My personal take on this is - the jury is still out. Yeah! It is always a nice "high", but as for any real beneficial effects ...I don't think I have seen any on the few occasions I have experimented. I have never been what could be considered a "user" because I did not like thew effect it had on my brain. My brain was always my strongest asset, so why would I ever want to put it at a disadvantage. Now that my brain is slowly turning to Lime Jell-o ...it just doesn't matter so much. Anyways - here is some information on some recent research on this subject. I really cannot endorse it, but it seems to be a current trend.
Health Editor, Sydney Morning Herald
|Yeah! That's me! In Michigan medical marihuana is legal. Not yet in Ohio.|
It makes most people a little foggy-headed, but scientists are investigating whether an active ingredient in cannabis could actually stave off dementia.
A team from Neuroscience Research Australia is in the early stages of research examining if one of the main active ingredients in cannabis, called cannabidiol, could reverse some of the symptoms of memory loss in animals.
Tim Karl, a senior research fellow with the group, said cannabidiol does not have the same psychoactive effects as the main component of marijuana, THC, but it has been found to have anti-inflammatory, antioxidant and other effects that could be beneficial for the brain.
“Back in the day cannabis was used for medical purposes, I'm talking 200 years, 100 years back, then at some point people discovered it had other effects and, as quite often happens in our society, people decided it was a bad drug,” he said.
“But it's not one compound, it is a mixture of 60 different compounds, and you just have to look at those different compounds because some of them might be good for you.”
His study involved injecting cannabidiol into mice that had been bred to have similar symptoms as those seen in Alzheimer's, as well as examining what would happen to brain cells treated with the drug.
Dr Karl found that when the mice were given the cannabidiol they showed drastic improvement on parts of the tests that were related to recognising and remembering objects and other mice.
“It basically brings the performance of the animals back to the level of healthy animals,” he said. “You could say it cured them, but we will have to go back and look at their brains to be sure.”
The study was done as part of the PhD of student David Cheng, who has also collected the brains of the mice and intends to examine them to see if they showed physical improvements.
As part of the research, which is being presented at the Australian Neuroscience Society annual meeting in Melbourne this week, the team also treated animal brain cells that produced a protein linked to the development of plaques in the brain in humans with Alzheimer's disease, amyloid precursor protein.
The cell research, done at the University of Wollongong, found treating the cells with cannabidiol also reduced the amount of the harmful protein that they produced.
Dr Karl said there had been case reports in medical literature of marijuana smokers who had developed Alzheimer's disease, only to find their smoking seemed to relieve some of their symptoms.
“Most of the components [of marijuana] are detrimental, they worsen your cognitive performance and have psychoactive effects… cannabidiol seems to not have any of these negative effects,” he said.
Marijuana cannabinoids slow brain degradation and aging, reverse dementia: here's how
Thursday, May 23, 2013 by: Ethan A. Huff, staff writer
(NaturalNews) The human brain contains an extensive network of special receptor sites that modulate nervous system function only when activated by the appropriate cannabinoid compounds, many of which are found in abundance in the marijuana plant. And emerging research continues to uncover the unique role these cannabinoids play in protecting brain function, which in turn helps deter the aging process and even reverse the damaging effects of Alzheimer's disease and other forms of dementia and cognitive abnormality.
One of the latest discoveries concerning cannabinoids involves their ability to act as antioxidants in the brain. Researchers from Germany found that the brain's cannabinoid system is fully capable of not only cleansing damaged brain cells from the brain, but also triggering the production of new brain cells within the brain, a concept that contradicts years of conventional thinking about how the brain works. Cannabinoids also supercharge mitochondria in the brain, which are the powerhouses of energy that maintain proper cell function.
Published in the journal Philosophical Transactions of the Royal Society B, these discoveries shed new insight on how natural marijuana cannabinoids hold the capacity to literally quell the brain inflammation responsible for causing cognitive decline, neural failure, and brain degeneration. By supplying these receptor sites with cannabinoids, patients may be able to overcome brain conditions like Alzheimer's disease, Parkinson's disease, Huntington's disease, and many other conditions, not to mention premature brain aging, all conditions for which modern science has failed to find real solutions.
"I've been trying to find a drug that will reduce brain inflammation and restore cognitive function in rats for over 25 years; cannabinoids are the first and only class of drugs that have ever been effective," said Gary Wenk, a professor of neuroscience, immunology and medical genetics at Ohio State University (OSU) who helped with the research. "I think that the perception about this drug is changing and in the future people will be less fearful," he added, referencing the fact that marijuana is still viewed mostly negatively by many people.
