Evaluation of the effectiveness of semaglutide in providing metabolic control and correction of insulin resistance in patients with type 2 diabetes mellitus in real clinical practice
https://doi.org/10.14341/DM13441
Abstract
RELEVANCE. Type 2 diabetes mellitus (DM2) is closely related to obesity and insulin resistance (IR). Semaglutide, glucagon-like peptide-1 receptor agonist, exhibits pronounced effects on glycemic control and body weight. However, data on its effect on various surrogate IR indices in routine practice are limited.
AIM. To evaluate the effect of semaglutide (Semavic®) on metabolic control and IR in patients with DM2 and obesity who did not achieve the target values of carbohydrate metabolism on metformin monotherapy.
MATERIALS AND METHODS. The prospective study included 31 patients aged 40–65 years with DM2, BMI 30.0–39.9 kg/m2 and HbA1c 7.0–9.0% on metformin treatment; 28 patients completed a 24-week follow-up. Anthropometric parameters, bioimpedance body composition, HbA1c, fasting glycemia, insulin, C-peptide, free fatty acids were evaluated. HOMA-IR, QUICKI, TyG, Adipo-IR indices, C peptide/insulin molar ratio were calculated.
RESULTS. After 24 weeks of semaglutide therapy, there was a significant decrease in body weight (median -8.2% or 8.58 kg), BMI, waist circumference and fat mass as well as an improvement in carbohydrate metabolism: HbA1c had decreased from 7.63% to 6.25%, 85.7% of patients reached HbA1c <7.0%. There was a significant decrease of HOMA-IR, TyG, Adipo-IR indices, an increase of QUICKI and C peptide/insulin molar ratio indicating a decrease of IR in the liver, adipose and peripheral tissues. Adverse events were observed in a small proportion of patients and were mainly of a temporary dyspeptic nature.
CONCLUSIONS. Semaglutide in patients with DM2 and obesity in real clinical practice provides a significant reduction of body weight, improves glycemic control and leads to multicomponent reduction of IR with a favorable safety profile.
About the Authors
D. M. AntsiferovaRussian Federation
Daria M. Antsiferova, MD, PhD student
37 Prechistenka street, 119034 Moscow
Competing Interests:
Авторы заявляют об отсутствии конфликта интересов.
A. S. Ametov
Russian Federation
Alexander S. Ametov, MD, PhD, Professor
Moscow
Competing Interests:
Авторы заявляют об отсутствии конфликта интересов.
O. M. Koteshkova
Russian Federation
Olga M. Koteshkova, MD, PhD
Moscow
Competing Interests:
Авторы заявляют об отсутствии конфликта интересов.
L. P. Romashkina
Russian Federation
Lada P. Romashkina, MD
Moscow
Competing Interests:
Авторы заявляют об отсутствии конфликта интересов.
M. B. Antsiferov
Russian Federation
Mikhail B. Antsiferov, MD, PhD, Professor
Moscow
Competing Interests:
Авторы заявляют об отсутствии конфликта интересов.
References
1. IDF Diabetes Atlas. 10th Edition. International Diabetes Federation, 2021
2. Boutari C, DeMarsilis A, Mantzoros CS. Obesity and diabetes. Diabetes Res Clin Pract. 2023;202:110773. doi: https://doi.org/10.1016/j.diabres.2023.110773
3. Mayorov AY, Urbanova KA, Galstyan GR, et al. Methods for guantificative assessment of insulin resistance. Obesity and metabolism. 2009;6(2):19-23. (In Russ.) doi: https://doi.org/10.14341/2071-8713-5313
4. Katz A, Nambi SS, Mather K, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab. 2000;85(7):2402-2410. doi: https://doi.org/10.1210/jcem.85.7.6661
5. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347-3351. doi: https://doi.org/10.1210/jc.2010-0288
6. Sorokina YA, Zanozina OV, Postnikova AD. The insulin resistance indices: necessity and opportunities in type 2 diabetes mellitus management. Clinical Medicine (Russian Journal). 2020;96(7):529-535. (In Russ.) doi: https://doi.org/10.30629/0023-2149-2020-98-7-529-535
7. Li Y, Li H, Chen X, Liang X. Association between various insulin resistance indices and cardiovascular disease in middle-aged and elderly individuals: evidence from two prospectives nationwide cohort surveys. Front Endocrinol (Lausanne). 2024;15:1483468. doi: https://doi.org/10.3389/fendo.2024.1483468
