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Sex differences in the TgF344-AD rat model: Investigating the behavior, pathology, and neuroanatomical structures

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Sex differences in the TgF344-AD rat model: Investigating the behavior, pathology, and neuroanatomical structures

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Content Sex differences in the TgF344-AD rat model:
Investigating the behavior, pathology, and neuroanatomical structures
By
Alicia Marguerite Reynoso Quihuis
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)
December 2024
Copyright 2024 Alicia Marguerite Reynoso Quihuis

ii
Dedication
To my mom and her mom, who created, cleared, and kept the path
open until the time was right for me to follow, and then lead my own
way.

iii
Table of Contents
Dedication ..……………………………………………………………………………………….ii
List of Tables …...………………………………………………………………………………...v
List of Figures ..…………………………………………………………………………………..vi
Abstract ……………………………………………………………………………………….…vii
Chapter One: Foundations and Sex-Specific Mechanisms in Alzheimer’s Disease:
A Historical and Contemporary Overview …………………………………...…………………..1
1. Alzheimer’s Disease …………………………………………………………………1
2. Risk factors of Alzheimer’s Disease ………………………………………………..2
2.1. Introduction ……………………………………………………………….2
2.2. Aging as a Primary Risk Factor …………………………………………..3
2.3. Female as a Secondary Risk Factor ………………………………………8
2.3.1. Lifestyle and Environmental Factors …………………………...8
2.3.2. Genetic Factors …………………………………………………10
2.3.3. Sex Hormone Factors …………………………………………..11
2.3.4. Impact of Estrogen on Neuroprotection ………………………..11
2.3.5. Menopause and Increased Alzheimer’s Disease Risk ………….13
2.3.6. Estrogen Replacement Therapy (ERT) …………………………13
2.3.7. Testosterone and Neuroprotection in Males ……………………14
2.4. Conclusion ………………………………………………………………..15
3. Alzheimer’s Disease Sex Differences in Humans ………………………………….16
3.1. Cognitive Sex Differences in Alzheimer’s Disease ……………………....17
3.2. Sex Differences in Alzheimer’s Disease Pathology ……………………....18
4. Rodent Models of Alzheimer’s Disease ……………………………………….……24
4.1. Mouse Models of Alzheimer’s Disease …………………………….……..24
4.2. Sex Differences in Alzheimer’s Disease: Challenges in
Mouse Models ………………………………………………………..……26
4.3. TgF344-AD: A Rat Model for Studying Sex Differences in
Alzheimer’s Disease ……………………………………….……………...27
4.4. The TgF344-AD: A Rat Model and Its Implications for
Translational Research ……………………………….…………………….30
4.5. Conclusions …………………………………..…………………………….31
5. Dissertation Objectives and Experimental Paradigms ……………………………...32

iv
Chapter Two: Investigating Sex Differences in Alzheimer’s Disease: Behavioral,
Biochemical, and Neuroanatomical Insights from the TgF344-AD Rat Model ………………35
Abstract ………………………………………………………………………………..35
1. Introduction …………………………………………………………………………36
2. Material and Methods ………………………………………………………………38
2.1. Animals …………………………………………………………………...38
2.2. Behavior …………………………………………………………………..39
2.2.1. Neurological Screen …………………………………………….39
2.2.2. Open Field Test …………………………………………………40
2.2.3. Novel Object Recognition Task ………………………………...40
2.2.4. Barnes Maze …………………………………………………….41
2.3. ELISAs ……………………………………………………………………41
2.4. Immunohistochemistry …………………………………………………...42
2.5. Ex-Vivo Magnetic Imaging Resonance …………………………………..44
2.5.1. Ex-vivo Brain Sample Preparation ……………………………..44
2.5.2. MRI Acquisition …………………………………………….….44
2.6. Statistical Analysis ………………………………………………………..45
3. Results ………………………………………………………………………………45
3.1. Cognitive Sex Differences ………………………………………………..45
3.1.1. Open Field ………………………………………………………45
3.1.2. Novel Object Recognition ………………………………………47
3.1.3. Barnes Maze …………………………………………………….48
3.2. Biochemistry and Immunohistochemistry ………………………………..49
3.2.1. Proinflammatory Panel …………………………………………49
3.2.2. Neurofilament-Light Chain ELISA …………………………….50
3.2.3. Detergent-Soluble Amyloid Peptides and
Immunohistochemistry ……………………………...……………51
3.3. Magnetic Resonance Imaging Anatomical Structures ……………………53
4. Discussion …………………………………………………………………………..55
4.1. Study Limitations …………………………………………………………62
4.2. Conclusions ……………………………………………………………….62
Chapter Three: Integrating Findings and Implications for Alzheimer’s Disease
and Broader Fields …...……………………………………………...…………..………………64
1. Summary of Dissertation Goals and Chapter 2 Findings ……………………...…...…64
2. Implications for Alzheimer’s Disease Research ……………….….…….……………65
3. Broader Implications Beyond Alzheimer’s Disease ………………….……..………..67
4. Future Directions ………….…………………….……………………….…………...68
5. Conclusions ……………………………………………………………….….….……70
References ……………………………………………………………………………………….71

v
List of Tables
1. Estrogen Loss in Females vs. Testosterone Decline in Males ………………….….…..……16
2. Females vs. Males Alzheimer’s Disease Pathological Features …………..……..……….…21
3. Common Rodent Models of Alzheimer’s Disease ………...……………..….………………28
4. Table 1. Protocol and Antibodies for Immunohistochemistry ……………………...……….43

vi
List of Figures
1. Incidence of Alzheimer’s Disease by Sex .………………………………………………...…5
2. Mortality Rates of Dementia, Vascular Dementia, and
Alzheimer’s Disease by Sex .…………………………...…………………...………...………7
3. Estrogen vs. Testosterone Hormone Production Across the Lifespan ……………….……12
4. Sex and Genotype Differences in Anxiety-like Behavior ………………..………………..47
5. Sex and Genotype Differences in Cognitive Behavioral ……………..…….……………..52
6. Sex and Genotype Differences in Biochemical Outcomes ……………..………………... 54
7. Sex and Genotype Differences in Detergent-Soluble
Amyloid Peptide Species ………………………………………………………………….…55
8. Sex and genotype differences in ex-vivo anatomical structures ……………..………………60

vii
Abstract
Alzheimer’s disease (AD) is a complex neurodegenerative disorder, predominantly
affecting the elderly, with a significantly higher prevalence and severity in females. This
introductory review chapter delves into the multifaceted sex-specific differences in AD,
encompassing biological, genetic, hormonal, and lifestyle factors. The pathology of AD is driven
by amyloid-β accumulation, tau tangles, neuroinflammation, and subsequent neuronal death,
with emerging evidence highlighting a sex disparity in these processes. Females exhibit a higher
amyloid-β burden, more extensive tau pathology, and a stronger inflammatory response, leading
to more rapid cognitive decline and disease progression. Hormonal changes, particularly the loss
of estrogen during menopause, exacerbate neurodegeneration in females, while testosterone’s
gradual decline in males presents a less immediate but still significant risk. In addition to
biological factors, lifestyle and environmental influences such as lower education levels and
occupational status among females further contribute to the heightened risk of AD. Moreover,
genetic factors like the APOE ε4 allele have been shown to increase AD susceptibility more
significantly in females. This review underscores the critical need for sex-specific research to
better understand these differences and to develop targeted therapeutic interventions. The
TgF344-AD rat model, which mirrors the human condition more accurately than traditional
mouse models, is introduced as a promising tool for investigating these sex differences. This
model offers the potential to explore the impact of estrogen loss and other age-related changes on
AD pathology, providing insights that could lead to the development of more effective,
personalized treatments. Ultimately, addressing the sex-specific mechanisms in AD pathogenesis
is essential for advancing therapeutic strategies and improving outcomes for both sexes,
particularly in the context of an aging global population.

1
Chapter One: Foundations and Sex-Specific Mechanisms in
Alzheimer’s Disease: A Historical and Contemporary Overview
1. Alzheimer’s Disease
Reflecting on the early 20th century, Dr. Alois Alzheimer’s seminal work laid the
foundation for our understanding of what would later be known as Alzheimer’s disease (AD). In
1907, he described his initial discovery in a 51-year-old female patient, Auguste Deter, followed
by a second case in 1911 involving a 56-year-old male patient, Johann F. It was not until 90
years later that this disease was shown to impact the sexes differently (Müller & Graeber, 1998).
As of 2024, over 110 years after Dr. Alzheimer’s initial publications, nearly 7 million
Americans have been diagnosed with AD, now the 7th leading cause of death in the United
States—a shift from its previous 6th position before 2019, influenced by the emergence of the
Coronavirus disease (Alzheimer’s Association, 2024). As the years have passed since Dr.
Alzheimer’s original observations, significant advancements have been made in understanding
the pathology of the disease.
AD is a prominent neurodegenerative disorder predominantly affecting older adults over
age 65. It is characterized by progressive cognitive decline, including memory loss and learning
impairments, (Alzheimer’s Association, 2024). AD pathogenesis, which can begin up to 20 years
before clinical symptoms emerge, is driven by multiple complex mechanisms, including cerebral
immune system dysregulation, accumulation of amyloid-β (A� ) plaques, neurofibrillary tangle
formation, neuroinflammation, and eventual neuronal cell death (Selkoe, 2001; Hardy & Selkoe,
2002; Glass et al., 2010; LaFerla, 2010).
Recent studies have underscored the importance of recognizing sex differences in the
development and progression of AD pathology. Biological, genetic, and hormonal factors

2
contribute to these differences, influencing the onset, symptoms, and progression of the disease.
Females are disproportionately affected by AD, in terms of 1) prevalence (the amount of those
diagnosed at a given time) 2) incidence (new cases over a given time period), and 3) severity of
the disease (Alzheimer’s Association, 2024; Li et al., 2022). This disparity could be partially
attributed to life factors associated with being female, hormonal changes post-menopause, and
genetic variations such as the presence of the ε4 allele of apolipoprotein E (APOE4), which has
been shown to have a stronger association with AD in females compared to males (Li et al.,
2022; Altmann et al., 2014; Mosconi et al., 2014).
Furthermore, sex-specific differences in brain structure and function, immune response,
and gender and lifestyle factors such as education, occupation, and social engagement also play a
crucial role in the differential impact of AD between males and females (Gatz et al., 2006;
Sundermann et al., 2017). Understanding these differences is essential for developing targeted
therapeutic strategies and personalized interventions aimed at mitigating the burden of AD.
This dissertation aims to delve deeper into the intricate mechanisms underlying AD with
a particular focus on the sex-specific aspects of its pathology and progression. By exploring the
molecular and behavioral factors that contribute to these differences, this research seeks to
enhance our understanding of AD and pave the way for more effective treatments and
preventative measures tailored to both males and females.
2. Risk Factors of Alzheimer’s Disease
2.1. Introduction
Since the inception of the Alzheimer’s Association in 1980, the organization has
consistently collected and published annual data reports within the United States’ (US)

3
population. For at least the past two decades, these reports have continually disclosed that
individuals aged 65-years-and-over, diagnosed with AD, are about two-thirds more likely to be
female than male. At this moment, approximately 4.2 million American females are living with
AD which is large enough to be compared to the entire population of Croatia or Panama.
Conversely, the other one-third diagnosed amounts to 2.7 million American males with AD,
equivalent to the populations of Jamaica or Qatar (World Population Review, 2023). Whether
these AD sex statistics are separated or combined, the reports make robustly evident that there is
a constantly increasing population of people dealing with a progressive, degenerative disease
with no cure; and with economical healthcare costs persistently rising and projected to reach
almost $1 trillion by the year 2050 (Skaria, 2022). Still, we cannot ignore this enduring sex
disparity in AD diagnosis rates which raises critical questions about the underlying causes of
these sex differences. Developing therapeutics to suitably address the disease begins with
acknowledging a sex difference.
2.2. Aging as a Primary Risk Factor
One of the most prominent risk factors for AD for both males and females is aging, a
factor extensively documented in multiple scientific studies and emphasized by the National
Institute on Aging. As individuals age, their risk of developing AD increases significantly
(Launer et al., 1999; Barron & Pike, 2012; Riedel et al., 2016; National Institute of Aging, 2023).
The most recent data from 2022 published by the Center for Disease Control’s National Center
for Health Statistics shows that the average life expectancy at birth is 80.2 years for females and
74.8 years for males. Despite this, males have experienced a greater increase in life expectancy

4
(1.3 years) compared to females (0.9 years) from the previous year’s figures suggesting males’
lifespan may be growing faster than females.
While many suggest that females have higher prevalence and incidence of AD, due in
part to their longer predicted lifespan, Riedel et al. provide a clearer picture of how that may not
be true at every life stage. In their 2016 publication, they combined data from the Cache County
Study (10,000 Utah participants observed over a 3-year span) with the Rotterdam Study (8,000
Dutch participants followed over 5 years) to characterize incidence of AD by sex. This finding
produced an incidence timeline that shows from the onset of 65-years-old, male participants have
higher incidence of AD than females (Figure 1). However, after age 75, that rate reverses with
females’ incidence risk increasing to 2-fold higher than age-matched males. Comparison
between the studies show, in the Cache County Study, male incidence rates begin to decrease at
93-years-old, but for females, incidence does not decline until 97 years-old. No distinction was
made between participants with all-cause dementia versus AD. The Rotterdam Study separated
out participants with AD and observed that male incidence rates decreased at around 90-yearsold while female rates continue to escalate. Moreover, the Rotterdam Study observed that
throughout all age groups, males have higher incidence rates for vascular dementia than females
(Miech et al., 2002; Riedel et al., 2016; Ruitenberg et al., 2001).
The EURODERM Studies sought to tease apart incidence rates of AD and vascular
dementia to determine the presence or absence of sex differences. The Cache County and
Rotterdam studies had modest samples sizes (8,000-10,000) and both studied participants in one
single but separate geographic locations. The EURODERM Studies rectified these limitations by
screening over 28,000, 65-years-and-older Europeans pooled together from across Denmark,
France, Netherlands and the United Kingdom. Their resulting analysis reaffirmed the originally

5
stated premise that females have an increased risk for developing AD, resulting in higher
incidence rates. Additionally, they also conveyed that their data revealed no sex differences in
risk for vascular dementia (Andersen et al., 1999).
Collectively considering findings from the Cache County, Rotterdam, and EURODERM
studies makes the case that there is a vital need to acknowledge the separation of vascular
dementia versus AD in their reporting analysis when trying to understand the sex difference risk
that comes with aging and AD. Buckley et al. attempted to understand this issue by looking at
death records and their correlation with dementia coding. Australian death records from 2006-
2014 were collected in a little less than 200,000 persons that had the coding of AD or dementia
on their death certificate. From this data, they were able to analyze that in all records labeled
65 70 75 80 85 90 95
0
25
50
75
100
125
Age in Years
Incidence Per 1,000 Person Years
Incidence of Alzheimer’s disease by Sex
Male
Female
Figure 1. Adapted from Riedel, B. C., et al., 2016. Incidence estimates of Alzheimer’s disease per
1,000 persons separated by sex. Data was taken from the Cache County Study and the
Rotterdam Study (Miech, R., et al., 2002, Ruitenberg, A., et al., 2001.) Combined studies’ reports
up until 75-years-old, males have higher incidences per persons. This is indicated by the blue
background. After the 75, the pink background emphasizes from that age and older, it is females
that have about a 2-fold higher risk for AD than males.

