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Horm Metab Res 2023; 55(02): 103-113
DOI: 10.1055/a-1931-4400
Original Article: Endocrine Care

Association Between the Variability of Glycated Hemoglobin and Retinopathy in Patients with Type 2 Diabetes Mellitus: A Meta-Analysis

Liping Zhai   *
1   Department of Endocrinology, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, China
,
Jun Lu   *
2   Department of Ophthalmology, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, China
,
Xinjian Cao
3   Department of Clinical Medicine, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, China
,
Jun Zhang
3   Department of Clinical Medicine, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, China
,
Yong Yin
3   Department of Clinical Medicine, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, China
,
Hu Tian
3   Department of Clinical Medicine, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, China
› Author Affiliations Funding information This study was supported by Jiangsu Traditional Medicine and Technology Development Program of China (MS2021085) and Taizhou Social Development Research Project, China (TS202011).
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Abstract

Visit-to-visit variability of glycated hemoglobin (HbA1c) is a marker of long-term glycemic fluctuation, which has been related to increased risk of macrovascular complications in patients with type 2 diabetes mellitus (T2DM). The association between HbA1c variability and retinopathy in patients with T2DM, however, has been inconsistent in previous studies. In order to fully evaluate the above association, we conducted a meta-analysis. Observational studies related to the aim of the meta-analysis were identified by search of PubMed, Web of Science, and Embase databases. Studies with HbA1c variability evaluated as the standard deviation (SD) and/or the coefficients of variation (CV) of HbA1c were included. The results were analyzed using a random-effects model that incorporated potential heterogeneity between studies. Twelve observational studies involving 44 662 T2DM patients contributed to the meta-analysis. Overall, 5150 (11.5%) patients developed retinopathy. Pooled results showed that compared to patients with lower HbA1c variability, T2DM patients with higher HbA1c-SD (relative risk [RR]: 1.48, 95% confidence interval [CI]: 1.24 to 1.78, p<0.001, I2=34%) and higher HbA1c-CV (RR: 1.29, 95% CI: 1.05 to 1.59, p=0.02, I2=0%) were both associated with higher risk of DR. For studies with HbA1c-SD, the association was not significantly affected by study characteristics such as country, study design, mean age, disease duration, adjustment of mean HbA1c, or quality scores (p for subgroup difference all>0.05). In conclusion, higher HbA1c variability may be associated with an increased risk of retinopathy in patients with T2DM.

Key words

HbA1c variability - diabetic retinopathy - type 2 diabetes - microvascular - meta-analysis

* These authors contributed equally to this work.




