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Published in final edited form as:
J Clin Exp Neuropsychol. 2009 January ; 31(1): 96–110. doi:10.1080/13803390802014594.
Vascular and cognitive functions associated with cardiovascular
disease in the elderly
Ronald A. Cohen1, Athena Poppas2, Daniel E. Forman3, Karin F. Hoth8, Andreana P.
Haley4, John Gunstad5, Angela L. Jefferson6, David F. Tate1, Robert H. Paul1,7, Lawrence
H. Sweet1, Mokato Ono1, Beth A. Jerskey1, and Marie Gerhard-Herman3
1Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University,
Providence, RI, USA
2Department of Cardiology, Rhode Island Hospital, Warren Alpert Medical School, Brown University,
Providence, RI, USA
3Division
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of Cardiology, Brigham and Women's Hospital and Geriatric Research, Education, and
Clinical Care, VA Boston Health Care System, Harvard Medical School, Boston, MA, USA
4Department
of Psychology, The University of Texas at Austin, Austin, TX, USA
5Department
of Psychology, Kent State University, Kent, OH, USA
6Department
of Neurology, Boston University School of Medicine, Boston, MA, USA
7Department
of Psychology, Behavioral Neuroscience, University of Missouri, St. Louis, MO, USA
8Department
of Medicine, National Jewish Medical and Research Center, and Department of
Psychiatry, University of Colorado, Denver, Colorado, USA
Abstract
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This study examines the relationship between systemic vascular function, neurocognitive
performance, and structural brain abnormalities on magnetic resonance imaging (MRI) among
geriatric outpatients with treated, stable cardiovascular disease and no history of neurological illness
(n = 88, ages 56–85 years). Vascular function was assessed by cardiac ejection fraction and output,
sequential systolic and diastolic blood pressures, flow mediated brachial artery reactivity (BAR), and
carotid intima media thickness (IMT). White matter hyperintensities (WMH) on MRI were quantified
and examined relative to cognitive and vascular function. Principal component analysis revealed two
primary vascular components: one associated with cardiac function, the other with atherosclerotic
burden/endothelial dysfunction. Both factors were significantly associated with cognitive function
and WMH volume. Reduced systolic variability and increased IMT were most strongly related to
reduced attention, executive function, and information-processing speed. These findings suggest the
possibility that systemic vascular indices may provide proxy measures of cerebrovascular
dysfunction and reinforce the importance of achieving greater understanding of interaction between
systemic vascular disease and brain dysfunction among elderly people with cardiovascular disease.
Address correspondence to Ronald A. Cohen, Centers for Behavioral and Preventive Medicine, The CORO Building, 5th Floor, One
Hoppin Street, Providence, RI 02903, USA (RCohen@lifespan.org).
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Keywords
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Cardiovascular disease; Cerebrovascular disease; White matter hyperintensities; Magnetic resonance
imaging; Flow mediated dilatation intima lamina thickness; Blood pressure variability; Cardiac
output; Cognition; Attention; Executive function; Psychomotor function
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Vascular cognitive impairments (VCI) occur on a continuum ranging from mild deficits among
patients with vascular risk factors such as cardiovascular disease (CVD) to the severe cognitive
dysfunction characteristic of vascular dementia (Bowler, Steenhuis, & Hachinski, 1999;
Rockwood, 2002; Roman, Erkinjuntti, Wallin, Pantoni, & Chui, 2002). Cardiovascular disease
was once thought to convey little risk to the brain given its capacity for vascular autoregulation
and sustained cerebral perfusion even under adverse hemodynamic conditions (Lassen,
1964). Brain dysfunction secondary to cardiovascular disease was usually attributed to acute
stroke during cardiac surgery (Newman et al., 2001), or in response to cardiac events (e.g.,
arrhythmia). Yet, patients with severe cardiovascular disease frequently exhibit cognitive
problems in the absence of clinically identified stroke (Moser et al., 1999; Paul et al., 2005),
particularly in cases of heart failure (Bennett & Sauve, 2003; Bornstein, Starling, Myerowitz,
& Haas, 1995), presumably reflecting the impact of reduced cardiac function on the aging brain
(Roman, 2004). We have previously shown that both cognitive dysfunction and structural brain
abnormalities on magnetic resonance imaging (MRI) are associated with reduced cardiac
output among patients with severe cardiovascular disease (Jefferson, Poppas, Paul, & Cohen,
2007a; Jefferson et al., 2007b) and abnormalities of systemic vascular function (Gunstad et al.,
2006a; Gunstad et al., 2005; Gunstad et al., 2006b; Haley et al., 2007a; Haley et al., 2007b;
Hoth et al., 2007).
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It seems intuitive that cerebral hypoperfusion secondary to impaired cardiac function should
contribute to CVD-associated cognitive impairments, particularly among patients with heart
failure. Yet, not all patients with CVD-associated cognitive problems have heart failure, nor
do all patients with heart failure experience brain dysfunction. Therefore, other factors beyond
cardiac output must also play a role. CVD-associated brain dysfunction is greatest among the
elderly (Rastas et al., 2006; Verhaegen, Borchelt, & Smith, 2003). There is now considerable
evidence that the homeostatic mechanisms involved in maintaining hemodynamic regulation
break down as people reach advanced age (Bakker et al., 2004; Murkin, 2002; Serrador,
Milberg, & Lipsitz, 2005). This is a significant problem as the cerebrovascular system must
adapt to momentary changes in hemodynamic state to insure sustained cerebral perfusion of
the brain. A variety of pathophysiological processes contribute to a breakdown of cerebral
hemodynamic function among the elderly with CVD, including factors that affect endothelial
function and smooth muscle reactivity (Kathiresan et al., 2006; Serrador et al., 2005; Verma,
Buchanan, & Anderson, 2003). Reduced vasodilatory responses to drug challenge (e.g.,
nitroglycerin, verapamil) or mechanically induced ischemia provides evidence of endothelial
dysfunction that occurs in association with both advanced age and vascular disease (Adams et
al., 1998; Bank & Kaiser, 1998; Bank et al., 1995). Dyslipidemia and associated atherosclerosis
also play an important role (Claus et al., 1996), as cardiovascular disease is known to contribute
to structural changes in cerebral blood vessels, including thickening of the intima media and
a breakdown in smooth muscle function (Akopov, Sercombe, & Seylaz, 1996; Tentolouris et
al., 2004, 2006; Vita, 2005; White, Deane, Vallance, & Markus, 1998). Chronic blood pressure
abnormalities also have detrimental mechanical effects on cerebral hemodynamics (Birns,
Markus, & Kalra, 2005; Lorberboym, Lampl, Kesler, Sadeh, & Gadot, 2001; Schmidt et al.,
2004). In sum, CVD-associated brain dysfunction among the elderly, without a clinical history
of acute stroke, likely depends on a complex interaction of impaired cardiac and cerebral
hemodynamic factors. Structural changes of both small and large cerebral vessels occur as the
result of atherosclerosis and associated thickening of the intima media. These changes are
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amplified when there has been a breakdown of endothelial function and vascular reactivity,
particularly in the context of chronic blood pressure disturbance.
