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Cross-sectional association of blood pressure, antihypertensive medications, MRI volumetric measures and cognitive function scores in an aging population

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Cross-sectional association of blood pressure, antihypertensive medications, MRI volumetric measures and cognitive function scores in an aging population

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Content CROSS-SECTIONAL ASSOCIATION OF BLOOD PRESSURE,
ANTIHYPERTENSIVE MEDICATIONS, MRI VOLUMETRIC MEASURES AND
COGNITIVE FUNCTION SCORES IN AN AGING POPULATION

By

Yin Jin

A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)

August 2016

Copyright 2016 Yin Jin
TABLE OF CONTENTS

DEDICATION iii
ACKNOWLEDGEMENTS iv
ABSTRACT v

INTRODUCTION 1

METHODS 3

RESULTS 9

DISCUSSION 16

CONCLUSION 23

REFERENCES 29

TABLES 30

DEDICATION

This work is dedicated to my parents, who always encouraged me to go on every
adventure.

ACKNOWLEDGEMENTS
I would like to express the deepest appreciation to my dear advisor Dr. Wendy Mack
for being a great mentor and for her personal support and great patience.
In addition, I would like to thank my committee members Dr. Helena Chui and Dr.
Ling Zheng for their valuable comments and guidance.

ABSTRACT
Background: Hypertension is a major risk factor for cognitive impairment and is
also associated with structural changes in the brain, particularly white matter
hypertensites (WMH) on MRI. Evidence for the possible associations of blood pressure
medications with dementia and associated brain pathology remains inconclusive. The
objectives of our study were to determine whether blood pressure and use of specific
classes of antihypertensive medications are associated with cognition and structural
magnetic resonance imaging (MRI) measures of brain atrophy in an elderly population of
individuals selected for no or mild cognitive impairment, with a range of vascular factors.
Methods: A total of 274 participants in the observational Aging Brain Study were
included in the analysis for relationships among blood pressure, anti-hypertensive
medications and cognitive function scores. An additional 30 participants were excluded
from analyses testing the relationships among blood pressure, anti-hypertensive
medications and MRI volumetric measures due to missing neuroimaging data. Baseline
measures included blood pressure, current medications, structural T1-weighted images
and four composite scales for cognitive function. Demographic information and other
measurements were included as covariates. We tested the relationship among blood
pressure (mean SBP, DBP, MAP and pulse pressure), five classes of antihypertensive
medications and dependent variables (MRI volumetric measures and cognitive function
scores), using separate multiple linear regression models for each dependent variable and
adjusting for age, educational level, race, gender, smoking status, Clinical Dementia
Rating (CDR) score and other measurements.
Results: SBP was significantly inversely associated with total grey matter and
total hippocampal volume (both P<0.035) and positively associated with volume of white
matter hyperintensites (WMH) (P=0.046). Elevated DBP was associated with higher
volume of WMH (P=0.0096). Pulse pressure was inversely associated with hippocampal
volume and grey matter volume (P=0.014 and 0.003, respectively). In models adjusting
for SBP, DBP and MAP, use of angiotensin II receptor blockers (ARBs) (n=41) was
positively associated with white matter volume (P<0.05), hippocampal volume
(P=0.045), and total grey matter volume in models adjusted for SBP (P=0.022). In
models adjusting for SBP, DBP, MAP and pulse pressure, use of ACE inhibitors was
significantly inversely related to grey matter volume (all P<0.02). Use of beta-blockers
was inversely associated with total grey matter volume in models including DBP, MAP
and pulse pressure (all P<0.05). In models with SBP, MAP and pulse pressure, beta
blockers were inversely related with the Global cognitive function score (all P<0.045).
Conclusion: In this Aging Brain population, blood pressure was associated with
reduced brain volumes, increased WMH and cognitive decline. Using ARBs may be
beneficial for older persons to prevent brain atrophy, while treatment with beta blockers
and ACE inhibitors could be risk factors for brain atrophy and cognitive impairment.
Further research will be needed to understand mechanisms of association among use of
specific classes of hypertensive medications, MRI measures of brain volumes and
cognition to prevent brain atrophy and cognitive decline.

INTRODUCTION
Dementing illness, defined as a chronic or persistent acquired disorder of the
mental processes marked by memory disorders, personality changes, and impaired
reasoning are among the most highly prevalent diseases of aging worldwide.
1
In 2015,
there were an estimated 47.5 million people with dementia worldwide. The number of
people affected is expected to double every 20 years up to about 80 million cases
worldwide in 2024 and 135.46 million in 2050.
2,3

Brain atrophy refers to the loss of brain volume and is associated with normal
aging, cognitive impairment, vascular disorders and neurodegenerative diseases. Atrophy
presents on magnetic resonance imaging (MRI) by widening of the sulci, narrowing of
the gyri, and enlargement of the ventricles.
4,5
Brain volumes can be quantified by
computerized algorithms (e.g., Freesurfer) and expressed as a percentage of intracranial
volume. Several studies have described prominent atrophic decline in the cortex in older
adults, particularly in the frontal and temporal region.
6,7
Atrophy of the medial temporal
lobe, including the hippocampus is characteristic of Alzheimer disease. So, white matter
hyperintensites (WMH) are also regarded as common phenomena in aging that increase
with hypertension.
8-10

There is growing evidence that vascular atherosclerosis is associated with both
brain atrophy and cognitive impairment, independent of Alzheimer disease
4
.
Hypertension, an independent risk factor for cerebral atherosclerosis, occurs in
approximately 40% of adults aged 25 and above worldwide. The number of people with
hypertension was estimated to be over 1 billion in 2008.
11
The association among
hypertension, brain atrophy, WMH, and cognitive impairment has been the focus of
much research in the past decades. Previous studies have indicated that hypertension is
inversely associated with executive dysfunction and is also associated with structural
changes in the brain.
12-14

Given the deleterious relationships of hypertension with cognition, dementia and
brain atrophy, the possible protective effects of specific classes of antihypertensive
medications (e.g., diuretics, beta blockers, angiotensin-converting enzyme inhibitors,
angiotensin receptor blockers, calcium channel blockers) on risk of dementia and brain
atrophy are of interest. Some studies have linked specific classes of antihypertensive
medications to reduced dementia risk.
15-17
However, there are conflicting conclusions
made by other studies.
18-20
The evidence for the associations between specific blood
pressure medications with dementia and associated brain pathology remains inconclusive.
18,19

The primary objective of our study was to determine whether blood pressure and
use of specific antihypertensive medications are associated with cognition and MRI
measures of brain atrophy in a population of individuals selected for no or mild cognitive
impairment, with a range of vascular factors.

