DEPARTMENT OF NEUROLOGY & ALZHEIMER CENTER, DEPARTMENT OF OPHTHALMOLOGY, VU UNIVERSITY MEDICAL CENTER, AMSTERDAM

Retinal biomarkers for early Alzheimer’s disease

Esmee Runhart

1-11-2016

Student number: 1976192

Faculty supervisor: dr. T.L. Ponsioen

Second supervisor: dr. P.J. Visser

Daily supervisors: drs. E. Konijnenberg, drs. H.T. Nguyen

Institution: VU University Medical Center

Department of Neurology & Alzheimer Center

Department of Ophthalmology

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Abstract

Characteristics of early Alzheimer’s disease (AD) include amyloid-beta (Aβ) pathology and

hippocampal atrophy. A curative treatment for AD is not available at this moment. An

explanation might be that patients in a late disease stage already have too much brain damage. Therefore, easy accessible AD biomarkers are necessary to identify cognitively healthy persons

in the earliest stage of AD. There is increasing evidence of changing retinal vasculature and

retinal nerve fiber layer (RNFL) thickness in AD patients. Here, the potential of retinal vasculature and RNFL as biomarkers for early AD was

studied in cognitively healthy elderly persons. Dynamic amyloid PET scans were acquired using [18F]Flutemetamol, to assess amyloid-beta non-displaceable binding potential (Aβ BPND)

in the posterior cingulate. MRI scans were acquired to assess hippocampal volume and

intracranial volume. Fundus images of 129 individuals were analyzed and retinal vascular parameters (RVPs), including calibers, tortuosity and fractal dimension, were measured using

Singapore I Vessel Assessment software. Peripapillary RNFL thickness of 120 individuals was measured using optical coherence tomography. Firstly, it is investigated whether RVPs can

predict Aβ pathology. Secondly, it is investigated whether RNFL thickness is associated with

hippocampal volume. Retinal venular changes were associated with Aβ BPND, after adjusting for age, gender

and cardiovascular risk factors. Higher Aβ BPND was associated with a smaller central retinal vein equivalent (β=0.004, p=0.049), a higher venular branching coefficient (β=0.342, p=0.024),

and a higher venular asymmetry factor (β=0.590, p=0.014). Additionally, a thinner RNFL in

the superior (β=8.60, p=0.002) and temporal superior (β=5.62, p=0.005) segment was associated with smaller left hippocampal volume, after adjusting for age, gender and

intracranial volume. Cognitively healthy individuals with retinal venular changes and thinner RNFL show

more cerebral Aβ pathology and decreased hippocampal volume respectively, suggesting these

characteristics are potential biomarkers for early AD.

Samenvatting

Kenmerken van vroege ziekte van Alzheimer (AD) zijn onder andere amyloïd-bèta (Aβ)

pathologie en hippocampus atrofie. Een curatieve behandeling voor AD is nog niet beschikbaar.

Een verklaring zou kunnen zijn dat patiënten in een laat ziektestadium al teveel schade hebben in het brein. Daarom zijn gemakkelijk verkrijgbare AD biomarkers nodig, zodat cognitief

gezonde personen in het vroegste ziekte stadium kunnen worden geïdentificeerd. Er is steeds meer bewijs voor veranderingen in retinale vasculatuur en retinale zenuwvezellaag (RNFL)

dikte in AD patiënten.

In deze studie werden de retinale bloedvaten en RNFL onderzocht als mogelijke biomarkers voor vroege identificatie van AD in cognitief gezonde, oudere personen. Met

gebruik van [18F]Flutemetamol werden dynamische amyloïd PET scans verkregen om amyloïd-bèta non-displaceable binding potential (Aβ BPND) in de cingularis posterior vast te stellen.

MRI scans werden verkregen om hippocampus volume en intracranieel volume vast te stellen.

Fundusfoto’s van 129 proefpersonen werden geanalyseerd en retinale vasculaire parameters (RVPs), waaronder kalibers, tortuositeit en vertakkingspatroon, werden gemeten door middel

van Singapore I Vessel Assessment software. Peripapillaire RNFL-dikte van 120 proefpersonen werd gemeten door middel van optical coherence tomography. Ten eerste werd onderzocht of

RVPs Aβ pathologie kunnen voorspellen. Ten tweede werd onderzocht of RNFL-dikte geassocieerd is met hippocampus volume.

Retinale venulaire veranderingen waren geassocieerd met Aβ BPND, na correctie voor

leeftijd, geslacht en cardiovasculaire risicofactoren. Hogere Aβ BPND was geassocieerd met een

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kleiner centrale retinale vene equivalent (β=0.004, p=0.049), een hogere venulaire vertakking coëfficiënt (β=0.342, p=0.024) en een hogere venulaire asymmetrie factor (β=0.590, p=0.014).

Daarnaast was een dunnere RNFL in het superior en temporaal superior segment geassocieerd met kleiner linker hippocampus volume, na correctie voor leeftijd, geslacht en intracranieel

volume.

Cognitief gezonde personen met retinale venulaire veranderingen en dunnere RNFL hebben respectievelijk meer cerebrale Aβ pathologie en kleiner hippocampus volume, wat

suggereert dat deze parameters potentiële biomarkers zijn voor vroege AD.

