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Long-term blood pressure variability across the clinical and biomarker spectrum of Alzheimer’s disease

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Long-term blood pressure variability across the clinical and biomarker spectrum of Alzheimer’s disease

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TITLE: Long-term blood pressure variability across the clinical and biomarker spectrum of
Alzheimer’s disease
by
Isabel Jean Sible

A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF ARTS
PSYCHOLOGY
August 2020

Copyright 2020 Isabel Jean Sible

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ACKNOWLEDGEMENTS
Funding
The study data analysis was supported by NIH/NIA grants (R01AG064228,
R01AG060049, R21AG055034, P50 AG005142, and P01 AG052350) and Alzheimer’s
Association grant AARG-17-532905. Data collection and sharing for this project was funded by
the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant
U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-
0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical
Imaging and Bioengineering, and through generous contributions from the following: AbbVie,
Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech;
BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.;
Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and
its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen
Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical
Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale
Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals
Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and
Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to
support ADNI clinical sites in Canada. Private sector contributions are facilitated by the
Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the
Northern California Institute for Research and Education, and the study is coordinated by the
Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data
are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
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TABLE OF CONTENTS

Acknowledgements………………………………………………………………………………..ii
List of Tables……………………………………………………………………………..……....iv
List of Figures……..………………………………………………………………………………v
Abstract…………………………………………………………………………………………...vi
Introduction………………………………………………………………………………………..1
Materials and Methods………………………………………………………………………….....3
Participants………………………………………………………………………………...3
Measures…………………………………………………………………………………..4
Statistical Analysis………………………………………………………………………….……..8
Results……………………………………………………………………………………………..9
Discussion………………………………………………………………………………………..12
Conclusion……………………………………………………………………………………….16
References………………………………………………………………………………………..18
Supplementary Materials………………………………………………………………………...41
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LIST OF TABLES
1. Table 1………………………………………………………………………………33-34
2. Table 2………………………………………………………………………………35-36
3. Supplementary Table 1…………………………………………………………………..45
4. Supplementary Table 2………………………………………………………………46-47
5. Supplementary Table 3………………………………………………………………48-49
6. Supplementary Table 4……………………………………………………………….50-51

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LIST OF FIGURES
1. Figure 1………………………………………………………………………………37-38
2. Figure 2……………………………………………………………………………….39-40
3. Supplementary Figure 1………………………………………………………………….52
4. Supplementary Figure 2………………………………………………………………53-54
5. Supplementary Figure 3………………………………………………………………55-56
6. Supplementary Figure 4………………………………………………………………57-58

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ABSTRACT AND KEY WORDS
Background
Elevated blood pressure is linked to cognitive impairment and Alzheimer’s disease biomarker
abnormality. However, blood pressure levels vary over time. Less is known about the role of
long-term blood pressure variability in cognitive impairment and Alzheimer’s disease
pathophysiology.
Objective
Determine whether long-term blood pressure variability is elevated across the clinical and
biomarker spectrum of Alzheimer’s disease.
Methods
Alzheimer’s Disease Neuroimaging Initiative participants (cognitively normal, mild cognitive
impairment, Alzheimer’s dementia [n=1421]) underwent baseline exam, including blood
pressure measurement at 0, 6, 12 months. A subset (n=318) underwent baseline lumbar puncture
to determine cerebral spinal fluid amyloid-β and phosphorylated tau levels. Clinical groups and
biomarker-confirmed Alzheimer’s disease groups were compared on blood pressure variability
over 12 months.
Results
Systolic blood pressure variability was elevated in clinically diagnosed Alzheimer’s dementia
(VIM: F2,1195 = 6.657, p = 0.001, η2 = 0.01) compared to cognitively normal participants (p =
.001), and in mild cognitive impairment relative to cognitively normal participants (p = .01).
Findings were maintained in biomarker-confirmed Alzheimer’s disease (VIM: F2,850 = 5.216, p =
0.006, η2 = 0.01), such that systolic blood pressure variability was elevated in biomarker-
confirmed dementia due to Alzheimer’s disease relative to cognitively normal participants (p =
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.005) and in biomarker-confirmed mild cognitive impairment due to Alzheimer’s disease
compared to cognitively normal participants (p = .04).
Conclusion
Long-term systolic blood pressure variability is elevated in cognitive impairment due to
Alzheimer’s disease. Blood pressure variability may represent an understudied aspect of vascular
dysfunction in Alzheimer’s disease with potential clinical implications.

Key Words: Blood Pressure; Alzheimer Disease; Amyloid; tau Proteins; Cognitive Dysfunction
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1
INTRODUCTION
A large body of research suggests a link between hypertension and cognitive decline,
with deleterious effects noted across cognitive domains that include memory, attention,
language, processing speed, and visuospatial perception [1–8]. Moreover, high blood pressure
(BP) has been associated with an increased risk for dementia [2,3,8], neuropathological changes
in patients with dementia [9–18], and in mouse models of Alzheimer’s disease (AD) [19]. On the
other hand, low BP has also been associated with increased dementia risk [2,3,20–24], and BP
levels have been shown to decrease with advancing clinical symptoms of AD [25]. Together
these studies underscore the potential importance of careful BP assessment and treatment in the
prevention of cognitive decline in older adults.
In addition to the importance of average BP levels, blood pressure variability (BPV) over
several months and years (e.g., long-term BPV) is thought to be a key index of cardiovascular
health [26]. Despite the potential value of BPV, the vast majority of prior observational research
and clinical trials have focused on static measures of BP. It is widely appreciated that BP is
dynamic and highly variable [26], and BP levels tend to fluctuate over multiple time-scales due
to a host of internal and external factors [26]. The inherent variability of BP limits the value and
reliability of average BP levels as a biomarker of neurocognitive dysfunction. These fluctuations
in BP also have important implications for brain health since variable pressure must be
counteracted by homeostatic mechanisms, including baroreflex function and cerebral
autoregulation, in order to ensure steady brain perfusion supporting normal neurological function
[27–31]. However, these homeostatic processes can be disrupted by a number of pathological
processes, including cerebrovascular remodeling due to chronic hypertension, leaving the brain
vulnerable to hypoperfusion injury [29,32–39].
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Elevated BPV has been reported in AD [40–42], and is recognized as a risk factor for
cognitive impairment and dementia in the general older adult population [43–49], even in those
with well-controlled average BP [50]. Several of these studies report the prognostic value of
BPV in predicting cognitive decline and dementia risk is beyond that of average BP
[40,44,46,48,51]. Prior research on BPV in cognitive impairment and AD has a number of
limitations, including investigating BPV in a combined group of mild-to-moderate AD patients
[40–42], combining cognitively unimpaired and mildly impaired participants into one “non-
demented” group [51], lack of characterization of older adult samples [43–47], and reliance on
clinical diagnosis without biomarker confirmation [40–44,49,51]. Thus, it remains unclear
whether increased BPV occurs in the more mild stages of cognitive impairment and whether it is
specific to one etiology [40–51]. Importantly, we are not aware of any studies investigating BPV
in mildly impaired participants relative to cognitively unimpaired older adults or to patients with
AD dementia. Although one study compared BPV in AD patients versus cognitively normal
controls [41], elevation of mildly impaired participants may be important for early diagnosis and
treatment implications. Moreover, examining BPV across the biomarker-confirmed AD clinical
spectrum could provide insight into disease-specific profiles. Prior studies have linked elevated
BP and cerebrovascular resistance to AD biomarker abnormality [52–56], but to our knowledge
no studies to date have examined BPV in patients with AD biomarker abnormality. The aim of
the current study was two-fold. First, we compared BPV in older adults with a clinical diagnosis
of AD dementia, mild cognitive impairment (MCI) or cognitively normal (CN). Second, we
compared BPV in older adults with biomarker-confirmed dementia due to AD, MCI due to AD,
and CN.