METHODS Design and Participants
Subjects were participants in a longitudinal multi-site research project, entitled the
Aging Brain Study. This project enrolled older adults with a spectrum of risk for vascular
disease from low to high (using the Framingham Coronary Risk Profile) and cognitive
function ranging from normal cognition to mild cognition impairment. Inclusion criteria
were age 70 or older and a Clinical Dementia Rating (CDR) score of 0 or 0.5. Exclusion
criteria were history of Korsakoff encephalopathy, alcohol abuse or dependence within
the past 5 years, substance abuse, head trauma with loss of consciousness>15 minutes,
parenchymal brain tumor, multiple sclerosis, Parkinson disease, communicating or non-
communicating hydrocephalus, amyotrophic lateral sclerosis, Axis I psychiatric disorder,
intracerebral hemorrhage, current pacemaker, vessel stent or ferromagnetic material in
soft tissue, and cognitive impairment and epilepsy due to conditions other than Alzheimer
or vascular diseases. Participants with B12 deficiency, hypothyroidism, HIV positive,
renal failure, liver failure, or respiratory failure were also excluded.
21,22
All eligible and
consenting subjects completed baseline and annual follow-ups, including physical
examination, laboratory testing, neuropsychological assessment and MRI scan. The
Aging Brain Study was approved by the institutional review boards at all participating
sites.
Demographics
Demographic and other information collected at baseline included age, gender, race,
education level and current smoking. Body mass index (BMI) was calculated as weight in
kilogram divided by square of height in meters.
Blood pressure
Blood pressure (right upper arm, in mmHg scale) was measured after the
participants were seated and rested at least 5 minutes. The average of three measurements
of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were computed as
mean SBP and DBP. Mean arterial pressure (MAP) was calculated using the formula
MAP = DBP + (1/3 × (SBP-DBP)). Pulse pressure was calculated as the difference
between SBP and DBP. Hypertension was defined as average systolic BP ≥140 mm Hg
and/or diastolic BP ≥90 mm Hg and/or use of antihypertensive medications. Indicator
variables for elevated blood pressure defined participants with systolic BP ≥140 mm Hg
and/or diastolic BP ≥90 mm Hg, regardless of their use of antihypertensive medications.
Baseline data for SBP, DBP, MAP and pulse pressure were used in the current analysis.
Anti-hypertensive medication
Current medications were ascertained at baseline and follow-up visits. Hypertensive
medications were classified into the following therapeutic classes: (1) diuretics
(chlorthalidone, chlorothiazide,furosemide,hydrochlorothiazide, indapamide, metolazone,
amiloride hydrochloride, spironolactone, triamterene, bumetanide), (2) beta-blockers
(acebutolol, atenolol, betaxolol, bisoprolol fumarate, carteolol hydrochloride, metoprolol
tartrate, metoprolol succinate, nadolol, penbutolol sulfate, pindolol, propranolol
hydrochloride, solotol hydrochloride, timolol maleate), (3) angiotensin-converting
enzyme (ACE) inhibitors(benazepril hydrochloride, captopril, enalapril maleate,
fosinopril sodium, lisinopril, moexipril, perindopril, quinapril hydrochloride, ramipril,
trandolapril), (4) angiotensin receptor blockers (ARBs) (candesartan, eprosartan
mesylate, irbesarten, losartin potassium, telmisartan, valsartan), and (5) calcium channel
blockers (amlodipine besylate, bepridil, diltiazem hydrochloride, felodipine, isradipine,
nicardipine, nifedipine, nisoldipine, verapamil hydrochloride).
23

MRI acquisition and processing
Structural T1-weighted MRI images were acquired by 3T or 4T MRI systems. 63
participants were recruited at the USC site using a 3T General Electric Signal HDx
system with an 8-channel head coil. 82 participants were scanned at the UCDavis site
using a 3T Siemens Magnetom Trio Syngo system with an 8-channel head coil and a 3T
Siemens Magnetom TrioTim system with an 8-channel head coil. 55 participants were
scanned at the San Francisco Veterans Administration Medical Center using a 4T
Siemens MedSpec Syngo System with an 8-channel head coil. 43 participants were
scanned at the UCSF Neuroscience Imaging Center using a 3T Siemens Magnetom
TrioTim system with a 12-channel head coil. FreeSurfer Version 5.1, a segmentation of
T1 weight image software, were used to obtain average reconstructed frontal, parietal,
temporal, occipital, insula, cerebral water matter and basal ganglia region of interest
(ROIs). White matter hyperintensites (WMH) segmentations were processed by a basic
skull strip of FLAIR image using MRIcro’s Brain Extraction toll (BET) 5.0. Intracranial
volume(ICV) was quantified using T-2 weighted images. Outputs were visually inspected
to ensure the segmentations correct.
22,24

Cognitive assessment
Cognitive functions were assessed with a cognitive battery used to compute four
composite scales: Verbal Memory, Nonverbal Memory, Executive Function, and Global
cognition. The Verbal Memory Scale used Trials 2-6 total recall on the Word List learning
test of the Memory assessment scale (MAS). The Nonverbal Memory Scale used the
Biber Visual Learning trials 4-5 and short and long delayed free recall on MAS. The
Executive measure representing of working memory was used Initiation–Perseveration
subscale of the Mattis Dementia Rating Scale, the FAS letter fluency test, WMS Digit
Span backward and Visual Memory Span backward. Wechsler Memory Scale-Revised
(WMS–R), Digit Span forward and backward, letter fluency and animal category fluency
and trials 1-2 on the Word List learning test of the MAS were donor items scale for the
Global cognition measure.
25

Other measurements
Very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and high-
density lipoprotein (HDL) cholesterol, total triglycerides, total cholesterol and glucose
levels were measured from fasting blood samples of participants. History of vascular
events such as stroke, myocardial infarction, coronary artery bypass or angioplasty, prior
carotid endarterectomy, artery stent, evidence of carotid atherosclerotic lesion or
stenosis > 15% and diabetes were identified based on participant self-report and medical
records. 90% of participants were genotyped for ApoE; subjects were categorized as
ApoE ε4-positive (carrying 1 or 2 ε4 alleles) or ApoE ε4-negative
Statistical analysis
All analyses were cross-sectional, using baseline data. Means and standard
deviations, as measures of central tendency and variability, were calculated for
continuous variables. Categorical variables were summarized as frequencies to and
percentages.
We tested the relationship among blood pressure (mean SBP, DBP, MAP and pulse
pressure), antihypertensive medications and MRI volumetric measures, using separate
multiple linear regression models for each MRI dependent variable (total grey matter
volume, total white matter volume, hippocampal volume and volume of white matter
lesions).
We also tested the relationships among blood pressure (mean SBP, DBP, MAP and
pulse pressure), antihypertensive medications and cognitive function scores. The
composite measures of Verbal Memory, Nonverbal Memory, Executive Function, and
Global cognition were treated as dependent variables in separate multiple linear
regressions.
Age, educational level, race, gender, smoking status, Clinical Dementia Rating
(CDR) score and other measurements were considered as potential confounders of the
association between outcome and interest variables. If a covariate changed the regression
coefficient of interest by more than 15%, we included this covariate as a confounder into
the final model. Effect modification was tested by adding interaction terms one by one
into the model; interaction terms with p<0.05 were retained in the final model. To avoid
possibly biased regression estimates, collinearity among independent variables was
checked before we built final models.
Assumptions of the linear regression associations were evaluated by checking
linearity, independence, normality and homoscedasticity. The dependent variable of
volume of white matter lesion did not meet linear regression model assumptions on
homoscedasticity and normality of residuals. We transformed the white matter lesion
variable on a natural log scale; linear regression assumptions were met using this
transformed variable. All data were analyzed using Statistical Analysis System (SAS)
software version 9.4.

