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Association between visit-to-visit variability of HbA1c and cognitive decline: a pooled analysis of two prospective population-based cohorts

  • Zhe-Bin Yu
  • Yao Zhu
  • Die Li
  • Meng-Yin Wu
  • Meng-Ling Tang
  • Jian-Bing WangEmail author
  • Kun ChenEmail author



The aim of this study was to investigate the association between visit-to-visit variability in HbA1c and cognitive function decline in the elderly population.


We performed a pooled analysis of two prospective population-based cohorts (the Health Retirement Study [HRS] and the English Longitudinal Study of Ageing [ELSA]). Cognitive function, including memory and executive function, were assessed at baseline and every 2 years, while HbA1c levels were assessed at baseline and every 4 years. Visit-to-visit variability (VVV) in HbA1c was calculated using the CV, SD and variation independent of the mean (VIM) during the follow-up period. Linear mixed models were used to evaluate the association between HbA1c variability and cognitive function decline with adjustment for demographics, mean HbA1c, education, smoking, alcohol consumption, BMI, baseline hypertension, baseline diabetes status and HDL-cholesterol.


The study enrolled 6237 participants (58.23% women, mean age 63.38 ± 8.62 years) with at least three measurements of HbA1c. The median follow-up duration was 10.56 ± 1.86 years. In the overall sample, compared with the lowest quartile of HbA1c variability, participants in the highest quartile of HbA1c variability had a significantly worse memory decline rate (−0.094 SD/year, 95% CI −0.185, −0.003) and executive function decline rate (−0.083 SD/year, 95% CI −0.125, −0.041), irrespective of mean HbA1c values over time. Among individuals without diabetes, each 1-SD increment in HbA1c CV was associated with a significantly higher rate of memory z score decline (−0.029, 95% CI −0.052, −0.005) and executive function z score decline (−0.049, 95% CI −0.079, −0.018) in the fully adjusted model.


We observed a significant association between long-term HbA1c variability and cognitive decline among the non-diabetic population in this study. The effect of maintaining steady glucose control on the rate of cognitive decline merits further investigation.


Cognitive decline Cognitive function Epidemiology Glucose variability HbA1c 



Cardiovascular disease


English Longitudinal Study of Ageing


Health and Retirement Study


Multiple imputation of chained equation


Variation independent of the mean


Visit-to-visit variability



We thank the University of Michigan for the use of data from the HRS Waves 8–13 from 2006–2016 and the UK Data Archive for the use of data from the ELSA Waves 2–7 from 2002–2015. The original data creators, depositors or copyright holders bear no responsibility for the current analysis or interpretation.

Contribution statement

KC is the guarantor of this work and had full access to all of the data in the study. KC and ZY conceptualised the study and designed the analysis plan. ZY performed all the statistical analyses and drafted the manuscript. JW provided supervision to ZY. All authors contributed to the acquisition, analysis or interpretation of data; provided critical revision of the manuscript for important intellectual content and approved the final version. KC is responsible for the integrity of the work as a whole.


The HRS data are sponsored by the National Institute on Aging (grant number U01AG009740), and the study is being conducted by the University of Michigan. The current study was funded by the Ministry of Science and Technology of the People’s Republic of China (No. 2015FY111600). The funders played no role in the study design, data collection, analysis, decision to publish or preparation of the manuscript.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2019_4986_MOESM1_ESM.pdf (463 kb)
ESM (PDF 463 kb)