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Table of contents

1. Introduction ......................................................................................................................... 4

2. Material and methods .......................................................................................................... 7

2.1 Study population .......................................................................................................... 7

2.2 Study design ................................................................................................................ 7

2.3 Amyloid PET scan ....................................................................................................... 7

2.3.1 PET tracer ............................................................................................................. 7

2.3.2 Dynamic PET scan teabreak protocol .................................................................. 7

2.4 Structural MRI ............................................................................................................. 8

2.5 Exploratory eye examination ....................................................................................... 8

2.6 Retinal photography and quantitative assessment of retinal vasculature .................... 8

2.7 Optical coherence tomography .................................................................................. 10

2.8 Other variables ........................................................................................................... 11

2.9 Statistical analysis...................................................................................................... 11

3. Results ............................................................................................................................... 12

3.1 Subject characteristics ............................................................................................... 12

3.2 Retinal vasculature .................................................................................................... 13

3.3 Retinal nerve fiber layer ............................................................................................ 14

4. Discussion ......................................................................................................................... 16

5. Conclusion ........................................................................................................................ 18

6. References ......................................................................................................................... 19

7. Appendices ........................................................................................................................ 24

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1. Introduction

Alzheimer’s disease (AD) is the most common type of dementia. The estimated number of

people with dementia was 35.6 million in 2010, and is expected to double every 20 years (1). This neurodegenerative disease, with multifactorial etiology, leads to progressive cognitive

decline. It has high impact on patients and society, and unfortunately a curative treatment has not been found at this moment.

The main neuropathological characteristics of AD are accumulation of extracellular

amyloid beta (Aβ) plaques and tau-protein related intra-neuronal neurofibrillary tangles (NFT) in the brain (2). Gold standard for AD diagnosis remains post mortem histopathological

examination for plaques and tangles. However, current techniques allow in vivo diagnosis of AD based on clinical criteria supported by evidence of abnormal biomarkers indicating Aβ

pathology and neuronal damage (3). Aβ pathology can be accurately determined using positron

emission tomography (PET) (4) or by measuring Aβ42 in cerebrospinal fluid (CSF) (5). Aβ42 together with total tau (t-tau), reflecting intensity of neurodegeneration, and phosphorylated tau

(p-tau), representing presence of neurofibrillary tangles, are the most important CSF biomarkers for AD (6). However, obtaining Aβ biomarkers is invasive and expensive, considering the

lumbar puncture procedure and PET scanner and tracer costs, respectively.

The accumulation of Aβ precedes clinical dementia by decades (7,8). The assumption that cognitively healthy individuals with abnormal levels of Aβ find themselves in the

preclinical stage of AD, has been incorporated in prominent guidelines for AD diagnosis (3,9). Biomarker studies have yielded hypotheses on how CSF and imaging biomarkers change over

the course of AD, from the preclinical stage to the earliest symptomatic stage (often referred to

as “mild cognitive impairment” (MCI)) to eventually dementia (8–10). Multiple studies found that abnormal Aβ is the first event, followed by abnormal tau, atrophy on structural magnetic

resonance imaging (MRI), brain glucose hypo metabolism on FDG PET and eventually by cognitive decline (10,11). Figure 1 describes this hypothetical evolution of biomarkers in AD.

So far, medication trials have not shown satisfying results of anti-Aβ drugs on cognitive outcome in patients already having clinical symptoms (MCI or dementia) (6). This might imply

irreversible damage in late disease stages, where cognitive decline is already present and extensive neurodegeneration has occurred. Therefore, studies in cognitively healthy subjects

with Aβ pathology (preclinical AD) are of great interest. Neurodegeneration, although a later biomarker in the course of AD than Aβ pathology,

precedes cognitive decline. Moreover, neurodegeneration assessed by atrophy on MRI, both

precedes and parallels cognitive decline (11,12). One region that is associated with risk to

Figure 1 Amyloid-first biomarker model (7).

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develop dementia is the hippocampus. Hippocampal atrophy was proven to be effective in predicting disease progression and AD diagnosis (12–14).

Methods of screening for early signs of AD are essential to identify cognitively healthy persons in the earliest disease stage, while interventions can be done to delay or even halt the

progression of early Aβ and NFT pathology. So in the future, secondary prevention can

hopefully avoid neurodegeneration and cognitive decline. These studies require easy accessible biomarkers for AD, as PET and lumbar puncture are invasive and expensive, and therefore are

of limited use in healthy elderly population screening. An accessible way of imaging neural tissue is through the eye. The retinal nerves and

blood vessels can easily be observed in detail in vivo. The eye and the brain have the same

embryologic origin; the retina and optic nerve extent from the diencephalon during embryonic development. The optic nerve is formed by axons of retinal ganglion cells directly extending to

the brain, more specifically to the thalamus and mesencephalon (15). Furthermore, anatomically and physiologically the retinal and cerebral microcirculation show a clear homology. This

includes a barrier function, auto-regulation, and relatively low-flow and high-oxygen-extraction

systems (16). The relation between the eye and the brain and recorded visual disturbances in AD (17,18) have inspired researchers to explore ocular manifestations of AD. At present,

multiple studies have shown change of the integrity of the retina in AD, including retinal vascular changes (19–23) and retinal nerve fiber layer (RNFL) thinning (24,25). Also, in a small

number of studies Aβ plaques have been seen within the retina of AD patients and AD

transgenic mice (26). It might be the case that the eye contains markers that are either specific to AD or can contribute to an AD risk analysis in combination with genetics, blood analysis or

brain imaging (22). Quantitative analysis of retinal vasculature is possible in fundus images by software that

measures retinal vascular parameters (RVPs), including arteriolar/venular caliber, tortuosity,

