COGNITIVE EFFECTS & Cannabis studies completed
Science & Research
1973- Study - Neuropsychological effects of marijuana.
1974 - Study ~ Marihuana Use and Psychosocial Adaptation.
1976 - Study ~ Operant acquisition of marihuana in man.
1981 - Study ~ Cognition and Long-Term Use of Ganja (Cannabis).
1985 - Study ~ Operant acquisition of marihuana by women.
1991 - Study ~ Flashback Following Use of Cannabis--a Review.
1992 - News ~ THREE THINGS MARIJUANA DOESN'T DO.
1998- Study - Cerebellar activity and disturbed time sense after THC.
1999 - Study ~ Enhancement of Memory in Cannabinoid Cb1 Receptor Knock-out Mice.
2001 - Study ~ Neuropsychological Performance in Long-term Cannabis Users.
2002 - Study ~ Cognitive Measures in Long-term Cannabis Users.
2002- News - Marijuana does not dent IQ permanently.
2003 - Study ~ CANNABINOIDS ALTER RECOGNITION MEMORY IN RATS.
2003 - News ~ Study: Brain Not Permanently Damaged by Marijuana.
2003 - News ~ Heavy Marijuana Use Doesn't Damage Brain.
2005- Study - Marijuana Effects On Human Forgetting Functions.
2008- Study - Review: executive functioning and cannabis use.
2008 - Study ~ Multiple sclerosis, cannabinoids, and cognition.
2010 - News ~ Key ingredient staves off marijuana memory loss.
2010 - News ~ Imbalance of Cannabis ChemicalsCauses Memory Issues.
2010 - News ~ Key ingredient dilutes marijuana's effect on memory.
2011 - Study ~ Sex, drugs, and cognition: effects of marijuana.
2011 - Study ~ Combined effects of THC and caffeine on working memory in rats.
2011 - News ~ Are smart kids more likely to use drugs?
2011 - News ~ Cannabinoid-1 Receptor Protects The Brain From Aging.
2012 - Study ~ Assessing topographical orientation skills in cannabis users.
2012 - Study ~ Neurocognitive functioning and cannabis use in schizophrenia.
2012 - News ~ Pot smoking not tied to middle-age mental decline.
Operant acquisition of marihuana by women.
J Pharmacol Exp Ther. 1985 Oct;235(1):162-71.
Marihuana acquisition and use patterns were studied in 21 women on a clinical research ward. Women could earn one 1-g marihuana cigarette or 50 cents in 30 min of performance on a second-order Fixed-Ratio 300 (Fixed-Interval 1 sec:S) schedule of reinforcement. A 7-day drug-free base line was followed by 21 days of marihuana availability and a postmarihuana drug-free period of 7 days. Five heavy marihuana users smoked an average of 6.1 (+/- 1.45) marihuana cigarettes per day and increased marihuana use significantly through time (P less than .001).
Seven moderate marihuana users smoked an average of 2.72 (+/- 0.16) marihuana cigarettes per day and used significantly less marihuana through time (P less than .01). Nine occasional marihuana users smoked less than one cigarette per day (0.90 +/- 0.22) and maintained stable patterns of marihuana use. Women who increased marihuana use during the premenstruum reported significantly greater premenstrual dysphoria on the Premenstrual Assessment Form than women whose marihuana use decreased or remained the same (P less than .05 to .01).
There were no marihuana dose-related effects on operant performance. The heavy, moderate and occasional marihuana smokers did not differ in operant purchase points earned, hours worked or money earned. Each marihuana dose-group earned an equivalent number of purchase points during the drug-free periods and the period of marihuana availability. Some subjects continued to work for money when smoking 15 to 20 marihuana cigarettes per day and periods of maximal operant work coincided with periods of maximal marihuana smoking (noon-midnight).(ABSTRACT TRUNCATED AT 250 WORDS)
Neurocognitive performance during acute THC intoxication in heavy and occasional cannabis users
|Ramaekers JG, Kauert G, Theunissen EL, Toennes SW, Moeller MR|
|Institution||Department of Neuropsychology and Psychopharmacology, Faculty of Psychology, Maastricht University, Maastricht, The Netherlands.|
|Source||J Psychopharmacol 2008 Aug 21.|
Abstract Performance impairment during Delta(9)-tetrahydrocannabinol (THC) intoxication has been well described in occasional cannabis users. It is less clear whether tolerance develops to the impairing effects of THC in heavy users of cannabis. The aim of the present study was to assess neurocognitive performance during acute THC intoxication in occasional and heavy users. Twenty-four subjects (12 occasional cannabis users and 12 heavy cannabis users) participated in a double-blind, placebo-controlled, two-way mixed model design. Both groups received single doses of THC placebo and 500 microg/kg THC by smoking.
Performance tests were conducted at regular intervals between 0 and 8 h after smoking, and included measures of perceptual motor control (critical tracking task), dual task processing (divided attention task), motor inhibition (stop signal task) and cognition (Tower of London). THC significantly impaired performance of occasional cannabis users on critical tracking, divided attention and the stop signal task.
