Genetic and Environmental Based Risk Model to Predict
Cognitive Decline in a Healthy Population
Candice Dunn
Senior Honors Thesis
Duke University
Fall/Spring Semester 2016-2017
Instructor: Kathleen A. Welsh-Bohmer, Ph.D.
Accompanied by: Michael W. Lutz, Ph.D., Daniel J. Hatch, Ph.D.
Duke Institute for Brain Sciences | Department of Neurology
Committee Members: Jeffrey Browndyke, Ph.D.
Duke Institute for Brain Sciences | Center for Cognitive Neuroscience
This thesis was submitted on 04/24/2017 as part of the requirement for Graduation with
Distinction in Neuroscience.
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Abstract
Over the years, research has attempted to understand the etiology and pathophysiology of
Alzheimer’s disease (AD), as well as the key risk factors involved in pathogenesis. While the
underlying biology of AD is better understood due to these findings, clinical trials focused on
AD treatment remain inconclusive, underscoring the importance of transitioning away from
treatment and towards prevention in order to address the growing prevalence rates of AD. Thus,
the overarching goal of this longitudinal study is to develop a risk model, utilizing six genetic
and environmental based risk factors related to AD, to predict cognitive change using
neuropsychological testing over a four-year period in a cognitively healthy population. While
most studies to date have tested their predictive accuracy using clinical diagnoses, we explore the
use of cognitive change as an endophenotype with high predictive accuracy, giving us more
power with small sample sizes while also catching the first signs of disease onset, as cognitive
deficit is a hallmark feature of AD. First, we explore the role of two individual genes in relation
to cognitive change: apolipoprotein E (APOE), a well-recognized gene in the literature and a
polymorphic, deoxythymidine tract in intron 6 of translocase of outer mitochondrial membrane
(TOMM40’523), a highly debated gene in the field. Second, we explore the role of minor risk
genes that have reached genome-wide significance using a polygenic risk score (PRS) that sums
their effect. Lastly, because no studies to date have taken environmental risk factors into
consideration, we explore the effect of three modifiable risk factors commonly associated with
AD: systolic blood pressure, body mass index and a self-reported count of five metabolic
conditions. We hypothesize that individuals with the highest genetic risk scores and the largest
number of environmental risk factors will show the most cognitive decline over four years.
Analyzing the role of these variables in four subdomains (APOE, PRS, TOMM40, metabolic
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health concerns), we present a method that provides insight for future studies limited by sample
size and cross-sectional data, while also presenting novel information regarding the effect of
TOMM40 and metabolic health concerns on AD risk. This approach may assist future clinical
trials in screening for healthy, high-risk individuals that may benefit from early intervention.
Lastly, our model could provide novel insights into the early pathogenesis of AD.
Introduction
Progressive neurodegenerative disease, such as Alzheimer’s disease (AD), causes
inescapable cognitive deficit and eventual loss of autonomy. Alzheimer’s disease is currently the
sixth leading cause of death in the United States, affecting a total of 5.4 million people and
costing $236 billion in patient care. By 2050, AD prevalence is estimated to rise to 16 million
victims and cost $1.1 trillion (Alzheimer’s Association Facts and Figures, 2016). Such a
mortality rate, in addition to its economic burden, are what led to former President Barack
Obama’s national call to increase funding for AD research in 2011 by signing the National
Alzheimer’s Project Act. Despite the increased focus on AD treatment, there are currently no
treatments available for slowing AD progression once dementia is present. This may be because
the pathological damage is too severe at this point in the disease (Salomone et al, 2012). Thus,
because AD is progressive in nature, there is an essential need to focus treatment at an earlier
stage of disease, such as Mild Cognitive Impairment (MCI), before symptomatic AD is
expressed. Delaying the onset of MCI due to AD could have a large public health impact and
reduce the incidence (i.e. new cases) of AD.
Recent studies within the last decade have helped clinicians understand the etiology,
pathophysiology and progression of AD by identifying several risk factors associated with the
disease. Such findings make it possible for clinical trials to screen for high-risk individuals by
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targeting cognitively healthy individuals who have not yet started to show symptoms of AD.
Building a risk model based upon these risk factors would allow for detection of individuals
likely to show cognitive decline and eventually develop AD dementia. Furthermore, such risk
models would allow clinicians to enroll high-risk individuals in clinical trials focused on primary
prevention to delay the age of onset of MCI due to AD (Romero et al, 2014).
Developing predictive models for delaying the onset of AD requires a good
understanding of the disease expression in addition to some knowledge of the contributing risk
factors. The sections that follow provide an overview of the clinical presentation of Alzheimer’s
disease and a discussion of the known contributing risk factors.
Alzheimer’s Disease
First described in 1906, AD has been studied for well over a century, allowing clinicians
to develop a clear sense of the chronology of key symptoms associated with progression from
early to late stages of AD. In no specific order, these are commonly referred to as the four A’s of
AD: amnesia, aphasia, agnosia and apraxia. Memory problems (largely episodic memory) are
typically the earliest signatures of AD and are often associated with neuronal changes seen in the
hippocampus. Thus amnesia, characterized by the inability to form new memories, is often the
first cognitive domain affected in the beginning stages of AD. Throughout progression,
difficulties in form vision and recognition (agnosia), communication (aphasia) and motor
integration and executive function skills (apraxia) are often a result of AD (Welsh-Bohmer,
2016).
