Amyloidogenic nanoplaques as a potential biomarker for Alzheimer’s disease

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Amyloidogenic nanoplaques as a potential biomarker for Alzheimer’s disease

Thesis for the degree of Philosophiae Doctor (PhD) Mari Aksnes

2021

Department of Geriatric Medicine Institute of Clinical Medicine

Faculty of Medicine

University of Oslo

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© Mari Aksnes, 2022

Series of dissertations submitted to the Faculty of Medicine, University of Oslo

ISBN 978-82-348-0007-8

reproduced or transmitted, in any form or by any means, without permission.

Cover: Hanne Baadsgaard Utigard.

Print production: Reprosentralen, University of Oslo.

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ACKNOWLEDGMENTS

First and foremost, I want to thank the patients included in the Norwegian Registry of Persons Assessed for Cognitive Symptoms (NorCog). The research presented in this thesis would not be possible without your contributions. I would also like to thank everyone involved in establishing and administering NorCog, as well as those who have contributed to including patients and collecting data.

The research presented in this thesis was primarily funded by the Olav Thon Foundation. I am grateful to the funding bodies for supporting this project. I want to thank my employer, the Department of Geriatric Medicine at the University of Oslo, and especially Torgeir Bruun Wyller, for welcoming me to the stimulating interdisciplinary environment and lovely atmosphere at ‘Loftet’. I am also appreciative of the PhD program at the Faculty of Medicine for granting me the opportunity to pursue a PhD.

I am deeply grateful to my main supervisor, Anne-Brita Knapskog. Thank you for taking the time to meet with a budding neuroscience researcher after a cold e-mail and for introducing me to this research project. I have appreciated our scientific discussions, your prompt feedback, your perspective as a clinician and your guidance at every major step of this journey. I hope to continue to work with you for several more years! I am also very grateful to my co-supervisor Nenad Bogdanović for the opportunity to work on this project. Your creativity and scientific knowledge is highly inspiring. You and Anne-Brita have made an excellent supervisor team.

I owe great thanks to my collaborators and co-authors at Karolinska Institutet: Vladana Vukojević, Ann Tiiman and Lars Terenius. Thank you for introducing me to the Thioflavin-T Fluorescence Correlation Spectroscopy method and patiently explaining the mechanisms and details. Thank you to Ann for completing several hours of experimental analysis for this project and to all of you for contributing with insightful comments on the results and resulting papers.

I also have to thank Karina Grasbekk, the greatest research nurse, for facilitating the collection of serum at the Memory Clinic and for organising the shipment of samples. Great thanks to all the Memory Clinic personnel who went the extra mile to secure serum samples for my project.

I want to thank my other co-authors for your contributions to and constructive feedback on my work. Thank you to Ebba Gløersen Müller for analysing the amyloid-PET scans for my first paper and for the interesting discussions on methodology with you and Mona-Elisabeth Revheim. Thank you to Trine Holt Edwin for providing clinical diagnoses and clinical dementia

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ratings for the patients in my first three papers and for all your help with small and big aspects of the PhD journey from the very beginning. Thank you to Hans Christian Dalsbotten Aass for doing the cytokine analyses for papers III and IV and for contributing to the interpretation of data and preparation of the manuscripts. I would also like to thank Leiv-Otto Watne and Kristi Henjum for helping to organise the cytokine analyses and for some great biomarker discussions through the years.

I owe great thanks to the NorCog team, especially Ingrid Tøndel Medbøen and Benedicte Bøhn, for your help with data extraction from NorCog and for answering all my questions about variables and data collection. I feel very lucky to have had you two and Yehani Wedatilake as my office mates for the last three years and I have thoroughly enjoyed the companionship and all our chats, both work-related and not.

Finally, I want to thank my friends and family for your support. Thank you to my fellow neuroscientists for sharing experiences as PhD-students and exciting discussions on our research and thank you to all my non-neuroscience friends for letting me go on about my work regardless of your interest in it. Thank you to my parents, brother and belle-mère for always believing in me, while also reminding me that work is not everything. Most of all, I want to thank my husband Anders for supporting me at every step of my academic journey, through the many highs and lows, always with love and patience.

Mari Aksnes

Oslo, October 2021

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SUMMARY

Background: Dementia is the decline of cognitive functions, e.g. memory, to a degree that limits a person’s ability to perform daily tasks. The most common cause of dementia is Alzheimer’s disease (AD), a progressive neurodegenerative disorder. Older age is the principal risk factor for AD, and therefore we expect more dementia cases with the demographic ageing of the global population. Currently, there is no cure for AD. The defining neuropathological features of AD are tau neurofibrillary tangles and extracellular amyloid-β (Aβ) plaques.

Biological markers (biomarkers) can be used to identify these features in patients several years before the first symptoms of dementia. Thus, biomarkers are important tools for early diagnosis of AD. The cause of AD is not known, but it is theorised that the aggregation of the peptide Aβ into oligomers and structured aggregates called amyloid is the primary cause. Yet, there are no established biomarkers for small soluble aggregated forms of Aβ.

Aims: The primary aim of this thesis was to assess the use of small amyloidogenic aggregates, called nanoplaques, as biomarkers for AD. Nanoplaques can be quantified in cerebrospinal fluid (CSF) and serum by the novel Thioflavin-T-Fluorescence Correlation Spectroscopy (ThT-FCS) assay. Specifically, we wanted to establish the relationship of CSF nanoplaque levels with established markers of AD neuropathology, and assess the relationship between CSF and serum nanoplaques with cytokines, markers of neuroinflammation. Furthermore, we aimed to assess whether CSF and serum nanoplaque levels differed significantly between patients with AD and other memory clinic patients.

Methods: We included CSF from 118 and serum from 49 memory clinic patients. Nanoplaque concentrations and sizes were quantified in CSF and serum by the ThT-FCS assay. A sub-set of 54 patients underwent ¹⁸F-Flutemetamol positron emission tomography (PET) imaging of brain amyloid. Core CSF biomarkers for AD, low CSF Aβ42, high total-tau (t-tau) and high phosphorylated-tau (p-tau) levels, were determined by enzyme-linked immunosorbent assays (n = 118 for the CSF patients, n = 26 for the serum patients). Levels of nine cytokines, eotaxin- 1, granulocyte stimulating factor, interleukin-6 (IL-6), IL-7, IL-8, monocyte chemoattractant protein-1, gamma-induced-protein 10, macrophage inflammatory protein-1α (MIP-1α) and MIP-1β, were quantified in the CSF of 111 patients and the serum of 48 patients using a custom- made nine-plex kit and read on a Luminex IS 200 instrument. We assessed whether the CSF nanoplaque levels were associated with ¹⁸F-Flutemetamol PET amyloid uptake, core CSF biomarkers or CSF levels of the nine cytokines. Moreover, we determined if serum nanoplaque

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levels were associated with serum levels of the nine cytokines. Finally, we assessed whether CSF nanoplaque levels differed between amyloid-positive and amyloid-negative patients, and whether CSF or serum nanoplaque levels differed between patients with clinical AD and patients with other disorders.

Main results: CSF nanoplaque levels were not associated with ¹⁸F-Flutemetamol PET amyloid uptake, but increased nanoplaque levels were associated with reduced levels of CSF Aβ42. We found no associations with p-tau or t-tau. CSF nanoplaque levels were increased among patients with non-detectable levels of MIP-1β and negatively associated with the cytokines IL-8 and MIP-1α. Nanoplaque levels were not associated with any cytokines in serum in our cohort. In the sub-set with ¹⁸F-Flutemetamol PET, nanoplaque levels did not differ between amyloid- positive and amyloid-negative patients, but they were significantly increased in patients with clinical AD. In the entire cohort of 118 patients, nanoplaque levels were significantly increased in amyloid-positive patients and patients with clinical AD, but quantification of nanoplaques did not improve classification of patients compared to the core biomarkers alone. Serum nanoplaque levels did not differ significantly between clinical AD and non-AD patients.

