Peculiarities of outpatient glycemic profile of patients with type 2 diabetes mellitus during innovative sugar-reducing therapy combined with metformin according to flash glucose monitoring data

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Abstract

Nowadays, innovative hypoglycemic metformin-combined therapy is recommended for a large number of patients with type 2 diabetes mellitus (T2 DM). Analysis of the ambulatory glycemic profile (AGP) and glycemic variability (GV) of patients can be informative when choosing the priority of prescribing dipeptidyl peptidase-4 (DPP-4) inhibitors or sodium-glucose cotransporter type 2 (SGLT-2) inhibitors.

The aim: to compare AGP and GA indexes in groups of patients with T2 DM receiving innovative glucose-lowering therapy combined with metformin.

Material and methods. The results of flash glucose monitoring (FMG) were assessed in 56 patients with type 2 diabetes mellitus aged from 45 to 60 years with an HbA1c level of ≤7,5% and disease duration of ≤5 years, receiving a combination of metformin and DPP-4 inhibitors or metformin and SGLT-2 inhibitors. For FMG, the FreeStyle Libre system (Abbott) was used.

Results. Mean sensor activity time was 86±9 (95%CI: 83–90) % and 85±9 (95%CI: 81–89) % in the groups of patients receiving metformin + DPP-4 inhibitors and metformin + SGLT-2 inhibitors, respectively. The time the glucose level was in the target range in the same groups was 91 [84–97] % versus 95 [87–97] %, the number and total duration of hypoglycemia episodes was 4 [1–16] times and 126 [45–186] minutes versus 3 [1–8] times and 90 [30–165] minutes, respectively. These differences were not statistically significant. When analyzing GV, statistically significant differences were observed only in relation to HBGI index: 1,12 [0,63–1,90] in the metformin + DPP-4 inhibitors group versus 2,17 [1,06–3,25] in the metformin + SGLT-2 inhibitors group (p=0,030). Differences in LI index (3,11 [1,76–3,85] vs. 4,45 [2,30–5,25]), J-index (15,23 [12,38–19,52] vs. 17,14 [13,99–23,43]) and ADDR (7,85 [3,22–13,00] index vs. 6,48 [4,30–14,61]) were not statistically significant (p >0,05).

Conclusion. In analyzed sample, among patients with type 2 diabetes mellitus receiving metformin + SGLT-2 inhibitors, the risk of hyperglycemia was significantly higher than in the group using metformin + DPP-4 inhibitors.

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About the authors

Anna S. Teplova

N.I. Pirogov Russian National Research Medical University of the Ministry of Healthcare of Russia

Author for correspondence.
Email: anna_kochina_@mail.ru
ORCID iD: 0000-0002-6826-5924

Assistant at the Department of Endocrinology of the Faculty of General Medicine

Russian Federation, Moscow

Tatyana Yu. Demidova

N.I. Pirogov Russian National Research Medical University of the Ministry of Healthcare of Russia

Email: t.y.demidova@gmail.com
ORCID iD: 0000-0001-6385-540X

MD, Professor, Head of the Department of Endocrinology of the Faculty of General Medicine

Russian Federation, Moscow

Kristina G. Lobanova

N.I. Pirogov Russian National Research Medical University of the Ministry of Healthcare of Russia

Email: miss.sapog@mail.ru
ORCID iD: 0000-0002-3656-0312
SPIN-code: 6044-1684

PhD in Medical Sciences, Assistant at the Department of Endocrinology of the Faculty of General Medicine