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Past studies have examined the influence of specific risk factors and pathophysiological
processes on the development of brain dysfunction. For example, chronic hypertension is
known to contribute to microvascular abnormalities, which has been shown to underlie
structural white matter damage and reduced cognitive function in large epidemiological studies
(Birkenhager, Forette, Seux, Wang, & Staessen, 2001; de Leeuw et al., 2004; den Heijer et al.,
2005; Vermeer, Koudstaal, Oudkerk, Hofman, & Breteler, 2002). We previously examined the
influence of specific cardiac and systemic vascular factors on cognitive function and MRI brain
abnormalities among the elderly with cardiovascular disease (Gunstad et al., 2005; Haley et
al., 2007a; Haley et al., 2007b; Hoth et al., 2007; Jefferson et al., 2007a; Jefferson et al.,
2007b; Paul et al., 2005). Yet, to date few studies have examined the simultaneous relationships
among cardiac output, blood pressure, and systemic hemodynamic function and measures of
brain function and structure. Past studies have investigated these relationships primarily by
examining the prevalence of specific risk factors in an epidemiological context (Claus et al.,
1996; DeCarli et al., 1999; de Leeuw et al., 2004; Vermeer et al., 2003). Most elderly people
with cardiovascular disease have a variety of underlying risk and etiological factors (Martins
e Silva & Saldanha, 2007; Terry et al., 2005; Tranche, Galgo, Mundet, & Sanchez-Zamorano,
2005). Accordingly delineating the basis of brain disturbances secondary to cardiovascular
disease, in the absence of large vessel stroke, requires simultaneous consideration of multiple
biomarkers of cardiac and systemic vascular function and structure. This motivated the current
study to explore the relative contributions of cardiac function (cardiac output and systolic blood
pressure) and systemic hemodynamic function to cognitive dysfunction and the brain
abnormalities in cardiovascular disease patients.
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We hypothesized that cognitive performance and MRI brain abnormalities would vary as a
function of both cardiac and systemic vascular function among elderly people with
cardiovascular disease with no prior history of acute stroke or other neurological brain disease.
Patients with both impaired cardiac function (reduced cardiac output, systolic variability) and
systemic hemodynamic dysfunction on brachial artery response (BAR) and thickening of the
carotid intima media (IMT) would show the weakest cognitive functioning and greater
structural abnormality on brain imaging. We studied 88 cardiovascular disease patients to test
the hypothesis that neurocognitive dysfunction secondary to cardiovascular disease is
associated with two primary vascular factors (cardiac function and systemic vascular health).
Patients underwent a vascular assessment that included measures of cardiac output, blood
pressure variability, carotid IMT, and BAR. In addition to the vascular assessment, a subset of
participants underwent structural brain MRI to assess the severity of white matter
hyperintensities (WMH), a proxy measure of cerebrovascular disease. The goal was to
determine which vascular factors were most strongly associated with cognitive status and MRI
brain abnormalities.
Method
Clinical sample
Participants in the study consisted of 88 elderly patients (age: 70.0 ± 7.7 years) with CVD,
recruited from the cardiology services at the Rhode Island Hospital and the Miriam Hospital
in Providence, RI. The majority of participants were identified from patients undergoing
noninvasive cardiovascular assessment for coronary heart disease and from the Rhode Island
Hospital Heart Failure Clinic (A.P.). Demographic and clinical characteristics of the sample
are presented in Table 1. Briefly, participants included 47 males (53%) and 41 females (47%)
who were clinically treated for cardiovascular disease. Racial/ethnic composition included
76.6% Caucasians, 9.4% African-American, and 1.8% Hispanic. The majority of the
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participants were married (65.8%), while 7.9% were divorced, 15.8% widowed, 10.5% never
married.
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All participants had a documented history of cardiovascular disease including at least one of
the following: coronary artery disease (CAD: 73.4%), myocardial infarction (42.9%), heart
failure (22.8%), chronic hypertension (76.0%), or cardiac arrhythmia (17.5%). A subset of
patients had previously undergone revascularization procedures: angioplasty (19.3%), stenting
(14.0%), coronary artery bypass graft (CABG: 32.8%). Determination of cardiovascular risk
factors were based on the self-report of all patients during the medical history interview, with
independent verification of the presence of specific risk factors derived from medical records
and/or discussion with the patients' physicians when possible. Pertinent risk factors and medical
histories of the sample included: 76.0% hypertension (76.0%), hypercholesterolemia (59.3%),
Type 2 diabetes (22.2%), and smoking (53.2%). A total of 36% of participants endorsed one
risk factor, 38% endorsed two, 20% endorsed three, and 6% had all four factors. All participants
were receiving ongoing treatment by a cardiologist and were diagnosed as having moderate to
severe cardiac disease. The mean New York Heart Scale severity
http://www.hcoa.org/hcoacme/ was 1.86 (SD = 0.73). Medication information for the sample
was as follows: 88% antihypertensives, 68% aspirin/antithrombotics, 75% lipid lowering
agents, 63% vitamins, 42% gastric acid inhibitors, 16% hypoglycemics, 18% vasodilators, and
15.9% psychiatric medications.
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Exclusion criteria included a history of neurological disease (e.g., stroke, seizure disorder,
Parkinson's disease, traumatic head injury with loss of consciousness for >10 minutes,
diagnosis of dementia), substance abuse, and major psychiatric illness (e.g., schizophrenia,
bipolar disorder). The study received local institutional review board approval, and all patients
gave signed informed consent prior to participation. All participants received monetary
compensation for their participation in the study.
Vascular assessment
Participants were asked to refrain from taking vasoactive medications (e.g., calcium channel
blockers, ACE inhibitors, and beta blockers), caffeinated beverages, and smoking for 6 hours
before the vascular assessment. Furthermore, all participants fasted for 6 hours prior to the
assessment. Prior to initiating vascular interrogation, patients remained supine for 15 minutes
in a quiet room.