Conclusions: This thesis has demonstrated that CSF nanoplaque levels are associated with CSF Aβ42, but not with amyloid uptake quantified by ¹⁸F-Flutemetamol PET, nor with CSF levels of p-tau and t-tau. This indicates that increased CSF nanoplaque levels are weakly associated with Aβ pathology, and could reflect alternative aspects of AD pathology. Further, we demonstrated that CSF, but not serum, nanoplaque levels were associated with reduced levels of the cytokines IL-8, MIP-1α and MIP-1β, potentially indicating that increased levels of these cytokines are neuroprotective. Finally, we found that CSF, but not serum, nanoplaque levels were significantly increased in AD patients. However, nanoplaque levels had worse discrimination for clinical AD and underlying AD pathology than the core CSF biomarkers. Overall, based on our results it seems unlikely that CSF or serum nanoplaque levels could be used as a single diagnostic biomarker for AD. Still, nanoplaque levels may provide important information on the amyloid aggregation process across the different stages of AD. Future research should extend our pilot study in serum to a larger cohort and explore whether nanoplaque levels in serum could be used to study AD pathophysiology in combination with other emerging blood- based biomarkers.

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SAMMENDRAG

Bakgrunn: Demens er en tilstand som påvirker kognitive funksjoner, som hukommelse, til den grad at det begrenser en persons evne til å utføre daglige gjøremål. Den vanligste årsaken til demens er Alzheimers sykdom (Alheimer’s disease, AD), en progressiv nevrodegenerativ lidelse. Økt alder er den primære risikofaktoren for AD, og derfor forventer vi at antall personer med demens kommer til å øke når den globale befolkningen blir eldre. Per i dag finnes det ingen kur for sykdommen. AD er definert av to nevropatologiske kjennetegn: nevrofibrillære floker av proteinet tau og avleiring av proteinet amyloid-β (Aβ) i plakk. Ved hjelp av biomarkører kan disse endringene identifiseres i pasienter flere år før de første symptomene på demens. Derfor er biomarkører viktige verktøy for tidlig diagnose av AD. Årsaken til AD er ukjent, men en teori er at aggregeringen av Aβ, altså dannelsen av strukturerte filamenter sammensatt av flere Aβ peptider, er hovedårsaken. Men, det finnes ingen etablerte biomarkører som måler løselige små aggregerte former av Aβ.

Mål: Hovedmålet til avhandlingen var å vurdere bruken av små, amyloidogene aggregater, kalt nanoplakk, som biomarkører for AD. Nanoplakk kan kvantifiseres i spinalvæske og serum ved bruk av den nye teknikken Thioflavin-T-fluorescens korrelasjonsspektroskopi (ThT-FCS). Mer spesifikt ønsket vi å etablere forholdet mellom nanoplakk i spinalvæske og etablerte biomarkører for AD nevropatologi, og forholdet mellom nanoplakk i spinalvæske og serum med cytokiner, markører for nevroinflammasjon. Videre ønsket vi å avgjøre hvorvidt nanoplakk-nivået i spinalvæske og serum kunne skille pasienter med AD fra andre hukommelsesklinikkpasienter.

Metode: Vi inkluderte spinalvæske fra 118 og serum fra 49 hukommelsesklinikkpasienter.

Nivået av og størrelsen til nanoplakk i spinalvæske og serum ble kvantifisert med ThT-FCS- teknikken. En gruppe på 54 pasienter fikk utført hjerneavbilding av amyloid-avleiringer med

18F-Flutemetamol positronemisjonstomografi (PET). Kjernebiomarkørene for AD i spinalvæske, redusert Aβ42, økt total tau (t-tau) og økt fosforylert tau (p-tau), ble målt med enzymkoblet immunadsorberende analyse (n = 118 for spinalvæske-pasientene og n = 26 for serum-pasientene). Nivået til ni cytokiner, eotaxin-1, granulocytt-stimulerende faktor, interleukin-6 (IL-6), IL-7, IL-8, monocyttkjemotaktisk protein-1, gamma-induserbart protein 10, makrofag inflammatorisk protein-1α (MIP-1α) og MIP-1β, ble kvantifisert i spinalvæske fra 111 pasienter og serum fra 48 pasienter med et skreddersydd multipleks-kit og tolket på et Luminex IS 200-instrument. Vi undersøkte om nivået av nanoplakk i spinalvæske var assosiert

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med resultatene fra ¹⁸F-Flutemetamol PET, kjernebiomarkørene i spinalvæske eller nivået av de ni cytokinene i spinalvæske. Videre undersøkte vi om nanoplakk-nivået i serum var assosiert med nivået av de ni cytokinene i serum. Til slutt undersøkte vi om nivået av nanoplakk i spinalvæske var forskjellig i pasienter med positive amyloid-biomarkører sammenlignet med pasienter med negative amyloid-biomarkører, og hvorvidt nivået av nanoplakk i spinalvæske eller serum skilte pasienter med klinisk AD fra andre hukommelsesklinikkpasienter.

Hovedresultater: Nivået av nanoplakk i spinalvæsken var ikke assosiert med amyloid målt med ¹⁸F-Flutemetamol PET, men økt nivå av nanoplakk var assosiert med redusert nivå av Aβ42

i spinalvæsken. Vi fant ingen assosiasjoner med p-tau eller t-tau. Pasienter med ikke-påviselige nivå av MIP-1β hadde mer nanoplakk i spinalvæsken og nanoplakk var negativt assosiert med cytokinene IL-8 og MIP-1α. Vi fant ingen assosiasjoner mellom cytokiner og nanoplakk i serum. I gruppen med ¹⁸F-Flutemetamol PET var det ingen forskjell i nanoplakk-nivå i spinalvæsken fra amyloid-positive og amyloid-negative pasienter, men signifikant høyere nanoplakk-nivå i pasienter med klinisk AD. I hele kohorten på 118 pasienter var det signifikant høyere nivå av nanoplakk i amyloid-positive pasienter og pasienter med klinisk AD, men kvantifisering av nanoplakk forbedret ikke klassifiseringen av pasientene sammenlignet med når kun kjernebiomarkørene ble brukt. Nivået av nanoplakk i serum var ikke signifikant forskjellig i pasienter med og uten klinisk AD.

Konklusjon: Denne avhandlingen har vist at nivået av nanoplakk i spinalvæsken er assosiert med nivået av Aβ42 i spinalvæsken, men ikke med amyloid målt med ¹⁸F-Flutemetamol PET eller nivå av t- eller p-tau i spinalvæsken. Dette indikerer at økt nanoplakk-nivå i spinalvæsken er svakt assosiert med Aβ-patologi, og muligens reflekterer andre aspekter av Alzheimers patofysiologi. Videre har vi vist at nivået av nanoplakk i spinalvæsken, men ikke i serum, var assosiert med lavere nivå av cytokinene IL-8, MIP-1α og MIP-1β, noe som kan tyde på at høyere nivå av disse cytokinene er beskyttende. Til slutt har vi vist at nivået av nanoplakk i spinalvæske, men ikke i serum, var høyere i pasienter med AD. Men, nivået av nanoplakk diskriminerte pasienter med klinisk AD og amyloid-positive pasienter dårligere enn kjernebiomarkørene for AD. Samlet sett tyder resultatene våre på at det er usannsynlig at nanoplakk-nivå i spinalvæske og serum kan bli brukt alene som en diagnostisk biomarkør for AD. Likevel er det mulig at vi kan få viktig informasjon om dannelse av amyloid i forskjellige stadium av AD ved å studere nanoplakk. Framtidig forskning bør utvide vår pilot-studie av nanoplakk i serum til en større kohort, og utforske om nanoplakk kan være nyttige biomarkører for å studere AD i kombinasjon med andre nye blod-baserte biomarkører.