RESULTS
Baseline characteristics of participant population
309 participants were recruited in this study; 36 were excluded from this analysis
due to missing data on blood pressure or antihypertensive medication use. An additional
30 participants were excluded from analyses testing the relationships among blood
pressure, antihypertensive medications and MRI volumetric measures due to missing
neuroimaging data. The average ± SD age of participants was 77.5 ± 6.3 years, 58.6%
were men and the average of education was 15.9 ± 2.9 years. The majority of participants
were non-Hispanic White (75.82%); the remaining ethnic composition was Hispanic
(3.7%), African American (8.4%), Asian (11.7%) and other (0.4%). Only 2.6% of
participants were current smokers at the study baseline. Among the 70.7% of participants
who were taking antihypertensive medications, the mean baseline SBP and DBP was
140.5 ± 19 mmHg and 72.2 ± 9.6 mmHg, respectively. Among participants who were not
taking antihypertensive medications, the mean SBP and DBP was137.4 ± 20.8 mmHg
and 74.4 ± 10.5mmHg, respectively. ACE inhibitors (41.12%) and beta blockers (38.9%)
were the most common antihypertensive medications used, while alpha blockers (8.8%)
were the least common antihypertensive medications used. The composition of other
antihypertensive medications was ARB (21%), diuretics (34%), and calcium channel
blocker (21%). SBP, DBP, MAP and PP were highly correlated to each other (P<0.001).
Additional baseline characteristics are shown in Table 1.
Association of blood pressure with MRI volumetric measures (without
inclusion of antihypertensive medications)
Table 2 shows the adjusted association between blood pressure and MRI
volumetric measures. Adjusted for age and LDL cholesterol, but not use of
antihypertensive medications, SBP was significantly inversely associated with total grey
matter volume (P=0.013); this association was maintained with exclusion of two
participants who were excluded due to outlying values of LDL based on jackknife
residuals (P=0.0059). SBP was significantly inversely associated with total hippocampal
volume (P=0.032), adjusted for age LDL cholesterol and CDR. The association of SBP
and total hippocampal volume differed by CDR (P for interaction =0.02). While SBP was
not significantly associated with hippocampal volume among subjects with CDR=0.5
(beta (SE) = 0.0001 (0.003); P=0.71), SBP was inversely associated with hippocampal
volume among subjects with CDR=0 (beta (SE) = -0.0007 (0.0003); P=0.02). SBP was
significantly positively associated with volume of WMH (P=0.046), adjusted for age and
glucose.
The association between MAP and grey matter volume was modified by both age
and CDR. There was a significant interaction between age (centered around the sample
mean of 78 years) and MAP (centered around the sample mean of 95 mmHg) (interaction
beta = 0.007, 95% CI:0.0004,0.01, P=0.04); the association of MAP with grey matter
volume increased with increasing age. At the 5
th
percentile of age (68 years), 25
th

percentile of age (73 years) and 50
th
percentile of age (78 years), MAP was significantly
negatively associated with grey matter volume (beta = -0.16, -0.13 and -0.09 respectively,
all P<0.003). In contrast, at the 75
th
percentile of age (83 years) and 95
th
percentile of age
(93 years), MAP was not associated with grey matter volume (beta = -0.05 and -0.02
respectively, both P>0.05). CDR also significantly interacted with MAP (interaction
P=0.004). The association of MAP with grey matter volume among subjects with
CDR=0.5 was -0.001 (SE=0.03, P=0.097). The association of MAP with grey matter
volume among subjects with CDR=0 was -0.08 (SE=0.03, P=0.017).
The association between MAP and hippocampal volume was modified by
education and gender. There was a significant interaction between education (centered
around the sample mean of 15 years) and MAP (centered around the sample mean of 95
mmHg) (interaction beta = -0.0003, 95% CI: -0.0005, -0.0000, P=0.02); the association
of MAP with hippocampal volume decreased with higher education level. At the 5
th

percentile of education (11 years) and at the 25
th
percentile of education (13 years), MAP
was not associated with hippocampal volume (beta=-0.001 and -0.0015 both P>0.05). In
contrast, at the 50
th
percentile of education (15 years), the 75th percentile of education
(17 years) and the 95th percentile of education (19 years), MAP was significantly
associated with hippocampal volume (beta = -0.002, -0.0025, -0.003, respectively, all
P<0.04). Gender also significantly interacted with MAP (interaction P=0.04). The
association of MAP with hippocampal volume among male subjects was -0.0007 (SE
=0.0006, P=0.24); the association of MAP with hippocampal volume among female
subjects was 0.0008 (SE = 0.0004, P=0.03).
In addition, elevated DBP was associated with higher volume of WMH
(P=0.0096). Pulse pressure was inversely associated with hippocampal volume and grey
matter volume (P=0.014 and 0.003, respectively).
Association of blood pressure, antihypertensive medications and MRI
volumetric measures
In models adjusting for SBP, DBP and MAP, use of angiotensin II receptor
blockers (ARBs) was positively associated with normal white matter volume (all P<0.05;
Table 3). Use of ARBs was also positively associated with hippocampal volume
(P=0.045) and total grey matter volume in models adjusted for SBP (P=0.022). In the
model including pulse pressure, use of ARBs was significantly positively associated with
hippocampal volume (P=0.02).
In models adjusting for SBP, DBP, MAP and pulse pressure, use of ACE
inhibitors was significantly inversely related to grey matter volume (all P<0.02). Use of
beta blockers was inversely associated with total grey matter volume in models including
DBP, MAP and pulse pressure (all P<0.05). No other antihypertensive medications were
associated with MRI volumetric measures (Table 3).
In sum, these results demonstrated that ACE inhibitors and beta blockers were
associated with decreased brain volumes (mainly total grey matter volume and
hippocampal volume), while ARBs were associated with higher brain volumes (total grey
matter volume, total normal white matter volume and hippocampal volume).
Association of blood pressure with cognitive function scores (without
antihypertensive medications)
Adjusted associations between blood pressure and cognitive function scores are
shown in Table 4. After adjusting for age, VLDL cholesterol, LDL cholesterol, HDL
cholesterol, triglycerides, glucose, race, ApoE ε4 status, history of stroke and diabetes,
the association of DBP with the Global function score was modified by HDL cholesterol.
There was a significant interaction between HDL (centered around the sample mean at 55
mg/dl) cholesterol and DBP (centered around the sample mean of 73 mmHg) (interaction
beta = -0.016, 95% CI: -0.03, -0.005, P=0.005). At the 60
th
(60 mg/dl) and 75
th

