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Development of biomarker profiles for early detection of women with an at-risk for Alzheimer's disease phenotype

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Development of biomarker profiles for early detection of women with an at-risk for Alzheimer's disease phenotype

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UNIVERSITY OF SOUTHERN CALIFORNIA

Development of Biomarker Profiles for Early Detection of
Women with an At-Risk for Alzheimer’s Disease Phenotype
By
Jamaica Rhae Rettberg

DOCTORAL DISSERTATION

SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

for the degree

DOCTOR OF PHILOSOPHY

Field of Neuroscience

Advisory Committee:
Roberta Diaz Brinton, Ph.D.
Enrique Cadenas, Ph.D.
Wendy Mack, Ph.D.
Howard Hodis, M.D.
1

DEDICATION

To my parents,
Cathy and Gary,
who always encouraged me to dream big.
I am so grateful for your guidance and your love.

and,

To Beau,
best friend and partner through it all.
Thank you for all your love and support
during this crazy adventure of pursuing my doctorate.
2
ACKNOWLEDGEMENTS

Many thanks to my advisor, Dr. Roberta Diaz Brinton. Her dedication to my research –
and to helping me grow as a scientist – has been tremendous. I have deep appreciation and
gratitude for her mentorship throughout my Ph.D, and I consider myself truly fortunate to have
had the opportunity to begin my scientific career under her guidance. I am grateful to my
committee members: Dr. Wendy Mack, Dr. Enrique Cadenas, and Dr. Howard Hodis, as well as
Dr. Helena Chui. Their collegiality, support, and advice have been instrumental in my research. I
am also grateful to the National Institute on Aging and the Southern California Clinical and
Translational Science Institute for financial support of my doctoral research and education.
Thanks as well to the wonderful STAR students I had the opportunity to work with, both for their
assistance with research and for teaching me mentorship skills. Finally, I am deeply grateful for
my labmates and colleagues, who have been a wonderful source of support and friendship over
the past six years.
3
ABSTRACT
Development of Biomarker Profiles for Early Detection of
Women with an At-Risk for Alzheimer’s Disease Phenotype

Alzheimer’s disease is a progressive, fatal neurodegenerative disorder for which there is
no preventative treatment or cure. Over 5 million Americans are currently living with sporadic
late-onset Alzheimer’s disease; of those diagnosed, 65% are women. Metabolic changes in the
brain are among the earliest features of the Alzheimer’s pathological cascade. Estrogen
positively regulates the bioenergetic system of the brain from glucose uptake to aerobic
glycolysis, mitochondrial function and ATP generation. Estrogen also regulates the peripheral
metabolic profile, and peripheral changes in metabolic homeostasis are coincident with
metabolic changes occurring in the brain. Loss of ovarian hormones at menopause could initiate
a bioenergetic and metabolic crisis, resulting in a metabolic phenotype consistent with increased
risk for AD.
To address this hypothesis, we conducted an unbiased principal components analysis
followed by k-means clustering of clinical data and bioenergetic indicators derived from plasma
from women in the Early vs. Late Intervention Trial with Estradiol (ELITE). Nine metabolic
biomarkers were assessed. Metabolic clusters were compared by early- vs. late-menopause, and
correlated with cognitive performance. Metabolic clusters were also compared longitudinally
between women randomized to hormone therapy (HT) vs. placebo, to investigate the effects of
HT usage on metabolic biomarkers as well as cognitive function.
Metabolic variables measured at baseline generated three distinct clusters. Women in one
cluster had a healthy metabolic profile; women in the second cluster were characterized by high
blood pressure; and women in the third cluster had an overall unhealthy metabolic profile.
4
Metabolic biomarkers within all profiles were very stable and differed significantly among
clusters over the five years of the trial. At baseline, women in the unhealthy metabolic cluster
showed a trend towards worse performance on tests of verbal memory than women in the healthy
cluster. Women in all clusters showed an improvement in cognitive testing over five years,
although women with the unhealthy metabolic phenotype had the greatest improvement, and
women with high blood pressure had the least improvement. Longitudinal changes in cognitive
function differed significantly between women in early and late menopause on select
neuropsychological tests. Hormone therapy had little overall effect on longitudinal cognitive
trajectories within the three clusters; however, women with the high blood pressure phenotype
showed the greatest metabolic and cognitive benefit from hormone therapy.
Alzheimer’s-related changes in the brain are known to begin years before clinically
detectable dementia; thus, identification of biomarkers indicating the earliest preclinical changes
is increasingly important. Overall, this systems-level approach demonstrates that metabolic
biomarkers, even within a healthy population, can be used to identify phenotypes consistent with
Alzheimer’s risk. This plasma-based biomarker panel represents an affordable, rapidly
deployable, and clinically relevant strategy to detect an at-risk phenotype of sporadic AD.

5
LIST OF ABBREVIATIONS
3xTgAD, triple-transgenic mouse model of Alzheimer’s disease
αERKO, ERα knockout
αKGDH, α-ketoglutarate dehydrogenase
Aβ, amyloid beta
1–42
, beta-amyloid
AD, Alzheimer’s disease
ANOVA, analysis of variance
ANCOVA, analysis of covariance
ArKO, aromatase knockout
BMI, body-mass index
BP, blood pressure
CEE, conjugated equine estrogen
CMRglu, cerebral metabolic rate of glucose uptake
COX, complex IV
CSF, cerebrospinal fluid
DBP, diastolic blood pressure
E
1
, estrone
E
2
, estradiol, 17β-estradiol
E
3
, estriol
ELITE, Early vs. Late Intervention Trial with Estradiol
Estrogen, 17b-estradiol unless otherwise specified
ER, estrogen receptor
FDG, fluorodeoxyglucose
HbA1c, hemoglobin A1c, glycated hemoglobin
HOMA-IR, homeostatic assessment of insulin resistance
HT, hormone therapy
IDE, insulin degrading enzyme
MCI, mild cognitive impairment
MMSE, mini-mental state exam
MPA, medroxyprogesterone acetate
mtDNA, mitochondrial DNA
OVX, ovariectomized, ovariectomy
OXPHOS, oxidative phosphorylation
PDH, pyruvate dehydrogenase
PET, positron emission tomography
rCBF, regional cerebral blood flow
ROS, reactive oxygen species
SBP, systolic blood pressure
SHBG, sex hormone-binding globulin
T2DM, Type II diabetes mellitus
6
TABLE OF CONTENTS

1. ALZHEIMER’S DISEASE ......................................................................................................9

2. ESTROGEN: A MASTER REGULATOR OF BIOENERGETIC SYSTEMS IN THE
BRAIN AND BODY ....................................................................................................................10
2.1 Introduction .........................................................................................................................10
2.2. Estrogen, estrogen receptors, and intracellular signaling pathways in the brain ...............11
2.2.1. Estrogen receptors alpha and beta: localization and splice variants .........................12
2.2.2. Membrane-embedded estrogen receptors ....................................................................14
2.2.3. Mitochondrial estrogen receptors ................................................................................16
2.3 Estrogen and brain bioenergetics ........................................................................................18
2.3.1 Estrogen regulation of glucose transport .....................................................................19
2.3.2 Estrogen regulation of glycolysis ..................................................................................21
2.3.3 Estrogen regulation of mitochondrial energy production ............................................22
2.3.4 Estrogen regulation of oxidative stress .........................................................................24
2.4 Mitochondrial relevance to Alzheimer’s disease ................................................................25
2.5 Estrogen regulation of whole-body metabolism .................................................................28
2.5.1. Estrogen, adiposity, and obesity .................................................................................30
2.5.2. Estrogen and insulin resistance ..................................................................................34
2.5.3. Estrogen and diabetes .................................................................................................37
2.5.4. Estrogen and leptin .....................................................................................................40
2.5.5. Estrogen and ghrelin ...................................................................................................43
2.5.6. Estrogen and adiponectin ...........................................................................................45
2.5.7. Estrogen and sex hormone binding globulin ..............................................................47
2.6. PET imaging of brain bioenergetic deficits in aging and Alzheimer’s disease .................49
2.7. Estrogen and hormone therapy, the timing hypothesis, and risk of Alzheimer’s disease .51

3. PREDICTING RISK FOR ALZHEIMER’S DISEASE: WHAT IS THE
CHALLENGE? ............................................................................................................................55

4. DEVELOPMENT OF BIOMARKER PROFILES IDENTIFYING “PHENOTYPES OF
RISK” ............................................................................................................................................58
4.1 The Early vs. Late Intervention Trial with Estradiol ..........................................................58
4.2 Biomarkers: systems biology vs. individual markers .........................................................62
4.3 Selection of the ELITE metabolic biomarker panel ...........................................................64
4.4 The nine metabolic biomarkers ...........................................................................................65
7
4.4.1 Glucose .........................................................................................................................65
4.4.2 HOMA Score .................................................................................................................66
4.4.3 β-Hydroxybutyrate ........................................................................................................67
4.4.4 HDL and LDL Cholesterol ...........................................................................................68
4.4.5 Triglycerides .................................................................................................................69
4.4.6 Hemoglobin A1c ............................................................................................................69
4.4.7 Blood Pressure ..............................................................................................................70

5. CHARACTERIZATION OF BASELINE METABOLIC PHENOTYPES ......................71
5.1 Introduction of Hypothesis .................................................................................................71
5.2 Statistical Methods ..............................................................................................................71
5.3 Results .................................................................................................................................72
5.3.1 Cluster demographics ...................................................................................................74
5.3.2 Cross-sectional analysis of metabolic phenotypes at baseline .....................................75
5.3.3 Cross-sectional analysis of metabolic phenotypes at baseline, stratified by
menopause cohort .................................................................................................................78
5.4 Discussion ...........................................................................................................................79

6. ASSOCIATION OF BASELINE METABOLIC PHENOTYPES WITH COGNITIVE
PERFORMANCE ........................................................................................................................81
6.1 Introduction of Hypothesis .................................................................................................81
6.2 Statistical Methods ..............................................................................................................81
6.3 Cognitive Factors and Individual Cognitive Tests ...............................................................82
6.3.1 Global Cognition ..........................................................................................................82
6.3.2 Verbal Memory .............................................................................................................82
6.3.3 Executive Function ........................................................................................................83
6.4 Results .................................................................................................................................84
6.4.1 Cross-sectional analysis of cognitive performance within phenotypes at baseline .....84
6.4.2 Cross-sectional analysis of cognitive performance within phenotypes at baseline,
stratified by menopause cohort ..............................................................................................89
6.5 Discussion ...........................................................................................................................93

7. LONGITUDINAL CHANGE IN METABOLIC PHENOTYPES ......................................95
7.1 Introduction of Hypothesis ..................................................................................................95
7.2 Statistical Methods ...............................................................................................................95
7.3 Results ..................................................................................................................................95
7.3.1 Longitudinal change in metabolic phenotypes ............................................................95
7.3.2 Cross-sectional analysis of metabolic phenotypes at study end ................................100
8
7.3.3 Longitudinal and cross-sectional changes in metabolic phenotypes, stratified by
menopause cohort ...............................................................................................................102
7.4 Discussion ..........................................................................................................................106

8. LONGITUDINAL CHANGE IN COGNITIVE PERFORMANCE .................................109
8.1 Introduction of Hypothesis ...............................................................................................109
8.2 Statistical Methods ............................................................................................................109
8.3 Results ................................................................................................................................110
8.3.1 Cross-sectional analysis of cognitive performance within phenotypes at study end .110
8.3.2 Longitudinal analysis of cognitive performance within phenotypes ..........................115
8.3.3 Cross-sectional analysis of cognitive performance within phenotypes at study end,
stratified by menopause cohort ...........................................................................................120
8.3.4 Longitudinal analysis of cognitive performance within phenotypes, stratified by
menopause cohort ................................................................................................................123
8.4 Discussion .........................................................................................................................127

9. PHENOTYPE MODIFICATION BY HORMONE THERAPY .......................................131
9.1 Introduction of Hypothesis ...............................................................................................131
9.2 Statistical Methods ............................................................................................................131
9.3 Results ................................................................................................................................132
9.3.1 Baseline comparisons between women randomized to placebo and women randomized
to hormone therapy ..............................................................................................................132
9.3.2 Cross-sectional analysis of metabolic phenotypes at study end, stratified by treatment
condition ..............................................................................................................................133
9.3.3 Longitudinal analysis of metabolic phenotypes, stratified by treatment condition ....134
9.3.4 Cross-sectional analysis of cognitive performance at study end, stratified by treatment
condition ..............................................................................................................................137
9.3.5 Longitudinal analysis of cognitive performance, stratified by treatment condition ...138
9.4 Discussion .........................................................................................................................141

10. CONCLUSIONS ..................................................................................................................143

11. REFERENCES .....................................................................................................................148

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1. ALZHEIMER’S DISEASE

Alzheimer’s disease (AD) is the most common form of dementia, characterized by a
progressive neurodegeneration that is ultimately fatal. More than 5 million people are living with
Alzheimer’s in the United States, and this number is expected to increase exponentially in the
coming years, reaching 8.4 million by 2030 (Thies and Bleiler, 2013). Based on the projected
incidence of AD, current estimates are that 11% of individuals over the age of 65 can be
expected to develop this disease during their lifetimes (Thies and Bleiler, 2013). AD is thought
to have a prodromal period lasting up to two decades; thus, at least 3 million people living today
are at risk for developing AD within the next 15 years, and that number is likely a low estimate
when mortality rates from other diseases are taken into account.
Of the 5.2 million Americans diagnosed with late-onset Alzheimer's disease, 3.4 million
are women and 1.8 million are men (Thies and Bleiler, 2013). Nearly all epidemiological studies
indicate higher prevalence and greater lifetime risk of Alzheimer’s disease in women (Gao et al.,
1998; Huang et al., 2014; Seshadri et al., 2006; Thies and Bleiler, 2013). Results from the
Framingham Study show a 17.2% estimated lifetime risk for women over the age of 65 to
develop AD, whereas the risk for men is 9.1% (Seshadri et al., 2006). While this prevalence
disparity between genders is well documented, the incidence rate of AD in women versus men is
controversial: some studies show higher incidence in women (Fratiglioni et al., 1997; Launer et
al., 1999; Ott et al., 1998) and some show equal incidence between the sexes (Edland et al.,
2002; Fillenbaum et al., 1998; Hebert et al., 2001). Interestingly, most studies that report higher
incidence of AD in women are from Europe and Asia; studies conducted in North America tend
to find no difference in incidence rates between men and women (Qiu et al., 2009). This suggests
that genetic background may play a role in Alzheimer’s risk.
10
The biological basis for gender differences in AD prevalence – and perhaps incidence –
has not been definitively established; however, both basic science research and clinical
observations suggest that menopausal loss of estrogen plays a significant role in brain aging, and
can increase Alzheimer’s disease risk in women (Brinton, 2008b; Paganini-Hill and Henderson,
1994; Rettberg et al., 2014). Estrogen is a systems-level regulator of both brain and whole-body
bioenergetics, with a decline in metabolism occurring coincidentally with loss of estrogen at
menopause (Rettberg et al., 2014). Substantial basic discovery research demonstrates that the
aging female brain undergoes profound shifts in metabolic capacity and function during the
transition leading to reproductive senescence (Rettberg et al., 2014; Yao et al., 2011). There is
also evidence that for some women the menopausal transition can initiate a process of
accelerated aging, characterized by metabolic dysfunction and cognitive decline. Although most
women will metabolically adapt to the bioenergetic and physiological changes of menopause,
those women with reduced adaptive ability may be at increased risk for the future development
of neurological disorders such as Alzheimer’s disease.

2. ESTROGEN: A MASTER REGULATOR OF BIOENERGETIC SYSTEMS IN THE
BRAIN AND BODY

2.1 Introduction
Estrogen is a systems-level signaling molecule that regulates and coordinates multiple
functions across organs, cells and genes. To achieve this integration, estrogen utilizes a repertoire
of receptors and signaling pathways to activate and regulate molecular and genomic responses
required for survival at the cellular, organismic and ultimately whole body level. Estrogen
integration and coordination of metabolism enables the development of peripheral biomarkers
which can serve as reporters of brain bioenergetics, thereby providing early detection of
11
populations at risk for neurodegenerative diseases associated with metabolic dysfunction, such as
Alzheimer’s disease. Reviewed in this section is estrogen action in the brain and the body with
particular emphasis on estrogen regulation of metabolism, and its clinical implications.
Throughout, estrogen is used to refer to 17β-estradiol (the predominant estrogen) whereas other
types of estrogens are specifically identified and typically are related to formulations of hormone
therapies.

2.2. Estrogen, estrogen receptors, and intracellular signaling pathways in the brain
Estrogens are steroid hormones primarily known for their role in promotion of female sex
characteristics and reproductive capability. There are three forms of estrogens in the female
body: estrone (E1), estradiol (E2), and estriol (E3). During a woman’s reproductive years, the
principal circulating estrogen is 17β-estradiol (E2); importantly, it is also the most potent form of
estrogen. In humans, estrogens are produced by the ovaries and adrenal glands, and circulate
throughout the body where they have effects on most organ systems, including brain, breast,
cardiovascular (heart and vasculature), immune, reproductive (ovaries and uterus), bladder, skin,
and bone (Kuiper et al., 1997).
Estrogen can cross the blood–brain barrier, and additionally, the brain can produce
estrogen endogenously from cholesterol (Balthazart and Ball, 2006; Garcia-Ovejero et al., 2005;
Prange-Kiel et al., 2003; Rune and Frotscher, 2005). Thus, along with its role in female
physiology and reproduction, decades of research have established that estrogen is a critical
signaling molecule within the brain (Brinton, 2008b). Estrogen receptors (ERs) are widely
distributed in the brain, are present on both neurons and glia, and are expressed by both sexes.
These receptors are highly evolutionarily conserved, with homologs in all vertebrate species.
12
Estrogen receptors are composed of two general classes: nuclear ERs and membrane
embedded/membrane associated ERs (mERs), both of which are present in the brain. There are
two isoforms of classical nuclear estrogen receptors: ERα (ESR1) (HUGO Gene Nomanclature
Committee, NCBI) and ERβ (ESR2) (HUGO Gene Nomenclature Committee, NCBI), which are
functionally distinct and differentially distributed throughout the brain. Coding regions for both
ERs are found on chromosome 6 (Menasce et al., 1993). The estrogen nuclear receptors exist
initially as monomers, and dimerize prior to translocation to the nucleus, where they regulate
transcription. In vitro evidence indicates the potential for heterodimers between ERα and ERβ
(Pettersson et al., 1997), although in vivo evidence of this phenomenon remains to be
established. In contrast to the nuclear receptors, membrane-associated estrogen receptors are
monomers of ERα and ERβ.
Classical estrogen signaling occurs as a result of the ER translocating to the nucleus,
where it binds the estrogen response element (ERE) to regulate gene expression. Additionally,
ERα can be alternatively spliced to generate three splice variants (GeneCards, ESR1), and ERβ
can be alternatively spliced to generate eleven splice variants (GeneCards, ESR2). Most splice
variants have been identified in breast or other cancer cell lines; because of the lack of genomic
control in these cell lines, the functionality of splice variants is controversial. In brain, however,
splice variants have been detected and have been associated with changes in estrogen
responsivity.

2.2.1. Estrogen receptors alpha and beta: localization and splice variants
In rat and mouse forebrain, ERα shows a wide pattern of distribution (Brinton, 2009;
Milner et al., 2001; Mitra et al., 2003; Shughrue, 2004; Shughrue et al., 1997); this is similar to
13
the human brain, where in situ hybridization studies show ERα is distributed throughout the
hypothalamus, forebrain, hippocampus (weakly), and amygdala (Mitra et al., 2003; Osterlund et
al., 2000a; Ostlund et al., 2003). ERβ is more narrowly distributed, with high concentrations seen
primarily in the hippocampus and cerebral cortex both in rodents and humans (Mitterling et al.,
2010; Osterlund et al., 2000b; Ostlund et al., 2003; Shughrue et al., 1997; Shughrue and
Merchenthaler, 2001). Within the hippocampus, both ERα and ERβ localize to dendritic spines,
which are sites of synapse formation that show a high degree of plasticity (Milner et al., 2005;
Milner et al., 2001). ERα and ERβ have both been shown to mediate hippocampal-dependent
learning tasks (Spencer et al., 2008); however, signaling through ERα and ERβ leads to
differential expression of synaptic proteins, indicating that these two receptors have distinct roles
within the hippocampus (Waters et al., 2009). In the rodent midbrain, ERβ is predominantly
localized to the substantia nigra, locus coeruleus, and raphe nuclei (Mitra et al., 2003; Shughrue
et al., 1997). ERα shows narrower distribution in the midbrain, and is primarily localized to the
periaqueductal gray (Mitra et al., 2003; Shughrue et al., 1997). In the hindbrain and cerebellum,
most ERα and ERβ immunostaining is within cell nuclei; the cerebellum shows no specific ERα
staining, although it does show staining for ERβ (Mitra et al., 2003; Shughrue et al., 1997).
The effect of aging on ERα and ERβ expression and signaling is still a developing area of
investigation, but a recent review thoroughly covers what is currently known (Foster, 2012). In
short, data suggest that in different areas of the hippocampus, the ERα/ERβ ratio changes with
age. In young and middle-aged rats, primates, and humans, ERβ is the dominant ER in the
hippocampus, although ERα is present in low quantities. With aging, nuclear ERα localization
increases in the dentate gyrus and CA3, but decreases in CA1 (Ishunina et al., 2007). ERα also
becomes less sensitive to E2 treatment as animals age; this is in contrast to ERβ, which shows
14
decreased levels with age but remains responsive to E2 treatment (Waters et al., 2011). Clinical
studies have shown a linear relationship between Mini Mental State Exam (MMSE) score and
ERα levels, but no relationship between MMSE and ERβ, in the frontal cortex of Alzheimer’s
patients (Kelly et al., 2008). The existence of variant isoforms of ERα that may influence
cognitive impairment has been proposed (Kelly et al., 2008); this was later observed in a cohort
of non-demented elderly (Yaffe et al., 2009). Thus the data show that decreased ERα levels and
responsiveness may mediate cognitive impairment and dementia; during aging, although ERβ
remains responsive to E2, it is unable to compensate for the loss of ERα.
With aging, there is also an increase in the expression of particular ERα splice variants in
the hippocampus that render much of the available ERα non-functional (Ishunina et al., 2007).
Interestingly, it has been shown that elderly women are more likely than elderly men to have
increased expression of ERα splice variants (Foster, 2012). ERβ splice variants are also present
in the brain (Mott and Pak, 2012). A recent study proposed that one dominant negative splice
variant, ERβ2, may mediate differential responses to E2 treatment early and late after
ovariectomy (OVX) in rats, such that only if estrogen treatment is initiated early after OVX is it
able to prevent induction of the dominant negative ERβ2 variant (Wang et al., 2012). In addition
to splice variants, there are several ERα polymorphisms that increase the risk of Alzheimer’s
disease (AD) specifically in women, particularly when associated with the APOE ε4 allele (Ryan
et al., 2013).

2.2.2. Membrane-embedded estrogen receptors
In addition to the classical cytoplasmic and nuclear ERs, there are also membrane-
embedded ERs, which rapidly initiate intracellular signaling pathways upon exposure to estrogen
15
(Micevych and Dominguez, 2009; Toran-Allerand et al., 2002). These membrane sites of ER
action activate both the Src/PI3K and Ras/Raf/MEKK/ERK signaling pathways, leading to
activation of CREB, and they have been identified as required for E2-inducible neuroprotection
(Levin, 2001; Mannella and Brinton, 2006; Zhao et al., 2005). G protein-coupled receptors that
associate with estrogen, such as G-protein coupled estrogen receptor 1 (GPER1; also called
GPR30) (HUGO Gene Nomenclature Committee, NCBI), have also been identified (Maggiolini
and Picard, 2010). This receptor has a high affinity for E2, and signaling thorough it activates
both cAMP/PKA and PI3K/Akt pathways (Maggiolini and Picard, 2010). 17β-estradiol binding
to ERs activates signaling cascades associated with neuronal survival and function, including
MAPK (Arevalo et al., 2012; Nilsen and Brinton, 2003; Singh et al., 2000), PI3K (Brinton,
2008a; Cheskis et al., 2008; Spencer-Segal et al., 2012)(Brinton, 2008a, Cheskis et al., 2008 and
Spencer-Segal et al., 2012), and PKC (Cordey et al., 2003). Activation of both the MAPK and
PI3K pathways leads to phosphorylation of CREB, which upregulates the transcription of
neuronal survival genes including the anti-apoptotic protein Bcl-2 (Nilsen and Brinton, 2003;
Nilsen et al., 2006; Pike, 1999; Stoltzner et al., 2001). Further, E2 activation of the PI3K
pathway has the potential to simultaneously activate the MAPK, PKC, Ca2+ influx and Akt
signaling pathways (Mannella and Brinton, 2006; Simoncini et al., 2000), and this activation has
been shown to mediate the neuroprotective effect of E2 (Mannella and Brinton, 2006).
Membrane-embedded ERs activate pathways that regulate calcium influx through L-type
Ca2+ channels, which in turns activates the PI3K/Src/ERK/CREB cascade (Mannella and
Brinton, 2006; Wu et al., 2005). This increases Bcl-2 expression, which potentiates the maximal
mitochondrial calcium uptake capacity (Murphy et al., 1996a). The increased mitochondrial
uptake of calcium induced by E2 could thus protect neurons against the adverse consequences of
16
excess cytoplasmic calcium. Our lab tested this hypothesis in a series of experiments analyzing
calcium dynamics between the cytosolic and mitochondrial compartments (Nilsen and Brinton,
2003). Results showed that E2 treatment led to increased mitochondrial sequestration of [Ca2+]
i

when neurons were exposed to excitotoxic glutamate, which was paralleled by a decrease in
cytoplasmic [Ca2+]
i
and an increase in expression levels of Bcl-2 (Nilsen and Brinton, 2003).
Importantly, despite the increased intramitochondrial calcium load, treatment with E2 was able
to preserve mitochondrial respiratory capacity (Nilsen and Brinton, 2003). In neurons derived
from the aged brain, E2 sustained calcium homeostasis comparable to the middle-aged level, and
prevented transition to the dysregulation associated with aged neurons (Brewer et al., 2006).

2.2.3. Mitochondrial estrogen receptors
Critically, multiple laboratories have also established the presence of mitochondrial ERs
(Irwin et al., 2012; Milner et al., 2005; Mitterling et al., 2010; Stirone et al., 2005; Yager and
Chen, 2007; Yang et al., 2004), which emphasizes the role that estrogen plays in regulating
cellular bioenergetics (Figure 1). The mitochondrial genome contains DNA sequences that
resemble half the palindromic nuclear ERE sequence (Demonacos et al., 1996). In an early
experiment, ERα and ERβ were shown to directly bind mitochondrial DNA (mtDNA) in vitro
through mitochondrial EREs, and the binding response was increased with exposure to 17β-
estradiol (Chen et al., 2004). Further work has demonstrated that ERβ localizes to the
mitochondria (Irwin et al., 2012; Simpkins et al., 2008). Considering the predominant location of
ERβ in brain mitochondria, it is reasonable to postulate that estrogen directly modulates
mitochondrial function via ERβ-mediated regulation of mtDNA transcription (Yang et al., 2004).
While mechanisms by which ERs coordinate the complex signaling pathways between the
17
membrane, mitochondria, and nucleus remain to be fully determined (Wagner et al., 2008)(
Wagner et al., 2008), it is remarkable that ERs are perfectly positioned to coordinate events at
the membrane with events in the mitochondria and nucleus (Figure 1) (Brinton, 2009; McEwen
et al., 2001; Milner et al., 2005; Milner et al., 2008; Milner et al., 2001; Yang et al., 2004).

Figure 1. Estrogen regulation of intracellular brain metabolism pathways.
Estrogen-induced signaling pathways in hippocampal and cortical neurons converge upon the mitochondria to
enhance glucose uptake and metabolism, aerobic glycolysis, tricarboxylic acid cycle (TCA)-coupled oxidative
phosphorylation and ATP generation. In parallel, E2 increases antioxidant defense and anti-apoptotic mechanisms.
Estrogen receptors at the membrane, in mitochondria, and within the nucleus are well positioned to regulate
coordinated mitochondrial and nuclear gene expression required for optimal bioenergetics.

18
2.3 Estrogen and brain bioenergetics
The human brain, despite comprising only 2% of the body’s mass, consumes 20% of the
body’s fuel for mitochondrial respiration and ATP generation. Thus the brain is singularly reliant
on efficient mitochondrial function, and is at risk for bioenergetic decline if mitochondrial
function is impaired. Estrogen has been shown to have beneficial effects on the entire
bioenergetic system of the brain from glucose transport into cells to glycolysis, the tricarboxylic
citric acid (TCA) cycle, oxidative phosphorylation (OXPHOS), and ATP production (Figure 2)
(Brinton, 2008b, 2009).

Figure 2. Estrogen regulation of the bioenergetic system.
Estrogen regulates many of the key enzymes involved in mitochondrial bioenergetics, including glucose
transporters, hexokinase (HK), pyruvate dehydrogenase (PDH), aconitase (Aco2), alpha ketoglutarate
dehydrogenase (aKGDH), succinate dehydrogenase (SDH), and Complexes I, III, and IV of the electron transport
chain.