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Echocardiogram—A complete, transthoracic echocardiogram was obtained from each
participant according to standards put forth by the American Society of Echocardiography.
From these data, cardiac output (CO) was derived. CO is the amount of blood in liters per
minute that is pumped from the heart to perfuse the systemic circulation. Because the flow is
pulsatile, CO is a function of stroke volume and heart rate. Stroke volume is a function of mean
velocity of blood flow from the left ventricle multiplied by the area of left ventricular outflow
tract measured from the 2D echo image: CO = (TVI × CSA) × HR, where TVI = time velocity
integral, CSA = cross-sectional area, and HR = heart rate. It is recorded with Doppler
echocardiography. While this method reflects a noninvasive procedure for obtaining CO,
previous research has shown that data generated from such noninvasive procedures strongly
correlate with invasive measures of CO (Moulinier et al., 1991).
Blood pressure—An automatic noninvasive blood pressure monitor Press-Mate 8800
(Colin Medical Instruments Corp, San Antonio, TX) was used to assess blood pressure (BP)
serially over the course of the vascular assessment. With the participant resting in the fasted
state in a quiet, darkened room, BP was measured in the left arm every 10 minutes for 2 hours.
Eight BP indices used in past studies were generated, including average resting pressure,
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standard deviation, and coefficient of variability (standard deviation divided by average resting
pressure) for both systolic and diastolic blood pressure. Pulse pressure and mean arterial
pressure were also collected. Mean diastolic and systolic pressures across the sequential blood
pressure samples were analyzed, along with the systolic and diastolic variability measures.
Carotid intima media thickness (IMT)—High-resolution B-mode carotid
ultrasonography was performed using a 7.5-MHz transducer and an Agilent 5500 machine
(Agilent, Andover, MA). IMT was calculated using scans of the far wall of the left common
carotid artery approximately 1 cm proximal to the carotid bulb. IMT was defined as the distance
between the luminal-endothelial interface and the junction between the media and the
adventitia. Automated, objective edge detection software was developed to measure IMT
thickness based on a validated technique described previously (Stadler et al., 1996).
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Brachial artery flow-mediated dilatation—Peripheral vascular autoregulation and
associated endothelial function were assessed by flow-mediated brachial artery vasodilatation
using well-established methods (Corretti et al., 2002; Stadler, Ibrahim, & Lees, 1997). Highfrequency B-mode ultrasound was used to visualize the brachial vessel. A Hewlett Packard
5500 ultrasound system, equipped with a linear array vascular (7.5-MHz) transducer was used
to acquire 2D and Doppler flows of each participant's left arm. Images were obtained in
longitudinal orientation approximately 5 cm above the antecubital fossa; straight segments, at
least 10 mm, were targeted for optimal assessments. Blood pressure was measured with an
automated Datascope accutor 3SAT (Paramus, NJ) in the contralateral arm after more than 10
minutes of rest in the supine position prior to the initiation of any vasoactive maneuvers.
To assess endothelial function, hyperemic (flow-mediated) vascular responses were assessed.
First, baseline images of brachial artery diameter and blood flow velocity were recorded for
one minute (sequential images, captured and digitized on each R-wave). Thereafter, a 4-cm
cuff (positioned on the mid-forearm) was inflated to 40 mg above the baseline systolic blood
pressure for 5 minutes so that mechanical ischemia was induced. The same brachial segment
was interrogated for 3 minutes after the cuff was deflated (the period of hyperemic flow). An
investigator who was blinded to participant characteristics performed analyses of the digital
images. Arterial diameter was determined using a validated software algorithm that
automatically calculates the average diameter over the selected segment (Yamauchi, Fukuda,
& Oyanagi, 2002). Flow-mediated vasodilatation was calculated as the percentage change in
diameter from baseline to the maximum diameter induced by reactive hyperemia.
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A total of 10 minutes after the hyperemic brachial assessment was completed, endothelial
independent-vascular function was assessed by measuring the same portion of the brachial
artery, before and 5 minutes after the administration of 0.3 mg sublingual nitroglycerin, a time
corresponding to peak vasodilatory responses to nitroglycerin. Nitroglycerin-mediated
vasodilation was calculated as the percentage change in arterial diameter from the
prenitroglycerine baseline to the diameter after nitroglycerin.
Neurocognitive assessment
All participants completed an extensive neuropsychological assessment including measures of
global cognitive functioning, language, memory, attention, executive functioning,
psychomotor speed, and visual-spatial ability. The battery included standard
neuropsychological instruments with known established reliability and validity (see Table 2)
(Yamauchi et al., 2002). All tests were administered and scored by trained research assistants
under the supervision of a licensed clinical neuropsychologist (R.A.C.), using standardized
procedures. The research assistants who administered the tests were blind to other clinical data
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from the vascular and neuroimaging assessments. The testing session was approximately 2
hours in duration.
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Neuropsychological test measures were grouped into one of five cognitive domains: (a) global
cognitive functioning, (b) language functions, (c) visual-spatial abilities, (d) learning and
memory functions, and (e) attention-executive-psychomotor functions. The following test
scores were included in each domain: (a) global: Mini-Mental State Examination (MMSE),
Dementia Rating Scale (DRS); (b) language: Boston Naming Test (BNT) total, Animals total;
(c) visual-spatial: Block Design total, Hooper Visual Organization Test (HVOT) total, Rey
Complex Figure Test (CFT) copy total; (d) memory: California Verbal Learning Test (CVLT)
sum of Trials 1–5, total immediate recall, delayed recall, and discrimination, CFT total
immediate and delayed recall and discrimination, Brief Visual Memory Test–Revised (BVMTR) sum of Trials 1–3, delayed recall and discrimination; (e) attention-executive-psychomotor
functions: Stroop Interference trial time, Trail Making A & B time, Controlled Oral Word
Association Test (COWAT) total, Letter Search total time, Digit Symbol Coding total, Digit
Span total, and Pegs-D time. The assignment of tests to particular cognitive domains was based
on the way these tests have been typically conceptualized in clinical neuropsychology (e.g.,
Lezak, 1995) and to maintain consistency with our previous studies of cardiovascular disease.
Given expectation of greatest cardiovascular disease effects on frontal-subcortical systems, the
battery was designed to assess attention and executive functioning using multiple measures.
