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AMERICAN
JOURNAL OF EPIDEMIOLOGY, Vol. 149, No.9 pages 794-800, 1999
CANNABIS
USE AND COGNITIVE DECLINE IN PERSONS UNDER 65 YEARS OF AGE
Constantine
G. Lyketsos, Elizabeth Garrett, Kung-Yee Liang, and James C. Anthony
ABSTRACT
The
purpose of this study was to investigate possible adverse effects of cannabis
use on cognitive decline after 12 years in persons under age 65 years.
This was a follow-up study of a probability sample of the adult household
residents of East Baltimore. The analyses included 1,318 participants in the
Baltimore, Maryland, portion of the Epidemiologic Catchment Area study who
completed the Mini-Mental State Examination
(MMSE) during three study waves in 1981, 1982, and 1993-1996.
Individual MMSE score differences between waves 2 and 3 were calculated
for each study participant. After
12 years, study participants' scores declined a mean of 1.20 points on the MMSE
(standard deviation 1.90), with 66% having scores that declined by at least one
point.
Significant
numbers of scores declined by three points or more (15% of participants in the
18--29 age group).
There
were no significant differences in cognitive decline between heavy users, light
users, and nonusers of cannabis.
There
were also no male-female differences in cognitive decline in relation to
cannabis use. The authors conclude that over long time periods, in persons under
age 65 years, cognitive decline occurs in all age groups. This decline is
closely associated with aging and educational level but does not appear to be
associated with cannabis use.
Am
J Epidemiol 1999;149:794-800
Cognitive capacity has multiple determinants, including genetic makeup,
nutritional status, health status, formal education, and age-related
developmental processes. This capacity generally reaches its peak in early
adulthood and then declines later in life (1).
Cognitive decline is a significant public health problem, given its
association with impaired functioning and increased mortality (1) and its close
link to dementia (2-4). Dementia is
defined as the occurrence of measurable, global cognitive decline sufficient to
impair functioning (5). The prevalence and incidence of dementia, now one of the
most common and serious diseases of the elderly, is rapidly increasing as the
world population ages (6, 7).
Epidemiologic studies of dementia and of cognitive decline have typically
investigated individuals over the age of 60 years. The expected prevalence of
dementia in these age groups is 2 percent or higher (6, 7), and prevalence might
be as high as 48 percent in those over age 85 (6, 7). In late life, dementing
processes hamper the study of cognitive decline as a phenomenon distinct from
dementia. Additionally, recent
research suggests (8) and scientific consensus concurs (9) that dementia is best
understood as the result of cumulative effects on the brain from diseases (e.g..
Alzheimer's disease or cerebrovascular disease) and other exposures (e.g.
alcohol or tobacco use), all occurring against background, possibly lifelong,
declines in cognition associated with aging itself.
However, epidemiologic knowledge regarding cognitive decline in persons
younger than age 65 is very limited. Indeed,
we could find only one published epidemiologic study of cognitive decline in
younger persons: the Seattle Longitudinal Study (10).
The Seattle Longitudinal Study followed a series of community-based
cohorts of individuals enrolled in a health maintenance organization. Sample
sizes for individual cohorts were between 500 and 997.
Participants were assessed according to a large number tests of
intelligence and cognitive capacity. The
main findings were that individual cognitive abilities did not change much
before age 60, with the exception of verbal fluency.
Because of attrition, the Seattle Longitudinal Study did not have
sufficient sample sizes to detect small cognitive declines in younger age
groups. Furthermore, very few individual participants were followed for spans of
more than 5 years.
The major correlate of cognitive decline is increasing age (10-14).
Higher educational level (14) and higher functioning (13) are associated with
less cognitive decline. Being
female or encountering stressful life events is not associated with cognitive
decline (II,13). Risk factors for
dementia include age, prior cognitive impairment, stroke, high blood pressure,
heart disease, diabetes mellitus, alcohol consumption, and depression (15-28). The use of nicotine via smoking has
also been associated with a lower risk for dementia, although this finding is
controversial (29). Being female
has not been associated with the incidence of dementia (15, 17).