6
with dementia, the average age of death was younger for males at 84.5-years-old compared to
females 87.6-years-of-old. When specifically looking at certificates that included AD on them,
females had higher rates of death than males (Figure 2C). This finding extended into the
categories across all dementia types and including mentions of “underlying or associated causes”
(Figure 2A). However, aligning with Riedel et al., it was reported that there were no sex
differences in dementia rates up until age 79 years, after which females had higher death rates.
When examining death rates with records marked with vascular dementia, or even those with
comprising ischemic heart diseases, from ages 60-to-89-years, males had a higher death rate than
females (Figure 2B). Buckley et al. concluded that females were more likely to have AD as
cause of death than males, especially after age 85-years (Buckley et al., 2019).
Despite the findings of Buckley et al. reiterating an increase mortality risk in females for
AD with age, it also indicated the need to look across age groups to get a better understanding of
the sex differences in the disease. Additionally, it called attention to the likelihood that medical
practitioners worldwide are not all using the same rubric to diagnose patients specifically with
AD and may be under scrutinizing contributions of comorbidities as well. Furthermore, some
patients may be misdiagnosed with other forms of dementia that could be contributing to the sex
differences in AD incidence and prevalence numbers. Regardless of the sex differences across
age groups, all studies validated that progressive aging remains the primary risk factor of AD.
Still, additional scrutiny and rigor across research investigations are warranted to clarify and
validate these sex difference findings.
The preceding discussion of major studies provides valuable insights into the field’s
current understanding of how aging influences the risk of developing AD and highlights
significant sex differences. However, these findings also underscore the importance of

7
considering cognitive trajectories as individuals age. McCarrey et al. (2016) conducted a study to
assess these cognitive trajectories and the corresponding sex differences. Their research builds
on earlier studies, dating back to the 1950s, which have documented that older males typically
excel in spatial tasks, while females tend to outperform in verbal and reasoning tasks (Bieri et al.,
1958; Witkin et al., 1954; Anastasi, 1958; Little, 1969). Using data from the Baltimore
Longitudinal Study of Aging, which included 1,000 to 2,000 participants followed over many
years, McCarrey et al. found that sex differences were evident at baseline in several cognitive
domains. Their longitudinal analyses revealed that males experience a faster rate of decline in
normal cognitive aging, particularly in mental competency, motor/sensory skills, and
visuospatial tasks. Interestingly, the study suggests that females may possess a cognitive
resilience advantage that benefits them as they age. While these findings appear to contradict the
higher cognitive impairment and risk for developing AD observed in females, McCarrey et al.
propose that females' greater longevity may ultimately heighten their risk relative to males
(McCarrey et al., 2016).
60 70 80 90 100
0
5
10
15
20
Age in Years
Rate Per 1,000 Person Years
Mortality Rates of
Alzheimer’s disease by Sex
Female
Male
60 70 80 90 100
0
20
40
60
80
100
Age in Years
Rate Per 1,000 Person Years
Mortality Rates of
Dementia by Sex Female
Male
60 70 80 90 100
0
2
4
6
8
10
Age in Years
Rate Per 1,000 Person Years
Mortality Rates of
Vascular Dementia by Sex
Female
Male
A. B. C.
Figure 2. Adapted from Buckley, R. F., et al., 2019. Mortality rates of (A.) All dementias, (B.) Vascular
dementia, and (C.) Alzheimer’s disease per 1,000 persons separated by sex. Data was taken from the
Australian death certificate records from 2006-2014. Blue background indicates age groups when
males had higher rates compared to females. Pink background indicates ages when females at higher
mortality rates compared to males.

8
Aging remains the most significant risk factor for AD, with sex differences playing a
significant role in the disease's progression and impact. The studies reviewed emphasize the need
for further research to better understand these differences, particularly how they manifest across
various age groups and cognitive domains. As we transition to the next section, it is essential to
explore how being female can serve as a secondary factor in developing AD. Understanding the
specific factors associated with female and male biology that contribute to this increased risk will
be vital in developing more precise, targeted, and effective interventions.
2.3. Female as a Secondary Risk Factor
As aging is considered the primary risk factor for developing AD, the argument could be
made that one of several secondary risk factors of AD is being born female. Numerous
characteristics can be placed under the umbrella of being biologically female that add to the
vulnerability of developing AD. Some of those contributors can be found in lifestyle and
environmental factors, genes, and the female’s primary sex hormone, estrogen.
2.3.1. Lifestyle and Environmental Factors
Lifestyle and environmental influences can play a role in the disparity in AD prevalence
between sexes. As mentioned in Section 2.2, females on average live longer than males, thereby
naturally increasing their lifetime risk of developing AD. Additionally, historical differences,
understood as society’s antiquated gender roles, in education and occupation have also exposed
females to different cognitive challenges and social engagements. These factors can then impact
each sex’s cognitive reserve—a concept referring to the brain's resilience to neuropathological
insult or impairment. Members of the Sex and Gender Differences Special Interest Group, a

9
faction of the Diversity and Disparities Professional Interest Area of International Society to
Advance AD and Treatment (ISTAART), reviewed sex and gender differences on AD risk in
peer-reviewed published literatures (Mielke et al., 2022). In their analysis from reviewing over
270 articles across the world, they continuously found that those with lower education were at
higher risk of developing AD. However, females remained at higher risks even when compared
with males with the same number of education years as males (Madigan, 2009; United Nations
Children’s Fund, 2020). The group considered that perhaps there could be an indirect risk for
females obtaining less education due to high levels of stress and or other related mental health
symptoms (Hasselgren et al., 2020). Corresponding to levels of education, job types can impact
AD risk as well. In the 2022 Mielke et al. paper, they found females that held what they deemed
as “low-control” job positions were susceptible to higher AD risks. Conversely, males in “highcontrol” job positions had lower risks (Andel et al., 2012; Hasselgre et al., 2018). As with all
studies, these results are up for debate due to the differing ways of accumulation and
stratification of data. Although recent generations show a trend towards greater sex parity in
employment areas, past disparities may still influence current prevalence rates among older
populations (Stern, 2002).
Despite the general trend of higher AD prevalence in females, there are notable
exceptions where males may be more susceptible. For instance, certain lifestyle factors
associated with vulnerability to cardiovascular diseases, which are known risk factors for AD,
are more prevalent in males. Males are more likely to engage in smoking and excessive alcohol
consumption behaviors, both of which increase AD risk. An overabundance of smoking and
alcohol-use can contribute to early vascular dementia, a condition that can coexist with, or even
mimic, AD (Anstey et al., 2007). In the vein of risky behaviors, males with a history of traumatic

10
brain injury are also at a significantly higher risk of developing AD later in life compared to
females with similar injuries (Gardner et al., 2014). These factors highlight that, while females
generally have higher rates of AD, certain conditions and behaviors can predispose males to the
disease, underscoring the complexity of AD risk factors.
2.3.2. Genetic Factors
Genetic factors also play a significant role in the sex-specific prevalence of AD. The
APOE ε4 allele, a well-known genetic risk factor for AD, has a more pronounced effect in
females than in males. Studies have shown that females carrying the APOE ε4 allele are at a
higher risk of developing AD compared to males with the same genetic variant (Farrer et al.,
1997; Payami et al., 1994) This suggests that the interplay between genetic predispositions and
sex-specific biological factors could be driving the observed differences in AD prevalence.
Several scientific researchers have published in-depth review articles delving into the sex
differences that can arise from being born with APOE ε2 and APOE ε3 versus APOE ε4
increased risk for females (Altmann et al., 2014; Ungar et al., 2014). Others have disputed this
finding by separating the data out by age groups, only from 55- to 70-years-old, females
maintaining a higher AD risk than males and then the sex difference disappears from thereafter
(Neu et al., 2017). Nevertheless, the APOE allele is beyond the scope of this present
dissertation, yet it is acknowledged that sex differences with females are at a higher risk in with
those genetic dispositions, and have been previously published, and can be read further in-depth
at the previously cited publications.

11
2.3.3. Sex Hormone Factors
Beyond life expectancy and environmental factors, hormonal differences significantly
influence the prevalence of AD between females and males. Estrogen, the primary gonadal
hormone in biologically born females, is widely recognized for its neuroprotective effects. (Pike
et al., 2009, Vest & Pike, 2013). Menopause, which results in declines in estrogen levels (Figure
3) and occurs in a female’s midlife, may partially explain the higher incidence of AD in females,
as the lack of estrogen's protective effects can lead to increased vulnerability to
neurodegenerative processes (Zárate et al., 2017).
2.3.4. Impact of Estrogen on Neuroprotection
Estrogen's neuroprotective effects are well-documented in scientific literature. Estrogen
interacts with estrogen receptors (ERα and ERβ) in the brain to modulate various cellular
mechanisms that contribute to neural health. These mechanisms include the regulation of
synaptic plasticity, reduction of oxidative stress, inhibition of apoptosis, and modulation of
inflammatory responses (Brinton, 2009). Estrogen enhances synaptic plasticity by increasing the
expression of synaptic proteins and promoting dendritic spine formation, which is crucial for
learning and memory functions (Woolley, 1998).
Moreover, estrogen has been shown to exert antioxidant properties, reducing the levels of
reactive oxygen species (ROS) and enhancing the expression of antioxidant enzymes. This
reduction in oxidative stress is vital, as oxidative damage is a prominent feature in the
pathogenesis of AD (Behl et al., 1995). Estrogen also inhibits apoptosis, the process of
programmed cell death, by regulating the expression of anti-apoptotic and pro-apoptotic genes.
This regulation helps maintain neuronal integrity and prevent neurodegeneration, and when

12
interrupted, can cause a major disruption in the brain’s immune homeostatic functions (Nilsen &
Brinton, 2003).
Additionally, estrogen modulates inflammatory responses in the brain. When unchecked
or disrupted, chronic inflammation can occur and is considered a hallmark of AD, contributing to
the progression of the disease. Estrogen reduces the production of pro-inflammatory cytokines
and enhances the release of anti-inflammatory molecules, thereby exerting an overall antiinflammatory effect (Vegeto et al., 2006). Along with inflammation, some studies have shown
that estrogen hormone replacement in postmenopausal females can lower A� accumulation by
increasing APP intracellular trafficking as well as prevent hyperphosphorylation by inducing
0 20 40 60 80 100
0
20
40
60
80
100
Age in Years
Average Hormone Production (%)
Estrogen vs Testosterone Hormone
Production Across the Lifespan
Female
Testosterone
Estrogen
Male
Menopause
ADAM
Figure 3. Estrogen hormone production adapted from Lephart, E. D. & Naftolin, F., 2021.
Female estrogen peaks prior to reaching 40, before undergoing menopause between 40 to
60 years-of-age. Testosterone hormone production adapted from Stárka, L., et al., 2009.
Testosterone peak of production typically occurs between 20-30 years for males before
ADAM occurs, thereby slowly declining across the rest of their lifespan.

13
dephosphorylation in neuron’s microtubules (Alvarez-de-la-Rosa et al., 2006; Ferreira &
Caceres, 1991; Xu et al., 1998; 2006)
2.3.5. Menopause and Increased Alzheimer’s Disease Risk
The transition into menopause results in a significant decline in estrogen levels,
presumably leading to the loss of its many neuroprotective effects. This hormonal change is
thought to increase female's susceptibility to AD. Studies have shown that females who undergo
surgical menopause at an early age, especially without estrogen replacement therapy (ERT), have
an increased risk of developing AD. Indeed, females that undergo natural menopause also exhibit
accelerated cognitive decline and higher AD incidence compared to pre-menopausal females
(Rocca et al., 2007).
The timing of estrogen loss is critical. Early menopause, either natural or surgical, has
been associated with a higher risk of AD due to the prolonged period of low estrogen levels. This
prolonged deficiency can exacerbate neuronal vulnerability and contribute to the development of
AD pathology (Geerlings et al., 2001; Hogervorst et al., 2001).
2.3.6. Estrogen Replacement Therapy (ERT)
Estrogen replacement therapy (ERT) has been explored as a potential intervention to
mitigate the increased AD risk associated with menopause. Observational studies have suggested
that ERT initiated around the time of menopause may reduce the risk of AD and slow cognitive
decline (Henderson, 2006). However, the effectiveness of ERT is influenced by the timing of
initiation. The "critical window hypothesis" posits that ERT is most beneficial when started near

14
the onset of menopause, commonly known as perimenopause, whereas delayed initiation may
not confer the same protective effects and could even be detrimental (Maki & Henderson, 2013).
Despite these findings, randomized controlled trials have yielded mixed results regarding
the efficacy of ERT in AD prevention. The Women's Health Initiative Memory Study (WHIMS)
reported that ERT initiated in females aged 65 and older did not reduce the risk of AD and was
associated with an increased risk of dementia (Shumaker et al., 2004). This discrepancy
highlights there is still much work to do and further required research to elucidate the optimal
timing and formulation of ERT for neuroprotection.
2.3.7. Testosterone and Neuroprotection in Males
In contrast to estrogen, those born biologically male have their primary gonadal hormone
as testosterone. As estrogen is neuroprotective for those born female, testosterone has also been
proven to be neuroprotective for biological males. Males do not undergo menopause, nor does
testosterone decrease to cessation, but levels decline at a slow rate typically beginning at the
average age of 30-years-old (Pike, 2009; Vest & Pike, 2013). Although the gradual decline in
testosterone can increase vulnerability to neurodegenerative diseases, the risk is not as sharply
elevated as it is with the sudden drop in estrogen levels in females. This process in males is
known as androgen deficiency in aging males (ADAM) (Figure 3). Despite a slower decline in
gonadal hormone, lower testosterone levels in aging males are still associated with cognitive
decline and increased AD risk (Moffat et al., 2004; Rosario et al., 2011).
Testosterone interacts with androgen receptors in the brain to promote neuronal survival,
enhance synaptic plasticity, and exert anti-inflammatory effects (Hammond et al., 2001).
Analogous to estrogen, testosterone has antioxidant properties, reducing oxidative stress and

15
protecting neurons from damage (Ahlbom et al., 2001). The gradual decline of testosterone with
age can contribute to increased vulnerability to neurodegenerative diseases (Moffat et al., 2004).
Males’ vulnerability with decreasing testosterone, like estrogen, has called on the need of
researchers to explore testosterone replacement treatment (TRT) as well. TRT is considered for
aging males with low testosterone levels to improve cognitive function and overall
well-being. However, the long-term effects of TRT on AD risk remain ongoing and under
investigation, further research is still needed to establish its efficacy and safety.
2.4. Conclusion
While both estrogen and testosterone have neuroprotective impacts, their decline and the
resultant impact on AD risk differ significantly between males and females. These evaluations
are summarized in Table 1. Overall, the declines of estrogen in females and testosterone in
males are thought to contribute to increased risk of AD through different mechanisms and rates
of hormonal change. The abrupt loss of estrogen during menopause presents a more immediate
and pronounced risk for females, whereas the gradual decline of testosterone poses a more
prolonged risk for males. Comprehending the multifaceted causes behind sex differences in AD
is essential for creating targeted, sex-specific interventions for AD prevention and treatment.
Recognizing the impact of hormonal changes, genetic factors, environmental and lifestyle
differences can help craft personalized early strategies and treatments, potentially reducing the
burden of AD on females and males. Future research must continue to unravel these complexities
to improve outcomes for both sexes and provide more targeted and effective care.

