Insulin resistance, brain atrophy, and cognitive performance in late middle-aged adults

Auriel A Willette, Guofan Xu, Sterling C Johnson, Alex C Birdsill, Erin M Jonaitis, Mark A Sager, Bruce P Hermann, Asenath La Rue, Sanjay Asthana, Barbara B Bendlin, Auriel A Willette, Guofan Xu, Sterling C Johnson, Alex C Birdsill, Erin M Jonaitis, Mark A Sager, Bruce P Hermann, Asenath La Rue, Sanjay Asthana, Barbara B Bendlin

Abstract

Objective: Insulin resistance dysregulates glucose uptake and other functions in brain areas affected by Alzheimer disease. Insulin resistance may play a role in Alzheimer disease etiopathogenesis. This longitudinal study examined whether insulin resistance among late middle-aged, cognitively healthy individuals was associated with 1) less gray matter in Alzheimer disease-sensitive brain regions and 2) worse cognitive performance.

Research design and methods: Homeostasis model assessment of insulin resistance, gray matter volume, and the Rey Auditory Verbal Learning Test (RAVLT) were acquired in 372 participants at baseline and a consecutive subset of 121 individuals ~4 years later. Voxel-based morphometry and tensor-based morphometry were used, respectively, to test the association of insulin resistance with baseline brain volume and progressive gray matter atrophy.

Results: Higher insulin resistance predicted less gray matter at baseline and 4 years later in medial temporal lobe, prefrontal cortices, precuneus, and other parietal gyri. A region-of-interest analysis, independent of the voxel-wise analyses, confirmed that higher insulin resistance was related to medial temporal lobe atrophy. Atrophy itself corresponded to cognitive deficits in the RAVLT. Temporal lobe atrophy that was predicted by higher insulin resistance significantly mediated worse RAVLT encoding performance.

Conclusions: These results suggest that insulin resistance in an asymptomatic, late middle-aged cohort is associated with progressive atrophy in regions affected by early Alzheimer disease. Insulin resistance may also affect the ability to encode episodic information by negatively influencing gray matter volume in medial temporal lobe.

Figures

Figure 1
Figure 1
The relationship between higher HOMA-IR and lower regional gray matter (GM) volume for baseline images among 372 participants. Coordinates correspond to sagittal cross-sections in MNI space. An orthogonal coronal image illustrates the location of these cross-sections in the brain. The result color map and color bar represent t values. A representative voxel in parahippocampus depicts the association. Brains are oriented in neurologic space. A.U., arbitrary units. L, left. (A high-quality digital representation of this figure is available in the online issue.)
Figure 2
Figure 2
The association between higher HOMA-IR and atrophy in regional gray matter (GM) volume over ~4 years for 121 participants with longitudinal data. Coordinates correspond to sagittal cross-sections in MNI space. An orthogonal coronal image illustrates the location of these cross-sections in the brain. The color map and bar represent t values. A representative voxel in calcarine gyrus depicts the association. Brains are oriented in neurologic space. A.U., arbitrary units. R, right. (A high-quality digital representation of this figure is available in the online issue.)

