.
43. Mansour Aly D, Dwivedi OP, Prasad RB, Käräjämäki A, Hjort R, Thangam M et al. Genome-wide association analyses highlight etiological differences underlying newly defined subtypes of diabetes. Nat Genet. 2021;53(11):1534–1542. https://doi.org/10.1038/s41588-021-00948-2.
44. Safai N, Ali A, Rossing P, Ridderstråle M. Stratification of type 2 diabetes based on routine clinical markers. Diabetes Res Clin Pract. 2018;141:275–283. https://doi.org/10.1016/j.diabres.2018.05.014.
45. Kahkoska AR, Geybels MS, Klein KR, Kreiner FF, Marx N, Nauck MA 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.
46. Lugner M, Gudbjörnsdottir S, Sattar N, Svensson AM, Miftaraj M, EegOlofsson K et al. Comparison between data-driven clusters and models based on clinical features to predict outcomes in type 2 diabetes: nationwide observational study. Diabetologia. 2021;64(9):1973–1981. https://doi.org/10.1007/s00125-021-05485-5.
47. Wang Y, Zou X, Cai X, Liu W, Chen L, Zhang R et al. Urinary C-peptide/creatinine ratio: A useful biomarker of insulin resistance and refined classification of type 2 diabetes mellitus. J Diabetes. 2021;13(11):893–904. https://doi.org/10.1111/1753-0407.13203.
48. Antonio-Villa NE, Fernández-Chirino L, Vargas-Vázquez A, FermínMartínez CA, Aguilar-Salinas CA, Bello-Chavolla OY. Prevalence Trends of Diabetes Subgroups in the United States: A Data-driven Analysis Spanning Three Decades From NHANES (1988–2018). J Clin Endocrinol Metab. 2022;107(3):735–742. https://doi.org/10.1210/clinem/dgab762.
49. Slieker RC, Donnelly LA, Fitipaldi H, Bouland GA, Giordano GN, Åkerlund M et al. Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study. Diabetologia. 2021;64(9):1982–1989. https://doi.org/10.1007/s00125-021-05490-8.
50. Anjana RM, Baskar V, Nair ATN, Jebarani S, Siddiqui MK, Pradeepa R et al. Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: a data-driven cluster analysis: the INSPIRED study. BMJ Open Diabetes Res Care. 2020;8(1):e001506. https://doi.org/10.1136/bmjdrc-2020-001506.
Review
For citations:
Kiseleva TA, Valeeva FV, Islamova DR, Nabiullina RM. Modern concepts of type 2 diabetes mellitus phenotypes. Meditsinskiy sovet = Medical Council. 2025;(16):169–176. (In Russ.) https://doi.org/10.21518/ms2025-396
JATS XML


