Raw test scores were converted to z-scores using sample means and standard deviations. For
measures on which the dependent measure was time for task completion (e.g., Trail Making),
z-scores were multiplied by – 1.0 to correct for the fact that a shorter duration indicates better
performance. This correction equated the direction of effects for all measures, enabling us to
average across the individual z-scores to derive a composite index score for each domain. Five
composite cognitive domain z-scores were calculated for each participant by averaging the zscores of all tests within that domain. Missing individual tests scores were replaced by 0, the
mean z-score of the sample. Demographic characteristics such as age, education, and gender
were entered into analyses as covariates to control for their potential influence on effects.
Cardiac disease risk severity was coded (0 = no risk; 1 = one risk factor; 2 = two risk factors;
etc.) based on medical history of hypertension, hypercholesterolemia, diabetes, and tobacco
use. Coronary artery disease (CAD) was coded as present if any one of the following conditions
was evident from the medical history: myocardial infarction, angina pectoris, coronary artery
bypass graft (CABG), or angioplasty/stent placement.
Brain MRI and WMH quantification
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Brain MRI was obtained using a Siemens Symphony 1.5 Tesla unit. Sagital T1-weighted (TR/
TE = 500/30) and T2-weighted (TR/TE = 2500/80) conventional spin-echo localizer images
and axial T1-, T2-, and fluid-attenuated inversion recovery (FLAIR; TR/TE = 6000/105, slice
thickness = 5-mm/2-mm gap) images were obtained for each participant. We used the FLAIR
sequence because of its ability to suppress cerebrospinal fluid (CSF) signal and the sensitivity
to visualize WMH, as described our previous studies (R. A. Cohen et al., 2002).
The FLAIR images were used to quantify WMH utilizing semiautomated threshold methods
with good intra- and interrater reliability. The intraclass correlations were conducted in order
to help us feel confident that the technicians were performing the semiautomatic brain
measurements in the same way each time. Each rater produced two traces for each of the MRI
indices in a small sample of participants (n = 10) at two different points in time. Intraclass
correlation (ICC) coefficients were determined using SPSS-14 (Shrout & Fleiss, 1979). The
derived Kappa correlation coefficients were consistently >.85, indicating that brain regions
were being measured by each rater in a consistent manner.
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The raw FLAIR imaging data was imported into the commercially available Mayo Clinic
software program ANAYLZE® (Biomedical Imaging Resource, Rochester, MN) where the
skull was stripped and the brain stem and cerebellum removed leaving only the total cerebral
brain parenchyma (WBV). Second, using the ANAYLZE® threshold tool, WMH in areas of
surrounding parenchyma were isolated for each patient. The total number of pixels reflecting
WMH was summed across the slices. Finally, WBV was calculated similarly using threshold
histogram values that were consistent with brain parenchyma. The WBV was used as a
correction factor for each of the hyperintensity values, and this ratio was the primary imaging
variable for this study. It also allows for a more direct comparison between patients controlling
for variability in brain size and generalized atrophy as the result of aging. We examined the
relationship of WBV to the vascular and cognitive indices.
Data analysis
Descriptive statistics were performed to characterize the sample with respect to demographic
and clinical characteristics, neurocognitive performance, and the vascular indices. Pearson
correlation coefficients were then calculated between each vascular and cognitive measure.
Composite z-scores were derived for each cognitive measure based on the study sample.
Cognitive domain index scores were then computed by summing across corrected z-scores for
all tests in a particular domain. Attention-executive, memory, language, and visual integrative
composite index scores were derived.
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The eight vascular indices (cardiac output, ejection fraction, mean systolic and diastolic blood
pressures, systolic and diastolic variance, flow mediated BAR, and IMT) were analyzed by
principal component analysis (PCA) and a maximum likelihood factor analysis (FA), with
subsequent confirmatory factor analysis based on structural equation modeling using Amos in
SPSS-14.0 (2007) used to test for goodness of fit (chi2). Three methods were used to determine
the optimal number of components or factors (Scree test, and Parallel Analysis, PA, and
minimum average partial, MAP, procedures). Loadings between retained components and
individual vascular indices >.40 were considered as statistically significant.
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Hierarchical multiple regression analyses were then performed using SPSS 14.0. Analysis of
the association between the vascular measures and cognitive function was conducted by
domain. The a priori hypothesis underlying this study was that significant associations would
be observed for the domain of attention, executive functioning, and information-processing
speed relative to the vascular indices, whereas that other cognitive domains would not strongly
associate with vascular function. These hypotheses led to planned set of regression analyses,
with the primary analysis of interest being the analysis of vascular function relative to
neurocognitive function. Vascular relationships with the other cognitive domains were
analyzed to provide comparison with the executive-attention domain. Accordingly, the
examination of associations between vascular function and the other cognitive domains were
conducted primarily as a contrast to this primary analysis, and effects were not expected for
these domains, alleviating the need for correction for multiple comparisons.
As hypothesized, many of the measures of attention and executive and psychomotor
functioning correlated significantly with the individual vascular indices (see Table 3).
Accordingly, hierarchical regression analyses were first conducted relative to the attentionexecutive domain (entry criteria: p < .05; removal criteria: p < .10), in which age, years of
education, Beck Depression Inventory (BDI) score, cardiovascular disease risk score, CAD
status, and medication classes were entered in the first step, followed by seven vascular indices
(average systolic blood pressure, BP, systolic variability, average diastolic BP, diastolic
variability, BAR, IMT, cardiac output). BAR to reactive hyperemia was entered into the
analyses, but not BAR in response to the nitroglycerine condition in order to avoid colinearity.
The composite indices for each cognitive domain were entered as dependent measures in a
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series of five separate regression analyses (attention/executive, learning/memory, language,
visual integration). The two vascular component scores from the PCA analysis were then
entered in a hierarchical regression with the executive index as the dependent measure and the
same covariates as those described above.
In a similar manner, the two MRI indices (WMH, WBV) were treated as dependent measures
in subsequent regression analyses, first with the individual vascular entered as independent
measures, and then in a second analysis using the two vascular component scores from the
PCA. Given that, as hypothesized, the vascular indices were associated with the attentionexecutive index, a series of subsequent analyses were conducted in which the vascular measures
were examined in relation to individual tests from this domain (e.g., Stroop, Trail Making,
etc.). A total of 23 cardiovascular disease patients both underwent MRI brain imaging and
completed all of the vascular and the neurocognitive tests. Others either chose not to participate
in this procedure or were excluded because of medical factors (e.g., pacemakers). In a final set
of analyses, the derived components from the PCA were examined by regression analysis
relative to each of the composite cognitive factors (Z-scores) and the MRI indices.
