Understanding the nature and magnitude of cognitive impairment in
people with major depressive disorder. Relationships to symptom
severity, functional disability, treatment response, comorbid
psychiatric and physical illness and cognitive genes.
Application to BMS for access to study data from Phase A and Phase
B components of study CN162007 entitled A Multicenter,
Randomized, Double-blind, Active-Controlled, Comparative, FixedDose, Dose Response Study of the Efficacy and Safety of BMS820836 in Patients with Treatment Resistant Major Depression.
Study team
Paul Maruff, PhD
Chief Science Officer CogState Ltd & Professor Florey Institute for
Neuroscience and Mental Health, University of Melbourne, Australia.
Adrian Schembri DPsych
Science Director - Research Division at Cogstate Ltd & Research Associate
for the School of Health Sciences, RMIT University, Melbourne, Australia.
Robb Pietrzak, PhD
Department of Psychiatry
Yale University
CT, USA.
Corresponding author
Paul Maruff
2/255 Bourke St
Melbourne, 3000
+61396641300
Australia
[email protected]
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Summary:
This application is in support of a request for access to data describing
cognitive, demographic, illness related and functional ability aspects of
patients with major depressive disorder who were recruited for study
CN162007. The application provides a rationale for this request on the
basis that the sample studied prior to randomization is one of the largest
and most well characterized groups of MDD ever studied with an extensive
battery of cognitive tests. This therefore provides a unique opportunity to
learn about the nature and magnitude of cognitive impairment in MDD and
the extent to which it is related demographic and other healthy variables as
well as the associations with functional disability. As the study team
consists of experts in cognitive function, psychiatry and statistical analyses
we believe we are in an excellent position to conduct these analyses and
report them to the broader scientific community. We believe this will have
benefit for understanding MDD.
Background
Cognitive impairment in depression.
Depressive disorders affect over 300 million people worldwide and are
projected to become the leading cause of disability1 Cognitive dysfunction is
a common symptom of major depressive disorder (MDD)2-4, with “difficulty
thinking, concentrating, or making decisions” among the features of an
MDD episode defined by the Diagnostic and Statistical Manual of Mental
Disorders IV (DSM-IV)5. Studies of cognition in individuals who meet clinical
criteria for MDD indicate that episodic memory, attention, and executive
function are the most commonly and severely impaired cognitive domains in
acute depression2-4,6,7.
Although such symptoms were initially thought to be due to low mood,
there is increasing evidence that cognitive dysfunction persists even in
individuals whose core depressive symptoms have resolved4,7-11. Most
depression rating scales do not capture cognitive symptoms and hence the
magnitude of this problem is underappreciated in routine practice in the
absence of formal neuropsychological testing. Despite these studies, there
is still no agreement on the nature and magnitude of cognitive impairment
in depression or about the extent to which such cognitive impairment is
related to levels of depressive symptoms, history of depressive illness,
comorbid medical or psychiatric disorders, central nervous system active
medicines or demographic variables.
Consequences of cognitive impairment in major depressive disorder
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One major consequence of cognitive dysfunction is functional disability12,13;
however, the true prevalence of cognitive dysfunction in MDD and its
relationship with functional disability have not been fully studied. Some
studies of major depression have identified associations between cognitive
dysfunction and functional disability, impaired work performance and
activities of daily living14-16 independent of residual depressive symptoms,
depression severity, psychosis or other comorbidities14. Others report that
the severity of depression is a stronger predictor of disability than cognitive
function.17.
One accepted reason for the lack of consensus in knowledge about the
nature and magnitude of cognitive impairment in depression, the extent to
which cognitive dysfunction is related to the depressive illness itself, to
other comorbid psychiatric illness or to other health related factors is
because no study of cognitive dysfunction in depression that has utilized a
large neuropsychological test battery has been conducted in samples of
more than approximately 100 patients. Hence the small samples studied to
date typically limit the power of these studies to detect any relationships
between depressive illness and cognitive dysfunction when they are small to
moderate in magnitude. Furthermore the small size of samples studied to
date has prohibited the use of multivariate statistical techniques that can be
used to understand unique variance in datasets that consist of large
numbers of different but potentially related measures. As a rule multivariate
models for understanding covariation with relatively large numbers of
variables require large data sets (i.e. with the rule of thumb of 10 cases per
variable).
