University of Southern California Dissertations and Theses

Renin-angiotensin system antihypertensive medicines with and without blood-brain-barrier crossing potential: effects on cognitive outcomes in the elderly

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Renin-angiotensin system antihypertensive medicines with and without blood-brain-barrier crossing potential: effects on cognitive outcomes in the elderly

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Content RENIN-ANGIOTENSIN SYSTEM ANTIHYPERTENSIVE MEDICINES WITH AND
WITHOUT BLOOD-BRAIN-BARRIER CROSSING POTENTIAL:
EFFECTS ON COGNITIVE OUTCOMES IN THE ELDERLY
by
Jean K. Ho
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
PSYCHOLOGY
August 2020
Copyright 2020 Jean K. Ho

ii
ACKNOWLEDGEMENTS
I would like to extend the biggest thanks of all to Dan Nation, for being unequivocally
the best PhD advisor anyone could ask for. Thank you to my committee, Mark (Hokchoi) Lai,
Stan Louie, and Stanley Huey, for your invaluable guidance, support, and refinement of this
project.
Thank you to my labmates (Aimée Gaubert, Anisa Marshall, Anna Blanken, Belinda
Yew, Elissa McIntosh, Jung Jang, Isabel Sible, Shubir Dutt, Yanrong Li). I was truly privileged
to work with you all. Thank you to my wonderful team of research assistants (Aye Waddy,
Delilah Leibowitz, Hannah Pavlov, Katherine Weir, Samantha Lask), who coded hundreds of
thousands of rows of medication data. Thank you to the research assistants who helped with
running of our VaSC Study (Alberto Jarrin-Lopez, Hijab Gulwani, Katherine Chang, Mihir
Somaiya, Natalie Koreie, Nicolette Mahboubian, Sophia Mortiz, Vanessa Ferral). Thank you to
the participants of the VaSC Study. Thank you to our collaborators from the School of Pharmacy
(Alick Tan, Kathleen Rodgers, Siyu Liu).
Thank you to all the PIs, authors, and statisticians of the various studies who responded
with such kindness and generosity to my request for data for this meta-analytic project. Thank
you to: Walter Kukull, Eric Larson, Bill Lee (ACT); Nobuyuki Sato (CAMUI); Elena LaCruz de
Diego (CARLA); Martha Clare Morris, Denis Evans, Todd Beck, Bo Yu (CHAP); Joan Lindsay,
Danielle Laurin, Pierre-Hugues Carmichael (CSHA); Hugh Hendrie, Sujuan Gao, Katie Lane
(IIDP); Enwu Liu (INSPIRED); Lewis Lipsitz (MBS); Phil St. John, Suzanne Tyas (MSHA);
Elizabeth Hudak, Jerri Edwards (SKILL); Frank Moriarty, Orna Donoghue (TILDA); Sebastian
Heinzel, Daniela Berg (TREND); Suzanne Judd, Mary Cushman, Margaret Stewart, Ya Yuan,

iii
and Jennifer Manly (REGARDS). Thank you to all the funding bodies which supported these
projects.
Thank you to all the participants who took part in the 14 studies included in our meta-
analysis: from Australia, the INSPIRED study; from Canada, the CSHA and MSHA studies;
from Germany, the CARLA and TREND studies; from Ireland, the TILDA study; from Japan,
the CAMUI study; and from the USA, the ACT, CHCS, CHAP, IIDP, MOBILIZE, SKILL, and
REGARDS studies.
Thank you to my USC cohort (Belinda Yew, Geoff Corner, Kelly Durbin, Sarah Stoycos
and Sylvanna Vargas) for all the laughter, good times, and support. I couldn’t have asked for
better people to go through this program with. Thank you to my dearest friends, Marquise Eloi,
Sonia Sabater, Christa Dang, and Chun Tao for the long dinners, video chats, and endless text
messages. I’m not the same person without you ladies. Thank you to the cities, coffee shops, and
couches of Los Angeles, Hollywood, Durham, and San Diego, where this work was done. Thank
you to all the NBA and Duke basketball players whose games provided stress relief and formed
the backdrop to hundreds of my worknights.
Thank you to my Mom and Dad, who deserve more than what a single sentence or
paragraph can convey. I hope I have made you proud.

iv
TABLE OF CONTENTS

ACKNOWLEDGEMENTS………………………………………………………………… ii
LIST OF TABLES………………………………………………………………………..... vi
LIST OF FIGURES……………………………………………………………………….... vii
ABSTRACT………………………………………………………………………................. viii
INTRODUCTION………………………………………………………………………….. 1
Blood pressure and cognition…………………………………………………………… 2
Antihypertensive medication use and cognitive outcomes……………………………… 3
Renin-angiotensin system……………………………………………………………….. 6
The classic RAS pathway: the ACE-Ang II-AT1R axis and its dysregulation in AD.. 7
The protective RAS pathway: the ACE2-Ang(1-7)-Mas axis and therapeutic effects 8
of activation…………………………………………………………………………..
METHODS (STUDY A) …………………………………………………………………... 10
Specific aims and hypotheses…………………………………………………………… 10
Search method…………………………………………………………………………... 11
Inclusion/exclusion criteria……………………………………………………………… 12
Determination of BBB-crossing potential……………………………………………….. 13
Data extraction…………………………………………………………………………... 18
Assessment of quality and publication bias………………………………………........... 19
Data analysis…………………………………………….................................................. 20
RESULTS (STUDY A) ……………………………………………...…………………….. 23
Clinical/demographic data……………………………………………............................. 23
Quality assessment and publication bias............................................................................ 23
Heterogeneity..................................................................................................................... 29
Cross-sectional results……………………………………………................................... 29
Longitudinal results……………………………………………....................................... 34
METHODS (STUDY B) ……………………………………………................................... 37
Specific aims and hypotheses……………………………………………........................ 37
Participants…………………………………………….................................................... 37
Blood draw, mass spectrometry, and flow cytometry…………………………………... 38

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Neuropsychological battery……………………………………………........................... 40
Statistical analyses……………………………………………......................................... 40
RESULTS (STUDY B) ……………………………………………..................................... 41
Demographic data…………………………………………............................................. 41
Neuropsychological data for assessment of cognitive status……………………............ 42
RAS metabolites and receptors………………………....................…………….............. 42
DISCUSSION...............……………………………………………..................................... 43
REFERENCES……………………………………………...……………………………… 52

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LIST OF TABLES

TABLE 1: Fourteen studies included in meta-analyses……………………........................... 16
TABLE 2: Characteristics of studies included in meta-analyses............................................. 17
TABLE 3: Neuropsychological measures used for the assessment of various cognitive
domains...................................................................................................................................

20
TABLE 4: Characteristics of the cohorts at baseline, after exclusion of dementia cases........ 24
TABLE 5: Differences between groups taking BBB-crossing drugs and non-BBB-crossing
drugs.........................................................................................................................................

25
TABLE 6: Baseline vascular risk factors................................................................................. 27
TABLE 7: Tests of heterogeneity........................................................................................... 29
TABLE 8: Demographic characteristics of VaSC Study participants..................................... 41
TABLE 9: Missing neuropsychological data........................................................................... 42

vii
LIST OF FIGURES

FIGURE 1: Simplified scheme of the Renin-Angiotensin System.......................................... 6
FIGURE 2: Flowchart of article selection................................................................................ 15
FIGURE 3: Begg’s funnel plot for examination of publication bias....................................... 28
FIGURE 4: Forest plot of mental status at baseline................................................................. 31
FIGURE 5: Forest plot of attention at baseline........................................................................ 31
FIGURE 6: Forest plot of executive function at baseline........................................................ 32
FIGURE 7: Forest plot of language at baseline....................................................................... 32
FIGURE 8: Forest plot of verbal memory (learning) at baseline............................................. 32
FIGURE 9: Forest plot of verbal memory (recall) at baseline................................................. 33
FIGURE 10: Forest plot of processing speed at baseline........................................................ 33
FIGURE 11: Forest plot of mental status over follow-up........................................................ 35
FIGURE 12: Forest plot of verbal memory (recall) over follow-up........................................ 35
FIGURE 13: Forest plot of attention over follow-up............................................................... 35
FIGURE 14: Forest plot of executive function over follow-up............................................... 36
FIGURE 15: Forest plot of verbal memory (learning) over follow-up.................................... 36

viii
Abstract

Hypertension is an established risk factor for cognitive decline and neurodegenerative
disease, possibly through its effects on cerebral perfusion and blood-brain barrier (BBB)
compromise. Work surrounding antihypertensive treatments have reported their possible salutary
effects on cognition and neuropathology. Reviews have highlighted that angiotensin II receptor
blockers (ARBs) and angiotensin-converting-enzyme (ACE)-inhibitors are linked to improved or
maintained cognition in later life. In a small number of studies, ARBs and ACE-inhibitors which
are able to cross the BBB have been linked to lower risk of dementia compared to non-BBB-
penetrant counterparts. However, no studies have examined these associations meta-analytically,
hampered by the fact that BBB-crossing potential is not a consideration in prescribing practice,
and also that few studies of older adults consider pharmacokinetic properties of drugs taken.
Our review fills this gap by acquiring individual participant data from 13 studies –
spanning 6 countries and published in the last 18 years – and coding for BBB-crossing potential
within participants using antihypertensive medications. As BBB-crossing potential was
unexamined in 13 of 14 studies meta-analyzed, the majority of effect sizes in our meta-analysis
are previously unpublished. In our international, cognitively intact sample, taking BBB-crossing
antihypertensive drugs was associated with better performance on memory recall over up to 3-
years of follow-up, relative to taking non-BBB-crossing medications. Taking non-BBB-crossing
medications was linked to better attention over time. In primary data analyses, we did not find
differences in renin-angiotensin-system metabolites among cognitively normal and cognitively
impaired older adults. Our meta-analytic results have implications for asymptomatic individuals
who may experience later cognitive benefit from changing their antihypertensive regimen.

