The relationship of montreal cognitive assessment scores to framingham coronary and stroke risk scores

doi:10.4236/ojpsych.2011.12008

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Open Journal of Psychiatry, 2011, 1, 49-55 OJPsych

doi:10.4236/ojpsych.2011.12008 Published Online July 2011 (http://www.SciRP.org/journal/OJPsych/).

Published Onl ine July 2011 in SciRes. http://www.scirp.org/journal/OJPsych

The relationship of montreal cognitive assessment scores to

framingham coronary and stroke risk scores

Myron Frederick Weiner 1,2*, Linda Susan Hynan1,3, Heidi Rossetti3, Matthew Wesley Warren1,

Colin Munro Cullum1,2

1Department of Psychiatry, Universit y of Texas Southwestern Medical Center, Dallas, USA;

2Department of Neurology, University of Texa s Southwestern Medical Center, Dallas, USA;

3 Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, USA.

E-mail: *[email protected];

Received 3 June 2011; revised 25 June 2011; accepted 3 July 2011.

ABSTRACT

We examined the relationship between a brief cogni-

tive screening measure and Framingham Coronary

and Stroke Risk scores. We administered the Mon-

treal Cognitive Assessment (MoCA) to pa r ticipants in

the Dallas Heart Study, a community-based mul-

tiethnic study investigating the development of athe-

rosclerosis. The composition of the group was 50%

Africa n American, 36% Caucasian and 14% Hispan-

ic. There were 765 subjects (mean age 51 years) who

had both Coronary and Stroke Risk scores and an

additional 144 subjects with only Coronary Risk

scores available. There was a small significant inverse

relationship between MoCA and Framingham Coro-

nary and Stroke Risk scores. MoCA scores were in-

fluenced by education, but were not influenced by

age or by the presence of one or more apoE4 alleles.

Keywords: Dementia; Montreal Cognitive Assessment;

Cognition; Cardiovascular Risk

1. INTRODUCTION

Because of the strong evidence for a relationship be-

tween cardiovascular risk factors and cognitive com-

promise [1], the authors were asked to provide a brief

measure of psychological function for the Dallas Heart

Study (DHS), a population-based study of the develop-

ment of atherosclerosis. Earlier studies of the relation-

ship between environmental and biological factors and

cognition in large population-based studies of elders

have produced some positive findings [2,3]. Specifically,

hypertension in late life has been associated with cogni-

tive decline [4,5] and thought possibly due to damaged

brain vasculature. Additionally, increased total choles-

terol has also been associated [6,7] and thoug h t to be due

to brain lipid dysregulation, although it is not always

shown [8 ]. Most stud ies of Type 2 diabete s have found a

positive association between impaired glucose metabol-

ism and dementia [9,10]. Ta k en together, metabolic syn-

drome with and without markers of inflammation have

been associated with cognitive declin e [11].

To facilitate participation, DHS subjects were seen in

a single day that included a variety of biological meas-

ures. Because of the heavy schedule, DHS investigators

required that our cognitive instrument be brief (15 mi-

nutes or less). We reviewed brief instruments, including

the Short Portable Mental Status Questionnaire [12], the

Mini-mental State Examination [13] and the Montreal

Cognitive Assessment (MoCA) [14]. We chose the

MoCA, a popular instrument for detecting mild cogni-

tive impairment and dementia in clinical settings, be-

cause of its greater sensitivity to memory and its inclu-

sion of items reflecting executive function [15,16]. We

hypothesized that MoCA scores would be related in-

versely to Framingham Risk scores for coronary artery

disease [17] and stroke [18], to older age and the pres-

ence of one or more apolipoprotein E4 (apoE4) alleles

and would correlate directly with years of edu c ation .

We compared Framingham Coronary and Stroke risk

scores obtained in 1999-2000 to MoCA scores obtained

in 2008 and 2009.

2. MATERIAL AND METHODS

The Dallas Heart Study (DHS) is a population-based

investigation designed to track the development of car-

diovascular disease; 50% of the sample is African

American [19]. The project, funded by the Donald W.

Reynolds Foundation, was initiated in 1999. The first

wave of examinees (DHS-1) who completed the entire

3-day study protocol (approximately 3000 subjects

ranging in age from 30 to 65 years), was not adminis-

M. F. Weiner et al. / Open Journal of Psychiatry 1 (2011) 49-55

tered a cognitive measure. DHS-2 examined 3,500 sub-

jects, largely returnees from DHS-1. Cognitive screening

was added to DHS-2 as part of a day-long visit that in-

cluded extensive demographic and family history infor-

mation, vital signs, EKG, measures of body fat, cardiac

wall thickness, aortic plaque, coronary artery calcifica-

tion, apoE genotype, and other measures. Additional

DHS-2 measures included 3 Tesla MRI imaging of the

brain, MRI determination of common carotid wall

thickness, and a measure of depressive symptoms.