16
3. Alzheimer’s Disease Sex Differences in Humans
In Section 1 and 2, the influences of aging and biological female sex have been shown to
increase the risks of developing AD. What about after the disease is present and progressing? For
instance, does the cognition loss and pathology of AD stop having sex differences, or do they
persist? In short, yes, the differences continue, however, the differences are not always
straightforward, and prior work often yields conflicting conclusions.
Table 1. Estrogen Loss in Females vs. Testosterone Decline in Males
Female Male
Primary Gonadal
Hormone: Estrogen Testosterone
Neuroprotective
Effects:
Estrogen has been shown to have
significant neuroprotective effects,
including reducing oxidative stress and
inflammation, promoting synaptic
plasticity, and enhancing mitochondrial
function
Testosterone provides neuroprotection by
reducing oxidative stress, promoting cell
survival, and enhancing mitochondrial
function
Hormonal Decline:
Females experience a significant decline
in estrogen levels during menopause,
leading to the loss of neuroprotective
benefits
Males experience a gradual decline in
testosterone levels starting around age
30, known as androgen deficiency in
aging males (ADAM)
Impact on
Alzheimer’s Risk:
The decline in estrogen levels during
menopause is associated with an
increased risk of Alzheimer's disease.
Early menopause and oophorectomy
(removal of ovaries) further elevate this
risk
Lower levels of testosterone in older
males have been linked to an increased
risk of developing Alzheimer's disease
Mechanism of
Action:
Estrogen reduces amyloid-beta
accumulation, promotes synaptic health,
and exerts anti-inflammatory effects. It
also enhances mitochondrial calcium
regulation and increases the expression
of anti-apoptotic proteins
Testosterone acts through androgen
receptors to reduce oxidative stress and
cell death. It also promotes mitochondrial
health and reduces neuroinflammation
Therapeutic
Interventions:
Hormone replacement therapy (HRT)
with estrogen has been investigated for
its potential to reduce Alzheimer's risk,
but results are mixed, and timing of
therapy initiation appears crucial
Testosterone replacement therapy is
being explored for its potential to mitigate
Alzheimer's risk in men, but further
research is needed to confirm its efficacy
and safety

17
3.1. Cognitive Sex Differences in Alzheimer’s Disease
Among the earliest symptoms AD patients exhibit are simple cognitive deficits in daily
tasks, particularly those relating to various forms of memory. In clinical settings, cognitive
decline in AD is assessed through several standardized tests that distinguish normal cognitive
aging from more rapid or severe cognitive impairments associated with AD.
In one important study, Filon et al. (2016) investigated sex differences in cognition of
AD. Using the Arizona Study of Aging and Neurodegenerative Disorders dataset (AZSAND),
they surveyed control and AD subjects annually. Controls were clarified as lacking dementia
while AD subjects were confirmed with postmortem pathologically defined AD. From a total of
over 1,000 participants, the average age at death was younger for males than females, with the
control groups having about 2 years longer than their sex-matched AD group. To clinically
measure cognition, participants were given the commonly utilized Mini-Mental State
Examination (MMSE), which consists of 11 questions that assess aspects of cognitive function
including communication. It does not evaluate emotion or mood-influenced memory. While the
age of dementia onset was not statistically different for males and females, MMSE scores
showed females moved to the severe clinical disease category at a faster rate despite having a
higher age of death coinciding with longer duration of life with the disease (Filon, et al., 2016).
McCarrey et al. (2016) looked only at cognitive trajectories of normal older adults and found
males are more likely than their female aged-match counterparts to decline at faster rates in
multiple mental exam tasks, contrary to the findings by Filon et al. Both Filon and McCarrey
conclude with the hypothesis that sex, as well as the disease itself, may possibly work in some
interconnected manner that biases females toward more rapid progression.

18
Sundermann and colleagues corroborated AD sex differences in cognition using the AD
Neuroimaging Initiative (ADNI) database. ADNI is a collective of researchers mostly located
across the US and Canada, but with centers across the world, that recruit adults between 55 and
90 years-of-age that fall into the groups of cognitively normal, mild cognitively impaired (MCI),
and those with early AD dementia. Participants undergo clinical assessments and
neurophysiological evaluations and partake in neuroimaging procedures. From this database,
Sundermann et al., has produced three separate publications specifically reiterating that at early
AD stages, females excel at baseline in verbal memory tasks compared to age-matched males.
However, with increasing AD-pathological burden, this advantage disappears in the subjects
leaving no differences between the sexes (Sundermann, et al., 2016, 2016, 2017).
As reviewed in Section 1 and 2, these cognitive results validate that AD has a strong sex
bias that needs to be continually investigated as publications come to inconsistent conclusions.
Moreover, the age of the subjects in these research publications play an important role of
displaying whether sex differences are present, or absent, when considering the disease’s
influence.
3.2. Sex Differences in Alzheimer's Disease Pathology
As many clinicians will let their patients know, an AD diagnosis traditionally is not
conclusively determined until postmortem pathological confirmation. While cognitive exams
mentioned in Section 3.1 can confirm deterioration of cognitive abilities, those results can signal
several different categories of a dementia diagnosis. To study brain pathology in living persons
with AD, researchers have made use of various neuroimaging techniques. One is Magnetic
Resonance Imaging (MRI) –a non-invasive procedure that uses magnetic fields and radio waves

19
to create an intricate picture of the body part being imaged, in this dissertation’s case, the brain.
Together with postmortem examinations, cerebral spinal fluid sampling, and various forms of
MRI and other neuroimaging approaches, researchers have found sex differences in the
pathology of the AD brain.
One of the most consistent findings across studies is the higher Aβ burden in females
compared to males. Positron emission tomography (PET) imaging studies, such as those
conducted by Jack et al. (2015), have demonstrated that females tend to accumulate more Aβ
plaques, particularly in the neocortex and hippocampus. These findings align with the earlier
work of Hua et al. (2010), who used data from the AD Neuroimaging Initiative (ADNI) to show
that females not only have a higher Aβ burden but also experience more rapid atrophy (as
detected by MRI) in key brain regions, including the hippocampus.
Further emphasizing the difference in Aβ burden, Sinforiani et al. (2010) explored how
these Aβ accumulations impact overall disease progression and outcomes. Their study suggests
that the increased Aβ burden in females could explain the faster cognitive decline observed in
females compared to males in AD. This observation is consistent with clinical data showing that
females are more likely to progress from mild cognitive impairment (MCI) to AD at a faster rate
than males.
In terms of tau pathology, another hallmark of AD, studies consistently show that females
exhibit more extensive tau tangles than males. Altmann et al. (2014) found that females with the
APOE ε4 allele, a key genetic risk factor for AD, have a higher density of tau tangles compared
to males with the same genetic profile. This observation is supported by Ardekani et al. (2016),
who reported that hippocampal atrophy progresses more rapidly in females, a phenomenon that
likely contributes to the more severe cognitive symptoms often observed in females. Moreover,

20
Skup et al. (2011) found that females experience greater gray matter atrophy, which is strongly
associated with tau pathology in AD. These findings are significant because tau tangles are
believed to spread in a specific fashion across the brain, leading to neuronal death and cognitive
decline.
Research by Fisher et al. (2018) adds an additional layer of understanding to these
findings by highlighting how sexual dimorphism in predisposition to AD can be traced back to
fundamental biological differences in tau pathology between males and females. This
dimorphism suggests that while males and females may develop AD through similar pathways,
the intensity and progression of tau-related brain damage may be significantly more severe in
females.
Cerebrospinal fluid (CSF) biomarkers also provide valuable insights into the sex
differences in AD pathology. Studies such as those by Sundelöf et al. (2008) have shown that
differences in plasma Aβ levels can be significant indicators of AD risk, particularly in male
populations. Barnes et al. (2005) found that females often have higher levels of tau in their CSF,
which correlates with more severe brain pathology and faster disease progression. This elevated
tau in the CSF could reflect the more extensive tau deposition in the brain, as observed in
imaging studies. Fisher et al. (2018) further highlighted that sex differences in predisposition to
AD could be partly due to these variations in CSF biomarkers, which may influence the
effectiveness of treatments targeting tau pathology.
Inflammation is another area where sex differences are pronounced in AD. Females tend
to exhibit higher levels of neuroinflammation, as evidenced by increased microglial activation.
Villa et al. (2018) found that microglial cells, which are the brain’s resident immune cells, show
sex-specific features that could influence their role in neuroinflammation and neurodegeneration.

21
This heightened inflammation may accelerate neurodegeneration, contributing to the faster
cognitive decline observed in females. Sinforiani et al. (2010) also noted that this stronger
inflammatory response in females could lead to more severe synaptic loss, particularly in regions
critical for memory, such as the hippocampus and frontal cortex. In contrast, males generally
show lower levels of microglial activation, which might explain their relatively slower disease
progression.
Table 2. Females vs. Males Alzheimer’s Disease Pathological Features
Pathological
Features Females Males
Amyloid-ȕ
Plaques:
Higher amyloid-ȕburden observed in
PET imaging, leading to more rapid
cognitive decline
Lower overall amyloid-ȕburden as
seen in PET imaging studies
Tau Pathology:
More extensive tau tangles,
particularly in limbic regions,
associated with APOE İallele
Less extensive tau tangles in cortical
regions
Brain Atrophy:
More diffuse atrophy across multiple
brain regions, including the frontal
and temporal lobes
Greater atrophy in the hippocampus
and entorhinal cortex
Neuronal Loss: More widespread neuronal loss
across different brain areas
Pronounced neuronal loss in
hippocampal areas
Glial Activation
(Astrocytes &
Microglia):
Higher levels of microglial activation,
indicating more neuroinflammatory
activity
Lower levels of activated microglia
observed in post-mortem studies
White Matter
Changes:
More diffuse white matter changes,
contributing to global cognitive
deficits
More pronounced white matter
lesions, particularly in periventricular
regions, linked to vascular risk factors
Cerebral Spinal
Fluid (CSF)
Biomarkers:
Higher CSF tau levels, correlating
with more severe pathology and
faster disease progression
Lower CSF tau levels compared to
females
Synaptic Loss:
More pronounced synaptic loss,
particularly in the hippocampus and
frontal cortex
Less extensive synaptic loss in
cortical regions
MRI Findings: More pronounced cortical thinning,
especially in the temporal lobe Less severe cortical thinning
PET Imaging
(Glucose
Metabolism):
Greater reductions in glucose
metabolism, particularly in the
parietal and temporal lobes,
contributing to faster cognitive decline
Lower reductions in glucose
metabolism in certain brain regions

22
The significance of neuroinflammation in AD pathology is further elaborated in the study
by Hua et al. (2010), where it was observed that the inflammatory response in females could be
exacerbated by hormonal changes, particularly during menopause. The reduction in estrogen
levels, which has neuroprotective properties, might lead to an increase in neuroinflammation,
thereby accelerating the disease process in postmenopausal females. This observation is critical
in understanding why females, particularly older ones, experience a more rapid decline than their
male counterparts.
White matter changes are another critical aspect of AD pathology where sex differences
are evident. Males often show more localized white matter lesions, particularly in periventricular
regions. These lesions are frequently associated with vascular risk factors, such as hypertension
and diabetes, which are more prevalent in males. Cavedo et al. (2018) found that these white
matter changes might contribute to the distinct cognitive profiles observed in male AD patients,
who often exhibit more vascular-related cognitive impairments. On the other hand, females tend
to have more diffuse white matter changes, suggesting that AD affects brain connectivity more
broadly in females. This diffuse white matter damage might contribute to the more global
cognitive deficits often seen in female patients, as reported by Fisher et al. (2018).
The importance of white matter integrity in cognitive function was further emphasized by
Barnes et al. (2005), who noted that the more diffuse nature of white matter changes in females
could contribute to the broader spectrum of cognitive decline observed in females with AD. This
finding suggests that interventions aimed at preserving white matter
integrity might be particularly beneficial for females in slowing the progression of AD.
Glucose metabolism, as measured by PET imaging, also differs between sexes in AD.
Mosconi et al. (2017) found that both sexes experience reductions in glucose metabolism in

23
regions such as the parietal and temporal lobes, which are crucial for memory and cognition.
However, these reductions are more severe in females, contributing to their more rapid cognitive
decline. Hua et al. (2010) further emphasized that age is a significant factor interacting with sex
in influencing glucose metabolism and other pathological features. Their study demonstrated that
older females, particularly those post-menopause, show even more pronounced atrophy and
cognitive decline compared to their male counterparts. This suggests that the interplay between
aging, hormonal changes, and sex-specific biology may profoundly influence AD progression.
These patho-biological findings highlight the importance of considering sex differences
in AD research and clinical practice. The more extensive Aβ-β burden, tau pathology, and
neuroinflammatory response in females suggest that females may benefit from earlier and more
aggressive intervention strategies. Conversely, the more localized brain atrophy and vascularrelated pathology in males indicate that treatments targeting vascular health might be particularly
beneficial for male patients. As the studies by Hua et al. (2010), Skup et al. (2011), Ardekani et
al. (2016), and others have shown, these sex differences are not merely superficial but reflect
fundamental differences in the biology of AD. Moreover, the research by Sinforiani et al. (2010)
emphasizes the impact of these differences on clinical outcomes, suggesting that personalized
treatment approaches that consider a patient’s sex and related biological factors could
significantly improve the effectiveness of interventions for AD. This perspective is particularly
important as it challenges the traditional one-size-fits-all approach to treating AD, advocating
instead for more tailored strategies that address the unique pathological features observed in
males and females.
In conclusion, while AD affects both males and females, the underlying pathology differs
significantly between the sexes, as abbreviated in Table 2. Females generally exhibit more