Publication History

Received: 14 July 2022

Accepted after revision: 15 August 2022

Article published online:
12 October 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Sabanayagam C, Banu R, Chee ML. et al. Incidence and progression of diabetic retinopathy: a systematic review. Lancet Diabetes Endocrinol 2019; 7: 140-149
    CrossrefPubMedGoogle Scholar
  • 2 Teo ZL, Tham YC, Yu M. et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology 2021; 128: 1580-1591
    CrossrefPubMedGoogle Scholar
  • 3 Lin KY, Hsih WH, Lin YB. et al. Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy. J Diabetes Investig 2021; 12: 1322-1325
    CrossrefPubMedGoogle Scholar
  • 4 Xu XH, Sun B, Zhong S. et al. Diabetic retinopathy predicts cardiovascular mortality in diabetes: a meta-analysis. BMC Cardiovasc Disord 2020; 20: 478
    CrossrefPubMedGoogle Scholar
  • 5 Xie J, Ikram MK, Cotch MF. et al. Association of diabetic macular edema and proliferative diabetic retinopathy with cardiovascular disease: a systematic review and meta-analysis. JAMA Ophthalmol 2017; 135: 586-593
    CrossrefPubMedGoogle Scholar
  • 6 Nusca A, Tuccinardi D, Albano M. et al. Glycemic variability in the development of cardiovascular complications in diabetes. Diabetes Metab Res Rev 2018; 34: e3047
    CrossrefPubMedGoogle Scholar
  • 7 Skrha J, Soupal J, Skrha J. et al. Glucose variability, HbA1c and microvascular complications. Rev Endocr Metab Disord 2016; 17: 103-110
    CrossrefPubMedGoogle Scholar
  • 8 Xia J, Yin C. Glucose variability and coronary artery disease. Heart Lung Circ 2019; 28: 553-559
    CrossrefPubMedGoogle Scholar
  • 9 Rodbard D. Glucose variability: a review of clinical applications and research developments. Diabetes Technol Ther 2018; 20: S25-S215
    CrossrefPubMedGoogle Scholar
  • 10 Umpierrez GE, Kovatchev BP. Glycemic variability: how to measure and its clinical implication for type 2 diabetes. Am J Med Sci 2018; 356: 518-527
    CrossrefPubMedGoogle Scholar
  • 11 Zhou Z, Sun B, Huang S. et al. Glycemic variability: adverse clinical outcomes and how to improve it?. Cardiovasc Diabetol 2020; 19: 102
    CrossrefPubMedGoogle Scholar
  • 12 Ceriello A, Monnier L, Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet Diabetes Endocrinol 2019; 7: 221-230
    CrossrefPubMedGoogle Scholar
  • 13 Wang T, Zhang X, Liu J. Long-term glycemic variability and risk of cardiovascular events in type 2 diabetes: a meta-analysis. Horm Metab Res 2022; 54: 84-93
    Article in Thieme ConnectPubMedGoogle Scholar
  • 14 Song J, Bai H, Xu H. et al. HbA1c variability and the risk of dementia in patients with diabetes: a meta-analysis. Int J Clin Pract 2022; 7706330
    PubMedGoogle Scholar
  • 15 Penno G, Solini A, Bonora E. et al. HbA1c variability as an independent correlate of nephropathy, but not retinopathy, in patients with type 2 diabetes: the renal insufficiency and cardiovascular events (RIACE) Italian multicenter study. Diabetes Care 2013; 36: 2301-2310
    CrossrefPubMedGoogle Scholar
  • 16 Foo V, Quah J, Cheung G. et al. HbA1c, systolic blood pressure variability and diabetic retinopathy in Asian type 2 diabetics. J Diabetes 2017; 9: 200-207
    CrossrefPubMedGoogle Scholar
  • 17 Cardoso CRL, Leite NC, Moram CBM. et al. 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 2018; 17: 33
    CrossrefPubMedGoogle Scholar
  • 18 Teliti M, Cogni G, Sacchi L. et al. Risk factors for the development of micro-vascular complications of type 2 diabetes in a single-centre cohort of patients. Diab Vasc Dis Res 2018; 15: 424-432
    CrossrefPubMedGoogle Scholar
  • 19 Slieker RC, van der Heijden A, Nijpels G. et al. Visit-to-visit variability of glycemia and vascular complications: the hoorn diabetes care system cohort. Cardiovasc Diabetol 2019; 18: 170
    CrossrefPubMedGoogle Scholar
  • 20 Song KH, Jeong JS, Kim MK. et al. Discordance in risk factors for the progression of diabetic retinopathy and diabetic nephropathy in patients with type 2 diabetes mellitus. J Diabetes Investig 2019; 10: 745-752
    CrossrefPubMedGoogle Scholar
  • 21 Li S, Nemeth I, Donnelly L. et al. Visit-to-visit HbA1c variability is associated with cardiovascular disease and microvascular complications in patients with newly diagnosed type 2 diabetes. Diabetes Care 2020; 43: 426-432
    CrossrefPubMedGoogle Scholar
  • 22 Scott ES, Januszewski AS, O’Connell R. et al. Long-term glycemic variability and vascular complications in type 2 diabetes: post hoc analysis of the FIELD study. J Clin Endocrinol Metab 2020; 105 dgaa361
    PubMedGoogle Scholar
  • 23 Dai D, Shen Y, Lu J. et al. Association between visit-to-visit variability of glycated albumin and diabetic retinopathy among patients with type 2 diabetes - A prospective cohort study. J Diabetes Complicat 2021; 35: 107971
    CrossrefPubMedGoogle Scholar
  • 24 Hu J, Hsu H, Yuan X. et al. HbA1c variability as an independent predictor of diabetes retinopathy in patients with type 2 diabetes. J Endocrinol Invest 2021; 44: 1229-1236
    CrossrefPubMedGoogle Scholar
  • 25 Kim HU, Park SP, Kim YK. Long-term HbA1c variability and the development and progression of diabetic retinopathy in subjects with type 2 diabetes. Sci Rep 2021; 11: 4731