Framingham Coronary Risk and Stroke Risk scores

were calculated from DHS-1 data. The Coronary Risk

score is based on a formula including age, total choles-

terol, cigarette smoking, high-density lipoprotein (HDL)

concentration, and systolic blood pressure (see Table 1).

Scores for men range from –10 to 37; for women, from

–8 to 44. When employed in an algorithm, these scores

indicate the likelihood (as a percent risk) of a coronary

event occurring within 10 years [20]. For this reason, we

modified Coronary Risk scoring by converting negative

scores to zero to create a continuous variable that could

be compared with cognitive test scores.

Ten-year Framingham Stroke Risk scoring differs

from Coronary Risk scoring in that it does not consider

total or HDL cholesterol but does include diabetes, his-

tory of heart disease, atrial fibrillation , and left ventricu-

lar hypertrophy (LVH) (see Ta ble 1). Scores range from

zero to 48 points in men and 2 to 48 points in women.

These scores indicate the likelihood (as a percent) of a

cerebrovascular event occurring within 10 years [18].

For this study, we used the raw score for the Stroke Risk

scale to create a continuous variable that could be com-

pared with cognitive test scores.

The MoCA is a 30-point, 10 - 15 minute cognitive test

that has been used primarily to detect mild cognitive

impairment and dementia in clinical populations [15,16].

The MoCA samples a wide range of cognitive abilities,

including orientation, attention, language, verbal memo-

ry, praxis, and mental flexibility. Existing norms are

based on a Canadian sample (N = 90) with a mean age in

the mid 70s and mean education of approximately 12

years. Although less is known about its psychometric

properties, the MoCA has a potential advantage over

other brief cognitive screening tests such as the 30-item

Mini-Mental State Examination (MMSE) [13] and the

10-item Short Portable Mental Status Questionnaire

(SPMSQ) [12] because of its greater sensitivity to more

subtle cognitive impairment [14,21].

Inclusion criteria: We included all subjects in the

DHS-1 database fluent in English who had both Fra-

mingham Coronary and Stroke Risk scores and MoCA

testing at the time of DHS-2.

Exclusion criteria: We excluded subjects with incom-

plete Coronary and Stroke Risk scores or with a history

of stroke or incomplete MoCA testing. All subjects in

DHS-1 and DHS-2 studies signed informed consent

docum e nt s ap proved by the UT Southwestern IRB.

Statistical methods:

For categorical variables, frequencies, percentages,

and 95% Confidence Intervals (95%CI) w ere calculated.

For continuous measures, means and standard deviations

(mean ± standard deviations) were calculated. The asso-

ciation between the continuous measures was examined

using the Pearson Product Moment correlation and par-

tial correlation coefficients. Two sets of multiple regres-

sion models predicting MoCA Total Scores from either

Coronary Risk or Stroke Risk and included the cova-

riates education and gender; gender was found to be

non-significant in all models and was excluded from

further modeling. Eight multiple regression models (four

for each type of risk score) predicting MoCA Scores

were fit to components of Coronary Risk and Stroke

Risk scores individually. The unadjusted models in-

cluded the components of each risk score and the ad-

justed models included education and gender in addition

to the components of the risk score. For both types of

models (adjusted and unadjusted), all components were

included in a model (full model) followed by a stepwise

procedure (p to enter and leave set at 0.05). Regression

weights, 95% confidence intervals (95% CI), and R are

reported for each model. No violations of the assump-

tions were found for any of the statistical tests performed.

SPSS V18 was used in all analyses and the level of sig-

nificance was set at p < 0.0 5.

3. RESULTS

Educational level ranged from 0 (no formal schooling) to

20 years, with a mean of 12.3 ± 2.3 years. Age ranged

from 18 to 65 years at the time of the first DHS-1 visit.

