Who Will Develop Dementia in a Cohort of Canadian Seniors
B. Hogan and Erika M. Ebly
Objectives: We examined whether easily attainable
variables were useful in predicting who became demented over
a five year period and determined the rates of incident dementia
for different categories of mild cognitive impairment. Methods:
This was a cohort study of subjects recruited nationally in
a population-based survey of Canadians 65 years and older (the
Canadian Study of Health and Aging). After standardized clinical
assessments, a subset of subjects (n=1782) was categorized as
not demented at time one. Identical study methods allowed a
reassessment of the cognitive status of surviving subjects (n=892)
five years later. Results: Three baseline variables
(Modified Mini Mental State (3MS) score, subject's age, and
an informant's report of the presence of memory problems) were
statistically significant predictors of the development of a
dementia. An equation incorporating these three variables had
a sensitivity of 79% and a specificity of 56% for predicting
dementia among survivors at time two. An equation substituting
the MMSE for the 3MS showed similar results. The various categories
of mild cognitive impairment examined showed significantly different
likelihoods for the subsequent development of a dementia. Some
categories with a higher dementia risk were characterized by
inclusion criteria requiring neuropsychological test scores
that were greater than one standard deviation (SD) below the
mean of age based normative data. Conclusion:
In the absence of extensive laboratory, radiologic or neuropsychological
tests, simple variables that can be easily determined in the
course of a single clinical encounter were useful in predicting
subjects with a higher risk of developing dementia. Attempts
to use neuropsychological results to predict the development
of dementia should look for significant impairments on age-standardized
Prédiction de la démence dans une cohorte de Canadiens
âgés. Objectifs: Nous avons évalué
si des variables facilement accessibles peuvent être utiles
pour prédire qui deviendra dément dans les cinq
prochaines années et nous avons déterminé
l'incidence de la démence pour différentes catégories
de déficits cognitifs légers. Méthodes:
Il s'agit d'une étude de cohorte portant sur
des sujets âgés de 65 ans et plus, recrutés
à travers le Canada dans le cadre d'une étude
de population (l'étude Canadienne sur la santé
et le vieillissement). Suite à une évaluation
clinique standardisée, un sous-groupe de sujets (n=1782)
ont été classifiés comme déments
au temps 1. Des méthodes d'étude identiques ont
permis une réévaluation du statut cognitif des
sujets survivants (n=892) cinq ans plus tard. Résultats:
Trois variables de l'évaluation initiale (le
score du mini mental modifié, l'âge du sujet et
les troubles de mémoire rapportés par un informateur)
étaient des prédicteurs significatifs du développement
d'une démence. Une équation incorporant ces trois
variables avait une sensibilité de 79% et une spécificité
de 56% pour prédire la démence parmi les survivants
au temps 2. Les différentes catégories de déficits
cognitifs légers examinés ont montré des
probabilités significativement différentes pour
le développement subséquent d'une démence.
Certains sous-groupes comportaient un risque plus élevé
de démence notamment ceux dont les scores des tests neuropsychologiques
étaient de plus d'une déviation standard sous
la moyenne normative pour l'âge. Conclusion:
En l'absence d'épreuves biologiques, radiologiques ou
neuropsychologiques poussées, des variables simples qui
peuvent être déterminées au cours d'une
seule entrevue clinique ont été utiles pour prédire
quels sujets avaient un risque plus élevé de développer
une démence. Si des tests neuropsychologiques sont utilisés
pour prédire le développement d'une démence
on devrait rechercher des déficits significatifs au moyen
d'épreuves standardisées pour l'âge.
J. Neurol. Sci. 2000; 27: 18-24
there are many studies on the epidemiology of dementia, there
are relatively few longitudinal studies that address progression
to dementia in individuals whose cognition was well characterized
at baseline. Identifying those at higher risk for the development
of dementia is an i mportant goal. Studies indicate
that up to 12% of normal older adults became cognitively impaired
over two years1 and that up to 80% of individuals
initially identified as cognitively impaired eventually progress
to dementia.2-5 While a number of variables have
been associated with the development of dementia,6-9
these factors are often derived from studies of pre-selected
groups such as those referred to memory clinics,8
and may not be relevant to the general population. They also
may not be readily available, requiring genetic testing, neuroimaging,
and/or neuropsychological testing.
second wave of the Canadian Study of Health and Aging (CSHA)
presented a unique opportunity to obtain follow-up data on the
cognitive status of a population-based cohort where cognition
was well characterized both at baseline (CSHA1) and at the time
of follow-up (CSHA2). The objectives of this study were to study
incident dementia in the population of survivors who were cognitively
normal or cognitively impaired but not demented at baseline.