Keywords: Hypertension, cognition, blood-brain-barrier, ARBs, ACE-inhibitors, dementia

1
Introduction

Hypertension is the most common and treatable risk factor in older adults, with over 60%
of the elderly population having blood pressure levels necessitating treatment (Centers for
Disease Control and Prevention, 2011). Hypertension is also an established risk factor for
cognitive decline, Alzheimer’s Disease (AD), and Vascular Dementia (VaD) (Duron & Hanon,
2008). These and other findings support the vascular hypothesis of AD, which postulates that
cerebrovascular disease and dysfunction, due to hypertension and other vascular risk factors,
underpin this dementing illness through effects on cerebral perfusion and blood-brain barrier
(BBB) compromise (de la Torre, 2002; Zlokovic, 2011). Therefore, work surrounding
antihypertensive treatments and their possible salutary effects on preserving cognition through
improved vascular health forms a promising area of research (Skoog & Gustafson, 2006).
In this work, we will first discuss mechanisms linking changes in blood pressure to
cognitive decline, independent of effects of clinical stroke. This will be followed by review of
the extant literature surrounding the potential for antihypertensive medications to attenuate
cognitive decline in older adults. Next, we discuss the functional properties of various
components of the renin-angiotensin-system (RAS), an enzyme-neuropeptide system which is
crucial in the homeostasis of blood pressure as well as processes of learning and memory. The
dissertation project, and its full methodology and results, will then be discussed. The project
comprised two parts, using (1) meta-analysis to investigate the potential of antihypertensive
medications (particularly, the BBB-penetrant variety) to slow decline in cognitive functioning, as
well as (2) primary analysis of prospective data to examine the differential links between various
RAS peptides on cognition.

2
1.1 Blood pressure and cognition
Hypertension and vascular risk factors have been linked to decreased performance on
various measures of cognition (Novak & Hajjar, 2010) which go beyond expected declines in
memory and processing speed associated with normal aging (Christensen, 2001). Studies
comparing hypertensives to normotensives indicate that hypertensives exhibit worse
performance on many domains of cognitive function, including episodic memory, working
memory, executive function (Saxby et al, 2003), attention, and psychomotor speed (Waldstein,
2003). Our previous work has found elevations in pulse pressure to be negatively correlated with
language ability and global cognitive function (Nation et al, 2010).
There are many pathophysiological mechanisms by which hypertension may promote
cognitive decline and eventual clinical expression of AD, including alterations in cerebral
hemodynamics and cerebrovascular disease (Iadecola & Davisson, 2008). Hypertension initiates
changes in the cellular architecture of cerebral arterioles, the primary source of vascular
resistance to pressure in the brain. These changes assist the arterioles in adapting to chronic
increases in pressure by lowering the amount of stress exerted on vessel walls. However, in
situations of chronic hypertension, these processes modify the hemodynamic and mechanical
properties of vessels, and lead to increased cerebrovascular resistance (Mathiassen et al, 2007),
decreased distensibility, and increased vessel stiffening (Izzard et al, 2006). This can further lead
to weakening of vessel walls, hemorrhage, and lacunar and microscopic infarcts (Lammie, 2002).
Functional implications of these vascular changes have been demonstrated in mouse
models, where arterial hypertension has been found to increase the permeability and dysfunction
of the BBB as well as reduce cerebral blood flow (CBF) (Gentile et al, 2009). These changes
may lead to increased influx and decreased efflux of amyloid-beta (Aβ) protein, the characteristic

3
hallmark of AD, across the BBB. This has been hypothesized as a potential mechanism
underlying the impaired clearance of neurotoxic Aβ from the brain, leading to its buildup
(Sagare, Bell, & Zlokovic, 2012). Further, increased growth and rearrangement of smooth
muscle cells in response to hypertension promote the formation of atherosclerotic plaques in the
brain, and narrowing of cerebral arteries. These in turn may affect the drainage of interstitial
fluid and solutes, including Aβ, along perivascular pathways (Iliff & Nedergaard, 2013). The
average CNS clearance rates of both Aβ1-42 and Aβ1-40 have been found to be slower in AD
patients compared to cognitively normal controls (Mawuenyega et al, 2010).
Hypertension occurs decades prior to the onset of AD symptoms, outside the probable
prodromal range (Kelley & Petersen, 2007), and exerts its effects on hemodynamics and
atherosclerotic mechanisms over many years. Hence, it may have the most impact on cognition
early on in the disease course (Bellew et al, 2004), and the degree of blood pressure control may
be a long-term determinant of cognitive function in those with hypertension (Waldstein &
Katzel, 2001).
1.2 Antihypertensive medication use and cognitive outcomes
Results from research linking blood pressure control to cognitive benefit have been
mixed. While the MRC-Elderly, SHEP, PROGRESS, SYST-EUR, SYST-EUR 2 and SPRINT
MIND trials indicated some benefit, the HYVET-COG, TRANSCEND and ONTARGET trials
and a Cochrane review found none (for reviews, see McGuinness et al., 2009 and Fournier et al.,
2009). Of the two large clinical intervention trials which found no benefit (ONTARGET and
TRANSCEND), it must be noted that the patients sampled had high vascular disease burden,
evidenced in established atherosclerotic cardiovascular disease or diabetes with end-organ
damage. Such individuals are likely to develop dementia with vascular etiology, as well as have

4
a greater ratio of vascular-to-AD pathology. The lack of distinction between dementia subtypes
in these studies may have obscured any beneficial effects which hypertensive treatment may
have had on specific types of dementia, which is important given the differential effects of
antihypertensive treatments on Aβ and AD pathology. The trials also relied heavily on achieved
scores and change scores on a single measure, the Mini-Mental State Examination (MMSE), in
order to diagnose cognitive impairment. A more comprehensive neuropsychological battery may
have been better suited to discern changes across a range of cognitive domains.
In studies which have suggested possible protective effects, treated hypertensives have
been found to have less AD neuropathology when compared to both untreated hypertensives and
normotensives (Hoffman et al, 2009). This was evidenced in lower neuritic plaque counts and
fewer neurofibrillary tangles of abnormally hyperphosphorylated tau protein (Hoffman et al,
2009). In comparison to individuals who have never taken any antihypertensive drugs,
individuals who have been treated with these medications have been found to be at decreased
risk of all dementia, with an 8% risk reduction for every year of use in individuals <75 years old,
and a 4% risk reduction in those >75 years, with similar estimates for AD (Haag et al, 2009). In
the SPRINT MIND trial, intensive blood pressure lowering to SBP < 120 mm Hg (compared to
standard treatment to <140 mm Hg) was linked to 19% reduction in cases of mild cognitive
impairment (MCI), which is considered the precursor to dementia (Kjeldsen, Narkiewicz,
Burnier & Oparil, 2018). A subset of this intensive treatment arm also showed smaller brain
white matter lesion volume compared to standard controls.
A recent meta-analysis of 19 randomized trials and 11 studies examining the relationships
among antihypertensive drug use, cognition, and dementia incidence provided support for the
perspective that antihypertensive treatment may have beneficial effects on cognition [effect size

5
0.05, 95% CI (0.02-0.07)] (Levi Marpillat et al, 2013). Of the many antihypertensive drug
classes available, drugs targeting the renin-angiotensin system (RAS), namely, angiotensin II
receptor blockers (ARBs) and angiotensin-converting-enzyme (ACE)-inhibitors, have been
highlighted as possibly conferring the greatest benefit (Levi Marpillat et al., 2013; Davies et al.,
2011).
It has been suggested that the use of certain ARBs and ACE-inhibitors – specifically,
ones which are able to cross the blood-brain-barrier (BBB) – might be linked to lower risks of
dementia and cognitive decline in older hypertensive adults, when compared to the use of their
non-BBB-penetrant counterparts. Analysis of data from the Cardiovascular Health Study of 5888
community-dwelling older adults found that the use of BBB-penetrating ACE-inhibitors was
associated with 65% less cognitive decline per year of exposure, compared to the use of other
anti-hypertensive drugs (Sink et al., 2009). Conversely, use of non-BBB-penetrant ACE-
inhibitors was associated with a greater risk of incident dementia (adjusted hazard ratio, 1.20;
95% CI, 1.00-1.43 per year of exposure) and greater odds of disability in independent activities
of daily living (adjusted odds ratio, 1.16; 95% CI, 1.03-1.30 per year of exposure), compared to
the use of other anti-hypertensive drugs (Sink et al., 2009). Our own research, using data from
the prospective Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, found that users of
BBB-crossing medications (ARBs or ACE-inhibitors) had better memory performance over a 3-
year-followup, as well as less white matter hyperintensities, compared to users of all other
antihypertensive medications (Ho & Nation, 2017). Nevertheless, results such as these need
confirmation by randomized controlled trials of BBB-crossing antihypertensive drugs in
dementia reduction, or support from meta-analysis of existing studies examining the benefit of
BBB-crossing potential.

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1.3 Renin-Angiotensin System (RAS)
The RAS (see simplified scheme in Figure 1) is instrumental in the maintenance of
cardiovascular function and fluid homeostasis in peripheral circulation, and represents one of the
most thoroughly investigated enzyme-neuropeptide systems. While commonly associated with
vascular-renal functions, pharmacological and genetic research studies have also established the
presence of a paracrine RAS within the central nervous system (CNS) which consists of identical
components and which acts largely independently of peripheral function (Wright & Harding,
1994). The RAS in the brain is believed to be involved in processes beyond mere
hydroelectrolytic homeostatic control; it has been implicated in processes of learning and
memory, neuronal differentiation, and nerve regeneration (Llorens & Mendelsohn, 2002), as well
as in the pathophysiology of various diseases, including AD (Wright & Harding, 2010).

Figure 1.

7
Simplified scheme of the renin-angiotensin system (RAS). Blue boxes indicate angiotensin metabolites,
grey circles indicate receptors, and red boxes indicate avenues at which antihypertensive drugs exert their
effects. The leftmost pathway (ACE-Ang II-AT1R) is the classic pathway of the RAS, blocked by the
action of ARBs and ACE-inhibitors. The rightmost pathway (ACE2-Ang-(1-7)-Mas) is the protective
pathway of the RAS, which represents new sites of therapeutic intervention.