19
2.3.1 Estrogen regulation of glucose transport
The family of GLUT receptors mediates glucose transport into the brain. In vivo, glucose
transporter-1 (GLUT-1) exists in two glycosylated isoforms: a 55 kD isoform that regulates
glucose transport from the blood vessels into the brain, and a 45 kD isoform that regulates
transport from the brain into glia. GLUT-1 is a low-affinity transporter, but is highly sensitive to
changes in glucose levels. Its expression levels are closely regulated by glucose availability and
demand; in particular, conditions of hypoglycemia lead to increased blood–brain barrier GLUT-1
expression (Carruthers et al., 2009; Simpson et al., 1999). Glucose transporter-3 (GLUT-3) is a
high-affinity GLUT isoform that is expressed on neuronal membranes, and allows for the
transport of glucose into the neuron. Glucose transporter-4 (GLUT-4) is also expressed on
neuronal membranes, and is unique because its translocation from the cytoplasmic compartment
to the cell membrane is regulated by insulin. Through this array of transporters, glucose enters
specific cellular compartments of the brain. Thus, tracking changes in expression or function of
these glucose transporters in each of the cellular compartments of the brain provides a window
into understanding adaptations occurring in brain that affect both its metabolic capacity and its
relationship to fuels provided from the periphery.
Ovariectomy induces a significant decline in multiple glucose transporters, including
GLUT-1, GLUT-3, and GLUT-4 (Cheng et al., 2001; Ding et al., 2013b; Shi and Simpkins,
1997). In both the rodent and the non-human primate, E2 treatment prevents the OVX-induced
decline in these glucose transporters (Cheng et al., 2001; Ding et al., 2012; Ding et al., 2013b;
Shi and Simpkins, 1997). Recently, our group demonstrated that loss of ovarian hormones with
reproductive aging induced a significant decline in brain glucose utilization, which could be
attributed to decreased neuronal glucose transporter expression, compromised hexokinase
20
activity, inactivation of the pyruvate dehydrogenase complex, and eventually a functionally
significant decrease in mitochondrial bioenergetic function (Ding et al., 2013a).
Estrogen regulation of the insulin-sensitive glucose transporter is particularly interesting,
as it requires a simultaneous increase in levels of E2/ER and the insulin/insulin receptor (IR)
system. In the rodent brain, coupling between estrogen and insulin involves insulin’s rodent
brain homolog insulin growth factor-1 (IGF-1) and its receptor (IGF-1R). The synergistic
coupling between ERs and IGF-1R has been extensively investigated (Arevalo et al., 2012;
Cardona-Gomez et al., 2002; Garcia-Segura et al., 2010; Garcia-Segura et al., 2000; Mendez and
Garcia-Segura, 2006; Mendez et al., 2006). IGF-1R and ERα can form a macromolecular
complex to enable downstream signaling functions, such as activation of the PI3K signaling
pathway that leads to phosphorylation and activation of Akt (Garcia-Segura et al., 2010; Mendez
et al., 2006). The phosphorylated (active) form of Akt has been shown to selectively co-localize
with GLUT-4 in IGF-1-expressing neurons, leading to an increase in glucose transport through
this regulation of GLUT-4 (Cheng et al., 2000). In the ovariectomized primate referenced above,
it was noted that IGF-1R mRNA is concentrated in neurons in a similar distribution to GLUT-3
and -4 (Cheng et al., 2001). The interaction between ER and IGF-1 provides compelling
evidence for the coordinated roles of IGF-1, the PI3K to Akt signaling pathway, and ER
signaling in estrogen-inducible promotion of neuronal metabolism and neuroprotection.
Interestingly, the above in vitro and in vivo studies correspond well with what has been
observed in humans. In patients with AD, low insulin levels, decreased expression of insulin
receptors and attenuated insulin signaling are detected in brain regions vulnerable to AD
pathology, particularly the hippocampus (De Felice et al., 2014; Schioth et al., 2012). It is
reasonable to hypothesize that attenuated insulin signaling could at least partially account for the
21
memory impairment seen early in the course of the disease. Alleviation of insulin deficits by
intranasal insulin administration was found improve cognitive function in preclinical animal
models, healthy controls, and patients with Alzheimer’s disease (Benedict et al., 2011; Holscher,
2014). Further analyses suggest that insulin effects can be modified by both sex and APOE
genotype, as well as by insulin dose (Claxton et al., 2013).
Soluble amyloid oligomers have been shown to disrupt insulin signaling by causing a loss
of insulin receptors (Zhao et al., 2008). In rodents, elevated brain amyloid beta1–42 (Aβ) levels
were associated with low circulating IGF-1, whereas increasing serum IGF-1 reduced Aβ burden
(Carro et al., 2002). In female rats, IGF-1 gene expression was consistently decreased following
both ovariectomy and reproductive senescence (Mao et al., 2012; Zhao et al., 2012). This was
paralleled by increased expression of genes involved in Aβ generation. In addition to its role in
promoting insulin/IGF-1 function, estrogen further promotes Aβ degradation and clearance by
upregulating insulin-degrading enzyme (IDE) and neprilysin gene and protein expression
(Jayaraman et al., 2012; Zhao et al., 2011b).

2.3.2 Estrogen regulation of glycolysis
In addition to facilitating glucose transport, estrogen also promotes neuronal aerobic
glycolysis. In rodents, E2 increases activity of the glycolytic enzymes hexokinase (soluble and
membrane-bound), phosphofructokinase and pyruvate kinase within 4h of exposure (Kostanyan
and Nazaryan, 1992). Hexokinases are critical enzymes, because they bind the voltage-dependent
anion channel (VDAC) on the mitochondrial outer membrane in order to link mitochondrial ATP
synthesis to glucose metabolism (Gottlob et al., 2001). This coupling is regulated by Akt, which
associates with hexokinase II (HKII) (Miyamoto et al., 2008); HKII activity is then required for
22
the antiapoptotic effect of Akt (Gottlob et al., 2001). E2 acts on this system from multiple angles,
both through activating Akt (Mannella and Brinton, 2006; Singh, 2001; Znamensky et al., 2003)
and through increasing HKII activity (Kostanyan and Nazaryan, 1992), and it is hypothesized
that estrogen may play a role in promoting the association of HKII and VDAC in neurons.

2.3.3 Estrogen regulation of mitochondrial energy production
A substantial number of the signaling pathways regulated by estrogen converge upon
mitochondria (Brinton, 2008a; Mannella and Brinton, 2006; Nilsen and Brinton, 2003, 2004;
Nilsen et al., 2006), and the upregulation of glucose transport and glycolysis mediated by
estrogen is complemented by its potentiation of mitochondrial bioenergetics. Using proteomic
analysis of brain mitochondria from female rats treated with E2, our lab identified several
metabolic enzymes whose activity and protein expression levels were regulated by E2, including
pyruvate dehydrogenase (PDH), aconitase, and ATP synthase (Nilsen et al., 2007). E2 treatment
increased the expression of several PDH subunits (Nilsen et al., 2007), which is important
because PDH is the primary regulatory enzyme that links glycolysis to the TCA cycle through
the generation of acetyl CoA. Further, in the brain PDH is responsible for directing acetyl-CoA
either to the TCA cycle or to be used for acetylcholine synthesis (Holmquist et al., 2007). E2
treatment increased expression of the Complex I β subunit 8 (Irwin et al., 2012). E2 also
increased activity (Nilsen et al., 2007; Yao et al., 2012) and expression (Nilsen et al., 2007) of
Complex IV (COX), which is consistent with previous findings (Bettini and Maggi, 1992;
Stirone et al., 2005). Lastly, E2 treatment led to increased expression of Complex V/ATP
synthase F1 subunits α and β (Nilsen et al., 2007). We had previously reported estrogen-induced
increases in ATP levels in primary neuronal cultures (Brinton et al., 2000), which coalesces well
23
with results seen in the proteomic analysis. Maximal mitochondrial respiratory rate in neurons
and glia was also increased by E2 treatment, and E2 treatment protected against electron
transport chain inhibitors (Yao et al., 2012). Thus the results of our analyses show that estrogen
produces a coordinated response of many mitochondrial enzymes, leading to optimal glucose
metabolism in the brain.
Further studies in our laboratory investigated the contributions of each of the estrogen
receptor isoforms, ERα and ERβ, to the promotion of mitochondrial bioenergetics (Irwin et al.,
2012). Using the ERα-selective agonist propylpyrazoletriol (PPT), which is 410-fold more
selective for ERα than ERβ (Stauffer et al., 2000), and the ERβ-selective agonist
diarylpropionitrile (DPN) is 70-fold more selective for ERβ than ERα (Meyers et al., 2001),
Irwin et al were able to probe the effects of signaling cascades activated through ERα and ERβ.
Hippocampal mitochondrial enzymes that showed increases in protein expression levels after
treatment with PPT included PDH subunit E1 and ATP synthase F1 subunit α. DPN upregulated
expression levels of COX subunit I, which is mtDNA-encoded. As PPT had no effect on COX
subunit I expression, this suggests that mtDNA transcription is regulated by ERβ-dependent
mechanisms. COX subunit IV expression and activity were upregulated by both PPT and DPN.
This subunit is encoded by nuclear DNA, which suggests that ERα and ERβ are independently
capable of upregulating specific mitochondrial proteins. Both agonists also reduced
mitochondrial lipid peroxides, consistent with previous findings of estrogen induction of the
antioxidants peroxiredoxin 5 and manganese superoxide dismutase (MnSOD) (Nilsen et al.,
2007; Yao et al., 2012). Assessment of mitochondrial respiration from the same samples
indicated that both PPT and DPN independently regulate mitochondrial function, often with
comparable results, leading to enhanced mitochondrial respiration (Irwin et al., 2012). Consistent
24
with the ERβ activation of mitochondrial function, analyses of an ERβ-selective formulation,
PhytoSERMS, showed that the formulation induced a significant rise in mitochondrial
respiration (Yao et al., 2013; Zhao et al., 2011a). While both ERα and ERβ promote
mitochondrial function, targeting ERβ generally results in greater efficacy of mitochondrial
respiration (Irwin et al., 2012; Yao et al., 2013).
Estrogen action at the mitochondria extends to protection against Aβ. Estrogen treatment
not only increases ATP production in healthy hippocampal neurons, but also sustained ATP
generation after the neurons were exposed to Aβ (Brinton et al., 2000). Ovariectomy in mice
leads to both a decline in mitochondrial bioenergetics and an elevation in mitochondrial Aβ, but
if estrogen treatment is started immediately following ovariectomy, both of these deleterious
events are prevented (Yao et al., 2012).

2.3.4 Estrogen regulation of oxidative stress
A byproduct of oxidative phosphorylation is the production of reactive oxygen species
(ROS). During normal (non-pathological) oxidative phosphorylation, mitochondria generate 1–
4% incompletely reduced oxygen which can react to form ROS (Cecarini et al., 2007; Sugioka et
al., 1988). Electron transport chain complexes I and III have been implicated as the primary
sources of ROS (Cadenas and Davies, 2000). Thus, a deficit in complex IV activity without a
corresponding decline in activity of complexes I and III – as described in AD patients in Section
2.4 – could lead to increased ROS leak (Atamna and Frey, 2007). Because mitochondria are the
main source of ROS production, they also suffer the greatest oxidative stress when excess ROS
are produced. Many components of the mitochondrial bioenergetic network are particularly
vulnerable to oxidative stress, e.g., lipid peroxidation of mitochondrial membranes and protein
25
damage on the electron transport chain, which increases the risk of severe mitochondrial
impairment that can lead to energetic failure of the cell and/or apoptosis (Blass, 2000; Lin and
Beal, 2006; Yao et al., 2004).
In vitro, estrogen has been shown to protect against DNA damage induced by hydrogen
peroxide (H2O2) and arachidonic acid (Moor et al., 2004; Tang and Subbiah, 1996). Estrogen
also increases expression of several antioxidant enzymes, including peroxiredoxin 5,
glutaredoxin and MnSOD (Nilsen and Brinton, 2004; Nilsen et al., 2007). In rodents,
ovariectomy led to an increase in lipid peroxides that was prevented by E2 treatment (Irwin et
al., 2008; Yao et al., 2012). Estrogen-induced rise in antioxidants and associated reduction in
free radicals, and lower oxidative damage to mitochondrial DNA, has been proposed as a
mechanism to explain the greater longevity of females relative to males (Borras et al., 2007;
Vina et al., 2005; Vina et al., 2006).

2.4 Mitochondrial relevance to Alzheimer’s disease
Mitochondria play a critical role in generating energy for the cell, and increasing
evidence links mitochondrial dysfunction to age-associated neurodegenerative disorders such as
Alzheimer’s disease (Brinton, 2008a; Moreira et al., 2006; Moreira et al., 2010; Mosconi et al.,
2011; Mosconi et al., 2009b; Swerdlow and Khan, 2009). Deficits in the activity of several key
enzymes involved in mitochondrial energy generation have been described in AD. Decreased
PDH activity in post-mortem brain homogenate from AD patients was one of the first deficits
described (Perry et al., 1980), and this has since been confirmed by a number of other groups
(Sheu et al., 1985; Sorbi et al., 1983; Yates et al., 1990). Microarray analyses also show a
downregulation of PDH gene expression in patients with incipient AD, providing further support
26
that a deficit in PDH activity is an early event in AD pathogenesis (Blalock et al., 2004). Activity
of α-ketoglutarate dehydrogenase (αKGDH), the rate-limiting enzyme of the TCA cycle, is also
deficient in post-mortem brain tissue from patients with AD (Butterworth and Besnard, 1990;
Gibson et al., 2000b; Gibson et al., 1988; Mastrogiacoma et al., 1996; Mastrogiacomo et al.,
1993). A decline in αKGDH activity is positively correlated with clinical dementia in sporadic
(non-genetic) forms of AD, but patients who are APOE ε4 carriers (Gibson et al., 2000a) or carry
the Swedish AβPP mutation KM670/671NL (Gibson et al., 1998) show the most reliable
correlation between decreased αKGDH activity and degree of dementia. The most consistently
documented deficit in mitochondrial enzyme function is decreased activity of COX, the
penultimate complex of the electron transport chain. Deficient COX activity has been identified
in post-mortem brain tissue (Cottrell et al., 2001; Kish et al., 1992; Maurer et al., 2000; Mutisya
et al., 1994; Parker et al., 1994b) as well as platelets (Bosetti et al., 2002; Cardoso et al., 2004;
Parker et al., 1990; Parker et al., 1994a; Valla et al., 2006a) and fibroblasts (Curti et al., 1997)
from AD patients. As further evidence that a mitochondrial deficit is an early event in AD
pathogenesis, the reduction of COX activity has been identified in peripheral tissues from
patients with mild cognitive impairment (MCI) (Swerdlow and Kish, 2002; Valla et al., 2006b).
Additionally, young adult APOE ε4 carriers without overt AD pathology showed lower COX
activity in the posterior cingulate than young adult non-carriers (Valla et al., 2010). Considering
that this deficit in COX activity is seen in peripheral tissues and not just in brain, it is unlikely to
be a result of neurodegeneration (Swerdlow, 2009). Indeed, the fact that COX deficiency is not
restricted to the brain suggests a systemic aspect to AD (Maruszak and Zekanowski, 2011;
Swerdlow, 2011).
27
In the “cybrid model” of AD, neural cultures that lack endogenous DNA (termed ρ0
cells) are fused with platelets that lack a nucleus, creating “cybrids” (Swerdlow et al., 1997).
Interestingly, when the cells are fused with platelets from AD patients, they exhibit
characteristics that match the findings from clinical AD specimens, including decreased COX
function that can be passed down through the cybrid lines (Swerdlow, 2007). This provides
evidence that mitochondria from AD patients have particular abnormalities that are carried
within mtDNA and are thus heritable, and which may explain the enzyme complex deficiencies
seen in individuals with AD.
Multiple in vitro and in vivo preclinical analyses have demonstrated a decline in
mitochondrial function prior to the onset of Alzheimer’s histopathological features. Results of
these analyses indicate decreased metabolic enzyme expression and activity, decreased cerebral
glucose metabolism, increased oxidative stress, and increased mitochondrial Aβ load (Chou et
al., 2011; Du et al., 2010; Hauptmann et al., 2009; Nicholson et al., 2010; Silva et al., 2011; Yao
et al., 2009). Thus this decline in brain mitochondrial function may serve as a biomarker of AD
risk as well as a therapeutic target.
An impairment of mitochondrial bioenergetics and oxidative phosphorylation is often
closely associated with increased free radical production and subsequent oxidative damage. As
mentioned above in Section 2.3.4, the consistently documented deficit in complex IV activity
which occurs without a corresponding decline in activity of complexes I and III could clog the
electron transport chain and lead to increased ROS leak (Atamna and Frey, 2007). Animal
models of AD show increased free radical generation and oxidative damage to cellular
components prior to the development of pathology (Nunomura et al., 2009; Pratico et al., 2001;
Rhein et al., 2009; Wang et al., 2005; Yao et al., 2009). Further, post-mortem analysis of brains
28
from Alzheimer’s patients show markers of increased oxidative stress, including lipid peroxides,
8-oxoguanine, and oxidized amino acids (Gibson and Shi, 2010; Nunomura et al., 2009; Reddy,
2006). Interestingly, an increase in oxidative stress has been demonstrated to increase β-amyloid
production in vitro and in vivo (Moreira et al., 2007; Nunomura et al., 2001).
Increasing evidence indicates that mitochondria are direct targets of Aβ. Aβ has been
shown to accumulate inside mitochondria, where it interacts with the mitochondrial protein Aβ-
binding-alcohol-dehydrogenase (ABAD), resulting in decreased COX activity and increased
oxidative stress (Lustbader et al., 2004; Reddy and Beal, 2008; Takuma et al., 2005). In AD
cybrids, Aβ-induced toxicity is exacerbated in parallel with mitochondrial dysfunction (Cardoso
et al., 2004). At the same time that Aβ exerts its toxicity upon the mitochondria, compromised
mitochondrial function – in particular, a decline in mitochondrial bioenergetics and increased
levels of ROS – further drives the degenerative process by increasing Aβproduction. The end
result is a vicious cycle in which excessive Aβ accumulation and sustained mitochondrial
dysfunction synergistically exacerbate each other, leading to activation of a multitude of
neurodegenerative pathways (Cardoso et al., 2004; Silva et al., 2011; Swerdlow et al., 2010; Yao
et al., 2011).

2.5 Estrogen regulation of whole-body metabolism
In addition to the brain, estrogen activates signaling pathways in nearly every tissue of
the body; hence these pathways would be affected by loss of ovarian hormones associated with
surgical or natural menopause (Figure 3).
29

Figure 3. Estrogen regulation of whole-body metabolism.
Estrogen receptors are expressed throughout peripheral systems involved in metabolism. Estrogen affects adipose
tissue distribution and risk of obesity, insulin resistance and risk of diabetes, and concentration of the adipokines
leptin, ghrelin, and adiponectin. Loss of estrogen at menopause leads to significant changes in many of these
systems, which can be stabilized with the use of hormone therapy. Adverse metabolic profiles, e.g. type 2 diabetes
and metabolic syndrome, can increase risk of developing Alzheimer’s disease.

Menopause is associated with a significant decline in the ovarian production of both 17β-
estradiol, the predominant estrogen during a woman’s reproductive years, and progesterone
(Brinton, 2010; Nejat and Chervenak, 2010; Soules et al., 2001). Clinically, menopause is
defined by one year of amenorrhea following the final menstrual period. Perimenopause
typically begins approximately 2 years before menopause, and it encompasses the early and late
transition stages as well as the first year after clinically-defined menopause (Soules et al., 2001).
30
This perimenopausal transition is characterized by widely fluctuating hormone levels (Soules et
al., 2001). In most women, the menopausal transition takes about 4 to 5 years (Woods and
Mitchell, 2004), with the average age of menopause at 51 years (ESHRE Capri Workshop
Group, 2006). After menopause, estradiol levels no longer fluctuate as they did during
perimenopause; instead, the ovaries gradually produce declining levels of estradiol and estrone
becomes the predominant circulating estrogen (Nejat and Chervenak, 2010). The dysregulation
of ovarian hormone secretion characteristic of the menopausal transition is in contrast to the male
andropause, in which testosterone levels decrease steadily over a number of years (Ferrari et al.,
2013).

2.5.1. Estrogen, adiposity, and obesity
Adiposity is a measure of the amount of adipose tissue (fat deposition) in the body.
Adipose tissue is preferentially stored in the form of subcutaneous adipocytes, but if energy
intake is too high, the body will store fat as intra-abdominal visceral adipose tissue. This visceral
fat is associated with most of the health risks of adiposity (Jensen, 2008). As adiposity increases,
an individual’s risk of developing insulin resistance, diabetes, hypertension, and cardiovascular
disease also increases (Pi-Sunyer, 2002; Poirier et al., 2006). Specifically, intra-abdominal
obesity is associated with the greatest risk of all of these diseases independent of total body
adiposity (Despres, 1993; Pouliot et al., 1992). Adipose tissue is the largest endocrine organ in
the body, and it releases factors called adipokines which are known to have effects on both the
periphery and the brain (Arnoldussen et al., 2014) (see 2.5.4, 2.5.5, and 2.5.6).
Adipose tissue has a different pattern of distribution in men and women. Age-matched
men tend to accumulate a greater amount of abdominal fat relative to premenopausal women,
31
who accumulate fat in a metabolically healthier gluteo-femoral pattern that is promoted by
estrogen (Carr, 2003; Krotkiewski et al., 1983). After menopause, when estrogen levels drop,
women experience a general increase in weight (Davis et al., 2012a; Pimenta et al., 2013) as well
as a redistribution of adipose tissue leading to increased abdominal fat deposition (Bjorkelund et
al., 1996; Toth et al., 2000; Zamboni et al., 1992). Importantly, the increased abdominal fat in
postmenopausal women tends to be visceral and not subcutaneous fat (Lovejoy et al., 2008). This
effect is also seen in healthy pre-menopausal women treated with gonadotropin-releasing
hormone (GnRH) agonists (Revilla et al., 1998; Yamasaki et al., 2001). Thus men have an
unhealthier adiposity profile compared to women before menopause, but after menopause the
prevalence of intra-abdominal adiposity rises significantly in women.
Estrogen receptors are present in adipose tissue, indicative of the potential for estrogen
regulation of adipocyte function (Mayes and Watson, 2004; Pallottini et al., 2008; Pedersen et
al., 1996). ERα and ERβ may differentially mediate estrogen’s effects on adipose tissue, with
ERα predominantly regulating adipose homeostasis via growth and proliferation of adipocytes,
and ERβ regulating sex-specific distribution of adipose tissue (Pallottini et al., 2008). Estrogen
signaling pathways mediated through ERα have been shown to upregulate α2A-adrenergic
receptor expression in subcutaneous but not visceral adipose tissue (Pedersen et al., 2004). The
α2A-adrenergic receptor controls anti-lipolytic pathways, promoting the accumulation of adipose
tissue. Thus E2 signaling can bias adipose distribution towards the “healthier” subcutaneous fat
depots vs. unhealthy visceral fat deposition (Pallottini et al., 2008). Differential ERα and ERβ
effects on adiposity have been investigated using ERα knockout (αERKO) mice, which develop
severe intra-abdominal obesity (Heine et al., 2000). This provides evidence that ERα positively
32
regulates adipose homeostasis and metabolism, whereas in the absence of ERα, unregulated
signaling through ERβ promotes an unhealthy adipose phenotype (Naaz et al., 2002).
Sex dimorphisms in the distribution of ERα and ERβ in adipose tissue have been
reported. Mature human adipocytes express both ERα and ERβ mRNA; expression of ERα is
identical between the sexes whereas mRNA levels of ERβ are higher in women (Dieudonne et
al., 2004). In human adipocytes isolated from subcutaneous and visceral fat depots from men and
premenopausal women, exposure to E2 resulted in upregulated ERα mRNA levels in both
adipocyte types in men, but only subcutaneous adipocytes in women. In men, E2 exposure did
not affect ERβ mRNA levels, but in women ERβ mRNA was upregulated – again, only in
subcutaneous adipocytes (Dieudonne et al., 2004). This suggests that men are more predisposed
towards ERα-dominant adipose regulation, whereas women have a more balanced ratio of ERα
and ERβ adipose regulation before menopause, which could explain sex-specific patterns of
adipose tissue distribution. After menopause, a shift in the ERα/ERβ ratio towards greater ERβ
signaling could mediate the postmenopausal increase in weight (Tomicek et al., 2011).
The issue of postmenopausal hormone therapy (HT) and changes in weight has been the
subject of controversy. A meta-analysis published in 2000 indicated no difference in
postmenopausal weight gain between women taking and not taking HT, but had insufficient data
to assess HT effects on body-mass index (BMI), waist-hip ratio, or fat mass (Norman et al.,
2000). However, a more recent meta-analysis conducted by the Endocrine Society reported that
HT was associated with less accumulation of weight, fat mass, and/or centrally located fat mass
(Santen et al., 2010). The 2000 meta-analysis was potentially influenced by its use of the term
“weight”; at menopause, there are large changes in both the distribution of body fat and the
proportions of fat to non-fat mass, all of which can affect weight in different ways (Santen et al.,
33
2010). Because increases in adiposity can be subcutaneous or visceral, women may experience a
gain in weight, but their overall adipose profile could be healthier. Consistent with this postulate,
HT use is associated with decreased abdominal/visceral fat (Haarbo et al., 1991; Salpeter et al.,
2006). Additionally, it may be that some types of HT lead to greater weight gain than others
(O'Sullivan et al., 1998), and weight changes may be less predictable in already obese women
(Santen et al., 2010). Overall, in non-overweight and non-obese women, results from the
Endocrine Society meta-analysis indicate that postmenopausal HT protects against weight gain,
and also promotes less adipose tissue deposition in visceral fat stores (Santen et al., 2010).
Adiposity exists along a continuum, and a high degree of adiposity is referred to as
obesity. Recent prevalence estimates show that approximately 70% of men and 62% of women
in the US are either overweight (BMI > 25) or obese (BMI > 30); although rates of obesity are
comparable between the sexes, more women fall into the category of extreme obesity (BMI > 40)
(Ogden et al., 2006). This is a critical problem because obesity, specifically visceral obesity, is
correlated with a large number of adverse cardiovascular outcomes. It is an established risk
factor for coronary heart disease, which explains why premenopausal women have a lower risk
of heart disease than age-matched men, but an increased risk after menopause (Authors, 2011;
Group, 2006). Obesity is also well recognized as the primary risk factor for both insulin
resistance (a pre-diabetic condition) and type 2 diabetes (Ahima, 2009; Pi-Sunyer, 2002).
Outcomes of multiple studies demonstrate that obesity is associated with an increased
risk of dementia, including late onset AD. Increased BMI in middle-aged populations (Kivipelto
et al., 2005; Whitmer et al., 2008; Whitmer et al., 2005) and older populations (Gustafson et al.,
2003) is predictive of higher risk of all forms of dementia. In the Baltimore Longitudinal Study
of Aging, both men and women showed associations between mid-life increases in BMI and
34
higher incidence of AD (Beydoun et al., 2008). Measurements of waist to hip ratio in middle-
aged and younger elderly individuals have also been shown to robustly correlate with higher risk
of late-onset AD (Luchsinger et al., 2011a; Luchsinger et al., 2007a), and measurements of waist
circumference correlate with decreased gray matter volume (Kurth et al., 2013). There are some
reports of no association between BMI and AD risk (Stewart et al., 2005) or of low BMI being
related to AD risk (Nourhashemi et al., 2003); however, those results are likely affected by the
age group being studied. Most research indicates that elevated BMI in middle age is related to an
increased risk of dementia, while such association diminishes in elderly populations (Fitzpatrick
et al., 2009; Luchsinger, 2008). Further strengthening the association between obesity and AD is
research showing that obesity is related with cortical atrophy in both cognitively normal obese
individuals (Pannacciulli et al., 2006; Raji et al., 2010) as well as MCI and AD patients (Ho et
al., 2010).

