Maternal exposure to Δ9-tetrahydrocannabinol impairs female offspring glucose homeostasis and endocrine pancreatic development in the rat
Bibliographic record
Abstract
Recent reports indicate that 7% of pregnant mothers in North America use cannabis. This is concerning given that in utero exposure to Δ9-tetrahydrocannabinol (Δ9-THC), the main psychoactive component in cannabis, causes fetal growth restriction and may alter replication and survival of pancreatic β-cells in the offspring. Accordingly, we hypothesized that maternal exposure to Δ9-THC during pregnancy would impair postnatal glucometabolic health of offspring. To test this hypothesis, pregnant Wistar rats were treated with daily intraperitoneal injections of either 3 mg/kg Δ9-THC or vehicle from gestational day 6 to birth. Offspring were subsequently challenged with glucose and insulin at 5 months of age to assess glucose tolerance and peripheral muscle insulin sensitivity. Female offspring exposed to Δ9-THC in utero were glucose intolerant, associated with blunted insulin response in muscle and increased serum insulin concentration 15 min after glucose challenge. Additionally, pancreata from male and female offspring were harvested at postnatal day 21 and 5 months of age for assessment of endocrine pancreas morphometry by immunostaining. This analysis revealed that gestational exposure to Δ9-THC reduced the density of islets in female, but not male, offspring at postnatal day 21 and 5 months, culminating in reduced β-cell mass at 5 months. These results demonstrate that fetal exposure to Δ9-THC causes female-specific impairments in glucose homeostasis, raising concern regarding the metabolic health of offspring, particularly females, exposed to cannabis in utero.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".