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ARTICLES IN THE THESIS

I. Amyloidogenic nanoplaques in cerebrospinal fluid: relationship to amyloid brain uptake and clinical Alzheimer’s disease in a memory clinic cohort Aksnes M, Müller EG, Tiiman A, Edwin TH, Terenius L, Revheim ME, Vukojević V, Bogdanović N, & Knapskog AB. Journal of Alzheimer’s Disease.

2020; 77: 831-842.

II. Comparison of cerebrospinal fluid amyloidogenic nanoplaques with core biomarkers of Alzheimer’s disease

Aksnes M*, Tiiman A*, Edwin TH, Terenius L, Bogdanović N, Vukojević V, &

Knapskog AB. Frontiers in Aging Neuroscience. 2021; 12: 608628. *Shared first authorship

III. Associations of cerebrospinal fluid amyloidogenic nanoplaques with cytokines in Alzheimer’s disease

Aksnes M, Aass HCD, Tiiman A, Edwin TH, Terenius L, Bogdanović N, Vukojević V, & Knapskog, AB. Translational Neurodegeneration. 2021; 10: 18.

IV. Serum amyloidogenic nanoplaques and cytokines in Alzheimer’s disease – pilot study in a small naturalistic memory clinic cohort

Aksnes M, Aass HCD, Tiiman A, Terenius L, Bogdanović N, Vukojević V, &

Knapskog, AB. Submitted.

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ABBREVIATIONS

AD Alzheimer’s disease

Aβ Amyloid-β

ADRDA Alzheimer’s Disease and Related Disorders Association AICD APP intracellular domain

APOE Apolipoprotein E APP Aβ precursor protein AUC Area under the curve BACE1 β-secretase

CERAD Consortium to Establish a Registry for Alzheimer’s Disease COWAT Controlled Word Association Test

CDR Clinical dementia rating CSF Cerebrospinal fluid

CT Computed tomography

CTF C-terminal fragment

ELISA Enzyme-linked immunosorbent assays

FDG Fluoredeoxyglucose

G-CSF Granulocyte colony-stimulating factor

IL Interleukin

IP-10 Interferon gamma-induced protein 10 MCI Mild cognitive impairment

MCP-1 Monocyte chemoattractant protein

MDS-OAβ Multimer Detection System-Oligomeric Aβ MEMFCS Maximum Entropy Method for FCS

MIP Macrophage inflammatory protein MMSE Mini Mental State Exam

MRI Magnetic resonance imaging NFL Neurofilament light chain

NIA-AA National Institute on Aging and the Alzheimer’s Association

NINCDS National Institute of Neurological and Communicative Disorders and Stroke NorCog Norwegian Registry of Persons Assessed for Cognitive Symptoms

OVE Observation Volume Element

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P-tau Phosphorylated tau PSEN Presenilin

ROC Receiver operating curve SCD Subjective cognitive decline

SD Standard Deviation

SIMOA Single-molecular array

sTREM Soluble triggering receptor expressed on myeloid cells tACC Temporal autocorrelation curve

TDP-43 TAR DNA-binding protein

ThT-FSC Thioflavin-T Fluorescence Correlation Spectroscopy TMT Trail Making Test

T-tau Total tau

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1 INTRODUCTION

1.1 DEMENTIA AND ALZHEIMER’S DISEASE

Dementia is the deterioration of higher order cognitive functions, such as memory and learning, to an extent that impedes a person’s capacity to live independently. The most common cause of dementia is Alzheimer’s disease (AD), a progressive neurodegenerative disorder. The primary risk factor for AD is old age [1]. Thus, due to global demographic ageing it is a pressing issue to improve the diagnosis, treatment and prevention of AD. In 2020 there were ~55 million people with dementia worldwide, and the number of cases is expected to rise towards 139 million by 2050 [2]. It is estimated that AD accounts for 60-80% of dementia cases [3].

In most cases, AD is sporadic: it develops without a known cause. However, in rare cases AD is caused by specific autosomal dominant genes, often called familial AD. In both sporadic and familial AD, dementia is preceded by a stage of mild cognitive impairment (MCI) where the ability to complete activities of daily living is preserved. Sporadic AD is a multifactorial disease; both genetic and environmental factors contribute to the disease risk, independently and/or concertedly. The best-established genetic risk factor for sporadic AD is the apolipoprotein E (APOE) isoform ε4 [4]. The main modifiable risk and protective factors include vascular risk factors (e.g. hypertension, atherosclerosis and hypercholesterolemia), smoking, alcohol consumption, physical exercise, diet, educational attainment, leisure activities and social life. Possibly, a third of AD cases could be attributed to modifiable risk factors [5].

The first description of the underlying biology of AD dates back to 1906, when German psychiatrist and neuropathologist Alois Alzheimer described the major neuropathological hallmarks of ‘presenile dementia’ [6]. His patient, Auguste D, had presented with progressive dementia symptoms and died at the age of 51. Post-mortem examination revealed an evenly atrophied brain with ‘striking changes of the neurofibrils’ and ‘minute military foci which are caused by the deposition of a special substance in the cortex’ [6]. These neuropathological alterations, known today as neurofibrillary tangles and extracellular amyloid-β (Aβ) plaques, remain the defining features of the disorder that now bears Alzheimer’s name [7].

Up until recently, it has not been possible to identify AD neuropathology in living patients, and thus, diagnosis has relied on clinical symptoms of dementia [8]. Today, technological advances in biochemistry and neuroimaging permit the identification of biological markers (biomarkers) indicating underlying AD neuropathology in living patients. Importantly, alterations in

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established biomarkers precede clinical symptoms by years or even decades [9, 10].

Consequently, updated diagnostic criteria suggest that biomarkers should be incorporated in the diagnosis of AD [11, 12]. Contemporary research criteria go further and propose that AD in living patients should be defined exclusively based on biomarkers [13].

1.2 NEUROPATHOLOGY DEFINES ALZHEIMER’S DISEASE

The AD brain is characterised by positive features, i.e. the presence of specific neuropathology, and negative features, i.e. the loss of specific features of a healthy brain [7], see Figure 1. The classic positive features are the Aβ plaques and neurofibrillary tangles described by Alzheimer.

These neuropathological hallmarks are accompanied by reactive central nervous system immune cells: microglia and astrocytes [14, 15]. Definite diagnosis of AD requires that these positive features are present post-mortem [16]. Typically, Aβ plaques are distributed throughout the cerebral cortex [17], while tangle formation primarily occurs in the limbic and association cortices [18]. The negative features of the AD brain are neuron and synapse loss, whose distribution tend to parallel tangle formation [19]. The pattern of cortical atrophy in AD is typically symmetric across the two brain hemispheres. Neuron and synapse loss are usually most evident in the medial temporal lobes, whereas the primary motor, sensory and visual cortices are relatively spared [7]. Cortical thinning in the entorhinal cortex as well as temporal

Figure 1. Healthy brain and brain with Alzheimer’s disease neuropathological changes. In the Alzheimer’s disease brain, the neuronal loss leads to gross brain atrophy that can be observed on the macroscopic level in terms of enlarged ventricles, narrowed gyri and widened sulci. On the microscopic level, extracellular amyloid-β (Aβ) plaques can be seen surrounding the neuron and intracellular neurofibrillary tangles can be observed in the soma.