2.5.2. Estrogen and insulin resistance
Insulin is pivotal to maintaining and sustaining glucose metabolism in the periphery and
in the brain (Cholerton et al., 2011; Craft, 2007; De La Monte, 2012). Insulin resistance is a state
in which the tissues that require blood glucose have a diminished response to insulin, and the
subsequent reduced clearance of glucose from blood feeds back onto the pancreas to increase
secretion of insulin to induce glucose uptake (Luchsinger, 2008). Insulin resistance is primarily
caused by obesity, particularly visceral obesity (Luchsinger et al., 2011b). Susceptibility to
developing insulin resistance is known to increase with age (Carr, 2003; Iozzo et al., 1999), but
there is less evidence that sex differentially affects development of insulin resistance. Fasting
insulin (Poehlman et al., 1995; Razay et al., 2007) and glucose (Dallongeville et al., 1995; Lynch
35
et al., 2002) levels have been shown to rise as estrogen levels decrease during the menopausal
transition, but this is proposed as a secondary response to the change in body fat distribution and
not a direct effect of estrogen decline (Wing et al., 1992; Wing et al., 1991). One study suggests
women may have differential insulin metabolism after menopause, with postmenopausal women
producing less insulin, but eliminating it more slowly (Walton et al., 1993).
The effects of estrogen signaling through ERs on insulin production have been studied in
vivo using aromatase knockout mice (ArKO), which lack the enzyme responsible for conversion
of androgens to estrogens (Jones et al., 2000). These animals develop insulin resistance by one
year of age; as in humans, this is likely due to increased visceral fat. Larger fat depots lead to
greater release of free fatty acids, which has the potential to disturb insulin dynamics in the liver
(Pallottini et al., 2008). A similar development of insulin resistance in conjunction with an
increase in fat mass is seen in ERα knockout (αERKO) mice (Bryzgalova et al., 2006; Heine et
al., 2000; Manrique et al., 2012). Ovariectomy provides a good model of postmenopausal insulin
resistance: loss of ovarian hormones induces an increase in body weight along with increased
plasma glucose levels and decreased plasma insulin response to glucose, and this can be reversed
through treatment with estrogen after ovariectomy (Bailey and Ahmed-Sorour, 1980; Ding et al.,
2012; Zhu et al., 2013). Still, in these models, the challenge remains to distinguish between
primary insulin resistance mediated by loss of estrogen and insulin resistance that develops
secondary to adipose deposition.
In human clinical research, the majority of studies have focused on the impact of
hormone therapy on incidence of type 2 diabetes (T2DM) as opposed to insulin resistance,
although several studies have investigated the effect of HT on insulin resistance. A meta-analysis
of studies of HT and insulin resistance indicated that postmenopausal HT significantly reduced
36
levels of insulin resistance (as measured by the homeostatic assessment of insulin resistance
(HOMA-IR) score) (Salpeter et al., 2006). Interestingly, two studies in non-human primates
found that specific hormone formulations may change estrogen’s beneficial effects on insulin
resistance; in these primates, a conjugated equine estrogen (CEE) formulation decreased body
mass and HOMA-IR, whereas addition of the progestin medroxyprogesterone acetate (MPA)
significantly increased body weight, fat mass, and HOMA-IR (Shadoan et al., 2003; Shadoan et
al., 2007).
Insulin can cross the blood–brain barrier (Park, 2001) and insulin receptors are expressed
throughout the brain with particularly high receptor densities in the hippocampus and entorhinal
cortex (Burns et al., 2011). Insulin resistance in non-diabetic (or pre-diabetic) individuals is
associated with increased risk of cognitive impairment and dementia (Craft, 2005; Xu et al.,
2007; Yaffe et al., 2004). Further, hyperinsulinemia as a response to insulin resistance has been
associated with increased risk of AD in multiple cross-sectional and longitudinal studies
(Kuusisto et al., 1997; Luchsinger et al., 2004; Peila et al., 2004; Razay and Wilcock, 1994;
Stolk et al., 1997). In a recent study, hyperinsulinemia a decade or more before death correlated
with presence and severity of amyloid plaques upon autopsy (Matsuzaki et al., 2010). HOMA-IR
correlates with decreased functional connectivity within the posterior cingulate cortex (Chen et
al., 2014). Additionally, HOMA-IR negatively correlates with hippocampal volume in
cognitively normal middle-aged women (Rasgon et al., 2011). Within the brain, insulin may lead
to decreased clearance of Aβ by competing for the insulin-degrading enzyme (Farris et al.,
2003). Insulin has also been shown to increase tau phosphorylation (Park, 2001).
Early preclinical analyses indicated that a decreased cerebral metabolic rate of glucose
uptake (CMRglu) is evident in rodent models of diabetes (Garris et al., 1984; Vannucci et al.,
37
1997). These findings have also been shown in humans: higher insulin resistance was associated
with decreased CMRglu in the same regions that show hypometabolism in AD (Baker et al.,
2011). This was replicated in a later study in which higher fasting glucose in nondiabetic
individuals was associated with decreased CMRglu in AD-affected brain regions (Burns et al.,
2013). These findings support earlier analyses in humans indicating that a decrease in the
cerebral metabolic rate of glucose uptake is one of the earliest events in the pathogenesis of AD
(Mosconi et al., 2006; Reiman et al., 1996b). Collectively, preclinical and clinical data provide
evidence of a link between development of insulin resistance and increased risk of AD, and
suggest that insulin resistance could be a biomarker of risk of AD or preclinical AD.

2.5.3. Estrogen and diabetes
The association between ovarian hormone loss and development of dysregulation of
glucose homeostasis dates back decades. In preclinical analyses, ovariectomy resulted in
increased plasma glucose levels, which was prevented by estrogen treatment (Bailey and
Ahmed-Sorour, 1980; El Seifi et al., 1981). Estrogen regulation of insulin and glucose
homeostasis is mediated through ERα receptors, which – in addition to their expression pattern in
adipose tissue –are also expressed in the liver, pancreatic beta cells, and skeletal muscle (Meyer
et al., 2011; Sutter-Dub, 2002). This allows for coordination of signals regulating metabolic
homeostasis in response to hormonal milieu. ERα in pancreatic beta cells has been proposed to
regulate insulin production in vivo, such that it has an anti-diabetic effect (Le May et al., 2006;
Wong et al., 2010). Consistent with these findings, αERKO mice show impaired glucose
tolerance and increased insulin resistance (Ribas et al., 2010). Several studies indicate a key role
of mERs in regulating pancreatic beta cell function (Nadal et al., 2004; Sutter-Dub, 2002).
38
Collectively, preclinical analyses indicate a role for estrogen in sustaining insulin and glucose
homeostasis that underlies a healthy metabolic profile.
However, the relationship between estrogen levels and diabetes following menopause is
complicated. Several studies have shown that, in women, higher endogenous estrogen levels
after menopause are associated with greater risk of developing insulin resistance and diabetes
(Ding et al., 2007; Kalish et al., 2003; Oh et al., 2002); further, higher endogenous estrogen
levels in diabetic postmenopausal women is associated with increased risk for dementia
(Carcaillon et al., 2014). An increase in diabetes risk factors was also seen in early studies using
high-estrogen HT (Wynn et al., 1979; Wynn and Doar, 1966). However, the Women’s Health
Initiative (WHI) (Margolis et al., 2004), Heart and Estrogen/Progestin Replacement Study
(Kanaya et al., 2003) and the Nurses’ Health Study (Manson et al., 1992) all reported that
women taking HT had fewer cases of incident diabetes. It should be noted that most of these
large epidemiological studies used combination hormone therapy (CEE + MPA), so it is also
unclear how well the results generalize to other HT regimens. Several recent prospective cohort
studies have also found reduced incidence of new-onset diabetes in women taking HT (de
Lauzon-Guillain et al., 2009; Pentti et al., 2009); this effect was much stronger for women taking
oral HT vs. transdermal HT (de Lauzon-Guillain et al., 2009) and for women who had long-term
HT exposure (Pentti et al., 2009). In the WHI trial, women with larger waist circumference
measurements showed a greater beneficial effect of HT on decreasing diabetes risk (Margolis et
al., 2004). An unresolved issue is whether HT has a direct effect on diabetes risk through
regulation of pancreatic ERα signaling, or an indirect effect through decreasing obesity.
Better concordance exists between outcomes of preclinical and clinical analyses of
dysregulated glucose metabolism and cognitive function. Diabetes is strongly linked to cognitive
39
impairment in both preclinical and clinical studies. Rats with streptozotocin-induced diabetes
have deficits in learning and memory (Baydas et al., 2003; Kucukatay et al., 2007; Lupien et al.,
2003; Stranahan et al., 2008; Tiwari et al., 2009; Ye et al., 2011). Both pigs and rats with
combined diabetes and hypercholesterolemia show increased blood–brain barrier permeability
and increased Aβ plaque deposition (Acharya et al., 2013). Diabetic animal models also show
decreased insulin uptake into the brain and consequent reduced levels of neuronal insulin (Banks
et al., 1997; Baskin et al., 1985; Kaiyala et al., 2000).
Persons with diabetes have a high risk of cognitive dysfunction, including amnestic MCI
(Luchsinger et al., 2007b; Solfrizzi et al., 2004) and all types of dementia (Brayne et al., 1998;
Brismar et al., 2007; Northam et al., 2006; Ott et al., 1999; Ristow, 2004; Stewart and Liolitsa,
1999). One study reported that diabetes was only related to risk of vascular dementia (VaD)
(MacKnight et al., 2002), whereas others have reported that diabetes is associated with increased
risk of developing both AD and VaD, but that the relative risk for VaD is greater than that for
AD (Luchsinger et al., 2005; Luchsinger et al., 2001; Yoshitake et al., 1995). However, the
majority of studies have found diabetes to be related to a higher risk of AD, particularly late-
onset AD (Arvanitakis et al., 2004; Cheng et al., 2011; Huang et al., 2014; Leibson et al., 1997;
Luchsinger et al., 2005; Peila et al., 2002). Two recent meta-analyses identified 15 separate
studies in which the association between AD and diabetes was investigated, and concluded that
diabetes is an independent risk factor for AD (Vagelatos and Eslick, 2013; Williams et al., 2010).
The presence of diabetes in patients who are newly diagnosed with AD is also related to greater
baseline cognitive impairment and more rapid progression of AD (Sanz et al., 2011).
The clear association between type 2 diabetes and risk of dementia emphasizes the
importance of strategies that reduce the risk of developing insulin resistance and dysregulation of
40
glucose homeostasis in both the periphery and the brain. Thus, clearly establishing the factors
that determine efficacy of estrogen or hormone therapy to reduce the risk of type 2 diabetes has
the potential to greatly impact both the incidence of both type 2 diabetes and associated
dementias.

2.5.4. Estrogen and leptin
Leptin is an adipokine secreted by adipose tissue that has a strong effect on regulating
energy intake and expenditure (Brennan and Mantzoros, 2006). Circulating levels of leptin are
directly proportional to the amount of adipose tissue in the body. Leptin functions through
binding leptin receptors in the hypothalamus, where it produces both an acute inhibition of
appetite due to food intake, and a more long-term inhibition based on the body’s fat stores
(Brennan and Mantzoros, 2006). Absence of leptin or genetic knockout of the leptin receptor
leads to extreme obesity, as demonstrated by the ob/ob mouse. Diseases such as obesity and
metabolic syndrome are associated with chronically high leptin levels (Brennan and Mantzoros,
2006); conversely, following a low-fat diet decreases circulating leptin (Dubuc et al., 1998).
Both pre- and post-menopausal women have higher leptin levels than men, which may be due to
endogenous estrogen levels or adipose tissue distribution (Dedeoglu et al., 2009; Rosenbaum et
al., 1996; Saad et al., 1997). In premenopausal women, leptin levels correlate with plasma levels
of estrogen; this correlation disappears after menopause, when it is confounded by a rise in
obesity (Hong et al., 2007).
Together with leptin receptors, ERα and ERβ are expressed in the hypothalamus,
although expression of ERα is greater than ERβ (Brown et al., 2010). There is evidence that
changing estrogen levels across the estrus cycle regulate leptin receptor expression at the mRNA
41
level, likely through an ERE located on the leptin receptor gene (Bennett et al., 1999). The result
of this regulation is higher sensitivity to leptin when estrogen levels are higher (Brown et al.,
2010). Crosstalk between leptin receptors and ERs leads to activation of the Stat3/Erk pathway
(Fusco et al., 2010; He et al., 2012). Shp2, a nonreceptor tyrosine phosphatase, has been
proposed to mediate this cross-talk between leptin receptors and ERs (He et al., 2012). Analyses
show that Shp2 associates with ERα, and that it is through this association that leptin and
estrogen synergistically activate the Erk signaling pathway. The interaction between leptin and
estrogen signaling pathways provides a mechanistic rationale for the observation that
postmenopausal women may have a reduced response to leptin once estrogen levels decrease,
which could lead to the increased obesity seen after menopause.
There is no consensus as to whether leptin levels increase, stay the same, or even
decrease after menopause, and each of these has been reported by multiple clinical studies
(reviewed by Dedeoglu et al. (2009)). This is likely due to age differences between the
populations studied in each trial. One group showed that total body fat and subcutaneous fat were
more predictive of leptin levels than visceral fat; as the menopausal transition is typically
associated with a redistribution of adipose tissue from subcutaneous to visceral fat stores, this
could account for decreased leptin after menopause (Dua et al., 1996). However, menopause is
also associated with more weight gain, which could lead to an increase in leptin levels.
Studies using rat ovariectomy models showed that after OVX, rats retain the sensitivity to
estrogen signaling effects on leptin, which is promising for studies of hormone therapy
(Machinal et al., 1999). Clinical studies have shown that women on HT typically have both
better maintenance of weight and healthier leptin levels (which could refer to an increase or
decrease in leptin depending on overall metabolic status), although it is unclear which of these
42
the principal driving force - or, again, if the driving forces are bidirectional (Dedeoglu et al.,
2009; Di Carlo et al., 2004). However, other studies show that once BMI is corrected for, the
association between HT and healthier leptin levels disappears (Bednarek-Tupikowska et al.,
2006; Gower et al., 2000).
A typical feature of AD is decreased BMI over the course of the disease. One hypothesis
is that this could be due to a disruption in energy homeostasis; thus, some recent studies have
investigated whether leptin levels are associated with AD. In vitro and in vivo, leptin has a
multitude of beneficial effects. A recent review summarized much of the preclinical research on
leptin as it relates to AD; critically, apart from leptin’s effects in the hypothalamus, there is a
significant amount of research showing that leptin has neurogenic and neuroprotective actions in
the hippocampus (Paz-Filho et al., 2010). In vitro, leptin activates the AMPK signaling pathway
(Greco et al., 2011), which is critical for stimulation of neuronal energy production, either
through glucose or lipid metabolism (Salminen et al., 2011). Further, there is also evidence that
Aβ production and tau phosphorylation can be mediated through the AMPK pathway (Greco et
al., 2009a; Greco et al., 2009b; Greco et al., 2008; Salminen et al., 2011). Additionally, leptin
decreased the amount of neurodegeneration caused by Aβ (Perez-Gonzalez et al., 2011). Similar
effects were seen both in the CRND8 mouse model of AD (Greco et al., 2010), and in rabbits
(Marwarha et al., 2010), where treatment with leptin decreased both amyloid burden and
phosphorylated tau in the hippocampus. Leptin has also been shown to reduce neuronal β-
secretase activity as well as APOE-dependent Aβ uptake (Fewlass et al., 2004). In the APP/PS1
mouse model of AD, leptin treatment increased proliferation of neuronal precursors in the
dentate gyrus subgranular zone (Perez-Gonzalez et al., 2011).
43
In healthy elderly individuals, higher leptin levels are associated with higher gray matter
volumes in the hippocampus (Narita et al., 2009) and less cognitive decline (Holden et al., 2009).
Longitudinal studies have shown that both women and men with higher baseline levels of leptin
have a decreased risk of incident AD (Lieb et al., 2009). Leptin levels decrease as severity of AD
increases, and AD patients typically have lower leptin levels than controls (Bigalke et al., 2011;
Khemka et al., 2014; Warren et al., 2012). This is primarily due to a decrease in BMI over the
course of the disease. However, it should be noted that some studies report no difference in leptin
levels between AD patients and controls (Theodoropoulou et al., 2012). This may be due to
differences in weight loss: one study reported that AD patients who experienced significant
weight loss had lower leptin levels than those whose weight remained stable (Power et al., 2001).

2.5.5. Estrogen and ghrelin
Ghrelin, another of the adipokines, is the counterpart to leptin. It is the only known
appetite-stimulating hormone, and it is predominantly produced by the stomach (Inui et al., 2004;
Nakazato et al., 2001). Ghrelin binds to the growth hormone secretagogue receptor (GHSR) in
the hypothalamus (De Vriese and Delporte, 2007), where it stimulates the release of growth
hormone. There are also ghrelin receptors in the hippocampus (Carlini et al., 2004), and ghrelin
has been shown to increase dendritic spine density and promote long-term potentiation (Diano et
al., 2006). There is not a clear consensus as to whether women naturally have higher ghrelin
levels or if levels are the same between the sexes (Makovey et al., 2007). However, both men
and women show an age-related decline in plasma ghrelin levels (Rigamonti et al., 2002) and
growth hormone release in response to ghrelin stimulation (Broglio et al., 2003).
44
Analyses of ovarian hormone regulation of ghrelin indicate that both plasma ghrelin
levels and ghrelin mRNA levels in stomach cells increased after ovariectomy, and these effects
were reversed with E2 treatment. Additionally, ghrelin has been shown to colocalize with ERα in
stomach (Matsubara et al., 2004). This suggests that estrogen is a negative regulator of ghrelin
synthesis and thus ghrelin levels would be expected to increase following menopause. However,
several studies have shown that HT has no effect on ghrelin levels in postmenopausal women
(Lambrinoudaki et al., 2008; Purnell et al., 2003). One study reported increased ghrelin levels in
receiving oral HT whereas transdermal HT had no effect (Kellokoski et al., 2005). Another study
found that ghrelin levels increased in general over a 1.5 year period; surprisingly, ghrelin levels
increased most sharply in those women who initiated and discontinued HT within the 1.5 years
of the study (Soni et al., 2011). Conversely, in a study of obese women with metabolic
syndrome, HT decreased ghrelin levels (Chu et al., 2006). These findings indicate that HT
induces an increase or decrease in ghrelin levels based on metabolic status.
In vitro, ghrelin improves glucose/insulin homeostasis by decreasing insulin resistance,
while also decreasing tau phosphorylation via activation of GSK3β (Chen et al., 2010). In vivo,
administration of ghrelin agonists has been shown to improve cognition (Atcha et al., 2009) and
neurogenesis (Moon et al., 2014; Moon et al., 2009). Additionally, ghrelin can protect against
synaptic loss and neuronal degeneration induced by Aβ injection into the hippocampus (Moon et
al., 2011).
Most studies show that plasma ghrelin levels are the same between AD patients and
controls (Proto et al., 2006; Theodoropoulou et al., 2012). However, when challenged with a
glucose load, male AD patients had a smaller area-under-the-curve measurement for ghrelin
45
levels (Theodoropoulou et al., 2012). Reduced ghrelin mRNA levels have been observed in the
temporal gyrus of patients with AD (Gahete et al., 2010).

2.5.6. Estrogen and adiponectin
Adiponectin is the predominant adipokine regulating overall body metabolism and
development of the metabolic syndrome (Hanley et al., 2007). It is produced by adipose tissue,
and regulates peripheral glucose and insulin levels (Diez and Iglesias, 2003; Dridi and Taouis,
2009). Plasma adiponectin levels are inversely correlated with glucose and insulin levels, and
lower adiponectin leads to insulin resistance and dyslipidemia (Cai et al., 2012; Dridi and
Taouis, 2009). Further, higher adiponectin levels are associated with lower risk of incident
diabetes in prospective studies (Li et al., 2009; Zhu et al., 2010). Adiponectin also appears to
play a role in inhibiting inflammatory processes (Hatzis et al., 2013). Adiponectin levels are
higher in females than males (Andreasson et al., 2012; Hotta et al., 2000) and increase with age
in both sexes (Andreasson et al., 2012; Obata et al., 2012). Unlike leptin and ghrelin – which
exert their peripheral effects via the hypothalamus – adiponectin effects appear to be primarily
peripherally mediated (Ukkola and Santaniemi, 2002).
Evidence points to ERα as a positive regulator of adiponectin levels in adipose tissue.
The balance between ERα and ERβ signaling in adipose tissue changes after menopause, with
ERβ becoming the dominant ER (Tomicek et al., 2011). Higher ERβ signaling leads to inhibition
of peroxisome proliferator activated receptor gamma (PPARγ), which regulates secretion of
adiponectin (Foryst-Ludwig et al., 2008). In vivo studies support this: in rats, OVX was
associated with increased ERβ, decreased PPARγ, decreased plasma adiponectin levels and also
decreased expression of one adiponectin receptor isoform (Tomicek et al., 2011). Further
46
evidence of positive regulation of adiponectin through ERα comes from a study using αERKO
mice; these animals had lower PPARγ levels and decreased adiponectin compared to WT mice
(Ribas et al., 2010). Similarly, in humans, a particular ERα gene polymorphism that is associated
with poorer prognosis after myocardial infarction is also associated with lower serum levels of
adiponectin (Yoshihara et al., 2009).
Although there is a slight increase in adiponectin with age, there does not appear to be a
significant difference in adiponectin levels associated specifically with menopause (Ahtiainen et
al., 2012) or loss of ovarian hormones (Benetti-Pinto et al., 2010). Soni et al found that
adiponectin levels increase slightly over 1.5 years in postmenopausal women, but this increase
was driven by an increase in adiposity (Soni et al., 2011). As with leptin and ghrelin, and effect
of HT on adiponectin is varied, likely because of different HT formulations in different
populations. Several studies show increased adiponectin levels in women taking HT
(Christodoulakos et al., 2008; Ruszkowska et al., 2013), whereas one study reported decreased
adiponectin levels with HT (Im et al., 2006).
It has been reported that individuals with MCI and AD have higher adiponectin levels
both in CSF and plasma (Khemka et al., 2014; Une et al., 2011), and that these increased levels
correlate with decreased MMSE scores (Khemka et al., 2014). However, other studies have
shown no difference in plasma adiponectin levels between AD patients and healthy controls
(Bigalke et al., 2011; Warren et al., 2012). The Framingham Heart Study found that a higher
baseline adiponectin levels was associated with increased risk of incident AD only in women
(van Himbergen et al., 2012). Thus it is unclear if adiponectin itself is associated with AD
development, or if it is more closely associated with metabolic disorders – such as obesity
47
(Ukkola and Santaniemi, 2002) or metabolic syndrome (Renaldi et al., 2009) – either of which
would increase an individual’s risk for AD.

2.5.7. Estrogen and sex hormone binding globulin
Sex hormone-binding globulin (SHBG) is a glycoprotein that binds to hormones as they
circulate in the bloodstream. It is produced in the liver, and its production is stimulated by
estrogen and testosterone (Plymate et al., 1988). SHBG has different affinities for different
hormones; it binds testosterone with a very high affinity, and its affinity is less for estrogen
(Wallace et al., 2013). Traditionally it was thought that only unbound (“free”) hormones were
able to act on their respective receptors, and through this mechanism SHBG could influence the
bioavailability of hormones. However, recent evidence indicates that there is also a G-protein-
coupled receptor which binds SHBG on cell membranes (Rosner et al., 2010; Wallace et al.,
2013). The SHBG receptor mechanism of action is not yet known; it may allow the bound
hormone to have intracellular effects without entering the cell (Rosner et al., 1999), or it may
allow the SHBG-hormone complex to enter the cell through endocytosis (Hammes et al., 2005).
SHBG binding to its receptor may also prompt expression of estrogen or testosterone receptors
on the cell membrane (Wallace et al., 2013). Signaling through the G-protein-coupled receptor
promotes intracellular release of cAMP and activation of PKA (Rosner et al., 2010).
Interestingly, SHBG bound to estrogen has agonist effects on the intracellular signaling cascade,
and SHBG bound to testosterone has antagonist effects (Rosner et al., 2010).
SHBG levels are closely associated with metabolism; in particular, several in vitro and in
vivo studies have shown that insulin can inhibit the synthesis of SHBG, decreasing the amount
available to bind hormones and increasing hormone bioavailability (Nestler, 1993; Plymate et al.,
48
1988). In humans, there tends to be an inverse relationship between plasma insulin level and
SHBG concentration (Akin et al., 2009; Preziosi et al., 1993), which implicates severity of
insulin resistance in determining SHBG levels. Insulin resistance and SHBG concentration have
been examined in a number of clinical studies, and it has been consistently shown that low
SHBG levels correlate with higher HOMA-IR scores (Akin et al., 2007; Akin et al., 2009; Davis
et al., 2012b; Kalish et al., 2003; Li et al., 2010a; Yasui et al., 2007). This relationship appears
independent of BMI (Davis et al., 2012b), but may be mediated by abdominal obesity (Akin et
al., 2009). SHBG is also associated with diabetes risk, and higher plasma levels of SHBG have
been linked to lower risk of developing T2DM (Ding et al., 2006; Ding et al., 2009; Kalyani et
al., 2009).
Women on average have higher SHBG levels than men (Bukowski et al., 2000; Soriguer
et al., 2012). It is unclear whether SHBG levels change with menopause after correcting for
changes in insulin levels. Multiple clinical studies have measured plasma SHBG concentrations
in pre- and post-menopausal women, and most (Akin et al., 2009; Key et al., 2011; Pasquali et
al., 1997) but not all (Burger et al., 2000) have found no effect of menopause status on SHBG
levels. The Study of Women’s Health Across the Nation found that overall, there were no
significant longitudinal changes in SHBG across the menopause transition, and any small
decreases in SHBG levels were driven by changes in adiposity (Wildman et al., 2012).
Interestingly, however, some studies have found that the inverse association between HOMA-IR
and SHBG levels is only seen in postmenopausal, not premenopausal, women (Akin et al., 2007,
Akin et al., 2009 and Davis et al., 2012b). After menopause, type of hormone therapy can also
affect SHBG levels; based on a number of small clinical studies, oral E2 therapy increases
49
plasma SHBG levels, whereas transdermal therapy has no effect (Selby et al., 1989; Taskinen et
al., 1996; Vehkavaara et al., 2000) (reviewed in (Goodman, 2012)).
Several studies have detected higher SHBG levels in persons with AD relative to healthy
controls (Hogervorst et al., 2004; Hoskin et al., 2004; Paoletti et al., 2004). In a recent
longitudinal study, elderly men and women who were dementia-free at baseline had a greater
risk of incident dementia and AD as their SHBG levels increased (Muller et al., 2010). The
increased risk remained significant even after correction for age and BMI; however, it is still
unclear whether this increased risk was associated with bioavailable testosterone, or whether it
was perhaps associated with another dementia risk factor (such as insulin resistance or diabetes).

2.6. PET imaging of brain bioenergetic deficits in aging and Alzheimer’s disease
Positron emission tomography (PET) scanning using fluorodeoxyglucose (FDG, a
radioisotope of glucose) is a strategy to assess metabolic activity of the brain. The FDG-PET
signal is particularly relevant to glucose required for synaptic activity (Landau et al., 2011). In
healthy normal individuals, some studies have shown no significant differences in the cerebral
metabolic rate of glucose uptake (CMRglu) between the genders (Miura et al., 1990; Tyler et al.,
1988), whereas others have shown that females have higher CMRglu rates than males
(Andreason et al., 1994; Yoshii et al., 1988). However, none of the studies above corrected for
estrogen and progesterone levels, and it has been shown that in premenopausal women there are
differences in glucose metabolism based on phase of the menstrual cycle (Reiman et al., 1996a).
Thus, it is difficult to discern whether estrogen and progesterone support the same levels of
CMRglu as testosterone. In normal aging, CMRglu declines with age, with the most significant
declines occurring in the prefrontal cortex (Chetelat et al., 2013; Kalpouzos et al., 2009; Pardo et
50
al., 2007; Reiman et al., 1996a). In the menopausal female brain, this age-related decline in
glucose metabolism in prefrontal cortex has been detected, as well as a decline in glucose
metabolism in the posterior cingulate that was specific to estrogen deprivation (Rasgon et al.,
2005). In a separate study, women had a greater age-related metabolic decline than men (Murphy
et al., 1996b).
A reduced rate of brain glucose metabolism is one of the most frequently documented
abnormalities in AD. Alzheimer’s patients show a specific pattern of abnormal glucose
metabolism in regions of the brain that are most vulnerable to development of pathology,
including the temporoparietal cortex, posterior cingulate, hippocampus, and precuneus (Bero et
al., 2011; Jagust et al., 2007; Mosconi et al., 2009b; Vaishnavi et al., 2010; Vlassenko et al.,
2010). In at-risk populations, a decline in cerebral glucose utilization appears decades prior to the
onset of clinical AD (Albert et al., 2011; Chen et al., 2011; de Leon et al., 2001; Mosconi et al.,
2009a; Reiman et al., 2004) and precedes brain atrophy (De Santi et al., 2001). Decreased
glucose uptake correlates with degree of cognitive impairment as measured by the MMSE or the
Alzheimer’s Disease Assessment Schedule—Cognition subscale (ADAS-Cog) (Habeck et al.,
2012; Minoshima et al., 1997), as well as with lower cerebrospinal fluid (CSF) Aβ42 and higher
total tau and phospho-tau levels (Petrie et al., 2009).
In persons with mild cognitive impairment, glucose hypometabolism accurately predicts
future clinical progression to AD (Chen et al., 2011; Chetelat et al., 2003; Herholz et al., 2011;
Landau et al., 2010; Petrie et al., 2009; Walhovd et al., 2010). The hallmark pattern of
hypometabolism in AD can be detected in the aging brain long before the diagnosis of AD
(Mosconi et al., 2008). In fact, FDG-PET measurements of glucose metabolism are more closely
51
related to changes in cognitive status than CSF or PET measurements of Aβ42 (Jagust et al.,
2009).
Consistent with clinical findings, multiple mouse models of AD display a pattern of
reduced metabolism in the posterior cingulate/retrosplenial cortex (Nicholson et al., 2010; Valla
et al., 2008; Valla et al., 2006b). Specifically, the triple transgenic Alzheimer’s disease
(3xTgAD) mouse, which has a measurable decline in mitochondrial enzyme activity at 3 months
and develops noticeable plaques by 6 months of age (Yao et al., 2009), has reduced metabolism
that is evident as early as 2 months of age (Nicholson et al., 2010). During reproductive
senescence, both the normal and 3xTgAD brain show a significant decline in glucose
metabolism, which is also evident in the ovariectomized mouse (Ding et al., 2013a and Ding et
al., 2013b).

