From subjective to objective cognitive decline Using biomarkers to understand the earliest stages of Alzheimer’s disease Jarith Laurien Ebenau

From subjective to objective cognitive decline Using biomarkers to understand the earliest stages of Alzheimer’s disease Jarith Laurien Ebenau

The studies described in this thesis were carried out at the Alzheimer Center Amsterdam, Department of Neurology, Vrije Universiteit Amsterdam Amsterdam UMC. Research of the Alzheimer Center is part of the Neurodegeneration program of Amsterdam Neuroscience. The Alzheimer Center is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. The SCIENCe project is funded by GieskesStrijbis fonds. Printing of this thesis was supported by stichting Alzheimer Nederland, stichting Alzheimer & Neuropsychiatry Foundation and Vrije Universiteit Amsterdam. ISBN: 978-94-6421-816-9 Layout and design: Wiebke Keck, Printed by: Ipskamp Printing © Jarith Laurien Ebenau, Amsterdam, The Netherlands, 2022. All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means without prior permission of the copyright holder, or, when applicable, with permission of the publishers of the scientific journals.

VRIJE UNIVERSITEIT FROM SUBJECTIVE TO OBJECTIVE COGNITIVE DECLINE Using biomarkers to understand the earliest stages of Alzheimer’s disease ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor 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 dinsdag 4 oktober 2022 om 13.45 uur in een bijeenkomst van de universiteit, De Boelelaan 1105 door Jarith Laurien Ebenau geboren te Groningen

promotoren: prof.dr. W.M. van der Flier prof.dr. B.N.M. van Berckel promotiecommissie: prof.dr. Y.A.L. Pijnenburg prof.dr. G. Chételat dr. R. Buckley prof.dr. R. Boellaard dr. N. Tolboom

TABLE OF CONTENTS CHAPTER 1 General introduction 7 CHAPTER 2 ATN classification and clinical progression in subjective cognitive decline: The SCIENCe project 21 CHAPTER 3 Grey zone amyloid burden affects memory function: the SCIENCe project 57 CHAPTER 4 Association of CSF, blood and imaging markers of neurodegeneration with clinical progression in people with subjective cognitive decline 79 CHAPTER 5 Risk of dementia in APOE ε4 carriers is mitigated by a polygenic risk score 105 CHAPTER 6 Cerebral blood flow, amyloid burden and cognition in cognitively normal individuals 131 CHAPTER 7 Longitudinal change in ATN biomarkers in cognitively normal individuals 155 CHAPTER 8 Summary and general discussion 179 APPENDIX Nederlandse samenvatting List of publications List of affiliations List of theses of the Alzheimer Center Dankwoord About the author 193 206 209 213 218 220

CHAPTER 1 General introduction

8 Chapter 1 Alzheimer’s disease as cause of dementia Worldwide, more than 55 million people are affected by dementia (1). Dementia refers to a clinical syndrome characterized by the progressive deterioration of cognitive functions with interference in daily functioning (2). It can be caused by several different diseases, and Alzheimer’s disease (AD) is its most common cause (1). According to the amyloid cascade hypothesis, accumulation of the protein amyloid-beta is the primary event in the pathogenesis of AD (3). It is thought to initiate a series of events including the formation of neurofibrillary tangles, inflammation and synaptic dysfunction, which ultimately leads to neuronal injury, neurodegeneration and cognitive decline (4, 5). The progression of cognitive symptoms is gradual, and therefore several predementia stages can be identified. Dementia is preceded by the stage of mild cognitive impairment (MCI), in which there are deficits in one cognitive domain but no significant interference in daily living yet (6). Subjective cognitive decline (SCD) might be an even earlier manifestation of neurodegenerative disease than MCI, since it refers to the stage in which there are no objective cognitive disorders yet, while individuals experience a self-perceived decline in cognition (Figure 1). However, not all individuals with MCI or SCD suffer from a neurodegenerative disease, since there is a myriad of other causes for both syndromes, nor will all individuals with SCD progress to MCI and dementia. Research interest is shifting increasingly to these early stages, in which pathology is beginning to accumulate, while there are no cognitive deficits yet, because these stages potentially provide a window of opportunity to halt the progression of the disease. However, how to accurately identify SCD patients with a neurodegenerative disease who are at risk at risk of future cognitive decline remains largely unknown. Figure 1. Course of cognitive decline. SCD = subjective cognitive decline, MCI = mild cognitive impairment. Adapted from Jessen et al. (2014) (7).