8. Yin H, Huang W, Yang B. Association between METS-IR index and obstructive sleep apnea: evidence from NHANES. Sci Rep. 2025;15(1):6654. doi: https://doi.org/10.1038/s41598-024-84040-9
9. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487-1495. doi: https://doi.org/10.2337/diacare.27.6.1487
10. Chen H, Sullivan G, Yue LQ, et al. QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am J Physiol Endocrinol Metab. 2003;284(4):E804-E812. doi: https://doi.org/10.1152/ajpendo.00330.2002
11. Yokoyama H, Emoto M, Fujiwara S, et al. Quantitative insulin sensitivity check index and the reciprocal index of homeostasis model assessment are useful indexes of insulin resistance in type 2 diabetic patients with wide range of fasting plasma glucose. J Clin Endocrinol Metab. 2004;89(3):1481-1484. doi: https://doi.org/10.1210/jc.2003-031374
12. Chen H, Sullivan G, Quon MJ. Assessing the predictive accuracy of QUICKI as a surrogate index for insulin sensitivity using a calibration model. Diabetes. 2005;54(7):1914-1925. doi: https://doi.org/10.2337/diabetes.54.7.1914
13. Simental-Mendía LE, Rodríguez-Morán M, GuerreroRomero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299-304. doi: https://doi.org/10.1089/met.2008.0034
14. Lee SH, Kwon HS, Park YM, et al. Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Cohort (CMC) study. PLoS One. 2014;9(2):e90430. doi: https://doi.org/10.1371/journal.pone.0090430
15. Shipovskaya AA, Kurbatova IV, Larina NA, Dudanova OP. Comparative characteristics of the triglyceride-glucose index and the homeostatic index of insulin resistance in various forms of non-alcoholic fatty liver disease. Meditsinskiy sovet = Medical Council. 2023;(18):59-67. (In Russ.) doi: https://doi.org/10.21518/ms2023-378
16. Volkova NI, Davidenko IY, Sorokina YA, et al. Methods for assessing insulin resistance in gestational diabetes mellitus. Medical Herald of the South of Russia. 2022;13(1):5-12. (In Russ.) doi: https://doi.org/10.21886/2219-8075-2022-13-1-5-12
17. Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, Fernández-Montero A, Martinez JA. Triglyceride-glucose index (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in patients with normal fasting glucose: The Vascular-Metabolic CUN cohort. Prev Med. 2016;86:99-105. doi: https://doi.org/10.1016/j.ypmed.2016.01.022
18. Park B, Lee HS, Lee YJ. Triglyceride glucose (TyG) index as a predictor of incident type 2 diabetes among nonobese adults: a 12-year longitudinal study of the Korean Genome and Epidemiology Study cohort. Transl Res. 2021;228:42-51. doi: https://doi.org/10.1016/j.trsl.2020.08.003
19. Sendur SN, Isgin Atici K, Turan Demirci B, et al. The TriglyceridesGlucose Index Shows a Stronger Correlation with Serum Adiponectin Levels than Homeostasis Model Assessment of Insulin Resistance and Quantitative Insulin Sensitivity Check Index. Metab Syndr Relat Disord. 2023;21(7):410-414. doi: https://doi.org/10.1089/met.2023.0054
20. Okamura T, Hashimoto Y, Hamaguchi M, Obora A, Kojima T, Fukui M. Triglyceride-glucose index is a predictor of incident chronic kidney disease: a population-based longitudinal study. Clin Exp Nephrol. 2019;23(7):948-955. doi: https://doi.org/10.1007/s10157-019-01729-2
21. Fritz J, Brozek W, Concin H, et al. The Triglyceride-Glucose Index and Obesity-Related Risk of End-Stage Kidney Disease in Austrian Adults. JAMA Netw Open. 2021;4(3):e212612. doi: https://doi.org/10.1001/jamanetworkopen.2021.2612
22. Quiroga B, Muñoz Ramos P, Sánchez Horrillo A, et al. Triglyceridesglucose index and the risk of cardiovascular events in persons with non-diabetic chronic kidney disease. Clin Kidney J. 2022;15(9):1705-1712. doi: https://doi.org/10.1093/ckj/sfac073
23. Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007-2010 Korean National Health and Nutrition Examination Survey. PLoS One. 2019;14(3):e0212963. doi: https://doi.org/10.1371/journal.pone.0212963