Results
Vascular function
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Cardiovascular disease patients in the sample exhibited a relatively wide range of findings on
the cardiac and vascular indices as shown in Table 1. Older patients (age >75 years) did not
differ from younger patients (age <75 years) on most vascular indices, so that we did not stratify
patients based on age. Instead age was entered as a covariate in subsequent analyses.
Both the PCA and FA analyses of the eight vascular indices revealed two primary vascular
components. The varimax-rotated patterns of variable loadings for the PCA and FA yielded
near identical results, strongly supporting a two-component solution. As shown in Table 4,
Component 1 was significantly associated with cardiac output, ejection fraction, and systolic
variance. Component 2 was most significantly associated with BAR, IMT, and diastolic
variance. This two-factor solution accounted for 72% of the overall variance of the vascular
indices. The reliability of three indices comprising the cardiac factor (Component 1) as
measured by Chronbach coefficient (α) statistic was .83, while the reliability of the three indices
comprising the hemodynamic factor (Component 2) was .89.
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A confirmatory FA analysis using structural equation modeling was conducted. The three
variables associated with the cardiac factor were allowed to load on Component 1, while the
other three variables were allowed to load on Component 2. A correlation was estimated
between latent constructs and each vascular variable's error variance. This analysis indicated
that a significant goodness of fit was the factor structure derived from the PCA and FA analyses,
χ2(5) = 22.6, p < .01.
Vascular and attention-executive functioning—Correlations between the five vascular
indices and the neuropsychological measures of attention, executive, and psychomotor
functioning, as well as the overall attention-executive index are provided in Table 3. Three of
the five vascular indices (IMT, BAR, systolic BP variance) were significantly associated with
the attention-executive index and also with various measures from this cognitive domain.
Cardiac output approached a significant correlation with the overall attention-executive index
(p = .06). Diastolic variance did not correlate with the executive index or any of the attentionexecutive measures.
Stepwise hierarchical regression analysis in which the five vascular indices were entered as
independent measures and the attention-executive index was entered as the dependent measure
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revealed a significant association, R = .45, F(3, 80) = 7.30, p = .001. Three vascular indices
were retained as significant correlates (systolic variance: β = .33; IMT: β = −.29; BAR: β = .
09). For this analysis, age, BDI severity, and medication class were not found to be significantly
associated with the attention-executive index.
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Vascular functioning was significantly associated with each of the individual cognitive
measures in the executive index measures. Trail Making A performance, R = .46, F(1, 82) =
6.13, p < .02, was most significantly associated with systolic BP variance (β = .33) and BAR
(β = .15). Trail Making B performance, R = .46, F(2, 81) = 9.92, p < .02, was most significantly
associated with systolic BP variance (β = .43) and IMT (β = .23). Letter Cancellation
performance, R = .39, F(2, 81) = 4.91, p = .01, was most significantly associated with IMT
(β = .30) and systolic BP variance (β = .22). Digit Span performance, R = .46, F(2, 81) = 5.23,
p = .01, was most significantly associated with IMT thickness (β = −2.8) and systolic BP
variance (β = .26). Stroop performance, R = .33, F(2, 81) = 5.25, p = .01, was most significantly
associated with BAR (β = −.20) and systolic BP variance (β = −.20). Digit Symbol Coding
performance, R = .36, F(2, 81) = 4.77, p = .01, was most significantly associated with IMT
thickness (β = −.26) and cardiac output (β = −.21). Psychomotor speed on the Grooved
Pegboard, R = .25, F(1, 82) = 5.23, p = .01, was most strongly associated with IMT (β = .25).
In summary, the vascular indices were significantly associated with all of the individual
measures of attention and executive and psychomotor functioning. IMT and systolic BP
variance were the vascular measures that tended to associate with most of the performance
measures.
Vascular components and attention-executive function—The two vascular
components derived from the PCA were then entered into a similar set of hierarchical regression
analyses in which age and the other clinical variables were treated as covariates, and the
executive index was treated as the dependent variable. A highly significant association between
vascular function and performance on the executive index was found, R = .67, F(1, 82) = 12.30,
p < .001. Both Component 1 (cardiac: β = .37) and Component 2 (systemic vascular: β = .47)
were retained as contributing to this association and together best accounted for the variance
in attention-executive function.
NIH-PA Author Manuscript
Vascular and other cognitive functions—The overall relationships between the eight
vascular indices and the other cognitive domain scores (language, visual, memory) were not
found to be statistically significant. In sum, BP variability, IMT, BAR, and cardiac output were
not strongly associated with cognitive functioning outside the domain of executive functioning,
attention, and psychomotor speed. The two component scores from the PCA analyses were
entered as independent measures in a similar set of hierarchical regression analyses using the
same covariates, though neither vascular component loaded on the other index scores for the
other cognitive domains.
Vascular function and structural brain findings—As shown in Table 3, several of the
vascular biomarkers significantly correlated with the structural brain MRI indices. Given that
a subset of the total sample completed both the MRI and vascular assessments, analyses were
conducted to determine whether the patients who underwent MRI differed from those who did
not. Patients in this subsample did not differ from those who did not undergo MRI scanning
on any demographic characteristics. The two groups did not differ with respect to age,
education, or gender. Cognitive function also did differ between the groups on multivariate
analysis of variance (MANOVA; p > .10). Furthermore, cardiac disease severity as measured
by the New York Heart Scale (NYHS) did not differ between groups, nor did rates for most
risk and etiological factors. Patients undergoing MRI assessment had a lower rate of cardiac
arrhythmia (p < .05), attributable to the exclusion of patients with pacemakers from MRI
assessment.
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BAR during the reactive hyperemia condition showed the strongest association with both total
WMH volume and also with corrected WMH volume (WMH/WBV, where WBV is whole
brain volume). Stepwise regression analysis revealed a strong association between the vascular
indices and total WMH volume, R = .77, F(2, 20) = 26.09, p = .0001. The vascular factors
contributing most significantly to this association were BAR during the reactive hyperemia
condition (β = −.74), though cardiac output was also retained as contributing some variance
(β = −.41). Whole brain volume (WBV) was also significantly associated with vascular
function, R = .67, F(2, 19) = 6.87, p = .01, with IMT (β = −.54) and systolic BP variance (β = .
42) most strongly associated with WBV.
