Biomarker Comparisons:
Results from U.S. ADNI-1
The ADNI Biostatistics Core
Laurel Beckett, Ph.D., Danielle Harvey, Ph.D.,
Hao Zhang, M.S., Julie Kuo, M.S. (UC Davis)
Michael Donohue, Ph.D., Anthony Gamst, Ph.D.
(UC San Diego)
John Kornak, Ph.D. (UC San Francisco)
Toronto April 2010
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Goal: Comparison of key biomarkers
• Many possible imaging summaries; we
restrict attention to selected subset.
• Focus is on biomarker performance in
context of clinical trials: 3 areas.
• Precision for detecting change.
• Clinical relevance of findings.
• Possible impact on trial design.
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Clinical characteristics of ADNI participants:
Baseline mean (SD) for cognitive scores
Variable
NC
MCI
AD
Sample size
229
398
192
ADAS-Cog total
6.17 (2.94)
11.6 (4.48)
18.5 (6.36)
MMSE
29.1 (1.0)
27.0 (1.8)
23.4 (2.0)
CDR Sum of Boxes
0.03 (0.12)
1.60 (0.88)
4.26 (1.65)
RAVLT 5-trial total
43 (9.2)
31 (9.1)
23 (7.5)
By group: normal control (NC), mild cognitive impairment
(MCI), Alzheimer’s (AD).
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Mean (SD) of annualized change for cognitive
test scores, by diagnostic group.
Variable
NC
MCI
AD
ADAS-Cog total
-0.54 (3.05)
1.05 (4.40)
4.37 (6.60)
MMSE
0.0095 (1.14)
-0.64 (2.5)
-2.4 (4.1)
CDR Sum of Boxes
0.07 (0.33)
0.63 (1.16)
1.62 (2.20)
RAVLT 5-trial total
0.29 (7.8)
-1.37 (6.6)
-3.62 (5.6)
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Mean (SD) of annualized change in biomarkers
Variable
NC
MCI
AD
CSF Aβ
-0.94 (18)
-1.4 (17)
-0.1 (14)
CSF Tau
3.45 (13)
2.34 (21)
1.24 (24)
PiB
0.098 (0.18)
-0.008 (0.18)
-0.004 (0.25)
Hypometabolic FDG-PET
(Utah)
-177 (1532)
752 (2950)
2993 (4040)
FDG-PET ROI-avg (UCB)
-0.006 (0.06)
-0.015 (0.064)
-0.055 (0.067)
FDG-PET CV-fROI
(Arizona)
-0.019 (0.037) -0.047 (0.03)
-0.081 (0.047)
Hippocampus (UCSF)
-40 (84)
-80 (91)
-116 (93)
Ventricles (UCSF)
848 (973)
1551 (1520)
2540 (1861)
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First consideration for biomarker comparison:
signal-to-noise ratio in estimates of change.
Important if you want to detect the impact of
treatment on disease progression!
We present this as the sample size needed for:
• Two-arm study (treatment vs control).
• Treatment reduces progression 25%.
• Measurements at baseline and 12 months.
• Alpha =0.05, Power=0.80, two-sided test.
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Comparisons of selected measures from each lab.
First compare MRI measures, then PET, then PET
winners to MRI winners.
Compare only on set of people who have data on
all the biomarkers in the comparison.
Analysis: rank test, blocked by participant,
normed data, followed by multiple comparisons.
Solid color bars show biomarkers not significantly
different from each other.
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1.5T MRI in AD’s (n=65), sample size / arm.