Two recent studies (30, 31) have
reported that lesser educational attainment is a risk factor for
dementia. However, this finding has
not been supported universally (17, 32, 33).
The relation between cognitive functioning or cognitive decline and use
of cannabis (marijuana) has received limited attention in epidemiologic studies.
Two cognitive effects of cannabis must be distinguished: acute effects,
those associated with intoxication, and residual effects, which persist after
the drug has left the central
nervous system (34). The latter effects
might be short term or long term. Cross-sectional studies, either experimentally
administering cannabis or comparing
users with nonusers, support the
existence of short term residual effects of cannabis use on attention, ability
to perform psychomotor tasks, and short term memory (34, 35).
These effects are more severe in women (36) and in heavy users of
cannabis as compared with light users (37).
To our knowledge, no study with published results has investigated the
long term effects of cannabis use on cognition in an epidemiologic sample.
According to Pope et al. (34), study designs best suited to addressing
this issue are naturalistic comparisons, in large epidemiologic samples, of
heavy users, light users, and nonusers of cannabis.
These studies must also account for the concurrent use of alcohol and
other drugs, both illicit and legal (e.g., nicotine).
In Addition such studies must adjust for other factors known to influence
cognition over time, such as age and education, and must investigate possible
interactions between the cognitive effects of cannabis use and gender (being
female).
We recently reported findings from a 13-year follow-up of 1,488 persons
of all ages who had participated in the Baltimore, Maryland, portion of the
Epidemiologic Catchment Area study (38). The
Mini-Mental State Examination (MMSE) (39), a widely used quantitative measure of
cognition, was administered to participants during wave 1 (1981) and during two
follow-up waves in 1982 and 1993-1996. The design of the study allowed us to
examine cognitive decline between waves 2 and 3 in a large epidemiologic sample.
We found that cognitive decline
occurred in all age groups. Age, education, and minority status were all significantly
associated with greater cognitive decline.
In this follow-up paper, we focus our investigation on persons under age 65 years.
To our knowledge, this is the first population study that has
investigated cognitive decline in this age group, in which the prevalence of
dementia is very low. This permits better study of cognitive decline as a
phenomenon distinct from dementia, as well as its associated risk factors.
We had two goals: 1) to further delineate the epidemiology of
age-specific cognitive decline in persons under 65 and
2) to investigate any long term association between cognitive decline and
use of cannabis using a design similar
to the one proposed by Pope et al. (34).
MATERIALS
AND METHODS
Baltimore
Epidemiologic Catchment Area follow-up
The Epidemiologic Catchment Area program has been described in detail
elsewhere (40, 41). The Baltimore arm of this five-site study first entered the
field in 1981, when the first wave of in-person assessments was completed.
A second wave of assessment (including wave 2 administration of the MMSE)
was conducted 1 year later, in 1982. The
Baltimore Epidemiologic Catchment Area target population consisted of the adult
household residents of eastern Baltimore City, an area with 175,211 inhabitants.
During wave 1, 4,238 individuals
were designated for interview by probability sampling methods, and 3,481 (82
percent) completed interviews. Of these persons, 2,695 completed interviews
during wave 2.
In 1993, all 3,481 initial participants were targeted for tracing and
interviewing. A total of 848
participants were found to have died; the remaining 2,633 were presumed to be
alive, but 415 of them could not be successfully traced.
Of the 2,218 persons located, 298 refused to participate, and 1,92O
completed interviews. Of these,
1,488 had completed the MMSE during all three waves, approximately 11.5 years
after wave 2. All study
participants signed informed consent statements approved by the Institutional
Review Board of the Johns Hopkins University School of Hygiene and Public
Health.