Regression analysis conducted with the vascular component scores from the PCA analysis also
revealed significant associations with each of the MRI indices. With respect to WMH, a
significant association was found, R = .78, F(1, 20) = 8.32, p < .01. Component 2 was most
strongly loaded on WMH volume (β = −.56) whereas Component 1 showed a weaker loading
(β = −.36). For WBV, a significant association was also found, R = .56, F(1, 20) = 6.25, p < .
05, with both components loading on WBV (Component 1: β = .42; Component 2: β = .40).
Discussion
NIH-PA Author Manuscript
The present study demonstrates that cardiovascular and systemic vascular functioning is
associated with cognitive performance and structural brain abnormalities on MRI in patients
with CVD. This is the first study to date to simultaneously examine measures of cardiac
function (cardiac output, systolic variability), along with measures of systemic vascular health
(BAR, IMT, diastolic variability) in relationship to measures of cognitive function and brain
MRI. These findings provide compelling evidence that cardiovascular disease patients with
the greatest systemic vascular dysfunction also have greater cognitive problems and structural
brain alterations, independent of any clinical evidence of stroke. These findings provide support
for current theories regarding the development of VCI in the absence of acute stroke (Bowler
et al., 1999; Rockwood, 2002).
NIH-PA Author Manuscript
This study was motivated by the hypothesis that brain dysfunction among cardiovascular
disease patients without prior history of acute stroke is a byproduct of chronic cerebral
hypoperfusion (Roman, 2004), which in turn may be influenced by the interaction of two
important vascular pathophysiological factors: (a) impaired cardiac output (Jefferson et al.,
2007a; Jefferson et al., 2007b); and (b) systemic hemodynamic dysfunction affecting
cerebrovascular autoregulation (Knopman, Mosley, Catellier, & Sharrett, 2005; Launer,
Feskens, Kalmijn, & Kromhout, 1996; Messier, Awad, & Gagnon, 2004; Messier &
Teutenberg, 2005; Moser et al., 2004; Serrador et al., 2005; Wong, Molyneaux, Constantino,
Twigg, & Yue, 2006). Simultaneous examination of cardiac output, blood pressure variability,
carotid IMT, and flow-mediated BAR in relationship to neurocognitive and MRI indices
provided a method for testing this hypothesis. As hypothesized these vascular indices were
strongly associated with cognitive function and MRI abnormalities. Furthermore, both cardiac
and systemic hemodynamic factors had significant influence on these effects.
The PCA analysis revealed a two-component solution, which was validated by several
methods. This result provides strong evidence that both impaired cardiac output and systemic
hemodynamic dysfunction is important in this cohort. The underlying vascular indices
examined in this cohort loaded on a cardiac factor and a systemic vascular factor. Furthermore,
the vascular indices loaded on these two factors as expected, with cardiac output, ejection
fraction, and systolic variability loading on the first “cardiac factor,” and IMT, BAR, and
diastolic variability loading on the second “systemic vascular factor,” providing construct
validity. The indices loading on Component 1 are all vascular measures known to be strongly
influenced by cardiac function, while those loading on Component 2 are known to relate
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strongly to systemic hemodynamic function. While the systemic hemodynamic indices are not
direct measures of cerebral vascular disturbance, the fact that they significantly associated with
neurocognitive and MRI abnormalities suggests that they have the potential to serve as proxy
measures of cerebrovascular health. Wall thickening of the carotid artery (IMT), decreased
vascular reactivity on tests of flow-mediated dilation (BAR), and, more generally, vessel
stiffening provides evidence of diminished integrity of systemic vascular structure and
function, including the possibility of endothelial or vascular smooth muscle dysfunction
(Andresen, Shari, & Bryan, 2006; Hassan et al., 2004; Lavi, Gaitini, Milloul, & Jacob, 2006;
Panza, Casino, Kilcoyne, & Quyyumi, 1993). Among people with severe CVD, the effects of
these systemic abnormalities on the brain would be compounded in the context of diminished
or unstable cardiac function. For example, a patient with heart failure who has greatly
diminished cardiac output would experience even greater adverse brain effects if they are
unable to sustain stable cerebrovascular hemodynamic function and autoregulation relative to
changes in their cardiac output. Conditions associated with rapid fluctuations in perfusion
would also likely contribute to further cerebrovascular damage due to mechanical effects of
flow changes.
NIH-PA Author Manuscript
Average diastolic and systolic blood pressures were not significantly associated with cognitive
function. This may seem a bit surprising given the association observed in past studies between
elevated blood pressure, cerebrovascular disease, and cognitive dysfunction. However, with
closer examination these findings are not too surprising, given that all patients in this sample
had significant cardiovascular disease, and the majority had a history of hypertension. The
sample was obtained from outpatient clinical settings, and all patients were being treated for
CVD. The majority were taking antihypertensive medications, and the sample had mean
diastolic and systolic blood pressures that were within the average range, though a small
proportion of patients remained hypertensive despite being treated. Past studies that have found
that associations between hypertension and cognition either have tended to involve large
community samples in which the majority of people were healthy or have involved
comparisons of hypertensive versus nonhypertensive patients (Elias, D'Agostino, Elias, &
Wolf, 1995; Elias, Elias, Sullivan, Wolf, & D'Agostino, 2003; Farmer et al., 1990; Lawlor et
al., 2005). The current findings suggest that blood pressure taken at the time of the assessment
may prove less useful in understanding vascular effects on the brain than are other vascular
indices among people being actively treated for their CVD. The fact that systolic blood pressure
variability was found to be positively associated with cognitive function is noteworthy. This
finding provides further support for the idea that capacity for hemodynamic reactivity is
essential for maintaining healthy brain function among cardiac patients with controlled
hypertension (HTN).
