Jeffrey Kaye

Layton Professor of Neurology & Biomedical Engineering

ORCATECH - Oregon Center for Aging & Technology

NIA - Layton Aging & Alzheimer's Disease Center

kaye@ohsu.edu

"… and the Related Dementias" -Multiple Targets, Multiple Outcomes

September 26, 2017 – Bethesda, MD

2017 Annual Regulatory Science Workshop

Summary

• The ‘other’ or ‘related dementias’ are prevalent

• They have been recognized by typical, but not invariant signs and symptoms emergent at variable pace

• The pathologies associated with these dementias are heterogeneous

• A cornucopia of biomarkers (imaging, fluid, physiologic) identify pathologic states; their relevance to progression and functional change in different use cases is evolving

• Biometric or digital biomarkers may add to the ability to identify more precisely, objectively and rapidly, meaningful relevant change in at risk or cognitively impaired patients

Differential Diagnosis of Dementia

Dementia

More Rapidly Evolving

Dementias

VascularDementias

Dementia with

Lewy Bodies

FrontotemporalDementias

Alzheimer’s Disease

Me

mo

ry

Progressive cognitive decline affecting daily function

Prevalence of Dementias

Lobo A, Launer LJ, Fratiglioni L, et al. Prevalence of dementia and major subtypes in Europe: a collaborative study of population-based cohorts. Neurology 2000;54 (11):S4–9

Seshadri, S, Wolf, PA, Beiser, A, et al. Lifetime risk of dementia and Alzheimer's disease: The impact of mortality on risk estimates in the Framingham Study. Neurology, 49:1498-1504,1997

Knapp & Prince, Dementia UK, 2007

Prevalence of Dementia in the United States: The Aging, Demographics, and Memory Study B.L. Plassman et. al. Neuroepidemiology. 2007 November; 29(1-2): 125–132

Dementia with Lewy Bodies

Alzforum.org, 2015

• Dementia• Fluctuating Cognition• Visual Hallucinations• REM Sleep Behavior Disorder• Parkinsonism• Autonomic dysfunction• Antipsychotic drug sensitivity• Hypersomnia• Apathy, Depression, Anxiety

McKeith, et al. Neurology, 2017, Diagnosis and management of dementia with Lewybodies. Fourth consensus report of the DLB Consortium

McKeith, et al. Diagnosis and management of dementia with Lewy bodies, Fourth consensus report of the DLB Consortium. Neurology, 2017

Dementia with Lewy Bodies

Vascular Cognitive Impairment & Dementia

Jellinger, Frontiers in Aging Neuroscience. 2013

Clinical Features of Vascular Dementia

CORTICAL SYNDROME• Medial frontal: executive dysfunction, abulia, apathy; akinetic mutism• Left parietal; aphasia, apraxia, agnosia• Right parietal: hemineglect (anosognosia, asomatognosia), confusion,

agitation, visuospatial, constructional difficulty• Medial temporal: anterograde amnesia

SUBCORTICAL SYNDROME• Focal motor signs• Early presence of gait disturbance, apraxic gait, parkinsonian gait• History of unsteadiness, falls• Urinary frequency, urgency,• Pseudobulbar palsy• Personality and mood changes (abulia, apathy, depression)• Cognitive disorder with relatively mild memory deficit, psychomotor

retardation, executive dysfunction

Vascular Dementia Criteria

1994 2013

DSM-5 Major Vascular Neurocognitive Disorder

PROBABLE IF Clinical features are consistent with vascular etiology:

• Onset related temporally to one or more cerebrovascular events

• Decline prominent in complex attention, processing speed, frontal-executive function

• Presence of cerebrovascular disease from history, exam, and/or neuroimaging accounting for deficits

O.A. Skrobot et al. / Alzheimer’s & Dementia 13 (2017) 624-633

Recent small subcortical

infarct

White matter hyper-

intensity LacunePerivascular

space

Cerebral microbleed

Modified from Wardlaw JM, Lancet Neurol. 2013

A B C D E F

MPRAGE T2* DTI FLAIR ASL rsfMRSilbert, Neuroimaging Core OHSU Aging & Alzheimer’s Center, 2017

Boespflug et al. Radiology, 2017

Imaging Markers of Vascular Disease or Damage

Westover et al., Estimating cerebral microinfarct burden from autopsy samples. Neurology, 2013

Based on in-depth examination (99 slides from 23 regions) of twobrains

The True Brain-Burden of Vascular Disease is Difficult to Estimate

Small vessel vascular disease & MCI prodrome

Carlson, et al., Neurology, 2008Silbert, et al., Neurology, 2012

Automated segmentation of WMH from FLAIR

Oregon Brain Aging Study

Multiple pathologies are common in non-demented AND demented cases

Cholerton, et al. Am J Path, 2016;186(3): 500-506 Sonnen, et al. Arch Neurol, 2011;68(8):1049–1056

RESIDUAL COGNITIVE DECLINE: 59%

Current biomarkers gauge pathology - Are there markers of clinically relevant change as well?