9 General introduction Subjective cognitive decline In 2014, the Subjective Cognitive Impairment Cohort (SCIENCe project) started at the Alzheimer Center Amsterdam, which studies individuals with SCD (Box 1)(8). Individuals with SCD are cognitively normal (i.e. performance on cognitive tests within normal limits) with a self-perceived (subjective) decline in any cognitive domain over time (7, 9). SCD can be caused by (a combination of) many different factors, such as depressive symptoms, sleeping disorders, stress-inducing life events or normal aging, making it a heterogeneous group of individuals. SCD could also be the first symptom of a neurodegenerative disease, most frequently AD. Since the pathogenesis of AD is a gradual process and accumulation of pathology starts decades before diagnosis of dementia, some cognitively normal individuals with SCD could already harbor the first pathological changes associated with this disease. As these subjects are difficult to identify, several so-called SCD plus clinical criteria have been proposed that are thought to increase the likelihood of a neurodegenerative disease in individuals with SCD. These include the self-perceived decline in memory rather than other domains of cognition, the onset of SCD within the last five years, age of onset ≥ 60 year, worries associated with SCD, feeling of worse cognitive performance than peers, confirmation of SCD by an informant and the presence of the APOE ε4 genotype (7). Individuals with SCD included in the SCIENCe project have presented to a memory clinic because of cognitive complaints, which makes them a clinically relevant population. They are searching for information about their biomarkers status and risk of progression to dementia, but it is often difficult for clinicians to provide answers, since these are largely unknown yet. ATN classification Over the past decade, there has been a change in the way AD is viewed by clinicians and researchers. Before availability of biomarkers, AD could only be diagnosed definitively after death by neuropathological examination, while a probable diagnosis in vivo relied mostly on clinical symptoms(2). Over the past decade, biomarker research has expanded greatly and many measures have become available that correlate well with neuropathology. This enables a biological approach for AD, which also helps to conduct studies aimed at a better understanding of the pathophysiological disease trajectories. It will furthermore benefit the search for therapeutic targets, since disease-modifying interventions must be focused on a biological target. In 2018, a new research framework was developed under the auspices of the National Institute on Aging and Alzheimer’s Association (NIA-AA) which groups biomarkers into three categories (ATN) (10). With this framework, the definition of AD was shifted from 1

10 Chapter 1 a syndrome to a biological construct. Each individual can be rated for the presence of abnormal amyloid-beta (A), hyperphosphorylated tau (T) and neurodegeneration (N), resulting in eight possible biomarker combinations, or ATN profiles. According to the ATN framework, individuals have AD when they have abnormal values for both amyloid and tau. Individuals with abnormal values for only amyloid are assigned the label Alzheimer’s pathologic change, and individuals with evidence of abnormal tau and/or neurodegeneration but normal amyloid values are labelled as having non-AD pathologic change. The ATN classification can be applied independently of clinical staging, including cognitively normal individuals, such as those with SCD. About 20-25% of cognitively normal individuals, with or without SCD, have abnormal amyloid values, placing them in the Alzheimer’s continuum (11). The ATN classification serves as a common language among researchers and provides a structured method to characterize a sample, which has been beneficial for the research field. However, there are several challenges yet to overcome. The ATN framework has emerged as research framework and is not intended (yet) for clinical diagnosis and prognosis. It still needs to be thoroughly examined through longitudinal cohort studies and randomized controlled trials and altered if needed, in order to use in general clinical practice. Furthermore, the value of the ATN classification system in cognitively normal individuals with or without SCD still needs to be elucidated. Box 1. The SCIENCe project The SCIENCe project, which stands for Subjective Cognitive Impairment Cohort, is an ongoing cohort study in the Alzheimer Center Amsterdam. In this study, the contribution of different factors to SCD is evaluated and the longitudinal trajectory in relation to biomarkers is studied. The study started in 2014 and has included over 450 participants to date. Individuals are generally first referred to the Alzheimer Center by their general physician or medical specialist because of cognitive complaints, and receive an extensive work-up. When criteria for MCI or dementia are not met and there is no other neurological or psychiatric disease that could explain the cognitive complaints, individuals are labelled SCDand are subsequently invited to participate in the SCIENCe project (see Figure 2). Participants contribute to research by undergoing annual neuropsychological examinations, and optional blood tests, lumbar punctures and positron emission tomography (PET) scans. Yearly, the diagnosis of SCD is re-evaluated. If there are signs of cognitive deterioration, participants are offered a referral to clinical care. The majority of the research conducted in this thesis involves individuals with SCD from the SCIENCe project.

11 General introduction Figure 2. Schematic overview of the SCIENCe project (8). A closer look at Alzheimer’s disease biomarkers Biomarkers of different modalities are used in the ATN classification. According to the framework, biomarkers for A include CSF amyloid-beta or amyloid burden on PET. Biomarkers for T include hyperphosphorylated (p-)tau in CSF or tau burden on PET. Biomarkers for N include atrophy on MRI, hypometabolism on fluorodeoxyglucose (FDG) PET, or total (t-)tau in CSF. In the following paragraphs, the different biomarkers and modalities will be discussed in more detail. Amyloid-targeting radioactive tracers such as [11C]PiB, [18F]florbetapir, [18F]flutemetamol and [18F]florbetaben allow for the simultaneous quantification of pathologic burden and visualization of its spatial distribution in the brain. For a large part of this thesis, [18F]florbetapir is used to quantify amyloid burden. In CSF, amyloid-beta 1-42 can be measured. Literature shows low values of CSF amyloid-beta and high amyloid burden 1