Participants
In these analyses, we included only those participants who were under age
65 at wave 1 and who completed the MMSE during all three study waves (n =
1,318).
Measurement of cognitive decline.
For
each participant, an MMSE score difference was calculated by subtracting the
wave 3 (1993-1996) MMSE score from the wave 2 (1982) MMSE score, The mean time
interval between the points at which these MMSEs were administered was 11.6
years (standard error 0.01 years). The median interval was 11.5 years, the 25th
percentile was 11.3 years, and the 75th percentile was 11.9 years. Change in
MMSE score between waves 2 and 3 Has the primary dependent variable in the
analyses.
Classification of participants
according to use of cannabis. Participants were separated into five groups
based on their self-reported drug use during all three waves of the study.
Group 1 ( nonusers) were those who reported in all three waves that they
had never used cannabis in any form (n = 806 (61 percent)).
Group 2 (light users) were participants who had used cannabis but had
never used it daily or more often for over 2 weeks (n = 235 (18 percent)).
Group 3 were light users who reported use of any other illicit substance
in any study wave (n = 131 (10 percent)). Group 4 (heavy-users) reported during
at least one study wave that they had used cannabis daily or more often for over
2 weeks (n = 137 (10 percent)). Group
5 were heavy users of cannabis who reported use of other illicit drugs as well
(n = 8 (1 percent)). Information on
cannabis use was missing for one participant.
Classification of participants
according to use of alcohol or tobacco.
On
the basis of the highest alcohol intake
reported for the past month during any of the three study waves, participants
were placed into three groups:
never drinkers (n = 67 (5 percent), light-to- moderate drinkers
(n = 778 (59 percent)), and
heavy drinkers, defined as those
who had had more than four drinks on any one day during the past month (n = 473
(36 percent)). With respect
to smoking, three groups were defined on the basis of self-report during any of
the three waves: never smokers (n =
347 (26 percent)): occasional
smokers (n = 573 (44 percent)); heavy
smokers, defined as those who smoked 20-39 cigarettes per day (or the
equivalent in cigars or pipefuls of tobacco (n = 310 (24 percent)) and very
heavy smokers, those who smoked two or more packs of cigarettes per day (or the
equivalent (n = 85 (6 percent)). Information on smoking was missing for three
participants.
Other variables associated with cognitive decline used as covariates.
Information
on other variables associated with cognitive decline was recorded at wave 1.
Gender was indicated as male or female.
Age was grouped as follows: 18-30, 31-40, 41-50, 51-60, and 61-64 years.
Minority status was indicated as African-American or Hispanic versus other
ethnicity (non-Hispanic white). Five
educational subgroups were developed: 0-8 years, 9-11 years, 12 years or General
Equivalency Diploma, 13-15 years, and 16 or more years, in conformance with
common educational landmarks (grade school, some high school, completed high
school or the equivalent, some college, and completed college).
It is possible that some study participants, especially those in younger
age groups at wave 1, completed their education after wave 1 and were thus
misclassified.
Analyses
Mean MMSE score changes between waves 2 and 3 (with 95 percent confidence
intervals) are reported in the tables for the entire cohort and for subgroups by
age. The proportions of individuals
who evidenced any increase, no change, a one-point decline, a two-point decline,
a three-point decline, or a four-point or greater decline are also reported by
age group. Mean change in MMSE score (with its 95 percent confidence interval)
by level of cannabis use was estimated for men and women separately.
The relation between level of cannabis use and MMSE score change between
waves 2 and 3 was examined in a series of linear regression models with MMSE
score change as the dependent variable and cannabis use as the independent
variable, with or without inclusion of the other covariates.
For both univariate and multiple regression models, the association of
cannabis use with change in MMSE score is reported in the form of regression
coefficients (with 95 percent confidence intervals). Subgroups were entered into
regression models individually as "dummy" variables to allow direct
comparisons of remission coefficients using one of the subgroups as the
reference category.
