2 Yr Longitudinal Studies

January 16, 2018 | Author: Anonymous | Category: Science, Health Science, Pediatrics
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Disclosures/Conflicts Consulting: GE Healthcare Bayer Abbott Elan/Janssen Synarc Genentech Merck

ADNI PET Achievements Literature-defined prespecified ROIs Statistically defined ROIs Multivariate approaches to prediction of conversion/decline Cross-sectional and longitudinal PIB studies Biomarker comparisons (PIB-CSF)

Statistically Defined ROIs in AD and MCI for Longitudinal Progression AD

12 month trial, 25% treatment effect (power = 0.8, a = 0.05, 2-tailed) 61 AD patients/arm

MCI

217 MCI patients/arm

Chen et al, Neuroimage 2010

26 MCI patients with a higher HCI 71 MCI patients with a lower HCI

21 MCI patients with a smaller hippo vol 76 MCI patients with a larger hippo vol

20 MCI patients with both a higher HCI & smaller hippo vol 38 MCI patients with neither a higher HCI or smaller hippo vol

Chen et al, submitted

Enrollment in ADNI PiB Studies to June 2010 (All Data Are Available On The LONI Website) Baseline – 103 Subjects at 14 PET Sites PiB Baseline Entry Times • NL: 19, 78±5 y/o, MMSE • 20 subjects at ADNI true baseline 29±1 • 69 subjects at ADNI 12 months • MCI: 65, 75±8 y/o, MMSE • 14 subjects at ADNI 24 months 27±2 1 Yr Longitudinal Studies – 80 Subjects • AD: 19, 73±9 y/o, MMSE • NL: 17/19 (89%) 22±3 • MCI: 50/65 (77%) • AD: 13/19 (68%) 2 Yr Longitudinal Studies – 39 Subjects

Total 224 PiB Scans

• NL: 11 • MCI: 26 • AD: 2 3 Yr Longitudinal Studies – 2 Subjects • NL: 2 • MCI: 0 • AD: 0

Mathis, Univ Pittsburgh

Baseline PiB 9/19 Normals PiB+ 47/65 MCI PiB+ 17/19 AD PiB+

Longitudinal PiB

MCI Converters (1-2 years)

21/47 PiB+ 3/18 PiBMathis, Univ Pittsburgh

Extent of Hypometabolism as a Predictor of MCI Conversion

Timing of conversion associated with more hypometabolic voxels

Foster, Univ Utah

ROI Generation Identification of ROIs from voxelwise analyses in the literature

Peak voxels plotted in MNI coordinates, smoothed, thresholded Post Cingulate Gyrus

L Inf Temporal Gyrus L Angular Gyrus

R Angular Gyrus

R Inf Temporal Gyrus

Jagust et al, Neurology 2009

FDG

AVLT

Combined = 12 fold higher risk of conversion

Landau et al, Neurology 2010

Prediction of Cognitive Decline in Normal ADNI Participants Define normal/abnormal cutoffs using external samples Classification of each subject as normal/abnormal on each marker Determine whether normal/abnormal status predicts cognitive change

Participants 92 cognitively normal ADNI participants (FDG-PET, structural MRI, and ApoE genotyping) Mean followup

2.7 +/- 0.8 yrs

Age Education Female ApoE4 carriers MMSE

75.8 +/- 4.8 yrs 15.9 +/- 3.2 yrs 39% 23% 28.9 +/- 1.1

FDG-PET (UC Berkeley) Sensitivity = 90% Specificity = 93%

Alzheimer’s patients N = 35 Age = 67.2 +/- 10.4 57% Female

Normal older subjects N = 39 Age = 73.1 +/- 5.8 62% Female

Mean FDG ROI uptake (relative to cerebellum/vermis region)

Hippocampal volumes (UCSF) Sensitivity = 94% Specificity = 95%

Alzheimer’s patients N = 51 Age = 78.6 +/- 8.5 43% Female

Normal older subjects N = 53 Age = 74.3 +/- 7.5 53% Female

Bilateral hippocampal volume (adjusted for total intracranial volume)

Normals stratified into high/low memory

No association between high/low performer status and status on any of the normal/abnormal markers

Median split of normals into high/low performers based on baseline performance on the Auditory Verbal Learning Test (free recall)

Neither group showed significant ADAS-cog change

Auditory Verbal Learning Test

Statistical analyses – multivariate Low performers Baseline

Parameter estimate

FDG-PET imaging

p-value

ns

Hippocampal volume

1.31 +/- 0.58

0.03

ApoE4 carrier status

0.99 +/- 0.66

0.03

ADAS-cog decline

age, sex, education

Abnormal hipp volume and ApoE4 carriers  2.3 pts/yr decline relative to normal

Defining the Technical Sources of Variability in ADNI PET Data What is the effect of changing scanners in a longitudinal study? How variable are longitudinal measurements on different scanners? How does instrument variation compare to site variation? What is the effect of processing on variation?

Effects of Scanner Switch in a Longitudinal Study

Rate of FDG Change (in ROI)

Stable Switch Stable Switch

Normals

MCI

Stable Switch

AD

Variability by Scanner HRRT

2 2

6

16

7

SD of Rate of Change

Normal

MCI

AD

The Future: ADNI2 and GO Cross-sectional and longitudinal studies of Ab deposition with AV-45

Comparison with other biomarkers in prediction/multivariate approaches Comparison with other biomarkers as outcomes Replication of statistical ROI approach using identical ROI Further investigate sources of variability

Susan Landau Bob Koeppe Eric Reiman Kewei Chen Chet Mathis

Julie Price Norman Foster Dan Bandy Danielle Harvey Norbert Schuff Mike Weiner

Acknowledgements

The ADNI Executive Committee, Site Investigators, Participants National Institute on Aging/Neil Buckholtz ISAB Alzheimer’s Association

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