Ultimately, there are three main stages defined by the pathology and presence of
cognitive decline: preclinical AD (silent disease), prodromal AD (often referred to as MCI) and
fully symptomatic AD (dementia) (Welsh-Bohmer, 2016). During preclinical AD, signs of
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disease are silent while evidence of biological changes of the disease are present. As the disease
progresses from preclinical AD to MCI, changes in cognition are present, although they may not
be disruptive to daily functioning (Albert et al 2011). Lastly, transitioning from MCI to fully
symptomatic AD is associated with major cognitive changes including impaired problemsolving, executive functioning, visuospatial functioning, language and attention (Attix and
Welsh-Bohmer, 2006). At this point in disease progression, function is now impaired and the
patient is diagnosed as having dementia.
Because cognitive change is not recognizable until the later stages, it is difficult to detect
individuals with preclinical AD. However, recent studies have identified criteria usable for
designing neuropsychological tests sensitive to detecting cognitive change during preclinical AD
that would otherwise be accepted as normal cognitive change (Dubois et al, 2015). Some studies
have shown it is possible to detect preclinical AD nearly a decade before diagnosis, largely with
respect to executive functioning and episodic memory (Welsh-Bohmer, 2016). Thus,
neuropsychological tests sensitive to recognizing impairments in these four cognitive domains at
all stages of disease (especially preclinical AD) are critical in clinical settings for proper
diagnosis and monitoring cognitive change over time.
Clinical diagnosis is now augmented by the identification of highly useful biomarkers
which are linked closely to the disease pathology. Proteins related to the amyloid plaques,
neurofibrillary tangles, and neuron death (beta amyloid, tau, and phospho-tau respectively) can
be measured in cerebrospinal fluid (CSF) and in functional neuroimaging studies to assist the
clinician in identifying true cases of early MCI due to AD and separating these cases from
healthy aged adults (Lutz, 2016). Recent studies have identified a temporal order for biomarker
occurrence in preclinical AD, starting with increased levels of CSF Aβ42, followed by amyloid
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positron emission tomography (PET), CSF tau, and finally by fluorodeoxyglucose (FDG)-PET
and MRI (both tracking closest to cognitive impairment) (Jack et al, 2013). Understanding the
temporal order of these biomarkers is helpful for clinicians wishing to understand the relative
timeline for individual’s progressing from preclinical to prodromal AD. Drawing from these
findings, subsequent studies have proposed the idea that amyloid deposits could form a decade
earlier than the first changes in cognition, further suggesting cognitive change occurs in result of
amyloid levels reaching a plateau (Toledo et al, 2014). By looking at the extent of amyloid
plaques and neurofibrillary tangles in the medial temporal lobe, these methods are sensitive to
detecting the underlying biological changes associated with AD progression. However, these
methods can be expensive, invasive and can be complicated to interpret in ambiguous cases.
Thus, biomarkers that are inexpensive, dichotomous and avoid invading participants are ideal
(Lutz, 2016). Genetic-based biomarkers are convenient, non-invasive, and can be quite useful
predictors of disease likelihood when used in older adults—where risk of disease will be
particularly worrisome. Genetic biomarkers have high specificity and, a low false-positive rate
(Breitner, 2016). Because false-positives require more expensive testing to sort out who is likely
truly affected by the condition, genetic risk algorithms may provide a low cost alternative,
effective for screening large groups of patients over time. In response, researchers have proposed
implementation of genetic-based biomarkers in clinical trials seeking to identify individuals at
high risk of developing AD.
Genetic Risk Factors for Alzheimer’s Disease
Among the several risk factors associated with AD, the two most important risk factors
are not modifiable. The first of these is age. AD is classified as a progressive neurodegenerative
disease that develops over a very long period of time. Older adults are therefore more likely to
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develop the disease than younger adults. While aging cannot be modified, we can try to better
understand the biology of aging and use this to our advantage to prevent age-associated diseases
like AD.
A second major risk factor is the inheritance of risk genes associated with the
development of AD. As previously discussed, genetics are responsible for a large proportion of
AD risk. The gene with the most significant role in predicting late-onset AD is apolipoprotein E
(APOE). This gene comes in three different isoforms (APOE: 𝜀2, 𝜀3, 𝜀4). The inheritance of the
𝜀4 variant confers high risk of AD in a dose dependent manner, such that inheritance of this
variant from each parent, resulting in a 𝜀4/ 𝜀4 genotype, places an individual at high risk of the
disease and an early age of onset; one copy of the gene (e.g. 𝜀4/ 𝜀3) places the individual at an
intermediate risk and no copies of the gene (i.e. 𝜀2/ 𝜀3 or 𝜀2/ 𝜀3) places them at a lower risk.
While other genes have now been identified as associated with later AD risk, none have as large
as an effect as APOE (Lambert 2013; Liu 2013). Genome-wide association studies (GWAS)
have identified minor risk genes that contribute to AD, including the following: BIN1, CLU,
ABCA7, CR1, PICALM, MS4A6A, CD33, EPHA1, CD2AP (Lambert 2013). In addition,
studies have identified and debated the role of a polymorphic, deoxythymidine tract in intron 6 of
translocase of outer mitochondrial membrane (TOMM40’523) (Crenshaw, 2013). Thus,
understanding the relationship between age and these various AD risk genes is important for
identifying high-risk individuals and determining who is at imminent risk of developing
symptomatic AD.
One way this has been handled by previous studies to determine genetic risk models of
AD is to use a polygenic risk score (PRS) that weighs the individual effect of each minor AD
risk gene by the number of risk alleles an individual has and summing them into a final “score”
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(Hayden et al, 2015). Recognizing minor risk genes would improve the predictive accuracy of
models by identifying individuals who are at risk of developing AD, although they may not have
the APOE risk allele (Escott-Price et al ,2015). Unlike the case for APOE, summation of these
genes is necessary because of their small effect on models when considered independently.