and fractals. The latter is a measure of complexity of the vascular branching pattern. Tortuosity and fractal dimension are measures of the efficiency of blood distribution in the retinal network,

with higher tortuosity values and smaller fractal dimension values more commonly associated with ill health (20,23). Change of retinal vascular calibers – either retinal vessel narrowing or

widening – reflect retinal microvascular dysfunction (20). RVP changes have shown to be

associated with multiple systemic factors and diseases (e.g., hypertension, diabetes, stroke, and heart disease) (27–29). Reported RVP changes in AD include venular narrowing (19,21,22) and

loss of vessel density, defined as decreased retinal fractals (21–23). Contrasting findings have been reported regarding change – both decrease and increase – in retinal arteriolar and venular

tortuosity in AD (21,22). Furthermore, Frost et al. found altered RVPs in cognitively healthy

subjects with Aβ pathology assessed by PET, specifically ‘venular branching asymmetry factor’ and ‘arteriolar length-to-diameter ratio’ (22). These results offer possibilities for RVPs as

biomarker for preclinical AD. Optical coherence tomography (OCT) is a non-invasive method for in-vivo

measurements of retinal layers, including the RNFL. OCT enables quantitative assessment of

retinal neuronal and axonal neurodegeneration, biomarkers that previously have been associated with AD (15). OCT enables exact measurement of retinal layers, including the

RNFL. This layer consists of axons of the retinal ganglion cells, which together form the optic nerve. RNFL thickness is therefore a measure of axonal loss and neurodegeneration in the

anterior visual pathway (30). Two recent meta-analyses of AD OCT studies found reduced

RNFL thickness in AD patients compared to cognitively healthy controls (24,31). Moreover, reduction of mean RNFL thickness was found in patients with MCI (24,31). However, AD

diagnosis in these studies was not supported by biomarkers. While involvement of the retina in AD has frequently been assessed, its potential as a

biomarker for preclinical AD has not been established. The aim of this study is to investigate

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RVPs and RNFL thickness as a risk factor for Aβ pathology and hippocampal atrophy in cognitively healthy elderly.

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2. Material and methods

2.1 Study population

This cross-sectional study is part of the European Medical Information Framework to develop

markers for early AD diagnosis (EMIF-AD). The current population consists of 100 cognitively healthy monozygotic twin pairs (200 subjects), recruited from the Netherlands Twin Register.

Inclusion criteria were age 60-100, normal cognition as assessed with Telephone Interview for Cognitive Status modified (TICS-m), Consortium to Establish a Registry for Alzheimer’s

Disease (CERAD) 10 word list learning and delayed recall and a Clinical Dementia Rating

(CDR) scale of 0, with memory subdomain of 0 and no depressive disorder present as assessed by 15-item Geriatric Depression Scale (GDS). Exclusion criteria comprise factors that may

influence retinal vasculature, RNFL and cognition, medical conditions that prohibit attendance at hospital visit sessions, and contra-indications for MRI. Exclusion and inclusion criteria are

further specified in Appendices 1. Glaucoma was defined as history of glaucoma or use of eye

pressure lowering medication. Subjects with history of ocular pathology influencing RNFL thickness (such as glaucoma or exudative macular degeneration), or evidence of these

conditions at ophthalmological examination (slit lamp, fundus photography or OCT) were excluded.

2.2 Study design

Subjects were screened for in- and exclusion criteria in a telephone interview. During a home visit cognition was assessed by an extensive neuropsychological testing battery to assess

whether inclusion criteria were fully met. Physical examination was performed, including

assessment of Body Mass Index (BMI), blood pressure and heart rate. Additionally, cardiovascular risk factors were recorded, such as smoking status, hypertension and

dyslipidemia. During a hospital visit to the VU University Medical Center other biomarkers were collected: i.e. 2-hour fasting blood draw, CSF collection by lumbar puncture, retinal

photography, OCT, ultrasound of the carotid artery, magnetic encephalography, dynamic

amyloid PET scan and structural MRI.

2.3 Amyloid PET scan

2.3.1 PET tracer

[18F]Flutemetamol (FMM) is a PET radiotracer which binds specifically to fibrillar Aβ (4). After

intravenous injection of FMM, the PET scanner detects photons generated as a result of decay of the positron-emitting radiotracer (32). The PET scanner thus reconstructs the tracer’s

distribution, allowing in vivo detection of fibrillar Aβ. The relatively long half-life of the

radionuclide fluorine-18 (T1/2=110 minutes) makes amyloid imaging widely available in research and clinical practice (4).

2.3.2 Dynamic PET scan teabreak protocol

PET-MRI was performed using a Philips Ingenuity Time-of-Flight PET-MRI camera (Royal Philips, Amsterdam, the Netherlands), after a bolus intravenous injection of 185 MBq

[18F]Flutemetamol (Cyclotron Research Center, University of Liège, Liège, Belgium). T1-weighted gradient pulse MRI was performed prior to each PET scan, for attenuation correction

of PET. All subjects were scanned under the same resting conditions (closed eyes, dimmed

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light), with immobilization of the head. First a 30 minute dynamic PET scan was acquired to monitor the time course of the radiotracer distribution directly after injection of FMM. This

dynamic scan was reconstructed into 18 frames with increasing frame length (6x5, 3x10, 4x60, 2x150, 2x300, 1x600 s). After 60 minutes rest outside the camera, the second part of the PET

scan was acquired from 90 to 110 minutes post injection (4x5-minute frames).