Marijuana cannabinoids help restore the body's own natural cellular abilities
Ever since researchers first discovered the existence of cannabinoid receptor systems in rat brains back in 1988, independent scientists largely unconcerned with the stigma of marijuana have been gradually forming a piecemeal understanding of how the system works, and how cannabinoids can help prevent and reverse all sorts of chronic conditions that result from a lack of these important compounds. Today, marijuana research is moving forward more strongly than ever, and this taboo plant is finally receiving proper recognition as the medical "superfood" that it truly is.
"Cannabis actually goes upstream, it provides feedback from the postsynaptic nerve to the presynaptic nerve, which is unheard of in neural chemistry," explains Dr. William Courtney, founder of the Cannabis International Foundation (CIF), which is actively working to consolidate all available and relevant science on marijuana's medical uses for the purpose of promoting a reformation in health maintenance, restoration, and wellness around the world.
"The phytocannabinoids from this plant augment the body's attempt to restore an increased function to a normal level. So [marijuana cannabinoids] mimic the regulatory system of cellular physiology."
Cannabis use and cognitive decline in persons under 65 years of age
AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol. 149, No.9 pages 794-800, 1999
Constantine G. Lyketsos, Elizabeth Garrett, Kung-Yee Liang, and James C. Anthony
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.
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.
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.
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.
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 %
18-30 545 41
31-40 319 24
41-50 179 14
51-60 185 14
61-64 90 7
Male 488 37
Female 830 63
Minority (African-American or Hispanic) 490 37
Nonminority (other) 828 63
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 (1.9 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=1318) -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 of cannabis use Number Mean score change in MMSE 95% confidence interval
Nonusers 251 -1.00 -0.73 to -1.27
Light users 104 -1.03 -0.67 to -1.39
Light users & use of drugs 47 -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
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 cannabis use
Model 1 (cannabis use)
Regression coefficient Standard error
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)
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.
This study was supported by grant 1R01-MH47447 from the National Institute of Mental Health for Baltimore Epidemiologic Catchment Area study follow-up.
1. Zec RF. The neuropsychology of aging. Exp Gerontol 1995;30:431-42
2. Bickel H, Cooper B. Incidence and relative risk of dementia in an urban elderly population: findings of a prospective field study. Psychol Med 1994;24: 179-92.
3. O'Brien JT, Beats B. Benign senescent forgetfulness and age associated memory impairment. In: Burns A. Levy R, eds. Dementia. New York. NY: Chapman and Hall Medical, 1994: 295-308.
4. Huppert FA, Brayne C. What is the relationship between dementia and normal aging? In: Huppert FA, Brayne C, O'Connor DW, eds. Dementia and normal aging. Cambridge, England: Cambridge University Press, 1994:3-11. 5. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. DSM-IV. 4th ed. Washington. DC: American Psychiatric Press. 1994.
6. Corrada M, Brookmeyer R, Kawas C. Sources of variability in prevalence rates of Alzheimer’s disease. Int J Epidemiol 1995;24:1000-5 7. Henderson AS, Alzheimer's disease In Its epidemiological context. Acta Neurol Scand Suppl 1993;149:1-3.
8. Snowdon DA, Greiner LH, Mortimer JA, et al. Brain infarction and the clinical expression of Alzheimer disease: The Nun Study. JAMA 1997; 277:813-17.
9. Small GW, Rabins PV, Barry PP, et al. Diagnosis and treatment of Alzheimer disease and related disorders. Consensus statement of the American Association for Geriatric Psychiatry, the Alzheimer’s Association, and the American Geriatrics Society.
10. Shaie KW, The course of adult intellectual development. Am Psychol 1994;49:304-13.
11. Grimby A, Berg S. Stressful life events and cognitive functioning in late life. Aging (Milano) 1995;7:35-9.
12. Hultsch DF, Hertzog C, Small BJ, et al. Short-term longitudinal change in cognitive performance in later life. Psychol Aging 1992;7:571-84.
13. Johansson B, Zarit SH, Berg S. Changes in cognitive functioning of the oldest old. J Gerontol 1992;47:P75-80.
14. Farmer ME, Kittner SJ, Rae DS, et al. Education and change in cognitive function. The Epidemiologic Catchment Area Study. Ann Epidemiol 1995;5:1-7.
15. Minami Y, Tsuji I, Fukao A, et al. Physical status and dementia risk, a three year prospective study in urban Japan. Int J Soc Psychiatry 1995;41:47-54.
16. Yoshitake T, Kiyohara Y, Kato I, et al. Incidence and risk factors of vascular dementia and Alzheimer's disease in a defined elderly Japanese population: The Hisayama Study. Neurology1995;45:1161-8.
17. Letenneur L, Commenges D. Dartigues JF, et al. Incidence of dementia and Alzheimer's disease in elderly community residents of south-western France. Int J Epidemiol 1994;23:1256-61.