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MATERIALS AND METHODS
Participants
Data used in the preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003
as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The
primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI),
positron emission tomography (PET), other biological markers, and clinical and
neuropsychological assessment can be combined to measure the progression of MCI and early
AD. Volunteer adults aged 55-91 (inclusive) were recruited from more than 50 sites across the
United States and Canada, and were enrolled if they had few depressive symptoms (Geriatric
Depression Scale score below 6), were free of significant neurological disease (apart from
suspected AD), and had low vascular risk (Hachinski Ischemic Score at or below 4). Further
information on recruitment and screening can be found on the ADNI website (www.adni-
info.org).
Ethics approval was obtained for each institution involved. This study was conducted
according to Good Clinical Practice guidelines, the Declaration of Helsinki, US 21 CFR Part 50-
Protection of Human Subjects, and Part 56- Institutional Review Boards, and pursuant to state
and federal HIPAA regulations. Institutional Review Boards were constituted following State
and Federal requirements at each participating location. Study protocols were approved by the
appropriate Boards and submitted to Regulatory Affairs at the ADNI Coordinating Center prior
to the start of the study. All participants and/or authorized representatives and study partners
provided written informed consent for the study prior to protocol-specific procedures. For more
information, see www.adni-info.org.

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For the present study, we included ADNI participants with an initial clinical evaluation
and health exam that included BP measurement at baseline, 6 months, and 12 months follow up.
A subset of these participants also underwent baseline lumbar puncture for evaluation of cerebral
spinal fluid (CSF) AD biomarkers. See Supplementary Table 1 for information on included
versus excluded participants in the present study.

Measures
Clinical group assessment
Baseline evaluation determined initial clinical diagnosis. Criteria for ADNI diagnoses of
MCI included: subjective memory complaint reported by the participant or informant; Mini-
Mental State Examination (MMSE) scores between 24 and 30 (inclusive); global Clinical
Dementia Rating (CDR) scale score of 0.5; scores on delayed recall of Story A of the Wechsler
Memory Scale Revised (WMS-R) Logical Memory II subtest that are below expected
performance based on years of education; general presentation that would disqualify for a
diagnosis of AD [57]. A diagnosis of AD dementia was assigned if the National Institute of
Neurological and Communicative Disorders and Stroke and Alzheimer’s Disease and Related
Disorders Association (NINCDS-ADRDA) criteria for probable AD were met, including MMSE
scores between 20 and 26 (inclusive), and CDR scores of 0.5 or 1 [58]. Participants were deemed
to be cognitively normal (CN) if neither diagnostic criteria were met.
Alternative diagnostic criteria for MCI were developed in efforts to reduce the known
high false-positive rate of MCI classification by the ADNI criteria [59,60]. Given our particular
interest in characterizing BPV during milder stages of disease, and the high potential for
misclassification, we conducted a cluster analysis of neuropsychological test performance among

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ADNI-defined MCI participants as previously described [60]. Briefly, neuropsychological test
scores (Rey Auditory Verbal Learning Test delayed memory recall, Rey Auditory Verbal
Learning Test delayed memory recognition, Animal fluency, Boston Naming Test, Trail Making
Test Parts A & B) covering three cognitive domains (memory, language, executive function)
were entered into a cluster analysis to derive three previously documented subtypes of MCI
(amnestic MCI, dysnomic MCI, and dysexecutive MCI), as well as a cluster-derived CN group
[60]. The ADNI-defined CN and the cluster-derived CN were combined into one CN group, and
the three cluster-derived MCI subtypes were combined into one MCI group [60]. Thus, our
primary analyses included participants with cluster-informed CN and MCI, and ADNI-defined
AD.
To contribute to the growing literature using this cluster method, as well as to validate
previous study findings of BPV using more conventional criteria [51], identical secondary
analyses were conducted in parallel with a sample of participants identified as CN, MCI, and AD
dementia using the conventional ADNI criteria (see Supplementary Materials). Potential
differences between the diagnostic schemes were examined, as the ADNI database has been used
in a previous study of BPV in CN and MCI participants based on ADNI diagnostic criteria [51].

Blood pressure measures
Physiological measures included brachial artery systolic BP and diastolic BP collected
during a health exam using a standardized ADNI protocol described elsewhere (www.adni-
info.org). Briefly, a calibrated mercury sphygmomanometer recorded BP from the dominant
forearm arranged at the horizontal level of the fourth intercostal space at the sternum while the
participant was seated and resting. BP assessment was conducted during each exam, including

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baseline, 6 months, and 12 months follow up. Average BP and BPV (standard deviation [SD],
coefficient of variation [CV] [100 x SD / mean], variation independent of mean [VIM]) were
calculated for each participant using the three BP measurements collected. VIM is a commonly
used index of long-term BPV and has no correlation with average BP levels over visits [42,61].
VIM was calculated using the formula: VIM = SD/meanx, where the power x was derived from
non-linear curve fitting (BP SD on y-axis against average BP on x-axis) using the nls package in
R Project [61,62].

Other physiological measures
Blood samples were collected by venipuncture and used to determine apolipoprotein E
(APOE)-ϵ4 carrier status [63]. Participants were categorized into those with or without at least
one copy of the APOE-ϵ4 allele.

Vascular risk factors
Vascular risk factor burden was determined by physical exam and clinical interview as
part of the general health evaluation at study entry. For the present study, general health
evaluation data were screened and coded for vascular risk factors most relevant to
cerebrovascular disease and cognition based on the Framingham Stroke Risk Profile [53,64–66].
Specifically, history of cardiovascular disease (i.e., myocardial infarction, intermittent
claudication, angina, heart failure, or other evidence of coronary disease), type 2 diabetes
mellitus, atrial fibrillation, evidence of carotid artery disease, and transient ischemic attack or
minor stroke were included as vascular risk factors. Each participant was determined to have low
vascular risk (i.e., the presence of ≤ 1 vascular risk factor) or high vascular risk (i.e., the presence

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of ≥ 2 vascular risk factors) based on prior studies linking vascular risk burden to
cerebrovascular pathology at autopsy [53,66]. Baseline body mass index (BMI) was calculated as
weight (kg) divided by height (meters) squared. Medications taken at baseline evaluation were
screened and participants were classified as those taking antihypertensive medication (all major
classes of hypertensive medications) versus those who were not taking antihypertensive
medication, as well as those who were taking acetylcholinesterase inhibitor (ChEIs) medication
versus those who were not taking ChEIs. Hypertensive status was determined based on mean BP.

Biomarker-confirmed Alzheimer’s disease diagnosis
A subset of participants underwent baseline lumbar puncture to obtain CSF samples for
assessment of amyloid-β (Aβ) and phosphorylated tau (Ptau) levels using methods detailed
elsewhere [67–70]. Briefly, Roche Elecsys Aβ CSF and Elecsys Ptau CSF immunoassays were
used to measure Aβ and Ptau levels in CSF aliquots following a Roche Study protocol at the
UPENN/ADNI Biomarker Laboratory. Acceptance criteria were met using previously described
analyte measuring ranges with lower to upper technical limits [67]. Using established guidelines
[69], participants with Aβ values at or above 980 pg/ml were characterized as Aβ negative and
participants with values below this cutoff were defined as Aβ positive. Participants were defined
as Ptau negative with values of Ptau at or below 21.8, and as Ptau positive with values above this
threshold.
To investigate BP in relation to cognitive impairment with biomarker-confirmed AD
pathophysiology, participants identified as MCI or AD dementia who had available biomarker
data were classified into one of two biomarker groups based on biomarker status of Aβ and Ptau

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per research recommendations for the diagnosis of AD [71,72]: MCI with two positive
biomarkers (MCIAβ+Ptau+), or AD dementia with two positive biomarkers (ADAβ+Ptau+).
To explore the contribution of specific biomarker burden on BPV, CN participants
identified using the cluster method who had available biomarker data were further classified into
intermediate biomarker groups for exploratory analyses (see Supplementary Methods).

STATISTICAL ANALYSIS
Systolic and diastolic BPV values (SD, CV, VIM) were not normally distributed and
were corrected through log transformation. Outliers of each BP measurement (mean, SD, CV,
VIM) were removed if they were greater than +/- 3 SD from the mean. One-way analysis of
variance (ANOVA) and chi-square tests were used to compare demographic variables (age, sex,
BMI, education, APOE-ϵ4 carrier status, antihypertensive medication use, ChEI use, vascular
risk level) among clinical and biomarker-confirmed AD groups. Analysis of covariance
(ANCOVA) models compared BP measurements across clinical and biomarker-confirmed AD
groups after covarying for age, sex, BMI, years of education, APOE-ϵ4 carrier status, vascular
risk level, antihypertensive medication use, and ChEI medication use. ANCOVA models also
included average BP over the 12 months as a covariate to account for the high degree of
correlation between average BP and some measures of BPV [26,61]. Potential interaction effects
of group by antihypertensive medication use on BPV, as well as group by average BP on BPV
were also examined. Post-hoc Least Significant Difference (LSD) tests and post-hoc chi-squared
tests were used in the case of significant main effects to determine specific group differences. All
analyses were 2-tailed with significance set at p < .05. Multiple comparison corrections (using
the False Discovery Rate [FDR] method) for significant main effects was set at p < .05 [73].