Description of study selected and patient population
Data from CN162007 can inform the debate about cognitive impairment in
MDD.
The study entitled, “A Multicenter, Randomized, Double-blind, ActiveControlled, Comparative, Fixed-Dose, Dose Response Study of the Efficacy
and Safety of BMS-820836 in Patients with Treatment Resistant Major
Depression”, clinical protocol number CN162007 recruited a very large
sample in order to test the efficacy of the experimental drug BMS-820836 in
patients classified as having treatment resistant depression. The design of
CN162007 is summarized in Figure 1 below which is taken directly from the
protocol document.
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Figure 1: Study design showing four phases of study CN162007
As is summarized in Figure 1 Study CN162007 was conducted in four
phases. In first phase patients were screened on inclusion/exclusion
criteria. In the second phase, approximately 800 patients who met clinical
criteria for MDD and who also met rigorous inclusion and exclusion (entitled
phase A in the protocol) were provided with treatment with antidepressants
for seven weeks (defined as phase B in the protocol). At the end of Phase B,
all patients were assessed on a large battery of cognitive tests and rating
scales for depressive symptoms, comorbid illness variables and physical
health. The patients with MDD were also classified as whether or not they
had responded adequately to the antidepressant therapy in Phase B.
Therefore at the end of phase B data relevant to understanding cognition
and its relationship to depressive illness were available for 800 people who
had been carefully diagnosed and described.
Therefore with the well described and large dataset from CN162007, it will
be possible to conduct detailed and appropriately powered statistical
analyses to investigate the nature of cognitive impairment in MDD, the
extent to which other aspects of the disorder influence its occurrence and
severity as well as the consequences of cognitive impairment for functional
disability.
The aim of this proposal is, in individuals who meet DSM-IV criteria for MDD
and who have received a treatment with an antidepressant drug for seven
weeks, to understand,
a) the nature and magnitude of cognitive impairment in people with
depression compared to normative data from healthy non-depressed
individuals,
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b) the extent to which the severity of depression is related to severity of
cognitive impairment,
c) the extent to which history of depressive illness impacts on the
magnitude of cognitive impairment,
d) the extent to which difficulties with cognitive function manifest in the
activities of daily living and functional disability in people with
depression,
e) whether a history of failure to respond to therapy with anti-depressant
medicine is associated with cognitive dysfunction,
f) whether patients diagnosed as treatment resistant depression after an
inadequate response to 7 weeks of prospective treatment with duloxetine
or escitalopram have a different cognitive profile to patients with a
response
g) the extent to which comorbid psychiatric disease moderates relationships
between cognitive function and depression,
h) whether comorbid physical disease or risk factors for major physical
disease might impact on cognitive function in depression,
i) how variation in polymorphisms of genes known to be important for
cognitive function (ApoE4, BDNF and COMT) can moderate relationships
between depression and cognitive impairment.
These aims can be addressed using a cross-sectional study design applied
to data collected from the sample during the week seven assessment (end
of phase B). Demographic data and potentially genetic data from the
baseline assessment will also be required for these analyses.
Sample
The study sample for this first part of the analysis will consist of all
individuals who satisfied the CN162007 study inclusion and exclusion
criteria and who completed the cognitive test battery at the week seven
assessment.
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Primary and Secondary endpoints for the proposed study
The primary and secondary endpoints for the proposed study will be drawn
from those that had been collected as specified in the protocol for study
CN162007. The theoretical constructs included in the aims section and the
operational definitions from the CN162007 which will be used as primary
and secondary endpoints are detailed in Tables 1 and 2 below.
The primary endpoint will be cognitive function defined as performance on
the four different cognitive tasks from the CogState battery. These are
detailed in Table 1.
Table 1: Description of the neuropsychological tests that form the CogState
battery used as the primary endpoint in this study.