Biometric monitoring devices for assessing end points in clinical trials: developing an ecosystem. Stephen P. Arneric´, Jesse M. Cedarbaum, Sean Khozin, Spyros Papapetropoulos, Derek L. Hill, Michael Ropacki, Jane Rhodes, Penny A. Dacks, Lynn D. Hudson, Mark Forrest Gordon, Volker D. Kern, Klaus Romero, George Vradenburg, Rhoda Au, Daniel R. Karlin, Maurizio F. Facheris, Cheryl J. Fitzer-Attas, Ottavio V. Vitolo, Jian Wang, Bradley M. Miller and Jeffrey A. Kaye. Nature Reviews Drug Discovery, 2017

These signs & symptoms would ideally track with major functional signs & symptoms

Digital Biomarkers

Buracchio et al. The Trajectory of Gait Speed Preceding Mild Cognitive Impairment Arch Neurol. 2010;67(8):980-986

Sabia, BMJ. 2017;; 357: j2709

Early MCI

Late MCI

Dodge et al. Trajectories of gait speed over time. Neurology, 2012

Physical Activity Walking

Cognition, Behavior

Jedynak, Neuroimage, 2012

Cognition, Behavior, (Motor Function)

Kaye, et al. Alzheimers Dement. 2014; Silbert et al., Alzheimers Dement, 2015; Seelye et al. Alzheimers Dement.: Diagnosis, Assessment & Disease Monitoring, 2015; Seelye et al. Alzheimer’s Disease & Assoc. Disorders, 2015

Frequent Behavior/M

ood Self-Report

30

20

10

14

16

18

20

0 4 8 12 16 20 24 28 32

Me

an

Days

on

Co

mp

ute

r

Months of Continuous Monitoring

Intact MCI

MM

SE

Chronobiological Behavior

Hayes, et al. Alzheimer Dis Assoc Disord. 2014Hayes, et al. IEEE Eng Med Biol Soc, 2010

No Differences Between Groups in Self-Report Measures

Self-Report Measure

Intact aMCI naMCI P value

Subjective DaytimeSleepiness

1.8 ± 0.2 1.5 ± 0.3 2.0 ± 0.3 0.69

SubjectiveInsomnia

1.3 ± 0.2 0.8 ± 0.3 1.6 ± 0.3 0.21

Subjective Restlessness

1.0 ± 0.1 0.4 ± 0.3 0.7 ± 0.2 0.34

Times up at night

1.1 ± 0.1 1.0 ± 0.3 1.0 ± 0.2 0.77

NA-MCI -

Normal -

A-MCI -

Disrupted InfradianRhythms in MCIReynolds, et al. AAIC, 2017

Activity, Mobility, Sleep

Kaye et al. Journals of Gerontology, 2011; Lyons et al. Frontiers in Aging Neuroscience, 2015

Technology Use Cases – Home-based Functional Domains

Secure Internet

Data Scientists University

CollaborationsPHARMA

Health Industry

ORCATECH Secure Data Backend -

Digital Data Repository

Safety & Cognition

Physiology & Health

Cognition&

BehaviorSocial EngagementCaregiving

Cognition&

Behavior

Computer Use/EMA

Safety & Cognition

Vehicle data port

iCONECT - MI/OR

CART - 202 Portland

CART - MARS Chicago

CART - PRISM Miami

CART - VA VISN 20

EVALUATE - AD

AIMS Transitions

Life Laboratory - BC

Life Laboratory Cohort

Studies/Cohorts

Studies XYZ

Wearable actigraphy, passive activity sensors

Activity, Mobility, Sleep

Social Engagement

Contact Sensors Doors Open/ClosePhone Activity/EMA

Caregiving

Technologies supporting Assessment & Intervention

Blood

Physiologic Sensing: Body Composition, Pulse, Vascular Resistance, Temperature, C02

MedTracker

Physiology & Health Neuroimaging

BiomarkerTarget

EnrichmentImaging, CSF, Plasma,

Genetics, other risk factors

Traditional Trial Enrichment:

Stratification accordingto increased risk of AD, LBD, VD...

Does not predict who/how progresses

Population

TargetPathologyGroup

6 months

High Efficiency Clinical Trials (Focus on Phase II)

BehavioralPhenotypeEnrichmentSleep hygiene,

Mobility, Computer Use, etc…

FurtherEnrichment:

Stratification accordingto who will progress.

Can predict who progresses

Disease Progression Group(s)

TargetPathology

Group

2-3 months

ContinuousAssessmentComputer use, Walking speed,

Activity, Mobility,etc…

Efficient LongitudinalAssessment:

Continuously assessed objective measures.

