WIESJE PELKMANS The contribution of biomarkers to understanding clinical progression IMAGING NEURODEGENERATION ACROSS THE ALZHEIMER’S DISEASE CONTINUUM
IMAGING NEURODEGENERATION ACROSS THE ALZHEIMER’S DISEASE CONTINUUM The contribution of biomarkers to understanding clinical progression Wiesje Pelkmans
Colofon The studies described in this thesis were carried out at the Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, the Netherlands. The Alzheimer Center Amsterdam is part of the Neurodegeneration research program of Amsterdam Neuroscience. Chapter 4 of this thesis described research that was carried out at the Clinical Memory Research Unit of Lund University, Sweden, and was funded by a research fellowship grant from Alzheimer Nederland (WE.15-2019-14). Printing costs of this thesis were sponsored by Alzheimer Nederland, Danone Nutricia Research, and Vrije Universiteit Amsterdam. Wiesje Pelkmans ISBN: 978-94-6361-733-8 Printing: Optima Layout and cover design: Wiesje Pelkmans Copyright 2022 ©Wiesje Pelkmans All rights reserved. No parts of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means without permission of the author.
VRIJE UNIVERSITEIT IMAGING NEURODEGENERATION ACROSS THE ALZHEIMER’S DISEASE CONTINUUM The contribution of biomarkers to understanding clinical progression ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor of Philosophy aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. J.J.G. Geurts, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Geneeskunde op maandag 3 oktober 2022 om 13.45 uur in een bijeenkomst van de universiteit, De Boelelaan 1105 door Wiesje Pelkmans geboren te Tilburg
promotoren: prof.dr. W.M. van der Flier prof.dr. F. Barkhof copromotor: dr. B.M. Tijms promotiecommissie: prof.dr. L. Reneman prof.dr. W.J. Niessen prof.dr. L. van der Weerd dr. J. Pereira dr. S.A.M. Sikkes dr. J.D. Gispert
Table of contents Chapter 1. General introduction 7 Chapter 2 Amyloid-β, cortical thickness, and subsequent 23 cognitive decline in cognitively normal oldest-old. Chapter 3 Association of CSF, blood and imaging markers of 67 neurodegeneration with clinical progression in people with subjective cognitive decline. Chapter 4 Tau-related grey matter network breakdown across 105 the Alzheimer’s disease continuum. Chapter 5 Grey matter T1-w/T2-w ratios are higher in 145 Alzheimer's disease Chapter 6 Grey matter network markers identify individuals 199 with prodromal Alzheimer’s disease who will show rapid clinical decline Chapter 7 Summary and general discussion 233 Appendix Nederlandse samenvatting (Dutch summary) 255 List of publications List of author affiliations List of PhD theses Alzheimer Center Amsterdam Portfolio Acknowledgements About the author
CHAPTER 1 General introduction
General introduction 9 GENERAL INTRODUCTION The Alzheimer’s disease continuum Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia. Traditionally, an AD diagnosis has been based on the patient’s clinical presentation, namely a progressive impairment of multiple cognitive domains that interferes with daily life activities (McKhann et al. 1984). A definitive biological diagnosis of AD could only be established by autopsy. Post-mortem studies, however, revealed that about one-third of clinical AD dementia cases did not exhibit the neuropathological changes associated with AD (Nelson et al. 2012; Elobeid et al. 2016). Over the past two decades, the development of biomarkers that can detect AD pathology in living individuals has transformed the field of AD research. The key pathological features of AD, namely amyloid-β (Aβ) plaques and tau neurofibrillary tangles, can be measured in vivo using positron emission tomography (PET), as well as in the cerebrospinal fluid (CSF), and accurate blood-based assays are on the horizon (Hansson 2021). Furthermore, structural magnetic resonance imaging (MRI) can provide estimates of regional neurodegeneration and patterns of brain atrophy. Consequently, it has become apparent that AD pathology begins to accumulate approximately 20 years prior to the onset of symptoms of dementi T a h . e amyloid cascade hypothesis proposes that aggregation of Aβ plays an initiating role in the onset of the disease by initiating a cascade of events leading to synaptic dysfunction, the formation of neurofibrillary tangles, inflammation, neuronal loss, cognitive dysfunction, and dementia (Jack et al. 2013; Selkoe and Hardy 2016; Busche and Hyman 2020). As a
Chapter 1 10 result, AD has been conceptualized as a biological construct along a continuum of preclinical, prodromal and dementia stages rather than a clinical symptomology. Therefore, AD can now be defined by markers indicating the underlying pathology, regardless of the clinical presentation. These insights have led to the publication of the 'ATN' research framework that binarizes three classes of AD biomarkers (i.e. A = Amyloid, T = hyperphosphorylated Tau, N = Neurodegeneration). A+T+ pathology can be used to define AD, while N biomarkers are not specific to AD but can be used for disease severity staging (Jack et al. 2018). Imaging correlates of neurodegeneration Synaptic loss and neuronal cell death are characteristic neurodegenerative features of AD that are closely related to cognitive decline. The grey matter of the brain is made up of neuronal cells and their connections, and loss of grey matter can be detected in vivo using structural MRI. Several biomarkers for grey matter integrity have been associated with cognitive impairments and can be used to accurately predict future deterioration in early disease stages (Dickerson and Wolk 2012; Tondelli et al. 2012; Mormino et al. 2014; Pettigrew et al. 2016; ten Kate et al. 2017; Bilgel et al. 2018). However, to date the mechanism of cognitive decline in AD cannot fully be explained by grey matter integrity. In addition, it remains unclear how neurodegeneration is associated with AD pathology across different disease stages. In this thesis we investigated the structural brain changes of four neurodegeneration measures that occur during the progression of Alzheimer’s disease: hippocampal volume, cortical thickness, grey matter networks, and cortical myelin. Furthermore, we studied their relationship with AD pathology markers and clinical progression.
