Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Variation of HbA1c affects cognition and white matter microstructure in healthy, young adults

Molecular Psychiatry volume 26, pages 1399–1408 (2021)Cite this article

Subjects

A Correction to this article was published on 13 March 2020

This article has been updated

Abstract

The metabolic serum marker HbA1c has been associated with both impaired cognitive performance and altered white matter integrity in patients suffering from diabetes mellitus. However, it remains unclear if higher levels of HbA1c might also affect brain structure and function in healthy subjects. With the present study we therefore aimed to investigate the relationship between HbA1c levels and cognitive performance as well as white matter microstructure in healthy, young adults. To address this question, associations between HbA1c and cognitive measures (NIH Cognition Toolbox) as well as DTI-derived imaging measures of white matter microstructure were investigated in a publicly available sample of healthy, young adults as part of the Humane Connectome Project (n = 1206, mean age = 28.8 years, 45.5% male). We found that HbA1c levels (range 4.1–6.3%) were significantly inversely correlated with measures of cognitive performance. Higher HbA1c levels were associated with significant and widespread reductions in fractional anisotropy (FA) controlling for age, sex, body mass index, ethnicity, and education. FA reductions were furthermore found to covary with measures of cognitive performance. The same pattern of results could be observed in analyses restricted to participants with HBA1c levels below 5.7%. The present study demonstrates that low-grade HbA1c variation below diagnostic threshold for diabetes is related to both cognitive performance and white matter integrity in healthy, young adults. These findings highlight the detrimental impact of metabolic risk factors on brain physiology and underscore the importance of intensified preventive measures independent of the currently applied diagnostic HbA1c cutoff scores.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1

Similar content being viewed by others

Change history

References

  1. World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2016.

  2. World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. Diabetes research and clinical practice. Geneva: World Health Organization; 2011.

  3. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care 2018;41:S13–S27.

  4. Seaquist ER. The final frontier: how does diabetes affect the brain? Diabetes. 2010;59:4–5.

    Article  CAS  Google Scholar 

  5. Ott A, Stolk RP, Hofman A, Van Harskamp F, Grobbee DE, Breteler MMB. Association of diabetes mellitus and dementia: the Rotterdam study. Diabetologia. 1996;39:1392–7.

    Article  CAS  Google Scholar 

  6. Geijselaers SLC, Sep SJS, Stehouwer CDA, Biessels GJ. Glucose regulation, cognition, and brain MRI in type 2 diabetes: a systematic review. Lancet Diabetes Endocrinol. 2015;3:75–89.

    Article  CAS  Google Scholar 

  7. Kodl CT, Seaquist ER. Cognitive dysfunction and diabetes mellitus. Endocr. Rev. 2008;29:494–511.

  8. Fox LA, Hershey T, Mauras N, Arbeláez AM, Tamborlane WV, Buckingham B, et al. Persistence of abnormalities in white matter in children with type 1 diabetes. Diabetologia. 2018;61:1538–47.

    Article  Google Scholar 

  9. Barnea-Goraly N, Raman M, Mazaika P, Marzelli M, Hershey T, Weinzimer SA, et al. Alterations in white matter structure in young children with type 1 diabetes. Diabetes Care. 2014;37:332–40.

    Article  Google Scholar 

  10. Van Duinkerken E, Schoonheim MM, IJzerman RG, Klein M, Ryan CM, Moll AC, et al. Diffusion tensor imaging in type 1 diabetes: decreased white matter integrity relates to cognitive functions. Diabetologia. 2012;55:1218–20.

    Article  Google Scholar 

  11. Kochunov P, Coyle T, Lancaster J, Robin DA, Hardies J, Kochunov V, et al. Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging. Neuroimage. 2010;49:1190–9.

    Article  CAS  Google Scholar 

  12. Bennett IJ, Madden DJ. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience. 2014;276:187–205.

    Article  CAS  Google Scholar 

  13. Bartzokis G, Lu PH, Tingus K, Mendez MF, Richard A, Peters DG, et al. Lifespan trajectory of myelin integrity and maximum motor speed. Neurobiol Aging. 2010;31:1554–62.

    Article  CAS  Google Scholar 

  14. Muetzel RL, Collins PF, Mueller BA, M. Schissel A, Lim KO, Luciana M. The development of corpus callosum microstructure and associations with bimanual task performance in healthy adolescents. Neuroimage 2008;39:1918–25.

  15. Kochunov P, Robin DA, Royall DR, Coyle T, Lancaster J, Kochunov V, et al. Can structural MRI indices of cerebral integrity track cognitive trends in executive control function during normal maturation and adulthood? Hum Brain Mapp. 2009;30:2581–94.

    Article  Google Scholar 

  16. Konrad A, Vucurevic G, Musso F, Stoeter P, Winterer G. Correlation of brain white matter diffusion anisotropy and mean diffusivity with reaction time in an oddball task. Neuropsychobiology. 2009;60:55–66.

