PRoBaND | Tracking Parkinsons Investigator Meeting Thursday , 16 May - - PDF document

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PRoBaND | Tracking Parkinsons Investigator Meeting Thursday , 16 May - - PDF document

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PRoBaND | Tracking Parkinsons Investigator Meeting Thursday , 16 May 2019 Barnes Wallis Building, University of Manchester 12:00 Registration & lunch 13 :00 Welcome & overview Donald Grosset, Chief investigator 1 3:15 Summary of study

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PRoBaND | Tracking Parkinson’s

Investigator Meeting Thursday, 16 May 2019 Barnes Wallis Building, University of Manchester

12:00 Registration & lunch 13:00 Welcome & overview Donald Grosset, Chief investigator 13:15 Summary of study data & queries Sofia Kanavou, University of Bristol 13:30 Heterogeneity in Parkinson's disease cognitive impairment Callum Smith, University of Glasgow 13:50 Update on genetics Huw Morris, University College London 14:15 Break 14:30 A GWAS of pain in Parkinson’s disease Nigel Williams, Cardiff University 15:00 The L-dopa response in Parkinson's disease Vanessa Pitz, University of Glasgow 15:20 Administration issues and updates Donald Grosset, Chief Investigator 15:40 Discussion, Q&A 16:00 Meeting closes

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Tracking Parkinson’s

A Brief Summary of Study data

Sofia Kanavou

Medical Statistician

Bristol Medical School: Population Health Sciences

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SLIDE 3
  • Data collection
  • Form completion rates
  • Withdrawals & cases with change in diagnosis
  • Common errors / things to avoid
  • Queries
  • Resolution rates
  • Database lockdown
  • Timeline

Outline

Bristol Medical School: Population Health Sciences

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SLIDE 4

Data collection

 Form completion rates*

Follow – up visits %CRFs on Database

(inc. withdrawn)

%CRFs on Database

(exc. withdrawn)

Baseline (V0/1) 100% 100% 6 months (V2) 93.75% 97.5% 12 months (V3) 90.9% 98.3% 18 months (V4) 86.45% 97.85% 24 months (V5) 79.3% 95.2% 30 months (V6) 74.9% 93.8% 36 months (V7) 70.7% 92.6% 42 months (V8) 49.3% 80.65% 54 months (V9) 41.25% 71.8% 72 months (V10) 7.1% 14.2%

Extended follow-up (ongoing) Locked-down visits; no further changes are expected up until V7

*Rates are calculated based on the Medication forms

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Data collection

 Withdrawals

  • 50.1% of recruited patients have withdrawn from the study (06/05/19).
  • Out of 1002 withdrawn subjects:
  • 817 (81.5%) have all relevant information recorded on the Registration form.
  • 18.4% missing withdrawal date– all come from 10/68 Centres (14.7%).
  • 34 (3.4%) had a change in their initial diagnosis
  • Approximately 1/3 of withdrawals are due to patients decline of follow-up

extension or Site’s difficulty to support it

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 Withdrawals

Data collection

Reasons (N= 1002) N (%) Change in diagnosis 34 (3.4%) Developed dementia 10 (1%) Patient died 124 (12.4%) Did not consent to study extension 111 (11.1%) Entered nursing/residential home 9 (0.9%) Intercurrent illness 75 (7.5%) Moved out of area 11 (1.1%) Patient choice 177 (17.6%) Site closed 218 (21.75%) Too disabled to attend 11 (1.1%) Withdrew consent 32 (3.2%) Other 190 (18.95%) 968 cases (96.6%)

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SLIDE 7

Data collection

 Change in diagnosis

“Other diagnosis” category includes:

  • MS
  • Corticobasal degeneration
  • Post polio syndrome
  • Spinal cord syndrome
  • SWEDD
  • SWEDD subdural

haematoma

  • SWEDD and

cerebrovascular tremor

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Data collection

  • Some interesting cases initially recorded under “Other” reasons:
  • Patient finds study unacceptable
  • Patient not on Parkinson’s medication
  • Patient choice – not entering study extension (2 cases)
  • Unspecified diagnosis change
  • Misdiagnosed - patient does not have PD

 Common errors / things to avoid

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SLIDE 9

Data collection

 Common errors / things to avoid

  • Cases initially recorded under “Other” reasons:
  • Patient finds study unacceptable
  • Withdrew after Baseline visit; would be good to have a few more details on memo

(i.e. why patient found the study to be unacceptable?)