24
severe Aβ and tau pathology, more pronounced brain atrophy, and a stronger neuroinflammatory
response, all of which contribute to a faster progression of the disease. Males, on the other hand,
show more localized brain changes, often related to vascular health. These differences
underscore the need for sex-specific approaches to AD research, diagnosis, and treatment,
ensuring that both males and females receive the most effective care based on their unique
biological profiles.
4. Rodent Models of Alzheimer’s Disease
4.1. Mouse Models of Alzheimer’s Disease
To intricately examine and develop sex-specific approaches for understanding and
treatment of AD, researchers have often turned to the use of rodent models. Over the past three
decades, transgenic mouse models have been invaluable in elucidating the pathophysiological
processes underlying AD. These models have been engineered to carry mutations associated with
familial AD (FAD), enabling researchers to study the molecular and cellular events that
contribute to AD progression. Despite their widespread use, it is essential to recognize the
limitations of these models, particularly in their ability to fully recapitulate the human condition
and in studying sex differences.
The PDAPP mouse model was among the first to demonstrate that overexpression of a
mutated form of the human Aβ precursor protein (APP) could lead to the formation of Aβ
plaques, a hallmark of AD pathology (Games et al., 1995). This model provided critical insights
into the role of Aβ in AD and sparked further research into APP processing and Aβ aggregation.
PDAPP mice begin to show Aβ plaques around six months of age, with extensive plaque
deposition by 12-15 months. The plaque pathology is associated with cognitive deficits,

25
particularly in tasks involving spatial memory, which is consistent with the cognitive decline
observed in AD patients (Chen et al., 2000). However, a significant limitation of the PDAPP
model is its inability to exhibit significant neuronal loss, a key feature of AD in humans. This
limitation has driven the development of additional models that aim to better replicate human
AD pathology.
The Tg2576 mouse model, another widely used transgenic model, expresses the Swedish
mutation (K670N/M671L) in APP, leading to a delayed onset of Aβ deposition compared to
PDAPP mice. Plaques in Tg2576 mice begin to form around 11-to-13 months-of-age, with
cognitive deficits emerging around 9-months. Although this model shares many pathological
features with PDAPP mice, including Aβ plaque formation and cognitive impairment, it also
fails to exhibit the full spectrum of AD pathology, particularly the development of neurofibrillary
tangles (NFTs) and significant neuronal loss (Hsiao et al., 1996).
To address the lack of NFTs in earlier models, the 3xTg-AD mouse model was
developed, which carries mutations in APP, presenilin 1 (PS1), and tau (P301L). This model is
significant because it is the first to exhibit both Aβ plaques and NFTs, providing a more
comprehensive representation of AD pathology. In 3xTg-AD mice, Aβ plaques begin to form
around six months of age, while NFTs develop later, mirroring the temporal sequence of events
observed in human AD. The presence of both Aβ and tau pathologies in this model allows
researchers to study the interactions between these two key features of AD, which are believed to
drive neurodegeneration synergistically (Oddo et al., 2003).
Despite these advancements, significant challenges remain in using mouse models to
study AD. One of the primary limitations is that most mouse models do not exhibit the degree of
neuronal loss observed in human AD. Neuronal loss is a critical component of the disease, as it is

26
directly associated with cognitive decline and functional impairments in patients. The lack of this
feature in many mouse models limits their utility in testing potential neuroprotective therapies
and understanding the mechanisms of cell death in AD (Oakley et al., 2006).
Furthermore, while these mouse models have been instrumental in studying the role of
Aβ and tau in AD, they often fail to replicate the complex interplay of genetic, environmental,
and hormonal factors that contribute to the disease in humans. For instance, in the APP23 mouse
model, the regulation of APP processing and tau phosphorylation is influenced by sex hormones,
which are known to have varying effects depending on hormone levels and the physiological
state of the organism (Yue et al., 2005). The majority of mouse models have been developed and
studied without adequately considering these sex differences, which is a significant gap in the
research given that females are more likely to develop AD and often experience more severe
symptoms (Mielke et al., 2014).
4.2 Sex Differences in Alzheimer’s Disease: Challenges in Mouse Models
Sex differences in AD are well-documented, with females being at a higher risk of
developing the disease and often experiencing faster cognitive decline compared to males (Li &
Singh, 2014). These differences are thought to be influenced by hormonal changes, particularly
the loss of estrogen during menopause, which may exacerbate neurodegenerative processes and
increase susceptibility to AD (Maki, 2013). Despite the importance of understanding these sexspecific factors, most transgenic mouse models have not been designed to explore these
differences, limiting their relevance to human AD.
Research on sex differences in AD mouse models has yielded some insights, although the
results are often inconsistent. For example, female Tg2576 mice have been reported to exhibit

27
more severe Aβ pathology and cognitive impairment than their male counterparts, suggesting
that sex hormones may influence Aβ production or clearance (Callahan et al., 2001). Similarly,
female 3xTg-AD mice have been shown to develop Aβ production, however findings of more
extensive tau pathology – also within female 3xTg-AD mice, which may be linked to the effects
of estrogen on tau phosphorylation and aggregation, have been found to have conflicting reports
(Carroll et al., 2010; Hirata-Fukae et al., 2008). However, the interpretation of these findings is
complicated by the fact that most mouse models do not adequately mimic the hormonal and
reproductive aging processes that occur in humans.
One of the significant challenges in studying sex differences in AD is the timing of
disease onset in most mouse models. Many transgenic mice develop AD-like symptoms in early
adulthood, before they reach reproductive senescence. This timing is problematic for studying
the impact of menopause and the associated loss of estrogen on AD progression, as it does not
accurately reflect the age-related hormonal changes that occur in females (Cohen et al., 2013).
As a result, there is a need for animal models that better represent the aging process and the
hormonal changes that contribute to sex differences in AD.
4.3 TgF344-AD: A Rat Model for Studying Sex Differences in Alzheimer’s Disease
The TgF344-AD rat model was developed to overcome the limitations of existing mouse
models and provide a more accurate representation of AD pathology, particularly for studying
sex differences. This model was created by introducing human Swedish mutant Aβ precursor
protein, APPswe, and Δ exon 9 mutant human presenilin 1 (PSEN1Δ9) mutations associated with
FAD into the genome of Fischer 344 rats, a strain known for its longevity and suitability for
aging studies (Cohen et al., 2013). These same constructs were previously used by Jankowsky et

28
al. in the C57Bl/6 back mouse model (2001). The use of rats instead of mice offers several
advantages, including a larger brain size, which facilitates more detailed neuroanatomical
studies, and a longer lifespan, which allows for the study of age-related changes in AD pathology
over a more extended period (Ellenbroek & Youn, 2016).
Table 3. Common Rodent Models of Alzheimer’s
Disease disease
Feature PDAPP Tg2576 3xTg-AD 5XFAD APP23 TgF344-AD
Species: Mouse Mouse Mouse Mouse Mouse Rat
APP Mutation: V717F
(FAD)
Swedish
(K670N/M6
71L)
Swedish +
PS1
(M146V) +
Tau
(P301L)
Swedish +
Florida
(I716V) +
London
(V717I)
Swedish
(APP751)
Swedish
(APPswe)
Presenilin
Mutation:
None None PS1
(M146V)
PS1
(M146L) +
PS1
(L286V)
None PS1 (ΔE9)
Tau Pathology
(NFTs): No No Yes No No Yes
Amyloid Plaque
Formation:
Yes (6-12
months)
Yes (11-13
months)
Yes (6
months)
Yes (2-3
months)
Yes (6
months)
Yes (6
months)
Neuronal Loss: No No Minimal Moderate Yes Significant
Neuroinflammation: Moderate Moderate High High Moderate High
Sex Differences
Studied: Limited Limited Yes Limited Limited Yes
(Robust)
Cognitive
Decline:
Yes
(Spatial
memory
deficits)
Yes
(Memory
deficits)
Yes
(Memory
and
cognitive
impairment)
Yes
(Early
impairment)
Yes
(Early
cognitive
deficits)
Yes (Agedependent,
correlated
with
pathology)
Onset of Ad-like
Symptoms:
Middle to
late
adulthood
Late
adulthood
Middle
adulthood
Early
adulthood
Middle
adulthood
Middle to
late
adulthood
Reproductive
Senescence
Impact:
Not
adequately
modeled
Not
adequately
modeled
Not
adequately
modeled
Not
adequately
modeled
Not
adequately
modeled
Modeled
(Menopause
-like state in
females)
Use in Translational
Research: Limited Limited Moderate Moderate Moderate High

29
One of the most significant advantages of the TgF344-AD rat model is its reported ability
to recapitulate the full spectrum of AD pathology, including Aβ plaques, early stage NFTs,
chronic neuroinflammation, and significant neuronal loss. These pathological features develop in
an age-dependent manner, with Aβ plaques appearing around six months, tau pathology and
neuroinflammation becoming prominent by 12-to-14 months, and neuronal loss occurring in
older animals (Cohen et al., 2013). This progression mirrors the temporal sequence of events
observed in human AD, making the TgF344-AD rat a valuable model for studying the disease.
Importantly, the TgF344-AD model also provides a unique opportunity to investigate sex
differences in AD. Unlike mouse models, which often manifest AD-like symptoms before
reproductive senescence, TgF344-AD rats develop cognitive impairments in middle to late
adulthood, coinciding with the natural transition to a menopause-like state in female rats (Cohen
et al., 2013). This timing is critical for studying the impact of estrogen loss on AD pathology and
cognitive decline, as it allows researchers to explore how hormonal changes associated with
aging contribute to the increased susceptibility and severity of AD in females.
While sex differences in AD are well-documented in clinical studies, the TgF344-AD rat
model offers the potential to explore these differences further. However, specific studies directly
comparing cognitive decline between male and female TgF344-AD rats are limited, and more
research is needed to conclusively determine whether females exhibit greater cognitive
impairments post-reproductive senescence.
Moreover, the TgF344-AD model allows for the investigation of the molecular and
cellular mechanisms underlying these sex differences. For instance, researchers can study how
estrogen modulates the expression of genes involved in Aβ production, tau phosphorylation, and
neuroinflammation, providing insights into the pathways that may contribute to the increased

30
risk of AD in females (Yue et al., 2005). Additionally, the larger brain size of rats compared to
mice facilitates more detailed neuroanatomical studies, allowing for the precise mapping of sexspecific changes in brain structure and function associated with AD (Ellenbroek & Youn, 2016).
4.4 The TgF344-AD Rat Model and Its Implications for Translational Research
The development of the TgF344-AD rat model represents a significant advancement in
the field of AD research, with important implications for translational studies. One of the key
challenges in developing effective treatments for AD is the failure of many promising therapies
in clinical trials, often due to key differences between animal models and human disease (Doody
et al., 2014). The TgF344-AD model addresses some of these challenges by providing a more
comprehensive representation of AD pathology, including the incorporation of sex differences
that are relevant to human patients.
For example, the TgF344-AD model can be used to test the efficacy of hormone
replacement therapies (HRT) in mitigating cognitive decline and neurodegeneration in postmenopausal females. By examining the effects of estrogen or other hormone treatments on the
progression of AD in female TgF344-AD rats, researchers can gain insights into the potential
benefits and risks of HRT in females at risk for AD (Maki, 2013). This model also allows for the
investigation of novel therapeutic targets that may be specific to one sex, providing a pathway
for the development of sex-specific treatments.
Additionally, the TgF344-AD rat model offers the possibility of longitudinal studies that
span the entire course of the disease, from early preclinical stages to severe cognitive
impairment. Such studies can provide valuable information on the timing of therapeutic
interventions, helping to identify windows of opportunity for treatment that could delay or
prevent the onset of AD symptoms (Cohen et al., 2013).

31
The TgF344-AD rat model also has the potential to bridge the gap between preclinical
studies and clinical trials. By providing a more accurate representation of AD pathology,
including sex differences, this model can help identify biomarkers that are predictive of disease
progression and response to treatment. These biomarkers can then be validated in human studies,
facilitating the translation of findings from animal models to clinical practice (Bateman et al.,
2012).
4.5 Conclusion
AD is a multifaceted neurodegenerative disorder that disproportionately affects females,
necessitating a deeper understanding of the sex-specific factors that influence its onset and
progression. While transgenic mouse models, as seen in Table 3, have provided valuable insights
into the molecular mechanisms underlying AD, their limitations in recapitulating the full
spectrum of the disease, particularly in terms of sex differences, have prompted the development
of alternative models.
The TgF344-AD rat model stands out as a tool for studying AD, offering a more accurate
representation of the disease’s pathology and a unique opportunity to investigate sex differences
in a clinically relevant manner. By incorporating both Aβ and tau pathologies, chronic
neuroinflammation, and significant neuronal loss, this model mirrors the progression of AD in
humans more closely than any mouse model. Additionally, its relevance to human aging and
menopause makes it an ideal candidate for studying the hormonal and genetic factors that
contribute to the increased risk and severity of AD in females.
Ongoing research with the TgF344-AD rat model holds the promise of uncovering the
cellular and molecular mechanisms driving sex differences in AD, ultimately contributing to the

32
development of sex-specific therapeutic strategies. As the field moves toward a more
personalized approach to medicine, the TgF344-AD model can play a crucial role in shaping the
future of AD research and treatment.
5. Dissertation Objectives and Experimental Paradigms
The preceding sections have critically reviewed the existing scientific literature,
establishing two significant gaps in the current understanding of AD: 1) a potential female bias
in the prevalence and progression of AD that remains inadequately understood, and 2) the
limitations of existing rodent models in replicating the full spectrum of AD phenotypes,
particularly in relation to aging and sex-specific differences. This dissertation seeks to address
these gaps by employing the TgF344-AD rat model, a novel transgenic model that more
accurately mirrors the human condition, to investigate sex differences in cognitive impairment,
biochemical markers, and AD-related pathology. Building on the foundation of previous
research, I hypothesize that female TgF344-AD rats will exhibit more pronounced cognitive
deficits, altered biochemical profiles, and greater pathological changes with age compared to
their age-matched male counterparts.
Chapter 1 laid the conceptual groundwork by reviewing evidence that suggests females
may develop more severe cognitive impairments with age and AD progression than males. For
instance, some studies have documented greater brain atrophy, earlier onset of cognitive decline,
and higher levels of Aβ and neurofibrillary tangles in females. However, the literature is not
unanimous, with some discrepancies potentially arising from variations in the age ranges studied,
methodological differences, and sample sizes. These inconsistencies highlight the critical need

33
for a robust translational model that can faithfully reproduce the sexual dimorphism observed in
human AD, both in terms of cognitive impairment and underlying pathology.
In this context, Chapter 2 describes analyses of sex differences using the TgF344-AD rat
model, which uniquely recapitulates the full spectrum of AD-like pathology, including Aβ
deposition, neuroinflammation, tauopathy, and neuronal loss in an age-dependent manner.
Unlike transgenic mouse models, which develop AD-related pathology prematurely and before
the onset of reproductive senescence, the TgF344-AD rat model progresses to AD pathology
more naturally, making it particularly well-suited for investigating the role of estrogen deficiency
and other age-related factors in AD.
The experimental design involved assessing sex differences in cognitive behavior,
biochemical markers, and AD-related pathology in TgF344-AD rats at two critical time points:
5-7 months of age, when minimal AD pathology is present, and 20-22 months of age, when the
pathology is more advanced and age-related changes are prominent. Behavioral tasks designed to
assess learning and memory were administered at both time points, followed by post-mortem
analyses that included the evaluation of inflammatory cytokines, a key biomarker of aging,
immunohistochemical analyses of AD hallmarks, and ex vivo MRI to assess anatomical brain
volume differences.
The findings from this dissertation significantly contribute to the field by: 1) establishing
sex differences in cognitive behavior within the TgF344-AD rat model, a novel and
translationally relevant model for AD; 2) providing a detailed correlation between these
behavioral differences and the underlying AD-like pathology; and 3) reinforcing the importance
of considering sex as a biological variable in preclinical studies, particularly in the context of
developing therapeutic strategies that address the observed sex bias in AD.