General introduction 11 Hippocampal volume and cortical thickness Neuronal loss in predominantly medial temporal regions, including the hippocampus, entorhinal cortex and surrounding parahippocampal cortex, is a consistent finding in AD patients and a predictor of decline in memory functioning (den Heijer et al. 2010; Verfaillie et al. 2016). As a result, macrostructural brain changes of atrophy and cortical thinning can typically be observed on MRI, which are widely used as diagnostic indicators of AD (Frisoni et al. 2010; McKhann et al. 2011). Aside from visual assessment, automated quantitative software can be used to calculate structural changes in the brain indicative of neuronal loss and/or a decrease in neuronal complexity (Bobinski et al. 1999). Even though hippocampal volume loss and regional cortical thinning are typical in individuals with Aβ aggregation, it is not specific to AD since there are multiple other pathologies that can cause hippocampal atrophy and cognitive impairments, including TDP-43, hippocampal sclerosis, and vascular damage (Wirth et al. 2013; Josephs et al. 2017; Flores et al. 2020; Yu et al. 2020). There is a significant increase in the prevalence of atrophy, Aβ- and non-Aβ pathologies with advancing age, with one or more pathologies evident in many individuals over the age of 60 and in nearly all individuals over the age of 80 (Fjell et al. 2014; Ossenkoppele et al. 2015; White et al. 2016; Spina et al. 2021). Therefore with increasing age, the association between pathological changes and cognitive decline becomes more complex. Additionally, there are many different approaches that are used to measure neurodegenerative changes and to define biomarker abnormalities (Mattsson-Carlgren et al. 2020). However, it is still unclear how these different measures are related and how they influence the prediction of clinical progression.
Chapter 1 12 Grey matter networks Normal cognitive functioning requires efficient information transfer between neuronal populations (Palop and Mucke 2016), and disruptions in brain networks have shown to contribute to cognitive dysfunction in many neurological disorders, including AD (Yu et al. 2021). The accumulation of Aβ plaques and neurofibrillary tangles can disrupt neuronal connectivity, resulting in a loss of neuronal network integrity. Therefore, AD can be regarded as a disconnection syndrome (Selkoe 2002; Delbeuck et al. 2003; Edwards 2019). Brain networks can be constructed through multiple neuroimaging techniques (e.g., sMRI, DTI, fMRI). The focus of this thesis is only on structural MRI networks whose nodes and edges (i.e. connections) are defined by structural covariance correlations of morphological information such as cortical thickness and/or volume. The organisation of connections in such grey matter networks can be quantified using graph theoretical metrics such as path length, clustering coefficient, and the small-world coefficient. These measures are indicative of how efficiently information is processed in a network. As compared to healthy individuals, AD brain networks show a loss of small world organisation, indicative of a more random network topology (Pievani et al. 2014; Stam 2014). The loss of integrity of grey matter networks has been associated with biomarkers of Aβ pathology in previous studies (Tijms et al. 2016; ten Kate et al. 2018; Voevodskaya et al. 2018). This suggests that changes in grey matter network topology occur in the presence of amyloid already in individuals with normal cognition. How tau pathology may contribute to the grey matter network alterations in AD is yet unclear. As elevated tau burden is thought to be more closely related to synaptic function and atrophy, than Aβ plaques
General introduction 13 (la Joie et al. 2020; Coomans et al. 2021; Iaccarino et al. 2021), tau pathology may contribute to impaired network organisation in AD. Furthermore, growing evidence suggests that disruptions in grey matter networks may be a sensitive early marker of disease progression, and have been associated with clinical progression to mild cognitive impairment or dementia and an increased risk of cognitive decline in predementia patients (Pereira et al. 2016; Dicks et al. 2018; Tijms et al. 2018; Verfaillie et al. 2018). As a consequence, grey matter network graph theory measures may serve as an early prognostic biomarker for subsequent disease progression. However, to identify individuals at risk for cognitive decline in the future, findings at the group level must be translated to subject-level applications. Cortical myelin Grey matter is composed predominantly of neuronal cell bodies and dendrites, but also includes myelinated axons. This insulating sheath that surrounds neuronal axons, is vulnerable to AD pathology and deteriorates with neuronal and axonal degeneration (Bartzokis 2011; Dean et al. 2017). Myelin integrity is considered essential for efficient neuronal communication by fine-tuning conduction speed and synchronization, thereby affecting brain connectivity (Nave and Werner 2014; Timmler and Simons 2019). According to Glasser and van Essen (2011), the ratio of T1-weighted and T2-weighted images can be used as a proxy measure of myelin content in grey matter. The contrast in T1-w and T2-w images is to a large extent driven by myelin, and by calculating the ratio between these images, the shared field inhomogeneities in the images are reduced and the contrast for myelin content is enhanced. Ongoing research has shown close spatial
