Open Journal of Psychiatry, 2011, 1, 49-55 OJPsych
doi:10.4236/ojpsych.2011.12008 Published Online July 2011 (
Published Onl ine July 2011 in SciRes.
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: *;
Received 3 June 2011; revised 25 June 2011; accepted 3 July 2011.
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
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.
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
Copyright © 2011 SciRes. OJPsych
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.
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
Copyright © 2011 SciRes. OJPsych
Table 1. Framingham 10-year coronary and stroke risk variables.
Variable Coronary Risk Stroke Risk
Systolic BP treated
Systolic BP untreated
Cigarettes X X
To tal cholesterol
HDL cholesterol
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
454 (49.9%)
366 (47.8%)
328 (36.1%)
294 (38.4%)
127 (14.0%)
105 (13.7%)
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%)
23.42 ± 3.95
23.61 ± 3.98
9.84 ± 5.29
6.04 ± 4.50
13.45 ± 2.95
13.56 ± 2.96
51.43 ± 9.58
51.36 ± 9.44
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
Copyright © 2011 SciRes. OJPsych
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) <0.00010.148 (–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
Smoker0.062 (–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) 0.00200.222 (–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) < 0.00010.176 (–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) 0.02110.178 (–0.361 to 0.006) 0.0574
(Full) Systolic BP –0.182 (–0.33 to 0.033) 0.01660.137 (–0.273 to –0.001) 0.0490
Diabetes –0.256 (–0.621 to 0.109) 0.16860.138 (–0.4 70 to 0.19 5) 0.4171
Smoker0.150 (–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
LVH0.375 (–0.591 to –0.158) 0.00070.311 (–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) LVH0.379 (–0.594 to –0.163) 0.00060.383 (–0.567 to –0.199) < 0.0001
Age –0.236 (–0.433 to –0.039) 0.01910.240 (–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
Copyright © 2011 SciRes. OJPsych
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
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
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
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
Copyright © 2011 SciRes. OJPsych
[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.
[1] Beeri, M.S., Ravona-Springer, R., Silverman, J.M. and
Haroutunian, V. (2009) The effects of cardiovascular risk
factors on cognitive compromise. Dialogues in Clinical
Neuroscience, 11, 201-212.
[2] Middleton, L.E., Barnes, D.E., Lui, L.Y. and Yaffe, K.
(2010) Physical activity over the life course and its asso-
ciation with cognitive performance and impairment in old
age. Journal of the American Geriatrics Society, 58,
1322-1326. doi:10.1111/j.1532-5415.2010.02903.x
[3] Yaffe, K., Vittinghoff, E., Lindquist, K., et al. (2010)
Posttraumatic stress disorder and risk of dementia among
us veterans. Archives of General Psychiatry, 67,
608-613. doi:10.1001/archgenpsychiatry.2010.61
[4] Tzourio, C. , D uf oui l, C ., D uc im etier e, P. and Alperovitch,
A. (1999) Cognitive decline in individuals with high
blood pressure: A longitudinal study in the elderly. Eva
study group. Epidemiology of vascular aging. Neurology,
53, 1948-1952.
[5] Mielke, M. M., Rosenbe r g, P. B., Tschanz, J., et al. (2007)
Vascular factors predict rate of progression in alzheimer
disease. Neurology, 69, 1850-1858.
[6] Solomon, A., Kareholt, I., Ngandu, T., et al. (2007) Serum
cholesterol changes after midlife and late-life cognition:
Twenty-one-year follow-up study. Neurology, 68, 751-756.
[7] Yaffe, K., Barr ett-Connor , E., Lin, F. and Grady , D . (2002)
Serum lipoprotein levels, statin use, and cognitive func-
tion in older women. Archives of Neurology, 59, 378-384.
[8] Reitz, C., Luchsinger, J., Tang, M.X., Manly, J. and
Mayeux, R. (2005) Impact of plasma lipids and time on
memory performance in healthy elderly withou t dementia.
Neurology, 64, 1378-1383.
[9] Whitmer, R. A., Sidney, S., Selby, J., Johnston, S. C. and
Yaffe, K. (2005) Midlife cardiovascular risk factors and
risk of dementia in late life. Neurology, 64, 277-281.
[10] Schnaider Beeri, M., Goldbour t, U., Silverman, J. M., et al.
(2004) Diabetes mellitus in midlife and the risk of de-
mentia three decades later. Neurology, 63, 1902-1907.
[11] Yaffe, K., Kanaya, A., Lindquist, K., et al. (2004) The
metabolic syndrome, inflammation, and risk of cognitive
decline. Journal of the American Medical Association,
292, 2237-2242. doi:10.1001/jama.292.18.2237
[12] Pfeiffer, E. (1975) A short portable mental status ques-
tionnaire for the assessment of organic brain deficit in
elderly patients. Journal of the American Geriatrics So-
ciety, 23, 433-441.