34
In conclusion, this dissertation not only underscores the prevalence and importance of sex
differences in AD but also validates the TgF344-AD rat model as a premier tool for investigating
these differences. The results suggest that targeting the underlying mechanisms of sex-specific
vulnerability in AD could lead to the development of more effective, personalized therapeutic
interventions, thereby advancing our understanding and treatment of this devastating disease.

35
Chapter Two: Investigating Sex Differences in Alzheimer’s Disease:
Behavioral, Biochemical, and Neuroanatomical Insights from the
TgF344-AD Rat Model
Abstract
Alzheimer’s disease (AD) is the leading cause of dementia, marked by cognitive decline,
memory loss, and learning impairments. The disease progresses through mechanisms such as
amyloid-β plaque accumulation, tau tangles, and chronic neuroinflammation. Epidemiological
studies have shown that females are disproportionately affected by AD, likely due to genetic
factors, including the APOE ε4 allele, and hormonal changes. This study investigates sex
differences in AD using the TgF344-AD rat model, with non-transgenic rats (wildtypes) serving
as controls. The research focuses on cognitive impairment, biochemical markers, and AD
pathology at two age points: 5-7 months and 20-22 months. Behavioral assessments, including
the open field test, novel object recognition task, and Barnes maze task, revealed significant sex
differences. Male wildtype rats exhibited lower anxiety levels in the open field test and
performed better in the Barnes maze, particularly in the younger age group. Biochemical
analyses showed higher levels of neurofilament light chain (NF-L), a marker of
neurodegeneration, in male transgenic rats, indicating greater neuronal injury compared to
females. However, no significant sex differences were observed in proinflammatory cytokines.
Ex vivo MRI revealed sex-specific neuroanatomical changes, with male wildtype rats having
smaller cingulate cortex volumes, but larger overall brain volumes than females. These findings
highlight the importance of considering sex as a biological variable in AD research. The
TgF344-AD rat model suggests that male rats may experience more severe AD pathology,

36
particularly in terms of neuronal injury and cortical atrophy, emphasizing the need for sexspecific therapeutic strategies.
1. Introduction
Alzheimer’s disease (AD) is the most prevalent form of dementia, characterized by
progressive cognitive decline, including memory loss and learning impairments (Alzheimer’s
Association, 2024). The pathogenesis of AD is driven by a multifaceted interplay of
mechanisms, including disruption of the cerebral immune system, accumulation of amyloid-β
(A�) plaques, formation of neurofibrillary tau tangles, chronic neuroinflammation, ultimately
leading to neuronal death (Glass et al., 2010; LaFerla, 2010; Selkoe, 2001; Hardy & Selkoe,
2002).
Recent published literature has underscored the importance of considering sex differences
in the progression and pathology of AD. For instance, epidemiological and clinical studies
indicate that females are disproportionately impacted by AD in 1) prevalence, 2) incidence, and
3) severity (Alzheimer’s Association, 2024; Li et al., 2022). Several factors, including genetics
like inheriting the APOE ε4 allele, and post-menopausal hormonal changes, are believed to
contribute to this disparity, with stronger associations observed in females than in males
(Altmann et al., 2014; Mosconi et al., 2014). Additionally, sex-specific differences in brain
structure and function, immune response, and lifestyle factors can further influence the impact of
AD between males and females (Gatz et al., 2006; Sundermann et al., 2017).
Given these differences, there is a critical need for improved rodent paradigms that
effectively model biological sex differences that may contribute to aspects of AD, particularly in

37
the context of aging. Traditional transgenic AD mouse models often fail to capture the full
spectrum of AD pathology, especially regarding sex-specific cognitive impairments and
underlying biochemical changes. The TgF344-AD rat model, a novel transgenic model that
mirrors the human condition more closely, offers a promising alternative. This model not only
recapitulates the hallmark pathologies of AD, such as Aβ deposition, neuroinflammation,
tauopathy, and neuronal loss, but does so in an age-dependent manner that is more reflective of
the natural progression of the disease in humans.
This study aims to systematically investigate the influence of sex differences in the
progression of Alzheimer's disease (AD) using the TgF344-AD rat model. Focusing on key
domains such as cognitive impairment, biochemical markers, and AD-related pathology, this
research leverages both behavioral and molecular assays. By examining two critical crosssectional age points—5-7 months and 20-22 months of age—this study seeks to capture early
and late-stage AD progression. In line with findings from human AD studies and prior rodent
research that indicate sex-specific vulnerabilities, we hypothesize that aged female TgF344-AD
rats will exhibit greater cognitive deficits, more pronounced biochemical alterations, and a higher
burden of AD-related neuropathology compared to their male counterparts. By addressing how
sex modulates these aspects of AD, the findings from this study aim to contribute to a more
nuanced understanding of the pathophysiological mechanisms driving sex-specific vulnerabilities
in AD, ultimately informing the development of more targeted and personalized therapeutic
strategies for male and female patients with AD. This research not only underscores the
importance of incorporating sex as a biological variable in AD studies but also seeks to provide a
translational framework for addressing sex-based disparities in clinical outcomes.

38
2. Material and Methods
2.1. Animals. TgF344-AD rats and their non-transgenic littermates were used from a colony
maintained at the University of Southern California (Cohen et al., 2013). TgF344-AD rats are a
rodent model of AD that express two human genes with autosomal dominant AD mutations: the
“Swedish” mutant amyloid precursor protein (APPsw) and the Δ exon 9 mutant presenilin-1
(PS1ΔE9) driven by the mouse prion promoter and generated on a Fischer 344 background.
Animals were bred using a heterozygous sire and a wildtype dam to obtain litters comprising
both transgenic (Tg; TgF344-AD+/-
) and wildtype (WT; TgF344-AD-/-
) offspring. Genotyping
was performed using ear punches and PCR, as previously described by Cohen et al. (2013). Both
Tg and WT littermates were group-housed under standard conditions with ad libitum access to
food and water, a 12-hour light-dark cycle, and a temperature- and humidity-controlled vivarium.
We bred two age groups: a younger Tg group with little-to-no AD-related pathology, 5-7-
months (5-7M), and an aged group expected full pathology, 20-22-months (20-22M). Each age
group has both a male and female WT and Tg group for a total of 4 groups per age. The 5-7M
groups had n values of female WT=11, male WT=13, female Tg=24, and male Tg=15. The 20-
22M groups had n values of female WT=9, male WT=6, female Tg=10, and male Tg=9.
In the aged rat groups (20-22 months of age), blood and hemi-brain samples were
collected from all animals one week after completing the behavioral tasks. Animals had
cardiocentesis from the heart’s vena cava to obtain blood samples. The sample was then placed
into 15mL conical tubes with 250uL of 0.1M EDTA. The tubes were quickly mixed before being
placed on ice. The blood samples were then centrifuged at 4°C, at 10,000xg for 15 minutes.
Serum was then removed from the top resulting layer aliquoted out into multiple 1.5mL
Eppendorf tubes. Serum samples were stored at -20°C until processing for proinflammatory

39
panel and neurofilament-light chain ELISAs. After retrieving blood samples, the animals were
perfused with ice-cold phosphate buffer saline, decapitated, and the brain was removed from the
skull. The brain was cut sagittally resulting in two hemibrains. One hemi brain was weighed,
snap frozen and stored at -80°C until homogenization for soluble-, and detergent-soluble amyloid
MSD panel. The other hemibrain was immersed in 4% paraformaldehyde for 24 hours and stored
at 4°C before being paraffin embedded for sectioning and staining. Henceforth, the experimenter
was blinded to sex, genotype, and age for all behavioral tasks performed and processing of serum
and brain samples.
All procedures were approved by the University of Southern California Institutional
Animal Care and Use Committee and adhered to National Institutes of Health guidelines and the
recommendations from the Association for Assessment and Accreditation of Laboratory Animal
Care International.
2.2. Behavior
2.2.1. Neurological Screen. Prior to behavioral testing at 5-7 months and 20-22 months of age,
animals underwent a neurological screen to ensure suitability for cognitive tasks. Visual acuity
was tested using a flashlight to observe pupil dilation. Olfactory response was assessed with
orange blossom lotion, auditory response by finger snapping near the ears, and tactile response
through paw pressure and ear twitch tests. Responses were marked as present or absent. Animals
exhibiting all reflexes were included in the study. No animals were excluded as all rodents
passed the neurological screening.

40
2.2.2. Open Field Test. The open field (OF) test was employed to measure locomotor activity
and anxiety-like behavior. Clear plexiglass boxes (40.5 cm x 40.5 cm x 38 cm) were used, and an
overhead video camera recorded the trials for analysis using Noldus Ethovision software
(Version 10.1; Leesburg, VA, USA). Rats were acclimated to the testing room for 30 minutes
before being placed in the center of the box and allowed to explore for 30 minutes. Data on
distance traveled, velocity, and time spent in specific regions were recorded. Boxes were cleaned
with 70% ethanol between trials. Data were analyzed in 5-minute increments over the 30-minute
period.
2.2.3. Novel Object Recognition Task. To evaluate short-term hippocampal learning and
memory, the novel object recognition (NOR) task was performed using a standard approach
(Mathiasen and DiCamillo, 2010) with minor modifications, as noted. Rats were placed in the
same boxes used for the OF test, with two identical objects in the center. Both objects in this trial
were upside down plastic cups (7.6cm diameter x 15.2 height). After 3 minutes of exploration
time, they were returned to their home cages, and the boxes and objects were cleaned with 70%
ethanol. After a 20-minute interval, rats were reintroduced to the box, now containing one
familiar and one novel object. The novel object was a pipette tip box (10.2 cm x 5.1cm x
6.35cm). Each animal was placed in the box, so the familiar object was located on the left side
and the novel object on the right side in front and at the opposing end of the box from where they
start. Exploration times with each object were recorded and the recognition index was calculated
as (time with novel object) / (total exploration time). Only rats with at least 30 seconds of total
object exploration were included in the analysis. No rats were excluded due to their performance.
Time exploring each object was recorded using Noldus Ethovison camera and software.

41
2.2.4. Barnes Maze Task. The Barnes maze task was used to assess spatial and long-term
hippocampal learning and memory. Behavior protocol was based on the established TgF344-AD
model publication with minor adjustments (Cohen et al., 2013; Gawel et al., 2019). The maze
consisted of a circular standing platform (100cm diameter x 80cm height) with 18 holes (9.5cm
diameter) around the periphery. Visual cues were provided to help the animals locate their
designated escape hole. Visual cues were made from white posterboard (2ft x 3ft) and black duct
tape (1.88in wide). The platform was cleaned with 70% ethanol before and after each trial. Each
rat had a unique escape hole for the first 7 days of testing. Rats underwent 3 trials per day for 4
consecutive days (learning phase), with each trial starting after a 15-second habituation period in
a PVC cylinder. The trial ended when the rat entered the escape hole or after 3 minutes, at which
point the rat was guided to the escape hole if necessary. A 48-hour delay was followed by a
probe trial to assess long-term memory. On days 8 and 9, cognitive flexibility was assessed by
moving the escape chamber to a new location, 180° relative to the initial location. Data recorded
included latency to locate the designated escape hole, errors made prior to entering correct
escape hole, distance traveled to correct escape hole, and average velocity during the trials until
ending in correct escape hole. All trials were recorded using Noldus Ethovision XT software.
2.3. ELISAs. Serum and hemibrains were collected from all animals one week after completion
of all behavior tasks at the 20-22month age range. Serum was assayed using ELISA kits for
neurofilament-light chain (Uman Diagnostics, Cat#20-8002) and proinflammatory cytokines
(Meso Scale Discovery, Cat# K15059D-1). All serum samples were run in duplicate and
according to the manufacturer’s protocol.

42
The non-fixed hemibrains were homogenized and assessed for soluble- and detergentsoluble amyloid (Meso Scale Discovery, Cat#K15200E). All animal samples were from the older
age cohort (20-22-months). Protocols for processing hemibrain homogenates for serial extraction
to obtain soluble and detergent-soluble amyloid fractions were adapted from Fryer et al. (2003)
and Zerbinatti et al. (2004, 2006) for the TgF344-AD rat model. Hemibrains were kept on dry ice
prior to homogenizing and individually weighed. Hemibrains were homogenized on ice with
30mL dounce homogenizers in 15X the brain weight in the homogenization buffer of 1X TBS
buffer and 3 inhibitors (Calbiochem set I, Cat #539131; and Sigma sets II and III, Cat #P5726
and P0044). Once homogenized, the brain and buffer solution were placed in 2mL
ultracentrifuge tubes. Tubes were ultracentrifuged at 4°C at 100,000 x g for 60 minutes. The
supernatant from this ultracentrifugation was aliquoted into Eppendorf tubes and stored in -80°C
until time for soluble-amyloid peptides ELISA plate. The pellet left in the ultracentrifuged tubes
was resolubilized in TBS with 1% Triton-X. This solution was then placed in new
ultracentrifuged tube. Tubes were placed on a mixer for 30 minutes at 4°C. The sample was
ultracentrifugated at 4°C at 100,000 x g for 60 minutes. The resulting supernatant was aliquoted
and stored at -80°C until time for detergent-soluble amyloid peptides ELISA plate. One hundred
micrograms of the soluble- and detergent-soluble protein (Quick Start Bradford protein assay
(Bio-Rad, Cat#500-0201)) were assayed on the amyloid MSD plate.
2.4. Immunohistochemistry. Fixed hemibrains from 20–22-month-old animals were sectioned
and stained to compare AD pathological markers: A� and microglia (IBA1) loads. The fixed
hemibrains were embedded in paraffin blocks and then coronally sectioned at 20 µm through the
dorsal hippocampus, a brain region critically involved in the consolidation of short-term and