We particularly wanted to identify factors associated with the
subsequent development of dementia. Although many variables
were collected as part of this study, we were interested in
whether those easily attainable could successfully predict cognitive
outcome in this population.
this population of undemented subjects was previously used by
us to examine vthe utility of various criteria proposed to define
mild cognitive impairment10-12 we will also report
follow-up data for these various categories of minor degrees
of cognitive impairment.
CSHA1 (1990-1991) was a national, population-based study designed
to examine the prevalence of dementia and other aspects of aging
in a representative sample of Canadians 65 years of age and
older.13 For community participants, the study had
a two-phase design. An approximately 45 minute long face-to-face
screening interview was administered by trained staff. This
gathered information on demographics, activities of daily living
(ADL),14,15 health status (including self-rated health)16
cognition was assessed by the Modified Mini-Mental State (3MS)
Appendix) where scores can range from 0-100. Higher scores
indicate better performance. The 3MS is a modification of the
Mini-Mental State Examination (MMSE).18 Four items
were added, the scoring system was refined and clearer instructions
for scoring were given.17 Compared to the MMSE the
3MS has a better sensitivity and specificity for detecting dementia.19
The 3MS can be administered within 20 minutes and requires no
special equipment. A MMSE score can be derived from
the version of the 3MS used in the CSHA. Individuals who scored
less than 78 on the 3MS and a randomly selected subset of those
scoring 78 or more were invited to a clinical examination.
clinical examination, described in detail elsewhere,13
was primarily designed to determine whether dementia was present
and to make a specific diagnosis as to the cause, if present.
The evaluation consisted of a standardized history, structured
informant (a third party identified by the subject as someone
who knew them well enough to provide the requested data) interview,
screening physical examination, repeat 3MS examination, neurological
examination, neuropsychological testing (if the 3MS score was
> 50) and select pre-determined laboratory tests.
The neuropsychological test battery was administered by a trained
psychometrician and interpreted by a neuropsychologist. The
battery included tests of memory, abstract thinking, judgement,
constructional abilities, language, familiar object recognition
and attention/psychomotor speed (see reference 13 for details).
Whenever available, neuropsychological testing was scored using
age, gender, and educational level adjusted norms.
were assigned to diagnostic categories at a consensus conference
that integrated all available data. Participants included the
physician(s), study nurse, psychometrician, and neuropsychologist
who had evaluated the subject. Patients were categorized as
no cognitive loss (NCL), cognitive impairment no dementia (CIND)
or demented at this conference. DSM-III-R criteria20
was used for the diagnosis of dementia. NINCDS-ADRDA criteria21
were used in making a diagnosis of possible or probable Alzheimer's
disease (AD). Cognitive diagnostic categories at baseline for
the 2,914 subjects who participated in the clinical examination
were NCL ( n= 921), CIND (n=861), and dementia (n=1132; n =
749 with a diagnosis of AD). CIND subjects were found to be
between cognitively normal subjects and those with dementia
in terms of age, 3MS score, general intellectual functioning
a nd performance of activities of daily living.22
In the second phase of the study (called CSHA2; 1996-1997) the
study cohort was recontacted. The same research methodology
with only minor modifications was used. In particular, the clinical
assessment, consensus conference, categorization of subjects,
and the criteria for dementia remained the same. We were unable
to include specific clinical diagnostic data from Newfoundland
subjects because a legal interpretation of the province's advance
directives legislation found it unacceptable for a proxy to
give consent to participate in a research study on behalf of
a person unable to give fully informed consent themselves.
examined follow-up data to determine the cognitive outcome and
survival of specific categories of mild cognitive impairment
(MCI)10-12 whose inclusion criteria captured at least
50 subjects at the time of cohort inception.22 The
inclusion criteria for these categories are summarized in the
footnote to Table 2. We did not utilize the exclusion criteria
for these categories in this report as their use would have
led to the loss of too many subjects. Further information on
how the various groupings were formed is described elsewhere.22
statistics (mean ±SD), analysis of variance, X2
(Minitab Statistical Software,Minitab,Inc., Pennsylvania)
and logistic regression (BMDP statistical software, California)
were performed as indicated in the text.
analyses were done first followed by logistic regressions.