The classic RAS pathway: the ACE-AngII-AT1R axis and its dysregulation in AD.
Antihypertensive drugs targeting the brain RAS (ARBs and ACE-inhibitors) act to block the
actions of one pathway of the RAS, involving ACE, angiotensin II (Ang II), and the angiotensin
II type 1 receptor (AT1R), together known as the classic pathway or the ACE-Ang II-AT1R axis
of the RAS. Low blood volume triggers this circuit: upregulating ACE, which increases the
production of Ang II, which in turn binds and activates AT1Rs. These effects increase blood
volume and peripheral resistance, both of which raise blood pressure (Gallo-Payet et al., 2011). It
must be noted that Ang II largely exerts its effects by binding to AT1Rs, which are present in
greater quantities than the type 2 receptor in adults (AT2R; Steckelings, Kaschina & Unger,
2005). AT1R activity includes the generation of free radicals and the activation of multiple
inflammatory pathways, all of which lead to tissue damage (Suzuki et al, 2003). However, in
situations in which Ang II binds to AT2Rs – such as when AT1Rs are blocked by ARBs –
greater AT2R activity takes place, which includes decreased vasoconstriction, increased cerebral
blood flow, and greater cerebral perfusion, which are protective against inflammation and
ischemia (Iwai et al, 2004).
Abnormalities in the classic brain RAS pathway observed in AD patients include
increases in ACE (Arregui, Perry, Rossor, & Tomlinson, 1982; Barnes et al, 1990; Miners et al,
2008; Miners et al, 2009), Ang II, and AT1Rs (Savaskan et al., 2001). ACE activity has been
positively associated with parenchymal Aβ in patients with AD (Miners et al, 2008). When
adjusted for neuronal damage, the main source of ACE within the brain, ACE protein levels have

8
been found to increase with Braak tangle stages, an indicator of AD severity (Miners et al, 2009).
It has been suggested, therefore, that neuronal production of ACE is increased in AD, possibly in
response to Aβ (Miners et al, 2009). Further, increases in ACE are linked to greater formation of
Ang II, which may play a role in the cognitive impairment observed in AD through its inhibitory
effect on acetylcholine release (Barnes et al., 1990). Accordingly, the administration of ACE
inhibitors, reducing the formation of Ang II, has been found to increase central in vivo release of
acetylcholine (Usinger et al., 1988). This mechanism, together with the cholinergic hypothesis of
memory (Bartus, Dean, Beer, & Lippa, 1982), have been suggested as the neurochemical
underpinnings for the beneficial effects on cognition observed with this antihypertensive
treatment. However, it must be noted that data from clinical trials of long-term administration of
inhibitors of cholinesterases (the enzymes which hydrolyze acetylcholine) have not found them
to be successful in reducing the risk or delaying the onset of AD (Raschetti, Alabanese,
Vanacore & Maggini, 2007). Therefore, increasing attention has been paid to other potential sites
of intervention, including the pathway next described.
The protective RAS pathway: the ACE2-Ang(1-7)-Mas axis and therapeutic effects
of activation. While research has traditionally focused on blocking the actions of the classic
pathway, there has been a shift in emphasis to the potential benefits of enhancing the functions of
more recently established angiotensin metabolites, which have shown opposing influence on the
vascular and proliferative effects of the classic pathway (Santos et al, 2003). These counter-
regulatory actions may have further therapeutic effects on cognition. Studies over the last decade
have identified additional alternate pathways by which angiotensinogen is processed,
independent of ACE (Haulica, Bild, & Serban, 2005). One of the more recently found RAS
constituents includes the first known homolog of ACE, known as ACE2 (Tipnis et al, 2000).

9
ACE2 is a carboxypeptidase which hydrolyzes Ang II to produce Ang-(1-7). Ang-(1-7) exerts its
effects through binding to the Mas receptor (Santos et al, 2003); altogether, these components
make up the ACE2-Ang-(1-7)-Mas axis, activation of which has been linked to beneficial
effects.
Ang-(1-7) acts as a counter-regulatory hormone to Ang II. Ang-(1-7) is a vasodilator
peptide which limits the pressor, angiogenic, and proliferative actions of Ang II (Iusuf, Henning,
van Gilst, & Roks, 2008). Upon binding to the Mas receptor, Ang-(1-7) elicits the release of
prostanoids and NO (Santos et al., 2000), which are crucial intercellular messengers in long term
potentiation and memory (Izquierdo & Medina, 1995; Ramakers & Storm, 2002).
Ang-(1-7) protects against brain damage. Neuroprotective effects of Ang-(1-7) have
been demonstrated in various animal models of disease, including models of hypertensive
encephalopathy, chronic cerebral hypoperfusion, and stroke (Jiang et al., 2013, Xie et al., 2014,
Jiang et al, 2014). In one study, intracerebroventricular (i.c.v.) infusion of Ang-(1-7) in
hypertensive rats inhibited effects of autophagy (Jiang et al., 2013), a cellular process increased
in conditions of stress which regulates the lysosomal degradation of damaged organelles
(Shintani & Kionsky, 2004). In a study of stroke, i.c.v. infusion of Ang-(1-7) significantly
increased the density of brain capillaries, improved regional cerebral blood flow, and decreased
infarct volume and neurological deficits following permanent middle cerebral artery occlusion
(MCAO) (Jiang et al., 2014). In these studies, neuroprotective effects were abolished or
attenuated by A-779, an antagonist of the Ang-(1-7) receptor, Mas, highlighting the importance
of the activation of Mas by Ang-(1-7) specifically.
Ang-(1-7) may be protective against Alzheimer’s disease pathology. There is increasing
evidence that levels of Ang-(1-7) are reduced in animal models of AD over the course of disease

10
progression (Chen et al., 2017). In a recent study using senescence-accelerated mouse prone
(SAMP8) mice, an animal model of AD, Jiang et al (2016) found that brain Ang-(1-7) levels
decreased over the course of disease, and were inversely correlated with tau
hyperphosphorylation. This finding was replicated in P301S mice, animal models of pure
tauopathy, suggesting that Ang-(1-7) may be implicated in the pathogenesis of AD through
effects on tau hyperphosphorylation.
ACE2 may metabolize neurotoxic neuropeptides involved in AD. While a subject of
burgeoning study, ACE2 has been recently found to convert a longer, neurotoxic species of
amyloid-β protein, Aβ43 (which accumulates in AD), to Aβ42. In combination with ACE, ACE2
converts Aβ42 further to Aβ40, a form which inhibits amyloid deposition and may itself be
neuroprotective (Liu et al., 2014). This same study also reported that ACE2 activity was lower in
AD patients than in normal controls (Liu et al., 2014).
It has been hypothesized that BBB-penetrant ARBs and ACE-inhibitors promote this
protective ACE2-Ang(1-7)-Mas axis, initiating and/or allowing for cognitive benefits beyond
mere peripheral blood pressure lowering. The next section describes the experimental design of
the present dissertation project, which allowed for evaluation of the hypothesized benefits of
BBB-crossing potential of drugs that may impact the ACE2-Ang(1-7)-Mas axis. Specifically,
the study compared cognitive outcomes between individuals taking ARBs or ACE-inhibitors that
were non-BBB-penetrant to those taking ARBs or ACE-inhibitors that were BBB-penetrant.

2. Methods – Study A
2.1 Specific Aims and Hypotheses
Study A aimed to conduct meta-analyses of existing literature in order to assess the
effects of BBB-crossing potential in blood pressure lowering treatments in hypertensive adults,

11
in randomized clinical trials, prospective cohort studies, and retrospective observational studies.
We hypothesized that BBB-crossing potential would be associated with lower cognitive
impairment, cognitive decline, and dementia.
2.2 Search Method
Guidelines from the Preferred Reporting Systems for Systematic Reviews and Meta-
Analyses (PRISMA) were utilized for this study (Moher et al., 2009) and systematic review
criteria were documented with the International Prospective Register of Systematic Reviews
(PROSPERO) system (registration number: CRD42018086511). The literature search was
conducted on January 31, 2018, with no restrictions placed on publication dates.
The initial search stage involved searches of the ALOIS database with the search terms
“(hyperten* or blood pressure)” and “Alzheim* or dement* or cognit*”. The ALOIS database
contains records of various major healthcare databases, including Medline, Embase, PsycInfo,
CINAHL, Literatura Latino Americana em Ciências da Saúde (LILACS), and ongoing trial
databases. The databases Web of Science, ProQuest Dissertations and Theses Global, and
ProQuest Central were also searched with the same search terms. Original research articles,
conference proceedings, and theses were included. Bibliographies of original and review articles
were screened for additional references.
One investigator (JKH) manually reviewed record titles and abstracts using broad
inclusion and exclusion criteria. If an article passed this first-level screening, the second-level
screening involved a full-text review. If studies contained insufficient information for the
calculation of effect sizes, personal communications with authors were made in attempts to
obtain data required for this computation. Communications also included requests for further
relevant studies, which were screened as above.