24. Beran A, Ayesh H, Mhanna M, et al. Triglyceride-Glucose Index for Early Prediction of Nonalcoholic Fatty Liver Disease: A MetaAnalysis of 121,975 Individuals. J Clin Med. 2022;11(9):2666. doi: https://doi.org/10.3390/jcm11092666
25. De León DD, Stanley CA. Determination of insulin for the diagnosis of hyperinsulinemic hypoglycemia. Best Pract Res Clin Endocrinol Metab. 2013;27(6):763-769. doi: https://doi.org/10.1016/j.beem.2013.06.005
26. Katz AI, Rubenstein AH. Metabolism of proinsulin, insulin, and C-peptide in the rat. J Clin Invest. 1973;52(5):1113-1121. doi: https://doi.org/10.1172/JCI107277
27. Nishikage S, Hirota Y, Takayoshi T, et al. Utility of the C-Peptide/Insulin Molar Ratio for Distinguishing Type A Insulin Resistance Syndrome From Type 2 Diabetes. J Clin Endocrinol Metab. 2025;110(10):e3383-e3390. doi: https://doi.org/10.1210/clinem/dgaf043
28. Søndergaard E, Jensen MD. Quantification of adipose tissue insulin sensitivity. J Investig Med. 2016;64(5):989-991. doi: https://doi.org/10.1136/jim-2016-000098
29. Søndergaard E, Espinosa De Ycaza AE, Morgan-Bathke M, Jensen MD. How to Measure Adipose Tissue Insulin Sensitivity. J Clin Endocrinol Metab. 2017;102(4):1193-1199. doi: https://doi.org/10.1210/jc.2017-00047
30. Jorge-Galarza E, Posadas-Romero C, Torres-Tamayo M, et al. Insulin Resistance in Adipose Tissue but Not in Liver Is Associated with Aortic Valve Calcification. Dis Markers. 2016;2016:9085474. doi: https://doi.org/10.1155/2016/9085474
31. Mu L, Li R, Lai Y, et al. Adipose insulin resistance is associated with cardiovascular risk factors in polycystic ovary syndrome. J Endocrinol Invest. 2019;42(5):541-548. doi: https://doi.org/10.1007/s40618-018-0949-2
32. Semnani-Azad Z, Connelly PW, Bazinet RP, et al. Adipose Tissue Insulin Resistance Is Longitudinally Associated With Adipose Tissue Dysfunction, Circulating Lipids, and Dysglycemia: The PROMISE Cohort. Diabetes Care. 2021;44(7):1682-1691. doi: https://doi.org/10.2337/dc20-1918
33. Zhou Q, Yan H, Jin A, et al. Adipose tissue specific insulin resistance and prognosis of nondiabetic patients with ischemic stroke. Diabetol Metab Syndr. 2023;15(1):246. doi: https://doi.org/10.1186/s13098-023-01235-2
34. Dedov I, Shestakova M, Sukhareva O, et al. 12th Edition. Diabetes mellitus. 2025;28(5S):1-175. (In Russ.) doi: https://doi.org/10.14341/DM20255S
35. Davies MJ, Aroda VR, Collins BS, et al. Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2022;65(12):1925-1966. doi: https://doi.org/10.1007/s00125-022-05787-2
36. Rossing P, Baeres FMM, Bakris G, et al. The rationale, design and baseline data of FLOW, a kidney outcomes trial with onceweekly semaglutide in people with type 2 diabetes and chronic kidney disease. Nephrol Dial Transplant. 2023;38(9):2041-2051. doi: https://doi.org/10.1093/ndt/gfad009
37. Ibrahim SS, Ibrahim RS, Arabi B, Brockmueller A, Shakibaei M, Büsselberg D. The effect of GLP-1R agonists on the medical triad of obesity, diabetes, and cancer. Cancer Metastasis Rev. 2024;43(4):1297-1314. doi: https://doi.org/10.1007/s10555-024-10192-9
38. Tsapas A, Avgerinos I, Karagiannis T, et al. Comparative Effectiveness of Glucose-Lowering Drugs for Type 2 Diabetes: A Systematic Review and Network Meta-analysis. Ann Intern Med. 2020;173(4):278-286. doi: https://doi.org/10.7326/M20-0864
39. Fruh SM. Obesity: Risk factors, complications, and strategies for sustainable long-term weight management. J Am Assoc Nurse Pract. 2017;29(S1):S3-S14. doi: https://doi.org/10.1002/2327-6924.12510
40. Warkentin LM, Majumdar SR, Johnson JA, et al. Weight loss required by the severely obese to achieve clinically important differences in health-related quality of life: two-year prospective cohort study. BMC Med. 2014;12:175. doi: https://doi.org/10.1186/s12916-014-0175-5
41. Rossijskaya associaciya endokrinologov, Obshchestvo bariatricheskih hirurgov. Klinicheskie rekomendacii «Ozhirenie», 2024 (In Russ.)