Lab
Variable
SS/arm
Alexander
L. Hippo Formation
313
Schuff - FS
Hippocampus
185
Dale
Ventricles
154
Studholme
TBM - % change
127
Schuff - FS
Ventricles
112
Dale
Whole brain
111
Studholme
TBM - CV % change
105
Fox
VBSI % change
97
Dale
Avg Hippocampus
94
Fox
BSI % change
66
Thompson
TBM - CV % change
55
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1.5T MRI in MCI (n=149), sample size per arm
Lab
Variable
SS/arm
Dale
Ventricles
377
Alexander
L. Hippo Formation
374
Schuff - FS
Hippocampus
280
Schuff - FS
Ventricles
263
Fox
VBSI % change
253
Studholme
TBM - % change
199
Studholme
TBM - CV % change
177
Fox
BSI % change
175
Dale
Avg Hippocampus
138
Dale
Whole Brain
127
Thompson
TBM - CV % change
84
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FDG PET in AD (n=36) - sample size per arm
Lab
Variable
SS/arm
Foster
hypometabolism1
638
Foster
hypometabolism2
549
Jagust
ROI-avg
412
Reiman
CV-fROI
96
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FDG PET in MCI - n=81, sample size per arm.
Lab
Variable
SS/arm
Jagust
ROI-avg
7649
Foster
hypometabolism1
1876
Foster
hypometabolism2
1280
Reiman
CV - fROI
280
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1.5T, FDG PET, cognitive tests, in AD, n=30
Lab
Modality
Variable
SS/arm
Cog.
MMSE
703
Cog.
ADAS-Cog
514
PET
Hypometab 2
508
Cog.
CDR SOB
495
PET
ROI-avg
396
Schuff - FS MRI
Ventricles
95
Reiman
PET
CV - fROI
91
Thompson
MRI
CV % change
53
Fox
MRI
BSI % change 50
Foster
Jagust
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1.5T vs FDG PET and cognitive tests, in MCI (n=69)
Lab
Jagust
Foster
Modality Variable
SS/arm
Cog.
MMSE
14315
PET
ROI-avg
4605
Cog.
ADAS-Cog
4528
Cog.
CDR-SOB
2473
PET
Hypometab 2 1629
Schuff-FS MRI
Ventricles
277
Reiman
PET
CV-fROI
249
Fox
MRI
BSI % ch.
177
Thompson
MRI
CV % ch.
73
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Summary of the precision/ sample size
comparisons:
Some of the FDG PET and 1.5T MRI measures
give very stable estimates of change, with good
signal-to-noise ratios.
A two-arm study with one of these measures as
outcomes would need substantially fewer people
than one with ADAS-COG or other standard
cognitive outcome measures.
But are these measures clinically relevant?
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We look at two measures of clinical relevance:
Relationship to cognitive change (one standard
clinical trial outcome).
• First, how well does baseline marker
predict change in cognition.
• Second, for key markers, how correlated is
change in marker with change in cognition.
Relationship of baseline marker level to
conversion from MCI to AD (another standard
trial outcome.)
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Predictors of longitudinal change in ADAS-Cog - NC
Predictor of
change/yr
Univariate Model
Multivariate Model*
p-value
Coefficient
p-value
Apoe4+
0.22
1.06
0.02
Yrs of education
0.19
-0.026
0.64
CSF Aβ
0.82
0.10
0.63
CSF tau
0.75
0.33
0.19
FDG-PET ROI-avg
(UCB)
0.076
0.05
0.77
Hippocampus
0.016
-0.25
0.27
Ventricles
0.45
0.18
0.31
* Sample size is very small for multivariate models (1/4 of overall sample)
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Predictors of longitudinal change in ADAS-Cog - MCI
Predictor of
change/yr
Univariate Model
Multivariate Model*
p-value
Coefficient
p-value
Apoe4+
0.005
0.57
0.24
Yrs of education
0.82
-0.004
0.96
CSF Aβ
<0.001
0.058
0.83
CSF tau
<0.001
0.20
0.16
FDG-PET ROI-avg
(UCB)
<0.001
-0.40
0.040
Hippocampus
<0.001
-0.014
0.94
Ventricles
<0.001
0.38
0.070
* Sample size is very small for multivariate models (1/4 of overall sample)
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Predictors of longitudinal change in ADAS-Cog - AD
Predictor of
change/yr
Univariate Model
Multivariate Model*
p-value
