Continuous glucose monitoring in patients with type 2 diabetes mellitus on oral hypoglycemic therapy

Introduction. Cardiovascular diseases (CVD) are the leading cause of mortality in patients with type 2 diabetes mellitus (T2DM). Continuous glucose monitoring (CGM)-derived parameters may serve as effective tools for assessing cardiovascular risk in T2DM.
Aim. To study CGM parameters in patients with T2DM receiving oral glucose-lowering therapy, depending on cardiovascular risk.
Materials and methods. The study included patients with T2DM receiving treatment with oral glucose-lowering drugs. All participants underwent clinical and laboratory examinations and CGM.
Results. A total of 86 patients with T2DM and disease duration > 1 year were included. Patients were divided into two groups based on the presence or absence of atherosclerotic cardiovascular disease (ASCVD) history. In Group 1 had a median HbA1c of 6.8%, in Group 2 it was 9.1% (p = 0.003). Low-density lipoprotein (LDL) levels were significantly higher in both subgroups of Group 1 (p = 0.004). Glomerular filtration rate (GFR) was significantly lower in Group 2 (p = 0.002). A discrepancy between HbA1c and the glucose management indicator (GMI) of more than 0.5% was observed in 72.9% patients. In Group 1, statistically significant age differences were observed depending on the GMI/HbA1c ratio. A reliable association was found between the frequency of level 3 hypoglycemia and cardiovascular risk grade (p = 0.012).
Conclusions. A discrepancy greater than 0.5% between HbA1c and GMI and a GMI/HbA1c ratio of less than 0.9, may indicate a high degree of glycation in patients with T2DM. The level 3 hypoglycemia in T2DM may represent a serious risk factor for ASCVD progression. GMI level is associated with hypoglycemic episodes and may serve as an indicator of hypoglycemia risk. A direct correlation between triglyceride (TG) and very low-density lipoprotein (VLDL) levels with GMI in patients with a history of ASCVD may provide insights into additional factors contributing to ASCVD progression in T2DM.

1. Anjana RM, Mohan V, Rangarajan S, Gerstein HC, Venkatesan U, Sheridan P et al. Contrasting Associations Between Diabetes and Cardiovascular Mortality Rates in Low-, Middle-, and High-Income Countries: Cohort Study Data From 143,567 Individuals in 21 Countries in the PURE Study. Diabetes Care. 2020;43:3094–3101. https://doi.org/10.2337/dc20-0886.

2. Emerging Risk Factors Collaboration; Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–2222. https://doi.org/10.1016/S0140-6736(10)60484-9.

3. Dedov II, Shestakova MV, Vikulova OK, Zheleznyakova AV, Isakov MA, Sazonova DV, Mokrysheva NG. Diabetes mellitus in the Russian Federation: dynamics of epidemiological indicators according to the Federal Register of Diabetes Mellitus for the period 2010–2022. Diabetes Mellitus. 2023;26(2):104–123. (In Russ.) https://doi.org/10.14341/DM13035.

4. American Diabetes Association Professional Practice Committee; 6. Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes – 2025. Diabetes Care. 2025;48(1):S128–S145. https://doi.org/10.2337/dc25-S006.

5. Beck RW, Bergenstal RM, Riddlesworth TD, Kollman C, Li Z, Brown AS, Close KL. Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials. Diabetes Care. 2019;42(3):400–405. https://doi.org/10.2337/dc18-1444.

6. Lu J, Ma X, Zhou J, Zhang L, Mo Y, Ying L et al. Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care.2018;41:2370–2376. https://doi.org/10.2337/dc18-1131.

7. Mayeda L, Katz R, Ahmad I, Bansal N, Batacchi Z, Hirsch IB et al. Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease. BMJ Open Diabetes Res Care. 2020;8(1):e000991. https://doi.org/10.1136/bmjdrc-2019-000991.

8. Zhang Z, Wang Y, Lu J, Zhou J. Time in tight range: A key metric for optimal glucose control in the era of advanced diabetes technologies and therapeutics. Diabetes Obes Metab. 2025;27(2):450–456. https://doi.org/10.1111/dom.16033.

