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Biomarker Values Predictive of
Cognitive Decline in MCI:Baseline or Progression?
Hiroko H. Dodge, PhD
Associate Professo r of Neuro logy
Directo r, Bio stat ist ics and Data Core, Layton Ag ing
and Alzheimers Disease Center
Oregon Health and Science Universi ty
Portland , OR
Adjun ct Research Associate Professo r
Michigan Alzheimers Disease Center
An n, Arbo r, MI
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Nothing to disclose
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CHANGE POINT MODELS
Silbert, Dodge et. al. Trajectory of white matterhyperintensity burden preceding mild cognitive
impairment. Neurology 2012;79:741-747
(PMC3421153)
Buracchio, Dodge et. al. The trajectory of gait
speed preceding MCI. Archives of Neurology.
Archives of Neurology, 2010; 67:980-986
(PMC2921227)
Dodge, Ganguli,et al. Terminal decline and
practice effects in non-demented older adults.
Neurology, 2011:77;722-730. (PMC3164394)
Statistical Approaches: Examples
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APPLICATION OF MIXED
EFFECTS MODEL
Erten-Lyons, Dodge et. al.
Neuropathological basis of age-
associated brain atrophy. JAMA
Neurology 70:616-622, 2013.
Dodge, Ganguli et. al. Cohort
Effects in Age-Associated
Cognitive Trajectories. Journal of
Gerontology: Medical Sciences.
In press.
Statistical Approaches: Examples
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LATENT TRAJECTORYANALYSIS
Dodge, Kayeet. al. In-
home walking speeds and
variability trajectories
associated with MildCognitive Impairment.
Neurology, 2012:
78(24):1946-1952.
(PMC3369505)
Statistical Approaches: Examples
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Biomarkers vs. cognitive
outcomes
Cognitiv
eFunctions
time (age)
Individual Specific Deviations from population
mean trajectory (random components)
Variability explained by
Age, education, (reserve
factors)
Baseline biomarker values
Rate of
progression in
biomarker values
Population
mean/average
trajectory
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Aim/Data
To examine which components ---baselinevalues or biomarker progressions--
explains more variability in cognitive
declines in memory and executivefunctions.
DATA: theAlzheimers Disease
Neuroimaging Initiative (ADNI 1). 526subjects with valid data in at least one of
our variables of interest were used in this
study.
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Cognitive Outcomes
Trajectory (slope) of cognitive functions:1. ADNI-memory (ADNI-Mem) and
2. ADNI-executive (ADNI-Exe).
(Crane et al., 2012; Gibbons et al., 2012)
The scores are psychometrically optimized
composite scores of memory and executivefunction, derived from items from ADNI
neuropsychological tests
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Approach: 2-stage
Stage 1. Individual-specific slope of thelongitudinal trajectory of each biomarker
was estimated using mixed effects models.
Longitudinal trajectories of biomarkers model
= + + + + ,
where ( , ) ~ 0, and
~
(0,2).
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Approach
Variability in cognitive declines explained
by subject-specific baseline biomarker
valueswas compared with variability
explained by biomarker progressions.
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Results
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Outcome: memoryNormal Group
Biomarker
_bl: baseline values
_prog: progression
% of variability
explained by
biomarkers
Standardizedeffect size
ttau_bl (1 sd=51) -5.40% -0.01ttau_prog
(1 sd=0.21) -3.00% 0.02Abeta42_bl
(1 sd=56) -7.70% 0Abeta42_prog
(1 sd=0.14) -14.80% 0.02A positive percentage indicates that the corresponding predictor
explains the variation in cognitive decline, while a negative
percentage indicates that inclusion of the predictor adds more
estimation error instead of improving model fitting.