All variables examined in the bivariate analyses
were included in the logistic regression models.
Age, education, 3MS score, gender, family history of dementia,
informant's report of the subject's memory problems, and OARS15,23
derived overall ADL status were considered as potential
explanatory variables for the development of dementia or AD.
Individuals with missing values for one or more
variables were excluded from the logistic analyses.
Memory problems were defined as being present when an informant
reported difficulties in response to one or both of the following
questions: "Does he/she have more difficulty remembering
short lists of items, e.g., shopping?" and/or "Does
he/she have difficulty remembering recent events, e.g., when
he/she last saw you, or what happened the day before?" Family
history of dementia was recorded by asking the informant if
relatives have trouble
with memory or became very confused and had to go into a home
to be looked after?" Age, education and 3MS score were used
as continuous variables. Other potential explanatory variables
were presented as categorical variables. Potential explanatory
variables from the logistic regression model were reported if
their p value was less than 0.10. Goodness of fit X2
(GOF) of the logistic models is described by providing Hosmer-Lemeshow
(HL) p values. Sensitivity and specificity of the logisitic
regression models are provided. Sensitivity represents the number
of cases with predicted probabilities greater than or equal
to 0.50. Specificity is the number of non-demented cases with
predicted probabilities < 0.50.
results of the logistic regression equation were
used to generate a receiver operator curve (ROC). The area under
the receiver operating curve is the probability of the model
identifying the subjects with a specific outcome
when randomly selected pairs of subjects with the outcome and
those without the outcome are compared.24
we derived empirically a simplified equation for
predicting dementia using the explanatory variables from the
logistic regression analyses. Sensitivity and specificity for
this simplified model were calculated using standard techniques.
As the MMSE is both shorter and more commonly used by physicians,
we compared the performance of the simplified equation
using either 3MS results or MMSE scores.
the 1,782 initially non-demented study subjects,
forty percent had died by the time of follow-up. Mortality was
higher in CIND than in NCL subjects (48.4% vs 30.5%,
p <.0001). Overall, approximately one quarter (26.9%) of
the non-demented survivors evaluated at time 2 had become demented.
One hundred and fifty-nine (18.9% of the total population of
survivors excluding Newfoundland subjects) were felt to have
probable or possible AD. Development
of dementia was significantly more common in subjects initially
categorized as CIND than in NCL (42.1% vs 14.7%,
p <.0001) subjects. The characteristics of those who
became demented and those who did not are shown in Table
1. A positive family history was the only factor
not associated with the development of dementia.
variables in the bivariate analyses (see Table 1) were considered
as potential explanatory variables in our logistic regression
analyses for dementia and AD. In NCL subjects, older age (b0=-5.8,
coef/se=-2.54; b1=.099, coef/se=4.1, OR=1.1 (1.05-1.16),
p <.0001) and decreased 3MS score (b2=-.048; coef/se=-3.51,
OR=.95 (.93-.98), p=.0004) were significant explanatory variables
for progression to dementia (HL p=.79). In CIND subjects, decreased
3MS score (b0=-5.86, coef/se=-2.54, b1=-.064,
coef/se=-3.6, OR=.94 (.90-.97), p=.0004) , older age (b2=.073,
coef/se=2.95, OR=1.08 (1.02-1.13), p=.004) and an informant's
report of memory problems (b3=.59, coef/se=1.72,
OR 1.81 (.92-3.6), p=.09) were significant explanatory variables
in the model to predict progression to dementia (HL
p=.96). When the two groups were combined, decreased 3MS score
(b0=-2.69, coef/se=-1.69; b1=-.069, coef/se=-6.7,
OR=.93 (.92-.95), p<.0001) , older age (b2=.081,
coef/se=4.83, OR=1.08 (1.05-1.12), p<.0001) and an informant's
report of memory problems (b3=.72, coef/se=3.3, OR
2.1 (1.3-3.2), p=.0007) were significant explanatory variables
in the model to predict progression to dementia (HL
p=.45). The sensitivity of this logistic regression model for
dementia was 27% and the specificity was 94%.