9. Ceriello A, Colagiuri S. International Diabetes Federation guideline for management of postmeal glucose: a review of recommendations. Diabet Med. 2008;25:1151–1156. https://doi.org/10.1111/j.1464-5491.2008.02565.x.

10. Zhang L, Sun XX, Tian Qs. Research progress on the association between glycemic variability index derived from CGM and cardiovascular disease complications. Acta Diabetol. 2024;61:679–692. https://doi.org/10.1007/s00592-024-02241-0.

11. Bergenstal RM, Beck RW, Close KL, Grunberger G, Sacks DB, Kowalski A et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care. 2018;41(11):2275–2280. https://doi.org/10.2337/dc18-1581.

12. Perlman JE, Gooley TA, McNulty B, Meyers J, Hirsch IB. HbA1c and glucose management indicator discordance: a real-world analysis. Diabetes Technol Ther. 2021;23(4):253–258. https://doi.org/10.1089/dia.2020.0501.

13. Hempe JM, Yang S, Liu S, Hsia DS. Standardizing the haemoglobin glycation index. Endocrinol Diabetes Metab. 2021;4(4):e00299. https://doi.org/10.1002/edm2.322.

14. Indyk D, Bronowicka-Szydełko A, Gamian A, Kuzan A. Advanced glycation end products and their receptors in serum of patients with type 2 diabetes. Sci Rep. 2021;11:13264. https://doi.org/10.1038/s41598-021-92630-0.

15. Puig-Jové C, Viñals C, Conget I, Quirós C, Vinagre I, Berrocal B et al. Association between the GMI/HbA1c ratio and preclinical carotid atherosclerosis in type 1 diabetes: impact of the fast-glycator phenotype across age groups. Cardiovasc Diabetol. 2025;24(1):75. https://doi.org/10.1186/s12933-025-02637-4.

16. Shah VN, Vigers T, Pyle L, Calhoun P, Bergenstal RM. Discordance between glucose management indicator and glycated hemoglobin in people without diabetes. Diabetes Technol Ther. 2023;25(5):324–328. https://doi.org/10.1089/dia.2022.0544.

17. Kröger J, Reichel A, Siegmund T, Ziegler R. Clinical Recommendations for the Use of the Ambulatory Glucose Profile in Diabetes Care. J Diabetes Sci Technol. 2020;14(3):586–594. https://doi.org/10.1177/1932296819883032.

18. Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon RO 3rd, Criqui M et al. Centers for Disease Control and Prevention; American Heart Association. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: A statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107(3):499–511. https://doi.org/10.1161/01.cir.0000052939.59093.45.

19. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group; Wilson DM, Xing D, Beck RW, Block J, Bode B, Fox LA et al. Hemoglobin A1c and mean glucose in patients with type 1 diabetes: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring randomized trial. Diabetes Care. 2011;34(3):540–544. https://doi.org/10.2337/dc10-1054.

20. Petrofsky JS, McLellan K, Prowse M, Bains G, Berk L, Lee S. The Effect of Body Fat, Aging, and Diates on Vertical and Shear Pressure in and under a Waist Belt and Its Effect on Skin Blood Flow. Diabetes Technol Ther. 2010;12:153–160. https://doi.org/10.1089/dia.2009.0123.

21. Mensh BD, Wisniewski NA, Neil BM, Burnett DR. Susceptibility of Interstitial Continuous Glucose Monitor Performance to Sleeping Position. J Diabetes Sci Technol. 2013;7:863–870. https://doi.org/10.1177/193229681300700408.

22. Petersen MC, Shulman GI. Mechanisms of Insulin Action and Insulin Resistance. Physiol Rev. 2018;98:2133–2223. https://doi.org/10.1152/physrev.00063.2017.

23. Didyuk O, Econom N, Guardia A, Livingston K, Klueh U. Continuous Glucose Monitoring Devices: Past, Present, and Future Focus on the History and Evolution of Technological Innovation. J Diabetes Sci Technol. 2020;15:676–683. https://doi.org/10.1177/1932296819899394.