Chapter 1 14 correspondence of the T1w/T2w ratio with cyto- and myeloarchitecture, regional gene expression, synaptic density, and other connectivity measures such as DTI (Ganzetti et al. 2015; Shafee et al. 2015; Huntenburg et al. 2017; Nieuwenhuys and Broere 2017; Burt et al. 2018; Ritchie et al. 2018; Fulcher et al. 2019). More advanced sequences such as the myelin water fraction (MWF) or quantitative magnetization transfer (qMT) can be considered more direct measures of myelin (Heath et al. 2018), but the T1w/T2w ratio has the advantage that it relies on standard sequences with short scan times, and might therefore be more suitable for clinical practice. To date, the ratio of T1-w/T2w images has not been investigated in AD patients and could serve as a promising measure of brain connectivity disruptions in AD. Conclusion With the rapid development of biomarkers for neurodegenerative diseases, we are now able to ask questions and provide answers about the interrelationships between specific biomarkers and their association with cognitive decline and progression to dementia. Such knowledge is essential for the development of effective treatment strategies for Alzheimer's disease. Furthermore, it is crucial for identifying individuals at an early disease stage at high risk of progression, which could assist in the selection of the right participants for disease-modifying clinical trials. Thesis aims The aim of this thesis is to improve understanding of the complex interrelationships between markers of AD pathology (i.e. amyloid and tau markers), structural brain changes (including measures of neuronal injury and brain network alterations), and their association with cognitive decline and clinical progression across different stages of AD.
General introduction 15 Three specific objectives are addressed in this thesis: 1. Improve understanding of the direct and indirect effects of amyloid pathology and different measures of neurodegeneration on future cognitive decline in individuals with normal cognition (Ch. 2, 3). 2. Understand how grey matter networks and myelin proxy measures change in relation to key pathological proteins in Alzheimer’s disease (Ch. 4, 5). 3. Test whether grey matter network measures, alone or combined with other prognostic biomarkers, can identify individuals with prodromal Alzheimer’s disease that will show rapid clinical decline (Ch. 6). Thesis outline In chapter 2 we examined the relationship between abnormal Ab and cognitive decline in cognitively normal individuals over the age of 90 and the extent to which such effects are mediated by cortical atrophy. In chapter 3 we compared different neurodegeneration markers and determined their predictive value for clinical progression in individuals with subjective cognitive decline. In chapter 4 we examined the relationship between tau deposition and alterations in whole-brain and regional grey matter networks in individuals across the AD spectrum. In chapter 5 we compared T1-w/T2-w ratio values between individuals with normal cognition and patients with AD dementia, and assessed to what extent neuronal injury, white matter hyperintensities, and cognitive functioning contributed to alterations in T1-w/T2-w values. In chapter 6 we explored whether grey matter networks can be used to identify individuals with prodromal AD who will progress rapidly.
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Chapter 1 20 Rosen HJ, Kramer JH, Miller BL, Seeley WW, Rabinovici GD, Grinberg LT. 2021. Comorbid neuropathological diagnoses in early versus late-onset Alzheimer’s disease. Brain. 144:2186–2198. Stam CJ. 2014. Modern network science of neurological disorders. Nature Reviews Neuroscience. 15:683–695. ten Kate M, Barkhof F, Visser PJ, Teunissen CE, Scheltens P, van der Flier WM, Tijms BM. 2017. Amyloid-independent atrophy patterns predict time to progression to dementia in mild cognitive impairment. Alzheimer’s Research & Therapy. 9:73. ten Kate M, Visser PJ, Bakardjian H, Barkhof F, Sikkes SAM, van der Flier WM, Scheltens P, Hampel H, Habert M-O, Dubois B, Tijms BM. 2018. Gray Matter Network Disruptions and Regional Amyloid Beta in Cognitively Normal Adults. Frontiers in Aging Neuroscience. 10:1–11. Tijms BM, Kate M ten, Wink AM, Visser PJ, Ecay M, Clerigue M, Estanga A, Garcia Sebastian M, Izagirre A, Villanua J, Martinez Lage P, van der Flier WM, Scheltens P, Sanz Arigita E, Barkhof F. 2016. Gray matter network disruptions and amyloid beta in cognitively normal adults. Neurobiology of Aging. 37:154–160. Tijms BM, ten Kate M, Gouw AA, Borta A, Verfaillie S, Teunissen CE, Scheltens P, Barkhof F, van der Flier WM. 2018. Gray matter networks and clinical progression in subjects with predementia Alzheimer’s disease. Neurobiology of Aging. 61:75–81. Timmler S, Simons M. 2019. Grey matter myelination. Glia. 67:2063–2070. Tondelli M, Wilcock GK, Nichelli P, de Jager CA, Jenkinson M, Zamboni G. 2012. Structural MRI changes detectable up to ten years before clinical Alzheimer’s disease. Neurobiology of Aging. 33:825.e25-825.e36. Verfaillie SCJ, Slot RE, Tijms BM, Bouwman F, Benedictus MR, Overbeek JM, Koene T, Vrenken H, Scheltens P, Barkhof F, van der Flier WM. 2018. Thinner cortex in patients with subjective cognitive decline is associated with steeper decline of memory. Neurobiology of Aging. 61:238–244. Verfaillie SCJ, Tijms B, Versteeg A, Benedictus MR, Bouwman FH, Scheltens P, Barkhof F, Vrenken H, van der Flier WM. 2016. Thinner temporal and parietal cortex is related to incident clinical progression to dementia in patients with subjective cognitive decline. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring. 5:43–52. Voevodskaya O, Pereira JB, Volpe G, Lindberg O, Stomrud E, van Westen D, Westman E, Hansson O. 2018. Altered structural network organization in cognitively normal individuals with amyloid pathology. Neurobiology of Aging. 64:15–24.