2.7. Estrogen and hormone therapy, the timing hypothesis, and risk of Alzheimer’s disease
It has been proposed that the loss of estrogen after menopause could significantly
contribute to cognitive decline and AD risk in women (Brinton, 2008b). Based on estrogen’s
myriad positive effects on mitochondrial energy production and regulation of Aβ accumulation,
it seems reasonable to think that estrogen replacement after menopause would have beneficial
effects on cognition in general, and might even be able to reduce the risk of AD. Epidemiological
studies and clinical trials of hormone therapy and cognition have shown mixed results
(Hogervorst et al., 2000; van Amelsvoort et al., 2001). The prevailing data indicate that although
HT cannot alter the progression of AD (Asthana et al., 2001; Asthana et al., 1999; Henderson et
al., 2000; Mulnard et al., 2000; Wang et al., 2000), it may have the potential to lower the risk of
women developing AD in the first place (Wise et al., 2001). Results from several longitudinal
52
studies of HT use in postmenopausal women indicate that long-term HT helps to preserve
memory (Jacobs et al., 1998; Maki et al., 2001; Resnick et al., 1997). Most studies also show that
HT is associated with better cognitive function in nondemented older women (Duka et al., 2000;
Jacobs et al., 1998; Kimura, 1995; Resnick et al., 1997; Robinson et al., 1994; Steffens et al.,
1999), although one found no effect of HT on cognitive function (Barrett-Connor and Kritz-
Silverstein, 1993). Multiple epidemiological studies – but not all - show that HT reduces the risk
of AD, which researchers suggest may be due to delayed disease onset (Henderson et al., 1994;
Kawas et al., 1997; Paganini-Hill and Henderson, 1996; Simpkins et al., 1994; Tang et al., 1996;
Yaffe et al., 1998; Zandi et al., 2002).
Results from several imaging studies support the idea that postmenopausal HT can
modulate brain bioenergetics, likely leading to the maintenance of cognitive function and
reduced risk of AD. In the earliest study of brain metabolism in healthy postmenopausal women
who were or were not receiving HT, patterns of regional cerebral blood flow (rCBF) were
measured during various memory-related tasks (Resnick et al., 1998). Women taking HT showed
a different pattern of rCBF than women not taking HT that was particularly evident in brain
regions involved in memory systems, and the women taking HT also had superior performance
on memory tasks (Resnick et al., 1998). In a follow-up study of the same women two years later,
HT users showed increased rCBF over time compared to nonusers in the hippocampus,
parahippocampal gyrus, and temporal lobe, regions that are critical for memory formation and
are also vulnerable to decreased glucose metabolism in preclinical AD (Maki and Resnick,
2000). As before, the HT users scored higher on a battery of memory tests than nonusers (Maki
and Resnick, 2000).
53
Glucose metabolism in cognitively intact women who were currently taking (ERT+) and
had never taken (ERT-) estrogen replacement therapy, as well as women with clinically
diagnosed AD, has also been studied using FDG-PET (Eberling et al., 2000). The ERT+ cohort
had the highest rates of glucose metabolism, and their metabolic rates were significantly higher
than those seen in the women with AD. Women in the ERT- cohort had glucose metabolism
rates that were intermediate to the two other groups, but their rates were not significantly
different from the women with AD (Eberling et al., 2000). This provides evidence that estrogen
depletion can affect glucose metabolism. Similarly, a longitudinal study compared glucose
metabolism between cognitively healthy women who did and did not take hormone therapy. At
baseline, the two groups of women showed no differences in metabolism; after two years, the
women not on hormone therapy had a significant decrease in glucose metabolism in the posterior
cingulate cortex – an area of the brain which shows metabolic decline in the earliest stages of
AD – whereas the women receiving estrogen therapy did not have a significant metabolic change
in this brain region (Rasgon et al., 2005). This preservation of brain metabolism was later shown
to be greatest in women taking 17β-estradiol-based hormone therapy formulations (Silverman et
al., 2011). Although these studies are observational, they provide evidence that peri- or
postmenopausal HT has a positive effect on brain bioenergetic function in women.
A challenge for the field was that results from the Women’s Health Initiative Memory
Study (WHIMS) opposed the idea that estrogen was protective against development of AD. In
the WHIMS study, both women in the estrogen-alone cohort (Shumaker et al., 2004) and women
in the estrogen + progesterone cohort (Shumaker et al., 2003) showed an increased risk of
developing AD. However, women in the WHIMS trial were significantly older than women in
many of the prior epidemiological studies: on average, they were 65 years old, which is at least a
54
decade past menopause. Additionally, the average BMI of women enrolled in the Women’s
Health Initiative study (the population from which the WHIMS women were drawn) was in the
overweight range, which likely would have put these women at a higher risk for metabolic
diseases such as diabetes, thus increasing their risk for AD (Stefanick et al., 2003). Further – as
discussed previously – the unhealthy metabolic status of these women could have potentially
attenuated any positive response to hormone therapy.
Thus, a “critical window” theory of hormone replacement has been suggested, based on
the concept that estrogen has beneficial effects if taken before or at the time of menopause when
neurological health is still intact, but detrimental effects if initiated years after menopause when
neurological health may have already begun to decline (Brinton, 2004, 2005). Model systems
that have been used to investigate the role of hormone replacement after menopause fall into two
distinct classes: preventative interventions in healthy organisms, and “restoration” interventions
in organisms with compromised neurological function (Brinton, 2005, 2008a, b). In our
laboratory, all systems that have led to the elucidation of neuroprotective effects of estrogen and
underlying mechanisms of action have typically used a prevention experimental paradigm
(Brinton, 2005, 2008a, b; Yao et al., 2012). Data from other researchers also supports this critical
window hypothesis: estrogen replacement in rats shortly after ovariectomy is associated with in
improvements in learning, whereas estrogen replacement after a long period of estrogen
deprivation is disadvantageous (Daniel et al., 2006; Gibbs, 2000). Further, this prevention
paradigm was investigated in women with surgical or pharmacological-induced menopause,
where the beneficial effects of HT shortly after menopause paralleled the effects seen in animal
model prevention studies (Phillips and Sherwin, 1992; Sherwin, 1988, 2012). An increasing body
of clinical literature also supports the concept of a critical window of estrogen benefit, which
55
aligns with the healthy cell bias of estrogen action (Bagger et al., 2005; Berent-Spillson et al.,
2010; Brinton, 2008b; Henderson et al., 2005; MacLennan et al., 2006; Maki, 2013; Maki et al.,
2011; Shao et al., 2012; Whitmer et al., 2011; Zandi et al., 2002).

3. PREDICTING RISK FOR ALZHEIMER’S DISEASE:
WHAT IS THE CHALLENGE?

Research has shown that the initial physiological changes in Alzheimer’s disease begin
approximately 20 years before clinical diagnosis (Thies and Bleiler, 2013). Both basic science
and failed clinical trials suggest that a successful intervention strategy for AD will need to be
initiated early, far in advance of significant impairment (Yao et al., 2011). The challenge, then, is
how to identify individuals at risk from within a healthy population, rather than waiting to
identify them when they show overt disease.
One promising way to identify individuals prior to clinical presentation of Alzheimer’s
disease is through the development of biomarkers. The most widely accepted trajectory of
Alzheimer’s biomarkers was
developed by Dr. Cliff Jack using
data from the Alzheimer’s Disease
Neuroimaging Initiative, and has
been recently updated (Jack and
Holtzman, 2013). This trajectory
provides a well-defined map of the
developmental time course of AD
pathological features; however,
Figure 4: Metabolic panel target zone relative to development of
Alzheimer's disease pathology and dementia (modified from Jack &
Holtzman 2013).
56
what is lacking from the ADNI biomarker trajectory is any insight into risk factors that might
predispose an individual to Alzheimer’s disease. It is imperative that biomarkers indicating the
earliest preclinical changes are identified, both to increase our understanding of early disease
trajectories and to identify individuals who could benefit from a preventative therapeutic
strategy. My doctoral research has focused on moving earlier in the time course of Alzheimer’s
disease: investigating risk phenotypes composed of metabolic biomarkers known to be
associated with future development of AD, and which occur prior to the changes in biomarkers
of pathology (Figure 4).
Investigating phenotypes of risk presents a different challenge when compared with
charting disease pathology. One way to address this challenge is to seek these phenotypes within
the population with the greatest lifetime risk of Alzheimer’s disease. As previously mentioned,
of the 5 million Americans who currently have AD, 65% are female. Discussed throughout
Section 2, the menopausal transition leads to significant systemic changes requiring the
postmenopausal female brain to adapt to a very different physiological state. Each year, around
1.5 million women in America enter perimenopause; by 2020, it is expected there will be 45
million women over the age of 55 (Brinton, 2010). The average life expectancy for women in the
US is approximately 81 years, and the average age of menopause is 51; thus, a majority of
women will live approximately a third of their lives after menopause. Identifying metabolic risk
factors within this population which detect a shift to dysregulated bioenergetics and impaired
metabolic state – and which may have consequences on cognitive health – has great potential for
advancing our understanding of the earliest biological events leading to AD and for developing
treatments targeted towards prevention of this disease.
57
Thus, the central hypothesis of my doctoral research is that the loss of ovarian hormones
at menopause initiates a bioenergetic crisis, leading to the emergence of different metabolic
phenotypes. A subset of these metabolic phenotypes will be associated with changes in cognitive
performance which are indicative of an at-risk phenotype of sporadic Alzheimer’s disease. The
concept of different bioenergetic and metabolic phenotypes emerging at menopause – some
possibly conferring risk for and some protecting against development of future cognitive decline
– has been relatively unexplored. Through a comprehensive characterization of the way in which
women undergo both chronological and endocrine aging during the menopausal transition, I
hope to gain insight into the trajectory of Alzheimer’s risk. Further, because this hypothesis is
based on physiological and metabolic changes which are not specific to women, it is expected
that this strategy will be applicable to male aging as well.

58
4. DEVELOPMENT OF BIOMARKER PROFILES IDENTIFYING “PHENOTYPES OF
RISK”

Given the effects of estrogen on both brain and body, as well as the feedback between
brain and body metabolic systems, I hypothesize that the pattern of female endocrine and
metabolic aging during menopause puts women at greater risk for future development of
cognitive decline and Alzheimer’s disease. In order to investigate this hypothesis, I collaborated
with Dr. Howard Hodis and Dr. Wendy Mack to investigate the association between metabolic
status and cognition in a population of postmenopausal women.

4.1 The Early vs. Late Intervention Trial with Estradiol
The Early vs. Late Intervention Trial with Estradiol (ELITE) is a randomized, double-
blind, placebo-controlled clinical trial headed by Dr. Howard Hodis (PI) and Dr. Wendy Mack
(Co-I) at the USC Atherosclerosis Research Unit (NCT00114517). Subject recruitment for
ELITE began in 2004, and final subject visits were completed at the end of 2012. The purpose of
the ELITE trial was to examine the effects of oral 17β-estradiol on the progression of early
(subclinical) atherosclerosis and cognitive decline in healthy postmenopausal women. The
primary outcome measure was the rate of change of distal common carotid artery far wall intima-
media thickness (IMT). A secondary outcome was change in neurocognitive function. The
hypothesis for ELITE was similar to the healthy cell bias of estrogen action discussed in Section
2.7; namely, that 17β-estradiol has the ability to reduce both progression of atherosclerosis and
cognitive decline if initiated soon after menopause when vascular and neural tissue are still
responsive to the effects of estrogen, but will have no effect on atherosclerosis progression or
cognitive decline if initiated many years following menopause.
59
A total of 643 women took part in the ELITE trial. Women were recruited into two
cohorts: Early Menopause (n = 271), which was defined as 6 months to 6 years postmenopausal,
and Late Menopause (n = 372), defined as 10 or more years postmenopausal. The inclusion
criteria for ELITE were relatively broad in order to include a diverse group of women, which
would give the study greater translational validity to the population. Women had to be
postmenopausal for at least 6 months (postmenopausal was defined by the absence of a period),
and then for less than 6 years or more than 10 years to fit with the two menopause cohorts.
Menopause status was confirmed by a serum estradiol level of 25 pg/ml or less. Exclusion
criteria were developed to ensure that only healthy women were enrolled in the trial. Women
were excluded if they had clinical signs, symptoms, or personal history of cardiovascular
disease; diabetes mellitus (fasting serum glucose 140 mg/dL or greater); uncontrolled
hypertension (diastolic blood pressure 110 mmHg or greater); untreated thyroid disease; plasma
triglyceride levels greater than 500 mg/dL; serum creatinine greater than 2.0 mg/dL; cirrhosis or
liver disease; or a life threatening disease with prognosis less than 5 years. Further, because the
study used hormone therapy, women were excluded who had a history of deep vein thrombosis
or pulmonary embolism, history of breast cancer, or were already taking hormone therapy. Last,
women who had a hysterectomy only and no oophorectomy were excluded because time from
menopause could not be determined.
Within each menopause cohort, women were randomized to receive either hormone
therapy or a placebo (Figure 5). The hormone therapy was an oral 17β -estradiol pill (1 mg QD).
Women who had not undergone a hysterectomy also used a vaginal 4% progesterone gel (or a
placebo gel) for the last ten days of each month. The progesterone gel was distributed in a
60
double-blind fashion along with the randomized treatment so that only women exposed to active
treatment would receive active progesterone.

Figure 5: Distribution of women enrolled in ELITE

Clinic visits in the ELITE trial occurred every 6 months, for a maximum of 5 years. At
each of the 6-month visits, clinical data were recorded such as age, height, weight, blood
pressure, and whether the women were experiencing menopausal vasomotor symptoms. Plasma
and serum samples were collected to measure levels of hormones, cholesterol, triglycerides,
hemoglobin A1c, lipoproteins, and adipokines, among others. Women took a comprehensive
battery of neuropsychological tests three times during the study: first at baseline prior to
randomization, again at 2.5 years (the 30-month Study Visit), and again at their final Study Visit.
The testing battery was designed by Dr. Victor Henderson, and was composed of 14
neuropsychological tests designed to measure six cognitive domains (Table 1).
61

Cognitive Domain Neuropsychological Tests
Global Cognition • Symbol-Digit Test
Executive Function; Attention;
Concentration; Working Memory
• Letter-Number Sequencing (WAIS-III)
• Trails-B Test
• Shipley Institute of Living, Abstraction Scale
• Verbal Fluency (Animal Naming)
Verbal Memory
• Word List Free Recall (CVLT-II)
o Immediate and Delayed Recall
• Paragraph Recall (Logical Memory)
o Immediate and Delayed Recall
Nonverbal Memory
• Faces Recall (WMS-R)
o Immediate and Delayed Recall
Visuospatial Ability/
Visuoconstruction
• Judgment of Line Orientation
• Block Design (WAIS-III)
Naming/Semantic Memory
• Boston Naming Test
• Verbal Fluency (Animal Naming)

Table 1: Neuropsychological Tests and Cognitive Domains Used in ELITE

Tests included in the ELITE neurocognitive battery overlap with those used in ADNI
(Crane et al., 2012) and relevant clinical trials such as KEEPS (Wharton et al., 2008), allowing
for comparisons between datasets. The neuropsychological tests in this battery are all commonly
used and well validated, and are often used in middle age and elderly populations to detect age-
associated change. Within this age range, these tests were not anticipated to have any ceiling
effects. Although cognitive tests have well-recognized practice effects, the 2.5 year period
between tests was expected to minimize practice effects and increase the likelihood of measuring
cognitive declines as well as stabilizations or improvements over time.
In summary, ELITE provides a rich set of data with which to characterize the metabolic
and cognitive aging of the women enrolled in the study.

62
4.2 Biomarkers: systems biology vs. individual markers
The phases of Alzheimer’s disease were recently revised and expanded to include clinical
AD, mild cognitive impairment, and preclinical AD (Albert et al., 2011; McKhann et al., 2011;
Sperling et al., 2011). As mentioned in Section 3, inclusion of the preclinical phase was of
particular significance because this emphasizes that the Alzheimer’s disease process starts
decades before the appearance of clinical symptoms. Importantly, the standards for staging the
phases of AD now include the use of diagnostic biomarkers.
The criteria for a biomarker of AD were proposed in 1998 by the Working Group on
Molecular and Biochemical Markers of Alzheimer's Disease, and specify that “the ideal
biomarker for AD should detect a fundamental feature of neuropathology and be validated in
neuropathologically confirmed cases; it should have a diagnostic sensitivity >80% for detecting
AD and a specificity of >80% for distinguishing other dementias; it should be reliable,
reproducible, non-invasive, simple to perform, and inexpensive” (Authors, 1998). A majority of
the biomarker research to date has centered on measurements of the two pathological hallmarks
of clinical AD: amyloid plaques (composed primarily of Aβ42) and neurofibrillary tangles
(composed of phosphorylated tau protein). However, these two biomarkers are more closely
associated with the neuropathology of Alzheimer’s, and their normal physiological levels and
fluctuations during preclinical disease stages are yet unresolved. The primary goal of biomarker
research is to be able to diagnose at-risk individuals far in advance of widespread
neurodegeneration, so that disease-modifying interventions will have greater likelihood of
delaying or preventing AD onset. Thus, cerebrospinal fluid levels of amyloid beta and tau may
not represent the ideal biomarkers of Alzheimer’s risk.
63
Further, conceptualization of Alzheimer’s disease is shifting from a disease initiated by a
single pathological entity to a failure of multiple interacting systems (Imtiaz et al., 2014; Yao et
al., 2011). A systems-level perspective predicts that failures at different points within the system
can differentially affect future risk of developing AD. As reviewed in Section 2, the process of
aging in the female involves a shift in bioenergetic systems. Within the brain, this is evidenced
by changes in substrate supply, substrate metabolism, and mitochondrial respiration.
Peripherally, changes in glucose/insulin homeostasis and adipokine regulation of energy intake
and expenditure are coincident with metabolic changes occurring in the brain (Rettberg et al.,
2014; Yao et al., 2011). The tight integration of these systems predicts that alterations within one
component of the system will obligate other components to adapt. These adaptations under
certain circumstances will compensate for the loss of estrogen regulation. However, such
adaptations are highly individualistic, and while some women will compensate very well for the
rest of their lives, women on the other end of the adaptive spectrum will be unable to compensate
(Yao et al., 2011).
Thus, successful preclinical biomarker development in Alzheimer’s disease would be
benefitted by an approach that investigates the physiological system as a whole, taking into
account systemic associations between biomarkers, and eventually defining these systemic
changes through a model which can be applied at the individual level. This systems biology
approach to biomarkers would allow for the characterization of phenotypes as normal or
pathological along a multimodal spectrum, rather than just along a single dimension. This could
lead to identification of a variety of “healthy” or “pathological” phenotypes, defined by different
networks of biomarkers and associated with different outcomes.

64
4.3 Selection of the ELITE metabolic biomarker panel
In developing a risk biomarker panel for the women enrolled in ELITE, the goal was to
identify biomarkers which covered all of the changes that occur during aging and menopause. It
was also important to select biomarkers that had the greatest potential for metabolically
differentiating the women. Some amount of correlation between biomarkers was expected, as
these metabolic systems are all interrelated; however, it was important to ensure that none of the
biomarkers were too closely correlated so as not to bias the results towards those markers. An
initial panel of potential biomarkers included 12 clinical and metabolic variables: glucose,
insulin, ketones, triglycerides, total cholesterol, HDL cholesterol, LDL cholesterol, HDL efflux,
the HOMA score, hemoglobin A1c, systolic blood pressure, and diastolic blood pressure.
Following a correlation analysis, insulin, total cholesterol, and HDL efflux were removed from
the list of biomarkers, and the final nine biomarkers used to determine risk phenotypes within the
ELITE population was:
1. Glucose
2. HOMA Score
3. β-hydroxybutyrate (ketones)
4. HDL cholesterol
5. LDL cholesterol
6. Triglycerides
7. Hemoglobin A1c
8. Systolic blood pressure
9. Diastolic blood pressure

65
Figure 6 shows how all of these biomarkers are associated with both metabolic and cognitive
changes following menopause, as well as with each other.

Figure 6: Menopausal changes in metabolic markers included in the ELITE biomarker development, as well as their
relationships to each other and to cognitive changes.

4.4 The nine metabolic biomarkers
4.4.1 Glucose
Glucose is one of three dietary monosaccharides (sugars). It is the primary fuel source
used by all cells of the body and thus necessary for survival; however, high blood glucose levels
can lead to the conditions of obesity, insulin resistance, and diabetes, as discussed in Section 2.
High (within a diabetic range) blood glucose levels are well known to be associated with a
decrease in cognition. However, recent studies have also shown that glucose levels which are
elevated – but still below the 126 mg/dl cut-off for diabetes - are also associated with greater
cognitive decline and progression to AD (Crane et al., 2013; Morris et al., 2014).
Brain Metabolism
LDL Cholesterol
Cardiovascular Risk
Diabetes
Risk
Cognitive Function
Visceral : Subcutaneous
Adipose Tissue
HDL Cholesterol
Triglycerides
Insulin
Resistance
E
2
Dysregulated Glucose
Homeostasis
Obesity
Blood Pressure
66
Glucose was measured in 5X diluted plasma using a kit manufactured by Cayman
Chemical (catalog no. 10009582). This assay uses a reaction that generates a colored dye to
indicate the concentration of glucose in the sample. All assays were performed exactly according
to the manufacturer’s protocol, and samples were run in triplicate. 96-well assay plates were read
on a BioTek Synergy H1 Hybrid Multi-Mode Microplate Reader, and analyzed using BioTek
microplate reader software and Microsoft Excel. Concentrations of glucose were quantified in
relation to a set standard curve, and controls were used to normalize data between plates. All
final measurements were compared to the full sample average, and any measurements outside 2
SD of the mean were re-run to confirm the results.

4.4.2 HOMA Score
The HOMA score is a measure of insulin resistance generated using the following
equation: [Insulin (µM) x Glucose (mmol/L)] / 22.5. Because the HOMA score takes fasting
glucose levels into account, it is a more sensitive measure of insulin resistance than measuring
plasma insulin alone. The association between insulin resistance and Alzheimer’s disease is
covered in detail in Section 2.5.2. Briefly, a higher HOMA score is associated with decreased
brain gray matter volume as well as increased risk of Alzheimer’s disease.
Insulin was measured in plasma using a kit manufactured by Alpco Diagnostics (catalog
no. 80-INSHU-E01.1). This assay uses a reaction that generates a colored dye to indicate the
concentration of insulin in the sample. All assays were performed exactly according to the
manufacturer’s protocol, and samples were run in triplicate. 96-well assay plates were read on a
BioTek Synergy H1 Hybrid Multi-Mode Microplate Reader, and analyzed using BioTek
microplate reader software and Microsoft Excel. Concentrations of insulin were quantified in
67
relation to a set standard curve, and controls were used to normalize data between plates. All
final measurements were compared to the full sample average, and any measurements outside 2
SD of the mean were re-run to confirm the results. The HOMA score was calculated using the
glucose measurements described in Section 4.4.1 and the insulin measurements described in the
current section.

4.4.3 β-Hydroxybutyrate
β-hydroxybutyrate is one of three ketone bodies generated by the liver. Ketones are most
commonly associated with type 2 diabetes, where high ketone levels can be a signal of poorly-
controlled insulin levels. However, the generation of ketones is also a normal physiological
response to conditions such as fasting, when primary fuel sources (glucose) are less readily
available (VanItallie and Nufert, 2003). Further, during states of glucose deprivation, ketones
serve as the primary alternative fuel source for the brain (Yao et al., 2011). Due to changes in
glucose transporters which occur following estrogen loss at menopause (reviewed in Section
2.3.1) as well as the glucose hypometabolism seen in Alzheimer’s disease (reviewed in Section
2.6), ketones have been proposed as an alternative fuel source which could be used to delay
progression of Alzheimer’s disease (Sharma et al., 2014; Yao et al., 2011).
β-hydroxybutyrate was measured in 3X diluted plasma using a kit manufactured by
Cayman Chemical (catalog no. 700190). This assay uses a reaction that generates a colored dye
to indicate the concentration of β-hydroxybutyrate in the sample. All assays were performed
exactly according to the manufacturer’s protocol, and samples were run in duplicate. 96-well
assay plates were read on a BioTek Synergy H1 Hybrid Multi-Mode Microplate Reader, and
analyzed using BioTek microplate reader software and Microsoft Excel. Concentrations of β-
68
hydroxybutyrate were quantified in relation to a set standard curve, and controls were used to
normalize data between plates. All final measurements were compared to the full sample
average, and any measurements outside 2 SD of the mean were re-run to confirm the results.

4.4.4 HDL and LDL Cholesterol
Cholesterol is a lipid that is a critical component of cell membranes, affecting both
membrane fluidity and structural integrity. Because cholesterol does not dissolve in water, it is
transported by lipoproteins, two of which are high-density lipoprotein (HDL) and low-density
lipoprotein (LDL). HDL plays a role in the process of reverse cholesterol transport, in which it
transports cholesterol to the liver for excretion; thus, higher HDL levels are beneficial. LDL is
the major cholesterol transport lipoprotein in the blood, and high levels of LDL are considered to
be detrimental. In both cross-sectional and longitudinal studies, high serum total cholesterol at
midlife is strongly associated with increased risk for Alzheimer’s disease (Kivipelto et al., 2001;
Shepardson et al., 2011; Whitmer et al., 2005). Higher levels of HDL cholesterol are beneficial;
thus, HDL cholesterol levels are inversely associated with risk of Alzheimer’s disease and
dementia (Zuliani et al., 2010). One study showed that a decrease in HDL levels over a five-year
period was associated with a decrease in memory (Singh-Manoux et al., 2008); another showed
that specifically in women, lower HDL levels at baseline were associated with poorer cognition
at a follow-up visit 12 years later (Komulainen et al., 2007). LDL levels are less closely
correlated with cognitive declines and Alzheimer’s risk (Dias et al., 2014), but one study did
show that higher levels of oxidized LDL were associated with greater cognitive impairment (Li
et al., 2010b).
69
HDL cholesterol was measured along with total cholesterol in serum. Blood draws and
cholesterol measurements were conducted by trained research staff. LDL cholesterol was
computed based on measurements of total cholesterol and HDL cholesterol.

4.4.5 Triglycerides
Triglycerides are another category of lipid in the blood. Often, plasma triglycerides are
associated with higher risk of Alzheimer’s disease through their inclusion as one of the
components of metabolic syndrome; however, higher triglyceride levels have been independently
linked to Alzheimer’s disease (Vikarunnessa et al., 2013). Elevated triglycerides have also been
associated with decreased cerebral blood flow (Birdsill et al., 2013), an early hallmark of
Alzheimer’s disease. In one study, lower triglyceride levels were associated with decreased
incidence of Alzheimer’s disease only in women (Ancelin et al., 2013).
Triglycerides were measured in serum. Blood draws and triglyceride measurements were
conducted by trained research staff.

4.4.6 Hemoglobin A1c
Hemoglobin A1c (HbA1c), also called glycated hemoglobin, is a form of hemoglobin
which is used to assess glycemic control: as blood glucose increases, HbA1c also increases.
Higher HbA1c levels are often seen in type 2 diabetes. Clinical studies have shown an
association between higher HbA1c levels and increased risk for cognitive decline and
Alzheimer’s disease in both diabetic (Gold et al., 2007; van Harten et al., 2007) and non-diabetic
(Roberts et al., 2014) individuals.
70
HbA1c was measured in serum. Blood draws and HbA1c measurements were conducted
by trained research staff.

4.4.7 Blood Pressure
Blood pressure is a measure of the force which circulating blood exerts upon arterial
walls, and is measured in two components: systolic and diastolic. High blood pressure, defined
as being greater than 120/80 mmHg, is referred to as hypertension. Midlife hypertension is
strongly associated with decreases in cognitive function (Launer et al., 1995), and is a known
risk factor for Alzheimer’s disease (Huang et al., 2014; Kivipelto et al., 2001; Skoog et al., 1996;
Solomon et al., 2014; Whitmer et al., 2005). Specifically, systolic blood pressure is very strongly
linked to cognitive decline and AD (Launer et al., 1995; Unverzagt et al., 2011). An association
with diastolic blood pressure appears weaker (Kivipelto et al., 2001).
Blood pressure was measured by trained nurses during each clinic visit.

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5. CHARACTERIZATION OF BASELINE METABOLIC PHENOTYPES

5.1 Introduction of Hypothesis
We hypothesize that by using a set of clinical and metabolic biomarkers, we will be able
to identify phenotypes of metabolic risk from within a healthy population of postmenopausal
women.

5.2 Statistical Methods
All metabolic, lipid and blood pressure variables were standardized prior to clustering.
Baseline averages and standard deviations (for each metabolic variable) from the entire ELITE
sample were used for standardization. A principal components analysis on the nine variables was
used to identify the number of potential clusters that best explained the variance between
individuals in the dataset. The principal components analysis on these nine variables identified
three potential clusters. Specifying three clusters, the average linkage method was used to obtain
the centroids for each cluster, and these were used as initial seeds for a nonhierarchical K-means
clustering algorithm. K-means clustering allowed us to partition our observations such that each
subject was clustered with all other subjects who were most similar on the nine metabolic
variables. The resulting three clusters were descriptively identified based on their means profile.
Once the clusters were defined, statistical analysis followed a stepwise approach,
progressing from simple group comparisons to more complex correlational analyses. Results of
the correlational analyses will be described in Sections 6 through 9. For Section 5, the clusters
were compared on demographic factors including age, years since menopause, early vs. late
menopause, race, and years of education using analysis of variance (ANOVA) and chi-square
tests. Primary metabolic analyses used ANOVA/ANCOVA (analysis of covariance) to compare
72
the means of each metabolic variable between each of the metabolic clusters. Comparisons
between clusters within each menopause stratum also used ANOVA and ANCOVA tests. Tests
used an overall 2-sided alpha of 0.05, correcting for multiple comparisons where necessary.
In order to longitudinally evaluate cognitive performance (Section 8), the full sample of
643 women in ELITE was restricted to those women who had completed cognitive testing at
baseline and again at one other time point. In total, 502 women had completed cognitive testing
to fit with these requirements, and these are the women included in the current analyses.

5.3 Results
Figure 7 shows a plot of the three metabolic clusters that emerged after analysis of nine
metabolic biomarkers. Each cluster had specific characteristic features, and the phenotypes have
been named as follows:
• Cluster 1 (green): Healthy Metabolic Phenotype, n = 209
o Characterized by low glucose, a low HOMA score, good cholesterol balance, low
triglycerides, and low blood pressure.
• Cluster 2 (red): High Blood Pressure Phenotype, n = 191
o Slightly less healthy on all metabolic parameters than the “Healthy” cluster.
Distinguishing feature was elevated systolic and diastolic blood pressure.
• Cluster 3 (blue): Poor Metabolic Phenotype, n = 102
o Elevated glucose, high HOMA score, high triglycerides and high HbA1c.

73

Figure 7: Plot showing the three clusters identified through k-means clustering.