24. Hempe JM, Yang S, Liu S, Hsia DS. Standardizing the haemoglobin glycation index. Endocrinol Diabetes Metab. 2021;4(4):e00299. https://doi.org/10.1002/edm2.299.

25. Azcoitia P, Rodríguez-Castellano R, Saavedra P, Alberiche MP, Marrero D, Wägner AM et al. Age and Red Blood Cell Parameters Mainly Explain the Differences Between HbA1c and Glycemic Management Indicator Among Patients With Type 1 Diabetes Using Intermittent Continuous Glucose Monitoring. J Diabetes Sci Technol. 2024;18(6):1370–1376. https://doi.org/10.1177/19322968231191544.

26. Liu H, Yang D, Deng H, Xu W, Lv J, Zhou Y et al. Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus. Ther Adv Endocrinol Metab. 2020;11. https://doi.org/10.1177/2042018820931664.

27. Perlman JE, Gooley TA, McNulty B, Meyers J, Hirsch IB. HbA1c and glucose management indicator discordance: a real-world analysis. Diabetes Technol Ther. 2021;23(4):253–258. https://doi.org/10.1089/dia.2020.0501.

28. Fellinger P, Rodewald K, Ferch M, Itariu B, Kautzky-Willer A, Winhofer Y. HbA1c and glucose management indicator discordance associated with obesity and type 2 diabetes in intermittent scanning glucose monitoring system. Biosensors. 2022;12(5):288. https://doi.org/10.3390/bios12050288.

29. Gomez-Peralta F, Choudhary P, Cosson E, Irace C, Rami-Merhar B, Seibold A. Understanding the Clinical Implications of Differences between Glucose Management Indicator and Glycated Haemoglobin. Diabetes Obes Metab. 2022;24:599–608. https://doi.org/10.1111/dom.14638.

30. Maran A, Morieri ML, Falaguasta D, Avogaro A, Fadini GP. The fast-glycator phenotype, skin advanced glycation end products, and complication Burden among people with type 1 diabetes. Diabetes Care. 2022;45(10):2439–2444. https://doi.org/10.2337/dc22-0980.

31. Sibianu M, Slevin M. The Pathogenic Role of C-Reactive Protein in Diabetes-Linked Unstable Atherosclerosis. Int J Mol Sci. 2025;26(14):6855. https://doi.org/10.3390/ijms26146855.

32. Standl E, Stevens SR, Lokhnygina Y, Bethel MA, Buse JB, Gustavson SM et al. Confirming the Bidirectional Nature of the Association Between Severe Hypoglycemic and Cardiovascular Events in Type 2 Diabetes: Insights From EXSCEL. Diabetes Care. 2020;43(3):643–652. https://doi.org/10.2337/dc19-1079.

33. Bonds DE, Miller ME, Bergenstal RM, Buse JB, Byington RP, Cutler JA et al. The association between symptomatic, severe hypoglycaemia and mortality in type 2 diabetes: retrospective epidemiological analysis of the ACCORD study. BMJ. 2010;340:b4909. https://doi.org/10.1136/bmj.b4909.

34. Zoungas S, Patel A, Chalmers J, de Galan BE, Li Q, Billot L et al. Severe hypoglycemia and risks of vascular events and death. N Engl J Med. 2010;363(15):1410–1418. https://doi.org/10.1056/NEJMoa1003795.

35. McCoy RG, Herrin J, Swarna KS, Deng Y, Kent DM, Ross JS et al. Effectiveness of glucose-lowering medications on cardiovascular outcomes in patients with type 2 diabetes at moderate cardiovascular risk. Nat Cardiovasc Res. 2024;3(4):431–440. https://doi.org/10.1038/s44161-024-00453-9.

36. Zhang X, Xu X, Jiao X, Wu J, Zhou S, Lv X. The effects of glucose fluctuation on the severity of coronary artery disease in type 2 diabetes mellitus. J Diabetes Res. 2013;2013:576916. https://doi.org/10.1155/2013/576916.