Detects individual relevantchange rapidly

Continuous Assessment:Clinical Progression

6 months

Precision Phenotyped

Thank You!

kaye@ohsu.edu

orcatech.org

“Something to make the world look sane again – Is there an app for that?”

Physical Activity & Mobility: Out of home assessment –Driving (and Cognition)

26

Table 2. Summary Driving Characteristics; (Per day; six month observation period)

Variable Total Intact MCI p-value

N 28 21 7

Mean # of (one-way) trips per day 4.2 (1.0) 4.1 (0.9) 4.7 (1.4) 0.19

Day-to-day variability in # of trips 2.1 (0.6) 2.1 (0.5) 2.3 (0.8) 0.49

Mean distance driven per day (miles) 20 (13) 22 (13) 14 (11) 0.06

Day-to-day variability in distance driven 26 (17) 31 (17) 13 (12) 0.01*

Mean time driven per day (hours) 0.9 (0.4) 0.9 (0.4) 0.8 (0.4) 0.26

Day-to-day variability in time driven 0.7 (0.3) 0.8 (0.3) 0.5 (0.2) <0.01**

Mean first clock starttime of driving per

day

11.1 (1.2) 11.3 (1.2) 10.6 (1.4) 0.43

Day-to-day variability in first starttime

(hrs)

2.8 (0.8) 2.8 (0.6) 2.7 (1.1) 0.78

Mean last clock starttime of driving per

day

15.4 (1.6) 15.2 (1.4) 15.9 (1.9) 0.33

Day-to-day variability in last starttime

(hrs)

3.2 (0.6) 3.2 (0.6) 3.2 (0.8) 0.79

Mean # of days monitored 206 (36) 208 (38) 201 (33) 0.70

% of days at least one trip was taken out

of all days monitored

52% 49% 60% 0.21

% of driving days with >=20 miles driven 26% 27% 21% 0.51

Mean time of highway driving per day

(seconds)

450 (506) 543 (533) 172 (288) 0.01*

Seelye et al. Journal of Alzheimer’s Disease, 2017

Night-time Behavior & Sleep

Hayes, et al. Alzheimer Dis Assoc Disord. 2014Hayes, et al. IEEE Eng Med Biol Soc, 2010

No Differences Between Groups in Self-Report Measures

Self-Report Measure

Intact aMCI naMCI P value

Subjective DaytimeSleepiness

1.8 ± 0.2 1.5 ± 0.3 2.0 ± 0.3 0.69

SubjectiveInsomnia

1.3 ± 0.2 0.8 ± 0.3 1.6 ± 0.3 0.21

Subjective Restlessness

1.0 ± 0.1 0.4 ± 0.3 0.7 ± 0.2 0.34

Times up at night

1.1 ± 0.1 1.0 ± 0.3 1.0 ± 0.2 0.77

ObjectiveMeasure

Intact aMCI naMCI P value

Movement in Bed (sensor firings)

9.4 ± 0.4 7.8 ± 0.9 10.9 ± 0.7 p < 0.05 (aMCI < naMCI)

Wake After Sleep Onset (mins)

27.2 ± 1.2 13.5 ± 2.6 20.6 ± 2.0 p < 0.001 (aMCI < intact,

naMCI)

Settling Time (mins)

2.5 ± 0.07 2.3 ± 0.15 3.1 ± 0.11 p < 0.001 (naMCI > intact,

aMCI)

Times up at night (# times)

2.1 ± 0.04 1.6 ± 0.10 1.9 ± 0.08 p < 0.001 (aMCI < intact,

naMCI)

Total Sleep Time (hrs)

8.3 ± 0.04 8.5 ± 0.09 8.5 ± 0.07 NS

NA-MCI -

Normal -

A-MCI -

Culturally Relevant Multi-modal Brain Health Program -SHARP: Sharing History through Active Reminiscence and Photo-imagery

PI: Raina Croff; NIA: P30AG008017,

P30AG024978, and Alzheimer’s Association

“In focusing attention on the mortality associated with Alzheimer disease, our goal is not to find a way to prolong the life of severely demented persons, but rather to call attention to our belief that senile as well as pre-senile forms of Alzheimer are a single disease, a disease whose etiology must be determined, whose course must be aborted, and ultimately a disease to be prevented.Robert Katzman, Archives of Neurology, 1976

Vascular disease burden predicting rate of cognitive decline

MRI Volumes predictive of cognitive decline: • CDR = 0 - percent total WM HSI volume• CDR = 0.5 - percent ventricular volume

Age/Time Matters

Braak & Del Tredici. The preclinical phase of the pathological process underlying

sporadic Alzheimer’s disease. Brain. 2015;138(10):2814-2833