Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline
Abstract
Objective:
Our aim was to test the association of vascular risk factor exposure in midlife with progression of MRI markers of brain aging and measures of cognitive decline.
Methods:
A total of 1,352 participants without dementia from the prospective Framingham Offspring Cohort Study were examined. Multivariable linear and logistic regressions were implemented to study the association of midlife vascular risk factor exposure with longitudinal change in white matter hyperintensity volume (WMHV), total brain volume (TBV), temporal horn volume, logical memory delayed recall, visual reproductions delayed-recall (VR-d), and Trail-Making Test B-A (TrB-A) performance a decade later.
Results:
Hypertension in midlife was associated with accelerated WMHV progression (p < 0.001) and worsening executive function (TrB-A score; p = 0.012). Midlife diabetes and smoking were associated with a more rapid increase in temporal horn volume, a surrogate marker of accelerated hippocampal atrophy (p = 0.017 and p = 0.008, respectively). Midlife smoking also predicted a more marked decrease in total brain volume (p = 0.025) and increased risk of extensive change in WMHV (odds ratio = 1.58 [95%confidence interval 1.07–2.33], p = 0.021). Obesity in midlife was associated with an increased risk of being in the top quartile of change in executive function (1.39 [1.02–1.88], p = 0.035) and increasing waist-to-hip ratio was associated with marked decline in TBV (10.81 [1.44–81.01], p = 0.021). Longitudinal changes in brain structure were significantly correlated with decline in memory and executive function.
Conclusions:
Midlife hypertension, diabetes, smoking, and obesity were associated with an increased rate of progression of vascular brain injury, global and hippocampal atrophy, and decline in executive function a decade later.
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REFERENCES
1.
Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol 2006;5:735–741.
2.
Tzourio C, Dufouil C, Ducimetiere P, Alperovitch A. Cognitive decline in individuals with high blood pressure: a longitudinal study in the elderly: EVA Study Group: Epidemiology of Vascular Aging. Neurology 1999;53:1948–1952.
3.
Alonso A, Mosley TH, Gottesman RF, Catellier D, Sharrett AR, Coresh J. Risk of dementia hospitalisation associated with cardiovascular risk factors in midlife and older age: the Atherosclerosis Risk in Communities (ARIC) study. J Neurol Neurosurg Psychiatry 2009;80:1194–1201.
4.
Jack CR, Shiung MM, Weigand SD, et al. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 2005;65:1227–1231.
5.
Prins ND, van Dijk EJ, den Heijer T, et al. Cerebral white matter lesions and the risk of dementia. Arch Neurol 2004;61:1531–1534.
6.
Kuller LH, Lopez OL, Newman A, et al. Risk factors for dementia in the Cardiovascular Health Cognition Study. Neuroepidemiology 2003;22:13–22.
7.
Elias MF, Beiser A, Wolf PA, Au R, White RF, D'Agostino RB. The preclinical phase of Alzheimer disease: a 22-year prospective study of the Framingham Cohort. Arch Neurol 2000;57:808–813.
8.
Amieva H, Jacqmin-Gadda H, Orgogozo JM, et al. The 9 year cognitive decline before dementia of the Alzheimer type: a prospective population-based study. Brain 2005;128:1093–1101.
9.
Dawber TR, Kannel WB. The Framingham Study: an epidemiological approach to coronary heart disease. Circulation 1966;34:553–555.
10.
Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study: design and preliminary data. Prev Med 1975;4:518–525.
11.
Splansky GL, Corey D, Yang Q, et al. The Third Generation Cohort of the National Heart, Lung, and Blood Institute's Framingham Heart Study: design, recruitment, and initial examination. Am J Epidemiol 2007;165:1328–1335.
12.
Debette S, Beiser A, Decarli C, et al. Association of MRI markers of vascular brain injury with incident stroke, mild cognitive impairment, dementia, and mortality: The Framingham Offspring Study. Stroke 2010;41:600–606.
13.