Furthermore, recent studies have shown model improvement when a polygenic risk score
capable of summing the effect of minor risk genes is used in conjunction with an APOE risk
score (Escott-Price et al, 2015). Utilized by several studies, genetic risk scores have been
incorporated in risk models to either predict cognitive decline or a later diagnosis of AD.
Regardless of the analyzed outcome, most studies have studied older individuals (age 65+),
including individuals who may already have symptoms compatible with early stages of AD.
Thus, additional studies are needed to test the predictive accuracy of genetic risk scores in a
cognitively healthy population.
Modifiable Risk Factors
In addition to risk factors for AD that are beyond control, such as age and genetics, there
are a number of modifiable risk variables that have been recognized for playing a later role in
developing fully symptomatic AD. These modifiable risk factors include common lifestyle
variables and health conditions such as hypertension, diabetes, obesity, smoking and high
cholesterol (Xu et al, 2015). Studies have supported the accuracy of the Framingham
cardiovascular risk profile (FCRP), based on common cardiovascular risk factors such as
diabetes, cholesterol levels, hypertension and obesity, in predicting cognitive decline in AD
patients (Viticchi et al, 2015). These studies further highlight the need to recognize modifiable
health variables as predictors of cognitive decline, particularly in patients who are at high risk of
AD based on age and genetics. Ultimately, because health concerns have a modifiable capacity
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and may be treatable, the implication of recognizing these risk factors is to increase public
awareness on variables that are able to be addressed with hopes of altering and/or reversing a
portion of their risk for AD prior to onset. Furthermore, these studies show the effect of
recognizing not only genetic risk factors on pathogenesis, but also environmental risk factors.
One way to incorporate environmental variables into studies using risk models is to
screen for health risks during baseline assessment. When health concerns are reported at
baseline, it may be possible to predict who is likely to experience cognitive change by
considering these conditions in relationship to each other and to the persons’ genetics. Therefore,
risk models that utilize genetic and environmental predictors, as well as age, would likely create
the most accurate prediction of later cognitive decline. Furthermore, improved models could
allow researchers to analyze gene-environment interactions that may provide insight on the
pathology of AD.
The last concern that must be addressed shares a common theme throughout all studies,
from those in the past to those that will take place in the future. Risk model efficacy can only be
determined with careful consideration of the dependent variable used for analysis. As this often
varies, many studies have used clinical diagnosis to test the predictive accuracy of their risk
models. While this is a sound method, it requires the tracking of a large number of patients
longitudinally for an unforeseen amount of time, increasing the costs of studies significantly.
Thus, as previously studies mentioned above have suggested, using cognitive change as the
dependent variable may offer an improved methodology for primary prevention studies working
with cognitively healthy individuals in the future. Endophenotypes, also known as intermediate
phenotypes, such as cognitive decline are important components of AD studies allowing greater
precision as the defining characteristics of early disease (Reitz, 2009). More importantly, using
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endophenotypes to detect symptoms of late-onset AD rather than the full clinical diagnosis,
allows for smaller sample sizes and increases the statistical power of studies (Blangero, 2003).
Thus, endophenotypes can be used as dependent variables in prevention studies, and are most
likely the best approach when working with cognitively healthy individuals.
Keeping these considerations in mind, we have developed an improved genetic and
environmental based risk model (GEBRM) that identifies healthy individuals with an increased
risk of developing AD. While genetic risk models have been proposed in the past, no models to
date (that we are aware of) have considered the combined effect of environmental risk factors.
Additionally, studies done in the past using similar genetic risk scores have analyzed participants
in the early stages of AD whereas we are looking to detect the first signs of disease in a healthy
population. To determine the efficacy of our improved risk model, baseline measurement of
cognitive functioning as well as monitoring cognitive change over time is essential. Because
impaired cognition is the hallmark symptom of AD, monitoring progressive cognitive decline is
an appropriate endophenotype capable of testing model accuracy. By following cognitively
healthy individuals prospectively four to five years, we can track cognitive change using a
battery of neuropsychological tests.
Successful results from our cohort will provide an improved risk model able to predict
the highest-risk individuals for AD, one of the top-10 deadliest diseases in the United States.
Additionally, it allows for analysis of gene-environment interactions and their significance in AD
development. We hypothesize that our GEBRM, incorporating six predictors into four
subdomains (APOE, PRS, TOMM40, metabolic health concerns) will improve risk models by
predicting cognitive decline with higher accuracy. Our improved GEBRM is predicted to have
more sensitivity than original models in targeting high-risk, healthy individuals because it
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incorporates the most significant single nucleotide polymorphisms (SNPs) associated with AD
risk as well as highly associated, modifiable health variables. We hypothesize that individuals
with the largest genetic risk scores (APOE, polygenic and TOMM40) and the most
environmental risk factors at baseline will be at greatest risk for future cognitive decline. Lastly,
we predict gene-environment interactions will reveal individuals at risk of AD that may not have
been recognized by models based solely on genetics, suggesting an environmental role in disease
development.
If our results reflect a successful model, our GEBRM will allow clinicians to detect and
recommend the highest-risk individuals for clinical trials focused on primary prevention. This
could transition clinical trials away from a focus on treatment and towards prevention. Because
evidence suggests that at least a portion of AD risk is modifiable, testing the efficacy of clinical
trial drugs on individuals at high-risk may improve success rates. If successful, starting therapies
before irreversible injury has occurred would have implications at a societal level, reducing the
incidence of disease and associated health care costs. Lastly, our model which looks at various
genetic and environmental risk factors associated with the very early cognitive signatures of AD,
could provide novel insights into the early pathogenesis of AD.