percentiles (70 mg/dl) of HDL, DBP was significantly negatively associated with the
Global cognitive function score (beta = -0.39 and -0.56, both P<0.007). In contrast, at the
50
th
(55 mg/dl), 40
th
(40 mg/dl) and 25
th
percentiles (30 mg/dl) of HDL, DBP was not
associated with the Global cognitive function score (beta= -0.12, 0.15, and 0.31
respectively, all P>0.1).
Following adjustment for age, education, VLDL cholesterol, LDL cholesterol
(centered around the sample mean at 95 mg/dl), HDL cholesterol, triglycerides, ApoE ε4
status, and history of stroke, the association between pulse pressure and Global cognition
was modified by LDL cholesterol (interaction beta=0.0045, 95% CI:0.001, 0.0075,
P=0.0072); the association of pulse pressure and Global cognition increased with higher
LDL cholesterol level. At the 5
th
percentile of LDL (50 mg/dl), pulse pressure was
significantly negatively associated with Global function score (beta = -0.22, P<0.05),
while at the 25
th
percentile (70 mg/dl), 50
th
percentile (95 mg/dl) and 75
th
percentile of
LDL (120 mg/dl) pulse pressure was not associated with the Global cognitive function
score (beta = -0.008, -0.01, 0.1 respectively, all P>0.05). ApoE ε4 genotype also
significantly interacted with pulse pressure (interaction P=0.024); the pulse pressure
association with the Global cognitive function score was lower in ApoE4 carriers
compared to non-carriers. The association of pulse pressure with the Global cognitive
function score among ApoE4 carriers was -0.29 (SE=0.07, P=0.01). The association of
pulse pressure with the Global cognitive function score among ApoE4 non-carriers was -
0.01 (SE=0.07, P=0.88).
Association of blood pressure, antihypertensive medication and cognitive
function scores
In models with SBP, MAP and pulse pressure respectively, beta blockers were
inversely related with the Global cognitive function score (all P<0.045). Other
antihypertensive medications were not significantly associated with cognitive scores
(Table 5)

DISCUSSION
There are four main findings in this cross-sectional study of elderly persons with
no or mild cognitive impairment and a range of vascular risk factors: (1) higher blood
pressure was associated with brain atrophy and higher volume of WMH; (2) ACE
inhibitors and beta blockers were associated with decreased brain volumes (mainly total
grey matter volume and hippocampal volume), while ARBs were positively associated
with brain volumes (total grey matter volume, total normal white matter volume and
hippocampal volume); (3) the association of DBP with Global cognitive function score
was modified by HDL cholesterol and the association between pulse pressure and Global
cognition was modified by LDL cholesterol and ApoE ε4 genotype; and (4) use of beta
blockers was related to reduced Global cognitive function score.
In this Aging Brain cohort with an average age of 77.5 years, blood pressure
(SBP, MAP and PP) was significantly associated with reduced brain volumes,
specifically affecting total grey matter and hippocampal volumes. Thus, elevated blood
pressure may promote brain atrophy. These results are supported by many previous
studies.
13,26,27
A possible mechanism is that dysfunction of the blood–brain barrier
(BBB) caused by chronic cerebral hypo-perfusion, induced by hypertension,
28
may lower
expression of GLUT, glucose transporter isoforms, in brain and thus reduce glucose
uptake
29
, leading to brain atrophy.
30
We also noted a positive association between
elevated blood pressure (SBP and DBP) and WMH, which are believed to represent
incomplete infarction of the white matter area, is related to atherosclerotic thickening of
the penetrating arteries as well as chronic cerebral hypo-perfusion.
31

32
Impaired cerebral
blood auto-regulations such as cerebral hypo-perfusion, that are related to the change in
atherosclerotic vessels, are also associated with hypertension.
28,33

In our analysis, the association between SBP and hippocampal volume was
modified by CDR. CDR has been found to correlate highly with brain atrophy, especially
medial temporal atrophy.
34
For the cognitively intact subjects, the effect of elevated blood
pressure on brain volumes changes might not be detected due to subclinical brain
atrophy. In contrast, the CDR=0.5 group with brain volume reduction increasing might be
more sensitive to find the association between elevated blood pressure and MRI
volumetric measures. This possible explanation also applies to the interaction between
MAP and CDR in their effect on grey matter volume. The association between MAP and
grey matter volume was also modified by age. Among subjects who were aged 78 or
lower, MAP was significantly negatively associated with grey matter volume. This
finding is consistent with previous study that higher level of blood pressure with greater
brain atrophy at “young old” (range defined as 60-69 years), who were shown the
greatest pathology on MRI.
35
. The association of MAP with hippocampal decreased with
higher education level. This result was proved by previous findings that higher education
was associated smaller hippocampal volumes.
36,37
Although it sounds counterintuitive,
the possible mechanism, higher education was associated with greater neural subtract in
late life, was supported by several reports.
38
. ApoE4 appears might also reduce the
potential protective effects of high education level, resulting in steeper cognitive decline
with age.
39
Gender significantly interacted with MAP in their effect on hippocampal
volume in our analysis. Sex-related difference in aortic stiffness might be a plausible
explanation for this interaction. Puntmann examined aortic stiffness for elderly people by
using MRI found that women had greater aortic stiffness than men.
40

The negative association of DBP with Global cognitive function score was
modified by HDL cholesterol level, indicating that HDL cholesterol level could affect
how DBP relates to cognitive function. Our results showed that the association between
DBP and Global cognitive function became more negative as HDL cholesterol level
increased. Among subjects with relatively higher HDL (levels more than 60mg/dl), DBP
was significantly negatively associated with the Global function score. However, at lower
percentiles of HDL (levels ranging from 30-55 mg/dl), DBP was not associated with
Global cognitive function. This effect modification might explain the inconsistent results
between DBP and cognitive function reported in many studies. Some studies reported an
inverse association of DBP with decline in cognitive functions in elderly adults.
14,41,42

For instance, the longitudinal study conducted by Kilander showed that low diastolic
blood pressure was associated with higher late-life cognitive performance
43
. Also a large
cross-sectional study in persons older than 45 showed that higher diastolic blood pressure
was related with impaired cognitive status
42
. However other studies have reported
opposite results.
41,44,45
Two longitudinal studies, for instance, indicated that low DBP is
associated with higher risk of cognitive impairment in elderly individuals over age 75.
44,45
No association between DBP and cognitive impairment in the aging population was
found in other studies.
46,47

The association between pulse pressure and Global cognition was modified by
LDL cholesterol and ApoE genotype. Pulse pressure was significantly negatively
associated with Global cognitive function among subjects with relatively low LDL
cholesterol levels (levels less than 50 mg/dl). However, pulse pressure was not associated
with Global function score at higher LDL cholesterol levels (ranging from 70 mg/dl-120
mg/dl). The pulse pressure association with Global cognition was lower in ApoE4
carriers compared to non-carriers. Among ApoE4 carriers, pulse pressure was
significantly negatively associated with Global cognitive function, while pulse pressure
was not associated with Global cognitive function among ApoE4 non-carriers. Our
finding relating pulse pressure and Global cognitive function may also have explained
conflicting results from previous studies. Several studies reported that higher pulse
pressure is associated with poor cognitive function.
14,48
The mechanism is likely due to
elevated vascular stiffness reflected by the elevated pulse pressure.
49
Arterial stiffness is
related to various cardiovascular and metabolic risk factors which could contribute to
reduced cognitive function.
14
In contrast, another Honolulu-Asia Aging study
demonstrated that mid- and late-life pulse pressure is not independently associated with
dementia risk.
50