  1. 1.
    Livingston G, Sommerlad A, Orgeta V et al (2017) Dementia prevention, intervention, and care. Lancet 390(10113):2673–2734. CrossRefGoogle Scholar
  2. 2.
    Biessels GJ, Strachan MW, Visseren FL, Kappelle LJ, Whitmer RA (2014) Dementia and cognitive decline in type 2 diabetes and prediabetic stages: towards targeted interventions. Lancet Diabetes Endocrinol 2(3):246–255. CrossRefGoogle Scholar
  3. 3.
    Forbes A, Murrells T, Mulnier H, Sinclair AJ (2018) Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study. Lancet Diabetes Endocrinol 6(6):476–486. CrossRefGoogle Scholar
  4. 4.
    Orsi E, Solini A, Bonora E et al (2018) Haemoglobin A1c variability is a strong, independent predictor of all-cause mortality in patients with type 2 diabetes. Diabetes Obes Metab 20(8):1885–1893. CrossRefGoogle Scholar
  5. 5.
    Wan EY, Fung CS, Fong DY, Lam CL (2016) 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 Complicat 30(7):1240–1247. CrossRefGoogle Scholar
  6. 6.
    Xu D, Fang H, Xu W, Yan Y, Liu Y, Yao B (2016) Fasting plasma glucose variability and all-cause mortality among type 2 diabetes patients: a dynamic cohort study in Shanghai. China. Sci Rep 6(1):39633. CrossRefGoogle Scholar
  7. 7.
    Echouffo-Tcheugui JB, Zhao S, Brock G, Matsouaka RA, Kline D, Joseph JJ (2019) Visit-to-visit glycemic variability and risks of cardiovascular events and all-cause mortality: the ALLHAT study. Diabetes Care 42(3):486–493. CrossRefGoogle Scholar
  8. 8.
    Hirakawa Y, Arima H, Zoungas S et al (2014) 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(8):2359–2365. CrossRefGoogle Scholar
  9. 9.
    Waden J, Forsblom C, Thorn LM et al (2009) A1C variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes. Diabetes 58(11):2649–2655. CrossRefGoogle Scholar
  10. 10.
    Jun JE, Lee SE, Lee YB et al (2017) Glycated albumin and its variability as an indicator of cardiovascular autonomic neuropathy development in type 2 diabetic patients. Cardiovasc Diabetol 16(1):127. CrossRefGoogle Scholar
  11. 11.
    Nazim J, Fendler W, Starzyk J (2014) Metabolic control and its variability are major risk factors for microalbuminuria in children with type 1 diabetes. Endokrynol Pol 65(2):83–89. CrossRefGoogle Scholar
  12. 12.
    Rodriguez-Segade S, Rodriguez J, Garcia Lopez JM, Casanueva FF, Camina F (2012) Intrapersonal HbA(1c) variability and the risk of progression of nephropathy in patients with type 2 diabetes. Diabet Med 29(12):1562–1566. CrossRefGoogle Scholar
  13. 13.
    Kilpatrick ES, Rigby AS, Atkin SL (2008) A1C variability and the risk of microvascular complications in type 1 diabetes: data from the Diabetes Control and Complications Trial. Diabetes Care 31(11):2198–2202. CrossRefGoogle Scholar
  14. 14.
    Hietala K, Waden J, Forsblom C et al (2013) HbA1c variability is associated with an increased risk of retinopathy requiring laser treatment in type 1 diabetes. Diabetologia 56(4):737–745. CrossRefGoogle Scholar
  15. 15.
    Hermann JM, Hammes HP, Rami-Merhar B et al (2014) HbA1c variability as an independent risk factor for diabetic retinopathy in type 1 diabetes: a German/Austrian multicenter analysis on 35,891 patients. PLoS One 9(3):e91137. CrossRefGoogle Scholar
  16. 16.
    Ravona-Springer R, Heymann A, Schmeidler J et al (2017) Hemoglobin A1c variability predicts symptoms of depression in elderly individuals with type 2 diabetes. Diabetes Care 40(9):1187–1193. CrossRefGoogle Scholar
  17. 17.
    Yang CP, Li CI, Liu CS et al (2017) Variability of fasting plasma glucose increased risks of diabetic polyneuropathy in T2DM. Neurology 88(10):944–951. CrossRefGoogle Scholar
  18. 18.
    Rawlings AM, Sharrett AR, Mosley TH, Ballew SH, Deal JA, Selvin E (2017) Glucose peaks and the risk of dementia and 20-year cognitive decline. Diabetes Care 40(7):879–886. CrossRefGoogle Scholar
  19. 19.
    Geijselaers SLC, Sep SJS, Stehouwer CDA, Biessels GJ (2015) Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol 3(1):75–89. CrossRefGoogle Scholar
  20. 20.
    Cukierman-Yaffe T, Gerstein HC, Williamson JD et al (2009) Relationship between baseline glycemic control and cognitive function in individuals with type 2 diabetes and other cardiovascular risk factors: the action to control cardiovascular risk in diabetes-memory in diabetes (ACCORD-MIND) trial. Diabetes Care 32(2):221–226. CrossRefGoogle Scholar
  21. 21.
    Kim C, Sohn JH, Jang MU et al (2015) Association between visit-to-visit glucose variability and cognitive function in aged type 2 diabetic patients: a cross-sectional study. PLoS One 10(7):e0132118. CrossRefGoogle Scholar
  22. 22.
    Cui X, Abduljalil A, Manor BD, Peng CK, Novak V (2014) Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes. PLoS One 9(1):e86284. CrossRefGoogle Scholar
  23. 23.
    Sonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, Weir DR (2014) Cohort profile: the health and retirement study (HRS). Int J Epidemiol 43(2):576–585. CrossRefGoogle Scholar
  24. 24.
    Steptoe A, Breeze E, Banks J, Nazroo J (2013) Cohort profile: the English longitudinal study of ageing. Int J Epidemiol 42(6):1640–1648. CrossRefGoogle Scholar