12 Chapter 1 on PET are associated with a greater risk of cognitive decline in cognitively normal individuals (12-18). Although imaging and CSF A biomarkers are not identical, they are shown to have a moderate to high agreement and relatively high correlation coefficients, implying they indeed measure the same pathological process (19, 20). However, there are difference and they could become abnormal at different time points, influencing their predictive value in different cognitive stages. Longitudinal studies with repeated biomarker measurements are needed to better understand which factors are associated with future amyloid accumulation, which would lead to a better understanding of the pathophysiology of AD. Furthermore, inherent to the ATN classification is the dichotomization of biomarkers. Often, a threshold is used to determine amyloid positivity, and additionally, PET scans can be ‘positive’ or ‘negative’ as determined by visual assessment. Although it can be useful in clinical and research settings, dichotomizing amyloid burden into a negative and positive status disregards the potential significance of early (subthreshold) amyloid pathology (21). The clinical relevance of a so-called ‘grey zone’ of amyloid burden still needs to be more thoroughly investigated. CSF p-tau is used most often as measure of T. Patients with AD dementia can be distinguished from controls by a profile with decreased concentrations of amyloidbeta and increased concentrations of p-tau (22-24). Since the emergence of tau tracers such as [18F]flortaucipir, it became possible to visualize and quantify tau load on PET, and tau PET research has expanded greatly during the past decade. Similar to A biomarkers, CSF and imaging T biomarkers are also shown to correlate fairly well (25). Neurodegeneration can have many different causes and is not specific for AD. Therefore, neurodegenerative markers are not necessary for the diagnosis, but rather have been suggested to provide pathologic staging information and predictive value. According to the framework, atrophy on MRI, hypometabolism on FDG PET or CSF t-tau can all be used to measure N. Regarding atrophy, especially atrophy of the medial temporal lobe and reduced cortical thickness in specific regions are hallmarks of (AD) dementia (26, 27). In addition to the biomarkers proposed in the framework, blood-based biomarkers are now available and have been suggested as non-invasive alternative markers for N (10, 28, 29). In contrast to biomarkers for A and T, N biomarkers are poorly correlated (20, 30-33). It still needs to be elucidated which N biomarker captures ‘neurodegeneration’ most accurately, but this proves to be difficult due to the lack of gold standard.

13 General introduction Although only the triad of amyloid, tau and neurodegeneration are represented in the ATN framework, other disease pathways might also play a role. In the ATN framework, for example, genetic risk factors are not addressed because they are no measure of pathology. However, the APOE gene has been associated with a higher rate of amyloid positivity and a higher risk of clinical progression, highlighting its relevance (34). In recent years, many other genes have been identified that likely play a role in the pathophysiology of AD, but these still need to be investigated in relation to AD biomarkers and clinical features (35). Furthermore, other processes such as changes in cerebral blood flow (CBF) are also suggested to play a role in the pathophysiology of AD (36, 37). With dynamic PET scans, it is possible to assess amyloid burden and CBF simultaneously in vivo, since it provides two unique parameters: binding potential (BPND) and R1. BPND is a measure of exact quantification of specific binding to amyloid-beta (38). R1 represents the ratio between the rate constant for ligand transfer from plasma to tissue (K1) in the target region and the reference region. This can be used as measure for relative CBF (rCBF) (39-41). CBF is shown to be abnormal in AD dementia and relates to changes in brain glucose metabolism and synaptic failure (42-44). It is currently unclear how CBF and other biomarkers such as amyloid burden are interrelated, especially in the early stages of the disease, as previous studies provide conflicting results and are hampered by small sample sizes (45-52). 1

14 Chapter 1 Aim and thesis outline In this thesis we studied the value of AD biomarkers for predicting clinical progression in SCD. More specifically, we aimed to: 1. Investigate the predictive value of biomarkers in the ATN classification by assessing the associations with cognitive decline and risk of clinical progression to MCI or dementia. 2. Explore the effects of different definitions of abnormality of AD biomarkers 3. Evaluate factors underlying the trajectories associated with AD biomarker abnormalities In chapter 2, we addressed the first aim by using the ATN classification to investigate the joint value of amyloid PET or CSF abeta (A), CSF p-tau (T) and medial temporal atrophy (N) on MRI. We investigated the relationship with cognitive decline and clinical progression. Chapters 3 and 4 address the second aim. In chapter 3 we explored how different thresholds for amyloid positivity based on amyloid burden on [18F]florbetapir PET relate to cognition and considered the possibility of a grey zone. In chapter 4 we used different modalities to define neurodegeneration and investigated the added value of each definition beyond amyloid-beta and p-tau. Chapters 5, 6 and 7 address the third aim. In chapter 5, we investigated the relationship between ATN biomarkers, the APOE gene and a polygenic risk score, and their association with clinical progression to dementia. In chapter 6, we investigated the relationship between the longitudinal trajectories of amyloid burden on PET and a measure of relative cerebral blood flow. Last, in chapter 7, we investigated longitudinal change in ATN classification and amyloid status.