Dynamic data was acquired using a so called teabreak protocol where plasma input curves are interpolated between a first and second dynamic PET scan. Scans were reconstructed

with row-action maximum likelihood algorithm (3D-Ramla), resulting in a voxel size of 2x2x2 mm and a spatial resolution of 5-7 mm full width at half maximum (FWHM). Post

reconstruction noise reduction was performed with a 3D Gaussian filter of 4 mm FWHM, prior

to calculation of parametric maps. The reconstructed data from the first and second PET scan were combined by correction for decay for the second scan and co-registration of the second

scan to the first scan. Each amyloid PET scan was qualitatively analyzed by an experienced physician (E.

Konijnenberg), and reported as visually amyloid positive or negative. Furthermore, quantitative

analysis was performed. For this, regions of interest (ROI) were applied to the dynamic PET data, using a MRI template based procedure (33). Subsequently, time activity curves (TAC)

were generated. These reflect pharmacokinetics by showing the distribution of the tracer in each ROI over time. The cerebellar cortex generally serves as a reference region in PET amyloid

imaging, because it is markedly free of fibrillar Aβ. So, TAC for the cerebellar cortex were used

as input function to analyze the TACs from several target ROIs (34). The dynamic data was also analyzed on pixel-by-pixel level using receptor parametric mapping (35,36), with the

amyloid-beta non-displaceable binding potential (Aβ BPND) as outcome measure. Aβ BPND reflects the amount of targets available for reversible binding of the tracer.

2.4 Structural MRI

Whole brain scans were obtained using a 3T Philips Achieva scanner using an 8-channel head coil (Royal Philips, Amsterdam, the Netherlands). Isotropic structural 3D T1-weighted images

were acquired using a sagittal turbo field echo (TFE) sequence (1.00 mm x 1.00 mm x 1.00 mm

voxels, repetition time (TR) = 7.9 ms, echo time (TE) = 4.5 ms, flip angle (FA) = 8 degrees). Hippocampal volumes were determined using FreeSurfer (37). Total intracranial volume was

also obtained with FreeSurfer, for correction of hippocampal volume in the statistical analysis.

2.5 Exploratory eye examination

Exploratory eye examination was performed, including slit lamp examination, and

measurement of intra-ocular pressure.

2.6 Retinal photography and quantitative assessment of retinal vasculature

Digital fundus images were obtained using a Topcon TRC 50DX type IA retinal camera

(Topcon Medical Systems, Inc., Oakland, USA) approximately 30 minutes after pupil dilation with 0.5% tropicamide. For our analyses a 50⁰ field optic disc-centered fundus image of each

eye was made. Right eye images of each participant were used; if the right eye images were

ungradable (due to insufficient image quality), measurements were performed on the left eye. Each fundus image was visually analyzed by an experienced physician (H.T. Nguyen),

to screen for exclusion criteria such as glaucoma or macular disease such as exudative macular degeneration. For quantitative analysis of retinal vasculature, Singapore I Vessel Assessment

(SIVA) software (version 3.0, National University of Singapore, Singapore) was used. With

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this software 13 RVPs in each fundus image were measured. RVPs include arteriolar/venular caliber, tortuosity and fractal dimension, specified in Table 1. Figure 2 is an example of an

image analyzed by the SIVA software, and depicts the retinal zones in which each RVP was measured. One trained grader (E.H. Runhart) was responsible for the visual evaluation of SIVA

automated measurements and performed manual intervention, if necessary. Manual

interventions could include adjusting the placement of the grid on the optic disc, correcting wrongly identified vessel type, and modifying widths of vessel segments. Figure S4 shows

grading of retinal vasculature using SIVA software. Average grading time was 30 minutes per image.

Vascular calibers were calculated for the six largest arterioles and six largest venules.

These measurements were then summarized as ‘central retinal artery equivalent’ (CRAE) and ‘central retinal vein equivalent’ (CRVE), using the revised Knudtson-Parr–Hubbard formula:

(38):

where w1 is the width of the narrower branch, w2 the width of the wider branch, and Ŵ is the

estimate of parent trunk arteriole or venule. CRAE and CRVE reflect average width of arterioles and venules, respectively. In these indexes, systemic vascular disease pathways that affect

either arterial or venous systems can be distinguished. Measurement of vessel calibers were

performed in zone B, in correspondence with prior AD research of retinal vasculature. Although caliber measurements of zone C were also available, a more reliable and consistent assessment

in zone B was found, as fundus images were of better quality in this zone. Branching coefficient (BC) and branching asymmetry factor (AF) were calculated

within the vessels with a first bifurcation in zone C. BC is calculated as

where w1, w2, and W are respectively the mean width of the narrower branch, the wider branch,

and the parent trunk (20). AF is a measure of symmetry between widths of branches after

bifurcating, defined as the square of the two branching vessel widths:

Larger values indicate more symmetry between the widths of the two daughter branching

vessels. Length diameter ratio (LDR) is the ratio of vessel length between two bifurcations and the diameter of the parent vessel at the first bifurcation.

Additionally, measures of tortuosity and fractal dimension were derived. Tortuosity is defined as the integral of the curvature square along the path of the vessel, normalized by the

total path length (20). Smaller values indicate straighter vessels. Fractal dimension was

calculated using the box-counting method, larger values reflecting a more complex branching pattern (39).

Arterioles: Ŵ = 0.88 * (w12 + w2

2)1/2

Venules: Ŵ = 0.95 * (w12 + w2

2)1/2

BC = (w12 + w2

2 / W2)

AF: (w12 / w2

2)

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Table 1 Description of the thirteen retinal vascular parameters measured for each fundus image, with the corresponding zone (figure 2) in which the parameter was calculated.

