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Endocrine

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Prognostic impact of visit-to-visit glycemic variability on the risks of major adverse cardiovascular outcomes and hypoglycemia in patients with different glycemic control and type 2 diabetes

  • Bao Sun
  • Fazhong He
  • Yongchao Gao
  • Jiecan Zhou
  • Lei Sun
  • Rong Liu
  • Heng Xu
  • Xiaoping Chen
  • Honghao Zhou
  • Zhaoqian Liu
  • Wei ZhangEmail author
Original Article
  • 71 Downloads

Abstract

Purpose

The prognostic impact of visit-to-visit glycemic variability on clinical outcomes in patients with different glycemic control and type 2 diabetes remains obscure. We investigated glucose variability and clinical outcomes for patients in the groups of Good glycemic control (GC), Insufficient glycemic control (IC), and Poor glycemic control (PC) in a prospective cohort study.

Methods

By using data from Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation (ADVANCE), 930 patients were enrolled from 61 centers in China and grouped into GC, IC, and PC according to their glycated hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG). Visit-to-visit glycemic variability was defined using the coefficient of variation (CV) of five measurements of HbA1c and FPG taken 3–24 months after treatment. Multivariable Cox proportional hazards models were employed to estimate adjusted hazard ratio (aHR).

Results

Among 930 patients in the intensive glucose control, 82, 538, and 310 patients were assigned to GC, IC, and PC, respectively. During the median of 4.8 years of follow-up, 322 patients were observed hypoglycemia and 244 patients experienced major adverse cardiovascular events (MACE). The CV of HbA1c and FPG was significantly lower for GC (6.0 ± 3.8, 11.2 ± 6.2) than IC (8.3 ± 5.6, 17.9 ± 10.6) and PC (9.5 ± 6.3, 19.3 ± 10.8). High glycemic variability was associated with a greater risk of MACE (aHR: 2.21; 95% confidence interval (CI): 1.61–3.03; p < 0.001) and hypoglycemia (aHR: 1.36; 95% CI: 1.04–1.79; p = 0.025) than low glycemic variability in total patients. The consistent trend was also found in subgroups of GC, IC, and PC.

Conclusions

This prospective cohort study showed that glycemic variability was significantly lower for GC than IC and PC. Furthermore, glycemic variability was associated with the risk of MACE and hypoglycemia in total patients and subgroups of different glycemic control.

Keywords

Visit-to-visit glycemic variability Different glycemic control Type 2 diabetes Major adverse cardiovascular events Hypoglycemia 

Abbreviations

GC

Good glycemic control

IC

Insufficient glycemic control

PC

Poor glycemic control

ADVANCE

Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation

HbA1c

Glycated hemoglobin A1c

FPG

Fasting plasma glucose

CV

Coefficient of variation

aHR

Adjusted hazard ratio

MACE

Major adverse cardiovascular events

SD

Standard deviations

BMI

Body mass index

Notes

Acknowledgements

We acknowledge the contributions of ADVANCE group at 61 centers in China. We also thank all patients and participants who have contributed to the register.

Funding

This research was funded by grants from National Key Research and Development Program (No. 2016YFC0905000), National Natural Science Foundation of China (Nos. 81522048, 81573511, and 81874329) and the Innovation Driven Project of Central South University (No. 2016CX024).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the local ethics committee and was in accordance with the 1964 Declaration of Helsinki and its later amendmentsmed consent.

Informed consent

All patients provide written informed consent.

Supplementary material

12020_2019_1893_MOESM1_ESM.docx (16 kb)
Supplementary table (We found some mistakes in the Supplementary Table 1. So we validated the original data and replaced it by a new Supplementary Table 1 in the attachments.)