42. Tao LC, Xu JN, Wang TT, et al. Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovasc Diabetol. 2022;21(1):68. doi: https://doi.org/10.1186/s12933-022-01511-x
43. Wang X, Hansen BC, Shi D, et al. Quantification of β-cell insulin secretory function using a graded glucose infusion with C-peptide deconvolution in dysmetabolic, and diabetic cynomolgus monkeys. Diabetol Metab Syndr. 2013;5(1):40. doi: https://doi.org/10.1186/1758-5996-5-40
44. Stafeev I, Agareva M, Michurina S, et al. Semaglutide 6-months therapy of type 2 diabetes mellitus restores adipose progenitors potential to develop metabolically active adipocytes. Eur J Pharmacol. 2024;970:176476. doi: https://doi.org/10.1016/j.ejphar.2024.176476
45. Ghusn W, Fansa S, Anazco D, et al. Weight loss and cardiovascular disease risk outcomes of semaglutide: a oneyear multicentered study. Int J Obes (Lond). 2024;48(5):662-667. doi: https://doi.org/10.1038/s41366-023-01456-5
46. Zhu R, Chen S. Proteomic analysis reveals semaglutide impacts lipogenic protein expression in epididymal adipose tissue of obese mice. Front Endocrinol (Lausanne). 2023;14:1095432. doi: https://doi.org/10.3389/fendo.2023.1095432
47. Vendrell J, El Bekay R, Peral B, et al. Study of the potential association of adipose tissue GLP-1 receptor with obesity and insulin resistance. Endocrinology. 2011;152(11):4072-4079. doi: https://doi.org/10.1210/en.2011-1070
48. El Bekay R, Coín-Aragüez L, Fernández-García D, et al. Effects of glucagon-like peptide-1 on the differentiation and metabolism of human adipocytes. Br J Pharmacol. 2016;173(11):1820-1834. doi: https://doi.org/10.1111/bph.13481
49. Gabery S, Salinas CG, Paulsen SJ, et al. Semaglutide lowers body weight in rodents via distributed neural pathways. JCI Insight. 2020;5(6):e133429. doi: https://doi.org/10.1172/jci.insight.133429
50. Niu S, Chen S, Chen X, et al. Semaglutide ameliorates metabolism and hepatic outcomes in an NAFLD mouse model. Front Endocrinol (Lausanne). 2022;13:1046130. doi: https://doi.org/10.3389/fendo.2022.1046130
51. Kapitza C, Dahl K, Jacobsen JB, Axelsen MB, Flint A. Effects of semaglutide on beta cell function and glycaemic control in participants with type 2 diabetes: a randomised, double-blind, placebo-controlled trial. Diabetologia. 2017;60(8):1390-1399. doi: https://doi.org/10.1007/s00125-017-4289-0
52. Bergmann NC, Davies MJ, Lingvay I, Knop FK. Semaglutide for the treatment of overweight and obesity: A review. Diabetes Obes Metab. 2023;25(1):18-35. doi: https://doi.org/10.1111/dom.14863
53. Ahmann AJ, Capehorn M, Charpentier G, et al. Efficacy and Safety of Once-Weekly Semaglutide Versus Exenatide ER in Subjects With Type 2 Diabetes (SUSTAIN 3): A 56-Week, Open-Label, Randomized Clinical Trial. Diabetes Care. 2018;41(2):258-266. doi: https://doi.org/10.2337/dc17-0417
54. DeVries JH, Desouza C, Bellary S, et al. Achieving glycaemic control without weight gain, hypoglycaemia, or gastrointestinal adverse events in type 2 diabetes in the SUSTAIN clinical trial programme. Diabetes Obes Metab. 2018;20(10):2426-2434. doi: https://doi.org/10.1111/dom.13396
Supplementary files
|
|
1. Figure 1. Insulin resistance and indices used for its assessment. | |
| Subject | ||
| Type | Исследовательские инструменты | |
View
(268KB)
|
Indexing metadata ▾ | |
|
|
2. Figure 2. Distribution of patients according to baseline and 24-week HbA1c levels. | |
| Subject | ||
| Type | Исследовательские инструменты | |
View
(153KB)
|
Indexing metadata ▾ | |
|
|
3. Figure 3. Changes in carbohydrate metabolism parameters and functional activity of pancreatic β-cells. | |
| Subject | ||
| Type | Исследовательские инструменты | |
View
(217KB)
|
Indexing metadata ▾ | |
|
|
4. Figure 4. Changes in anthropometric parameters at baseline and after 24 weeks of therapy. | |
| Subject | ||
| Type | Исследовательские инструменты | |
View
(236KB)
|
Indexing metadata ▾ | |
|
|
5. Figure 5. Changes in insulin resistance during semaglutide therapy. | |
| Subject | ||
| Type | Исследовательские инструменты | |
View
(217KB)
|
Indexing metadata ▾ | |
Review
For citations:
Antsiferova D.M., Ametov A.S., Koteshkova O.M., Romashkina L.P., Antsiferov M.B. Evaluation of the effectiveness of semaglutide in providing metabolic control and correction of insulin resistance in patients with type 2 diabetes mellitus in real clinical practice. Diabetes mellitus. 2026;29(1):40-49. (In Russ.) https://doi.org/10.14341/DM13441
JATS XML
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).








