12
Three types of studies were included: (1) randomized, double-blind trials, in which
pharmacological interventions to lower blood pressure were administered for over six months,
(2) prospective cohort studies, and (3) retrospective observational studies. Six months was
chosen as the cutoff duration for treatment time in RCTs in accordance with other reviews which
determined this to be the minimum amount of treatment time required for benefits to be achieved
(McGuinness, Todd, Passmore & Bullock, 2009). This duration was not applied to exclude
prospective cohort studies or retrospective observational studies due to the infrequent recording
of treatment duration by studies, as well as inaccurate reporting by participants (e.g. reporting
medication use prior to a medication being available).
Given that multiple publications from individual research groups reported findings from
the same participant samples, authors from the most recent publication using the participant
sample group were contacted, and the most recent data obtained through these authors were used
for analyses.
2.3 Inclusion/Exclusion Criteria
Titles and abstracts were screened and assessed according to the inclusion criteria that a
study: (1) involves human participants, (2) involves adults aged > 50 with hypertension, (3)
assesses the effects of antihypertensive drugs affecting the renin-angiotensin-system: ARBs or
ACE-inhibitors, (4) assesses at least one neuropsychological outcome, (5) provides sufficient
information in the publication or through contact with the authors to allow for calculation of
effect sizes.
Exclusion criteria included (1) studies focused on another condition (e.g. diabetes
mellitus), (2) studies in populations with particular diagnoses in which antihypertensive
medications were primarily used for other effects other than lowering blood pressure (e.g. studies

13
of vasoactive medications in participants with systolic heart failure for their cardiac remodeling
effects), (3) non-pharmacologic interventions for blood pressure control, (4) studies with less
than 6-month follow-up, (5) studies for which all medications used were from only one category
(all were BBB-crossing or non-BBB-crossing), given that no comparison between the categories
could be made. Editorials, correspondence, commentaries, and case reports/series were excluded.
The selection of studies included in the meta-analysis are shown in Figure 2. After full
inclusion and exclusion criteria were applied, 67 studies remained. Of these, only three studies
had published summary statistics that could be used for the computation of effect sizes for
groups of participants taking BBB-crossing drugs and non-BBB-crossing drugs. Thus, all other
primary authors and/or study consortiums were contacted, and raw medication and
neuropsychological data were requested. Thirteen studies from six countries (Australia, Canada,
Germany, Ireland, Japan, and the USA) agreed to participate in our meta-analysis and supplied
full raw data. Two research groups provided medication data coded for BBB-crossing potential,
and the remaining 11 medication datasets were coded by a team of five research assistants. Each
dataset was coded twice, and discrepancies were resolved by the first author (JKH). Details of
each study are available in Tables 1 and 2. All participating studies had at least one published
paper; no authors of unpublished sources (e.g. theses) responded to requests for data.
2.4 Determination of BBB-crossing potential
Determination of BBB-crossing potential was made following previous literature. With
regard to ACE-inhibitors, we followed the categories by Sink et al. (2009), who assessed studies
of tissue-specific ACE-activity after administration of ACE-inhibitors, as well as tissue-specific
imaging of radio-labeled ACE-inhibitors. Captopril, fosinopril, lisinopril, perindopril, ramipril,
and trandolapril were classified as BBB-crossing ACE-inhibitors, and benazepril, enalapril,

14
moexipril, and quinapril were classified as non-BBB-crossing ACE-inhibitors. With regard to
ARBs, after similar review of the literature, we classified telmisartan and candesartan as BBB-
crossing, and olmestartan, eprosartan, irbesartan, and losartan as non-BBB-crossing. There had
to be at least 2 positive autoradiographic studies and not more than 1 negative autoradiographic
study for an ARB to be classified as BBB-crossing.

15

Figure 2.
Flowchart depicting article selection through all inclusion and exclusion criteria. ADNI = Alzheimer’s
Disease Neuroimaging Initiative, NACC = National Alzheimer’s Coordinating Center.

16
Study
abbreviation
Full study name Citation Study location
1 ACT Adult Changes in Thought Kukull et al (2002) USA (WA)
2 CHCS Cardiovascular Health Cognition
Substudy
Sink et al (2009) USA (NC, MD,
CA, PA)
3 CHAP Chicago Health and Aging Project Morris et al (2000) USA (IL)
4 IIDP Indianapolis-Ibadan Dementia Project Liu et al (2013) USA (IN)
a

5 MBS Maintenance of Balance, Independent
Living, Intellect, and Zest in the
Elderly Study (MOBILIZE) Boston
Study
Leveille (2008) USA (MA)
6 SKILL Staying Keen in Later Life Hudak et al (2013) USA (KY, AL)
7 REGARDS Reasons for Geographic and Racial
Differences in Stroke
Gillett et al (2015) USA (NC, SC,
GA, TN, MS,
AL, LA, AR)
8 CAMUI Combination of Antihypertensive
Therapy in the Elderly, Multicenter
Investigation
Sato et al (2013) Japan
9 CARLA Cardiovascular Disease, Living and
Ageing in Halle (CARLA)
Lacruz et al (2016) Germany
10 CSHA Canadian Study of Health and Aging Lindsay et al (2002) Canada
11 INSPIRED Investigating Services Provided in the
Residential Environment for
Dementia
Liu et al (2017) Australia
12 MSHA Manitoba Study of Health and Aging Tyas et al (2001) Canada
13 TILDA The Irish Longitudinal Study on
Aging
Kenny (2013) Ireland
14 TREND Tübinger Evaluation of Risk Factors
for Early Detection of
Neurodegeneration
Heinzel et al (2014) Germany

Table 1.
Fourteen studies included in meta-analyses.
a
The original study had data from Ibadan, Nigeria; however, none of the Nigerian participants took
antihypertensive medications. Therefore, only participants from Indianapolis were included in our
analyses.

17

Study
abbreviation
Study description Study type Adapted
NOS
a
rating
1 ACT Population-based, longitudinal study Prospective 7/8
2 CARLA Population-based, longitudinal study Prospective 7/8
3 CHAP Population-based, longitudinal study Prospective 8/8
4 CHCS Population-based, longitudinal study Prospective 8/8
5 CSHA Population-based, longitudinal study Prospective 7/8
6 IIDP Population-based, longitudinal study Prospective 8/8
7 MSHA Population-based, longitudinal study Prospective 6/8
8 REGARDS Population-based, longitudinal study Prospective 7/8
9 TILDA Population-based, longitudinal study Prospective 7/8
10 TREND Longitudinal study of an enriched sample of
individuals with prodromal risk factors for
Alzheimer’s disease and Parkinson’s disease
Prospective 6/8
11 CAMUI Randomized, open-label, parallel comparison
study in hypertensive patients
Randomized
trial
8/8
12 MBS Population-based, cross-sectional study Retrospective 6/7
13 INSPIRED Cross-sectional study of 17 nursing homes Retrospective 7/7
14 SKILL Population-based, cross-sectional study Retrospective 7/7

Table 2.
Characteristics of studies included in meta-analyses.
a
NOS = Newcastle-Ottawa Scale, which was developed to evaluate the quality of non-randomized studies
in meta-analyses. Given that we selected for studies with at least 6 month follow-up, we did not assess the
adequacy of follow-up duration. As opposed to the usual 9, the maximum score was 8 for longitudinal
studies and 7 for cross-sectional studies.

18
Data from two studies – the Canadian Study of Health and Aging (CSHA; Lindsay et al.,
2002) and Manitoba Study of Health and Aging (MSHA; Tyas et al., 2001) were pooled and
analyzed as one. This was due to the small sample sizes that used the medications of interest
(MSHA n=15, CSHA n=11), which was likely due to the fact that these drugs were newer and
less prescribed in the early 1990s, during data collection. Further, both studies used the same
cognitive assessment, and the MSHA was a parallel study to the CSHA, with one-quarter of
MSHA participating in the CSHA. Duplicate participants were dropped before analyses.
2.5 Data Extraction
Data were extracted from published reports as well as through personal communications
with authors. From each study, the following were extracted: (1) study design, (2) objectives, (3)
setting, (4) demographic variables (sex, age), (5) comorbidities (burden of comorbidity, number
of medications at baseline), (6) baseline cognitive function, (7) subject eligibility and exclusion
criteria, (8) number of subjects per group, (9) years of enrollment, (10) duration of follow-up,
(11) the study and comparator interventions (i.e., antihypertensive medications used), (12)
relevant co-interventions, (13) change in cognitive function, and (14) adverse events (changes in
quality of life, all-cause mortality).
The summary statistics required for each study and each outcome for continuous data
were (1) means and standard deviations of cognitive measures at baseline and at follow-up, if
available, (2) the standard error of the mean change, if follow-up was available, and (3) the
number of patients per group at each assessment. A weighted estimate of the effect across studies
was computed.

19
2.6 Assessment of quality and publication bias
Given that our meta-analysis only included one randomized trial, the methodological
quality of included studies was assessed using an adapted version of the Newcastle-Ottawa Scale
(NOS), which was developed to evaluate the quality of non-randomized studies in meta-analyses.
Given that we selected for studies with at least 6 month follow-up, we did not assess the
adequacy of follow-up duration. Thus, the maximum score that a longitudinal study could
receive on the NOS was 8, as opposed to the usual 9. For our purposes, “outcome” was
operationalized as cognitive performance, and “exposure” was operationalized as treatment with
antihypertensive medication. Studies were assessed for selection (4 points for representativeness
of participants, ascertainment of exposure and non-exposure, and demonstration that dementia
was absent at the start of the study), comparability (2 points for whether the study controlled for
one or more important covariates) and outcome (2 points for whether the study conducted
independent, blind assessment or used record linkage, and whether there was a description of
subjects lost to follow-up). The maximum score that a cross-sectional study could receive on the
NOS was 7, after excluding the criterion on subjects lost to follow-up.
The presence of publication bias was assessed by visual inspection of the Begg’s funnel
plot and Egger’s regression test. Two studies were excluded from this analysis as they did not
have baseline effect sizes; one study did not have baseline cognitive data available in the
published report (Sink et al., 2009), and the other study utilized a six-item cognitive screener,
which, after the exclusion of participants with dementia, provided a small range of scores (4-6)
which would not have been meaningful to interpret (Gillett et al, 2015).

20
2.7 Data Analysis
Outcomes included: (1) neuropsychological performance on measures of mental status,
memory, language, executive function, attention, and processing speed, and (2) cognitive change
from baseline in all these domains. If multiple tests were used to assess a cognitive domain for
any given study, the multiple effect sizes were not averaged. Rather, the most sensitive test for
each cognitive domain was selected based on previous literature (Lezak, Howieson, & Loring,
2012; Strauss, Sherman & Spreen, 2006), and studies contributed one effect size per cognitive
domain. Tests used for the assessment of each domain are displayed in Table 3.