THC did not affect the performance of heavy cannabis users except in the stop signal task, i.e. stop reaction time increased, particularly at high THC concentrations. Group comparisons of overall performance in occasional and heavy users did not reveal any persistent performance differences due to residual THC in heavy users. These data indicate that cannabis use history strongly determines the behavioural response to single doses of THC
Non-acute (residual) neurocognitive effects of cannabis use: a meta-analytic study
J Int Neuropsychol Soc. 2003 Jul;9
Department of Psychiatry, University of California, San Diego, CA 92103, USA. [email protected]
The possible medicinal use of cannabinoids for chronic diseases emphasizes the need to understand the long-term effects of these compounds on the central nervous system. We provide a quantitative synthesis of empirical research pertaining to the non-acute (residual) effects of cannabis on the neurocognitive performance of adult human subjects. Out of 1,014 studies retrieved using a thorough search strategy, only 11 studies met essential a priori inclusion criteria, providing data for a total of 623 cannabis users and 409 non- or minimal users. Neuropsychological results were grouped into 8 ability domains, and effect sizes were calculated by domain for each study individually, and combined for the full set of studies. Using slightly liberalized criteria, an additional four studies were included in a second analysis, bringing the total number of subjects to 1,188 (i.e., 704 cannabis users and 484 non-users). With the exception of both the learning and forgetting domains, effect size confidence intervals for the remaining 6 domains included zero, suggesting a lack of effect. Few studies on the non-acute neurocognitive effects of cannabis meet current research standards; nevertheless, our results indicate that there might be decrements in the ability to learn and remember new information in chronic users, whereas other cognitive abilities are unaffected. However, from a neurocognitive standpoint, the small magnitude of these effect sizes suggests that if cannabis compounds are found to have therapeutic value, they may have an acceptable margin of safety under the more limited conditions of exposure that would likely obtain in a medical setting.
- [PubMed - indexed for MEDLINE]
Review: executive functioning and cannabis use
Almeida PP, Novaes MA, Bressan RA, Lacerda AL
Rev Bras Psiquiatr 2008 Mar; 30(1):69-76.
OBJECTIVE: Cannabis is the most used illicit drug worldwide, however only a few studies have examined cognitive deficits related to its use. Clinical manifestations associated with those deficits include amotivational syndrome, impairment in cognitive flexibility, inattention, deficits in abstract reasoning and concept formation, aspects intimately related to the executive functions, which potentially exert a central role in substance dependence. The objective was to make a review about consequences of cannabis use in executive functioning.
METHOD: This review was carried out on reports drawn from MedLine, SciELO, and Lilacs.
DISCUSSION: In studies investigating acute use effects, higher doses of tetrahydrocannabinol are associated to impairments in performance of nonsevere users in planning and control impulse tasks. However, chronic cannabis users do not show those impairments. Although demonstration of residual effects of cannabis in the executive functioning is controversial, persistent deficits seem to be present at least in a subgroup of chronic users after 28 days of abstinence.
CONCLUSIONS: The neuropsychological studies found did not have as a main aim the evaluation of executive functioning. A criterial selection of standardized neuropsychological tests, more appropriate study designs as well as concomitant investigations with structural and functional neuroimaging techniques may improve the understanding of eventual neurotoxicity associated with cannabis use.
Neuropsychological effects of marijuana
- Weekly Reports for JANUARY 4, 1946. Public Health Rep. 1946 Jan 04;61(1):1–36. [PMC free article] [PubMed]
- Public Health Weekly Reports for JANUARY 4, 1946. Public Health Rep. 1946 Jan 4;61(1):1–36. [PMC free article] [PubMed]
- Isbell H, Jasinski DR. A comparison of LSD-25 with (-)-delta-9-trans-tetrahydrocannabinol (THC) and attempted cross tolerance between LSD and THC. Psychopharmacologia. 1969;14(2):115–123. [PubMed]
- Weil AT, Zinberg NE, Nelsen JM. Clinical and psychological effects of marihuana in man. Science. 1968 Dec 13;162(3859):1234–1242. [PubMed]
- Manno JE, Kiplinger GF, Haine SE, Bennett IF, Forney RB. Comparative effects of smoking marihuana or placebo on human motor and mental performance. Clin Pharmacol Ther. 1970 Nov–Dec;11(6):808–815. [PubMed]
- Abel EL. Marihuana and memory: acquisition or retrieval? Science. 1971 Sep 10;173(4001):1038–1040. [PubMed]
- Kiplinger GF, Manno JE, Rodda BE, Forney RB. Dose-response analysis of the effects of tetrahydrocannabinol in man. Clin Pharmacol Ther. 1971 Jul–Aug;12(4):650–657. [PubMed]
- Meyer RE, Pillard RC, Shapiro LM, Mirin SM. Administration of marijuana to heavy and casual marijuana users. Am J Psychiatry. 1971 Aug;128(2):198–204. [PubMed]
- Clark LD, Hughes R, Nakashima EN. Behavioral effects of marihuana. Experimental studies. Arch Gen Psychiatry. 1970 Sep;23(3):193–198. [PubMed]
- Melges FT, Tinklenberg JR, Hollister LE, Gillespie HK. Marihuana and temporal disintegration. Science. 1970 May 29;168(935):1118–1120. [PubMed]
- Waskow IE, Olsson JE, Salzman C, Katz MM. Psychological effects of tetrahydrocannabinol. Arch Gen Psychiatry. 1970 Feb;22(2):97–107. [PubMed]
- Jones RT, Stone GC. Psychological studies of marijuana and alcohol in man. Psychopharmacologia. 1970 Aug 19;18(1):108–117. [PubMed]
- Goode E. Drug use and grades in college. Nature. 1971 Nov 26;234(5326):225–227. [PubMed]
Cerebellar activity and disturbed time sense after THC
Brain Res. 1998 Jun 29;797(2):183-9.
