PPMI Cognitive-Behavioral Working Group Daniel Weintraub, MD PPMI [PDF]

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PPMI Cognitive-Behavioral Working Group

PPMI Annual Meeting - May 13-14, 2015

Daniel Weintraub, MD

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Membership

Daniel Weintraub – WG Chair Tanya Simuni – Steering Committee Shirley Lasch – IND Chris Coffey, Chelsea Caspell-Garcia – Statistics Core

Dag Aarsland Roy Alcalay Paolo Barone Melanie Braddabur David Burn Cindy Casacelli Lama Chahine William Cho Thomas Comery Autilia Cozzolino Johnna Devoto Chris Dodds Jamie Eberling Alberto Espay Stewart Factor Hubert Fernandez Regan Fong Douglas Galasko Sandeep Gupta Keith Hawkins David Hewitt Jim Leverenz Irene Litvan Anita McCoy Susanne Ostrowitzki Bernard Ravina Alistair Reith Irene Richard Liana Rosenthal Holly Shill Andrew Siderowf John Sims Gretchen Todd Eduardo Tolosa Matt Troyer Michael Ward Michele York

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Overview

Review of assessments
Baseline manuscript from CBWG
Preliminary longitudinal results
Individuals’ work

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Study Assessments

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Cognitive Assessments

Global - Montreal Cognitive Assessment (MoCA)
Memory - Hopkins Verbal Learning Test (HVLT)
Visuospatial - Benton Judgment of Line Orientation

(JOLO)

Working memory - Letter-Number Sequencing

(LNS)

Executive - Semantic fluency (animals, fruits,

vegetables)

Attention - Symbol-Digit Modalities Test (SDMT)

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Behavioral Assessments

Geriatric Depression Scale (GDS-15)
State-Trait Anxiety (STAI)

– State and trait subscales

Questionnaire for Impulsive-Compulsive

Disorders in Parkinson's Disease (QUIP)

– Screening instrument for ICDs and related behaviors

MDS-UPDRS Part I (psychosis, apathy, etc.)

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Steps for Determining Annual Cognitive Diagnosis in PPMI

1. Investigator determines presence of cognitive decline from pre-PD state based on clinical interview and knowledge of patient 2. Investigator determines presence of significant functional impairment due to cognitive deficits interfering with routine instrumental activities of daily living (IADLs) 3. Subject has neuropsychological testing at study visit 4. Categorization of normal cognition, MCI, or dementia made centrally based on steps #1, #2 and #3

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Baseline CBWG Manuscript

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20% of PD patients screen positive for MCI and

close to 10% meet cognitive test-based criteria

Multiple NPS (e.g., depression, anxiety and apathy)

more common in untreated PD patients compared with general population

Rates of NPS associated with DRT (e.g., psychosis

and ICDs) either low or similar to controls

Weintraub et al. Movement Disorders (10.1002/mds.26170).

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Preliminary Longitudinal Results: Cognition and Biomarkers

Courtesy Chelsea Caspell-Garcia, MS

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Cohort Size (data submitted as of 4/13/15)

Baseline Year 1 Year 2 Year 3 # Seen (%) # Seen (%) # Seen (%) # Seen (%) GROUPS: PD Subjects 423 (100%) 393 (96%) 318 (91%) 145 (87%) Healthy Controls 196 (100%) 185 (98%) 158 (95%) 125 (93%) TOTAL 619 (100%) 578 (97%) 476 (92%) 270 (90%)

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Psychiatric and Cognitive Outcomes

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Baseline DAT as Predictor of Global Cognition and Depression Over Time

Univariate Univariate Variable Estimate (95% CI) p-value Contralateral Caudate

0.251 (-0.60, 0.09)

0.1529 Ipsilateral Caudate

0.140 (-0.46, 0.18)

0.3968 Contralateral Putamen

0.241 (-0.99, 0.51)

0.5272 Ipsilateral Putamen

0.090 (-0.60, 0.42)

0.7257 Contralateral Striatum

0.166 (-0.42, 0.09)

0.2017 Ipsilateral Striatum

0.076 (-0.29, 0.14)

0.4826 Mean Caudate

0.207 (-0.55, 0.14)

0.2408 Mean Putamen

0.173 (-0.84, 0.49)

0.6093 Mean Striatum

0.250 (-0.74, 0.24)

0.3127

Depression

Analyses adjusted for age, gender, education, APOE e4 status, and PD medication use.

Cognition

Univariate Univariate Variable Estimate (95% CI) p-value Contralateral Caudate 0.303 (-0.12, 0.72) 0.1577 Ipsilateral Caudate 0.270 (-0.13, 0.67) 0.1842 Contralateral Putamen

0.303 (-1.22, 0.61)

0.5156 Ipsilateral Putamen 0.469 (-0.16, 1.10) 0.1415 Contralateral Striatum 0.132 (-0.18, 0.44) 0.4091 Ipsilateral Striatum 0.199 (-0.06, 0.46) 0.1368 Mean Caudate 0.308 (-0.12, 0.73) 0.1551 Mean Putamen 0.278 (-0.54, 1.10) 0.5046 Mean Striatum 0.378 (-0.22, 0.98) 0.2142

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Baseline AD CSF Biomarkers as Predictors of Global Cognitive Decline

Univariate Univariate Variable Estimate (95% CI) p-value A-Beta 1-42 0.0017 (-0.0004, 0.0037) 0.11 t-tau

0.0008 (-0.0029, 0.0013)

0.45 p-tau 0.0008 (-0.0012, 0.0027) 0.45 t-tau/A-Beta 1-42

0.0023 (-0.0045, -0.0002)

0.03

Analyses adjusted for age, gender, education, APOE e4 status, and PD medication use.