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17 General introduction 27. Dickerson BC, Bakkour A, Salat DH, Feczko E, Pacheco J, Greve DN, et al. The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cerebral cortex (New York, NY : 1991). 2009;19(3):497-510. 28. Rajan KB, Aggarwal NT, McAninch EA, Weuve J, Barnes LL, Wilson RS, et al. Remote Blood Biomarkers of Longitudinal Cognitive Outcomes in a Population Study. Ann Neurol. 2020;88(6):1065-76. 29. Verberk IMW, Laarhuis MB, van den Bosch KA, Ebenau JL, van Leeuwenstijn M, Prins ND, et al. Serum markers glial fibrillary acidic protein and neurofilament light for prognosis and monitoring in cognitively normal older people: a prospective memory clinic-based cohort study. The Lancet Healthy Longevity. 2021. 30. Alexopoulos P, Kriett L, Haller B, Klupp E, Gray K, Grimmer T, et al. Limited agreement between biomarkers of neuronal injury at different stages of Alzheimer’s disease. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2014;10(6):684-9. 31. Jack JCR, Wiste HJ, Weigand SD, Knopman DS, Mielke MM, Vemuri P, et al. Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings. Brain : a journal of neurology. 2015;138(12):3747-59. 32. Toledo JB, Weiner MW, Wolk DA, Da X, Chen K, Arnold SE, et al. Neuronal injury biomarkers and prognosis in ADNI subjects with normal cognition. Acta neuropathologica communications. 2014;2:26. 33. Vos SJB, Gordon BA, Su Y, Visser PJ, Holtzman DM, Morris JC, et al. NIA-AA staging of preclinical Alzheimer disease: discordance and concordance of CSF and imaging biomarkers. Neurobiology of aging. 2016;44:1-8. 34. Ali JI, Smart CM, Gawryluk JR. Subjective Cognitive Decline and APOE ɛ4: A Systematic Review. Journal of Alzheimer’s Disease. 2018;65:303-20. 35. de Rojas I, Moreno-Grau S, Tesi N, Grenier-Boley B, Andrade V, Jansen IE, et al. Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores. Nature communications. 2021;12(1):3417. 36. Korte N, Nortley R, Attwell D. Cerebral blood flow decrease as an early pathological mechanism in Alzheimer’s disease. Acta Neuropathol. 2020;140(6):793-810. 37. Calsolaro V, Edison P. Neuroinflammation in Alzheimer’s disease: Current evidence and future directions. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2016;12(6):719-32. 38. Golla SS, Verfaillie SC, Boellaard R, Adriaanse SM, Zwan MD, Schuit RC, et al. Quantification of [(18)F]florbetapir: A test-retest tracer kinetic modelling study. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2019;39(11):2172-80. 39. Meyer PT, Hellwig S, Amtage F, Rottenburger C, Sahm U, Reuland P, et al. Dualbiomarker imaging of regional cerebral amyloid load and neuronal activity in dementia with PET and 11C-labeled Pittsburgh compound B. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2011;52(3):393-400. 1

18 Chapter 1 40. Ottoy J, Verhaeghe J, Niemantsverdriet E, De Roeck E, Wyffels L, Ceyssens S, et al. (18)F-FDG PET, the early phases and the delivery rate of (18)F-AV45 PET as proxies of cerebral blood flow in Alzheimer’s disease: Validation against (15)O-H2O PET. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2019. 41. Hays CC, Zlatar ZZ, Wierenga CE. The Utility of Cerebral Blood Flow as a Biomarker of Preclinical Alzheimer’s Disease. Cellular and molecular neurobiology. 2016;36(2):167-79. 42. Zlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nature reviews Neuroscience. 2011;12(12):723-38. 43. Chen Y, Wolk DA, Reddin JS, Korczykowski M, Martinez PM, Musiek ES, et al. Voxellevel comparison of arterial spin-labeled perfusion MRI and FDG-PET in Alzheimer disease. Neurology. 2011;77(22):1977-85. 44. Musiek ES, Chen Y, Korczykowski M, Saboury B, Martinez PM, Reddin JS, et al. Direct comparison of fluorodeoxyglucose positron emission tomography and arterial spin labeling magnetic resonance imaging in Alzheimer’s disease. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2012;8(1):51-9. 45. Fazlollahi A, Calamante F, Liang X, Bourgeat P, Raniga P, Dore V, et al. Increased cerebral blood flow with increased amyloid burden in the preclinical phase of alzheimer’s disease. Journal of magnetic resonance imaging : JMRI. 2020;51(2):505-13. 46. Sojkova J, Beason-Held L, Zhou Y, An Y, Kraut MA, Ye W, et al. Longitudinal cerebral blood flow and amyloid deposition: an emerging pattern? Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2008;49(9):1465-71. 47. Mattsson N, Tosun D, Insel PS, Simonson A, Jack CR, Jr., Beckett LA, et al. Association of brain amyloid-β with cerebral perfusion and structure in Alzheimer’s disease and mild cognitive impairment. Brain : a journal of neurology. 2014;137(Pt 5):1550-61. 48. Bilgel M, Beason-Held L, An Y, Zhou Y, Wong DF, Resnick SM. Longitudinal evaluation of surrogates of regional cerebral blood flow computed from dynamic amyloid PET imaging. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2020;40(2):288-97. 49. Albrecht D, Isenberg AL, Stradford J, Monreal T, Sagare A, Pachicano M, et al. Associations between Vascular Function and Tau PET Are Associated with Global Cognition and Amyloid. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2020;40(44):8573-86. 50. Michels L, Warnock G, Buck A, Macauda G, Leh SE, Kaelin AM, et al. Arterial spin labeling imaging reveals widespread and Aβ-independent reductions in cerebral blood flow in elderly apolipoprotein epsilon-4 carriers. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2016;36(3):581-95. 51. Bangen KJ, Clark AL, Edmonds EC, Evangelista ND, Werhane ML, Thomas KR, et al. Cerebral Blood Flow and Amyloid-β Interact to Affect Memory Performance in Cognitively Normal Older Adults. Front Aging Neurosci. 2017;9:181. 52. Funaki K, Nakajima S, Noda Y, Wake T, Ito D, Yamagata B, et al. Can we predict amyloid deposition by objective cognition and regional cerebral blood flow in patients with subjective cognitive decline? Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society. 2019;19(4):325-32.