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Reported values for ANOVA and ANCOVA models include F-value (F), p-value (p), and partial
eta-squared (η2). Reported values for interaction effects include F-value (F) and p-value (p).
Reported values for chi-squared tests include x2 values (x2) and p-values (p). See Supplementary
Materials for identical secondary and exploratory analyses and results. All analyses were carried
out in R Project [62].

RESULTS
Primary analyses of clinical groups included 1421 participants identified through cluster
analysis as CN, MCI, and AD who had valid BP measurements taken at a health exam at
baseline, 6 months, and 12 months follow up. A subset of 318 participants with valid CSF Aβ
and Ptau data from lumbar puncture were included in primary analyses of biomarker-confirmed
AD groups (Supplementary Figure 1).

Demographic Findings
Clinical groups
As summarized in Table 1, there were significant differences among clinical groups by
sex, BMI, years of education, APOE-ϵ4 carrier status, antihypertensive medication use, and ChEI
use.

Biomarker-confirmed Alzheimer’s disease groups
As shown in Table 2, biomarker-confirmed AD groups were significantly different on
BMI, years of education, APOE-ϵ4 carrier status, and ChEI use.

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Demographic findings for secondary and exploratory groups showed a similar pattern
(see Supplementary Results and Supplementary Tables 2-4).

Blood Pressure Variability Findings
Clinical groups
After controlling for age, sex, BMI, years of education, APOE-ϵ4 carrier status, vascular
risk level, antihypertensive medication use, ChEI use, and average BP, there were significant
differences among the clinical groups on systolic BPV (SD: F2,1195 = 5.829, p = 0.003, η2 = 0.01;
CV: F2,1194 = 4.447, p = 0.01, η2 = 0.007; VIM: F2,1195 = 6.657, p = 0.001, η2 = 0.01). Post-hoc
comparisons indicated participants with a clinical diagnosis of AD showed significantly higher
systolic BPV than CN for all measures of variability (SD: p = .002; CV: p = .004; VIM: p =
.001). MCI exhibited greater systolic BPV relative to CN on SD (p = .03) and VIM (p = .01) but
not CV (p = .08) measures of variability. Clinically diagnosed AD did not significantly differ
from MCI on systolic BPV (SD: p = .21; CV: p = .20; VIM: p = .28) (Figure 1a).
There were no statistically significant differences in diastolic BPV among the clinical
groups (SD: F2,1197 = 2.281, p = .10; CV: F2,1197 = 1.904, p = .15; VIM: F2,1197 = 1.992, p = .14)
(Figure 1b). There were also no significant interaction effects of clinical group by
antihypertensive medication use on BPV (systolic: SD: F2,1193 = 2.203, p = .11; CV: F2,1192 =
2.040, p = .13; VIM: F2,1193 = 2.241, p = .11; diastolic: SD: F2,1195 = 0.014, p = .99; CV: F2,1195 =
0.069, p = .93; VIM: F2,1195 = 1.465, p = .23), or of clinical group by average BP on BPV
(systolic: SD: F2,1193 = 1.222, p = .30; CV: F2,1192 = 1.264, p = .29; VIM: F2,1193 = 1.855, p = .16;
diastolic: SD: F2,1195 = 0.459, p = .63; CV: F2,1195 = 0.145, p = .87; VIM: F2,1195 = 0.717, p = .49).

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Secondary analyses of clinical groups showed a similar pattern of BPV findings (see
Supplementary Results and Supplementary Figure 2).

Biomarker-confirmed Alzheimer’s disease groups
Biomarker-confirmed AD groups differed significantly on systolic BPV (SD: F2,850 =
3.955, p = 0.02, η2 = 0.009; VIM: F2,850 = 5.216, p = 0.006, η2 = 0.01; trending CV: F2,850 =
2.928, p = 0.05, η2 = 0.007), such that ADAβ+Ptau+ participants showed significantly higher
systolic BPV than CN participants for all measures of variability (SD: p = .007; CV: p = .02;
VIM: p = .005). MCIAβ+Ptau+ participants exhibited significantly higher systolic BPV relative to
CN on VIM (p = .04) but not SD (p = .21) or CV (p = .38) measures of variability. There were
no statistically significant differences in systolic BPV between ADAβ+Ptau+ and MCIAβ+Ptau+ (SD:
p = .19; CV: p = .19; VIM: p = .43) (Figure 2a).
Biomarker-confirmed AD groups did not differ significantly on diastolic BPV (SD: F2,851
= 0.236, p = .79; CV: F2,853 = 0.175, p = .84; VIM: F2,851 = 1.331, p = .27) (Figure 2b). There
were also no significant interaction effects of biomarker-confirmed AD group by
antihypertensive medication use on BPV (systolic: SD: F2,848 = 0.452, p = .64; CV: F2,848 =
0.293, p = .75; VIM: F2,848 = 0.184, p = .83; diastolic: SD: F2,849 = 0.446, p = .64; CV: F2,851 =
0.448, p = .64; VIM: F2,849 = 0.505, p = .60) , or of biomarker-confirmed AD group by average
BP on BPV (systolic: SD: F2,848 = 0.738, p = .48; CV: F2,848 = 0.815, p = .44; VIM: F2,848 =
1.366, p = .26; diastolic: SD: F2,849 = 0.972, p = .38; CV: F2,851 = 0.253, p = .78; VIM: F2,849 =
0.084, p = .92).
Secondary analyses of biomarker-confirmed AD groups showed a similar pattern of BPV
findings (see Supplementary Results and Supplementary Figure 3).

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Exploratory analyses of CN intermediate biomarker groups revealed no significant
differences in systolic or diastolic BPV by level of AD biomarker burden (see Supplementary
Results and Supplementary Figure 4).
All primary analyses of systolic BPV findings survived FDR correction.

DISCUSSION
Study findings suggest that long-term systolic BPV over one year is elevated in older
adults with cognitive impairment due to AD. The present investigation replicated the previously
published elevation of systolic BPV in clinically diagnosed AD compared to age-matched
controls [41], and extended prior work by using multiple CSF biomarkers to confirm systolic
BPV elevation in older adults with a pathological diagnosis of dementia due to AD [71,72]. With
regard to more mild levels of symptoms, prior studies of BPV have combined clinical groups
[40,42,51], obscuring whether BPV is elevated in MCI. The present study is the first to
demonstrate increased systolic BPV at the MCI stage and to further demonstrate that increased
systolic BPV specifically applies to MCI in the presence of AD biomarker abnormality.
Participant groups did not significantly differ by diastolic BPV in any analyses. Exploratory
analyses also revealed no significant differences in systolic or diastolic BPV among cognitively
unimpaired participants with varying levels of AD biomarker burden. This suggests that systolic
BPV is linked to AD-related cognitive impairment rather than AD in the absence of cognitive
dysfunction, a finding that is consistent with recent studies implicating vascular factors in AD-
related cognitive decline specifically [74]. Importantly, there were also no significant
interactions between BPV and antihypertensive use, or between BPV and average BP levels.
Thus, results indicate increased systolic BPV, but not diastolic BPV, may occur in the early

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stages of cognitive decline in AD. Findings were similar using conventional criteria but were
more consistent using cluster-derived groups, a pattern previously observed when using refined
MCI classifications to investigate biomarker associations in the ADNI cohort [75]. Study
findings provide novel insights into the timeline of BPV elevation with respect to MCI diagnosis
and AD pathology, which may have important diagnostic and treatment implications.
The present study does not investigate possible mechanisms responsible for the observed
increase in systolic BPV in MCI and AD, but it has been hypothesized that variable BP may
induce variability of cerebral perfusion and impact brain health and cognition. Over time,
chronic high fluctuations in BP may outreach the homeostatic mechanisms that work to steady
changing BP levels, making the brain more vulnerable to waxing and waning levels of cerebral
blood flow [76]. Erratic levels of cerebral perfusion threaten the brain’s need for continuous
circulation of oxygen and glucose, and may lead to cerebrovascular injury and disrupted
functioning [28,30,37,39,76–79].
Another possible explanation for the study findings is that long-term arterial stiffening
may be responsible for both inflated BPV [32,80–87] and AD-related cognitive decline [88].
Arterial stiffness may increase BPV through distinct mechanisms involving changes in the
timing and buffering of the pulse wave as it is propagated throughout the arterial tree and
reflected to the heart [89]. Additionally, arterial stiffness may cause biophysical injury to the
brain by passing pulsatile forces into the vulnerable cerebrovasculature, but also by interfering
with clearance of toxic proteins along perivascular and/or lymphatic spaces [90,91]. Therefore,
arterial stiffness may be responsible for the observed correlation between BPV and brain health.
Future studies that directly investigate mechanisms are needed to disentangle these relationships.