18. Hebert LE, Scherr PA, Beckett LA, et al. Age-specific incidence of Alzheimer's disease in a community population. JAMA 1995;273:1354-9
19. Payami H, Montee K, Kaye J. Evidence for familial factors that protect against dementia and outweigh the effect of increasing age. Am J Hum Genet 1994;54:650-7.
20. Boothby H, Blizard R, Livingston G, et al. The Gospel Oak Study stage III: the incidence of dementia. Psvchol Med 1994;24:89-95.
21. Shen YC, Li G, Li YT, et al. Epidemiology of age-related dementia in China. Chin Med J (Engl) 1994;107:60-4.
22. Paykel ES, Brayne C, Huppert FA, et al. Incidence of dementia In a population older than 75 yearz In the United Kingdom. Arch Gen Psychiatry 1994;51:325-32.
23. Morgan K, Lilley JM, Arie T, et al. Incidence of dementia in a representative Bntish sample. Br J Psychiatry: 1993;163:467-70.
24. Bachman DL, Wolf PA, Linn RT, et al. Incidence of dementia and probable Alzheimer's disease in a general population: The Framingham Study. Neurology 1993;43:515-19.
25. Hagnell O, Franck A, Grasbeck A, et al. Vascular dementia in the Lundby study. 1. A prospective, epidemiological study of incidence and risk from 1957 to 1972. Neuropsychobiology 1992;26:43-9.
26. Copeland JR, Davidson IA, Dewey ME, et al. Alzheimer's disease, other dementias, depression and pseudodementia: prevalence, incidence and three-year outcome in Liverpool Br J Psychiatry 1992;161:23o-9.
27. Mann AH. Livingston G, Boothby H. et al. The Gospel Oak Study: the prevalence and incidence of dementia in an inner city area of London. Neuroepidemioloy 1992;11(suppl 1):76-9.
28. Li G, Shen YC, Chen CH, et al. A three-year fullow-up srudy of age-related dementia in an urban area of Beijing. Acta Psychiatr Scand 1991;83:99-104.
29. Lee PN. Smoking and Alzhetmer's disease: a review of the epidemiological evidence. Neuroepidemioloy 1994;13:131-44.
30. Stern Y, Gurland B, Tatemichi TK, et al. Influence of education and occupation on the incidence of Alzheimer's disease. JAMA‘94;271:1004-10.
31. Ott A, Breteler MM. van Harskamp F, et al. Prevalence of Alzheimer's disease and vascular dementia: association with education. The Rotterdam Study. BMJ 1995;310:970-3.
32. Gilleard CJ. Education and Alzheimer's disease: a review of recent international epidemiological studies. Aging Ment Health 1997;1:33-46.
33. Beard CM, Kokmen E, Offord KP, et al. Lack of association between Alzheimer's disease and education, occupation, marital status, or living arrangement. Neurology 1992;42: 2063-8.
34. Pope HG Jr, Gruber AJ, Yurgelun-Todd D. The residual neuropsychological effects of cannabis: the current status of research. Drug Alcohol Depend 1995;38:25-34.
35. Fletcher JM, Page JB, Francis DJ, et al. Cognitive correlates of long-term cannabis use In Costa Rican men. Arch Gen Psychiatry 1996;53: 1O51-7.
36. Pope HG Jr, Jacobs A, Mialet JP, et al. Evidence for a sex-specific residual effect of cannabis on visuospatial memory. Psychother Psychosom 1997;66: 179-84.
37. Pope HG Jr, Yurgelun-Todd D. The residual cognitive effects of heavy marijuana use in college students. JAMA 1996;275: 521-7.
38. Lyketsos CG, Chen LS, Anthony JC. Cognitive decline in adulthood: an 11.5-year follow-up of the Baltimore Epidemiologic Catchment Area study. Am J Psychiatry 1999:156:58-65.
39. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189-98.
40. Eaton WW, Anthony JC, Gallo J, et al. Natural history of Diagnostic Interview Schedule/DSM-IV major depression: the Baltimore Epidemiologic Catchment Area follow-up. Arch Gen Psychiatry 1997:54:993-9.
41. Eaten WW, Kessler LG, eds. Epidemiologic field methods in psychiatry: the NIMH Epidemiologic Catchment Area program. Orlando, FL: Academic Press, Inc. 1985.
42. McCullagh P. Regression models for ordinal data (with discussion). J R Stat Soc [B] 1980;42:109-42.
43. Rebok G, Brandt J, Folstein M. Longitudinal cognitive decline in patients with Alzheimer's disease. J Geriatr Psychiatry Neurol 1990;3:91-7.
44. Schmand B, Lindeboom J, Launer L, et al. What is a significant score change on the Mini-Mental State Examination? Int J Geriatr Psychiatry: 1995;10:411-14.
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