Visit-to-visit glycated hemoglobin A1c variability in adults with type 2 diabetes: A systematic review and meta-analysis
Open accessAbstract
BACKGROUND: Current practice uses the latest measure of glycated hemoglobin (HbAlc) to facilitate clinical decision-making. Studies have demonstrated that HbAlc variability links the risk of death and complications of diabetes. However, the role of HbAlc variability is unclear in clinical practice. This systematic review summarized the evidence of visit-to-visit HbAlc variability regarding different metrics in micro- and macro-vascular complications and death in people with type 2 diabetes. METHODS: We searched PubMed, EMBASE (via OVID), and Cochrane Central Register (CENTRAL, via OVID) for studies investigating the association between HbAlc variability and adverse outcomes in patients with type 2 diabetes and performed random-effects meta-analysis stratified by HbAlc variability metrics in terms of standard deviation (SD), coefficient of variation (CV), and HbAlc variability score (HVS). RESULTS: In people with type 2 diabetes, the highest quantile of all three HbAlc variability metrics (HbAlc-standard deviation [HbAlc-SD], HbAlc-coefficient of variance [HbAlc-CV], and HVS) is associated with increased risks of all-cause mortality, cardiovascular events, progression to chronic kidney disease, amputation, and peripheral neuropathy. For example, the hazard ratio of HbAlc-SD on all-cause mortality was l.89 with 95% confidence interval (95% CI) l.46-2.45 (HbAlc-CV l.47, 95% CI l.26-l.72; HVS l.67, 95% CI l.34-2.09). CONCLUSIONS: High HbAlc variability leads to micro- and macro-vascular complications of type 2 diabetes and related death. People with type 2 diabetes and high HbAlc variability need additional attention and care for the potential adverse outcomes.
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Low quality of care may result in visit-to-visit HbA1c variability among type 2 diabetes mellitus patients and relatively fewer outpatient visits.
“Low quality of care may result in visit-to-visit HbA1c variability among T2DM patients and relatively fewer outpatient visits.”
From Clinical Inertia in the Management of Type 2 Diabetes Mellitus: A Systematic Review (2023) · cites this paper as [45]
The Discussion (page unavailable) says high HbA1c variability in people with type 2 diabetes may reflect lower quality of care. It does not establish that low-quality care causes the variability, and the passages found do not link it to fewer outpatient visits. (LLM, conf=0.85)
Experimental — accuracy not yet measuredLow quality of care may increase the risk of type 2 diabetes mellitus patients receiving suboptimal preventive care for their diabetes.
“It may increase the risk of those patients receiving suboptimal preventive care for their diabetes [45,46].”
From Clinical Inertia in the Management of Type 2 Diabetes Mellitus: A Systematic Review (2023) · cites this paper as [45]
The Discussion says high HbA1c variability may reflect lower quality of care, but it does not address whether lower-quality care increases the risk of receiving suboptimal preventive care. A search for preventive care and related terms found no passage assessing that claim; the Results instead report risks of diabetes complications. (LLM, conf=0.72)
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Cites
Severe hypoglycemia and risks of vascular events and death
Zoungas, S., Patel, A., Chalmers, J., de Galan, B. E., Li, Q., Billot, L., Woodward, M., Ninomiya, T., Neal, B., MacMahon, S., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1003795 · Cited by 1,471 · see citing works
6. Glycemic Targets: <i>Standards of Medical Care in Diabetes—2022</i>
American Diabetes Association Professional Practice Committee (2022) · Diabetes Care · doi:10.2337/dc22-s006 · Cited by 1,403 · see citing works
Bonds, D. E., Miller, M. E., Bergenstal, R. M., Buse, J. B., Byington, R. P., Cutler, J. A., Dudl, R. J., Ismail-Beigi, F., Kimel, A. R., Hoogwerf, B., et al. (2010) · BMJ · doi:10.1136/bmj.b4909 · Cited by 933 · see citing works
6. Glycemic Targets: <i>Standards of Medical Care in Diabetes—2021</i>
American Diabetes Association (2021) · Diabetes Care · doi:10.2337/dc21-s006 · Cited by 847 · see citing works
Glycaemic variability in diabetes: clinical and therapeutic implications
Ceriello, A., Monnier, L., & Owens, D. (2019) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(18)30136-0 · Cited by 588 · see citing works
Zoungas, S., Arima, H., Gerstein, H. C., Holman, R. R., Woodward, M., Reaven, P., Hayward, R. A., Craven, T., Coleman, R. L., & Chalmers, J. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30104-3 · Cited by 529 · see citing works
Grant, R. L. (2014) · BMJ · doi:10.1136/bmj.f7450 · Cited by 515 · cited 1× · see citing works
“We converted the odds ratios (ORs) and risk ratios (RRs) to HRs for the studies that did not report the HRs using established methods([16,17]) (details in Appendix 3, http://links.”