To validate the findings from the linear regression models, we also
constructed a series of proportional odds logit models (42) relating diseases or
substance use to MMSE score change. These were bivariate or multivariate
“analogs” to the linear models. The dependent variable was “change in MMSE
score,” grouped as follows: any increase, no change, a one-point decline, a
two-point decline, a three-point decline, or a four-point or greater decline.
Findings from these models were similar to those obtained from the linear
models. For simplicity, we report
only findings from the linear models.
RESULTS
Table 1 provides a description of the study cohort at wave 1 with regard
to sociodemographic variables. It
also shows mean MMSE scores at each study wave.
TABLE
1. Sociodemographic characteristics at the Baltimore Epidemiologic Catchment
Area study cohort at wave 1 (n = 1,318) and mean MMSE scores at waves 1-3
Variable
Number
%
Age (years)
18-30
545
41
31-40
319
24
41-50
179
14
51-60
185
14
61-64
90
7
Gender
Male
488
37
Female
830
63
Race
Minority (African-American
or Hispanic)
490
37
Nonminority (other) 828
63
Education (years)
0-8
161
12
9-11
280
21
12/GED
541
41
13-15
211
16
16 or more 125
10
Mean
MMSE score
Wave 1(1981)
28.65 (1.90 standard deviation)
Wave 2 (1982)
28.65 (1.81 standard deviation)
Wave 3 (1993-1996) 27.46 (2.23 standard deviation)
Cognitive
decline between waves 2 and 3
Table 2 shows the mean change in MMSE score between waves 2 and 3 for
every age group. It also shows the
proportions of participants in each age group with specific changes in MMSE
score, as described above. Persons in all age groups had mean declines greater
than zero, with two thirds declining in score by at least one point. The mean
decline and the proportion of persons with declining scores increased steadily
with age, as expected. It is
noteworthy that in every age group there was a notable proportion of
participants whose score declined three points or more-- a change of a magnitude
that merits clinical attention (43, 44). These
estimated declines must be considered in the context of MMSE measurement error,
the MMSE ceiling effect, and normal variation in MMSE scores over time (see
Discussion).
TABLE
2. Mean change in Mini-Mental State Examination (MMSE) score from wave 2 (1982)
to wave 3 (1993-1996) and proportions of participants evidencing specific MMSE
score changes, by age group, Baltimore Epidemiologic Catchment Aiea study
follow-up
Age
group (years)
Change in MMSE score
Mean change 95% Confidence Interval
18-30
(n = 545)
-0..98
-0.83 to-1.13
31-40
(n = 319)
-1.08
-0.89 to-1.27
41-50
(n = 179)
-1.25
-0.92 to-1.58
51-60
(n = 185)
-1.52
-1.20 to -1.84
61-64
(n = 90)
-2.12
-1.52 to-2.72
All
ages (n =1,318)
-1.20
-1.10 to -1.30
(EDITORIAL
NOTE: Only the first part of TABLE 2 is included to save space.)
Association
between cannabis use and score decline
Table 3 displays estimated mean changes in MMSE score according to level
of cannabis use for men and women separately.
Women who were nonusers of cannabis had scores that declined more than
those of men who were nonusers. However,
within male-female groups, there were no evident differences in score decline by
cannabis use for either men or women.
TABLE
3. Mean change in Mini-Mental State Examination (MMSE) score between wave 2
(1982) end wave 3 (1993-1996) in men and women, by level of cannabis use,
Baltimore Epidemiologic Catchment Area study follow-up
Gender and level
Mean score 95% confidence
of cannabis use
Number
change in MMSE interval
Men
Nonusers
251
-1.00
-0.73 to -1.27
Light users
104
-1.03
-0.67 to -1.39
Light users & use of drugs
48
-1.06
-0.57 to -1.55
Heavy users
82
-0.84
-0.46 to -1.22
Heavy users & use of drugs
3
-0.33
+5.93 to -6.59
Women
Nonusers
555
-1.46
-1.29 to -1.63
Light users
131
-1.04
-0.71 to -1.37
Light users & use of drugs
83
-1.07
-0.77 to -1.37
Heavy users
55
-1.15
-0.47 to -1.83
Heavy users & use of drugs
8
-0.60
+3.09 to -4.29
Table 4 displays results from the linear regression models with MMSE
change between waves 2 and 3 used as the dependent variable. The numbers shown
in the table are regression coefficients estimating the relative change in MMSE
score for a given group of cannabis users relative to nonusers. Model 1 included only cannabis use as the covariate.