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Alternatively, neurodegenerative effects on cortical control of autonomic nervous system
regulation may cause amplification of BPV [92]. Specifically, the insular cortex, anterior
cingulate gyrus, and amygdala regulate autonomic nervous system activity [92], and
neurodegeneration in these areas is related to autonomic disruption [93–99]. In addition, the
locus coeruleus plays a major role in regulating autonomic activity, and is an early site of AD
pathology [100]. Together these studies suggest that AD pathology impacting central nervous
system control of autonomic activity may influence BP and BPV.
While increased BPV is a cardiovascular risk factor in the general population, it may be
particularly detrimental in AD since these patients already show decreased cerebral perfusion
[101], increased cerebrovascular resistance [56], and autonomic abnormalities [2]. Individuals
with AD pathology and elevated BPV may be especially promising candidates for therapeutic
intervention. Despite the known dynamic nature of BP, most BP therapies focus on modifying
average levels [102,103]. Thus, the potential role of BPV with regard to informing diagnosis,
treatment and/or prevention of neuropathological processes warrants further investigation.
Dysregulated BP has garnered enormous attention from both the clinical and scientific
communities, in part because BP is a highly modifiable risk factor for cardiovascular [104] and
cognitive outcomes [103,105]. It is also increasingly recognized that early intervention offers the
highest likelihood to alter disease trajectories to prevent dementia [105]. It has been estimated
that BP control, particularly in midlife, could significantly reduce the world-wide prevalence of
AD [106]. Given the importance of BP as an early modifiable risk factor, understanding the
potential role of BPV during the early stages of cognitive impairment may be of great value for
early diagnostic and treatment studies.

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The present investigation has a number of strengths. First, the study used both CSF Aβ
and Ptau biomarkers to confirm AD diagnosis. In doing so, we were able to characterize long-
term BPV in cognitive impairment specifically due to AD. Second, the study compared distinct
groups of rigorously defined CN, MCI and AD on BPV, providing insights into the
characterization of BPV elevation across the spectrum of AD. Third, the study utilized multiple
diagnostic methods for distinguishing between CN and MCI participants. Fourth, while some
studies on long-term BPV in cognitive impairment and dementia measured BPV over more than
12 months, consistent with other studies [40,45], the present study revealed elevation across
groups over just one year, which may suggest the immediate influence variable BP may have in
aging adults. Fifth, the study accounted for medication use known to affect BP levels (e.g.,
antihypertensive agents), and autonomic nervous system activity perhaps especially in patients
with cognitive impairment (e.g., ChEIs) [107]. The present study cannot address the role of
specific antihypertensive medication classes in the observed clinical and pathological
associations with BPV; however, it should be noted that some antihypertensive medications may
influence BPV more than others. Specifically, some studies have reported different class effects
on risk of stroke [102,108]. The literature is mixed in terms of which specific antihypertensive
medication class may have the greatest impact on cognition [109–111], and may vary based on
BP dependent and independent effects since some medications may cross the blood-brain barrier
to directly influence the central nervous system [112]. A final study strength is that the study had
a large sample size and drew from a well-characterized participant pool, which included a
detailed panel of vascular health.
Limitations of the present study include the fact that some details of BP measurement
were not explicitly standardized across sites and the study utilized three measurements of BP to

16
calculate BPV. Although it has been recommended to use more than three BP measurements to
estimate BPV for predicting cardiovascular risk (i.e., stroke) [113], one longitudinal study
predicted cognitive impairment and dementia using just three BP measurements [44]. Another
study limitation is the demographic differences between included and excluded participants. As
summarized in Supplementary Table 1, the included participants were older, less educated,
contain a smaller percent of males, and differ by distribution of baseline clinical diagnosis, when
compared to participants excluded from the study. It should be noted that ADNI protocols
changed mid-study from collecting BP measurement every six months to collecting it every 12
months, which may further influence these differences. Participants excluded from the present
study due to missing BP data were largely excluded because they were newly enrolled and only
had baseline BP recorded at this point in their study involvement. Other limitations of the present
investigation include the cross-sectional nature of the study design and the lack of direct
measures of arterial stiffening or other vascular mediators linking BPV to the AD spectrum, such
as cerebral perfusion or autoregulation. Future studies will investigate the role these processes
may have in the progression to dementia. Future studies will also investigate how
cerebrovascular autoregulation and neuropathological factors may moderate the relationship
between BPV and cognitive impairment. While the present study included a substantial number
of participants with MCI, an even larger sample size would allow evaluation of MCI subtypes
(i.e., amnestic, dysnomic, dysexecutive) and intermediate biomarker categories to further
determine the timing and etiology of BPV elevation.

CONCLUSION

17
Study findings indicate that systolic BPV is elevated in cognitive impairment due to AD.
Importantly, these findings are independent of average BP levels, which have historically been
the main focus of BP research [2] and clinical trials [103]. Given the high overlap of vascular
pathology and neurodegeneration [9], the interest to study BPV in the context of cognitive aging
is growing. Beyond understanding how BPV is characterized in the general aging population,
studying the role of BPV in at-risk populations with AD pathology and neurodegeneration may
reveal understudied targets for vascular contributions to cognitive impairment and dementia.

18
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33
Table 1
Clinical and Demographic Data for Clinical Groups
CN
(n=681)
MCI
(n=479)
AD
(n=261)
F or 𝒙 2 p-value
Clinical/Demographic
Age, yrs 73.9 (6.8) 73.6 (7.2) 75.4 (7.6) 2.954 .05
Sex (n,% Male)c 358
(52.6%)
295
(61.6%)
145
(55.6%)
9.333 .009
BMI, kg/m2abc 27.4 (4.9) 26.6 (4.3) 25.6 (4.3) 14.915 < .001
Education, yrsa 16.3 (2.7) 15.9 (2.9) 15.2 (2.9) 12.409 < .001
APOE-ϵ4 carriers
(n,%)abc
232
(34.1%)
260
(54.3%)
178
(68.2%)
102.950 < .001
Hypertensive (n,%) 317
(46.6%)
236
(49.3%)
134
(51.3%)
1.936 .38
Medication Use, (n,%)
Antihypertensive
agentsab
128
(18.8%)
107
(22.3%)
77 (29.5%) 12.683 .002
ACE inhibitors 38 (29.7%) 34 (31.8%) 24 (31.2%)
Alpha-blockers 7 (18.3%) 5 (4.7%) 7 (9.1%)
Angiotensin II
inhibitors
29 (22.7%) 23 (21.5%) 19 (24.7%)
Calcium channel-
blockers
37 (28.9%) 19 (17.8%) 15 (19.5%)
Central agonists 0 (0.0%) 2 (1.9%) 0 (0.0%)
Combined alpha-beta-
blockers
2 (1.6%) 5 (4.7%) 0 (0.0%)
Diuretics 15 (11.7%) 19 (17.8%) 10 (13.0%)
Vasodilators 0 (0.0%) 0 (0.0%) 2 (2.6%)
ChEIsabc 38
(5.6%)
117
(24.4%)
112
(42.9%)
187.38 < .001
Mean BP, mmHg

34
Systolic 132.4
(13.0)
133.2
(13.4)
133.8
(13.5)
0.601 .55
Diastolic 73.5 (7.9) 74.0 (7.5) 74.2 (6.7) 0.852 .43
Baseline Vascular Risk
Factors, (n,%)

