Prenatal exposure to THC vapor influences feeding, bodyweight and glucose metabolism under both basal conditions and following high fat diet
Bibliographic record
Abstract
Abstract 4-20% of people report using cannabis during pregnancy, thereby it is essential to assess the associated risks. There is some evidence that prenatal cannabis exposure (PCE) may be associated with increased risk for development of obesity and diabetes later in life, however this has not been well explored under controlled conditions. The aim of this study was to use a translational THC vapor model in rodents to characterize the effects of PCE on adiposity, glucose metabolism, and feeding patterns in adulthood, with focus on potential sex differences. Pregnant Sprague Dawley rats were exposed to vaporized THC (100mg/ml) or control (polyethylene glycol vehicle) across the entire gestational period. Adult offspring from PCE or control litters were subjected to measures of adiposity, glucose metabolism and feeding behavior. Rats were then placed onto special diets (60% high-fat diet [HFD] or control 10% low fat diet [LFD]) for 4-months, then re-subjected to adiposity, glucose metabolism and feeding behavior measurements. PCE did not influence maternal weight or food consumption but was associated with transient decreased pup weight. PCE did not initially influence bodyweight or adiposity, but PCE did significantly reduce the rate of bodyweight gain when animals were maintained on special diets (HFD/LFD), regardless of which diet. Further, PCE had complex effects on glucose metabolism and feeding behavior that were both sex and diet dependent. No effects of PCE were found on plasma leptin or insulin, or white adipose tissue mass. Overall, this data enhances current understanding of the potential impacts of PCE.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".