Seshadri S, Wolf PA, Beiser A, et al. Lifetime risk of dementia and Alzheimer's disease. The impact of mortality on risk estimates in the Framingham Study. Neurology 1997;49:1498–1504.
14.
Wolf PA, D'Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the Framingham Study. Stroke 1991;22:312–318.
15.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. Washington, DC: American Psychiatric Association; 1994.
16.
Chobanian AV, Bakris GL, Black HR, et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003;42:1206–1252.
17.
Debette S, Beiser A, Hoffmann U, et al. Visceral fat is associated with lower brain volume in healthy middle-aged adults. Ann Neurol 2010;68:136–144.
18.
Davis PC, Gearing M, Gray L, et al. The CERAD experience, part VIII: neuroimaging-neuropathology correlates of temporal lobe changes in Alzheimer's disease. Neurology 1995;45:178–179.
19.
Au R, Seshadri S, Wolf PA, et al. New norms for a new generation: cognitive performance in the Framingham Offspring Cohort. Exp Aging Res 2004;30:333–358.
20.
Bryan RN, Manolio TA, Schertz LD, et al. A method for using MR to evaluate the effects of cardiovascular disease on the brain: the cardiovascular health study. AJNR Am J Neuroradiol 1994;15:1625–1633.
21.
Gottesman RF, Coresh J, Catellier DJ, et al. Blood pressure and white-matter disease progression in a biethnic cohort: Atherosclerosis Risk in Communities (ARIC) study. Stroke 2010;41:3–8.
22.
Korf ES, van Straaten EC, de Leeuw FE, et al. Diabetes mellitus, hypertension and medial temporal lobe atrophy: the LADIS study. Diabet Med 2007;24:166–171.
23.
den Heijer T, Vermeer SE, van Dijk EJ, et al. Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI. Diabetologia 2003;46:1604–1610.
24.
Fukuda H, Kitani M. Cigarette smoking is correlated with the periventricular hyperintensity grade of brain magnetic resonance imaging. Stroke 1996;27:645–649.
25.
Longstreth WT, Arnold AM, Manolio TA, et al. Clinical correlates of ventricular and sulcal size on cranial magnetic resonance imaging of 3,301 elderly people: The Cardiovascular Health Study Collaborative Research Group. Neuroepidemiology 2000;19:30–42.
26.
Longstreth WT, Arnold AM, Beauchamp NJ, et al. Incidence, manifestations, and predictors of worsening white matter on serial cranial magnetic resonance imaging in the elderly: the Cardiovascular Health Study. Stroke 2005;36:56–61.
27.
Ikram MA, Vrooman HA, Vernooij MW, et al. Brain tissue volumes in the general elderly population: The Rotterdam Scan Study. Neurobiol Aging 2008;29:882–890.
28.
Brody AL, Mandelkern MA, Jarvik ME, et al. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol Psychiatry 2004;55:77–84.
29.
Plassman BL, Williams JW, Burke JR, Holsinger T, Benjamin S. Systematic review: factors associated with risk for and possible prevention of cognitive decline in later life. Ann Intern Med 2010;153:182–193.
30.
Knopman DS, Mosley TH, Catellier DJ, Coker LH. Fourteen-year longitudinal study of vascular risk factors, APOE genotype, and cognition: the ARIC MRI Study. Alzheimers Dement 2009;5:207–214.
31.
Carmelli D, Swan GE, Reed T, et al. Midlife cardiovascular risk factors, ApoE, and cognitive decline in elderly male twins. Neurology 1998;50:1580–1585.
32.
Kuo HK, Jones RN, Milberg WP, et al. Effect of blood pressure and diabetes mellitus on cognitive and physical functions in older adults: a longitudinal analysis of the advanced cognitive training for independent and vital elderly cohort. J Am Geriatr Soc 2005;53:1154–1161.
33.
Lu FP, Lin KP, Kuo HK. Diabetes and the risk of multi-system aging phenotypes: a systematic review and meta-analysis. PLoS ONE 2009;4:e4144.
