Association of midlife stroke risk with structural brain integrity and memory performance at older ages: a longitudinal cohort study

Enikő Zsoldos, Abda Mahmood, Nicola Filippini, Sana Suri, Verena Heise, Ludovica Griffanti, Clare E Mackay, Archana Singh-Manoux, Mika Kivimäki, Klaus P Ebmeier, Enikő Zsoldos, Abda Mahmood, Nicola Filippini, Sana Suri, Verena Heise, Ludovica Griffanti, Clare E Mackay, Archana Singh-Manoux, Mika Kivimäki, Klaus P Ebmeier

Abstract

Cardiovascular health in midlife is an established risk factor for cognitive function later in life. Knowing mechanisms of this association may allow preventative steps to be taken to preserve brain health and cognitive performance in older age. In this study, we investigated the association of the Framingham stroke-risk score, a validated multifactorial predictor of 10-year risk of stroke, with brain measures and cognitive performance in stroke-free individuals. We used a large (N = 800) longitudinal cohort of community-dwelling adults of the Whitehall II imaging sub-study with no obvious structural brain abnormalities, who had Framingham stroke risk measured five times between 1991 and 2013 and MRI measures of structural integrity, and cognitive function performed between 2012 and 2016 [baseline mean age 47.9 (5.2) years, range 39.7-62.7 years; MRI mean age 69.81 (5.2) years, range 60.3-84.6 years; 80.6% men]. Unadjusted linear associations were assessed between the Framingham stroke-risk score in each wave and voxelwise grey matter density, fractional anisotropy and mean diffusivity at follow-up. These analyses were repeated including socio-demographic confounders as well as stroke risk in previous waves to examine the effect of residual risk acquired between waves. Finally, we used structural equation modelling to assess whether stroke risk negatively affects cognitive performance via specific brain measures. Higher unadjusted stroke risk measured at each of the five waves over 20 years prior to the MRI scan was associated with lower voxelwise grey and white matter measures. After adjusting for socio-demographic variables, higher stroke risk from 1991 to 2009 was associated with lower grey matter volume in the medial temporal lobe. Higher stroke risk from 1997 to 2013 was associated with lower fractional anisotropy along the corpus callosum. In addition, higher stroke risk from 2012 to 2013, sequentially adjusted for risk measured in 1991-94, 1997-98 and 2002-04 (i.e. 'residual risks' acquired from the time of these examinations onwards), was associated with widespread lower fractional anisotropy, and lower grey matter volume in sub-neocortical structures. Structural equation modelling suggested that such reductions in brain integrity were associated with cognitive impairment. These findings highlight the importance of considering cerebrovascular health in midlife as important for brain integrity and cognitive function later in life (ClinicalTrials.gov Identifier: NCT03335696).

Keywords: Framingham stroke risk; brain health; cardiovascular health; cognition; structural brain integrity.

© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.

Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
The association of midlife Framingham stroke risk and lower grey matter density (top) and FA at older ages. Rows I correspond to Model I (baseline model, uncorrected Framingham association with grey and white matter integrity). Rows II correspond to Model II (corrected model, Framingham stroke risk and grey and white matter integrity corrected for confounders). Rows III correspond to Model III (longitudinal model, analyses with significant results for associations between FSRS between 2012 and 2013, and voxelwise GM, and FA were repeated using scanner type and FSRS between 1991 and 1994, 1997 and 1999, 2002 and 2004 and 2007 and 2009 as a confounder). Blue represents regions significant at P < 0.05, threshold-free cluster enhancement, corrected for multiple comparisons. Coordinates are in MNI space. L = left; M = mean; P = posterior.
Figure 2
Figure 2
Framingham stroke-risk predicted changes in white matter microstructure (FA) are primary to white matter lesions and secondary to Wallerian degeneration. First row shows lower FA associated with Framingham stroke risk. Second row shows first row controlled for percentage grey matter (an estimate of Wallerian degeneration). Third row shows first row controlled for white matter hyperintensity volume. Blue represents regions significant at P < 0.05, threshold-free cluster enhancement, corrected for multiple comparisons. Coordinates are in MNI space. L = left; P = posterior.
Figure 3
Figure 3
Structural equation modelling results. Framingham stroke risk in later waves was best associated with white matter hyperintensity and hippocampal volume, which, in turn, was associated with memory performance in older life. Covariances are not shown.