Cardiovascular
disease
74 (10.9%) 60 (12.5%) 33 (12.6%) 0.992 .61
Atrial Fibrillation 28 (4.1%) 2.3% (11) 1.8% (12) 3.618 .16
Type 2 diabetes mellitus 42 (6.2%) 44 (9.2%) 19 (7.3%) 3.750 .15
Carotid artery disease 3 (0.4%) 6 (1.3%) 1 (0.4%) 3.124 .21
TIA/minor stroke 16 (2.4%) 9 (1.9%) 7 (2.7%) 0.552 .76
Means and standard deviations shown unless otherwise indicated.
Significant differences (p < .05) among clinical groups are identified in boldface type.
a indicates a Least Significant Difference-corrected pairwise difference between AD and CN at p
< .05
b indicates a Least Significant Difference-corrected pairwise difference between AD and MCI at
p < .05
c indicates a Least Significant Difference-corrected pairwise difference between MCI and CN at
p < .05
Abbreviations: ACE = angiotensin-converting enzyme; APOE = apolipoprotein E; BP = blood
pressure; BMI = body mass index; ChEIs = acetylcholinesterase inhibitors; TIA = transient
ischemic attack; CN = cognitively normal; MCI = Mild Cognitive Impairment; AD =
Alzheimer’s disease

35
Table 2
Clinical and Demographic Data for Biomarker-Confirmed AD Groups
CN
(n=681)
MCIAβ+Ptau+
(n=185)
ADAβ+Ptau+
(n=133)
F or 𝒙 2 p-value
Clinical/Demographic
Age, yrs 73.9 (6.8) 73.6 (7.1) 74.3 (8.0) 0.557 .57
Sex (n,% Male) 358
(52.6%)
99 (53.5%) 71 (53.4%) 0.069 .97
BMI, kg/m2ac 27.4 (4.9) 26.1 (4.1) 25.3 (4.3) 13.138 < .001
Education, yrsa 16.3 (2.7) 16.1 (2.8) 15.4 (2.8) 5.022 .007
APOE-ϵ4 carriers (n,%)ac 232
(34.1%)
132
(72.4%)
103
(77.4%)
143.260 < .001
Hypertensive (n,%) 317
(46.6%)
91
(49.2%)
68
(51.1%)
0.991 .61
Medication Use, (n,%)
Antihypertensive agents 128
(18.8%)
33 (17.8%) 30 (22.6%) 1.259 .53
ACE inhibitors 38 (29.7%) 12
(36.4%)
11 (36.7%)
Alpha-blockers 7 (18.3%) 1 (3.0%) 0 (0.0%)
Angiotensin II inhibitors 29 (22.7%) 6 (18.2%) 7 (23.3%)
Calcium channel-
blockers
37 (28.9%) 5 (15.2%) 9 (30.0%)
Central agonists 0 (0.0%) 0 (0.0%) 0 (0.0%)
Combined alpha-beta-
blockers
2 (1.6%) 0 (0.0%) 0 (0.0%)
Diuretics 15 (11.7%) 9 (27.3%) 3 (10.0%)
Vasodilators 0 (0.0%) 0 (0.0%) 0 (0.0%)
ChEIsabc 38 (5.6%) 49 (26.5%) 47 (35.3%) 118.25 < .001
Mean BP, mmHg

36
Systolic 132.4 (13.0) 133.3 (13.6) 133.1 (12.4) 0.319 .73
Diastolic 73.5 (7.9) 73.4 (6.9) 74.4 (6.4) 1.407 .35
Baseline Vascular Risk
Factors, (n,%)

Cardiovascular disease 74 (10.9%) 26 (14.1%) 19 (14.3%) 2.233 .33
Atrial Fibrillation 28 (4.1%) 4 (0.5%) 1 (3.0%) 5.851 .05
Type 2 diabetes mellitus 42 (6.2%) 14 (7.6%) 8 (6.0%) 0.515 .77
Carotid artery disease 3 (0.4%) 3 (1.6%) 1 (0.8%) 2.923 .23
TIA/minor stroke 16 (2.4%) 4 (2.2%) 4 (3.0%) 0.261 .88
Means and standard deviations shown unless otherwise indicated.
Significant differences (p < .05) among biomarker-confirmed AD groups are identified in
boldface type.
a indicates a Least Significant Difference-corrected pairwise difference between ADAβ+Ptau+ and
CN at p < .05
b indicates a Least Significant Difference-corrected pairwise difference between ADAβ+Ptau+ and
MCIAβ+Ptau+ at p < .05
c indicates a Least Significant Difference-corrected pairwise difference between MCIAβ+Ptau+ and
CN at p < .05
Abbreviations: ACE = angiotensin-converting enzyme; Aβ = amyloid-β; APOE = apolipoprotein
E; BP = blood pressure; BMI = body mass index; ChEIs = acetylcholinesterase inhibitors; Ptau =
phosphorylated tau; TIA = transient ischemic attack; CN = cognitively normal; MCI = Mild
Cognitive Impairment; AD = Alzheimer’s disease

37
Figure 1. Systolic BPV by Clinical Group
1a. Systolic BPV was greatest in clinically diagnosed AD overall and compared to CN for all
measures of BPV. Clinically diagnosed MCI exhibited greater systolic BPV relative to CN on
SD and VIM measures of BPV. 1b. Clinical groups did not significantly differ by diastolic BPV.
Boxplot lines display minimum, 1st quartile, median, 3rd quartile, and maximum. Abbreviations:
BPV = blood pressure variability; CN = cognitively normal; MCI = Mild Cognitive Impairment;
AD = Alzheimer’s disease; SD = standard deviation; CV = coefficient of variation; VIM =
variation independent of mean

38
1a Systolic BPV

1b Diastolic BPV

39
Figure 2. Systolic BPV by Biomarker-Confirmed AD Group
2a. Systolic BPV was greatest in biomarker-confirmed dementia due to AD overall and
compared to CN for all measures of BPV. Biomarker-confirmed MCI due to AD exhibited
greater systolic BPV relative to CN as measured by VIM. 2b. Biomarker-confirmed AD groups
did not significantly differ by diastolic BPV. Boxplot lines display minimum, 1st quartile,
median, 3rd quartile, and maximum. Aβ+Ptau+ indicates positive biomarkers for both Aβ and
Ptau. Abbreviations: BPV = blood pressure variability; Aβ = amyloid-β; Ptau = phosphorylated
tau CN = cognitively normal; MCI = Mild Cognitive Impairment; AD = Alzheimer’s disease; SD
= standard deviation; CV = coefficient of variation; VIM = variation independent of mean

40
2a Systolic BPV

2b Diastolic BPV
41
Supplementary Material 1
SUPPLEMENTARY METHODS 2
Biomarker-confirmed Alzheimer’s disease diagnosis 3
To explore BP in relation to AD biomarker abnormality in CN participants, study 4
participants identified as CN using cluster analysis who had available biomarker data were 5
classified into one of four biomarker groups based on biomarker status of Aβ and Ptau per 6
research recommendations for the diagnosis of AD [71,72]: CN with two negative biomarkers 7
(CNAβ-Ptau-), CN with Aβ abnormality only (CNAβ+Ptau-), CN with Ptau abnormality only (CNAβ- 8
Ptau+), or CN with two positive biomarkers (CNAβ+Ptau+). 9
10
SUPPLEMENTARY RESULTS 11
Secondary analyses of ADNI clinical groups included 1436 participants who met ADNI 12
diagnostic criteria for CN, MCI, and AD and had valid BP measurements taken at a health exam 13
at baseline, 6 months, and 12 months follow up. A subset of 374 participants with valid CSF Aβ 14
and Ptau data from lumbar puncture were included in secondary analyses of ADNI biomarker- 15
confirmed AD groups. Exploratory analyses of CN intermediate biomarker groups included 396 16
participants. 17
18
Demographic Findings 19
Clinical groups 20
As summarized in Supplementary Table 2, there were significant differences among 21
secondary clinical groups on age, sex, BMI, years of education, APOE-ϵ4 carrier status, 22
antihypertensive medication use, and ChEI use. 23