Methods
Cohort
The Joseph and Kathleen Bryan Alzheimer’s Disease Research Center (Bryan ADRC)
provided data necessary for this study. Data for the GEBRM was collected from a prior study
(PREPARE) comprised of a healthy volunteer cohort (n=1244) in the community of Durham,
NC. The cohort used in PREPARE was originally drawn from the Alzheimer’s Disease
Prevention Registry (ADPR, n=2311). Participants ranging from ages 55 to 93 enrolled in the
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registry in response to community outreach events. Additionally, registrants represented an
ethnically diverse population and gave permission to be contacted for future prevention studies
(Romero 2014). PREPARE was one of the first studies to draw from the ADPR and was
designed to assess the genetic and health conditions related to AD in the community. The
protocol lasted around one hour. Vital signs were taken, family history was collected, a blood
sample was drawn and a battery of neuropsychological tests were administered.
Longitudinal data were collected by following subjects prospectively from baseline (2011
to 2013) to a second time point four to five years later (2015-2017). Participants were contacted
four years after baseline data collection to measure cognitive change via the same
neuropsychological testing. The second wave of data collection is ongoing. At the time of
analysis, the data collection was halfway complete (n=480). This study primarily focuses on
Caucasians due to known allele frequency differences between races recognized in the literature.
Furthermore, because we could not determine which TOMM40 allele is risk or protective for
African Americans (AA) due to ambiguous findings in the literature, we could not accurately
address genetic risk in AA. However, regression models were run cross-sectionally to compare
results across our population at baseline and models with and without AA were analyzed for
similarities. All models were controlled for age, sex and education confounders. An extensive
discussion of AA exclusion will be addressed in the discussion section of this paper.
Genotyping
Blood samples were obtained and DNA was extracted and genotyped at Polymorphic DNA
Technologies. Those who did not wish to have their blood taken could continue with the study,
but were not further analyzed in our results. The 11 targeted genotypes included the following:
APOE, TOMM40, CR1, BIN1, CD2AP, EPHA1, CLU, MS4A6A, PICALM, ABCA7, CD33.
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Errors in genotypic assays were recorded as missing values. Participants missing the APOE
genotype (n=112) were excluded from the study because of APOE’s strong weight in the full risk
model. Without APOE, risk scores would be affected drastically, introducing bias into our
analysis. Participants with more than two missing genotypes other than APOE were excluded
from analysis to avoid skewed results. For the participants with 1-2 missing values who
remained in the study, their missing value was assigned a “0” and thus did not add, or remove,
from their polygenic risk score. This scenario affected a small number of participants, allowing
missing values to be attributed to random chance.
Health Problems Self-Report Questionnaire
Our study was restricted to cognitively healthy individuals for the earliest and most sensitive
measurement of the onset of mild cognitive impairment (MCI) due to Alzheimer’s Disease (AD)
(if it was to occur). Our sample enrolled in ADPR under self-reported healthy conditions. For
PREPRARE, participants self-reported demographic information, family medical history as well
as personal health concerns. The health questionnaire included seven domains of health issues
with boxes to check off “yes”, “no”, or “don’t know” in response. The seven response domains
included health problems in the following categories: metabolic, cardiovascular, cancer,
respiratory, gastrointestinal, bone/joint and neurological concerns. Participants were specifically
asked: “Has a doctor ever told you that you have, or have ever had any of the following [health
problems]?” One question specifically targeted prior AD diagnosis. Patients who self-reported
prior diagnosis of Alzheimer’s Disease (n=2) were not included in our longitudinal analysis.
These impaired individuals were included in our baseline analyses to permit generalizability of a
representative sample of people who self-report as healthy at registration. Additionally, clinical
diagnosis was not possible for checking the validity of these responses. The final longitudinal
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sample of 480 individuals were those who had completed their second cognitive testing session
by the time data analysis began and were not diagnosed with AD at baseline.
Description of Variables
APOE & TOMM40 Risk Score
Equation 1 was used to determine the APOE ε4 and the rs10524523 poly-T variant in
intron 6 of the translocase of the outer mitochondrial membrane gene (TOMM40) risk scores.
The SNPs used to detect the APOE
Equation 1: 𝑙𝑜𝑔10 (𝑂𝑅) × 𝑛𝑢𝑚𝑏𝑒𝑟_𝑚𝑖𝑛𝑜𝑟_𝑎𝑙𝑙𝑒𝑙𝑒𝑠
genotype was rs429358 for the minor
risk allele (ε4). Originally, the TOMM40 risk score was included in the polygenic risk score
because of recent findings in the literature supporting its association with AD. However, after
our original analysis showed model saturation, TOMM40 was analyzed independently.
Categorical designation of the alleles for TOMM40 rs10524523 were based on the length of the
poly-T repeat and followed standard definition as given in Roses et al 2010 namely S, L, VL.
The odds ratio (OR) used for each gene is depicted by Table 1.
Polygenic Risk Score
One polygenic risk score (PRS) summed the individual effect of 9 minor risk genes. The
nine risk genes used in the PRS included the following (Gene, SNP): (CR1, rs6656401); (BIN1,
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rs6733839); (CD2AP, RS10948363); (EPHA1, rs11771145); (CLU, rs9331896); (MS4A6A,
rs983392); (PICALM, rs10792832); (ABCA7, rs4147929); (CD33, rs3865444). The polygenic
risk score was calculated according to Equation 2. The only notable difference between
Equation 1 and Equation 2 is the summation function. Details regarding OR for each gene is
further explained in Table 1.