General introduction 21 White LR, Edland SD, Hemmy LS, Montine KS, Zarow C, Sonnen JA, Uyehara-Lock JH, Gelber RP, Ross GW, Petrovitch H, Masaki KH, Lim KO, Launer LJ, Montine TJ. 2016. Neuropathologic comorbidity and cognitive impairment in the Nun and Honolulu-Asia Aging Studies. Neurology. 86:1000–1008. Wirth M, Villeneuve S, Haase CM, Madison CM, Oh H, Landau SM, Rabinovici GD, Jagust WJ. 2013. Associations Between Alzheimer Disease Biomarkers, Neurodegeneration, and Cognition in Cognitively Normal Older People. JAMA Neurology. 25:289–313. Yu L, Boyle PA, Dawe RJ, Bennett DA, Arfanakis K, Schneider JA. 2020. Contribution of TDP and hippocampal sclerosis to hippocampal volume loss in older-old persons. Neurology. 94:e142–e152. Yu M, Sporns O, Saykin AJ. 2021. The human connectome in Alzheimer disease — relationship to biomarkers and genetics. Nature Reviews Neurology. 17:545– 563.
CHAPTER 2 Amyloid-β, cortical thickness, and subsequent cognitive decline in cognitively normal oldest-old Wiesje Pelkmans, Nienke Legdeur, Mara ten Kate, Frederik Barkhof, Maqsood M. Yaqub, Henne Holstege, Bart N. M. van Berckel, Philip Scheltens, Wiesje M. van der Flier, Pieter Jelle Visser, Betty M. Tijms Published in Annals of Clinical and Translational Neurology, 2021. 8(2) p.348–358. DOI: 10.1002/acn3.51273
Chapter 2 24 Abstract Objective: To investigate the relationship between amyloid-β (Aβ) deposition and markers of brain structure on cognitive decline in oldest-old individuals with initial normal cognition. Methods: We studied cognitive functioning in four domains at baseline and change over time in fifty-seven cognitively intact individuals from the EMIF- AD 90+ study. Predictors were Aβ status determined by [18F]-flutemetamol PET (normal = Aβ- vs. abnormal = Aβ+), cortical thickness in 34 regions and hippocampal volume. Mediation analyses were performed to test whether effects of Aβ on cognitive decline were mediated by atrophy of specific anatomical brain areas. Results: Subjects had a mean age of 92.7 ± 2.9 years, of whom 19 (33%) were Aβ+. Compared to Aβ-, Aβ+ individuals showed steeper decline on memory (β ± SE = -0.26 ± 0.09), and processing speed (β ± SE = -0.18 ± 0.08) performance over 1.5 years (p < .05). Furthermore, medial and lateral temporal lobe atrophy was associated with steeper decline in memory and language across individuals. Mediation analyses revealed that part of the memory decline observed in Aβ+ individuals was mediated through parahippocampal atrophy. Interpretation: These results show that Aβ abnormality even in the oldest old with initially normal cognition is not part of normal aging, but is associated with a decline in cognitive functioning. Other pathologies may also contribute to decline in the oldest old as cortical thickness predicted cognitive decline similarly in individuals with and without Aβ pathology.