42
Biomarker-confirmed Alzheimer’s disease groups 1
As shown in Supplementary Table 3, there were significant differences among secondary 2
biomarker-confirmed AD groups by age, BMI, years of education, APOE-ϵ4 carrier status, and 3
ChEI use. 4
As summarized in Supplementary Table 4, there were significant differences among 5
exploratory groups by age, BMI, and APOE-ϵ4 carrier status. 6
7
Blood Pressure Variability Findings 8
Clinical groups 9
Similar to primary clinical groups, secondary clinical groups significantly differed on 10
systolic BPV (SD: F2,1211 = 4.222, p = 0.02, η2 = 0.007; CV: F2,1214 = 4.312, p = .01, η2 = 0.007; 11
VIM: F2,1211 = 4.202, p = 0.02, η2 = 0.007), such that the clinically diagnosed AD group 12
exhibited significantly higher systolic BPV when compared to the CN group on all measures of 13
variability (SD: p = .004; CV: p = .003; VIM: p = .004) and the MCI group based on SD (p = 14
.049) but not CV (p = .06) or VIM (p = .07). The MCI group did not significantly differ from the 15
CN group on systolic BPV (SD: p = .15; CV: p = .13; VIM: p = .11) (Supplementary Figure 16
2a). 17
As with the primary clinical groups, there were no statistically significant differences in 18
diastolic BPV among the secondary clinical groups (SD: F2,1215 = 2.217, p = .11; CV: F2,1214 = 19
2.161, p = .12; VIM: F2,1215 = 1.826, p = .16) (Supplementary Figure 2b). There were also no 20
significant interaction effects of secondary clinical group by antihypertensive medication use on 21
BPV (systolic: SD: F2,1209 = 2.581, p = .08; CV: F2,1212 = 2.602, p = .08; VIM: F2,1209 = 2.970, p 22
= .05; diastolic: SD: F2,1213 = 0.175, p = .84; CV: F2,1212 = 0.229, p = .80; VIM: F2,1213 = 0.089, p 23

43
= .92), or of secondary clinical group by average BP on BPV (systolic: SD: F2,1209 = 1.664, p = 1
.19; CV: F2,1212 = 1.106, p = .33; VIM: F2,1209 = 1.669, p = .19; diastolic: SD: F2,1213 = 0.334, p = 2
.72; CV: F2,1212 = 0.326, p = .72; VIM: F2,1213 = 1.837, p = .16). 3
4
Biomarker-confirmed Alzheimer’s disease groups 5
Similar to primary biomarker-confirmed AD groups, secondary biomarker-confirmed AD 6
groups significantly differed on systolic BPV (SD: F2,669 = 3.356, p = 0.04, η2 = 0.01; CV: F2,667 7
= 3.180, p = .04, η2 = 0.009; VIM: F2,669 = 4.005, p = 0.02, η2 = 0.01). ADAβ+Ptau+ participants 8
showed higher systolic BPV compared to CN on all measures of variability (SD: p = .01; CV: p 9
= .01; VIM: p = .01). The MCIAβ+Ptau+ group exhibited higher systolic BPV compared to CN on 10
VIM (p = .04) but not SD (p = .13) or CV (p = .20) measures of variability. There were no 11
statistically significant differences in systolic BPV between ADAβ+Ptau+ and MCIAβ+Ptau+ (SD: p = 12
.26; CV: p = .20; VIM: p = .46) (Supplementary Figure 3a). 13
Secondary biomarker-confirmed AD groups did not differ significantly by diastolic BPV 14
(SD: F2,668 = 0.029, p = .97; CV: F2,668 = 0.034, p = .97; VIM: F2,668 = 1.222, p = .30). 15
(Supplementary Figure 3b). There were also no significant interaction effects of secondary 16
biomarker-confirmed AD group by antihypertensive medication use on BPV (systolic: SD: F2,667 17
= 0.739, p = .48; CV: F2,665 = 0.625, p = .54; VIM: F2,667 = 0.495, p = .61; diastolic: SD: F2,666 = 18
0.747, p = .47; CV: F2,666 = 0.619, p = .54; VIM: F2,666 = 0.499, p = .61), or of secondary 19
biomarker-confirmed AD group by average BP on BPV (systolic: SD: F2,667 = 2.501, p = .08; 20
CV: F2,665 = 1.892, p = .15; VIM: F2,667 = 2.767, p = .06; diastolic: SD: F2,666 = 0.444, p = .64; 21
CV: F2,666 = 0.087, p = .92; VIM: F2,666 = 0.222, p = .80). 22

44
Exploratory CN intermediate biomarker groups did not significantly differ by systolic 1
BPV (SD: F3,324 = 1.460, p = .23; CV: F3,326 = 1.267, p = .29; VIM: F3,324 = 0.894, p = .45) 2
(Supplementary Figure 4a) or diastolic BPV (SD: F3,326 = 0.448, p = .72; CV: F3,327 = 0.366, p 3
= .78; VIM: F3,326 = 0.694, p = .56) (Supplementary Figure 4b). There were also no significant 4
interaction effects of exploratory group by antihypertensive medication use on BPV (systolic: 5
SD: F3,321 = 0.573, p = .63; CV: F3,323 = 0.686, p = .56; VIM: F3,321 = 0.439, p = .73; diastolic: 6
SD: F3,323 = 2.577, p = .05; CV: F3,324 = 2.629, p = .05; VIM: F3,323 = 2.353, p = .07), or of 7
exploratory group by average BP on BPV (systolic: SD: F3,321 = 0.348, p = .79; CV: F3,323 = 8
0.260, p = .85; VIM: F3,321 = 0.075, p = .97; diastolic: SD: F3,323 = 0.875, p = .45; CV: F3,324 = 9
0.865, p = .46; VIM: F3,323 = 1.225, p = .30). 10
All secondary analyses of systolic BPV findings survived FDR correction. 11

45
Supplementary Table 1 1
Clinical and Demographic Data for Included and Excluded Study Participants 2
Included
(n=1436)
Excluded
(n=730)
F or 𝒙 2 p-value
Clinical/Demographic
Age, yrs 73.9 (7.1) 72.1 (7.4) 28.430 < .001
Sex (n,% Male) 807 (56.2%) 346 (47.4%) 14.829 < .001
Education, yrs 16.0 (2.8) 16.2 (2.7) 4.718 .03
Baseline Diagnosis (n,%) 22.621 < .001
CN 408 (28.4%) 134 (18.4%)
MCI 763 (53.1%) 478 (65.5%)
AD 265 (18.5%) 114 (15.6%)
N/A -- 4 (0.6%)
Means and standard deviations shown unless otherwise indicated. 3
Significant differences (p < .05) between included and excluded participants are identified in 4
boldface type. 5
Abbreviations = CN = cognitively normal; MCI = Mild Cognitive Impairment; AD = 6
Alzheimer’s disease; N/A = no baseline diagnosis available 7

46
Supplementary Table 2 1
Clinical and Demographic Data for Secondary Clinical Groups 2
CN
(n=408)
MCI
(n=763)
AD
(n=265)
F or 𝒙 2 p-value
Clinical/Demographic
Age, yrsbc 74.9 (5.7) 73.2 (7.5) 75.4 (7.6) 9.110 .< .001
Sex (n,% Male)abc 210
(51.5%)
450
(59.0%)
147
(55.5%)
6.156 .046
BMI, kg/m2ab 27.2 (4.6) 27.0 (4.7) 25.6 (4.3) 10.407 < .001
Education, yrsab 16.4 (2.7) 16.0 (2.8) 15.2 (2.9) 11.877 < .001
APOE-ϵ4 carriers
(n,%)abc
109
(26.7%)
389
(50.1%)
181
(68.3%)
120.400 < .001
Hypertensive (n,%) 190
(46.6%)
370
(48.5%)
135
(50.1%)
1.313 .52
Medication Use, (n,%)
Antihypertensive
agentsab
88 (21.6%) 148
(19.4%)
78 (29.4%) 11.627 .003
ACE inhibitors 26 (29.6%) 44 (29.7%) 22 (28.2%)
Alpha-blockers 6 (6.8%) 12 (8.1%) 8 (10.3%)
Angiotensin II
inhibitors
15 (17.1%) 33 (22.3%) 18 (23.1%)
Calcium channel
blockers
22 (25.0%) 29 (19.6%) 15 (19.2%)
Central agonists 0 (0.0%) 2 (1.4%) 0 (0.0%)
Combined alpha-beta-
blockers
2 (2.3%) 7 (4.7%) 0 (0.0%)
Diuretics 17 (19.3%) 21 (14.2%) 11 (14.1%)
Vasodilators 0 (0.0%) 0 (0.0%) 4 (5.1%)
ChEIsabc 0 (0.0%) 159
(20.8%)
113
(42.6%)
194.08 < .001
Mean BP, mmHg