Theoretical
Operational
Neuropsychological
Primary
variable
definition from
paradigm
performance
protocol
measure
CN162007
Psychomotor
CogState Detection
Simple reaction time
Average speed
function
task
of responses
Visual attention
CogState
Choice reaction time
Average speed
Identification task
of responses
Executive
Groton Maze
Hidden pathway maze
Number of
function
Learning Task
learning
errors made
Memory
International
Verbal list learning
Number of
Shopping List Task
correct words
learned
The secondary endpoints will be defined from the variables that describe the
characteristics of the sample. These are detailed in Table 2.
Table 2: Operational definitions of theoretical constructs used in the aims of the
Part 1 of the proposed study.
Theoretical variable
Operational definition from protocol CN162007
Cognitive function
Performance on the cognitive tests in the CogState battery
Demographic information
Age, gender, race, employment status
Severity of depression
Score on HAMD, MADRS scales
History of depressive
Data from psychiatric history on number of depressive
illness
episodes and length of time
Comorbid psychiatric
Data from psychiatric history pertinent to bipolar disorder,
illness
anxiety disorder etc.
Activities of daily living in
Score on Sheehan Disability Scale (SDS) & Cognitive and
people/functional disability. Physical Function Questionnaire (CPFQ)
History of anti-depressant
Data from outcome of classification from phase B of the
medicine failure.
study
Comorbid physical diseases Data from physical exam and physical measurements,
or risk factors for major
smoking history and presence of other CNS active drugs
physical disease
TRD
As defined by the study protocol
Cognitive genes
Baseline data for ApoE4, BDNF and COMT gene
polymorphisms
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Statistical analysis plan
All data analyses will be conducted using SAS or SPSS statistical analysis
packages. For all analyses the level of Type I error required for statistical
significance will be p<0.05. In order to protect against false positive
findings being interpreted, measures of effect size will be computed for all
group differences and associations. Where the magnitude of differences or
associations is trivial (i.e. d<0.2), relationships will not be interpreted
regardless of the level of statistical significance detected.
The statistical approach to be taken in order to address each of the study
aims is detailed in Table 3.
Study Aim
Determine the nature
and magnitude of
cognitive impairment in
people with depression
compared to normative
data from healthy nondepressed individuals
Determine the extent
to which the severity of
depression is related to
severity of cognitive
impairment
Determine the extent
to which history of
depressive illness
impacts on the
magnitude of cognitive
impairment
Determine the extent
to which difficulties
with cognitive function
manifest in the
activities of daily living
and functional disability
in people with
depression
Determine whether a
history of failure to
respond to therapy
Statistical methods
Compare performance on the four cognitive tests in the
CogState battery to age matched controls using
independent group’s t-tests.
Express magnitude of impairment in performance using
Cohen’s d for each measure.
Compute associations between scores on each
neuropsychological test and level of depressive symptoms
from MADRS and HAM-D using Pearson correlations.
Stage history of depressive illness in terms of number of
episodes.
Classify episodes into groups depending on distribution of
values.
Compare performance between individuals with MDD
grouped according to number of episodes using ANOVA
with covariates for demographic or illness variables also
related to number of episodes (i.e. age, gender, current
level of depressive symptoms).
Compute associations between performance on each
neuropsychological test and scores on SDS and subscales
of CPFQ using Pearson’s correlations.
Classify group of patients with MDD according to their
classification of response to therapy at the week seven
assessment (i.e. responder, inadequate response).
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with anti-depressant
medicine is associated
with cognitive
dysfunction
Determine the extent
to which comorbid
psychiatric disease
moderates
relationships between
cognitive function and
depression
Determine whether
comorbid physical
diseases or risk factors
for major physical
disease might impact
on cognitive function in
depression
Compare performance on neuropsychological tests
between the two groups using a series of t-tests and
express magnitude of differences using Cohen’s d.
Covariates may be added to this analysis, such as age,
gender, level of depressive symptoms at baseline (in
which case ANOVA with covariates will be used).
Classify MDD group into subgroups according to the
presence of bipolar disorder, anxiety disorder (or other
psychiatric disorders identified). Compare performance
on cognitive tests between individuals with MDD and no –
comorbid psychiatric disorder to those with MDD with
comorbid bipolar disorder, MDD with comorbid anxiety
disorder.