  • Patient not on Parkinson’s medication
  • This could trigger a re-evaluation of patient’s diagnosis, but should not be a reason to

withdraw from study

  • Patient choice – not entering study extension (2 cases)
  • Subject I: Patient had already completed Visit 10 – conflicting information
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SLIDE 10

Data collection

 Common errors / things to avoid

  • Cases initially recorded under “Other” reasons:
  • Patient choice – not entering study extension (2 cases)
  • Subject II: Patient had a new diagnosis recorded on the form; reason for withdrawal

should have been “change in diagnosis”

  • Unspecified diagnosis change
  • Even with unspecified new diagnosis, this should have been recorded as “change in

diagnosis”

  • Misdiagnosed - patient does not have PD
  • This should have been recorded as “change in diagnosis”
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SLIDE 11

 Resolution rates

Queries

  • 3616 queries have been recorded so far - 90.4% have been

resolved.

  • Majority of queries were with regards to Medication (34.9%),

Diagnostic factors (9.3%), Registration (7.4%) and UPDRS Clinician form (7%).

  • So far 70% of all participating centres dealt with ≥90% of their

queries.

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SLIDE 12

Bristol Medical School: Population Health Sciences

Queries

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SLIDE 13

Bristol Medical School: Population Health Sciences

Queries

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SLIDE 14

Queries

  • July 2017 - March 2019 → 592 queries entered on database
  • 66.6% were resolved
  • Out of 198 outstanding queries:
  • 35.9% refer to 18-month visit
  • 17.2% refer to 36-month visit
  • Queries focus mostly on:
  • Medications (46.6%); UPDRS Clinician (12.8%); MoCA (12.5%)

Bristol Medical School: Population Health Sciences

 Last 2 years in the study

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SLIDE 15

Bristol Medical School: Population Health Sciences

Database lockdown

 Timeline

Follow-up visit First visit* Last visit* Database Lockdown 18 months (V4) 03/05/2013 01/12/2016 12/01/2018 24 months (V5) 21/01/2014 14/02/2018 01/03/2019 30 months (V6) 06/06/2014 06/06/2018 01/03/2019 36 months (V7) 05/01/2015 12/09/2018 01/03/2019

*Taken from Medication form extracted in March 2019

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Bristol Medical School: Population Health Sciences

Database lockdown

 Timeline

Follow-up visit First visit* Last visit* Database Lockdown 18 months (V4) 10/01/2013 04/12/2016 12/01/2018 24 months (V5) 26/03/2013 05/12/2017 01/03/2019 30 months (V6) 10/01/2014 01/12/2017 01/03/2019 36 months (V7) 04/01/2015 04/12/2017 01/03/2019 42 months (V8) 22/09/2015 (ongoing) 01/09/2019 54 months (V9) 29/06/2016 (ongoing) 01/03/2020 72 months (V10) 17/01/2018 (ongoing) 01/03/2021

Estimated (likely to change) Done; datasets annually amended based on any updates recorded throughout the year

*Taken from Medication form extracted in March 2019

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SLIDE 17

Bristol Medical School: Population Health Sciences

  • Based on Medications forms recorded on Database
  • ≥90% forms entered in 3 years of follow-up
  • similar rates expected for forms completed at the clinic (UPDRS,

MoCa)

  • Rates drop in follow-up extension
  • Around 1/3 did not participate after 3-year follow-up

Summary

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SLIDE 18

Bristol Medical School: Population Health Sciences

  • Withdrawals & changed diagnosis always recorded on relevant fields the

Registration form

  • Queries are being handled more quickly
  • Few specific centres that are not very responsive
  • Lock-down of 3 year follow-up period
  • Data will be reviewed and amended annually
  • Current data version 1

Summary

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Heterogeneity in Parkinson’s disease cognitive impairment

Callum Smith

3rd year Ph.D. student

  • Dr. Donald Grosset  Dr. Breda Cullen
  • Prof. Jonathan Cavanagh  Dr. Matthew Sheridan
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  • Parkinson’s disease (PD) is primarily a

movement disorder.