Equation 2:
Environmental Risk Variables
Environmental risk variables are a new addition to AD risk models, offering a novel
approach that differentiates our research from models proposed in the past. A total of three health
variables were used as predictors. All three environmental risk variables recognize metabolic
health concerns, of which included the following: Diabetes, High Cholesterol, Thyroid Disease,
Hypertension (High Blood Pressure) and Obesity. Metabolic risk factors were considered both
categorically and continuously. We focused on metabolic concerns rather than health variables
from the health domains mentioned in the methods section above (Health Problems Self-Report
Questionnaire) because of evidence in the literature supporting their association with AD
(Vittichi, 2015). The three health variables recognized by our model included the following: a
continuous body mass index (BMI) variable, a continuous systolic blood pressure (SBP)
variable, and a categorical variable that counted the number of metabolic health concerns each
subject reported at baseline (from 0-5). Baseline measurements of SBP were used while BMI
was calculated from the weight and height originally reported by participants using the
𝑚𝑎𝑠𝑠
equation: (ℎ𝑒𝑖𝑔ℎ𝑡 2 ) ∗ 703.
Creating the Full Model
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The final risk model recognized 6 predictors: APOE4 risk score, PRS, TOMM40 risk score, a
self-reported (SR) count (0-5) of metabolic health concerns, BMI and SBP. The predictors were
separated into four subdomains: APOE (ε4), PRS, TOMM40, Health Variables. The health
variables subdomain recognized the SR count, BMI and SBP in one model. The genetic risk
score calculations were based on risk alleles at SNP variants reported from the literature. An OR
> 1 represented a risk allele and an OR < 1 represented a protective allele. When the OR <1, the
major allele was depicted as the “higher risk” allele. The log10 transformation used in Equation
1 and Equation 2 was necessary for transforming the data for symmetry across the x-axis so that
risk alleles were to raise an individual’s risk score above the x-axis (positive) and protective
alleles were to lower an individual’s risk score below the x-axis (negative). Large, positive risk
scores represented the highest risk participants while negative risk scores represented individuals
who were relatively protected.
Cognitive Measurements
A battery of neuropsychological tests was administered to measure cognitive functioning,
testing a range of cognitive domains to ensure the various number of AD symptoms were tracked
longitudinally [executive function, episodic memory, attention, visuospatial skills, language,
orientation and naming]. The battery administered included three neuropsychological tests:
Montreal Cognitive Assessment (MoCA), the Word List Memory (WLM) test from the
Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) battery, and the Trail B
Making Test. CERAD WLM consisted of multiple components including the following: CERAD
Word List Learning (WLM-learning) task (WLMTOT) (3 trials), CERAD Delayed Recall
(WLMCRT), CERAD Delayed Recognition (WLRG) (Yes and No). The MoCA test is scored on
a scale of 0-30 and is consistently used in clinical settings for detecting MCI by testing executive
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function skills, attention, language, memory, orientation and visuospatial skills. Ultimately, large
scores represent healthy individuals. The Trail B Making test is often used to test executive
function skills by timing an individual’s ability to task-switch. It is scaled from 0-300 seconds
and those who exceeded the limit were coded with a time of 300 seconds to limit the number of
missing values. Thus, Trails B is reverse-scaled as large times may represent disruptions in
cognition and small times represent healthy individuals. Lastly, CERAD is used for detecting
dementia and/or MCI and tests memory domains. WLM-learning scores range from 0-30,
representing a combined score from three individual trials (0-10) while delayed recall and each
of the recognition yes/no trials ranged from 0-10. High scores represent better performance. The
description of each neuropsychological exam defined here is further discussed by Hayden et al
2014.
Participants with below-average performance on neuropsychological tests at baseline
were identified using the interquartile rule for outliers and were analyzed for suspected onset of
mild cognitive impairment (MCI) due to AD. Using the first quartile (Q1) and third quartile
(Q3), the interquartile range was calculated (IQR = Q3-Q1) and multiplied by 1.5. Subtracting
this value from Q1, below average outliers were determined if participants scored outside this
range: [Q1 – (IQR)*1.5]. However, due to the nature of our cohort which screened for
participants who self-reported as healthy, these subjects were not removed from analysis as these
subjects are representative of a community population in which our findings are expected to be
applied. Additionally, participants who scored above average were analyzed as outliers if they
scored outside the range of 1.5*IQR above Q3: [Q3 + (IQR)*1.5]. These participants remained
in the sample for similar reasons as the low-scoring outliers. The goal of our study was to collect
two cognitive data points for the entire sample (n=1244). But with a shortened timeline, we were
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only able to collect a subset of the total sample (n=480) by the four-year time point. Due to
exclusions, this left 352 individuals to analyze longitudinally. This small sample size reduces the
power of our statistical analysis, but is typical for exploratory analysis and will be improved
upon by the end of data collection in the summer of 2017. The second data point was collected
four years after baseline using the same battery of neuropsychological tests. Participants who did
not wish to continue with the study, who passed away since baseline, or who had missing
information were excluded from analysis because two data points were necessary to test the
accuracy of our risk model for prediction of cognitive change.
Statistical Analysis
Patients were de-identified and responses were numerically coded for statistical analysis
using SAS/STAT Software. Stepwise linear regression was used to assess the relationship
between AD risk factors and cognitive change. All regression models were run with assistance
from a postdoctoral fellow with training in biostatistics. Regression models co-varied for age,
sex, race and education. Because our data analysis was exploratory, there is a need to fully
clarify our results once the complete data-set is collected by the summer of 2017.
