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Official websites use. Share sensitive information only on official, secure websites. First study to triangulate evidence using observational and genetic epidemiological approaches. Patterns of tobacco and cannabis use were derived using longitudinal latent class analysis. Mendelian randomization was used to examine whether associations were causal. Although studies have examined the association between tobacco and cannabis use in adolescence with subsequent cognitive functioning, study designs are usually not able to distinguish correlation from causation. Separate patterns of tobacco and cannabis use were derived using longitudinal latent class analysis based on measures assessed on five occasions from age 13—18 in a large UK population cohort Avon Longitudinal Study of Parents and Children. Cognitive functioning measures comprised of working memory, response inhibition, and emotion recognition assessed at 24 years of age. Mendelian randomization was used to examine the possible causal relationship between smoking initiation, lifetime cannabis use and cognitive functioning. We found evidence of a relationship between tobacco and cannabis use and diminished cognitive functioning for each of the outcomes in the observational analyses. Mendelian randomization analyses were imprecise and did not provide additional support for the observational results. There was some evidence to suggest that adolescent tobacco and cannabis use were associated with deficits in working memory, response inhibition and emotion recognition. Better powered genetic studies are required to determine whether these associations are causal. Tobacco and cannabis use during adolescence, when the brain is still developing and undergoing considerable structural and function changes De Bellis et al. The association between adolescent tobacco and cannabis use and subsequent cognitive functioning has received particular attention because certain cognitive functions e. Due to the prolonged neurodevelopmental period and the potential for the endocannabinoid and nicotinic cholinergic signalling systems to be involved in altering development Galve-Roperh et al. Nonetheless, there is still uncertainty regarding the nature of the association between tobacco and cannabis use and neurocognitive function. A recent review of prospective studies of the association between cannabis use and cognition in young people Gonzalez et al. However, studies often fail to control for neurocognitive measures prior to cannabis use Jacobus et al. A recent study Meier et al. Findings from two recent longitudinal studies of adolescents Castellanos-Ryan et al. The direction of association between tobacco and cognitive functioning is also unclear as there is a lack of epidemiological studies that have prospectively examined this relationship. Evidence from animal studies suggests that nicotine exposure may have more deleterious developmental effects during adolescence, when the brain is thought to be more vulnerable Slotkin, Furthermore, human studies suggest that nicotine has a more potent effect when consumed in late adolescence compared to in adulthood Azam et al. They also found diminished cognitive functioning in individuals who started starting smoking after 18 years of age. The literature is further complicated by the differential effects of acute, chronic, and withdrawal from chronic nicotine on cognitive functioning. Studies have reported beneficial effects of acute nicotine Heishman et al. In an effort to strengthen the evidence, we used data from the Avon Longitudinal Study of Parents and Children ALSPAC , a large UK prospective birth cohort, to investigate whether patterns of adolescent tobacco and cannabis use were prospectively associated with cognitive functioning at 24 years of age. Separate measures of tobacco and cannabis use were assessed on six occasions across adolescence allowing distinct classes of tobacco and cannabis use to be established. As young people do not initiate tobacco or cannabis at the same time Degenhardt et al. As a next step we used genetic variants that are separately associated with smoking initiation and lifetime cannabis use to perform Mendelian randomization MR to improve causal inference Lawlor et al. The aims were to investigate 1 whether separate patterns of tobacco smoking and cannabis use assessed between 13—18 years were associated with working memory, response inhibition, and emotion recognition assessed at age 24, and 2 whether tobacco use and cannabis use were associated with these cognitive outcomes using MR. Of these initial pregnancies, there was a total of 14, foetuses, resulting in 14, live births and 13, children who were alive at 1 year of age. When the oldest children were approximately 7 years of age, an attempt was made to bolster the initial sample with eligible cases who had failed to join the study originally. The total sample size for analyses using any data collected after the age of 7 years is therefore 15, pregnancies, resulting in 15, foetuses. Of this total sample 14, were alive at 1 year of age Boyd et al. Of these, offspring were invited to attend the year clinic assessment. A detailed overview of our study population, including attrition at the different measurement occasions is presented in Supplementary Material Fig. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following recommendations of the ALSPAC Ethics and Law Committee at the time. Consent for biological samples was collected in accordance with the Human Tissue Act Information on tobacco and cannabis use were collected on six occasions via questionnaire Q or during attendance at a study clinic C. Patterns of tobacco use have been described in detail elsewhere Howe et al. There was good agreement that a four-class solution was adequate in explaining the heterogeneity in tobacco based on model fit criteria see Table S1a. Patterns of cannabis use have been described in detail elsewhere Taylor et al. There was good agreement that a four-class solution was adequate in explaining the heterogeneity in cannabis use based on model fit criteria see Table S1b. Data collection for the online questionnaires was collected and managed by REDcap electronic data capture tools Harris et al. Further information on all three cognitive tasks is presented in Supplementary material. The N -back task 2-back condition was used to assess working memory. The N -back task Kirchner, is widely used to measure working memory. High scores on number of hits indicating more accurate identification, while high scores on false alarms indicating less accurate identification were examined as secondary outcomes. The Stop Signal Task Logan et al. The task consisted of trials, which included a ratio of trials without stop signals to trials with stop signals. Mean response times were calculated. An estimate of stop signal reaction time SSRT was calculated using the median of the inhibition function approach Band et al. SSRT used as the primary outcome as it is a reliable measure of inhibitory control, with shorter reaction times indicating faster inhibition. SSRT data were available for participants. Individual Stop Signal indices i. Emotion recognition was assessed using a six alternative forced choice 6AFC emotion recognition task Penton-Voak et al. In each trial, participants were presented with a face displaying one of six emotions: anger, disgust, fear, happiness, sadness, or surprise. Participants were required to select the descriptor that best described the emotion that was present in the face, using the computer mouse. Emotion intensity varied across 8 levels within each emotion from the prototypical emotion to an almost neutral face. Each individual stimulus was presented twice, giving a total of 96 trials. An overall measure of emotion recognition the number of facial emotions accurately identified was used as the primary outcome. Each of the individual emotions were examined as secondary outcomes. Confounders comprised of established risk factors for cognitive functioning that could plausibly have a relationship with earlier substance use. Potential confounders included: income, maternal education, socioeconomic position, housing tenure, sex, and maternal smoking during first trimester in pregnancy. Finally, a measure of alcohol use asking whether they had ever had a whole drink of alcohol was collected at age 13 years up to the first assessment of smoking and cannabis use. Further information is presented in Supplementary Material. Different tobacco and cannabis phenotypes were used across different analytic methods. Tobacco and cannabis class membership was related to covariates using the Bolck-Croon-Hagenaars Bolck et al. This approach uses the weights derived from the latent classes to reflect measurement error in the latent class variable. Linear regression was used to examine the association between the cognitive outcomes and latent class membership controlling for the confounding variables. Analyses were carried out using Mplus 8. Missing data was dealt with in three steps. For a detailed description of missingness at each timepoint see Tables S2a and S2b. Next, multiple imputation was based on participants for both tobacco and cannabis models who had information on at least one of the cognitive outcomes. The imputation model based on datasets contained performance on all of the cognitive tasks, all measures of tobacco and cannabis use, and potential confounding variables, as well as a number of auxiliary variables known to be related to missingness e. Finally, inverse probability weighting was used where estimates of prevalence and associations were weighted to account for probabilities of non-response to attending the clinic. See Table S3 for a detailed description of attrition for completing the cognitive assessments at age 24 years. See Tables S4a and S4b for a detailed description of confounding factors associated with tobacco and cannabis use class membership. See Table S5 for a detailed description of sample characteristics. Our aim was to triangulate the findings from the observational analyses with one- and two-sample MR analyses. However, due to insufficient power in the two-sample MR analyses, we will primarily focus on the one-sample MR results. Two-sample MR are still included as a set of sensitivity analyses as they allow us to conduct some of the pleiotropy robust methods e. Information on genotyping and quality control are presented in the Supplementary Material. One-sample MR analyses using two-stage least squares regression models with robust standard errors was used to examine the to examine the polygenic risk score constructed using genome-wide significant SNPs for smoking initiation SNPs Liu et al. Each of the cognitive outcomes were then regressed on the fitted values from the stage 1 for tobacco and cannabis use in the second stage. The three key assumptions in MR are 1 the genetic instrument is robustly associated with the exposure of interest; 2 confounders of the exposure-outcome association are not associated with the genetic instrument; and 3 the genetic instrument is not associated with the outcome other than through its association with the exposure; see Lawlor et al. Power calculations conducted for one-sample MR analyses using mRnd Brion et al. Two-sample MR analysis was used to test the hypothesised causal effect of smoking initiation and lifetime cannabis use on cognitive functioning. See Supplementary material for further details. Fully adjusted associations between patterns of tobacco use from 13—18 years and cognitive functioning outcomes at age 24 are presented in Table 1. All associations were supported by significant Wald test values indicating a significant difference between the groups. Results demonstrating various levels of adjustment are presented in the Supplementary Material Tables S6a-S6c. Smoking patterns from 13 to 18 years and cognitive functioning at age 24 fully adjusted models. In the secondary analyses, there was some evidence to suggest that early-onset regular tobacco smoking was associated with fewer correct hits on the N -back task in the fully adjusted models Table S7. There was evidence to suggest that late-onset regular tobacco users were associated with poorer Go and Stop accuracy in the fully adjusted models Table S8. Fully adjusted associations between patterns of cannabis use from 13—18 years and cognitive functioning outcomes at age 24 are presented in Table 2. Results demonstrating various levels of adjustment are presented in the Supplementary Material Tables S10ac. Patterns of cannabis use from 13 to 18 years and cognitive functioning at age 24 fully adjusted models. In the secondary analyses, there was evidence to suggest that early-onset cannabis users were associated with worse Go and Stop accuracy compared to non-cannabis users, in the fully adjusted models Table S There was no evidence of an association between specific response inhibition measures in the sensitivity analyses Table S Information testing whether the genetic instruments are associated with the confounders are presented in the Supplementary Material Tables S14a and S14b. Results from the one-sample MR provided little evidence to suggest that smoking initiation or lifetime cannabis use were causal risk factors for deficits in cognitive functioning Table 3. One-sample MR analyses of the effects of smoking initiation on cognitive functioning standardised coefficients. Overall, the two-sample MR methods provided some evidence to suggest that SNPs associated with smoking initiation and SNPs associated with lifetime cannabis use were a causal risk factor for deficits in cognitive functioning. This observational study provided evidence to suggest an association between tobacco and cannabis use across adolescence and subsequent cognitive functioning. Early- and late-onset regular tobacco smokers demonstrated poorer working memory and poorer ability to recognise emotions; while, early-onset regular tobacco smokers had slower ability to inhibit responses compared to non-tobacco smokers. Early-onset regular cannabis users had poorer working memory performance and slower ability to inhibit responses compared to non-cannabis users. Our results remained largely consistent when controlling for prior measures of substance use and cognition allowing for clear temporality between exposure and outcomes. Genetic analyses were imprecise and did not provide sufficient evidence for a possible causal association between smoking initiation and lifetime cannabis use and cognitive functioning in the ALSPAC sample. It is likely that these analyses were underpowered. To our knowledge, this is the first study to assess the relationship between separate tobacco and cannabis use in adolescents, and subsequent cognitive functioning using a combination of observational and genetic epidemiological approaches. Those who initiated regular use at earlier and later ages demonstrated poorer performance on the cognitive tasks. There was some evidence to suggest cannabis use with associated with emotion-specific impairments in emotion recognition. This is in line with previous research suggesting cannabis users may have poorer recognition of negative emotions Bossong et al. Our results also tentatively suggest that recognition deficits may be related to specific patterns of cannabis use, with different patterns in early- and late-onset use. The observational findings contribute to a literature of mixed findings regarding the direction of association between tobacco and cannabis exposure and subsequent cognition by suggesting that adolescent tobacco and cannabis use precede observed reductions in cognitive function. These findings support studies that have demonstrated effects may depend on the frequency, duration, and age at onset of use Boccio and Beaver, ; Castellanos-Ryan et al. Our study extends previous findings in a number of ways. First, the observational study was better powered than most of the previous studies as it used data from over participants providing information spanning birth to 24 years of age. Second, identifying heterogeneous patterns of tobacco and cannabis use across this crucial period allows individuals who follow markedly different developmental trajectories to be captured Chen and Kandel, ; Degenhardt et al. Third, the cognitive measures were assessed at a time when they are expected to have reached maturity in some individuals Davidson et al. Examining mature levels of cognitive functioning reduces the possibility that cognitive functioning is influencing earlier tobacco and cannabis use, effects that cannot be disentangled in purely cross-sectional studies. Further, our ability to control for earlier measures of cognitive functioning and substance use, prior to the baseline measures of tobacco and cannabis use helps to rule out the possibility of reverse causation. Fourth, our study sought to examine specificity in cognitive functioning, by using well-validated tests to probe different domains of cognitive functioning instead of focusing on general intelligence. Finally, we sought to triangulate our results by using one- and two-sample MR approaches to assess tobacco and cannabis use as causal risk factors for cognitive functioning. This approach can help to overcome the main sources of bias from classical observational approaches, by providing a more reliable estimate of the likely underlying causal relationship. There are limitations to this study that should be considered. We attempted to minimize the effect of drop-out by using multiple imputation, FIML, and inverse probability weighting which assume MAR missing patterns. Although it is not possible to test the MAR assumption, it was made more plausible as a number of SES variables were found to predict whether participants attended the clinic or not Table S1. Second, tobacco and cannabis use were self-reported. However, there is evidence to suggest that self-reported assessments are reliable and valid methods Boykan et al. Third, while the longitudinal approach for each substance used in this study has a number of advantages over using measures at a single timepoint, it was not possible to examine cannabis use without tobacco use as most cannabis users use cannabis in combination with tobacco Amos et al. We therefore cannot rule out the possibility that observed associations between cannabis use and cognitive functioning are exacerbated by the combined use of cannabis and tobacco. Fourth, different measures of tobacco and cannabis use for the observational and MR analyses were used. To our knowledge it is not currently possible to use a nominal exposure as was used in the observational analyses and consequently the effect sizes are not directly comparable. Fifth, it is likely that both the one- and two-sample MR analyses are underpowered. However, findings using weak instruments tend to bias findings towards the null in the two-sample setting and toward the outcome-risk association in the one-sample setting Davies et al. Sixth, the main limitation of one- and two-sample MR is that the quality of the pooled results in the GWAS consortia is dependent on the individual studies. Another limitation is that the same sample may contribute to both GWAS i. This will bias the MR estimate towards the observed estimate. However, as the MR found no clear evidence for an effect, this suggests it was not biased by overlapping samples. See Lawlor and colleagues Lawlor et al. Finally, it is possible that the direction of the association could work in both ways, that is, impairments in cognitive functioning may precede and increase the risk of developing tobacco and cannabis use Anokhin and Golosheykin, ; Castellanos-Ryan et al. Overall, there was observational evidence that adolescent tobacco and cannabis use were associated with subsequent cognitive functioning, highlighting impairments in a range of cognitive domains, including working memory, response inhibition and emotion recognition. Our findings lend support to the developmental vulnerability hypothesis, in that, tobacco and cannabis use in adolescence, when the brain is undergoing critical development, may have neurotoxic effects. Better powered genetically informed studies are required to determine whether these associations are causal. In order to rule out the possibility of deficient cognitive functioning preceding substance in adolescence, future research should use an equally robust approach to examine the alternate hypothesis. This study lends support to public health strategies and interventions aimed at reducing tobacco and cannabis exposure in young people. All authors have participated in the preparation of the manuscript and approve of its submission. CS is employed by Cambridge Cognition. MRM is co-director of Jericoe Ltd, which produces software for the assessment and modification of emotion recognition. We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. Drug Alcohol Depend. Find articles by Liam Mahedy. Find articles by Robyn Wootton. Find articles by Steph Suddell. Find articles by Caroline Skirrow. Find articles by Matt Field. Find articles by Jon Heron. Find articles by Matthew Hickman. 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