Amyloid Imaging and Longitudinal Clinical Progression in Dementia With Lewy Bodies

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Amyloid Imaging and Longitudinal Clinical Progression in Dementia with Lewy Bodies Paul C. Donaghy PhD , Michael J. Firbank PhD , Alan J. Thomas PhD , Jim Lloyd PhD , George Petrides MBBS , Nicola Barnett MSc , Kirsty Olsen MSc , John T.O’Brien DM PII: DOI: Reference:

S1064-7481(19)30589-5 https://doi.org/10.1016/j.jagp.2019.12.009 AMGP 1389

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The American Journal of Geriatric Psychiatry

Received date: Revised date: Accepted date:

15 October 2019 16 December 2019 18 December 2019

Please cite this article as: Paul C. Donaghy PhD , Michael J. Firbank PhD , Alan J. Thomas PhD , Jim Lloyd PhD , George Petrides MBBS , Nicola Barnett MSc , Kirsty Olsen MSc , John T.O’Brien DM , Amyloid Imaging and Longitudinal Clinical Progression in Dementia with Lewy Bodies, The American Journal of Geriatric Psychiatry (2019), doi: https://doi.org/10.1016/j.jagp.2019.12.009

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Title: Amyloid imaging and longitudinal clinical progression in dementia with Lewy bodies Authors: Paul C. Donaghy PhD1,*, Michael J. Firbank PhD1, Alan J. Thomas PhD1, Jim Lloyd PhD2, George Petrides MBBS2, Nicola Barnett MSc1, Kirsty Olsen MSc1, John T. O'Brien DM3 1

Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK

2

Nuclear Medicine Department, Newcastle upon Tyne Hospitals National Health Service Foundation

Trust, Newcastle upon Tyne, UK 3

Department of Psychiatry, University of Cambridge, Cambridge, UK

*

Corresponding Author: Dr Paul Donaghy; Level 3 Biomedical Research Building, Campus for Ageing

and Vitality, Newcastle upon Tyne NE4 5PL, UK; Tel.: +44(0)191 208 1318; Email:

Conflicts of interest and sources of funding PCD – none MJF – none AJT – none JL – none GP has received honoraria for educational presentations and scan reporting fees from GE Healthcare. NB – none KO – none JOB has acted as a consultant for Lilly and received research support from Avid for studies of AV1451. 1

Highlights 

We sought to determine whether amyloid deposition was associated with more rapid clinical decline over one year in dementia with Lewy bodies.

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Amyloid deposition on visual rating was associated with greater decline in MMSE and daily function over 1 year.

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This study provides further evidence for a link between amyloid deposition and clinical progression in DLB.

Abstract Objective: Significant amyloid deposition is present in approximately half of all cases of dementia with Lewy bodies (DLB). We sought to determine whether amyloid deposition was associated with more rapid clinical decline over one year. Methods: 28 participants had a baseline clinical assessment and amyloid PET scan, followed by a further clinical assessment after one year. Changes in clinical measures were compared with amyloid deposition assessed by visual rating and cortical standardized uptake value ratio (SUVR). Results: Amyloid deposition on visual rating was associated with greater decline in MMSE and daily function over one year. There was no correlation between cortical SUVR and clinical measures. Conclusions: This study provides further evidence for a link between amyloid deposition and clinical progression in DLB. Pathologies such as amyloid, and their interaction with α-synuclein, remain possible treatment targets in DLB. Objective Significant amyloid deposition is present in approximately half of all cases of dementia with Lewy bodies (DLB).1 We have previously demonstrated that amyloid deposition is associated with hippocampal atrophy and medial temporal hypoperfusion, but that clinical presentation is not significantly different in those with and without amyloid deposition in cross-sectional analysis.2,3 We 2

sought to determine whether amyloid deposition was associated with differences in disease progression in the same cohort. We hypothesised that DLB participants with amyloid deposition would have more rapid decline in measures of cognition and function over one year.