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Outcome: memory Normal GroupBiomarker
_bl: baseline values
_prog: progression
% of variability
explained by
biomarkers
Standardized
effect sizeFDG-PET_bl
(1 sd=0.15) -0.60% 0.04FDG-PET_prog
(1 sd=0.18)
1.50%
0.1
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Outcome: memoryNormal Group
Biomarker
_bl: baseline
_prog: progression
% of variability
explained by
biomarkers
Standardized
effect sizePrecuneus
Thickness/icv_bl
(1 sd=2.3E-07) -2.36% 0.01PrecuneusThickness/icv_prog
(1 sd=0.10) -0.48% -0.01Medial Temporal
Thickn./icv_bl(1 sd=2.3E-07) -3.62% -0.01Medial Temporal
Thickn./icv_prog
(1 sd=0.10)-0.05% 0.1
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Outcome: memoryAmong MCI*
Biomarker
_bl: baseline values
_prog: progression
% of variability
explained by
biomarkers
Standardizedeffect size
ttau_bl (1 sd=51) -1.90% 0ttau_prog
(1 sd=0.21) 0.30% -0.04Abeta42_bl
(1 sd=56) 5.10% -0.15Abeta42_prog
(1 sd=0.14) 10.30% -0.48FDG-PET_bl
(1 sd=0.15) 12.20% 0.09FDG-PET_prog
(1 sd=0.18) 12.70% 0.08
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Outcome: memory Among MCIBiomarker
_bl: baseline values
_prog: progression
% of variability
explained by
biomarkers
Standardized effect
sizeLog_wmh/icv_bl
(1 sd=1.57) 0.50% -0.03Log_wmh/icv_prog
(1 sd=0.05) 0.10% 0.01Hpcv/icv_bl
(1 sd=3.7E-04) 9.00% 0.07Hpcv/icv_prog
( 1 sd=0.07) 19.80% 0.08Ventricles/icv_bl
(1 sd=5.8E-03) 8.70% -0.04Ventricles/icv_prog
(1 sd=0.11) 39.40% -0.12Total brain/icv_bl
(1 sd=0.04) 2.40% 0.06Total brain/icv_prog(1 sd=0.09) 16.00% 0.05
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Outcome: memory Among MCIBiomarker
_bl: baseline
_prog: progression
% of variability
explained by
biomarkers
Standardized
effect sizePrecuneus
Thickness/icv_bl
(1 sd=2.3E-07) 3.49% 0.09PrecuneusThickness/icv_prog
(1 sd=0.10) 5.38% 0.07Medial Temporal
Thickness/icv_bl(1 sd=2.3E-07) 4.36% 0.09Medial Temporal
Thickness/icv_prog
(1 sd=0.10)25.52% 0.11
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Outcome: memory Among ADBiomarker
_bl: baseline values
_prog: progression
% of variability
explained by
biomarkers
Standardizedeffect size
ttau_bl (1 sd=51) -7.90% -0.19ttau_prog
(1 sd=0.21) -17.80% -0.08Abeta42_bl
(1 sd=56) -8.60% -0.05Abeta42_prog
(1 sd=0.14) 6.60% -0.04FDG-PET_bl
(1 sd=0.15) 30.00% 0.1FDG-PET_prog
(1 sd=0.18) 84.00% 0.12
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Outcome: memory Among ADBiomarker
_bl: baseline values
_prog: progression
% of variability
explained by
biomarkers
Standardized effect
sizeLog_wmh/icv_bl
(1 sd=1.57) -4.70% 0.1Log_wmh/icv_prog
(1 sd=0.05) 3.00% 0.1Hpcv/icv_bl
(1 sd=3.7E-04) 4.70% -0.07Hpcv/icv_prog
( 1 sd=0.07) 26.00% 0.15Ventricles/icv_bl
(1 sd=5.8E-03)
4.20%
0.11
Ventricles/icv_prog
(1 sd=0.11) 63.80% -0.18Total brain/icv_bl
(1 sd=0.04) -4.50% 0.05Total brain/icv_prog(1 sd=0.09) 26.00% 0.17
O t
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Outcome: memoryAmong AD
Biomarker
_bl: baseline_prog: progression
% of
variability
explained bybiomarkers
Standardized
effect sizePrecuneus Thickness/icv_bl
(1 sd=2.3E-07) 5.14% 0.07PrecuneusThickness/icv_prog
(1 sd=0.10) 5.13% 0.08Medial Temporal
Thickness/icv_bl(1 sd=2.3E-07) 6.14% 0.09Medial Temporal
Thickness/icv_prog
(1 sd=0.10)64.65% 0.17
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Conclusions
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A number of studies have shown support for
the hypothetical AD progression model
developed by Jack et al (Jack et al., 2013;Jack et al., 2010).
Our study results also coincides with the
model; Across diagnostic groups, thepercentages of variability in cognitive declines
explained by functional (FDG-PET) or
structural (brain morphometric) biomarkers(either their baseline values or progressions)
increased significantly as disease progressed
from normal to AD.
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For most biomarkers, biomarker progressions
were more predictive of (associated with)memory decline than baseline values.
This suggests that clinical trials which require
recruiting at risk subjects could be improvedby using progression rather than baseline
values in biomarkers to enrich the study
subjects.
Future studies are warranted to estimate the
incremental effectiveness of improving clinical
trial statistical power by using biomarker
progression criteria
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Special Thanks to:
Michigan ADCRoger Albin, MD
Robert Koeppe, PhD
Oregon Health &
Science Univers i tyADC
Jeffrey Kaye, MD
Lisa Silbert, MD
UC DavisDaniel Harvey, PhD
Univers i ty o fPi t tsburgh
Mary Ganguli MD
Funding Sources
Michigan ADC pi lotgrant
R13 AG030995, FridayHarbor AdvancedPsychometric Work
Shop 2011
MCI Symposium