NCL subjects, older age (b0=-8.1, coef/se=-2.88;
b1=.119, coef/se=4.1, OR=1.13 (1.06-1.19), p <.0001)
and decreased 3MS score (b2=-.046; coef/se=-2.85,
OR=.96 (.92-.99), p=.004) were significant explanatory variables
for progression to AD (HL p=.58). In CIND subjects, decreased
3MS score (b0=-5.86, coef/se=-2.54, b1=-.064,
coef/se=-3.6, OR=.94 (.90-.97), p=.0004), older age (b2=.073,
coef/se=2.95, OR=1.08 (1.02-1.13), p=.004) and an informant's
report of a family history of dementia (b3=.59, coef/se=1.72,
OR 1.81 (.92-3.6), p=.09) were significant explanatory variables
in the model to predict progression to AD (HL p=.45). When the
two groups were combined, decreased 3MS score (b0=-3.5,
coef/se=-1.9, b1=-.072, coef/se=-6.2, OR=.93 (.91-.95),
p<.0001) , older age (b2=.087, coef/se=4.5,OR=1.09
(1.05-1.13), p<.0001), an informant's report of memory problems
(b3=.72, coef/se=2.9, OR 2.1 (1.3-3.4), p=.004),
and an informant's report of a family history of dementia (b4=.54,
coef/se=1.8, OR 1.7 (.94-3.1), p=.07) were significant explanatory
variables in the model to predict progression to
AD (HL p=.86). The sensitivity of this logistic regression model
for AD was 45% and the specificity was 89%.
ROC for the logistic regression model for predicting
dementia in NCL subjects was 0.73; the ROC for the
model predicting dementia in CIND subjects was also
0.73. The ROC for the combined group was 0.78.
ROC for predicting AD was 0.74 for NCL, .75 for
CIND and .81 for the combined group.
the results of the logistic regression as a guide,
with a trial and error approach, we constructed a simplified
equation for predicting progression to dementia.
The derived equation was: (100-3MS score)+ (.25 * age) + 10
(if memory problems were reported by an informant). The
results of this equation led to an ROC of 0.72 for NCL, 0.71
for CIND and 0.77 for the entire group (NCL and CIND)
in predicting progression to dementia. In the combined
group, using a score of 44 or greater to indicate a positive
result, yielded a sensitivity of 79.2% with a specificity
equation incorporating the MMSE was: (100-(MMSE/30
* 100) + (.25 * age) + 10 (if memory problems were
reported by an informant). The results of the equation using
the MMSE led to an ROC of .73 for NCL, 0.67
for CIND, and .77 for the entire group (NCL and CIND)
in predicting the progression to dementia. In the
combined group, using a score of 44 or greater as a cutoff,
yielded a sensitivity of 72.9% with a specificity
of the various categories of Mild Cognitive Impairment
rates of developing dementia among the survivors of different
categories of MCI were significantly different (p=.0007),
ranging from 20 to 51 per cent (see Table
2). MCI (ICD-10-Type 3), MCI (DSM-IIIR-Type2), LLF and AACD
showed the highest conversion rates. The subsequent rates
of dementia found for these four categories were not significantly
different (p=.88). The rate for developing AD ranged
from 11 to 38 per cent (p=.003) but there were no significant
differences between the four categories with the
highest rates (p=0.76). Mortality rates were also
significantly different between groupings (p=.002)
and ranged from 30.1 to 53.8 per cent.
results suggest that simple, easily obtained variables
may be useful in predicting the likelihood of the development
of dementia in seniors over a five year period. Our results,
from a large population-based cohort, confirmed earlier work
that showed that age,25,26 Mini-Mental Status Examination
score27 and caregiver identification of memory difficulties28,29
were important risk factors for the development of dementia.
These three easily determined factors led to a predictive equation
of reasonable sensitivity.
variables that have been associated with a higher risk of progression
include subitems from the 3MS/MMSE (such as recall and time
orientation)30 and educational attainment.31
We used the total 3MS score rather than components of
the 3MS. While associated with the outcome of interest, the
components of the 3MS did not perform as well as the total 3MS
score. Educational level was associated with the subsequent
development of dementia in NCL subjects, but it was not a significant
explanatory variable in the logistic models. There was no evidence
in our study that family history was an important predictor
for the progression to dementia though it was associated with
a higher risk of developing AD.