19 General introduction 1

CHAPTER 2 ATN classification and clinical progression in subjective cognitive decline: The SCIENCe project Jarith L. Ebenau, Tessa Timmers, Linda M.P. Wesselman, Inge M.W. Verberk, Sander C.J. Verfaillie, Rosalinde E.R. Slot, Argonde C. van Harten, Charlotte E. Teunissen, Frederik Barkhof, Karlijn A. van den Bosch, Mardou S.S.A. van Leeuwenstijn, Jori Tomassen, Anouk den Braber, Pieter Jelle Visser, Niels D. Prins, Sietske A.M. Sikkes, Philip Scheltens, Bart N.M. van Berckel, Wiesje M. van der Flier Neurology. 2020 Jul 7;95(1):e46-e58

22 Chapter 2 ABSTRACT Objective To investigate the relationship between the ATN classification system (amyloid, tau, neurodegeneration) and risk of dementia and cognitive decline in individuals with subjective cognitive decline (SCD). Methods We classified 693 participants with SCD (60 ± 9 years, 41% women, Mini-Mental State Examination score 28 ± 2) from the Amsterdam Dementia Cohort and Subjective Cognitive Impairment Cohort (SCIENCe) project according to the ATN model, as determined by amyloid PET or CSF β-amyloid (A), CSF p-tau (T), and MRI-based medial temporal lobe atrophy (N). All underwent extensive neuropsychological assessment. For 342 participants, follow-up was available (3 ± 2 years). As a control population, we included 124 participants without SCD. Results Fifty-six (n = 385) participants had normal Alzheimer disease (AD) biomarkers (A–T– N–), 27% (n = 186) had non-AD pathologic change (A–T–N+, A–T+N–, A–T+N+), 18% (n = 122) fell within the Alzheimer continuum (A+T–N–, A+T–N+, A+T+N–, A+T+N+). ATN profiles were unevenly distributed, with A–T+N+, A+T–N+, and A+T+N+ containing very few participants. Cox regression showed that compared to A–T–N–, participants in A+ profiles had a higher risk of dementia with a dose–response pattern for number of biomarkers affected. Linear mixed models showed participants in A+ profiles showed a steeper decline on tests addressing memory, attention, language, and executive functions. In the control group, there was no association between ATN and cognition. Conclusions Among individuals presenting with SCD at a memory clinic, those with a biomarker profile A–T+N+, A+T–N–, A+T+N–, and A+T+N+ were at increased risk of dementia, and showed steeper cognitive decline compared to A–T–N– individuals. These results suggest a future where biomarker results could be used for individualized risk profiling in cognitively normal individuals presenting at a memory clinic.

23 ATN classification in subjective cognitive decline INTRODUCTION The research framework for Alzheimer’s disease (AD) diagnosis, developed under the auspices of the National institute on Aging and Alzheimer’s Association (NIAAA), proposes to categorize individuals based on biomarker evidence of pathology using the so-called ‘ATN classification’ (1). According to the ATN classification system, each individual is rated for the presence of beta-amyloid (CSF Aβ or amyloid PET, ‘A’), hyperphosphorylated tau (CSF p-tau or tau PET, ‘T’) and neurodegeneration (atrophy on structural MRI, FDG PET or CSF total tau, ‘N’), resulting in eight possible biomarker combinations. Several former studies have applied the ATN classification scheme (2-8). Of these, two have used a cross-sectional design in cognitively unimpaired participants (2, 3), and one focused on biomarker inconsistencies in healthy controls, MCI and dementia patients (4). Three former studies had a longitudinal design, evaluating the association between ATN and cognitive decline in a nondemented elderly (cognitively normal and/or MCI) (5-7). Subjective cognitive decline (SCD) is characterized by self-perceived decline in cognition, but comparable cognitive performance to peers (9, 10). In SCD, abnormal amyloid, abnormal tau and signs of neurodegeneration are associated with an increased risk of cognitive decline (11-17). Longitudinal studies investigating the ATN classification scheme in relation to clinical progression in SCD, which has been described as stage 2 in the diagnostic framework (1), are not yet available. We aimed to examine (i) the distribution and clinical correlates of the ATN biomarker profiles in individuals presenting with SCD at a memory clinic, and (ii) to investigate the ATN predictive value for risk of clinical decline over time. METHODS Population We included 693 participants with SCD from the Amsterdam Dementia Cohort and the ‘Subjective Cognitive Impairment Cohort’ (SCIENCe) project at the Alzheimer Center Amsterdam (18-20). All participants underwent a standardized diagnostic workup, which consisted of a neurological, physical and neuropsychological evaluation, and brain MRI (18, 19). We used the Geriatric Depression Scale (GDS) to assess depressive symptoms (21, 22). Participants were labeled SCD in a multidisciplinary consensus meeting when clinical and cognitive testing was normal and criteria for MCI, dementia 2

24 Chapter 2 or other neurological or psychiatric conditions (e.g. major depression, schizophrenia) were not met (10, 23). Follow-up diagnoses were available for 342 participants (3±2 years). These participants were on average three years older, but otherwise comparable to the entire sample of 693 participants. At follow-up, diagnoses were re-evaluated as SCD, MCI, AD dementia, or other types of dementia (frontal temporal dementia (FTD), primary progressive aphasia (PPA), vascular dementia, Dementia with Lewy bodies (DLB)) (24-27). The clinical endpoints were (i) progression to dementia, and (ii) progression to MCI or dementia. Participants were included for the current project when MRI and CSF were available within one year of the diagnosis. Standard Protocol Approvals, Registrations, and Patient Consents The research is in accordance with the ethical consent by the VU University and with the Helsinki Declaration of 1975. For all patients written informed consent was available. Neuropsychological assessment All participants received an extensive standardized neuropsychological assessment (18). We used the Mini Mental State Examination (MMSE) for global cognition. To asses memory we used the Visual Association Test version A (VAT-A) and total immediate and delayed recall of the Dutch version of the Rey auditory verbal learning task (RAVLT). For language we used category fluency (animals). To assess attention, we used the Trail Making Test A (TMT-A), the forward condition of the Digit Span and Stroop task I and II (naming and color naming). To assess executive functioning we used the TMT-B, Digit Span (backwards) and Stroop task III (color-word). Raw test scores for TMT and Stroop were log transformed, because the data were right-skewed, and subsequently inverted, such that a lower score implies worse performance. The proportion of missing tests ranged from 7.6% for the TMT-A to 19.1% for the Stroop III. In total, 1424 neuropsychological investigations of 693 patients were available (299 ≥2; range 2-12, median 3). MRI All participants underwent an MRI-scan of the brain (Siemens Avanto, n=7; GE Discovery MR750, n=14; Impax, n=119; 3T Philips Ingenuity TF PET/MR system, n=123; 1.5T GE Signa HDxt, n=21; 3.0T GE Signa HDxt, n=262; 1.5T Siemens Sonata, n=27; 3T Toshiba Vantage Titan, n=119; Vision, n=1). The protocol included 3D T1-weighted images, 3D T2-weighted images and 3D T2-weighted fluid-attenuated inversionrecovery (FLAIR) images (18). Visual rating of medial temporal lobe atrophy (MTA)