47
Systolic 132.8
(12.5)
132.8
(13.8)
133.9
(13.7)
0.316 .73
Diastolic 73.3 (7.8) 73.8 (7.8) 74.2 (6.8) 0.805 .45
Baseline Vascular Risk
Factors, (n,%)

Cardiovascular disease 45 (11.0%) 91 (11.9%) 33 (12.5%) 0.353 .84
Atrial Fibrillation 17 (4.2%) 22 (2.9%) 12 (4.5%) 2.183 .34
Type 2 diabetes mellitus 25 (6.1%) 62 (8.1%) 19 (7.2%) 1.574 .46
Carotid artery disease 3 (0.5%) 6 (0.9%) 1 (0.4%) 1.180 .55
TIA/minor stroke 9 (2.2%) 17 (2.2%) 7 (2.6%) 0.171 .92
Means and standard deviations shown unless otherwise indicated. 1
Significant differences (p < .05) among secondary clinical groups are identified in boldface type. 2
a indicates a Least Significant Difference-corrected pairwise difference between AD and CN at p 3
< .05 4
b indicates a Least Significant Difference-corrected pairwise difference between AD and MCI at 5
p < .05 6
c indicates a Least Significant Difference-corrected pairwise difference between MCI and CN at 7
p < .05 8
Abbreviations: ACE = angiotensin-converting enzyme; APOE = apolipoprotein E; BP = blood 9
pressure; BMI = body mass index; ChEIs = acetylcholinesterase inhibitors; TIA = transient 10
ischemic attack; CN = cognitively normal; MCI = Mild Cognitive Impairment; AD = 11
Alzheimer’s disease 12

48
Supplementary Table 3 1
Clinical and Demographic Data for Secondary Biomarker-Confirmed AD Groups 2
CN
(n=408)
MCIAβ+Ptau
+
(n=240)
ADAβ+Ptau+
(n=134)
F or 𝒙 2 p-value
Clinical/Demographic
Age, yrsc 74.9 (5.7) 73.5 (7.1) 74.3 (7.9) 3.314 .049
Sex (n,% Male) 210
(51.5%)
133
(55.4%)
712
(53.7%)
0.973 .62
BMI, kg/m2ac 27.2 (4.6) 26.1 (4.0) 25.3 (4.3) 10.504 < .001
Education, yrsa 16.4 (2.7) 16.0 (2.8) 15.4 (2.7) 5.065 .007
APOE-ϵ4 carriers (n,%)ac 109
(26.7%)
176
(73.3%)
104
(77.6%)
181.600 < .001
Hypertensive (n,%) 190
(46.6%)
111
(46.3%)
68 (50.8%) 0.978 .61
Medication Use, (n,%)
Antihypertensive agents
(n,%)
88 (21.6%) 41 (17.1%) 30 (22.4%) 2.299 .32
ACE inhibitors 26 (29.6%) 14 (34.2%) 11 (36.7%)
Alpha-blockers 6 (6.8%) 2 (4.9%) 0 (0.0%)
Angiotensin II
inhibitors
15 (17.1%) 8 (19.5%) 7 (23.3%)
Calcium channel
blockers
22 (25.0%) 7 (17.1%) 9 (30.0%)
Central agonists 0 (0.0%) 0 (0.0%) 0 (0.0%)
Combined alpha-beta
blockers
2 (2.3%) 0 (0.0%) 0 (0.0%)
Diuretics 17 (19.3%) 10 (24.4%) 3 (10.0%)
Vasodilators 0 (0.0%) 0 (0.0%) 0 (0.0%)
ChEIsabc 0 (0.0%) 57 (23.8%) 47 (35.1%) 140.42 < .001
Mean BP, mmHg

49
Systolic 132.8
(12.5)
133.4
(13.5)
133.2
(12.4)
0.245 .78
Diastolic 73.3 (7.8) 73.6 (7.1) 74.3 (6.4) 0.727 .48
Baseline Vascular Risk
Factors, (n,%)

Cardiovascular disease 45 (11.0%) 34 (14.2%) 19 (14.2%) 1.757 .42
Atrial Fibrillation 17 (4.2%) 2 (0.8%) 4 (3.0%) 5.883 .05
Type 2 diabetes
mellitus
25 (6.1%) 18 (7.5%) 8 (6.0%) 0.548 .76
Carotid artery disease 3 (0.5%) 3 (1.3%) 1 (0.8%) 1.147 .56
TIA/minor stroke 9 (2.2%) 4 (1.7%) 4 (3.0%) 0.707 .70
Means and standard deviations shown unless otherwise indicated. 1
Significant differences (p < .05) among secondary biomarker-confirmed AD groups are 2
identified in boldface type. 3
Aβ+Ptau+ indicates positive biomarkers for both Aβ and Ptau. 4
a indicates a Least Significant Difference-corrected pairwise difference between ADAβ+Ptau+ and 5
CN at p < .05 6
b indicates a Least Significant Difference-corrected pairwise difference between ADAβ+Ptau+ and 7
MCIAβ+Ptau+ at p < .05 8
c indicates a Least Significant Difference-corrected pairwise difference between MCIAβ+Ptau+ and 9
CN at p < .05 10
Abbreviations: ACE = angiotensin-converting enzyme; Aβ = amyloid-β; APOE = apolipoprotein 11
E; BP = blood pressure; BMI = body mass index; ChEIs = acetylcholinesterase inhibitors; Ptau = 12
phosphorylated tau; TIA = transient ischemic attack; CN = cognitively normal; MCI = Mild 13
Cognitive Impairment; AD = Alzheimer’s disease 14

50
Supplementary Table 4 1
Clinical and Demographic Data for Exploratory Groups 2
CNAβ-Ptau-
(n=161)
CNAβ+Ptau-
(n=102)
CNAβ-Ptau+
(n=42)
CNAβ+Ptau+
(n=91)
F or 𝒙 2 p-value
Clinical/Demographic
Age, yrsaef 71.9 (6.8) 73.9 (6.7) 74.3 (8.0) 76.1 (7.8) 6.686 < .001
Sex (n,% Male) 78
(48.5%)
56
(54.9%)
20
(47.6%)
51 (56.0%) 2.068 .56
BMI, kg/m2ab 27.8 (4.4) 27.9 (6.4) 27.3 (5.1) 25.7 (3.8) 3.798 .01
Education, yrs 16.3 (2.6) 16.6 (2.8) 16.0 (2.5) 15.9 (2.7) 1.327 .27
APOE-ϵ4 carriers
(n,%)abce
32
(19.6%)
45
(44.1%)
13
(31.0%)
60 (65.9%) 55.159 < .001
Hypertensive (n,%) 70
(43.5%)
45
(44.1%)
23
(54.8%)
43 (47.3%) 1.766 .62
Medication Use,
(n,%)

Antihypertensive
agents (n,%)
29
(18.0%)
16
(15.7%)
7 (16.7%) 13 (14.3%) 0.642 .89
ACE inhibitors 8 (27.6%) 7 (43.8) 3 (42.9%) 6 (46.2%)
Alpha blockers 0 (0.0%) 1 (6.3%) 0 (0.0%) 0 (0.0%)
Angiotensin II
inhibitors
7 (24.1%) 2 (12.5%) 1 (14.3%) 2 (15.4%)
Calcium channel
blockers
6 (20.7%) 4 (25.0%) 3 (42.9%) 4 (30.8%)
Central agonists 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)
Combined alpha-beta
blockers
3 (10.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%)
Diuretics 5 (17.2%) 2 (12.5%) 0 (0.0%) 1 (7.7%)
Vasodilators
ChEIs 8 (5.0%) 6 (5.9%) 2 (4.8%) 6 (6.6%) 0.364 .95
Mean BP, mmHg

