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Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Greaves, Ronda / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter

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Volume 57, Issue 2


The frequency of testing for glycated haemoglobin, HbA1c, is linked to the probability of achieving target levels in patients with suboptimally controlled diabetes mellitus

Christopher J. Duff
  • Department of Clinical Biochemistry, University Hospitals of North Midlands, Stoke-on-Trent, Staffordshire, UK
  • Institute for Applied Clinical Sciences, University of Keele, Stoke-on-Trent, Staffordshire, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ivonne Solis-Trapala
  • Institute for Applied Clinical Sciences, University of Keele, Stoke-on-Trent, Staffordshire, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Owen J. Driskell
  • Department of Clinical Biochemistry, University Hospitals of North Midlands, Stoke-on-Trent, Staffordshire, UK
  • Institute for Applied Clinical Sciences, University of Keele, Stoke-on-Trent, Staffordshire, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ David Holland / Helen Wright
  • Department of Clinical Biochemistry, University Hospitals of North Midlands, Stoke-on-Trent, Staffordshire, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jenna L. Waldron / Clare Ford / Jonathan J. Scargill / Martin Tran
  • Department of Clinical Biochemistry, University Hospitals of North Midlands, Stoke-on-Trent, Staffordshire, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Fahmy W.F. Hanna
  • Department of Diabetes and Endocrinology, University Hospital of North Midlands, Stoke-on-Trent, Staffordshire, UK
  • Centre for Health and Development, Staffordshire University, Stoke-on-Trent, Staffordshire, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ R. John Pemberton / Adrian Heald
  • The School of Medicine and Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Anthony A. FryerORCID iD: https://orcid.org/0000-0001-8678-0404
Published Online: 2018-10-02 | DOI: https://doi.org/10.1515/cclm-2018-0503



We previously showed, in patients with diabetes, that >50% of monitoring tests for glycated haemoglobin (HbA1c) are outside recommended intervals and that this is linked to diabetes control. Here, we examined the effect of tests/year on achievement of commonly utilised HbA1c targets and on HbA1c changes over time.


Data on 20,690 adults with diabetes with a baseline HbA1c of >53 mmol/mol (7%) were extracted from Clinical Biochemistry Laboratory records at three UK hospitals. We examined the effect of HbA1c tests/year on (i) the probability of achieving targets of ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) in a year using multi-state modelling and (ii) the changes in mean HbA1c using a linear mixed-effects model.


The probabilities of achieving ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) targets within 1 year were 0.20 (95% confidence interval: 0.19–0.21) and 0.10 (0.09–0.10), respectively. Compared with four tests/year, having one test or more than four tests/year were associated with lower likelihoods of achieving either target; two to three tests/year gave similar likelihoods to four tests/year. Mean HbA1c levels were higher in patients who had one test/year compared to those with four tests/year (mean difference: 2.64 mmol/mol [0.24%], p<0.001).


We showed that ≥80% of patients with suboptimal control are not achieving commonly recommended HbA1c targets within 1 year, highlighting the major challenge facing healthcare services. We also demonstrated that, although appropriate monitoring frequency is important, testing every 6 months is as effective as quarterly testing, supporting international recommendations. We suggest that the importance HbA1c monitoring frequency is being insufficiently recognised in diabetes management.

This article offers supplementary material which is provided at the end of the article.

Keywords: diabetes mellitus; glycaemic target; glycated haemoglobin; monitoring; test utilisation


  • 1.

    American Diabetes Association. Standards of medical care in diabetes. Diabetes Care 2018;41 Suppl 1:S1–153.PubMedGoogle Scholar

  • 2.

    National Institute for Health and Clinical Excellence. Type 2 diabetes in adults: management (NG28). (Last updated: July 2016). https://www.nice.org.uk/guidance/ng28. Accessed: 11 May 2018.

  • 3.

    National Institute for Health and Clinical Excellence. Type 1 diabetes in adults: diagnosis and management (NG17). (Last updated: July 2016). https://www.nice.org.uk/guidance/ng17. Accessed: 11 May 2018.

  • 4.

    Akan P, Cimrin D, Ormen M, Kume T, Ozkaya A, Ergor G, et al. The inappropriate use of HbA1c testing to monitor glycaemia: is there evidence in laboratory data? J Eval Clin Pract 2007;13:21–4.CrossrefPubMedGoogle Scholar

  • 5.