NIH-PA Author Manuscript
The fact that the vascular indices were associated not only with cognitive function but also
with brain MRI indices is significant, as it supports the hypothesis that vascular dysfunction
secondary cardiovascular disease may cause structural brain alterations. For the most part, the
relationships with respect to the cognitive and MRI were theoretically consistent as one would
expect attention, executive, and psychomotor function to be most affected in patients with
greater WMH (Almeida et al., 2005; Bakker et al., 1999; Breteler et al., 1994; Chalela, Wolf,
Maldjian, & Kasner, 2001; R. Cohen, 1993; R. A. Cohen et al., 2002; DeCarli et al., 2001;
Deicken, Reus, Manfredi, & Wolkowitz, 1991; de Leeuw et al., 1999; Fernando & Ince,
2004; Gunstad et al., 2005; Knuiman, Folsom, Chambless, Liao, & Wu, 2001; Lazarus,
Prettyman, & Cherryman, 2005; R. H. Paul et al., 2005; Roman et al., 2002; Yamauchi et al.,
2002). Yet, the findings with respect to the brain MRI indices were somewhat more
complicated, although not altogether surprising. Peripheral vascular reactivity measured by
flow-mediated dilatation of the brachial artery, was significantly associated with greater
volume of brain WMH corrected for WBV, suggesting a possible linkage between
cardiovascular disease, diminished endothelial health, and brain changes. Yet, carotid IMT and
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cardiac output were associated with WBV, but not WMH, and systolic variability was actually
significantly associated with both corrected WMH volume and WBV. Therefore, vascular brain
effects may not be limited to white matter damage traditionally associated with microvascular
disease, but may relate to cortical atrophy as well. This possibility needs to be explored in
future studies examining cortical and subcortical morphometry in relationship to
cardiovascular disease vascular dysfunction in larger samples.
NIH-PA Author Manuscript
Several studies by our group have shown relationships between individual vascular indices and
brain functioning in CVD. For example, cardiac output was found to be associated with
executive functioning and attention performance (Jefferson et al., 2007a; Jefferson et al.,
2007b). BP variability was also found to be associated with cognitive function (Gunstad et al.,
2005). Diminished vascular reactivity on BAR to both pharmacological manipulation and
reactive hyperemia (Hoth et al., 2007; Moser et al., 2004) and vessel wall thickness as measured
by carotid IMT (Haley et al., 2007a) were also found to be associated cognitive functioning
and structural brain abnormalities in CVD. These findings suggest that in addition to cardiac
function, endothelial health may be important to preservation of cognitive function in the
context of cardiovascular disease in the elderly. Our previous analyses suggest that
relationships between endothelial function and brain WMH are significant even after
controlling for the effects of age and hypertension (Hoth et al., 2007), suggesting that
endothelial dysfunction was associated with the presence of structural brain abnormalities
beyond these other vascular risk factors. Also, endothelial-dependent vasodilatation is
influenced by multiple factors in addition to hypertension, such as hypercholesterolemia and
obesity. Therefore, systemic indices, such as BAR and IMT that are sensitive to vascular wall
thickening and stiffness, may provide integrated measure of large conduit systemic vascular
autoregulation and atherosclerotic burden (Celermajer, Sorensen, Bull, Robinson, & Deanfield,
1994; Corretti et al., 2002).
NIH-PA Author Manuscript
Consistent with our past studies of vascular dementia (R. A. Cohen et al., 2002; Moser et al.,
2001) and also cardiovascular disease (Haley et al., 2007a; Hoth et al., 2007; Paul et al.,
2006), the relationship of the vascular and MRI indices to cognitive function was almost
entirely observed on timed tasks requiring rapid processing speed. In contrast, there were few
effects observed for tasks without processing-speed demands. Accordingly, the current
findings suggest that processing speed is the function most associated with abnormalities of
systemic vascular function and also with white matter abnormalities on MRI. This finding fits
with expectation given that cerebral microvascular abnormalities associated with severe
cardiovascular disease are known to have the greatest effects on the white matter of the brain
and frontal-subcortical systems (Paul, Cohen, Ott, & Salloway, 2004). It does not appear that
these processing-speed effects are primarily attributable to motor speed disturbances for several
reasons. Performance on the Grooved Pegboard Test was less strongly associated with vascular
function than some of the other attention-executive measures. Also, findings from our recent
functional brain imaging studies of cardiovascular disease patients indicate that the vascular
indices used in the current study were strongly associated with processing speed related to
working-memory performance and associated brain activation on a 2-back task having minimal
motor demands (Haley et al., 2007b).
The study has some limitations that warrant mentioning. By study design, the sample was
composed of patients whose cardiovascular disease severity ranged from mild to severe, with
some heterogeneity in etiology. For example, patients with severe cardiovascular disease
tended to have greater heart failure. Most patients had histories of HTN, atherosclerosis, and
other cardiovascular disease risk factors, though the exact mix of these factors varied across
individuals. A study of this type without sufficient sample heterogeneity would not be feasible,
as patients would not differ sufficiently on the vascular variables of interest. By including
patients with cardiovascular disease heterogeneity and a range of severity, we were able to
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Cohen et al.
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observe a broad range of disturbance across the vascular indices, enabling simultaneous
examination of the contribution of multiple vascular factors. While this heterogeneity has a
cost of reducing some experimental control achievable with a more homogeneous group, it has
the benefit of making our sample representative of the population of elderly cardiovascular
disease patients in the real world. Accordingly, sample heterogeneity offers many advantages
when trying to account for relative effects of multiple risk and etiological influences typical in
cardiovascular disease.
Another limitation stems from the fact that the majority of patients in the sample were actively
being treated with medications for their CVD, most notably drugs to control hypertension.
Since it is not realistic to take patients off of their medications, it would be impossible to
completely eliminate this potential confound. Yet, there appears to be little systemic influence
of particular medications based on analyses conducted by comparing cognitive performance
by medication class and also when medications were treated as covariates. Also, this study
examined the association among vascular factors, neurocognitive function, and structural brain
abnormalities at a single point in time. While this enabled tests of specific hypotheses, it does
not permit conclusions regarding causality. Future prospective longitudinal studies are needed
with experimental designs aimed at examining the influence of specific vascular factors on the
development of brain dysfunction.
NIH-PA Author Manuscript
Findings regarding the relationship between the vascular function and structural MRI brain
abnormalities were based on a subset of patients, raising the possibility that this subgroup was
not representative of the full sample. Yet, patients who underwent MRI did not differ from the
group as a whole with respect to demographic, neurocognitive, cardiac disease severity, or
most vascular risk and etiological factors, mitigating against the smaller sample size for the
MRI analyses being a significant concern. Furthermore, associations among systemic vascular
function, structural brain abnormalities, and neurocognitive performance were entirely
consistent with a priori hypotheses.
NIH-PA Author Manuscript
In summary, the current findings suggest that cardiac and systemic hemodynamic dysfunction
likely contribute to the development of brain dysfunction among cardiovascular disease
patients without prior evidence of stroke or other neurological brain disease. The results point
to the possible involvement of atherosclerotic load and endothelial and vascular smooth muscle
function as factors that may underlie systemic and ultimately cerebral hemodynamic function.