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3 and parietal regions can be recognised on a magnetic resonance imaging (MRI) scan [20]. In later stages, there are apparent widened sulci, narrowed gyri and enlarged ventricles [21].

Synaptic loss is extensive in AD and exacerbates the cortical atrophy [22].

1.2.1 Neurofibrillary tangles

The main constituent of intracellular neurofibrillary tangles, hereby tangles, is the microtubule- associated protein tau. Tau is primarily expressed in cortical axons where it binds tubulin in microtubules and promotes their assembly and stability [23], thereby contributing to normal axon function. Tau is a phosphoprotein with more than 30 potential phosphorylation sites; its phosphorylation is regulated through interactions between multiple kinases and phosphatases [24]. Tangles form when abnormally misfolded and hyperphosphorylated tau aggregates into paired helical filaments, then further to neurofibrillary tangles [25]. Because hyperphosphorylated tau cannot bind tubulin, this process causes microtubule instability and disassembly. Accordingly, tangles are always accompanied by neuropil treads, likely originating from the breakdown of dendrites and axons from the tangle-bearing neurons [7].

Tangles have three distinct morphological stages: pre-tangles, mature intracellular tangles and extracellular ‘ghost’ tangles [26, 27]. Pre-tangles are typically found in the somatic compartment of otherwise healthy-looking neurons. Mature tangles consist of fibrillar aggregates, often displace the soma of the neuron and extend to distorted neuronal processes.

Ghost tangles appear extracellularly after the death of tangle-bearing neurons and are ostensibly specific for AD [28]. Typically, tangle formation begins in the transentorhinal region before spreading to the entorhinal cortex, hippocampus and eventually neocortex [18].

1.2.2 Amyloid-β and extracellular plaques

In AD, extracellular plaques result from the accumulation, aggregation and deposition of the Aβ peptide. Morphologically, there are two types of Aβ plaques: diffuse and dense-core plaques. Diffuse plaques are large with poorly defined borders and do not have damaging effects on the surrounding neuropil. They are common in the brains of cognitively healthy older people, while dense-core plaques are often found in patients with AD [7]. Dense-core plaques are small with clearly delineated borders, exert damage on surrounding neuropil and are surrounded by dystrophic neuronal, astrocytic and microglial processes. They contain amyloid that can be stained with structure-specific dyes such as Congo red, Thioflavin, or their derivatives [29]. Amyloid are strictly ordered aggregates comprised of arrays of intermolecular

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β-sheets that run parallel to the long axis of the fibrils; this is known as a cross-β structure [30].

Of note, while amyloid is often used colloquially to refer to the Aβ peptide, not all Aβ peptides aggregate to amyloid and other proteins can also aggregate to this secondary structure. Amyloid deposition begins in the neocortex, before progressing gradually to the entorhinal layers, then to the hippocampus and the amygdala, the basal ganglia, the brainstem nuclei and finally the cerebellum [17]. It is hypothesised that the spread of pathology in AD is driven by the toxic conformations of Aβ and/or tau that are produced during the disease [31]. Spread of pathology appears to follow the seeding-nucleation principle [32]. According to this principle, a stable

‘seed’ or nucleus of a polymerised protein is formed in a slow thermodynamically unfavourable nucleation phase. This is followed by a rapid elongation stage, where the seeds grow through incorporation of monomeric peptides into the polymer. While the mechanism is poorly understood, it is supposed that large polymers then fragment and generate more seeds, hence propagating the reaction. The misfolding and aggregation process also entails the rearrangement of the protein structure into a series of β-strands. By virtue of hydrogen bonding and hydrophobic interactions, these strands are stabilised and create binding points for the folded or partially folded protein, forcing it to adapt into the cross-β sheet polymeric structure [33].

This seeding property allows the misfolded aggregates to spread the misfolding and aggregation process throughout the brain, similar to the spread of infectious prion proteins [34].

Aβ is a product of the proteolytic processing of the Aβ precursor protein (APP). APP is a transmembrane protein with a large extracellular domain, the N-terminus, and a short intracellular tail, the C-terminus [35]. There are two APP processing pathways: the non- amyloidogenic pathway and the amyloidogenic pathway (Figure 2). In the non-amyloidogenic pathway, α-secretase cleaves a large soluble fragment, APPsα, from the αAPP C-terminal fragment (CTF). Further, γ-secretase cleavage in the transmembrane domain produces the non- toxic peptides p3 and APP intracellular domain (AICD) from αAPP CTF [36]. Importantly, cleavage by α-secretase precludes the formation of Aβ, as the α-secretase cleavage site is within the Aβ-domain. In the amyloidogenic pathway, cleavage by β-secretase (BACE1) produces APPsβ and βAPP CTF, and subsequent cleavage of βAPP CTF by γ-secretase produces AICD and Aβ. The exact cleavage site determines the amino acid length of the Aβ peptide [37], ranging from 38 to 43 residues [36], with Aβ40 and Aβ42 being most prevalent. The longer varieties, i.e. Aβ42 and Aβ43,are more neurotoxic and prone to aggregate [38, 39]. These peptides aggregate in a cascade from monomers to dimers, trimers, oligomers, protofibrils and finally to the amyloid fibrils that deposit into plaques [40, 41].

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1.3 DISEASE MECHANISMS

While AD neuropathology is well characterised and extensively studied, the initial trigger of sporadic AD remains debated. Competing theories centre different aspects of AD pathology and explore diverse mechanisms by which one initial pathological event can induce and aggravate other pathological processes. Here, I will emphasise the amyloid cascade hypothesis and the roles of neuroinflammation in AD pathology. Theories that posit amyloid accumulation as a secondary event or by-product of other pathological processes, such as the mitochondrial cascade hypothesis [42] and tau-centred hypotheses [43, 44], are beyond the scope of this thesis.

1.3.1 Amyloid cascade hypothesis

Since its conception around 1991-1992 [45-48], the amyloid (or Aβ) cascade hypothesis has become the primary model of AD aetiology. In short, the model proposes that Aβ-deposition is the initial pathological event in AD and that neurofibrillary tangle formation, synaptic dysfunction and neuroinflammation are all downstream [49]. The hypothesis proposes that Aβ could cause cell death either through an innate toxicity [50] or by increasing neuronal sensitivity

Figure 2. The non-amyloidogenic and amyloidogenic pathway. In the non-amyloidogenic pathway, APP is first cleaved by α-secretase to release APPsα from the cell membrane, and then the remaining αAPP CTF is cleaved by γ-secretase to form the peptides AICD and p3. In the amyloidogenic pathway, APP is first cleaved by β-secretase to release APPsβ from the cell membrane, and then the remaining βAPP CTF is cleaved by γ-secretase to form AICD and Aβ. Aβ, amyloid-β, AICD, APP intracellular domain; APP, Aβ precursor protein; CTF, C- terminal fragment

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to damage induced by pathologically high levels of excitatory neurotransmitters [51].

Moreover, Aβ could lead to tangle formation by disrupting calcium homeostasis and increasing intracellular calcium concentration as this is known to affect tau and tau phosphorylation [48].