34.
Walther K, Birdsill AC, Glisky EL, Ryan L. Structural brain differences and cognitive functioning related to body mass index in older females. Hum Brain Mapp 2010;31:1052–1064.
35.
Fergenbaum JH, Bruce S, Lou W, Hanley AJ, Greenwood C, Young TK. Obesity and lowered cognitive performance in a Canadian First Nations population. Obesity 2009;17:1957–1963.
36.
Waldstein SR, Giggey PP, Thayer JF, Zonderman AB. Nonlinear relations of blood pressure to cognitive function: the Baltimore Longitudinal Study of Aging. Hypertension 2005;45:374–379.
37.
van Oijen M, Okereke OI, Kang JH, et al. Fasting insulin levels and cognitive decline in older women without diabetes. Neuroepidemiology 2008;30:174–179.
38.
Euser SM, Sattar N, Witteman JC, et al. A prospective analysis of elevated fasting glucose levels and cognitive function in older people: results from PROSPER and the Rotterdam Study. Diabetes 2010;59:1601–1607.
39.
van den Heuvel DM, ten Dam VH, de Craen AJ, et al. Increase in periventricular white matter hyperintensities parallels decline in mental processing speed in a non-demented elderly population. J Neurol Neurosurg Psychiatry 2006;77:149–153.
40.
Lamar M, Resnick SM, Zonderman AB. Longitudinal changes in verbal memory in older adults: distinguishing the effects of age from repeat testing. Neurology 2003;60:82–86.
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Copyright © 2011 by AAN Enterprises, Inc.
Publication History
Received: January 2, 2011
Accepted: April 5, 2011
Published online: August 1, 2011
Published in print: August 2, 2011
Disclosure
Dr. Debette reports no disclosures. Dr. Seshadri serves as an Associate Editor for the Journal of Alzheimer's Disease and on the editorial board of Stroke and receives research support from the NIH (NIA, NINDS, NHLBI). Dr. Beiser receives publishing royalties for Introductory Applied Statistics (Brooks Cole, 2005) and receives research support from the NIH (NIA, NINDS, NHLBI). Dr. Au receives/has received research support from the NIH (NIA, NINDS) and the Wing Tat Lee Fund. J.J. Himali reports no disclosures. Dr. Palumbo serves as a consultant for the NIH/NIDCD; receives salary support from the NIH/NIA; and receives research support from the VA Boston Healthcare System R&D Service (via an IPA to Boston University School of Medicine) to chair the Institutional Review Board. Dr. Wolf receives publishing royalties from the 5th edition of Stroke: Pathophysiology, Diagnosis, and Management (Elsevier, 2008) and receives research support from the NIH (NHLBI, NINDS, NIA). Dr. DeCarli serves as Editor-in-Chief of Alzheimer Disease and Associated Disorders; serves as a consultant for Takeda Pharmaceutical Company Limited and Avanir Pharmaceuticals; and receives research support from Merck Serono and the NIH (NIA, NHLBI).
Authors
Author Contributions
Dr. Debette: drafting/revising the manuscript, analysis or interpretation of data. Dr. Seshadri: drafting/revising the manuscript, study concept or design, acquisition of data, study supervision, obtaining funding. Dr. Beiser: drafting/revising the manuscript, study concept or design, analysis or interpretation of data, acquisition of data, statistical analysis. Dr. Au: drafting/revising the manuscript, acquisition of data. J.J. Himali: analysis or interpretation of data, statistical analysis. Dr. Palumbo: drafting/revising the manuscript, analysis or interpretation of data, acquisition of data. Dr. Wolf: drafting/revising the manuscript, study concept or design, analysis or interpretation of data, acquisition of data, statistical analysis, study supervision, obtaining funding. Dr. DeCarli: drafting/revising the manuscript, study concept or design, analysis or interpretation of data, acquisition of data, study supervision, obtaining funding.
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