25 ATN classification in subjective cognitive decline was performed on coronal T1-weighted images averaging scores for the left and right sides (range 0-4) (28). Posterior atrophy (PA) was rated using sagittal, axial and coronal planes of T1 and FLAIR weighted images averaging scores for the left and right sides (range 0-3) (29). Global cortical atrophy (GCA) was rated using axial FLAIR images (range 0-3) (30). The severity of white matter hyperintensities (WMH) was determined on the FLAIR sequence using the Fazekas scale (range 0-3) (31). Lacunes were defined as deep lesions (3-15 mm) with CSF-like signal on all sequences. They were counted and dichotomized into absent (0) of present (≥ 1 lacune). Microbleeds were defined as small dot-like hypointense lesions on T2-weighted images. They were also counted and dichotomized into absent (0) or present (≥ 1 microbleed). An experienced neuroradiologist reviewed all scans. CSF CSF was obtained by lumbar puncture between the L3/L4, L4/L5 or L5/S1 intervertebral space by a 25-gauge needle and syringe and collected in polypropylene tubes (32). Aβ-42, total tau and tau phosphorylated threonine 181 (p-tau) were measured using sandwich ELISA’s (Innotest beta-amyloid1-42 n=579, Innotest hTAU-Ag and Innotest PhosphoTAU-181p) (33). CSF Aβ levels were adjusted for the drift in CSF biomarker analyses that occurred over the years (34). For nine participants, we used Elecsys for analyses of Aβ. These values were transformed to the Innotest-equivalent values by the following formula: Elecsys Aβ (pg/ml) = -365 + 1.87 * Innotest Aβ (pg/ml) (35). PET For n=105 participants, amyloid PET was performed in research context using the tracers [18F]florbetapir (n=19), [18F]florbetaben (n=65), [18F]flutemetamol (n=10) or [11C]- PIB (Pittsburgh compound-B, n=11). The tracers were administered intravenously through a cannula. PET scans were acquired on the following systems: Gemini TF PET-CT, Ingenuity TF PET-CT, and Ingenuity PET/MRI (Philips Healthcare, Best, The Netherlands). During scans, laser beams were used to monitor head movement. For [18F]florbetapir imaging, participants were injected with a tracer dose of approximately 370 megabecquerel (MBq) [18F]florbetapir (20). 90-minutes dynamic PET emission scans were obtained simultaneously starting with tracer injection. For [18F]florbetaben imaging, participants were injected with a tracer dose of approximately 300 MBq [18F] florbetaben (36). The image acquisition window extended from 90 to 110 minutes (4 x 5 minute frames) after dose injection. For [18F]flutemetamol imaging, participants were injected with approximately 191 MBq [18F]flutemetamol (37). The image acquisition window extended from 90 minutes to 110 minutes (4 x 5 minute frames) after dose injection. For [11C]PIB imaging, participants were injected with a tracer dose of approximately 365 MBq [11C]PIB in younger participants and approximately 382 MBq 2

26 Chapter 2 [11C]PIB in older participants (38, 39). 90-minutes dynamic PET emission scans were obtained immediately starting with tracer injection. All scans were visually rated as ‘positive’ or ‘negative’ by a trained nuclear medicine physician. ATN classification We used amyloid PET (n=105) or CSF Aβ (n=588) to determine whether a participant was A- or A+. If both measures were available, the PET result was used. CSF concentrations were considered amyloid positive <813 pg/mL (34). For tau (T), we used CSF p-tau concentrations. Values were considered p-tau positive > 52 pg/ml (40). We used the average MTA to determine neurodegeneration (N). For participants <65 years of age, an average MTA score ≥1 was considered positive, for participants ≥65 years of age, an average MTA score ≥1.5 was considered positive (41). Because a number of ATN profiles only contained very few participants, we also clustered the eight biomarkers profiles into three categories. The A-T-N- profile was labelled as the ‘normal AD biomarker’ category. We clustered the remaining A- profiles (A-T-N+, A-T+N- and A-T+N+) as ‘non-AD pathologic change’ and we clustered all A+ profiles (A+T-N-, A+T-N+, A+T+N-, A+T+N+) as ‘Alzheimer’s continuum’ (1). Control group For comparison, we also included a control group without subjective cognitive decline, recruited from the EMIF-AD PreclinAD study (42). We included 124 participants, including 53 monozygotic twin pairs and 18 singletons. For 119 participants, A was determined by visual read of [18F]Flutemetamol PET. Levels of CSF Aβ 40 and Aβ42, and p-tau were analyzed using kits from ADx Neurosciences/Euroimmun. CSF concentrations were considered amyloid positive when the CSF Aβ42/40 ratio was < 0.065 (n=5 for whom amyloid PET was not available). To determine T, we used the 75th percentile of p-tau ( ≥ 86 pg/mL). N was determined using average MTA ( <65 years - MTA ≥1; ≥65 years - MTA ≥1.5 considered positive). Neuropsychological testing procedures for the control group were largely similar to procedures for participants with SCD. For 121 participants, follow-up assessments were available (2±0 years). Statistics We compared demographic and clinical variables between the eight ATN biomarker profiles. For continuous variables, we used Analysis of Variance (ANOVA) and Kruskal-Wallis where appropriate, and post-hoc Tukey’s test if the assumption for homogeneity of variances was met, and Games-Howell if the assumption was not met. For dichotomous variables we used Fisher’s exact test, and post-hoc looked at