In contrast to our expectation, among low LDL cholesterol and/or high HDL
cholesterol subjects, elevated blood pressure (DBP and PP) was associated with Global
cognitive function decrease. We hypothesized that high LDL and/or Low HDL mediated
atherosclerosis together with high blood pressure, which induced modification to
endothelium, contribute to cognitive scores decrease. However, there was a research in
aging population have a similar result with our finding, which higher LDL cholesterol
were associated with more memory score.
51
It is possible that elder person, especially
with high LDL cholesterol, may less susceptible to the adverse effect of high cholesterol.
Stains were taken by subjects diagnosed hyperlipidemia could be another explanation.
Stains have been associated with positive effect on cardiovascular risk factors which may
prevent from impaired cognition. Pulse pressure was significantly associated with Global
cognitive function score among ApoE4 carriers in our analysis. The interaction between
ApoE4 and blood pressure on white matter hyperintesites, which related to cognitive
decline and dementia, also been found by pervious study.
34
Brain atrophy, especially
hippocampal atrophy, is greater and cognitive dysfunction more serious in aging
population carrying ApoE4.
52,53
ApoE4 carriers might be more easily subject to
neurodegeneration and more risk of atherosclerosis, along with longstanding
hypertension, eventually resulting in cognitive impairment.
54
.
In this Aging Brain cohort, beta blockers were linked to lower volumes of total
grey matter, total white matter and hippocampus. Beta blockers may decrease brain
glucose metabolism, which in turn may increase the risk of brain atrophy and subsequent
cognitive decline.
55
However, this evidence of an effect of beta blockers on brain glucose
metabolism is from a small experiment in subjects with essential tremor disease; this
association should be validated in a larger study in the general population. Our data
further indicated that participants using ARBs had higher brain volumes, coinciding with
a previous studies reporting that those treated with ARBs had less amyloid deposition and
less atrophy rates,
56,57
and those using ACE inhibitors had lower brain volumes than
participants not using these medications. Although both of these two types of
antihypertensive medications block the renin-angiotensin system (RAS) to reduce blood
pressure, ACE inhibitors can also increase the bradykinin level.
58
Bradykinin was
identified as an initial mediator to brain inflammation, which enhances vascular
permeability and thus leads to BBB dysfunction, which might cause brain atrophy in the
aging population
59,60
. Differential effects of these two classes of anti-hypertensive
medications on the bradykinin level might contribute to the differential effect for ARBs
and ACE inhibitors that we observed on brain volumes. This hypothesis should be tested
in future research. Another potential superior ARBs protective effect was provided by
Hajjar that unlike ACE inhibitors, ARBs could stimulate the angiotensin receptors type 2
(AT2), which may protect endothelia cells and neuros.
61

Use of beta blockers in our study was associated with lower Global cognitive
function in models adjusting for DBP, MAP and pulse pressure. This result is consistent
with research indicating that beta blockers may cause or exacerbate cognitive impairment
in the elderly.
18
Beta blockers also have been reported as one type of drug that induced
cognitive impairment in the aging population.
62
Beta blockers might influence the
adrenergic signaling in the hippocampus, negatively impacting memory functions in the
elderly.
19
The relationship between beta blockers and cognition is also linked with our
previous finding associating use of beta blockers with lower total grey matter volume.
Grey matter volume is related to cognition in elderly people and brain degenerative
disease in many studies;
63,64
our data indicate that beta blockers is negatively associated
with both. Although our study did not show significant associations between ARBs and
cognitive function scores, previous studies conducted by Hajjar and Garrick
demonstrated that patients receiving ARBs could have a lower rate of MMSE decline and
reduce risk of dementia in the aging population.
65

66
These results support our finding
relating use of ARBs to higher brain volumes.
The major limitation of our study is the cross-sectional study design. Serial
cognitive and MRI volumetric change analysis could better assess a possible causal effect
of antihypertensive medications on brain atrophy and cognition. Secondly, our
medication data assessed current use only. Expanded medication data related to duration
and prior use of other antihypertensive medications would strengthen these analyses.
Thirdly, multiple outcome measures were used in our study so that the P value should
have been adjusted upward to prevent the results from incorrectly declaring a statistical
significance.
CONCLUSION
In this Aging Brain population, blood pressure was associated with reduced brain
volumes, increased WML and cognitive decline. Using ARBs may be beneficial for older
persons to prevent brain atrophy, while treatment with beta blockers and ACE inhibitors
could be risk factors for brain atrophy and cognitive impairment. Further research will be
needed to understand mechanisms of association among use of specific hypertensive
medications, MRI measures of brain volumes and cognition to prevent brain atrophy and
cognitive decline.

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Tables
Table 1: Demographic Characteristics (n=273)
1

Demographic
Characteristics
No anti-hypertensive medications
(n=80)
Anti-hypertensive medications
(N=193)
Elevated blood
pressure
2
(n=29)
Normal blood
pressure
(n=51)
Elevated blood
pressure
2

(n=99)
Normal blood
pressure
(n=94)
Age, yrs 77.9 ±6.8 75.8±6.2 79.1±6.2 75.5±6.4
Male, n (%) 16 (55.2) 24 (47.1) 55(55.6) 65 (69.2)
Education, yrs 15.9±2.8 16.4±3.0 15.7±2.7 15.8±3.2
Race, n (%)
Non-Hispanic
Whites
23 (79.3) 42 (82.4) 67(67.7) 75 (79.8)
Hispanic 2 (6.9) 2 (3.3) 3(3.0) 3 (3.2)
African Americans 1 (3.45) 2 (3.29) 14(14.14) 6 (6.38)
Asian 3 (10.3) 5(9.8) 15(15.2) 9 (9.6)
Other 0 (0) 0(0) 0(0) 1 (1.1)
BMI, kg/m 27.5±9.5 26.7±5.2 27.8±5.1 28.2±5.4
VLDL cholesterol,
mg/dl
20.4±9.3 21.3±8.1 22.1±8.5 22.8±14
LDL cholesterol,
mg/dl
110±40.5 94.2±28.8 97.7±32.5 87.17±31.3
HDL cholesterol,
mg/dl

57.3±17.5 61.4±17.8 56.1±17.0 54.6±16.5
Total cholesterol
, mg/dl
187.7±46.0 177.4±37.0 176.7±39.6 164.2±39.7
Triglycerides
, mg/dl
111.0±47.3 98.7±40.9 120.8±80.9 116.6±89.0
Glucose, mg/dl
109.1±31.5 98.2± 20.2 107.1± 30.4 107.5±37.6
Diabetes, n (%) 7 (24.1) 11 (21.6) 33(33.3) 31 (33.0)
Stroke
3
, n (%) 2 (6.9) 13(25.5) 24 (24.2) 31(33.0)
MI
3
, n (%) 4 (13.8) 5 (9.8) 11 (11.2) 17(18.5)
CABG
3
, n (%) 7 (24.1) 9 (18) 21 (21.2) 18 (19.2)
CEA
3
, n (%) 3 (10.3) 2 (4) 3 (3.0) 4 (4.3)
CAS
3
, n (%) 6 (20.7) 5 (10.0) 12 (12.1) 22 (23.4)
CALS
3
, n (%) 1 (3.5) 3 (6.1) 6 (6.1) 4 (4.3)
CDR=0.5, n(%) 18 (62.1) 17 (33.3) 34 (34.3) 39 (41.5)
CDR=0, n (%) 11 (37.9) 34 (66.7) 65 (65.7) 55 (58.5)
Current Smoking, n
(%)
0 (0) 0 (0) 3 (3.0) 4 (4.3)
APOE e4 present, n
(%)