Domain Test used k
a

Attention Trail Making Test A 3
Color Trails 1 1
Executive Function Trail Making Test B 3
Color Trails 2 1
Language Animal Fluency 3
Category Fluency 1
Memory (Learning) CERAD Word List Learning 3
HVLT Immediate Recall 2
Word List Learning 1
Memory (Recall) CERAD Word List Recall 3
HVLT Delayed Recall 2
Word List Learning 1
Mental Status Cognitive Abilities Screening Instrument (CASI) 1
Mini-Mental State Examination (MMSE) 7
Modified MMSE 2
Cambridge Mental Disorders of the Elderly Examination
(CAMDEX)
1
Psychogeriatric Assessment-Cognitive Impairment (PAS-Cog) 1
Processing Speed Modified Symbol-Digit Modality Test 1
Digit Symbol Test 1

Table 3.
Neuropsychological measures used for the assessment of various cognitive domains.
a
k = number of included studies at baseline

This meta-analysis necessitated the combination of data from studies which used similar,
but not identical, rating scales for outcome assessment. Effect sizes (Hedge’s g) were calculated

21
using mean differences, analyses of variance, or t tests comparing two treated groups, (i)
participants taking anti-hypertensive medications which cross the BBB (the “Crossing group”)
and (ii) participants taking anti-hypertensive medications which do not (the “Non-crossing
group”). Given that pooled studies used different rating scales, the measure of group difference
for all outcome measures was the standardized mean difference (SMD) i.e., the absolute mean
difference divided by the pooled standard deviation. SMDs can be intuitively interpreted as the
groups differing by, for example, one-tenth (g = 0.10) or one (g = 1) standard deviation. Cohen
(1969) suggests the following interpretations of g: g = 0.20 considered a small effect, g = 0.50
considered a medium effect, and g = 0.80 considered a large effect.
For the purposes of consistency, effect sizes were calculated by subtracting the scores of
the Non-crossing group from the Crossing group. On most tests, a higher score indicates better
performance; thus, a positive effect size indicates that the Crossing group performed better on a
test than the Non-crossing group, while a negative effect size indicates that the Crossing group
group performed worse on a test than the Non-crossing group. On timed tests in which greater
time taken reflected poorer performance (e.g. Trail Making Test), scores were inverted (i.e.
multiplied by -1) to allow for consistent analysis and interpretation of results.
For longitudinal analyses, cognitive change was measured in change scores, computed by
subtracting the baseline score from the score at follow-up assessment. SMDs were computed as
the absolute mean difference in change scores divided by the pooled standard deviation of
change scores. In calculation of change scores, another covariate was added to every model:
each individual’s baseline score on the measure being studied, with the exception of the
Cardiovascular Health Cognition Substudy (CHCS; Sink et al., 2009), for which these data were
unavailable. A positive effect size indicates that the Crossing group performed better than the

22
Non-crossing group on a test over follow-up, while a negative effect size indicated the reverse.
The most amount of data available was at 3 years of follow-up; thus, this cutoff was used for
longitudinal analyses. Prior to analyses, participants who had suspected dementia at baseline, as
determined using individual study criteria, were dropped.
Heterogeneity between studies was examined using the I
2
and Q statistics, and p < 0.10
was considered to indicate significant heterogeneity (Deeks, Higgins & Altman, 2008; Higgins,
Thompson, Deeks, & Altman, 2003). An I
2
-statistic above 40% was considered as representing
heterogeneity that may be substantial enough to impact the meta-analysis, following Cochrane
guidelines (Higgins et al., 2019). Maximum likelihood random effects meta-regression analyses
(i.e., moderator analyses) were considered for each cognitive domain to explore any possible
sources of heterogeneity based on mean age, mean educational attainment, and percentage of
male participants (Thompson & Higgins, 2002; van Houwelingen et al., 2002; Viechtbauer,
2006). Meta-regression was only considered when there were ten or more studies contributing to
a meta-analysis (Higgins et al., 2019), and only one cognitive domain (mental status) met this
cutoff. Given that the amount of heterogeneity in this meta-analysis was inconsequential (0.0%),
meta-regression was not conducted.
All results are reported using a random-effects model. The Knapp-Hartung-Sidik-
Jonkman (HKSJ) adjustment was used for confidence intervals and the Sidik-Jonkman estimator
was used for the tau-squared estimator. Analyses were conducted using SPSS for Mac OS X
version 21.0 (SPSS, Armonk, NY: IBM Corp), R version 3.6.2 software (R Core Team, 2014)
and the meta (Balduzzi, Rücker, & Schwarzer, 2019), dmetar (Harrer, Cuijpers, Furukawa, &
Ebert, 2019), and metafor (Viechtbauer, 2010) packages. The Meta-Essentials tool (Suurmond,

23
van Rhee, & Hak, 2017) was used to conduct Egger’s regression test and produce Begg’s funnel
plot.
4. Results – Study A
4.1 Clinical/demographic data
Demographic characteristics for 12,849 participants with baseline demographic data are
shown in Table 4. Differences between those who took BBB-crossing and non-BBB-crossing
drugs are shown in Table 5. Baseline vascular risk factors are displayed in Table 6.
4.2 Quality assessment and publication bias
Study quality was high across our participating cohorts. As shown in Table 2, four of
eleven longitudinal studies scored 8/8 points on the NOS scale. Five studies scored 7/8, with all
losing 1 point for having no mention of blinded assessment, with the exception of CSHA, which
lost 1 point for using written self-report medication data, as opposed to ascertainment through
records or structured interview. Two studies scored 6/8, losing points for having no mention of
blinded assessment as well as using self-report data (MSHA) and being non-representative of the
general older adult community (TREND). The TREND study used an enriched sample of
individuals who were partially selected for having prodromal risk factors for Alzheimer’s disease
and Parkinson’s disease. Two of three cross-sectional studies scored 7/7 points. The remaining
study, the Investigating Services Provided in the Residential Environment for Dementia
(INSPIRED) study, was docked one point for being non-representative of the general older adult
community, as its participants comprised nursing home residents as opposed to community-
dwelling older adults.

24
Study Age, y Sex, no. (%) Education, y Race /
ethnicity
(%)
APOEe4, no.
(%)
Range Mean
(SD)
Female Male Range Mean
(SD)
E4
carrier
Missing
data
ACT 60-90 73.6 (6.0) 861
(55%)
689
(45%)
10-20 14.8
(2.9)
88% White,
4% Asian,
3% Black,
5% Others
343
(22%)
196
(13%)
CAMUI

63-89 74.4 (5.9) 41
(51%)
39
(49%)
-- -- Japanese NA NA
CARLA

60-87 73.3 (7.2) 129
(39%)
204
(61%)
9-20 14.7
(2.5)
NA NA NA
CHAP

64-93 72.9 (5.8) 458
(64%)
258
(36%)
7-20 13.2
(2.6)
53% Black,
47% White
232
(32%)
17 (2%)
CSHA and
MSHA

65-90 79.1 (5.6) 18
(69%)
8
(31%)
3-18 10.1
(3.3)
European
descent
4
(15%)
10
(39%)
IIDP

65-89 72.4 (6.3) 135
(68%)
65
(32%)
3-16 10.1
(2.8)
NA NA NA
INSPIRED

66-98 86.1 (7.6) 82
(79%)
22
(21%)
10-16 11.9
(2.2)
NA NA NA
MBS

69-92 78.4 (5.2) 110
(55%)
90
(45%)
4-18 14.6
(2.7)
82% White,
13% Black,
7% Others
NA NA
SKILL

62-96 73.7 (6.0) 99
(55%)
80
(45%)
8-20 14.0
(2.4)
87% White,
12% Black,
1% Others
NA NA
TILDA

50-80 66.5 (8.7) 472
(47%)
540
(53%)
5-18 10.6
(4.2)
NA NA NA
TREND

50-84 66.7 (6.6) 192
(46%)
228
(54%)
9-20 14.1
(2.7)
NA NA NA
REGARDS

50-94 66.8 (8.5) 4317
(54%)
3712
(46%)
12-16 14.1
(1.7)
55% White,
45% Black
NA NA

Table 4.
Characteristics of the cohorts at baseline, after exclusion of dementia cases. One study, the
Cardiovascular Health Cognition Substudy, did not have baseline demographic data on the sample taking
relevant medications. NA = Not available.

25

26

27

28
Figure 3 shows Begg’s funnel plot for examination of publication bias for studies with
baseline cognitive assessments. The symmetrical distribution of studies on either side of the
overall effect line indicates that publication bias was unlikely. The Egger test was not significant
for publication bias (p = 0.65).

Figure 3.
Begg’s funnel plot for examination of publication bias.

29
4.3 Heterogeneity
Heterogeneity between studies for each cognitive domain was examined using the I
2
and
Q statistics. Q statistics are presented in Table 7, and I
2
and associated p-values are presented in
individual figures. Results indicated no significant heterogeneity between studies that
necessitated meta-regression analyses.
Cognitive domain (baseline) Q d.f. p-value for Q
Attention 1.72 3 0.63
Executive Function 1.88 3 0.60
Language 0.54 3 0.91
Memory (Learning) 2.72 5 0.74
Memory (Recall) 7.83 5 0.17
Mental Status 6.24 10 0.79
Processing Speed 1.27 1 0.26
Cognitive domain (longitudinal) Q d.f. p-value for Q
Attention 0.00 1 0.95
Executive Function 1.05 1 0.31
Language 0.59 3 0.90
Memory (Learning) 1.98 3 0.58
Memory (Recall) 0.73 3 0.87
Mental Status 4.11 4 0.39

Table 7.
Tests of heterogeneity. The Q-statistic is the weighted sum of squared differences between observed
effects and the weighted average effect. A low p-value indicates there is an undetermined degree of
heterogeneity that may be worth exploring in moderator analyses.

4.4 Cross-sectional Results
Effect sizes were calculated for mental status at baseline assessment (Figure 4) as well as
the domains of attention, executive function, language, processing speed, memory learning and
recall (Figures 5-10). Overall, the effect sizes ranged from a minimum of -0.16 for processing
speed, to a maximum of 0.04 for attention. Results suggested that there was no significant
difference between the group using non-BBB-crossing drugs and the group using BBB-crossing
drugs across any cognitive domains at baseline: attention (g = 0.04, 95% CI [-0.10, 0.18], p =

30
0.40), executive function (g = 0.04, 95% CI [-0.12, 0.19], p = 0.45), language (g = 0.02, 95% CI
[-0.01, 0.06], p = 0.12), mental status (g = -0.05, 95% CI [-0.13, 0.02], p = 0.13), verbal memory
(learning), (g = 0.0007, 95% CI [-0.01, 0.08], p = 0.98), verbal memory (recall), (g = -0.04, 95%
CI [-0.23, 0.15], p = 0.60), and processing speed (g = -0.16, 95% CI [-2.05, 1.74], p = 0.48).