    CrossrefPubMedGoogle Scholar
  • 26 Park JY, Hwang JH, Kang MJ. et al. Effects of glycemic variability on the progression of diabetic retinopathy among patients with type 2 diabetes. Retina 2021; 41: 1487-1495
    CrossrefPubMedGoogle Scholar
  • 27 Page MJ, Moher D, Bossuyt PM. et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 2021; 372: n160
    CrossrefPubMedGoogle Scholar
  • 28 Higgins J, Thomas J, Chandler J. et al. Cochrane handbook for systematic reviews of interventions version 6.2. The Cochrane Collaboration. 2021 www.training.cochrane.org/handbook
    PubMedGoogle Scholar
  • 29 Wells GA, Shea B, O’Connell D. et al. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2010 http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
    PubMed
  • 30 Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002; 21: 1539-1558
    CrossrefPubMedGoogle Scholar
  • 31 Patsopoulos NA, Evangelou E, Ioannidis JP. Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation. Int J Epidemiol 2008; 37: 1148-1157
    CrossrefPubMedGoogle Scholar
  • 32 Egger M, Davey Smith G, Schneider M. et al. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629-634
    CrossrefPubMedGoogle Scholar
  • 33 Gorst C, Kwok CS, Aslam S. et al. Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care 2015; 38: 2354-2369
    CrossrefPubMedGoogle Scholar
  • 34 Prazny M, Skrha J, Soupal J. et al. [Glycemic variability and microvascular complications of diabetes]. Cas Lek Cesk; 156: 308–313
    PubMed
  • 35 Papachristoforou E, Lambadiari V, Maratou E. et al. Association of glycemic indices (hyperglycemia, glucose variability, and hypoglycemia) with oxidative stress and diabetic complications. J Diabetes Res 2020; 7489795
    PubMedGoogle Scholar
  • 36 Li W, Maloney RE, Aw TY. High glucose, glucose fluctuation and carbonyl stress enhance brain microvascular endothelial barrier dysfunction: Implications for diabetic cerebral microvasculature. Redox Biol 2015; 5: 80-90
    CrossrefPubMedGoogle Scholar
  • 37 Ruan Y, Jiang S, Musayeva A. et al. Oxidative stress and vascular dysfunction in the retina: therapeutic strategies. Antioxidants (Basel) 2020; 9: 761
    CrossrefPubMedGoogle Scholar
  • 38 Chalke SD, Kale PP. Combinational approaches targeting neurodegeneration, oxidative stress, and inflammation in the treatment of diabetic retinopathy. Curr Drug Targets 2021; 22: 1810-1824
    CrossrefPubMedGoogle Scholar
  • 39 Sun J, Xu Y, Sun S. et al. Intermittent high glucose enhances cell proliferation and VEGF expression in retinal endothelial cells: the role of mitochondrial reactive oxygen species. Mol Cell Biochem 2010; 343: 27-35
    CrossrefPubMedGoogle Scholar
  • 40 Picconi F, Parravano M, Sciarretta F. et al. Activation of retinal Muller cells in response to glucose variability. Endocrine 2019; 65: 542-549
    CrossrefPubMedGoogle Scholar
  • 41 Zhao Q, Zhou F, Zhang Y. et al. Fasting plasma glucose variability levels and risk of adverse outcomes among patients with type 2 diabetes: a systematic review and meta-analysis. Diabetes Res Clin Pract 2019; 148: 23-31
    CrossrefPubMedGoogle Scholar
  • 42 Zhou JJ, Koska J, Bahn G. et al. Fasting glucose variation predicts microvascular risk in ACCORD and VADT. J Clin Endocrinol Metab 2021; 106: 1150-1162
    CrossrefPubMedGoogle Scholar
  • 43 Sartore G, Chilelli NC, Burlina S. et al. Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes. Acta Diabetol 2013; 50: 437-442
    CrossrefPubMedGoogle Scholar
  • 44 Lu J, Ma X, Zhou J. et al. Association of time in range, as assessed by ontinuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care 2018; 41: 2370-2376
    CrossrefPubMedGoogle Scholar
  • 45 Lu J, Ma X, Zhang L. et al. Glycemic variability assessed by continuous glucose monitoring and the risk of diabetic retinopathy in latent autoimmune diabetes of the adult and type 2 diabetes. J Diabetes Investig 2019; 10: 753-759
    CrossrefPubMedGoogle Scholar
  • 46 Pratley RE, Rosenstock J, Heller SR. et al. Reduced glucose variability with glucose-dependent versus glucose-independent therapies despite similar glucose control and hypoglycemia rates in a randomized, controlled study of older patients with type 2 diabetes mellitus. J Diabetes Sci Technol 2018; 12: 1184-1191
    CrossrefPubMedGoogle Scholar
  • 47 Suzuki R, Eiki JI, Moritoyo T. et al. Effect of short-term treatment with sitagliptin or glibenclamide on daily glucose fluctuation in drug-naive Japanese patients with type 2 diabetes mellitus. Diabetes Obes Metab 2018; 20: 2274-2281
    CrossrefPubMedGoogle Scholar
  • 48 Osonoi T, Saito M, Tamasawa A. et al. Effects of sitagliptin or mitiglinide as an add-on to acarbose on daily blood glucose fluctuations measured by 72 h subcutaneous continuous glucose monitoring in Japanese patients with type 2 diabetes: a prospective randomized study. Expert Opin Pharmacother 2014; 15: 1325-1335
    CrossrefPubMedGoogle Scholar
  • 49 Li H, Xu X, Wang J. et al. A randomized study to compare the effects of once-weekly dulaglutide injection and once-daily glimepiride on glucose fluctuation of type 2 diabetes mellitus patients: a 26-week follow-up. J Diabetes Res 2019; 2019: 6423987
    PubMedGoogle Scholar