Lower A-Beta 1-42 associated with lower MoCA scores (marginal).
Higher t-tau/A-Beta 1-42 associated with lower MoCA scores.

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Draft Planned Analyses

Baseline Change in Cognitive STATUS From Baseline

Change in Individual Cognition SCORES From Baseline MoCA score MoCA <26 Any 2 tests >1.5 SD below mean NEW MoCA <26 (last point) LAST MoCA >3 point decrease from BL NEW any last 2 tests >1.5 SD below mean NEW MCI diagnosis NEW dementia diagnosis Cognitive Clinical Outcome N/A N (%) N (%) N (%) N (%) N (%) N (%) N (%) N/A Biomarker Baseline Change BL to Year 1 CSF

1. A-syn
2. t-tau
3. ptau181
4. AB1-42
5. t-tau/AB1-42
6. ptau181/AB1-42
7. p-tau181/t-tau

CSF

1. A-syn
2. t-tau
3. ptau181
4. AB1-42
5. t-tau/AB1-42

6.ptau181/AB1-42

7. p-tau181/t-tau

Plasma

1. Urate
2. α-synuclein
3. IGF

Structural MRI

1. Major ROI’s
2. Cortical thickness
3. Subcortical

Structural MRI

1. Major ROI’s
2. Cortical thickness
3. Subcortical

DTI

1. FA (anisotropy)
2. MD (diffusivity)

DTI

1. FA (anisotropy)
2. MD (diffusivity)

DAT

1. Mean striatal
2. Mean putamen
3. Mean caudate
4. Ipsi. caudate
5. Contra. caudate
6. Ipsi. putamen
7. Contra. putamen

DAT

1. Mean striatal
2. Mean putamen
3. Mean caudate
4. Ipsi. caudate
5. Contra. caudate
6. Ipsi. putamen
7. Contra. putamen

Genetics

1. APOE
2. GBA
3. LRRK
4. Synuclein (SNCA)
5. MAPT
6. COMT
7. HLA
8. KLOTHO

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Individuals’ Work

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Early Disease Course: Depression

de al Riva et al. Neurology 2014;83:1096-1103.

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Early Disease Course: Psychosis

Psychosis (% present) BL 12 months 24 months Change in PD

ver time

Change between groups

ver time

PD 3.1% (13/423) 5.4 % (14/261) 10.4% (10/96) 11.64 (2), p=0.003 1.49 (2), 0.59 HC 0.5% (1/195) 0% (0/145) 2.4% (2/83) Fischer test, p 0.076 0.003 0.038

de al Riva et al. Neurology 2014;83:1096-1103.

Variable UPDRS Part I Hallucinations and Psychosis item PD Subjects (N = 423) Healthy Controls (N = 196) Statistic (Chi- square) df p-value Negative 410 (97%) 194 (99%) 3.95 1 0.047 Any positive score 13 (3%) 1 (1%)

“The frequency of new-onset psychosis was nearly three times as high in the DRT group compared with the untreated group.”

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Early Disease Course: Global Cognition

de al Riva et al. Neurology 2014;83:1096-1103.

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Impact of DRT Initiation

de al Riva et al. Neurology 2014;83:1096-1103.

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Change in DAT Availability and Incident ICD Behaviors

All subjects Subjects on DRT OR P OR P Baseline DAT binding Right caudate 1.07 .82 1.12 .71 Left caudate .905 .70 .94 .84 Right putamen .77 .58 .99 .99 Left putamen .55 .18 .78 .63 Mean total striatal .82 .64 .99 .98 Change in DAT binding (baseline-year 1) Right caudate 2.75 .08 4.03 .01 Left caudate 1.58 .35 1.78 .26 Right putamen 2.37 .33 3.28 .25 Left putamen 1.66 .48 2.52 .24 Mean total striatal 4.04 .14 6.90 .04 DAT binding (post-baseline) Right caudate .66 .31 .47 .07 Left caudate .66 .31 .62 .32 Right putamen .17 .04 .06 .01 Left putamen .17 .03 .15 .07 Mean total striatal .36 .09 .25 .04

Smith et al. (unpublished data ).

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Smith et al. (unpublished data). In collaboration with Julia Kraemmer and JC Corvol.

(Logistic regression model)

Genes implicating serotonin, dopamine and

pioid systems. Another

model implicated noradrenergic system.

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CBWG Members: Published or In Progress

Dag Aarsland and colleagues

– Lebedev et al. Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic

deficits. Frontiers in systems neuroscience, 2014.

– Siepel et al. Cognitive executive impairment and dopaminergic deficits in de novo Parkinson's disease. Movement Disorders, 2014. – Pereira et al. Initial cognitive decline is associated with cortical thinning in early Parkinson disease. Neurology, 2014. – Pereira et al. Aberrant cerebral network topology and mild cognitive impairment in early Parkinson’s disease. Human Brain Mapping, 2015. – Skogseth et al. Associations between cerebrospinal fluid biomarkers and cognition in early Parkinson’s disease (submitted). – Pereira et al. Cerebrospinal fluid Aβ1-42 levels are associated with functional network disruption in early Parkinson’s disease (submitted).

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Liu et al. Neurology 2015;8:1-9.

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Neuropsychiatric Symptoms in SWEDDs

Sprenger et al. Movement Disorders 2015 (10.1002/mds.26204).

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CPWG Members: Sampling of Ongoing Work

Lama Chahine - Baseline sleep and daytime sleepiness

symptoms as predictors of cognitive decline

Alberto Espay - Differential effect of dopaminergic

medications on depression and anxiety symptoms

Maria Teresa Pellecchia and Paolo Barone - Insulin-

like growth factor-1 (IGF) as biomarker for early cognitive impairment

Roy Alcalay - CSF β-amyloid 1-42 predicting