References

    1. Allan CL, Sexton CE, Kalu UG, McDermott LM, Kivimaki M, Singh-Manoux A, et al.Does the Framingham Stroke Risk Profile predict white-matter changes in late-life depression? Int Psychogeriatr 2012; 24: 524–31.
    1. Allan CL, Zsoldos E, Filippini N, Sexton CE, Topiwala A, Valkanova V, et al.Lifetime hypertension as a predictor of brain structure in older adults: cohort study with a 28-year follow-up. Br J Psychiatry 2015; 206: 308–15.
    1. Bastos-Leite AJ, van der Flier WM, van Straaten EC, Staekenborg SS, Scheltens P, Barkhof F.. The contribution of medial temporal lobe atrophy and vascular pathology to cognitive impairment in vascular dementia. Stroke 2007; 38: 3182–5.
    1. Bergmann O, Spalding KL, Frisen J.. Adult neurogenesis in humans. Cold Spring Harb Perspect Biol 2015; 7: a018994.
    1. Brandt J. The Hopkins Verbal Learning Test: development of a new memory test with six equivalent forms. Clin Neuropsychol 1991; 5: 125–42.
    1. Buckner RL. Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 2004; 44: 195–208.
    1. Burton EJ, Barber R, Mukaetova-Ladinska EB, Robson J, Perry RH, Jaros E, et al.Medial temporal lobe atrophy on MRI differentiates Alzheimer’s disease from dementia with Lewy bodies and vascular cognitive impairment: a prospective study with pathological verification of diagnosis. Brain 2009; 132: 195–203.
    1. Cox SR, Lyall DM, Ritchie SJ, Bastin ME, Harris MA, Buchanan CR, et al.Associations between vascular risk factors and brain MRI indices in UK Biobank. Eur Heart J 2019; 40: 2290–300.
    1. Craik FIM, Bialystok E.. Cognition through the lifespan: mechanisms of change. Trends Cogn Sci 2006; 10: 131–8.
    1. D’Agostino RB, Wolf PA, Belanger AJ, Kannel WB.. Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study. Stroke 1994; 25: 40–3.
    1. de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LMP, Heijboer R, et al.Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001; 70: 9–14.
    1. Debette S, Beiser A, DeCarli C, Au R, Himali JJ, Kelly-Hayes M, 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: 600–6.
    1. Debette S, Markus HS.. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 2010; 341: c3666.
    1. Debette S, Seshadri S, Beiser A, Au R, Himali JJ, Palumbo C, et al.Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 2011; 77: 461–8.
    1. den Heijer T, van der Lijn F, Koudstaal PJ, Hofman A, van der Lugt A, Krestin GP, et al.A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 2010; 133: 1163–72.
    1. Douaud G, Smith S, Jenkinson M, Behrens T, Johansen-Berg H, Vickers J, et al.Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 2007; 130: 2375–86.
    1. Dregan A, Stewart R, Gulliford MC.. Cardiovascular risk factors and cognitive decline in adults aged 50 and over: a population-based cohort study. Age Ageing 2013; 42: 338–45.
    1. Elkins JS, O'Meara ES, Longstreth WT, Carlson MC, Manolio TA, Johnston SC.. Stroke risk factors and loss of high cognitive function. Neurology 2004; 63: 793–9.
    1. Filippini N, Zsoldos E, Haapakoski R, Sexton CE, Mahmood A, Allan CL, et al.Study protocol: The Whitehall II imaging sub-study. BMC Psychiatry 2014; 14: 159.
    1. Fotenos AF, Mintun MA, Snyder AZ, Morris JC, Buckner RL.. Brain volume decline in aging: evidence for a relation between socioeconomic status, preclinical Alzheimer disease, and reserve. Arch Neurol 2008; 65: 113–20.
    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. Gorelick PB, Scuteri A, Black SE, Decarli C, Greenberg SM, Iadecola C, et al.Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2011; 42: 2672–713.
    1. Griffanti L, Zamboni G, Khan A, Li L, Bonifacio G, Sundaresan V, et al.BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage 2016; 141: 191–205.
    1. Hachinski V; World Stroke Organization. Stroke and potentially preventable dementias proclamation: updated world stroke day proclamation. Stroke 2015; 46: 3039–40.
    1. Hayes AF. Introduction to mediation, moderation, and conditional process analysis, 2nd edn New York: The Guilford Press; 2018.
    1. Hohman TJ, Samuels LR, Liu D, Gifford KA, Mukherjee S, Benson EM, et al.Stroke risk interacts with Alzheimer’s disease biomarkers on brain aging outcomes. Neurobiol Aging 2015; 36: 2501–8.
    1. Jack CR, Shiung MM, Weigand SD, O'Brien PC, Gunter JL, Boeve BF, et al.Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 2005; 65: 1227–31.
    1. Jeerakathil T, Wolf PA, Beiser A, Massaro J, Seshadri S, D’Agostino RB, et al.Stroke risk profile predicts white matter hyperintensity volume: the Framingham Study. Stroke 2004; 35: 1857–61.