31
Mental Status at Baseline

Figure 4.
Forest plot showing the effect size estimates for individuals who took BBB-crossing drugs vs. non-BBB-
crossing drugs. Size of squares denote relative weighting of each study in the model. The blue diamond
indicates overall effect. Placement of the diamond to the left of the vertical line (i.e. negative effect size)
indicates that the non-BBB-crossing group outperformed the BBB-crossing group, and vice versa. There
were no significant differences among individuals who took BBB-crossing drugs vs. non-BBB-crossing
drugs on measures of mental status at baseline.

Attention at Baseline

Figure 5.
Forest plot. There were no significant differences among individuals who took BBB-crossing drugs vs.
non-BBB-crossing drugs on measures of attention at baseline.

32

Executive Function at Baseline

Language at Baseline

Verbal Memory (Learning) at Baseline

Figures 6-8.
Forest plots. There were no significant differences among individuals who took BBB-crossing drugs vs.
non-BBB-crossing drugs on measures of executive function, language, or memory (learning) at baseline.

33
Verbal Memory (Recall) at Baseline

Processing Speed at Baseline

Figure 9-10.
Forest plots. There were no significant differences among individuals who took BBB-crossing drugs vs.
non-BBB-crossing drugs on measures of memory (recall) and processing speed at baseline.

34
4.5 Longitudinal Results
Effect sizes were calculated for change in scores over time on mental status measures as
well as the domains of attention, executive function, language, verbal memory (learning) and
verbal memory (recall); see Figures 11-15. The maximum effect size was 0.07 (95% CI [0.01,
0.12], p = 0.03) for verbal recall, suggesting that using BBB-crossing drugs was associated with
better performance on recall over 3-year follow-up, relative to using non-BBB-crossing drugs.
The minimum effect size was -0.17 (95% CI [-0.23, -0.10], p = 0.02) for attention, suggesting
that using non-BBB-crossing drugs was associated with better performance on attention
measures over time, relative to using BBB-crossing drugs.
BBB-crossing-potential did not significantly affect performance over time on mental
status (g = -0.01, 95% CI [-0.22, 0.20], p = 0.88) or other cognitive domains: executive function
(g = -0.01, 95% CI [-0.22, 0.20], p = 0.88), language (g = -0.01, 95% CI [-0.06, 0.04], p = 0.41),
or verbal memory (learning), (g = 0.05, 95% CI [-0.05, 0.15], p = 0.19).

35
Mental Status over Follow-up

Verbal Memory (Recall) over Follow-up

Attention over Follow-up

Figure 11-13.
Forest plots. Individuals who took BBB-crossing drugs outperformed those who took non-BBB-crossing
drugs on a measure of verbal memory (recall) over time. Individuals who took non-BBB-crossing drugs
outperformed those who took BBB-crossing drugs on a measure of attention over time. There were no
significant differences among the two groups on measures of mental status over follow-up.

36
Executive Function over Follow-up

Verbal Memory (Learning) over Follow-up

Figure 14-15.
Forest plots. There were no significant differences among the two groups on measures of executive
function or verbal memory (learning) over follow-up.

37
5. Methods – Study B
5.1 Specific Aims and Hypotheses
Study B utilized prospective data collection for primary data analysis, in order to:
a) Assess whether levels of RAS constituents [Ang II, AT1R, AT2R, Ang-(1-7), Mas] differ
between cognitively normal participants and participants with MCI. These were assessed
from blood samples, which can provide estimates of free peptides in circulation, as well as
receptor levels on white blood cells.
b) Assess whether the ratio of Ang II to Ang-(1-7) differs between cognitively normal
participants and participants with MCI. This ratio may be considered as a proxy measure of
beneficial ACE2 activity, given that Ang II is the primary substrate in its production of Ang-
(1-7).
Our hypotheses were that:
a) Levels of Ang II and AT1R would be higher in participants with MCI than in cognitively
normal participants, while levels of Ang-(1-7), Mas and AT2R would be higher in
cognitively normal participants than in the MCI group. In order words, levels of putatively
protective RAS elements [Ang-(1-7), Mas, AT2R] would be correlated with superior
cognitive functioning, while the opposite would be true for Ang II and AT1R.
b) The ratio of Ang II to Ang-(1-7) would be lower in participants with MCI than in cognitively
normal participants, indicating less beneficial ACE2 and Ang-(1-7) action and potentially
more deleterious Ang II effects.
5.2 Participants
The study protocol was approved by the University of Southern California (USC)
Institutional Review Board and all participants provided written informed consent. Study

38
procedures were carried out in the Vascular Senescence and Cognition (VaSC) Laboratory
within USC’s Department of Psychology. Participants were recruited from the community by
word-of-mouth, flyers, and outreach events. Inclusion criteria included: age 55 years or older,
independently living, and no history of clinical stroke, dementia, learning disability, traumatic
brain injury or other systemic or neurological illnesses or treatment that may affect cognitive
functioning. Participants were classified as either cognitively normal or having MCI following
neuropsychological testing. Criteria for MCI were scoring >1 standard deviation below norm-
referenced scores on >1 measure within a cognitive domain, or >2 measures across domains
(Bondi et al., 2014).
5.3 Blood draw, mass spectrometry, and flow cytometry
A blood draw was performed following an overnight fast and prior to neuropsychological
testing. Peripheral blood assays were conducted in a blinded fashion without knowledge
regarding participants’ cognitive functioning. To 150 μL of plasma sample, 40 μL of 1000
ng/mL NorLeu3-Angiotensin(1-7) was added as the internal standard. Samples are then
alkalinized by adding 700 μL 5% ammonium hydroxide solution, then layered onto S solid phase
extraction cartridges (Oasis 1cc MAX P/No.186001883, Waters, MA, USA) that are conditioned
and washed. Analytes were eluted using 1 mL methanol with 2% formic acid and evaporated to
dryness using dried and filtered steady stream of nitrogen gas. Residue was reconstituted using
50 μL of 0.2% formic acid, where 45 μL was injected into the LC-MS system consisting of
Shimadzu LC-20AD HPLC (Shimadzu, Japan) and an API 4000 mass spectrometer with
TurboIonspray ionization using the positive mode (Ab Sciex, MA, USA). Analytes were
separated using Hypersil Gold C18 column (P/No. 25005-052130, Thermo Scientific, USA) with
the dimensions of 50 x 2.1 mm x 5 μm. Mobile phase A is water with 0.5% formic acid, while

39
mobile phase B is acetonitrile with 0.5% formic acid. A gradient program was used, where the
concentration of mobile phase B starts from 10% at the beginning to 90% in 5 min and held for
another 2 min. Each analyte was determined using multiple reaction monitoring.
For the flow cytometry process, peripheral blood mononuclear cells were transferred to
stained and unstained tubes. The stained tube was incubated with 5 μL of Human BD Fc Block
(BD Biosciences) for 10–15 min at RT, and 1 μL of each of the following antibodies were added:
(1) CD34-PE-Vio770 (clone: AC136, Miltenyi Biotec), (2) CD133-VioBright FITC (clone:
AC133, Miltenyi Biotec), (3) CD309-PerCP/Cy5.5 (clone: 7D4-6, BioLegend), (4) angiotensin II
type 1a receptor-PE (polyclonal, Bioss), (5) AGTR-2-APC (clone: 364805, R&D Systems), and
(6) Mas1-Alexa Fluor 350 (polyclonal, Bioss). Tubes were incubated in the dark for 30 min at 4
°C. Samples are washed twice with 3 mL PBS + 2% FBS, centrifuged at 300 x g for 8 min, and
fixed with 2% formaldehyde in PBS until analysis. Samples were acquired on a BD LSR II flow
cytometer and analyzed on FlowJo software. Within the lymphocyte gate, 100,000 events were
recorded for the blank tube and the entire sample is recorded for the stained tube. Fluorescence
compensation was automatically calculated using AbC Total Antibody Compensation Bead Kit
(Thermo Fisher). Fluorescence-minus-one (FMO) controls were used to set positive/negative
gates for each fluorochrome in the lymphocyte gate. Cells were gated to exclude platelets and
debris (R1). A pulse geometry gate (R2) on FSC-A vs. FSC-H was applied to isolate single cells.
Within the lymphocyte gate (R3), single gates were drawn for CD34+ (R4), CD133+ (R5), and
CD309+ (R6) above their respective FMO bounds. These gates were sequentially applied into
the lymphocyte gate to count CD34+CD133+CD309+ cells.