Method
Flagged first, then by citations
Research works printed but not linked: 23
- [2]Grunberger G Sherr J Allende M Blevins T Bode B Handelsman Y. American association of clinical endocrinology clinical practice guideline: the use of advanced technology in the management of persons with diabetes mellitus. Endocr Pract 2021; 27: 505– 537. doi: 10.1016/j.eprac.2021.04.008. 34116789 · DOI · Paper page
- [3]Ray KK Seshasai SR Wijesuriya S Sivakumaran R Nethercott S Preiss D. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a metaanalysis of randomised controlled trials. Lancet 2009; 373: 1765– 1772. doi: 10.1016/S0140-6736(09)60697-8. 19465231 · DOI · Paper page
- [5]Fang HJ Zhou YH Tian YJ Du HY Sun YX Zhong LY. Effects of intensive glucose lowering in treatment of type 2 diabetes mellitus on cardiovascular outcomes: a meta-analysis of data from 58,160 patients in 13 randomized controlled trials. Int J Cardiol 2016; 218: 50– 58. doi: 10.1016/j.ijcard.2016.04.163. 27236108 · DOI · Paper page
- [6]Hu J Hsu H Yuan X Lou K Hsue C Miller JD. HbA1c variability as an independent predictor of diabetes retinopathy in patients with type 2 diabetes. J Endocrinol Invest 2021; 44: 1229– 1236. doi: 10.1007/s40618-020-01410-6. 32897535 · DOI
- [7]Wan EYF Fung CSC Fong DYT Lam CLK. Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes mellitus - A retrospective population-based cohort study. J Diabetes Complications 2016; 30: 1240– 1247. doi: 10.1016/j.jdiacomp.2016.05.024. 27318537 · DOI
- [8]Penno G Solini A Zoppini G Orsi E Fondelli C Zerbini G. 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 2013; 12: 98 doi: 10.1186/1475-2840-12-98. 23829205 PMC3750360 · DOI
- [9]Penno G Solini A Bonora E Fondelli C Orsi E Zerbini G. 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. doi: 10.2337/dc12-2264. 23491522 PMC3714498 · DOI
- [11]Gorst C Kwok CS Aslam S Buchan I Kontopantelis E Myint PK. Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care 2015; 38: 2354– 2369. doi: 10.2337/dc15-1188. 26604281 · DOI · Paper page
- [12]Li S Nemeth I Donnelly L Hapca S Zhou K Pearson ER. 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. doi: 10.2337/dc19-0823. 31727686 · DOI
- [13]Forbes A Murrells T Mulnier H Sinclair AJ. Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study. Lancet Diabetes Endocrinol 2018; 6: 476– 486. doi: 10.1016/S2213-8587(18)30048-2. 29674135 · DOI
- [14]Ceriello A Rizvi AA Rizzo M. Glycemic variability, glycated hemoglobin, and cardiovascular complications: still a dilemma in clinical practice. Adv Ther 2021; doi: 10.1007/s12325-021-01942-0. Online ahead of print. 34757599 · DOI
- [15]Stroup DF Berlin JA Morton SC Olkin I Williamson GD Rennie D. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000; 283: 2008– 2012. doi: 10.1001/jama.283.15.2008. 10789670 · DOI · Paper page
- [17]VanderWeele TJ. Optimal approximate conversions of odds ratios and hazard ratios to risk ratios. Biometrics 2020; 76: 746– 752. doi: 10.1111/biom.13197. 31808145 · DOI
- [18]Borenstein M Hedges LV Higgins JP Rothstein HR. A basic introduction to fixed-effect and random-effects models for metaanalysis. Res Synth Methods 2010; 1: 97– 111. doi: 10.1002/jrsm.12. 26061376 · DOI · Paper page
- [19]Cheng D Fei Y Liu Y Li J Xue Q Wang X. HbA1C variability and the risk of renal status progression in diabetes mellitus: A meta-analysis. PLoS One 2014; 9: e115509 doi: 10.1371/journal.pone.0115509. 25521346 PMC4270779 · DOI
- [20]Ceriello A Rossi MC De Cosmo S Lucisano G Pontremoli R Fioretto P. Overall quality of care predicts the variability of key risk factors for complications in type 2 diabetes: an observational, longitudinal retrospective study. Diabetes Care 2019; 42: 514– 519. doi: 10.2337/dc18-1471. 30765432 · DOI
- [21]Bralic Lang V Bergman Markovic B Vrdoljak D. The association of lifestyle and stress with poor glycemic control in patients with diabetes mellitus type 2: a Croatian nationwide primary care cross-sectional study. Croat Med J 2015; 56: 357– 365. doi: 10.3325/cmj.2015.56.357. 26321029 PMC4576750 · DOI
- [22]Osborn CY Mayberry LS Kim JM. Medication adherence may be more important than other behaviours for optimizing glycaemic control among low-income adults. J Clin Pharm Ther 2016; 41: 256– 259. doi: 10.1111/jcpt.12360. 26939721 PMC4871756 · DOI
- [23]Noyes JD Soto-Pedre E Donnelly LA Pearson ER. Characteristics of people with high visit-to-visit glycaemic variability in type 2 diabetes. Diabet Med 2018; 35: 262– 269. doi: 10.1111/dme.13435. 28755478 PMC5811920 · DOI
- [24]Tourkmani AM Alharbi TJ Rsheed AMB AlRasheed AN AlBattal SM Abdelhay O. Hypoglycemia in type 2 diabetes mellitus patients: a review article. Diabetes Metab Syndr 2018; 12: 791– 794. doi: 10.1016/j.dsx.2018.04.004. 29678605 · DOI
- [25]Desouza CV Bolli GB Fonseca V. Hypoglycemia, diabetes, and cardiovascular events. Diabetes Care 2010; 33: 1389– 1394. doi: 10.2337/dc09-2082. 20508232 PMC2875462 · DOI
- [28]Lin CC Chen CC Chen FN Li CI Liu CS Lin WY. Risks of diabetic nephropathy with variation in hemoglobin A1c and fasting plasma glucose. Am J Med 2013; 126: 1017.e1–1017.e10. doi: 10.1016/j.amjmed.2013.04.015. 23993260 · DOI
- [29]Reddy MA Zhang E Natarajan R. Epigenetic mechanisms in diabetic complications and metabolic memory. Diabetologia 2015; 58: 443– 455. doi: 10.1007/s00125-014-3462-y. 25481708 PMC4324095 · DOI
Cited by
Sorted by citations
Clinical Inertia in the Management of Type 2 Diabetes Mellitus: A Systematic Review
Almigbal, T. H., Alzarah, S. A., Aljanoubi, F. A., Alhafez, N. A., Aldawsari, M. R., Alghadeer, Z. Y., & Alrasheed, A. A. (2023) · Medicina · doi:10.3390/medicina59010182 · Cited by 58 · see citing works
“It may increase the risk of those patients receiving suboptimal preventive care for their diabetes [45,46].”
Background
- Authors
- Qu, F., Shi, Q., Wang, Y., Shen, Y., Zhou, K., Pearson, E. R., & Li, S.
- Affiliations
- Sichuan University (CN)
- University of Chinese Academy of Sciences (CN)
- University of Dundee (GB)
- Corresponding author
- Li, Sheyu
- Venue
- Chinese Medical Journal
- Published
- 2022-03-03
- Volume
- 135
- Issue
- 19
- Pages
- 2294-2300
- ISSN
- 0366-6999 (ISSN-L)
- 2542-5641
- PMC
- PMC9771337
- OpenAlex
- W4220769701
- Cited by
- 37
- References
- 7
- Citations by year
- 2026: 11 · 2025: 7 · 2024: 10 · 2023: 7 · 2022: 2
- FWCI
- 0.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Chinese Medical Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Sichuan University (ZYGD18022)
- West China Hospital, Sichuan University (ZYGD18022, 1.3.5)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- HbA1c, type 2 diabetes, all-cause mortality, cardiovascular events, chronic kidney disease progression, amputation, peripheral neuropathy, microvascular complications, macrovascular complications, coefficient of variation, standard deviation
- MeSH terms
- Adult, Blood Glucose, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Glycated Hemoglobin, Humans, Risk Factors
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