A combination of several scientific discoveries inspired the original formulation of the amyloid hypothesis. First, the APP gene was localised to chromosome 21, which is present in triplicate in trisomy 21. As individuals with trisomy 21 invariably present with AD neuropathology after 50 years of age [52], it was hypothesised that this pathology results from an overproduction of Aβ from three APP copies. Second, the mutations that cause familial AD were identified and linked to the production or processing of Aβ. Familial AD is caused by missense mutations in either the APP gene, or the presenilin 1 or presenilin 2 (PSEN1 or PSEN2) genes [53]. Mutations in the APP gene result in overproduction of Aβ, mutations in PSEN1 result in a relative increase in the C-terminal-extended Aβ42 and Aβ43 peptides and mutations in PSEN2 cause increased self-aggregation of these Aβ species [49]. Remarkably, a rare mutation (APP A673T) that reduces β-secretase cleavage and causes less aggregation-prone Aβ peptides decreases the risk of AD [54, 55]. Third, genetic risk factors for sporadic AD, such as APOE, are also linked to Aβ. APOE does not affect Aβ production per se, but influences its clearance from the brain.

The ε2 allele promotes Aβ clearance and is protective against AD [56], whereas the ε4 allele reduces the clearance of Aβ relative to the other alleles [57]; the ε3 allele is considered neutral.

Furthermore, the APOE isoforms differentially bind to Aβ and modulate its fibrillation [49].

APOE ε4 in particular appears to promote the aggregation of Aβ into amyloid [58, 59].

Despite the genetic evidence supporting the amyloid cascade hypothesis, it is still debated. One primary criticism is the poor correlation of Aβ plaque burden with neurofibrillary tangles, neuronal loss and clinical symptoms of AD [60, 61]. Moreover, it has been posited that Aβ deposition could be a consequence of AD neurodegeneration rather than its cause, and that the formation of Aβ plaques could be neuroprotective [62]. At the same time, it is well established that Aβ must self-aggregate in order to become neurotoxic [63]. Therefore, updated iterations of the amyloid cascade hypothesis, such as the amyloid-oligomer hypothesis, centre aggregated forms of Aβ that have not yet been deposited into plaques [64]. Initially, Aβ oligomers were viewed merely as an intermediate state for the generation of amyloid plaques [64]; currently, they are considered the primary pathogenic species. Oligomeric forms of Aβ are sufficient to induce cognitive impairment in the absence of plaques both in animal models of AD and in rare cases of familial AD caused by the Osaka mutation (APP E693) [64, 65]. However, misfolded oligomers are a heterogeneous group, ranging from dimers (composed of two monomers) to

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7 large protofibrillary structures (composed of hundreds of monomers); it is unclear which oligomeric structures are the most relevant for AD pathogenesis [66].

A further criticism of the amyloid cascade hypothesis is the fact that while invariably present in AD, Aβ pathology is also frequently observed in the brains of cognitively intact individuals [67]. It should be noted that these individuals often have diffuse plaques, have a lower Aβ oligomer to plaque ratio, and seldom undergo cognitive testing before death [68]. In an attempt to reconcile the idea that Aβ accumulation triggers the pathologic cascade in AD while it is also present in healthy ageing, a recent model has suggested that Aβ levels must exceed a critical threshold in order to exert neurotoxic effects [69]. This model proposes that the balance between Aβ production and clearance shifts in healthy ageing, but that Aβ levels do not exceed the critical threshold. In sporadic AD, the reduction in Aβ clearance is more severe than in physiological ageing, leading to accumulation of Aβ beyond the critical threshold, and in familial AD, Aβ production is increased faster and earlier.

1.3.2 Neuroinflammation

The term neuroinflammation denotes the inflammation-like responses mediated by astrocytes and microglia to disease, infection or injury in the central nervous system. Neuroinflammation can have both favourable and damaging effects on the brain [70]. In the AD pathogenic process, neuroinflammation has been proposed as the link between Aβ deposition, neuronal damage and clinical symptoms. Generally, one distinguishes between acute and chronic neuroinflammation:

the acute neuroinflammatory response minimises neuronal injury by causing activated microglia to engulf damaged cells, while chronic neuroinflammation is a self-sustaining response that persists long after the initial insult. Chronic neuroinflammation often has deleterious effects on the brain [71]. Continuous activation of microglia and the resulting release of inflammatory mediators are key drivers of chronic neuroinflammation [71, 72].

Microglia are a specific class of phagocytes: cells that engulf and consume microorganisms and damaged tissue. Microglia are distributed throughout the central nervous system, and when activated by pathologic triggers such as protein aggregates, they extend their processes to the injury site and migrate there before initiating an immune response [73]. In the AD brain, microglia often surround Aβ plaques [74]. Microglia play a dual role in the AD pathologic process: they may eliminate Aβ aggregates by phagocytosis and delay the progression of amyloid pathology, but continued microglial activation can increase Aβ production and decrease Aβ clearance through the release of inflammatory molecules [70, 75]. Activated

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microglia secrete various molecules such as cytokines, free radicals and complement proteins that are associated with the hypothesised amyloid cascade. Cytokines are a diverse group of molecules often grouped as pro- and anti-inflammatory cytokines, despite the fact that most cytokines can exert both pro- or anti-inflammatory effects depending on the time and site of activation [76]. Pro-inflammatory cytokines contribute to microglia activation and to the generation and deposition of Aβ, while anti-inflammatory cytokines inhibit microglia activation and disrupt Aβ generation and deposition. Pro-inflammatory cytokines downregulate gene expression of Aβ-degrading enzymes, thus inhibiting Aβ clearance and promoting Aβ accumulation [75]. Moreover, certain cytokines upregulate the expression of BACE1, causing increased production of Aβ peptides [73].

There is a reciprocity in the relationship between microglia and Aβ; while chronic neuroinflammation promotes the accumulation and aggregation of Aβ, this in turn triggers microglial activation [77]. Indeed, both the production of Aβ, as well as its removal and/or deposition into plaques have been linked to oxidative stress, neuroinflammation and cell death [70, 78]. Aβ might exert its neurotoxic effects by directly activating microglia, and increased Aβ aggregation results in increased markers of microglial activation in model systems [79].

Aβ42 aggregates can bind microglial cell-surface receptors, provoking the secretion of the cytokine interleukin-1β (IL-1β); IL-1β has in turn been shown to modulate APP expression and aggravate plaque formation [73]. Recent findings suggest that especially larger Aβ aggregates exert neurotoxic effects by inducing neuroinflammation and cytokine release [80, 81].

1.4 BIOMARKERS FOR ALZHEIMER’S DISEASE

A biomarker is a feature that can be objectively measured and assessed as an indicator of a normal physiological process, a pathological process, or a response to a therapeutic intervention [82]. For AD, a biomarker should enable monitoring and staging of the pathophysiological process [83]. A biomarker should have more than 80% sensitivity (correctly classify at least 80% of those with AD) and specificity (correctly classify at least 80% of those with other disorders as non-AD patients). Moreover, an ideal biomarker should be reliable, simple to measure, noninvasively collected, inexpensive, easily integrated into clinical practice and have a clearly defined cut-off point [84, 85]. As mentioned in section 1.1, biomarker changes in AD precede clinical symptoms and dementia by years or decades [9, 10]. Biomarkers are thus essential to verify the AD pathophysiological process in living patients with symptoms, and especially at the earliest pre-symptomatic stages of the disease.