  • However, non-motor symptoms are common.
  • Cognitive decline, including dementia, is

especially common and debilitating.

  • In many respects, cognitive impairment is highly

variable in PD.

Cognitive decline in PD

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Variation in cognitive decline in PD Genetic Neuropathological Clinical

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Variation in cognitive decline in PD Genetic Neuropathological Clinical

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Systematic review (1)

Major protein pathologies:

  • Alpha-synuclein (Parkinson’s)
  • Amyloid-beta (Alzheimer’s; top image)
  • Tau (Alzheimer’s; bottom image)
  • TDP-43 (frontotemporal dementia, LATE)

Vascular pathology (e.g. stroke). All of these pathologies are common autopsy findings in people with PD.

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Systematic review (2)

Objective: To describe the neuropathology of dementia in PD using a systematic review of autopsy studies.

  • Five databases were systematically searched for relevant
  • articles. 1566 potentially eligible articles were found.
  • Of these, 44 reports met inclusion criteria.
  • These involved 2002 PD cases, 57.2% with dementia.
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Systematic review (3)

0% 20% 40% 60% 80% 100% All studies, n=535 Sierra, n=10 ᵃRuffmann, n=55 ᵃKotzbauer, n=32 Kalaitzakis, n=12 Jendroska, n=23 ᵇJellinger 2008, n=32 ᵇJellinger 2002, n=66 Irwin, n=92 Horvath, n=109 Compta, n=29 Braak, n=79 Ballard, n=28 Aarsland, n=18 Absent Mild Moderate Severe

Figure 2. Severity of tau pathology in PD cases with dementia.

0% 20% 40% 60% 80% 100% All studies, n=538 Sierra, n=10 ᵃKotzbauer, n=32 ᵃKempster, n=69 Jendroska, n=23 ᵇJellinger 2002, n=66 ᵇJellinger 2008, n=32 Irwin, n=92 Horvath, n=109 de Vos, n=12 Braak, n=79 Ballard, n=28 Aarsland, n=18 Absent Mild Moderate Severe

Figure 1. Severity of amyloid-beta pathology in PD cases with dementia.

TDP-43 and cerebrovascular pathologies were not significantly more common in PD cases with dementia compared to those without. However…

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Variation in cognitive decline in PD Genetic Neuropathological Clinical

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Many PD patients have other disease pathologies (e.g. Alzheimer’s) in the brain. In life, different diseases are associated with different clinical profiles. These profiles are summarised in diagnostic criteria for each disease.

Clinical project (1)

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Clinical project (2)

Objective: To see how many cognitively impaired PD patients meet diagnostic criteria for other cognitive disorders.

  • 45 patients kindly took part. Each nominated one relative.
  • Each completed a detailed neuropsychological assessment

and various questionnaires. Medical notes were accessed.

  • This data was transferred to an expert panel for a consensus

diagnosis, referencing current diagnostic criteria.

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Preliminary results show that many PD patients meet criteria for other cognitive disorders. Most patients show a typical PD profile. Alzheimer’s is the most common coexistent disease, occurring in about a third.

Clinical project (3)

0% 20% 40% 60% 80% 100% Lewy Alzheimer Vascular Frontotemporal Probable Possible Not

Figure 3. Percentage of cognitively impaired PD patients meeting criteria for each disease.

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Variation in cognitive decline in PD Genetic Neuropathological Clinical

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Genetics project (1)

Many genetic variants affect the risk of dementia. Two of the most widely studied are APOE e4, the strongest risk factor for Alzheimer’s, and MAPT H1, which is implicated in several diseases. Both have been associated with cognitive decline in PD, but results are inconclusive. Need for large samples.