Table 2 lists the average values for each variable within the three phenotypes. Of note is
that few metabolic variables are in a clinical disease range – which is to be expected, given that
these measurements were taken at baseline when women had just gone though
inclusion/exclusion. However, particularly in the Poor Metabolic phenotype, many of the
average values are at the outer edge of a normal range, indicating a potential shift towards an
unhealthier metabolic state.

74

Table 2: Average values and SD for each metabolic biomarker within each phenotype

A plot of z-scores (Figure 8) shows each metabolic biomarker using the same scale, and provides
an easier visual interpretation of the raw data above.

Figure 8: Plot of Z-scores for each of the individual metabolic biomarkers

5.3.1 Cluster demographics
Table 3 lists demographic information for each of the clusters. There were no significant
differences in age at randomization, years since menopause, or menopause cohort membership
between the clusters. Women in the Healthy phenotype had significantly more years of education
than women in the Poor Metabolic phenotype (p < 0.05); however, the difference in average
years of education was approximately 7 months, and the significant result was likely due to large
Healthy High BP Poor Metabolic
Glucose (mg/dL) 80.60 (7.58) 80.28 (7.46) 91.55 (9.77)
Insulin Resistance (HOMA Score) 0.98 (0.48) 1.16 (0.46) 2.62 (1.12)
Ketones (mM) 0.12 (0.06) 0.10 (0.03) 0.10 (0.04)
HDL Cholesterol (mg/dL) 74.96 (17.88) 65.40 (15.67) 52.08 (10.77)
LDL Cholesterol (mg/dL) 129.95 (29.64) 137.11 (29.11) 144.96 (33.43)
Triglycerides (mg/dL) 80.41 (26.99) 97.31 (33.36) 166.59 (65.59)
HbA1c (%) 5.60 (0.38) 5.52 (0.40) 5.80 (0.45)
Systolic Blood Pressure (mmHg) 105.83 (8.96) 125.27 (10.26) 121.18 (10.74)
Diastolic Blood Pressure (mmHg) 67.95 (5.52) 80.86 (5.82) 76.31 (7.73)
75
sample size. Nonetheless, years of education have been controlled for in all relevant analyses.
All women in the ELITE trial were highly educated; only 3% of the sample population had less
than 12 years of education. Overall there was a significant difference in the racial makeup of the
three phenotypes (Chi square, p < 0.005): significant differences in cluster membership were
seen between Caucasian and Hispanic women (p < 0.0005) and there was a trend towards a
significant difference in cluster membership between Hispanic and Asian women (p < 0.10).

TOTAL
SAMPLE
HEALTHY HIGH BP
POOR
METABOLIC
(n) 502 209 (41.6%) 191 (38.1%) 102 (20.3%)
Age 60.57 (6.95) 60.03 (7.25) 60.87 (6.83) 61.10 (6.50)
Years Since Menopause 10.41 (7.75) 9.88 (7.41) 10.43 (7.72) 11.53 (8.43)
Early Menopause (n) 216 95 82 39
Late Menopause (n) 286 114 109 63
Years of Education 16.15 (2.17) 16.35 (2.16) 16.16 (2.09) 15.73 (2.29)
Ethnicity
Caucasian (n) 355 157 140 58
African American (n) 41 14 18 9
Hispanic (n) 63 17 21 25
Asian (n) 43 21 12 10

Table 3: Demographics of the total ELITE sample and each of the three phenotypes

5.3.2 Cross-sectional analysis of metabolic phenotypes at baseline
At baseline, nearly all metabolic biomarkers were significantly different between the
three clusters. Glucose was significantly higher in the Poor Metabolic cluster than in the Healthy
cluster (p < 0.0005) and the High BP cluster (p < 0.0005). There was no significant difference in
glucose levels between the Healthy and Poor Metabolic clusters. The HOMA score was
significantly higher in the Poor Metabolic cluster than the Healthy cluster (p < 0.0005) and the
High BP cluster (p < 0.0005), and the HOMA score in the High BP cluster was significantly
higher than in the Healthy cluster (p < 0.0005). Ketones were significantly higher in the Healthy
76
cluster than the High BP cluster (p < 0.0005) and Poor Metabolic cluster (p < 0.05). There was
no significant difference in ketone levels between the High BP and Poor Metabolic clusters.
Triglycerides were significantly higher in the Poor Metabolic cluster than in both the Healthy
cluster (p < 0.0005) and the High BP cluster (p < 0.0005), and triglycerides in the High BP
cluster were significantly higher than in the Healthy cluster (p < 0.0005). HDL cholesterol was
significantly higher in the Healthy cluster than in both the High BP cluster (p < 0.0005) and the
Poor Metabolic cluster (p < 0.0005), and HDL cholesterol was also higher in the High BP cluster
than in the Poor Metabolic cluster (p < 0.0005). LDL cholesterol was significantly lower in the
Healthy cluster than in the Poor Metabolic cluster (p < 0.005); there was a trend towards lower
LDL cholesterol in the Healthy cluster compared to the High BP cluster (p < 0.10), and no
significant difference in LDL levels between the High BP cluster and the Poor Metabolic cluster.
HbA1c levels were significantly higher in the Poor Metabolic cluster than in the Healthy cluster
(p < 0.005) and the High BP cluster (p < 0.0005), and HbA1c levels in the High BP cluster were
significantly lower than in the Healthy cluster (p < 0.05). Systolic blood pressure was
significantly higher in the High BP cluster than in the Healthy cluster (p < 0.0005) and the Poor
Metabolic cluster (p < 0.005), and systolic blood pressure in the Poor Metabolic cluster was
significantly higher than in the Healthy cluster (p < 0.0005). Diastolic blood pressure was
significantly higher in the High BP cluster than in the Healthy cluster (p < 0.0005) and the Poor
Metabolic cluster (p < 0.0005), and diastolic blood pressure in the Poor Metabolic cluster was
significantly higher than in the Healthy cluster (p < 0.0005).
Results of the significant differences between metabolic biomarkers at baseline are
depicted in Figure 9, and summarized in Table 4. Although differences in the average values of
each biomarker between the clusters appear minimal (Table 4), the clustering method robustly
77
differentiates between the three metabolic phenotypes. Between the Healthy and Poor Metabolic
clusters, levels of all metabolic biomarkers are significantly different. Between the Healthy and
High BP clusters, levels of all biomarkers are significantly different with the exception of
glucose. Between the High BP and Poor Metabolic clusters, levels of all biomarkers are
significantly different with the exception of ketones and LDL cholesterol.

Figure 9: Significant differences between metabolic biomarkers at baseline. Significance values:
◊
p < 0.10,
*p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant differences are adjusted for
menopause cohort and treatment condition.

0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"
Metabolic"
Diastolic)BP)(V0))
0"
50"
100"
150"
Healthy" High"BP" Poor"
Metabolic"
Systolic)BP)(V0))
0"
2"
4"
6"
Healthy" High"BP" Poor"
Metabolic"
HbA1c&(V0)&
0"
50"
100"
150"
200"
Healthy" High"BP" Poor"
Metabolic"
Triglycerides+(V0)+
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"
Metabolic"
HDL$Cholesterol$(V0)$
0"
40"
80"
120"
160"
Healthy" High"BP" Poor"
Metabolic"
LDL#Cholesterol#(V0)#
0.00#
0.05#
0.10#
0.15#
Healthy# High#BP# Poor#
Metabolic#
Ketones'(V0)'
0.00#
0.50#
1.00#
1.50#
2.00#
2.50#
3.00#
Healthy# High#BP# Poor#
Metabolic#
HOMA%Score%(V0)%
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"
Metabolic"
Glucose((V0)(
***
***
***
***
***
***
*
***
***
***
***
***
***
**
◊
***
**
*
***
***
**
*** ***
***
78

Table 4: Significant differences between metabolic phenotypes.

5.3.3 Cross-sectional analysis of metabolic phenotypes at baseline, stratified by menopause
cohort
Very few significant differences on levels of individual biomarkers between women in
early menopause and women in late menopause were seen at baseline. This is to be expected,
because women were clustered into metabolic phenotypes independent of menopause cohort
membership. Thus all women who had a healthy metabolic profile, regardless of early or late
menopause, would have clustered together in the Healthy cluster, and so forth.
Figure 10 shows the significant differences between metabolic biomarkers at baseline,
stratified by menopause cohort. In the Poor Metabolic cluster, triglyceride levels were
significantly lower in the early menopause women than in the late menopause women (p <
0.005). In the High BP cluster, there was a trend towards significantly higher HDL cholesterol
levels in the late menopause women than in the early menopause women (p < 0.10). All other
differences were non-significant.
Healthy vs.
High BP
Healthy vs.
Poor Metabolic
High BP vs.
Poor Metabolic
Glucose NS p < 0.0001 p < 0.0001
HOMA Score p < 0.0001 p < 0.0001 p < 0.0001
Ketones p < 0.0001 p < 0.01 NS
HDL Cholesterol p < 0.0001 p < 0.0001 p < 0.0001
LDL Cholesterol p < 0.10 p < 0.001 NS
Triglycerides p < 0.0001 p < 0.0001 p < 0.0001
HbA1c p < 0.05 p < 0.005 p < 0.0001
Systolic B.P. p < 0.0001 p < 0.0001 p < 0.005
Diastolic B.P. p < 0.0001 p < 0.0001 p < 0.0001
79

Figure 10: Significant differences between metabolic phenotypes, stratified by menopause cohort. Significance
values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant differences
between metabolic biomarkers are adjusted for treatment condition.

5.4 Discussion
We hypothesized that this set of nine clinical metabolic biomarkers could be used to
detect women at metabolic risk from within a healthy population. Using k-means clustering,
three distinct metabolic phenotypes were identified. These metabolic phenotypes, labeled as
Healthy, High BP, and Poor Metabolic, are defined by presence (High BP and Poor Metabolic)
or absence (Healthy) of patterns of metabolic variables individually known to be risk factors for
Alzheimer’s disease. The Healthy and Poor Metabolic clusters were significantly different from
each other on all nine metabolic biomarkers; Healthy and High BP clusters were significantly
0"
50"
100"
150"
200"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Trig.,'Early'vs.'Late'(V0)'
0"
20"
40"
60"
80"
100"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
HDL,%Early%vs.%Late%(V0)%
**
◊
0"
20"
40"
60"
80"
100"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Glucose,)Early)vs.)Late)(V0))
0.0#
0.5#
1.0#
1.5#
2.0#
2.5#
3.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
HOMA,&Early&vs.&Late&(V0)&
0.00#
0.05#
0.10#
0.15#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Ketones,(Early(vs.(Late((V0)(
0"
25"
50"
75"
100"
125"
150"
175"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
LDL,$Early$vs.$Late$(V0)$
0"
2"
4"
6"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
HbA1c,'Early'vs.'Late'(V0)'
0"
20"
40"
60"
80"
100"
120"
140"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
SBP,%Early%vs.%Late%(V0)%
0"
20"
40"
60"
80"
100"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
DBP,%Early%vs.%Late%(V0)%
80
different on all nine biomarkers except glucose; High BP and Poor Metabolic were significantly
different on all nine biomarkers except ketones and LDL cholesterol. Within each cluster, there
were very few significant differences seen between women in early menopause and women in
late menopause. There were also no significant differences in cluster membership between
women in early menopause and women in late menopause, indicating that even in late
menopause there are women who show successful menopausal adaptive capability and who are
thus grouped into the Healthy cluster.
When looking at the levels of each individual biomarker, it is readily apparent that there
is a wide amount of variability within the study population. This highlights the fact that,
particularly when characterizing a healthy population, using a single marker to predict risk is not
feasible. Each individual biomarker gives too little information (or has too much variability) to
provide the necessary degree of separation between healthy individuals and those in need of a
preventative strategy this early in the disease process. When the nine biomarkers are aggregated,
we are able to identify heterogeneous metabolic phenotypes that give perspective on biomarkers
of risk as well as preventative interventions.

81
6. ASSOCIATION OF BASELINE METABOLIC PHENOTYPES WITH COGNITIVE
PERFORMANCE

6.1 Introduction of Hypothesis
We hypothesize that metabolic phenotype membership is associated with cognitive
performance at baseline, and that these associations may be modified by time since menopause.

6.2 Statistical Methods
All statistical analyses were based on the three clusters identified in Section 5. To assess
results from our cognitive testing battery evaluated at baseline (prior to randomization), each
cognitive test was assessed individually; additionally, composite scores were generated for three
cognitive domains (global cognition, executive function, and verbal memory). The composite
scores for executive function and verbal memory were generated from a linear sum of the test
scores within each domain, with each standard test score inversely weighted by its correlation
with other cognitive tests. The composite score for global cognition was similarly calculated as a
weighted average, but included all 14 individual tests in the testing battery.
Statistical methods were similar to those described in Section 5. ANCOVA was used to
test for the overall differences among metabolic clusters on each cognitive factor and cognitive
test; adjusting covariates included menopause cohort, treatment condition, race, and education.
Additionally, pairwise comparisons on the means of cognitive tests/factors by metabolic clusters
were examined, with adjustments for multiple comparisons. Comparisons between clusters
stratified by menopause stratum also used ANCOVA tests. Tests used an overall 2-sided alpha of
0.05, correcting for multiple comparisons where necessary.

82
6.3 Cognitive Factors and Individual Cognitive Tests
Clinical research has recently begun emphasizing the previously under-rated predictive
ability of neurocognitive tests, especially at early stages of neurodegenerative diseases (Snyder,
2013). In ELITE, the cognitive testing battery was designed to measure a wide array of cognitive
functions, and included tests shown to detect age-associated cognitive changes. This cognitive
battery included 3 cognitive factors and 14 individual cognitive tests. The three cognitive factors
– global cognition, verbal memory, and executive function – and the individual cognitive tests
that fell within each of these factors are described below:

6.3.1 Global Cognition
The cognitive factor for Global Cognition is a weighted average of all 14 individual
cognitive tests, and gives an overarching view of cognitive performance. It takes five cognitive
domains into account: verbal memory, non-verbal memory, executive function/working memory,
naming/semantic memory, and visuospatial ability. As such, it is expected that lower factor
scores for Global Cognition would be associated with greater risk for cognitive decline and
Alzheimer’s disease (Backman et al., 2005; van Harten et al., 2013; Wilson et al., 2012).
The individual cognitive test which fell into the domain of Global Cognition was the
Symbol-Digit test. The Symbol-Digit test has been shown to predict incident dementia (Tierney
et al., 2010) as well as conversion to AD from MCI (Tabert et al., 2006).

6.3.2 Verbal Memory
The cognitive factor for Verbal Memory is a weighted average of the following cognitive
tests: CVLT-II (immediate and delayed recall) and Logical Memory (immediate and delayed
83
recall). Verbal memory tests are a common measure of episodic memory, which is the process of
storing and retrieving new information. Declines in verbal episodic memory are consistent with
the entorhinal cortex and hippocampal degeneration that occurs early in the Alzheimer’s disease
process, and are well established to be an early indicator of Alzheimer’s disease (Lowry et al.,
2014; Okonkwo et al., 2014). Clinical research has shown that verbal memory tests may provide
a successful means by which to identify individuals who will go on to develop Alzheimer’s
while they are still in a preclinical stage (Backman et al., 2005; Backman et al., 2001; Driscoll et
al., 2006; Nestor et al., 2004; Tierney et al., 2005), and also predict which individuals will
transition from MCI to AD (Gomar et al., 2011).
Specifically, the CVLT is a test of word list recall, and the Logical Memory test is a test
of paragraph recall. The CVLT is a standard clinical tool for assessing episodic memory, and is
sensitive to subtle memory impairments (Beck et al., 2012). The Logical Memory test is shown
to be sensitive to the effects of hormone therapy (Sherwin and Henry, 2008).

6.3.3 Executive Function
The cognitive factor for Executive Function is a weighted average of the following
cognitive tests: Letter-Number Sequencing, Trails-B, and Shipley Institute of Living.
Executive function deficits are seen in patients with MCI or mild Alzheimer’s disease (Harrison
et al., 2014; Weiler et al., 2014), and lower scores on tests of executive function have been
shown to predict progression to Alzheimer’s disease (Ewers et al., 2013; Li et al., 2013). Tests
measuring executive function require cross-talk between the hippocampus and frontal cortex, and
thus impairments on these tests might be expected later in the disease process than impairments
in verbal memory. Indeed, one study found that decreased scores on tests of executive function
84
were associated with gray matter atrophy in the frontal cortex in MCI patients who converted to
AD (Morgen et al., 2013).
The Letter-Number sequencing test distinguishes between patients with MCI and healthy
controls, and testing ability becomes worse as the MCI patients progress to AD (Kessels et al.,
2011). Healthy controls in the ADNI cohort who had CSF biomarkers related to AD showed
worse performance on the Trails-B test than those without CSF AD biomarkers (Schott et al.,
2010); additionally, the Trails-B test is predictive of progression to AD regardless of ethnicity
(Weissberger et al., 2013).

In three of the cognitive domains: non-verbal memory, visuospatial ability, and
naming/semantic memory, there were few cross-sectional or longitudinal effects of metabolic
phenotypes on individual tests within that domain. The one exception was the test of Animal
Naming, which fell under the domain of Naming/Semantic Memory; however, this test can also
be categorized within the domain of executive function or as a test of verbal fluency. For the
purposes of brevity, results for Section 6 will focus on the three cognitive factor scores and the
individual test scores within each domain, with the addition of the Animal Naming test.
Significant differences on any of the other individual cognitive tests will be noted as they occur.

6.4 Results
6.4.1 Cross-sectional analysis of cognitive performance within phenotypes at baseline
Cognitive Factors: Initially, analysis of significant differences between clusters was
conducted using two adjustment factors: menopause cohort and treatment condition. After
adjusting for these two factors, women in the Healthy cluster performed significantly better than
85
women in the Poor Metabolic cluster within the factor scores of Global Cognition (p < 0.05),
Verbal Memory (p < 0.005), and Executive Function (p < 0.05). There were no significant
differences in cognitive performance between women in the Healthy and High BP clusters, or
women in the High BP and Poor Metabolic clusters. After further adjusting the results for race
and education, women in the Healthy cluster showed a trend towards better cognitive
performance than women in the Poor Metabolic cluster on the Verbal Memory factor score (p <
0.01). Differences on the Global Cognition and Executive Function factors lost significance after
adjusting for race and education. All results are shown in Figure 11.

Verbal Memory: After adjusting for menopause cohort and treatment condition, women
in the Healthy cluster performed significantly better than women in the Poor Metabolic cluster
on three individual tests of verbal memory: Logical Memory, Immediate Recall (p < 0.005),
Logical Memory, Delayed Recall (p < 0.10), and CVLT-II, Delayed Recall (p < 0.05). There was
no significant difference between women in the Healthy cluster and women in the Poor
Metabolic cluser on the CVLT-II, Immediate Recall test. There were no significant differences in
cognitive performance on individual tests of verbal memory between women in the Healthy and
High BP clusters, or women in the High BP and Poor Metabolic clusters. After further adjusting
the results for race and education, only the difference between women in the Healthy and Poor
Metabolic clusters on the Logical Memory, Immediate Recall test remained significant (p <
0.05). All results are shown in Figure 12.

86
Adjusted for Menopause and Treatment Further Adjusted for Race and Education

Figure 11: Significant differences between phenotypes on the three cognitive factor scores. Results after adjusting
for menopause cohort and treatment are shown on the left; results after adding adjustments for race and education
are shown on the right. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent
SEM.

!0.80%
!0.60%
!0.40%
!0.20%
0.00%
0.20%
0.40%
0.60%
Healthy% High%BP% Poor%Metabolic%
Global&Cogni+on&(V0)&
*
!0.80%
!0.60%
!0.40%
!0.20%
0.00%
0.20%
0.40%
0.60%
Healthy% High%BP% Poor%Metabolic%
Global&Cogni+on&(V0)&
!0.50%
!0.40%
!0.30%
!0.20%
!0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
Healthy% High%BP% Poor%Metabolic%
Verbal'Memory'(V0)'
**
!0.50%
!0.40%
!0.30%
!0.20%
!0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
Healthy% High%BP% Poor%Metabolic%
Verbal'Memory'(V0)'
◊
!0.60%
!0.50%
!0.40%
!0.30%
!0.20%
!0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
Healthy% High%BP% Poor%Metabolic%
Execu&ve(Func&on((V0)(
*
!0.60%
!0.50%
!0.40%
!0.30%
!0.20%
!0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
Healthy% High%BP% Poor%Metabolic%
Execu&ve(Func&on((V0)(
87
Adjusted for Menopause and Treatment Further Adjusted for Race and Education

Figure 12: Significant differences between phenotypes on tests of verbal memory. Results after adjusting for
menopause cohort and treatment are shown on the left; results after adding adjustments for race and education are
shown on the right. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent
SEM.
0.00#
1.00#
2.00#
3.00#
4.00#
5.00#
6.00#
Healthy# High#BP# Poor#Metabolic#
Logical(Memory,(Immediate((V0)(
**
0.00#
1.00#
2.00#
3.00#
4.00#
5.00#
6.00#
Healthy# High#BP# Poor#Metabolic#
Logical(Memory,(Immediate((V0)(
*
0.00#
1.00#
2.00#
3.00#
4.00#
5.00#
6.00#
Healthy# High#BP# Poor#Metabolic#
Logical(Memory,(Delayed((V0)(
◊
0.00#
1.00#
2.00#
3.00#
4.00#
5.00#
6.00#
Healthy# High#BP# Poor#Metabolic#
Logical(Memory,(Delayed((V0)(
0.00#
5.00#
10.00#
15.00#
20.00#
25.00#
30.00#
35.00#
Healthy# High#BP# Poor#Metabolic#
CVLT,&Immediate&(V0)&
0.00#
5.00#
10.00#
15.00#
20.00#
25.00#
30.00#
35.00#
Healthy# High#BP# Poor#Metabolic#
CVLT,&Immediate&(V0)&
0.00#
2.00#
4.00#
6.00#
8.00#
10.00#
12.00#
Healthy# High#BP# Poor#Metabolic#
CVLT,&Delayed&(V0)&
*
0.00#
2.00#
4.00#
6.00#
8.00#
10.00#
12.00#
Healthy# High#BP# Poor#Metabolic#
CVLT,&Delayed&(V0)&
88
After adjusting for menopause cohort and treatment condition, women in the Healthy
cluster performed significantly better than women in the Poor Metabolic cluster on the Symbol-
Digit Test (p < 0.005). This difference remained significant after adjusting for race and
education. There were no significant differences on the Symbol-Digit Test between women in
the Healthy and High BP clusters, or women in the High BP and Poor Metabolic clusters. No
significant differences were seen between any of the clusters on the Animal Naming Test. All
results are shown in Figure 13.

Adjusted for Menopause and Treatment Further Adjusted for Race and Education

Figure 13: Significant differences between phenotypes. Results after adjusting for menopause cohort and treatment
are shown on the left; results after adding adjustments for race and education are shown on the right. Significance
values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM.

0.00#
10.00#
20.00#
30.00#
40.00#
50.00#
60.00#
Healthy# High#BP# Poor#Metabolic#
Symbol'Digit,(V0),
**
0.00#
10.00#
20.00#
30.00#
40.00#
50.00#
60.00#
Healthy# High#BP# Poor#Metabolic#
Symbol'Digit,(V0),
*
0.00#
5.00#
10.00#
15.00#
20.00#
25.00#
30.00#
35.00#
Healthy# High#BP# Poor#Metabolic#
Animal'Naming'Test'(V0)'
0.00#
5.00#
10.00#
15.00#
20.00#
25.00#
30.00#
35.00#
Healthy# High#BP# Poor#Metabolic#
Animal'Naming'Test'(V0)'
89
There were no significant differences between any of the clusters on the individual tests
of Executive Function (Letter-Number Sequencing, Trails-B, or Shipley). Results of all
significant cognitive performance analyses at baseline are summarized in Table 5. Prior to
adjusting for race and education, women in the Healthy cluster performed significantly better
than women in the Poor Metabolic cluster. After adjusting for race and education, subtle but
significant differences in tests of global cognition and verbal memory were seen between women
in the Healthy cluster and women in the Poor Metabolic cluster.

Table 5: Comparison of significant differences between phenotypes on cognitive factors and individual cognitive
tests. Results after adjusting for menopause cohort and treatment are shown on the left; results after adding
adjustments for race and education are shown on the right. NS = non-significant.

6.4.2 Cross-sectional analysis of cognitive performance within phenotypes at baseline, stratified
by menopause cohort
Overall within each cluster, women in the early menopause cohort performed better on
the cognitive tests than women in the late menopause cohort.
Adjusted for Menopause Cohort and
Treatment
Adjusted for Menopause Cohort, Treatment,
Race, and Education
High BP vs.
Healthy
Poor Met.
vs. Healthy
High BP vs.
Poor Met
High BP vs.
Healthy
Poor Met.
vs. Healthy
High BP vs.
Poor Met
Global
Cognition
NS

p < 0.05

NS

NS

NS

NS

Verbal
Memory
NS

p < 0.005 NS NS

p < 0.10 NS

Executive
Function
NS

p < 0.05

NS

NS

NS

NS

Logical Mem.
Immed.
NS

p < 0.005

NS

NS

p < 0.05

NS

Logical
Memory Del.
NS

p < 0.10 NS

NS

NS

NS

CVLT
Immediate
NS

NS NS NS

NS

NS

CVLT
Delayed
NS

p < 0.05

NS NS

NS

NS

Symbol-
Digit
NS

p < 0.005

NS

NS

p < 0.05

NS

Animal
Naming
NS

NS

NS

NS

NS

NS

90
Global Cognition: There were no significant differences between women early and late
in menopause within any of the clusters on the Global Cognition factor. Within the High BP
cluster, women in early menopause had significantly better cognitive performance on the
Symbol-Digit test (p < 0.005). All analyses are adjusted for treatment, race, and education.
Results are shown in Figure 14.

Figure 14: Significant differences between phenotypes, stratified by menopause cohort. Significance values:

◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM.

Verbal Memory: Within the Healthy cluster, women in early menopause showed a trend
towards higher scores on the Verbal Memory factor than women in late menopause (p < 0.10).
On the CVLT-II, in both the immediate recall and delayed recall conditions, women in early
menopause had significantly better cognitive performance than women in late menopause in both
the Healthy (p < 0.05) and High BP (p < 0.10) clusters. All analyses are adjusted for treatment,
race, and education. Results are shown in Figure 15.

!1.00%
!0.50%
0.00%
0.50%
1.00%
Early% Late% Early% Late% Early% Late%
Healthy% High%BP% Poor%Met%
Global&Cogni+on,&Early&vs.&Late&(V0)&
0.0#
10.0#
20.0#
30.0#
40.0#
50.0#
60.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Symbol'Digit,-Early-vs.-Late-(V0)-
**
91

Figure 15: Significant differences between phenotypes, stratified by menopause cohort. Significance values:

◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM.

!0.80%
!0.60%
!0.40%
!0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
Early% Late% Early% Late% Early% Late%
Healthy% High%BP% Poor%Met%
Verbal'Memory,'Early'vs.'Late'(V0)'
◊
0.0#
1.0#
2.0#
3.0#
4.0#
5.0#
6.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Log$Mem$Imm,$Early$vs.$Late$(V0)$
0.0#
1.0#
2.0#
3.0#
4.0#
5.0#
6.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Log$Mem$Del,$Early$vs.$Late$(V0)$
0.0#
5.0#
10.0#
15.0#
20.0#
25.0#
30.0#
35.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
CVLT%Imm,%Early%vs.%Late%(V0)%
*
◊
0.0#
5.0#
10.0#
15.0#
20.0#
25.0#
30.0#
35.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
CVLT%Del,%Early%vs.%Late%(V0)%
*
◊
92
Executive Function: Women in early menopause showed a trend towards higher scores
on the Executive Function factor than women in late menopause in both the High BP (p < 0.005)
and Poor Metabolic (p < 0.05) clusters. Early menopause women in the Poor Metabolic cluster
showed a trend towards better performance on the Trails-B test than women in late menopause (p
< 0.10). Early menopause women in the High BP cluster had better performance on the Letter-
Number Sequencing test than women in late menopause (p < 0.05). In both the High BP (p <
0.10) and Poor Metabolic (p < 0.10) clusters, early menopause women had a trend towards better
cognitive performance on the Shipley Institute of Living Test than women in late menopause. All
analyses are adjusted for treatment, race, and education. Results are shown in Figure 16.