Department of Psychiatry and Radiology, Duke University Medical Center, Box 3972, Durham, NC 27710, USA. [email protected]
Because marijuana continues to be the most commonly used illicit drug, its effects on the brain function are of major interest. We utilized positron emission tomography (PET) and magnetic resonance imaging (MRI) to study the effects of delta-9-tetrahydrocannabinol (THC) infusion on brain blood flow and its behavioral correlates in 46 volunteers. Consistent with previous reports, there was a significant increase in cortical and cerebellar blood flow following THC, but not all subjects showed this effect. Those who showed a decrease in cerebellar CBF also had a significant alteration in time sense. The relationship between decreased cerebellar flow and impaired time sense is of interest because the cerebellum has been linked to an internal timing system.
Copyright 1998 Elsevier Science B.V. All rights reserved.
- [PubMed - indexed for MEDLINE]
Marijuana Effects On Human Forgetting Functions
J Exp Anal Behav. 2005 January; 83(1): 67–83.
Influence of cannabis use trajectories, grade repetition and family background on the school-dropout rate at the age of 17 years in France
Legleye S, Obradovic I, Janssen E, Spilka S, Le Nézet O, Beck F
Eur J Public Health 2010 Apr; 20(2):157-63.
BACKGROUND: Research has shown that cannabis use contributes to school dropout, but few studies have distinguished the age at onset of use from the age at progression to daily use neither their interaction with grade repetition.
METHODS: This study is based on a French representative cross-sectional survey (N = 29,393 teenagers aged 17 years) and uses retrospective data. The influence of drug-use patterns <16 years of age on school-dropout rates (5.3%) are modelled with logistic regressions among boys and girls.
RESULTS: The main factors associated with dropout were a low family socio-economic status, early grade repetition, single-parent families and daily tobacco smoking (ORa > or = 2.6). The link with the move to daily cannabis use was more evident when it occurred <14 years of age (ORa = 2.05 for boys and 3.41 for girls) rather than at > or =15 years (ORa = 1.45 for both sexes). The onset of cannabis use was not significant when occurring <14 years of age, but was linked to school attainment when occurring at age 15-16 years (ORa = 0.80 for boys and 0.64 for girls). Results are similar for alcohol use. Repeating a grade before beginning to use cannabis increased the dropout rates compared with the opposite sequence. Girls were more affected by early grade repetition and by early and daily cannabis use.
CONCLUSION: Cannabis use is rarely a trigger for grade repetition but can have either damaging or positive effects on school attainment depending of the level of use. Positive social competence reflected by peer initiation should be investigated to understand this paradoxical effect.
Is moderate substance use associated with altered executive functioning in a population-based sample of young adults?
|Piechatzek M, Indlekofer F, Daamen M, Glasmacher C, Lieb R, Pfister H, Tucha O, Lange KW, Wittchen HU, Schütz CG|
|Institution||Ludwig Maximilian University, Munich, Germany.|
|Source||Hum Psychopharmacol 2009 Dec; 24(8):650-65.|
|Abstract||BACKGROUND: Substance use (SU) has been linked with impaired cognitive functioning. Evidence comes mainly from clinical studies or studies examining heavy users. Though, the majority of users are not involved in heavy use. This study investigates the association between moderate use and cognition in a population-based sample. |
METHODS: A total of 284 young adults with ecstasy, cannabis or alcohol use and a control group were sampled from the EDSP database for participation in the Munich Assessment of Young Adults (MAYA) study. Subjects completed a comprehensive battery of neuropsychological tests (executive functions, working memory and impulsivity). Multiple linear regression models were conducted to examine the relationship between use and cognitive performance.
RESULTS: Increased ecstasy consumption was associated with increased error-proneness (Stroop task, CANTAB ID/ED-shift, spatial working memory). More frequent cannabis use and more extensive alcohol consumption were associated with a higher degree of impulsiveness.
CONCLUSIONS: Based on mild to moderate SU, little indication of differences in executive functioning was found. For ecstasy use, an increased error-proneness was revealed. The subtle differences in relatively young individuals warrant further investigation in prospective long-term studies to identify subjects at risk, and to examine effects of prolonged patterns of use on executive functioning.
|Pub Type(s)||Journal Article|
Research Support, Non-U.S. Gov't
Heavy cannabis use without long-term effect on global intelligence
Canadian researcher compared the intelligence quotient (IQ) of 15 current heavy users of cannabis, 9 current light users, 9 former regular users and 37 non-users in a group of 70 young people. Participants had been followed since birth and now were 17-20 years old.
Current marijuana use was significantly correlated in a dose-related fashion with a decline in IQ when compared to the IQ measured at age 9-12. In current heavy users the IQ showed a decrease of 4.1 points, compared to gains in IQ points for light current users (5.8), former users (3.5) and non-users (2.6).
The authors concluded that current cannabis use "had a negative effect on global IQ score only in subjects who smoked 5 or more joints per week" and that "marijuana does not have a long-term negative impact on global intelligence."
Former users had smoked marijuana regularly in the past but not for at least 3 months. Current heavy use was defined as smoking at least 5 joints per week. Light users smoked less than 5 joints per week.
(Source: Fried P, et al. Current and former marijuana use: preliminary findings of a longitudinal study of effects on IQ in young adults. CMAJ 2002;166(7):887-91)
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 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
|Minority (African-American or Hispanic)||490||37|
|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. 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 & use of drugs||0.25||0.19|
|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. 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 that are more severe among women and heavy users. 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. 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, 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.