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9 There is extensive research on markers of AD pathology both in biological fluids and using different neuroimaging modalities. Biological fluids such as blood and cerebrospinal fluid (CSF) may reflect pathophysiological processes in the brain [86]. One strength of fluid-based biomarkers is that several markers reflecting diverse pathophysiological processes may be analysed in the same sample. CSF directly interacts with the brain extracellular space and can reflect biochemical processes that occur there, making it an ideal source of AD biomarkers [86].

Neuroimaging biomarkers have similar sensitivity and specificity as CSF biomarkers [87]. A major advantage of imaging biomarkers is that they also provide topographical information, i.e.

they can localise pathology to specific brain regions.

1.4.1 Established biomarkers

In CSF, there are three core biomarkers for AD: CSF Aβ42, total tau (t-tau) and phosphorylated tau (p-tau). These markers are commonly measured by standardised enzyme-linked immunosorbent assays (ELISA). The combination of decreased levels of Aβ42 and increased levels of t-tau and p-tau is dubbed the ‘AD signature’ [88] and has been validated against post- mortem brain pathology [89]. This AD signature accurately separates AD from healthy controls and predicts the conversion of MCI to dementia with up to 95% sensitivity and 87% specificity [90, 91]. The core CSF biomarkers each have a neuroimaging counterpart thought to reflect the same pathophysiological process. These established biomarkers are increasingly incorporated into the diagnosis and definition of AD, as will be discussed in sections 1.5.2 and 1.5.3.

Markers of amyloid pathology: CSF Aβ peptides and amyloid-positron emission tomography CSF Aβ peptides and amyloid-positron emission tomography (PET) are established markers of amyloid pathology in AD. Aβ42, the principal form in amyloid plaques, is consistently reduced in the CSF and inversely correlated with plaque load as measured by amyloid-PET or determined post-mortem. Compared to controls, CSF Aβ42 is reduced by approximately 50% in AD patients [92, 93], separating patients from controls with a sensitivity of 80% and a specificity of 82% [87]. It is hypothesised that levels are reduced in the CSF because the sequestration of Aβ42 to plaques is quicker than its clearance to CSF. Reduced levels of CSF Aβ42 have been found in some other conditions, but is largely specific to AD [88, 94]. While much less researched than Aβ42, the longer Aβ43 peptide appears to have similar diagnostic accuracy for AD [95]. Shorter Aβ peptides such as Aβ38 and Aβ40 are sequestered less into plaques and their CSF levels are relatively stable throughout the AD pathophysiological process

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[92]. Nonetheless, the Aβ42/Aβ40 ratio is superior at distinguishing AD from other dementia disorders compared to Aβ42 alone [85].

Amyloid-PET permits the visualisation of brain amyloid deposition in vivo and has been validated against post-mortem examinations [96]. This method provides information on the distribution of amyloid plaques throughout the brain [97], which is advantageous as it permits the identification of patients with atypical amyloid deposition [98, 99]. Amyloid-PET positivity is associated with increased risk of AD [100]. This method has high negative predictive value, as a person with cognitive impairment and negative amyloid-PET is not likely to have AD [101]. The positive predictive value is, however, more moderate; about one third of people aged over 60 years have a positive amyloid-PET scan [102]. The first amyloid tracer was the carbon- based ¹¹C Pittsburgh compound B [103, 104], but more recently fluorine-based tracers with longer half-lives, such as ¹⁸F-Flutemetamol, ¹⁸F-Florbetapir and ¹⁸F-Florbetapen, have been developed. All of these tracers are derived from the histologic dye Thioflavin-T (ThT), which binds to fibrillar amyloid with high affinity [105]. Consequently, while CSF analysis measures soluble and mostly monomeric forms of Aβ peptides, amyloid-PET measures fibrillar amyloid deposits. Nonetheless, the agreement between amyloid-PET positivity and Aβ42 levels in CSF is quite high [94]. Although CSF Aβ42 appears to detect cerebral amyloid pathology earlier than amyloid-PET [106], they are often viewed as interchangeable measures of brain amyloid pathology [107]. Of note, neither of these approaches permit the quantification of soluble oligomeric and aggregated forms of Aβ, which are highly attractive biomarkers for AD [64].

Markers of tau-pathology: CSF phosphorylated tau and tau-positron emission tomography The markers of neurofibrillary tangle pathology in AD are CSF p-tau and tau-PET. In the standardised ELISAs for AD, p-tau is tau phosphorylated at threonine 181 [108]. Increased levels of p-tau in the CSF are thought to reflect the phosphorylation state of tau and the presence of neurofibrillary tangles in the brain; CSF p-tau levels analysed before death correlate with neurofibrillary tangle pathology post-mortem [109]. Unlike t-tau, p-tau levels are normal in patients with non-AD disorders such as Creutzfeldt-Jakob disease and cerebral ischemia [110].

Moreover, p-tau separates AD dementia from other dementias, such as dementia with Lewy bodies, vascular dementia or frontotemporal dementia [111].

Novel tau-PET tracers have enabled the visualisation of neurofibrillary tangle pathology in vivo.

Like amyloid-PET, tau-PET allows the localisation of pathology and the possibility to track the spread of pathology to new brain regions. The most established tau-PET tracer is ¹⁸F-

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11 flortaucipir, also called ¹⁸F-AV-1451 or ¹⁸F-T807, which binds to paired helical filaments in neurofibrillary tangles [112, 113]. This tracer differentiates AD patients from controls [114, 115], and there is some evidence that tau-PET could be a useful tool for the differential diagnosis of AD and other disorders with tau pathology [116], but more research is needed [117]. Compared to CSF p-tau, tau-PET more accurately separates patients with mild to moderate AD dementia from controls, but the modalities perform similarly for the separation of patients at earlier AD stages [118]. This suggests that CSF p-tau plateaus before the dementia stage of AD, while the tau-PET signal continues to increase, likely reflecting the continuous accumulation of neurofibrillary tangles throughout the disease process. In a recent longitudinal study, the cortical tau quantified by tau-PET was shown to increase significantly over an 18- month period, but only in amyloid-positive patients [113]. As of now, tau-PET is not widely available and rarely used in clinical practice.

Established markers of neurodegeneration

Together with CSF t-tau, MRI and ¹⁸F-Fluoredeoxyglucose (FDG)-PET scans may provide biomarker data on neurodegeneration in AD. Increased CSF t-tau levels indicate axonal degeneration and neuronal cell death [119]. CSF t-tau levels are increased in AD patients compared to controls and can identify AD with a sensitivity of 82% and a specificity of 90%

[87]. Yet, t-tau levels are also increased in non-AD disorders such as Creutzfeldt-Jakob disease, traumatic brain injury and stroke [110, 120, 121], and thus not specific for AD. However, p-tau and t-tau levels are often highly correlated in AD patients [e.g. 118].

Atrophy and neuronal cell loss can be visualised with a structural MRI; this method has similar diagnostic performance to the core CSF biomarkers [87]. T1-weigthed MRI scans are most used in the assessment of AD, as this method provides excellent separation between grey and white matter, and can reveal subtle changes in grey matter volume [122]. Reduced hippocampal volume is highly predictive of AD [123], but is also found in other disorders such as depression [122]. Entorhinal cortex volume may already be reduced in patients with only subjective memory complaints [124]. When combined with other measures such as age and cognitive tests, both entorhinal and hippocampal volume reductions have been found to predict conversion from MCI to AD dementia [125], but some findings suggest that medial temporal lobe atrophy does not predict conversion in adjusted analyses [126]. Whole brain atrophy rates correlate well with performance decline on cognitive tests [127, 128]. FDG-PET can be used to visualise glucose metabolism in the brain. The tracer ¹⁸F-FDG is a glucose analogue that is taken up by

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neurons and metabolised to FDG-6-phosphate at the same rate as glucose [122]. Reduced glucose metabolism indicates neurodegeneration and precedes cognitive symptoms [9, 129]. In AD, hypometabolism is typically seen bilaterally in the hippocampi, the medial temporal lobe structures and the posterior cingulate, with spreading to the whole cortex at later disease stages [129]. FDG-PET separates patients with AD from patients with other neurodegenerative diseases with high sensitivity and specificity [122].