Russian Federation, Moscow

References

  1. Schwartz S.S., Epstein S., Corkey B.E. et al. The time is right for a new classification system for diabetes: Rationale and implications of the β-cell-centric classification schema. Diabetes Care. 2016; 39(2): 179–86. https://dx.doi.org/10.2337/dc15-1585.
  2. Демидова Т.Ю. Выбор пероральных сахароснижающих средств: современный взгляд на проблему. Проблемы эндокринологии. 2012; 58(6): 53–59. [Demidova T.Yu. The choice of oral hypoglycemic agents: the current view of the problem. Problemy endokrinologii = Problems of Endocrinology. 2012; 58(6): 53–59 (In Russ.)]. EDN: RVAMZJ.
  3. Дедов И.И., Шестакова М.В., Майоров А.Ю. с соавт. Алгоритмы специализированной медицинской помощи больным сахарным диабетом. Под редакцией И.И. Дедова, М.В. Шестаковой, А.Ю. Майорова. 11-й выпуск. Сахарный диабет. 2023; 26(S2): 1–231. [Dedov I.I., Shestakova M.V., Mayorov A.Yu. et al. Standards of specialized diabetes care. 11th edition. Edited by Dedov I.I., Shestakova M.V., Mayorov A.Yu. Sakharnyy diabet = Diabetes Mellitus. 2023; 26(S2): 1–231 (In Russ.)]. https://dx.doi.org/10.14341/DM13042.
  4. del Prato S., Indovina F., Falcetta P. Сахарный диабет 2 типа. Комбинированная терапия на старте заболевания. Сахарный диабет. 2018; 21(5): 386–394. [Del Prato S., Indovina F., Falcetta P. Type 2 diabetes mellitus. From the start – combination therapy. Sakharnyy diabet = Diabetes Mellitus. 2018; 21(5): 386–394 (In Russ.)]. https://dx.doi.org/10.14341/DM9867. EDN: YPVIMX.
  5. Шестакова М.В., Анциферов М.Б., Аметов А.С. с соавт. Какие новые возможности для клинической практики открывает исследование VERIFY и какова его ценность для пациентов с впервые выявленным СД 2 типа? Совместное заключение по итогам экспертного совета. 6 ноября 2019 г. Сахарный диабет. 2020; 23(1): 106–110. [Shestakova M.V., Antsiferov M.B., Ametov A.S. et al. What are new opportunities for clinical practice the VERIFY study opens and which values for native diabetes patients? Joint conclusion on the advisory board results. November 6, 2019. Sakharnyy diabet = Diabetes Mellitus. 2020; 23(1): 106–110 (In Russ.)]. https://dx.doi.org/10.14341/DM12404. EDN: LWOPWW.
  6. Анциферов М.Б. Результаты UKPDS и их значение в совершенствовании специализированной помощи больным диабетом. Сахарный диабет. 1999; (4): 23–28. [Antsiferov M.B. UKPDS results and their implications for improving specialist diabetes care. Sakharnyy diabet = Diabetes Mellitus. 1999; (4): 23–28 (In Russ.)]. EDN: QILMUH.
  7. Action to Control Cardiovascular Risk in Diabetes Study Group. Gerstein H.C., Miller M.E., Byington R.P. et al. Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med. 2008; 358(24): 2545–59. https://dx.doi.org/10.1056/NEJMoa0802743.
  8. Riddle M.C., Ambrosius W.T., Brillon D.J. et al.; Action to Control Cardiovascular Risk in Diabetes Investigators. Epidemiologic relationships between A1C and all-cause mortality during a median 3.4-year follow-up of glycemic treatment in the ACCORD trial. Diabetes Care. 2010; 33(5): 983–90. https://dx.doi.org/10.2337/dc09-1278.
  9. Martinez M., Santamarina J., Pavesi A. et al. Glycemic variability and cardiovascular disease in patients with type 2 diabetes. BMJ Open Diabetes Res Care. 2021; 9(1): e002032. https://dx.doi.org/10.1136/bmjdrc-2020-002032.
  10. Mendez C.E., Mok K.T., Ata A. et al. Increased glycemic variability is independently associated with length of stay and mortality in noncritically ill hospitalized patients. Diabetes Care. 2013; 36(12): 4091–97. https://dx.doi.org/10.2337/dc12-2430.
  11. Huang R.l., Wang H.l., Shen Z. et al. Increased glycemic variability evaluated by continuous glucose monitoring is associated with osteoporosis in type 2 diabetic patients. Front Endocrinol (Lausanne). 2022; 13: 861131. https://dx.doi.org/10.3389/fendo.2022.861131.
