Variation of HbA1c affects cognition and white matter microstructure in healthy, young adults.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
04 2021
04 2021
Historique:
received:
07
01
2019
accepted:
10
06
2019
revised:
03
06
2019
pubmed:
31
8
2019
medline:
15
5
2021
entrez:
31
8
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31467393
doi: 10.1038/s41380-019-0504-3
pii: 10.1038/s41380-019-0504-3
doi:
Substances chimiques
Glycated Hemoglobin A
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1399-1408Subventions
Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States
Commentaires et corrections
Type : ErratumIn
Références
World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2016.
World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. Diabetes research and clinical practice. Geneva: World Health Organization; 2011.
American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care 2018;41:S13–S27.
Seaquist ER. The final frontier: how does diabetes affect the brain? Diabetes. 2010;59:4–5.
doi: 10.2337/db09-1600
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.
doi: 10.1007/s001250050588
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.
doi: 10.1016/S2213-8587(14)70148-2
Kodl CT, Seaquist ER. Cognitive dysfunction and diabetes mellitus. Endocr. Rev. 2008;29:494–511.
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.
doi: 10.1007/s00125-018-4610-6
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.
doi: 10.2337/dc13-1388
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.
doi: 10.1007/s00125-012-2488-2
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.
doi: 10.1016/j.neuroimage.2009.09.052
Bennett IJ, Madden DJ. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience. 2014;276:187–205.
doi: 10.1016/j.neuroscience.2013.11.026
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.
doi: 10.1016/j.neurobiolaging.2008.08.015
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.
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.
doi: 10.1002/hbm.20689
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.
doi: 10.1159/000236445
McLaren L. Socioeconomic status and obesity. Epidemiol. Rev. 2007;29:29–48.
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.
Galobardes B, Shaw M, Lawlor DA, Lynch JW, Smith GD. Indicators of socioeconomic position (part 1). J. Epidemiol. Community Health. 2006;60:7–12.
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.
doi: 10.1016/j.neuroimage.2013.05.041
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.
doi: 10.1016/j.neuroimage.2012.02.018
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.
doi: 10.1212/WNL.0b013e3182872e5f
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.
doi: 10.1017/S1355617714000241
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.
doi: 10.1371/journal.pone.0015710
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.
doi: 10.1002/mrm.24623
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.
doi: 10.1016/j.neuroimage.2013.04.127
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.
doi: 10.1016/j.neuroimage.2006.02.024
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.
doi: 10.1016/j.psyneuen.2018.03.007
Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:83–98.
doi: 10.1016/j.neuroimage.2008.03.061
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.
doi: 10.1016/j.neuroimage.2007.07.053
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.
doi: 10.1016/j.neuroimage.2006.12.021
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.
doi: 10.1093/cercor/bhr246
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 .
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 .
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 .
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.
doi: 10.1016/j.metabol.2017.08.009
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.
doi: 10.1136/jech-2016-207588
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.
doi: 10.3233/JAD-2012-120106
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.
doi: 10.1111/j.1399-5448.2008.00431.x
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.
doi: 10.1155/2009/329632
Osborn O, Olefsky JM. The cellular and signaling networks linking the immune system and metabolism in disease. Nat Med. 2012. 363–74.
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.
doi: 10.2337/dc12-0702
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.
doi: 10.2337/db06-1656
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 .
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 .
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.
doi: 10.1016/j.jad.2016.05.047
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.
doi: 10.1016/j.bbi.2016.12.020
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.
Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013;80:807–15.
doi: 10.1016/j.neuron.2013.10.044
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.
doi: 10.1016/j.neurobiolaging.2010.01.014
Brito NH, Noble KG. Socioeconomic status and structural brain development. Front. Neurosci. 2014;8. https://doi.org/10.3389/fnins.2014.00276 .
Pechey R, Monsivais P. Socioeconomic inequalities in the healthiness of food choices: exploring the contributions of food expenditures. Prev Med. 2016;88:203–9.
doi: 10.1016/j.ypmed.2016.04.012