Figure 16: Significant differences between phenotypes, stratified by menopause cohort. Significance values:

◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM.
!1.00%
!0.50%
0.00%
0.50%
1.00%
Early% Late% Early% Late% Early% Late%
Healthy% High%BP% Poor%Met%
Exec.&Func*on,&Early&vs.&Late&(V0)&
**
*
0.00#
50.00#
100.00#
150.00#
200.00#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Trails'B,*Early*vs.*Late*(V0)*
◊
0.00#
2.00#
4.00#
6.00#
8.00#
10.00#
12.00#
14.00#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Le#er%Number*Seq.,*Early*vs.*Late*(V0)*
*
0.00#
5.00#
10.00#
15.00#
20.00#
25.00#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Shipley,)Early)vs.)Late)(V0))
◊
◊
93
6.5 Discussion
In Section 6, we investigated whether the set of clinical metabolic biomarkers developed
in Section 5 could identify women at risk for Alzheimer’s disease from within a healthy
population. Risk for Alzheimer’s disease was based on decreased performance on the cognitive
testing battery. Specifically, three cognitive domains were investigated – global cognition, verbal
memory, and executive function – as well as individual cognitive tests within those domains.
Results showed a clear trend of performance on all cognitive tests, with women in the Healthy
cluster performing best, followed by women in the High BP cluster, and women in the Poor
Metabolic cluster performing worst. Women in the Healthy cluster showed significantly better
cognitive performance than women in the Poor Metabolic cluster on nearly all cognitive factors
and tests after adjusting for menopause cohort and treatment condition; after further adjusting for
race and education there were fewer significant differences, but significant differences remained
on tests of verbal memory and global cognition.
Given that this is an overall healthy and highly educated population, we were less
concerned with identifying dementia outright than we were with seeking small declines in
cognitive function that might portend development of dementia. Thus, only subtle differences in
cognitive performance were expected at baseline. It is remarkable that such significant
differences were seen between women in the Healthy and Poor Metabolic phenotypes, even after
adjusting for all potential confounders. A decline in verbal memory is known to be a very early
sign of cognitive decline, and we found significant differences between groups in this study both
on individual tests of verbal memory as well as within the verbal memory domain.
There was an overall trend for women in early menopause to have superior cognitive
performance when compared within women in late menopause. Interestingly, women in late
94
menopause showed different patterns of decreased cognitive performance compared to early
menopause women within each metabolic phenotype. Significant differences between early
menopause and late menopause women in the Healthy cluster were only seen on tests of verbal
memory. Late menopause women in the High BP cluster performed significantly worse than
early menopause on nearly every cognitive test and cognitive domain. In the Poor Metabolic
cluster, significant differences between early and late menopause women were only seen within
the domain of executive function.
Thus, even within a healthy group of women, our biomarker analysis identifies
heterogeneous phenotypes that are associated with significant differences in cognitive
performance. In particular, one of the clusters – the Poor Metabolic phenotype – is defined by
Alzheimer’s risk factors and shows significantly lower cognitive performance compared to the
Healthy phenotype. This indicates that there may be signals of Alzheimer’s risk within the Poor
Metabolic phenotype. Over the five years of the trial, there is the potential for this phenotype to
undergo further deterioration; however, because many of the risk factors are modifiable, there is
also the potential for improvement.

95
7. LONGITUDINAL CHANGE IN METABOLIC PHENOTYPES

7.1 Introduction of Hypothesis
We hypothesize that the three metabolic phenotypes will have different rates of
longitudinal change over the five years of the trial.

7.2 Statistical Methods
Measurements of longitudinal change in metabolic biomarkers focused on three time
points – baseline, 2.5 years (Visit 30), and end of study – to match with cognitive assessment
times. Modeling each metabolic biomarker separately as the longitudinal dependent variable,
data were analyzed using mixed effects linear models, testing the effects of metabolic cluster as
well as menopause cohort. The regression coefficient for time (years) since randomization
estimated the slope of cognitive change (in units/year). In the mixed model, random effects were
specified for the regression intercept (biomarker value at baseline) and slope (annual biomarker
change) to allow for subject-specific deviations around the average baseline and slope of change.
Interaction terms of treatment condition and menopause cohort with time tested whether the
slopes significantly differed by these variables. All analyses were adjusted for menopause cohort
and treatment condition. Tests used an overall 2-sided alpha of 0.05, correcting for multiple
comparisons where necessary.

7.3 Results
7.3.1 Longitudinal change in metabolic phenotypes
All phenotypes showed significant changes in metabolic biomarkers over the five years
of the study. The only biomarker which did not change significantly in any of the three clusters
96
was glucose. Within the Healthy cluster, ketones decreased significantly (p < 0.0005). HDL
levels increased significantly (p < 0.0005), and LDL levels decreased significantly (p < 0.0005).
Plasma triglycerides increased significantly (p < 0.0005), as did percent HbA1c (p < 0.0005),
and there was a trend towards an increased HOMA score (p < 0.10). Both systolic and diastolic
blood pressure increased significantly (p < 0.05). Thus on a majority of biomarkers, the Healthy
cluster got slightly more unhealthy over time; cholesterol levels were the only biomarkers which
showed an improvement in the Healthy cluster.
Within the High BP cluster, cholesterol levels also improved: HDL levels increased
significantly (p < 0.0005) and LDL levels decreased significantly (p < 0.0005). Ketones showed
a significant decrease (p < 0.005). HbA1c levels increased slightly but significantly (p < 0.0005).
Both systolic and diastolic blood pressure decreased significantly (p < 0.0005). Overall, the High
BP cluster showed the least amount of significant longitudinal changes from baseline.
Within the Poor Metabolic cluster, a majority of the biomarkers showed longitudinal
improvement. As in the Healthy and High BP clusters, cholesterol levels improved: HDL
cholesterol increased significantly (p < 0.0005) and LDL cholesterol decreased significantly (p <
0.0005). Ketones decreased significantly (p < 0.05). The HOMA score showed a significant
decrease (p < 0.005), as did plasma triglycerides (p < 0.0005); however, HbA1c levels rose
significantly (p < 0.0005). Both systolic and diastolic blood pressure decreased slightly but
significantly (p < 0.05).
Results of the significant longitudinal changes for the nine metabolic biomarkers are
depicted in Figure 17, and summarized in Table 6. Within Table 6, significant increases and
decreases in each biomarker are color-coded to identify whether these changes are in a healthy
(green) or an unhealthy (red) direction.
97

Figure 17: Significant longitudinal differences within phenotypes. Within each phenotype, the darker-colored bar
represents the baseline visit and the lighter color represents the end-of-study visit. Significance values:
◊
p < 0.10,
*p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences between metabolic biomarkers are adjusted for
menopause cohort and treatment condition.

Longitudinal changes within metabolic phenotypes were significantly different from each
other on six of the nine biomarkers. For glucose, HDL, and HbA1c, there were no significant
differences in longitudinal slope between the phenotypes. The longitudinal change in the HOMA
score within the Poor Metabolic cluster was significantly different from the longitudinal change
in both the Healthy cluster (p < 0.0005) and High BP cluster (p < 0.005). Ketones showed a trend
towards a greater longitudinal decrease in the Healthy cluster when compared to the High BP
cluster (p < 0.10). Longitudinal change in triglyceride levels within the Poor Metabolic cluster
was significantly different from the longitudinal change in both the Healthy cluster (p < 0.0005)
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
Glucose(Longitudinal(
0.0#
0.5#
1.0#
1.5#
2.0#
2.5#
3.0#
Healthy# High#BP# Poor#Met#
HOMA%Longitudinal%
0.00#
0.05#
0.10#
0.15#
Healthy# High#BP# Poor#Met#
Ketones'Longitudinal'
0"
50"
100"
150"
200"
Healthy" High"BP" Poor"Met"
Triglycerides+Longitudinal+
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
HDL$Longitudinal$
0"
20"
40"
60"
80"
100"
120"
140"
160"
Healthy" High"BP" Poor"Met"
LDL#Longitudinal#
0.0#
2.0#
4.0#
6.0#
8.0#
Healthy# High#BP# Poor#Met#
HbA1c&Longitudinal&
0"
20"
40"
60"
80"
100"
120"
140"
Healthy" High"BP" Poor"Met"
SBP$Longitudinal$
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
DBP$Longitudinal$
◊
**
***
** *
***
***
***
***
***
***
***
***
***
*** ***
***
*
*
***
*
*
98

Table 6: Significant longitudinal changes within phenotypes. Green text represents a change in a positive (healthy)
direction, and red text indicates a change in a negative (unhealthy) direction. Significance values:
◊
p < 0.10,
*p < 0.05, **p < 0.005, ***p < 0.0005.

and High BP cluster (p < 0.0005); further, the longitudinal changes between the Healthy and
High BP clusters were also significantly different (p < 0.05). The longitudinal change in HDL
levels within the Poor Metabolic cluster was significantly different from the longitudinal change
in both the Healthy cluster (p < 0.05) and High BP cluster (p < 0.05). Longitudinal change in
systolic blood pressure was significantly different between the Healthy and High BP clusters (p
< 0.0005), between the High BP and Poor Metabolic clusters (p < 0.05), as well as between the
Healthy and Poor Metabolic clusters (p < 0.0005). Similarly, longitudinal change in diastolic
blood pressure was significantly different between the Healthy and High BP clusters (p <
0.0005), between the High BP and Poor Metabolic clusters (p < 0.0005), as well as between the
Healthy and Poor Metabolic clusters (p < 0.005). Overall, a majority of the significant
differences in longitudinal change of biomarkers were driven by the fact that the Poor Metabolic
cluster tended to become healthier over time, and the Healthy and High BP clusters tended to
become less healthy. Significant differences in longitudinal metabolic changes between clusters
are depicted in Figure 18, and summarized in Table 7.
Healthy( High(B.P.( Poor(Metabolic(
Glucose( Stable' Stable' Stable'
HOMA(Score( Increases'
◊'
Stable' Decreases'**'
Ketones( Decreases'***' Decreases'**' Decreases'*'
HDL(Cholesterol( Increases'***' Increases'***' Increases'***'
LDL(Cholesterol( Decreases'***' Decreases'***' Decreases'***'
Triglycerides( Increases'***' Stable' Decreases'***'
HbA1c( Increases'***' Increases'***' Increases'***'
Systolic(B.P.( Increases'*' Decreases'***' Decreases'*'
Diastolic(B.P.( Increases'*' Decreases'***' Decreases'*'
99

Figure 18: Significant longitudinal differences between phenotypes. Within each cluster, the darker-colored bar
represents the baseline visit and the lighter color represents the end-of-study visit. Significance values:
◊
p < 0.10,
*p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences between metabolic biomarkers are adjusted for
menopause cohort and treatment condition.

Table 7: Significant longitudinal changes between phenotypes. NS = non-significant.

0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
Glucose(Longitudinal(
0.0#
0.5#
1.0#
1.5#
2.0#
2.5#
3.0#
Healthy# High#BP# Poor#Met#
HOMA%Longitudinal%
0.00#
0.05#
0.10#
0.15#
Healthy# High#BP# Poor#Met#
Ketones'Longitudinal'
0"
50"
100"
150"
200"
Healthy" High"BP" Poor"Met"
Triglycerides+Longitudinal+
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
HDL$Longitudinal$
0"
20"
40"
60"
80"
100"
120"
140"
160"
Healthy" High"BP" Poor"Met"
LDL#Longitudinal#
0.0#
2.0#
4.0#
6.0#
8.0#
Healthy# High#BP# Poor#Met#
HbA1c&Longitudinal&
0"
20"
40"
60"
80"
100"
120"
140"
Healthy" High"BP" Poor"Met"
SBP$Longitudinal$
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
DBP$Longitudinal$
**
*** ◊
*
*
***
*
***
***
***
*
***
***
**
Healthy(vs.((
High(BP(
Healthy(vs.((
Poor(Metabolic(
High(BP(vs.((
Poor(Metabolic(
Glucose(
NS# NS# NS#
HOMA(Score(
NS# p#<#0.0005# p#<#0.005#
Ketones(
p#<#0.10# NS# NS#
HDL(Cholesterol(
NS# NS# NS#
LDL(Cholesterol(
NS# p#<#0.05# p#<#0.05#
Triglycerides(
p#<#0.05# p#<#0.0005# p#<#0.0005#
HbA1c(
NS# NS# NS#
Systolic(B.P.(
p#<#0.0005# p#<#0.0005# p#<#0.05#
Diastolic(B.P.(
p#<#0.0005# p#<#0.005# p#<#0.0005#
100
7.3.2 Cross-sectional analysis of metabolic phenotypes at study end
Despite significant longitudinal changes within the three phenotypes over the five years
of the study, cross-sectional analysis of biomarker profiles at study end reveals that the
phenotypes remain significantly different from each other on a majority of the nine biomarkers.
The Healthy and Poor Metabolic clusters, which at baseline were significantly different on all
nine biomarkers, still have significantly different levels of glucose (p < 0.0005), HOMA score (p
< 0.0005), HDL (p < 0.0005), triglycerides (p < 0.0005), HbA1c (p < 0.0005), systolic blood
pressure (p < 0.0005), and diastolic blood pressure (p < 0.0005). Significant differences in levels
of ketones and LDL cholesterol that were seen at baseline between the Healthy and Poor
Metabolic clusters are no longer significant at the end of the study. The Healthy and High BP
clusters were significantly different on all biomarkers except glucose at baseline; at study end,
they remain significantly different on the HOMA score (p < 0.05), HDL cholesterol (p < 0.0005),
triglycerides (p < 0.05), systolic blood pressure (p < 0.0005), and diastolic blood pressure (p <
0.0005). Baseline significant differences in levels of ketones, LDL cholesterol, and HbA1c are
no longer significant at study end. The Poor Metabolic and High BP clusters were significantly
different on all biomarkers except ketones and LDL cholesterol at baseline; at study end, they
still have significantly different levels of glucose (p < 0.0005), HOMA score (p < 0.0005), HDL
cholesterol (p < 0.0005), triglycerides (p < 0.0005), and HbA1c (p < 0.0005). Baseline
differences in systolic and diastolic blood pressure are no longer significant at study end.
Significant differences on the nine metabolic biomarkers between the clusters are depicted in
Figure 19. Table 8 provides a summary of significant differences at study end compared to
significant differences at baseline.
101

Figure 18: Significant cross-sectional differences between phenotypes at study end. Significance values:
◊
p < 0.10,
*p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant differences between metabolic
biomarkers are adjusted for menopause cohort and treatment condition.

0"
20"
40"
60"
80"
Healthy" High"BP" Poor"Metabolic"
Diastolic)BP)(End))
0"
40"
80"
120"
160"
Healthy" High"BP" Poor"
Metabolic"
Systolic)BP)(End))
0.0#
2.0#
4.0#
6.0#
8.0#
Healthy# High#BP# Poor#Metabolic#
HbA1c&(End)&
0"
50"
100"
150"
200"
Healthy" High"BP" Poor"
Metabolic"
Triglycerides+(End)+
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"
Metabolic"
HDL$Cholesterol$(End)$
0.0#
0.5#
1.0#
1.5#
2.0#
2.5#
3.0#
Healthy# High#BP# Poor#Metabolic#
HOMA%Score%(End)%
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"
Metabolic"
Glucose((End)(
***
***
***
***
*
***
***
***
***
***
*
***
***
***
***
***
***
0.00#
0.02#
0.04#
0.06#
0.08#
0.10#
0.12#
Healthy# High#BP# Poor#
Metabolic#
Ketones'(End)'
0"
50"
100"
150"
Healthy" High"BP" Poor"
Metabolic"
LDL#Cholesterol#(End)#
102

Table 8: Significant differences between phenotypes at baseline compared with study end.

7.3.3 Longitudinal and cross-sectional changes in metabolic phenotypes, stratified by
menopause cohort
Within both the early and late menopause cohorts, women in all three phenotypes show
significant longitudinal changes on nearly all of the metabolic biomarkers similar to what was
seen in the full sample. In the early menopause cohort, women in the Healthy cluster have a
significant longitudinal change in the HOMA score (p < 0.05), ketones (p < 0.0005), HDL (p <
0.0005), LDL (p < 0.0005), triglycerides (p < 0.0005), and HbA1c (p < 0.0005). They also have
a trend towards significant longitudinal changes in glucose (p < 0.10) and systolic blood pressure
(p < 0.10). Women in the High BP cluster have a significant longitudinal change in ketones (p <
0.005), HDL (p < 0.0005), LDL (p < 0.0005), triglycerides (p < 0.05), HbA1c (p < 0.0005),
systolic blood pressure (p < 0.0005), and diastolic blood pressure (p < 0.0005). They also have a
Healthy(vs.(High(BP( Healthy(vs.(Poor(Met.( High(BP(vs.(Poor(Met.(
Glucose(
Baseline( NS* p*<*0.0005* p*<*0.0005*
Study*End( NS* p*<*0.0005* p*<*0.0005*
HOMA(Score(
Baseline( p*<*0.0005* p*<*0.0005* p*<*0.0005*
Study*End( p*<*0.05* p*<*0.0005* p*<*0.0005*
Ketones(
Baseline( p*<*0.0005* p*<*0.01* NS*
Study*End( NS* NS* NS*
HDL(Cholesterol(
Baseline( p*<*0.0005* p*<*0.0005* p*<*0.0005*
Study*End( p*<*0.0005* p*<*0.0005* p*<*0.0005*
LDL(Cholesterol(
Baseline( p*<*0.01* p*<*0.005* NS*
Study*End( NS* NS* NS*
Triglycerides(
Baseline( p*<*0.0005* p*<*0.0005* p*<*0.0005*
Study*End( p*<*0.05* p*<*0.0005* p*<*0.0005*
HbA1c(
Baseline( p*<*0.05* p*<*0.005** p*<*0.0005*
Study*End( NS* p*<*0.0005* p*<*0.0005*
Systolic(Blood(
Pressure(
Baseline( p*<*0.0005* p*<*0.0005* p*<*0.005*
Study*End( p*<*0.0005* p*<*0.0005* NS*
Diastolic(Blood(
Pressure(
Baseline( p*<*0.0005* p*<*0.0005* p*<*0.0005*
Study*End( p*<*0.0005* p*<*0.0005* NS*
103
trend towards significant longitudinal changes in glucose (p < 0.10) and the HOMA score (p <
0.10). Women in the Poor Metabolic cluster have a significant longitudinal change in the HOMA
score (p < 0.005), HDL (p < 0.05), LDL (p < 0.0005), triglycerides (p < 0.05), HbA1c (p < 0.05),
and a trend towards a significant difference in systolic blood pressure (p < 0.10). Results are
shown in Figure 20.

Figure 20: Significant longitudinal differences within phenotypes in the early menopause women. Within each
phenotype, the darker-colored bar represents the baseline visit and the lighter color represents the end-of-study
visit. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences between
metabolic biomarkers are adjusted treatment condition.

In the late menopause cohort, fewer longitudinal changes were seen within all three
clusters. Women in the Healthy cluster had a significant longitudinal change in ketones (p <
0.0005), HDL (p < 0.0005), LDL (p < 0.0005), HbA1c (p < 0.005), systolic blood pressure (p <
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
Glucose(Longitudinal,(Early(
0.0#
0.5#
1.0#
1.5#
2.0#
2.5#
3.0#
Healthy# High#BP# Poor#Met#
HOMA%Longitudinal,%Early%
0.00#
0.05#
0.10#
0.15#
Healthy# High#BP# Poor#Met#
Ketones'Longitudinal,'Early'
0"
50"
100"
150"
200"
Healthy" High"BP" Poor"Met"
Trig.&Longitudinal,&Early&
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
HDL$Longitudinal,$Early$
0"
40"
80"
120"
160"
Healthy" High"BP" Poor"Met"
LDL#Longitudinal,#Early#
0.0#
2.0#
4.0#
6.0#
Healthy# High#BP# Poor#Met#
HbA1c&Longitudinal,&Early&
0"
20"
40"
60"
80"
100"
120"
140"
Healthy" High"BP" Poor"Met"
SBP$Longitudinal,$Early$
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
DBP$Longitudinal,$Early$
◊
◊
*
◊
**
***
**
***
*
*
***
***
*
*** ***
***
***
***
*
◊
***
◊
***
104
0.05), and diastolic blood pressure (p < 0.05), as well as a trend towards a significant
longitudinal change in triglycerides (p < 0.10). Women in the High BP cluster had a significant
longitudinal change in HDL (p < 0.0005), LDL (p < 0.0005, HbA1c (p < 0.0005), systolic blood
pressure (p < 0.0005), and diastolic blood pressure (p < 0.0005). Women in the Poor Metabolic
cluster has a significant longitudinal change in ketones (p < 0.05), HDL (p < 0.0005), LDL (p <
0.0005), triglycerides (p < 0.0005), HbA1c (p < 0.0005), as well as trends towards significant
longitudinal changes in systolic (p < 0.10) and diastolic (p < 0.10) blood pressure. Results are
shown in Figure 21.

Figure 21: Significant longitudinal differences within phenotypes in the late menopause women. Within each
phenotype, the darker-colored bar represents the baseline visit and the lighter color represents the end-of-study
visit. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences between
metabolic biomarkers are adjusted treatment condition.

0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
Glucose(Longitudinal,(Late(
0.0#
0.5#
1.0#
1.5#
2.0#
2.5#
3.0#
Healthy# High#BP# Poor#Met#
HOMA%Longitudinal,%Late%
0.00#
0.05#
0.10#
0.15#
Healthy# High#BP# Poor#Met#
Ketones'Longitudinal,'Late'
0"
40"
80"
120"
160"
200"
Healthy" High"BP" Poor"Met"
Trig.&Longitudinal,&Late&
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
HDL$Longitudinal,$Late$
0"
40"
80"
120"
160"
Healthy" High"BP" Poor"Met"
LDL#Longitudinal,#Late#
0.0#
2.0#
4.0#
6.0#
Healthy# High#BP# Poor#Met#
HbA1c&Longitudinal,&Late&
0"
20"
40"
60"
80"
100"
120"
140"
Healthy" High"BP" Poor"Met"
SBP$Longitudinal,$Late$
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
DBP$Longitudinal,$Late$
*
***
***
***
***
***
***
*** ***
◊
** ***
***
***
*
*
*** ◊
◊
105
At baseline, we saw very few differences between levels of each biomarker in the early
menopause women and the late menopause women within each cluster (Section 5.3.3). As a
result of the early menopause women undergoing a greater number of longitudinal changes on
the nine metabolic biomarkers, there were some significant differences between women early
and late in menopause within each cluster by the end of the study. Similar to at baseline, there
remained a trend for early menopause women in the High BP cluster to have lower HDL levels
than late menopause women (p < 0.10). Significant differences in levels of triglycerides between
early menopause and late menopause women were seen in the Healthy cluster (p < 0.05), the
High BP cluster (p < 0.005), and the Poor Metabolic cluster (p < 0.05). Significant differences in
levels of HbA1c between early menopause and late menopause women were also seen in the
Healthy cluster (p < 0.05), the High BP cluster (p < 0.05), and the Poor Metabolic cluster (p <
0.05). All other biomarkers were not significantly different between the early and late
menopause women within each cluster. Results are shown in Figure 22.
When comparing longitudinal changes between clusters, results were similar to those
seen for the whole sample: because the Healthy and High BP clusters tended to become less
healthy over time, and the Poor Metabolic cluster became slightly more healthy, there were many
significant differences in longitudinal slope between the Poor Metabolic and Healthy clusters, as
well as between the Poor Metabolic and High BP clusters. No specific effect of menopause
cohort membership was seen on comparisons of longitudinal slopes between clusters.

106

Figure 22: Significant differences between early and late menopause women within metabolic phenotypes.
Significance values: ◊p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant
differences between metabolic biomarkers are adjusted for treatment condition.

7.4 Discussion
We hypothesized that the three metabolic phenotypes would have different rates of
longitudinal change over the five years of the trial. Results showed that women in the Healthy
cluster got slightly but significantly less healthy by the end of the study. Women in the High BP
cluster had fewer overall longitudinal changes, but similar to the Healthy women, they got
slightly but significantly less healthy. The biggest positive change in the High BP cluster was a
significant decrease in blood pressure over the course of the trial. Interestingly, women in the
0.0#
2.0#
4.0#
6.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
HbA1c,'Early'vs.'Late'(End)'
0"
50"
100"
150"
200"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Trig.,'Early'vs.'Late'(End)'
0"
20"
40"
60"
80"
100"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
HDL,%Early%vs.%Late%(End)%
◊
*
*
**
* *
*
0"
20"
40"
60"
80"
100"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Glucose,)Early)vs.)Late)(End))
0.0#
0.5#
1.0#
1.5#
2.0#
2.5#
3.0#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
HOMA%,%Early%vs.%Late%(End)%
0.00#
0.05#
0.10#
0.15#
Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Ketones,(Early(vs.(Late((End)(
0"
20"
40"
60"
80"
100"
120"
140"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
LDL,$Early$vs.$Late$(End)$
0"
20"
40"
60"
80"
100"
120"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
SBP,%Early%vs.%Late%(End)%
0"
20"
40"
60"
80"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
DBP,%Early%vs.%Late%(End)%
107
Poor Metabolic cluster showed a significant improvement on a majority of the metabolic
biomarkers by the end of the study.
The improvement in the Poor Metabolic phenotype was an unexpected result. It is
possible that women in the Poor Metabolic cluster are showing a mild “clinical trial effect,”
which refers to the well-documented benefit experienced simply by participating in a trial. There
are several driving forces behind the clinical trial effect, including the benefits of frequent clinic
visits, patient behavior changes as a result of knowing that they are in a clinical trial, and the fact
that individuals who choose to enroll in a clinical trial typically represent a population with
favorable attitudes towards managing their health. In the ELITE trial, women received the
benefits of clinic visits every 6 months as well as having to keep periodic diet and exercise
diaries. It may be that women with a Poor Metabolic phenotype received a greater benefit from
this increased awareness of their health and lifestyle than Healthy or High BP women; it might
also be that there was more room for metabolic improvement within the Poor Metabolic cohort,
leading to the result of significant longitudinal change on most of their metabolic biomarkers.
Regardless, these results emphasize the importance of routine medical care, particularly for
individuals who are less healthy.
Overall, a greater number of biomarkers showed longitudinal changes in the early
menopause women. Women in the early menopause, High BP cluster showed significant
longitudinal change on all nine biomarkers; women in the Healthy cluster on eight and women in
the Poor Metabolic cluster on six. Levels of metabolic biomarkers were more stable in the late
menopause women: the Healthy and Poor Metabolic clusters showed longitudinal change on 7
biomarkers, and High BP showed longitudinal change on 5. Again returning to the importance of
preventive care, these results show that interventions aimed at preventing metabolic decline may
108
be more successful earlier in menopause, when there is greater potential for metabolic
modification and improvement.
A cross-sectional analysis at the end of the study revealed that although women in the
Healthy and High BP clusters became slightly less healthy during the trial, and women in the
Poor Metabolic cluster became slightly more healthy, these changes did not lead to the Poor
Metabolic women ever reaching a state of metabolic health similar to the Healthy women. As is
clear in both Figure 19 and Table 8, there remained very significant differences in levels of each
metabolic biomarker between the three clusters at the end of the trial. Thus, although individual
biomarkers may show fluctuations at each trial visit, the three metabolic phenotypes are very
stable over time. These results highlight the power of using a panel of biomarkers: by doing so,
we are able to measure overall systemic change rather than focusing on change within one
individual biomarker. Further, access to longitudinal measurements which reflect duration of
exposure to a state of poor (or good) metabolic health can provide a more accurate measurement
of the effects of exposure to each phenotype of metabolic risk.

109
8. LONGITUDINAL CHANGE IN COGNITIVE PERFORMANCE

8.1 Introduction of Hypothesis
We hypothesize that metabolic phenotype membership will have an association with
longitudinal cognitive performance.

8.2 Statistical Methods
Similar to the longitudinal metabolic analysis described in Section 7, measurements of
longitudinal change in cognitive performance focused on the three time points at which the
neurocognitive battery was taken – baseline, 2.5 years (Visit 30), and end of study. Modeling
each cognitive composite/test variable separately as the longitudinal dependent variable, data
were analyzed using mixed effects linear models, testing the effects of metabolic cluster as well
as menopause cohort. The regression coefficient for time (years) since randomization estimated
the slope of cognitive change (in units/year). In the mixed model, random effects were specified
for the regression intercept (cognitive value at baseline) and slope (annual cognitive change) to
allow for subject-specific deviations around the average baseline and slope of change.
Interaction terms of metabolic cluster and menopause cohort with time tested whether the slopes
significantly differed by these variables. All analyses were adjusted for menopause cohort,
randomized treatment, race, and education. Tests used an overall 2-sided alpha of 0.05,
correcting for multiple comparisons where necessary.

110
8.3 Results
8.3.1 Cross-sectional analysis of cognitive performance within phenotypes at study end
Cognitive Factors: At study end, there were no significant differences between any of
the clusters on the Global Cognition factor score. The Healthy cluster had a significantly higher
Verbal Memory factor score than the Poor Metabolic cluster after adjusting for menopause
cohort and treatment; however, significance was lost after further adjusting for race and
education. The Healthy cluster had a trend towards a higher Executive Function factor score than
the Poor Metabolic cluster after adjusting for menopause cohort and treatment; again,
significance was lost after further adjusting for race and education. There were no significant
differences between the Healthy and High BP clusters, or the High BP and Poor Metabolic
clusters, on either the Verbal Memory or Executive Function factor score. Results are shown in
Figure 23.
Verbal Memory: After adjusting for menopause cohort and treatment condition, women
in the Healthy cluster performed significantly better than women in the Poor Metabolic cluster
on two individual tests of verbal memory: Logical Memory, Immediate Recall (p < 0.05) and
Logical Memory, Delayed Recall (p < 0.05). There were no significant differences between
women in the Healthy cluster and women in the Poor Metabolic cluster on either the Immediate
Recall or Delayed recall version of the CVLT; however, women in the Healthy cluster performed
significantly better than women in the High BP cluster on the CVLT, Delayed Recall test (p <
0.05). After further adjusting the results for race and education, the difference between Healthy
and Poor Metabolic on Logical Memory, Delayed Recall remained significant (p < 0.05). The
difference between women in the Healthy and High BP clusters in the CVLT, Delayed Recall
test showed a trend towards significance (p < 0.10). Results are shown in Figure 24.
111
Adjusted for Menopause and Treatment Further Adjusted for Race and Education

Figure 23: Significant differences between phenotypes on the three cognitive factor scores. Results after adjusting
for menopause cohort and treatment are shown on the left; results after adding adjustments for race and education
are shown on the right. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent
SEM.