  12. Bailey T.S., Bhargava A., De Vries J.H. et al. Within-day variability based on 9-point profiles correlates with risk of overall and nocturnal hypoglycemia in adults with type 1 (T1D) and type 2 diabetes (T2D). Diabetes. 2017; 66(Suppl 1): A107.
  13. Jangam S., Hayter G., Dunn T. Reduction in glycemic variability is correlated with reductions in both hypoglycemia and hyperglycemia risk in type 1 and type 2 subjects. Diabetes Technol Ther. 2016; 18(Suppl 1): A42.
  14. Strain W.D., Paldanius P.M. Diabetes, cardiovascular disease and the microcirculation. Cardiovasc Diabetol. 2018; 17(1): 57. https://dx.doi.org/10.1186/s12933-018-0703-2.
  15. Yokota S., Tanaka H., Mochizuki Y. et al. Association of glycemic variability with left ventricular diastolic function in type 2 diabetes mellitus. Cardiovasc Diabetol. 2019; 18(1): 166. https://dx.doi.org/10.1186/s12933-019-0971-5.
  16. Zinman B., Marso S.P., Poulter N.R. et al. Day-to-day fasting glycaemic variability in DEVOTE: Associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2). Diabetologia. 2018; 61(1): 48–57. https://dx.doi.org/10.1007/s00125-017-4423-z.
  17. Takahashi H., Iwahashi N., Kirigaya J. et al. Glycemic variability determined with a continuous glucose monitoring system can predict prognosis after acute coronary syndrome. Cardiovasc Diabetol. 2018; 17(1): 116. https://dx.doi.org/10.1186/s12933-018-0761-5.
  18. Siegelaar S.E., Kerr L., Jacober S.J. et al. A decrease in glucose variability does not reduce cardiovascular event rates in type 2 diabetic patients after acute myocardial infarction: A reanalysis of the HEART2D study. Diabetes Care. 2011; 34(4): 855–57. https://dx.doi.org/10.2337/dc10-1684.
  19. FLAT-SUGAR Trial Investigators . Glucose variability in a 26-week randomized comparison of mealtime treatment with rapid-acting insulin versus GLP-1 agonist in participants with type 2 diabetes at high cardiovascular risk. Diabetes Care. 2016; 39(6): 973–81. https://dx.doi.org/10.2337/dc15-2782.
  20. Danne T., Nimri R., Battelino T. et al. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017; 40(12): 1631–40. https://dx.doi.org/10.2337/dc17-1600.
  21. Service F.J., Molnar G.D., Rosevear J.W. et al. Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes. 1970; 19(9): 644–55. https://dx.doi.org/10.2337/diab.19.9.644.
  22. McDonnell C.M., Donath S.M., Vidmar S.I. et al. A novel approach to continuous glucose analysis utilizing glycemic variation. Diabetes Technol Ther. 2005; 7(2): 253–63. https://dx.doi.org/10.1089/dia.2005.7.253.
  23. Ryan E.A., Shandro T., Green K. et al. Assessment of the severity of hypoglycemia and glycemic lability in type 1 diabetic subjects undergoing islet transplantation. Diabetes. 2004; 53(4): 955–62. https://dx.doi.org/10.2337/diabetes.53.4.955.
  24. Wojcicki J.M. «J»-index. A new proposition of the assessment of current glucose control in diabetic patients. Horm Metab Res. 1995; 27(1): 41–42. https://dx.doi.org/10.1055/s-2007-979906.
  25. Kovatchev B.P., Cox D.J., Kumar A. et al. Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data. Diabetes Technol Ther. 2003; 5(5): 817–28. https://dx.doi.org/10.1089/152091503322527021.
  26. Molnar G.D., Taylor W.F., Ho M.M. Day-to-day variation of continuously monitored glycaemia: A further measure of diabetic instability. Diabetologia. 1972; 8(5): 342–48. https://dx.doi.org/10.1007/BF01218495.
  27. Kovatchev B.P., Otto E., Cox D. et al. Evaluation of a new measure of blood glucose variability in diabetes. Diabetes care. 2006; 29(11): 2433–38. https://dx.doi.org/10.2337/dc06-1085.

Supplementary files

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1. JATS XML
2. Fig. 1. Example of outpatient glycemic profile in a patient treated with the combination of metformin + sodium-glucose cotransporter inhibitor type 2

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3. Fig. 2. Example of outpatient glycemic profile of a patient treated with the combination of metformin + dipeptidyl peptidase-4 inhibitor

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