27 ATN classification in subjective cognitive decline standardized residuals with values < -1.96 or > 1.96 considered significant. We used Chi-squared test to compare the distribution of ATN profiles between the SCD and control group. We performed Cox proportional hazards analyses to evaluate the association between eight-profile ATN classification (A-T-N-: reference) and clinical progression to dementia. Analyses were adjusted for age, sex and education. In additional analyses, we used progression to MCI or dementia as outcome. To explore the additive effect of A-status and memory function, we ran an additional analysis after constructing a new, four-level variable ((1) A-, high memory (baseline RAVLT-delayed recall z-score ≤ -1.0); (2) A-, low memory (z-score > -1.0); (3) A+, high memory; (4) A+, low memory). Analyses were adjusted for age, sex and education. As reference category we used (1) A-, high memory. Subsequently, we assessed the associations between ATN classification and cognitive decline using linear mixed models (LMM). ATN profiles (included as dummies, with the A-T-N- profile as reference), time and the interactions between ATN profiles and time were included as independent variables, age, sex and education were included as covariates, and cognitive test scores were used as dependent variables. Intercept and time were included as random factors. Separate models were run for 11 individual neuropsychological tests. We used the false discovery rate (FDR) method to correct for multiple testing with q set at 0.05. For the analyses in the control group without SCD, we additionally added family as a random factor to account for within twin pair dependence. All analyses were done using SPSS version 22. P-values <0.05 were considered significant. Kaplan Meier curves and figures showing association between ATN and cognitive decline were made with R studio 3.4.2. Data availability Any data not published within the article may be shared upon request. 2

28 Chapter 2 RESULTS Baseline demographics and clinical features At baseline, the 693 SCD participants were on average 60±9 years old, 283(41%) were female, and MMSE was 28±2. With 385 (56%) participants, the majority of the participants was negative for each of the three biomarkers (A-T-N-, normal AD biomarkers). Figure 1 and Table 1 show the distribution, demographics and clinical characteristics of all biomarker profiles (three category comparison provided in Supplemental Table e-1, available from Dryad: bg79cnp71). Participants were older in A+ profiles and in profiles with a higher number of biomarkers affected. There were no significant differences in sex, education, MMSE or GDS scores between ATN profiles. APOE ε4 varied by ATN biomarker profiles, with A+ profiles containing the highest number of APOE ε4 carriers. Comparing biomarker values between ATN profiles, we found that total-tau (not included in our ATN-definition) was lowest in A-T-N-, and was higher in T+ groups, but not in N+ groups. P-tau and total-tau strongly correlated with each other (Spearman’s rho 0.90, p≤0.00). GCA scores were higher in N+ profiles (Spearman’s rho MTA~GCA: 0.35, p≤0.00). There was no difference in any of the MRI measures of small vessel disease between groups. Participants in the control group without SCD were on average 9 years older than our SCD sample and more often female (52% versus 41%, (Supplemental Table e-2, available from Dryad: The distribution of ATN profiles differed between the two cohorts (p=0.00). The prevalence of A-T-N- was similar in both cohorts, but N+ biomarker profiles were more prevalent in the control group (23% versus 11%), while the A+ was somewhat more common in our SCD sample (18% versus 14%). In the control group, age differed across ATN profiles, with on visual inspection a stronger age effect than in the SCD sample. ATN profiles also differed on MMSE.