MoCA scores ranged from 7 to 30 points and were

available in 968 subjects; 952 of those also had Fra-

mingham Coronary Risk data available and 808 of these

same subjects had Stroke Risk scores. The 23 subjects

whose ethnicity was indicated as “Other” and the 20

subjects with a history of stroke were dropped from the

analysis, leaving a total of 909 subjects with Coronary

Risk scores and 765 of these same subjects with Stroke

Risk scores. The ethnic composition of this sample was

49.9% African American, 36.1% Caucasian, and 14.0%

Hispanic; 42% were men (Ta ble 2). There were 16 sub-

jects who completed the MoCA in Spanish, however,

removing these 16 subjects from analyses did not affect

results. The distribution of E4 alleles was 27 .6% for one

allele and 4.2% for two alleles, with an overall E4 allele

frequenc y of 1 8.1%.

For the group as a whole, there was a small but sig-

M. F. Weiner et al. / Open Journal of Psychiatry 1 (2011) 49-55

Table 1. Framingham 10-year coronary and stroke risk variables.

Variable Coronary Risk Stroke Risk

Age

Systolic BP treated

Systolic BP untreated

Cigarettes X X

To tal cholesterol

HDL cholesterol

Diabetes

Cardiovascular disease

Atrial fibrillation X

Left ventricular hypertrophy

Table 2. Categorical and continuous measures for coronary and stroke risk cohorts.

Coronary Ri s k (N = 909) Stroke Risk (N = 765)

Measure Value Statistic1

Ethnicity

Black

454 (49.9%)

366 (47.8%)

White

328 (36.1%)

294 (38.4%)

Hispanic

127 (14.0%)

105 (13.7%)

Gender

Male

382 (42.0%)

324 (42.4%)

ApoE4 Alleles

610 (68.2%)

515 (68.5%)

1 247 (27.6%) 210 (27.9%)

38 (4.2%)

27 (3.6%)

MoCA Score

23.42 ± 3.95

23.61 ± 3.98

Coronary Risk Score

9.84 ± 5.29

6.04 ± 4.50

Education (yrs)

13.45 ± 2.95

13.56 ± 2.96

Age (yrs)

51.43 ± 9.58

51.36 ± 9.44

Age (yrs) median, range

IRQ range

51.1, 26.4 – 72.8,

44.3 – 58.8

51, 26.8 – 72.8,

4.4 – 58.6

Statistics are frequency (%) for categorical measures and mean ± standard deviation for continuous measures unless otherwise indicated.

nificant inverse relationship between Coronary Risk

scores in the DHS-1 cohort and MoCA scores obtained

approximately 8 years later [ (907) = –0.201, p < 0.001].

Table 3 shows the multiple regression models used to

predict MoCA scores from the components for Coronary

and Stroke Risk Scores. When examining components of

the Coronary Risk, score, age and systolic blood pres-

sure were significant in the unadjusted full model while

education and gender were significant in addition to age

and systolic BP in the adjusted full model. The unad-

justed stepwise model again resulted in both age and

systolic BP found to be significant; however, only age

and education were found significant in the adjusted

stepwise model.

Of the 909 subjects with Coronary Risk scores, 765

also had Stroke Risk scores for comparison with MoCA

scores. The ethnicity, gender, age and apoE4 allele dis-

tribution (allele frequency = 18.5%) of this group was

essentially the same as the Coronary Risk group (see

Table 2). For the group as a whole, there was again a

significant correlation between MoCA and Stroke Risk

scores [r(763) = –0.215, p < 0.001]. On examining

components of the Stroke Risk score, age, systolic blood

pressure (BP), and left ventricular hypertrophy (LVH)

were found to be significant in the unadjusted full model

while systolic BP, LVH and education were found to be

significant in the adjusted full model. The unadjusted

stepwise model again resulted in age, systolic BP, and

LVH found to be significant; however, only age, LVH

and education were found significant in the adjusted

stepwise model.

Controlling for the effect of education on MoCA

scores, the partial correlation between MoCA scores and

Framingham Coronary Risk and Stroke Risk scores was

r(905) = –0.205 (p < 0.001 and r(761) = –0.157 (p <

0.001) respectively. Thus, after adjusting for educatio n, a

1-point increase in Coronary Risk score was associated

with a 0.138-point drop in MoCA score while a 1-point

M. F. Weiner et al. / Open Journal of Psychiatry 1 (2011) 49-55

Table 3. Models predicting MoCA from components of coronary and stroke risk.