Zec RF. The neuropsychology of aging. Exp Gerontol 1995;30:431-42
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.
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.
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.
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.
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.
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.
Shaie KW, The course of adult intellectual development. Am Psychol 1994;49:304-13.
Grimby A, Berg S. Stressful life events and cognitive functioning in late life. Aging (Milano) 1995;7:35-9.
Hultsch DF, Hertzog C, Small BJ, et al. Short-term longitudinal change in cognitive performance in later life. Psychol Aging 1992;7:571-84.
Johansson B, Zarit SH, Berg S. Changes in cognitive functioning of the oldest old. J Gerontol 1992;47:P75-80.
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.
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.
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.
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.
Hebert LE, Scherr PA, Beckett LA, et al. Age-specific incidence of Alzheimer's disease in a community population. JAMA 1995;273:1354-9
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.
Shen YC, Li G, Li YT, et al. Epidemiology of age-related dementia in China. Chin Med J (Engl) 1994;107:60-4.
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.
Morgan K, Lilley JM, Arie T, et al. Incidence of dementia in a representative Bntish sample. Br J Psychiatry: 1993;163:467-70.
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.
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.
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.
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.
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.
Lee PN. Smoking and Alzhetmer's disease: a review of the epidemiological evidence. Neuroepidemioloy 1994;13:131-44.
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.
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.
Gilleard CJ. Education and Alzheimer's disease: a review of recent international epidemiological studies. Aging Ment Health 1997;1:33-46.
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.
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.
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.
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.
Pope HG Jr, Yurgelun-Todd D. The residual cognitive effects of heavy marijuana use in college students. JAMA 1996;275: 521-7.
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.
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.
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.
Eaten WW, Kessler LG, eds. Epidemiologic field methods in psychiatry: the NIMH Epidemiologic Catchment Area program. Orlando, FL: Academic Press, Inc. 1985.
McCullagh P. Regression models for ordinal data (with discussion). J R Stat Soc [B] 1980;42:109-42.
Rebok G, Brandt J, Folstein M. Longitudinal cognitive decline in patients with Alzheimer's disease. J Geriatr Psychiatry Neurol 1990;3:91-7.
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
Differential Effects of THC or CBD-rich Cannabis Extracts on Working Memory in Rats
Paola Fadda, Lianne Robinson, Walter Fratta, Roger G. Pertwee, Gernot Riedel
Department of Biomedical Science, School of Medical Sciences, College of Life Sciences and Medicine, University of Aberdeen,
Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK
B.B. Brodie Department of Neuroscience and Center of Excellence ‘‘Neurobiology of Dependence’’, University of Cagliari,
Cittadella Universitaria Monserrato, Cagliari, Italy
Received 22 April 2004; received in revised form 29 June 2004; accepted 17 August 2004
Cannabinoid receptors in the brain (CB1) take part in modulation of learning, and are particularly important for working and short-term memory. Here, we employed a delayed-matching-to-place (DMTP) task in the open-field water maze and examined
the effects of cannabis plant extracts rich in either D9-tetrahydrocannabinol (D9-THC), or rich in cannabidiol (CBD), on spatial working and short-term memory formation in rats. D9-THC-rich extracts impaired performance in the memory trial (trial 2) of
the DMTP task in a dose-dependent but delay-independent manner. Deficits appeared at doses of 2 or 5 mg/kg (i.p.) at both 30 s and 4 h delays and were similar in severity compared with synthetic D9-THC. Despite considerable amounts of D9-THC present,
CBD-rich extracts had no effect on spatial working/short-term memory, even at doses of up to 50 mg/kg. When given concomitantly, CBD-rich extracts did not reverse memory deficits of the additional D9-THC-rich extract. CBD-rich extracts also did
not alter D9-THC-rich extract-induced catalepsy as revealed by the bar test. It appears that spatial working/short-term memory is not sensitive to CBD-rich extracts and that potentiation and antagonism of D9-THC-induced spatial memory deficits is dependent on the ratio between CBD and D9-THC....read full pdf
# 2004 Elsevier Ltd. All rights reserved.
Keywords: Cannabis extract; THC; Cannabidiol; Working memory;
Marijuana does not dent IQ permanently
April 2002 by Alison Motluk
Smoking marijuana does not have a long-term effect on intelligence, say researchers in Canada who have followed volunteers from before birth to early adulthood.
Heavy pot smokers did experience a dip in their intelligence quotient (IQ). But people who had once smoked heavily and then given up were right back up to normal, the study found. Light smokers appeared no different from non-smokers.
What the researchers do not know is if decades of pot-smoking could have a more lasting impact. Looking at long-term users in their 30s or 40s could show different results, admits Peter Fried, at Carleton University in Ottawa, who led the study. "Perhaps the nervous system isn't as flexible then," he says.
"You can't argue with what they're saying," says William Campbell, President of the Canadian Society of Addiction Medicine. "It doesn't surprise me or disappoint me."
Fried and his team followed 70 middle-class kids from the womb. IQ tests were taken at around age 10 and then again between 18 and 20. As adults, the participants were asked about a range of behaviours, including pot smoking. They also under went urine analysis to check their answers.
At the time of the second questionnaire, nine had been heavy users in the past but had not smoked for over three months. Fifteen were still smoking cannabis heavily - at least five joints a week and nine were current light users who smoked a few joints weekly. The rest of the volunteers had never been regular users, so had either never smoked marijuana or had done so less than once a week.
Only the heavy current users had experienced a decline in their IQ scores over the 10-year period - about four points. Light users, former users and abstainers all saw their IQ scores climb between two and six points.