Model Assessment
Our model was largely saturated by the number of predictor variables originally
hypothesized, causing us to analyze our predictors in four subdomains rather than at once. These
subdomains were split into the following: APOE (𝜀4) risk score, PRS, TOMM40 risk score and
combined metabolic health variables (self-report metabolic score, SBP and BMI), allowing us to
further analyze the effects of these variables on cognition independently. Unfortunately, this did
not allow us to test for interaction as we originally planned. The gene TOMM40 was analyzed
independently from the PRS due to the known interaction between variants in this gene and the
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APOE coding SNPs. Every individual was analyzed at baseline while those with cognitive test
results at baseline and follow-up were analyzed further to test the model’s accuracy in predicting
cognitive change.
Results
Cross-sectional Subdomains at Baseline
The final cross-sectional sample included 1,064 non-African American individuals,
ranging in age from 55 to 89. Results are recorded in Table 2. When age, sex and education were
controlled for, the APOE 𝜀4 risk score was significantly related to baseline cognitive
performance measured by MoCA (-0.490, p=0.0152), Trails B (9.873, p=0.008), WLMCRT (0.496, p=0.008) and WLRG (No) (-0.097, p=0.007) in the expected direction. Keeping in mind
that Trails B is reverse-scaled, the APOE 𝜀4 risk score predicted baseline cognitive performance
on four of the six cognitive tests used, matching reports in the literature. Unlike the APOE 𝜀4
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risk score, the PRS was not significantly related at baseline to any of the neuropsychological
tests.
When age, sex and educated were controlled as confounders, TOMM40 predicted
baseline MoCA (9.889, p=0.0042), but in the unexpected direction (short (S) allele was
associated with lower values of MoCA relative to the VL allele). Because APOE and TOMM40
so closely relate we controlled for APOE to test for independent effects of TOMM40. When
APOE 𝜀4 was controlled for as a confounder, the results remained significant and still in the
unexpected direction (8.252, p=0.0279). Trails B was significantly related to TOMM400 (145.69, p=0.022) when age, sex and education were controlled, but was non-significant (-105.72,
p=0.129) when APOE 𝜀4 was controlled. Surprisingly, WLRG (Yes) showed a non-significant
trend when demographics were controlled (1.897, p=0.08), but became significant (2.58,
p=0.028) in relationship to TOMM40 and in the unexpected direction when APOE 𝜀4 was
controlled. This finding was cross-validated by running a model that controlled for APOE by
only looking at APOE 𝜀3/3 individuals (3.20, p=0.02).
Lastly, the SR health concerns showed significant results (-0.046, p=0.0128) in
relationship to WLRG (Yes) when age, sex and education confounders were controlled. These
results are representative of cognitive deficit at baseline. All other models were non-significant
when demographics were controlled.
Cognitive Change Subdomains
The final sample used for cognitive change was limited to 352 non-African American
participants from the ages of 55 to 89. Results are recorded in Table 3. Cognitive change was
modelled by looking at time point two as the dependent variable while controlling for time point
one. It was critical to control for time point one to adjust for the baseline differences in cognition
21
between individuals. More specifically, individuals that scored a perfect MoCA score (30) at
baseline did not have room for improvement, but had more room for decline than someone who
originally scored a 28 on MoCA. By the time our second data point started collection, four years
had gone by since the first scores were collected.
The APOE 𝜀4 showed a significant relationship to cognitive decline on several tests
when age, sex and education were controlled, specifically: MoCA (-1.010, p=0.009), Trails B
(17.13, p=0.002), WLMTOT (-1.771, p=0.001), WLMCRT (-0.902, p=0.004) and WLRG (No)
(-0.284, p=0.009). When demographics were controlled, the PRS was not significantly related to
cognitive decline. It is important to note that although non-significant, PRS predicted change in
the unexpected direction on all tests besides WLMTOT. This will be further addressed in our
discussion.
22
TOMM40 significantly predicted cognitive change on Trails B (-231.82, p=0.012) and
WLMTOT (18.32, p=0.037) when age, sex and education were controlled. However, these
results were in the unexpected direction and became non-significant when APOE was controlled.
It is important to mention that all cognitive change predictions were in the unexpected direction
for TOMM40. This will also be explored in the discussion of our paper.
SBP predicted cognitive change (-0.217, 0.022) on Trails B when age, sex and education
were controlled. However, this was the only significant finding from the three health variables
included in our metabolic health variable subdomain for predicting cognitive change over time.
Discussion
We believe it is important to start this discussion with our rationale behind the sample
exclusions utilized in this study. As suggested by previous studies, risk allele frequencies have
shown differences between races (Reitz et al, 2013). Thus, including African Americans in
analysis may result in confounding results because of different inheritance of some of the key
genetic regions or “haplotypes” (Hayden et al, 2015). Recent analyses of the TOMM40 poly-T
gene showed that there is a genetic admixture of the TOMM40 genotypes amongst African
Americans depending on their ancestry (European versus African) (Roses et al, 2014). Because
of the complexity of the genetics and the yet undetermined risk relationship between TOMM40
and AD in African Americans, we could not determine which allele type would be considered of
high or low risk. This is an area of interest that is being actively explored in the Bryan ADRC.