Parameter Description Retinal zone

CRAE Central retinal artery equivalent B

CRVE Central retinal vein equivalent B

AVR Arteriole–venular ratio (CRAE/CRVE) B

FDa Fractal dimension of arteriolar network C

FDv Fractal dimension of venular network C

TORTa Curvature tortuosity arteriole C

TORTv Curvature tortuosity venule C

BCa Branching coefficient arteriole C

BCv Branching coefficient venule C

AFa Asymmetry factor arteriole (or asymmetry ratio) C

AFv Asymmetry factor venule (or asymmetry ratio) C

LDRa Length diameter ratio arteriole C

LDRv Length diameter ratio venule C

Figure 2 Retinal zones utilized for retinal vascular analysis. Zone B is defined as the region from 0.5 to 1.0 disc diameters away from the disc margin and zone C is defined as the region from 0.5 to 2.0 disc diameters away from the disc margin.

2.7 Optical coherence tomography

Spectral Domain OCT (Spectralis®, Heidelberg Engineering, Heidelberg, Germany) was

performed to acquire macular scans and optic disc ring scans from each eye. In OCT, reflected light from retinal tissue is used to produce detailed cross-sectional and 3D images of the retina.

Peripapillary RNFL thickness was measured in eight segments (Figure 3). Average RNFL thickness of the left and right eye was used.

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Figure 3 Peripapillary RNFL measurement in eight segments using OCT.

2.8 Other variables

Aβ pathology in persons without dementia is associated with age, so we adjusted for age in both

analyses (40). Factors associated with RVP changes include age, gender, hypertension, diabetes mellitus, dyslipidemia and smoking (27,28,41). To adjust for the possible effect of these factors

in the retinal vascular analysis, a Framingham 10-year risk score for cardiovascular disease was

calculated, combining the following cardiovascular risk factors: age, gender, diabetes, smoking, treated and untreated systolic blood pressure, total cholesterol, HDL cholesterol (42).

2.9 Statistical analysis

Statistical analyses were performed using SPSS Statistics software, version 23.0 (SPSS, Inc., Chicago, USA). Aβ pathology on PET, defined as Aβ BPND, was the continuous outcome

measure in retinal vascular analysis. Hippocampal volume was the continuous outcome measure in RNFL analysis. Left and right hippocampal volume were compared using a paired

sample t-test. Because the study population was composed of monozygotic twins, generalized

estimating equations in SPSS with the exchangeable and robust function were used to correct for family relatedness (43). Gender and age were included as covariates in both analyses, with

the additional covariate Framingham risk score in retinal vascular analysis and intracranial volume in retinal nerve fiber layer thickness. By evaluating b-values, the effect of the retinal

measurements on Aβ pathology and hippocampal volume were examined. To explore

diagnostic value of RVPs for Aβ pathology, receiver operating characteristic (ROC) curves were used. Visual amyloid status was chosen as outcome measure for ROC analysis, as a clear

cut-off value for Aβ BPND has not been validated.

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3. Results

3.1 Subject characteristics

After exclusion of fundus images with poor quality or unfit format for SIVA (n=44), 129

subjects suitable for retinal vasculature analysis were assessed. The selection process has been described in Figure S1. Subjects had a median age of 68.6 years (IQR 64.3-75.5) and 75 (58%)

was female. The scores for the individual Framingham risk score items for these subjects are shown in Table S1.

After exclusion of subjects with poor quality OCT scan (n=2), glaucoma (n=11),

exudative macular degeneration (n=5), myelinated RNFL (n=2) and epiretinal membrane (n=1), 120 subjects were included for RNFL analysis (Figure S1). Median age of these subjects was

lower compared to the SIVA group, 65.4 years (IQR 62.6-72.7), 54% was female. Subject characteristics for SIVA and OCT groups are presented separately in Table 2.

Table 2 Characteristics of subjects included for analyses.

Retinal vasculature analysis Total (%) or mean ± SD*

RNFL analysis Total (%) or mean ± SD*

Age

Gender, female

Education, years

GDS (ref <11)

TICS-m (ref >22)

68.6 (64.3-75.5)

75 (58.1%)

15.2 ± 5.0

0 (0-1)

28.2 ± 2.9

65.4 (62.6-72.7)

65 (54.2%)

15.2 ± 4.7

0 (0-1)

28.6 ± 3.0

Cognition MMSE, median

CERAD recall (ref > -1.5 SD)

15 Word verbal learning task

delayed recall

29 (29-30)

7.4 ± 1.3

8.4 ± 2.8

29 (29-30)

7.6 ± 1.2

8.6 ± 2.8

Imaging Aβ positive (visual)¤

Aβ BPND posterior cingulate

Medial temporal lobe atrophy

- Left hemisphere

- Right hemisphere

Global cortical atrophy

Fazekas score

Hippocampal volume

- Left hemisphere

- Right hemisphere

18 (16.1%)

0.26 (0.20-0.35)

0.58 ± 0.79

0.64 ± 0.79

0.77 ± 0.74

1.18 ± 0.85

15 (12.7%)

0.54 ± 0.76

0.59 ± 0.73

0.69 ± 0.71

1.11 ± 0.82

3658 ± 555

3845 ± 496

Factors considered relevant to RVPs or RNFL

Framingham risk score

Intra-ocular pressure, mmHg¤¤ - OD

- OS

22.8 (15.7-33.0)

13.6 ± 2.6

13.8 ± 3.0

Abbreviations: GDS Geriatric Depression Scale, ref reference value, TICS-m Telephone Interview for Cognitive Status

modified, MMSE Mini Mental State Examination, CERAD Consortium to Establish a Registry for Alzheimer’s Disease 10 word list, Aβ BPND amyloid non-displaceable binding potential, RVPs retinal vascular parameters, RNFL retinal nerve fiber

layer, OD right eye, OS left eye.