43
long-term memory. Six hippocampal sections, each spaced approximately 100 µm apart, were
immunolabeled using the antibodies and protocol provided in Table 1. Stained hippocampus
slide images were imaged on an Olympus BX50 microscope with DP74 camera using the
CellSens software (Olympus). At 10X magnification, hippocampus images of sections: CA1,
CA2, CA3, CA 4/ Hilus, and the Dentate Gyrus were taken across 4 tissue sections per subject
brain. Then using Fiji ImageJ software, hippocampus area sections were converted to 8-bit,
followed by thereshold to designate positive and negative immunostaining. The A� and,
separately IBA1, load was calculated per hippocampus sections by calculating the percentage of
total pixels that were marked with positive immunostaining. This protocol of immunostained
image analysis quantification was followed directly from Mehtods and Protocols by Christensen
& Pike, 2020.
Table 1. Protocol and Antibodies for Immunohistochemistry
Protocol Step Reagent Time
Dewax Dewax solution (Leica) 30 seconds
Pretreatment /
Retrieval
1. Epitope Retrieval 1 (Citrate, Low pH 6.0)
2. Epitope Retrieval 2 (EDTA, High pH 9.0)
1. 5, 20
minutes
2. 20, 30
minutes
Antibody
1. Anti-Beta-Amyloid – 1:2000 (BioLegend,
Cat#SIG-39169)
2. Anti-IBA1 1:1000 (Fujifilm, Cat#019-19741)
15 minutes
Post Primary Rabbit-Anti-Mouse (DS9800) 8 minutes

44
Polymer HRP Anti-Rabbit-HRP (Leica, DS9800) 8 minutes
Peroxide Block H2O2 Peroxide Block (Leica, DS9800) 8 minutes
DAB DAB (Leica, DS9800) 10 minutes
2.5. Ex-Vivo Magnetic Imaging Resonance
2.5.1. Ex-vivo Brain Sample Preparation. Following cardiac perfusion with 4%
paraformaldehyde (PFA), the brain was left intact inside the skull after removing the head, most
of the surrounding skin, and cartilaginous tissue. The sample was postfixed in 4% PFA for 24h.
After 24h, the sample was rinsed with PBS and immersed in 5mM gadoliniumdiethylenetriamine pentaacetic acid (Gd-DTPA, BioPAL, Inc.) in PBS for 7 days. After GdDTPA incubation, the sample was rinsed with PBS and immersed in Galden HT170 (TMC
Industries, Cat#20170) in a 50 mL centrifuge tube for imaging.
2.5.2. MRI Acquisition. MRI was performed using an MR Solutions Powerscan Preclinical
System 7T horizontal 24 cm bore scanner (MR Solutions, Guildford, UK). The magnet was
equipped with a standard gradient set (~600 mT/m maximum gradient) and a 35 mm internal
diameter quadrature volume coil. A three-dimension fast spin echo (3D FSE) sequence was used
to image the ex vivo brain samples with the following parameters: TR = 200 ms, TE = 39 ms,
echo train number = 7, echo spacing = 13 ms, sample period = 200 µs, field of view = 28 mm x
28 mm x 14mm, matrix size = 250 x 256 x 128, number of averages = 8. Fiji software was used
to resize matrix size to 256 x 256 x 128, with a spatial resolution of 0.11 mm x 0.11 mm x 0.11
mm, to extract the brain, and to manually define and compute volumes of regions of interest

45
(ROI), such as the hippocampus and cingulate cortex (Schindelin et al., 2012). The Paxinos and
Watson rat brain atlas made available by a tool by Matt Gaidica was used as reference for
defining the ROIs (Paxinos & Watson, 2006).
2.6. Statistical Analysis
Experimental data are all reported in mean ± standard error of the mean (SEM). Data was
analyzed in GraphPad Prism 10 (GraphPad Software, Inc., La Jolla, CA). When analyzing
behavioral data, when data was presented in 5-minute binned trial times (OF), or across multiple
days (Barnes maze), a 3-way analysis of variance (ANOVA) was conducted with Tukey’s posthoc were applicable. Other analysis was conducted using ordinary 1-way ANOVA with Tukey’s
or t-test post hoc analysis where noted. Additionally, 1-way ANOVA was used for NOR,
biochemistry ELISAs, immunohistochemistry stains, and MRI anatomical volumes as well.
Statistical significance was set at p < 0.05.

3. Results
3.1. Cognitive Sex Differences
3.1.1. Open Field
To examine sex differences in anxiety-like behavior, as well as general locomotion, with
aging and progression of AD-pathology we used the open field behavioral task (Seibenhener &
Wooten, 2015). These measures of anxiety-like behavior and motor movement can be influenced
by the limbic area and neocortical regions of the brain (Stafstrom, 2006). First, looking at
anxiety-like behavior as indicated by time spent in the center of the open field box, at 5-7M male

46
Tg were more likely to groups spend longer time periods in the center of the box (Figure 1A, B).
While no significance between all groups in total time in center (Figure 1B) emerged, when we
broke the 30-minute trial into 5-minute-time bins, a sex (F(1,354)=20.58, p<0.001) and genotype
(F(1,354)=11.97, p=0.0006) main effect was seen, with male and female Tg spending more time
in center than their WT sex match (Figure 1A). In post-hoc comparisons, at the older age of 20-
22M, the male WT spent the most time in center compared to all other groups (Figure 1E).
However, this was only significant when compared to female WT (p=0.0168, 95%
C.I.=[27.44,348.2]) and female Tg (p=0.0115, 95% C.I.=[35.81,350.1]). Again, looking at the
time in center data broken into 5-minute-binned increments there was a significant sex X
genotype interaction (F(5,150)=2.674, p=0.0240) for the 20-22M age groups (Figure 1D). Upon
closer observation, while it initially male WT were exhibiting less anxious behavior at their older
age, when their performance compared to the the young male WT, their total and binned time
spent in the center did not significantly deviate much, unlike both female groups and male Tg
whose time dropped drastically (Figure 1B, E).
Next, we assessed locomotor ability measures like distance traveled and time spent in
movement during the behavioral task. There was no significant difference between any groups in
both 5-7M or 20-22M ages in distance and movement total values (Figure 1C, F). When
analyzing the 5-minute-time-bins at 5-7M, a highly significant sex X genotype interaction
(F(1,354)=37.24 , p<0.001) took place driven by male Tg that both spend more time in the center
and traveled more across the time bins (Figure 1C). Additionally at the same age, a time X sex
X genotype interaction (F(5,295)=3.559, p=0.0039) is noted with male Tg having more
movement during the task than male WT, as well as female WT moving more over time than
female Tg. At 20-22M, the female Tg travel more distance and have more movement across time

47
bins. No significant effect of sex or genotype was seen for movement, however, there was a time
X genotype interaction for distance traveled (F(5,150)=2.423, p=0.0381) (Figure 1F). Overall,
male WTs performance was least impacted by increased aging, while female Tg displayed the
most anxiety-like behavior with increased aging and AD pathology. As expected, all animals’
outcome measured values decreased from young to older age.
3.1.2. Novel Object Recognition
We used the novel object recognition task to measure hippocampal-dependent short-term
memory in the TgF344-AD rat model. This type of memory falls under declarative-episodic or -
recognition memory as it requires the animal to identify the difference between a previous
Figure 1. Sex and genotype differences in the anxiety-like behavioral outcomes across
age in males (blue) and females (red) of the TgF344-AD rat model (transgenic, Tg;
hatched bars, dotted lines) and littermate controls (Wildtype, WT; solid bars, solid lines).
(A-F) Open field performance broken into 5-minute time bins of the full 30-minute task
and (B, E) total 30-minute performance. (A-C) 5-7M and (D-F) 20-22M time spent in the
center of the maze and distance traveled across task time. * Asterisks in (A) denotes
significance between Male Tg vs Female Tg p=0.0254.
A
1 2 3 4 5 6
0
4000
8000
12000
5 Minute Time Bins
Distance (cm)
5-7M Distance Female WT
Female Tg
Male WT
Male Tg
Sex x Genotype
P<0.0001
C
1 2 3 4 5 6
0
20
40
60
80
100
120
5 Minute Time Bins
Time in Center (seconds)
20-22M Time in Center
Female WT
Female Tg
Male WT
Male Tg
Sex x Genotype P=0.0150
Sex P=0.0008
Genotype P=0.0121
D
J
1 2 3 4 5 6
0
500
1000
1500
5 Minute Time Bins
Distance (cm)
20-22M Distance Female WT
Female Tg
Male WT
Male Tg
Time x Genotype P=0.0328
E F
Female WT Female Tg Male WT Male Tg
0
200
400
600
800
1000
Time in Center (seconds)
20-22M Total Time in Center
0.0008
0.0004 0.0092
G
B
Female WT Female Tg Male WT Male Tg
0
500
1000
1500
2000
Total Time in Center (seconds)
5-7M Total Time in Center
H
K L
I

48
familiar object it was shown to a newer novel object it encounters for the first time (Antunes &
Biala, 2012; Ennaceur & de Souza Silva, 2018). One-way ANOVA analysis showed no
significant effect across all groups (Figure 2A, B). When combining sexes with their respective
genotype groups of WT vs Tg, t-test analysis resulted in the 20-22M WTs significantly explored
the novel object more than their sex-mixed Tg (t(32)=2.164; p=0.0379) (Figure 2D). No
significant differences were found in 5-7M WT vs Tg (Figure 2C). Therefore, despite no sex
differences found at both age groups, the progressive AD pathology still impacted the short-term
memory in the 20-22M Tg group.
3.1.3. Barnes Maze
To assess spatial learning and memory, and cognitive flexibility in a hippocampaldependent task we used the Barnes maze (Barnes, 1979; Gawel et al., 2019; Rosenfeld &
Ferguson, 2014). Days 1-4, the learning phase of the task, were when animals were trained to
find their designated escape hole using the salient spatial cues on the wall. Animals were
considered to have learned the behavioral task by showing decreases in errors and latency to
their unique escape hole with each increasing learning day. On days 5 and 6 there was a 48-hour
wait period, when the animals did not get tested and remained in their home cage. Then on day 7,
their long-term memory was tested, known as their test, or probe, day. Days 8-9 (REV1-REV2)
examined cognitive flexibility with the escape hole rotated 180° from its original location for
each animal.
In the 5-7M group, 3-way ANOVA analysis resulted in a significant sex effect with male
WT and Tg finding their escape holes in less time than their female counterparts (Figure 2E).
Assessing average velocity analysis found a time X genotype interaction (F(6,300)=5.676,

49
p<0.001), as well as a genotype (F(1,50)=8.572, p=0.0051) and sex (F(1,50)=13.31, p=0.0006)
effect (Figure 2F).
At the 20-22M age, we found significance differences in the average velocity to find the
escape hole. There was both a time X genotype (F=6,180)=4.546, p=0.003) interaction as well as
a main effect of genotype (F(1,30)=12.52, p=0.0013) for the older animals (Figure 2H). The WT
groups averaged faster velocity to their holes than the Tg animals. Lastly, there was a trend for a
genotype effect (F=(1,29)=3.809, p=0.0607) in latency in this age that did not quite meet
significance (Figure 2G). This latency trend is the opposite of the average velocity where the Tg
groups take longer time to find their hole than their sex-matched WTs. There were no significant
differences, or trends, seen for distance traveled and errors made between any groups.
Tukey’s post-hoc multiple comparisons did not reveal any significant differences
between all groups in errors, latency, distance and average velocity for ages 5-7M and 20-22M.
The one post-hoc multiple comparison significance found on REV1 (Day 8) was female WT
with faster velocity to their escape hole than male Tg at 5-7M of age (p=0.0299, 95%
C.I.=[0.3918,12.49]. This significance was absent by reversal day 2 (Day 9) and was not seen at
20-22M.
3.2. Biochemistry and Immunohistochemistry
3.2.1. Proinflammatory Panel
The innate immune system has shown to be a contributing factor to the progression of
AD pathology (Frost, Jonas, & Li, 2019; Heneka, Golenbock & Latz, 2015; Labzin, Heneka, &
Latz, 2018). Thus, we tested the animal serum levels using a multiplex ELISA approach to
simultaneously measure 10 cytokine levels in the 20-22M age groups. None of the cytokines in

50
the panel showed statistical significance or any trend across all groups. Figure 3A shows an
example of TNF-a, a cytokine produced by monocytes/macrophages upon acute inflammation.
Additionally, Figure 3B, C shows the other cytokines from the ELISA panel that are produced
by monocytes like IL-1� and IL-6 and they did not reflect any sort of pattern that could
distinction between all groups (Xie, Van Hoecke, & Vandenbroucke, 2022). Again, the other
measured cytokines, IFN-�, IL-4, IL-5, KC GRO, IL-10, and IL-13, were found to be nonsignificant across groups.
3.2.2. Neurofilament-Light Chain ELISA
To assess whether there were sex differences in the TgF344-AD rat model in a biomarker
of neurodegeneration we used a neurofilament light chain (NF-L) ELISA plate on the 20-22M
age groups. Previous studies have shown increasing NF-L levels are a sensitive marker of
neuronal damage due to progressive axonal injury that correlates with many central nervous
system diseases (Gaetani et al., 2019; Gaiottino et al., 2013). In the case of AD, studies have
shown higher NF-L levels found in blood serum and CSF precede even the earliest clinical
diagnosis compared to levels in a normal aging adult (DeJong et al., 2007; Paterson et al., 2018).
One-way ANOVA analysis of the 20-22M serum samples showed a significant with p<0.001
between the mean NF-L concentration values (F(3,35)=17.20) (Figure 3D). Tukey post-hoc
comparison resulted in significant difference between both male and female WT (p=0.0004, 95%
C.I.=[-176.2,-44.6]), and male and female Tg (p=0.0005, 95% C.I.=[-169.4,-41.10] where the
males of in each genotype had higher NF-L levels. However, the Tg displayed higher NF-L
levels than their WT sex match (t(37)=2.393, p=0.0219 (Figure 3E). Additionally of note,

51
female WT were found to have lowest levels of all groups and male Tg had the highest by highly
statistical margin (p<0.0001, 95% C.I.=[98.78, 227.0]).
3.2.3. Detergent-Soluble Amyloid Peptides Panel and Immunohistochemistry
We investigated sex differences in our 20-22M Tg animals on their accumulation of three
A� peptides from brain homogenates in a multiplex panel to complement findings of 4G8