Model 2 included cannabis use and use of alcohol and tobacco.
Model 3 included cannabis use plus age, gender, education, minority
status, alcohol use, and tobacco use. Model
4 included cannabis use plus all of the variables from models 2 and 3. Both light and heavy users of cannabis evidenced less
cognitive decline than nonusers, although this finding was not statistically
significant at the conventional level of p < 0.05 (model 1). After adjustment for the other variables in models 2-4, there
was no association between cannabis use and cognitive decline.
TABLE
4. Regression coefficients
indicating relative differences in Mini-Mental State Examination (MMSE) score
change between wave 2 (1982) and wave 3 (1993-1996), by level of cannabis use,
in four regression models, Baltimore Epidemiologic Catchment Area study
follow-up
Level of
Model 1 (cannabis use)
cannabis use
Regression coefficient Standard
Error
Nonusers
Light users
0.28*
0.15
Light users & use of drugs
0.25
0.19
Heavy users
0.35*
0.18
Heavy users & use of drugs
0.81
0.71
* p < 0.10
(EDITORIAL
NOTE: Models 2, 3 and 4 were not included in this table, see note at end of this
article)
DISCUSSION
Cognitive decline is an age-related phenomenon that affects persons of
all ages, including those under age 30 years.
It becomes more pronounced with increasing age and is most evident in
persons over age 59. A significant
proportion (>15 percent) of persons in all population age groups evidence
declines that approach clinical significance. We offer two interpretations of
this finding.
One is that cognitive decline might be an inevitable phenomenon of aging, perhaps modified by genetic
makeup, education, nutrition, disease, and environmental exposure.
Another is that the declines are the result of slowly progressive
neurodegenerative diseases (such as Alzheimer's disease) which might be lifelong
in evolution but do not lead to clinical symptoms until much later in
life (8). While these two lines of reasoning are not mutually exclusive, the
relation between age and cognitive decline across all age groups reported here
lends greater support to the former.
To our knowledge, this was the first long term prospective study in the
United States that had a community sample large enough to investigate the
relationship between cannabis use and cognitive decline in persons under age 65
years. Other studies have found
short term residual effects of cannabis use on memory and cognition (34, 35)
that are more severe among women (36) and heavy users (37).
However, our data suggest that over the long term cannabis use is not
associated with greater declines in cognition among men, women, or heavy users.
The study design we used included several of the features proposed by
Pope et al. (34) as critical to addressing the long term effects of cannabis on
cognition: naturalistic follow-up,
a large sample size, a population basis, comparison of light cannabis use with
heavy use, and the construction of models accounting for the effects of gender
and use of illicit drugs, alcohol, and tobacco. Therefore, these results would
seem to provide strong evidence of the absence of a long term residual effect of
cannabis use on cognition.
Notable limitations of this study include loss to follow-up and
mortality. Cognitive functioning at
base-line was a predictor of both mortality and loss to follow-up in the
Epidemiologic Catchment Area study (40). Additionally,
it is possible that some cannabis users in the study may have used cannabis on
the day the MMSE was administered. Given
the acute effects on cannabis on cognition (34), this would have tended to
reduce their MMSE score on that day. This
may have adversely affected accurate measurement of MMSE score changes over
time.