A+N+ and cognitive decline in 90+ 25 Introduction Even tough amyloid-β (Aβ) plaques are considered the pathological hallmark of Alzheimer’s disease (AD; Montine et al. 2012; Jack et al. 2018), Aβ pathology is observed frequently in cognitively normal (CN) adults. Furthermore, post-mortem and in vivo studies have shown that the prevalence of abnormal Aβ in CN individuals increases with age from 16% at the age of 60, up to 44% of CNs in their 90’s (Jansen et al. 2015; Elobeid et al. 2016), illustrative of the complex relationship of Aβ deposition with cognitive functioning. While previous studies have reported that Aβ pathology in CN individuals is related to subtle cognitive deficits (Visser et al. 2009; Hedden et al. 2013; Jansen et al. 2018), and an increased risk for cognitive decline and dementia (Vos et al. 2013; Donohue et al. 2017; Ebenau et al. 2020). The relationship of Aβ pathology and cognitive decline in the oldest-old, i.e. individuals of 90 years and older, is however less clear. Recent longitudinal studies in the oldest-old suggest a steeper cognitive decline in non-demented Aβ+ compared to Aβ- individuals aged older than 90 (Kawas et al. 2013; Lopez et al. 2014; Zhao et al. 2018), although one study did not find such an association (Balasubramanian et al. 2012). Another driver of cognitive decline is cortical atrophy, in particular medial temporal lobe atrophy, which has been observed frequently in the oldestold as well (Lopez et al. 2014; Zhao et al. 2018). Medial temporal lobe atrophy is considered a key feature of Aβ pathology, but in the oldest-old other causes of medial temporal lobe atrophy are common, such as hippocampal sclerosis, cerebrovascular disease, TDP-43 pathology, and aging-related tau astrogliopathy (ARTAG; Kawas et al. 2015; Robinson et al. 2018). It still remains unclear how abnormal Ab is related to cognitive decline in cognitively normal individuals over age 90 and to what extent such effects are mediated by cortical atrophy. In this study we investigated
Chapter 2 26 if Aβ pathology is associated with cognitive decline in CN oldest-old. Additionally, where this association was present, we further investigated whether the effect of Ab on cognitive decline was independent or mediated by cortical thickness. Methods Participants Individuals with normal cognition who underwent an amyloid positron emission tomography (PET) were selected from the Innovative Medicine Initiative European Medical Information Framework for AD (EMIF-AD) 90+ Study conducted at the Amsterdam University Medical Center (UMC). Individuals were recruited through general practitioners or via advertisements, see Legdeur et al. (2018) for detailed description of this cohort and overall study design. Normal cognition was defined as a Clinical Dementia Rating (CDR) score of zero, and a Mini-Mental State Examination (MMSE) score of ≥ 26. MRI acquisition and processing 3D-T1 weighted and 3D sagittal fluid-attenuated inversion recovery (FLAIR) images were acquired on a Philips 3T Achieva scanner using an 8-channel head coil and a sagittal turbo gradient-echo sequence (T1: 1.00 mm3 isotropic voxels, repetition time (TR) = 7.9 ms, echo time (TE) = 4.5 ms, flip angle (FA) = 8 degrees; FLAIR: 1.12 mm3 isotropic voxels, TR = 4800 ms, TE = 279 ms, and inversion time = 1650 ms). Cortical thickness was estimated from 3D T1 MRI using FreeSurfer (v5.3; https://surfer.nmr.mgh.harvard.edu). Non-brain tissue was removed, followed by transformation to MNI space, segmentation and creation of cortical surface meshes (Fischl 2012). The cortical thickness values were
A+N+ and cognitive decline in 90+ 27 summarized in anatomical regions according to the Desikan–Killiany atlas implemented in FreeSurfer. To reduce dimensionality of the data we averaged cortical thickness values for each brain region across hemispheres, resulting in 34 cortical regions of interest (ROIs). Hippocampal volume was obtained with FMRIB’s Software Library (FSL) FIRST (v5.0.1), as reported previously in Patenaude et al. (2011). White matter hyperintensities (WMH) segmentation was performed using a previously established algorithm based on a three-level Gaussian mixture model to model healthy tissues and lesions (Sudre et al. 2015). Because of a skewed distribution, WMH volume was log transformed. Resulting images were visually checked for quality, and data from four subjects had to be excluded due to gross registration or segmentation errors. Amyloid PET Dynamic [18F] flutemetamol amyloid-PET scans were performed on a Philips Ingenuity TF PET-MRI scanner (Philips Medical Systems, Cleveland, Ohio, USA). The tracer was produced by General Electric (GE) Healthcare at the Cyclotron Research Center of the University of Liège (Liège, Belgium). First, a 30 min dynamic emission scan was started simultaneously with a bolus intravenous injection of 185 MBq [18F] flutemetamol. The second part of the scan was performed from 90 to 110 min post injection. Immediately prior to each part of the PET scan a dedicated MR sequence was performed for attenuation correction. During scanning, the head was immobilized to reduce movement artifacts. Data from the two scan parts were coregistered and combined into a single 4D image using VINCI Software 2.56 (https://vinci.sf.mpg.de) and in-house built software for decay correction of the second part. Parametric nondisplaceable binding potential (BPND) images were generated from the entire image set using the receptor