    Lyon AW, Higgins T, Wesenberg JC, Tran DV, Cembrowski GS. Variation in frequency of haemoglobin A1c (HbA1c) testing: population studies used to assess compliance with clinical practice guidelines and use of HbA1c to screen for diabetes. J Diab Sci Technol 2009;3:411–7.CrossrefGoogle Scholar

  • 6.

    Laxmisan A, Vaughan-Sarrazin M, Cram P. Repeated hemoglobin A1C ordering in the VA health system. Am J Med 2011;124:342–9.PubMedCrossrefWeb of ScienceGoogle Scholar

  • 7.

    Driskell OJ, Holland D, Hanna FW, Jones PW, Pemberton RJ, Tran M, et al. Inappropriate requesting of glycated hemoglobin (HbA1c) is widespread: assessment of prevalence, impact of national guidance, and practice-to-practice variability. Clin Chem 2012;58:906–15.PubMedCrossrefGoogle Scholar

  • 8.

    Pivovarov R, Albers DJ, Hripcsak G, Sepulveda JL, Elhadad N. Temporal trends of hemoglobin A1c testing. J Am Med Inform Assoc 2014;21:1038–44.PubMedCrossrefGoogle Scholar

  • 9.

    McCoy RG, Van Houten HK, Ross JS, Montori VM, Shah ND. HbA1c overtesting and overtreatment among US adults with controlled type 2 diabetes, 2001–13: observational population based study. Br Med J 2015;351:h6138.Google Scholar

  • 10.

    Paul CL, Piterman L, Shaw JE, Kirby C, Barker D, Robinson J, et al. Patterns of type 2 diabetes monitoring in rural towns: how does frequency of HbA1c and lipid testing compare with existing guidelines? Aust J Rural Health 2016;24:371–7.PubMedCrossrefWeb of ScienceGoogle Scholar

  • 11.

    Driskell OJ, Holland D, Waldron JL, Ford C, Scargill JJ, Heald A, et al. Reduced testing frequency for glycated haemoglobin, HbA1c, is associated with deteriorating diabetic control. Diabetes Care 2014;37:2731–7.CrossrefGoogle Scholar

  • 12.

    Scargill JJ, Livingston M, Holland D, Duff CJ, Fryer AA, Heald AH. Monitoring thyroid function in patients on levothyroxine. Assessment of conformity to national guidance and variability in practice. Exp Clin Endocrinol Diabetes 2017;125:625–33.Web of ScienceCrossrefPubMedGoogle Scholar

  • 13.

    Parcero AF, Yaeger T, Bienkowski RS. Frequency of monitoring hemoglobin A1C and achieving diabetes control. J Prim Care Community Health 2011;2:205–8.CrossrefPubMedGoogle Scholar

  • 14.

    Phan TL, Hossain J, Lawless S, Werk LN. Quarterly visits with glycated hemoglobin monitoring: the sweet spot for glycemic control in youth with type 1 diabetes. Diabetes Care 2014;37:341–5.CrossrefWeb of SciencePubMedGoogle Scholar

  • 15.

    Fu C, Ji L, Wang W, Luan R, Chen W, Zhan S, et al. Frequency of HbA1c monitoring was inversely associated with glycemic control of patients with type 2 diabetes mellitus. J Endocrinol Invest 2012;35:269–73.PubMedGoogle Scholar

  • 16.

    Loh TP, Tan KM, Saw S, Sethi SK. Glycated haemoglobin: what is the diagnostic yield at shortened testing intervals? Diabetes Res Clin Pract 2011;94:e40–2.PubMedCrossrefWeb of ScienceGoogle Scholar

  • 17.

    Kalbfleisch JD, Lawless JF. The analysis of panel data under a Markov assumption. J Am Statistical Assoc 1985;80:863–71.CrossrefGoogle Scholar

  • 18.

    R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2016. https://www.R-project.org/. Accessed: 11 May 2018.

  • 19.

    Jackson CH. Multi-state models for panel data: the msm package for R. J Stat Softw 2011;38:1–29.Google Scholar

  • 20.

    Bates D, Maechler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw 2015;67:1–48.Google Scholar

  • 21.

    Anichini R, Cosimi S, Di Carlo A, Orsini P, De Bellis A, Seghieri G, et al. Gender difference in response predictors after 1-year exenatide therapy twice daily in type 2 diabetic patients: a real world experience. Diabetes Metab Syndr Obes 2013;6: 123–9.PubMedGoogle Scholar

  • 22.

    Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up. Diabetes Care 2016;39:686–93.PubMedWeb of ScienceGoogle Scholar

  • 23.

    Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 2008;359:1577–89.Web of SciencePubMedCrossrefGoogle Scholar

  • 24.

    Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med 2008;358:580–91.Web of SciencePubMedCrossrefGoogle Scholar

  • 25.

    Mannucci E, Monami M, Dicembrini I, Piselli A, Porta M. Achieving HbA1c targets in clinical trials and in the real world: a systematic review and meta-analysis. J Endocrinol Invest 2014;37:477–95.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 26.

    Balkau B, Calvi-Gries F, Freemantle N, Vincent M, Pilorget V, Home PD. Predictors of HbA1c over 4 years in people with type 2 diabetes starting insulin therapies: the CREDIT study. Diabetes Res Clin Pract 2015;108:432–40.PubMedCrossrefWeb of ScienceGoogle Scholar

  • 27.

    Virtue MA, Furne JK, Nuttall FQ, Levitt MD. Relationship between GHb concentration and erythrocyte survival determined from breath carbon monoxide concentration. Diabetes Care 2004;27:931–5.CrossrefPubMedGoogle Scholar

  • 28.

    Lupescu A, Bissinger R, Goebel T. Enhanced suicidal erythrocyte death contributing to anemia in the elderly. Cell Physiol Biochem 2015;36:773–83.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 29.

    Stratton IM, Adler AI, Neil HA. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. Br Med J 2000;321:405–12.CrossrefGoogle Scholar

  • 30.

    Sood R, Sood A, Ghosh AK. Non-evidence-based variables affecting physicians’ test-ordering tendencies: a systematic review. Neth J Med 2007;65:167–77.PubMedGoogle Scholar

  • 31.

    Smellie WS, Galloway MJ, Chinn D, Gedling P. Is clinical practice variability the major reason for differences in pathology requesting patterns in general practice? J Clin Pathol 2002;55:312–4.PubMedCrossrefGoogle Scholar

  • 32.

    Yasaitis LC, Bubolz T, Skinner JS, Chandra A. Local population characteristics and hemoglobin A1c testing rates among diabetic medicare beneficiaries. PLoS One 2014;9:e111119.PubMedCrossrefWeb of ScienceGoogle Scholar

  • 33.

    Fryer AA, Smellie WS. Managing demand for laboratory tests: a laboratory toolkit. J Clin Pathol 2013;66:62–72.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 34.

    Svensson E, Baggesen LM, Thomsen RW, Lyngaa T, Pedersen L, Nørrelund H, et al. Patient-level predictors of achieving early glycaemic control in type 2 diabetes mellitus: a population-based study. Diabet Med 2016;33:1516–23.CrossrefPubMedGoogle Scholar

  • 35.

    Fitzmaurice GM, Laird NM, Ware JH. Applied longitudinal analysis, 2nd ed. Hoboken, NJ: Wiley, 2011.Google Scholar

  • 36.

    Glymour MM, Weuve J, Berkman LF, Kawachi I, Robins JM. When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol 2005;162:267–78.CrossrefPubMedGoogle Scholar

About the article

Corresponding author: Prof. Anthony A. Fryer, Department of Clinical Biochemistry, Keele University, Institute for Applied Clinical Sciences, University Hospitals of North Midlands, Newcastle Road, Stoke-on-Trent, Staffordshire ST4 6QG, UK, Phone: +44 1782 674245, Fax: +44 844 244 8602

Received: 2018-05-11

Accepted: 2018-09-04

Published Online: 2018-10-02

Published in Print: 2018-12-19

Author contributions: O.J.D., I.S-T, C.J.D. and A.A.F. wrote the initial draft of the manuscript, performed the data analysis and provided clinical advice and critique from a clinical laboratory scientist perspective. I.S-T. developed the statistical modelling, conducted the statistical analysis, contributed to the interpretation of results and drafted the manuscript. J.L.W., J.J.S, and M.T. performed the data extraction from the three centres. H.W. supported data preparation for analysis. C.F. provided clinical advice and critique from a clinical laboratory scientist perspective. A.H. and F.W.H. provided clinical advice and critique from a clinical/research diabetologist perspective. R.J.P. provided a patient perspective and ensured the team had a patient-centred focus. All authors reviewed and edited the manuscript. A.A.F. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: The study was supported by a National Institute for Health Research Healthcare Scientist Fellowship award to O.J.D. (HCS/08/011, Funder Id: 10.13039/501100000659), supervised by A.A.F.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organisation played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Citation Information: Clinical Chemistry and Laboratory Medicine (CCLM), Volume 57, Issue 2, Pages 296–304, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/cclm-2018-0503.

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