ANOVA will be used to compare groups and magnitude of
difference from MDD no comorbid psychiatric disorder will
be expressed using Cohen’s d.
The main area of investigation for this is whether the
cardiovascular risk factors, by themselves, or combined
increase the magnitude of cognitive impairment in MDD.
The quantification of risk factors will depend on the
distribution and number of risk factors and physical
diseases identified in the sample.
Risk factor composite scores (where risk factors are given
a score and then summed) may be used as an
independent variable in analyses examining the
magnitude of association between risk factor burden and
cognitive function. For these type of analyses Pearson
correlations will be computed.
Determine how
variation in
polymorphisms of the
ApoE4, BDNF and
COMT genes moderate
relationships between
depression and
cognitive impairment
If one illness is represented in sufficient numbers (i.e.
cardiovascular disease) then this may be examined as a
specific category in ANOVA models (compared to MDD
without the disease).
If available, ApoE4 genotype will be classified as Apoe4
positive or negative.
BDNF val66met will be classified in patients as Val/Val
and met/met homozygotes and val/met heterozygotes.
Individuals with MDD will be classified according to their
polymorphism on the ApoE4 and BDNF val66met genes
and performance on the neuropsychological tests
compared between groups using ANOVA. The magnitude
of differences between groups will be expressed using
Cohen’s d.
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Publication plan
Depending on the results of the study, the publication plan will be:
a) The first publication will describe the relationships between cognitive
function and depressive symptomatology and demographic
characteristics. The aim of this publication is to consider the extent to
which cognitive impairment can be classified in individual patients with
depression. This will be submitted to a journal in the area of general
psychiatry such as JAMA Psychiatry, Biological Psychiatry or British
Journal of Psychiatry.
b) The second publication will consider the stability of cognitive impairment
in people with depression.
Qualifications and experience of the research team (include
Curricula Vitae)
The study team will consist of
Paul Maruff, PhD:
Background. Paul Maruff is a neuropsychologist with specialization in
neuropsychological aspects of neuropsychiatric disease. He is Chief Science
Officer and founder of CogState Ltd, the company that worked with BMS on
CN162007. Dr Maruff is also Professor at the Florey Institute for
Neuroscience and Mental Health at the University of Melbourne, Australia.
Dr Maruff has published over 250 articles relating to cognitive impairment in
international peer reviewed journals.
Role in this study: Dr Maruff will oversee the project, organize tasks within
the study team be responsible for statistical analyses and reporting and will
develop communications arising from the results.
Adrian Schembri DPsych.
Adrian Schembri is a board certified clinical psychologist with expertise in
depression and anxiety disorders. Dr Schembri has published widely on the
interrelationships between psychiatric symptoms and
Robb Pietrzak, PhD.
Background: Robert Pietrzak is Associate Professor in the department of
psychiatry at Yale University New Haven, CT, USA. Dr Pietrzak is an expert
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in psychiatric epidemiology and public health. He is also expert on the
application of modern and sophisticated multivariate statistical techniques
to understanding issues of comorbidity in both psychiatric and physical
illness.
Role in this study: Dr Pietrzak will advise on statistical analyses and the
interpretation thereof. Dr Pietrzak will be conduct quality assurance
analyses of statistical approaches developed by Dr Schembri. Dr Pietrzak
will also be involved in drafting communications from the study and in the
presentation of information relating to the project.
Curricula Vitae
Detailed curricula vitea for all non-BMS staff that are named as
investigators on this grant are included at the end of this document.
Source of research funding
The cost of statistical analysis (including data transfer and data cleaning),
data interpretation and data reporting will be conducted by CogState
scientists or graduate students supervised by Maruff or Schembri.
Any potential conflicts of interest
Paul Maruff is chief scientific officer and full time employee at CogState Ltd
the company that provided the cognitive tests used in the study.
Adrian Schembri is a full time employee of CogState Ltd. the company that
provided the cognitive tests used in the study
Robb Pietrzak has no conflicts to declare.
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