Methods Participants Participants with dementia were recruited prospectively between June 2013 and February 2016 from secondary care services in the North of England. All participants were ≥60 years old and had a diagnosis of probable DLB confirmed by two clinicians based on contemporaneous diagnostic criteria,4 with an MMSE ≥12. Participants were recruited prior to the publication of the 2017 diagnostic criteria for DLB,5 but all DLB participants met the updated criteria for a diagnosis of probable DLB. Post-mortem diagnosis was used where available. Participants were excluded if they had a major concurrent psychiatric illness; severe physical illness; contraindications to PET-CT imaging or MRI; a history of other significant neurological illness including stroke or previous experimental treatment with an amyloid-targeting agent. Participants with capacity gave their written informed consent to take part in the study. For those who lacked capacity, their participation in the study was discussed with a consultee in accordance with the Mental Capacity Act. The study received ethical approval from the National Research Ethics Service Committee North East - Newcastle & North Tyneside 2 (13/NE/0064). Baseline cognitive and clinical assessment Participants had a clinical and cognitive assessment carried out at baseline and 1 year. This included the Addenbrooke’s Cognitive Examination-Revised (ACE-R), FAS verbal fluency, Graded Naming Test (GNT), Bristol and Instrumental Activities of Daily Living Scales (BADL, IADL), Neuropsychiatric 3

Inventory (NPI), Geriatric Depression Scale (GDS) and the Revised Unified Parkinson’s disease Rating Scale Motor Sub-scale (MDS-UPDRS). Imaging Imaging was performed at baseline. Details of the MRI and PET acquisition and analysis have been published elsewhere2 and will briefly be summarised here. Amyloid imaging was carried out using a Siemens Biograph-40 PET-CT scanner. Participants were given a 370MBq intravenous injection of 18FFlorbetapir (Amyvid). PET imaging was carried out for 15 minutes, commencing 30-50 minutes after injection. Images were reconstructed using iterative reconstruction (4 iterations, 16 subsets), with a 168x168 matrix size, 2.04x2.04mm pixel size, 3mm slice thickness, and 3mm post-reconstruction Gaussian filter. Attenuation correction was performed utilising CT scan data. MRI scans included a 3D sagittal magnetisation-prepared rapid gradient echo (MPRAGE) sequence (3T Achieva scanner; Philips Medical Systems). Amyloid PET images were visually rated as positive or negative based on the manufacturer’s criteria by a panel of five raters (PCD, MJF, GP, JL, JTO). MR images were segmented into white matter, grey matter and CSF using SPM 8 (www.fil.ion.ucl.ac.uk/spm/software/spm8/). The amyloid PET image was co-registered with the native space MRI. A mean cortical standardised uptake value ratio (SUVR) was derived from the unweighted mean of frontal, temporal, parietal and cingulate regions relative to the whole cerebellum.6 Statistical Analysis Statistical analysis was completed using IBM SPSS Statistics software (version 22; http://www03.ibm.com/software/products/en/spss-statistics). Demographic comparisons were carried out using t-tests or Mann-Whitney tests; χ2 or Fisher’s Exact tests were used for categorical variables. IADL and BADL scores were combined to make a composite function z-score where positive scores indicated greater decline in function over time. Change over time in a variable was calculated as the

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difference between baseline and year one, divided by the time between assessments. Associations with clinical and imaging variables were assessed using linear regression with age, years in education and baseline score as covariates. Sex was also included as a covariate in comparisons using Florbetapir SUVR. Results 37 participants underwent baseline assessment including amyloid PET imaging. 28 eligible participants completed at least part of the follow-up assessment (four deceased, two unable to complete follow-up due to physical illness, one withdrew, two excluded due to non-DLB pathological diagnosis). Five DLB cases were confirmed by post-mortem diagnosis. 23 participants had a structural MRI to allow quantification of Florbetapir SUVR. 13 participants had negative amyloid scans and 15 had positive scans on visual rating. There were no significant differences between these groups in age (amyloid negative 74.9±6.2 v amyloid positive 75.1±7.5 years; t(26)=-0.1, p=0.91), sex (23% v 20% female; Fisher’s Exact (FE) p>0.99), baseline ACE score (63.7±14.4 v 67.9±16.9; t(26)=-0.7, p=0.49), baseline MMSE score (21.4±4.8 v 22.7±4.8; t(26)=0.7, p=0.47), baseline function z-score (-0.12±1.20 v -0.27±0.93; t(26)=0.4, p=0.72), time between baseline and follow-up assessment (12.5±0.7 v 12.4±0.8 months; U=92.5, p=0.82), treatment with cholinesterase inhibitors (85% v 93%; FE p=0.58), memantine (15% v 13%; FE p>0.99), antipsychotics (15% v 7%; FE p=0.58) or levodopa (39% v 33%; FE p>0.99). The amyloid negative group had fewer years in education (9.7±3.8 v 12.6±3.3; U=47.0, p=0.02). There were no differences between the amyloid positive and negative groups in baseline scores in the clinical and cognitive scales analysed. In simple analysis without covariates, amyloid positive cases had a greater decline in function (z-score of functional decline: amyloid positive 0.35±0.70 v 0.23±0.74 amyloid negative; t(26)=-2.1, p=0.045). There was negative correlation between amyloid SUVR and change in ACE total (r(20)=-0.51, p=0.02) and MMSE (r(20)=-0.63, p=0.002). The results of