Given that a lower level of cognitive functioning was associated with
greater cognitive decline, these estimates of decline may be underestimates. The
assessment of cannabis use was based on self-reports and was not confirmed with
biologic measures or controlled in an experimental setting.
This may have led to underestimation of cannabis use in persons with poor
memory.
Another important limitation of the study is that the MMSE is not a very
sensitive measure of cognitive decline, even though it specifically tests memory
and attention. Thus, small or subtle effects of cannabis use on cognition or
psychomotor speed may have been missed. The MMSE is not intended for the purpose
for which it was used in this study, and it contains some items that assess
neurologic function as well as cognition. Additionally, MMSE item analysis was
not performed in this study. Given
the MMSE's ease of use and widespread application, it was the most practical
instrument available for brief assessment of cognitive functioning at the time
the multisite Epidemiologic Catchment Area study was planned in the late 1970s.
Also, given its limited sensitivity, declines noted on
the MMSE are probably under estimates of true
declines.
Other limitations of the MMSE include the fact that small errors. such as
forgetting the present day's date, may be due to measurement error and not to
true decline. Measurement error on
the MMSE might be caused by a
variety of factors, including the ambient environment in which the test is
taken, the respondent’s mood or emotional state, the respondent’s adequacy
of sleep the night before, the time of day at
which the test is given, and other factors.
However, such errors ought to be random and not systematic (equally
distributed between study waves), so the effect
on mean estimates should "average out across the population and across
waves of assessment.
MMSE scores in this study exhibited a ceiling effect, given that most
participants scored in the 27-30 range during wave 1.
However, the ceiling effect was limited to a minority of participants,
those who scored 30 points at baseline, since most declines were small.
Finally,
the small but tangible beneficial "practice effect" of repeated
testing on MMSE score would tend to lead to higher, not lower, MMSE scores at
follow-up.
We conclude that cognitive decline occurs across all age groups. with a
significant proportion of persons of all ages showing declines near clinically
significant levels after 12 years. Such
decline is not associated with cannabis use in either men or women.
A better understanding of predictors of cognitive decline in persons
under age 65 years might lead to interventions designed to slow or arrest such
decline. This in turn might reduce the incidence of dementia at older ages.
ACKNOWLEDGMENTS
This study was supported by grant 1R01-MH47447 from the National
Institute of Mental Health for Baltimore Epidemiologic Catchment Area study
follow-up.
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J Epidemiol Vol, 149, No. 9. 1999 pages 794-800
Copyright:
1999 Johns Hopkins University School of Hygiene and Public Health
EDITORIAL
NOTE: Models 2, 3 and 4 were not included in Table 4, partly because there is no
specific discussion of how these models were mathematically created.
They begin to compensate for other variables, however, it is not fair to
lower the existing differences between cannabis users and nonusers - by
compensating for alcohol and tobacco use. Since
these variables accelerate cognitive decline, particularly alcohol according to
this article and many other sources, one questions whether they should be used
to diminish this important finding of lower cognitive decline among marijuana
smokers as compared to nonusers. Many
people would argue that the use of cannabis helps cut down on the use of these
two legal drugs, and this is part of its beneficial effect - rather than
something that must be subtracted away from it.
Also,
the p < 0.10 probability for “Light users” and “Heavy users” means
that there is a greater than a ten to one chance that this observed difference
is real or an actual difference. This
is less than the p < 0.05 probability often used in research, which is
greater than a twenty to one chance that this observed difference actually
reflects a similar real difference in the population and thus, is
“statistically significant”.
Since
this is the first major study to be published in this area of marijuana
research, more studies are needed to see if this observed trend of lowered
cognitive decline continues in marijuana smoking populations in the future.
At least the authors report that there is absolutely no evidence that
marijuana causes a long-term decline in mental functioning - as the false
assertions that marijuana did indeed cause brain damage were popular in
legislative circles in the 1980’s and were used to increase marijuana
penalties.