[13] Folstein, M.F., Folstein, S.E. and McHugh, P.R. (1975)
“Mini-mental state. A practical method for grading the
cognitive state of patients for the clinician. Journal of
Psychiatric R es earch, 12, 189-198.
[14] Nasreddine, Z.S., Phillips, N. A ., Bedirian, V., et al. (2005)
The montreal cognitive assessment, moca: A brief
screening tool for mild cognitive impairment. Journal of
the American Geriatrics Soci ety, 53, 695-699.
[15] Smith, T., Gildeh, N. and Holmes , C. (2007) The montreal
cognitive assessment: Validity and utility in a memory
clinic setting. Canadian Journal of Psychiatry, 52,
[16] Hoops, S., Nazem, S., Siderowf, A. D., et al. (2009) Va-
lidity of the MoCA and mmse in the detection of mci and
dementia in parkinson disease. Neurology, 73,
1738-1745. doi:10.1212/WNL.0b013e3181c34b47
[17] D'Agostino, R. B., Sr., Grundy, S., Sullivan, L. M. and
Wilson, P. (2001) Validation of the framingham coronary
heart disease prediction scores: Results of a multiple eth-
nic groups investigation. Journal of the American Medical
Association, 286, 180-187. doi:10.1001/jama.286.2.180
[18] D’Agostino, R.B., Wolf, P.A., Belanger, A.J. and Kannel,
W.B. (1994) Stroke risk profile: Adjustment for antihy-
pertensive medication. The framingham study. Stroke, 25,
40-43. doi:10.1161/01.STR.25.1.40
[19] Victor , R. G., Hal ey, R. W., W illett, D . L., et al. (2004) The
dallas heart study: A population-based probability s ampl e
for the multidisciplinary study of ethnic differences in
cardiovascular health. American Journal of Cardiology,
93, 1473-1480.
[20] Wilson, P.W., D’Agostino, R.B., Levy, D., et al. (1998)
Prediction of coronary heart disease using risk factor
categories. Circulation, 97, 1837-1847.
[21] Aggarwal, A. and Kean, E. (2010) Comparison of the
folstein mini mental state examination (mmse) to the
montreal cognitive assessment (MoCA) as a cognitive
screening tool in an inpatient rehabilitation setting. Neu-
roscience and Medicine, 1, 39-42.
[22] Bernstein, I.H., Lacritz, L., Barlow, C.E., Weiner, M.F.
and Defina, L. F. (2011) Psychometric evaluation of the
montreal cognitive assessment (MoCA) in three diverse
samples. Clinical Neuropsychology, 25, 119-126.
[23] McLennan, S.N., Mathias, J.L., Brennan, L.C. and Ste-
wart, S. (2010) Validity of the montreal cognitive as-
sessment (MoCA) as a screening test for mild cognitive
impairment (MCI) in a cardiovascular population. Journal
of Geriatric Psychiatry and Neurology, 24, 33-38.
[24] Kuczynski, B., Jagust, W., Chui, H.C. and Re ed, B. (2009)
An inverse association of cardiovascular risk and frontal
M. F. Weiner et al. / Open Journal of Psychiatry 1 (2011) 49-55
Copyright © 2011 SciRes. OJPsych
lobe glucose metabolism. Neurology, 72, 738-743.
[25] Zade, D., Beiser, A., McGlinchey, R., et al. (2010) Inter-
active effects of apolipoprotein E type 4 genotype and
cerebrovascular risk on neuropsychological performance
and structural brain changes. Journal of Stroke and Ce-
rebrovascular Diseases, 19, 261-268.
[26] Fillenbaum, G.G., Landerman, L.R., Blazer, D.G., et al.
(2001) The relationship of APOE genotype to cognitive
functioning in older African-American and caucasian
community residents. Journal of the American Geriatrics
Society, 49, 1148-1155.
[27] Blair, C.K., Folsom, A.R., Knopman, D.S., et al. (2005)
APOE genotype and cognitive decline in a middle-aged
cohort. Neurology, 64, 268-276.
[28] Wisdom, N.M., Callahan, J.L. and Hawkins, K.A. (2011)
The effects of apolipoprotein e on non-im paired cognitive
functioning: A meta-analysis. Neurobiology of Aging, 32,
63-74. doi:10.1001/jama.282.1.40
[29] Haan, M.N., Shemanski, L., Jagust, W.J., Manolio, T.A.
and Kuller, L. (1999) The role of APOE epsilon4 in
modulating effects of other risk factors for cognitive de-
cline in elderly persons. Journal of the American Medical
Association, 282, 40-46. doi:10.1001/jama.282.1.40
[30] Qiu, C. and Fratiglioni, L. (2010) Apolipoprotein E epsi-
lon4 status and cognitive decline with and without de-
mentia. Archives of Neurology, 67, 1036.
[31] Kozauer, N.A., Mielke, M.M., Chan, G.K., Rebok, G.W.
and Lyketsos, C.G. (2008) Apolipoprotein e genotype and
lifetime cogni tive decline. International Psychogeriatrics,
20, 109-123. doi:10.1017/S104161020700587X