29 ATN classification in subjective cognitive decline Table 1. Baseline demographics, PET, CSF, and MRI values in eight-profile ATN classification A-T-NN=385 (55.6%) A-T-N+ N=39 (5.6%) A-T+NN=137 (19.8%) A-T+N+ N=10 (1.4%) A+T-NN=43 (6.2%) A+T-N+ N=14 (2.0%) A+T+NN=54 (7.8%) A+T+N+ N=11 (1.6%) P-value Demographics Age, mean (SD) 57.2 (8.3) 59.0 (9.0) 61.1 (8.7) 60.0 (7.5) 62.8 (7.1) 65.8 (9.3) 66.7 (6.6) 68.8 (9.9) 0.00a Sex, n female (%) 155 (40.3%) 11 (28.2%) 55 (40.1%) 4 (40.0%) 20 (46.5%) 5 (35.7%) 29 (53.7%) 4 (36.4%) 0.41 Education, mean (SD) 5.4 (1.3) 5.5 (1.1) 5.5 (1.3) 4.7 (1.3) 5.3 (1.4) 5.8 (1.4) 5.5 (1.2) 5.3 (0.9) 0.41 MMSE, mean (SD) 28 (2) 28 (2) 28 (2) 27 (2) 28 (1) 28 (2) 28 (1) 28 (2) 0.46 APOE ε4 carriers, n (%) 103 (27.2%) 13 (35.1%) 51 (38.6%) 3 (30.0%) 25 (59.5%) 8 (57.1%) 35 (68.6%) 7 (63.6%) 0.00a Depressive symptoms GDS score, mean (SD) 3.1 (2.5) 3.1 (3.3) 3.2 (3.0) 4.2 (2.0) 2.5 (2.5) 3.2 (4.5) 2.6 (3.2) 2.0 (1.8) 0.63 Amyloid PET Amyloid PET, n positive/ total (N=105) 0/54 0/8 0/17 0/2 8/8 1/1 14/14 1/1 0.00a CSF Aβ, mean (SD) (N=588) 1125.2 (154.9) 1116.4 (203.9) 1213.2 (204.3) 1149.1 (244.7) 698.9 (100.5) 690.4 (122.0) 654.3 (105.6) 620.0 (103.0) 0.00a P-tau, mean (SD) 38.1 (8.6) 36.8 (11.0) 66.0 (12.1) 76.8 (40.9) 41.3 (9.7) 32.1 (9.1) 87.3 (31.9) 95.5 (55.4) 0.00a Total Tau, mean (SD) 215.8 (74.0) 210.1 (67.5) 400.2 (130.9) 532.4 (375.1) 251.3 (80.8) 182.2 (70.0) 629.9 (329.9) 622.4 (337.9) 0.00a 2

30 Chapter 2 Table 1. Baseline demographics, PET, CSF, and MRI values in eight-profile ATN classification (continued) A-T-NN=385 (55.6%) A-T-N+ N=39 (5.6%) A-T+NN=137 (19.8%) A-T+N+ N=10 (1.4%) A+T-NN=43 (6.2%) A+T-N+ N=14 (2.0%) A+T+NN=54 (7.8%) A+T+N+ N=11 (1.6%) P-value MRI MTA, mean (SD)b 0.2 (0.3) 1.2 (0.3) 0.2 (0.3) 1.3 (0.4) 0.3 (0.4) 1.4 (0.4) 0.3 (0.4) 1.5 (0.5) 0.00a GCA, mean (SD) 0.2 (0.4) 0.7 (0.6) 0.4 (0.6) 0.5 (0.9) 0.3 (0.5) 0.6 (0.6) 0.4 (0.5) 0.6 (0.8) 0.00a PA, mean (SD) b 0.4 (0.6) 0.7 (0.8) 0.6 (0.7) 0.6 (0.8) 0.5 (0.5) 0.6 (0.5) 0.7 (0.6) 1.1 (0.8) 0.00a Fazekas, mean (SD) 0.5 (0.6) 0.7 (0.8) 0.6 (0.6) 0.8 (0.8) 0.8 (0.7) 1.1 (0.9) 0.9 (0.7) 0.9 (0.8) 0.00a Lacunes, n (%)c 12 (3.1%) 2 (5.1%) 8 (5.8%) 1 (10.0%) 0 (0.0%) 1 (8.3%) 4 (7.4%) 1 (9.1%) 0.13 Microbleeds, n (%)c 3 9 (10.3%) 8 (21.1%) 16 (11.9%) 0 (0.0%) 8 (18.6%) 3 (23.1%) 13 (24.1%) 5 (45.5%) 0.00a Abbreviations: Aβ = β-amyloid; GCA=global cortical atrophy; GDS=geriatric depression scale; MMSE=mini-mental state examination; MTA=medial temporal atrophy; p-tau = phosphorylated threonine 181; PA=parietal atrophy. Analyses were performed using analysis of variance and Fisher’s exact test. Education is rated using the Dutch Verhage system (43). a: p-value <0.05. b: average between left and right side. c: values are dichotomized into 0 counts and ≥1 counts. N shown is number of participants with ≥1 counts.

31 ATN classification in subjective cognitive decline Figure 1. Distribution of the ATN biomarker profiles in subjective cognitive decline Pie chart illustrates the distribution of the 8-profile and 3-category ATN classification. AD = Alzheimer’s disease. Risk of dementia Table 2 shows that after 3±2 years of follow-up, 16 (21%) participants in A+ profiles showed incident dementia (AD n=14; non-AD n=2), compared to 2 (1%) participants in A-T-N- (non-AD n=2) . The Supplemental e-Box, available from Dryad , provides a case description of the two participants in respectively A-T+N- and A-T+N+ that progressed to AD dementia and two participants in A-T-N- that progressed to dementia. Both A- participants that progressed to AD dementia became A+T+N+ on follow-up. The participants who initially were A-T-N- progressed to primary progressive aphasia and to possible FTD. Cox proportional hazard analyses showed that compared to A-T-N-, participants in the A+ profiles were at increased risk of dementia with an incremental increase in hazard rate (A+T-N- HR 9.7 (1.6-59.3), A+T+N- HR 20.2 (3.7-110.2) and A+T+N+ HR 62.3 (9.5-408.4); Figure 2). Within the A- profiles, participants in A-T+N+ were at increased risk of dementia (HR 18.5 (1.6-211.4)), but participants in A-T+N- and A-T-N+ were not. When we repeated the analyses with clinical progression to MCI or dementia as outcome, a similar pattern emerged, although the Hazard Ratios were lower, caused by a higher number of participants progressing to MCI in the reference profile. 2