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regression with amyloid, age, years in education and baseline score are presented in Table 1. Sex was also included as a covariate in SUVR analysis. A positive amyloid PET scan was associated with greater decline in MMSE over one year (amyloid positive -2.7±2.4 v -0.9±3.8 amyloid negative; Beta=-0.45, t(22)=-2.6, p=0.02), greater decline in function (z-score change in function 0.35±0.70 v 0.23±0.75; Beta=0.42, t(23)=2.1, p<0.05) and the development of fewer depressive symptoms in the GDS (-0.6±1.6 v 1.6±2.8; Beta=-0.56, t(21)=-3.0, p=0.01). There were no significant correlations with amyloid SUVR. Conclusions We hypothesised that amyloid deposition in DLB would be associated with more rapid clinical decline. Amyloid positivity on visual rating was associated with greater decline in MMSE and daily function over one year. There were no significant associations between clinical progression and amyloid deposition assessed using semi-quantitative measurement of Florbetapir SUVR. This may be in part because power to detect differences was lower in the SUVR analysis, as five participants did not have a baseline MRI scan due to contraindications. Visual rating of amyloid positivity is defined by the loss of grey-white matter contrast in two cortical areas or greater grey matter signal than white matter signal in a single cortical area. In contrast, mean cortical SUVR is a measure of average florbetapir binding over frontal, temporal, parietal and cingulate regions. Therefore, focal amyloid deposition may cause an abnormal scan on visual rating with a relatively normal value for mean cortical florbetapir SUVR. We analysed the relationship of several variables with amyloid deposition in DLB. As such, there is a risk of type 1 error and these findings require validation in independent cohorts. We were unable to statistically analyse the effect of medications on rates of decline because few participants were not on treatment with cholinesterase inhibitors and few were receiving antipsychotics.

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This is the largest cohort of DLB to report longitudinal clinical findings following amyloid imaging. Our findings are in keeping with two previous studies that have investigated the link between amyloid deposition and disease progression in DLB. Reduced CSF amyloid has been previously shown to correlate with more rapid decline in MMSE after adjustment for age, sex and baseline score.7 Higher 11C-PiB binding has previously been shown to correlate with rate of increase of functional impairment measured by percentage change in the CDR sum of boxes.8 Neither of these studies reported a significant association between amyloid and these measures at baseline. We have previously reported the baseline findings of this cohort, demonstrating no clinical differences between the groups in cross-sectional analysis, but evidence of increased medial temporal lobe atrophy and hypoperfusion in those with amyloid deposition.2,3 From these studies it appears that amyloid deposition is not related to clinical presentation in DLB in cross-sectional analysis but is associated with medial temporal lobe changes along with cognitive and functional decline in longitudinal analysis. It remains unclear whether these findings are due to amyloid and α-synuclein affecting the brain independently, or whether amyloid and α-synuclein interact with one another, and potentially with associated pathologies such as tau. There is some evidence of interactions between Alzheimer’s disease pathology and α-synuclein from animal and pathology studies. Amyloid deposition has been found to be associated with greater Lewy body pathology in human brains at post-mortem.9 Transgenic mice studies have demonstrated that amyloid, α-synuclein and tau can synergistically promote each other’s deposition.10 In the absence of an α-synuclein imaging ligand, the relationship between amyloid and α-synuclein is difficult to assess in vivo. However, with the availability of tau imaging, the relationship between tau and amyloid in DLB should become clear in the coming years. Crucially, coincidental pathologies such as amyloid and tau, and their interaction with α-synuclein, remain potential treatment targets in DLB. Acknowledgements