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Genetics project (2)

Objective: To evaluate the association of variants

  • f APOE and MAPT to cognitive decline in PD.
  • 1016 recent-onset Tracking Parkinson’s participants were

analysed.

  • APOE and MAPT variants were linked to MoCA score at

baseline, 18 months, and 36 months.

  • These genes were also linked to the magnitude of change in

MoCA score between each of these visits (i.e. rate of decline).

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SLIDE 33

Genetics project (3)

APOE e4 was associated with poorer cognitive function at 36 months. APOE e4 was strongly related to the rate of cognitive decline in early PD. The deleterious effects of APOE e4 on cognition

  • nly appeared in men.

MAPT was not associated with any outcome.

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Conclusions

  • 1. In PD, the pathology, clinical presentation,

and genetics of cognitive decline vary.

  • 1. Coexistent Alzheimer’s pathology has a

substantial contribution to dementia in PD.

  • 1. Various coexistent pathologies cause

different cognitive profiles to emerge in life.

  • 2. Genetic factors associated with Alzheimer’s

also contribute to cognitive decline in PD.

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Implications for treatment

  • 1. Clinicians should not assume that cognitive

decline in PD is purely the result of PD itself.

  • 1. Trials of new treatments targeted directly

against abnormal proteins must take into account the variability within PD.

  • 1. The optimal treatment strategy for cognitive

symptoms must be tailored to the individual.

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And thanks for your continuing work on Tracking Parkinson’s! Thanks to all patients, relatives, supervisors, and funders. If you have any questions, please ask.

Thanks for listening!

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Update on PROBAND genetics

UCL Institute of Neurology, National Hospital Queen Square, Royal Free London Huw Morris for PROBAND Consortium

h.morris@ucl.ac.uk

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Prospective UK multi-centre study 2011-2016 Natural history and genetics study

2247 people with Parkinson’s, 300 siblings Linkage to MRI, proteomic, brain bank, and pain studies

PROBAND/Tracking Parkinson’s cohort

Malek et al, Tracking Parkinson's: Study Design and Baseline Patient Data. J Parkinsons Dis. 2015;5(4):947-59.

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  • no effect
  • normal variability
  • increased chance of disease
  • high chance of disease

Genetic variation

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Orientation

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Types of risk factor

Common: Occur as > 5% Cases vs Controls; Intermediate: Occur in 0.1-5% Rare: Occur as <0.1 % of variants - may cause familial disease

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PD as a single gene disorder - rare variants autosomal dominant

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PD as a single le g gen ene d disorder - rare v e variants ts autos

  • som
  • mal d

dominant

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PD as a single le g gen ene d disorder - rare v e variants ts autos

  • som
  • mal d

dominant

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PD as a single le g gen ene d disorder - rare v e variants ts autos

  • som
  • mal r

reces cessive

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PD as a single le g gen ene d disorder - rare v e variants ts autos

  • som
  • mal r

reces cessive

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PD as a single le g gen ene d disorder - rare v e variants ts autos

  • som
  • mal r

reces cessive - phen enoty

  • types
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PD as a single le g gen ene d disorder - rare v e variants ts autos

  • som
  • mal r

reces cessive - phen enoty

  • types
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  • Three well defined AR genes – PARK2 (parkin), PARK6 (DJ-1), PARK7

(PINK1)

  • AR EOPD – “pure” PD: long disease course, no dementia, prominent

L-DOPA induced dyskinesias

  • AD-PD – LRRK2, Synuclein – may have personal or family history of

early dementia, may not have a family history

PD PD as a single g gene d disorder

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Intermediate risk - GBA – 142 patients with Group 1 and Group 2 GBA Mutations

1 E326K homozygote who did not have GD

Background frequency UK GD single het – 1% E326K – 2.5%

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Results – GBA Motor features

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Results – GBA Family history

Our data suggests that single GBA mutations are not a cause of familial PD

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GBA Results – Cognition 1.5 years after diagnosis

Sub-analysis of L444P did not show higher dementia rate Sub-analysis of MOCA domains e.g., visuo-spatial did not show any differences