Chapter 2 28 parametric mapping and cerebellar grey matter as reference tissue (Gunn et al. 1997; Wu and Carson 2002). Global cortical BPND was calculated as the volume weighted average BPND of 22 regions located within frontal, parietal, temporal, posterior cingulate, and medial temporal lobes (Tolboom et al. 2009). Dynamic BPND images were used for visual assessment of [18F] flutemetamol as negative (Ab-) or positive (Ab+) by the consensus of three readers, who had been trained according to the manufacturers image interpretation methodology and were blinded to the clinical and demographic data (Collij et al. 2019). Neuropsychological assessment A trained neuropsychologist administered cognitive tests within the following cognitive domains: memory, language, processing speed, and executive functioning. For each cognitive domain, tests were combined into a composite score. For memory we included the CERAD 10 words test (delayed recall; Morris et al. 1989), the Wechsler Logical Memory Test (delayed recall; Wechsler et al. 2009), the Rey Complex Figure Test (delayed copy; Meyers et al. 1996), and the Visual Association Test A (Lindeboom et al. 2002). For language, we used the 2-minute Animal Fluency score (Zhao et al. 2013), and the Graded Naming Test (McKenna and Warrington 1980). For processing speed, we included the Digit Symbol Substitution Test from theWechsler Adult Intelligence Scale-Revised (WAIS-R; Wechsler 1981), the Trail Making Test A (Reitan 1958), and the WAIS-III Digit span forward (Wilde and Strauss 2002). For executive functioning we included the Trail Making Test B (Reitan 1958), the WAIS-III Digit span backward (Wilde and Strauss 2002), Letter Fluency (one minute per letter, three letters; Zhao et al. 2013), and the Clock drawing test (Teunisse et al. 1991). For a subset (n = 43; 75.4%), neuropsychological tests were repeated once circa 1.5 years
A+N+ and cognitive decline in 90+ 29 (1.0y – 2.8y) after baseline assessment. For each test we calculated Z-scores using the baseline mean and standard deviation of the total group. We created composite scores by averaging test Z-scores for each cognitive domain. Trail Making Test A& B scores were inverted so that for all cognitive tests lower scores indicated worse performance. APOE genotyping For all participants, blood samples were collected for DNA analysis. DNA was extracted using the QIAamp® DNA Blood Mini Kit (QIAGEN GmbH, Hilden, Germany). Apolipoprotein e (APOE) genotype was determined using TaqMan assays (ThermoFisher Scientific, Foster City, CA, USA) on a QuantStudio-12 K-Flex system. We classified individuals as APOE ε4 carriers or non-carriers according to their genotype status at rs429358. For three participants, APOE data was missing. Statistical analysis We compared demographical characteristics of the Ab- and Ab+ groups using X2 tests for categorical variables and ANOVA for continuous variables. We ran four linear mixedmodels (LMM) with subject specific intercepts, and fixed slopes: Model 1 tested the effect of baseline Ab status on cognitive decline with cognitive domain score as the outcome (Model 1: Cognition ~ Time * A b ). Model 2 tested the effect of Ab status on cortical thickness of all FreeSurfer ROIs and on hippocampal volume (Model 2: ROI ~ A b ). Model 3 tested the effect of cortical thickness and hippocampal volume on cognitive decline (Model 3: Cognition ~ Time * ROI). Model 4 tested the combined the effects of Ab pathology, cortical thickness, and their interactions with each other on cognitive decline (Model 4: Cognition ~ Time * ROI * A b ). Interaction terms were removed when not significant (p > .05). All models included sex,
Chapter 2 30 education, WMH, and age as covariates, and for hippocampal volume, total intracranial volume was added as a covariate. LMM were corrected for multiple testing using a false discovery rate (FDR) procedure (Benjamini and Yekutieli 2001). Effects that did not survive FDR correction are shown as puncorrected. When Aβ status was significantly associated with both cognitive decline and thickness and/or volume in certain ROIs, we performed causal mediation analyses to assess whether the association between Aβ status and cognitive decline was mediated by grey matter brain atrophy. Mediation analyses provides us with a better understanding of the complex pathways of Ab deposition towards cognitive decline. To estimate the average causal mediation effect three linear models were fitted: the first model has cognitive decline as the outcome of interest as the dependent variable and thickness/volume as predictor, while controlling for Aβ; the second model that has the mediator variable thickness/volume as the dependent variable and Aβ status as predictor; the third model averages direct and indirect effects of Aβ status on cognitive decline and are estimated based on the quasi-Bayesian Monte Carlo approximation (1000 simulations). Mediation analyses was performed only in individuals who had repeated assessment of cognitive function (n = 43). Finally, we further investigated the effect of other factors known to be associated with cognitive decline, including APOE ε4 genotype, education, and vascular damage. Statistical analyses were performed in R (v4.0.2) using the “lme4” package (v1.1), “mediation” package (v4.5; Tingley et al. 2014), and group estimates were obtained using the “emmeans” package (v1.5).