Fried concedes that while IQ may be spared, memory and attention may be harder hit and is examining the effect now: "The most-often stated reason for quitting was they felt their short-term memory was affected."
Canadian Medical Association Journal (vol 166, p 887)
Current and former marijuana use: preliminary findings of a longitudinal study of effects on IQ in young adults
CMAJ • April 2, 2002; 166 (7)
© 2002 Canadian Medical Association or its licensors
Background: Assessing marijuana's impact on intelligence quotient (IQ) has been hampered by a lack of evaluation of subjects before they begin to use this substance. Using data from a group of young people whom we have been following since birth, we examined IQ scores before, during and after cessation of regular marijuana use to determine any impact of the drug on this measure of cognitive function.
Methods: We determined marijuana use for seventy 17- to 20-year-olds through self-reporting and urinalysis. IQ difference scores were calculated by subtracting each person's IQ score at 9–12 years (before initiation of drug use) from his or her score at 17–20 years. We then compared the difference in IQ scores of current heavy users (at least 5 joints per week), current light users (less than 5 joints per week), former users (who had not smoked regularly for at least 3 months) and non-users (who never smoked more than once per week and no smoking in the past two weeks).
Results: Current marijuana use was significantly correlated (p < 0.05) in a dose- related fashion with a decline in IQ over the ages studied. The comparison of the IQ difference scores showed an average decrease of 4.1 points in current heavy users (p < 0.05) compared to gains in IQ points for light current users (5.8), former users (3.5) and non-users (2.6).
Interpretation: Current marijuana use had a negative effect on global IQ score only in subjects who smoked 5 or more joints per week. A negative effect was not observed among subjects who had previously been heavy users but were no longer using the substance. We conclude that marijuana does not have a long-term negative impact on global intelligence. Whether the absence of a residual marijuana effect would also be evident in more specific cognitive domains such as memory and attention remains to be ascertained.
Marijuana produces well-documented, acute cognitive changes that last for several hours after the drug has been ingested. Whether it produces cognitive dysfunction beyond this period of acute intoxication is much more difficult to establish. Approaches to investigating long-lasting effects include clinical assessment of long-term users, observations of subcultures in countries where long-term daily use of cannabis has been the cultural norm for decades and marijuana administration studies in which subjects with a history of use ranging from infrequent to extensive are given the drug in controlled laboratory settings after various periods of abstinence. As discussed in several reviews of the literature, the findings have been equivocal.
Most studies that examined heavy marijuana users for possible cognitive dysfunction lasting beyond the acute intoxication period assessed subjects after an abstinence period of only a day or two. The fact that cannabinoid metabolites have been detected in the urine of long-term marijuana users after weeks or even months of abstinence compromises the interpretation of these studies. To account for potential pre-existing differences between users and non-users, studies have typically matched the comparison group with the user group in terms of non-marijuana variables.Suggestions for improving study designs have emphasized both the need for comparison groups to be as similar as possible to the drug-using group and the need for a prolonged abstinence period. The most desirable procedure would involve a longitudinal, prospective design in which cognitive measures were available for all non-using and using subjects before and after marijuana consumption had been initiated by the users.
The Ottawa Prenatal Prospective Study (OPPS), underway since 1978, satisfies these criteria. This study permits both within-subject and between-subject comparisons among relatively low-risk non-users and users before, during and after quitting regular marijuana use. The primary objective of the OPPS is the neuropsychologic assessment of children exposed prenatally to marijuana or cigarettes. Women who used and did not use marijuana and cigarettes volunteered to participate during their pregnancy, and their children, now between the ages of 17 and 20 years, have been assessed since birth. Details of the recruitment of the largely middle-class families, the assessment procedures and the findings for the children from birth to adolescence have been summarized elsewhere.
The objectives of the current study were as follows: to determine if current, regular marijuana use is predictive of decline in IQ from pre-usage levels, to determine if a differential effect on IQ occurs with heavy versus light current, regular marijuana use, and to determine if any IQ effects persist after subjects cease using marijuana for at least 3 months.
A potential pool of 74 young adults with urinalysis results, self-reports of marijuana use and a broad measure of IQ obtained at both a preteen (9–12 years) and a young adult (17–20 years) assessment was available. Two subjects with inconsistencies between the self- report of marijuana use and the urine screening results were excluded, as were one subject who tested positive for cocaine and another who was taking methylphenidate. Consequently, the final sample comprised 70 subjects whose self-report of marijuana use and absence of hard drug use had been validated by urinalysis results.
During the preteen period and before initiation of marijuana use, IQ was measured by means of the Wechsler Intelligence Scale for Children-III (WISC). When the subjects were young adults, IQ was evaluated with the Wechsler Adult Intelligence Scale-III (WAIS). The outcome variable for the examination of potential marijuana effects was an IQ difference score, derived by subtracting the preteen WISC IQ score from the young adult WAIS IQ score. Thus a positive difference score reflects an increase in IQ over the approximately 10-year period, whereas a negative score reflects a decrease.
Marijuana use was determined by 2 procedures that were part of an extensive neuropsychologic battery given to the 17– to 20-year-olds. The first consisted of a questionnaire completed by the subject, which asked for details of current and past marijuana use, as well as other drug use. The second was a urine sample analyzed for the presence of cannabinoids, amphetamines, opiates, cocaine and cotinine (a metabolite of nicotine). All metabolite concentrations were adjusted for creatinine to control for urine dilution. Although these procedures did not assess the strength of the marijuana used by the OPPS subjects, an estimate was suggested by Health Canada's analysis of marijuana seized by police between 1996 and 1999, which revealed an average of 5% to 6% tetrahydrocannabinol (THC).