5 (17.6) 11 (25.0) 18 (20.7) 23 (26.1)
Blood Pressure
Measurements

SBP, mmHg 179.8±15.0 124.6±9.9 155.1±13.8 125.3±9.1
DBP, mmHg 81.3±10.6 70.5±8.2 75.1±9.3 69.2±8.9
MAP, mmHg 107.5±9.9 88.6±7.6 101.8±8.8 87.9±7.4
PP, mmHg 78.6±15.5 54.1±9.5 79.9 ±14.0 56.1±10.9
Antihypertensive
medications, n (%)

Diuretics 39(39.4) 28 (29.8)
Calcium antagonists 37(37.4) 27 (28.7)
ACE inhibitors 38(38.4) 42(44.7)
Angiotensin II
receptor blockers

19(19.2) 21 (22.3)
Alpha blockers 5 (5.1) 12 (12.8)
Beta blockers 32 (32.3) 43 (45.7)
Cognitive function
scores

Verbal Memory 94.2±19.9 100.3±19.4 97.0±19.5 97.7±17.6
Nonverbal Memory 90.2±18.7 100.3±16.4 96.9±16.1 95.3±12.5
Executive Function 92.2 ±17.8 96.5 ±19.2 94.0 ±16.1 93.6 ±14.2
Global Function 96.8±15.7 101.3±18.6 96.1±16.5 98.3±15.3
MRI volumetric
measures, mm
N=27 N=45 N=89 N=82
Grey matter volume 38.9±4.6 39.7±4.5 37.6±3.7 37.4±3.4
White matter volume 30.33±3.5 31.13±3.7 29.64±3.3 30 ±2.9
Hippocampal volume 0.4 ±0.06 0.4 ±0.06 0.4 ±0.05 0.4 ±0.05
Volume of white
matter lesions
0.5±0.7 0.4±0.6 0.5±0.5 0.5±0.8
1.All continuous variables are described by the mean ± SD
2. Elevated blood pressure defined participants with systolic BP ≥140 mm Hg and/or diastolic BP
≥90 mm Hg

3. STROKE Clinical diagnosis of stroke; MI: Clinical diagnosis of myocardial infarction; CABG:
Prior coronary artery bypass or angioplasty; CEA: Prior carotid endarterectomy; CAS Prior
coronary, carotid, or other artery stent; CALS Evidence of carotid atherosclerotic lesion or
stenosis

Table 2 Adjusted associations between blood pressure and MRI volumetric measures (without adjustment for antihypertensive medications) Grey matter volume White matter volume Hippocampal volume Volume of white matter
lesions (on log scale)
β (95% CI) P-value β (95% CI) P-value β (95% CI) P-value β (95% CI) P-value
SBP Main
effect
-0.037
(-0.063, -0.011 )
0.0059 -0.0017
(-0.023, 0.02)
0.88 -0.002
(-0.005,-0.0002)
0.032 0.010
(0.00035, 0.039)
0.046
Interaction
term
SBP*CDR 0.002
(0.00003,0.003)
0.02
DBP Main
effect
-0.13
(-0.064, 0.04 )
0.64 -0.01
(-0.055, 0.034)
0.64 0.0008
(-0.0036,0.002)
0.18 0.11
(0.027, 0.19)
0.0096
Interaction
term
DBP*Gender -0.002
(-0.003, -0.0001)
0.03
MAP Main
effect
-0.091
(-0.15, -0.032 )
0.0025 -0.0002
(-0.04, 0.037)
0.99 -0.002
(-0.004,-0.0001)
0.038 0.27
(-0.0056, 0.060)
0.10
Interaction
term
MAP *CDR 0.26
(0.085,0.43)
0.004
MAP*
Education
-0.01
(-0.025,-
0.0008)
0.037
MAP *gender
-0.001
(-0.003.-0.00004)
0.04
MAP*age 0.007
(0.0004,0.01)
0.04
MAP*education
-0.0003
(-0.0005, -0.00001)
0.02
Pulse
pressure
Main
effect
-0.045
(-0.074, -0.015 )
0.003 -0.0053
(-0.029, 0.018)
0.66 -0.00054
(-0.00097,-0.00010)
0.014 0.02
(-0.003, 0.043)
0.09
Interaction
term

* All regression models were adjusted for baseline age, education, race, gender, smoking status, Clinical Dementia Rating (CDR)
score and other measurements.
Table 3 Adjusted associations between antihypertensive medications and MRI volumetric measures
(with blood pressure adjustment)
Grey matter volume White matter volume Hippocampal
volume
Volume of white matter
lesions (on log scale)
β (95% CI) P-value β (95% CI) P-value β (95% CI) P-value β (95% CI) P-value
SBP-
adjusted

Diuretics
-0.51
(-1.57,0.54)
0.34 -0.25
(-1.11,0.61)
0.57 0.0020
(-0.0014,0.018)
0.81 -0.06
(-0.84 ,0.72)
0.88
Calcium
antagonists
0.22
(-0.79,1.23)
0.67 -0.58
(-1.40,0.25)
0.17 0.0026
(-0.013,0.019)
0.75 -0.56
(-1.35,0.22)
0.29
ACE
inhibitors
-1.43
(-2.44,-0.41)
0.0061 -0.53
(-1.37,0.31)
0.22 -0.0073
(-0.023,0.0087)
0.37 0.58
(-0.16,1.33)
0.125
Angiotensin
II receptor
blockers
1.56
(0.22,2.85)
0.022 1.12
(00564,2.19)
0.039 0.022
(0.0005,0.043)
0.045 -0.58
(-1.56,0.40)
0.24
Alpha
blockers
-1.08
(-2.89, 0.74)
0.24 -0.84
(-2.36, 0.67)
0.27 -0.016
(-0.045, 0.013)
0.28 0.62
(-0.77, 2.00)
0.38
Beta
blockers
-0.95
(-2.05,0.15)
0.09 -0.24
(-1.15,0.67)
0.066 0.00072
(-0.016,0.017)
0.93 0.18
(-0.62,0.98)
0.66
DBP-
adjusted

Diuretics
-0.45
(-1.52,0.62)
0.62 -0.06
(-0.95,0.83)
0.89 0.0024
(-0.014.018)
0.77 -0.17
(-0.94,0.6)
0.66
Calcium
antagonists
-0.29
(-1.31,0.72)
0.57 -0.76
(-1.60,0.09)
0.08 0.0014
(-0.014,0.017)
0.85 -0.45
(-1.22, 0.32)
0.32
ACE
inhibitors
-1.43
(-2.47,-0.39)
0.007 -0.66
(-1.53,0.21)
0.14 -0.0039
(-0.020,0.012)
0.62 0.68
(-0.072,1.44)
0.076
Angiotensin
II receptor
blockers
1.13
(-0.20,2.46)
0.085 1.29
(0.19,2.39)
0.022 0.015
(-0.0059,0.035)
0.16 -0.52
(-1.47,0.44)
0.28
Alpha
blockers
-0.33
(-2.18, 1.53)
0.73 -0.55
(-2.12 1.03)
0.49 -0.0058
(-0.034, 0.022)
0.69 0.68
(-0.68, 2.03)
0.32
Beta
blockers
-1.11
(-2.22-,0.049)
0.049 -0.26
(-1.18,0.67)
0.55 -0.00036
(-0.016,0.015)
0.96 0.35
(-0.46,1.17)
0.39
MAP-
Adjusted