*If data is not normally distributed, median and inter quartile range is presented. ¤ 112 of 129 subjects for retinal vasculature analysis and 118 of 120 subjects for RNFL analysis had a visual Aβ rating at time

of analysis. ¤¤ Intra-ocular pressure was measured in 61 subjects for RNFL analysis, due to logistical limitations early in the study.

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3.2 Retinal vasculature

Figure 4 and Figure 5 show the distribution of Aβ BPND in the posterior cingulate cortex. High

values of Aβ BPND correspond with visually positive rated Aβ status (orange). Aβ negative rated subjects are shown in green and subjects without visual score in blue. The level of Aβ BPND

was log transformed because of a right-skewed distribution (Figure S2).

Figure 4 Distribution of CRVE and amyloid binding Figure 5 Distribution of CRAE and amyloid binding potential. potential.

Figure 6 CRVE compared in visually amyloid positive and Figure 7 CRAE compared in visually amyloid positive and negative subjects. negative subjects.

Smaller venular calibers, higher venular branching coefficient and higher venular asymmetry

factor were associated with higher Aβ BPND in the posterior cingulate (Table 3). The effects

remained present after adjusting for age, gender and Framingham risk score. Retinal arteriolar parameters were not associated with Aβ BPND. The difference between retinal venular and

arteriolar parameters in association with amyloid pathology is shown in Figure 6 and Figure 7. Aβ negative subjects showed a larger CRVE (161.6 ± 18.1) compared to Aβ positive subjects

(147.4 ± 19.0, p = 0.003), while there was no difference in CRAE between Aβ negative (104.2

± 12.3) and positive subjects (99.7 ± 12.7, p = 0.162).

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Table 3 RVPs associated with Aβ BPND.

RVP β SE p-value

Central retinal vein equivalent

Model 1 -0.004 0.0019 0.044

Model 2 -0.004 0.0018 0.049

Branching coefficient venule

Model 1 0.329 0.151 0.030

Model 2 0.342 0.152 0.024

Asymmetry factor venule

Model 1 0.603 0.248 0.015

Model 2 0.590 0.239 0.014 Only retinal vascular parameters (RVPs) that were significant showing effect on

amyloid binding potential (Aβ BPND) in GEE analysis are shown (P<0.05). The beta indicates the effect size, and the direction of the association is given by its

positive or negative value. Model 1: adjusted for age and gender; model 2:

adjusted for Framingham risk score.

To explore sensitivity and specificity of RVPs, subjects with Aβ BPND above 0.45 were regarded as visually positive if visual rating was not done (Figure 4 and Figure 5). The area

under the curve was 0.667 for CRVE (p=0.014), 0.662 for BCv (p=0.017), 0.551 for AFv

(p=0.456). At a cut-off value of 156 µm for CRVE, sensitivity was 68% and specificity 61% (Figure 8). At a cut-off value of 160 µm, sensitivity was 82%, specificity 49%.

3.3 Retinal nerve fiber layer

The left hippocampus (3648 ± 546) was smaller than the right (3812 ± 497, p=0.000). Therefore

left and right hippocampal volume were considered as separate outcome measures in RNFL

thickness analysis. Distribution of left and right hippocampal volume is shown in Figure S3.

Figure 9 Correlation between superior retinal nerve fiber layer (RNFL) thickness and left hippocampal volume (left) and right hippocampal volume (right).

Figure 8 Sensitivity and specificity of

central retinal vein equivalent.

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Figure 10 Correlation between temporal superior retinal nerve fiber layer (RNFL) thickness and left hippocampal volume (left) and right hippocampal volume (right).

A thinner superior and temporal superior RNFL was associated with a smaller hippocampal

volume (Figure 9 and Figure 10). Effects remained present after adjusting for age and gender

(Table 4).

Table 4 Retinal nerve fiber layer thickness associated with hippocampal volume.

RNFL segment Right hippocampal volume Left hippocampal volume

β SE P-value β SE P-value

Superior 3.88 2.60 0.135 8.60 2.73 0.002

Temporal superior 2.65 1.84 0.150 5.62 2.00 0.005 Only segments that were significant showing effect on hippocampal volume (P<0.05) in GEE analysis are shown. The beta

indicates the effect size, and the direction of the association is given by its positive or negative value. Adjusted for age, gender and intracranial volume. Average RNFL thickness of both eyes.

