Association between cannabis use and risk of diabetes mellitus type 2: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Cannabis consumption exerts multiple effects on metabolism via various pathways, including glucose regulation and insulin secretion. Studies concerning the association between cannabis use and diabetes mellitus type 2 are discrepant. OBJECTIVE: This study was conducted to evaluate the association between cannabis use and type 2 diabetes mellitus (T2DM). SEARCH METHODS: We searched PubMed, Scopus, Embase, Proquest, Web of Science, and Cochrane Library with no time, language or study types restriction until July 1, 2022, using various forms of "cannabis" and "diabetes mellitus" search terms. SELECTION CRITERIA: Randomized control trials, cohort, and case-control studies investigating the relationship between cannabis consumption and diabetes mellitus type 2 were included. DATA COLLECTION AND ANALYSIS: The Newcastle-Ottawa scale was used to assess the quality of studies. We pooled odds ratio (OR) with 95% confidence interval (CI) using the random-effects model, generic inverse variance method, DerSimonian and Laird approach. MAIN RESULTS: A meta-analysis of seven studies, containing 11 surveys and 4 cohorts, revealed that the odds of developing T2DM in individuals exposed to cannabis was 0.48 times (95% CI: 0.39 to 0.59) lower than in those without cannabis exposure. CONCLUSIONS: A protective effect of cannabis consumption on the odds of diabetes mellitus type 2 development has been suggested. Yet given the considerable interstudy heterogeneity, the upward trend of cannabis consumption and cannabis legalization is recommended to conduct studies with higher levels of evidence.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".