40
5.4 Neuropsychological battery
After the blood draw and breakfast, participants underwent neuropsychological testing.
Cognitive assessment included the following measures: (1) WMS-R Logical Memory II subtest:
immediate and delayed recall; (2) Rey Auditory Verbal Learning Test (RAVLT): total immediate
recall score for Trials 1-6, delayed recall score, recognition score; (3) WMS-IV Visual
Reproduction: immediate and delayed recall, recognition score; (4) Stroop task; (5) Trail Making
Test (TMT): parts A and B, times to completion; (6) Wechsler Adult Intelligence Scale-Revised
(WAIS-R) Digit Span: forward and backward scores and spans; (7) Animal Fluency: total score,
(8) Fruit and Vegetable Fluency: total score; (9) Controlled Oral Word Association test (FAS);
(10) Boston Naming Test (BNT): total score or Multilingual Naming Test (MINT): total score;
(11) WAIS-IV Block Design; (12) WMS-IV Visual Reproduction Copy; (13) Judgment of Line
Orientation (JLO): total correct score.
These tests were selected to assess various domains of cognition: memory (WMS-R
Logical Memory II, RAVLT, WMS-IV Visual Reproduction recall and recognition), attention
(Stroop, Trails A, WAIS-R Digit Span forward), executive function (Trails B, WAIS-R Digit
Span backward), and language (Animal fluency, Fruit and Vegetable fluency, FAS,
BNT/MINT), and visuospatial function (WAIS-IV Block Design, WMS-IV Visual Reproduction
Copy, JLO).
5.5 Statistical analyses
Data were screened for departures from normality using indices of skewness and kurtosis.
Data were log-transformed as needed in order to satisfy the assumption of normality for
parametric tests. Groups were compared on demographic variables using chi-square tests for
nominal variables and one-way analyses of variance (ANOVAs) for continuous variables. Levels

41
of RAS metabolites [Ang II, Ang-(1-7)] and receptors (AT1R, AT2R, and Mas), as well as a
proxy measure of ACE2 [i.e., the ratio of Ang II to Ang-(1-7)], derived from mass spectrometry
and flow cytometry, were compared using analyses of covariance (ANCOVAs). The covariates
for ANCOVAs included age, sex, and education. The groups compared were cognitively normal
participants and participants with MCI, and all analyses will be two-tailed with significance set
at p < .05. Given that this was an exploratory study, with no prior studies having investigated the
relation of RAS metabolites to neuropsychological function, we did not apply multiple
comparison corrections.
6. Results – Study B
6.1 Demographic data
Demographic characteristic of VaSC Study participants are displayed in Table 8. The
Cognitively Normal group was significantly more educated than the MCI group, F(1, 86) = 7.10,
p < 0.01. In post-hoc analyses, when only White and Black participants were examined, there
was a statistically significant relationship between race/ethnicity and cognitive group, χ
2
(1,
N=54) = 13.111, p = .001. There were no significant differences between the groups on the
percentage of male participants, χ
2
(1, N=88) = 3.47, p = .08, or age, F(1, 86) = 1.67, p =.20.
Total Sample
(N=88)
Cognitively Normal
(n=67)
MCI
(n=21)
Age 70.11 (7.71) 69.52 (6.81) 72.00 (10.02)
Sex (% male) 39.8 34.3 57.1
Education 15.92 (2.75) 16.34 (2.29) 14.57 (3.61)
Race/ethnicity 58% White, 22%
Black, 9% Asian,
3% Other
67% White, 13%
Black, 8% Hispanic,
9% Asian, 3% Other
29% White, 48%
Black, 14% Hispanic,
5% Asian, 5% Other

Table 8.
Demographic characteristics of VaSC Study participants. MCI = Mild Cognitive Impairment.

42
6.2 Neuropsychological data for assessment of cognitive status
As shown in Table 9, we only had a small amount of missing neuropsychological data
used in determining cognitive status of participants. No missing data were imputed.

Test Condition / Measure
Percentage
missing
Memory RAVLT Delayed Recall
0.0%

WMS-4 Logical Memory Delayed Recall
0.0%

WMS-4 Visual
Reproduction Delayed Recall
0.0%
Attention/Executive
Function Stroop Interference
2.0%

Trails A
0.0%

Trails B
0.0%
Language D-KEFS FAS
0.0%

D-KEFS Animals
0.0%

Boston Naming Test Spontaneously Correct
1.0%
Visuospatial WAIS-4 Block Design Total Correct
0.0%

WMS-4 Visual
Reproduction Copy
3.0%

Judgment of Line
Orientation Total Correct
0.0%

Table 9.
Missing neuropsychological data.
WMS-4, Wechsler Memory Test – 4
th
Edition; WAIS-4, Wechsler Adult Intelligence Scale – 4
th
Edition;
D-KEFS, Delis – Kaplan Executive Functioning System; RAVLT, Rey Auditory Verbal Learning Test

6.3 RAS metabolites and receptors
Levels of RAS metabolites and receptors were collected on a subset (n=52) of
participants. After controlling for covariates, there were no significant differences found between
the Cognitively Normal and MCI groups on either of the RAS metabolites: Ang II, F(1, 28) =
2.29, p = .14, or Ang-(1-7), F(1, 30) = 1.05, p = .31. There were no significant differences
between the groups on levels of RAS receptors: AT1R, F(1, 28) = 2.29, p = .14, AT2R, F(1, 28)

43
= 2.78, p = .11, or Mas, F(1, 23) = 0.28, p = .60, or the proxy measure of ACE2, F(1, 15) = 3.51,
p = .08.

7. Discussion
The link between treating hypertension and slowing cognitive decline in older age has
been well-reviewed (e.g. McGuinness, Todd, Passmore, & Bullock, 2006). Meta-analyses and
systematic reviews (e.g. Levi Marpillat et al., 2013; Fournier et al., 2009), as well as large-scale
individual studies (e.g. Davies et al., 2011; Li et al., 2010), have highlighted how ARBs and
ACE-inhibitors are linked to improved or maintained cognition in later life, above and beyond
any benefit conferred by their counterparts. ARBs and ACE-inhibitors modulate blood pressure
through their effects on homeostatic-related receptors located within the vasculature, whereas
other classes of drugs reduce blood pressure through diverse mechanisms such as decreasing
heart rate (e.g. beta-blockers) or reducing blood volume (e.g. diuretics).
ARBs and ACE-inhibitors have beneficial peripheral effects on neurons as well as
systemic vascular resistance, but these alone may not be enough to confer protective effects on
cognition. This is because their blood-pressure-lowering effects only ameliorate one risk factor
(i.e., hypertension) in individuals who may have many others (e.g. genetic risk for
neurodegeneration or other vascular risk burden) that, altogether, may perpetuate disease.
Independent of their peripheral effects, however, the ARBs and ACE-inhibitors that cross the
BBB may affect the brain and exert additional salutary effects that include increasing blood flow,
protecting against inflammation, vascular oxidative stress, and brain damage. For example,
blockade of AT1Rs in cerebrovascular endothelial cells (Zhou et al., 2006) by ARBs may reduce
proinflammatory cell migration to the brain parenchyma and vasculature, activity which is likely

44
replicated in surrounding microglia and neuronal cells, thus reducing glial and neuronal damage.
In animal models, a BBB-crossing ARB (candesartan) has been found to have numerous anti-
inflammatory effects, including (1) reducing production and release of centrally-acting
proinflammatory cytokines, (2) repressing nuclear transcription factors’ activation in the brain,
(3) reducing gene expression of proinflammatory cytokines and prostanoid receptors, and (4)
decreasing microglia activation in vivo (Benicky et al., 2011). These effects were widespread
and found beyond well-known areas for circulating proinflammatory factors (i.e. the
hypothalamic paraventricular nucleus and subfornical organ); they were also observed in the
prefrontal cortex, hippocampus, and amygdala (Benicky et al., 2011).
With regard to ACE-inhibitors, a BBB-crossing ACE-inhibitor (zofenopril) has been
found to significantly and dose-dependently decrease intracellular reactive oxygen species (ROS)
and superoxide formation induced by oxidized low-density lipoprotein and tumor necrosis
factor-α in human endothelial cells (Cominacini et al., 2002), likely aiding cellular defense
against free radicals. Similar findings have been reported for captopril, another BBB-crossing
ACE-inhibitor (Barbagallo, Dominguez, & Resnick, 1999), while enalapril, a non-BBB-crossing
ACE-inhibitor, has not been found to have these beneficial effects (Cominacini et al., 2002).
Taken together, given their neuroprotective effects of lowering inflammation and oxidative
stress, BBB-penetrant ARBs and ACE-inhibitors may work to potentially reduce cerebrovascular
dysfunction overall.
Thus, evaluation of the ability of ARBs and ACE-inhibitors to cross the BBB represents a
key direction for researching possible benefits on cognition, as they may be able to exert
beneficial effects on the neural milieu. To our knowledge, there have only been three studies that
have examined the associations between BBB-crossing potential and cognition. These studies

45
indicated that use of the BBB-crossing drugs was linked to less cognitive decline (Wharton et al.,
2015; Sink et al., 2009) and better memory (Ho & Nation, 2017) over time, compared to use of
the non-BBB-crossing drugs.
Despite these encouraging findings, there have been no studies to date which have
examined these associations meta-analytically. This undertaking has been hampered by the fact
that, with the exception of these three studies, none of the many empirical studies examining
hypertension and cognition have considered the pharmacokinetic properties of these drugs. This
is because BBB-crossing potential is not a consideration made in prescribing practices.
Importantly, this meant that our study benefited from “randomization in nature” with regard to
whether or not participants were taking BBB-crossing or non-BBB-crossing antihypertensive
drugs. Thus, although we only included one randomized trial in our meta-analysis, all
participants were, in a sense, “randomly” prescribed a BBB-crossing or non-BBB-crossing
medication.
We acquired individual participant data from 13 studies spanning six countries and
classified medications for BBB-crossing potential. Our review contributes to the literature
through meta-analysis of studies published over the past 18 years which quantifies the effects of
BBB-crossing potential on cognitive performance in hypertensive older adults. As these
associations were entirely unexamined in 13 of the 14 original papers from these studies, the
majority of the findings in our meta-analysis are previously unpublished. The only published
study examining the effects of BBB-crossing potential on cognition was by Sink et al (2009).
As hypothesized, our meta-analysis indicated that, in our international sample, taking
BBB-crossing antihypertensive drugs was associated with better performance on verbal memory
recall over up to 3-years of follow-up, relative to taking non-BBB-crossing medications. This is

46
consistent with our prior observations in a smaller sample of American and Canadian
participants (Ho & Nation, 2017), in which users of BBB-crossing drugs had better memory
recall performance over 3-year follow-up compared to users of non-BBB-crossing drugs, and
also displayed less white matter hyperintensities compared to this group. These findings
highlight the benefits of these drugs on memory recall. Similar to our smaller study, we did not
find any difference on learning performance over time, suggesting that the use of these drugs is
linked to better retention and delayed recall of what is learned, as opposed to more efficient
encoding of new information.
Importantly, this finding implicates memory consolidation mechanisms that are
underpinned by medial temporal and diencephalic circuitry (Zola-Morgan & Squire, 1990). As
we have reviewed previously (Ho & Nation, 2018), animal studies examining the cognitive
effects of the protective ACE2-Ang(1-7)-Mas axis show that Mas activity may enhance
hippocampal function. This is the axis which BBB-penetrant drugs are hypothesized to promote
and which has been demonstrated in human brains (Xia & Lazartigues, 2008, Metzger et al.,
1994). Ang-(1-7) improves the performance of mice and rats on spatial working memory tasks
(Uekawa et al., 2016; Xie et al., 2014) which implicate the hippocampus, a medial temporal lobe
structure. Thus, it is possible that the use of BBB-penetrant drugs aids memory consolidation
through promotion of the salutary effects of Mas and Ang-(1-7) on the hippocampus. Of note,
the highest amounts of Mas in human brains are found in the hippocampus, with lower amounts
in the cerebellum and cortex (Metzger et al., 1994).
Remarkably, we were able to observe these subtle memory differences in a meta-analytic
sample free of dementia and suspected cognitive impairment based on screening exams. Thus,
our findings have clear implications for individuals who are still cognitively healthy, but who