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Avid Radiopharmaceuticals, Inc., a wholly owned subsidiary of Eli Lilly and Company, enabled use of the 18F-florbetapir by providing tracer and funding scanner time, but was not involved in data analysis or interpretation. This research was supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre. References 1. Ossenkoppele R, Jansen WJ, Rabinovici GD, et al: Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA : the journal of the American Medical Association 2015; 313:1939-1949 2. Donaghy PC, Firbank MJ, Thomas AJ, et al: Clinical and imaging correlates of amyloid deposition in dementia with Lewy bodies. Movement disorders : official journal of the Movement Disorder Society 2018; 33:1130-1138 3. Mak E, Donaghy PC, McKiernan E, et al: Beta amyloid deposition maps onto hippocampal and subiculum atrophy in dementia with Lewy bodies. Neurobiology of aging 2019; 73:74-81 4. McKeith IG, Dickson DW, Lowe J, et al: Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology 2005; 65:1863-1872 5. McKeith IG, Boeve BF, Dickson DW, et al: Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 2017; 89:88-100 6. Landau S,Jagust W. Florbetapir Processing Methods [online]. Available at: adni.bitbucket.org. Accessed 13/01/2015. 7. Abdelnour C, van Steenoven I, Londos E, et al: Alzheimer's disease cerebrospinal fluid biomarkers predict cognitive decline in lewy body dementia. Movement disorders : official journal of the Movement Disorder Society 2016; 31:1203-1208 8. Sarro L, Senjem ML, Lundt ES, et al: Amyloid-beta deposition and regional grey matter atrophy rates in dementia with Lewy bodies. Brain : a journal of neurology 2016; 139:2740-2750 9. Pletnikova O, West N, Lee MK, et al: Abeta deposition is associated with enhanced cortical alpha-synuclein lesions in Lewy body diseases. Neurobiology of aging 2005; 26:1183-1192 10. Clinton LK, Blurton-Jones M, Myczek K, et al: Synergistic Interactions between Abeta, tau, and alpha-synuclein: acceleration of neuropathology and cognitive decline. The Journal of neuroscience : the official journal of the Society for Neuroscience 2010; 30:7281-7289

Table 18

Visual rating

ACE Total

F-Florbetap

Beta (95% CI)

t (df); p value

Beta (95% CI)

-0.34 (-0.73-0.07)

t (22) =-1.7; p = 0.097

-0.39 (-0.93-0.15)

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MMSE

-0.45 (-0.81 - -0.09)

t (22) = -2.6; p = 0.02

-0.40 (-0.94-0.14)

FAS verbal fluency

0.21 (-0.23-0.65)

t (19) = 1.0; p = 0.33

0.18 (-0.36-0.72)

Graded naming test

0.23 (-0.19-0.65)

t (20) = 1.1; p = 0.27

0.01 (-0.60-0.62)

Function z-score

0.42 (0.001-0.84)

t (23) = 2.1; p = 0.05

-0.02 (-0.65-0.62)

NPI Total

-0.07 (-0.55-0.41)

t (23) = -0.3; p = 0.75

-0.37 (-0.97-0.22)

CAF

-0.01 (-0.38-0.36)

t (23) = -0.1; p = 0.94

-0.27 (-0.68-0.14)

MDS-UPDRS

-0.03 (-0.45-0.39)

t (22) = -0.2; p = 0.87

-0.05 (-0.55-0.46)

GDS

-0.56 (-0.95- -0.16)

t (21) = -3.0; p = 0.01

-0.24 (-0.82-0.35)

Table 1. The association between amyloid deposition and change in cognitive and clinical variables over one year. General linear model relating change in score to amyloid deposition assessed using visual rating or SUVR. Age, years in education and baseline score were included as covariates. Sex was also included as a covariate in the SUVR analysis. ACE – Addenbrooke’s Cognitive Examination (Revised); MMSE – Mini Mental State Examination; NPI – Neuropsychiatric Inventory; CAF – Clinician Assessment of Fluctuations; MDS-UPDRS – Unified Parkinson’s Disease Rating Scale (Movement Disorders Society Revised Version); GDS – Geriatric Depression Scale. Significant results in bold.

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