In fact, to explore the extent to which the results from the African Americans in this
sample compared to the rest of the participants, we ran models that included African Americans
to see if there were similarities within our sample. The minor allele frequencies for African
Americans were very close to the allele frequencies seen in our full sample, suggesting that in
23
this sample the two groups are more similar than different (perhaps reflecting some shared
European ancestry). Furthermore, because our exploratory linear regression models including
African Americans suggested similar results on multiple subdomains, there is a critical need to
further study and define disease-specific OR for minority ethnicities before we can interpret
these results.
The final exclusion necessary for interpretation of our results required assessing missing
values. Because APOE largely contributes to AD, missing APOE values were not able to be
assigned interpretable missing values, which required us to exclude these individuals. In
conjunction to this possible assay problem, participants who had more than two minor risk genes
missing in their PRS could not contribute interpretable results. Although one of two missing
values can be assigned a missing value of “0” without largely affecting their score, more than
two “zero” scores may begin to skew the PRS in the wrong direction because of the meaning of
zero in our calculations. Since our PRS was transformed using log10, a missing value of zero is
best interpretated as a baseline score, with no protection and no risk contribution. Thus, these
exclusions were necessary for proper interpretation of genetic risk scores.
Altogether, our results for the APOE risk score are in line with the literature, supporting
APOE as the most important risk factor, other than age, associated with AD. In the APOE
subdomain, the absence of an effect with WLRG (Yes) was expected and is likely due to the
ceiling effects on the measure. Unlike our hypothesis, the PRS did not show any significant
results and the estimate for PRS followed a trend in the unexpected direction. This could be due
to several methodological reasons. First, studies in the past assessing PRS have used different
dependent variables, such as clinical diagnosis, instead of cognitive change to test the predictive
accuracy of their model. Thus, because our sample is representative of a cognitively healthy
24
population, it is possible the polygenic risk score is age-dependent and unable to predict the early
signatures of AD until the disease can develop further or a larger sample size is used. Secondly,
the OR used in our PRS calculations were collected from the International Genomics of
Alzheimer’s Project, a two-stage study (meta-analysis) based on genome-wide association
studies (GWAS) conducted in 2013. Thus, it is likely that there are other genes related to AD
that have not yet been identified. The structure of the PRS as a simple sum of terms that are the
product of the number of risk alleles multiplied by log OR precludes statistical evaluation of
interactions between genes.
Interestingly, our findings with TOMM40 were counterintuitive. Because our results
reported a significant, unexpected trend for WLRG (Yes) when APOE 𝜀4 was controlled for, we
further analyzed this finding. As TOMM40 has been highly debated in the field since its
discovery, studies originally predicted the S/S genotype for the ‘523 variant to be protective
against AD amongst APOE 𝜀3/3 individuals. When analyzing the effect of TOMM40,
individuals with long poly-T variants have been identified as having a higher risk for late-onset
AD (Roses et al, 2010). Thus, it is important to control for APOE genotype when analyzing the
‘523 variant. This is done by limiting analysis to APOE 𝜀3/3 individuals, eliminating the effect
of APOE 𝜀4. Following this rationale, we cross-validated our results by limiting our sample to
APOE 𝜀3/3 individuals instead of adding APOE 𝜀4 as a cofounder into our model. Our
counterintuitive results remained significant.
Evaluating studies that have done this in the past, when measuring cognitive decline in a
healthy population, individuals with the S/S genotype have been reported to perform better on
episodic memory, attention and executive function domains than heterozygotes and those with
no S alleles (Hayden et al, 2012). However, recent findings based on larger sample sizes have
25
challenged this original discovery. One study revealed faster cognitive decline among APOE
𝜀3/3 individuals with the S/S genotype for the ‘523 variant in comparison to those with one or no
S alleles (Yu et al, 2017). Thus, because we had access to these recent findings for our study, we
used this knowledge with the most updated criteria to code TOMM40 with a risk allele OR of
1.06. This OR was calculated by a practicing geneticist in the Bryan ADRC.
The specific risk allele for the TOMM40’523 variant has been identified as S or VL in
the literature from studies that have examined measures of cognition, age of onset of AD, risk for
AD or hippocampal thinning (Yu et al, 2017; Burggren et al, 2017). The largest study of the
association of this variant with cognitive decline (Yu et al, 2017) showed that in APOE 3/3
individuals, the S allele was associated with increased cognitive decline relative to the VL allele.
For the subdomains considered in our study, we observed a statistically-significant relationship
in the opposite direction. Specifically, that cognitively healthy individuals with the S/S genotype
may have a protective effect against cognitive decline at baseline. It may be possible that
TOMM40 has a protective effect at a younger age that disappears with time. However, because
the analysis of change in cognition with respect to the TOMM40’523 variant was nonsignificant, our study has not resolved this ambiguity of the TOMM40’523 risk allele, although it
does contribute additional data on the association of this variant with cognitive scores and
cognitive decline.
The inconsistency of findings on TOMM40’523 association with neurocognitive
measures across studies highlights the need for understanding the effect of TOMM40 poly-T on
cognitive decline in relation to age and the interaction with other AD risk genes, specifically
APOE. Recent biomarker studies that worked with healthy, older APOE 𝜀3 controls and reported
an association between entorhinal cortex (EC) thickness and poly-T repeat length showed that
26
individuals with longer poly-T repeat lengths (VL) have a thinner EC, further supporting the
hypothesis that longer poly-T lengths contribute to AD risk in cognitively healthy individuals
(Burggren et al, 2017). Additional measurements for cognition performance in the PREPARE
cohort over time may help to resolve the ambiguity of which TOMM40’523 allele is associated
with risk for cognitive decline. For Alzheimer’s genetics research, this is a future aim for studies
wishing to understand the effect of genetic risk on AD.
