Gestational exposure to cannabidiol leads to glucose intolerance in 3-month-old male offspring
Bibliographic record
Abstract
Reports in North America suggest that up to 20% of young women (18-24 years) use cannabis during pregnancy. This is concerning given clinical studies indicate that maternal cannabis use is associated with fetal growth restriction and dysglycemia in the offspring. Preclinical studies demonstrated that prenatal exposure to Δ9-tetrahydrocannabinol, the main psychoactive component of cannabis, in rat dams led to female-specific deficits in β-cell mass and glucose intolerance/insulin resistance. Yet to date, the contributions of cannabidiol (CBD), the primary nonpsychoactive compound in cannabis, remain elusive. This study aimed to define the effects of in utero cannabidiol (CBD) exposure on postnatal glucose regulation. Pregnant Wistar rat dams received daily intraperitoneal injections of either a vehicle solution or 3 mg/kg of CBD from gestational day (GD) 6 to parturition. CBD exposure did not lead to observable changes in maternal or neonatal outcomes; however, by 3 months of age male CBD-exposed offspring exhibited glucose intolerance despite no changes in pancreatic β/α-cell mass. Transcriptomic analysis on the livers of these CBD-exposed males revealed altered gene expression of circadian rhythm clock machinery, which is linked to systemic glucose intolerance. Furthermore, alterations in hepatic developmental and metabolic processes were also observed, suggesting gestational CBD exposure has a long-lasting detrimental effect on liver health throughout life. Collectively, these results indicate that exposure to CBD alone in pregnancy may be detrimental to the metabolic health of the offspring later in life.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".