Coronary Risk

Unadjusted Model Coefficients Adjusted Model Coefficients

Model Predictors B (95% CI)/R p-value B (95% CI)/R p-value

1 Age –0.130 (–0.187 to –0.073) <0.0001 –0.148 (–0.199 to –0.096) <0.0001

(Full) Cholesterol 0.002 (–0.126 to 0.130) 0.9786 –0.008 (–0.122 to 0. 106) 0.8944

Smoker –0.062 (–0.171 to 0. 047) 0.2639 0.035 (–0.063 to 0.133) 0.4845

HDL –0.008 (–0.244 to 0. 229) 0.9496 0.116 (–0.104 to 0.335) 0.3004

Systolic BP –0.282 (–0.460 to –0.103) 0.0020 –0.222 (–0.39 to –0.054) 0.0095

Education -- -- 0.608 (0.531 to 0.6 86) < 0.0001

Gender (M) -- -- –0.683 (–1.185 to –0.18) 0.0078

Model R 0.225 < 0.0001 0.500 < 0.0001

2 Age –0.123 (–0.177 to –0.07) < 0.0001 –0.176 (–0.22 to –0.131) < 0.0001

(Stepwise) Systolic BP –0.290 (–0.466 to –0.113) 0.0013 -- --

Education -- -- 0.603 (0.527 to 0.6 79) < 0.0001

Model R 0.222 < 0.0001 0.490 < 0.0001

Stroke Risk

Unadjusted Model Coefficients Adjusted Model Coefficients

Model Predictors B (95% CI)/R p-value B (95% CI)/R p-value

1 Age –0.236 (–0.437 to –0.036) 0.0211 –0.178 (–0.361 to 0.006) 0.0574

(Full) Systolic BP –0.182 (–0.33 to –0.033) 0.0166 –0.137 (–0.273 to –0.001) 0.0490

Diabetes –0.256 (–0.621 to 0.109) 0.1686 –0.138 (–0.4 70 to 0.19 5) 0.4171

Smoker –0.150 (–0.358 to 0.058) 0.1574 0. 07 0 (–0.122 to 0.262) 0.4762

CVD 0.105 (–0.165 to 0.374) 0.4457 0.076 (–0 .170 to 0.321) 0.5459

Afib 0.095 (–0.245 to 0.435) 0.5835 0.055 (–0 .256 to 0.365) 0.7296

LVH –0.375 (–0.591 to –0.158) 0.0007 –0.311 (–0.5 09 to –0.113) 0.0021

Education -- -- 0. 5 56 (0.470 to 0.6 4 1) < 0.0001

Gender (M) -- -- –0.353 (–0.862 to 0.157) 0.1749

Model R 0.245 < 0.0001 0.477 < 0.0001

2 Systolic BP –0.204 (–0.348 to –0.059) 0.0057 -- --

(Stepwise) LVH –0.379 (–0.594 to –0.163) 0.0006 –0.383 (–0.567 to –0.199) < 0.0001

Age –0.236 (–0.433 to –0.039) 0.0191 –0.240 (–0.413 to –0.067) 0.0067

Education -- -- 0. 5 54 (0.471 to 0.6 3 8) < 0.0001

Model R 0.232 < 0.0001 0.469 < 0.0001

increase in Stroke Risk score was associated with a

0.124-p oi nt decrea se.

In order to further explore the potential role of age,

the above analyses were repeated using data from the

subset of 493 subjects aged 50 years or older in the Co-

ronary Risk group and 413 subjects in the Stroke Risk

group (mean age ± standard deviation = 58.66 ± 5.97

years for the Coronary Risk group and 58.52 ± 5.97

years for the Stroke Risk group). The re was a v ery small

significant correlation between MoCA scores and Coro-

M. F. Weiner et al. / Open Journal of Psychiatry 1 (2011) 49-55

nary Risk scores [r(491) = –0.139, p = 0.002] and Stroke

Risk scores [r(411) = –0.214, p < 0.001] in these older

subjects. Controlling for the effect of education on Mo-

CA scores, the partial correlation between Framingham

Coronary Risk and Stroke Risk scores in older subjects

was r(4 89) = –0.059 (p = .191) and r(409) = –0.110 (p =

0.026) respectively. Thus, after adjusting for education

in older subjects, a 1-point increase in Coronary Risk

score was associated with a 0.067-point drop in MoCA

score while a 1-point increase in Stroke Risk score was

associated with a 0.082-point decrease.

There were no significant differences in MoCA scores,

Coronary Risk s cores, or Stroke Risk scor es for subjects

with or without apoE4 alleles (p = 0.110, p = 0.312, and

p = 0.874, respectively) in the entire cohort or among

subjects age 50 + (p = 0.103, p = 0.623, and p = 0.526,

respectively). Also , there were no significant d ifferences

between men and women in the relationship between

MoCA and Coronary or Stroke Risk scores when ex-

amined for the entire sample or the older group (data not

shown).