In relation to our results regarding metabolic health factors, few models were significant
at baseline and only one model showed longitudinal, significant results for SBP predicting
cognitive decline. Because our results were muted when sex, age and education were controlled,
our exploratory findings are difficult to interpret. It appears as if SBP may contribute to baseline
cognition, although this is highly dependent upon demographic variables. Our longitudinal
finding suggests that SBP predicts a cognitive improvement on Trails B, by decreasing the total
time required to complete the task. While this finding is seemingly counterintuitive, it is possible
that this finding could be real, suggesting higher SBP or treatment of hypertension increases
cognition. This highlights the importance for studies with a larger timeline (lasting throughout
the stages of AD), samples with a broader age range and neuropsychological tests able to
measure other cognitive domains in order to better understand this relationship. Because our
health variables are self-reported and were only collected at baseline, future studies should work
towards incorporating reliable and verifiable health conditions. Furthermore, it is possible that
previous findings in the literature suggesting the importance of metabolic and cardiovascular
health variables on AD are an effect of AD, rather than a cause, as they have worked with
diagnosed AD patients. Thus, there is a need for strong, consistent methods capable of measuring
modifiable health variables in cognitively healthy individuals. More specifically, our results
27
suggest a need for developing methods capable of measuring the effect of SBP on preclinical
AD.
Limitations
Our study utilizes a small sample size in the final analysis due to time restrictions from an
outreach standpoint, threatening the power of our statistical results. Additionally, our small
sample size did not allow for full model analysis as model saturation was an issue. Increasing the
sample size (perhaps by the end of data collection) may improve the power and generalizability
of our results.
Secondly, our risk model is only truly applicable for a Caucasian population, although it
may provide insight for future research on minority ethnicities due to similar allele frequencies
reported in our sample. Although in additional models we looked at cognitive change when
African Americans remained in the sample, the small sample size of African Americans (n=72)
and those of an unspecified race (n=9) limited this analysis. Most importantly, the odds ratios
utilized by our model are most applicable to a Caucasian population and may not present
converging results for other ethnicities.
Thirdly, the neuropsychological tests used in analysis were sensitive at detecting change
in a variety of cognitive domains; However, it is possible results may be improved by using
composite scores that are weighted with memory measures so profoundly involved in AD and
tied closely to the underlying pathogenesis.
Additionally, the use of self-reported health conditions may be inaccurate and include
reporting biases. More objective measures of these health conditions would be preferred but were
not available. Additionally, these measures are coded as categorical variables. Continuous
measures, such as blood pressure, weight and height provide a greater breadth of measures and
28
possibly better resolution of how that variable is related to another, such as cognitive decline. For
example, checking “yes” for high cholesterol can represent an individual who just reaches the
limit for being considered to have high cholesterol. However, it will also represent an individual
with a level well-over the limit. Thus, these individuals with different weighted effects of high
cholesterol will be given the same risk value in our model. A more extensive health concerns list,
as well as the option for patients to include health documents may also address this concern in
the future.
Lastly, our study is the first of its kind and in result, has not been replicated. Our sample
does not allow to be split for independent verification, making it more challenging to run the
model again for consistent results. Our predictive accuracy could further be tested by following
up with cognitive testing 10 years after baseline with participants (if the sample size would
permit this). However, for these reasons, our research can only be exploratory.
Conclusions
Our main goal was to explore a comprehensive risk model in a cognitively healthy
population that would be useful in predicting cognitive decline. The risk factors we examined
included known genetic risks as well as suggested medical risk conditions associated with both
cardiovascular disease and AD risk. Initial findings did not allow us to combine these risk factors
into a joint model to test for interactions, but the independent subdomains were able to provide
insight into key risk factors related to the earliest signatures of Alzheimer’s disease. Consistent
with the literature, APOE is the most significant risk factor able to predict baseline cognition as
well as cognitive change over time. Unlike recent findings and our hypothesis, a polygenic risk
score was unable to predict cognition at any point in time in a cognitively healthy population.
This suggests the need for exploration of gene-gene interactions within diverse populations,
29
especially in the preclinical stages of AD, to better understand the role of minor risk genes in
aging and disease onset.
Additionally, our results disproved recent findings supporting the risk effect of the ‘S’
allele from the TOMM40 genotype. Interestingly, our results suggest an age-dependent effect
that could represent the protective nature of the ‘S’ allele at younger ages. This finding points to
the importance of further exploring the relationship between TOMM40, APOE and age in
diverse, healthy populations to better understand genetic risk of AD.
Lastly, our study looked at the effect of modifiable risk factors related to metabolic health
concerns on AD risk. Although results were largely inconclusive, they suggest the need for
robust methods able to track the progression of health concerns with changes in time and
cognition. This is critical before a relationship between health concerns and cognition can clearly
be understood in healthy individuals. However, the few metabolic health variables that showed
significant results are suggestive of an environmental role in the progression of AD—one that is
controllable and may be treated or prevented if detected early.
Overall, future aims of our study wish to combine these risk factors into one model able
to detect their combined effect on cognition and to investigate possible gene-environment
interactions on AD risk. The broad goal of these types of studies is to better understand the risk
factors associated with AD to detect the highest-risk individuals in a cognitively healthy
population for early intervention. This would have clinical significance by providing clinicians
with the methodology necessary to identify individuals worthy of primary prevention clinical
trials to decrease the prevalence and incidence of AD long-term. Lastly, these studies help to
understand the pathogenesis of disease and may increase the likeliness of developing a treatment
for Alzheimer’s disease further down the road.
30
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