16

4. Discussion

Major findings of this study are the association between retinal venular changes and cerebral

Aβ BPND, and between RNFL thickness and hippocampal volume. This study demonstrated that retinal venular changes, defined as smaller CRVE and

higher BCv and AFv, are associated with higher Aβ BPND in the posterior cingulate in

cognitively healthy subjects. So far, no previous studies have investigated retinal vascular changes with respect to Aβ BPND in a cognitively healthy population. In a few studies retinal

vascular changes were investigated by fundus image analysis comparing AD to healthy controls. Smaller venular calibers in AD subjects were decribed, and inconsistent change in

fractal dimension and tortuosity (21–23). Interestingly, one study compared RVPs with respect

to visual rating of Aβ in a small group of cognitively healthy subjects, and found a higher AFv in Aβ positive subjects, which is in concordance with our findings (22). The findings in our

study strengthen previous evidence of abnormal retinal vasculature as a marker for early AD. A possible pathophysiological explanation for the altered vasculature is deposition of

Aβ in retinal vessel walls analogous to deposition of Aβ in cerebral vasculature. This so called

cerebral amyloid angiopathy (CAA), is a known concomitant factor in AD (44,45). A self-reinforcing pathway is suggested of parenchymal Aβ pathology leading to CAA, causing

microbleeds and vascular dysfunction, and eventually reduced clearance of Aβ (44,46). Aβ has been detected in retinal vessel walls of AD transgenic mouse models (47). Although Aβ is less

frequently found in veins than in arteries (48), Aβ deposition in veins preceded deposition in

arteries in AD transgenic mouse models (49). Moreover, microvascular change in AD might not only be mediated by Aβ in vessel walls, but also by mural cell loss on venules (50).

Ultimately, the possibility that vascular dysfunction precedes significant Aβ deposition needs to be considered, as soluble Aβ can cause abnormal vascular reactivity in the absence of

vascular deposition (45). Thus far, the pathophysiological basis of retinal venular changes as

well as cerebral vascular changes in relation to Aβ pathology remains unclear. An easy accessible biomarker for AD will facilitate further research on this topic. Our findings add to

the growing evidence that RVPs are a promising, easy accessible biomarker for identification of individuals with early AD, but longitudinal research is needed.

Despite the fact that Aβ pathology plays an earlier role in the course of the disease, also

neurodegeneration, assessed using MRI, precedes cognitive decline. We therefore also investigated whether retinal neurodegeneration reflects neurodegeneration in the brain.

Hippocampal volume was chosen as indicator of cerebral neurodegeneration, although hippocampal atrophy is not specific for AD. However, it was proven to be effective in

predicting disease progression and AD diagnosis (12–14). In this study was shown that thinning

of the peripapillary RNFL in the superior and temporal superior segments is associated with smaller hippocampal volume of the left hemisphere in cognitively healthy persons. RNFL

thinning has previously been associated with AD and MCI (24,31). With respect to heterogeneity in segments showing RNFL thinning: a comprehensive meta-analysis reported

superior and inferior quadrants demonstrated the greatest thinning in patients with AD

compared to HC, whereas nasal and temporal quadrants demonstrated significant thinning in few studies (24). To our knowledge no earlier studies have been done investigating RNFL

thickness with respect to hippocampal volume, as an indicator of possible early AD. The discrepancy between left and right hippocampal volume has been observed in previous studies,

with more asymmetry in MCI than in AD (51). The finding that retinal neurodegeneration is

associated with hippocampal volume in cognitively healthy elderly subjects, offers promises as an indicator for subjects at increased risk of AD, however longitudinal studies are necessary to

assess the pathophysiological background of this association. Retinal and cerebral neurodegeneration might share a similar pathophysiology,

analogous to the similarities between retinal and cerebral vascular changes in AD, as described

17

above. CAA has been suggested as the pathology that might connect neurodegenerative and vascular changes in the brain (46). Cerebral small vessel disease is thought to contribute to brain

atrophy through endothelial damage, arteriosclerosis, and hypoperfusion, resulting in ischemia and finally tissue loss (46,52). Another hypothesis is retrograde neuronal degeneration down

the optic nerve in the diseased brain with Aβ and atrophy disrupting connections within the

visual tract (53). Either explanation implies a complex relationship between Aβ, vascular changes and neurodegeneration in retina and brain.

This study has been carried out recognizing the following limitations: 1) The causal and temporal relationship between retinal changes, elevated Aβ BPND and decreased hippocampal

volume cannot be examined due to the cross-sectional study design; 2) As a consequence of

correction for familiarity of the monozygotic twin subjects, some loss of power was experienced; 3) In spite of following a standardized protocol, the retinal vasculature analysis is

influenced by subjective grader input, variance in image quality, and physiological vasculature dynamics such as cardiac cycle (54). Because of the semi-automated analysis, identification of

the optic disc and tracking of retinal vessels is partly done manually. Data did not suffer from

intergrader measurement errors because one grader performed manual intervention, including an extra check after all images had been graded. Regarding image quality, this factor might

have influenced some of the RVPs that were not found associated with Aβ BPND. For example LDRa and LDRv were not assessable in 36 and 30 subjects respectively, because calculation

requires a minimum of two bifurcations of one vessel. Also, venules are more distinct on fundus

images than arterioles are; 4) Furthermore, loss of images for analysis due to poor quality was encountered because of suboptimal pupillary dilatation after instillation of tropicamide; 5) To

conclude, visual fields were not tested in our subjects, and intra-ocular pressure was added to the protocol later due to study logistics. Hence, it was not possible to examine whether retinal

thinning was correlated to possible unknown glaucoma or intra-ocular pressure. However,

subjects were screened for history of glaucoma or use of intra-ocular pressure lowering drugs. In this study Aβ BPND in the posterior cingulate was chosen as the Aβ outcome measure,

as this region is known for early Aβ deposition (55). Aβ BPND is particularly interesting for the purpose of finding biomarkers for early AD, because of its potential to examine RVPs in

subjects in the process of Aβ accumulation long before clinical symptoms become manifest.