Coefficient
p-value
Apoe4+
0.30
-0.39
0.82
Yrs of education
0.15
0.05
0.79
CSF Aβ
0.26
-1.39
0.12
CSF tau
0.004
0.43
0.17
FDG-PET ROI-avg
(UCB)
<0.001
-2.12
0.005
Hippocampus
0.79
-0.08
0.90
Ventricles
0.95
0.43
0.47
* Sample size is very small for multivariate models (1/4 of overall sample)
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Correlation Between Change in imaging and
Change in ADAS Comparisons: MCI (n=69)
Lab
Modality Variable
Fox
MRI
BSI % change 0.36
Jagust
PET
ROI-avg
-0.34
Schuff-FS
MRI
Ventricles
0.32
Reiman
PET
CV-fROI
-0.31
Foster
PET
Hypometab 2
0.19
Thompson
MRI
CV-% change
-0.15
Toronto April 2010
Correlation
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Correlation Between Change in imaging and
Change in ADAS Comparisons: AD (n=30)
Lab
Modality Variable
Fox
MRI
BSI % change 0.45
Schuff-FS
MRI
Ventricles
0.42
Reiman
PET
CV-fROI
-0.32
Thompson
MRI
CV-% change
-0.31
Jagust
PET
ROI-avg
-0.23
Foster
PET
Hypometab 2
0.09
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Correlation
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Predictors of time to conversion from MCI to AD:
multivariate results, accelerated failure time model.
Variable
Coefficient
p-value
Bl ADAS-Cog
-0.101
0.002
Bl FAQ
-0.092
0.002
Using ACH EI
-0.060
0.046
Bl Hippcampus
0.058
0.078
Bl MMSE
0.053
0.083
p-value*
MCI-MRI cohort
0.003
MCI-MRI-FDG cohort
Bl FAQ
-0.073
0.043
Bl ADAS-Cog
-0.074
0.059
Bl Hippocampus
0.070
0.070
0.025
Bl FDG-ROI-avg
0.071
0.091
0.015
* p-values excluding all clinical variables from the model
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Summary of clinical relevance of biomarkers
1. Evidence for surrogate marker potential (as
replacement or secondary endpoints in trials)
• The most promising markers have better
precision than standard performance tests.
• Correlation with cognitive change is
moderate at best, but then cognitive
measures are noisy.
• We do not have any data on their ability to
capture treatment effects.
• So far: not enough to convince the FDA!
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2. Alternative ideas for using biomarkers in
clinical trials:
• As better entry criteria for early
studies: promising! They do predict, and
multivariate data look even more promising.
• As stratification tools: again, very
promising, as some of them predict quite
well in MCI (not enough data yet on NC.)
• As covariates to adjust regression models
for cognitive change or conversion: quite
promising. Donohue and Gamst results.
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Analytic sample size for mixed models: effects of
two biomarker strategies (restrict to Aβ abnormal,
include hippocampal volume as covariate)
Study
group
MCI All
people
% Tx
Effect
Power Out
come
40%
80%
25
80
40
80
25
80
MCI-Aβ
ADAS
CDR
ADAS
CDR
ADAS
CDR
ADAS
N per group
No covar Hippo
294
279
224
199
753
714
572
507
196
194
106
101
501
495
CDR
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256
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Summary of results from Donohue/ Gamst work:
• Restricting trial to MCI with dysfunctional
CSF Aβ level at baseline sharply reduces
sample size needed to see difference in rate
of change (also to see conversion, but that
takes bigger sample size, not shown.)
• Inclusion of hippocampal volume as a
covariate decreases sample size somewhat.
Marker combinations might help even more.
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Thank you for the opportunity to speak to
the Steering Committee!
On behalf of all the ADNI Biostatistics team:
UC Davis: Laurel Beckett, Danielle Harvey,
Hao Zhang, Julie Kuo.
UC San Diego: Mike Donohue, Anthony Gamst.
UC SF: John Kornak.
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