1.4.2 Emerging biomarkers

Novel CSF biomarkers

There is extensive research on additional CSF biomarkers that reflect oligomeric Aβ and alternate aspects of AD pathology such as neurodegeneration, synaptic dysfunction and inflammation, see Dhiman and colleagues for a recent review [130]. The quantification of Aβ oligomers has resulted in conflicting findings, with reports of both increased and decreased levels of CSF Aβ oligomers in AD compared to non-AD patients [131, 132]. Neurofilament light chain protein (NFL) is a marker of neurodegeneration. NFL is one of three key components of the neuronal cytoskeleton and is mainly located within myelinated axons. Elevated NFL in CSF reflects axonal degeneration [133]. Increased CSF levels of NFL are not specific to AD, but seen in a range of neurodegenerative diseases, such as multiple sclerosis and frontotemporal dementia [134, 135]. Neurogranin, a calmodulin-binding postsynaptic protein located to the neuronal dendrites, is emerging as a biomarker of synaptic dysfunction [130]. Reduced levels of neurogranin are seen in the AD brain from early stages [130]. Conversely, CSF neurogranin levels are increased in AD; this appears to be specific in AD and has not been demonstrated in other disorders such as frontotemporal dementia, dementia with Lewy bodies or Parkinson’s disease [136]. Several markers of neuroinflammation have been explored as biomarkers for AD [130, 137-140], and while many markers differ between AD and normal controls or other neurodegenerative diseases, none appear specific for AD.

Blood-based biomarkers

Established CSF and neuroimaging biomarkers are robust indicators of AD pathophysiology, but these methods have important limitations that restrict their use in general clinical practice, population screening and serial sampling. While routine in clinical neurology and many geriatric departments, CSF collection by lumbar puncture is invasive and rarely performed by general practitioners [141]. PET imaging is costly and only available at specialised nuclear

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13 medicine centres. In contrast, blood sampling is inexpensive and routine in clinical practice, and blood-based biomarkers for AD are therefore highly anticipated.

However, measuring biomarkers of central nervous system disorders in blood is less straight forward than measurements in CSF. The concentration of brain-derived proteins and molecules is much lower in blood than in CSF and these low levels of brain-derived proteins must be quantified within a matrix of other proteins that are expressed at much higher levels [141].

Therefore, ultra-sensitive assays are required to assess blood-based biomarkers for central nervous system diseases. Furthermore, if a biomarker is not exclusively expressed in the brain, it is problematic to determine whether changes in biomarker concentrations reflect systematic changes or changes in the brain. This is relevant to AD specifically, as Aβ is also expressed peripherally [142]. Finally, endogenous antibodies may also interact with any non-human monoclonal antibodies in immunoassays, thus interfering with the results [143].

The first attempts at quantifying AD biomarkers in blood resulted in conflicting findings [92].

However, recently developed ultra-sensitive assays, such as the single-molecule array (SIMOA), and improved biological characterisation of patient cohorts has led to much progress in the field [144]. Blood NFL levels have been shown to reliably reflect neurodegeneration in several disorders, including AD [142]. In terms of AD-specific biomarkers, several independent studies have suggested that reduced blood plasma Aβ42/Aβ40 ratio indicate brain amyloid pathology [142, 145, 146]. Some preliminary evidence highlights plasma Aβ oligomers as sensitive markers of AD pathology [147, 148]. Nonetheless, a series of recent publications have cemented plasma p-tau181 and p-tau217 as highly promising blood-based AD biomarkers.

Plasma p-tau181 and p-tau217 are strongly associated with established biomarkers of AD, discriminate AD from other neurodegenerative disorders with high accuracy [149, 150] and predict neurodegeneration and brain atrophy over time [151].

1.4.3 Biomarker staging

In order to apply AD biomarkers to screening, diagnosis or disease monitoring, it is necessary to understand their dynamics across the disease stages. In 2010, Jack and colleagues published an influential hypothetical model describing these dynamics [9]; an updated model was published in 2013 [10], see Figure 3. These models were founded on four core principles: First, biomarkers were assumed to become abnormal in a temporally ordered manner, with Aβ markers preceding markers of tau pathology, followed by markers of neurodegeneration and finally clinical symptoms [152-154]. Second, Aβ pathology was deemed to be necessary, but

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not sufficient, to cause AD. Third, all biomarkers were proposed to develop non-linearly in a sigmoidal relationship with time, with steeper curves for later-changing biomarkers [10, 155].

Finally, the models assume that the time lag between the first biomarker changes and the first clinical symptoms would be individual and that the cognitive outcomes would depend on the individual risk of cognitive impairment [156, 157]. As new biomarkers are discovered and our understanding of the dynamics of both established and emerging AD biomarkers is expanding, attempts are made to refine and update this model by adjusting the curves and incorporating new markers. See for example Zetterberg & Bendlin’s 2021 review for a model including plasma markers and emerging CSF markers such as NFL and neurogranin [158].

Figure 3. Model of AD biomarker dynamics. Aβ, amyloid-β; CSF, cerebrospinal fluid; MRI, magnetic resonance imaging, FDG, ¹⁸F-Fluoredeoxyglucose; PET, positron emission tomography. Reprinted from The Lancet Neurology, Volume 12, Jack et al., “Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers”, pages 207-215, Copyright (2013), with permission from Elsevier.

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1.5 DIAGNOSIS OF ALZHEIMER’S DISEASE

The first diagnostic criteria for Alzheimer’s disease were published by the National Institute of Neurological and Communicative Disorders and Stroke (NINCDS) and the Alzheimer’s Disease and Related Disorders Association (ADRDA) workgroup in 1984. These criteria have relatively high sensitivity (81%) and specificity (70%) for the diagnosis of probable or possible AD based on clinical symptoms of dementia [159]. However, the NINCDS-ADRDA criteria do not accurately represent our current understanding of AD pathophysiology or its clinical manifestation. While the NINCDS-ADRDA criteria assume a close correspondence between clinical symptoms and brain pathology, it is now established that AD pathology precedes clinical symptoms in a long preclinical period. Moreover, it is now clear that AD diagnostic criteria should acknowledge atypical clinical presentations, provide guidance for differential diagnosis of non-AD dementias and acknowledge MCI as a pre-dementia stage in AD [11].

Novel diagnostic criteria, presented in section 1.5.2 attempt to address these needs.

1.5.1 Stages of cognitive impairment

To qualify as dementia, the cognitive decline must be advanced to the extent that it significantly influences activities of daily living, such as paying bills or managing medications. However, the onset of dementia is preceded by a gradual worsening of cognitive functions that does not significantly interfere with daily activities [160]. This intermediary stage between normal ageing and dementia is termed MCI. A shared feature of these phases is that the cognitive decline is more severe than what would be expected in association with normal ageing only. It is hypothesised that MCI is preceded by a stage of subjective cognitive decline (SCD) [161].