52
Figure 2. Sex and genotype differences in the cognitive behavioral outcomes across age in
males (blue) and females (red) of the TgF344-AD rat model (transgenic, Tg; hatched bars,
dotted lines) and littermate controls (Wildtype, WT; solid bars, solid lines). (A, B) Short-term
novel object recognition index in (A, C) 5-7M groups and (B, D) 20-22M. (E-H) Barnes maze
performance across all days of testing. Days 1-4 were the learning phase, TEST Day is day 7
after a 48-hour task delay for long-term memory testing, and REV1 and REV2 are days 8-9
when the escape hole was rotated across the maze to test cognitive flexibility. (E, F) 5-7M
and (G, H) 20-22M performance in (E, G) latency and (D, H) average velocity to escape hole.
Data show mean values +SEM. The n.s. abbreviation in (G) indicates non-significant p-value.
1 2 3 4 TEST REV1 REV2
0
10
20
30
40
Days
Velocity (cm/s)
5-7M Velocity Female WT
Female Tg
Male WT
Time x Genotype P<0.0001 Male Tg
Genotype P=0.0051
Sex P=0.0006
F
0
50
100
Test Recognition Index
5-7M Test Recognition Index
Female Male
Wildtype
Transgenic
0
50
100
Test Recognition Index
20-22M Test Recognition Index
Female Male
Wildtype
Transgenic
A
0
50
100
Test Recognition Index
5-7M Test Recognition Index
Female Male
Wildtype
Transgenic
0
50
100
Test Recognition Index
20-22M Test Recognition Index
Female Male
Wildtype
Transgenic
B
1 2 3 4 TEST REV1 REV2
0
10
20
30
40
Days
Velocity (cm/s)
20-22M Velocity Female Tg
Male WT
Male Tg
Female WT
Time x Genotype P=0.0003
Genotype P=0.0013
H
1 2 3 4 TEST REV1 REV2
0
50
100
150
200
Days
Latency (sec)
5-7M Latency
Female WT
Female Tg
Male WT
Sex P=0.0022 Male Tg
E
0
50
100
Test Recognition Index
5-7M Test Recognition Index
Wildtypes Transgenics
C
0
50
100
Test Recognition Index
20-22M Test Recognition Index
Wildtypes Transgenics
D 0.0379
G
1 2 3 4 TEST REV1 REV2
0
50
100
150
200
Days
Latency (sec)
20-22M Latency
Genotype n.s.; p=0.0607

53
amyloid plaque immunohistochemistry results. The accumulation of A� comes from amyloid
precursor protein (APP) cleavage by � −, � −, or � −secretases which can lead to the production
of the common peptides: A�38 (considered a negative regulator of AD), A�40 (the most
common A� peptide found in the brain), or A� 42, with A�42 being more prone to plaque
formation and found in higher amounts in AD patients (Gouras, Olsson, & Hansson, 2015;
Hartmann, 2013; Näslund et al., 1994; Quartey et al., 2021). The MSD panel we used tested for
all three of these A� species concentrations. T-test analysis from the results of detergent-soluble
A� found no statistical significance in all peptide species between male and female Tg (Figure
A-C).
3.3. Magnetic Resonance Imaging Anatomical Structures
Finally, we used MRI to compare the impact of AD pathology on brain volumes and
anatomical structures of the cingulate cortex (CC) and hippocampus. The CC of the brain is
involved in information processing and regulation. Degeneration of this area is commonly found
in AD (Kelly et al., 2009; Yuan et al., 2022). The hippocampus has many roles involved in
memories and spatial navigation and this structure becomes damaged early in AD (Anand &
Dhika, 2012; Jack et al., 2000; Schuff et al., 2009). Creating new memories becomes much
harder after pathology has impacted this area. Lastly, the whole brain typically suffers from
neuronal loss with progressive pathology increasing universal atrophy (Pini et al., 2016; Schott et
al., 2008; Spulber et al., 2010). We compared MRI tracing analysis of these ex-vivo brain
structures across all groups at 20-22M (Figure 5A). In the CC region, a one-way ANOVA
analysis resulted in overall significance across all groups (F(3, 30)=9.478, p<0.0001). Tukey’s

54
post hoc comparison between female and male WT with females having larger CC volumes than
male WT (p=0.0332, C.I.=[-0.01094, -3.517*10-5
]) (Figure 5B). Furthermore, there was high
significance between female and male Tg with female Tg having larger CC volumes (p=0.0006,
95% C.I.=[-0.001397,-0.0003373]). Within sex, there were no significance between either
female WT and Tg or the same for males. Surprisingly, the hippocampus volumes had no
statistically significant differences (Figure 5C). Similar to CC, the whole brain volumes had no
Figure 3. Sex and genotype differences in biochemical outcomes in the TgF344-AD rat model
in 20-22M males (blue) and females (red) of the TgF344-AD rat model (transgenic, Tg;
hatched bars, dotted lines) and littermate controls (Wildtype, WT; solid bars, solid lines). (A-C)
Results from ELISA blood proinflammatory cytokine levels across all groups at 20-22M. (A)
TNF-a, (B) IL-1b, and (C) IL-6. (D-E) Neurofilament-Light chain biomarker serum levels in (D)
all 20-22M age groups and (E) separated into mixed sex wildtype and transgenic groups. Data
show mean values +SEM.
0
5
10
15
20
25
Concentration (pg/mL)
TNF-Alpha
Female Male
Wildtype
Transgenic
0
10
20
30
40
50
Concentration (pg/mL)
IL-1Beta
Female Male
Wildtype
Transgenic
IL-ȕ
Wildtype Transgenic
0
100
200
300
400
Concentration (pg/mL) 20-22M Neurofilament Light Chain
0.0219
NF-L Levels
0
100
200
300
400
Concentration (pg/mL) 20-22M Neurofilament Light Chain
Female Male
Wildtype
Transgenic
0.0004
<0.0001
0.0005
NF-L Levels
0
200
400
600
800
Concentration (pg/mL)
IL-6
Female Male
Wildtype
Transgenic
0
200
400
600
800
Concentration (pg/mL)
IL-6
Female Male
Wildtype
Transgenic
A B C
D E

55
significant differences within sex, but the two WT sexes were significantly different with males
having larger volumes (p<0.0001, 95% C.I.=[165.6, 328.1]). The male and female Tg whole
brain volumes were significantly different with males again being larger (p<0.0001, 95%
C.I.=[129.2, 291.7]) (Figure 5D).
4. Discussion
The findings from this study provide significant insights into sex-specific differences in
the progression of AD using the TgF344-AD rat model. Our study revealed distinct behavioral,
biochemical, and neuroanatomical differences between male and female rats, particularly
concerning anxiety-like behavior, neurodegeneration, and brain structure. These results align
with and, in some cases, diverge from previous research on the TgF344-AD model, highlighting
the complex nature of AD pathology.
In the open field test, our results showed that male WT rats exhibited lower anxiety-like
behavior compared to females, particularly in the younger cohort (5-7M). This finding contrasts
0
50
100
150
Detergent-Soluble Aȕ 1-38 (pg/mL)
Detergent Soluble Aȕ 1-38
Female Tg Male Tg
0
250
500
750
1000
Detergent-Soluble Aȕ 1-40 (pg/mL)
Detergent-Soluble Aȕ 1-40
Female Tg Male Tg
0
100
200
300
Detergent-Soluble Aȕ 1-42 (pg/mL)
Detergent-Soluble Aȕ 1-42
Female Tg Male Tg
A B C
Figure 4. Sex and genotype differences in detergent-soluble amyloid species in the TgF344-
AD rat model with males (blue) and females (red) of the TgF344-AD rat model (transgenic, Tg;
hatched bars). (A-B) Concentrations from brain homogenates in 20-22M transgenic groups
with peptide (A) Ab1-30 and (B) Ab1-40, and (C) Ab1-42. Data show mean values +SEM.

56
with the study by Cohen et al. (2013), where no significant sex differences were reported in the
locomotor activity of TgF344-AD rats at 15M. However, our observation aligns with Saré et al.
(2020), who reported sex-specific variations in activity levels, with female TgF344-AD rats
showing higher anxiety-like behavior as they age. This suggests that sex-specific differences in
anxiety may become more pronounced with aging and the progression of AD pathology.
Additionally, in the study by Saré et al., it was suggested that hormonal changes, particularly the
decline in estrogen, might exacerbate anxiety behaviors in female rats, which could explain the
increased anxiety observed in our aged female cohort. This highlights the potential influence of
hormonal factors on behavioral outcomes in AD models. Interestingly in the male WTs
locomotor and anxiety-like behavior performance, Vasquez et al. (1983) published that male
Fischer 344 rats have little impact on these two output measures when transitioning from young,
5M, to old, 25M, validating these male WT rats’ findings. Moreover, the aged male Tgs
evolution from least anxious as young age to more anxiety-like behavior comparable to both
female groups endorse the AD pathological impact in the TgF344-AD model in a sex-specific
way.
The novel object recognition (NOR) task in our study did not reveal significant
differences between sexes in short-term memory, although WT rats showed a preference for the
novel object. This is consistent with the findings of Morrone et al. (2020), who also observed no
significant cognitive impairment in young TgF344-AD rats (12-13M) using the NOR task.
However, our study diverges from Cohen et al. (2013), who reported marked cognitive decline in
TgF344-AD rats by 24M. This discrepancy may be attributed to differences in the age at which
cognitive assessments were conducted and the specific behavioral paradigms employed. It also
underscores the potential influence of subtle methodological variations on outcomes in cognitive

57
testing. The contrasting findings suggest that cognitive decline in TgF344-AD rats may be highly
dependent on the age and specific memory tasks used, with older ages and more complex tasks
possibly revealing more pronounced deficits.
In the Barnes maze task, we observed that male WT rats outperformed females in terms
of latency and velocity to locate the escape hole, particularly during the learning phase. This
finding partially aligns with the study by Berkowitz et al. (2018), which demonstrated that both
male and female TgF344-AD rats exhibit spatial memory deficits in the Morris Water Maze by
10-11M. However, the absence of strong cognitive dysfunction in our Barnes maze results may
suggest that this task is less sensitive to early cognitive deficits in TgF344-AD rats compared to
the Morris Water Maze, which is known to be more demanding and reliant on hippocampal
function. The Morris Water Maze, as shown by Webster et al. (2014), may better capture spatial
memory deficits in AD models, particularly when assessing long-term memory and cognitive
flexibility, areas where TgF344-AD rats have previously shown impairments.
Biochemically, our investigation revealed unexpectedly elevated levels of neurofilament
light chain (NF-L), a well-established marker of neurodegeneration, in male TgF344-AD rats.
This finding suggests more severe neuronal injury in males compared to females, which was
unanticipated and contradicts our initial hypothesis that female Tg rats would exhibit greater
cognitive decline and a higher burden of neurodegeneration. The increased concentration of NFL in male Tg rats challenges much of the existing literature on Alzheimer's disease (AD) sex
differences, which frequently suggests that females are more susceptible to the pathological
hallmarks of AD. In a Huntington’s disease (HD) study, a neurodegenerative disease known to
equally affect males and females, by Sampedro et al. (2021) looking at NF-L levels, in both sex
patients of HD found females to have higher levels of NF-L which they correlated to higher

58
amounts of brain atrophy. This underscores the importance of considering NF-L as a sensitive
biomarker that may reveal more subtle or sex-specific aspects of neurodegeneration that are
otherwise overlooked in broader assessments of disease pathology. In the context of our study,
the higher NF-L concentrations observed in male TgF344-AD rats point to a nuanced
understanding of sex differences in AD, suggesting that the male cohort may experience more
pronounced neuronal injury, despite the commonly held assumption of female vulnerability to
AD-related pathology.
However, our study did not find significant sex differences in proinflammatory cytokine
levels, which contrasts with the work by Heneka et al. (2015), who highlighted the role of
neuroinflammation as a critical driver of AD progression. This proposes that while
neuroinflammation is a hallmark of AD, its manifestation may not be uniformly influenced by
sex in the TgF344-AD model. Interestingly, the lack of significant sex differences in cytokine
levels might also be due to the timing of sample collection. Inflammation can be transient, with
cytokine levels fluctuating throughout the disease course, as noted by Xie et al. (2022).
Therefore, future studies should consider longitudinal sampling to capture dynamic changes in
inflammation markers over time. Additionally, different brain regions may exhibit varying levels
of neuroinflammation, which may not be fully captured in serum cytokine levels alone.
Investigating region-specific inflammation through techniques such as immunohistochemistry or
localized cytokine assays could provide deeper insights into sex differences in AD-related
inflammation.
Neuroanatomical analyses using ex vivo MRI revealed sex-specific differences in brain
volumes, with male WT and Tg rats having smaller cingulate cortex volumes but larger whole
brain volumes compared to females. These findings align with previous research indicating that

59
male rodents may experience more pronounced cortical atrophy in models of AD, particularly in
of AD progression. This implies that while neuroinflammation is a hallmark of AD, its
manifestation may not be uniformly influenced by sex in the TgF344-AD model.
One relevant study by Sperling et al. (2014), discusses cortical atrophy in Alzheimer’s
models and emphasizes the vulnerability of the cingulate cortex and other cortical regions to AD
pathology. Although this study primarily focuses on human AD patients, its findings regarding
the pattern of cortical atrophy are mirrored in rodent models, including the TgF344-AD rat
model. This suggests that male rodents, like male humans, may experience more severe cortical
atrophy, potentially leading to greater cognitive deficits in tasks reliant on these brain regions.
Additionally, Li and Singh (2014) provide evidence that sex differences in
neuroanatomical changes are evident in transgenic models of AD, with males often exhibiting
greater brain volume loss in critical areas such as the hippocampus and cortex. This divergences
with our finding that hippocampal volumes were relatively preserved across sexes in our study,
which could be due to the age of the animals or specific stages of disease progression being
assessed. This preservation might suggest that hippocampal atrophy occurs later or less severely
in the TgF344-AD model compared to other regions like the cingulate cortex. Furthermore,
Cosgrove et al. (2007) explored sex differences in brain aging and found that males tend to
experience more rapid loss of cortical thickness in areas implicated in AD, such as the prefrontal
cortex and cingulate gyrus. These observations support the idea that sex differences in brain
structure may underlie the differential progression of cognitive decline in AD models, as
observed in our study.
Interestingly, while our study did not find significant differences in hippocampal volumes
between sexes, Fjell et al. (2009) reported that hippocampal atrophy is a prominent feature of

60
AD progression, though it may occur at different rates depending on sex and genetic background.
This discrepancy advises that further research is needed to fully understand the interaction
between sex and specific brain regions in AD.
0.00
0.05
0.10
0.15
0.20
Hippocampus
Volume (m
m3)
Female
Wildtype
Transgenic
Male
C
A
D
0
500
1000
1500
2000
2500
Whole Brain
Volume (m
m3)
Female Male
Wildtype
Transgenic
<0.0001
<0.0001
<0.0001
<0.0001
0.000
0.002
0.004
0.006
0.008
Volume (m
m3)
Cingulate Cortex
Female Male
Wildtype
Transgenic
0.0332
0.0126
0.0018
0.0006
B
Figure 5. Sex and genotype differences in ex-vivo anatomical structures in 20-22M males
(blue) and females (red) of the TgF344-AD rat model (transgenic, Tg; hatched bars) and
littermate controls (Wildtype, WT; solid bars): cingulate cortex, hippocampus and whole brain
analyzed from scanned MRI images. (A) Example of the rat brain slices scanned and traced
from the 7T scanner. This example is from a female Tg rat. The magenta color represents the
outline of the whole brain and yellow outlines are exclusions of the brain typically due to fluid
space in between the tissue. (B) Analysis of the temporal cingulate cortex volume group
comparison, followed by (C) the hippocampus and (D) whole brain. Data show mean values
+SEM.

