Associations between cannabis use, tobacco use and co-use with brain volume: a systematic review and meta-analysis
Bibliographic record
Abstract
Background and Aims Cannabis is the most widely used illicit drug worldwide and is often co-used with tobacco, the leading cause of preventable death. Although cannabis and tobacco have distinct neurobiological actions, their associations with brain volumes are unclear. We aimed to systematically review brain volumes associated with cannabis use, tobacco use, and their co-use. Design Systematic review and meta-analysis (CRD42022356982). Setting SCOPUS, PubMed and PsycINFO were searched up to 5th September 2024 Participants Searches yielded 103 studies: 57 investigated cannabis use, 45 investigated tobacco use, and one investigated tobacco and cannabis co-use. Measurements We extracted adjusted and unadjusted estimates. Random effects meta-analyses were stratified by exposure and study design across 33 brain regions. Risk of bias was assessed using a modified version of the Newcastle-Ottawa scale. Findings Meta-analysis of adjusted estimates from cross-sectional studies indicated smaller amygdala volumes (k = 17, g = 0.13, 95%CI [0.03, 0.23]) in people who use cannabis compared to controls. Relative to controls, people who smoked tobacco had smaller volumes in the amygdala (k = 5, g = 0.17, 95%CI [0.04, 0.31]), insula (k = 5, g = 0.17, 95%CI [0.06, 0.27]), pallidum (k = 5, g = 0.17, 95%CI [0.13, 0.21]) and total grey matter volume (TGMV) (k = 7, g = 0.17, 95%CI [0.04, 0.30]). Longitudinal studies indicated a larger decrease in TGMV in people who smoke tobacco (k = 5, g = 0.05, 95%CI [0.01, 0.10]) relative to controls. Conclusions There was evidence that cannabis use was associated with smaller volume in the amygdala. Tobacco use was associated with smaller amygdala, insula, pallidum and total grey matter volume.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".