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Common variants progression

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Common variants Progression

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Parkinson’s Families Project ct

  • 42 study sites in UK
  • 827 participants recruited
  • Genetic analysis - Neurochip; MLPA
  • 555 samples from PD/parkinsonism patients

genotyped

  • 5.2% carrying pathogenic mutations
  • 3% carry GBA mutations (N370S or T369M on

Neurochip)

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SLIDE 58

Manuela Tan Miriam Pollard Patients, families, Study sites and teams and funders Nigel Williams Kate Bresner

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Update on PROBAND genetics

UCL Institute of Neurology, National Hospital Queen Square, Royal Free London Huw Morris for PROBAND Consortium

h.morris@ucl.ac.uk

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A GWAS of pain in Parkinson’s disease

Nigel Williams (Cardiff University) and Monty Silverdale (University of Manchester)

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Pain in Parkinson’s Disease

  • PI: Monty Silverdale (University of Manchester)
  • CoPIs: Nigel Williams, Huw Morris, Donald Grosset
  • Study was based on 1021 individuals with Parkinson’s Disease from the

Proband Cohort

  • McGill pain questionnaire (quantitative 1-40)
  • All samples genotyped whole genome SNP array
  • 85% of patients reported pain
  • the most common subtypes of PD pain are musculoskeletal, radicular and dystonic
  • Negligible correlation between
  • the severity of motor impairment and the severity of musculoskeletal or dystonic

pain

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GWAS: Reduced Pain vs Pain

  • Rationale:
  • As pain is inevitable subjective analysing the data as a quantitative trait might not

be the best approach?

  • Individuals who consistently score low on multiple pain scales are most likely to

be those who are actually in the least pain (either due to a high pain threshold or having reduced pain).

  • 2 pain scales used:
  • McGill (0-40)
  • VAS Severity (0-10) over last month
  • Reduced Pain = PD patients with McGill <3 and VAS Severity <2
  • N=315
  • Pain = all other PD patients
  • N=706
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Pain in Parkinson’s Disease GWAS (Proband Cohort Only)

Analysis: Leon Hubbard

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Pain in Parkinson’s Disease GWAS: Association analysis Replication

  • An additional 297 Parkinson patients from the Oxford Monument

cohort (Caleb Weber, Richard Wade-Martins, Michelle Hu) had been assessed for Pain

  • Identical protocol
  • Genotypes were available
  • Meta-analysis was then conducted at Cardiff
  • Combined sample size = 1318 (high pain =898 vs low pain =420)
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Pain in Parkinson’s Disease GWAS (Combined analysis of Proband and Oxford Cohorts)

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*** the genetic variant associated with migraine/cold sensitivity is independent of the variant associated with Pain in PD Transient receptor potential melastatin 8 ion channel (TRPM8)

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GWAS: Reduced Pain vs Pain

TRPM8 GDF10

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Conclusion

  • This approach has identified plausible candidates for reduced

pain in PD

  • Future work is required to establish the biological mechanisms
  • Explore relationship with Pain in PD and Depression
  • PhD studentship at Cardiff (Hannah Hendry)
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The L-dopa response in Parkinson’s disease

Vanessa Pitz PhD student in Neuroscience

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Overview

  • The L-dopa response
  • Factors potentially affecting the response
  • The PPMI study
  • Results
  • Conclusion

2

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The L-dopa response

  • Levodopa (L-dopa) gold standard treatment for PD
  • Measured with standardized clinical rating scales (MDS UPDRS) 1
  • Embedded in MDS diagnostic criteria 2

3

1Goetz C et al, Mov Disord 2008, 24, 2129-70 2Postuma RB et al, Mov Disord 2015, 30, 1591-99

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The L-dopa response

Motor examination

  • MDS UPDRS 3
  • L-dopa challenge test
  • UPDRS 3 scale >30% cut-off 2
  • MDS UPDRS 3 scale ≥24.5% cut-off 3

4

2Postuma RB et al, Mov Disord 2015, 30, 1591-99 3Merello M et al, Parkinsonism Relat Disord, 2011, 17, 705-707

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The L-dopa response

Motor complications

  • MDS UPDRS 4 scale
  • Dyskinesia
  • Motor fluctuations
  • Dystonia

5

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The L-dopa response

L-dopa in the the MDS criteria 2

6

2Postuma RB et al, Mov Disord 2015, 30, 1591-99

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What could affect the response?