47
may nevertheless be at early and undetectable stages of neurodegenerative diseases. In the most
common neurodegenerative disease, AD, deficits in memory recall are often the greatest
impairments observed, compared to decline in other domains (Grober & Kawas, 1997). Our
overall effect size was small (g = 0.06); however, this is somewhat expected, given the
cognitively intact nature of our participants. Interestingly, the Reasons for Geographic and Racial
Differences in Stroke (REGARDS) study contributed the most participants (and therefore had the
highest-weighted effect size) to this finding, and was made up of participants with the highest
BMI scores as well as histories of diabetes – vascular risk factors which put them at greater risk
for poorer cognition. The BBB-crossing group within this study also had a significantly higher
proportion of participants with stroke compared to the non-BBB-crossing group. Thus, it is
notable that we were able to replicate our previously reported findings of benefit in memory
recall in a large, international, and cognitively unimpaired sample – one in which the greatest
contributing study was one that had increased vascular risk for developing cognitive issues.
Our findings were somewhat mixed, as the non-BBB-crossing group displayed better
attention over follow-up, compared to the BBB-crossing group. Reasons for this difference is
unclear, and has not been found in other studies utilizing the same measure. Attentional
processes may be impacted by spurious factors unrelated to neuropathological processes
including test engagement, stress and depression. Although attention can be impacted by
neuropathology affecting frontal-subcortical networks, such effects would typically also be
observed on even more sensitive measures of executive function and processing speed. It is,
therefore, a curiosity that we did not observe differences in these other more sensitive cognitive
domains in participants taking non-BBB crossing drugs relative to BBB-crossing drugs.

48
Vascular risk data was not available for all studies; however, The Irish Longitudinal
Study on Aging (TILDA; Kenny, 2013), which contributed the highest weight to the attention
finding (55%) over follow-up, had a significantly greater proportion of participants with prior
stroke in the BBB-crossing group than participants in the non-BBB-crossing group. Prior stroke
increases the risk for deficits in attention (Lincoln, Majid, & Weyman, 2000), and this increased
risk in the BBB-crossing group may partly explain this finding. It is possible that our differential
results of better memory in the BBB-crossing group and better attention in the non-BBB-
crossing group are therefore due to differences in Alzheimer’s- vs. vascular-associated
neuropsychological impairment among the studies used in analysis of each domain.
Strengths of our meta-analysis included exhaustive search of the literature which allowed
for a large sample size (N=12,849 at baseline) that enabled the quantitative assessment of the
associations between antihypertensive drug use and cognitive change. We also largely avoided
the computation of effect sizes using data from differing adjusted models, as we used raw
cognitive data (as opposed to published summary statistics) and were able to use the same
covariates (age, sex, and educational attainment) in most analyses. Since we were not restricted
to the use of published summary statistics, we were also able to perform moderator analyses
which ruled out the effects of these covariates on our results. In longitudinal analyses, we were
able to control for individual variation by including each individual’s baseline score as a
covariate in the regression model. These analyses produced adjusted estimates of effects together
with standard errors, which give the least biased and most precise estimates of the effect sizes.
Limitations of our study include the possibility of publication bias, which is inherent in
any meta-analysis, but which we attempted to overcome by sourcing for unpublished studies
(e.g. dissertations, conferences), as well as the inevitable inclusion of underpowered studies,

49
which is not uncommon in meta-analyses: 66% of Cochrane meta-analyses themselves are
underpowered (Turner, Bird, & Higgins, 2013). Nevertheless, we highlight that the majority of
the data in our analyses were unpublished findings, as we were examining relatively novel
associations within established datasets. Additionally, we do not have data on the dose, potency,
or distribution of these drugs throughout the body (e.g. whether they are homogenously
distributed or concentrated in certain tissues). We were also unable to control for differences in
vascular risk among our participants, as these data were inconsistently recorded in some of the
original studies, given that vascular contributions to dementia were not always a focus. Further,
in a number of our analyses, one study contributed more weight than all other studies combined.
Nevertheless, with regard to the Attention analyses, the effect sizes of the two contributing
studies were comparable (-0.17 and -0.16). With regard to the Memory (Recall) analyses, the
effect sizes of the studies contributing the least weight (0.10, 0.10, 0.13) were twice that of the
REGARDS study (0.05, which contributed 70% of weight). The fact that our overall effect and
finding of better recall over time was heavily weighted by the smallest effect size among our
studies is therefore indeed remarkable.
Possible mechanisms behind the cognitive effects observed in relation to ARB and ACE-
inhibitor use are not entirely clear, but their effects on the RAS are likely implicated. Links
between RAS metabolites and cognition in humans have additionally yet to be fully elucidated,
which we aimed to do in our second study (Study B). Experimental studies in animal models
have demonstrated improved memory performance following administration of Ang-(1-7) in
models of AD (Uekawa et al., 2016) as well as models of chronic cerebral hypoperfusion (Xie et
al., 2014). With regard to effects on AD-specific pathology, Zhu et al (2011) found that infusions
of Ang II and the stimulation of AT1Rs promoted amyloidgenesis in vivo, an effect which was

50
fully abolished with infusion of losartan (an ARB).
Given these links between RAS peptides, their receptors, and cognition, we investigated
whether levels of Ang II and Ang-(1-7) as well as their receptors (AT1R, AT2R, and Mas)
differed in humans, particularly between cognitively normal participants and participants
showing cognitive impairment. Results from this study did not show differences among these
groups in levels of these RAS constituents. However, since we are limited to blood-based
measures, we cannot say for certain what the tissue levels of RAS metabolites and receptors may
be, and only inferences can be drawn. Other limitations of this study include its observational
nature and characteristics of the sample, which consisted of individuals who were healthy, living
independently, and who were free of stroke and neurological illness at the time of study
enrollment. Vascular risk burden is likely higher in the general population; hence, the results
may be best interpreted as independent of comorbid, confounding vascular disease.
Nevertheless, this study is not without its strengths. It benefits from our assessment of the
relationships among RAS peptides and receptors and cognition in participants with normal
cognition as well as patients with MCI, the assumed preclinical phase of AD. Inclusion of
participants at both stages of disease progression allowed us to examine whether there were
differential associations depending on disease severity. Additionally, the Ang-(1-7) peptide and
its effects on cognition have only, to our knowledge, been examined in two animal studies
(Uekawa et al., 2016; Xie et al., 2014), which reported that Ang-(1-7) showed much protective
effect against biological hallmarks of AD in animals. Our study adds to the existing literature as
the first study examining levels of Ang-(1-7) and associations with cognitive ability in humans.
Given that current pharmaceutical treatments for dementia have only had modest effects
on symptom improvement, modifying risk factors such as hypertension represents the most

51
promising line of work toward dementia prevention (Middleton & Yaffe, 2009). New drug
development necessitates enormous investments of time and money, making the repurposing of
existing medications a key research goal. Our meta-analysis found that in a large, global, and
cognitively intact sample, the use of BBB-crossing ARBs and ACE-inhibitors was linked to
better memory recall over 3-years of follow-up compared to their non-BBB-crossing
counterparts. This finding has clear implications for individuals who remain symptom-free, but
who may experience later cognitive benefit from simply changing their antihypertensive
regimen.

52
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Abstract (if available)

Abstract Hypertension is an established risk factor for cognitive decline and neurodegenerative disease, possibly through its effects on cerebral perfusion and blood-brain barrier (BBB) compromise. Work surrounding antihypertensive treatments have reported their possible salutary effects on cognition and neuropathology. Reviews have highlighted that angiotensin II receptor blockers (ARBs) and angiotensin-converting-enzyme (ACE)-inhibitors are linked to improved or maintained cognition in later life. In a small number of studies, ARBs and ACE-inhibitors which are able to cross the BBB have been linked to lower risk of dementia compared to non-BBB-penetrant counterparts. However, no studies have examined these associations meta-analytically, hampered by the fact that BBB-crossing potential is not a consideration in prescribing practice, and also that few studies of older adults consider pharmacokinetic properties of drugs taken. Our review fills this gap by acquiring individual participant data from 13 studies—spanning 6 countries and published in the last 18 years—and coding for BBB-crossing potential within participants using antihypertensive medications. As BBB-crossing potential was unexamined in 13 of 14 studies meta-analyzed, the majority of effect sizes in our meta-analysis are previously unpublished. In our international, cognitively intact sample, taking BBB-crossing antihypertensive drugs was associated with better performance on memory recall over up to 3 years of follow-up, relative to taking non-BBB-crossing medications. Taking non-BBB-crossing medications was linked to better attention over time. In primary data analyses, we did not find differences in renin-angiotensin-system metabolites among cognitively normal and cognitively impaired older adults. Our meta-analytic results have implications for asymptomatic individuals who may experience later cognitive benefit from changing their antihypertensive regimen.

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Creator Ho, Jean Kay (author)

Core Title Renin-angiotensin system antihypertensive medicines with and without blood-brain-barrier crossing potential: effects on cognitive outcomes in the elderly

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 07/22/2021

Defense Date 05/18/2020

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

Tag ACE-inhibitors,ARBs,blood-brain-barrier,cognition,dementia,hypertension,OAI-PMH Harvest

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Huey, Stanley Jr. (committee chair), Lai, Mark Hokchoi (committee member), Louie, Stan (committee member), Nation, Daniel (committee member)

Creator Email jean.ho@alumni.duke.edu,jeankayho@gmail.com

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