Marijuana measures treated as continuous variables were self-report of mean number of joints currently smoked per week, self-report of length of time (months) that marijuana had been smoked and total estimated number of joints smoked (mean number of joints smoked per week multiplied by number of weeks of use). The mean number of joints currently smoked per week was also treated as a categorical variable, as follows: the subjects were grouped as light current regular users, heavy current regular users, former regular users or non-users.
Categorization of the current marijuana users as light or heavy users was based on both the self-report and the urinalysis data. The urinalysis data were bimodally distributed: 11 subjects had cannabinoid to creatinine ratios between 4 and 54 ng/mg, and 13 subjects had ratios between 147 and 705 ng/mg. These 2 groups of subjects were used to validate the categorization based on self-reports. Defining heavy regular use as at least 5 joints per week (n = 15) and light regular use of any amount less than 5 joints at least once a week (n = 9) optimized concordance with the bimodal urine division as indicated by 2 analysis. Eight (73%) of the 11 subjects with the lower metabolite values smoked fewer than 5 joints per week, and 12 (92%) of the 13 subjects with the higher metabolite values smoked an average of 5 or more joints per week (p = 0.001).
Of the 70 subjects, 37 were non-users who had never used marijuana regularly (where regular use was defined as at least once a week) and who had not used any marijuana in the past 2 weeks; 9 were former users who had smoked marijuana regularly in the past but had not smoked for at least 3 months before the young adult assessment; 9 were light current users; and 15 were heavy current users.
The assessments were conducted in laboratories at Carleton University, Ottawa. Given that the testing sessions commenced in the early morning and that all subjects reported no use of marijuana on the day of testing, it is unlikely that the subjects were assessed while in an acute state of intoxication.
The validity of self-reporting for current marijuana use was examined with 2 approaches. The initial selection of the 70 subjects involved a criterion of concordance between self-reports of marijuana use and urine screening results (see above). The second measure of concordance was a high correlation between reported current marijuana use and the cannabinoid to creatinine ratio found with urinalysis (r = 0.70, p < 0.001). Although self-reports of earlier use could not be directly confirmed pharmacologically, their reliability is enhanced by the validity of the self-reporting for current marijuana use.
In examining the relation between marijuana use and IQ difference scores, we considered a variety of potentially confounding variables, including variables related to socioeconomic status, such as family income and parental education; the subject's education level (number of years of education at the time of the young adult assessment); age and sex of the subject; mother's age at the time of the subject's birth; maternal use of cigarettes, marijuana and alcohol during pregnancy; and the subject's use of tobacco and alcohol and exposure to secondhand marijuana smoke. In the subsequent analyses, we controlled for any potential confounding factor that was related to both the marijuana independent variable (at ? = 0.1) and the IQ difference score (at ? = 0.05).
Hierarchical regression (a statistical approach to measure the impact of marijuana use after considering potential confounders) was used to examine the predictive relation of quantity (both mean number of joints per week and total joints over lifetime) and duration (period of use) of current marijuana use to the IQ difference score. Differential effects on the IQ difference score of light current use, heavy current use and former use as contrasted to non-use were examined with Dunnett's 2-sided multiple comparison procedure with analysis of variance (ANOVA) and analysis of covariance (ANCOVA) when required to control for confounding variables.
Analyses in which number of joints smoked per week was used both as a continuous and as a categorical variable revealed significant associations of this variable with the IQ difference score.
When number of joints smoked per week was treated as a continuous variable, regression analyses revealed a significant negative association with the IQ difference score (r = –0.24, p < 0.05) after accounting for potentially confounding variables. In these analyses, no predictive relation with the IQ difference score was found for the self-reported period of marijuana use or the estimated total number of joints smoked.
For analyses in which number of joints smoked per week was treated as a categorical variable, ANOVA with Dunnett's procedure indicated that the mean IQ difference score for the heavy current user group was significantly different from that for non-users (–4.0 v. 2.6, p < 0.05), whereas no significant differences were evident in comparisons with the light current users and former users (5.8 v. 2.6 and 3.5 v. 2.6 respectively) (Table 1). The characteristics of the 4 groups (light current users, heavy current users, former users and non-users) are presented in Table 1. Of particular importance to the present study is the fact that preteen IQ, assessed before marijuana use, did not differ across the groups. Although some characteristics did differ across the 4 groups (such as father's and mother's education), none of these was associated with the IQ difference score; therefore, they were not used as covariates.
Although there was no overall difference in IQ difference score between former users and non-users, a subgroup of former users, those who had used at least 5 joints per week (heavy use), was analyzed separately; again, there was no significant difference relative to non-users (t-test, p = 0.7). This lack of a negative impact among the former heavy users is striking, as they had smoked, on average, an estimated 5793 joints over 3.2 years (mean of 37 joints per week); in contrast, the current heavy users had smoked, on average, an estimated 2386 joints over 3.1 years (mean of 14 joints per week).