  25. 25.
    Graig R, Deverill C, Pickering K (2006) Quality control of blood saliva and urine analytes. In: Spronston K, Mindell J (eds) Health Survey for England 2004: methodology and documentation, vol 2. The Information Centre, LondonGoogle Scholar
  26. 26.
    Eileen MC, Jessica DF, Jung Ki K et al (2013) Documentation of biomarkers in the 2006 and 2008 health and retirement study. Institute for Social Research, University of Michigan, Ann Arbor, MichiganGoogle Scholar
  27. 27.
    Rothwell PM (2010) Limitations of the usual blood-pressure hypothesis and importance of variability, instability, and episodic hypertension. Lancet 375(9718):938–948. CrossRefGoogle Scholar
  28. 28.
    Baars MA, van Boxtel MP, Dijkstra JB et al (2009) Predictive value of mild cognitive impairment for dementia. The influence of case definition and age. Dement Geriatr Cogn Disord 27(2):173–181. CrossRefGoogle Scholar
  29. 29.
    Dregan A, Stewart R, Gulliford MC (2013) Cardiovascular risk factors and cognitive decline in adults aged 50 and over: a population-based cohort study. Age Ageing 42(3):338–345. CrossRefGoogle Scholar
  30. 30.
    Bates D, Machler M, Bolker WS (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48CrossRefGoogle Scholar
  31. 31.
    Li TC, Yang CP, Tseng ST et al (2017) Visit-to-visit variations in fasting plasma glucose and HbA1c associated with an increased risk of Alzheimer disease: Taiwan diabetes study. Diabetes Care 40(9):1210–1217. CrossRefGoogle Scholar
  32. 32.
    Rizzo MR, Marfella R, Barbieri M et al (2010) Relationships between daily acute glucose fluctuations and cognitive performance among aged type 2 diabetic patients. Diabetes Care 33(10):2169–2174. CrossRefGoogle Scholar
  33. 33.
    Ceriello A, Esposito K, Piconi L et al (2008) Oscillating glucose is more deleterious to endothelial function and oxidative stress than mean glucose in normal and type 2 diabetic patients. Diabetes 57(5):1349–1354. CrossRefGoogle Scholar
  34. 34.
    Salkind SJ, Huizenga R, Fonda SJ, Walker MS, Vigersky RA (2014) Glycemic variability in nondiabetic morbidly obese persons: results of an observational study and review of the literature. J Diabetes Sci Technol 8(5):1042–1047. CrossRefGoogle Scholar
  35. 35.
    Hanefeld M, Sulk S, Helbig M, Thomas A, Köhler C (2014) Differences in glycemic variability between normoglycemic and prediabetic subjects. J Diabetes Sci Technol 8(2):286–290. CrossRefGoogle Scholar
  36. 36.
    Bancks MP, Carnethon MR, Jacobs DR Jr et al (2018) Fasting glucose variability in young adulthood and cognitive function in middle age: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Diabetes Care 41(12):2579–2585. CrossRefGoogle Scholar
  37. 37.
    Wang A, Liu X, Xu J, Han X et al (2017) Visit-to-visit variability of fasting plasma glucose and the risk of cardiovascular disease and all-cause mortality in the general population. J Am Heart Assoc 6(12).
  38. 38.
    Ghouse J, Skov MW, Kanters JK et al (2019) Visit-to-visit variability of hemoglobin a in people without diabetes and risk of major adverse cardiovascular events and all-cause mortality. Diabetes Care 42(1):134–141. CrossRefGoogle Scholar
  39. 39.
    Kim JA, Lee JS, Chung HS et al (2018) Impact of visit-to-visit fasting plasma glucose variability on the development of type 2 diabetes: a nationwide population-based cohort study. Diabetes Care 41(12):2610–2616. CrossRefGoogle Scholar
  40. 40.
    Zhou JJ, Schwenke DC, Bahn G, Reaven P (2018) Glycemic variation and cardiovascular risk in the Veterans Affairs Diabetes Trial. Diabetes Care 41(10):2187–2194. CrossRefGoogle Scholar
  41. 41.
    Sinclair AJ, Paolisso G, Castro M, Bourdel-Marchasson I, Gadsby R, Rodriguez Mañas L (2011) European Diabetes Working Party for Older People 2011 clinical guidelines for type 2 diabetes mellitus. Executive summary. Diabetes Metab 37(Suppl 3):S27–S38. CrossRefGoogle Scholar
  42. 42.
    Monnier L, Mas E, Ginet C et al (2006) Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295(14):1681–1687. CrossRefGoogle Scholar
  43. 43.
    Arnold SE, Arvanitakis Z, Macauley-Rambach SL et al (2018) Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol 14(3):168–181. CrossRefGoogle Scholar
  44. 44.
    Del Guerra S, Grupillo M, Masini M et al (2007) Gliclazide protects human islet beta-cells from apoptosis induced by intermittent high glucose. Diabetes Metab Res Rev 23(3):234–238. CrossRefGoogle Scholar
  45. 45.
    U.K. Prospective Diabetes Study Group (1995) U.K. prospective diabetes study 16. Overview of 6 years’ therapy of type II diabetes: a progressive disease. Diabetes 44(11):1249–1258. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Epidemiology and Health Statistics, Department of Public HealthZhejiang University School of MedicineZhejiangChina
  2. 2.Research Center for Air Pollution and HealthZhejiang UniversityZhejiangChina
  3. 3.Cancer Institute, The Second Affiliated Hospital/Department of Public HealthZhejiang University School of MedicineZhejiangChina

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