Subjective cognitive decline

SCD is defined as a subjectively experienced cognitive decline in the absence of objective cognitive impairment [161]. SCD has been put forth as the very earliest clinical manifestation of AD, but whether SCD should be considered an early, subclinical stage of AD is debated [162]. Several studies have found that SCD is associated with altered AD biomarkers and increased risk of cognitive impairment [161], but one study found that only 14.4% of SCD patients had positive core CSF biomarkers of AD, whereas there was a high prevalence of neuropsychiatric symptoms linked to stress and depression [163]. Furthermore, while patients with SCD, especially in memory clinic cohorts, have higher incidence of dementia than healthy

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controls, the risk-difference is larger for non-AD dementias than AD and the majority of individuals with SCD remain cognitively normal at follow-up [164].

Mild cognitive impairment

In AD, dementia is preceded by MCI, a stage of cognitive decline that is less severe, but nonetheless beyond what is expected given the person’s age and educational attainment. The distinction between MCI and dementia depends on the extent to which activities of daily living are affected. However, due to the gradual nature of cognitive decline, the transition points between clinical stages are difficult to ascertain [11, 165]. The prevalence of MCI is uncertain, and reports range from 5 to 37% [166]. This is likely in part due to the heterogeneous nature of MCI; not all MCI is due to AD [167, 168]. The conversion rate from MCI to AD dementia has been reported as 5-15% per year, and up to 18.2% for amnestic MCI, dramatically higher than the incidence of 1-2% per year in general population [167, 169].

Dementia

Dementia refers to a chronic and often progressive syndrome that affects higher order cognitive functions, such as memory and learning, reasoning and judgment, spatial orientation and visual processing, language, or personality and behaviour. The underlying cause of dementia typically affects its clinical presentation. In AD, the most common presentation is amnestic, with primary cognitive deficits connected to memory and learning. However, atypical or non-amnestic presentations of AD also occur, with primary deficits in language, visuospatial processing or executive function. Dementia may range in severity from quite mild to severe; the disease severity develops gradually and can be scored on the clinical dementia rating (CDR) scale [170]. Dementia cannot be diagnosed if the cognitive decline can be explained by other disorders, such as delirium (acute cognitive dysfunction) or psychiatric disorders.

1.5.2 Clinical diagnostic criteria

Updated diagnostic criteria for AD have been put forth by two groups: The International Working Group for New Research Criteria for the diagnosis of AD [12, 98, 162, 171] and The National Institute on Aging and the Alzheimer’s Association (NIA-AA) [11, 165, 172, 173].

Both sets of criteria incorporate biomarkers and recognise that AD can be diagnosed independently of dementia. Here, the focus will be on the NIA-AA criteria, which have been used in this thesis.

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17 The NIA-AA presented diagnostic criteria for the diagnosis of MCI due to AD and AD dementia in two publications [11, 173]. Here, post-mortem AD neuropathology and in vivo AD biomarkers are considered evidence of the ‘AD pathophysiological process’. The clinical syndrome arising from this pathophysiological process is termed AD dementia, and the criteria distinguish between probable AD dementia, possible AD dementia and AD dementia with evidence of the AD pathophysiological process [11]. The latter requires biomarker data, while probable and possible AD dementia are diagnosed based on clinical criteria. Similarly, MCI is diagnosed based on clinical criteria, but may be classified for research purposes as MCI unlikely due to AD or MCI due to AD with intermediate or high likelihood based on biomarkers [173].

The NIA-AA criteria for probable AD dementia require that dementia is present and that the following criteria are fulfilled: insidious onset, clearly progressively worsening cognition, initial presentation of cognitive deficits that are either amnestic or nonamnestic. Exclusion criteria are evidence of substantial concomitant cerebrovascular disease; prominent features of non-AD dementia; primary progressive aphasia; or other concurrent active neurological disease, non-neurological medical comorbidity or use of medication that could have a substantial effect on cognition. All patients who would have been diagnosed with ‘probable AD’ using the NINCDS-ADRDA-criteria also meet the NIA-AA criteria for probable AD dementia. Possible AD dementia is diagnosed if the clinical course is atypical, i.e. progressive decline cannot be documented or onset is sudden, or if the clinical presentation is aetiologically mixed, i.e. the patient has evidence of one or more concomitant conditions that preclude diagnosis of probable AD dementia. This definition of possible AD dementia differs from the previous criteria; all patients who would have been diagnosed with ‘possible AD’ using the NINCDS-ADRDA-criteria would not necessarily meet the NIA-AA criteria. It is important to note that while biomarker evidence increases the certainty of AD as the cause of cognitive impairment, it is not required to meet the NIA-AA criteria for AD dementia.

Pathophysiologically proven AD dementia can still only be diagnosed based on post-mortem neuropathological examination.

1.5.3 Redefining Alzheimer’s disease – NIA-AA research framework

In 2018, NIA-AA published a research framework for AD [13]. This framework represents a radical shift in the conceptualisation of the disease, redefining AD as a purely biological construct divorced from clinical symptoms. This research framework was introduced with the intention of providing AD researchers with a common language and frame of reference.

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Because the accumulation of biomarkers appears to be a continuous process that begins before symptom onset, it was proposed that AD should be conceptualised as a continuum, rather than the three clinically distinct entities preclinical AD, MCI and dementia.

In the NIA-AA research framework, AD is defined by biomarkers in vivo and by neuropathological assessment post-mortem; clinical symptoms (or the absence of symptoms) do not influence the definition. The rationale for excluding clinical symptoms from the definition, is that they are neither sensitive nor specific for the underlying biology that defines AD, and cannot identify presymptomatic individuals [174]. It is well established that typical AD symptoms do not ‘rule in’ AD neuropathology and that the absence of symptoms does not

‘rule out’ the underlying pathology. Therefore, the authors argue that a purely biological definition is better suited to elucidate the underlying mechanisms that cause cognitive impairment in AD [13].

In the research framework, biomarkers are organised in three general groups using an unbiased descriptive classification scheme [175]: A, reflecting aggregated Aβ or an associated pathologic state; T, reflecting aggregated tau or an associated pathologic state; and N, reflecting neurodegeneration. In line with the widely held view that amyloid biomarkers represent the earliest evidence of AD, amyloid positivity is a required feature for AD. All amyloid-positive individuals are considered to be on the “Alzheimer’s continuum”. However, because both Aβ and tau deposits are required to fulfil the neuropathological criteria for AD [99], this is also a requirement for the definition of AD in vivo. Markers of neurodegeneration, N, are not specific to AD and not included in the definition of the disease, but they are used to stage the disease severity. When applying normal/abnormal cut-off points for all biomarker groups, there are eight possible biomarker profiles organised into three biomarker categories, see Table 1. The framework can be extended to incorporate new biomarker groups as they become available, for example introducing an ‘I’ group for inflammatory markers. In addition to the biomarker profile, each patient will have a cognitive stage based on clinical symptoms: cognitively unimpaired, MCI, mild-, moderate- or severe dementia.

Since its publication 2018, the NIA-AA research framework has been widely incorporated in research. Yet, there is still some controversy. Critics of the amyloid cascade hypothesis question the centrality of Aβ pathology in the framework [176]. Moreover, the separation of the clinical and biological features of AD has been termed ‘a messy divorce’ [177]. It is argued that while clinical symptoms are not sensitive or specific for AD pathophysiology, the opposite is also true: the AD biomarker profile might not be sufficiently sensitive or specific for the clinical

Amyloidogenic nanoplaques as a potential biomarker for Alzheimer’s disease