In the present work, the use of commensurable IQ measures obtained before and after initiation of marijuana use permitted examination of the consequences of marijuana use in the context of pre-drug performance. Of all the marijuana and non-marijuana variables considered, only the quantity of current marijuana use, in terms of number of joints smoked per week, was negatively related to change in IQ from preteen to young adult. Not associated with change in IQ were duration of marijuana use, the total quantity of marijuana used and former use of marijuana. In addition, variables such as socioeconomic status (family income and parental education), age of mother at time of subject's birth, subject's prenatal exposure to drugs (nicotine, marijuana and alcohol), preteen IQ score, age, sex, academic history, other drug use and passive marijuana exposure were not predictive of change in IQ score.
The IQ difference score for the heavy current users differed from that for non-users, but no such differences were apparent between light current users and non-users. The clinical significance for an individual of such an effect on IQ scores is difficult to ascertain, but the impact on society might be substantial. IQ scores are considered normally distributed, with a mean of 100 and a standard deviation of 15, and it is therefore estimated that 2.3% of individuals will score 70 or below (2 standard deviations [SD]), and 6.7% will score 77.5 or below (1.5 SD) on global intelligence tests. These are cutoff points at which intervention and special education have typically been provided. Any factors in a population that result in a 4-point decrease in IQ, as was found with the heavy current marijuana users, would increase to 5.5% the proportion of individuals with an IQ of 70 or below and to 11.0% those with an IQ of 77.5 or below. A corresponding decrease in proportions would be expected on the other end of the distribution (people with higher IQ scores). For comparison, an IQ decrement of 5 points has been observed in children exposed prenatally to 3 alcoholic drinks per day, of 3.75 points in offspring exposed prenatally to cocaine and of 2.6 points after low lead exposure.
The IQ deficit among heavy current users in the present study likely reflected residue of the drug in their bodies. Assuming use of at least 5 joints per week by subjects in this group and given the elimination half-life of THC in the plasma of long-term marijuana users, such quantities and patterns of smoking are likely to result in an accumulation of THC in the body.
Although the heavy current users experienced a decrease in IQ score, their scores were still above average at the young adult assessment (mean 105.1). If we had not assessed preteen IQ, these subjects would have appeared to be functioning normally. Only with knowledge of the change in IQ score does the negative impact of current heavy use become apparent.
There were no differences in IQ score at the preteen assessment among the future groups of users and the future non-users. This finding suggests that, at least in a low-risk, white, predominantly middle-class sample, IQ score before any marijuana use is not a predictor of future marijuana use.
We investigated the possibility of a longer-lasting deficit, perhaps representing a neurotoxic consequence on the central nervous system (CNS), using data for the former users. The mean IQ difference score for the former users did not differ significantly from that for the non-users, which suggests a lack of long-term effects. Similarly, there was no negative impact on IQ difference among former heavy users relative to non-users (in contrast to the situation for current heavy users). This lack of a long-lasting negative impact suggests the absence of any CNS alteration as reflected by global IQ performance.
Both the negative effects of use of at least 5 joints weekly and the lack of long-term effects found in this study should be interpreted cautiously. The relatively small number of subjects for whom data were available, the length of time that the drug was used, the estimated total number of joints smoked and the young age of the subjects may serve, individually or collectively, to moderate effects. Smoking at least 5 joints weekly should not be interpreted as a definitive threshold, as subjects were at low risk for other factors that could have a negative synergistic effect on IQ score. It is also important to emphasize that broad intellectual functioning may be less vulnerable to the consequences of marijuana use than more specific cognitive domains, such as attention and memory.
The popularity of marijuana among youth has been increasing during the past 4 years,and pressure on governmental agencies to assess the medical uses of the drug and to reassess the legal status of the drug has been growing. These trends emphasize the need to continue investigating the cognitive consequences of both current and previous marijuana use.
This article has been peer reviewed.
Contributors: Peter Fried has been the Director of Ottawa Prenatal Prospective Study since its inception. He designed the protocol for the study, as well as the questionnaire that was administered to the subjects. He also made a substantial contribution to the writing and editing of the manuscript. Barbara Watkinson was the primary statistical analyst and made a substantial contribution to the writing and editing of the manuscript. Deborah James assisted in the analyses and made a substantial contribution to the writing and editing of the manuscript. Robert Gray was in charge of data management and extrapolated the data reported in this manuscript. He also assisted in writing the manuscript.
Acknowledgements: This work was supported by a grant to Peter Fried from the National Institute on Drug Abuse, Washington, DC. The authors thank Heather Linttell for her testing of the subjects over the past 15 years and all the families who have participated in the Ottawa Prenatal Prospective Study for the past 2 decades. The urinalysis was carried out under the direction of Dr. Sherry Perkins, Head, Division of Biochemistry, Ottawa Hospital, Ottawa.
Competing interests: None declared.
- Hall W, Solowij N, Lemon J. The health and psychological consequences of cannabis use. Canberra: Australian Government Publishing Service; 1994.
- Klonoff H. Acute psychological effects of marijuana in man, including acute cognitive, psychomotor and perceptual effects on driving. In: Fehr KO, Kalant H, editors. Cannabis and health hazards. Toronto: Addiction Research Foundation; 1983. p. 433-74.
- Beardsley PM, Kelly TH. Acute effects of cannabis on human behavior and central nervous system functions. In: Kalant H, Corrigall W, Hall W, Smart R, editors. The health effects of cannabis. Toronto: Addiction Research Foundation; 1999. p. 129-69.
- Kolansky H, Moore RT. Toxic effects of chronic marihuana use. JAMA 1972; 222:35-41.
[Abstract/Free Full Text]
- Lundqvist T. Specific thought patterns in chronic cannabis smokers observed during treatment. Life Sci 1995;56:2141-4.[Medline]
- Schwartz RH, Gruenewald PJ, Klitzner M, Fedio P. Short-term memory impairment in cannabis-dependent adolescents. Am J Dis Child 1989;143:1214-9.