Moreover the measure of Aβ used in this study, Aβ BPND, is not influenced by heterogeneous flow effects, as this is corrected for as opposed to the more widely used standardized uptake

value ratio (4). For future research it would be interesting to examine association between RVPs and

other cerebral regions for Aβ deposition, for example the occipital lobe as a part of the visual

tract, or other regions known for early Aβ deposition like the medial frontal regions (55). Additionally, in future, automated visual field testing could be added to the protocol, and

segmentation of retinal layers measured using OCT, would allow for analysis of different neuronal layers. Whether the subgroups of subjects with RVP changes and thinner RNFL are

at increased risk of developing MCI and AD, remains to be determined at follow up, just like

specificity of these retinal parameters. Ultimately, analysis of cause and effect, including underlying influences of genetic and environmental factors in these monozygotic twins (56),

will be possible once follow up has been completed.

18

5. Conclusion

In conclusion, the results of this study in cognitively healthy persons suggest that retinal

parameters, RVPs as well as RNFL thickness, might have some use in detecting preclinical AD. Accumulating evidence shows that retinal vascular changes and retinal neurodegeneration

indicate vascular changes and neurodegeneration in the brain. This could be relevant for

exploring treatment options for AD, as treatment studies require cheap and easy accessible biomarkers to identify cognitively healthy individuals at risk for Aβ pathology. Because retinal

biomarkers seem to be associated with AD pathology even in cognitively healthy persons, these measures could be useful to select subjects for anti-Aβ medication trials. Finally, as similarities

between retinal and cerebral structures are striking, further studies of retinal vasculature and

neurodegeneration in (preclinical) AD will hopefully add to the understanding of the complex pathophysiology of AD. Specificity and potential as predictor for disease progression to MCI

and AD, is hopefully to be determined at follow up.

19

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24

7. Appendices

7.1 Appendix 1

Inclusion criteria

- Age 60-100 years - Telephone Interview for Cognitive Status modified (TICS-m) >22

- Geriatric Depression Scale (GDS) (15 item) <11 (57)

- Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) 10 word list immediate and delayed recall > -1.5 SD of age adjusted normative data (58)

- Clinical Dementia Rating (CDR) scale total of 0 and memory sub domain of 0 (59)

Exclusion criteria

- Clinical diagnosis of mild cognitive impairment or probable AD

- Uncontrolled diabetes mellitus - Glaucoma (only for RNFL analysis)

- Macular disease such as exudative macular degeneration, diabetic macular edema,

epiretinal membranes or myelinated RNFL (only for RNFL analysis) - Significant cataract

- Cataract surgery within the prior six months

- Known thyroid disease without treatment - Severe head trauma, with loss of consciousness

- Brain tumour (past, present)

- Schizophrenia, bipolar disorders, or recurrent psychotic disorders

- Stroke resulting in physical impairment - Neurodegenerative disorders (e.g. Huntington disease, cortical basal degeneration,

multiple system atrophy, Creutzfeldt-Jakob disease, primary progressive aphasia,

Parkinson’s disease) - Epilepsy, currently using antiepileptic drugs

- Brain infection (e.g. herpes simplex encephalitis)

- Cancer with terminal life expectancy - Known B12 vitamin deficiency without treatment

- History of recreational drug use

- Alcohol consumption: >35 units per week - Physical morbidity or illness which will not permit attendance at visit sessions

- Contraindication for MRI (e.g. metal implants, pacemaker)

- Medications that may impair cognition, at the discretion of the investigator, e.g. high dose benzodiazepine, lithium carbonate, antipsychotics including atypical agents, high

dose antidepressants, Parkinson’s disease medicines

25

7.2 Appendix 2

Figure S1 Flowchart of in- and excluded subjects in retinal vasculature analysis and RNFL analysis.

Table S1 Description of factors considered relevant in retinal vasculature analysis.

Factor Total (%) or mean (SD)*

Hypertension

Blood pressure (mmHg)

Antihypertensive medication use

Diabetes type 1

Diabetes type 2

HbA1c (mmol/mol)

Hypercholesterolemia

Total cholesterol (mmol/l)

HDL/LDL (mmol/l)

Triglycerides (mmol/l)

BMI Smoking pack years

56 (43.4%)

155/83 (± 21/±10)

55 (42.6%)

0 (0.0%)

8 (6.2%)

38.1 (± 4.9)

49 (38.0%)

5.5 (± 1.3)

1.6/3.2 (±0.6/±1.1)

1.4 (± 0.7)

25.6 (± 3.5) 1 (0-10)

Telephone screening

n=215

Home and hospital visit

n=193

MRI for analysis

n=143

OCT

n=141

OCT for analysis

n=120

21 excluded

2 poor quality

11 glaucoma

5 exudative macular degeneration

2 myelinated retinal nerve fiber layer

1 epiretinal membrane

PET for analysis

n=179

Fundus images

n= 173

Fundus images for SIVA analysis

n=129

44 excluded

16 poor quality

28 unfit format for SIVA

50 excluded

7 MRI not yet acquired

42 MRI not yet processed

1 MRI not acquired according to protocol

14 excluded

11 PET not yet acquired

3 PET not acquired according to protocol

22 excluded

9 hospital visit too late for inclusion

9 screen failures

4 lost to follow up

26

Figure S2 Distribution of amyloid-beta binding potential.

Figure S3 Distribution of left and right hippocampal volume.

27

7.3 Appendix 3 Figure S4 Grading of retinal vasculature using Singapore I Vessel Assessment software.

Arterioles: Ŵ = 0.88 *

(w12 +

w22)1/2

Venules: Ŵ

= 0.95 * (w1

2 +

w22)1/2