Diuretics
-0.58
(-1.63,0.46)
0.27 -0.18
(-1.01,0.66)
0.67 0.0033
(-0.013,0.019)
0.87 -0.030
(-0.82,0.76)
0.94

Calcium
antagonists
-0.43
(-1.02,0.94)
0.93 -0.67
(-1.47,0.14)
0.11 0.0018
(-0.013,0.017)
0.81 -0.5
(-1.29,0.29)
0.21
ACE
inhibitors
-1.21
(-2.22,-0.2)
0.019 -0.4
(-1.23,0.44)
0.35 0.00053
(-0.015,0.016)
0.94 0.65
(-0.12,1.42)
0.096
Angiotensin
II receptor
blockers
1.05
(-0.22,2.35)
0.11 1.09
(0.04,2.14)
0.042 0.013
(-0.0078,0.033)
0.23 -0.60
(-1.59,0.39)
0.23
Alpha
blockers
-1.21
(-3.02, 0.60)
0.19 -0.68
(-2.19, 0.84)
0.38 -0.0081
(-0.036, 0.020)
0.57 0.64
(-0.76, 2.04)
0.37
Beta
blockers
-1.10
(-2.19,-0.02)
0.047 -0.11
(-1.00,0.78)
0.80 0.0029
(-0.013,0.097)
0.72 0.26
(-0.55,1.07)
0.53
Pulse
pressure-
adjusted

Diuretics
-0.44
(-1.53,0.65)
0.43 -0.28
(-1.12,0.57)
0.52 0.0040
(-0.012,0.02)
0.62 -0.10
(-0.89,0.68)
0.70
Calcium
antagonists
0.0024
(-1.04,1.04)
0.99 -0.74
(-1.54,0.067)
0.072 0.00072
(-0.015,0.016)
0.93 -0.56
(-1.35,0.22)
0.16
ACE
inhibitors
-1.71
(-2.73,-0.69)
0.001 -0.73
(-1.54,0.083)
0.078 -0.0099
(-0.026,0.0057)
0.21 0.57
(-0.18,1.32)
0.22
Angiotensin
II receptor
blockers
1.13
(-0.14,2.39)
0.08 0.82
(-0.16,1.80)
0.10 0.023
(0.0036,0.042)
0.02 -0.61
(-1.59,0.37)
0.22
Alpha
blockers
-1.21
(-3.1, 0.69)
0.21 -0.96
(-2.48, 0.56)
0.21 -0.01
(-0.039, 0.018)
0.48 0.51
(-0.87, 1.90)
0.47
Beta
blockers
-1.42
(-2.48,-0.36)
0.0089 -0.58
(-1.42,0.26)
0.17 -0.0063
(-0.022,0.001)
0.42 0.15
(-0.65,0.95)
0.71
* All regression models were adjusted for baseline age, education, race, gender, smoking
status, Clinical Dementia Rating (CDR) score and other measurements.
Table 4 Adjusted associations between blood pressure and cognitive function score (without adjustment for antihypertensive medications)
Global function score Verbal memory score Nonverbal memory measure score Executive function score
β (95% CI) P-value β (95% CI) P-value β (95% CI) P-value β (95% CI) P-value
SBP Main
effect
-0.044
(-0.14,0.05)
0.37 0.016
(-0.096,0.13)
0.78 0.0062
(-0.089.0.10)
0.90 -0.035
(-0.13,0.06)
0.47
Interaction
DBP Main
effect
-0.12
(-0.32,0.09)
0.25 0.062
(-0.17,0.30)
0.60 0.43
(-0.16.1.01)
0.15 -0.17
(-0.39,0.052)
0.13
Interaction
term
DBP*HDL -0.016
(-0.03,-0.005)
0.005 DBP*Gender 0.39
(0.001.0.078)
0.049
MAP Main
effect
-0.13
(-0.30,0.047)
0.15 0.043
(-0.16,0.25)
0.68 -0.065
(-0.024.0.10)
0.44 -0.16
(-0.35,0.02)
0.082
Interaction
Pulse
pressure
Main
effect
-0.008
(-0.14,0.12)
0.90 -0.034
(-0.18,0.11)
0.64 0.024
(-0.093.0.14)
0.69 -0.032
(-0.15,0.087)
0.60
Interaction
term
Pulse
pressure*ApoE
-2.09
(-0.55,-0.028)
0.024

Pulse
pressure*LDL
0.0045
(0.001,0.0075)
0.0072
* All regression models were adjusted for baseline age, education, race, gender, smoking status, Clinical Dementia Rating (CDR)
score and other measurements.
Table 5 Adjusted associations between antihypertensive medications and cognitive function scores
(with blood pressure adjustment)
Global function score Verbal memory score Nonverbal memory
measure score
Executive function score

β (95% CI)

P-value

β (95% CI)

P-value

β (95% CI)

P-value

β (95% CI)

P-value

SBP-
adjusted

Diuretics
2.40
(-1.82,6.61)
0.29 0.53
(-4.27,5.33)
0.82 1.35
(-2.68,5.38)
0.51 1.06
(-3.19 5.32)
0.49
Calcium
antagonists
1.18
(-2.83,5.19)
0.56 -3.89
(-8.43,0.66)
0.09 -1.32
(-5.17,2.52)
0.50 3.1
(-0.89,7.1)
0.09
ACE
inhibitors
1.04
(-2.95.5.03
0.45 1.78
(-2.83.6.38)
0.45 0.015
(-3.82,3.85)
0.99 0.67
(-3.35,4.67)
0.83
Angiotensin
II receptor
blockers
-1.12
(-6.46,4.22)
0.68 -0.64
(-6.74,5.45)
0.84 0.15
(-4.99,5.25)
0.95 0.66
(-4.7,6.01)
0.81
Alpha
blockers
2.7
(-4.62,10.01)
0.29 4.58
(-3.85,13.00)
0.29 0.84
(-6.12, 7.79)
0.81 2.08
(-5.2, 9.35)
0.57
Beta
blockers
-4.34
(-8.58,-0.10)
0.045 -3.6
(-8.45,1.24)
0.14 0.8
(-3.3,4.9)
0.70 0.21
(-4.09,4.52)
0.92
DBP-
adjusted