61
These neuroanatomical findings underscore the importance of considering sex differences
in AD research. The greater cortical atrophy observed in males, particularly in the cingulate
cortex, may contribute to the more severe cognitive deficits seen in tasks requiring executive
function and spatial memory. Future research should further investigate the progression of
neuroanatomical changes in the TgF344-AD model across different stages of disease and explore
potential interventions that address these sex-specific vulnerabilities.
To conclude this study, our hypothesis of female Tg being worse off in cognitive
impairment, biochemical markers, and AD-related pathology with age compared to male Tg was
in fact, incorrect. Previous studies had alluded to females being at higher risk for prevalence,
incidence and severity of AD, yet our TgF344-AD rat model data counters this idea. There were
no sex differences in the Tg’s anxiety-like behavior, short-term memory, long-term memory,
proinflammatory cytokines and detergent-soluble A� species. Conversely, the significant sex
differences we did find in the older age group were in NF-L levels and MRI CC volumes that
resulted in the male Tg being more impaired than female Tg. This unexpected finding suggests
that the TgF344-AD rat model may reveal underlying mechanisms of sex differences in AD that
are not apparent in human epidemiological studies. The greater impairment observed in males
could provide a unique opportunity to investigate male-specific vulnerabilities and resilience
factors in AD pathology. Understanding why male Tg rats are more impaired could lead to new
insights into the disease's progression and potential therapeutic targets that are sex-specific,
offering a more tailored approach to treating or preventing AD.

62
4.1. Study Limitations
This study encountered a few limitations during experimentation. First, a few sample
sizes were limited, particularly the aged WT groups, due to deciding to include these for
comparison against the Tg later in the study. Secondly, while originally intended to be a
longitudinal study, previous unpublished data indicated that the animals were still capable of
remembering previous tested behavioral tasks regardless of potential increased pathology with
aging. Other researchers should take this under consideration especially if not including any sort
of therapeutic to address the AD pathology. Finally, while originally published as a rat model
having neurofibrillary tau tangles, we were unable to address any sex differences
immunohistochemically as our staining procedure resulted in no stained tau in the hippocampal
region for both male and female Tg (Cohen et al., 2013). It is possible that this pathology has
been lost along the way from extensive breeding, but exhaustive attempts to stain tangles led to
no results which also may have impacted behavioral performances.
4.2. Conclusions
Overall, our findings highlight the importance of considering sex as a biological variable
in AD research. In our particular study, it highlights the need to take a closer look at male AD
pathology and the importance the TgF344-AD model may play in being able to better understand
how sex impacts this disease. While our results align with some studies, they also diverge in key
areas, underscoring the need for further research to unravel the complex interplay between sex,
aging, and AD pathology. Future studies should continue to explore the underlying mechanisms
driving these differences, particularly focusing on hormonal influences, genetic factors, and the

63
role of neuroinflammation, to inform the development of personalized therapeutic strategies for
AD.
Additionally, it would be beneficial to conduct longitudinal studies in the TgF344-AD
model to track the progression of behavioral, biochemical, and neuroanatomical changes over
time. Such studies could provide more detailed insights into how sex differences manifest at
various stages of AD and help identify critical windows for intervention. Exploring the impact of
hormone replacement therapy, as well as the genetic underpinnings of sex differences in AD,
could further advance our understanding and lead to more effective, sex-specific treatment
approaches.

64
Chapter 3: Integrating Findings and Implications for Alzheimer’s
Disease and Broader Fields
1. Summary of Dissertation Goals and Chapter 2 Findings
The primary objective of this dissertation was to investigate the role of sex differences in the
progression and pathology of Alzheimer’s disease (AD) using the TgF344-AD rat model. This
research was inspired by the growing recognition that males and females may experience distinct
trajectories in cognitive decline, biochemical markers, and neuroanatomical changes associated
with AD. By undertaking a comprehensive examination of these differences at two critical age
points (5-7M and 20-22M), this dissertation aimed to enhance our understanding of how sex
influences AD pathology, with the potential to guide the development of sex-specific therapeutic
strategies.
The findings in Chapter 2 revealed significant sex-specific differences in the TgF344-AD rat
model, particularly concerning behavioral assessments, biochemical markers, and
neuroanatomical structures. Some of the key results were highlighted in the behavioral
assessments, biochemical markers, and the neuroanatomical changes.
When evaluating behavioral results of the study, we found male wildtype (WT) rats
demonstrated lower anxiety levels and superior performance in cognitive tasks. This was seen in
the Barnes Maze as well, with male WT outperforming their female counterparts, especially at
younger ages. This observation aligns with previous research suggesting that males may exhibit
different behavioral responses due to sex-specific neurobiological factors.
Moving onto to assessing biochemical markers, the study found higher levels of
neurofilament light chain (NF-L), a marker of neurodegeneration, in male transgenic (Tg) rats,

65
indicating greater neuronal injury compared to females. However, no significant sex differences
were observed in proinflammatory cytokines, which are commonly associated with AD
progression.
Finally, when we looked at neuroanatomical changes by ex vivo MRI scanning and tracing,
we revealed sex-specific changes in brain structure. We found male WT and Tg rats exhibiting
smaller cingulate cortex volumes but larger overall brain volumes than females. These findings
emphasize the importance of considering sex as a biological variable in AD research.
Collectively, these results highlight the complex interplay between sex, aging, and AD
pathology. The divergence between male and female responses suggests that therapeutic
approaches to AD should be tailored to address these sex-specific vulnerabilities.
2. Implications for Alzheimer’s Disease Research
The findings from Chapter 2 have significant implications for the field of AD research. The
observed sex differences underscore the necessity of incorporating sex as a biological variable in
AD studies, as these differences may influence both the onset and progression of the disease.
For instance, when addressing behavioral differences and cognitive decline, the differences
in our behavioral responses between male and female rats suggest that cognitive decline in AD
may manifest differently based on sex. For example, males' superior performance in spatial
tasks, such as the Barnes Maze, may indicate that cognitive interventions could be more effective
if tailored to the specific cognitive strengths and weaknesses associated with each sex. This is
consistent with findings from human studies, which show that females with AD tend to have
more severe cognitive impairments in verbal memory tasks, while males may experience more
significant deficits in spatial memory (Cherrier et al., 2005; Sundermann et al, 2017).

66
When considering our results toward biochemical markers and neurodegeneration, we found
elevated levels of NF-L in male Tg rats suggesting males may be more susceptible to certain
types of neuronal damage. This finding is in line with research showing that NF-L is a sensitive
marker of neurodegeneration and is elevated in various neurodegenerative diseases, including
AD. The lack of significant differences in proinflammatory cytokines between sexes suggests
that while neuroinflammation is a key feature of AD, its role may not differ substantially
between males and females. Additionally, the TgF344-AD rat model may also not be an
acceptable model to study neuroinflammation of AD. In previous studies, it has been shown in
AD, the proinflammatory cytokines: IL-1, IL-6, IL-10, and TNF-alpha are typically elevated in
AD pathology (Kummer et al., 2021; Porro et al., 2020; Rani et al., 2023; Torres-Acosta et al.,
2020; Xie et al., 2015) Although, Caldwell et al. (2021), found no significant sex differences in
proinflammatory cytokines in normal, mild cognitive impairment and AD patients. Additionally,
the females’ performance on verbal memory recall was more determined by estrogen’s levels
influencing cytokines than proinflammatory cytokine levels alone. Nevertheless, the differential
response in neurodegeneration markers highlights the need for sex-specific approaches in
developing biomarkers for early diagnosis and monitoring of AD progression.
The neuroanatomical differences observed in the TgF344-AD model, particularly in the
cingulate cortex and hippocampus, are critical for understanding sex-specific vulnerabilities in
AD. Females, who exhibited greater cortical atrophy, may be at higher risk for more severe
cognitive deficits that could lead to paranoid delusions or in cognitive tasks requiring executive
function and spatial memory. Interestingly, when AD males with paranoid delusions underwent
MRI to measure regional volume and cortical thickness, this was not associated with any
changes in those regions, contrary to the female data results (Whitehead et al., 2011). These

67
findings are consistent with studies showing that females with AD tend to have more extensive
cortical atrophy, which is associated with a faster progression of cognitive decline. This suggests
that therapeutic strategies aimed at preserving brain structure, particularly in regions like the
hippocampus and cingulate cortex, may need to be sex-specific to be effective.
3. Broader Implications Beyond Alzheimer’s Disease
The insights gained from this dissertation extend beyond AD, offering valuable perspectives
for other fields of research. Understanding sex differences in neurodegenerative processes can
have wide-ranging implications for the development of treatments for various neurological
disorders.
The findings from the TgF344-AD rat model can inform research on other neurodegenerative
diseases, such as Parkinson’s disease (PD) and multiple sclerosis (MS), where sex differences
also play a crucial role in disease progression and response to treatment. For instance, research
has shown that males with Parkinson's disease tend to experience more severe motor symptoms,
while females are more likely to suffer from non-motor symptoms such as depression and
anxiety (Haaxma et al., 2007; Miller & Cronin-Golomb, 2010). Similarly, in multiple sclerosis,
females are more likely to be diagnosed, but males tend to have a more aggressive disease course
(Casetta et al., 2009; Voskul, 2020). By exploring the underlying mechanisms of these sex
differences in AD, researchers may uncover new targets for therapy in other neurodegenerative
conditions.
The role of primary gonadal hormones, particularly estrogen and testosterone, in
neuroprotection is another area where the findings from this dissertation can be applied. The
decline in estrogen levels during menopause is known to exacerbate neurodegenerative

68
processes, making postmenopausal females more susceptible to diseases like AD (Bove et al.,
2014; Christensen & Pike, 2015; Tang et al., 1996). Understanding the interplay between
hormonal changes and neurodegeneration can lead to the development of hormone-based
therapies that could potentially mitigate the risk of developing AD and other neurodegenerative
diseases in aging populations.
The emphasis on sex differences in this dissertation aligns with the broader trend toward
personalized medicine, where treatments are tailored to individual patient characteristics,
including sex. By recognizing that males and females may respond differently to the same
therapeutic interventions, researchers and clinicians can develop more effective, personalized
treatment plans. This approach is particularly relevant in the context of AD, where sex-specific
factors such as hormonal status, genetic predispositions, and lifestyle choices can significantly
influence disease outcomes.
Finally, the findings have implications for public health and policy, particularly in terms of
resource allocation and the development of targeted intervention programs. As the global
population ages, the burden of neurodegenerative diseases like AD is expected to increase. By
incorporating sex differences into public health strategies, policymakers can ensure that
resources are allocated in a way that addresses the specific needs of both males and females,
potentially leading to more effective prevention and treatment programs.
4. Future Directions
The findings from this dissertation open several avenues for future research. Highlighted
below are key research areas that warrant further investigation to further understand the full
potential of the TgF344-AD rat model.

69
1. Longitudinal Studies in the TgF344-AD Model: Conducting longitudinal studies in the
TgF344-AD model could provide more detailed insights into how sex differences
manifest at various stages of AD. By tracking behavioral, biochemical, and
neuroanatomical changes over time, researchers can identify critical windows for
intervention and develop more targeted therapeutic strategies.
2. Exploration of Hormonal Therapies: Given the role of hormones in modulating AD
progression, future research should explore the potential of hormone replacement therapy
(HRT) as a means of mitigating neurodegenerative processes in postmenopausal females.
Investigating the timing, dosage, and long-term effects of HRT could lead to more
effective strategies for preventing or delaying the onset of AD in at-risk populations.
3. Genetic and Epigenetic Factors: The interplay between genetic factors, such as the
APOE ε4 allele, and sex-specific biological processes remains an area of significant
interest. Future studies should explore how genetic and epigenetic modifications
influence the risk and progression of AD in males and females. Understanding these
mechanisms could lead to the development of new biomarkers and therapeutic targets.
4. Cross-Species Comparisons: Comparing the findings from the TgF344-AD model with
other animal models of AD, as well as with human clinical data, could provide valuable
insights into the generalizability of the observed sex differences. Such comparisons could
help refine the TgF344-AD model and enhance its utility in preclinical research.
5. Application to Other Neurological Disorders: Finally, applying the insights gained
from this dissertation to other neurological disorders could help identify common
pathways that are modulated by sex. By exploring the similarities and differences
between AD and other conditions like PD, MS, and amyotrophic lateral sclerosis (ALS),

70
researchers may uncover new strategies for treating a broad range of neurodegenerative
diseases.
5. Conclusion
This dissertation highlights the critical importance of considering sex as a biological variable in
Alzheimer’s disease research. The findings from Chapter 2 underscore the complex interplay
between sex, aging, and AD pathology, with significant implications for both basic research and
clinical practice. By expanding our understanding of how sex influences neurodegenerative
processes, this research paves the way for more effective, personalized therapeutic strategies that
can improve outcomes for both males and females. Moreover, the broader implications of these
findings extend to other fields of neuroscience, offering new perspectives on the role of sex
differences in health and disease.

71
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Abstract (if available)

Abstract Alzheimer’s disease (AD) is a complex neurodegenerative disorder, predominantly affecting the elderly, with a significantly higher prevalence and severity in females. This introductory review chapter delves into the multifaceted sex-specific differences in AD, encompassing biological, genetic, hormonal, and lifestyle factors. The pathology of AD is driven by amyloid-β accumulation, tau tangles, neuroinflammation, and subsequent neuronal death, with emerging evidence highlighting a sex disparity in these processes. Females exhibit a higher amyloid-β burden, more extensive tau pathology, and a stronger inflammatory response, leading to more rapid cognitive decline and disease progression. Hormonal changes, particularly the loss of estrogen during menopause, exacerbate neurodegeneration in females, while testosterone’s gradual decline in males presents a less immediate but still significant risk. In addition to biological factors, lifestyle and environmental influences such as lower education levels and occupational status among females further contribute to the heightened risk of AD. Moreover, genetic factors like the APOE ε4 allele have been shown to increase AD susceptibility more significantly in females. This review underscores the critical need for sex-specific research to better understand these differences and to develop targeted therapeutic interventions. The TgF344-AD rat model, which mirrors the human condition more accurately than traditional mouse models, is introduced as a promising tool for investigating these sex differences. This model offers the potential to explore the impact of estrogen loss and other age-related changes on AD pathology, providing insights that could lead to the development of more effective, personalized treatments. Ultimately, addressing the sex-specific mechanisms in AD pathogenesis is essential for advancing therapeutic strategies and improving outcomes for both sexes, particularly in the context of an aging global population.

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Creator Quihuis, Alicia Marguerite Reynoso (author)

Core Title Sex differences in the TgF344-AD rat model: Investigating the behavior, pathology, and neuroanatomical structures

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Degree Conferral Date 2024-08

Publication Date 10/02/2024

Defense Date 10/01/2024

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

Tag aging,Alzheimer's disease,dementia,learning and memory,neurodegenerative disease,Neuroscience,OAI-PMH Harvest,sex differences

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Advisor Jakowec, Michael (committee chair), Chen, Jeannie (committee member), Pike, Christian (committee member)

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