However, these findings must be viewed as an initial step in understanding these relationships,
as the causal bases of these vascular effects cannot be fully ascertained from this cross-sectional
analysis. Future large-scale prospective studies will be important to determine the direction of
this relationship. The findings underscore the potential importance of interventions to improve
systemic vascular function, given the possible deleterious effects of cerebrovascular changes.
Future research combining measures of vascular function, neuroimaging, and functional
outcomes in longitudinal experimental designs will help to clarify the relative roles of
diminished cardiac and systemic vascular function, including endothelial health, in the
development of VCI and associated structural and functional brain abnormalities.
Acknowledgments
This work was supported in part by Grant R01-AG017975 (R.A.C.) from the National Institute of Health.
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TABLE 1
Characteristics of sample
NIH-PA Author Manuscript
Mean
Range
Age (years)
72.2 (7.7)
56–85
Education (years)
14.3 (2.7)
9–20
Mini Mental Status Exam Score
28.8 (1.1)
26–30
Beck Depression Inventory
4.2 (2.5)
1–9
129.3 (19.5)
87.2–186.8
Demographic–clinical variables
Vascular indices
Systolic BP variability: Mean (mm Hg)
Systolic BP: Standard deviation (mm Hg)
8.3 (5.7)
0.0–28.6
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Diastolic BP variability: Mean (mm Hg)
68.5 (10.1)
52.2–90.4
Diastolic BP: Standard deviation (mm Hg)
5.8 (3.2)
0.0–15.2
Ejection fraction
0.62 (0.13)
0.25–0.80
Cardiac output (l/m)
4.6 (1.2)
2.5–9.6
IMT (mm)
0.88 (0.13)
0.61–1.19
BAR–reactive hyperemia
5.9 (4.5)
0–14.6
BAR–nitroglycerine (% increase)
15.6 (6.8)
3.5–25.7
0.68 (1.22)
0.02–5.47
1226.8 (155.9)
886.9–1574.9
Brain MRI indices (n = 23)
WMH/WBV (ratio)
3
WBV (mm )
Note. Standard deviations in parentheses. BP = blood pressure. IMT = intima media thickness. BAR = brachial artery response. WMH = white matter
hyperintensities. WBV = whole brain volume.
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TABLE 2
Neuropsychological performance among cardiovascular disease patients
Test measures by domain
Mean (SD)
Range
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Global cognitive functioning
MMSE
28.4 (1.7)
23–30
DRS
137.3 (5.3)
116–144
BNT
54.7 (5.1)
36–60
Animals
19.8 (5.3)
8–36
HVOT
23.5 (3.5)
17–30
CFT–Copy
30.6 (5.6)
13–36
Block Design
32.1 (11.1)
10–60
Language
Visual-spatial
Memory
CVLT Long Delay
8.9 (3.5)
0–10
CFT–Delay
14.2 (7.3)
0–34
BVMT-R Delay
7.1 (3.1)
0–12
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Attention-executive-psychomotor
Trail Making A
39.0 (12.9)
16–93
Trail Making B
100.9 (53.0)
34.–300
Letter Cancellation
92.9 (25.8)
49–200
Stroop Interference Trial
30.9 (9.5)
10–72
COWAT
40.1 (12.7)
12–75
GPB-dominant
93.9 (25.1)
56.–206
Digit Span
17.5 (3.7)
8–28
Symbol Coding
54.9 (14.5)
18–98
Note. Tests included in the neuropsychological battery are listed along with the mean and standard deviation and range of raw scores for the total sample.
MMSE = Folstein Mini-Mental State Examination; DRS = Dementia Rating Scale-2; WAIS-III FSIQ = Wechsler Adult Intelligence Scale-III Full Scale
Intelligence Quotient; BNT = Boston Naming Test; Animals = Category Fluency-Animal Naming; HVOT = Hooper Visual Organization Test; CFT =
Rey Complex Figure Test; Block Design = Wechsler Adult Intelligence Scale–Third Edition (WAIS-III) Block Design; CVLT = California Verbal Learning
Test; BVMT-R = Brief Visual Memory Test–Revised; COWAT = Controlled Oral Word Association Test; GPB = Grooved Pegboard; Digit Span and
Digit Symbol = WAIS-III subtests. As indicated by the range of scores for most tests, CVD patients differed rather dramatically in their performance
across the sample.
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TABLE 3
Associations among vascular, cognitive, and MRI indices
IMT
Systolic variability
BAR
CO
Diastolic variability
Executive index
−.34***
.19*
.19*
.13#
.07
COWAT
−.20*
−.06
.28**
−.19**
−.06
Digit Span
−.26***
−.04
.21*
.04
.05
GPB
.23**
.10
−.12
.06
.09
Letter Cancellation
.33***
.15*
−.18*
−.15*
.02
Stroop
−.29**
−.16*
.25**
−.09
.06
Coding
−.31***
−.12#
.17*
−.12#
−.09
TMT A
.14#
.14*
.04
.07
.07
TMT B
.32***
.16*
.06
.10
−.03
Language index
.08
.03
.08
.02
.01
Visual index
.09
.07
.04
.08
.07
Memory index
.10
.11
.05
.10
.07
Total WMH
−.63**
.22
−.14
.01
−.15
WMH/WBV
−.49**
.28
.18
.01
−.14
.26
−.29*
−.35**
−.32*
−.23#
Cohen et al.
J Clin Exp Neuropsychol. Author manuscript; available in PMC 2009 September 8.
Cognitive
MRI
WBV
Note. Statistically significant associations are reported in bold. Columns are in the order of their strength of association with the attention-executive index (sum of Z-scores for each measure in this
domain). COWAT = Controlled Oral Word Association Test. GPB = Grooved Pegboard. TMT A = Trail Making Test A. TMT B = Trail Making Test B. MRI = magnetic resonance imaging. WMH =
white matter hyperintensities. WBV = whole brain volume.
*
p < .05.
**
p < .01.
***
p < .001.
#
Effect approached statistical significance (p = .06–08).
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TABLE 4
Principal component analysis of vascular indices
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Vascular measure
CO
Component 1
(Cardiac)
Component 2
(Systemic vascular)
.86
—
Systolic variance
.76
—
Ejection fraction
.56
—
IMT
—
.80
Diastolic variance
—
.68
BAR: Flow mediated
—
.48
Systolic BP: mean
—
—
Diastolic BP: mean
—
—
Note. CO = cardiac output. IMT = intima media thickness. BAR = brachial artery response. BP = blood pressure.
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