4. DISCUSSION

The uniqueness of our study is that it examines vascular

risk factors in a population-based sample that includes

50% African Americans. It is possible that the risk fac-

tors we examined were not sufficiently sensitive to

detect and quantify the effects of subclinical atheroscle-

rosis. We have now begun exploring the relationship

between more direct biological measures of atheroscle-

rosis including the concentration of atherosclero-

sis-related inflammatory substances such as CRP and

direct measures of atherosclerosis such as coronary ar-

tery calcium.

Our cognitive measure, the MoCA, was designed to

be used in clinical settings in which there is great vari a-

tion in cognitive function [22]. It has been suggested that,

as a screening tool, it may have limited v alue in popula-

tions where prevalence of mild cognitive impairment is

low [23]. However, the range of MoCA scores in this

study was 7-30 (mean ± standard deviation = 23.38 ± 4).

Other investigators have found more robust relationships

between cardiovascular disease and cognitive function

using more detailed neurocognitive measures [24,25]

and also with very crude measures. For example, one

study found that SPMSQ scores were lower in the pres-

ence of apoE4 [26]. These subjects had lower initial

SPMSQ scores, and there was increased disparity be-

tween E4 carriers and non-E4 carriers over a period of 4

years.

We found that the influence of Coronary or Stroke

Risk scores on MoCA scores did not increase with age.

Because the mean age of this study population was rela-

tively young, it may be that the impact of coronary and

stroke risk factors are limited at th is age, indicating such

patients either need continued fo llow up at a later time or

more sensitive tests early on. We also did not find the

negative effect of the apoE4 allele on cognition found in

the another study [27] or in a meta-analysis of 77 studies

in which apoE4 carriers performed more poorly on tests

of global cognitive function, and the disparity between

E4 and non-E4 carriers increased with age [28].

One study found significant interactions between the

presence of E4 and verbal memory, verbal organization,

nonverbal memory, set shifting and complex attention in

a community-based group of subjects with an average

age of 61 years, but systolic blood pr essure was the only

individual risk factor significantly related to these cogni-

tive measures [25]. Because of the disparity of our find-

ings from those of others in the literature, we reviewed

data from non-demented older adults persons followed

yearly at the UT Southwestern Alzheimer’s Disease

Center (ADC). We examined MMSE data from all 219

subjects who had both MMSE scores and apoE4 allele

determination, of whom 81 (40%) had one or more

apoE4 alleles. We found no significant difference in

MMSE scores in no n-demented subjects w ith or without

an apoE4 allele.

Our findings concerning the impact of coronary and

stroke risk factors and E4 on cognition may be related to

differences in the populations studied and in the psy-

chological measure employed. Both our “young” (mean

age = 51 years) and our “old” (mean age = 58 years)

cohorts were relatively young in relation to the sample

examined by Haan et al. [29] and the more recent me-

ta-analytic study [28]. The relationship of apoE4 to cog-

nition in other studies may be partially explainable by

the possible inclusion in older populations of persons

with incipient Alzheimer disease [30], which is less

likely in our DHS sample. Another study, which ex-

amined the relationship of the apoE4 allele to MMSE

scores in persons 659 persons followed over 22 years in

a large community-based study, found no relationship

between apoE4 status and MMSE scores, but there was a

significant difference in delayed recall in persons < 65

years of age [31]. They suggested survival bias as an

explanation of the difference in apoE 4 influence on

cognition.

Other studies have suggested that vascular disease in-

fluences performance on cognitive tasks associated with

frontal lobe function more than those associated with

other cortical areas [24]. The MoCA contains few items

relating to this cognitive domain, and score ranges for

those items are limited. Supporting this explanation is

the finding that a delayed recall test was more sensitive

than the MMSE in detecting cognitive decline in elders

M. F. Weiner et al. / Open Journal of Psychiatry 1 (2011) 49-55

[31]. We plan to re-test a subgroup of DHS-2 subjects

with additional brief measures that may increase sensi-

tivity to executive and memory function and we will also

follow them prospectively to examine the rate of cogni-

tive change.

Although the MoCA samples many of the same cog-

nitive areas than less sensitive instruments, several less

sensitive instruments have shown stronger correlations

of global cognitive function with cardiovascular risk

factors, including age and apoE status. Our failure to

find correlations of similar strength may be attributable

to some limitation of the MoCA, the relative youth of

the population we studied or differences between per-

sons with presymptomatic and symptomatic cardiovas-

cular disease.

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