51
Systolic 132.1
(14.0)
129.9
(12.5)
134.3
(14.2)
134.0
(12.9)
1.718 .16
Diastolic 73.4 (7.9) 74.3 (7.8) 71.8 (8.0) 74.0 (7.4) 0.642 .59
Baseline Vascular
Risk Factors, (n,%)

Cardiovascular
disease
14 (8.7%) 14
(13.7%)
3 (7.1%) 12 (13.2%) 2.753 .43
Atrial Fibrillation 3 (1.9%) 5 (4.9%) 2 (4.8%) 3 (3.3%) 2.153 .54
Type 2 diabetes
mellitus
7 (4.3%) 8 (7.8%) 5 (11.9%) 5 (5.5%) 3.779 .29
Carotid artery disease 0 (0.0%) 1 (1.0%) 0 (0.0%) 1 (1.1%) 2.128 .55
TIA/minor stroke 3 (1.9%) 5 (5.0%) 1 (2.4%) 2 (2.2%) 2.341 .51
Means and standard deviations shown unless otherwise indicated. 1
Significant differences (p < .05) among exploratory groups are identified in boldface type. 2
Aβ-Ptau- indicates negative biomarkers for both Aβ and Ptau. 3
Aβ+Ptau- indicates a positive Aβ biomarker and a negative Ptau biomarker. 4
Aβ-Ptau+ indicates a negative Aβ biomarker and a positive Ptau biomarker. 5
Aβ+Ptau+ indicates positive biomarkers for both Aβ and Ptau. 6
a indicates a Least Significant Difference-corrected pairwise difference between CNAβ+Ptau+ and 7
CNAβ-Ptau- at p < .05 8
b indicates a Least Significant Difference-corrected pairwise difference between CNAβ+Ptau+ and 9
CNAβ+Ptau- at p < .05 10
c indicates a Least Significant Difference-corrected pairwise difference between CNAβ+Ptau+ and 11
CNAβ-Ptau+ at p < .05 12
d indicates a Least Significant Difference-corrected pairwise difference between CNAβ+Ptau- and 13
CNAβ-Ptau+ at p < .05 14
e indicates a Least Significant Difference-corrected pairwise difference between CNAβ+Ptau- and 15
CNAβ-Ptau- at p < .05 16
f indicates a Least Significant Difference-corrected pairwise difference between CNAβ-Ptau+ and 17
CNAβ-Ptau- at p < .05 18
Abbreviations: ACE = angiotensin-converting enzyme; Aβ = amyloid-β; APOE = apolipoprotein 19
E; BP = blood pressure; BMI = body mass index; ChEIs = acetylcholinesterase inhibitors; Ptau = 20
phosphorylated tau; TIA = transient ischemic attack; CN = cognitively normal 21
52
Supplementary Figure 1. Flow Diagram of Primary Analyses Groups 1
Primary analyses included 1421 participants in the clinical groups and 318 participants in the 2
biomarker-confirmed AD groups. 3
4
5

53
Supplementary Figure 2. Systolic BPV by Secondary Clinical Group 1
Supplementary 2a. Systolic BPV was greatest in clinically diagnosed AD overall and compared 2
to CN for all measures of BPV. Clinically diagnosed AD exhibited greater systolic BPV relative 3
to MCI based on VIM measure of BPV. Supplementary 2b. Secondary clinical groups did not 4
significantly differ by diastolic BPV. Boxplot lines display minimum, 1st quartile, median, 3rd 5
quartile, and maximum. Abbreviations: BPV = blood pressure variability; CN = cognitively 6
normal; MCI = Mild Cognitive Impairment; AD = Alzheimer’s disease; SD = standard 7
deviation; CV = coefficient of variation; VIM = variation independent of mean 8
9

54
Supplementary 2a Systolic BPV 1
2
Supplementary 2b Diastolic BPV 3
4
5
55
Supplementary Figure 3. Systolic BPV by Secondary Biomarker-Confirmed AD Group 1
Supplementary 3a. Systolic BPV was greatest in biomarker-confirmed dementia due to AD 2
overall and compared to CN for all measures of BPV. Biomarker-confirmed MCI due to AD 3
exhibited greater systolic BPV relative to CN based on VIM measure of BPV. Supplementary 4
3b. Secondary biomarker-confirmed AD groups did not significantly differ by diastolic BPV. 5
Boxplot lines display minimum, 1st quartile, median, 3rd quartile, and maximum. Aβ+Ptau+ 6
indicates positive biomarkers for both Aβ and Ptau. Abbreviations: BPV = blood pressure 7
variability; Aβ = amyloid-β; Ptau = phosphorylated tau; CN = cognitively normal; MCI = Mild 8
Cognitive Impairment; AD = Alzheimer’s disease; SD = standard deviation; CV = coefficient of 9
variation; VIM = variation independent of mean 10

56
Supplementary 3a Systolic BPV 1
2
Supplementary 3b Diastolic BPV 3
4
57
Supplementary Figure 4. Systolic BPV by Exploratory Group 1
Supplementary 4a. Exploratory groups did not significantly differ on systolic BPV. 2
Supplementary 4b. Exploratory groups did not significantly differ on diastolic BPV. Boxplot 3
lines display minimum, 1st quartile, median, 3rd quartile, and maximum. Aβ-Ptau- indicates 4
negative biomarkers for both Aβ and Ptau. Aβ+Ptau- indicates a positive Aβ biomarker and a 5
negative Ptau biomarker. Aβ-Ptau+ indicates a negative Aβ biomarker and a positive Ptau 6
biomarker. Aβ+Ptau+ indicates positive biomarkers for both Aβ and Ptau. Abbreviations: BPV = 7
blood pressure variability; Aβ = amyloid-β; Ptau = phosphorylated tau; CN = cognitively 8
normal; SD = standard deviation; CV = coefficient of variation; VIM = variation independent of 9
mean 10
58
Supplementary 4a Systolic BPV 1
2
Supplementary 4b Diastolic BPV 3
4

Abstract (if available)

Abstract Background: Elevated blood pressure is linked to cognitive impairment and Alzheimer’s disease biomarker abnormality. However, blood pressure levels vary over time. Less is known about the role of long-term blood pressure variability in cognitive impairment and Alzheimer’s disease pathophysiology. ❧ Objective: Determine whether long-term blood pressure variability is elevated across the clinical and biomarker spectrum of Alzheimer’s disease. ❧ Methods: Alzheimer’s Disease Neuroimaging Initiative participants (cognitively normal, mild cognitive impairment, Alzheimer’s dementia [n=1421]) underwent baseline exam, including blood pressure measurement at 0, 6, 12 months. A subset (n=318) underwent baseline lumbar puncture to determine cerebral spinal fluid amyloid-β and phosphorylated tau levels. Clinical groups and biomarker-confirmed Alzheimer’s disease groups were compared on blood pressure variability over 12 months. ❧ Results: Systolic blood pressure variability was elevated in clinically diagnosed Alzheimer’s dementia (VIM: F2,1195 = 6.657, p = 0.001, η2 = 0.01) compared to cognitively normal participants (p = .001), and in mild cognitive impairment relative to cognitively normal participants (p = .01). Findings were maintained in biomarker-confirmed Alzheimer’s disease (VIM: F2,850 = 5.216, p = 0.006, η2 = 0.01), such that systolic blood pressure variability was elevated in biomarker-confirmed dementia due to Alzheimer’s disease relative to cognitively normal participants (p = .005) and in biomarker-confirmed mild cognitive impairment due to Alzheimer’s disease compared to cognitively normal participants (p = .04). ❧ Conclusion: Long-term systolic blood pressure variability is elevated in cognitive impairment due to Alzheimer’s disease. Blood pressure variability may represent an understudied aspect of vascular dysfunction in Alzheimer’s disease with potential clinical implications.

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Asset Metadata

Creator Sible, Isabel Jean (author)

Core Title Long-term blood pressure variability across the clinical and biomarker spectrum of Alzheimer’s disease

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 02/06/2021

Defense Date 03/12/2020

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

Tag aging,Alzheimer's disease,Amyloid,blood pressure variability,cognitive impairment,OAI-PMH Harvest,Tau

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Nation, Daniel A. (committee chair), Schwartz, David (committee chair), Beam, Chris (committee member), John, Richard (committee member)

Creator Email ijsible@gmail.com,sible@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-358778

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Legacy Identifier etd-SibleIsabe-8893.pdf

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

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

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