[Abstract/Free Full Text]
- Rubin V, Comitas L. Psychological assessment. In: Rubin V, Comitas L, editors. Ganja in Jamaica: a medical anthropological study of chronic marijuana use. The Hague: Mouton; 1975. p. 111-9.
- Carter WE, Coggins W, Doughty PL. Cannabis in Costa Rica: a study of chronic marihuana use. Philadelphia: Institute for the Study of Human Issues; 1980.
- Varma VJ, Malhotra AK, Dang R, Das K, Nehra R. Cannabis and cognitive functions: a prospective study. Drug Alcohol Depend 1988;21:147-52.[Medline]
- Pope HG Jr, Yurgelun-Todd D. The residual cognitive effects of heavy marijuana use in college students. JAMA 1996;275:521-7.
[Abstract/Free Full Text]
- Jones RT, Benowitz N. The 30 day trip — clinical studies of cannabis tolerance and dependence. In: Braude MC, Szara S, editors. The pharmacology of marijuana. New York: Raven Press; 1976. p. 627-42.
- Chait LD. Subjective and behavioral effects of marijuana the morning after smoking. Psychopharmacology (Berl) 1990;100:328-33.
- 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.[Medline]
- Solowij N. Long-term effects of cannabis on the central nervous system. In: Kalant H, Corrigall W, Hall W, Smart R, editors. The health effects of cannabis. Toronto: Addiction Research Foundation; 1999. p. 195-265.
- Block RI, Farnham S, Braverman S, Noyes R Jr, Ghoneim MM. Long-term marijuana use and subsequent effects on learning and cognitive functions related to school achievement: preliminary study. NIDA Res Monogr 1990; 101: 96-111.[Medline]
- Block RI, Ghoneim MM. Effects of chronic marijuana use on human cognition. Psychopharmacology (Berl) 1993;110:219-28.
- Ellis GM Jr, Mann MA, Judson BA, Schramm NT, Taschian A. Excretion patterns of cannabinoid metabolites after last use in a group of chronic users. Clin Pharmacol Ther 1985;38:572-8.[Medline]
- Cridland JS, Rottanberg D, Robins AH. Apparent half-life of excretion of cannabinoids in man. Hum Toxicol 1983;2:641-4.[Medline]
- Heustis MA, Mitchell JM, Cone EJ. Detection times of marijuana metabolites in urine by immunoassay and GC–MS. J Anal Toxicol 1995;19:443-9.[Medline]
- Millsaps CL, Azrin RL, Mittenberg W. Neuropsychological effects of chronic cannabis use on the memory and intelligence of adolescents. J Child Adolesc Subst Abuse 1994;3:47-55.
- Fried PA. Behavioral evaluation of the older infant and child. In: Slikker W Jr, Chang LW, editors. Handbook of developmental neurotoxicology. San Diego: Academic Press; 1998. p. 469-86.
- Fried PA, Smith AM. A literature review of the consequences of prenatal marijuana exposure. An emerging theme of deficiency in aspects of executive function. Neurotoxicol Teratol 2001;23:1-11.
- Wechsler D. Wechsler Intelligence scale for Children. 3rd ed. New York: The Psychological Corporation; 1991.
- Wechsler D. Wechsler Adult Intelligence Scale. 3rd ed. San Antonio (TX): The Psychological Corporation; 1997.
- Jacobson SW, Jacobson JL. Prospective, longitudinal assessment of developmental neurotoxicity. Environ Health Perspect 1996;104:275-83.
- Dunnett CW. A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 1955;50:1096-121.
- Lester BM, LaGasse LL, Seifer R. Cocaine exposure and children: the meaning of subtle effects. Science 1998;282:633-4.
[Free Full Text]
- Streissguth AP, Barr HM, Sampson PD, Darby BL, Martin DC. IQ at age 4 in relation to maternal alcohol use and smoking during pregnancy. Dev Psychol 1989;25:3-11.
- Bellinger DC. Interpreting the literature on lead and child development: the neglected role of the "experimental system." Neurotoxicol Teratol 1995;17:201-12.[Medline]
- Johansson E, Agurell S, Hollister LE, Halldin MM. Prolonged apparent half-life of D1-tetrahydrocannabinol in plasma of chronic marijuana users. J Pharm Pharmacol 1988;40:374-5.[Medline]
- Martin BR, Cone EJ. Chemistry and pharmacology of cannabis. In: Kalant H, Corrigal W, Hall W, Smart R, editors. The health effects of cannabis. Toronto: Addiction Research Foundation; 1999. p. 21-68.
- Ogborne AC, Smart RG, Adlaf EM. Self-reported medical use of marijuana: a survey of the general population. CMAJ 2000:162;1685-6. Available: www.cma.ca/cmaj/vol-162/issue-12/1685.htm
- Johnston LD, O'Malley PM, Bachman JG. The Monitoring the Future national survey results on adolescent drug use: overview of key findings, 2000. Bethesda (MD): National Institute on Drug Abuse; 2001. NIH Publ No. 01-4923. Available: http://monitoringthefuture.org/pubs/monographs/overview2000.pdf (accessed 20 Feb 2002)
- Marijuana: federal smoke clears, a little [editorial] CMAJ 2001;164:1397. Available: www.cma.ca/cmaj/vol-164/issue-10/1397.asp
[Free Full Text]
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