0"
0.2"
0.4"
0.6"
0.8"
1"
1.2"
Healthy" High"BP" Poor"Met"
Global&Cogni+on&(End)&
0"
0.2"
0.4"
0.6"
0.8"
1"
1.2"
Healthy" High"BP" Poor"Met"
Global&Cogni+on&(End)&
0"
0.2"
0.4"
0.6"
0.8"
1"
Healthy" High"BP" Poor"Met"
Verbal'Memory'(End)'
*
0"
0.2"
0.4"
0.6"
0.8"
1"
Healthy" High"BP" Poor"Met"
Verbal'Memory'(End)'
!0.4%
!0.2%
0%
0.2%
0.4%
Healthy% High%BP% Poor%Met%
Execu&ve(Func&on((End)(
◊
!0.4%
!0.2%
0%
0.2%
0.4%
Healthy% High%BP% Poor%Met%
Execu&ve(Func&on((End)(
112
Adjusted for Menopause and Treatment Further Adjusted for Race and Education

Figure 24: Significant differences between phenotypes on the tests of verbal memory. Results after adjusting for
menopause cohort and treatment are shown on the left; results after adding adjustments for race and education are
shown on the right. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent
SEM.
0"
1"
2"
3"
4"
5"
6"
Healthy" High"BP" Poor"Met"
Logical(Memory,(Immed.((End)(
*
0"
1"
2"
3"
4"
5"
6"
Healthy" High"BP" Poor"Met"
Logical(Memory,(Immed.((End)(
0"
1"
2"
3"
4"
5"
6"
Healthy" High"BP" Poor"Met"
Logical(Memory,(Del.((End)(
*
0"
1"
2"
3"
4"
5"
6"
Healthy" High"BP" Poor"Met"
Logical(Memory,(Del.((End)(
*
0"
10"
20"
30"
40"
Healthy" High"BP" Poor"Met"
CVLT,&Immediate&(End)&
0"
10"
20"
30"
40"
Healthy" High"BP" Poor"Met"
CVLT,&Immediate&(End)&
0"
10"
20"
30"
40"
Healthy" High"BP" Poor"Met"
CVLT,&Delayed&(End)&
*
0"
10"
20"
30"
40"
Healthy" High"BP" Poor"Met"
CVLT,&Delayed&(End)&
◊
113
Executive Function: After adjusting for menopause cohort and treatment condition,
women in the Healthy cluster performed significantly better than women in the Poor Metabolic
cluster on two individual tests of executive function: the Trails-B test (p < 0.05) and the Shipley
Institute of Living test (p < 0.005). After further adjusting the results for race and education, both
of these differences became non-significant. There were no significant differences between
women in the Healthy and High BP clusters on any individual tests of executive function.
Results are shown in Figure 25.

Adjusted for Menopause and Treatment Further Adjusted for Race and Education

Figure 25: Significant differences between phenotypes on tests of executive function. Results after adjusting for
menopause cohort and treatment are shown on the left; results after adding adjustments for race and education are
shown on the right. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent
SEM.

0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
Trails'B)Test)(End))
*
0"
20"
40"
60"
80"
100"
Healthy" High"BP" Poor"Met"
Trails'B)Test)(End))
0"
5"
10"
15"
20"
Healthy" High"BP" Poor"Met"
Shipley((End)(
**
0"
5"
10"
15"
20"
Healthy" High"BP" Poor"Met"
Shipley((End)(
114
After adjusting for menopause cohort and treatment condition, women in the Poor
Metabolic cluster performed significantly better than women in the High BP cluster on Animal
Naming (p < 0.05), and there was a trend towards bettter performance in the Healthy cluster
compared to the High BP cluster (p < 0.10). Both of these significant differences/trends remained
after further adusting the results for race and education. Results are shown in Figure 26.

Adjusted for Menopause and Treatment Further Adjusted for Race and Education

Figure 26: Significant differences between phenotypes on animal naming. Results after adjusting for menopause
cohort and treatment are shown on the left; results after adding adjustments for race and education are shown on
the right. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM.

Overall, many of the differences both on cognitive factor scores and individual cognitive
test scores that were seen between the Healthy and Poor Metabolic clusters at baseline became
slightly less significant by the end of the study. Interestingly, at the end of the study there were
also significant differences between the Healthy and High BP clusters, as well as between the
Poor Metabolic and High BP clusters, that were not seen at baseline. Thus, the relationships
between the clusters on tests of cognitive function shifted throughout the five years of the study.
A full summary of the significant differences between clusters with each of the adjustment
conditions can be found in Table 9. In order to fully understand how each of the clusters have
changed relative to the others, it is necessary to look at the longitudinal results.
0"
10"
20"
30"
40"
Healthy" High"BP" Poor"Met"
Animal'Naming'Test'(End)'
*
◊
0"
10"
20"
30"
40"
Healthy" High"BP" Poor"Met"
Animal'Naming'Test'(End)'
*
◊
115

Table 9: Comparison of significant differences between phenotypes on each cognitive factor and
individual cognitive test, at baseline and end of study. Results after adjusting for menopause
cohort and treatment are shown on the left; results after adding adjustments for race and
education are shown on the right.

8.3.2 Longitudinal analysis of cognitive performance within phenotypes
Over the five years of the clinical trial, cognitive performance improved significantly in
all three phenotypes. This result was somewhat unexpected – we had anticipated that cognitive
performance would decrease longitudinally, but considering that the study population is
composed of relatively young, well-educated women, it appears that they were able to learn
Adjusted(for(Menopause(Cohort(and(Treatment( Adj.(Meno(Cohort,(Treatment,(Race,(Educa9on(
High(BP(vs.(
Healthy(
Poor(Met.((
vs.(Healthy(
High(BP(vs.(
Poor(Met(
High(BP(vs.(
Healthy(
Poor(Met.((
vs.(Healthy(
High(BP(vs.(
Poor(Met(
Global(
Cogni9on(
Baseline( NS
*
p*<*0.05
*
0
*
NS
*
NS
*
NS
*
Study*End( NS
*
NS* 0
*
NS
*
NS
*
NS
*
Verbal(
Memory(
Baseline( NS
*
p*<*0.005* 0* NS
*
p*<*0.10* NS
*
Study*End( NS
*
p*<*0.05
*
0* NS
*
NS
*
NS
*
Execu9ve(
Func9on(
Baseline( NS
*
p*<*0.05
*
0
*
NS
*
NS
*
NS
*
Study*End( NS
*
p*<*0.10* 0
*
NS
*
NS
*
NS
*
Logical(Mem.(
Immediate(
Baseline( NS
*
p*<*0.005
*
0
*
NS
*
p*<*0.05
*
NS
*
Study*End( NS
*
p*<*0.05
*
0
*
NS
*
NS
*
NS
*
Logical(Mem.(
Delayed(
Baseline( NS
*
p*<*0.10* 0
*
NS
*
NS
*
NS
*
Study*End( NS
*
p*<*0.05
*
0
*
NS
*
p*<*0.05
*
NS
*
CVLT(
Immediate(
Baseline( NS
*
NS* 0* NS
*
NS
*
NS
*
Study*End( NS
*
NS* 0* NS
*
NS
*
NS
*
CVLT(Delayed(
Baseline( NS
*
p*<*0.05
*
0* NS
*
NS
*
NS
*
Study*End( p*<*0.05
*
NS* 0* p*<*0.05
*
NS* NS
*
TrailsJB(Test(
Baseline( NS
*
p*<*0.05
*
0* NS
*
NS
*
NS
*
Study*End( NS
*
p*<*0.05
*
0* NS
*
NS* NS
*
LeKerJ
Number(Seq.(
Baseline( NS
*
NS
*
0* NS
*
NS
*
NS
*
Study*End( NS
*
NS
*
0* NS
*
NS
*
NS
*
Shipley(
Baseline( NS
*
p*<*0.005* 0* NS
*
NS
*
NS
*
Study*End( NS
*
p*<*0.005* 0* NS
*
NS* NS
*
SymbolJDigit(
Test(
Baseline( NS
*
p*<*0.005
*
0
*
NS
*
p*<*0.05
*
NS
*
Study*End( NS
*
p*<*0.05
*
0
*
NS
*
NS
*
NS
*
Animal(
Naming(
Baseline( NS
*
NS
*
0
*
NS
*
NS
*
NS
*
Study*End( p*<*0.10* NS
*
0
*
p*<*0.10* NS
*
NS
*
116
testing strategies despite the 2.5 year intervals between neuropsychological assessments.
Therefore, instead of comparing rate of cognitive decline, the majority of the longitudinal results
are reported as rate of improvement. Longitudinal changes in cognitive function will be
determined based on which groups show greater capacity for learning and which show less. All
longitudinal results are reported after adjusting for menopause cohort, treatment condition, race,
and education; there were very few differences between the results obtained using 4 adjusting
factors and those obtained when adjusting only for menopause cohort and treatment condition.

Cognitive Factors: Global Cognition improved significantly in the Healthy cluster (p <
0.0005), High BP cluster (p < 0.0005), and Poor Metabolic cluster (p < 0.0005). There was no
significant difference in rate of improvement between any of the clusters. Verbal Memory
improved significantly in the Healthy cluster (p < 0.0005), High BP cluster (p < 0.0005), and
Poor Metabolic cluster (p < 0.0005). There was no significant difference in rate of improvement
between any of the clusters. There were no significant improvements in any of the clusters on the
Executive Function factor score, nor were there differences in the rate of improvement between
the clusters. Results are shown in Figure 27.

Global Cognition: The Healthy cluster showed a significant longitudinal decrease in
performance on the Symbol-Digit test (p < 0.05). The High BP cluster showed a trend towards
decreased longitudinal performance (p < 0.10). The Poor Metabolic group showed a slight but
non-significant improvement. There was no significant difference in rate of longitudinal change
between the clusters. Results are shown in Figure 28.
117

Figure 27: Significant longitudinal differences within phenotypes. Within each phenotype, the darker-colored bar
represents the baseline visit and the lighter color represents the end-of-study visit. Significance values:
◊
p < 0.10,
*p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences are adjusted for race, education, menopause cohort
and treatment condition.

Figure 28: Significant longitudinal differences on the symbol-digit test within phenotypes. Within each phenotype,
the darker-colored bar represents the baseline visit and the lighter color represents the end-of-study visit.
Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences are adjusted for race,
education, menopause cohort and treatment condition.

!0.50%
0.00%
0.50%
1.00%
Healthy% High%BP% Poor%Met%
Global&Cogni+on,&Longitudinal&&
***
***
***
!0.40%
!0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
Healthy% High%BP% Poor%Met%
Verbal'Memory,'Longitudinal'
***
***
***
!0.40%
!0.30%
!0.20%
!0.10%
0.00%
0.10%
0.20%
0.30%
Healthy% High%BP% Poor%Met%
Execu&ve(Func&on,(Longitudinal(
0.0#
10.0#
20.0#
30.0#
40.0#
50.0#
60.0#
Healthy# High#BP# Poor#Met#
Symbol'Digit,-Longitudinal-
◊ *
118
Verbal Memory: On the Logical Memory, Immediate Recall test, both the High BP
cluster (p < 0.10) and Poor Metabolic cluster (p < 0.10) showed a trend towards improved
longitudinal performance. The Healthy cluster showed a trend towards increased longitudinal
performance on the Logical Memory, Delayed Recall test (p < 0.10). There were no significant
differences in rate of improvement between the clusters on either the Immediate Recall or
Delayed Recall version of the Logical Memory test. Performance on the CVLT, Immediate
Recall improved significantly in the Healthy cluster (p < 0.0005), High BP cluster (p < 0.0005),
and Poor Metabolic cluster (p < 0.0005). Performance on the CVLT, Delayed Recall improved
significantly in the Healthy cluster (p < 0.0005), High BP cluster (p < 0.0005), and Poor
Metabolic cluster (p < 0.0005). There were no significant differences in rate of improvement
between the clusters on either the Immediate Recall or Delayed Recall version of the CVLT.
Results are shown in Figure 29.

Executive Function: None of the clusters showed any significant longitudinal change in
performance on the Executive Function factor score, or on any of the individual tests of
executive function. There were no significant differences in rate of longitudinal change in
performance between the clusters.

On the Animal Naming test, both the Healthy (p < 0.005) and Poor Metabolic (p < 0.005)
clusters had significant longitudinal improvement. The Animal Naming test had the only rate of
longitudinal change that was different between clusters: the slope for the Poor Metabolic cluster
was significantly greater than the slope for the High BP cluster (p < 0.05). Results are shown in
Figure 30.
119

Figure 29: Significant longitudinal differences on tests of verbal memory within phenotypes. Within each phenotype,
the darker-colored bar represents the baseline visit and the lighter color represents the end-of-study visit.
Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences are adjusted for race,
education, menopause cohort and treatment condition.

Figure 30: Significant longitudinal differences on tests of verbal memory within phenotypes. Within each phenotype,
the darker-colored bar represents the baseline visit and the lighter color represents the end-of-study visit.
Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences are adjusted for race,
education, menopause cohort and treatment condition.
0.0#
1.0#
2.0#
3.0#
4.0#
5.0#
6.0#
Healthy# High#BP# Poor#Met#
Logical(Memory(Imm.(Longitudinal(
◊
◊
0.0#
1.0#
2.0#
3.0#
4.0#
5.0#
6.0#
Healthy# High#BP# Poor#Met#
Logical(Memory(Del.(Longitudinal(
◊
0.0#
10.0#
20.0#
30.0#
40.0#
Healthy# High#BP# Poor#Met#
CVLT%Immediate,%Longitudinal%
***
***
***
0.0#
5.0#
10.0#
15.0#
Healthy# High#BP# Poor#Met#
CVLT%Delayed,%Longitudinal%
***
***
***
0.0#
10.0#
20.0#
30.0#
40.0#
Healthy# High#BP# Poor#Met#
Animal'Naming,'Longitudinal'
**
**
0.0#
10.0#
20.0#
30.0#
40.0#
Healthy# High#BP# Poor#Met#
Animal'Naming,'Longitudinal'
*
120

Although there were not many significant differences in longitudinal slopes between the
clusters, the data do show an interesting pattern: on nearly every individual cognitive test, the
Poor Metabolic cluster had the highest slope score and thus the largest rate of improvement over
the course of the trial. Further, the High BP cluster tended to have the lowest slope score,
indicating the smallest rate of improvement.

8.3.3 Cross-sectional analysis of cognitive performance within phenotypes at study end,
stratified by menopause cohort
In Section 8.3.1, after adjusting for menopause cohort, treatment condition, race, and
education, there were few significant differences between clusters. Within each cluster, however,
there were some tests which had significant differences between women early in menopause and
women late in menopause. Notably, women in the early menopause cohort showed superior
cognitive performance to women in the late menopause cohort on every single cognitive factor
score and every individual cognitive test. Within the Healthy cluster at the end of the study,
women in early menopause performed better than women in late menopause on the cognitive
factors of Global Cognition (p < 0.05), Verbal Memory (p < 0.05), and Executive Function (p <
0.10), as well as the CVLT Immediate Recall (p < 0.005) and Delayed Recall (0.05) tests. Within
the High BP cluster at the end of the study, women in early menopause performed better than
women in late menopause on the cognitive factors of Global Cognition (p < 0.05) and Executive
Function (p < 0.005). Early menopause women performed better than late menopause women on
the Symbol-Digit test (p < 0.05), Letter-Number Sequencing (p < 0.005), CVLT Immediate
Recall (p < 0.10), CVLT Delayed Recall (p < 0.05), and Animal Naming (p < 0.05). Within the
Poor Metabolic cluster at the end of the study, women in early menopause performed better than
121
women in late menopause on the Executive Function cognitive factor (p < 0.05). Women in early
menopause had a trend towards superior cognitive performance on the Symbol Digit test (p <
0.10), the Trails-B test (p < 0.10), and Letter-Number Sequencing (p < 0.10). Early menopause
women had significantly better cognitive performance on the Shipley Institute of Living test (p <
0.05). Results are shown in Figure 31.

Figure 31: Significant differences between phenotypes, stratified by menopause cohort. Significance values:

◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant differences are adjusted
for race, education, and treatment condition.
!0.50%
0.00%
0.50%
1.00%
1.50%
2.00%
Early% Late% Early% Late% Early% Late%
Healthy% High%BP% Poor%Met%
Global&Cog.,&Early&vs.&Late&(End)&
!0.50%
0.00%
0.50%
1.00%
1.50%
Early% Late% Early% Late% Early% Late%
Healthy% High%BP% Poor%Met%
Verbal'Memory,'Early'vs.'Late'(End)'
!1.00%
!0.50%
0.00%
0.50%
1.00%
Early% Late% Early% Late% Early% Late%
Healthy% High%BP% Poor%Met%
Exec.&Func*on,&Early&vs.&Late&(End)&
0"
10"
20"
30"
40"
50"
60"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Symbol'Digit,-Early-vs-Late-(End)-
0"
20"
40"
60"
80"
100"
120"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Trails'B)Test,)Early)vs.)Late)(End))
0"
2"
4"
6"
8"
10"
12"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Le#er%Number,+Early+vs.+Late+(End)+
0"
5"
10"
15"
20"
Early" Late" Early" Late" Early" Late"
Healthy" High"BP" Poor"Met"
Shipley,)Early)vs.)Late)(End))
0.0#
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Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Log$Mem$Imm,$Early$vs.$Late$(End)$
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Early# Late# Early# Late# Early# Late#
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Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
CVLT%Imm,%Early%vs.%Late%(End)%
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Early# Late# Early# Late# Early# Late#
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CVLT%Del,%Early%vs.%Late%(End)%
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20.0#
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Early# Late# Early# Late# Early# Late#
Healthy# High#BP# Poor#Met#
Animal'Naming,'Early'vs.'Late'(End)'
*
*
* ◊
**
*
*
◊
◊
** ◊
*
**
◊
* *
*
122
A comparison of significant differences between women in early menopause and women
in late menopause at baseline and study end is shown in Table 10. This comparison shows that
women in early menopause outperformed women in late menopause on some cognitive factors
and cognitive tests at baseline, but the number of tests where early menopause women
significantly outperform late menopause women increases substantially by the end of the study.

Table 10: Significant differences between early and late menopause women within each phenotype, compared
between baseline and end of study. Significant differences are adjusted for race, education, and treatment condition.

Healthy( High(BP( Poor(Metabolic(
Early(vs.(Late( Early(vs.(Late( Early(vs.(Late(
Global(Cogni:on(
Baseline( NS* NS* NS*
Study*End( p*<*0.05* p*<*0.05* NS*
Verbal(Memory(
Baseline( p*<*0.10* NS* NS*
Study*End( p*<*0.05* NS* NS*
Execu:ve(Func:on(
Baseline( NS* p*<*0.005* p*<*0.05*
Study*End( p*<*0.10* p*<*0.005* p*<*0.05*
SymbolADigit(Test (
Baseline( NS* p*<*0.005* NS*
Study*End( NS* p*<*0.05* p*<*0.10*
TrailsAB(Test (
Baseline( NS* NS* p*<*0.10*
Study*End( NS* NS* p*<*0.10*
LeDerANumber(
Sequencing(
Baseline( NS* p*<*0.05* NS*
Study*End( NS* p*<*0.005* p*<*0.10*
Shipley(
Baseline( NS* p*<*0.10* p*<*0.10*
Study*End( NS* NS* p*<*0.05*
Logical(Memory(
Immediate(Recall(
Baseline( NS* NS* NS*
Study*End( NS* NS* NS*
Logical(Memory(
Delayed(Recall(
Baseline( NS* NS* NS*
Study*End( NS* NS* NS*
CVLT(Immediate(Recall(
Baseline( p*<*0.05* p*<*0.10* NS*
Study*End( p*<*0.005* p*<*0.10* NS*
CVLT(Delayed(Recall(
Baseline( p*<*0.05* p*<*0.05* NS*
Study*End( p*<*0.05* p*<*0.05* NS*
Animal(Naming(
Baseline( NS* NS* NS*
Study*End( NS* p*<*0.05* NS*
123
8.3.4 Longitudinal analysis of cognitive performance within phenotypes, stratified by menopause
cohort
Longitudinal results had shown that the Poor Metabolic cluster had the greatest increase
in cognitive performance from baseline to study end. An analysis of longitudinal cognitive
performance revealed that this significant increase was primarily driven by the early menopause
women within the Poor Metabolic cluster.

Cognitive Factors: All three clusters showed greater improvements in cognitive
performance in the early menopause cohort compared to the late menopause cohort. On the
Global Cognition factor score, the early menopause women had a significant increase within the
Healthy cluster (p < 0.0005), the High BP cluster (p < 0.0005), and the Poor Metabolic cluster (p
< 0.0005). Late menopause women showed a significant increase in Global Cognition within the
Healthy cluster (p < 0.005), the High BP cluster (p < 0.05), and the Poor Metabolic cluster (p <
0.0005). None of the slope differences between clusters were significantly different from each
other. On the factor score of Verbal Memory, early menopause women had a significant increase
in the Healthy cluster (p < 0.0005), the High BP cluster (p < 0.0005), and the Poor Metabolic
cluster (p < 0.0005). Late menopause women showed a significant increase in Verbal Memory
within the Healthy cluster (p < 0.0005) and the High BP cluster (p < 0.005). There were no
significant differences in longitudinal slope scores between the clusters. In all three clusters,
early menopause women had no significant longitudinal change in the Executive Function factor
score. Late menopause women in the High BP cluster had a significant longitudinal decrease in
Executive Function (p < 0.05). In late menopause women, there was a significant difference in
the longitudinal Executive Function slope score between the High BP women (whose
124
performance decreased significantly) and the Poor Metabolic women (whose performance
increased non-significantly). Results are shown in Figure 32.

Figure 32: Significant longitudinal differences within phenotypes, stratified by early and late menopause. Within
each phenotype, the darker-colored bar represents the baseline visit and the lighter color represents the end-of-
study visit. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences between
are adjusted for race, education, and treatment condition.
!0.50%
0.00%
0.50%
1.00%
1.50%
Healthy% High%BP% Poor%Met%
Global&Cog.&Longitudinal,&Early&
***
***
**
!0.60%
!0.40%
!0.20%
0.00%
0.20%
0.40%
0.60%
Healthy% High%BP% Poor%Met%
Global&Cog&Longitudinal,&Late&
**
**
*
!1.00%
!0.50%
0.00%
0.50%
1.00%
1.50%
Healthy% High%BP% Poor%Met%
Verbal'Memory'Longitudinal,'Early'
***
***
***
!0.30%
!0.20%
!0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
Healthy% High%BP% Poor%Met%
Verbal'Mem.'Longitudinal,'Late'
**
**
0.00#
0.10#
0.20#
0.30#
0.40#
0.50#
0.60#
0.70#
Healthy# High#BP# Poor#Met#
Exec.&Func*on&Longitudinal,&Early&
!0.70%
!0.60%
!0.50%
!0.40%
!0.30%
!0.20%
!0.10%
0.00%
0.10%
Healthy% High%BP% Poor%Met%
Exec.&Func*on&Longitudinal,&Late&
*
*
125
Verbal Memory: In both early and late menopause women, there were no significant
differences in longitudinal rates of cognitive change between the clusters on either the Immediate
Recall or the Delayed Recall version of the Logical Memory test. Early menopause women in all
three clusters showed a significant longitudinal improvement (p < 0.0005) on the CVLT
Immediate Recall test; the greatest rate of improvement was seen in the Poor Metabolic women.
Longitudinal rate of change was significantly different between early menopause women in the
Poor Metabolic and High BP clusters (p < 0.05), and showed a trend towards significance
between the women in the Poor Metabolic and Healthy clusters (p < 0.10). Women in late
menopause in the Healthy cluster (p < 0.0005) and High BP cluster (p < 0.0005) showed a
significant longitudinal improvement in performance on the CVLT Immediate Recall test. Again,
the longitudinal rate of change was significantly different between late menopause women in the
Poor Metabolic and High BP clusters (p < 0.05), and between women in the Poor Metabolic and
Healthy clusters (p < 0.05); however, the significant differences in longitudinal change in the late
menopause were driven by women in the Poor Metabolic cluster showing the smallest
longitudinal improvement in performance, whereas in early menopause the significant
differences were driven by women in the Poor Metabolic cluster showing the largest longitudinal
improvement in performance. Results are shown in Figure 32.
On the CVLT Delayed Recall test, women in all three clusters both early and late in
menopause showed a significant longitudinal improvement in performance. In the early
menopause women, longitudinal rate of improvement was significantly higher in the Poor
Metabolic cluster than in the High BP cluster (p < 0.05) and the Healthy cluster (p < 0.05). In the
late menopause women, there were no significant differences in longitudinal rate of
improvement between the clusters. Results are shown in Figure 33.
126

Figure 33: Significant longitudinal differences within phenotypes (left) and between phenotypes (right), stratified by
early and late menopause. Within each phenotype, the darker-colored bar represents the baseline visit and the
lighter color represents the end-of-study visit. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005,
***p < 0.0005. Significant differences between are adjusted for race, education, and treatment condition.
0.0#
10.0#
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Healthy# High#BP# Poor#Met#
CVLT%Immediate%Longitudinal,%Early%
***
***
***
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Healthy# High#BP# Poor#Met#
CVLT%Immediate%Longitudinal,%Early%
◊
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CVLT%Immediate%Longitudinal,%Late%
*** ***
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*
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CVLT%Delayed%Longitudinal,%Early%
***
***
***
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Healthy# High#BP# Poor#Met#
CVLT%Delayed%Longitudinal,%Early%
*
*
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Healthy# High#BP# Poor#Met#
CVLT%Delayed%Longitudinal,%Late%
***
***
**
0.0#
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4.0#
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8.0#
10.0#
12.0#
Healthy# High#BP# Poor#Met#
CVLT%Delayed%Longitudinal,%Late%
127
There were no significant differences in longitudinal rates of cognitive change between
the clusters on the Symbol-Digit test, the Trails-B test, Letter-Number Sequencing, the Shipley
Institute of Living test in either the early menopause or late menopause women. In early
menopause women, there was a significant improvement on the Animal Naming test in the
Healthy cluster (p < 0.05), as well as a trend towards improvement in the High BP cluster (p <
0.10) and the Poor Metabolic cluster (p < 0.10). There were no significant differences in
longitudinal rates of cognitive change between the clusters in early menopause. Late menopause
women showed a significant improvement on Animal Naming in the Poor Metabolic cluster (p <
0.05), as well as a trend towards improvement in the Healthy cluster (p < 0.10). Late menopause
women in the High BP cluster had a slight but non-significant decrease in performance. The
longitudinal rate of change on Animal Naming was significantly different between late
menopause women in the Poor Metabolic cluster and in the High BP cluster (p < 0.05). Results
are shown in Figure 34.

8.4 Discussion
We hypothesized that metabolic phenotype membership at baseline would be associated
with longitudinal differences in cognitive performance at the end of the study. Our initial
hypothesis was that women would show a decrease in cognitive performance over time, but
interestingly, women within all three metabolic phenotypes showed a significant longitudinal
improvement on nearly every single cognitive factor and individual cognitive test. The greatest
amount of longitudinal improvement was seen in women in the Poor Metabolic phenotype, as
indicated by larger longitudinal slope scores. Women in the High BP phenotype had the smallest
amount of longitudinal improvement.
128

Figure 34: Significant longitudinal differences within phenotypes (left) and between phenotypes (right), stratified by
early and late menopause. Within each phenotype, the darker-colored bar represents the baseline visit and the
lighter color represents the end-of-study visit. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005,
***p < 0.0005. Significant differences between are adjusted for race, education, and treatment condition.

The differences in rates of improvement seen between the clusters – specifically the
differences between the Poor Metabolic cluster and the Healthy cluster – may have been due to a
mild ceiling effect with the tests included in the neurocognitive battery. As discussed in Section
4.2, these cognitive tests were selected with the expectation that they would not show a ceiling
effect; however, the expectation was also that cognitive performance would be highest at
baseline and would decrease at future assessment times. In the Healthy women, performance was
already quite high at the baseline, and considering that performance continued to improve from
that point, it is possible that the tests were not challenging enough and performance began to
reach a maximal level. As a result of the Poor Metabolic women starting from a lower cognitive
0.0#
10.0#
20.0#
30.0#
40.0#
Healthy# High#BP# Poor#Met#
Animal'Naming'Longitudinal,'Early'
*
◊ ◊
0.0#
10.0#
20.0#
30.0#
40.0#
Healthy# High#BP# Poor#Met#
Animal'Naming'Longitudinal,'Early'
0.0#
5.0#
10.0#
15.0#
20.0#
25.0#
30.0#
35.0#
Healthy# High#BP# Poor#Met#
Animal'Naming'Longitudinal,'Late'
◊
*
0.0#
5.0#
10.0#
15.0#
20.0#
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30.0#
35.0#
Healthy# High#BP# Poor#Met#
Animal'Naming'Longitudinal,'Late'
*
129
performance point at baseline, they had more room for improvement, which may be why we see
the greatest amount of cognitive change within this cluster. Another possibility is that the
cognitive tests included in this battery were not sensitive enough for the type of cognitive
impairments that women might have suffered from. As a result of their high level of education,
this is a population in which is would be more difficult to measure subtle cognitive changes
because they do still have the mental capacity to learn testing strategies. An additional
neurocognitive assessment, perhaps given another five years after the end of the trial, could be a
useful method to evaluate whether these women begin experiencing greater changes in cognitive
performance as they age, and also to decrease the strategy learning effects which may have been
present with a 2 to 2.5 year interval between assessment times.
Of note is that although women in the Poor Metabolic cluster showed a significant
improvement in cognitive performance over time, women in the High BP cluster showed the
smallest amount of improvement (and on some tests, the High BP women did in fact show a
decrease in cognitive performance). However, our investigation of longitudinal change in
metabolic phenotypes showed that women in both the High BP and Poor Metabolic clusters
become more metabolically healthy over time. One hypothesis is that the Poor Metabolic cluster
represents a modifiable phenotype: as these women experienced an improvement in metabolic
health, this was reflected by an improvement in cognitive health. This is supported by our results
showing that the Poor Metabolic phenotype had the greatest amount of both metabolic and
cognitive improvement. The High BP women, although they were able to improve their
metabolic status (and in particular, to decrease their blood pressure), did not show an associated
increase in cognitive performance.
130
The concept of modifiable phenotypes within the ELITE study matches very well with
recent results from the Kronos Early Estrogen Prevention Study (KEEPS). The KEEPS
researchers performed a clustering analysis of midlife vascular risk factors that was similar to the
clustering performed in ELITE, through which they identified a “high risk” cluster and a “low
risk” cluster (Wharton et al., 2014). An investigation of individual risk factors within the clusters
showed that slightly elevated systolic blood pressure had an association with poorer cognitive
performance at baseline. From this the researchers concluded that increased SBP, even within a
normotensive range, might have the greatest ability to adversely affect cognitive health.
Further support for the idea that the Poor Metabolic phenotype may receive the greatest
cognitive benefit as a result of metabolic improvement comes from the Whitehall II study
(Akbaraly et al., 2010). In this study, researchers investigated cognitive function in patients with
persistent metabolic syndrome (MetS), non-persistent metabolic syndrome, and healthy controls.
Although patients with MetS had lower cognitive performance at baseline, longitudinal results at
10 years showed that individuals with non-persistent MetS had cognitive performance equal to
the healthy controls at the end of the trial. Only those participants whose MetS persisted through
the 10 years of the trial showed significantly decreased cognitive performance at follow-up.
In summary, each of our three metabolic phenotypes is associated with a different
trajectory of longitudinal cognitive performance. Critically, these results highlight the
importance of modifiable metabolic risk factors, with regards to the potential effects that
metabolic modifications may have on cognitive health.