References

    1. de la Monte SM, Wands JR. Review of insulin and insulin-like growth factor expression, signaling, and malfunction in the central nervous system: relevance to Alzheimer’s disease. J Alzheimers Dis 2005;7:45–61
    1. Craft S, Watson GS. Insulin and neurodegenerative disease: shared and specific mechanisms. Lancet Neurol 2004;3:169–178
    1. Convit A, Wolf OT, Tarshish C, de Leon MJ. Reduced glucose tolerance is associated with poor memory performance and hippocampal atrophy among normal elderly. Proc Natl Acad Sci USA 2003;100:2019–2022
    1. Schrijvers EM, Witteman JC, Sijbrands EJ, Hofman A, Koudstaal PJ, Breteler MM. Insulin metabolism and the risk of Alzheimer disease: the Rotterdam Study. Neurology 2010;75:1982–1987
    1. Blalock EM, Grondin R, Chen KC, et al. Aging-related gene expression in hippocampus proper compared with dentate gyrus is selectively associated with metabolic syndrome variables in rhesus monkeys. J Neurosci 2010;30:6058–6071
    1. Willette AA, Bendlin BB, Colman RJ, et al. Calorie restriction reduces the influence of glucoregulatory dysfunction on regional brain volume in aged rhesus monkeys. Diabetes 2012;61:1036–1042
    1. Cersosimo E, DeFronzo RA. Insulin resistance and endothelial dysfunction: the road map to cardiovascular diseases. Diabetes Metab Res Rev 2006;22:423–436
    1. Henneberg N, Hoyer S. Desensitization of the neuronal insulin receptor: a new approach in the etiopathogenesis of late-onset sporadic dementia of the Alzheimer type (SDAT)? Arch Gerontol Geriatr 1995;21:63–74
    1. Zhao WQ, Chen H, Quon MJ, Alkon DL. Insulin and the insulin receptor in experimental models of learning and memory. Eur J Pharmacol 2004;490:71–81
    1. Gasparini L, Gouras GK, Wang R, et al. Stimulation of beta-amyloid precursor protein trafficking by insulin reduces intraneuronal beta-amyloid and requires mitogen-activated protein kinase signaling. J Neurosci 2001;21:2561–2570
    1. Zhao WQ, Lacor PN, Chen H, et al. Insulin receptor dysfunction impairs cellular clearance of neurotoxic oligomeric abeta. J Biol Chem 2009;284:18742–18753
    1. Planel E, Tatebayashi Y, Miyasaka T, et al. Insulin dysfunction induces in vivo tau hyperphosphorylation through distinct mechanisms. J Neurosci 2007;27:13635–13648
    1. Rasgon NL, Kenna HA, Wroolie TE, et al. Insulin resistance and hippocampal volume in women at risk for Alzheimer’s disease. Neurobiol Aging 2011;32:1942–1948
    1. Raji CA, Ho AJ, Parikshak NN, et al. Brain structure and obesity. Hum Brain Mapp 2010;31:353–364
    1. Tan ZS, Beiser AS, Fox CS, et al. Association of metabolic dysregulation with volumetric brain magnetic resonance imaging and cognitive markers of subclinical brain aging in middle-aged adults: the Framingham Offspring Study. Diabetes Care 2011;34:1766–1770
    1. Benedict C, Brooks SJ, Kullberg J, et al. Impaired insulin sensitivity as indexed by the HOMA score is associated with deficits in verbal fluency and temporal lobe gray matter volume in the elderly. Diabetes Care 2012;35:488–494
    1. Burns JM, Honea RA, Vidoni ED, Hutfles LJ, Brooks WM, Swerdlow RH. Insulin is differentially related to cognitive decline and atrophy in Alzheimer’s disease and aging. Biochim Biophys Acta 2012;1822:333–339
    1. Ursache A, Wedin W, Tirsi A, Convit A. Preliminary evidence for obesity and elevations in fasting insulin mediating associations between cortisol awakening response and hippocampal volumes and frontal atrophy. Psychoneuroendocrinology 2012;37:1270–1276
    1. Raz N. Diabetes: brain, mind, insulin-what is normal and do we need to know? Nat Rev Endocrinol 2011;7:636–637
    1. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage 2000;11:805–821
    1. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–419
    1. Baker LD, Cross DJ, Minoshima S, Belongia D, Watson GS, Craft S. Insulin resistance and Alzheimer-like reductions in regional cerebral glucose metabolism for cognitively normal adults with prediabetes or early type 2 diabetes. Arch Neurol 2011;68:51–57
    1. Kriegeskorte N, Lindquist MA, Nichols TE, Poldrack RA, Vul E. Everything you never wanted to know about circular analysis, but were afraid to ask. J Cereb Blood Flow Metab 2010;30:1551–1557
    1. Mesulam M. Principles of Behavior and Cognitive Neurology. Oxford, Oxford University Press, 2000
    1. Jack CR, Jr, Petersen RC, Xu YC, et al. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 1999;52:1397–1403
    1. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;14:21–36
    1. Sager MA, Hermann B, La Rue A. Middle-aged children of persons with Alzheimer’s disease: APOE genotypes and cognitive function in the Wisconsin Registry for Alzheimer’s Prevention. J Geriatr Psychiatry Neurol 2005;18:245–249
    1. Johnson SC, La Rue A, Hermann BP, et al. The effect of TOMM40 poly-T length on gray matter volume and cognition in middle-aged persons with APOE ε3/ε3 genotype. Alzheimers Dement 2011;7:456–465
    1. Bendlin BB, Ries ML, Canu E, et al. White matter is altered with parental family history of Alzheimer’s disease. Alzheimers Dement 2010;6:394–403
    1. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984;34:939–944
    1. Haroon E, Raison CL, Miller AH. Psychoneuroimmunology meets neuropsychopharmacology: translational implications of the impact of inflammation on behavior. Neuropsychopharmacology 2012;37:137–162
    1. Spreen O, Strauss E. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary. 2nd ed. New York, Oxford University Press, 1998
    1. Gomar JJ, Bobes-Bascaran MT, Conejero-Goldberg C, Davies P, Goldberg TE, Alzheimer’s Disease Neuroimaging Initiative Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer’s disease neuroimaging initiative. Arch Gen Psychiatry 2011;68:961–969
    1. Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839–851
    1. Kipps CM, Duggins AJ, Mahant N, Gomes L, Ashburner J, McCusker EA. Progression of structural neuropathology in preclinical Huntington’s disease: a tensor based morphometry study. J Neurol Neurosurg Psychiatry 2005;76:650–655
    1. Hayasaka S, Phan KL, Liberzon I, Worsley KJ, Nichols TE. Nonstationary cluster-size inference with random field and permutation methods. Neuroimage 2004;22:676–687
    1. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51:1173–1182
    1. McDonald CR, McEvoy LK, Gharapetian L, et al. Alzheimer’s Disease Neuroimaging Initiative Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology 2009;73:457–465
    1. Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol 1997;42:85–94
    1. Braak H, Thal DR, Ghebremedhin E, Del Tredici K. Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J Neuropathol Exp Neurol 2011;70:960–969

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