    Article  Google Scholar 

  17. McLaren L. Socioeconomic status and obesity. Epidemiol. Rev. 2007;29:29–48.

  18. Weintraub S, Dikmen SS, Heaton RK, Tulsky DS, Zelazo PD, Bauer PJ et al. Cognition assessment using the NIH Toolbox. Neurology 2013;80:S49–S53.

  19. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Smith GD. Indicators of socioeconomic position (part 1). J. Epidemiol. Community Health. 2006;60:7–12.

  20. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62–79.

    Article  Google Scholar 

  21. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, et al. The human connectome project: a data acquisition perspective. NeuroImage. 2012;62:2222–31.

    Article  Google Scholar 

  22. Gershon RC, Wagster MV, Hendrie HC, Fox NA, Cook KF, Nowinski CJ. NIH toolbox for assessment of neurological and behavioral function. Neurology. 2013;80:S2–6.

    Article  Google Scholar 

  23. Heaton RK, Akshoomoff N, Tulsky D, Mungas D, Weintraub S, Dikmen S, et al. Reliability and validity of composite scores from the NIH toolbox cognition battery in adults. J Int Neuropsychol Soc. 2014;20:588–98.

    Article  Google Scholar 

  24. Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, Glasser MF, et al. Multiplexed echo planar imaging for sub-second whole brain fmri and fast diffusion imaging. PLoS One. 2010;5:e15710.

    Article  CAS  Google Scholar 

  25. Sotiropoulos SN, Moeller S, Jbabdi S, Xu J, Andersson JL, Auerbach EJ, et al. Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: reducing the noise floor using SENSE. Magn Reson Med. 2013;70:1682–9.

    Article  CAS  Google Scholar 

  26. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the human connectome project. Neuroimage. 2013;80:105–24.

    Article  Google Scholar 

  27. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:1487–505.

    Article  Google Scholar 

  28. Repple J, Opel N, Meinert S, Redlich R, Hahn T, Winter NR, et al. Elevated body-mass index is associated with reduced white matter integrity in two large independent cohorts. Psychoneuroendocrinology. 2018;91:179–85.

    Article  Google Scholar 

  29. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:83–98.

    Article  Google Scholar 

  30. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage. 2008;39:336–47.

    Article  Google Scholar 

  31. Kochunov P, Thompson PM, Lancaster JL, Bartzokis G, Smith S, Coyle T, et al. Relationship between white matter fractional anisotropy and other indices of cerebral health in normal aging: tract-based spatial statistics study of aging. Neuroimage. 2007;35:478–87.

    Article  CAS  Google Scholar 

  32. Herting MM, Maxwell EC, Irvine C, Nagel BJ. The impact of sex, puberty, and hormones on white matter microstructure in adolescents. Cereb Cortex. 2012;22:1979–92.

    Article  Google Scholar 

  33. Opel N, Redlich R, Kaehler C, Grotegerd D, Dohm K, Heindel W, et al. Prefrontal gray matter volume mediates genetic risks for obesity. Mol Psychiatry. 2017;22:703–10. http://www.ncbi.nlm.nih.gov/pubmed/28348383.

  34. Opel N, Redlich R, Dohm K, Zaremba D, Goltermann J, Repple J, et al. Mediation of the influence of childhood maltreatment on depression relapse by cortical structure: a 2-year longitudinal observational study. The Lancet Psychiatry 2019. https://doi.org/10.1016/S2215-0366(19)30044-6.

  35. Mackey S, Chaarani B, Kan KJ, Spechler PA, Orr C, Banaschewski T, et al. Brain Regions Related to Impulsivity Mediate the Effects of Early Adversity on Antisocial Behavior. Biol Psychiatry 2017. https://doi.org/10.1016/j.biopsych.2015.12.027.

  36. Alfaro FJ, Gavrieli A, Saade-Lemus P, Lioutas VA, Upadhyay J, Novak V. White matter microstructure and cognitive decline in metabolic syndrome: a review of diffusion tensor imaging. Metabolism. 2018;78:52–68.

    Article  CAS  Google Scholar 

  37. Pappas C, Andel R, Infurna FJ, Seetharaman S. Glycated haemoglobin (HbA1c), diabetes and trajectories of change in episodic memory performance. J Epidemiol Community Health. 2017;71:115–20.

    Article  Google Scholar 

  38. Ravona-Springer R, Moshier E, Schmeidler J, Godbold J, Akrivos J, Rapp M, et al. Changes in glycemic control are associated with changes in cognition in non-diabetic elderly. J Alzheimer’s Dis. 2012;30:299–309.

    Article  CAS  Google Scholar 

  39. Malone JI, Hanna S, Saporta S, Mervis RF, Park CR, Chong L, et al. Hyperglycemia not hypoglycemia alters neuronal dendrites and impairs spatial memory. Pediatr Diabetes. 2008;9:531–9.

    Article  Google Scholar 

  40. Hernández-Fonseca JP, Rincón J, Pedreañez A, Viera N, Arcaya JL, Carrizo E, et al. Structural and ultrastructural analysis of cerebral cortex, cerebellum, and hypothalamus from diabetic rats. Exp Diabetes. Res 2009;2009:1–12.

    Article  Google Scholar 

  41. Osborn O, Olefsky JM. The cellular and signaling networks linking the immune system and metabolism in disease. Nat Med. 2012. 363–74.

  42. Wang X, Bao W, Liu J, OuYang Y-Y, Wang D, Rong S, et al. Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2013;36:166–75.

    Article  CAS  Google Scholar 

  43. Fontana L, Eagon JC, Trujillo ME, Scherer PE, Klein S. Visceral fat adipokine secretion is associated with systemic inflammation in obese humans. Diabetes. 2007;56:1010–3.

    Article  CAS  Google Scholar 

  44. Frodl T, Carballedo A, Hughes MM, Saleh K, Fagan A, Skokauskas N, et al. Reduced expression of glucocorticoid-inducible genes GILZ and SGK-1: high IL-6 levels are associated with reduced hippocampal volumes in major depressive disorder. Transl Psychiatry. http://www.ncbi.nlm.nih.gov/pubmed/22832853.

  45. van Velzen LS, Schmaal L, Milaneschi Y, van Tol M-J, van der Wee NJA, Veltman DJ, et al. Immunometabolic dysregulation is associated with reduced cortical thickness of the anterior cingulate cortex. Brain Behav Immun. 2017;60:361–8. http://www.ncbi.nlm.nih.gov/pubmed/27989860.

  46. Benedetti F, Poletti S, Hoogenboezem TA, Mazza E, Ambrée O, de Wit H, et al. Inflammatory cytokines influence measures of white matter integrity in bipolar disorder. J Affect Disord. 2016;202:1–9.

    Article  CAS  Google Scholar 

  47. Poletti S, de Wit H, Mazza E, Wijkhuijs AJM, Locatelli C, Aggio V, et al. Th17 cells correlate positively to the structural and functional integrity of the brain in bipolar depression and healthy controls. Brain Behav Immun. 2017;61:317–25.

    Article  CAS  Google Scholar 

  48. Racine AM, Merluzzi AP, Adluru N, Norton D, Koscik RL, Clark LR et al. Association of longitudinal white matter degeneration and cerebrospinal fluid biomarkers of neurodegeneration, inflammation and Alzheimer’s disease in late-middle-aged adults. Brain Imaging Behav 2019;13:41–52.

  49. Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013;80:807–15.

    Article  CAS  Google Scholar 

  50. Kochunov P, Williamson DE, Lancaster J, Fox P, Cornell J, Blangero J, et al. Fractional anisotropy of water diffusion in cerebral white matter across the lifespan. Neurobiol Aging. 2012;33:9–20.

    Article  CAS  Google Scholar 

  51. Brito NH, Noble KG. Socioeconomic status and structural brain development. Front. Neurosci. 2014;8. https://doi.org/10.3389/fnins.2014.00276.

  52. Pechey R, Monsivais P. Socioeconomic inequalities in the healthiness of food choices: exploring the contributions of food expenditures. Prev Med. 2016;88:203–9.

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5–1 and DA1151/5–2 to UD; SFB-TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Funding

VA is a member of the advisory board of, or has given presentations on behalf of, the following companies: Astra-Zeneca, Janssen-Organon, Lilly, Lundbeck, Servier, Pfizer, Otsuka, and Trommsdorff. These affiliations are of no relevance to the work described in the paper. H. Kugel has received consultation fees from MR: comp GmbH, Testing Services for MR Safety.

Author information

Author notes

  1. These authors contributed equally: Jonathan Repple, Greta Karliczek

Authors and Affiliations

  1. Department of Psychiatry, University of Münster, Münster, Germany

    Jonathan Repple, Greta Karliczek, Susanne Meinert, Katharina Förster, Dominik Grotegerd, Janik Goltermann, Ronny Redlich, Volker Arolt, Bernhard T. Baune, Udo Dannlowski & Nils Opel

  2. Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia

    Bernhard T. Baune

  3. The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia

    Bernhard T. Baune

Authors
  1. Jonathan Repple
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Greta Karliczek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Susanne Meinert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katharina Förster
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dominik Grotegerd
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Janik Goltermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ronny Redlich
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Volker Arolt
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bernhard T. Baune
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Udo Dannlowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Nils Opel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JR and GK made substantial contributions to the conception, design of the work, and drafted the work. SM, KF, DG, and JG made substantial contributions to the analysis of this work and the interpretation of data. VA, BB, and UD have substantially revised the work. NO made substantial contributions to the conception, design of the work, and drafted the work. All authors approved the submitted version and agreed both to be personally accountable for the author’s own contributions and that questions related to the accuracy or integrity of any part of the work are appropriately investigated.

Corresponding author

Correspondence to Nils Opel.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Repple, J., Karliczek, G., Meinert, S. et al. Variation of HbA1c affects cognition and white matter microstructure in healthy, young adults. Mol Psychiatry 26, 1399–1408 (2021). https://doi.org/10.1038/s41380-019-0504-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-019-0504-3

This article is cited by

Search

Advanced search

Quick links