A+N+ and cognitive decline in 90+ 31 Results Demographics Participants (n = 57) had an average age of 92.7 years, ranging from 88-102 years, were more often female (63%), and 33% had a visually rated abnormal amyloid PET scan (Table 1). The Aβ+ (n=19) and Aβ- (n=38) groups did not differ in age, sex, APOE ε4 carriership, vascular burden, or years of education. Moreover, the two groups did not differ in availability of follow-up data, nor the time between test assessments. More years of education was associated with better performance on tests related to processing speed, executive functioning, and less steep decline in memory and executive functioning over time (pFDR<.05; Supplementary Table 3). Higher WMH volumes were associated with worse performance on language, processing speed, and faster decline on memory (pFDR<.05). No association between age, sex, or APOE ε4 carriership and cognitive performance or cognitive decline was observed. Table 1. Subject characteristics according to Aβ status. Aβ- Aβ+ Total p-value (n = 38) (n = 19) (n = 57) Sex, f (%) 23 (60.5) 13 (68.4) 36 (63.2) .771 Age, (y) 92.51 (3.13) 93.00 (2.56) 92.67 (2.94) .554 Education, (y) 12.78 (4.53) 11.50 (4.30) 12.35 (4.46) .312 Aβ load (BPND) 0.15 (0.12) 0.56 (0.28) 0.29 (0.27) <.001* APOEε4 carrier (%) 2 (5.7) 3 (16.7) 5 (9.4) .426 WMH volume 9.59 (0.89) 9.71 (0.94) 9.63 (0.90) .635 T0 – T1 difference, (y) 1.57 (0.57) 1.82 (0.64) 1.66 (0.60) .191 T1 availability (%) 27 (71.1) 16 (84.2) 43 (75.4) .446 Deceased at T1 (%) 10 (26.3) 2 (10.5) 12 (21.1) .301 Data are presented as mean (SD), or n (%). Aβ = amyloid-β; BPND =nondisplaceable binding potential; APOE = apolipoprotein e; WMH = White matter hyperintensities (log); T0 = baseline; T1 = follow-up; p<0.05.
Chapter 2 32 Associations of amyloid status and cortical thickness with cognitive decline First, we tested the effects of Aβ status on cognitive decline over time (Model 1). At baseline, Aβ+ individuals tended to show worse performance on memory and language, although this did not reach significance (Fig. 1). Over time, Aβ+ individuals showed steeper decline in memory (β ± SE = -0.26 ± 0.09), and processing speed (β ± SE = -0.18 ± 0.08) than Aβ- individuals(puncorrected< .05; Fig. 1). Also, a steeper decline in language performance (β ± SE = -0.15 ± 0.08) was observed at trend level (Supplementary Table 1a).
A+N+ and cognitive decline in 90+ 33 Figure 1. Cognitive performance over time in relation to Aβ status. Spaghetti plots showing individual longitudinal change on memory, language, processing speed, andexecutiveperformanceaccording toAβstatus (Amyloid- isnormalAβ;Amyloid+ is abnormal Aβ). All scores were z-scored and for processing speed and executive functioning inversedsuch that lower scores indicateworse impairment.
Chapter 2 34 Next, we tested effects of Aβ status on cortical thickness (Model 2). Individuals with Aβ+ showed thinner parahippocampal cortex and a thinner medial orbitofrontal cortex compared to Aβ- individuals (puncorrected< .05; Fig. 2). Hippocampal volume did not differ between Aβ- and Aβ+ individuals (Supplementary Table 1c). Then we studied effects of cortical thickness on cognitive decline over time (Model 3). A thinner cortex in predominantly anterior cingulate and multiple lateral temporal regions, including the entorhinal, parahippocampal, fusiform, and superior temporal cortex, was associated with a steeper decline in memory (pFDR<.05; Fig. 3), with no interaction effects of Aβ status. In addition, smaller hippocampal volume was associated with worse memory and processing speed performance at baseline and a faster decline in memory over time (puncorrected<.05), independent of Aβ status. Moreover, thinner superior frontal and lateral temporal regions, including the fusiform, inferior and superior temporal, posterior cingulate, and supramarginal cortex, were associated with a steeper decline in language performance (puncorrected <.05), of which only the posterior cingulate cortex survived the correction for multiple comparisons. Additionally, a few cortical regions showed that thicker cortex was associated with a steeper decline in executive functioning (puncorrected<.05; Fig. 3). Figure2. Amyloidassociations with regional cortical thickness. Betas are provided for regionswith thinner cortex in abnormal Aβ individuals compared to normal Aβ individuals at puncorrected <.05.