Diuretics
3.32
(-1.01,7.66)
0.13 1.85
(-2.97,6.68)
0.45 2.44
(-1.7,6.60)
0.25 -0.71
(-5.32,3.89)
0.76
Calcium
antagonists
0.99
(-3.07,5.08)
0.63 -3.69
(-8.31,0.92)
0.12 -1.64
(-5.63,2.34)
0.42 2.7
(-1.69,,7.1)
0.22
ACE
inhibitors
1.28
(-2.82.5.40)
0.54 2.16
(-2.54.6.85)
0.37 0.47
(-3.54,4.47)
0.82 -1.19
(-5.61,3.24)
0.60
Angiotensin
II receptor
blockers
-0.39
(-5.81,5.04)
0.89 -0.28
(-6.49,5.93)
0.93 1.02
(-4.34,6.38)
0.70 1.93
(-3.93,7.79)
0.52
Alpha
blockers
2.54
(-4.92,9.99)
0.50 5.78
(-2.81,14.37)
0.29 4.99
(-2.28, 12.26)
0.18 3.38
(-4.54, 11.31)
0.4
Beta
blockers
-3.62
(-8.06,0.68)
0.09 -5.0
(-9.41,0.41)
0.07 -0.03
(-4.23,4.17)
0.98 -1.71
(-6.4,2.98)
0.47
MAP-
Adjusted

)

Diuretics
1.71
(-2.48,5.91)
0.42 1.63
(-3.26,6.54)
0.51 2.3
(-1.83,6.44)
0.27 0.99
(-3.42,5.40)
0.66
Calcium
antagonists
1.03
(-3.00,5.06)
0.61 -3.67
(-8.35,1.01)
0.12 -2,19
(-6.15,1.77)
0.28 3.15
(-1.09,7.39)
0.14
ACE
inhibitors
-0.026
(-4.02.3.97)
0.99 1.30
(-3.45.6.04)
0.59 0.40
(-3.60,4.41)
0.84 -1.17
(-5.38,,3.05)
0.59
Angiotensin
II receptor
blockers
-1.46
(-6.83,3.92)
0.59 0.022
(-6.25,6.30)
0.99 0.12
(-5.18,5.43)
0.96 1.34
(-4.45,7.04)
0.64
Alpha
blockers
2.34
(-5.05,9.73)
0.53 6.22
(-2.44,14.88)
0.16 4.46
(-2.70, 11.63)
0.22 1.98
(-5.48, 9.75)
0.62
Beta
blockers
-4.92
(-9.17,-0.66)
0.023 -4.54
(-9.49,0.41)
0.07 0.15
(-4.12,4.42)
0.94 -1.51
(-6.02,3.01)
0.51
Pulse
pressure-
adjusted

Diuretics
2.10
(-2.17,6.36)
0.33 0.82
(-4.44,6.08)
0.76 1.86
(-2.43,6.15)
0.39 1.48
(-3.90,5.86)
0.54
Calcium
antagonists
0.73
(-3.32,4.78)
0.72 -3.29
(-8.3,1.72)
0.13 -1.17
(-5.25,2.91)
0.57 3.16
(0.94,7.28)
0.13
ACE
inhibitors
-0.17
(-4.26.3.91)
0.93 -1.43
(-6.42.3.57)
0.57 -0.78
(-4.83,3.28)
0.71 -1.19
(4.35,3.98)
0.93
Angiotensin
II receptor
blockers
-1.81
(-7.17,3.54)
0.51 1.72
(-4.93,8.37)
0.61 1.0
(-4.45,6.44)
0.72 0.87
(-4.63,6.37)
0.75
Alpha
blockers
1.02
(-6.28,8.32)
0.78 1.81
(-7.28,10.90)
0.16 2.73
(-4.59, 10.05)
0.46 3.16
(-4.24, 10.57)
0.4
Beta
blockers
-4.44
(-8.71,-0.18)
0.041 -4.67
(-10.07,0.55)
0.079 -0.72
(-5.05,3.61)
0.94 -0.66
(-5.05,3.74)
0.77
*All regression models were adjusted for baseline age, education, race, gender, smoking
status, Clinical Dementia Rating (CDR) score and other measurements.

Abstract (if available)

Abstract Background: Hypertension is a major risk factor for cognitive impairment and is also associated with structural changes in the brain, particularly white matter hypertensites (WMH) on MRI. Evidence for the possible associations of blood pressure medications with dementia and associated brain pathology remains inconclusive. The objectives of our study were to determine whether blood pressure and use of specific classes of antihypertensive medications are associated with cognition and structural magnetic resonance imaging (MRI) measures of brain atrophy in an elderly population of individuals selected for no or mild cognitive impairment, with a range of vascular factors. ❧ Methods: A total of 274 participants in the observational Aging Brain Study were included in the analysis for relationships among blood pressure, anti-hypertensive medications and cognitive function scores. An additional 30 participants were excluded from analyses testing the relationships among blood pressure, anti-hypertensive medications and MRI volumetric measures due to missing neuroimaging data. Baseline measures included blood pressure, current medications, structural T1-weighted images and four composite scales for cognitive function. Demographic information and other measurements were included as covariates. We tested the relationship among blood pressure (mean SBP, DBP, MAP and pulse pressure), five classes of antihypertensive medications and dependent variables (MRI volumetric measures and cognitive function scores), using separate multiple linear regression models for each dependent variable and adjusting for age, educational level, race, gender, smoking status, Clinical Dementia Rating (CDR) score and other measurements. ❧ Results: SBP was significantly inversely associated with total grey matter and total hippocampal volume (both P<0.035) and positively associated with volume of white matter hyperintensites (WMH) (P=0.046). Elevated DBP was associated with higher volume of WMH (P=0.0096). Pulse pressure was inversely associated with hippocampal volume and grey matter volume (P=0.014 and 0.003, respectively). In models adjusting for SBP, DBP and MAP, use of angiotensin II receptor blockers (ARBs) (n=41) was positively associated with white matter volume (P<0.05), hippocampal volume (P=0.045), and total grey matter volume in models adjusted for SBP (P=0.022). In models adjusting for SBP, DBP, MAP and pulse pressure, use of ACE inhibitors was significantly inversely related to grey matter volume (all P<0.02). Use of beta-blockers was inversely associated with total grey matter volume in models including DBP, MAP and pulse pressure (all P<0.05). In models with SBP, MAP and pulse pressure, beta blockers were inversely related with the Global cognitive function score (all P<0.045). ❧ Conclusion: In this Aging Brain population, blood pressure was associated with reduced brain volumes, increased WMH and cognitive decline. Using ARBs may be beneficial for older persons to prevent brain atrophy, while treatment with beta blockers and ACE inhibitors could be risk factors for brain atrophy and cognitive impairment. Further research will be needed to understand mechanisms of association among use of specific classes of hypertensive medications, MRI measures of brain volumes and cognition to prevent brain atrophy and cognitive decline.

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Asset Metadata

Creator Jin, Yin (author)

Core Title Cross-sectional association of blood pressure, antihypertensive medications, MRI volumetric measures and cognitive function scores in an aging population

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

Publication Date 08/03/2016

Defense Date 06/30/2016

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

Tag antihypertensive medications,blood pressure,brain MRI,cognition,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Mack, Wendy (committee chair), Chui, Helena (committee member), Zheng, Ling (committee member)

Creator Email qjqqqqpgm@gmail.com,yinjin@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-294662

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Legacy Identifier etd-JinYin-4577.pdf

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