scores on select neuropsychological tests,32 specific
neuroradiologic features,33 comorbidity,34,35
and Apo E genotype36 have also been associated with
subsequent cognitive decline, but they may not be feasible to
obtain in routine clinical practice. Studies from other CSHA
investigators will report on the role of neuropsychology and
genetic testing for apolipoprotein E allele in predicting the
development of dementia in this cohort. In this report we deliberately
chose to deal only with variables that could be easily determined
in the course of a single clinical encounter by a non-specialist
practitioner without access to laboratory/radiological investigations.
follow-up data on the different categories of cognitive impairment
show that the inclusion criteria for some categories were more
successful than others at describing impaired individuals at
higher risk for progression to dementia. Some of
the groups at higher risk for dementia were characterized by
the requirement for neuropsychological test scores that were
greater than 1 standard deviation (SD) below the mean
of age-based normative data (e.g., AACD, LLF). The CSHA
study diagnosis of CIND, while less rigorously determined, had
a similarly high rate of progression to dementia. The neuropsychologists
who participated in the CSHA generally required scores of at
least 1 SD below the norms for a neuropsychological
test (taking into account educational background, sensory impairments,
test characteristics, and the pattern of results) before they
would declare that an impairment was present. We used their
opinion to determine the presence of impairments
for ICD-10-types 2 and 3 and DSM-III-R type 2. Groups at lower
risk were those who were judged impaired when compared to young
adults (AAMI) or whose scores fell within 1 SD of
the mean for age based norms (ACMI). The rate of progression
to dementia in these groups was similar to CSHA1 subjects categorized
as NCL. These results suggest that appropriate criteria for
significant cognitive impairment (defined as a high likelihood
of progression to dementia) using neuropsychological test performance
should define impairment as a score of at least 1 SD below an
were present in our study. We report on a population that was
predominately Caucasian (98.5%). The l ong lapse
before the follow-up examination (mean five years) and the high
mortality rate resulted in the loss of valuable information.
Forty percent of the subjects had died during these five years.
Principally because of the way subjects were selected for the
clinical examination, the annual mortality rate of approximately
80 per 1000 was higher than one would expect to find for a similarly
aged unselected Canadian population. The reported age-specific
death rate for Canadians aged 75-79 in 1991 was 46.7 per 1000
and 45.5 per 1000 in 1992.37 In addition, those who
refused to undergo the clinical assessment may have done so
because of a reluctance to have an already suspect cognitive
state assessed.38 These limitations might weaken
the generalizability of our results. The specificity of
our predictive instrument (using 43/44 as the cut point) was
poor. The predictive equation using the MMSE worked as well
as the one utilizing the 3MS score. Our study suggests that
for determining the risk of a future dementia the
3MS offers no clear advantage over the MMSE.
a pre-selected population (non-demented patients referred by
their family physician with a 3+ month history of symptomatic
memory problems that interfered with daily functioning) and
using a battery that included neuropsychological test results,
Tierney et al reported a sensitivity of 75.9% and a specificity
of 93.6% in the prediction of probable AD.8 Our
specificity (89%) for predicting probable and possible
AD derived in a representative sample of Canadians 65 years
and older may be more applicable to the general population.
our population-based study we found that simple measures were
able to select those at higher risk for developing dementia.
Our simplified approach may be useful in i dentifying
a higher risk group for more intensive monitoring and/or the
administration of safe, inexpensive interventions (e.g., secondary
prevention by risk factor modification or by the use of antioxidants,
non-steroidal anti-inflammatory agents or estrogen replacement
therapy in women). Further studies are needed to prove the utility
of these potential interventions. The different categories of
cognitive impairment as described in the literature have significantly
different outcomes. The accuracy of predicting outcomes will
likely be improved by incorporating laboratory, radiological,
and neuropsychological data but at the cost of ease of use.
data reported in this article were collected as part of the
Canadian Study of Health and Aging. The core study was funded
by the Seniors' Independence Research Program, through the National
Health Research and Development Program (NHRDP) of Health Canada
(project no. 6606-3954-MC (S)). Additional funding was provided
by Pfizer Canada Incorporated through the Medical Research Council/
Pharmaceutical Manufacturers Association of Canada Health Activity
Program, NHRDP (project no. 6603-1417-302 (R)), Bayer Incorporated,
and the British Columbia Health Research Foundation (projects
no. 38 (93-2) and no. 34 (96-1)). The study was coordinated
through the University of Ottawa and the Division of Aging and
Seniors, Health Canada.
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J. Neurol. Sci. 2000; 27: 18-24