    1. Kaffashian S, Dugravot A, Brunner EJ, Sabia S, Ankri J, Kivimaki M, et al.Midlife stroke risk and cognitive decline: a 10-year follow-up of the Whitehall II cohort study. Alzheimers Dement 2013a; 9: 572–9.
    1. Kaffashian S, Dugravot A, Elbaz A, Shipley MJ, Sabia S, Kivimaki M, et al.Predicting cognitive decline: a dementia risk score vs. the Framingham vascular risk scores. Neurology 2013b; 80: 1300–6.
    1. Kohler S, Thomas AJ, Lloyd A, Barber R, Almeida OP, O’Brien JT.. White matter hyperintensities, cortisol levels, brain atrophy and continuing cognitive deficits in late-life depression. Br J Psychiatry 2010; 196: 143–9.
    1. Laakso MP, Partanen K, Riekkinen P, Lehtovirta M, Helkala EL, Hallikainen M, et al.Hippocampal volumes in Alzheimer’s disease, Parkinson’s disease with and without dementia, and in vascular dementia: an MRI study. Neurology 1996; 46: 678–81.
    1. Li L, Simoni M, Kuker W, Schulz UG, Christie S, Wilcock GK, et al.Population-based case-control study of white matter changes on brain imaging in transient ischemic attack and ischemic stroke. Stroke 2013; 44: 3063–70.
    1. Molendijk ML, van Tol MJ, Penninx BW, van der Wee NJ, Aleman A, Veltman DJ, et al.BDNF val66met affects hippocampal volume and emotion-related hippocampal memory activity. Transl Psychiatry 2012; 2: e74.
    1. Moran C, Beare R, Wang W, Callisaya M, Srikanth V; Alzheimer's Disease Neuroimaging Initiative (ADNI). Type 2 diabetes mellitus, brain atrophy, and cognitive decline. Neurology 2019; 92: e823–30.
    1. Nagy Z, Jobst KA, Esiri MM, Morris JH, King EM, MacDonald B, et al.Hippocampal pathology reflects memory deficit and brain imaging measurements in Alzheimer’s disease: clinicopathologic correlations using three sets of pathologic diagnostic criteria. Dementia 1996; 7: 76–81.
    1. Nishtala A, Piers RJ, Himali JJ, Beiser AS, Davis-Plourde KL, Saczynski JS, et al.Atrial fibrillation and cognitive decline in the Framingham Heart Study. Heart Rhythm 2018; 15: 166–72.
    1. Pase MP, Davis-Plourde K, Himali JJ, Satizabal CL, Aparicio H, Seshadri S, et al.Vascular risk at younger ages most strongly associates with current and future brain volume. Neurology 2018; 91: e1479–86.
    1. Pase MP, Satizabal CL, Seshadri S.. Role of improved vascular health in the declining incidence of dementia. Stroke 2017; 48: 2013–20.
    1. Santos MAO, Bezerra LS, Carvalho A, Brainer-Lima AM.. Global hippocampal atrophy in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Trends Psychiatry Psychother 2018; 40: 369–78.
    1. Singh-Manoux A, Kivimaki M, Glymour MM, Elbaz A, Berr C, Ebmeier KP, et al.Timing of onset of cognitive decline: results from Whitehall II prospective cohort study. BMJ 2012; 344: d7622.
    1. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al.Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004; 23 (Suppl 1): S208–19.
    1. Smith SM, Nichols TE.. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009; 44: 83–98.
    1. Spalding KL, Bergmann O, Alkass K, Bernard S, Salehpour M, Huttner HB, et al.Dynamics of hippocampal neurogenesis in adult humans. Cell 2013; 153: 1219–27.
    1. Suri S, Topiwala A, Chappell MA, Okell TW, Zsoldos E, Singh-Manoux A, et al.Association of midlife cardiovascular risk profiles with cerebral perfusion at older ages. JAMA Netw Open 2019; 2: e195776.
    1. Uiterwijk R, Staals J, Huijts M, de Leeuw PW, Kroon AA, van Oostenbrugge RJ.. Framingham Stroke Risk Profile is related to cerebral small vessel disease progression and lower cognitive performance in patients with hypertension. J Clin Hypertens 2018; 20: 240–5.
    1. van Oijen M, de Jong FJ, Witteman JC, Hofman A, Koudstaal PJ, Breteler MM.. Atherosclerosis and risk for dementia. Ann Neurol 2007; 61: 403–10.
    1. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al.Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013; 12: 822–38.
    1. Wechsler D. Test of premorbid functioning. UK version (TOPF UK). Bloomington, MN: Pearson Inc; 2011.
    1. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE.. Permutation inference for the general linear model. Neuroimage 2014; 92: 381–97.
    1. Zsoldos E, Brain markers of cumulative stress response and allostatic load in the ageing whitehall II cohort. Oxford: University of Oxford; 2017.
    1. Zsoldos E, Filippini N, Mahmood A, Mackay CE, Singh-Manoux A, Kivimaki M, et al.Allostatic load as a predictor of grey matter volume and white matter integrity in old age: the Whitehall II MRI study. Sci Rep 2018; 8: 6411.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa