Limited psychological and social effects of lifetime cannabis use frequency: Evidence from a 30-year community study of 4,078 twins

BACKGROUND

Cannabis use is associated with outcomes like income, legal problems, and psychopathology. This finding rests largely on correlational research designs, which rely at best on statistical controls for confounding. Here, we control for unmeasured confounders using a longitudinal study of twins.

METHOD

In a sample of 4,078 American adult twins first assessed decades ago, we used cotwin control mixed effects models to evaluate the effect of lifetime average frequency of cannabis consumption measured on substance use, psychiatric, and psychosocial outcomes.

RESULTS

On average, participants had a lifetime cannabis frequency of about one to two times per month, across adolescence and adulthood. As expected, in individual-level analyses, cannabis use was significantly associated with almost all outcomes in the expected directions. However, when comparing each twin to their cotwin, which inherently controls for shared genes and environments, we observed within-pair differences consistent with possible causality in three of the 22 assessed outcomes: cannabis use disorder symptoms (βW-Pooled = .15, SE = .02, p = 1.7 × 10-22), frequency of tobacco use (βW-Pooled = .06, SE = .01, p = 1.2 × 10-5), and illicit drug involvement (βW-Pooled = .06, SE = .02, p = 1.2 × 10-4). Covariate specification curve analyses indicated that within-pair effects on tobacco and illicit drug use, but not cannabis use disorder, attenuated substantially when covarying for lifetime alcohol and tobacco use.

CONCLUSIONS

The cotwin control results suggest that more frequent cannabis use causes small increases in cannabis use disorder symptoms, approximately 1.3 symptoms when going from a once-a-year use to daily use. For other outcomes, our results are more consistent with familial confounding, at least in this community population of twins. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Associations between sleep medication use and alcohol consumption over 36 years in Finnish twins

BACKGROUND

Sleep medication use is an indicator of underlying sleep problems that might be induced by various factors such as alcohol use. However, the longitudinal relationship between drinking and sleep problems remains poorly understood. We investigated associations between sleep medication and alcohol use throughout adulthood, and examined the role of familial and potential confounding factors contributing to these associations.

METHODS

We used information of zygosity and self-report questionnaire data over a follow-up period of 36 years from the Older Finnish Twin Cohort (N=13,851).

RESULTS

Logistic regression analyses suggested consistent associations between sleep medication use and heavy/binge drinking at all four time points (OR range =1.36-3.18, P <0.05), implying that increased drinking is associated with increased sleep medication use over time. Cross-lagged path analyses suggested that moderate/heavy and binge drinking predict sleep medication use at most time points (OR range = 1.15-1.94, P <0.05), whilst sleep medication use predicts subsequent abstaining from alcohol (OR range =2.26-2.47, P <0.05). Within-pair analyses implied that familial factors play a role, and quantitative genetic modelling estimated genetic factors to explain approximately 80% of the lifetime association of sleep medication use with moderate/heavy and binge drinking.

CONCLUSIONS

Drinking is associated with sleep medication use throughout adulthood. Further, our results suggest that drinking is likely to predict sleep medication use, thereby potentially constituting a risk factor for sleep problems, and that genetic factors contribute to the association. These findings are important in terms of better understanding the development of sleep and alcohol use disorders.

A large-scale genome-wide association study meta-analysis of cannabis use disorder

BACKGROUND

Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder.

METHODS

To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations.

FINDINGS

We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia.

INTERPRETATION

These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder.

FUNDING

National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits

Cigarette smoking is the leading cause of preventable morbidity and mortality. Genetic variation contributes to initiation, regular smoking, nicotine dependence, and cessation. We present a Fagerström Test for Nicotine Dependence (FTND)-based genome-wide association study in 58,000 European or African ancestry smokers. We observe five genome-wide significant loci, including previously unreported loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416), and extend loci reported for other smoking traits to nicotine dependence. Using the heaviness of smoking index from UK Biobank (N = 33,791), rs2714700 is consistently associated; rs1862416 is not associated, likely reflecting nicotine dependence features not captured by the heaviness of smoking index. Both variants influence nearby gene expression (rs2714700/MAGI2-AS3 in hippocampus; rs1862416/TENM2 in lung), and expression of genes spanning nicotine dependence-associated variants is enriched in cerebellum. Nicotine dependence (SNP-based heritability = 8.6%) is genetically correlated with 18 other smoking traits (rg = 0.40-1.09) and co-morbidities. Our results highlight nicotine dependence-specific loci, emphasizing the FTND as a composite phenotype that expands genetic knowledge of smoking.

Psychometric validation of measures of alcohol expectancies, retrospective subjective response, and positive drinking consequences for use with adolescents

The Anticipated Effects of Alcohol Scale (AEAS), the Subjective Effects of Alcohol Scale, and the Positive Drinking Consequences Questionnaire (PDCQ) are psychometrically sound measures of alcohol expectancies (expectancies), subjective response to alcohol, and positive drinking consequences, respectively, for use with adults. Prior research using these measures suggests that expectancies, subjective response, and positive drinking consequences are related yet distinct determinants of drinking. The current study presents psychometric evaluations of these measures for use with adolescents including confirmatory factor analyses (CFA) of the previously identified latent structures, internal consistency, and test-criterion relationships. Legally, alcohol cannot be administered to adolescents, so we assessed retrospective subjective response (during the first drinking episode ever [SEAS First] and the most recent drinking episode [SEAS Recent]). The sample comprised 248 Connecticut high school students (53.6% male; mean age 16.50 [1.19] years; 71.4% White) who completed an anonymous survey. CFA confirmed the latent factor structures for each measure. The AEAS, SEAS First, SEAS Recent and the PDCQ were internally consistent (mean α AEAS=0.83; SEAS First=0.88; SEAS Recent=0.89, PDCQ=0.87). AEAS subscales evidenced moderate overlap with corresponding SEAS First subscales (mean=0.36) and SEAS Recent subscales (mean=0.46) and modest overlap with the PDCQ (mean=0.17). Expectancies, subjective response, and positive drinking consequences also accounted for significant variance in monthly drinking, lifetime maximum number of drinks consumed, and alcohol-related problems. In sum, the AEAS, the retrospective SEAS, and the PDCQ are psychometrically sound measures for use with adolescents.