7

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The PPMI study

  • Launched in 2011
  • 423 PD patients
  • Drug-naïve for antiparkinsonian medication

Study inclusion criteria

  • PD diagnosis for < 2 years at screening
  • H&Y stage I or II at baseline
  • 30 years or older at diagnosis
  • Dopamine transporter deficit (positive SPECT)
  • Not expected to require PD medication within next 6 months

8

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SLIDE 77

The PPMI study

9

423 PD patients enrolled 85.8% (363/423) patients assessed 50.1% (182/363) challenge test

3.5 years disease duration Defined ‘on’ and ‘off’ state

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SLIDE 78

The Tracking Parkinson’s study

10

2,000 PD patients enrolled 2,000 patients assessed 51.3% (1026/2000) challenge test

3.5 years disease duration Defined ‘on’ and ‘off’ state

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SLIDE 79

Motor improvement

11

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Demographics

12

Variable Tracking Parkinson’s PPMI Number of patients, n 928* 181 Change in diagnosis**, n 7/935 (0.7%) Age at diagnosis, years 66.1 (9.1) 62.1 (9.7) Age at study entry, years 67.5 (9.0) 62.2 (9.7) Disease duration at challenge, years 3.4 (0.9) 3.6 (0.6)

* = n of patients with L-dopa challenge test at visit 5 and UPDRS 4 assessment at visit 4, ** = 4x MSA, 1x likely vascular, 1x LBD, 1x post polio syndrome

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Treatment

13

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Treatment

14 LEDD = Levodopa-equivalent daily dose, data are mean (SD) or median (IQR)

Variable Tracking Parkinson’s PPMI % response L-dopa challenge 30.8 (15.6, 45.5) 24.7 (11.1, 40.7) Treatment duration, years 3.3 (0.9) 2.1 (0.6) L-dopa dose in challenge, mg 110.9 (45.0) 132.6 (62.9) LEDD at challenge 520.1 (235.7) 546.7 (369.2)

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SLIDE 83

15 Data are mean (SD)

MDS UPDRS 4

Variable Tracking Parkinson’s PPMI MDS UPDRS 4 1.1 (2.1) 1.1 (2.0) Dyskinesia, n (%) 33 (3.6) 14 (7.7) Motor fluctuations, n (%) 38 (4.1) 47 (25.8) Dystonia, n (%) 65 (7.0) 17 (9.3)

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SLIDE 84

Conclusions

16

  • Variation in L-dopa responsiveness shown in both studies
  • Link between L-dopa responsiveness and motor complications

confirmed in PPMI, analysis underway in Tracking

  • Next steps:
  • What causes this variation?
  • Does this tie in to disease subtypes?
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SLIDE 85

Thank you for your attention

17

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SLIDE 86

Tracking Parkinson’s (PRoBaND) L-dopa challenge Study timelines

May 2019

Donald Grosset Consultant Neurologist and Honorary Professor

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SLIDE 87

If you have not done a challenge test (for any reason*), record a ‘standard’ UPDRS 3 score, and put it in this Baseline section

*Reasons include:

  • Already did challenge test at V5
  • Not yet on L-dopa
  • Patient forgot instructions! 
  • Researcher forgot! 
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SLIDE 88
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SLIDE 89

Study timelines

  • Recruitment Feb 2012 to May 2014
  • All ongoing cases are at 5 years+
  • Funding is currently to Nov 2020
  • We are seeking a no-cost extension, aiming for Nov 2021
  • We will seek new funding beyond that, aiming for Nov 2024
  •  Please plan to keep your site active, and your patients enrolled
  • Parkinson’s is a long-term condition, and we need prolonged follow-

up

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SLIDE 90

Tracking Parkinson’s: Thank You All