A+N+ and cognitive decline in 90+ 35 Figure 3. Associations of cortical thickness with baseline and decline in cognitive functioning, by Aβ status. Effect of cortical thickness on baseline (left) and annual change (right) onmemory, language, processing speed, and executive functioning across all subjects and by Aβ status. Top left: thinner caudal* and rostral* anterior cingulate, entorhinal*, fusiform*, inferior, superior* and middle* temporal, insula*, isthmus cingulate, parahippocampal*, and temporal pole* was associated with a faster decline in memory. Top right: thinner caudal middle frontal, entorhinal, fusiform, inferior and superior temporal, medial orbitofrontal, posterior cingulate*, superior frontal, and supramarginal cortex was associatedwith faster decline in language. Aβ+ individuals with thinner pars triangularis and parahippocampal cortex showed worse language performance compared to Aβ- individuals. Aβ- individuals with thinner postcentral and a superior parietal cortex showed faster decline in language performance compared to Aβ+ individuals. Bottom left: Aβ+ individuals with thinner parahippocampal and a thicker inferior parietal, pars triangularis, pars opercularis showed worse processing speed performance compared to Aβ- individuals. Aβ+ individuals with thicker cuneus, frontal pole, postcentral, rostral middle frontal, superior frontal, and superior parietal cortex showed a faster decline on processing speed compared to Aβ- individuals. Bottom right: thicker lateral occipital, pars opercularis, and postcentral regions were associated with faster decline in executive functioning. Aβ+ individuals with thicker precuneus showeda faster decline inexecutive functioning compared toAβ- individuals. Beta estimates in red indicate thinner cortex is associated with steeper decline on cognitive test
Chapter 2 36 score, blue indicates a thicker cortex is associatedwith steeper decline on cognitive test score. Dataarepresentedforregionssignificantwithanuncorrected p-value<0.05. ∗Indicatesregion significant at pFDR <0.05. Finally, we examined the interactions of Aβ status and brain structure measures on the rate of cognitive decline (Model 4). Aβ+ individuals with a thinner parahippocampal and a thicker pars triangularis (β ± SE = 1.90 ± 0.91; β ± SE = 4.04 ± 1.87; puncorrected <.05; Fig. 3) showed worse language performance. While also thinner postcentral and superior parietal cortex (β ± SE = 1.01 ± 0.37; β ± SE = 0.94 ± 0.36; puncorrected <.05) was associated with faster language decline in Aβ- individuals. Furthermore, we observed that thicker cortex in occipital and parietal regions was associated with slower processing speed and steeper decline over time in Aβ+ individuals (puncorrected<.05; Fig. 3; see supplementary Table 1 for full LMM results). We repeated all analyses excluding n = 12 individuals that passed away before the second neuropsychological assessment took place, and observed overall largely similar results (see supplementary Table 2). Mediation analyses Next, we investigated whether the effect of Aβ pathology on cognitive performance was mediated by cortical thickness (indirect effect) or not (independent effect) for cortical regions that were associatedwith abnormal Aβ (i.e., parahippocampal gyrus and medial orbitofrontal cortex). Memory decline associated with Aβ+ was fully mediated by parahippocampal thinning (36.4%; p < .05; Fig. 4). Decline in language performance associated with Aβ+ was partially mediated by parahippocampal thinning (21.6%; p < .05), and partially independent (78.4% p < .05) after controlling for the presence of parahippocampal atrophy. The effect of Aβ on decline in processing speed, and executive functioning were independent of
A+N+ and cognitive decline in 90+ 37 parahippocampal thickness. Moreover, Aβ associated cognitive decline was independent of medial orbitofrontal thickness for all cognitive domains (p < .05; Fig. 4).
Chapter 2 38
A+N+ and cognitive decline in 90+ 39 Figure4.Mediationanalysisshowinghowcortical thinningmediatestheeffectofAβon longitudinal decline in cognitive functioning. The total effect of Aβ onmemory, language, processingspeed, andexecutivefunctioningovertime(left).MediationeffectofAβtroughPHC thickness on memory, language, processing speed, and executive functioning over time (middle). Mediation effect of Aβ trough mOFC thickness on memory, language, processing speed, and executive functioning over time (right). The figure shows regression coefficients with a 95%confidence interval. a, the effect of Aβ on cortical thickness; b, the effect of cortical thickness on cognitive decline when controlling for Aβ; c, the total effect of Aβ on cognitive decline(withoutcontrollingformediationeffects);c', thedirecteffectofAβoncognitivedecline when adjusting for mediation; c-c', the mediation effect. * = p<.05; Aβ = amyloid-beta; PHC = parahippocampal cortex;mOFC=medial orbitofrontal cortex. Discussion In this study of oldest-old with initially intact cognition, we found that abnormal Aβ was associated with steeper decline in memory and processing speed performance over 1.5 years. Our findings support the notion that both Aβ pathology and brain atrophy have detrimental effects on cognitive functioning among cognitively normal individuals that are separate from normal ageing. These results suggest that Aβ abnormality is indicative of a neurodegenerative process, that also in the oldest-old with apparent high reserve and maintenance mechanisms lead to cognitive decline. In addition, non-Aβ pathologies may also contribute to decline in the oldest-old as a thinner medial and lateral temporal cortex was related to subsequent decline in memory and language irrespective of Aβ pathology, indicating that other, possibly Aβ independent pathological processes might also be involved in cognitive decline in the oldest-old. Numerous studies have reported on the role of Aβ pathology in very early cognitive decline (Petersen et al. 2016; Bilgel et al. 2018; Clark et al. 2018), and we further extend on those findings by showing that the detrimental effect of Aβ is also present in nonagenarians with initially intact cognition. Our baseline results are in line with other cross-sectional data showing only a subtle effect of Aβ pathology in preclinical AD on cognition
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