References

  1. 1.
    International Diabetes Federation. IDF Diabetes Atlas, 8 edn. http://www.diabetesatlas.org (2017). Accessed 1 Dec 2017.
  2. 2.
    A.K. Wright, E. Kontopantelis, R. Emsley, I. Buchan, N. Sattar, M.K. Rutter, D.M. Ashcroft, Life expectancy and cause-specific mortality in type 2 diabetes: a population-based cohort study quantifying relationships in ethnic subgroups. Diabetes Care 40(3), 338–345 (2017).CrossRefGoogle Scholar
  3. 3.
    UK Prospective Diabetes Study (UKPDS) Group. UK Prospective Diabetes Study (UKPDS) GroupIntensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet (Lond., Engl.) 352(9131), 837–853 (1998).Google Scholar
  4. 4.
    D.M. Nathan, S. Genuth, J. Lachin, P. Cleary, O. Crofford, M. Davis, L. Rand, C. Siebert, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329(14), 977–986 (1993).CrossRefGoogle Scholar
  5. 5.
    B. Hemmingsen, S.S. Lund, C. Gluud, A. Vaag, T. Almdal, C. Hemmingsen, J. Wetterslev, Intensive glycaemic control for patients with type 2 diabetes: systematic review with meta-analysis and trial sequential analysis of randomised clinical trials. BMJ 343, d6898 (2011).CrossRefGoogle Scholar
  6. 6.
    H.C. Gerstein, M.E. Miller, R.P. Byington, D.C. Goff Jr., J.T. Bigger, J.B. Buse, W.C. Cushman, S. Genuth, F. Ismail-Beigi, R.H. Grimm Jr., J.L. Probstfield, D.G. Simons-Morton, W.T. Friedewald, Effects of intensive glucose lowering in type 2 diabetes. N. Engl. J. Med. 358(24), 2545–2559 (2008).CrossRefGoogle Scholar
  7. 7.
    D.E. Bonds, M.E. Miller, R.M. Bergenstal, J.B. Buse, R.P. Byington, J.A. Cutler, R.J. Dudl, F. Ismail-Beigi, A.R. Kimel, B. Hoogwerf, K.R. Horowitz, P.J. Savage, E.R. Seaquist, D.L. Simmons, W.I. Sivitz, J.M. Speril-Hillen, M.E. Sweeney, The association between symptomatic, severe hypoglycaemia and mortality in type 2 diabetes: retrospective epidemiological analysis of the ACCORD study. BMJ 340, b4909 (2010).CrossRefGoogle Scholar
  8. 8.
    B. Sun, F. He, L. Sun, J. Zhou, J. Shen, J. Xu, B. Wu, R. Liu, X. Wang, H. Xu, X. Chen, H. Zhou, Z. Liu, W. Zhang, Cause-specific risk of major adverse cardiovascular outcomes and hypoglycemic in patients with type 2 diabetes: a multicenter prospective cohort study. Endocrine (2018).  https://doi.org/10.1007/s12020-018-1715-0.
  9. 9.
    M. Kuroda, T. Shinke, H. Otake, D. Sugiyama, T. Takaya, H. Takahashi, D. Terashita, K. Uzu, N. Tahara, D. Kashiwagi, K. Kuroda, Y. Shinkura, Y. Nagasawa, K. Sakaguchi, Y. Hirota, W. Ogawa, K. Hirata, Effects of daily glucose fluctuations on the healing response to everolimus-eluting stent implantation as assessed using continuous glucose monitoring and optical coherence tomography. Cardiovasc. Diabetol. 15, 79 (2016).CrossRefGoogle Scholar
  10. 10.
    D. Tschope, P. Bramlage, S. Schneider, A.K. Gitt, Incidence, characteristics and impact of hypoglycaemia in patients receiving intensified treatment for inadequately controlled type 2 diabetes mellitus. Diabetes Vasc. Dis. Res. 13(1), 2–12 (2016).CrossRefGoogle Scholar
  11. 11.
    Y. Hirakawa, H. Arima, S. Zoungas, T. Ninomiya, M. Cooper, P. Hamet et al., Impact of visit-to-visit glycemic variability on the risks of macrovascular and microvascular events and all-cause mortality in type 2 diabetes: the ADVANCE trial. Diabetes Care 37, 2359–2365 (2014).CrossRefGoogle Scholar
  12. 12.
    L. Nalysnyk, M. Hernandez-Medina, G. Krishnarajah, Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature. Diabetes Obes. Metab. 12(4), 288–298 (2010).CrossRefGoogle Scholar
  13. 13.
    B. Zinman, S.P. Marso, N.R. Poulter, S.S. Emerson, T.R. Pieber, R.E. Pratley, M. Lange, K. Brown-Frandsen, A. Moses, A.M. Ocampo Francisco, J. Barner Lekdorf, K. Kvist, J.B. Buse, Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2). Diabetologia 61(1), 48–57 (2018).CrossRefGoogle Scholar
  14. 14.
    A. Patel, S. MacMahon, J. Chalmers, B. Neal, L. Billot, M. Woodward, M. Marre, M. Cooper, P. Glasziou, D. Grobbee, P. Hamet, S. Harrap, S. Heller, L. Liu, G. Mancia, C.E. Mogensen, C. Pan, N. Poulter, A. Rodgers, B. Williams, S. Bompoint, B.E. de Galan, R. Joshi, F. Travert, Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 358(24), 2560–2572 (2008).CrossRefGoogle Scholar
  15. 15.
    Rationale and design of the ADVANCE study: a randomised trial of blood pressure lowering and intensive glucose control in high-risk individuals with type 2 diabetes mellitus, Action in diabetes and vascular disease: PreterAx and DiamicroN modified-release controlled evaluation. J. Hypertens. Suppl. 19(4), S21–S28 (2001)Google Scholar
  16. 16.
    F. He, M. Liu, Z. Chen, G. Liu, Z. Wang, R. Liu, J. Luo, J. Tang, X. Wang, X. Liu, H. Zhou, X. Chen, Z. Liu, W. Zhang, Assessment of human tribbles homolog 3 genetic variation (rs2295490) effects on type 2 diabetes patients with glucose control and blood pressure lowering treatment. EBioMedicine 13, 181–189 (2016).CrossRefGoogle Scholar
  17. 17.
    J. Gu, Y.Q. Fan, J.F. Zhang, C.Q. Wang, Association of hemoglobin A1c variability and the incidence of heart failure with preserved ejection fraction in patients with type 2 diabetes mellitus and arterial hypertension. Hell. J. Cardiol. 59(2), 91–97 (2018).CrossRefGoogle Scholar
  18. 18.
    J. Gu, Y.Q. Fan, J.F. Zhang, C.Q. Wang, Impact of long-term glycemic variability on development of atrial fibrillation in type 2 diabetic patients. Anatol. J. Cardiol. 18(6), 410–416 (2017).Google Scholar
  19. 19.
    C. Stettler, S. Allemann, P. Juni, C.A. Cull, R.R. Holman, M. Egger, S. Krahenbuhl, P. Diem, Glycemic control and macrovascular disease in types 1 and 2 diabetes mellitus: meta-analysis of randomized trials. Am. Heart J. 152(1), 27–38 (2006).CrossRefGoogle Scholar
  20. 20.
    I.M. Stratton, A.I. Adler, H.A. Neil, D.R. Matthews, S.E. Manley, C.A. Cull, D. Hadden, R.C. Turner, R.R. Holman, Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321(7258), 405–412 (2000).CrossRefGoogle Scholar
  21. 21.
    M.S. Kirkman, M. McCarren, J. Shah, W. Duckworth, C. Abraira, The association between metabolic control and prevalent macrovascular disease in Type 2 diabetes: the VA Cooperative Study in diabetes. J. Diabetes Complicat. 20(2), 75–80 (2006).CrossRefGoogle Scholar
  22. 22.
    C.R.L. Cardoso, N.C. Leite, C.B.M. Moram, G.F. Salles, Long-term visit-to-visit glycemic variability as predictor of micro- and macrovascular complications in patients with type 2 diabetes: The Rio de Janeiro Type 2 Diabetes Cohort Study. Cardiovasc. Diabetol. 17(1), 33 (2018).  https://doi.org/10.1186/s12933-018-0677-0.CrossRefGoogle Scholar
  23. 23.
    P.E. Cryer, Glycemic goals in diabetes: trade-off between glycemic control and iatrogenic hypoglycemia. Diabetes 63(7), 2188–2195 (2014).  https://doi.org/10.2337/db14-0059. CrossRefGoogle Scholar
  24. 24.
    S.E. Inzucchi, R.M. Bergenstal, J.B. Buse, M. Diamant, E. Ferrannini, M. Nauck, A.L. Peters, A. Tsapas, R. Wender, D.R. Matthews, Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 38(1), 140–149 (2015).CrossRefGoogle Scholar
  25. 25.
    K.J. Lipska, H. Krumholz, T. Soones, S.J. Lee, Polypharmacy in the aging patient: a review of glycemic control in older adults with type 2 diabetes. J. Am. Med Assoc. 315(10), 1034–1045 (2016).CrossRefGoogle Scholar
  26. 26.
    D. Cheng, Y. Fei, Y. Liu, J. Li, Q. Xue, X. Wang, N. Wang, HbA1C variability and the risk of renal status progression in Diabetes Mellitus: a meta-analysis. PLoS ONE 9(12), e115509 (2014).CrossRefGoogle Scholar
  27. 27.
    C. Gorst, C.S. Kwok, S. Aslam, I. Buchan, E. Kontopantelis, P.K. Myint, G. Heatlie, Y. Loke, M.K. Rutter, M.A. Mamas, Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care 38(12), 2354–2369 (2015).CrossRefGoogle Scholar
  28. 28.
    G. Penno, A. Solini, G. Zoppini, E. Orsi, C. Fondelli, G. Zerbini, S. Morano, F. Cavalot, O. Lamacchia, R. Trevisan, M. Vedovato, G. Pugliese, Hemoglobin A1c variability as an independent correlate of cardiovascular disease in patients with type 2 diabetes: a cross-sectional analysis of the renal insufficiency and cardiovascular events (RIACE) Italian multicenter study. Cardiovasc. Diabetol. 12, 98 (2013).CrossRefGoogle Scholar
  29. 29.
    E.S. Kilpatrick, A.S. Rigby, S.L. Atkin, The effect of glucose variability on the risk of microvascular complications in type 1 diabetes. Diabetes Care 29(7), 1486–1490 (2006).CrossRefGoogle Scholar
  30. 30.
    R. Borg, J.C. Kuenen, B. Carstensen, H. Zheng, D.M. Nathan, R.J. Heine, J. Nerup, K. Borch-Johnsen, D.R. Witte, HbA(1)(c) and mean blood glucose show stronger associations with cardiovascular disease risk factors than do postprandial glycaemia or glucose variability in persons with diabetes: the A1C-Derived Average Glucose (ADAG) study. Diabetologia 54(1), 69–72 (2011).CrossRefGoogle Scholar
  31. 31.
    C.E. Mendez, K.T. Mok, A. Ata, R.J. Tanenberg, J. Calles-Escandon, G.E. Umpierrez, Increased glycemic variability is independently associated with length of stay and mortality in noncritically ill hospitalized patients. Diabetes Care 36(12), 4091–4097 (2013).CrossRefGoogle Scholar
  32. 32.
    S. Zoungas, J. Chalmers, B. Neal, L. Billot, Q. Li, Y. Hirakawa, H. Arima, H. Monaghan, R. Joshi, S. Colagiuri, M.E. Cooper, P. Glasziou, D. Grobbee, P. Hamet, S. Harrap, S. Heller, L. Lisheng, G. Mancia, M. Marre, D.R. Matthews, C.E. Mogensen, V. Perkovic, N. Poulter, A. Rodgers, B. Williams, S. MacMahon, A. Patel, M. Woodward, Follow-up of blood-pressure lowering and glucose control in type 2 diabetes. N. Engl. J. Med. 371(15), 1392–1406 (2014).CrossRefGoogle Scholar
  33. 33.
    H.C. Gerstein, M.E. Miller, F. Ismail-Beigi, J. Largay, C. McDonald, H.A. Lochnan, G.L. Booth, Effects of intensive glycaemic control on ischaemic heart disease: analysis of data from the randomised, controlled ACCORD trial. Lancet (Lond., Engl.) 384(9958), 1936–1941 (2014).CrossRefGoogle Scholar
  34. 34.
    M. Yamazaki, G. Hasegawa, S. Majima, K. Mitsuhashi, T. Fukuda, H. Iwase, M. Kadono, M. Asano, T. Senmaru, M. Tanaka, M. Fukui, N. Nakamura, Effect of repaglinide versus glimepiride on daily blood glucose variability and changes in blood inflammatory and oxidative stress markers. Diabetol. Metab. Syndr. 6, 54 (2014).CrossRefGoogle Scholar
  35. 35.
    Chinese Diabetes Society. China guideline for type 2 diabetes (2013). China J. Diabetes Mellitus 22(8), 447–498 (2014).Google Scholar
  36. 36.
    K. Omori, H. Nomoto, A. Nakamura, Reduction in glucose fluctuations in elderly patients with type 2 diabetes using repaglinide: a randomized controlled trial of repaglinide vs sulfonylurea. J. Diabetes Investig. (2018).  https://doi.org/10.1111/jdi.12889.
  37. 37.
    N.A. Thornberry, B. Gallwitz, Mechanism of action of inhibitors of dipeptidyl-peptidase-4 (DPP-4). Best Pract. Clin. Endocrinol. Metab. 23(4), 479–486 (2009).CrossRefGoogle Scholar
  38. 38.
    L. Monnier, E. Mas, C. Ginet, F. Michel, L. Villon, J.P. Cristol, C. Colette, Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. J. Am. Med Assoc. 295(14), 1681–1687 (2006).CrossRefGoogle Scholar
  39. 39.
    A. Ceriello, M.A. Ihnat, “Glycaemic variability”: a new therapeutic challenge in diabetes and the critical care setting. Diabet. Med. 27(8), 862–867 (2010).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Clinical Pharmacology, Xiangya HospitalCentral South UniversityChangshaPeople’s Republic of China
  2. 2.Hunan Key Laboratory of Pharmacogenetics, Department of Clinical Pharmacology, Institute of Clinical PharmacologyCentral South UniversityChangshaPeople’s Republic of China
  3. 3.Data Analysis Technology Lab, School of Mathematics and StatisticsHenan UniversityKaifengPeople’s Republic of China
  4. 4.Department of Laboratory Medicine, National Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China HospitalSichuan UniversityChengduPeople’s Republic of China

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