131
9. PHENOTYPE MODIFICATION BY HORMONE THERAPY

9.1 Introduction of Hypothesis
We hypothesize that metabolic phenotype association with longitudinal cognitive
performance will be modified by hormone therapy. Further, the effects of hormone therapy will
differ between women in early menopause and women in late menopause.

9.2 Statistical Methods
Following the cross-sectional and longitudinal analyses described in Sections 5 – 8,
metabolic biomarker profile and cognitive function were evaluated in relation to hormone
therapy. Cross-sectional analyses used ANCOVA models similar to those described in Sections 5
and 6. Longitudinal analyses used mixed effects linear models similar to those described in
Sections 7 and 8. For both cross-sectional and longitudinal analyses, cluster by randomized
treatment interaction terms were added to test effect modification by hormone therapy. Cluster
by treatment by menopause interaction terms were added to test whether hormone therapy
differentially affected cognition and/or metabolic biomarkers between menopause cohorts
(earlier vs. later randomization to hormone therapy) in any of the metabolic clusters. For
longitudinal analyses, the placebo group was used as an estimate of the expected rate of
cognitive change. All metabolic analysis adjusted for menopause cohort and treatment; all
cognitive analyses adjusted for menopause cohort, treatment condition, race, and education.
Tests used an overall 2-sided alpha of 0.05, correcting for multiple comparisons where
necessary.

132
9.3 Results
9.3.1 Baseline comparisons between women randomized to placebo and women randomized to
hormone therapy
At baseline prior to randomization, metabolic biomarkers and cognitive performance
showed no significant differences between women randomized to placebo and women
randomized to hormone therapy.

Metabolic Biomarkers: Within each of the three clusters, there were no significant
differences in any of the metabolic biomarkers between women randomized to placebo and
women randomized to hormone therapy. In the High BP cluster, there was a trend towards
women in the HT stratum to have higher HDL levels (p < 0.10). When women were stratified
into early and late menopause to test the interaction of menopause cohort and treatment
condition, there were no significant differences within any of the clusters on any of the nine
metabolic biomarkers.

Cognitive Performance: After adjusting for menopause cohort, race, and education,
there were no significant differences on baseline cognitive performance between women
randomized to placebo and women randomized to HT in any of the three clusters. There was a
trend for women randomized to HT in the Healthy cluster to have a higher Verbal Memory factor
score than women randomized to placebo (p < 0.10). When women were stratified into early and
late menopause to test the interaction of menopause cohort and treatment condition, there were
no significant differences within any of the clusters on any of the cognitive factor scores or
individual cognitive tests.
133
9.3.2 Cross-sectional analysis of metabolic phenotypes at study end, stratified by treatment
condition
At the end of the study, there were no significant differences between women randomized
to HT and women randomized to placebo in any of the three clusters, on any of the nine
metabolic biomarkers. Table 11 summarizes the levels of each biomarker within each treatment
stratum in the three clusters, as well as the difference in each metabolic biomarker between
women randomized to placebo and to HT. Although the differences were not significant, there is
an overall trend towards women in the High BP cluster having metabolic improvement when
taking HT versus placebo. In both the Healthy and Poor Metabolic clusters, HT had a beneficial
effect on the HOMA score, LDL cholesterol, HbA1c, and systolic blood pressure. In the High
BP cluster, HT had a beneficial effect on every single biomarker except HbA1c, and the
difference in mean HbA1c levels between women on placebo and women on HT was negligible.

Table 11: Average levels of metabolic biomarkers within HT-treated and placebo conditions at the end of the study.
The difference score refers to the placebo value subtracted from the HT value. None of the differences between HT-
treated and placebo-treated women within the three clusters were significant.

A treatment by menopause cohort interaction analysis reveals no significant effect of HT
on metabolic biomarkers specifically within early or late menopause women in the three clusters.
HEALTHY HIGH B.P. POOR METABOLIC
Placebo HT Diff. Placebo HT Diff. Placebo HT Diff.
Glucose
81.13 81.47 0.34 81.50 80.05 -1.45 91.51 93.59 2.08
HOMA
1.13 0.98 -0.15 1.37 1.11 -0.26 2.54 2.51 -0.03
Ketones
0.10 0.10 0.00 0.10 0.09 -0.01 0.10 0.09 -0.01
HDL
80.20 78.81 -1.39 69.17 74.40 5.23 56.97 56.66 -0.31
LDL
118.60 113.54 -5.06 122.70 120.10 -2.60 120.78 114.34 -6.44
Triglycerides
86.82 93.25 6.43 99.91 98.93 -0.98 137.19 143.29 6.10
HbA1c
5.77 5.66 -0.11 5.64 5.65 0.01 6.13 5.95 -0.18
SBP
108.32 107.98 -0.34 117.35 117.30 -0.05 118.33 117.23 -1.10
DBP
69.16 69.54 0.38 75.36 73.43 -1.93 73.69 74.20 0.51
134

9.3.3 Longitudinal analysis of metabolic phenotypes, stratified by treatment condition
There were few significant differences in longitudinal change of the metabolic
biomarkers between placebo-treated and HT-treated women in the three clusters. Within the
Healthy cluster, women in the placebo stratum had a significantly smaller longitudinal increase
in plasma triglycerides compared to women in the HT stratum (p < 0.05). Within the High BP
cluster, women in the HT stratum had a significantly smaller longitudinal increase in the HOMA
score (p < 0.05) and LDL cholesterol (p < 0.005) than women in the placebo stratum. Within the
Poor Metabolic cluster, women in the HT stratum had a significantly larger longitudinal increase
in HDL (p < 0.05) and a significantly smaller longitudinal increase in HbA1c (p < 0.05). Overall,
HT-treated women in the High BP and Poor Metabolic clusters showed more metabolic
improvement than placebo-treated women; in the Healthy cluster, longitudinal change in
metabolic biomarkers was relatively unaffected by randomization to HT or placebo. Results
comparing longitudinal change in metabolic biomarkers within clusters are shown in Figure 35.
Results comparing longitudinal change in metabolic biomarkers within each phenotype between
women randomized to HT and women randomized to placebo are shown in Figure 36.
A further analysis of treatment effect by menopause cohort within the three clusters
revealed differential effects of HT in the early and late menopause women. Within the Healthy
cluster, there were no significant differences in any of the metabolic biomarkers between early
menopause women randomized to HT and those randomized to placebo. The Healthy, late
menopause women also showed little effect of HT: women in the HT-treated condition had a
135

Figure 35: Significant longitudinal differences within phenotypes, stratified by treatment condition. Significance
values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant differences
between metabolic biomarkers are adjusted for menopause cohort.

trend towards a larger increase in triglycerides than women in the placebo condition (p < 0.10),
and women in the HT-treated condition had a smaller decrease in ketones than women in the
placebo condition (p < 0.05). Thus, HT appeared to be mildly detrimental for late menopause
women in the Healthy cluster, whereas it had no effect on early menopause women within the
Healthy cluster. Within the High BP cluster, early menopause women in the HT-treated
condition had a trend towards a smaller increase in the HOMA score (p < 0.10), a larger decrease
in LDL levels (p < 0.10), and a smaller increase in HbA1c (p < 0.10). The High BP, late
menopause women treated with HT had a significantly greater longitudinal decrease in LDL
0.0#
20.0#
40.0#
60.0#
80.0#
100.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Glucose(Longitudinal,(P(vs.(HT(
0.00#
0.50#
1.00#
1.50#
2.00#
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3.00#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
HOMA%Longitudinal,%Placebo%vs.%HT%
0.00#
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0.14#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Ketones'Longitudinal,'P'vs.'HT'
0.0#
50.0#
100.0#
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P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Triglycerides+Longitudinal,+P+vs.+HT+
0.0#
20.0#
40.0#
60.0#
80.0#
100.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
HDL$Longitudinal,$Placebo$vs.$HT$
0.0#
50.0#
100.0#
150.0#
200.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
LDL#Longitudinal,#Placebo#vs.#HT#
0.0#
1.0#
2.0#
3.0#
4.0#
5.0#
6.0#
7.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
HbA1c&Longitudinal,&Placebo&vs.&HT&
0"
20"
40"
60"
80"
100"
120"
140"
P" HT" P" HT" P" HT"
Healthy" High"BP" Poor"Met"
SBP$Longitudinal,$Placebo$vs.$HT$
0"
20"
40"
60"
80"
100"
P" HT" P" HT" P" HT"
Healthy" High"BP" Poor"Met"
DBP$Longitudinal,$Placebo$vs.$HT$
◊
*
◊
**
***
**
** *
***
***
◊
***
***
***
*** ***
***
*** ***
**
***
** **
*** ***
***
*** ***
*
*
*** ***
◊
*
◊
*** ***
◊
136

Figure 36: Significant longitudinal differences between HT and placebo-treated women within each phenotype.
Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant
differences between metabolic biomarkers are adjusted for menopause cohort.

levels compared to the placebo-treated women (p < 0.05). Overall within the High BP cluster,
HT had a beneficial effect on metabolic biomarkers in both early and late menopause women.
Within the Poor Metabolic cluster, early menopause women in the placebo condition had a trend
towards a different longitudinal change in glucose levels than women in the HT condition:
whereas glucose levels decreased in the placebo-treated women over the five years of the trial,
glucose increased in the HT-treated women (p < 0.10). The Poor Metabolic, late menopause
women treated with HT had a significant improvement in HDL levels (p < 0.05) and HbA1c (p <
0.05) compared to the placebo-treated women. Overall within the Poor Metabolic cluster, HT
0.0#
20.0#
40.0#
60.0#
80.0#
100.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Glucose(Longitudinal,(P(vs.(HT(
0.00#
0.50#
1.00#
1.50#
2.00#
2.50#
3.00#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
HOMA%Longitudinal,%Placebo%vs.%HT%
0.00#
0.02#
0.04#
0.06#
0.08#
0.10#
0.12#
0.14#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Ketones'Longitudinal,'P'vs.'HT'
0.0#
50.0#
100.0#
150.0#
200.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Triglycerides+Longitudinal,+P+vs.+HT+
0.0#
20.0#
40.0#
60.0#
80.0#
100.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
HDL$Longitudinal,$Placebo$vs.$HT$
0.0#
50.0#
100.0#
150.0#
200.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
LDL#Longitudinal,#Placebo#vs.#HT#
0.0#
1.0#
2.0#
3.0#
4.0#
5.0#
6.0#
7.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
HbA1c&Longitudinal,&Placebo&vs.&HT&
0"
20"
40"
60"
80"
100"
120"
140"
P" HT" P" HT" P" HT"
Healthy" High"BP" Poor"Met"
SBP$Longitudinal,$Placebo$vs.$HT$
0"
20"
40"
60"
80"
100"
P" HT" P" HT" P" HT"
Healthy" High"BP" Poor"Met"
DBP$Longitudinal,$Placebo$vs.$HT$
*
*
*
**
*
◊
137
had a beneficial effect on metabolic biomarkers only in the late menopause women. A summary
of the treatment by menopause interaction results is shown in Table 12.

Table 12: Interaction of HT with menopause cohort in the three metabolic phenotypes. Significance values:

◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005.

An overall review of the effect of hormone treatment by menopause cohort within each of
the clusters reveals no effect or a slight negative effect in the Healthy cluster, a beneficial effect
in both early and late menopause women in the High BP cluster, and a beneficial effect only for
the late menopause women in the Poor Metabolic cluster.

9.3.4 Cross-sectional analysis of cognitive performance at study end, stratified by treatment
condition
Although there was very little overall effect of hormone therapy on cognitive
performance at the end of the study, some interesting results were seen in the High BP cluster.
On the Trails-B test, there was a significant effect of HT by cluster (p < 0.05): in both the High
BP and Poor Metabolic clusters, women treated with HT had significantly better cognitive
HEALTHY' HIGH'BP' POOR'METABOLIC'
Early' Late' Early' Late' Early' Late'
Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT'
Glucose'
NS# NS# NS# NS# ◊#Placebo#be,er# NS#
HOMA'
NS# NS# ◊#HT#be,er# NS# NS# NS#
Ketones'
NS# *#HT#be,er# NS# NS# NS# NS#
HDL'
NS# NS# NS# NS# NS# *#HT#be,er#
LDL'
NS# NS# ◊#HT#be,er# *#HT#be,er# NS# NS#
Triglycerides'
NS# ◊#Placebo#be,er# NS# NS# NS# NS#
HbA1c'
NS# NS# ◊#HT#be,er# NS# NS# *#HT#be,er#
SBP'
NS# NS# NS# NS# NS# NS#
DBP'
NS# NS# NS# NS# NS# NS#
138
performance compared with women randomized to placebo. Results also showed a trend towards
an effect of HT by cluster on both tests of visuospatial ability: the Judgment of Line Orientation
test (p < 0.10) and the Block Design test (p < 0.10). In both cases, women in the High BP cluster
randomized to HT performed better than women in the High BP cluster randomized to placebo.
Results are shown in Figure 37.

Figure 37: Significant differences between women treated with HT and placebo within phenotypes. Significance
values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Error bars represent SEM. Significant differences are
adjusted for race, education, and menopause cohort.

Within all three metabolic phenotypes, there was no significant effect of hormone therapy
on cognitive factor scores. There was no significant effect of HT on any individual tests of verbal
memory. A treatment-by-menopause cohort interaction analysis revealed no differential effects
of hormone therapy between women in early menopause and women in late menopause within
the three metabolic phenotypes.

9.3.5 Longitudinal analysis of cognitive performance, stratified by treatment condition
As discussed in Section 8, women within all three metabolic phenotypes showed a
significant longitudinal improvement on a majority of the cognitive factors and cognitive tests.
The rate of improvement showed very little modification by hormone therapy: that is, women
within both the placebo and HT-treated conditions in all three clusters showed significant
0.0#
20.0#
40.0#
60.0#
80.0#
100.0#
120.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Trails'B)Test)
0.0#
5.0#
10.0#
15.0#
20.0#
25.0#
30.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Line%Orienta*on%
0.0#
5.0#
10.0#
15.0#
20.0#
25.0#
30.0#
35.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Block&Design&
◊
◊
*
*
139
longitudinal improvement on most of the cognitive tests. On the CVLT, Delayed Recall test,
placebo-treated women in both the High BP (p < 0.10) and Poor Metabolic (p < 0.05) clusters
had greater longitudinal improvement than women in the HT stratum, although women in the HT
condition also showed longitudinal improvement. Conversely, HT-treated women in the High BP
cluster showed a longitudinal improvement on the Judgment of Line Orientation test over the
trial whereas women in the placebo condition showed a slight decline in performance; the
difference in longitudinal slope between the two clusters was significantly different (p < 0.05).
Results of the two tests where significant differences were seen are shown in Figure 38.

Figure 37: Significant longitudinal changes in cognitive performance between women treated with HT and placebo
within phenotypes. Significance values:
◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005. Significant differences
are adjusted for race, education, and menopause cohort.

A treatment-by-menopause cohort interaction showed no clear pattern of differential HT
effects in early or late menopause. Women randomized to HT had a significant longitudinal
improvement compared with women randomized to placebo on the following tests: Judgment of
Line Orientation in the Healthy, early menopause group (p < 0.05), the High BP, late menopause
group (p < 0.10), and the Poor Metabolic, late menopause group (p < 0.10); and the Trails-B test
in the High BP phenotype, late menopause (p < 0.10). Women randomized to placebo had
significant longitudinal improvement compared with women randomized to HT on the following
0.0#
5.0#
10.0#
15.0#
20.0#
25.0#
30.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
Line%Orienta*on%Longitudinal,%Placebo%vs.%HT%
0.0#
2.0#
4.0#
6.0#
8.0#
10.0#
12.0#
14.0#
P# HT# P# HT# P# HT#
Healthy# High#BP# Poor#Met#
CVLT%Longitudinal,%Placebo%vs.%HT%
* ◊ *
140
tests: Block Design, Healthy phenotype, early menopause (p < 0.10), verbal memory, Healthy
Phenotype, late menopause (p < 0.10), Judgment of Line Orientation, Healthy phenotype, late
menopause (p < 0.10), CVLT Delayed Recall, High BP phenotype, early menopause (p < 0.005)
and Poor Metabolic phenotype, early menopause (p < 0.05). Results are summarized in Table 13.

Table 13: Interaction of HT with menopause cohort in the three metabolic phenotypes. Significance values:

◊
p < 0.10, *p < 0.05, **p < 0.005, ***p < 0.0005.

In summary, in the Healthy cluster, women randomized to placebo generally showed a
greater longitudinal increase in cognitive performance than women randomized to HT. In the
High BP cluster, randomization to HT was beneficial for longitudinal cognitive function in the
late menopause women. Results in the Poor Metabolic cluster were mixed. Overall there were
HEALTHY' HIGH'BP' POOR'METABOLIC'
Early' Late' Early' Late' Early' Late'
Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT' Placebo'vs.'HT'
Global'
NS# NS# NS# NS# NS# NS#
Verbal'
NS#
◊#Placebo#greater#
improvement#
NS# NS# NS# NS#
Execu?ve'
NS# NS# NS# NS# NS# NS#
SymbolBDigit'
NS# NS# NS# NS# NS# NS#
TrailsBB'
NS# NS# NS#
◊#HT#greater#
improvement#
NS# NS#
LeFerBNumber'
NS# NS# NS# NS# NS# NS#
Shipley'
NS# NS# NS# NS# NS# NS#
Line'Orient.'
*#HT#greater#
improvement#
◊#Placebo#greater#
improvement#
NS#
◊#HT#greater#
improvement#
NS#
◊#HT#greater#
improvement#
Block'Design'
◊#Placebo#greater#
improvement#
NS# NS# NS# NS# NS#
Log'Mem'Imm'
NS# NS# NS# NS# NS# NS#
Log'Mem'Del'
NS# NS# NS# NS# NS# NS#
CVLT'Imm'
NS# NS# NS# NS# NS# NS#
CVLT'Del'
NS# NS#
**Placebo#greater#
improvement#
NS#
*#Placebo#greater#
improvement#
NS#
Animal'
NS# NS# NS# NS# NS# NS#
141
very few significant differences, making the longitudinal cognitive performance results
somewhat difficult to interpret.

9.4 Discussion
Upon reviewing the effects of hormone therapy on both metabolism and cognitive
performance, it is apparent that while some women may benefit from postmenopausal HT, others
may experience null or detrimental effects. With regards to metabolic phenotypes, women within
each cluster who were randomized to placebo or HT had no difference in levels of the nine
metabolic biomarkers at baseline. As detailed in Section 7, women in the Healthy cluster
generally became less healthy over the five years of the trial, blood pressure levels decreased in
the High BP women, and women in the Poor Metabolic cluster became healthier on a majority of
the biomarkers. HT had an overall null effect (and in some cases, a slight negative effect) on
longitudinal biomarker trajectories in the Healthy cluster. A beneficial longitudinal effect of HT
was seen on metabolic biomarkers in the High BP cluster, both in early and late menopause
women. A beneficial longitudinal effect of HT was also seen on metabolic biomarkers in the
Poor Metabolic cluster; here, the effect was limited to women in the late menopause cohort.
The effect of HT on longitudinal changes in metabolic biomarkers was similar to the
effect seen on longitudinal cognitive function. At randomization, prior to initiation of HT or
placebo treatment, women in all three phenotypes showed no differences in cognitive
performance. As detailed in Section 8, cognitive performance improved significantly in all
phenotypes on nearly every cognitive test. Although there were few cognitive tests which
showed a specific benefit of HT, women in the High BP cluster treated with HT showed a greater
longitudinal improvement on some cognitive tests than women taking placebo. A menopause
142
cohort-by-treatment interaction analysis revealed that this effect was limited to the late
menopause women.
The cognitive test which showed the greatest sensitivity to HT was the Judgment of Line
Orientation test, which is somewhat unexpected. Substantial clinical research shows that women
perform worse then men on tests of visuospatial ability; whereas testosterone is beneficial to
visuocognitive performance, estrogen is generally thought to be detrimental (Caparelli-Daquer et
al., 2009; Goyette et al., 2012). Thus, it is likely that another factor is driving this result. The
longitudinal metabolic improvement in the High BP women randomized to HT would be
expected to have an effect on cognitive function, and may be causing the significant longitudinal
cognitive improvements seen in this phenotype. Women in the High BP phenotype have a
number of cardiovascular risk factors, and there is a well-characterized association between
cardiovascular disease and cognitive decline. As HT treatment improves levels of these risk
factors over the course of the trial, cognitive performance would be expected to improve as well.
These results further emphasize the importance of metabolic status as a key factor in
evaluating the risk/benefit profile of hormone therapy. Whereas the full population showed few
significant effects of HT on longitudinal cognitive performance, stratification using a set of
metabolic biomarkers allowed us to identify particular phenotypes which receive greater
metabolic and cognitive benefit from HT intervention.

143
10. CONCLUSIONS

The central hypothesis of my doctoral research is that the loss of ovarian hormones at
menopause initiates a state of bioenergetic crisis, leading to the emergence of different metabolic
phenotypes. My goal was to develop a panel of biomarkers composed of nine indicators common
to clinical practice, which could be used to identify Alzheimer’s risk phenotypes prior to the
transition to a clinical disease state. Further, I sought to characterize longitudinal metabolic and
cognitive trajectories within each phenotype as well as modifying factors. This systems levels
approach was expected to provide a more clinically-relevant strategy to reliably detect an at-risk
phenotype of cognitive decline and sporadic AD.
Using cluster analysis, I showed that within a healthy population of women there were
heterogeneous metabolic phenotypes that were associated with significant baseline differences in
cognitive performance. Although levels of individual biomarkers within each phenotype varied
over time, the phenotypes themselves remained stable. The metabolic phenotypes were
associated with different longitudinal trajectories of cognitive performance, which were further
affected by early or late menopause and by randomization to hormone therapy.
Women who were categorized into the Healthy phenotype entered the study with normal
levels of each of the nine biomarkers, and although their metabolic health declined slightly, they
remained healthy throughout the five years of the trial. Their cognitive performance at baseline
was excellent, and significantly better within the domains of global cognition and verbal memory
than women in the Poor Metabolic cluster. At the end of the study, these women displayed a
longitudinal improvement in performance on nearly all cognitive tests, and their cognitive
performance remained highest when compared to the other two phenotypes. Randomization to
144
hormone therapy had a null or slightly negative effect on both longitudinal metabolic health and
longitudinal cognitive performance in the Healthy phenotype.
Women who were categorized into the High BP phenotype were generally healthy at the
beginning of the trial, but were characterized by elevated systolic and diastolic blood pressure.
They had average cognitive performance at baseline, with scores on most tests falling between
the Healthy and Poor Metabolic women. Their metabolic health remained the most stable over
the five years of the trial, although their blood pressure decreased by the end of the study.
Similar to the Healthy women, they showed longitudinal improvement on many cognitive tests;
however, their rate of improvement was smallest among the phenotypes. Women in the High BP
phenotype received the greatest amount of metabolic and cognitive benefit from hormone
therapy, suggesting that this phenotype might be the most sensitive to estrogen effects.
Women who were categorized into the Poor Metabolic phenotype began the study with
slightly unhealthy levels of the nine biomarkers. Their cognitive performance at baseline was
lowest among the three phenotypes, and they showed significantly lower performance within the
domains of global cognition and verbal memory compared with the Healthy women. They
experienced a significant longitudinal increase in metabolic health, and levels of nearly all
biomarkers changed in a positive direction; despite this, their metabolic phenotype remained
significantly different from that of the women in the Healthy cluster. However, they had the
highest rate of longitudinal cognitive improvement, suggesting that improvement in metabolic
health over the five years of the trial had a beneficial effect on cognitive performance. Hormone
therapy was associated with improved metabolic status specifically in the late menopause
women, but there was no strong effect of hormone therapy on longitudinal cognitive trajectories
within the Poor Metabolic phenotype.
145
An important characteristic of these nine biomarkers is that they are all modifiable
factors. Results from this study demonstrate that metabolic modification over a five-year period,
particularly within the Poor Metabolic phenotype, was associated with a significant longitudinal
improvement in cognitive performance. It has been suggested that instead of a one-drug, one-
target approach for systemic diseases such as AD, small modifications in several risk factors
might be adequate to decrease overall risk (Solomon et al., 2014). Our results support this
hypothesis: within the Poor Metabolic phenotype, significant cognitive benefit occurred despite
the fact that levels of each individual metabolic variable did not improve to a level that was
comparable to the Healthy phenotype. Thus, metabolic dysregulation provides a promising target
for an intervention/prevention strategy that may be successful early in the AD process. While it
is possible that high blood pressure can be targeted as a prevention strategy, our results show that
early screening and intervention are critical, because the long-term cognitive effects of elevated
blood pressure may be significant.
The study results also highlight the importance of ethnicity in determining metabolic
status and disease risk. Although the clusters were driven entirely by levels of the nine metabolic
biomarkers, there was a significant difference in the racial composition of the clusters: Caucasian
and Asian women were more likely to fall within the Healthy phenotype, African American
women were more likely to fall within the High BP phenotype, and Hispanic women were more
likely to fall within the Poor Metabolic phenotype. These results match closely with what is seen
in the epidemiological literature. Data from the 2013 Alzheimer’s Association Facts & Figures
report show that at nearly every age, African Americans and Hispanics have a higher risk of
dementia (Thies and Bleiler, 2013). A recent study found that Hispanic ethnicity was associated
with a younger age of dementia onset even after accounting for hypercholesterolemia,
146
hypertension, and diabetes (Fitten et al., 2014). Thus, when evaluating phenotypes of risk for
Alzheimer’s disease, it is important to be aware of the underlying genetic metabolic risk
conferred by particular racial backgrounds.
Although age is well recognized as the greatest risk factor for Alzheimer’s, the complete
etiology of this disease has yet to be fully elucidated. By identifying pathways and systems
which are perturbed early, we will gain greater insight into the preclinical disease process. It is
unlikely that a single indicator will identify an at-risk phenotype prior to the transition to a
defined clinical disease state. A multifactorial approach to biomarker development is much
stronger than an approach which uses single biomarkers – not only because it gives a superior
overall view of the system, but also because this systems approach has greater clinical relevance.
In early 2011, the National Alzheimer’s Plan Act (NAPA) was signed into law, with the
goal of developing an effective treatment for AD by 2025. Several of the NAPA
recommendations focused on biomarker development. The Act calls for development of
biomarkers that can be more easily obtained in large populations, as well as for biomarkers
which can be used to identify disease subtypes. The concept of identifying phenotypes which
appear prior to disease onset – and which may have predictive ability for disease trajectory or for
successful intervention strategies – is in close concordance with the goals of NAPA.
When facing a disease such as Alzheimer’s with a long preclinical period, early
identification of persons at risk is challenging, but critical. My goal was to develop a validated
biomarker panel that identifies persons at risk for later development of MCI or Alzheimer's at a
stage when prevention is both possible and effective. Results of my doctoral research
demonstrate that metabolic biomarkers, even within a healthy population, can be used to identify
phenotypes of risk for cognitive decline and Alzheimer’s disease. Importantly, these are
147
commonly used clinical analyses, which could be easily integrated into routine clinical care
because they require no invasive techniques or specialized equipment. This biomarker panel
could thus provide a rapidly deployable and inexpensive screening tool for phenotypes of
Alzheimer’s risk, giving it great potential for translation into the clinical domain.

148
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Abstract (if available)

Abstract Alzheimer’s disease is a progressive, fatal neurodegenerative disorder for which there is no preventative treatment or cure. Over 5 million Americans are currently living with sporadic late‐onset Alzheimer’s disease

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Rettberg, Jamaica Rhae (author)

Core Title Development of biomarker profiles for early detection of women with an at-risk for Alzheimer's disease phenotype

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Publication Date 01/11/2015

Defense Date 04/25/2014

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag aging,Alzheimer's disease,biomarker,Female,metabolism,OAI-PMH Harvest,phenotype

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Brinton, Roberta D. (committee chair), Cadenas, Enrique (committee member), Hodis, Howard N. (committee member), Mack, Wendy Jean (committee member)

Creator Email jrettberg@gmail.com,rettberg@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-436359

Unique identifier UC11287169

Identifier etd-RettbergJa-2651.pdf (filename),usctheses-c3-436359 (legacy record id)

Legacy Identifier etd-RettbergJa-2651.pdf

Dmrecord 436359

Document Type Dissertation

Format application/pdf (imt)

Rights Rettberg, Jamaica Rhae

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA